Litcius/Paper detail

Rare Genetic Variants of Large Effect Influence Risk of Type 1 Diabetes

Vincenzo Forgetta, Despoina Manousaki, Roman Istomine, Stephanie Ross, Marie-Catherine Tessier, Luc Marchand, Min Li, Hui‐Qi Qu, Jonathan P. Bradfield, Struan F.A. Grant, Håkon Håkonarson, Andrew D. Paterson, Ciriaco A. Piccirillo, Constantin Polychronakos, J. Brent Richards

2020Diabetes151 citationsDOIOpen Access PDF

Abstract

Most replicated genetic determinants for type 1 diabetes are common (minor allele frequency [MAF] >5%). We aimed to identify novel rare or low-frequency (MAF <5%) single nucleotide polymorphisms with large effects on risk of type 1 diabetes. We undertook deep imputation of genotyped data followed by genome-wide association testing and meta-analysis of 9,358 type 1 diabetes case and 15,705 control subjects from 12 European cohorts. Candidate variants were replicated in a separate cohort of 4,329 case and 9,543 control subjects. Our meta-analysis identified 27 independent variants outside the MHC, among which 3 were novel and had MAF <5%. Three of these variants replicated with Preplication < 0.05 and Pcombined < Pdiscovery. In silico analysis prioritized a rare variant at 2q24.3 (rs60587303 [C], MAF 0.5%) within the first intron of STK39, with an effect size comparable with those of common variants in the INS and PTPN22 loci (combined [from the discovery and replication cohorts] estimate of odds ratio [ORcombined] 1.97, 95% CI 1.58–2.47, Pcombined = 2.9 × 10−9). Pharmacological inhibition of Stk39 activity in primary murine T cells augmented effector responses through enhancement of interleukin 2 signaling. These findings provide insight into the genetic architecture of type 1 diabetes and have identified rare variants having a large effect on disease risk.

Topics & Concepts

Minor allele frequencyType 2 diabetesPTPN22GeneticsSingle-nucleotide polymorphismBiologyGenome-wide association studyOdds ratioGenetic associationImputation (statistics)AlleleCase-control studyGenotypeDiabetes mellitusGeneMedicineInternal medicineEndocrinologyMissing dataComputer scienceMachine learningDiabetes and associated disordersPancreatic function and diabetesGenetic Associations and Epidemiology