This page lists datasets currently available in the T2D Knowledge Portal. Filter by data type using the menu at the top, or click on the links to see more details about individual sets.

Dataset

Dataset version: dv1

Publications

Analysis of protein-coding genetic variation in 60,706 humans.
Lek M, et al.
Nature. 2016 Aug 18;536(7616):285-91. doi: 10.1038/nature19057

Project

Exome Aggregation Consortium (ExAC) Learn more >

ExAC is a coalition of investigators seeking to aggregate and harmonize exome sequencing data from a wide variety of large-scale sequencing projects, and to make summary data available for the wider scientific community.

Accessing ExAC data in the T2D Knowledge Portal

ExAC data are available:

  • Via the Variant Finder tool, on the Additional search options tab
  • Linked from genes displayed on the genomic map beneath LocusZoom plots.

Dataset

Download URL: ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/phase1/analysis_results/integrated_call_sets/ALL.wgs.integrated_phase1_v3.20101123.snps_indels_sv.sites.vcf.gz
Download date: 07/26/2015
Data set version: dv1; 1000 Genomes Phase 1

Publications

A global reference for human genetic variation.
1000 Genomes Project Consortium, et al.
Nature. 2015 Oct 1;526(7571):68-74. doi: 10.1038/nature15393

Dataset subjects

Number of subjectsCohortEthnicity
2461000 Genomes: African American
African American
2861000 Genomes: East Asian
East Asian
3791000 Genomes: European
European
1811000 Genomes: Hispanic
Hispanic

Project

The International Genome Sample Resource (IGSR) Learn more >

The IGSR maintains human variation and genotype reference data from the 1000 Genomes project, which aimed to find most genetic variants with frequencies of at least 1% in the populations studied. The project generated whole-genome sequence data from 2,504 healthy individuals from 26 populations.

Accessing 1000 Genomes data in the T2D Knowledge Portal

1000 Genomes data are available:

Dataset

The 17K exome sequence analysis dataset includes all samples in the 13K exome sequence analysis dataset.

Publications

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

Dataset phenotypes

  • type 2 diabetes

Dataset Subjects

ProjectCasesControlsCohort (Click to view selection criteria for cases and controls) Ethnicity
T2D-GENES500526Jackson Heart Study Candidate Gene Association Resource African American
T2D-GENES518530Wake Forest Study African American
T2D-GENES526561Korea Association Research Project (KARE) and Korean National Institute of Health (KNIH) East Asian
T2D-GENES486592Singapore Diabetes Cohort Study and Singapore Prospective Study Program East Asian
T2D-GENES506355Longevity Genes in Founder Populations (Ashkenazi) European
T2D-GENES484498Metabolic Syndrome in Men Study (METSIM) European
T2D-GENES272218San Antonio Mexican American Family Studies, Texas Latino
T2D-GENES749704Starr County, Texas Latino
T2D-GENES531538London Life Sciences Population (LOLIPOP) South Asian
T2D-GENES563585Singapore Indian Eye Study South Asian
GoT2D472486Finland-United States Investigation of NIDDM Genetics (FUSION) European
GoT2D9790KORAgen Study Helmholtz zentrum München (KORA) European
GoT2D322320UK Type 2 Diabetes Genetics Consortium (UKT2D) European
GoT2D478443PPP-Malmo-Botnia Study European
SIGMA551547UNAM/INCMNSZ Diabetes Study (UIDS) Latino
SIGMA509459Diabetes in Mexico Study (DMS) Latino
SIGMA270526Mexico City Diabetes Study (MCDS) Latino
SIGMA487443Multiethnic Cohort (MEC) Latino
Total83218421

Projects

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." 

 

Genetics of Type 2 Diabetes (GoT2D) Learn more >

The GoT2D consortium aims to understand the allelic architecture of type 2 diabetes through whole-genome sequencing, high-density SNP genotyping, and imputation. The reference panel based on this work is intended as a comprehensive inventory of low-frequency variants in Europeans, including SNPs, small insertions and deletions, and structural variants.

 

Slim Initiative in Genomic Medicine for the Americas (SIGMA) Learn more >

The SIGMA partnership aims to understand the genomic basis of type 2 diabetes in Mexican and Latin American populations. 

Overview of analysis

Samples were subdivided into 15 sub-groups according to ancestry and study of origin. Each sub-group was analyzed independently, with sub-group specific principal components and genetic relatedness estimates. Association tests were performed with both a linear mixed model, as implemented in EMMAX, using covariates for sequencing batch, and the Firth test, as implemented in EPACTS, using covariates for principal components and sequencing batch. Related samples were excluded from the Firth analysis but maintained in the EMMAX analysis. Variants were then filtered from each sub-group analysis, according to low call rate, differential case-control missingness, or deviation from Hardy-Weinberg equilibrium (as computed separately for each sub-group). Association statistics were then combined via a fixed-effects inverse-variance weighted meta-analysis, at both the level of ancestry as well as across all samples. On the Portal, results are shown for each subgroup, each ancestry, and all samples. P-values are taken from the EMMAX analysis, while effect sizes are taken from the Firth analysis.

 

Accessing 17K exome sequence analysis data in the T2D Knowledge Portal

17K exome sequence analysis data are available:

  • On Gene Pages in the Variants & Associations table
  • On Variant Pages in the Associations at a glance section and in the Association statistics across traits table
  • Via the Variant Finder tool, for the phenotype T2D
  • Via the Genetic Association Interactive Tool (GAIT), which enables custom association analysis for either single variants (available on Variant Pages) or for the set of variants in and near a gene (Interactive burden test, available on Gene Pages)
  • From the View full genetic association results for a phenotype search on the Portal home page: first select one of the dataset phenotypes, and then on the resulting page, select the 17K exome sequence analysis dataset.

Dataset

Samples in the 13K exome sequence analysis dataset are a subset of those in the 17K exome sequence analysis dataset.

Publication

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

Dataset phenotypes

  • HbA1c
  • fasting glucose
  • fasting insulin
  • cholesterol
  • HDL cholesterol
  • LDL cholesterol
  • diastolic blood pressure
  • systolic blood pressure

Dataset subjects

ProjectCasesControlsCohort (Click to view selection criteria for cases and controls) Ethnicity
T2D-GENES500526Jackson Heart Study Candidate Gene Association Resource African American
T2D-GENES518530Wake Forest Study African American
T2D-GENES526561Korea Association Research Project (KARE) and Korean National Institute of Health (KNIH) East Asian
T2D-GENES486592Singapore Diabetes Cohort Study and Singapore Prospective Study Program East Asian
T2D-GENES506355Longevity Genes in Founder Populations (Ashkenazi) European
T2D-GENES484498Metabolic Syndrome in Men Study (METSIM) European
T2D-GENES272218San Antonio Mexican American Family Studies, Texas Latino
T2D-GENES749704Starr County, Texas Latino
T2D-GENES531538London Life Sciences Population (LOLIPOP) South Asian
T2D-GENES563585Singapore Indian Eye Study South Asian
GoT2D472486Finland-United States Investigation of NIDDM Genetics (FUSION) European
GoT2D9790KORAgen Study Helmholtz zentrum München (KORA) European
GoT2D322320UK Type 2 Diabetes Genetics Consortium (UKT2D) European
GoT2D478443PPP-Malmo-Botnia Study European
Total65046446

Project

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."

 

Project

Genetics of Type 2 Diabetes (GoT2D) Learn more >

The GoT2D consortium aims to understand the allelic architecture of type 2 diabetes through whole-genome sequencing, high-density SNP genotyping, and imputation. The reference panel based on this work is intended as a comprehensive inventory of low-frequency variants in Europeans, including SNPs, small insertions and deletions, and structural variants.

