AMP T2D-GENES quantitative trait exome sequence analysis: African American ancestry


Exome sequencing of 20,791 cases of type 2 diabetes and 24,440 controls.
Flannick J, et al.
Nature. 2019 May 22. doi: 10.1038/s41586-019-1231-2

Sequence data and association statistics from 12,940 type 2 diabetes cases and controls.
Flannick J, Fuchsberger C, Mahajan A, et al.
Sci Data. 2017 Dec 19;4:170179. doi: 10.1038/sdata.2017.179

The genetic architecture of type 2 diabetes.
Fuchsberger C, Flannick J, Teslovich TM, Mahajan A, Agarwala V, Gaulton KJ, et al.
Nature 2016 Aug 4;536(7614):41-7. doi: 10.1038/nature18642

Association of a low-frequency variant in HNF1A with type 2 diabetes in a Latino population.
SIGMA Type 2 Diabetes Consortium, et al.
JAMA. 2014 Jun 11;311(22):2305-14. doi: 10.1001/jama.2014.6511

Whole-exome sequencing of 2,000 Danish individuals and the role of rare coding variants in type 2 diabetes.
Lohmueller KE, et al.
Am J Hum Genet. 2013 Dec 5;93(6):1072-86. doi: 10.1016/j.ajhg.2013.11.005


  • Adiponectin
  • BMI
  • Diastolic blood pressure
  • Fasting glucose
  • Fasting insulin
  • HbA1c
  • HDL cholesterol
  • Height
  • HOMA-B
  • LDL cholesterol
  • Leptin
  • Serum creatinine
  • Systolic blood pressure
  • Total cholesterol
  • Triglycerides
  • Triglyceride-to-HDL ratio
  • Waist circumference
  • Waist-hip ratio


Project Cases Controls Cohort (Click to view selection criteria for cases and controls) Ancestry
T2D-GENES 522 531 Wake Forest School of Medicine Study African American
T2D-GENES 476 513 Jackson Heart Study African American
T2D-GENES 1,291 1,242 BioMe Biobank Program (BioMe) African American
ESP 467 1,374 Exome Sequencing Project (ESP) African American


Exome Sequencing project (ESP) Learn more >

The Exome Sequencing Project (ESP) aims to discover novel genes and mechanisms contributing to heart, lung and blood disorders by pioneering the application of next-generation sequencing of the protein coding regions of the human genome across diverse, richly-phenotyped populations and to share these datasets and findings with the scientific community to extend and enrich the diagnosis, management and treatment of heart, lung and blood disorders.

Type 2 Diabetes Genetic Exploration by Next-Generation Sequencing in Ethnic Samples (T2D-GENES) Learn more >

T2D-GENES (Type 2 Diabetes Genetic Exploration by Next-generation sequencing in multi-Ethnic Samples) is a large collaborative effort to find genetic variants that influence risk of type 2 diabetes. With funding from NIDDK, the group is pursuing three projects: (1) deep whole-exome sequencing in 10,000 people from five ethnicities (African-American, East Asian, South Asian, European, and Hispanic); (2) deep whole-genome sequencing of 600 individuals selected from extended Mexican American pedigrees; and (3) a trans-ethnic fine-mapping "mega-meta-analysis."

Overview of analysis and results

The T2D analysis reported in Flannick et al. 2019 was extended to 24 quantitative traits. Briefly, single-variant association analyses were conducted for each trait (n=24) at the sample sub-group level (n=25). We analyzed samples using the EMMAX test via the EPACTS software package. A genomic relationship matrix (GRM) was included to account for relatedness across samples. In all cases, multiallelic sites were categorized as a single "non-reference" allele.

For each of the 25 sub-studies × 24 traits (n=600) single-variant analyses, quantile-quantile (QQ)-plots of all and common (MAC>20) alleles were used to assess whether the regression models were well-calibrated (i.e., comparison of regression results to expected results under a null-model). Stringencies of the variant filters were increased based on digression from the null model. Variants were filtered based on sequencing quality or excessive heterozygosity. Covariates to adjust for subject ascertainment and/or differing sequencing technologies across sub-studies were added where appropriate. Phenotype-specific outliers were removed where appropriate. Following all sub-study level quality control, a 25-group fixed-effect inverse-variance weighted meta-analysis was then conducted using METAL.

contributing community
Dataset ID