EpiGraphDB API endpoints
This document is auto generated from EpiGraphDB API specification.
Topic endpoints
GET /mr
Return information related to Mendelian randomisation Specify at least one of `exposure_trait` and `outcome_trait` or both.
Params
{'exposure_trait': typing.Union[str, NoneType],
'outcome_trait': typing.Union[str, NoneType],
'pval_threshold': <class 'float'>}
1. Query for exposure trait
Script
import requests
url = 'https://api.epigraphdb.org/mr'
params = {'exposure_trait': 'Body mass index'}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'MATCH (exposure:Gwas)-[mr:MR_EVE_MR]->(outcome:Gwas) '
'WHERE exposure.trait = "Body mass index" AND mr.pval < '
'1e-05 RETURN exposure {.id, .trait}, outcome {.id, '
'.trait}, mr {.b, .se, .pval, .method, .selection, '
'.moescore} ORDER BY mr.pval ;',
'total_seconds': 0.528185},
'results': [{'exposure': {'id': 'ieu-a-974', 'trait': 'Body mass index'},
'mr': {'b': 0.193038232711978,
'method': 'FE IVW',
'moescore': 1.0,
'pval': 0.0,
'se': 0.00223608210517218,
'selection': 'DF'},
'outcome': {'id': 'ebi-a-GCST005062',
'trait': 'Fibrinogen levels'}},
{'exposure': {'id': 'ebi-a-GCST006368',
'trait': 'Body mass index'},
'mr': {'b': 0.53327738343657,
'method': 'FE IVW',
'moescore': 0.93,
'pval': 0.0,
'se': 0.0104448807074039,
'selection': 'DF + HF'},
'outcome': {'id': 'ukb-b-20188',
'trait': 'Arm fat percentage (left)'}},
{'exposure': {'id': 'ieu-a-2', 'trait': 'Body mass index'},
'mr': {'b': 0.439223555551985,
'method': 'FE IVW',
'moescore': 0.9,
'pval': 0.0,
'se': 0.00988946480369914,
'selection': 'Tophits'},
'outcome': {'id': 'ukb-b-4650',
'trait': 'Comparative body size at age 10'}},
{'exposure': {'id': 'ieu-a-2', 'trait': 'Body mass index'},
'mr': {'b': 0.6739010545861329,
'method': 'FE IVW',
'moescore': 0.92,
'pval': 0.0,
'se': 0.0178415889107993,
'selection': 'DF + HF'},
'outcome': {'id': 'ukb-b-2303',
'trait': 'Body mass index (BMI)'}},
{'exposure': {'id': 'ieu-a-2', 'trait': 'Body mass index'},
'mr': {'b': 0.44864951007464,
'method': 'FE IVW',
2. Query for outcome trait
Script
import requests
url = 'https://api.epigraphdb.org/mr'
params = {'outcome_trait': 'Body mass index'}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'MATCH (exposure:Gwas)-[mr:MR_EVE_MR]->(outcome:Gwas) '
'WHERE outcome.trait = "Body mass index" AND mr.pval < '
'1e-05 RETURN exposure {.id, .trait}, outcome {.id, '
'.trait}, mr {.b, .se, .pval, .method, .selection, '
'.moescore} ORDER BY mr.pval ;',
'total_seconds': 0.162546},
'results': [{'exposure': {'id': 'ukb-b-4540',
'trait': 'Methods of admission to hospital '
'(recoded): Emergency admission: '
'Non-injury'},
'mr': {'b': -1.16333513628466,
'method': 'FE IVW',
'moescore': 1.0,
'pval': 0.0,
'se': 0.0295084277124307,
'selection': 'DF'},
'outcome': {'id': 'ieu-a-1089', 'trait': 'Body mass index'}},
{'exposure': {'id': 'ukb-b-4263',
'trait': 'Number of full brothers'},
'mr': {'b': 0.7033029662996549,
'method': 'FE IVW',
'moescore': 1.0,
'pval': 0.0,
'se': 0.0014276237828675,
'selection': 'DF'},
'outcome': {'id': 'ieu-a-974', 'trait': 'Body mass index'}},
{'exposure': {'id': 'ukb-b-11632',
'trait': 'Treatment/medication code: atenolol'},
'mr': {'b': 0.5209304643689739,
'method': 'FE IVW',
'moescore': 1.0,
'pval': 0.0,
'se': 0.0109074036615998,
'selection': 'DF'},
'outcome': {'id': 'ieu-a-974', 'trait': 'Body mass index'}},
{'exposure': {'id': 'ukb-a-63',
'trait': 'Non-cancer illness code self-reported: '
'heart attack/myocardial infarction'},
'mr': {'b': -0.8420743366880229,
'method': 'FE IVW',
'moescore': 1.0,
'pval': 0.0,
'se': 0.0198091394625712,
'selection': 'DF'},
'outcome': {'id': 'ieu-a-974', 'trait': 'Body mas
3. Query for both exposure and outcome
Script
import requests
url = 'https://api.epigraphdb.org/mr'
params = {'expsoure_trait': 'Body mass index', 'outcome_trait': 'Coronary heart disease'}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'MATCH (exposure:Gwas)-[mr:MR_EVE_MR]->(outcome:Gwas) '
'WHERE outcome.trait = "Coronary heart disease" AND '
'mr.pval < 1e-05 RETURN exposure {.id, .trait}, outcome '
'{.id, .trait}, mr {.b, .se, .pval, .method, '
'.selection, .moescore} ORDER BY mr.pval ;',
'total_seconds': 0.093968},
'results': [{'exposure': {'id': 'ubm-a-496',
'trait': 'IDP dMRI TBSS ICVF Superior cerebellar '
'peduncle R'},
'mr': {'b': 0.13610546823221,
'method': 'FE IVW',
'moescore': 1.0,
'pval': 0.0,
'se': 0.000331892872942469,
'selection': 'DF'},
'outcome': {'id': 'ebi-a-GCST000998',
'trait': 'Coronary heart disease'}},
{'exposure': {'id': 'ubm-a-2677',
'trait': 'volume Right-Cerebellum-Cortex'},
'mr': {'b': -0.22925403960399401,
'method': 'FE IVW',
'moescore': 1.0,
'pval': 0.0,
'se': 0.00569335833058816,
'selection': 'DF'},
'outcome': {'id': 'ebi-a-GCST000998',
'trait': 'Coronary heart disease'}},
{'exposure': {'id': 'ubm-a-496',
'trait': 'IDP dMRI TBSS ICVF Superior cerebellar '
'peduncle R'},
'mr': {'b': 0.13610546823221,
'method': 'FE IVW',
'moescore': 1.0,
'pval': 0.0,
'se': 0.000331892872942469,
'selection': 'DF'},
'outcome': {'id': 'ieu-a-8', 'trait': 'Coronary heart disease'}},
{'exposure': {'id': 'ubm-a-2677',
'trait': 'volume Right-Cerebellum-Cortex'},
'mr': {'b': -0.22925403960399401,
'method': 'FE IVW',
'moescore': 1.0,
'pval': 0.0,
'se': 0.00569335833058816,
'selection': 'DF'},
'outcome': {'id': 'ieu-a-8', 'trait': 'Co
GET /obs-cor
Returns observational correlates for a trait. Args: - `trait`: A trait name, e.g. "body mass index" - `cor_coef_threshold`: Coefficient correlation threshold
Params
{'cor_coef_threshold': <class 'float'>, 'trait': <class 'str'>}
1. Query
Script
import requests
url = 'https://api.epigraphdb.org/obs-cor'
params = {'trait': 'Waist circumference'}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'MATCH '
'(trait:Gwas)-[obs_cor:OBS_COR]-(assoc_trait:Gwas) '
'WHERE trait.trait = "Waist circumference" AND '
'abs(obs_cor.cor) > 0.8 RETURN trait {.id, .trait}, '
'assoc_trait {.id, .trait}, obs_cor {.cor} ORDER BY '
'obs_cor.cor DESC ;',
'total_seconds': 0.026421},
'results': [{'assoc_trait': {'id': 'ukb-b-11842', 'trait': 'Weight'},
'obs_cor': {'cor': 0.878521630374856},
'trait': {'id': 'ukb-b-9405', 'trait': 'Waist circumference'}},
{'assoc_trait': {'id': 'ukb-b-11842', 'trait': 'Weight'},
'obs_cor': {'cor': 0.878521630374856},
'trait': {'id': 'ukb-b-9405', 'trait': 'Waist circumference'}},
{'assoc_trait': {'id': 'ukb-b-12039', 'trait': 'Weight'},
'obs_cor': {'cor': 0.871360904191991},
'trait': {'id': 'ukb-b-9405', 'trait': 'Waist circumference'}},
{'assoc_trait': {'id': 'ukb-b-12039', 'trait': 'Weight'},
'obs_cor': {'cor': 0.871360904191991},
'trait': {'id': 'ukb-b-9405', 'trait': 'Waist circumference'}},
{'assoc_trait': {'id': 'ukb-b-19953',
'trait': 'Body mass index (BMI)'},
'obs_cor': {'cor': 0.802693232651605},
'trait': {'id': 'ukb-b-9405', 'trait': 'Waist circumference'}},
{'assoc_trait': {'id': 'ukb-b-19953',
'trait': 'Body mass index (BMI)'},
'obs_cor': {'cor': 0.802693232651605},
'trait': {'id': 'ukb-b-9405', 'trait': 'Waist circumference'}}]}
2. Adjust for correlation coefficient
Script
import requests
url = 'https://api.epigraphdb.org/obs-cor'
params = {'trait': 'Waist circumference', 'cor_coef_threshold': 0.2}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'MATCH '
'(trait:Gwas)-[obs_cor:OBS_COR]-(assoc_trait:Gwas) '
'WHERE trait.trait = "Waist circumference" AND '
'abs(obs_cor.cor) > 0.2 RETURN trait {.id, .trait}, '
'assoc_trait {.id, .trait}, obs_cor {.cor} ORDER BY '
'obs_cor.cor DESC ;',
'total_seconds': 0.025044},
'results': [{'assoc_trait': {'id': 'ukb-b-11842', 'trait': 'Weight'},
'obs_cor': {'cor': 0.878521630374856},
'trait': {'id': 'ukb-b-9405', 'trait': 'Waist circumference'}},
{'assoc_trait': {'id': 'ukb-b-11842', 'trait': 'Weight'},
'obs_cor': {'cor': 0.878521630374856},
'trait': {'id': 'ukb-b-9405', 'trait': 'Waist circumference'}},
{'assoc_trait': {'id': 'ukb-b-12039', 'trait': 'Weight'},
'obs_cor': {'cor': 0.871360904191991},
'trait': {'id': 'ukb-b-9405', 'trait': 'Waist circumference'}},
{'assoc_trait': {'id': 'ukb-b-12039', 'trait': 'Weight'},
'obs_cor': {'cor': 0.871360904191991},
'trait': {'id': 'ukb-b-9405', 'trait': 'Waist circumference'}},
{'assoc_trait': {'id': 'ukb-b-19953',
'trait': 'Body mass index (BMI)'},
'obs_cor': {'cor': 0.802693232651605},
'trait': {'id': 'ukb-b-9405', 'trait': 'Waist circumference'}},
{'assoc_trait': {'id': 'ukb-b-19953',
'trait': 'Body mass index (BMI)'},
'obs_cor': {'cor': 0.802693232651605},
'trait': {'id': 'ukb-b-9405', 'trait': 'Waist circumference'}},
{'assoc_trait': {'id': 'ukb-b-2303',
'trait': 'Body mass index (BMI)'},
'obs_cor': {'cor': 0.795709331174756},
'trait': {'id': 'ukb-b-9405', 'trait': 'Waist circumference'}},
{'assoc_trait': {'id': 'ukb-b-2303',
'trait': 'Body mass index (BMI)'},
'obs_cor': {'cor': 0.795709331174756},
'trait': {'id': 'ukb-b-9405', 'trait': 'Waist circumference'}},
{'assoc_trait': {'id': 'ukb-b-20044', 'trait': 'Trunk fat mass'},
'obs_cor': {'cor': 0.7381030697878149},
GET /genetic-cor
Returns genetic correlates for a trait. Args: - `trait`: A trait name, e.g. Whole body fat mass - `cor_coef_threshold`: correlation coefficient threshold
Params
{'cor_coef_threshold': <class 'float'>, 'trait': <class 'str'>}
1. Query
Script
import requests
url = 'https://api.epigraphdb.org/genetic-cor'
params = {'trait': 'Waist circumference'}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'MATCH (trait:Gwas)-[gc:GEN_COR]-(assoc_trait:Gwas) '
'WHERE trait.trait = "Waist circumference" AND '
'abs(gc.rg) > 0.8 RETURN trait {.id, .trait}, '
'assoc_trait {.id, .trait}, gc { .rg, .rg_SE, .Z, .p, '
'.rg_intercept, .rg_intercept_SE, .h2, .h2_SE, '
'.h2_intercept, .h2_intercept_SE } ORDER BY gc.rg DESC '
';',
'total_seconds': 0.043816},
'results': [{'assoc_trait': {'id': 'ukb-a-287',
'trait': 'Arm fat mass (left)'},
'gc': {'Z': 278.9,
'h2': 0.203,
'h2_SE': 0.006928,
'h2_intercept': 1.055,
'h2_intercept_SE': 0.0172,
'p': 9.999999999999994e-309,
'rg': 0.9541,
'rg_SE': 0.003421,
'rg_intercept': 0.9111,
'rg_intercept_SE': 0.01661},
'trait': {'id': 'ukb-a-382', 'trait': 'Waist circumference'}},
{'assoc_trait': {'id': 'ukb-b-8338',
'trait': 'Arm fat mass (left)'},
'gc': {'Z': 278.9,
'h2': 0.203,
'h2_SE': 0.006928,
'h2_intercept': 1.055,
'h2_intercept_SE': 0.0172,
'p': 9.999999999999994e-309,
'rg': 0.9541,
'rg_SE': 0.003421,
'rg_intercept': 0.9111,
'rg_intercept_SE': 0.01661},
'trait': {'id': 'ukb-a-382', 'trait': 'Waist circumference'}},
{'assoc_trait': {'id': 'ukb-b-7212',
'trait': 'Leg fat mass (left)'},
'gc': {'Z': 330.4,
'h2': 0.203,
'h2_SE': 0.006928,
'h2_intercept': 1.055,
'h2_intercept_SE': 0.0172,
'p': 9.999999999999994e-309,
'rg': 0.9541,
'rg_SE': 0.002888,
'rg_intercept': 0.9081,
'rg_intercept_SE': 0.01695},
'trait': {'id': 'ukb-a-382', 'trait': 'Waist circumference'}},
{'
2. Adjust for correlation coefficient
Script
import requests
url = 'https://api.epigraphdb.org/genetic-cor'
params = {'trait': 'Waist circumference', 'cor_coef_threshold': 0.2}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'MATCH (trait:Gwas)-[gc:GEN_COR]-(assoc_trait:Gwas) '
'WHERE trait.trait = "Waist circumference" AND '
'abs(gc.rg) > 0.2 RETURN trait {.id, .trait}, '
'assoc_trait {.id, .trait}, gc { .rg, .rg_SE, .Z, .p, '
'.rg_intercept, .rg_intercept_SE, .h2, .h2_SE, '
'.h2_intercept, .h2_intercept_SE } ORDER BY gc.rg DESC '
';',
'total_seconds': 0.153649},
'results': [{'assoc_trait': {'id': 'ukb-a-287',
'trait': 'Arm fat mass (left)'},
'gc': {'Z': 278.9,
'h2': 0.203,
'h2_SE': 0.006928,
'h2_intercept': 1.055,
'h2_intercept_SE': 0.0172,
'p': 9.999999999999994e-309,
'rg': 0.9541,
'rg_SE': 0.003421,
'rg_intercept': 0.9111,
'rg_intercept_SE': 0.01661},
'trait': {'id': 'ukb-a-382', 'trait': 'Waist circumference'}},
{'assoc_trait': {'id': 'ukb-b-8338',
'trait': 'Arm fat mass (left)'},
'gc': {'Z': 278.9,
'h2': 0.203,
'h2_SE': 0.006928,
'h2_intercept': 1.055,
'h2_intercept_SE': 0.0172,
'p': 9.999999999999994e-309,
'rg': 0.9541,
'rg_SE': 0.003421,
'rg_intercept': 0.9111,
'rg_intercept_SE': 0.01661},
'trait': {'id': 'ukb-a-382', 'trait': 'Waist circumference'}},
{'assoc_trait': {'id': 'ukb-b-7212',
'trait': 'Leg fat mass (left)'},
'gc': {'Z': 330.4,
'h2': 0.203,
'h2_SE': 0.006928,
'h2_intercept': 1.055,
'h2_intercept_SE': 0.0172,
'p': 9.999999999999994e-309,
'rg': 0.9541,
'rg_SE': 0.002888,
'rg_intercept': 0.9081,
'rg_intercept_SE': 0.01695},
'trait': {'id': 'ukb-a-382', 'trait': 'Waist circumference'}},
{'
GET /confounder
Get confounder / intermediate / collider evidence between traits: `type` accepts 1 of the 4 options: - confounder: `confounder -> exposure -> outcome <-confounder` - intermediate: `intermediate <- exposure -> outcome <- confounder` - reverse_intermediate: `intermediate -> exposure -> outcome -> confounder` - collider: `collider <- exposure -> outcome -> collider`
Params
{'exposure_trait': typing.Union[str, NoneType],
'outcome_trait': typing.Union[str, NoneType],
'pval_threshold': <class 'float'>,
'type': <enum 'ConfounderType'>}
1. Confounder (default)
Script
import requests
url = 'https://api.epigraphdb.org/confounder'
params = {'exposure_trait': 'Body mass index', 'outcome_trait': 'Coronary heart disease'}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'MATCH (cf:Gwas)-[r1:MR_EVE_MR]-> (exposure:Gwas '
'{trait: "Body mass index"}) '
'-[r2:MR_EVE_MR]->(outcome:Gwas {trait: "Coronary heart '
'disease"}) <-[r3:MR_EVE_MR]-(cf:Gwas) WHERE r1.pval < '
'1e-05 AND r2.pval < 1e-05 AND r3.pval < 1e-05 AND '
'cf.id <> exposure.id AND cf.id <> outcome.id AND '
'exposure.id <> outcome.id AND cf.trait <> '
'exposure.trait AND cf.trait <> outcome.trait AND '
'exposure.trait <> outcome.trait RETURN exposure {.id, '
'.trait}, outcome {.id, .trait}, cf {.id, .trait}, r1 '
'{.b, .se, .pval, .selection, .method, .moescore}, r2 '
'{.b, .se, .pval, .selection, .method, .moescore}, r3 '
'{.b, .se, .pval, .selection, .method, .moescore} ORDER '
'BY r1.p;',
'total_seconds': 0.591121},
'results': [{'cf': {'id': 'ukb-b-8338', 'trait': 'Arm fat mass (left)'},
'exposure': {'id': 'ieu-a-974', 'trait': 'Body mass index'},
'outcome': {'id': 'ieu-a-8', 'trait': 'Coronary heart disease'},
'r1': {'b': 0.8370181564895791,
'method': 'Simple median',
'moescore': 0.87,
'pval': 2.517982692176079e-112,
'se': 0.0371642062477166,
'selection': 'HF'},
'r2': {'b': 0.32847610101648,
'method': 'FE IVW',
'moescore': 0.85,
'pval': 6.96632705468264e-06,
'se': 0.0730805223133059,
'selection': 'DF'},
'r3': {'b': 0.320258689822155,
'method': 'FE IVW',
'moescore': 0.67,
'pval': 1.45279464451578e-06,
'se': 0.0664765123257088,
'selection': 'DF'}},
{'cf': {'id': 'ukb-b-6704', 'trait': 'Arm fat mass (right)'},
'exposure': {'id': 'ieu-a-974', 'trait': 'Body mass index'},
'outcome': {'id': 'ieu-a-8', 'trait': 'Coronary heart disease'},
'r1': {'b': 0.800980400009612,
2. Intermediate
Script
import requests
url = 'https://api.epigraphdb.org/confounder'
params = {'exposure_trait': 'Body mass index', 'outcome_trait': 'Coronary heart disease', 'type': 'intermediate'}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'MATCH (cf:Gwas)<-[r1:MR_EVE_MR]- (exposure:Gwas '
'{trait: "Body mass index"}) '
'-[r2:MR_EVE_MR]->(outcome:Gwas {trait: "Coronary heart '
'disease"}) <-[r3:MR_EVE_MR]-(cf:Gwas) WHERE r1.pval < '
'1e-05 AND r2.pval < 1e-05 AND r3.pval < 1e-05 AND '
'cf.id <> exposure.id AND cf.id <> outcome.id AND '
'exposure.id <> outcome.id AND cf.trait <> '
'exposure.trait AND cf.trait <> outcome.trait AND '
