Validating the doubly weighted genetic risk score for the prediction of type 2 diabetes in the Lifelines and Estonian Biobank cohorts

Abstract

As many cases of type 2 diabetes (T2D) are likely to remain undiagnosed, better tools for early detection of high‐risk individuals are needed to prevent or postpone the disease. We investigated the value of the doubly weighted genetic risk score (dwGRS) for the prediction of incident T2D in the Lifelines and Estonian Biobank (EstBB) cohorts. The dwGRS uses an additional weight for each single nucleotide polymorphism in the risk score, to correct for “Winner's curse” bias in the effect size estimates. The traditional (single‐weighted genetic risk score; swGRS) and dwGRS were calculated for participants in Lifelines ( n = 12,018) and EstBB ( n = 34,129). The dwGRS was found to have stronger association with incident T2D (hazard ratio [HR] = 1.26 [95% confidence interval: 1.10–1.43] and HR = 1.35 [1.28–1.42]) compared to the swGRS (HR = 1.21 [1.07–1.38] and HR = 1.25 [1.19–1.32]) in Lifelines and EstBB, respectively. Comparing the 5‐year predicted risks from the models with and without the dwGRS, the continuous net reclassification index was 0.140 (0.034–0.243; p = .009 Lifelines), and 0.257 (0.194–0.319; p < 2 × 10 −16 EstBB). The dwGRS provided incremental value to the T2D prediction model with established phenotypic predictors. It clearly distinguished the risk groups for incident T2D in both biobanks thereby showing its clinical relevance.

A Principal Component Informed Approach to Address Polygenic Risk Score Transferability Across European Cohorts

“…Associated variants generally have a small effect on the biological outcome ( Wray et al, 2007 ; Visscher et al, 2017 ) and are often combined into a polygenic risk score (PRS) to estimate a person’s genetic susceptibility for a trait or disease ( Wray et al, 2014 ). PRSs have already demonstrated their clinical potential by detecting individuals in high-risk groups for several diseases such as type 2 diabetes, cardiovascular diseases, Alzheimer’s disease, breast cancer, prostate cancer, and colorectal cancer ( Lecarpentier et al, 2017 ; Khera et al, 2018 ; Schumacher et al, 2018 ; Läll et al, 2019 ; Pärna et al, 2020 ) sometimes reaching risk detection equal to monogenic mutations ( Khera et al, 2018 ).…”

“…Likewise, GWAS results deriving from large consortia such as GIANT have been shown to still carry residual population stratification, despite PCA correction in the original studies ( Berg et al, 2019 ). In addition, there is still a lack of consensus on whether PC adjustment should be applied only to the discovery or also to the target cohort ( Pärna et al, 2020 ; Choi et al, 2020 ; Läll et al, 2017 ; Abdellaoui et al, 2019 ; Privé et al, 2022 ; Wünnemann et al, 2019 ).…”

“…In addition, there is still a lack of consensus on whether PC adjustment should be applied only to the discovery or also to the target cohort (Pärna et al, 2020;Choi et al, 2020;Läll et al, 2017;Abdellaoui et al, 2019;Privé et al, 2022;Wünnemann et al, 2019).…”

An Improved Genome-Wide Polygenic Score Model for Predicting the Risk of Type 2 Diabetes

“…Besides the well-known behavioral and environmental factors, T2D has a strong genetic component ( Zimmet et al, 2014 ). Genome-wide association studies (GWASs) have successfully identified many common genetic variants that confer T2D susceptibility ( Burton et al, 2007 ; Scott et al, 2007 ; Palmer et al, 2012 ; Visscher et al, 2017 ; Pärna et al, 2020 ). However, all of these common genetic variants discovered by GWAS can only be able to account for a small proportion of the total heritability ( McCarthy, 2010 ; Herder and Roden, 2011 ; Prasad and Groop, 2015 ) and thus lead to low predictive power.…”

Economic evaluation of using polygenic risk score to guide risk screening and interventions for the prevention of type 2 diabetes in individuals with high overall baseline risk

“…For example, the Finnish diabetes risk score (FINDRISC) is a widely used risk assessment tool estimating the 10-year overall risk of developing T2D based on traditional risk factors, such as age, body mass index, physical activity, and family history of diabetes ( Lindström and Tuomilehto, 2003 ). Besides above well-known conventional risk factors, T2D has a strong genetic component ( Wray et al, 2013 ; Khera et al, 2019 ; Pärna et al, 2020 ). Therefore, over the last decade, development of novel genomic-based polygenic risk scores (PRSs) has increased the potential of using genomic information in the risk screening and prevention of T2D ( Padilla-Martínez et al, 2020 ).…”