APIs

In keeping with the AMP T2D mission of democratizing access to genetic data and facilitating worldwide research, we are developing software (APIs) so that researchers can query summary statistics and other results programmatically. The currently available APIs are listed on this page and may be accessed here.

Genetic association datasets stored at the AMP T2D Data Coordinating Center (DCC) at the Broad Institute are described in detail on the Datasets page of the Type 2 Diabetes Knowledge Portal.

If you cite in a scientific publication results generated using these data and APIs, please do so in the following format:

Type 2 Diabetes Knowledge Portal. Year/Month/Date of access; http://www.type2diabetesgenetics.org/.

If your analysis uses specific datasets, please also cite the original publications for those datasets.

 

Currently available APIs 

Variants by chromosome region and phenotype

Retrieves genetic associations and variant annotations.

  • specify a chromosomal region and a phenotype; retrieve p-values, standard error, effect size, predictions of variant impact, and more, from the largest dataset available for that phenotype. Key to phenotype abbreviations:
    • T2D, type 2 diabetes
    • body mass index, BMI
    • fasting glucose, FG
    • fasting insulin, FI
    • triglycerides, TG
    • LDL cholesterol, LDL
    • HDL cholesterol, HDL
    • waist/hip ratio, WHR
    • two-hour insulin, 2hrI
    • hip circumference, HIPC
    • chronic kidney disease, CKD
    • coronary artery disease, CAD
    • height, HEIGHT
    • waist circumference, WAIST

 

DEPICT

Retrieve results generated by application of the DEPICT software (Pers, TH, et al., 2015) to the datasets in the DCC.

  • start with a gene and retrieve gene sets of which it is a member that are enriched for associations with a phenotype, along with the significance of the predicted associations
  • start with a phenotype and retrieve tissues enriched for associations with it, along with the significance of the predicted associations
  • start with a region and a phenotype; retrieve a list of potential effector genes within the region for that phenotype, with p-values
  • start with a phenotype and retrieve gene sets enriched (at a specified significance) for associations with it, with p-values

 

eCAVIAR

Retrieve results generated by application of the eCAVIAR software (Hormozdiari, F, et al., 2016) to the datasets in the DCC.

  • start with a gene and a phenotype; retrieve variants in the gene that represent colocalized GWAS and eQTL signals in a given tissue, along with the colocalization posterior probability of the prediction

 

GTEx

Retrieve tissue-specific gene expression data downloaded from GTEx.

 

Knockout

Retrieve mouse null mutant phenotypes downloaded from the Knockout Mouse Project as curated at the Mouse Genome Informatics (MGI) database.

  • for a specified human gene, retrieve the gene ID and name of the mouse homolog, as determined using the Homologene algorithm, and the Mammalian Phenotype Ontology ID and term for phenotype(s) displayed by mice that are null mutant for that gene

 

LD score

Retrieve results generated by application of LD score regression (Finucane, H, et al., 2015) to the datasets in the DCC.

  • start with a phenotype; retrieve a list of tissues enriched for associations with that phenotype along with effect sizes and p-values for the associations

 

MAGMA

Retrieve results generated by application of the MAGMA software (Finucane, H, et al., 2015) to the datasets in the DCC.

  • start with a phenotype; retrieve a list of gene-level association results for that phenotype, with a p-value for each gene

 

GREGOR

Retrieve results generated by application of the GREGOR software (Schmidt, EM et al., 2015) to the datasets in the DCC.

  • start with a phenotype; retrieve tissues enriched for trait associations, with their enrichment p-values and effect sizes per chromatin state and ancestry

 

Meta-analysis

Retrieve bottom-line meta-analysis results generated by application of the METAL software to the datasets in the DCC, as documented here. METAL software, developed at the University of Michigan, performs meta-analysis while accounting for sample overlap between datasets.

  • for a variant, retrieve "bottom-line" p-values and effect sizes for each phenotype in the datasets stored at the DCC

 

Phenotype list

Retrieve a list of all phenotypes for which there are genetic associations in the datasets stored at the DCC.

 

Region 

Retrieve chromatin state annotations near a variant or list of variants.

  • for a variant or list of variants, retrieve regional chromatin states and the tissues in which they are active

 

Tissue list

Retrieve a list of all tissues for which there are annotations in the datasets or results displayed in the Knowledge Portal Network.

 

Variant prioritization

Retrieve meta-analyzed genetic associations and variant annotations.

  • specify a phenotype and a genomic region; for variants in the region associated with the phenotype, retrieve p-values, standard error, effect size, and several predictions of variant impact. The p-values retrieved are bottom-line meta-analysis results generated by application of the METAL software, accounting for sample overlap, to the datasets in the DCC, as documented here.