Accessing 13K exome sequence analysis data in the T2D Knowledge Portal

13K exome sequence analysis data are available:

  • On Variant Pages in the Associations at a glance section and in the Association statistics across traits table
  • Via the Variant Finder tool, for the phenotypes listed above

Publication

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

Dataset phenotypes

  • type 2 diabetes

Dataset subjects

CasesControlsCohortEthnicity
493486Finland-United States Investigation of NIDDM Genetics (FUSION) Study European
101104Kooperative Gesundheitsforschung in der Region Augsburg (KORA) European
322322UK Type 2 Diabetes Genetics Consortium (UKT2D) European
410419Malmo-Botnia Study European
Total: 1326Total: 1331

Project

Genetics of Type 2 Diabetes (GoT2D) Learn more >

The GoT2D consortium aims to understand the allelic architecture of type 2 diabetes through whole-genome sequencing, high-density SNP genotyping, and imputation. The reference panel based on this work is intended as a comprehensive inventory of low-frequency variants in Europeans, including SNPs, small insertions and deletions, and structural variants.

Accessing GoT2D WGS data in the T2D Knowledge Portal

GoT2D WGS data are available:

  • On Gene Pages in the Variants & Associations table
  • On Variant Pages in the Associations at a glance section and in the Association statistics across traits table
  • Via the Variant Finder tool, for the phenotype T2D
  • From the View full genetic association results for a phenotype search on the Portal home page: first select one of the dataset phenotypes, and then on the resulting page, select the GoT2D WGS dataset.

Publication

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

Dataset phenotypes

  • type 2 diabetes

Dataset subjects

CasesControlsCohortEthnicity
132455MT. SINAI BioMe Biobank Platform (BioMe (Affy)) European
2551647MT. SINAI BioMe Biobank Platform (BioMe (Illumina)) European
677697Diabetes Gene Discovery Group (DGDG) European
8991057Diabetes Genetics Initiative (DGI) European
3896013Estonian Genome Center, University of Tartu (EGCUT- OMNI) European
801768Estonian Genome Center, University of Tartu (EGCUT- 370) European
6737660Framingham Heart Study (FHS) European
10601090Finland-United States Investigation of NIDDM Genetics (FUSION) Study European
46244668InterAct European
9932985KORAgen Study Helmholtz zentrum München European
111838Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS) European
166953Uppsala Longitudinal Study of Adult Men (ULSAM) European
15862938Wellcome Trust Case Control Consortium (WTCCC) European
Total: 11,645Total: 32,769

Project

Genetics of Type 2 Diabetes (GoT2D) Learn more >

The GoT2D consortium aims to understand the allelic architecture of type 2 diabetes through whole-genome sequencing, high-density SNP genotyping, and imputation. The reference panel based on this work is intended as a comprehensive inventory of low-frequency variants in Europeans, including SNPs, small insertions and deletions, and structural variants.

Accessing GoT2D WGS + replication data in the T2D Knowledge Portal

GoT2D WGS + replication data are available:

  • On Gene Pages in the Variants & Associations table
  • On Variant Pages in the Associations at a glance section and in the Association statistics across traits table
  • Via the Variant Finder tool, for the phenotype T2D
  • From the View full genetic association results for a phenotype search on the Portal home page: first select one of the dataset phenotypes, and then on the resulting page, select the GoT2D WGS + replication dataset.

Publications

Genetic fine mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci.
Gaulton KJ, et al.
Nat Genet. 2015 Dec;47(12):1415-25. doi: 10.1038/ng.3437.

Low-frequency and rare exome chip variants associate with fasting glucose and type 2 diabetes susceptibility.
Wessel J, et al.
Nat Commun. 2015 Jan 29;6:5897. doi: 10.1038/ncomms6897.

Genetic background of patients from a university medical center in Manhattan: implications for personalized medicine.
Tayo BO, et al.
PLoS One. 2011 May 4;6(5):e19166. doi: 10.1371/journal.pone.0019166.

Dataset phenotypes

  • type 2 diabetes
  • fasting glucose adjusted for age-sex
  • HbA1c adjusted for age-sex
  • HbA1c adjusted for age-sex-BMI

Dataset subjects

CasesControlsCohort (Click to view selection criteria for cases and controls)Ethnicity
2,2936,880The Charles Bronfman Institute for Personalized Medicine BioMe Biobank Mixed

Project

The Charles Bronfman Institute for Personalized Medicine (IPM) BioMe Biobank is a consented, EMR-linked medical care setting biorepository of the Mount Sinai Medical Center (MSMC) drawing from a population of over 70,000 inpatients and 800,000 outpatient visits annually. MSMC serves diverse local communities of upper Manhattan, including Central Harlem, East Harlem, and Upper East Side with broad health disparities.

The BioMe Biobank was founded in September 2007 and as of September 2016, > 34,000 participants were enrolled. IPM BioMe Biobank populations include 28% African American, 38% Hispanic Latino predominantly of Caribbean origin, and 23% Caucasian/White. Enrolled participants consent to be followed throughout their clinical care (past, present, and future) at Mount Sinai in real-time, integrating their genomic information with their electronic health records for discovery research and clinical care implementation. The BioMe Biobank disease burden is reflective of health disparities with broad public health impact. Biobank operations are fully integrated in clinical care processes, including direct recruitment from clinical sites waiting areas and phlebotomy stations by dedicated Biobank recruiters independent of clinical care providers, prior to or following a clinician standard of care visit. Recruitment currently occurs at a broad spectrum of over 30 clinical care sites.

Funding acknowledgments

The BioMe Biobank is supported by the Andrea and Charles Bronfman Philanthropies.

Experiment summary

The BioMe AMP T2D GWAS sample set is comprised of 13,034 unique individuals. The major ancestry groups are admixed American (41.5%) and African American (38%). Subjects of European ancestry comprise 19.8% of the set, and 0.006% are South Asian.

Samples were genotyped on at least one of three platforms: Illumina Exome Array, which assayed nearly 200,000 autosomal variants; Illumina GWAS Array, which assayed over 844,000 autosomal variants; and Affymetrix GWAS Array, which assayed over 837,000 autosomal variants.

T2D case and control definition algorithms were developed by a multidisciplinary team of scientists, clinicians, and software specialists. Comprehensive documentation of the algorithms can be found at www.phekb.org/phenotype/type-2-diabetes-mellitus. This algorithm has been validated with excellent performance statistics; 100% sensitivity and > 98% positive predictive value for cases, and ≥ 98% sensitivity and ≥ 98% positive predictive value for controls.

In addition to T2D status, the phenotypes considered in this initial analysis were fasting glucose level, measured in blood samples taken from patients who had not had food or drink for at least 8 hours, and HbA1c level, which is an indicator of average blood glucose levels over a three-month period. Phenotypes were recorded during routine medical visits, and reflect the value at the time of the most recent visit at which that measurement was taken (or, where applicable, at the last non-pregnant visit at which that measurement was taken).

Overview of analysis and results

Data were analyzed by the Analysis Team at the Accelerating Medicines Partnership Data Coordinating Center (AMP-DCC), Broad Institute. After sample quality control (excluding samples flagged for non-type 2 diabetes, and removing duplicates where samples had been assayed on multiple platforms), results from the two GWAS arrays were combined in meta-analysis and results from the exome array were analyzed separately. A single set of p-values was produced using a MEGA analysis strategy, including all samples in a single association test.

