TOPMed Omics of Type 2 Diabetes and Quantitative Traits
NIH Reporter: NIH NIDDK UM1DK078616
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.
Aim 1: Test WGS-wide for known and new T2D and QT- associated loci in five ancestry groups.
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).
Aim 2: Test omic measures individually and in multilevel network models of T2D pathobiology.
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.
Aim 3: Integrate TOPMed WGS, omics with AMP T2D DGA, T2DKP for variant-to-function analyses.
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 data. We are well positioned to achieve the Aims of the proposal, with the intention to find new approaches to address the global epidemic of T2D in all populations at risk.