Evaluation of Bayesian Linear Regression Models as a Fine Mapping tool

Merina Shrestha, Zhonghao Bai, Tahereh Gholipourshahraki, Astrid Johannesson Hjelholt, Mads Kjølby, Palle Duun Rohde, Peter Sørensen

Publikation: Working paper/PreprintPreprint


Bayesian linear regression (BLR) models consider the underlying genetic architecture of complex phenotypes by specifying different prior distributions for SNP effects allowing heterogenous distribution of the true genetic signals. Our goal is to evaluate BLR models with BayesC and BayesR prior distributions for fine mapping on simulated and real binary and quantitative phenotypes, and compare them to the state-of-the-art external models: FINEMAP, SuSIE-RSS, SuSIE-Inf and FINEMAP-Inf. Evaluation of models was based on the F1 classification score for simulations, and predictive accuracy for the UK Biobank (UKB) phenotypes. We used over 533K genotyped SNPs (simulations) and 6.6 million imputed SNPs (UKB phenotypes), from over 335K White British Unrelated samples in the UK biobank. We simulated phenotypes from low (GA1) to moderate (GA2) polygenicity, heritability(h2) of 30% and 10%, causal SNPs (π) of 0.1% and 1% sampled genome-wide, and prevalence (PV) of 5% and 15%. Summary statistics and in-sample linkage disequilibrium were used to fit models in regions defined by lead SNPs. BayesR improved the F1 score, averaged across all simulations, by by 27.26%, 26.96%, 18.40%, 15.42%, and 13.32% relative to FINEMAP-Inf, BayesC, FINEMAP, SUSIE-RSS and SUSIE-Inf. Prediction R2, averaged across all the UKB quantitative phenotypes, with BayesR decreased by 5.32% and 3.71% compared to SuSIE-Inf and FINEMAP-Inf, whereas increased by 7.93% and 8.3% compared to SuSIE-RSS and BayesC. Prediction AUC, averaged across all the UKB binary phenotypes, with BayesR increased by 0.40%, 0.16%, 0.08%., and 0.05% relative to SuSIE-RSS, BayesC, FINEMAP-Inf, and SuSIE-Inf. These findings suggest that the performance of the BLR models was comparable to the state-of-the-art external models. The performance of BayesR prior was closely aligned with SuSIE-Inf and FINEMAP-Inf models. Results from both simulations and application of the models in the UKB phenotypes suggest that the BLR models are efficient fine mapping tools.

Antal sider30
StatusUdgivet - sep. 2023


Dyk ned i forskningsemnerne om 'Evaluation of Bayesian Linear Regression Models as a Fine Mapping tool'. Sammen danner de et unikt fingeraftryk.