T2D associations were adjusted either for age and sex, or for age, sex, and BMI. Among the top 20 variants for T2D association, rs7903146, within the well-known T2D risk gene TCF7L2, was the only variant reaching genome-wide significance. An analysis of previously known associations revealed four that were nominally significant. Additionally, about half of the previously associated variants that were analyzed had odds ratios with the same direction of effect as previously published for those variants (Morris AP, et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes.)

Fasting glucose associations were calculated from inverse normalized age- and sex-adjusted residuals. None of the top results reached genome-wide significance. Four previously identified risk variants reached nominal significance in this analysis. Overall, 80% of the previously identified variants that were analyzed were shown to have the same direction of effect as the known result (Dupuis J, et al. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk.; Scott RA, et al. Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways.).

HbA1c associations were calculated from inverse normalized age- and sex-adjusted residuals, both with and without additional adjustment for BMI. The analysis of HbA1c performed similarly to fasting glucose, with no genome-wide associations in the top 20. No previously identified variants reached nominal significance. In total, 62.5% of all variants that were previously identified (Soranzo N, et al. Common variants at 10 genomic loci influence hemoglobin A1(C) levels via glycemic and nonglycemic pathways.) had directions of effect that were consistent with published results.

Detailed reports

AMP-DCC Data Analysis Report (download PDF)

Genotype Data Quality Control Report (download PDF)

Accessing BioMe AMP T2D GWAS data in the T2D Knowledge Portal

BioMe AMP T2D GWAS data are available for the phenotypes listed above, at these locations in the Portal:

  • On Gene Pages in the Variants & Associations table and the Minor allele frequencies across datasets table
  • On Variant Pages in the Associations at a glance section and in the Association statistics across traits table
  • Via the Variant Finder tool
  • From the View full genetic association results for a phenotype search on the Portal home page: first select one of the dataset phenotypes, and then on the resulting page, select the BioMe AMP T2D GWAS dataset.

Future plans for BioMe AMP T2D GWAS data in the T2D Knowledge Portal

BioMe data will be analyzed for associations of variants with additional phenotypes, and these results will be made available in the Portal via the pages and tools listed above.

External Links to BioMe AMP T2D GWAS data

These data are also available in dbGaP under the following accessions: phs000388.v1.p1; phs000948.v1.p1; phs000925.v1.p1.

Dataset

Download URL: http://www.cardiogramplusc4d.org/data-downloads/
Download date: October 2016

Publications

A comprehensive 1,000 Genomes-based genome-wide association meta-analysis of coronary artery disease.
Nikpay M, et al.
Nat Genet. 2015 Oct;47(10):1121-30. doi: 10.1038/ng.3396

Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease.
Schunkert H, et al.
Nat Genet. 2011 Mar 6;43(4):333-8. doi: 10.1038/ng.784

Dataset phenotypes

  • coronary artery disease

Project

CARDIoGRAMplusC4D Consortium Learn more >

 

 

 

Experiment summary

CARDIoGRAMplusC4D 1000 Genomes-based GWAS is a meta-analysis of GWAS studies of mainly European, South Asian, and East Asian, descent imputed using the 1000 Genomes phase 1 v3 training set with 38 million variants. The study interrogated 9.4 million variants and involved 60,801 CAD cases and 123,504 controls.

Accessing CARDIoGRAM GWAS data in the T2D Knowledge Portal

CARDIoGRAM GWAS data are available at these locations in the Portal:

  • On Gene Pages in the Variants & Associations table when the "coronary artery disease" phenotype is selected
  • On Variant Pages in the Associations at a glance section and in the Association statistics across traits table
  • Via the Variant Finder tool
  • From the View full genetic association results for a phenotype search on the Portal home page, for the "coronary artery disease" phenotype.

Dataset

These datasets draw upon the same sample set: CKDGen GWAS; CKDGen GWAS - stratified UACR associations; and CKDGen GWAS - stratified serum creatinine associations.

Download URL - Download URL: https://fox.nhlbi.nih.gov/CKDGen/
Download date: October 2016
Dataset version: dv2

Publications

Genome-wide Association Studies Identify Genetic Loci Associated With Albuminuria in Diabetes.
Teumer A, et al.
Diabetes. 2016 Mar;65(3):803-17. doi: 10.2337/db15-1313

Dataset phenotypes

  • urinary albumin-to-creatinine ratio
  • urinary albumin-to-creatinine ratio: individuals with type 2 diabetes
  • urinary albumin-to-creatinine ratio: individuals without type 2 diabetes

Project

Chronic Kidney Disease Genetics Consortium (CKDGen)

CKDGen is a consortium of researchers investigating genetic loci associated with kidney function and disease.

Experiment summary

Summary meta-analysis data are provided for the phenotypes listed above. Unless otherwise specified, analyses were performed among participants of European ancestry. Analyses were performed on the entire group except for those indicated to include only participants with or without T2D.

Accessing CKDGen GWAS - stratified UACR associations data in the T2D Knowledge Portal

CKDGen GWAS - stratified UACR associations data are available at these locations in the Portal:

  • On Gene Pages in the Variants & Associations table when the "Urinary albumin-to-creatinine ratio" phenotype is selected
  • On Variant Pages in the Associations at a glance section and in the Association statistics across traits table
  • Via the Variant Finder tool
  • From the View full genetic association results for a phenotype search on the Portal home page, for the "Urinary albumin-to-creatinine ratio" phenotype.

Dataset

These datasets draw upon the same sample set: CKDGen GWAS; CKDGen GWAS - stratified UACR associations; and CKDGen GWAS - stratified serum creatinine associations.

Download URL - Download URL: https://fox.nhlbi.nih.gov/CKDGen/
Download date: October 2016
Dataset version: dv2

Publications

Genetic associations at 53 loci highlight cell types and biological pathways relevant for kidney function.
Pattaro C, et al.
Nat Commun. 2016 Jan 21;7:10023. doi: 10.1038/ncomms10023

Dataset phenotypes

  • eGFR-creat (serum creatinine)
  • eGFR-creat (serum creatinine): individuals with type 2 diabetes
  • eGFR-creat (serum creatinine): individuals without type 2 diabetes
  • eGFR-creat (serum creatinine): African Americans

Project

Chronic Kidney Disease Genetics Consortium (CKDGen)

CKDGen is a consortium of researchers investigating genetic loci associated with kidney function and disease.

Experiment summary

Summary meta-analysis data are provided for the phenotypes listed above. Unless otherwise specified, analyses were performed among participants of European ancestry. Analyses were performed on the entire group except for those indicated to include only participants with or without T2D, or participants of African ancestry.

Accessing CKDGen GWAS - stratified serum creatinine associations data in the T2D Knowledge Portal

CKDGen GWAS - stratified serum creatinine associations data are available at these locations in the Portal:

  • On Gene Pages in the Variants & Associations table when the "eGFR-creat (serum creatinine)" phenotype is selected
  • On Variant Pages in the Associations at a glance section and in the Association statistics across traits table
  • Via the Variant Finder tool
  • From the View full genetic association results for a phenotype search on the Portal home page, for the "eGFR-creat (serum creatinine)" phenotype.

Dataset

These datasets draw upon the same sample set: CKDGen GWAS; CKDGen GWAS - stratified UACR associations; and CKDGen GWAS - stratified serum creatinine associations.