'exposure.trait <> outcome.trait RETURN exposure {.id, '
'.trait}, outcome {.id, .trait}, cf {.id, .trait}, r1 '
'{.b, .se, .pval, .selection, .method, .moescore}, r2 '
'{.b, .se, .pval, .selection, .method, .moescore}, r3 '
'{.b, .se, .pval, .selection, .method, .moescore} ORDER '
'BY r1.p;',
'total_seconds': 1.299568},
'results': [{'cf': {'id': 'ukb-d-KNEE_ARTHROSIS',
'trait': 'Gonarthrosis [arthrosis of knee](FG)'},
'exposure': {'id': 'ieu-a-974', 'trait': 'Body mass index'},
'outcome': {'id': 'ieu-a-8', 'trait': 'Coronary heart disease'},
'r1': {'b': 0.0161424198250729,
'method': 'Simple median',
'moescore': 0.9,
'pval': 2.90885870510003e-06,
'se': 0.00345133490341152,
'selection': 'Tophits'},
'r2': {'b': 0.32847610101648,
'method': 'FE IVW',
'moescore': 0.85,
'pval': 6.96632705468264e-06,
'se': 0.0730805223133059,
'selection': 'DF'},
'r3': {'b': -6.80711894634798,
'method': 'FE IVW',
'moescore': 1.0,
'pval': 1.7008004818256495e-142,
'se': 0.267833586160006,
'selection': 'DF'}},
{'cf': {'id': 'ukb-d-ICDMAIN_ANY_ENTRY',
'trait': 'Any ICDMAIN event in hilmo or causes of death'},
'exposure': {'id': 'ieu-a-974', 'trait': 'Body mass index'},
3. Reverse intermediate
Script
import requests
url = 'https://api.epigraphdb.org/confounder'
params = {'exposure_trait': 'Body mass index', 'outcome_trait': 'Coronary heart disease', 'type': 'reverse_intermediate'}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'MATCH (cf:Gwas)-[r1:MR_EVE_MR]-> (exposure:Gwas '
'{trait: "Body mass index"}) '
'-[r2:MR_EVE_MR]->(outcome:Gwas {trait: "Coronary heart '
'disease"}) -[r3:MR_EVE_MR]->(cf:Gwas) WHERE r1.pval < '
'1e-05 AND r2.pval < 1e-05 AND r3.pval < 1e-05 AND '
'cf.id <> exposure.id AND cf.id <> outcome.id AND '
'exposure.id <> outcome.id AND cf.trait <> '
'exposure.trait AND cf.trait <> outcome.trait AND '
'exposure.trait <> outcome.trait RETURN exposure {.id, '
'.trait}, outcome {.id, .trait}, cf {.id, .trait}, r1 '
'{.b, .se, .pval, .selection, .method, .moescore}, r2 '
'{.b, .se, .pval, .selection, .method, .moescore}, r3 '
'{.b, .se, .pval, .selection, .method, .moescore} ORDER '
'BY r1.p;',
'total_seconds': 1.183184},
'results': [{'cf': {'id': 'ukb-b-12646',
'trait': 'Treatment speciality of consultant (recoded): '
'General medicine'},
'exposure': {'id': 'ieu-a-974', 'trait': 'Body mass index'},
'outcome': {'id': 'ieu-a-8', 'trait': 'Coronary heart disease'},
'r1': {'b': 0.7733674238392042,
'method': 'FE IVW',
'moescore': 1.0,
'pval': 1.0539751258843899e-60,
'se': 0.047052819153257,
'selection': 'DF'},
'r2': {'b': 0.32847610101648,
'method': 'FE IVW',
'moescore': 0.85,
'pval': 6.96632705468264e-06,
'se': 0.0730805223133059,
'selection': 'DF'},
'r3': {'b': 0.00984847041991893,
'method': 'FE IVW',
'moescore': 0.75,
'pval': 1.72104244684849e-07,
'se': 0.0018840844963074301,
'selection': 'HF'}},
{'cf': {'id': 'ukb-b-11632',
'trait': 'Treatment/medication code: atenolol'},
'exposure': {'id': 'ieu-a-974', 'trait': 'Body mass in
4. Collider
Script
import requests
url = 'https://api.epigraphdb.org/confounder'
params = {'exposure_trait': 'Body mass index', 'outcome_trait': 'Coronary heart disease', 'type': 'collider'}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'MATCH (cf:Gwas)<-[r1:MR_EVE_MR]- (exposure:Gwas '
'{trait: "Body mass index"}) '
'-[r2:MR_EVE_MR]->(outcome:Gwas {trait: "Coronary heart '
'disease"}) -[r3:MR_EVE_MR]->(cf:Gwas) WHERE r1.pval < '
'1e-05 AND r2.pval < 1e-05 AND r3.pval < 1e-05 AND '
'cf.id <> exposure.id AND cf.id <> outcome.id AND '
'exposure.id <> outcome.id AND cf.trait <> '
'exposure.trait AND cf.trait <> outcome.trait AND '
'exposure.trait <> outcome.trait RETURN exposure {.id, '
'.trait}, outcome {.id, .trait}, cf {.id, .trait}, r1 '
'{.b, .se, .pval, .selection, .method, .moescore}, r2 '
'{.b, .se, .pval, .selection, .method, .moescore}, r3 '
'{.b, .se, .pval, .selection, .method, .moescore} ORDER '
'BY r1.p;',
'total_seconds': 2.045604},
'results': [{'cf': {'id': 'ukb-b-13488',
'trait': 'Treatment/medication code: perindopril'},
'exposure': {'id': 'ieu-a-974', 'trait': 'Body mass index'},
'outcome': {'id': 'ieu-a-8', 'trait': 'Coronary heart disease'},
'r1': {'b': 0.0072192910127228,
'method': 'RE IVW',
'moescore': 0.87,
'pval': 2.0269797155019801e-07,
'se': 0.00111303222094769,
'selection': 'HF'},
'r2': {'b': 0.32847610101648,
'method': 'FE IVW',
'moescore': 0.85,
'pval': 6.96632705468264e-06,
'se': 0.0730805223133059,
'selection': 'DF'},
'r3': {'b': 0.00384275108363339,
'method': 'FE IVW',
'moescore': 0.82,
'pval': 3.45610209569087e-11,
'se': 0.00057997675976848,
'selection': 'HF'}},
{'cf': {'id': 'ukb-b-12651',
'trait': 'Diagnoses - secondary ICD10: E78.0 Pure '
'hypercholesterolaemia'},
'exposure': {'id': 'ieu-a-974', 'trait': 'Body mass
GET /drugs/risk-factors
Drugs for common risk factors of diseases
Params
{'pval_threshold': <class 'float'>, 'trait': <class 'str'>}
1. Query
Script
import requests
url = 'https://api.epigraphdb.org/drugs/risk-factors'
params = {'trait': 'Coronary heart disease'}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'MATCH (trait:Gwas {trait: "Coronary heart '
'disease"})<-[mr:MR_EVE_MR]-(assoc_trait:Gwas) '
'-[gwas_to_variant:GWAS_TO_VARIANT]->(variant:Variant) '
'-[:VARIANT_TO_GENE]->(gene:Gene) '
'<-[:CPIC|:OPENTARGETS_DRUG_TO_TARGET]-(drug:Drug) '
'WHERE trait.trait <> assoc_trait.trait AND mr.pval < '
'1e-08 AND gwas_to_variant.pval < 1e-8 RETURN trait '
'{.id, .trait}, assoc_trait {.id, .trait}, variant '
'{.name}, gene {.name}, drug {.label}, mr {.b, .se, '
'.pval, .selection, .method, .moescore} ORDER BY '
'mr.pval ;',
'total_seconds': 0.372227},
'results': [{'assoc_trait': {'id': 'ukb-a-12', 'trait': 'Nap during day'},
'drug': {'label': 'SB-649868'},
'gene': {'name': 'HCRTR2'},
'mr': {'b': 2.43741650603288,
'method': 'FE IVW',
'moescore': 1.0,
'pval': 0.0,
'se': 0.008056364023149541,
'selection': 'DF'},
'trait': {'id': 'ieu-a-9', 'trait': 'Coronary heart disease'},
'variant': {'name': 'rs2653349'}},
{'assoc_trait': {'id': 'ukb-a-12', 'trait': 'Nap during day'},
'drug': {'label': 'LEMBOREXANT'},
'gene': {'name': 'HCRTR2'},
'mr': {'b': 2.43741650603288,
'method': 'FE IVW',
'moescore': 1.0,
'pval': 0.0,
'se': 0.008056364023149541,
'selection': 'DF'},
'trait': {'id': 'ieu-a-9', 'trait': 'Coronary heart disease'},
'variant': {'name': 'rs2653349'}},
{'assoc_trait': {'id': 'ukb-a-12', 'trait': 'Nap during day'},
'drug': {'label': 'SUVOREXANT'},
'gene': {'name': 'HCRTR2'},
'mr': {'b': 2.43741650603288,
'method': 'FE IVW',
'moescore': 1.0,
'pval': 0.0,
'se': 0.008056364023149541,
'selection': 'DF'},
'trait': {'id': 'ieu-a-9', 'trait': 'Coronar
GET /pathway
Pathway-based stratification of instruments
Params
{'pval_threshold': <class 'float'>, 'trait': <class 'str'>}
1. Query
Script
import requests
url = 'https://api.epigraphdb.org/pathway'
params = {'trait': 'LDL cholesterol'}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'MATCH (gwas:Gwas {trait: "LDL cholesterol"}) '
'-[gwas_to_variant:GWAS_TO_VARIANT]->(variant:Variant)-[variant_to_gene:VARIANT_TO_GENE]-> '
'(gene:Gene)-[gene_to_protein:GENE_TO_PROTEIN]->(protein:Protein) '
'-[protein_in_pathway:PROTEIN_IN_PATHWAY]->(pathway:Pathway) '
'WHERE gwas_to_variant.pval < 1e-05 AND gene.name is '
'not null RETURN gwas {.id, .trait}, gwas_to_variant '
'{.beta, .se, .pval, .eaf, .samplesize}, variant '
'{.name}, gene {.name}, protein {.uniprot_id}, pathway '
'{.id, .name} ORDER BY gwas_to_variant.pval ;',
'total_seconds': 0.092851},
'results': [{'gene': {'name': 'TOMM40'},
'gwas': {'id': 'ieu-a-300', 'trait': 'LDL cholesterol'},
'gwas_to_variant': {'beta': -0.4853,
'eaf': 0.03166,
'pval': 1e-200,
'samplesize': 139198.0,
'se': 0.0119},
'pathway': {'id': 'R-HSA-9663891', 'name': 'Selective autophagy'},
'protein': {'uniprot_id': 'O96008'},
'variant': {'name': 'rs7254892'}},
{'gene': {'name': 'TOMM40'},
'gwas': {'id': 'ieu-a-300', 'trait': 'LDL cholesterol'},
'gwas_to_variant': {'beta': -0.4853,
'eaf': 0.03166,
'pval': 1e-200,
'samplesize': 139198.0,
'se': 0.0119},
'pathway': {'id': 'R-HSA-9612973', 'name': 'Autophagy'},
'protein': {'uniprot_id': 'O96008'},
'variant': {'name': 'rs7254892'}},
{'gene': {'name': 'TOMM40'},
'gwas': {'id': 'ieu-a-300', 'trait': 'LDL cholesterol'},
'gwas_to_variant': {'beta': -0.4853,
'eaf': 0.03166,
'pval': 1e-200,
'samplesize': 139198.0,
'se': 0.0119},
'pathway': {'id': 'R-HSA-9609507',
'name': 'Protein localization'},
GET /xqtl/multi-snp-mr
xQTL multi SNP MR results Search by exposure_gene, outcome_trait, or both. - qtl_type: eQTL, pQTL
Params
{'exposure_gene': typing.Union[str, NoneType],
'mr_method': <enum 'MrMethodInput'>,
'outcome_trait': typing.Union[str, NoneType],
'pval_threshold': <class 'float'>,
'qtl_type': <enum 'QtlTypeInput'>}
1. Query by exposure gene
Script
import requests
url = 'https://api.epigraphdb.org/xqtl/multi-snp-mr'
params = {'exposure_gene': 'PLAU'}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'MATCH (gene:Gene)-[r:XQTL_MULTI_SNP_MR]->(gwas:Gwas) '
'WHERE gene.name = "PLAU" AND gene.name is not null AND '
'r.mr_method = "IVW" AND r.qtl_type = "eQTL" AND r.p < '
'1e-05 RETURN gene {.ensembl_id, .name}, gwas {.id, '
'.trait}, r {.beta, .se, .p} ORDER BY r.p ;',
'total_seconds': 0.101615},
'results': [{'gene': {'ensembl_id': 'ENSG00000122861', 'name': 'PLAU'},
'gwas': {'id': 'ukb-a-337',
'trait': 'Forced expiratory volume in 1-second (FEV1)'},
'r': {'beta': 0.0421645947955484,
'p': 2.3281682783539e-06,
'se': 0.008928206414949279}}]}
2. Query by outcome trait
Script
import requests
url = 'https://api.epigraphdb.org/xqtl/multi-snp-mr'
params = {'outcome_trait': "Crohn's disease"}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'MATCH (gene:Gene)-[r:XQTL_MULTI_SNP_MR]->(gwas:Gwas) '
'WHERE gwas.trait = "Crohn\'s disease" AND gene.name is '
'not null AND r.mr_method = "IVW" AND r.qtl_type = '
'"eQTL" AND r.p < 1e-05 RETURN gene {.ensembl_id, '
'.name}, gwas {.id, .trait}, r {.beta, .se, .p} ORDER '
'BY r.p ;',
'total_seconds': 0.034624},
'results': [{'gene': {'ensembl_id': 'ENSG00000125462', 'name': 'C1orf61'},
'gwas': {'id': 'ieu-a-12', 'trait': "Crohn's disease"},
'r': {'beta': -1.98228326767424,
'p': 4.7061328100262e-32,
'se': 0.16821468966705502}},
{'gene': {'ensembl_id': 'ENSG00000148396', 'name': 'SEC16A'},
'gwas': {'id': 'ieu-a-12', 'trait': "Crohn's disease"},
'r': {'beta': -0.496190474958369,
'p': 6.068514061391429e-19,
'se': 0.055809597735565986}},
{'gene': {'ensembl_id': 'ENSG00000120708', 'name': 'TGFBI'},
'gwas': {'id': 'ieu-a-12', 'trait': "Crohn's disease"},
'r': {'beta': -0.8785646251229459,
'p': 8.876167446378721e-16,
'se': 0.109254130145635}},
{'gene': {'ensembl_id': 'ENSG00000128656', 'name': 'CHN1'},
'gwas': {'id': 'ieu-a-12', 'trait': "Crohn's disease"},
'r': {'beta': 0.8776293616492471,
'p': 1.09814235013335e-15,
'se': 0.109493432815241}},
{'gene': {'ensembl_id': 'ENSG00000113441', 'name': 'LNPEP'},
'gwas': {'id': 'ieu-a-12', 'trait': "Crohn's disease"},
'r': {'beta': 0.330519978528724,
'p': 2.43591728961006e-15,
'se': 0.0417488939483079}},
{'gene': {'ensembl_id': 'ENSG00000168610', 'name': 'STAT3'},
'gwas': {'id': 'ieu-a-12', 'trait': "Crohn's disease"},
'r': {'beta': 0.6139865880190749,
'p': 2.19421869461152e-14,
'se': 0.0803783886796071}},
{'gene': {'ensembl_id': 'ENSG00000118971', 'name': 'CCND2'},
'gwas': {'id': 'ieu-a-12', 'trait': "Crohn's disease"},
'r': {'beta'
GET /xqtl/single-snp-mr
xQTL single SNP MR results Search by exposure_gene, outcome_trait, variant, or all of them. - qtl_type: eQTL, pQTL
Params
{'exposure_gene': <class 'str'>,
'outcome_trait': <class 'str'>,
'pval_threshold': <class 'float'>,
'qtl_type': <enum 'QtlTypeInput'>,
'variant': <class 'str'>}
1. Query by exposure gene
Script
import requests
url = 'https://api.epigraphdb.org/xqtl/single-snp-mr'
params = {'exposure_gene': 'PLAU'}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'MATCH '
'(variant:Variant)-[s:XQTL_SINGLE_SNP_MR_SNP_GENE]-> '
'(gene:Gene)-[r:XQTL_SINGLE_SNP_MR_GENE_GWAS]->(gwas:Gwas) '
'WHERE gene.name = "PLAU" AND gene.name is not null AND '
'variant.name = r.rsid AND r.qtl_type = "eQTL" AND r.p '
'< 1e-05 RETURN gene {.ensembl_id, .name}, gwas {.id, '
'.trait}, r {.beta, .se, .p, .rsid} ORDER BY r.p ;',
'total_seconds': 0.089812},
'results': [{'gene': {'ensembl_id': 'ENSG00000122861', 'name': 'PLAU'},
'gwas': {'id': 'ieu-a-12', 'trait': "Crohn's disease"},
'r': {'beta': -0.418305477297394,
'p': 4.72411883038432e-13,
'rsid': 'rs2227551',
'se': 0.0578328522072584}},
{'gene': {'ensembl_id': 'ENSG00000122861', 'name': 'PLAU'},
'gwas': {'id': 'ieu-a-294',
'trait': 'Inflammatory bowel disease'},
'r': {'beta': -0.287952021853336,
'p': 1.2777334577962101e-09,
'rsid': 'rs2227551',
'se': 0.0474372098378583}}]}
2. Query by outcome trait
Script
import requests
url = 'https://api.epigraphdb.org/xqtl/single-snp-mr'
params = {'outcome_trait': "Crohn's disease"}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'MATCH '
'(variant:Variant)-[s:XQTL_SINGLE_SNP_MR_SNP_GENE]-> '
'(gene:Gene)-[r:XQTL_SINGLE_SNP_MR_GENE_GWAS]->(gwas:Gwas) '
'WHERE gwas.trait = "Crohn\'s disease" AND gene.name is '
'not null AND variant.name = r.rsid AND r.qtl_type = '
'"eQTL" AND r.p < 1e-05 RETURN gene {.ensembl_id, '
'.name}, gwas {.id, .trait}, r {.beta, .se, .p, .rsid} '
'ORDER BY r.p ;',
'total_seconds': 0.116427},
'results': [{'gene': {'ensembl_id': 'ENSG00000252010', 'name': 'SCARNA5'},
'gwas': {'id': 'ieu-a-12', 'trait': "Crohn's disease"},
'r': {'beta': -1.17330229026402,
'p': 2.8355306794367294e-77,
'rsid': 'rs10210302',
'se': 0.0630580538945707}},
{'gene': {'ensembl_id': 'ENSG00000162594', 'name': 'IL23R'},
'gwas': {'id': 'ieu-a-12', 'trait': "Crohn's disease"},
'r': {'beta': 1.46163646089095,
'p': 1.9953408712365396e-75,
'rsid': 'rs2064689',
'se': 0.0795347351592587}},
{'gene': {'ensembl_id': 'ENSG00000085978', 'name': 'ATG16L1'},
'gwas': {'id': 'ieu-a-12', 'trait': "Crohn's disease"},
'r': {'beta': 1.2079615384271298,
'p': 2.8610995188664594e-72,
'rsid': 'rs10192702',
'se': 0.0671884714816885}},
{'gene': {'ensembl_id': 'ENSG00000168918', 'name': 'INPP5D'},
'gwas': {'id': 'ieu-a-12', 'trait': "Crohn's disease"},
'r': {'beta': -3.5729246848269502,
'p': 1.4757206411231692e-71,
'rsid': 'rs13418066',
'se': 0.199744172231942}},
{'gene': {'ensembl_id': 'ENSG00000167208', 'name': 'SNX20'},
'gwas': {'id': 'ieu-a-12', 'trait': "Crohn's disease"},
'r': {'beta': -3.68434918236263,
'p': 1.1674850313199198e-68,
'rsid': 'rs113593279',
'se': 0.21039410884541696}},
{'gene': {'ensembl_id': 'ENSG00000115415', 'name': 'STAT1'},
'gwas': {'id': 'ieu-a-12',
3. Query by variant
Script
import requests
url = 'https://api.epigraphdb.org/xqtl/single-snp-mr'
params = {'variant': 'rs1250566'}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'MATCH '
'(variant:Variant)-[s:XQTL_SINGLE_SNP_MR_SNP_GENE]-> '
'(gene:Gene)-[r:XQTL_SINGLE_SNP_MR_GENE_GWAS]->(gwas:Gwas) '
'WHERE variant.name = "rs1250566" AND gene.name is not '
'null AND variant.name = r.rsid AND r.qtl_type = "eQTL" '
'AND r.p < 1e-05 RETURN gene {.ensembl_id, .name}, gwas '
'{.id, .trait}, r {.beta, .se, .p, .rsid} ORDER BY r.p '
';',
'total_seconds': 0.039038},
'results': [{'gene': {'ensembl_id': 'ENSG00000167037', 'name': 'SGSM1'},
'gwas': {'id': 'ieu-a-12', 'trait': "Crohn's disease"},
'r': {'beta': 2.14504951732656,
'p': 1.27027097631945e-25,
'rsid': 'rs1250566',
'se': 0.205002386936228}},
{'gene': {'ensembl_id': 'ENSG00000104490', 'name': 'NCALD'},
'gwas': {'id': 'ieu-a-12', 'trait': "Crohn's disease"},
'r': {'beta': 2.44887688594423,
