Identification of genetic effects underlying type 2 diabetes in South Asian and European populations.
Marie LohWeihua ZhangHong Kiat NgKatharina T SchmidAmel LamriLin TongMeraj AhmadJung-Jin LeeMaggie C Y NgLauren E PettyCassandra N SpracklenFumihiko TakeuchiMd Tariqul IslamFarzana JasmineAnuradhani KasturiratneMuhammad G KibriyaKaren L MohlkeGuillaume ParéGauri PrasadMohammad ShahriarMiao Ling CheeH Janaka de SilvaJames C EngertHertzel C GersteinK Radha ManiSabanayagam CharumathiMarijana VujkovicAnanda Rajitha WickremasingheTien Yin WongChittaranjan S YajnikSalim YusufHabibul AhsanDwaipayan BharadwajSonia S AnandJennifer E BelowMichael BoehnkeDonald W BowdenGiriraj R ChandakChing-Yu ChengNorihiro KatoAnubha MahajanXueling S SimMark I McCarthyAndrew P MorrisJaspal S KoonerDanish SaleheenJohn C ChambersPublished in: Communications biology (2022)
South Asians are at high risk of developing type 2 diabetes (T2D). We carried out a genome-wide association meta-analysis with South Asian T2D cases (n = 16,677) and controls (n = 33,856), followed by combined analyses with Europeans (n eff = 231,420). We identify 21 novel genetic loci for significant association with T2D (P = 4.7 × 10 -8 to 5.2 × 10 -12 ), to the best of our knowledge at the point of analysis. The loci are enriched for regulatory features, including DNA methylation and gene expression in relevant tissues, and highlight CHMP4B, PDHB, LRIG1 and other genes linked to adiposity and glucose metabolism. A polygenic risk score based on South Asian-derived summary statistics shows ~4-fold higher risk for T2D between the top and bottom quartile. Our results provide further insights into the genetic mechanisms underlying T2D, and highlight the opportunities for discovery from joint analysis of data from across ancestral populations.
Keyphrases
- genome wide
- dna methylation
- gene expression
- type diabetes
- genome wide association
- copy number
- systematic review
- insulin resistance
- cardiovascular disease
- glycemic control
- healthcare
- skeletal muscle
- electronic health record
- high throughput
- transcription factor
- deep learning
- randomized controlled trial
- machine learning
- bioinformatics analysis
- genome wide analysis