Download URL - eGFRcrea: http://www.nhlbi.nih.gov/sites/www.nhlbi.nih.gov/files/CKDGen-eGFRcrea_meta_post.csv
Download URL - eGFRcyse: http://www.nhlbi.nih.gov/sites/www.nhlbi.nih.gov/files/CKDGen-eGFRcys_meta_post.csv
Download URL - CKD: http://www.nhlbi.nih.gov/sites/www.nhlbi.nih.gov/files/CKDGen-CKD_meta_post.csv
Download URL - UACR: http://www.nhlbi.nih.gov/sites/www.nhlbi.nih.gov/files/CKDGen-UACR_meta_post.csv
Download URL - MA: http://www.nhlbi.nih.gov/sites/www.nhlbi.nih.gov/files/CKDGen-MA_meta_post.csv
Download date: 5/10/2016
Dataset version: dv1

Publications

New loci associated with kidney function and chronic kidney disease.
Köttgen A, et al.
Nat Genet. 2010 May;42(5):376-84. doi: 10.1038/ng.568

Dataset phenotypes

  • chronic kidney disease
  • eGFR-cys (serum cystatin C)
  • microalbuminuria

Project

Chronic Kidney Disease Genetics Consortium (CKDGen)

CKDGen is a consortium of researchers investigating genetic loci associated with kidney function and disease.

Accessing CKDGen GWAS data in the T2D Knowledge Portal

CKDGen GWAS data are available at these locations in the Portal:

  • On Gene Pages in the Variants & Associations table when any of the phenotypes listed above is selected
  • On Variant Pages in the Associations at a glance section and in the Association statistics across traits table
  • Via the Variant Finder tool
  • From the View full genetic association results for a phenotype search on the Portal home page, for the phenotypes listed above.

Dataset

Download URL: http://diagram-consortium.org/downloads.html
Download date: 5/10/2016
Dataset version: dv1, dv2 (as European subset)

Publications

Genetic fine mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci.
Gaulton KJ, et al.
Nat Genet. 2015 Dec;47(12):1415-25. doi: 10.1038/ng.3437

Dataset phenotypes

  • type 2 diabetes

Project

DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Learn more >

The DIAGRAM consortium performs large-scale studies to characterize the genetic basis of type 2 diabetes, primarily focusing on samples of European descent.

Experiment summary

With data updates in October 2016, total sample size for DIAGRAM data in the Portal increased to 149,821. New gender-stratified subsets were added as well as Metabochip and fine-mapping results.

Please see this page for more details on the DIAGRAM datasets. Dataset names on the DIAGRAM download page correspond to names used in the Portal as follows:

DIAGRAM name: Stage 1 GWAS: Summary Statistics
Portal name: DIAGRAM GWAS

DIAGRAM name: Stage 1 GWAS & Stage 2 Metabochip: Summary Statistics
Portal name: DIAGRAM GWAS + MetaboChip

DIAGRAM name: Stage 1 GWAS & Stage 2 Metabochip: Sex-Specific Summary Statistics
Portal name: DIAGRAM GWAS + MetaboChip: females; DIAGRAM GWAS + MetaboChip: males

DIAGRAM name: Stage 2 Metabochip: Summary Statistics
Portal name: DIAGRAM MetaboChip

DIAGRAM name: Trans-ethnic T2D GWAS meta-analysis
Portal name: DIAGRAM Transethnic meta-analysis

DIAGRAM name: DIAGRAM Metabochip meta-analysis of T2D: Credible Sets
Portal name: DIAGRAM MetaboChip fine mapping

Accessing DIAGRAM trans-ehtnic meta-analysis data in the T2D Knowledge Portal

DIAGRAM trans-ethnic meta-analysis data are available at these locations in the Portal:

  • On Gene Pages in the Variants & Associations table
  • On Variant Pages in the Associations at a glance section and in the Association statistics across traits table
  • Via the Variant Finder tool
  • From the View full genetic association results for a phenotype search on the Portal home page, for the "type 2 diabetes" phenotype.

Data set

Download URL: https://www.broadinstitute.org/collaboration/giant/index.php/GIANT_consortium_data_files
Publication:

PMID: 20881960 Hundreds of variants clustered in genomic loci and biological pathways affect human height.
Lango Allen H, Estrada K, Lettre G, Berndt SI, Weedon MN, Rivadeneira F, Willer CJ, Jackson AU, Vedantam S, Raychaudhuri S, Ferreira T, Wood AR, Weyant RJ, Segrè AV, Speliotes EK, Wheeler E, Soranzo N, Park JH, Yang J, Gudbjartsson D, Heard-Costa NL, Randall JC, Qi L, Vernon Smith A, Mägi R, Pastinen T, Liang L, Heid IM, Luan J, Thorleifsson G, Winkler TW, Goddard ME, Sin Lo K, Palmer C, Workalemahu T, Aulchenko YS, Johansson A, Zillikens MC, Feitosa MF, Esko T, Johnson T, Ketkar S, Kraft P, Mangino M, Prokopenko I, Absher D, Albrecht E, Ernst F, Glazer NL, Hayward C, Hottenga JJ, Jacobs KB, Knowles JW, Kutalik Z, Monda KL, Polasek O, Preuss M, Rayner NW, Robertson NR, Steinthorsdottir V, Tyrer JP, Voight BF, Wiklund F, Xu J, Zhao JH, Nyholt DR, Pellikka N, Perola M, Perry JR, Surakka I, Tammesoo ML, Altmaier EL, Amin N, Aspelund T, Bhangale T, Boucher G, Chasman DI, Chen C, Coin L, Cooper MN, Dixon AL, Gibson Q, Grundberg E, Hao K, Juhani Junttila M, Kaplan LM, Kettunen J, König IR, Kwan T, Lawrence RW, Levinson DF, Lorentzon M, McKnight B, Morris AP, Müller M, Suh Ngwa J, Purcell S, Rafelt S, Salem RM, Salvi E, Sanna S, Shi J, Sovio U, Thompson JR, Turchin MC, Vandenput L, Verlaan DJ, Vitart V, White CC, Ziegler A, Almgren P, Balmforth AJ, Campbell H, Citterio L, De Grandi A, Dominiczak A, Duan J, Elliott P, Elosua R, Eriksson JG, Freimer NB, Geus EJ, Glorioso N, Haiqing S, Hartikainen AL, Havulinna AS, Hicks AA, Hui J, Igl W, Illig T, Jula A, Kajantie E, Kilpeläinen TO, Koiranen M, Kolcic I, Koskinen S, Kovacs P, Laitinen J, Liu J, Lokki ML, Marusic A, Maschio A, Meitinger T, Mulas A, Paré G, Parker AN, Peden JF, Petersmann A, Pichler I, Pietiläinen KH, Pouta A, Ridderstråle M, Rotter JI, Sambrook JG, Sanders AR, Schmidt CO, Sinisalo J, Smit JH, Stringham HM, Bragi Walters G, Widen E, Wild SH, Willemsen G, Zagato L, Zgaga L, Zitting P, Alavere H, Farrall M, McArdle WL, Nelis M, Peters MJ, Ripatti S, van Meurs JB, Aben KK, Ardlie KG, Beckmann JS, Beilby JP, Bergman RN, Bergmann S, Collins FS, Cusi D, den Heijer M, Eiriksdottir G, Gejman PV, Hall AS, Hamsten A, Huikuri HV, Iribarren C, Kähönen M, Kaprio J, Kathiresan S, Kiemeney L, Kocher T, Launer LJ, Lehtimäki T, Melander O, Mosley TH Jr, Musk AW, Nieminen MS, O'Donnell CJ, Ohlsson C, Oostra B, Palmer LJ, Raitakari O, Ridker PM, Rioux JD, Rissanen A, Rivolta C, Schunkert H, Shuldiner AR, Siscovick DS, Stumvoll M, Tönjes A, Tuomilehto J, van Ommen GJ, Viikari J, Heath AC, Martin NG, Montgomery GW, Province MA, Kayser M, Arnold AM, Atwood LD, Boerwinkle E, Chanock SJ, Deloukas P, Gieger C, Grönberg H, Hall P, Hattersley AT, Hengstenberg C, Hoffman W, Lathrop GM, Salomaa V, Schreiber S, Uda M, Waterworth D, Wright AF, Assimes TL, Barroso I, Hofman A, Mohlke KL, Boomsma DI, Caulfield MJ, Cupples LA, Erdmann J, Fox CS, Gudnason V, Gyllensten U, Harris TB, Hayes RB, Jarvelin MR, Mooser V, Munroe PB, Ouwehand WH, Penninx BW, Pramstaller PP, Quertermous T, Rudan I, Samani NJ, Spector TD, Völzke H, Watkins H, Wilson JF, Groop LC, Haritunians T, Hu FB, Kaplan RC, Metspalu A, North KE, Schlessinger D, Wareham NJ, Hunter DJ, O'Connell JR, Strachan DP, Wichmann HE, Borecki IB, van Duijn CM, Schadt EE, Thorsteinsdottir U, Peltonen L, Uitterlinden AG, Visscher PM, Chatterjee N, Loos RJ, Boehnke M, McCarthy MI, Ingelsson E, Lindgren CM, Abecasis GR, Stefansson K, Frayling TM, Hirschhorn JN.
Nature. 2010 Oct 14;467(7317):832-8. doi: 10.1038/nature09410. Epub 2010 Sep 29.