'p': 1.27027097631945e-25,
'rsid': 'rs1250566',
'se': 0.234039169201657}},
{'gene': {'ensembl_id': 'ENSG00000167037', 'name': 'SGSM1'},
'gwas': {'id': 'ieu-a-294',
'trait': 'Inflammatory bowel disease'},
'r': {'beta': 1.5369178644553605,
'p': 4.77192691073105e-20,
'rsid': 'rs1250566',
'se': 0.16762013140528198}},
{'gene': {'ensembl_id': 'ENSG00000104490', 'name': 'NCALD'},
'gwas': {'id': 'ieu-a-294',
'trait': 'Inflammatory bowel disease'},
'r': {'beta': 1.75460874141047,
'p': 4.77192691073105e-20,
'rsid': 'rs1250566',
'se': 0.19136204647104105}},
{'gene': {'ensembl_id': 'ENSG00000167037', 'name': 'SGSM1'},
'gwas': {'id': 'ukb-b-19732',
'trait': 'Non-cancer illness code, self-reported: '
'hypothyroidism/myxoedema'},
'r': {'beta': -0.0378122558152413,
'p': 1.5992576203615e-07,
'rsid': 'rs1250566',
POST /xqtl/single-snp-mr/gene-by-variant
Get the list of genes associated by an instrument SNP, nested per SNP
Params
{'data': <class 'app.apis.xqtl.models.GeneByVariantRequest'>}
1. Query
Script
import requests
url = 'https://api.epigraphdb.org/xqtl/single-snp-mr/gene-by-variant'
data = {'variant_list': ['rs9272544', 'rs242797']}
r = requests.post(url, json=data)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'MATCH '
'(variant:Variant)-[vg:XQTL_SINGLE_SNP_MR_SNP_GENE]-(gene:Gene) '
'-[xqtl:XQTL_SINGLE_SNP_MR_GENE_GWAS]-(gwas:Gwas) WHERE '
"variant.name IN ['rs9272544', 'rs242797'] AND "
"xqtl.rsid = variant.name AND xqtl.qtl_type = 'eQTL' "
'WITH variant.name AS variant, collect(DISTINCT '
'gene.name) AS gene_list RETURN variant, gene_list, '
'size(gene_list) AS n_genes',
'total_seconds': 0.037819},
'results': [{'gene_list': ['CSRP2BP', 'NENF'],
'n_genes': 2,
'variant': 'rs9272544'},
{'gene_list': ['PPP1R3D', 'FAM217B'],
'n_genes': 2,
'variant': 'rs242797'}]}
GET /prs
Polygenic risk scores between GWAS traits
Params
{'pval_threshold': <class 'float'>, 'trait': <class 'str'>}
1. Query
Script
import requests
url = 'https://api.epigraphdb.org/prs'
params = {'trait': 'Body mass index'}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'MATCH (trait:Gwas)-[prs:PRS]-(assoc_trait:Gwas) WHERE '
'trait.trait = "Body mass index" AND prs.p < 1e-05 '
'RETURN trait {.id, .trait}, assoc_trait {.id, .trait}, '
'prs { .beta, .se, .p, .r2, .nsnps, .n, .model } ORDER '
'BY prs.p ;',
'total_seconds': 0.051964},
'results': [{'assoc_trait': {'id': 'ukb-b-9685',
'trait': 'Trunk predicted mass'},
'prs': {'beta': 0.063792367,
'model': 'lm',
'n': 328779,
'nsnps': 251,
'p': 1e-314,
'r2': 0.004351404,
'se': 0.001060756},
'trait': {'id': 'ieu-a-2', 'trait': 'Body mass index'}},
{'assoc_trait': {'id': 'ukb-a-293',
'trait': 'Trunk predicted mass'},
'prs': {'beta': 0.063792367,
'model': 'lm',
'n': 328779,
'nsnps': 251,
'p': 1e-314,
'r2': 0.004351404,
'se': 0.001060756},
'trait': {'id': 'ieu-a-2', 'trait': 'Body mass index'}},
{'assoc_trait': {'id': 'ukb-b-17409',
'trait': 'Trunk fat-free mass'},
'prs': {'beta': 0.06422349,
'model': 'lm',
'n': 328812,
'nsnps': 251,
'p': 1e-314,
'r2': 0.004408133,
'se': 0.00106428},
'trait': {'id': 'ieu-a-2', 'trait': 'Body mass index'}},
{'assoc_trait': {'id': 'ukb-a-292',
'trait': 'Trunk fat-free mass'},
'prs': {'beta': 0.06422349,
'model': 'lm',
'n': 328812,
'nsnps': 251,
'p': 1e-314,
'r2': 0.004408133,
'se': 0.00106428},
'trait': {'id': 'ieu-a-2', 'trait': 'Body mass index'}},
{'assoc_trait': {'id': 'ukb-b-20044', 'trait': 'Trunk fat mass'},
'prs': {'beta': 0.136499121,
'model': 'lm'
POST /protein/ppi
For the list of proteins, returns their **directly** associated proteins in protein-protein-interactions
Params
{'data': <class 'app.apis.protein.models.PpiRequest'>}
1. Query
Script
import requests
url = 'https://api.epigraphdb.org/protein/ppi'
data = {'uniprot_id_list': ['P30793', 'Q9NZM1', 'O95236']}
r = requests.post(url, json=data)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'MATCH '
'p=(protein:Protein)-[r:STRING_INTERACT_WITH]-(assoc_protein:Protein) '
"WHERE protein.uniprot_id IN ['P30793', 'Q9NZM1', "
"'O95236'] RETURN protein {.uniprot_id}, assoc_protein "
'{.uniprot_id}',
'total_seconds': 0.030312},
'results': [{'assoc_protein': {'uniprot_id': 'O14638'},
'protein': {'uniprot_id': 'P30793'}},
{'assoc_protein': {'uniprot_id': 'P20711'},
'protein': {'uniprot_id': 'P30793'}},
{'assoc_protein': {'uniprot_id': 'O43556'},
'protein': {'uniprot_id': 'P30793'}},
{'assoc_protein': {'uniprot_id': 'P00374'},
'protein': {'uniprot_id': 'P30793'}},
{'assoc_protein': {'uniprot_id': 'Q32Q12'},
'protein': {'uniprot_id': 'P30793'}},
{'assoc_protein': {'uniprot_id': 'P22392'},
'protein': {'uniprot_id': 'P30793'}},
{'assoc_protein': {'uniprot_id': 'P05187'},
'protein': {'uniprot_id': 'P30793'}},
{'assoc_protein': {'uniprot_id': 'P22102'},
'protein': {'uniprot_id': 'P30793'}},
{'assoc_protein': {'uniprot_id': 'P07101'},
'protein': {'uniprot_id': 'P30793'}},
{'assoc_protein': {'uniprot_id': 'Q9BY32'},
'protein': {'uniprot_id': 'P30793'}},
{'assoc_protein': {'uniprot_id': 'P50583'},
'protein': {'uniprot_id': 'P30793'}},
{'assoc_protein': {'uniprot_id': 'O95197'},
'protein': {'uniprot_id': 'P30793'}},
{'assoc_protein': {'uniprot_id': 'P05186'},
'protein': {'uniprot_id': 'P30793'}},
{'assoc_protein': {'uniprot_id': 'Q05932'},
'protein': {'uniprot_id': 'P30793'}},
{'assoc_protein': {'uniprot_id': 'Q9NZB8'},
'protein': {'uniprot_id': 'P30793'}},
{'assoc_protein': {'uniprot_id': 'Q5MY95'},
'protein': {'uniprot_id': 'P30793'}},
{'assoc_protein': {'uniprot_id': 'Q9BX66'},
'protein': {'uniprot_id': 'P30793'}},
{'assoc_protein': {'uniprot_id': 'P49961'},
'protein': {'uniprot_id': 'P30793'}},
{'assoc_protein': {'uni
POST /protein/ppi/pairwise
For the list of proteins, returns a graph edgelist where they are connected via protein-protein-interactions, with configurable middle steps
Params
{'data': <class 'app.apis.protein.models.PpiGraphRequest'>}
1. Default query (direct interaction)
Script
import requests
url = 'https://api.epigraphdb.org/protein/ppi/pairwise'
data = {'uniprot_id_list': ['P30793', 'Q9NZM1', 'O95236', 'P32456', 'Q13536', 'Q9NRQ5', 'O60674', 'O14933', 'P32455', 'P40306', 'P42224', 'P28838', 'P23381']}
r = requests.post(url, json=data)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'MATCH '
'p=(protein:Protein)-[r:STRING_INTERACT_WITH*1..1]-(assoc_protein:Protein) '
"WHERE protein.uniprot_id IN ['P30793', 'Q9NZM1', "
"'O95236', 'P32456', 'Q13536', 'Q9NRQ5', 'O60674', "
"'O14933', 'P32455', 'P40306', 'P42224', 'P28838', "
"'P23381'] AND assoc_protein.uniprot_id IN ['P30793', "
"'Q9NZM1', 'O95236', 'P32456', 'Q13536', 'Q9NRQ5', "
"'O60674', 'O14933', 'P32455', 'P40306', 'P42224', "
"'P28838', 'P23381'] RETURN protein.uniprot_id AS "
'protein, assoc_protein.uniprot_id AS assoc_protein, '
'length(p) AS path_size',
'total_seconds': 0.030356},
'results': [{'assoc_protein': 'P32456', 'path_size': 1, 'protein': 'P42224'},
{'assoc_protein': 'P32456', 'path_size': 1, 'protein': 'P42224'},
{'assoc_protein': 'P32455', 'path_size': 1, 'protein': 'P42224'},
{'assoc_protein': 'P32455', 'path_size': 1, 'protein': 'P42224'},
{'assoc_protein': 'O60674', 'path_size': 1, 'protein': 'P42224'},
{'assoc_protein': 'O60674', 'path_size': 1, 'protein': 'P42224'},
{'assoc_protein': 'P28838', 'path_size': 1, 'protein': 'P42224'},
{'assoc_protein': 'P28838', 'path_size': 1, 'protein': 'P42224'},
{'assoc_protein': 'O14933', 'path_size': 1, 'protein': 'P42224'},
{'assoc_protein': 'O14933', 'path_size': 1, 'protein': 'P42224'},
{'assoc_protein': 'P42224', 'path_size': 1, 'protein': 'P32456'},
{'assoc_protein': 'P42224', 'path_size': 1, 'protein': 'P32456'},
{'assoc_protein': 'P32455', 'path_size': 1, 'protein': 'P32456'},
{'assoc_protein': 'P32455', 'path_size': 1, 'protein': 'P32456'},
{'assoc_protein': 'P42224', 'path_size': 1, 'protein': 'P32455'},
{'assoc_protein': 'P42224', 'path_size': 1, 'protein': 'P32455'},
{'assoc_protein': 'P32456', 'path_size': 1, 'protein': 'P32455'},
{'assoc_protein': 'P32456', 'path_size': 1, 'protein': 'P32455'},
{'assoc_protein': 'O14933', 'path_size': 1, 'protein': 'P32455'},
{'assoc_protei
2. With at most 1
intermediate protein
Script
import requests
url = 'https://api.epigraphdb.org/protein/ppi/pairwise'
data = {'uniprot_id_list': ['P30793', 'Q9NZM1', 'O95236', 'P32456', 'Q13536', 'Q9NRQ5', 'O60674', 'O14933', 'P32455', 'P40306', 'P42224', 'P28838', 'P23381'], 'n_intermediate_proteins': 1}
r = requests.post(url, json=data)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'MATCH '
'p=(protein:Protein)-[r:STRING_INTERACT_WITH*1..2]-(assoc_protein:Protein) '
"WHERE protein.uniprot_id IN ['P30793', 'Q9NZM1', "
"'O95236', 'P32456', 'Q13536', 'Q9NRQ5', 'O60674', "
"'O14933', 'P32455', 'P40306', 'P42224', 'P28838', "
"'P23381'] AND assoc_protein.uniprot_id IN ['P30793', "
"'Q9NZM1', 'O95236', 'P32456', 'Q13536', 'Q9NRQ5', "
"'O60674', 'O14933', 'P32455', 'P40306', 'P42224', "
"'P28838', 'P23381'] RETURN protein.uniprot_id AS "
'protein, assoc_protein.uniprot_id AS assoc_protein, '
'length(p) AS path_size',
'total_seconds': 1.015432},
'results': [{'assoc_protein': 'P42224', 'path_size': 2, 'protein': 'P42224'},
{'assoc_protein': 'P42224', 'path_size': 2, 'protein': 'P42224'},
{'assoc_protein': 'P42224', 'path_size': 2, 'protein': 'P42224'},
{'assoc_protein': 'P42224', 'path_size': 2, 'protein': 'P42224'},
{'assoc_protein': 'P42224', 'path_size': 2, 'protein': 'P42224'},
{'assoc_protein': 'P42224', 'path_size': 2, 'protein': 'P42224'},
{'assoc_protein': 'P42224', 'path_size': 2, 'protein': 'P42224'},
{'assoc_protein': 'P42224', 'path_size': 2, 'protein': 'P42224'},
{'assoc_protein': 'P42224', 'path_size': 2, 'protein': 'P42224'},
{'assoc_protein': 'P42224', 'path_size': 2, 'protein': 'P42224'},
{'assoc_protein': 'P42224', 'path_size': 2, 'protein': 'P42224'},
{'assoc_protein': 'P42224', 'path_size': 2, 'protein': 'P42224'},
{'assoc_protein': 'P42224', 'path_size': 2, 'protein': 'P42224'},
{'assoc_protein': 'P42224', 'path_size': 2, 'protein': 'P42224'},
{'assoc_protein': 'P42224', 'path_size': 2, 'protein': 'P42224'},
{'assoc_protein': 'P42224', 'path_size': 2, 'protein': 'P42224'},
{'assoc_protein': 'P42224', 'path_size': 2, 'protein': 'P42224'},
{'assoc_protein': 'P42224', 'path_size': 2, 'protein': 'P42224'},
{'assoc_protein': 'P42224', 'path_size': 2, 'protein': 'P42224'},
{'assoc_protei
POST /protein/in-pathway
For the list of proteins, returns their associated pathway data
Params
{'data': <class 'app.apis.protein.models.PathwayRequest'>}
1. Query
Script
import requests
url = 'https://api.epigraphdb.org/protein/in-pathway'
data = {'uniprot_id_list': ['O14933', 'O60674', 'P32455']}
r = requests.post(url, json=data)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'MATCH '
'p=(protein:Protein)-[r:PROTEIN_IN_PATHWAY]-(pathway:Pathway) '
"WHERE protein.uniprot_id IN ['O14933', 'O60674', "
"'P32455'] RETURN protein.uniprot_id AS uniprot_id, "
'count(p) AS pathway_count, collect(pathway.id) AS '
'pathway_reactome_id',
'total_seconds': 0.026342},
'results': [{'pathway_count': 17,
'pathway_reactome_id': ['R-HSA-983169',
'R-HSA-983168',
'R-HSA-977225',
'R-HSA-936440',
'R-HSA-913531',
'R-HSA-73894',
'R-HSA-73893',
'R-HSA-5656169',
'R-HSA-392499',
'R-HSA-168928',
'R-HSA-168256',
'R-HSA-168249',
'R-HSA-1280218',
'R-HSA-1280215',
'R-HSA-1169410',
'R-HSA-1169408',
'R-HSA-110313'],
'uniprot_id': 'O14933'},
{'pathway_count': 62,
'pathway_reactome_id': ['R-HSA-983231',
'R-HSA-982772',
'R-HSA-9679506',
'R-HSA-9679191',
'R-HSA-9670439',
'R-HSA-9669938',
'R-HSA-9656223',
'R-HSA-9649948',
'R-HSA-9607240',
'R-HSA-913531',
'R-HSA-912526',
'R-HSA-9027284',
'R-HSA-9027283',
'R-HSA-9027277',
'R-HSA-9027276',
'R-HSA-9020956',
GET /gene/druggability/ppi
For a gene, search for its associated druggable genes via protein-protein-interaction (INTACT and STRING)
Params
{'gene_name': <class 'str'>}
1. Query
Script
import requests
url = 'https://api.epigraphdb.org/gene/druggability/ppi'
params = {'gene_name': 'IL23R'}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'MATCH (g1:Gene)-[:GENE_TO_PROTEIN]-(p1:Protein) '
'-[:INTACT_INTERACTS_WITH | STRING_INTERACT_WITH]- '
'(p2:Protein)-[:GENE_TO_PROTEIN]-(g2:Gene) WHERE '
"g1.name = 'IL23R' AND g2.name IS NOT NULL AND "
'g2.druggability_tier IS NOT NULL RETURN DISTINCT g1 '
'{.name}, p1 {.uniprot_id}, p2 {.uniprot_id}, g2 '
'{.name, .druggability_tier} ORDER BY '
'g2.druggability_tier, g2.name',
'total_seconds': 0.036028},
'results': [{'g1': {'name': 'IL23R'},
'g2': {'druggability_tier': 'Tier 1', 'name': 'CSF2'},
'p1': {'uniprot_id': 'Q5VWK5'},
'p2': {'uniprot_id': 'P04141'}},
{'g1': {'name': 'IL23R'},
'g2': {'druggability_tier': 'Tier 1', 'name': 'IFNA1'},
'p1': {'uniprot_id': 'Q5VWK5'},
'p2': {'uniprot_id': 'P01562'}},
{'g1': {'name': 'IL23R'},
'g2': {'druggability_tier': 'Tier 1', 'name': 'IFNG'},
'p1': {'uniprot_id': 'Q5VWK5'},
'p2': {'uniprot_id': 'P01579'}},
{'g1': {'name': 'IL23R'},
'g2': {'druggability_tier': 'Tier 1', 'name': 'IL10'},
'p1': {'uniprot_id': 'Q5VWK5'},
'p2': {'uniprot_id': 'P22301'}},
{'g1': {'name': 'IL23R'},
'g2': {'druggability_tier': 'Tier 1', 'name': 'IL12B'},
'p1': {'uniprot_id': 'Q5VWK5'},
'p2': {'uniprot_id': 'P29460'}},
{'g1': {'name': 'IL23R'},
'g2': {'druggability_tier': 'Tier 1', 'name': 'IL12RB1'},
'p1': {'uniprot_id': 'Q5VWK5'},
'p2': {'uniprot_id': 'P42701'}},
{'g1': {'name': 'IL23R'},
'g2': {'druggability_tier': 'Tier 1', 'name': 'IL13'},
'p1': {'uniprot_id': 'Q5VWK5'},
'p2': {'uniprot_id': 'P35225'}},
{'g1': {'name': 'IL23R'},
'g2': {'druggability_tier': 'Tier 1', 'name': 'IL15'},
'p1': {'uniprot_id': 'Q5VWK5'},
'p2': {'uniprot_id': 'P40933'}},
{'g1': {'name': 'IL23R'},
'g2': {'druggability_tier': 'Tier 1', 'name': 'IL17A'},
'p1': {'uniprot_i
GET /gene/literature
For a gene, search for its literature evidence related to a SemMedDB term
Params
{'gene_name': <class 'str'>, 'object_name': <class 'str'>}
1. Query
Script
import requests
url = 'https://api.epigraphdb.org/gene/literature'
params = {'gene_name': 'IL23R', 'object_name': 'Inflammatory Bowel Diseases'}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'MATCH '
'(gene:Gene)-[:TERM_TO_GENE]-(lt_gene:LiteratureTerm)-[st:SEMMEDDB_PREDICATE]->(lt:LiteratureTerm) '
"WHERE gene.name = 'IL23R' AND lt.name =~ "
'"(?i).*Inflammatory Bowel Diseases.*" WITH gene, '
'lt_gene, st, lt MATCH '
'(triple:LiteratureTriple)-[triple_to_lit:SEMMEDDB_TO_LIT]-(l:Literature) '
'WHERE triple.subject_id = lt_gene.id AND '
'triple.object_id = lt.id AND triple.predicate = '
'st.predicate RETURN gene {.name}, st {.predicate}, lt '
'{.id, .name, .type}, collect(l.id) AS pubmed_id',
'total_seconds': 0.041697},
'results': [{'gene': {'name': 'IL23R'},
'lt': {'id': 'C0021390',
'name': 'Inflammatory Bowel Diseases',
'type': ['dsyn']},
'pubmed_id': ['23131344'],
'st': {'predicate': 'PREDISPOSES'}},
{'gene': {'name': 'IL23R'},
'lt': {'id': 'C0021390',
'name': 'Inflammatory Bowel Diseases',
'type': ['dsyn']},
'pubmed_id': ['21155887', '17484863'],
'st': {'predicate': 'NEG_ASSOCIATED_WITH'}},
{'gene': {'name': 'IL23R'},
'lt': {'id': 'C0021390',
'name': 'Inflammatory Bowel Diseases',
'type': ['dsyn']},
'pubmed_id': ['31728561'],
'st': {'predicate': 'CAUSES'}},
{'gene': {'name': 'IL23R'},
'lt': {'id': 'C0021390',
'name': 'Inflammatory Bowel Diseases',
'type': ['dsyn']},
'pubmed_id': ['21155887',
'18383521',
'18383363',
'25159710',
'18341487',
'18047540',
'19575361',
'17484863',
'19496308',
'18698678',
'18088064',
'19175939',
'19817673',
'29248579',
GET /gene/drugs
Get the associated drugs for a gene.
