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GWAS Across Ancestries Uncovers Novel Genetic Loci Linked to Glycemic Traits

NEW YORK — A trans-ancestral analysis has uncovered additional loci associated with glycemic traits among people without type 2 diabetes.

Glycemic traits like fasting blood glucose levels, glucose levels two hours after oral challenge, and glycated hemoglobin levels are commonly used to diagnose and monitor type 2 diabetes as well as cardiometabolic health. While previous genome-wide association studies have linked more than 120 loci to glycemic traits, these analyses largely relied on participants of European ancestry.

Researchers from the Meta-Analyses of Glucose and Insulin-related traits Consortium, which was established in 2007, have sought to expand not only the size of their study cohorts but also boost the diversity of their genetic ancestry.

"It is quite well known that genetic studies have been biased towards European ancestry populations, therefore we and others have attempted to increase diverse representation, as we felt we were missing important knowledge regarding populations in the world that are at increased risk of type 2 diabetes," senior author Inês Barroso, a professor at the University of Exeter, said in an email. "As we are moving towards more precision medicine approaches to type 2 diabetes, it is really important to have representation of the diverse population of the world, otherwise we will just continue to exacerbate existing healthcare inequalities."

As she and her colleagues reported in Nature Genetics on Monday, their trans-ancestral analysis of more than 280,000 individuals without diabetes uncovered more than 240 loci associated with glycemic traits. They further estimated that if their analysis had only included individuals of European ancestry, they would have missed about two dozen of these novel loci.

For their analysis, the researchers aggregated GWAS data on up to 281,416 individuals without diabetes for whom fasting glucose, fasting insulin, two-hour glucose after oral challenge, and glycated hemoglobin levels were available. About 30 percent of the individuals were of non-European ancestry: about 13 percent had East Asian, 7 percent Hispanic, 6 percent African American, 3 percent South Asian, and 2 percent sub-Saharan African ancestry. Within this cohort, more than 49 million variants were genotyped or imputed.

By conducting meta-analyses of each of the four glycemic traits within each ancestry group and in trans-ancestry meta-analyses, the researchers uncovered 242 loci associated with those traits. Of these, 99 loci had not previously been linked to either glycemic traits or type 2 diabetes.

Three of the single-ancestry novel loci were unique to individuals of non-European ancestry, the researchers noted. For instance, a locus near LOC100128993 was linked to fasting insulin levels among individuals of African-American ancestry, while a locus within PIK3C2G was linked to fasting glucose levels among individuals of Hispanic ancestry.

The researchers further estimated that if they had instead relied upon a similarly sized cohort of only individuals of European ancestry, they would have identified 24 fewer novel loci.

The trans-ancestry approach also enabled the researchers to have better fine-mapping resolution and narrow in on variants that are even more likely to be causal. By combining this improved fine-mapping with expression quantitative trait loci and other analyses, the researchers identified new candidate causal variants, including one linked to fasting insulin levels that affects the expression of INSR, which encodes the insulin receptor, in subcutaneous adipose and another linked to fasting glucose levels that is associated with the expression of SLC2A1, a major glucose transporter, in the blood.

Barroso noted that their findings also highlighted tissue and cell types that could influence glycemic traits, and which variants and biological pathways may have greater effects on the development of type 2 diabetes. "This understanding is being used to develop stratified genetic scores that may allow us to better stratify patients in future, and better target treatment to patients," she added.

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