 

PMID: 22982992 FTO genotype is associated with phenotypic variability of body mass index.


 Yang J, Loos RJ, Powell JE, Medland SE, Speliotes EK, Chasman DI, Rose LM, Thorleifsson G, Steinthorsdottir V, Mägi R, Waite L, Smith AV, Yerges-Armstrong LM, Monda KL, Hadley D, Mahajan A, Li G, Kapur K, Vitart V, Huffman JE, Wang SR, Palmer C, Esko T, Fischer K, Zhao JH, Demirkan A, Isaacs A, Feitosa MF, Luan J, Heard-Costa NL, White C, Jackson AU, Preuss M, Ziegler A, Eriksson J, Kutalik Z, Frau F, Nolte IM, Van Vliet-Ostaptchouk JV, Hottenga JJ, Jacobs KB, Verweij N, Goel A, Medina-Gomez C, Estrada K, Bragg-Gresham JL, Sanna S, Sidore C, Tyrer J, Teumer A, Prokopenko I, Mangino M, Lindgren CM, Assimes TL, Shuldiner AR, Hui J, Beilby JP, McArdle WL, Hall P, Haritunians T, Zgaga L, Kolcic I, Polasek O, Zemunik T, Oostra BA, Junttila MJ, Grönberg H, Schreiber S, Peters A, Hicks AA, Stephens J, Foad NS, Laitinen J, Pouta A, Kaakinen M, Willemsen G, Vink JM, Wild SH, Navis G, Asselbergs FW, Homuth G, John U, Iribarren C, Harris T, Launer L, Gudnason V, O'Connell JR, Boerwinkle E, Cadby G, Palmer LJ, James AL, Musk AW, Ingelsson E, Psaty BM, Beckmann JS, Waeber G, Vollenweider P, Hayward C, Wright AF, Rudan I, Groop LC, Metspalu A, Khaw KT, van Duijn CM, Borecki IB, Province MA, Wareham NJ, Tardif JC, Huikuri HV, Cupples LA, Atwood LD, Fox CS, Boehnke M, Collins FS, Mohlke KL, Erdmann J, Schunkert H, Hengstenberg C, Stark K, Lorentzon M, Ohlsson C, Cusi D, Staessen JA, Van der Klauw MM, Pramstaller PP, Kathiresan S, Jolley JD, Ripatti S, Jarvelin MR, de Geus EJ, Boomsma DI, Penninx B, Wilson JF, Campbell H, Chanock SJ, van der Harst P, Hamsten A, Watkins H, Hofman A, Witteman JC, Zillikens MC, Uitterlinden AG, Rivadeneira F, Zillikens MC, Kiemeney LA, Vermeulen SH, Abecasis GR, Schlessinger D, Schipf S, Stumvoll M, Tönjes A, Spector TD, North KE, Lettre G, McCarthy MI, Berndt SI, Heath AC, Madden PA, Nyholt DR, Montgomery GW, Martin NG, McKnight B, Strachan DP, Hill WG, Snieder H, Ridker PM, Thorsteinsdottir U, Stefansson K, Frayling TM, Hirschhorn JN, Goddard ME, Visscher PM. 

Nature. 2012 Oct 11;490(7419):267-72. doi: 10.1038/nature11401.




Data set phenotypes
  • BMI
  • Waist-hip ratio
  • Waist circumference
  • Hip circumference
  • Height
Project

Genetic Investigation of ANthropometric Traits (GIANT) consortium Learn more >

An international collaboration that seeks to identify genetic loci that modulate human body size and shape, including height and measures of obesity.

Experiment summary

Data were updated in October 2016, including the addition of European cohorts for BMI and height (published in the Yang et al. article cited above).

Data set

Download URL: http://csg.sph.umich.edu//abecasis/public/lipids2013/
Publication:

Biological, clinical and population relevance of 95 loci for blood lipids.
Teslovich TM, Musunuru K, Smith AV, Edmondson AC, Stylianou IM, Koseki M, Pirruccello JP, Ripatti S, Chasman DI, Willer CJ, Johansen CT, Fouchier SW, Isaacs A, Peloso GM, Barbalic M, Ricketts SL, Bis JC, Aulchenko YS, Thorleifsson G, Feitosa MF, Chambers J, Orho-Melander M, Melander O, Johnson T, Li X, Guo X, Li M, Shin Cho Y, Jin Go M, Jin Kim Y, Lee JY, Park T, Kim K, Sim X, Twee-Hee Ong R, Croteau-Chonka DC, Lange LA, Smith JD, Song K, Hua Zhao J, Yuan X, Luan J, Lamina C, Ziegler A, Zhang W, Zee RY, Wright AF, Witteman JC, Wilson JF, Willemsen G, Wichmann HE, Whitfield JB, Waterworth DM, Wareham NJ, Waeber G, Vollenweider P, Voight BF, Vitart V, Uitterlinden AG, Uda M, Tuomilehto J, Thompson JR, Tanaka T, Surakka I, Stringham HM, Spector TD, Soranzo N, Smit JH, Sinisalo J, Silander K, Sijbrands EJ, Scuteri A, Scott J, Schlessinger D, Sanna S, Salomaa V, Saharinen J, Sabatti C, Ruokonen A, Rudan I, Rose LM, Roberts R, Rieder M, Psaty BM, Pramstaller PP, Pichler I, Perola M, Penninx BW, Pedersen NL, Pattaro C, Parker AN, Pare G, Oostra BA, O'Donnell CJ, Nieminen MS, Nickerson DA, Montgomery GW, Meitinger T, McPherson R, McCarthy MI, McArdle W, Masson D, Martin NG, Marroni F, Mangino M, Magnusson PK, Lucas G, Luben R, Loos RJ, Lokki ML, Lettre G, Langenberg C, Launer LJ, Lakatta EG, Laaksonen R, Kyvik KO, Kronenberg F, König IR, Khaw KT, Kaprio J, Kaplan LM, Johansson A, Jarvelin MR, Janssens AC, Ingelsson E, Igl W, Kees Hovingh G, Hottenga JJ, Hofman A, Hicks AA, Hengstenberg C, Heid IM, Hayward C, Havulinna AS, Hastie ND, Harris TB, Haritunians T, Hall AS, Gyllensten U, Guiducci C, Groop LC, Gonzalez E, Gieger C, Freimer NB, Ferrucci L, Erdmann J, Elliott P, Ejebe KG, Döring A, Dominiczak AF, Demissie S, Deloukas P, de Geus EJ, de Faire U, Crawford G, Collins FS, Chen YD, Caulfield MJ, Campbell H, Burtt NP, Bonnycastle LL, Boomsma DI, Boekholdt SM, Bergman RN, Barroso I, Bandinelli S, Ballantyne CM, Assimes TL, Quertermous T, Altshuler D, Seielstad M, Wong TY, Tai ES, Feranil AB, Kuzawa CW, Adair LS, Taylor HA Jr, Borecki IB, Gabriel SB, Wilson JG, Holm H, Thorsteinsdottir U, Gudnason V, Krauss RM, Mohlke KL, Ordovas JM, Munroe PB, Kooner JS, Tall AR, Hegele RA, Kastelein JJ, Schadt EE, Rotter JI, Boerwinkle E, Strachan DP, Mooser V, Stefansson K, Reilly MP, Samani NJ, Schunkert H, Cupples LA, Sandhu MS, Ridker PM, Rader DJ, van Duijn CM, Peltonen L, Abecasis GR, Boehnke M, Kathiresan S.
Nature. 2010 Aug 5;466(7307):707-13. doi: 10.1038/nature09270.


