# TOPMed Omics of Type 2 Diabetes and Quantitative Traits

> **NIH NIH UM1** · MASSACHUSETTS GENERAL HOSPITAL · 2021 · $902,027

## Abstract

Project Summary/Abstract
Type 2 diabetes continues to spread globally due to unhealthy environment interacting with genetics. Recent genetic
discoveries of >700 variants at >400 loci associated with type 2 diabetes (T2D) and its related quantitative traits (QTs:
fasting glucose (FG), insulin (FI) and hemoglobin A1c (A1c)) give insight into new T2D pathobiology. However, most
discoveries have been in whites; studies in minority groups disproportionately affected by T2D are needed. Also, most
associations are in the non-coding genome, indicating that whole genome sequence (WGS) analysis is needed for full
variant and effector gene characterization. The NHLBI Trans-Omics for Precision Medicine (TOPMed) study includes
WGS from 21,493 cases of prevalent T2D and 63,541 controls from five populations (41,557 Euro, 23,203 AA, 16,213
Latino, 2,867 Asian, 1,194 Samoan Adiposity Study) from 28 cohorts and up to 54,407 non-T2D individuals with FG,
FI or HbA1c, as well as age of T2D onset, level of glycemic control and longitudinal follow-up for incident T2D
events. In this project Aim 1 is to test WGS-wide in five ancestry groups for known and new common and rare variants
associated T2D and QTs. We will conduct analyses in the NHLBI BioData Catalyst. Replication of novel variants is
available in >1 million individuals of diverse ancestry from six biobanks with T2D (UKBB, BioME, BioVU, Partners
BB, REGARDS, MVP) with TOPMed-imputed genomic array data. For health translation, we will group T2D genetic
risk variants into polygenic risk scores (PRSs) that predict future T2D or characterize specific physiological axes, and
use variants in Mendelian Randomization (MR) tests of disease causality. Next, TOPMed has blood omic measures
from five ancestry groups that may also identify novel biological networks relevant to T2D pathobiology, including
whole blood DNA methylation (measured by sequencing or microarrays, N=11,131), transcriptomics (RNA-seq)
(N=8,334), proteomics (SomaLogic aptamers or Olink proteomics, N=7,897) and metabolomics (liquid
chromatography/mass spectroscopy, N=11,631). In Aim 2, we will test omic signatures associated with T2D and QTs
individually and in multidimensional omic and genomic network models of the pathobiology of T2D. Finally, in Aim 3
we plan to integrate TOPMed WGS and omic results with bespoke cell or tissue-specific (beta cell, islet, liver, fat and
muscle) omic and epigenomic annotation (ATAC-seq, RNA-seq, Hi-C, ChIP-seq) in the Accelerating Medicine
Partnership (AMP) T2D Diabetes epiGenome Atlas, and with hundreds of additional genomic trait associations in the
AMP T2D Knowledge Portal (T2DKP) for ‘in silico variant-to-function’ and phenomic studies. Complete functional
mapping with blood and tissue-specific omic integration of the human T2D and QT genome is on the horizon. Our
multidisciplinary, multicenter team has a proven track record in genetics and omic discovery. We are actively working
with TOPMed, AMP T2D DGA and T2DKP ...

## Key facts

- **NIH application ID:** 10121260
- **Project number:** 2UM1DK078616-13
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** JAMES B MEIGS
- **Activity code:** UM1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $902,027
- **Award type:** 2
- **Project period:** 2008-04-01 → 2025-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10121260

## Citation

> US National Institutes of Health, RePORTER application 10121260, TOPMed Omics of Type 2 Diabetes and Quantitative Traits (2UM1DK078616-13). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10121260. Licensed CC0.

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