T2D-GENES (Type 2 Diabetes Genetic Exploration by Next-generation sequencing in multi-Ethnic Samples) Consortium

T2D-GENES logo

T2D-GENES is a large collaborative effort to find genetic variants that influence risk of type 2 diabetes. With funding from NIDDK, the group pursued three projects as listed below. The T2D-GENES Consortium also funded and guided the construction of the Portal.

The T2D-GENES consortium has contributed to these datasets in the T2D Knowledge Portal:

  • 13K exome sequence analysis
  • AMP T2D-GENES exome sequence analysis
  • AMP T2D-GENES quantitative trait exome sequence analysis

Project 1: Deep whole-exome sequencing in 10,000 people from five ethnicities

The goal of Project 1 was to discover how variation in the protein-coding portion of the genome contributes to type 2 diabetes risk. The project’s dataset is unusually large and diverse, with exomes from 10,000 people across five ethnicities, including 1,000 type 2 diabetes cases and 1,000 controls from each:

  • African-American (samples from Wake Forest University and the Jackson Heart Study)
  • South Asian (UK LOLIPOP; Singapore)
  • East Asian (Korea; Singapore)
  • Hispanic (Starr County; San Antonio)
  • European (Finns (METSIM); Ashkenazim)

This diversity of ancestries allows scientists to find new genetic variants in populations that have otherwise been under-studied. The project also examined exomes to identify the transcripts most likely to be involved in type 2 diabetes pathogenesis. In addition, T2D-GENES researchers closely examined genomic locations that have been implicated in single-gene and syndromic forms of type 2 diabetes (such as MODY), evaluating them for association with traits that are related to the disease (such as fasting glucose levels). Ultimately, Project 1 is intended to answer major questions about the genetic architecture of type 2 diabetes and how natural selection has shaped it, and to spur the development of new statistical and analytical methods that can be used in genomic studies of other diseases.

Project 2: Deep whole-genome sequencing of 600 individuals selected from extended Mexican American pedigrees

Project 2 aimed to identify low-frequency and rare variants (those seen in less than five and less than .05 percent of the population, respectively) influencing type 2 diabetes risk. The project's dataset includes whole-genome sequence information on 1,043 people from 20 Mexican-American extended families in which type 2 diabetes is unusually common. The research participants were selected from two studies: the San Antonio Family Heart Study (SAFHS) and the San Antonio Family Diabetes/Gallbladder Study (SAFDGS), collectively referred to as the San Antonio Mexican American Family Studies (SAMAFS). About 600 partipants underwent high-quality whole-genome sequencing, with an average of 50x coverage. The remaining 440 participants had genotypes imputed genome-wide based on their family members' information and data from the 1000 Genomes Project.

Studies of large, complex pedigrees, such as Project 2, are especially well-suited for the study of rare variants. Finding rare variants in the population at large requires extremely large sample sizes; often, the variants may be seen only once, making it difficult to reliably determine their effects on phenotype. However, by studying large pedigrees, scientists can increase their chances of finding multiple individuals who carry the same rare variants (because those variants run in their families). Therefore, Project 2's approach provides a way to identify low-frequency and rare variants -- both at known GWAS signals and novel genomic loci -- that may contribute to type 2 diabetes risk.

Project 3: Trans-ethnic fine-mapping "mega-meta-analysis"

Genome-wide association studies (GWAS) have implicated dozens of genomic regions in type 2 diabetes risk, but in many cases it is unclear which variants in those regions actually influence the underlying biology of disease, and which variants are merely near the disease-causing variants but do not themselves contribute to pathophysiology. The goal of Project 3 was to precisely identify the causal variants by (1) focusing on genomic regions previously implicated in type 2 diabetes risk; (2) inferring the existence of surrounding low-frequency variants by imputing relevant data from the 1000 Genomes Project; and (3) using a range of statistical approaches to determine which of these variants are most likely to cause disease.

A "mega-meta-analysis," Project 3 involved data from 26,488 type 2 diabetes patients and 83,964 non-diabetic controls, with follow-up in 21,491 patients and 55,647 controls. The project was a collaboration between five consortia with research participants from different continental ancestry groups: AGEN-T2D (East Asians), DIAGRAM (Europeans), SAT2D (South Asians), MAT2D (Mexican Americans), and MEDIA (African Americans).

This diversity of ancestries is especially important to the study design. Variants that are tightly correlated by location in some ancestry groups (e.g., Europeans) may travel more independently in other groups (e.g., African Americans). Therefore, examining data from many different groups can help distinguish between true causal variants and those that are merely along for the ride.


    T2D-GENES Consortium Members

    Albert Einstein College of Medicine Gil Atzmon
    Nir Barzilai
    Ben Glaser
    Boston University School of Public Health Josée Dupuis
    Han Chen
    Broad Institute David Altshuler
    Noël Burtt
    Jason Flannick
    Pierre Fontanillas
    Alisa Manning
    University of Exeter Tim Frayling
    George Washington University Kathleen Jablonski
    Harvard University/MGH Belinda Cornes
    Jose Florez
    James Meigs
    University of California, San Francisco Mark Seielstad
    Imperial College London John Chambers
    Jaspal Kooner
    Weihua Zhang
    Korean National Institute of Health Yoon Shin Cho
    Young Jin Kim
    Jong-Young Lee
    National University of Singapore Daniel Ng
    E Shyong Tai
    Yik Ying Teo
    University of Oxford/UK Mark McCarthy
    Nicola Beer
    Teresa Ferreira
    Cecilia Lindgren
    Anubha Mahajan
    Andrew Morris
    Inga Prokopenko
    Manuel Rivas
    Erasmus University Medical Center Sara Willems
    Seoul National University Jayoung Kim
    Ik-Soo Huh
    Jaehoon Lee
    Taesung Park
    Sohee Oh
    Singapore Eye Research Institute Kamran Ikram
    University of Chicago Graeme Bell
    Mathew Barber
    Nancy Cox
    Eric Gamazon
    Hae Hyung Im
    University of Kuopio, Finland Markku Laakso
    University of Maryland School of Medicine Toni Pollin
    Texas Biomedical Research Institute John Blangero
    Ravi Duggirala
    The University of Michigan Michael Boehnke
    Gonçalo Abecasis
    Christian Fuchsberger
    Hyun Min Kang
    Adam Locke
    Laura Scott
    Xueling Sim
    Tanya Teslovich
    University of Mississippi Medical Center Solomon Musani
    Jim Wilson
    The University of Texas Health Science Center Craig Hanis
    Heather Highland
    Taylor Maxwell
    Wake Forest University School of Medicine Don Bowden
    Maggie Ng
    Nicholette Palmer
    Wellcome Trust Sanger Institute Aaron Day-Williams
    Ele Zeggini
    The University of Texas Health Science Center - San Antonio Donna M. Lehman
    Hanna E. Abboud
    Ralph A. DeFronzo
    Christopher P. Jenkinson