 PMID: 24097068

 Discovery and refinement of loci associated with lipid levels.


 Global Lipids Genetics Consortium, Willer CJ, Schmidt EM, Sengupta S, Peloso GM, Gustafsson S, Kanoni S, Ganna A, Chen J, Buchkovich ML, Mora S, Beckmann JS, Bragg-Gresham JL, Chang HY, Demirkan A, Den Hertog HM, Do R, Donnelly LA, Ehret GB, Esko T, Feitosa MF, Ferreira T, Fischer K, Fontanillas P, Fraser RM, Freitag DF, Gurdasani D, Heikkilä K, Hyppönen E, Isaacs A, Jackson AU, Johansson A, Johnson T, Kaakinen M, Kettunen J, Kleber ME, Li X, Luan J, Lyytikäinen LP, Magnusson PK, Mangino M, Mihailov E, Montasser ME, Müller-Nurasyid M, Nolte IM, O'Connell JR, Palmer CD, Perola M, Petersen AK, Sanna S, Saxena R, Service SK, Shah S, Shungin D, Sidore C, Song C, Strawbridge RJ, Surakka I, Tanaka T, Teslovich TM, Thorleifsson G, Van den Herik EG, Voight BF, Volcik KA, Waite LL, Wong A, Wu Y, Zhang W, Absher D, Asiki G, Barroso I, Been LF, Bolton JL, Bonnycastle LL, Brambilla P, Burnett MS, Cesana G, Dimitriou M, Doney AS, Döring A, Elliott P, Epstein SE, Eyjolfsson GI, Gigante B, Goodarzi MO, Grallert H, Gravito ML, Groves CJ, Hallmans G, Hartikainen AL, Hayward C, Hernandez D, Hicks AA, Holm H, Hung YJ, Illig T, Jones MR, Kaleebu P, Kastelein JJ, Khaw KT, Kim E, Klopp N, Komulainen P, Kumari M, Langenberg C, Lehtimäki T, Lin SY, Lindström J, Loos RJ, Mach F, McArdle WL, Meisinger C, Mitchell BD, Müller G, Nagaraja R, Narisu N, Nieminen TV, Nsubuga RN, Olafsson I, Ong KK, Palotie A, Papamarkou T, Pomilla C, Pouta A, Rader DJ, Reilly MP, Ridker PM, Rivadeneira F, Rudan I, Ruokonen A, Samani N, Scharnagl H, Seeley J, Silander K, Stancáková A, Stirrups K, Swift AJ, Tiret L, Uitterlinden AG, van Pelt LJ, Vedantam S, Wainwright N, Wijmenga C, Wild SH, Willemsen G, Wilsgaard T, Wilson JF, Young EH, Zhao JH, Adair LS, Arveiler D, Assimes TL, Bandinelli S, Bennett F, Bochud M, Boehm BO, Boomsma DI, Borecki IB, Bornstein SR, Bovet P, Burnier M, Campbell H, Chakravarti A, Chambers JC, Chen YD, Collins FS, Cooper RS, Danesh J, Dedoussis G, de Faire U, Feranil AB, Ferrières J, Ferrucci L, Freimer NB, Gieger C, Groop LC, Gudnason V, Gyllensten U, Hamsten A, Harris TB, Hingorani A, Hirschhorn JN, Hofman A, Hovingh GK, Hsiung CA, Humphries SE, Hunt SC, Hveem K, Iribarren C, Järvelin MR, Jula A, Kähönen M, Kaprio J, Kesäniemi A, Kivimaki M, Kooner JS, Koudstaal PJ, Krauss RM, Kuh D, Kuusisto J, Kyvik KO, Laakso M, Lakka TA, Lind L, Lindgren CM, Martin NG, März W, McCarthy MI, McKenzie CA, Meneton P, Metspalu A, Moilanen L, Morris AD, Munroe PB, Njølstad I, Pedersen NL, Power C, Pramstaller PP, Price JF, Psaty BM, Quertermous T, Rauramaa R, Saleheen D, Salomaa V, Sanghera DK, Saramies J, Schwarz PE, Sheu WH, Shuldiner AR, Siegbahn A, Spector TD, Stefansson K, Strachan DP, Tayo BO, Tremoli E, Tuomilehto J, Uusitupa M, van Duijn CM, Vollenweider P, Wallentin L, Wareham NJ, Whitfield JB, Wolffenbuttel BH, Ordovas JM, Boerwinkle E, Palmer CN, Thorsteinsdottir U, Chasman DI, Rotter JI, Franks PW, Ripatti S, Cupples LA, Sandhu MS, Rich SS, Boehnke M, Deloukas P, Kathiresan S, Mohlke KL, Ingelsson E, Abecasis GR. 

Nat Genet. 2013 Nov;45(11):1274-83. doi: 10.1038/ng.2797.




Data set phenotypes
  • Cholesterol
  • LDL cholesterol
  • Triglycerides
  • HDL cholesterol
  • Diastolic blood pressure
  • Systolic blood pressure
Project

Global Lipids Genetics Consortium (GLGC) Learn more >

A consortium to study the genetic determinants of blood low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and triglycerides.

Experiment summary

With updates to the data in October 2016, the sample size for GLGC GWAS increased to 188,577. Associations for triglyceride, cholesterol, HDL, and LDL levels were updated from a joint analysis of GWAS and MetaboChip data, described here and published in Willer et al. 2013 (see above).

Data set

Download URL: https://www.magicinvestigators.org/downloads/


Publications



Genome-Wide Association Study of the Modified Stumvoll Insulin Sensitivity Index Identifies BCL2 and FAM19A2 as Novel Insulin Sensitivity Loci.
Walford G, Gustafsson S, Rybin D, et al.
Diabetes. 2016 Oct;65(10):3200-11. doi: 10.2337/db16-0199




Common variants at 10 genomic loci influence hemoglobin A1(C) levels via glycemic and nonglycemic pathways.
Soranzo N, et al.
Diabetes. 2010 Dec;59(12):3229-39. doi: 10.2337/db10-0502.


Data set phenotypes

  • type 2 diabetes
  • HbA1c
  • fasting glucose
  • two-hour glucose
  • HOMA-B
  • fasting insulin
  • two-hour insulin
  • HOMA-IR
  • proinsulin levels
  • insulin sensitivity index (ISI)

Project

Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) Learn more >

MAGIC (the Meta-Analyses of Glucose and Insulin-related traits Consortium) represents a collaborative effort to combine data from multiple GWAS to identify additional loci that impact on glycemic and metabolic traits.