Params
{'gene_name': <class 'str'>}
1. Query
Script
import requests
url = 'https://api.epigraphdb.org/gene/drugs'
params = {'gene_name': 'TFRC'}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'MATCH '
'(gene:Gene)-[r:OPENTARGETS_DRUG_TO_TARGET|CPIC]-(drug:Drug) '
"WHERE gene.name = 'TFRC' RETURN gene {.name}, r, "
'type(r) AS r_source, drug {.label}',
'total_seconds': 0.020943},
'results': [{'drug': {'label': 'A27.15'},
'gene': {'name': 'TFRC'},
'r': {'action_type': 'ANTAGONIST', 'phase': '1'},
'r_source': 'OPENTARGETS_DRUG_TO_TARGET'},
{'drug': {'label': 'E2.3'},
'gene': {'name': 'TFRC'},
'r': {'action_type': 'INHIBITOR', 'phase': '1'},
'r_source': 'OPENTARGETS_DRUG_TO_TARGET'}]}
GET /ontology/gwas-efo
Map Gwas trait to EFO term, via `GWAS_NLP_EFO`
Params
{'efo_term': typing.Union[str, NoneType],
'fuzzy': <class 'bool'>,
'score_threshold': <class 'float'>,
'trait': typing.Union[str, NoneType]}
1. Default (fuzzy matching)
Script
import requests
url = 'https://api.epigraphdb.org/ontology/gwas-efo'
params = {'trait': 'body mass', 'efo_term': 'body mass'}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'MATCH (gwas:Gwas)-[r:GWAS_NLP_EFO]-(efo:Efo) WHERE '
'r.score > 0.75 AND gwas.trait =~ "(?i).*body mass.*" '
'AND efo.value =~ "(?i).*body mass.*" RETURN gwas {.id, '
'.trait}, r {.score}, efo {.type, .value, .id} ORDER BY '
'r.score DESC',
'total_seconds': 0.080094},
'results': [{'efo': {'id': 'http://www.ebi.ac.uk/efo/EFO_0004340',
'type': 'typed-literal',
'value': 'body mass index'},
'gwas': {'id': 'ieu-a-95', 'trait': 'Body mass index'},
'r': {'score': 1.0}},
{'efo': {'id': 'http://www.ebi.ac.uk/efo/EFO_0004340',
'type': 'typed-literal',
'value': 'body mass index'},
'gwas': {'id': 'ieu-a-1089', 'trait': 'Body mass index'},
'r': {'score': 1.0}},
{'efo': {'id': 'http://www.ebi.ac.uk/efo/EFO_0004340',
'type': 'typed-literal',
'value': 'body mass index'},
'gwas': {'id': 'bbj-a-3', 'trait': 'Body mass index'},
'r': {'score': 1.0}},
{'efo': {'id': 'http://www.ebi.ac.uk/efo/EFO_0004340',
'type': 'typed-literal',
'value': 'body mass index'},
'gwas': {'id': 'ieu-a-835', 'trait': 'Body mass index'},
'r': {'score': 1.0}},
{'efo': {'id': 'http://www.ebi.ac.uk/efo/EFO_0004340',
'type': 'typed-literal',
'value': 'body mass index'},
'gwas': {'id': 'ieu-a-2', 'trait': 'Body mass index'},
'r': {'score': 1.0}},
{'efo': {'id': 'http://www.ebi.ac.uk/efo/EFO_0004340',
'type': 'typed-literal',
'value': 'body mass index'},
'gwas': {'id': 'bbj-a-2', 'trait': 'Body mass index'},
'r': {'score': 1.0}},
{'efo': {'id': 'http://www.ebi.ac.uk/efo/EFO_0004340',
'type': 'typed-literal',
'value': 'body mass index'},
'gwas': {'id': 'ieu-a-94', 'trait': 'Body mass index'},
'r': {'score': 1.0}},
{'efo': {'id': 'http://www.ebi.ac.uk/efo/EFO_0
2. Exact matching
Script
import requests
url = 'https://api.epigraphdb.org/ontology/gwas-efo'
params = {'trait': 'Body mass index', 'fuzzy': False}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'MATCH (gwas:Gwas)-[r:GWAS_NLP_EFO]-(efo:Efo) WHERE '
'r.score > 0.75 AND gwas.trait = "Body mass index" '
'RETURN gwas {.id, .trait}, r {.score}, efo {.type, '
'.value, .id} ORDER BY r.score DESC',
'total_seconds': 0.02598},
'results': [{'efo': {'id': 'http://www.ebi.ac.uk/efo/EFO_0004340',
'type': 'typed-literal',
'value': 'body mass index'},
'gwas': {'id': 'ieu-a-1089', 'trait': 'Body mass index'},
'r': {'score': 1.0}},
{'efo': {'id': 'http://www.ebi.ac.uk/efo/EFO_0004340',
'type': 'typed-literal',
'value': 'body mass index'},
'gwas': {'id': 'ieu-a-974', 'trait': 'Body mass index'},
'r': {'score': 1.0}},
{'efo': {'id': 'http://www.ebi.ac.uk/efo/EFO_0004340',
'type': 'typed-literal',
'value': 'body mass index'},
'gwas': {'id': 'ieu-a-95', 'trait': 'Body mass index'},
'r': {'score': 1.0}},
{'efo': {'id': 'http://www.ebi.ac.uk/efo/EFO_0004340',
'type': 'typed-literal',
'value': 'body mass index'},
'gwas': {'id': 'ebi-a-GCST004904', 'trait': 'Body mass index'},
'r': {'score': 1.0}},
{'efo': {'id': 'http://www.ebi.ac.uk/efo/EFO_0004340',
'type': 'typed-literal',
'value': 'body mass index'},
'gwas': {'id': 'ebi-a-GCST006368', 'trait': 'Body mass index'},
'r': {'score': 1.0}},
{'efo': {'id': 'http://www.ebi.ac.uk/efo/EFO_0004340',
'type': 'typed-literal',
'value': 'body mass index'},
'gwas': {'id': 'bbj-a-2', 'trait': 'Body mass index'},
'r': {'score': 1.0}},
{'efo': {'id': 'http://www.ebi.ac.uk/efo/EFO_0004340',
'type': 'typed-literal',
'value': 'body mass index'},
'gwas': {'id': 'ieu-a-835', 'trait': 'Body mass index'},
'r': {'score': 1.0}},
{'efo': {'id': 'http://www.ebi.ac.uk/efo/EFO_0004340',
'type': 'typed-li
GET /ontology/disease-efo
Map Disease label to EFO term, via `MONDO_MAP_EFO`
Params
{'disease_label': typing.Union[str, NoneType],
'efo_term': typing.Union[str, NoneType],
'fuzzy': <class 'bool'>}
1. Query
Script
import requests
url = 'https://api.epigraphdb.org/ontology/disease-efo'
params = {'disease_label': 'leukemia', 'efo_term': 'leukemia'}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'MATCH (disease:Disease)-[r:MONDO_MAP_EFO]-(efo:Efo) '
'WHERE disease.label =~ "(?i).*leukemia.*" AND '
'efo.value =~ "(?i).*leukemia.*" RETURN disease {.id, '
'.label}, efo {.type, .value, .id}',
'total_seconds': 0.072188},
'results': [{'disease': {'id': 'http://purl.obolibrary.org/obo/MONDO_0010643',
'label': 'acute leukemia (disease)'},
'efo': {'id': 'http://www.ebi.ac.uk/efo/EFO_1000068',
'type': 'typed-literal',
'value': 'Acute Leukemia'}},
{'disease': {'id': 'http://purl.obolibrary.org/obo/MONDO_0020322',
'label': 'acute biphenotypic leukemia'},
'efo': {'id': 'http://www.ebi.ac.uk/efo/EFO_1000828',
'type': 'typed-literal',
'value': 'B- and T-cell mixed leukemia'}},
{'disease': {'id': 'http://purl.obolibrary.org/obo/MONDO_0019461',
'label': 'B-cell prolymphocytic leukemia'},
'efo': {'id': 'http://www.ebi.ac.uk/efo/EFO_1000102',
'type': 'typed-literal',
'value': 'B-Cell Prolymphocytic Leukemia'}},
{'disease': {'id': 'http://purl.obolibrary.org/obo/MONDO_0004947',
'label': 'B-cell acute lymphoblastic leukemia'},
'efo': {'id': 'http://www.ebi.ac.uk/efo/EFO_0000094',
'type': 'typed-literal',
'value': 'B-cell acute lymphoblastic leukemia'}},
{'disease': {'id': 'http://purl.obolibrary.org/obo/MONDO_0006115',
'label': 'blast phase chronic myelogenous leukemia, '
'BCR-ABL1 positive'},
'efo': {'id': 'http://www.ebi.ac.uk/efo/EFO_1000131',
'type': 'typed-literal',
'value': 'Blast Phase Chronic Myelogenous Leukemia, '
'BCR-ABL1 Positive'}},
{'disease': {'id': 'http://purl.obolibrary.org/obo/MONDO_0006147',
'label': 'chronic eosinophilic leukemia, not '
'otherwise specified'},
'efo': {'id': 'http://www.ebi.ac
GET /ontology/gwas-efo-disease
Map Gwas trait to Disease label, via Efo term.
Params
{'disease_label': typing.Union[str, NoneType],
'efo_term': typing.Union[str, NoneType],
'fuzzy': <class 'bool'>,
'score_threshold': <class 'float'>,
'trait': typing.Union[str, NoneType]}
1. By trait and disease_label
Script
import requests
url = 'https://api.epigraphdb.org/ontology/gwas-efo-disease'
params = {'trait': 'infectious disease', 'disease_label': 'infectious disease', 'score_threshold': 0.7}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'MATCH (gwas:Gwas)-[ge:GWAS_NLP_EFO]-(efo:Efo) '
'-[ed:MONDO_MAP_EFO]-(disease:Disease) WHERE ge.score > '
'0.7 AND gwas.trait =~ "(?i).*infectious disease.*" AND '
'disease.label =~ "(?i).*infectious disease.*" RETURN '
'gwas {.id, .trait}, ge {.score}, efo {.type, .value, '
'.id}, disease {.id, .label}',
'total_seconds': 0.089583},
'results': [{'disease': {'id': 'http://purl.obolibrary.org/obo/MONDO_0005550',
'label': 'infectious disease'},
'efo': {'id': 'http://www.ebi.ac.uk/efo/EFO_0005741',
'type': 'typed-literal',
'value': 'infectious disease'},
'ge': {'score': 0.7459719},
'gwas': {'id': 'finn-a-AB1_OTHER_INFECTIONS',
'trait': 'Other infectious diseases'}},
{'disease': {'id': 'http://purl.obolibrary.org/obo/MONDO_0005550',
'label': 'infectious disease'},
'efo': {'id': 'http://www.ebi.ac.uk/efo/EFO_0005741',
'type': 'typed-literal',
'value': 'infectious disease'},
'ge': {'score': 0.7459719},
'gwas': {'id': 'ukb-b-7375',
'trait': 'Main speciality of consultant (recoded): '
'Infectious diseases'}},
{'disease': {'id': 'http://purl.obolibrary.org/obo/MONDO_0005786',
'label': 'Hepadnaviridae infectious disease'},
'efo': {'id': 'http://www.ebi.ac.uk/efo/EFO_0007301',
'type': 'typed-literal',
'value': 'Hepadnaviridae infectious disease'},
'ge': {'score': 0.7315803000000001},
'gwas': {'id': 'finn-a-AB1_OTHER_INFECTIONS',
'trait': 'Other infectious diseases'}},
{'disease': {'id': 'http://purl.obolibrary.org/obo/MONDO_0005786',
'label': 'Hepadnaviridae infectious disease'},
'efo': {'id': 'http://www.ebi.ac.uk/efo/EFO_0007301',
'type': 'typed-literal',
'value': 'Hepadnaviridae infectious disease'},
'ge': {'score':
2. By trait and efo_term
Script
import requests
url = 'https://api.epigraphdb.org/ontology/gwas-efo-disease'
params = {'trait': 'insomnia', 'efo_term': 'insomnia', 'score_threshold': 0.7}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'MATCH (gwas:Gwas)-[ge:GWAS_NLP_EFO]-(efo:Efo) '
'-[ed:MONDO_MAP_EFO]-(disease:Disease) WHERE ge.score > '
'0.7 AND gwas.trait =~ "(?i).*insomnia.*" AND efo.value '
'=~ "(?i).*insomnia.*" RETURN gwas {.id, .trait}, ge '
'{.score}, efo {.type, .value, .id}, disease {.id, '
'.label}',
'total_seconds': 0.074328},
'results': [{'disease': {'id': 'http://purl.obolibrary.org/obo/MONDO_0013600',
'label': 'insomnia (disease)'},
'efo': {'id': 'http://www.ebi.ac.uk/efo/EFO_0004698',
'type': 'typed-literal',
'value': 'insomnia'},
'ge': {'score': 0.8038048000000001},
'gwas': {'id': 'ebi-a-GCST006488',
'trait': 'Insomnia complaints'}},
{'disease': {'id': 'http://purl.obolibrary.org/obo/MONDO_0013600',
'label': 'insomnia (disease)'},
'efo': {'id': 'http://www.ebi.ac.uk/efo/EFO_0004698',
'type': 'typed-literal',
'value': 'insomnia'},
'ge': {'score': 0.9299452},
'gwas': {'id': 'ukb-a-13', 'trait': 'Sleeplessness / insomnia'}},
{'disease': {'id': 'http://purl.obolibrary.org/obo/MONDO_0013600',
'label': 'insomnia (disease)'},
'efo': {'id': 'http://www.ebi.ac.uk/efo/EFO_0004698',
'type': 'typed-literal',
'value': 'insomnia'},
'ge': {'score': 0.8038048000000001},
'gwas': {'id': 'ebi-a-GCST006487',
'trait': 'Insomnia complaints'}},
{'disease': {'id': 'http://purl.obolibrary.org/obo/MONDO_0013600',
'label': 'insomnia (disease)'},
'efo': {'id': 'http://www.ebi.ac.uk/efo/EFO_0004698',
'type': 'typed-literal',
'value': 'insomnia'},
'ge': {'score': 0.9299452},
'gwas': {'id': 'ukb-b-3957',
'trait': 'Sleeplessness / insomnia'}},
{'disease': {'id': 'http://purl.obolibrary.org/obo/MONDO_0013600',
'label': 'insomnia (d
3. By efo_term and disease_label
Script
import requests
url = 'https://api.epigraphdb.org/ontology/gwas-efo-disease'
params = {'efo_term': 'insomnia', 'disease_label': 'insomnia', 'score_threshold': 0.7}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'MATCH (gwas:Gwas)-[ge:GWAS_NLP_EFO]-(efo:Efo) '
'-[ed:MONDO_MAP_EFO]-(disease:Disease) WHERE ge.score > '
'0.7 AND efo.value =~ "(?i).*insomnia.*" AND '
'disease.label =~ "(?i).*insomnia.*" RETURN gwas {.id, '
'.trait}, ge {.score}, efo {.type, .value, .id}, '
'disease {.id, .label}',
'total_seconds': 0.072451},
'results': [{'disease': {'id': 'http://purl.obolibrary.org/obo/MONDO_0013600',
'label': 'insomnia (disease)'},
'efo': {'id': 'http://www.ebi.ac.uk/efo/EFO_0004698',
'type': 'typed-literal',
'value': 'insomnia'},
'ge': {'score': 0.8038048000000001},
'gwas': {'id': 'ebi-a-GCST006488',
'trait': 'Insomnia complaints'}},
{'disease': {'id': 'http://purl.obolibrary.org/obo/MONDO_0013600',
'label': 'insomnia (disease)'},
'efo': {'id': 'http://www.ebi.ac.uk/efo/EFO_0004698',
'type': 'typed-literal',
'value': 'insomnia'},
'ge': {'score': 0.9299452},
'gwas': {'id': 'ukb-a-13', 'trait': 'Sleeplessness / insomnia'}},
{'disease': {'id': 'http://purl.obolibrary.org/obo/MONDO_0013600',
'label': 'insomnia (disease)'},
'efo': {'id': 'http://www.ebi.ac.uk/efo/EFO_0004698',
'type': 'typed-literal',
'value': 'insomnia'},
'ge': {'score': 0.8038048000000001},
'gwas': {'id': 'ebi-a-GCST006487',
'trait': 'Insomnia complaints'}},
{'disease': {'id': 'http://purl.obolibrary.org/obo/MONDO_0013600',
'label': 'insomnia (disease)'},
'efo': {'id': 'http://www.ebi.ac.uk/efo/EFO_0004698',
'type': 'typed-literal',
'value': 'insomnia'},
'ge': {'score': 0.9299452},
'gwas': {'id': 'ukb-b-3957',
'trait': 'Sleeplessness / insomnia'}},
{'disease': {'id': 'http://purl.obolibrary.org/obo/MONDO_0013600',
'label': 'insomnia
GET /literature/gene
For a gene, search for its literature evidence related to a SemMedDB term
Params
{'gene_name': <class 'str'>, 'object_name': <class 'str'>}
1. Query
Script
import requests
url = 'https://api.epigraphdb.org/literature/gene'
params = {'gene_name': 'IL23R', 'object_name': 'Inflammatory bowel disease'}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'MATCH '
'(gene:Gene)-[:TERM_TO_GENE]-(lt_gene:LiteratureTerm)-[st:SEMMEDDB_PREDICATE]->(lt:LiteratureTerm) '
"WHERE gene.name = 'IL23R' AND lt.name =~ "
'"(?i).*Inflammatory bowel disease.*" WITH gene, '
'lt_gene, st, lt MATCH '
'(triple:LiteratureTriple)-[triple_to_lit:SEMMEDDB_TO_LIT]-(l:Literature) '
'WHERE triple.subject_id = lt_gene.id AND '
'triple.object_id = lt.id AND triple.predicate = '
'st.predicate RETURN gene {.name}, st {.predicate}, lt '
'{.id, .name, .type}, collect(l.id) AS pubmed_id',
'total_seconds': 0.040453},
'results': [{'gene': {'name': 'IL23R'},
'lt': {'id': 'C0021390',
'name': 'Inflammatory Bowel Diseases',
'type': ['dsyn']},
'pubmed_id': ['23131344'],
'st': {'predicate': 'PREDISPOSES'}},
{'gene': {'name': 'IL23R'},
'lt': {'id': 'C0021390',
'name': 'Inflammatory Bowel Diseases',
'type': ['dsyn']},
'pubmed_id': ['21155887', '17484863'],
'st': {'predicate': 'NEG_ASSOCIATED_WITH'}},
{'gene': {'name': 'IL23R'},
'lt': {'id': 'C0021390',
'name': 'Inflammatory Bowel Diseases',
'type': ['dsyn']},
'pubmed_id': ['31728561'],
'st': {'predicate': 'CAUSES'}},
{'gene': {'name': 'IL23R'},
'lt': {'id': 'C0021390',
'name': 'Inflammatory Bowel Diseases',
'type': ['dsyn']},
'pubmed_id': ['21155887',
'18383521',
'18383363',
'25159710',
'18341487',
'18047540',
'19575361',
'17484863',
'19496308',
'18698678',
'18088064',
'19175939',
'19817673',
'29248579',
GET /literature/gwas
Search for literature evidence of a Gwas trait via semmed. - `semmed_triple_id`: search for a specific semmed triple id (see EpiGraphDB documentation) - `semmed_predicates`: list of predicates for **whitelist** - `by_gwas_id`: False. If True search by `Gwas.id` - `fuzzy`: True. By default fuzzy match trait name. - `skip`: pagination
Params
{'by_gwas_id': <class 'bool'>,
'fuzzy': <class 'bool'>,
'gwas_id': typing.Union[str, NoneType],
'limit': <class 'int'>,
'pval_threshold': <class 'float'>,
'semmed_predicates': typing.List[str],
'semmed_triple_id': typing.Union[str, NoneType],
'skip': <class 'int'>,
'trait': typing.Union[str, NoneType]}
1. Search by trait name
Script
import requests
url = 'https://api.epigraphdb.org/literature/gwas'
params = {'trait': 'Sleep duration', 'fuzzy': False}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'MATCH '
'(gwas:Gwas)-[gs:GWAS_TO_LITERATURE_TRIPLE]->(triple:LiteratureTriple) '
'-[sl:SEMMEDDB_TO_LIT]->(lit:Literature) WHERE '
'gwas.trait = "Sleep duration" AND gs.pval < 0.001 WITH '
'gwas, triple, lit, gs MATCH '
'(gwas)-[gl:GWAS_TO_LITERATURE]-(lit) RETURN gwas {.id, '
'.trait}, gs {.pval, .localCount}, triple {.id, .name, '
'.predicate}, lit {.id} SKIP 0 LIMIT 50',
'total_seconds': 0.03176},
'results': [{'gs': {'localCount': 2, 'pval': 4.129090490384296e-06},
'gwas': {'id': 'ieu-a-1088', 'trait': 'Sleep duration'},
'lit': {'id': '30683158'},
'triple': {'id': 'C0851578:COEXISTS_WITH:C0018099',
'name': 'Sleep Disorders COEXISTS_WITH Gout',
'predicate': 'COEXISTS_WITH'}},
{'gs': {'localCount': 2, 'pval': 2.4785163389458493e-06},
'gwas': {'id': 'ieu-a-1088', 'trait': 'Sleep duration'},
'lit': {'id': '22425576'},
'triple': {'id': 'C0028754:PREDISPOSES:C0751249',
'name': 'Obesity PREDISPOSES Chronic Insomnia',
'predicate': 'PREDISPOSES'}},
{'gs': {'localCount': 2, 'pval': 1.8541137780475587e-05},
'gwas': {'id': 'ieu-a-1088', 'trait': 'Sleep duration'},
'lit': {'id': '12527604'},
'triple': {'id': 'C0030054:TREATS:C0018802',
'name': 'Oxygen TREATS Congestive heart failure',
'predicate': 'TREATS'}},
{'gs': {'localCount': 2, 'pval': 1.8541137780475587e-05},
'gwas': {'id': 'ieu-a-1088', 'trait': 'Sleep duration'},
'lit': {'id': '2817624'},
'triple': {'id': 'C0030054:TREATS:C0018802',
'name': 'Oxygen TREATS Congestive heart failure',
'predicate': 'TREATS'}},
{'gs': {'localCount': 2, 'pval': 4.129090490384296e-06},
'gwas': {'id': 'ieu-a-1088', 'trait': 'Sleep duration'},
'lit': {'id': '19194159'},
'triple': {'id': 'C0033487:STIMULATES:C0628385',
'name'
2. Search by id
Script
import requests
url = 'https://api.epigraphdb.org/literature/gwas'
params = {'gwas_id': 'ieu-a-1088', 'by_gwas_id': True}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'MATCH '
'(gwas:Gwas)-[gs:GWAS_TO_LITERATURE_TRIPLE]->(triple:LiteratureTriple) '
'-[sl:SEMMEDDB_TO_LIT]->(lit:Literature) WHERE gwas.id '
'= "ieu-a-1088" AND gs.pval < 0.001 WITH gwas, triple, '
'lit, gs MATCH (gwas)-[gl:GWAS_TO_LITERATURE]-(lit) '
'RETURN gwas {.id, .trait}, gs {.pval, .localCount}, '
'triple {.id, .name, .predicate}, lit {.id} SKIP 0 '
'LIMIT 50',
'total_seconds': 0.036351},
'results': [{'gs': {'localCount': 2, 'pval': 4.129090490384296e-06},
'gwas': {'id': 'ieu-a-1088', 'trait': 'Sleep duration'},
'lit': {'id': '30683158'},
'triple': {'id': 'C0851578:COEXISTS_WITH:C0018099',
'name': 'Sleep Disorders COEXISTS_WITH Gout',
'predicate': 'COEXISTS_WITH'}},
{'gs': {'localCount': 2, 'pval': 2.4785163389458493e-06},
'gwas': {'id': 'ieu-a-1088', 'trait': 'Sleep duration'},
'lit': {'id': '22425576'},
'triple': {'id': 'C0028754:PREDISPOSES:C0751249',
'name': 'Obesity PREDISPOSES Chronic Insomnia',
'predicate': 'PREDISPOSES'}},
{'gs': {'localCount': 2, 'pval': 1.8541137780475587e-05},
'gwas': {'id': 'ieu-a-1088', 'trait': 'Sleep duration'},
'lit': {'id': '12527604'},
'triple': {'id': 'C0030054:TREATS:C0018802',
'name': 'Oxygen TREATS Congestive heart failure',
'predicate': 'TREATS'}},
{'gs': {'localCount': 2, 'pval': 1.8541137780475587e-05},
'gwas': {'id': 'ieu-a-1088', 'trait': 'Sleep duration'},
'lit': {'id': '2817624'},
'triple': {'id': 'C0030054:TREATS:C0018802',
'name': 'Oxygen TREATS Congestive heart failure',
'predicate': 'TREATS'}},
{'gs': {'localCount': 2, 'pval': 4.129090490384296e-06},
'gwas': {'id': 'ieu-a-1088', 'trait': 'Sleep duration'},
'lit': {'id': '19194159'},
'triple': {'id': 'C0033487:STIMULATES:C0628385',
'name': 'Pro
3. Search by id and semmed triple id
Script
import requests
url = 'https://api.epigraphdb.org/literature/gwas'
params = {'gwas_id': 'ieu-a-1088', 'semmed_triple_id': 'C0060135:INTERACTS_WITH:C0001962', 'by_gwas_id': True}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'MATCH '
'(gwas:Gwas)-[gs:GWAS_TO_LITERATURE_TRIPLE]->(triple:LiteratureTriple) '
'-[sl:SEMMEDDB_TO_LIT]->(lit:Literature) WHERE gwas.id '
'= "ieu-a-1088" AND triple.id = '
'"C0060135:INTERACTS_WITH:C0001962" AND gs.pval < 0.001 '
'WITH gwas, triple, lit, gs MATCH '
'(gwas)-[gl:GWAS_TO_LITERATURE]-(lit) RETURN gwas {.id, '
'.trait}, gs {.pval, .localCount}, triple {.id, .name, '
'.predicate}, lit {.id} SKIP 0 LIMIT 50',
'total_seconds': 0.027241},
'results': [{'gs': {'localCount': 2, 'pval': 2.4785163389458493e-06},
'gwas': {'id': 'ieu-a-1088', 'trait': 'Sleep duration'},
'lit': {'id': '15481247'},
'triple': {'id': 'C0060135:INTERACTS_WITH:C0001962',
'name': 'felbamate INTERACTS_WITH Ethanol',
'predicate': 'INTERACTS_WITH'}}]}
4. Search by trait name and filter predicate
Script
import requests
url = 'https://api.epigraphdb.org/literature/gwas'
params = {'trait': 'Sleep duration', 'semmed_predicates': ['COEXISTS_WITH', 'TREATS'], 'fuzzy': False}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'MATCH '
'(gwas:Gwas)-[gs:GWAS_TO_LITERATURE_TRIPLE]->(triple:LiteratureTriple) '
'-[sl:SEMMEDDB_TO_LIT]->(lit:Literature) WHERE '
'gwas.trait = "Sleep duration" AND gs.pval < 0.001 AND '
"triple.predicate IN ['COEXISTS_WITH','TREATS'] WITH "
'gwas, triple, lit, gs MATCH '
'(gwas)-[gl:GWAS_TO_LITERATURE]-(lit) RETURN gwas {.id, '
'.trait}, gs {.pval, .localCount}, triple {.id, .name, '
'.predicate}, lit {.id} SKIP 0 LIMIT 50',
'total_seconds': 0.03823},
'results': [{'gs': {'localCount': 2, 'pval': 4.129090490384296e-06},
'gwas': {'id': 'ieu-a-1088', 'trait': 'Sleep duration'},
'lit': {'id': '30683158'},
'triple': {'id': 'C0851578:COEXISTS_WITH:C0018099',
'name': 'Sleep Disorders COEXISTS_WITH Gout',
'predicate': 'COEXISTS_WITH'}},
{'gs': {'localCount': 2, 'pval': 1.8541137780475587e-05},
'gwas': {'id': 'ieu-a-1088', 'trait': 'Sleep duration'},
'lit': {'id': '12527604'},
'triple': {'id': 'C0030054:TREATS:C0018802',
'name': 'Oxygen TREATS Congestive heart failure',
'predicate': 'TREATS'}},
{'gs': {'localCount': 2, 'pval': 1.8541137780475587e-05},
'gwas': {'id': 'ieu-a-1088', 'trait': 'Sleep duration'},
'lit': {'id': '2817624'},
'triple': {'id': 'C0030054:TREATS:C0018802',
'name': 'Oxygen TREATS Congestive heart failure',
'predicate': 'TREATS'}},
{'gs': {'localCount': 2, 'pval': 2.7170377494970012e-05},
'gwas': {'id': 'ieu-a-1088', 'trait': 'Sleep duration'},
'lit': {'id': '14655910'},
'triple': {'id': 'C1436328:TREATS:C0751249',
'name': 'Eszopiclone TREATS Chronic Insomnia',
'predicate': 'TREATS'}},
{'gs': {'localCount': 2, 'pval': 2.7170377494970012e-05},
'gwas': {'id': 'ieu-a-1088', 'trait': 'Sleep duration'},
'lit': {'id': '19300573'},
'triple': {'i
GET /literature/gwas/pairwise
Return information of traits in a Subject-Predicate-Object association graph. Args: - `blacklist` (True) and `semmantic_types`: The list of [semmantic types](https://mmtx.nlm.nih.gov/MMTx/semanticTypes.shtml) to exclude (`blacklist`=True) or include (`blacklist`=False). Leave `semmantic_types` blank to disable this. - `by_gwas_id` (False): If True search by `Gwas.id`. - `fuzzy` (True): By default fuzzy match trait name.
