Woman injecting insulin, daily diabetes care during COVID-19
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Results from a large international study across diverse populations provides important information that should improve type 2 diabetes risk prediction.

Type 2 diabetes rates have increased significantly in recent years affecting more than 35 million people in the U.S. alone. Despite being known as a condition linked to poor diet and insufficient exercise, there is a significant genetic component to type 2 diabetes that can impact whether or not someone will develop the condition.

Much research is ongoing that aims to help improve risk prediction for type 2 diabetes, for example, by recording genetic variants linked to the condition and by creating polygenic risk scores using these variants. However, the majority of previous genome wide association studies and biobanks have been dominated by White populations from European backgrounds.

This can be problematic from a genetic point of view as variants such as single nucleotide polymorphisms (SNPs) that are linked to disease often vary considerably in type and distribution between populations.

To try and improve genetics-based risk predictions, the DIAMANTE (DIabetes Meta-ANalysis of Trans-Ethnic association studies) Consortium carried out a detailed meta-analysis of 122 genome wide association studies including almost 181,000 people with type 2 diabetes and 1.16 million people with the condition. The cohort included was diverse including 51% European, 28.4% East Asian, 8.3% South Asian, 6.6% African, and 5.6% Hispanic individuals.

As reported in Nature Genetics, the research team identified 237 areas of the genome with links to type 2 diabetes, with 338 specific association signals. “We have now identified 117 genes that are likely to cause Type 2 diabetes, 40 of which have not been reported before,” explains Anubha Mahajan, a co-author and University of Oxford professor.

The scientists say their findings are useful for a number of reasons. Firstly, because it allowed them to check if association signals were shared across populations; secondly it allowed them to see how much variations there was for each association in different populations; thirdly because causal variants could be studied and mapped in more detail; and finally because it allowed them to calculate population-specific weights for certain variants to evaluate how much risk is linked to them in different groups.

“Our findings matter because we’re moving toward using genetic scores to weigh up a person’s risk of diabetes,” says co-author Cassandra Spracklen, assistant professor of biostatistics and epidemiology in the UMass Amherst School of Public Health and Health Sciences.

“Up to now, over 80% of genomic research of this type has been conducted in white European-ancestry populations, but we know that scores developed exclusively in individuals of one ancestry don’t work well in people of a different ancestry.”

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