Experiment summary

October 2016 updates

As a result of updates in October 2016, the number of samples for MAGIC data in the Portal increased to 133,010.

Fasting glucose and fasting insulin associations in the MAGIC GWAS dataset were updated based on the analysis published by Manning et al. 2012 (see above). Fasting glucose results account for BMI and were generated from an analysis of 29 studies in up to 58,074 non-diabetic participants. Fasting insulin results also account for BMI and were generated from an analysis of 26 studies in up to 51,750 non-diabetic participants.

Fasting glucose, 2 hr glucose, and fasting insulin results in the MAGIC Metabochip dataset were updated based on the analysis published by Scott et al. 2012 (see above). Results for fasting glucose are from models adjusted for age and sex, and from up to 133,010 non-diabetic participants from 66 studies. Fasting insulin results are for ln-transformed fasting insulin as the outcome and are adjusted for age, sex and are reported both with and without BMI adjustment. These results are from up to 108,557 individuals from 56 studies. Results for 2h-glucose are from models adjusted for age and sex and from up to 42,854 individuals from 20 studies.

January 2017 updates

Results of variant associations with the modified Stumvoll Insulin Sensitivity Index (ISI) were added for 16,753 non-diabetic individuals. Associations were adjusted in one of three ways: for age and sex; for age, sex, and BMI; or according to a model that analyzed the combined influence of the genotype effect adjusted for BMI and the interaction effect between the genotype and BMI on ISI.

Data set

Publication:

Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near ODZ4.
Psychiatric GWAS Consortium Bipolar Disorder Working Group.
Nat Genet. 2011 Sep 18;43(10):977-83. doi: 10.1038/ng.943.
Publication:
A mega-analysis of genome-wide association studies for major depressive disorder.
Major Depressive Disorder Working Group of the Psychiatric GWAS Consortium, Ripke S, Wray NR, Lewis CM, Hamilton SP, Weissman MM, Breen G, Byrne EM, Blackwood DH, Boomsma DI, Cichon S, Heath AC, Holsboer F, Lucae S, Madden PA, Martin NG, McGuffin P, Muglia P, Noethen MM, Penninx BP, Pergadia ML, Potash JB, Rietschel M, Lin D, Müller-Myhsok B, Shi J, Steinberg S, Grabe HJ, Lichtenstein P, Magnusson P, Perlis RH, Preisig M, Smoller JW, Stefansson K, Uher R, Kutalik Z, Tansey KE, Teumer A, Viktorin A, Barnes MR, Bettecken T, Binder EB, Breuer R, Castro VM, Churchill SE, Coryell WH, Craddock N, Craig IW, Czamara D, De Geus EJ, Degenhardt F, Farmer AE, Fava M, Frank J, Gainer VS, Gallagher PJ, Gordon SD, Goryachev S, Gross M, Guipponi M, Henders AK, Herms S, Hickie IB, Hoefels S, Hoogendijk W, Hottenga JJ, Iosifescu DV, Ising M, Jones I, Jones L, Jung-Ying T, Knowles JA, Kohane IS, Kohli MA, Korszun A, Landen M, Lawson WB, Lewis G, Macintyre D, Maier W, Mattheisen M, McGrath PJ, McIntosh A, McLean A, Middeldorp CM, Middleton L, Montgomery GM, Murphy SN, Nauck M, Nolen WA, Nyholt DR, O'Donovan M, Oskarsson H, Pedersen N, Scheftner WA, Schulz A, Schulze TG, Shyn SI, Sigurdsson E, Slager SL, Smit JH, Stefansson H, Steffens M, Thorgeirsson T, Tozzi F, Treutlein J, Uhr M, van den Oord EJ, Van Grootheest G, Völzke H, Weilburg JB, Willemsen G, Zitman FG, Neale B, Daly M, Levinson DF, Sullivan PF.
Mol Psychiatry. 2013 Apr;18(4):497-511. doi: 10.1038/mp.2012.21.

Data set phenotypes
  • Bipolar disorder
  • Major depressive disorder
  • Schizophrenia
Project

Psychiatric GWAS Consortium (PGC) Learn more >

PGC conducts meta-analyses of genome-wide association study data, focusing on autism, attention-deficit hyperactivity disorder, bipolar disorder, major depressive disorder, and schizophrenia.

Data set

Publication:

Sequence variants in SLC16A11 are a common risk factor for type 2 diabetes in Mexico.
SIGMA Type 2 Diabetes Consortium, Williams AL, Jacobs SB, Moreno-Macías H, Huerta-Chagoya A, Churchhouse C, Márquez-Luna C, García-Ortíz H, Gómez-Vázquez MJ, Burtt NP, Aguilar-Salinas CA, González-Villalpando C, Florez JC, Orozco L, Haiman CA, Tusié-Luna T, Altshuler D.
Nature. 2014 Feb 6;506(7486):97-101. doi: 10.1038/nature12828.
Publication:
Association of a low-frequency variant in HNF1A with type 2 diabetes in a Latino population.
SIGMA Type 2 Diabetes Consortium, Estrada K, Aukrust I, Bjørkhaug L, Burtt NP, Mercader JM, García-Ortiz H, Huerta-Chagoya A, Moreno-Macías H, Walford G, Flannick J, Williams AL, Gómez-Vázquez MJ, Fernandez-Lopez JC, Martínez-Hernández A, Jiménez-Morales S, Centeno-Cruz F, Mendoza-Caamal E, Revilla-Monsalve C, Islas-Andrade S, Córdova EJ, Soberón X, González-Villalpando ME, Henderson E, Wilkens LR, Le Marchand L, Arellano-Campos O, Ordóñez-Sánchez ML, Rodríguez-Torres M, Rodríguez-Guillén R, Riba L, Najmi LA, Jacobs SB, Fennell T, Gabriel S, Fontanillas P, Hanis CL, Lehman DM, Jenkinson CP, Abboud HE, Bell GI, Cortes ML, Boehnke M, González-Villalpando C, Orozco L, Haiman CA, Tusié-Luna T, Aguilar-Salinas CA, Altshuler D, Njølstad PR, Florez JC, MacArthur DG.
JAMA. 2014 Jun 11;311(22):2305-14. doi: 10.1001/jama.2014.6511.
Publication:
Rare variants in PPARG with decreased activity in adipocyte differentiation are associated with increased risk of type 2 diabetes.
Majithia AR, Flannick J, Shahinian P, Guo M, Bray MA, Fontanillas P, Gabriel SB; GoT2D Consortium; NHGRI JHS/FHS Allelic Spectrum Project; SIGMA T2D Consortium; T2D-GENES Consortium, Rosen ED, Altshuler D.
Proc Natl Acad Sci U S A. 2014 Sep 9;111(36):13127-32. doi: 10.1073/pnas.1410428111.

Data set phenotypes
  • Type 2 diabetes

Data Set Subjects

CasesControlsCohortEthnicity
8151138UNAM/INCMNSZ Diabetes Study (UIDS) Latino
690472Diabetes in Mexico Study (DMS) Latino
287613Mexico City Diabetes Study (MCDS) Latino
20562143Multiethnic Cohort (MEC) Latino
Total: 3,848Total: 4,366
Project

Slim Initiative in Genomic Medicine for the Americas (SIGMA) Learn more >

The SIGMA partnership aims to understand the genomic basis of type 2 diabetes in Mexican and Latin American populations.