Params
{'assoc_gwas_id': typing.Union[str, NoneType],
'assoc_trait': typing.Union[str, NoneType],
'blacklist': <class 'bool'>,
'by_gwas_id': <class 'bool'>,
'fuzzy': <class 'bool'>,
'gwas_id': typing.Union[str, NoneType],
'limit': <class 'int'>,
'pval_threshold': <class 'float'>,
'semmantic_types': typing.List[str],
'skip': <class 'int'>,
'trait': typing.Union[str, NoneType]}
1. Search by trait name
Script
import requests
url = 'https://api.epigraphdb.org/literature/gwas/pairwise'
params = {'trait': 'Sleep duration', 'assoc_trait': 'Coronary heart disease', 'pval_threshold': 0.1, 'blacklist': True, 'semmantic_types': ['nusq', 'dsyn'], 'limit': 10, 'fuzzy': True}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'MATCH '
'(gwas:Gwas)-[gs1:GWAS_TO_LITERATURE_TRIPLE]->(s1:LiteratureTriple) '
'-[:SEMMEDDB_OBJ]->(st:LiteratureTerm)<-[:SEMMEDDB_SUB]- '
'(s2:LiteratureTriple)<-[gs2:GWAS_TO_LITERATURE_TRIPLE]-(assoc_gwas:Gwas) '
'WHERE gwas.trait =~ "(?i).*sleep duration.*" AND '
'assoc_gwas.trait =~ "(?i).*coronary heart disease.*" '
'AND gs1.pval < 0.1 AND gs2.pval < 0.1 AND all(type in '
"st.type where NOT type IN ['nusq','dsyn']) MATCH "
'(s1)-[:SEMMEDDB_SUB]-(st1:LiteratureTerm) MATCH '
'(s2)-[:SEMMEDDB_OBJ]-(st2:LiteratureTerm) RETURN gwas '
'{.id, .trait}, gs1 {.pval, .localCount}, st1 {.name}, '
's1 {.id, .subject_id, .object_id, .predicate}, st '
'{.name, .type}, s2 {.id, .subject_id, .object_id, '
'.predicate}, st2 {.name}, gs2 {.pval, .localCount}, '
'assoc_gwas {.id, .trait} SKIP 0 LIMIT 10',
'total_seconds': 31.490255},
'results': [{'assoc_gwas': {'id': 'ieu-a-9',
'trait': 'Coronary heart disease'},
'gs1': {'localCount': 3, 'pval': 5.30766406829485e-09},
'gs2': {'localCount': 2, 'pval': 0.0016273514466348749},
'gwas': {'id': 'ukb-b-4424', 'trait': 'Sleep duration'},
's1': {'id': 'C0074414:INHIBITS:C0166417',
'object_id': 'C0166417',
'predicate': 'INHIBITS',
'subject_id': 'C0074414'},
's2': {'id': 'C0166417:NEG_ASSOCIATED_WITH:C0010054',
'object_id': 'C0010054',
'predicate': 'NEG_ASSOCIATED_WITH',
'subject_id': 'C0166417'},
'st': {'name': 'PPAR gamma', 'type': ['aapp', 'gngm']},
'st1': {'name': 'sevoflurane'},
'st2': {'name': 'Coronary Arteriosclerosis'}},
{'assoc_gwas': {'id': 'ieu-a-9',
'trait': 'Coronary heart disease'},
'gs1': {'localCount': 3, 'pval': 5.30766406829485e-09},
'gs2': {'localCount': 2, 'pval': 0.0016273514466348749},
2. Search by Gwas.id
Script
import requests
url = 'https://api.epigraphdb.org/literature/gwas/pairwise'
params = {'gwas_id': 'ieu-a-1088', 'assoc_gwas_id': 'ieu-a-6', 'by_gwas_id': True, 'pval_threshold': 0.1, 'blacklist': True, 'semmantic_types': ['nusq', 'dsyn'], 'limit': 10, 'fuzzy': False}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'MATCH '
'(gwas:Gwas)-[gs1:GWAS_TO_LITERATURE_TRIPLE]->(s1:LiteratureTriple) '
'-[:SEMMEDDB_OBJ]->(st:LiteratureTerm)<-[:SEMMEDDB_SUB]- '
'(s2:LiteratureTriple)<-[gs2:GWAS_TO_LITERATURE_TRIPLE]-(assoc_gwas:Gwas) '
'WHERE gwas.id = "ieu-a-1088" AND assoc_gwas.id = '
'"ieu-a-6" AND gs1.pval < 0.1 AND gs2.pval < 0.1 AND '
"all(type in st.type where NOT type IN ['nusq','dsyn']) "
'MATCH (s1)-[:SEMMEDDB_SUB]-(st1:LiteratureTerm) MATCH '
'(s2)-[:SEMMEDDB_OBJ]-(st2:LiteratureTerm) RETURN gwas '
'{.id, .trait}, gs1 {.pval, .localCount}, st1 {.name}, '
's1 {.id, .subject_id, .object_id, .predicate}, st '
'{.name, .type}, s2 {.id, .subject_id, .object_id, '
'.predicate}, st2 {.name}, gs2 {.pval, .localCount}, '
'assoc_gwas {.id, .trait} SKIP 0 LIMIT 10',
'total_seconds': 0.058762},
'results': [{'assoc_gwas': {'id': 'ieu-a-6',
'trait': 'Coronary heart disease'},
'gs1': {'localCount': 2, 'pval': 8.663661090846101e-06},
'gs2': {'localCount': 2, 'pval': 0.0016273514466348749},
'gwas': {'id': 'ieu-a-1088', 'trait': 'Sleep duration'},
's1': {'id': '146:INTERACTS_WITH:C0001962',
'object_id': 'C0001962',
'predicate': 'INTERACTS_WITH',
'subject_id': '146'},
's2': {'id': 'C0001962:COEXISTS_WITH:C0003138',
'object_id': 'C0003138',
'predicate': 'COEXISTS_WITH',
'subject_id': 'C0001962'},
'st': {'name': 'ethanol', 'type': ['orch', 'phsu']},
'st1': {'name': 'ADRA1D'},
'st2': {'name': 'Antacids'}},
{'assoc_gwas': {'id': 'ieu-a-6',
'trait': 'Coronary heart disease'},
'gs1': {'localCount': 2, 'pval': 0.00043840087836640037},
'gs2': {'localCount': 2, 'pval': 0.0016273514466348749},
'gwas': {'id': 'ieu-a-1088', 'trait': 'Sleep duration'},
's1': {'id': 'C0020592
3. Whitelist semmantic types
Script
import requests
url = 'https://api.epigraphdb.org/literature/gwas/pairwise'
params = {'gwas_id': 'ieu-a-1088', 'assoc_gwas_id': 'ieu-a-6', 'by_gwas_id': True, 'pval_threshold': 0.1, 'blacklist': False, 'semmantic_types': ['aapp', 'orch'], 'limit': 10, 'fuzzy': False}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'MATCH '
'(gwas:Gwas)-[gs1:GWAS_TO_LITERATURE_TRIPLE]->(s1:LiteratureTriple) '
'-[:SEMMEDDB_OBJ]->(st:LiteratureTerm)<-[:SEMMEDDB_SUB]- '
'(s2:LiteratureTriple)<-[gs2:GWAS_TO_LITERATURE_TRIPLE]-(assoc_gwas:Gwas) '
'WHERE gwas.id = "ieu-a-1088" AND assoc_gwas.id = '
'"ieu-a-6" AND gs1.pval < 0.1 AND gs2.pval < 0.1 AND '
"all(type in st.type where type IN ['aapp','orch']) "
'MATCH (s1)-[:SEMMEDDB_SUB]-(st1:LiteratureTerm) MATCH '
'(s2)-[:SEMMEDDB_OBJ]-(st2:LiteratureTerm) RETURN gwas '
'{.id, .trait}, gs1 {.pval, .localCount}, st1 {.name}, '
's1 {.id, .subject_id, .object_id, .predicate}, st '
'{.name, .type}, s2 {.id, .subject_id, .object_id, '
'.predicate}, st2 {.name}, gs2 {.pval, .localCount}, '
'assoc_gwas {.id, .trait} SKIP 0 LIMIT 10',
'total_seconds': 0.168488},
'results': [{'assoc_gwas': {'id': 'ieu-a-6',
'trait': 'Coronary heart disease'},
'gs1': {'localCount': 3, 'pval': 9.783804421427983e-05},
'gs2': {'localCount': 2, 'pval': 0.0016273514466348749},
'gwas': {'id': 'ieu-a-1088', 'trait': 'Sleep duration'},
's1': {'id': 'C0001962:CONVERTS_TO:C0000966',
'object_id': 'C0000966',
'predicate': 'CONVERTS_TO',
'subject_id': 'C0001962'},
's2': {'id': 'C0000966:STIMULATES:7124',
'object_id': '7124',
'predicate': 'STIMULATES',
'subject_id': 'C0000966'},
'st': {'name': 'Acetaldehyde', 'type': ['orch']},
'st1': {'name': 'ethanol'},
'st2': {'name': 'TNF'}}]}
GET /pqtl/
Returns the MR and other results related to pQTL - `query`: Protein or trait name e.g., ADAM19 or Inflammatory bowel disease - `rtype`: Results type - "simple": Basic summary, - "mrres": MR results, - "sglmr": Single SNP MR results, - "inst": SNP information, - "sense": Sensitivity analysis, - pvalue: MR pvalue threshold - searchflag: - "proteins": Searches for a protein e.g., if query=ADAM19 - "traits": Searches for a specific trait e.g., if query=Inflammatory bowel disease
Params
{'pvalue': <class 'float'>,
'query': <class 'str'>,
'rtype': <enum 'RtypeInput'>,
'searchflag': <enum 'SearchflagInput'>}
1. Search protein
Script
import requests
url = 'https://api.epigraphdb.org/pqtl/'
params = {'query': 'ADAM15', 'rtype': 'simple', 'searchflag': 'proteins'}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'MATCH (e:Exposure)<-[n:SENS_EXP]-(s)-[m:SENS_OUT]-> '
'(o:Outcome)<-[r:MR]-(e:Exposure)<-[i:INST_EXP]-() '
'WHERE e.expID = "ADAM15" AND r.pvalue < toFloat("0.5") '
'AND s.rs_ID = i.rs_ID RETURN DISTINCT s.expID AS '
'expID, s.outID AS outID, s.outID_mrbase AS '
'outID_mrbase, r.nsnp AS nsnp, r.pvalue AS pvalue, '
's.rs_ID AS rsID, s.direction AS direction, '
's.steiger_pvalue AS steiger_pvalue, s.coloc_prob AS '
'coloc_prob, r.beta AS beta, r.method AS method, '
'i.trans_cis AS trans_cis, toFloat(r.mr_hetero_pvalue) '
'AS q_pvalue, s.ld_score AS ld_check, r.se AS se ORDER '
'BY pvalue, outID;',
'total_seconds': None},
'results': [{'beta': -0.243289811709403,
'coloc_prob': None,
'direction': 'NA',
'expID': 'ADAM15',
'ld_check': None,
'method': 'Wald ratio',
'nsnp': 1,
'outID': 'Non-cancer illness code self-reported: high '
'cholesterol',
'outID_mrbase': 'UKB-a:108',
'pvalue': 3.74006279226831e-06,
'q_pvalue': None,
'rsID': 'rs7949566',
'se': 0.0525994735936868,
'steiger_pvalue': None,
'trans_cis': 'trans'},
{'beta': 0.0510281875,
'coloc_prob': None,
'direction': 'NA',
'expID': 'ADAM15',
'ld_check': None,
'method': 'Wald ratio',
'nsnp': 1,
'outID': 'Forced expiratory volume in 1-second (FEV1)',
'outID_mrbase': 'UKB-a:337',
'pvalue': 0.00010395449708144,
'q_pvalue': None,
'rsID': 'rs7949566',
'se': 0.013147625,
'steiger_pvalue': None,
'trans_cis': 'trans'},
{'beta': 0.0444993125,
'coloc_prob': None,
'direction': 'NA',
'expID': 'ADAM15',
'ld_check': None,
'method': 'Wald ratio',
'nsnp': 1,
'outID': 'Forced v
2. Search trait
Script
import requests
url = 'https://api.epigraphdb.org/pqtl/'
params = {'query': 'Coronary heart disease', 'rtype': 'simple', 'searchflag': 'traits'}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'MATCH '
'(o:Outcome)<-[n:SENS_OUT]-(s)-[m:SENS_EXP]->(e:Exposure)-[r:MR]-> '
'(o:Outcome)<-[INST_OUT]-()-[i:INST_EXP]->() WHERE '
'o.outID = "Coronary heart disease" AND r.pvalue < '
'toFloat("0.5") AND s.rs_ID = i.rs_ID AND s.expID = '
'i.expID RETURN DISTINCT s.expID AS expID, s.outID AS '
'outID, s.outID_mrbase AS outID_mrbase, r.nsnp AS nsnp, '
'r.pvalue AS pvalue, s.rs_ID AS rsID, s.direction AS '
'direction, s.steiger_pvalue AS steiger_pvalue, '
's.coloc_prob AS coloc_prob, r.beta AS beta, r.method '
'AS method, i.trans_cis AS trans_cis, '
'toFloat(r.mr_hetero_pvalue) AS q_pvalue, s.ld_score AS '
'ld_check, r.se AS se ORDER BY pvalue, expID;',
'total_seconds': None},
'results': [{'beta': 0.252303585657371,
'coloc_prob': None,
'direction': 'TRUE',
'expID': 'LPA',
'ld_check': 1.0,
'method': 'Wald ratio',
'nsnp': 1,
'outID': 'Coronary heart disease',
'outID_mrbase': '7',
'pvalue': 5.38664852468177e-39,
'q_pvalue': None,
'rsID': 'rs55730499',
'se': 0.0193149800796813,
'steiger_pvalue': 0.0,
'trans_cis': 'cis'},
{'beta': 0.554633333333333,
'coloc_prob': None,
'direction': 'TRUE',
'expID': 'B2M',
'ld_check': 0.8514,
'method': 'Wald ratio',
'nsnp': 1,
'outID': 'Coronary heart disease',
'outID_mrbase': '7',
'pvalue': 2.68751026338001e-10,
'q_pvalue': None,
'rsID': 'rs10774625',
'se': 0.0878166666666667,
'steiger_pvalue': 5.34156745501946e-08,
'trans_cis': 'trans'},
{'beta': 0.344278820375335,
'coloc_prob': 0.9850814995,
'direction': 'TRUE',
'expID': 'VCAM1',
'ld_check': None,
'method': 'Wald ratio',
'nsnp': 1,
GET /pqtl/pleio/
Returns the number or the list of associated proteins in the database - `rsid`: SNP rs_ID e.g., rs1260326 - `prflag`: - "count" - "proteins"
Params
{'prflag': <enum 'PrflagInput'>, 'rsid': <class 'str'>}
1. Query
Script
import requests
url = 'https://api.epigraphdb.org/pqtl/pleio/'
params = {'rsid': 'rs1260326', 'prflag': 'proteins'}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'MATCH (i:Instruments)-[INST_EXP]->(e:Exposure) WHERE '
'i.rs_ID = "rs1260326" RETURN DISTINCT e.expID AS '
'expID;',
'total_seconds': None},
'results': [{'expID': 'FST'}, {'expID': 'SAA1'}, {'expID': 'KLKB1'}]}
GET /pqtl/list/
Returns either the list of all proteins or traits in the database - "searchable_entities": {"flag": "Search for 'outcomes' or 'exposures'"}
Params
{'flag': <enum 'ListFlagInput'>}
1. List outcomes
Script
import requests
url = 'https://api.epigraphdb.org/pqtl/list/'
params = {'flag': 'outcomes'}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'match (o:Outcome) return distinct o.outID as outID;',
'total_seconds': None},
'results': [{'outID': 'Cancer code self-reported: basal cell carcinoma'},
{'outID': 'Cancer code self-reported: malignant melanoma'},
{'outID': 'Cancer code self-reported: small intestine or small '
'bowel cancer'},
{'outID': 'Breast cancer (Combined Oncoarray; iCOGS; GWAS meta '
'analysis)'},
{'outID': 'Serum creatinine (eGFRcrea)'},
{'outID': 'Serum cystatin C (eGFRcys)'},
{'outID': 'Eye problems or disorders: Cataract'},
{'outID': 'Eye problems or disorders: Diabetes related eye '
'disease'},
{'outID': 'Eye problems or disorders: Glaucoma'},
{'outID': 'Eye problems or disorders: Injury or trauma resulting '
'in loss of vision'},
{'outID': 'Hearing difficulty or problems: Yes'},
{'outID': 'Subjective well being'},
{'outID': 'Femoral neck bone mineral density'},
{'outID': 'Lumbar spine bone mineral density'},
{'outID': 'ER-positive Breast cancer (Combined Oncoarray; iCOGS; '
'GWAS meta analysis)'},
{'outID': 'ER-negative Breast cancer (Combined Oncoarray; iCOGS; '
'GWAS meta analysis)'},
{'outID': 'Endometrioid ovarian cancer'},
{'outID': 'Forced vital capacity (FVC)'},
{'outID': 'Red blood cell count'},
{'outID': 'Primary sclerosing cholangitis '},
{'outID': 'Non-cancer illness code self-reported: ankylosing '
'spondylitis'},
{'outID': 'Non-cancer illness code self-reported: anxiety or '
'panic attacks'},
{'outID': 'Non-cancer illness code self-reported: arthritis '
'(nos)'},
{'outID': 'Non-cancer illness code self-reported: asthma'},
{'outID': 'Non-cancer illness code self-reported: bladder '
'problem (not cancer)'},
{'outID': 'Non-cancer illness code self-reported: bone disorder'},
{'outID': 'Non-cancer illness code self-reported: chro
2. List exposures
Script
import requests
url = 'https://api.epigraphdb.org/pqtl/list/'
params = {'flag': 'exposures'}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'match (e:Exposure) return distinct e.expID as expID;',
'total_seconds': None},
'results': [{'expID': 'A1CF'},
{'expID': 'ACP1'},
{'expID': 'ACP2'},
{'expID': 'ACP5'},
{'expID': 'ADA2'},
{'expID': 'ADAM15'},
{'expID': 'ADAM23'},
{'expID': 'ADAMTS13'},
{'expID': 'ADAMTS5'},
{'expID': 'ADGRE2'},
{'expID': 'AFM'},
{'expID': 'AGER'},
{'expID': 'AGRP'},
{'expID': 'AGT'},
{'expID': 'AHSG'},
{'expID': 'AIFM1'},
{'expID': 'AKR1A1'},
{'expID': 'AKR1B1'},
{'expID': 'AKR1C1'},
{'expID': 'ALCAM'},
{'expID': 'ALDH3A1'},
{'expID': 'AMBP'},
{'expID': 'AMH'},
{'expID': 'AMY1A'},
{'expID': 'ANG'},
{'expID': 'ANGPTL1'},
{'expID': 'ANGPTL3'},
{'expID': 'ANXA1'},
{'expID': 'ANXA2'},
{'expID': 'APCS'},
{'expID': 'APMAP'},
{'expID': 'APOA5'},
{'expID': 'ARFIP1'},
{'expID': 'ARHGEF10'},
{'expID': 'ART3'},
{'expID': 'ART4'},
{'expID': 'ASAH2;ASAH2B'},
{'expID': 'ASMTL'},
{'expID': 'ASPH'},
{'expID': 'ASPN'},
{'expID': 'ATF6'},
{'expID': 'ATP1B2'},
{'expID': 'ATP2A3'},
{'expID': 'ATP4B'},
{'expID': 'B2M'},
{'expID': 'B3GAT3'},
{'expID': 'B3GNT2'},
{'expID': 'B4GALT1'},
{'expID': 'B4GALT2'},
{'expID': 'B4GAT1'},
{'expID': 'BCAR3'},
{'expID': 'BCL10'},
{'expID': 'BGLAP'},
{'expID': 'BMP6'},
{'expID': 'BPI'},
{'expID': 'BPIFB1'},
{'expID': 'BST1'},
{'expID': 'C10orf10'},
{'expID': 'C17orf78'},
{'expID': 'C1QTNF5'},
{'expID': 'C1S'},
{'expID': 'C6orf89'},
{'expID': 'C7'},
{'expID': 'C8A;C8B;C8G'},
{'expID': 'C8orf33'},
{'expID': 'CA10'},
{'expID': 'CA13'},
{
GET /covid-19/ctda/list/{entity}
List entities
Params
{'entity': <enum 'CovidXqtlList'>}
1. List exposure genes
Script
import requests
url = 'https://api.epigraphdb.org/covid-19/ctda/list/gene'
params = None
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False, 'query': None, 'total_seconds': None},
'results': [{'id': 'ENSG00000204435', 'name': 'CSNK2B'},
{'id': 'ENSG00000095303', 'name': 'PTGS1'},
{'id': 'ENSG00000126001', 'name': 'CEP250'},
{'id': 'ENSG00000075413', 'name': 'MARK3'},
{'id': 'ENSG00000096968', 'name': 'JAK2'},
