NEW YORK – A team led by researchers at the University of Oxford has published findings from a multi-ancestry genome-wide association meta-analysis focused on type 2 diabetes (T2D), uncovering more than 300 T2D-related signals that were subsequently fine-mapped to suspected causal genes and used to develop cross-ancestry polygenic risk scores.
"Multi-ancestry genetic risk scores enhanced transferability of T2D prediction across diverse populations," co-senior and co-corresponding authors Andrew Morris and Mark McCarthy and their colleagues wrote in Nature Genetics on Thursday, noting that their work "provides a step toward more effective clinical translation of T2D GWAS to improve global health for all, irrespective of genetic background."
Morris is affiliated with the University of Oxford's Wellcome Centre for Human Genetics, the University of Liverpool, the University of Tartu's Estonian Genome Centre, the University of Manchester, and the NIHR Manchester Biomedical Research Centre. McCarthy was affiliated with the University of Oxford's Oxford Centre for Diabetes, the Wellcome Centre for Human Genetics, and the NIHR Oxford Biomedical Research Centre at the time of the study and is currently with Roche/Genentech.
As part of the "Diabetes Meta-Analysis of Trans-Ethnic association studies" (DIAMANTE) consortium, the researchers brought together genotyping data for 180,834 individuals with T2D and nearly 1.2 million unaffected controls, performing a multi-ancestry GWAS and ancestry-specific GWAS analyses. Almost 49 percent of participants were of European descent, they noted, though the cohort also included participants with East Asian, South Asian, African, Hispanic, or admixed ancestry.
"Increasing diversity in genetic research will ultimately provide a more comprehensive and refined view of the genetic contribution to complex human traits, powering understanding of the molecular and biological processes underlying common diseases," the authors concluded, "and offering the most promising opportunities for clinical translation of GWAS findings to improve global public health."
Based on profiles for more than 19.8 million biallelic SNPs on autosomal chromosomes, the team narrowed in on genome-wide significant associations with T2D at 277 genetic loci, including 237 that met more stringent association significance criteria and 11 not reported in the past.
That set included 338 independent variant associations, though subsequent fine-mapping analyses that incorporated genome, functional, and regulatory annotation insights suggested that more than 54 percent of T2D-related signals related to single SNPs at the locus in question.
With such fine-mapping clues, the investigators noted, it should be possible to more fully tease out the molecular processes that contribute to T2D and to track down causal genes from the associated SNP set.
"[W]e demonstrate the value of analyses conducted on diverse populations to understand how T2D-associated variants impact downstream molecular and biological processes underlying the disease and advance clinical translation of GWAS findings for all, irrespective of genetic background," the researchers reported.
Likewise, because the current analysis includes individuals from a range of ancestral backgrounds, the team got a look at associations that span different population groups, ultimately coming up with a multi-ancestry T2D genetic risk score (GRS) that outperformed ancestry-specific GRS when it came to predicting disease across the cohort and in data for more than 129,200 Finnish FinnGen participants.
"Our study demonstrates the advantages of a GRS derived from multi-ancestry meta-regression for T2D prediction across five major ancestry groups," the authors wrote, adding that "we built on our expanded collection of distinct multi-ancestry association signals to demonstrate evidence of positive selection of T2D-risk alleles in African populations that may have been driven by the promotion of energy storage and use through adaptation to the local environment."