Publications

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

Dataset phenotypes

  • type 2 diabetes

Dataset subjects

CasesControlsCohortEthnicity
186110882Oxford-based UK T2D case-control European
17151793The Diabetes Audit and Research in Tayside Scotland (GoDarts) European
111850Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS) European
160941Uppsala Longitudinal Study of Adult Men (ULSAM) European
7734385Metabolic Syndrome in Men Study (METSIM) European
6461380FIN-D2D 2007 European
81477The Dose Responses to Exercise Training (DR's EXTRA) Study European
11121494National FINRISK 2007 Study (FINRISK 2007) European
981486Finland-United States Investigation of NIDDM Genetics (FUSION) Study European
3114688Prevalence, Prediction and Prevention of diabetes (PPP) (Finnish) European
26010Diabetes Registry Vaasa (DIREVA) (Finnish) European
19280All New Diabetics In Scania (ANDiS) (Swedish) European
4405173Malmö Diet and Cancer (MDC) (Swedish) European
31920Scania Diabetes Registry (SDR) (Swedish) European
13341754Nurses' Health Study (NHS) European
11131298Health Professional Follow-Up Study (HPFS) European
8821506Estonian Genome Center, University of Tartu (EGCUT) European
14461567EFSOCH and DARE European
9592779Cooperative Health Research in the Region of Augsburg [KORA] European
58644996Danish T2D case-control European
960965GLACIER European
6911157EPIC-Norfolk (T2D cases) and the Fenland study (cohort) European
Total: 29,161Total: 48,571

Projects

DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Learn more >

The DIAGRAM consortium performs large-scale studies to characterize the genetic basis of type 2 diabetes, primarily focusing on samples of European descent.

Genetics of Type 2 Diabetes (GoT2D) Learn more >

GoT2D led an effort to aggregate data for a meta-analysis of low-frequency variants in coding regions that influence risk of in type 2 diabetes and related traits such as LDL cholesterol levels. The study was based on data from high-density SNP genotyping with a custom array (the exome chip) in 82,000 people, of which 16,000 were funded by GoT2D. The dataset includes ~35,000 type 2 diabetes cases and ~51,000 controls, all of European ancestry.

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."

Dataset phenotypes

  • type 2 diabetes
  • fasting glucose
  • fasting insulin

Dataset Subjects

CasesControlsCohortEthnicity
5402,913Massachusetts General Hospital Cardiology and Metabolic Patient cohort (CAMP MGH) Mixed

Project

This work was performed at Pfizer Inc. and Massachusetts General Hospital as part of a public-private partnership to generate genotype data for a cardiometabolic and prediabetic cohort.

Experiment summary

The MGH Cardiology and Metabolic Patient (CAMP MGH) cohort comprises 3,857 subjects recruited between 2008 and 2012. Approximately 86% of subjects were of European ancestry; 10% were African American; 2% were admixed American; 1% were East Asian; and 1% were South Asian. Two thirds of the subjects were drawn from patients who had appointments with a physician in the MGH Heart Center, while one third were recruited independent of any hospital visit. All subjects had plasma and serum samples collected, as well as blood for genomic DNA. Subjects with known diabetes had vascular reactivity measurements (FMD of brachial artery), while subjects without known diabetes had an oral glucose tolerance test. Exome Core Chip genotyping was performed on all subjects.

Overview of analysis results

Data were analyzed by the Analysis Team at the Accelerating Medicines Partnership Data Coordinating Center (AMP-DCC), Broad Institute. After removing related samples and samples flagged for non-type 2 diabetes, 3,453 samples (540 T2D cases and 2,913 controls) were analyzed. Two different statistical models were applied to analyze associations of variants with type 2 diabetes, fasting glucose levels, and fasting insulin levels.

The strongest association with T2D, at genome-wide significance, was observed for the variant rs9468919 on chromosome 6 near HLA-C. Detection of an association with this locus was unexpected because it is known to be associated with T1D, but type 1 diabetics had been removed from the sample set before analysis. To evaluate this result further, we investigated the linkage disequilibrium (LD) relationships of this SNP with others in the region. rs9468919 is not in LD with any variants known to be associated with T1D, but is in moderate LD with SNPs in the region that are reported to be associated with T2D. Supporting its potential association with T2D, this SNP displays a T2D association of nominal significance (p = 0.055) in the DIAGRAM Transethnic meta-analysis dataset.

While we cannot fully explain this signal, it is unlikely that the result at rs9468919 is due to T1D sample contamination, known confounders, or to poor quality of the SNP genotyping. In any first-pass, cohort-specific analysis such as this, some results may be inflated or even spurious due to limited sample size or cohort-specific effects. However, the high quality of the data, QC, and analysis methods applied are consistent with all standards within the T2D genetics community.

Other than the HLA-C signal, additional signals of nominal significance were detected at previously reported T2D-associated loci.

A variant associated with fasting glucose levels at genome-wide significance was detected near MTNR1B on chromosome 11. Additional signals of nominal significance were detected at loci previously reported to be associated with fasting glucose levels, and several rare variants with significant effects were also seen.

A variant associated with fasting insulin levels at genome-wide significance was detected near CHMP4C on chromosome 8. However, since the minor allele count for this variant was only 8 in this dataset, the result could be spurious. A signal of nominal significance was detected at the IGF1 locus, previously reported to be associated with fasting insulin levels.

Detailed reports

AMP-DCC Data Analysis Report (download PDF)

Genotype Data Quality Control Report (download PDF)

Accessing CAMP data in the T2D Knowledge Portal

CAMP data are available:

  • On Gene Pages in the Variants & Associations table
  • On Variant Pages in the Associations at a glance section and in the Association statistics across traits table
  • Via the Variant Finder tool, for the phenotypes T2D, fasting glucose, and fasting insulin
  • Via the Genetic Association Interactive Tool (GAIT), which enables custom association analysis for either single variants (available on Variant Pages) or for the set of variants in and near a gene (Interactive burden test, available on Gene Pages)
  • From the View full genetic association results for a phenotype search on the Portal home page: first select one of the dataset phenotypes, and then on the resulting page, select the CAMP GWAS dataset.

Future plans for CAMP data in the T2D Knowledge Portal

  • Associations of variants with T2D, fasting glucose, and fasting insulin from the CAMP data will be made available via LocusZoom, an interactive visualization of variants and associations that is displayed on both Gene Pages and Variant Pages
  • CAMP data will be analyzed for associations of variants with additional phenotypes, and these results will be made available in the Portal via the pages and tools listed above.

Data set phenotypes
  • Type 2 diabetes

Data Set Subjects

CasesControlsCohortEthnicity
8151138UNAM/INCMNSZ Diabetes Study (UIDS) Latino
690472Diabetes in Mexico Study (DMS) Latino
287613Mexico City Diabetes Study (MCDS) Latino
20562143Multiethnic Cohort (MEC) Latino
Total: 3,848Total: 4,366
Project

Slim Initiative in Genomic Medicine for the Americas (SIGMA) Learn more >

The SIGMA partnership aims to understand the genomic basis of type 2 diabetes in Mexican and Latin American populations.

Publications

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

Dataset phenotypes

  • fasting glucose
  • fasting insulin

Projects

DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Learn more >

The DIAGRAM consortium performs large-scale studies to characterize the genetic basis of type 2 diabetes, primarily focusing on samples of European descent.

Genetics of Type 2 Diabetes (GoT2D) Learn more >

GoT2D led an effort to aggregate data for a meta-analysis of low-frequency variants in coding regions that influence risk of in type 2 diabetes and related traits such as LDL cholesterol levels. The study was based on data from high-density SNP genotyping with a custom array (the exome chip) in 82,000 people, of which 16,000 were funded by GoT2D. The dataset includes ~35,000 type 2 diabetes cases and ~51,000 controls, all of European ancestry.

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."

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