{'id': 'ENSG00000198056', 'name': 'PRIM1'},
{'id': 'ENSG00000163820', 'name': 'FYCO1'},
{'id': 'ENSG00000233276', 'name': 'GPX1'},
{'id': 'ENSG00000172992', 'name': 'DCAKD'},
{'id': 'ENSG00000109501', 'name': 'WFS1'},
{'id': 'ENSG00000135930', 'name': 'EIF4E2'},
{'id': 'ENSG00000077348', 'name': 'EXOSC5'},
{'id': 'ENSG00000102967', 'name': 'DHODH'},
{'id': 'ENSG00000132842', 'name': 'AP3B1'},
{'id': 'ENSG00000137806', 'name': 'NDUFAF1'},
{'id': 'ENSG00000165661', 'name': 'QSOX2'},
{'id': 'ENSG00000138829', 'name': 'FBN2'},
{'id': 'ENSG00000166170', 'name': 'BAG5'},
{'id': 'ENSG00000135506', 'name': 'OS9'},
{'id': 'ENSG00000090621', 'name': 'PABPC4'},
{'id': 'ENSG00000082781', 'name': 'ITGB5'},
{'id': 'ENSG00000169972', 'name': 'PUSL1'},
{'id': 'ENSG00000072274', 'name': 'TFRC'},
{'id': 'ENSG00000136485', 'name': 'DCAF7'},
{'id': 'ENSG00000104388', 'name': 'RAB2A'},
{'id': 'ENSG00000155393', 'name': 'HEATR3'},
{'id': 'ENSG00000115486', 'name': 'GGCX'},
{'id': 'ENSG00000064763', 'name': 'FAR2'},
{'id': 'ENSG00000011523', 'name': 'CEP68'},
{'id': 'ENSG00000014138', 'name': 'POLA2'},
{'id': 'ENSG00000114030', 'name': 'KPNA1'},
{'id': 'ENSG00000163959', 'name': 'SLC51A'},
{'id': 'ENSG00000007923', 'name': 'DNAJC11'},
{'id': 'ENSG00000127948', 'name': 'POR'},
{'id': 'ENSG00000204386', 'name': 'NEU1'},
{'id': 'ENSG00000156599', 'name': 'ZDHHC5'},
{'id': 'ENSG00000239732', 'name': 'TLR9'},
{'id': 'ENSG00000149428', 'name': 'HYOU1'},
{'id': 'ENSG00000164008', 'name': 'C1orf50'},
{'id': 'ENSG00000116406', 'name': 'EDEM3'},
{'id': 'ENSG00000163964', 'nam
2. List outcome gwas
Script
import requests
url = 'https://api.epigraphdb.org/covid-19/ctda/list/gwas'
params = None
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False, 'query': None, 'total_seconds': None},
'results': [{'id': '1058', 'name': 'Celiac disease'},
{'id': '833', 'name': 'Rheumatoid arthritis'},
{'id': 'SAIGE-557',
'name': 'Intestinal malabsorption (non-celiac)'},
{'id': '1025', 'name': 'Multiple sclerosis'},
{'id': '89', 'name': 'Height'},
{'id': '1112', 'name': 'Primary sclerosing cholangitis'},
{'id': 'UKB-a:336', 'name': 'Forced vital capacity (FVC)'},
{'id': 'UKB-b:19732',
'name': 'Non-cancer illness code, self-reported: '
'hypothyroidism/myxoedema'},
{'id': '1004', 'name': 'Age at menopause'},
{'id': 'UKB-b:18194',
'name': 'Non-cancer illness code, self-reported: ankylosing '
'spondylitis'},
{'id': 'UKB-b:14177',
'name': 'Vascular/heart problems diagnosed by doctor: High blood '
'pressure'},
{'id': 'SAIGE-593', 'name': 'Hematuria'},
{'id': '970', 'name': 'Ulcerative colitis'},
{'id': 'UKB-b:18113',
'name': 'Non-cancer illness code, self-reported: asthma'},
{'id': 'UKB-b:6519', 'name': 'Worrier / anxious feelings'},
{'id': 'UKB-a:249', 'name': 'Weight'},
{'id': 'UKB-b:18275',
'name': 'Hearing difficulty/problems with background noise'},
{'id': 'UKB-b:20544', 'name': 'Nervous feelings'},
{'id': 'UKB-a:360',
'name': 'Systolic blood pressure automated reading'},
{'id': 'SAIGE-715', 'name': 'Other inflammatory spondylopathies'},
{'id': '1008', 'name': 'Platelet count'},
{'id': 'UKB-b:10912',
'name': 'Non-cancer illness code, self-reported: high '
'cholesterol'},
{'id': 'UKB-b:9981', 'name': 'Sensitivity / hurt feelings'},
{'id': '282', 'name': 'Psoriasis'},
{'id': 'UKB-a:580',
'name': 'Diagnoses - main ICD10: R04 Haemorrhage from '
'respiratory passages'},
{'id': '16', 'name': 'Childhood intelligence'},
{'id': 'SAIGE-070.4', 'name': 'Chronic hepatitis'},
{'id': 'UKB-b:17324', 'name': 'Eye problem
3. List tissues
Script
import requests
url = 'https://api.epigraphdb.org/covid-19/ctda/list/tissue'
params = None
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False, 'query': None, 'total_seconds': None},
'results': [{'name': 'Colon_Sigmoid'},
{'name': 'Whole_blood'},
{'name': 'Testis'},
{'name': 'Stomach'},
{'name': 'Lung'},
{'name': 'Colon_Transverse'},
{'name': 'Plasma'},
{'name': 'Small_Intestine_Terminal_Ileum'},
{'name': 'Kidney_Cortex'},
{'name': 'Kidney_Glomerular'},
{'name': 'Kidney_Tubulointerstitial'}]}
GET /covid-19/ctda/single-snp-mr/{entity}
Single SNP MR
Params
{'entity': <enum 'CovidXqtlSingleSnpMrEntity'>,
'pval_threshold': <class 'float'>,
'q': typing.Union[str, NoneType]}
1. By exposure gene
Script
import requests
url = 'https://api.epigraphdb.org/covid-19/ctda/single-snp-mr/gene'
params = {'q': 'ENSG00000102967'}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False, 'query': None, 'total_seconds': None},
'results': [{'SNP': 'rs7186207',
'b': 0.205406736,
'exposure_gene_name': 'DHODH',
'exposure_id': 'ENSG00000102967',
'outcome_id': '301',
'outcome_trait': 'Total cholesterol',
'p': 2.37e-14,
'samplesize': 185717.1,
'se': 0.026925977,
'tissue': 'Lung',
'xQTL': 'eQTL'},
{'SNP': 'rs7186207',
'b': 0.06979717,
'exposure_gene_name': 'DHODH',
'exposure_id': 'ENSG00000102967',
'outcome_id': '301',
'outcome_trait': 'Total cholesterol',
'p': 2.37e-14,
'samplesize': 185717.1,
'se': 0.009149441999999999,
'tissue': 'Testis',
'xQTL': 'eQTL'},
{'SNP': 'rs3213422',
'b': 0.067614573,
'exposure_gene_name': 'DHODH',
'exposure_id': 'ENSG00000102967',
'outcome_id': '301',
'outcome_trait': 'Total cholesterol',
'p': 7.07e-14,
'samplesize': 170451.0,
'se': 0.009031549,
'tissue': 'Colon_Sigmoid',
'xQTL': 'eQTL'},
{'SNP': 'rs3213422',
'b': 0.087059556,
'exposure_gene_name': 'DHODH',
'exposure_id': 'ENSG00000102967',
'outcome_id': '301',
'outcome_trait': 'Total cholesterol',
'p': 7.07e-14,
'samplesize': 170451.0,
'se': 0.011628893999999999,
'tissue': 'Colon_Transverse',
'xQTL': 'eQTL'},
{'SNP': 'rs3213422',
'b': 0.104335773,
'exposure_gene_name': 'DHODH',
'exposure_id': 'ENSG00000102967',
'outcome_id': '301',
'outcome_trait': 'Total cholesterol',
'p': 7.07e-14,
'samplesize': 170451.0,
'se': 0.013936546999999999,
'tissue': 'Stomach',
'xQTL': 'eQTL'},
{'SNP': 'rs3213422',
'b': 0.271522488,
'exposure_gene_name': 'DHODH',
'exposure_id': 'ENSG00000102967',
'outcome_id': '301',
'outcome_trait': '
2. By outcome gwas
Script
import requests
url = 'https://api.epigraphdb.org/covid-19/ctda/single-snp-mr/gwas'
params = {'q': '7', 'pval_threshold': 0.01}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False, 'query': None, 'total_seconds': None},
'results': [{'SNP': 'rs75143203',
'b': -0.40230111,
'exposure_gene_name': 'PTGS1',
'exposure_id': 'ENSG00000095303',
'outcome_id': '7',
'outcome_trait': 'Coronary heart disease',
'p': 0.004701989,
'samplesize': 184305.0,
'se': 0.142318238,
'tissue': 'Whole_blood',
'xQTL': 'eQTL'},
{'SNP': 'rs2273703',
'b': -0.081138410904698,
'exposure_gene_name': 'MARK3',
'exposure_id': 'ENSG00000075413',
'outcome_id': '7',
'outcome_trait': 'Coronary heart disease',
'p': 0.00851448921892339,
'samplesize': 184305.0,
'se': 0.0308398887715727,
'tissue': 'Colon_Transverse',
'xQTL': 'eQTL'},
{'SNP': 'rs704',
'b': 0.063704366,
'exposure_gene_name': 'JAK2',
'exposure_id': 'ENSG00000096968',
'outcome_id': '7',
'outcome_trait': 'Coronary heart disease',
'p': 0.00809804,
'samplesize': 184305.0,
'se': 0.024057952999999997,
'tissue': 'Plasma',
'xQTL': 'pQTL'},
{'SNP': 'rs4855839',
'b': -0.20039510483486603,
'exposure_gene_name': 'GPX1',
'exposure_id': 'ENSG00000233276',
'outcome_id': '7',
'outcome_trait': 'Coronary heart disease',
'p': 0.000348838069844257,
'samplesize': 184305.0,
'se': 0.0560382700380748,
'tissue': 'Colon_Transverse',
'xQTL': 'eQTL'},
{'SNP': 'rs10416743',
'b': 0.281005897204525,
'exposure_gene_name': 'EXOSC5',
'exposure_id': 'ENSG00000077348',
'outcome_id': '7',
'outcome_trait': 'Coronary heart disease',
'p': 0.00106793296194576,
'samplesize': 184305.0,
'se': 0.0858822564326926,
'tissue': 'Lung',
'xQTL': 'eQTL'},
{'SNP': 'rs7186207',
'b': 0.229155448,
'exposure_gene_name': 'DHODH',
'exposure_id': 'E
3. By tissue
Script
import requests
url = 'https://api.epigraphdb.org/covid-19/ctda/single-snp-mr/tissue'
params = {'q': 'Lung'}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False, 'query': None, 'total_seconds': None},
'results': [{'SNP': 'rs7150141',
'b': -0.167833129473753,
'exposure_gene_name': 'MARK3',
'exposure_id': 'ENSG00000075413',
'outcome_id': 'UKB-a:500',
'outcome_trait': 'Heel bone mineral density (BMD) T-score '
'automated',
'p': 5.95892942072884e-13,
'samplesize': 194398.0,
'se': 0.0233055650046683,
'tissue': 'Lung',
'xQTL': 'eQTL'},
{'SNP': 'rs7150141',
'b': -0.110348346566112,
'exposure_gene_name': 'MARK3',
'exposure_id': 'ENSG00000075413',
'outcome_id': 'UKB-a:248',
'outcome_trait': 'Body mass index (BMI)',
'p': 8.552081799386999e-10,
'samplesize': 336107.0,
'se': 0.017988665889829,
'tissue': 'Lung',
'xQTL': 'eQTL'},
{'SNP': 'rs7150141',
'b': -0.339465618318829,
'exposure_gene_name': 'MARK3',
'exposure_id': 'ENSG00000075413',
'outcome_id': '22',
'outcome_trait': 'Schizophrenia',
'p': 2.38420737056187e-05,
'samplesize': 82315.0,
'se': 0.0803375625149276,
'tissue': 'Lung',
'xQTL': 'eQTL'},
{'SNP': 'rs7150141',
'b': -0.39662147980342605,
'exposure_gene_name': 'MARK3',
'exposure_id': 'ENSG00000075413',
'outcome_id': 'SAIGE-317',
'outcome_trait': 'Alcohol-related disorders',
'p': 4.32206723251463e-05,
'samplesize': None,
'se': 0.0969840844774802,
'tissue': 'Lung',
'xQTL': 'eQTL'},
{'SNP': 'rs7150141',
'b': -0.0277843479267843,
'exposure_gene_name': 'MARK3',
'exposure_id': 'ENSG00000075413',
'outcome_id': 'UKB-b:14177',
'outcome_trait': 'Vascular/heart problems diagnosed by doctor: '
'High blood pressure',
'p': 4.56805562049628e-05,
'samplesize': 461880.0,
'se': 0.0068154117842900205,
'tissue': 'Lung
GET /covid-19/ctda/multi-snp-mr/{entity}
Multi SNP MR
Params
{'entity': <enum 'CovidXqtlMultiSnpMrEntity'>,
'pval_threshold': <class 'float'>,
'q': typing.Union[str, NoneType]}
1. By exposure gene
Script
import requests
url = 'https://api.epigraphdb.org/covid-19/ctda/multi-snp-mr/gene'
params = {'q': 'ENSG00000102967'}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False, 'query': None, 'total_seconds': None},
'results': [{'b': 0.095030259,
'exposure_gene_name': 'DHODH',
'exposure_id': 'ENSG00000102967',
'method': 'IVW',
'outcome_id': '7',
'outcome_trait': 'Coronary heart disease',
'p': 0.00034289300000000004,
'samplesize': 184305.0,
'se': 0.026540807000000003,
'tissue': 'Colon_Transverse',
'xQTL': 'eQTL'},
{'b': 0.100579717,
'exposure_gene_name': 'DHODH',
'exposure_id': 'ENSG00000102967',
'method': 'IVW',
'outcome_id': '798',
'outcome_trait': 'Myocardial infarction',
'p': 0.0006516790000000001,
'samplesize': 171875.0,
'se': 0.029502953,
'tissue': 'Colon_Transverse',
'xQTL': 'eQTL'}]}
2. By outcome gwas
Script
import requests
url = 'https://api.epigraphdb.org/covid-19/ctda/multi-snp-mr/gwas'
params = {'q': '7', 'pval_threshold': 0.01}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False, 'query': None, 'total_seconds': None},
'results': [{'b': 0.095030259,
'exposure_gene_name': 'DHODH',
'exposure_id': 'ENSG00000102967',
'method': 'IVW',
'outcome_id': '7',
'outcome_trait': 'Coronary heart disease',
'p': 0.00034289300000000004,
'samplesize': 184305.0,
'se': 0.026540807000000003,
'tissue': 'Colon_Transverse',
'xQTL': 'eQTL'}]}
3. By tissue
Script
import requests
url = 'https://api.epigraphdb.org/covid-19/ctda/multi-snp-mr/tissue'
params = {'q': 'Lung'}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False, 'query': None, 'total_seconds': None},
'results': [{'b': -0.0515283199819485,
'exposure_gene_name': 'DCAKD',
'exposure_id': 'ENSG00000172992',
'method': 'IVW',
'outcome_id': 'UKB-a:360',
'outcome_trait': 'Systolic blood pressure automated reading',
'p': 4.7688066457417e-09,
'samplesize': 317754.0,
'se': 0.008800670078142859,
'tissue': 'Lung',
'xQTL': 'eQTL'},
{'b': -0.0340715341232658,
'exposure_gene_name': 'DCAKD',
'exposure_id': 'ENSG00000172992',
'method': 'IVW',
'outcome_id': 'UKB-a:359',
'outcome_trait': 'Diastolic blood pressure automated reading',
'p': 5.41864181544449e-08,
'samplesize': 317756.0,
'se': 0.00626661035654878,
'tissue': 'Lung',
'xQTL': 'eQTL'},
{'b': -0.00700797598617659,
'exposure_gene_name': 'DCAKD',
'exposure_id': 'ENSG00000172992',
'method': 'IVW',
'outcome_id': 'UKB-b:18113',
'outcome_trait': 'Non-cancer illness code, self-reported: asthma',
'p': 1.4273690003140198e-05,
'samplesize': 462933.0,
'se': 0.00161488675437382,
'tissue': 'Lung',
'xQTL': 'eQTL'},
{'b': -0.007424694051918659,
'exposure_gene_name': 'DCAKD',
'exposure_id': 'ENSG00000172992',
'method': 'IVW',
'outcome_id': 'UKB-b:14486',
'outcome_trait': 'Non-cancer illness code, self-reported: '
'osteoarthritis',
'p': 0.000240802671540149,
'samplesize': 462933.0,
'se': 0.0020220590754309697,
'tissue': 'Lung',
'xQTL': 'eQTL'},
{'b': 0.000706262189885184,
'exposure_gene_name': 'DCAKD',
'exposure_id': 'ENSG00000172992',
'method': 'IVW',
'outcome_id': 'UKB-b:15198',
'outcome_trait': 'Non-cancer illness code, self-reported: '
'fibromyalgia',
'p': 0.00026046667042737903,
'samplesize': 4
GET /xqtl_multi_ancestry_pwmr/list/{entity}
List entities
Params
{'entity': <enum 'Entity'>}
1. Get list of GWAS envolved in the study
Script
import requests
url = 'https://api.epigraphdb.org/xqtl_multi_ancestry_pwmr/list/gwas'
params = None
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False, 'query': None, 'total_seconds': None},
'results': [{'_id': 'gbmi-a-00001-afr-b',
'_name': 'Asthma',
'author': 'Wei Zhou',
'build': 'HG38/GRCh38',
'category': 'Binary',
'consortium': 'GBMI (Global biobank Meta-analysis initiative)',
'mr': 1,
'ncase': 5054,
'ncontrol': 27599,
'nsnp': 24660533,
'population': 'African',
'sample_size': 32653,
'sex': 'Males/Females',
'trait': 'Asthma',
'unit': 'log odds ratio',
'year': 2021},
{'_id': 'gbmi-a-00001-nfe-b',
'_name': 'Asthma',
'author': 'Wei Zhou',
'build': 'HG38/GRCh38',
'category': 'Binary',
'consortium': 'GBMI (Global biobank Meta-analysis initiative)',
'mr': 1,
'ncase': 100736,
'ncontrol': 1118479,
'nsnp': 47156680,
'population': 'European',
'sample_size': 1219215,
'sex': 'Males/Females',
'trait': 'Asthma',
'unit': 'log odds ratio',
'year': 2021},
{'_id': 'gbmi-a-00002-afr-b',
'_name': 'Chronic obstructive pulmonary disease (COPD)',
'author': 'Wei Zhou',
'build': 'HG38/GRCh38',
'category': 'Binary',
'consortium': 'GBMI (Global biobank Meta-analysis initiative)',
'mr': 1,
'ncase': 1978,
'ncontrol': 27699,
'nsnp': 23889093,
'population': 'African',
'sample_size': 29677,
'sex': 'Males/Females',
'trait': 'Chronic obstructive pulmonary disease (COPD)',
'unit': 'log odds ratio',
'year': 2021},
{'_id': 'gbmi-a-00002-nfe-b',
'_name': 'Chronic obstructive pulmonary disease (COPD)',
'author': 'Wei Zhou',
'build': 'HG38/GRCh38',
'category': 'Binary',
'consortium': 'GBMI (Global biobank Meta-analysis initiative)',
'mr': 1,
'ncase': 51231,
'ncontrol': 749346,
'nsnp': 45532064,
'population': 'Eu
2. Get list of genes envolved in the study
Script
import requests
url = 'https://api.epigraphdb.org/xqtl_multi_ancestry_pwmr/list/gene'
params = None
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False, 'query': None, 'total_seconds': None},
'results': [{'_id': 'ENSG00000168685',
'_name': 'IL7R',
'chr': 5,
'description': 'P16871;Interleukin-7 receptor subunit alpha'},
{'_id': 'ENSG00000146701',
'_name': 'MDH2',
'chr': 7,
'description': 'P40926;Malate dehydrogenase/ mitochondrial'},
{'_id': 'ENSG00000023892',
'_name': 'DEF6',
'chr': 6,
'description': 'Q9H4E7;Differentially expressed in FDCP 6 '
'homolog'},
{'_id': 'ENSG00000109854',
'_name': 'HTATIP2',
'chr': 11,
'description': 'Q9BUP3;Oxidoreductase HTATIP2'},
{'_id': 'ENSG00000174080',
'_name': 'CTSF',
'chr': 11,
'description': 'Q9UBX1;Cathepsin F'},
{'_id': 'ENSG00000178096',
'_name': 'BOLA1',
'chr': 1,
'description': 'Q9Y3E2;BolA-like protein 1'},
{'_id': 'ENSG00000164879',
'_name': 'CA3',
'chr': 8,
'description': 'P07451;Carbonic anhydrase 3'},
{'_id': 'ENSG00000254087',
'_name': 'LYN',
'chr': 8,
'description': 'P07948;Tyrosine-protein kinase Lyn/ isoform B'},
{'_id': 'ENSG00000135447',
'_name': 'PPP1R1A',
'chr': 12,
'description': 'Q13522;Protein phosphatase 1 regulatory subunit '
'1A'},
{'_id': 'ENSG00000215845',
'_name': 'TSTD1',
'chr': 1,
'description': 'Q8NFU3;Thiosulfate '
'sulfurtransferase/rhodanese-like '
'domain-containing protein 1'},
{'_id': 'ENSG00000091483',
'_name': 'FH',
'chr': 1,
'description': 'P07954;Fumarate hydratase/ mitochondrial'},
{'_id': 'ENSG00000138430',
'_name': 'OLA1',
'chr': 2,
'description': 'Q9NTK5;Obg-like ATPase 1'},
{'_id': 'ENSG00000181634',
'_name': 'TNFSF15',
'chr': 9,
'description': 'O95150;Tumor necrosis factor ligand supe
GET /xqtl_multi_ancestry_pwmr/xqtl_pwas_mr/{entity}
Main MR evidence
Params
{'entity': <enum 'Entity'>,
'pval_threshold': <class 'float'>,
'q': typing.Union[str, NoneType]}
1. Query study results by GWAS
Script
import requests
url = 'https://api.epigraphdb.org/xqtl_multi_ancestry_pwmr/xqtl_pwas_mr/gwas'
params = {'q': 'gbmi-a-00001-nfe-b', 'pval_threshold': 0.01}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False, 'query': None, 'total_seconds': None},
'results': [{'ancestry': 'European',
'b': 0.135224441,
'chr': 5,
'ci_low': None,
'ci_upp': None,
'description': 'P16871;Interleukin-7 receptor subunit alpha',
'gene_id': 'ENSG00000168685',
'gene_name': 'IL7R',
'gwas_id': 'gbmi-a-00001-nfe-b',
'gwas_name': 'Asthma',
'ldcheck': 1,
'method': 'Wald_ratio',
'moescore': 1,
'nsnp': 1,
'pleiotropy': None,
'protein': 'IL-7 Ra',
'pval': 0.003705908,
'pwcoco': 0,
'se': 0.046594299000000006,
'selection': 'DF+HF',
'seqid': 'SeqId_5089_11'},
{'ancestry': 'European',
'b': -0.256949822,
'chr': 6,
'ci_low': None,
'ci_upp': None,
'description': 'Q9H4E7;Differentially expressed in FDCP 6 '
'homolog',
'gene_id': 'ENSG00000023892',
'gene_name': 'DEF6',
'gwas_id': 'gbmi-a-00001-nfe-b',
'gwas_name': 'Asthma',
'ldcheck': 1,
'method': 'Wald_ratio',
'moescore': 1,
'nsnp': 1,
'pleiotropy': None,
'protein': 'DEFI6',
'pval': 6.429999999999999e-07,
'pwcoco': 1,
'se': 0.051617302999999996,
'selection': 'DF+HF',
'seqid': 'SeqId_14090_23'},
{'ancestry': 'European',
'b': -0.184344691,
'chr': 9,
'ci_low': None,
'ci_upp': None,
'description': 'O95150;Tumor necrosis factor ligand superfamily '
'member 15',
'gene_id': 'ENSG00000181634',
'gene_name': 'TNFSF15',
'gwas_id': 'gbmi-a-00001-nfe-b',
'gwas_name': 'Asthma',
'ldcheck': 1,
'method': 'Wald_ratio',
'moescore': 1,
'nsnp': 1,
'pleiotropy': None,
'protein': 'TNFSF15',
'pval': 0.00021721,
'pwcoco': 0,
'se': 0.049848337,
'selec
2. Query study results by gene
Script
import requests
url = 'https://api.epigraphdb.org/xqtl_multi_ancestry_pwmr/xqtl_pwas_mr/gene'
params = {'q': 'ENSG00000168685', 'pval_threshold': 0.01}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False, 'query': None, 'total_seconds': None},
'results': [{'ancestry': 'European',
'b': 0.135224441,
'chr': 5,
'ci_low': None,
'ci_upp': None,
'description': 'P16871;Interleukin-7 receptor subunit alpha',
'gene_id': 'ENSG00000168685',
'gene_name': 'IL7R',
'gwas_id': 'gbmi-a-00001-nfe-b',
'gwas_name': 'Asthma',
'ldcheck': 1,
'method': 'Wald_ratio',
'moescore': 1,
'nsnp': 1,
'pleiotropy': None,
'protein': 'IL-7 Ra',
'pval': 0.003705908,
'pwcoco': 0,
'se': 0.046594299000000006,
'selection': 'DF+HF',
'seqid': 'SeqId_5089_11'}]}
GET /opengwas/search/id
GWAS recommender for the IEU OpenGWAS Database. For the input gwas_id returns a list of OpenGWAS studies curated in EpiGraphDB.
Params
{'gwas_id': <class 'str'>,
'limit': <class 'int'>,
'return': <class 'app.apis.opengwas.GwasRecommenderRes'>}
1. Recommend OpenGWAS datasets by id, ieu-a-2
Script
import requests
url = 'https://api.epigraphdb.org/opengwas/search/id'
params = {'gwas_id': 'ieu-a-2'}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'empty': False,
'results': [{'id': 'ieu-b-40', 'trait': 'body mass index'},
{'id': 'ieu-a-1089', 'trait': 'Body mass index'},
{'id': 'ieu-a-974', 'trait': 'Body mass index'},
{'id': 'ieu-a-95', 'trait': 'Body mass index'},
{'id': 'ebi-a-GCST004904', 'trait': 'Body mass index'},
{'id': 'ebi-a-GCST006368', 'trait': 'Body mass index'},
{'id': 'bbj-a-2', 'trait': 'Body mass index'},
{'id': 'ieu-a-835', 'trait': 'Body mass index'},
{'id': 'ieu-a-2', 'trait': 'Body mass index'},
{'id': 'ieu-a-785', 'trait': 'Body mass index'},
{'id': 'bbj-a-1', 'trait': 'Body mass index'},
{'id': 'bbj-a-3', 'trait': 'Body mass index'},
{'id': 'ieu-a-94', 'trait': 'Body mass index'},
{'id': 'ieu-a-85', 'trait': 'Extreme body mass index'},
{'id': 'ukb-b-2303', 'trait': 'Body mass index (BMI)'},
{'id': 'ukb-b-19953', 'trait': 'Body mass index (BMI)'},
{'id': 'ukb-a-248', 'trait': 'Body mass index (BMI)'},
{'id': 'ebi-a-GCST004770', 'trait': 'Lean body mass'},
{'id': 'ukb-b-13354', 'trait': 'Whole body fat-free mass'},
{'id': 'ukb-a-266', 'trait': 'Whole body fat-free mass'},
{'id': 'ukb-a-265', 'trait': 'Whole body fat mass'},
{'id': 'ukb-b-19393', 'trait': 'Whole body fat mass'},
{'id': 'ukb-a-292', 'trait': 'Trunk fat-free mass'},
{'id': 'ukb-b-17409', 'trait': 'Trunk fat-free mass'},
{'id': 'ukb-a-267', 'trait': 'Whole body water mass'},
{'id': 'ukb-b-14540', 'trait': 'Whole body water mass'},
{'id': 'ukb-b-20044', 'trait': 'Trunk fat mass'},
{'id': 'ukb-a-291', 'trait': 'Trunk fat mass'},
{'id': 'ieu-a-999', 'trait': 'Body fat'},
{'id': 'ebi-a-GCST003435', 'trait': 'Body fat percentage'}]}
2. Recommend OpenGWAS datasets by id, ieu-a-10
Script
import requests
url = 'https://api.epigraphdb.org/opengwas/search/id'
params = {'gwas_id': 'ieu-a-10'}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'empty': False,
'results': [{'id': 'ebi-a-GCST004132', 'trait': "Crohn's disease"},
{'id': 'ebi-a-GCST003044', 'trait': "Crohn's disease"},
{'id': 'ieu-a-30', 'trait': "Crohn's disease"},
{'id': 'ieu-a-11', 'trait': "Crohn's disease"},
{'id': 'ieu-a-14', 'trait': "Crohn's disease"},
{'id': 'ieu-a-10', 'trait': "Crohn's disease"},
{'id': 'ieu-a-12', 'trait': "Crohn's disease"},
{'id': 'ieu-a-13', 'trait': "Crohn's disease"},
{'id': 'finn-a-K11_CROHN', 'trait': 'Crohn disease'},
{'id': 'ieu-a-975', 'trait': "Paget's disease"},
{'id': 'finn-a-CHRONSMALL',
'trait': "Crohn's disease of small interstine"},
{'id': 'ieu-a-983', 'trait': "Hirschsprung's disease"},
{'id': 'ieu-a-812', 'trait': "Parkinson's disease"},
{'id': 'ieu-a-818', 'trait': "Parkinson's disease"},
{'id': 'finn-a-G6_PARKINSON_EXMORE',
'trait': "Parkinson's disease"},
{'id': 'finn-a-G6_PARKINSON', 'trait': "Parkinson's disease"},
{'id': 'ieu-a-297', 'trait': "Alzheimer's disease"},
{'id': 'finn-a-AD', 'trait': "Alzheimer's disease"},
{'id': 'ieu-a-824', 'trait': "Alzheimer's disease"},
{'id': 'ieu-a-298', 'trait': "Alzheimer's disease"},
{'id': 'finn-a-CHRONLARGE',
'trait': "Crohn's disease of large intestine"},
{'id': 'ieu-b-7', 'trait': 'Parkinson’s disease'},
{'id': 'ukb-b-6548',
'trait': "Illnesses of mother: Parkinson's disease"},
{'id': 'finn-a-PDSTRICT_EXMORE',
'trait': "Parkinson's disease, strict definition"},
{'id': 'finn-a-PDSTRICT',
'trait': "Parkinson's disease, strict definition"},
{'id': 'ukb-b-956',
'trait': "Illnesses of father: Parkinson's disease"},
{'id': 'finn-a-G6_ALZHEIMER', 'trait': 'Alzheimer disease'},
{'id': 'ieu-b-2', 'trait': 'Alzheimer’s disease'},
{'id': 'ukb-b-16943',
'trait': "Illnesses of siblings: Parkinson's disease"},
{'id': 'finn-a-AD_EXMORE',
'trait': "Alzheimer's disease (more excluded)"}]}
Utility endpoints
GET /ping
Test that you are connected to the API.
Params
{'dependencies': <class 'bool'>, 'return': <class 'bool'>}
1. Default
Script
import requests
url = 'https://api.epigraphdb.org/ping'
params = None
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
True
GET /builds
Params
{'return': typing.Dict[str, typing.Any]}
1. Default
Script
import requests
url = 'https://api.epigraphdb.org/builds'
params = None
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'epigraphdb': {'api': '1.0-private',
'database': '1.0',
'overall': '1.0',
'web_app': None},
'pqtl': '3.0'}
GET /meta/schema
Schema of EpiGraphDB Graph.
Params
{'graphviz': <class 'bool'>,
'overwrite': <class 'bool'>,
'plot': <class 'bool'>}
1. Default
Script
import requests
url = 'https://api.epigraphdb.org/meta/schema'
params = {'graphviz': False, 'plot': False}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'connections': [{'count': 2461,
'from_node': 'Drug',
'rel': 'OPENTARGETS_DRUG_TO_DISEASE',
'to_node': 'Disease'},
{'count': 5763,
'from_node': 'Gene',
'rel': 'GENE_TO_DISEASE',
'to_node': 'Disease'},
{'count': 8247,
'from_node': 'Disease',
'rel': 'MONDO_MAP_UMLS',
'to_node': 'LiteratureTerm'},
{'count': 2819,
'from_node': 'Disease',
'rel': 'MONDO_MAP_EFO',
'to_node': 'Efo'},
{'count': 2463,
'from_node': 'Pathway',
'rel': 'PATHWAY_CHILD_OF',
'to_node': 'Pathway'},
{'count': 121873,
'from_node': 'Protein',
'rel': 'PROTEIN_IN_PATHWAY',
'to_node': 'Pathway'},
{'count': 16435,
'from_node': 'LiteratureTerm',
'rel': 'TERM_TO_GENE',
'to_node': 'Gene'},
{'count': 1969,
'from_node': 'LiteratureTerm',
'rel': 'MEDRXIV_PREDICATE',
'to_node': 'LiteratureTerm'},
{'count': 32651,
'from_node': 'LiteratureTriple',
'rel': 'BIORXIV_OBJ',
'to_node': 'LiteratureTerm'},
{'count': 32657,
'from_node': 'LiteratureTriple',
'rel': 'BIORXIV_SUB',
'to_node': 'LiteratureTerm'},
{'count': 5584547,
'from_node': 'LiteratureTerm',
'rel': 'SEMMEDDB_PREDICATE',
'to_node': 'LiteratureTerm'},
{'count': 32648,
'from_node': 'LiteratureTerm',
'rel': 'BIORXIV_PREDICATE',
'to_node': 'LiteratureTerm'},
{'count': 5584547,
'from_node': 'LiteratureTriple',
'rel': 'SEMMEDDB_SUB',
'to_node': 'LiteratureTerm'},
{'count': 5556,
'from_node': 'Gwas',
'rel': 'METAMAP_LITE',
'to_node': 'LiteratureTerm'},
{'count': 5584547,
2. Graphviz format
Script
import requests
url = 'https://api.epigraphdb.org/meta/schema'
params = {'graphviz': True, 'plot': False}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
('digraph "EpiGraphDB schema" {\n'
'\tgraph [rankdir=LR]\n'
'\tnode [shape=record]\n'
'\tDisease [label=< <B> Disease 38,960 </B> | _name: STRING indexed | doid: '
'LIST indexed | umls: LIST | label: STRING indexed | efo: LIST | '
'definition: STRING | _source: LIST | id: STRING indexed unique | _id: '
'STRING indexed | icd9: LIST | mesh: LIST | icd10: LIST >]\n'
'\tPathway [label=< <B> Pathway 2,441 </B> | _name: STRING indexed | name: '
'STRING indexed | _source: LIST | id: STRING indexed unique | _id: STRING '
'indexed | url: STRING >]\n'
'\tLiteratureTerm [label=< <B> LiteratureTerm 108,905 </B> | _name: STRING '
'indexed | name: STRING indexed | _source: LIST | id: STRING indexed '
'unique | _id: STRING indexed | type: LIST indexed >]\n'
'\tGene [label=< <B> Gene 57,737 </B> | druggability_tier: STRING indexed | '
'_name: STRING indexed | small_mol_druggable: STRING | start: INTEGER | '
'description: STRING | ensembl_id: STRING indexed unique | type: STRING | '
'chr: STRING indexed | bio_druggable: STRING | adme_gene: STRING | name: '
'STRING indexed | _source: LIST | end: INTEGER | _id: STRING indexed | '
'biomart_source: STRING >]\n'
'\tLiteratureTriple [label=< <B> LiteratureTriple 5,609,945 </B> | '
'subject_id: STRING indexed | predicate: STRING indexed | _name: STRING '
'indexed | name: STRING indexed | _source: LIST | id: STRING indexed '
'unique | _id: STRING indexed | object_id: STRING indexed >]\n'
'\tLiterature [label=< <B> Literature 3,995,672 </B> | issn: STRING | '
'_name: STRING indexed | year: INTEGER | _source: LIST | id: STRING '
'indexed | _id: STRING indexed | dp: STRING | title: STRING | edat: '
'STRING | doi: STRING >]\n'
'\tProtein [label=< <B> Protein 20,280 </B> | name: STRING indexed | '
'_source: LIST | uniprot_id: STRING indexed unique | _id: STRING indexed | '
'_name: STRING indexed >]\n'
'\tVariant [label=< <B> Variant 99,005 </B> | ref: STRING | _name: STRING '
'indexed | pos: INTEGER indexed | build: STRING | name: STRING indexed '
'unique | alt: STRING | _source: LIST | _id: STRING indexed | chr: '
'STRING indexed >]\n'
'\tEfo [label=< <B> Efo 25,390 </B> | _name: STRING indexed | _source: '
'LIST | id: STRING indexed unique | _id: STRING indexed | type: STRING | '
'value: STR
GET /meta/nodes/list
List meta nodes
Params
{}
1. Query
Script
import requests
url = 'https://api.epigraphdb.org/meta/nodes/list'
params = None
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
['Disease',
'Drug',
'Efo',
'Gene',
'Gwas',
'Literature',
'LiteratureTerm',
'Pathway',
'Protein',
'Tissue',
'Variant']
GET /meta/nodes/id-name-schema
Show the current id / name schema for meta nodes.
Params
{}
1. Query
Script
import requests
url = 'https://api.epigraphdb.org/meta/nodes/id-name-schema'
params = None
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'Disease': {'id': 'id', 'name': 'label'},
'Drug': {'id': 'label', 'name': 'label'},
'Efo': {'id': 'id', 'name': 'value'},
'Gene': {'id': 'ensembl_id', 'name': 'name'},
'Gwas': {'id': 'id', 'name': 'trait'},
'Literature': {'id': 'id', 'name': 'id'},
'LiteratureTerm': {'id': 'id', 'name': 'name'},
'LiteratureTriple': {'id': 'id', 'name': 'name'},
'Pathway': {'id': 'id', 'name': 'name'},
'Protein': {'id': 'uniprot_id', 'name': 'uniprot_id'},
'Tissue': {'id': 'id', 'name': 'name'},
'Variant': {'id': 'name', 'name': 'name'}}
GET /meta/rels/list
List meta rels.
Params
{}
1. Query
Script
import requests
url = 'https://api.epigraphdb.org/meta/rels/list'
params = None
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
['BIORXIV_OBJ',
'BIORXIV_PREDICATE',
'BIORXIV_SUB',
'BIORXIV_TO_LIT',
'CPIC',
'EFO_CHILD_OF',
'EXPRESSED_IN',
'GENE_TO_PROTEIN',
'GEN_COR',
'GWAS_EFO_EBI',
'GWAS_NLP',
'GWAS_NLP_EFO',
'GWAS_TO_LITERATURE',
'GWAS_TO_LITERATURE_TRIPLE',
'GWAS_TO_VARIANT',
'MEDRXIV_OBJ',
'MEDRXIV_PREDICATE',
'MEDRXIV_SUB',
'MEDRXIV_TO_LIT',
'METAMAP_LITE',
'MONDO_MAP_EFO',
'MONDO_MAP_UMLS',
'MR_EVE_MR',
'OBS_COR',
'OPENTARGETS_DRUG_TO_DISEASE',
'OPENTARGETS_DRUG_TO_TARGET',
'PATHWAY_CHILD_OF',
'PROTEIN_IN_PATHWAY',
'PRS',
'TERM_TO_GENE',
'SEMMEDDB_OBJ',
'SEMMEDDB_PREDICATE',
'SEMMEDDB_SUB',
'SEMMEDDB_TO_LIT',
'STRING_INTERACT_WITH',
'OPENGWAS_TOPHITS',
'VARIANT_TO_GENE',
'XQTL_MULTI_SNP_MR',
'XQTL_SINGLE_SNP_MR_GENE_GWAS',
'XQTL_SINGLE_SNP_MR_SNP_GENE',
'GENE_TO_DISEASE']
GET /meta/nodes/{meta_node}/list
List nodes under a meta node. - `limit`: If you supply full_data to be True, the limit is 2,000, otherwise the limit is 10,000 - `full_data`: When False, only return the id and name fields for a node. For the specific id and name fields, refer to /meta/nodes/id-name-schema.
Params
{'full_data': <class 'bool'>,
'limit': <class 'int'>,
'meta_node': <enum 'EpigraphdbMetaNodesFull'>,
'offset': <class 'int'>}
1. List Gwas nodes (only id and name)
Script
import requests
url = 'https://api.epigraphdb.org/meta/nodes/Gwas/list'
params = {'full_data': False, 'limit': 5}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'MATCH (n:Gwas) RETURN n.id AS id, n.trait AS name SKIP '
'0 LIMIT 5',
'total_seconds': 0.022856},
'results': [{'id': 'ieu-b-43',
'name': 'frontotemporal dementia (TDP subtype)'},
{'id': 'ieu-b-40', 'name': 'body mass index'},
{'id': 'ieu-b-39', 'name': 'diastolic blood pressure'},
{'id': 'ieu-b-38', 'name': 'systolic blood pressure'},
{'id': 'ieu-b-35', 'name': 'C-Reactive protein level'}]}
2. List Gwas nodes (full data)
Script
import requests
url = 'https://api.epigraphdb.org/meta/nodes/Gwas/list'
params = {'full_data': True, 'limit': 5}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'MATCH (n:Gwas) RETURN n SKIP 0 LIMIT 5',
'total_seconds': 0.020222},
'results': [{'n': {'_id': 'ieu-b-43',
'_name': 'frontotemporal dementia (TDP subtype)',
'_source': ['OpenGWAS-2020-10-13'],
'author': 'Van Deerlin,V',
'build': 'HG19/GRCh37',
'category': 'Binary',
'id': 'ieu-b-43',
'mr': '1',
'ncase': '515.0',
'ncontrol': '2509.0',
'nsnp': '494577',
'pmid': '20154673.0',
'population': 'European',
'sample_size': '3024.0',
'sex': 'Males and Females',
'subcategory': 'Psychiatric / neurological',
'trait': 'frontotemporal dementia (TDP subtype)',
'unit': 'log odds ratio',
'year': '2010.0'}},
{'n': {'_id': 'ieu-b-40',
'_name': 'body mass index',
'_source': ['OpenGWAS-2020-10-13'],
'author': 'Yengo, L',
'build': 'HG19/GRCh37',
'category': 'Continuous',
'consortium': 'GIANT',
'id': 'ieu-b-40',
'mr': '1',
'nsnp': '2336260',
'pmid': '30124842.0',
'population': 'European',
'sample_size': '681275.0',
'sex': 'Males and Females',
'subcategory': 'Anthropometric',
'trait': 'body mass index',
'unit': 'SD',
'year': '2018.0'}},
{'n': {'_id': 'ieu-b-39',
'_name': 'diastolic blood pressure',
'_source': ['OpenGWAS-2020-10-13'],
'author': 'Evangelou, E',
'build': 'HG19/GRCh37',
'category': 'Continuous',
'consortium': 'International Consortium of Blood Pressure',
'id': 'ieu-b-39',
'mr': '1',
'nsnp': '7160619',
'pmid': '30224653.0',
'population': 'European',
GET /meta/nodes/{meta_node}/search
Use `id` for exact match, and use `name` for fuzzy match. - full_data: If False, only returns basic info (id, name).
Params
{'full_data': <class 'bool'>,
'id': typing.Union[str, NoneType],
'limit': <class 'int'>,
'meta_node': <class 'str'>,
'name': typing.Union[str, NoneType]}
1. Search Gwas nodes by id
Script
import requests
url = 'https://api.epigraphdb.org/meta/nodes/Gwas/search'
params = {'id': 'ieu-a-2'}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'MATCH (node: Gwas {id: "ieu-a-2"}) RETURN node LIMIT '
'10;',
'total_seconds': 0.021117},
'results': [{'node': {'_id': 'ieu-a-2',
'_name': 'Body mass index',
'_source': ['OpenGWAS-2020-10-13'],
'author': 'Locke AE',
'build': 'HG19/GRCh37',
'category': 'Risk factor',
'id': 'ieu-a-2',
'mr': '1',
'nsnp': '2555511',
'pmid': '25673413.0',
'population': 'Mixed',
'sample_size': '339224.0',
'sd': '4.77',
'sex': 'Males and Females',
'subcategory': 'Anthropometric',
'trait': 'Body mass index',
'year': '2015.0'}}]}
2. Search Gwas nodes by name
Script
import requests
url = 'https://api.epigraphdb.org/meta/nodes/Gwas/search'
params = {'name': 'body mass'}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'MATCH (node: Gwas) WHERE node.trait =~ "(?i).*body '
'mass.*" RETURN node LIMIT 10;',
'total_seconds': 0.027794},
'results': [{'node': {'_id': 'ieu-a-1089',
'_name': 'Body mass index',
'_source': ['OpenGWAS-2020-10-13'],
'author': 'Wood',
'build': 'HG19/GRCh37',
'category': 'Risk factor',
'id': 'ieu-a-1089',
'mr': '0',
'note': 'Dominance model? If so then not necessarily of '
'value for MR; Results from interim Biobank '
'release enriched for smokers; could lead to '
'bias through collider issues in MR',
'nsnp': '8654252',
'pmid': '26961502.0',
'population': 'European',
'sample_size': '120286.0',
'sex': 'Males and Females',
'subcategory': 'Anthropometric',
'trait': 'Body mass index',
'year': '2016.0'}},
{'node': {'_id': 'ieu-a-974',
'_name': 'Body mass index',
'_source': ['OpenGWAS-2020-10-13'],
'author': 'Locke AE',
'build': 'HG19/GRCh37',
'category': 'Risk factor',
'id': 'ieu-a-974',
'mr': '1',
'nsnp': '2494613',
'pmid': '25673413.0',
'population': 'European',
'sample_size': '171977.0',
'sd': '4.77',
'sex': 'Females',
'subcategory': 'Anthropometric',
'trait': 'Body mass index',
'year': '2015.0'}},
{'node': {'_id': 'ieu-a-95',
'_name': 'Body mass index',
'_source': ['OpenGWAS-2020-10-13'],
'author': 'Randall JC',
'build': 'HG19/GRCh37',
'category': 'Risk factor',
'id': 'ieu-a-95',
GET /meta/nodes/{meta_node}/search-neighbour
Search the neighbour nodes adjacent to the query node.
Params
{'id': typing.Union[str, NoneType],
'limit': <class 'int'>,
'meta_node': <class 'str'>}
1. Search neighbour nodes of a Gwas node
Script
import requests
url = 'https://api.epigraphdb.org/meta/nodes/Gwas/search-neighbour'
params = {'id': 'ieu-a-2'}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'MATCH (neighbour)-[rel]-(node: Gwas {id: "ieu-a-2"}) '
'WITH neighbour, rel, node, CASE WHEN '
'LABELS(neighbour)[0] = "Gwas" THEN { meta_node: '
'LABELS(neighbour)[0], meta_rel: TYPE(rel), node_data: '
'neighbour {.id, .trait} } ELSE { meta_node: '
'LABELS(neighbour)[0], meta_rel: TYPE(rel), node_data: '
'neighbour } END AS neighbour_data RETURN { meta_node: '
'LABELS(node)[0], id: node.id, neighbour: '
'collect(neighbour_data)[0..(50-1)] } AS data',
'total_seconds': 0.376298},
'results': [{'data': {'id': 'ieu-a-2',
'meta_node': 'Gwas',
'neighbour': [{'meta_node': 'Efo',
'meta_rel': 'GWAS_NLP_EFO',
'node_data': {'_id': 'http://www.ebi.ac.uk/efo/EFO_0005936',
'_name': 'underweight body '
'mass index '
'status',
'_source': ['EpiGraphDB '
'v0.3'],
'id': 'http://www.ebi.ac.uk/efo/EFO_0005936',
'type': 'typed-literal',
'value': 'underweight body '
'mass index '
'status'}},
{'meta_node': 'Efo',
'meta_rel': 'GWAS_NLP_EFO',
'node_data': {'_id': 'http://www.ebi.ac.uk/efo/EFO_0007041',
'_name': 'obese body mass '
'index status',
'_source': ['EpiGraphDB '
'v0.3'],
GET /meta/rels/{meta_rel}/list
List relationships under a meta relationship.
Params
{'limit': <class 'int'>,
'meta_rel': <enum 'EpigraphdbMetaRels'>,
'offset': <class 'int'>}
1. List MR (MR EvE) relationships
Script
import requests
url = 'https://api.epigraphdb.org/meta/rels/MR_EVE_MR/list'
params = None
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'MATCH (n)-[r: MR_EVE_MR]-(m) RETURN n, r, m SKIP 0 '
'LIMIT 10',
'total_seconds': 0.026431},
'results': [{'m': {'_id': 'prot-a-1171',
'_name': 'Polypeptide N-acetylgalactosaminyltransferase 2',
'_source': ['OpenGWAS-2020-10-13'],
'author': 'Sun BB',
'build': 'HG19/GRCh37',
'category': 'Immune system',
'id': 'prot-a-1171',
'mr': '1',
'nsnp': '10534735',
'pmid': '29875488.0',
'population': 'European',
'sample_size': '3301.0',
'sex': 'Males and Females',
'subcategory': 'Protein',
'trait': 'Polypeptide N-acetylgalactosaminyltransferase 2',
'year': '2018.0'},
'n': {'_id': 'ukb-d-XIII_MUSCULOSKELET',
'_name': 'Diseases of the musculoskeletal system and '
'connective tissue',
'_source': ['OpenGWAS-2020-10-13'],
'author': 'Neale lab',
'build': 'HG19/GRCh37',
'category': 'Binary',
'id': 'ukb-d-XIII_MUSCULOSKELET',
'mr': '1',
'ncase': '77099.0',
'ncontrol': '284095.0',
'nsnp': '13586589',
'population': 'European',
'sample_size': '361194.0',
'sex': 'Males and Females',
'trait': 'Diseases of the musculoskeletal system and '
'connective tissue',
'year': '2018.0'},
'r': {'_source': ['MR-EvE-2021-03-10'],
'b': -0.851781600575194,
'ci_low': -2.60590765161164,
'ci_upp': 0.9023444504612571,
'method': 'FE IVW',
'moescore': 1.0,
'nsnp': 2,
'pval': 0.341223071383756,
'se': 0.8949622709369641,
'selection': 'DF'}},
{'m': {'_id': 'prot-a-1171',
'_name': 'Polypeptide N-acetylgalactosaminyltransferase 2'
GET /meta/paths/search
Params
{'id_source': <class 'str'>,
'id_target': <class 'str'>,
'limit': <class 'int'>,
'max_path_length': <class 'int'>,
'meta_node_source': <class 'str'>,
'meta_node_target': <class 'str'>}
1. Search pair-wise rels between two Gwas
Script
import requests
url = 'https://api.epigraphdb.org/meta/paths/search'
params = {'meta_node_source': 'Gwas', 'meta_node_target': 'Gwas', 'id_source': 'ieu-a-2', 'id_target': 'ieu-a-10', 'max_path_length': 1, 'limit': 3}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'MATCH p=(n:Gwas {id: "ieu-a-2"}) -[*1..1]- (m:Gwas '
'{id: "ieu-a-10"}) WITH nodes(p) AS node_list, '
'relationships(p) AS rel_list RETURN [ x in node_list | '
'{ _id: id(x), meta_node: labels(x)[0], node_value: x } '
'] AS node_data, [ i in range(0, size(rel_list) - 1) | '
'{ meta_rel: type(rel_list[i]), rel_index: i, rel__id: '
'id(rel_list[i]), rel_value: rel_list[i], '
'head_meta_node: labels(startNode(rel_list[i]))[0], '
'head_node__id: id(startNode(rel_list[i])), '
'end_meta_node: labels(endNode(rel_list[i]))[0], '
'end_node__id: id(endNode(rel_list[i])) } ] AS rel_data '
'LIMIT 3;',
'total_seconds': 0.042102},
'results': [{'node_data': [{'_id': 218133,
'meta_node': 'Gwas',
'node_value': {'_id': 'ieu-a-2',
'_name': 'Body mass index',
'_source': ['OpenGWAS-2020-10-13'],
'author': 'Locke AE',
'build': 'HG19/GRCh37',
'category': 'Risk factor',
'id': 'ieu-a-2',
'mr': '1',
'nsnp': '2555511',
'pmid': '25673413.0',
'population': 'Mixed',
'sample_size': '339224.0',
'sd': '4.77',
'sex': 'Males and Females',
'subcategory': 'Anthropometric',
'trait': 'Body mass index',
'year': '2015.0'}},
{'_id': 384631,
'meta_node': 'Gwas',
'node_value': {'_id': 'ieu-a-10',
'_name
POST /mappings/gene-to-protein
Return protein uniprot_id from associated genes. - `gene_name_list`: List of HGNC symbols of the genes (default). - `gene_id_list`: List of Ensembl gene IDs (when `by_gene_id == True`)
Params
{'data': <class 'app.apis.mappings.models.GeneToProteinRequest'>}
1. (default) By HGNC symbols
Script
import requests
url = 'https://api.epigraphdb.org/mappings/gene-to-protein'
data = {'gene_name_list': ['GCH1', 'MYOF']}
r = requests.post(url, json=data)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'MATCH '
'(gene:Gene)-[gp:GENE_TO_PROTEIN]-(protein:Protein) '
"WHERE gene.name IN ['GCH1', 'MYOF'] RETURN gene "
'{.ensembl_id, .name}, protein {.uniprot_id}',
'total_seconds': 0.022568},
'results': [{'gene': {'ensembl_id': 'ENSG00000131979', 'name': 'GCH1'},
'protein': {'uniprot_id': 'P30793'}},
{'gene': {'ensembl_id': 'ENSG00000138119', 'name': 'MYOF'},
'protein': {'uniprot_id': 'Q9NZM1'}}]}
2. By Ensembl IDs
Script
import requests
url = 'https://api.epigraphdb.org/mappings/gene-to-protein'
data = {'gene_id_list': ['ENSG00000162594', 'ENSG00000113302'], 'by_gene_id': True}
r = requests.post(url, json=data)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'MATCH '
'(gene:Gene)-[gp:GENE_TO_PROTEIN]-(protein:Protein) '
"WHERE gene.ensembl_id IN ['ENSG00000162594', "
"'ENSG00000113302'] RETURN gene {.ensembl_id, .name}, "
'protein {.uniprot_id}',
'total_seconds': 0.024281},
'results': [{'gene': {'ensembl_id': 'ENSG00000162594', 'name': 'IL23R'},
'protein': {'uniprot_id': 'Q5VWK5'}},
{'gene': {'ensembl_id': 'ENSG00000113302', 'name': 'IL12B'},
'protein': {'uniprot_id': 'P29460'}}]}
GET /nlp/query/text
Return EpiGraphDB entities that matches the input text via text embeddings. - `asis`: If False, apply builtin preprocessing to `text` - `include_meta_nodes`: Leave as is to search in all meta entities, otherwise limit to the supplied list
Params
{'asis': <class 'bool'>,
'include_meta_nodes': typing.List[str],
'limit': <class 'int'>,
'text': <class 'str'>}
1. Query
Script
import requests
url = 'https://api.epigraphdb.org/nlp/query/text'
params = {'text': 'Coronary heart disease', 'asis': True, 'include_meta_nodes': ['Gwas', 'Disease'], 'limit': 10}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False, 'query': None, 'total_seconds': 0.079659},
'results': {'clean_text': 'Coronary heart disease',
'results': [{'id': 'ebi-a-GCST000998',
'meta_node': 'Gwas',
'name': 'Coronary heart disease',
'score': 0.9116211000000001,
'text': 'coronary heart disease'},
{'id': 'ieu-a-8',
'meta_node': 'Gwas',
'name': 'Coronary heart disease',
'score': 0.9116211000000001,
'text': 'coronary heart disease'},
{'id': 'ieu-a-6',
'meta_node': 'Gwas',
'name': 'Coronary heart disease',
'score': 0.9116211000000001,
'text': 'coronary heart disease'},
{'id': 'ieu-a-7',
'meta_node': 'Gwas',
'name': 'Coronary heart disease',
'score': 0.9116211000000001,
'text': 'coronary heart disease'},
{'id': 'ieu-a-9',
'meta_node': 'Gwas',
'name': 'Coronary heart disease',
'score': 0.9116211000000001,
'text': 'coronary heart disease'},
{'id': 'ukb-d-I9_CHD',
'meta_node': 'Gwas',
'name': 'Major coronary heart disease event',
'score': 0.8311637999999999,
'text': 'major coronary heart disease event'},
{'id': 'finn-a-I9_CHD',
'meta_node': 'Gwas',
'name': 'Major coronary heart disease event',
'score': 0.8311637999999999,
'text': 'major coronary heart disease event'},
{'id': 'http://purl.obolibrary.org/obo/MONDO_0005267',
'meta_node': 'Disease',
'name': 'heart disease',
'score': 0.8085366,
'text': 'heart disease'},
{'id': 'http://purl.obolibrary.o
GET /nlp/query/entity
Return EpiGraphDB entities that matches the query entity via text embeddings.
Params
{'entity_id': <class 'str'>,
'include_meta_nodes': typing.List[str],
'limit': <class 'int'>,
'meta_node': <class 'str'>}
1. Query
Script
import requests
url = 'https://api.epigraphdb.org/nlp/query/entity'
params = {'entity_id': 'ieu-a-2', 'meta_node': 'Gwas', 'include_meta_nodes': ['Efo'], 'limit': 5}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False, 'query': None, 'total_seconds': 0.045785},
'results': [{'id': 'http://www.ebi.ac.uk/efo/EFO_0004340',
'meta_node': 'Efo',
'name': 'body mass index',
'score': 1.0,
'text': 'body mass index'},
{'id': 'http://www.ebi.ac.uk/efo/EFO_0005851',
'meta_node': 'Efo',
'name': 'height-adjusted body mass index',
'score': 0.9553389999999999,
'text': 'height-adjusted body mass index'},
{'id': 'http://www.ebi.ac.uk/efo/EFO_0007041',
'meta_node': 'Efo',
'name': 'obese body mass index status',
'score': 0.9128609999999999,
'text': 'obese body mass index status'},
{'id': 'http://www.ebi.ac.uk/efo/EFO_0005935',
'meta_node': 'Efo',
'name': 'overweight body mass index status',
'score': 0.9039048000000001,
'text': 'overweight body mass index status'},
{'id': 'http://www.ebi.ac.uk/efo/EFO_0004995',
'meta_node': 'Efo',
'name': 'lean body mass',
'score': 0.9010530000000001,
'text': 'lean body mass'}]}
GET /nlp/query/entity/encode
Return the text embeddings of the queried entity
Params
{'entity_id': <class 'str'>, 'meta_node': <class 'str'>}
1. Query
Script
import requests
url = 'https://api.epigraphdb.org/nlp/query/entity/encode'
params = {'entity_id': 'ieu-a-2', 'meta_node': 'Gwas'}
r = requests.get(url, params=params)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False, 'query': None, 'total_seconds': 0.020282},
'results': [-0.23766601085662842,
0.12512199580669403,
-0.09448466449975967,
-0.126140296459198,
0.2749579846858978,
0.2646183371543884,
0.18217907845973969,
-0.18841862678527832,
0.011102299205958843,
0.20915333926677704,
0.14648906886577606,
-0.3002133369445801,
0.20739607512950897,
0.1166803240776062,
-0.058740634471178055,
0.417758971452713,
-0.1102587953209877,
0.17899031937122345,
0.2807776629924774,
0.2152116745710373,
0.022434568032622337,
-0.1390741467475891,
0.17028461396694183,
-0.06438823789358139,
-0.16778212785720825,
-0.043247636407613754,
-0.04925861954689026,
0.0266922265291214,
-0.06209079921245575,
-0.08959973603487015,
0.03844459727406502,
-0.18151147663593292,
0.12086880207061768,
-0.1565229892730713,
-0.19269810616970062,
0.08463457226753235,
0.009711295366287231,
0.053418029099702835,
-0.2929460108280182,
-0.6527466773986816,
-0.050607699900865555,
0.09547837823629379,
0.04387316107749939,
-0.05470770224928856,
-0.04110092297196388,
-0.05396467074751854,
0.10817847400903702,
-0.07165583223104477,
-0.1626873016357422,
-0.0497497022151947,
0.019376112148165703,
0.02835729904472828,
-0.2107655256986618,
-0.11753445863723755,
-0.004698334261775017,
-0.025601664558053017,
-0.16406656801700592,
0.18529647588729858,
-0.1575743407011032,
0.017800167202949524,
-0.05577133223414421,
0.13604873418807983,
-0.001458028913475573,
-0.3792083263397217,
-0.05305369198322296,
0.046654969453811646,
-0.033491265028715134,
POST /cypher
Send a cypher query to EpiGraphDB Graph.
Params
{'data': <class 'app.apis.cypher.models.CypherRequest'>}
1. Query
Script
import requests
url = 'https://api.epigraphdb.org/cypher'
data = {'query': 'MATCH (n:Gwas)-[r:MR_EVE_MR]-(m:Gwas) WHERE r.pval < 1e-5 RETURN properties(n), properties(r), properties(m) LIMIT 10'}
r = requests.post(url, json=data)
r.raise_for_status()
r.json()
Results
{'metadata': {'empty_results': False,
'query': 'MATCH (n:Gwas)-[r:MR_EVE_MR]-(m:Gwas) WHERE r.pval < '
'1e-5 RETURN properties(n), properties(r), '
'properties(m) LIMIT 10',
'total_seconds': 0.035328},
'results': [{'properties(m)': {'_id': 'ukb-d-K11_APPENDIX',
'_name': 'Diseases of appendix',
'_source': ['OpenGWAS-2020-10-13'],
'author': 'Neale lab',
'build': 'HG19/GRCh37',
'category': 'Binary',
'id': 'ukb-d-K11_APPENDIX',
'mr': '1',
'ncase': '2953.0',
'ncontrol': '358241.0',
'nsnp': '11121577',
'population': 'European',
'sample_size': '361194.0',
'sex': 'Males and Females',
'trait': 'Diseases of appendix',
'year': '2018.0'},
'properties(n)': {'_id': 'prot-a-1171',
'_name': 'Polypeptide '
'N-acetylgalactosaminyltransferase 2',
'_source': ['OpenGWAS-2020-10-13'],
'author': 'Sun BB',
'build': 'HG19/GRCh37',
'category': 'Immune system',
'id': 'prot-a-1171',
'mr': '1',
'nsnp': '10534735',
'pmid': '29875488.0',
'population': 'European',
'sample_size': '3301.0',
'sex': 'Males and Females',
'subcategory': 'Protein',
'trait': 'Polypeptide '
'N-acetylgalactosaminyltransferase 2',
'year': '2018.0'},
'properties(r)': {'_source': ['MR-EvE-2021-03-10'],
'b': 5.0276653757678,
'ci_low':