Comparison of Methods Utilizing Sex-Specific PRSs Derived From GWAS Summary Statistics

Abstract

The polygenic risk score (PRS) is calculated as the weighted sum of an individual's genotypes and their estimated effect sizes, which is often used to estimate an individual's genetic susceptibility to complex traits and disorders. It is well known that some complex human traits or disorders have sex differences in trait distributions, disease onset, progression, and treatment response, although the underlying mechanisms causing these sex differences remain largely unknown. PRSs for these traits are often based on Genome-Wide Association Studies (GWAS) data with both male and female samples included, ignoring sex differences. In this study, we present a benchmark study using both simulations with various combinations of genetic correlation and sample size ratios between sexes and real data to investigate whether combining sex-specific PRSs can outperform sex-agnostic PRSs on traits showing sex differences. We consider two types of PRS models in our study: single-population PRS models (PRScs, LDpred2) and multiple-population PRS models (PRScsx). For each trait or disorder, the candidate PRSs were calculated based on sex-specific GWAS data and sex-agnostic GWAS data. The simulation results show that applying LDpred2 or PRScsx to sex-specific GWAS data and then combining sex-specific PRSs leads to the highest prediction accuracy when the genetic correlation between sexes is low and the sample sizes for both sexes are balanced and large. Otherwise, the PRS generated by applying LDpred2 or PRScs to sex-agnostic GWAS data is more appropriate. If the sample sizes between sexes are not too small and very unbalanced, combining LDpred2-based sex-specific PRSs to predict on the sex with a larger sample size and combining PRScsx-based sex-specific PRSs to predict on the sex with a smaller size are the preferred strategies. For real data, we considered 19 traits from Genetic Investigation of ANthropometric Traits (GIANT) consortium studies and UK Biobank with both sex-specific GWAS data and sex-agnostic GWAS data. We found that for waist-to-hip ratio (WHR) related traits, accounting for sex differences and incorporating information from the opposite sex could help improve PRS prediction accuracy. Taken together, our findings in this study provide guidance on how to calculate the best PRS for sex-differentiated traits or disorders, especially as the sample size of GWASs grows in the future.

Keywords: genome-wide association study; human genetics; polygenic risk score; risk prediction; sex-specific.

FIGURE 1

Overview of the 11 PRSs considered. The inputs are female-specific GWAS summary statistics, male-specific GWAS summary statistics, and sex-agnostic GWAS summary statistics; PRS methods include PRScs, LDpred2, and PRScsx; validation samples are used to tune hyperparameters of PRSs and find the optimal weights of sex-specific PRSs; testing samples are to assess prediction accuracy.

FIGURE 2

Comparisons of PRSs on WHR and WHRadjBMI, WHRadjBMI.SNPadjPA and WHRadjBMI.SNPadjSMK, and WCadjBMI.SNPadjPA and WCadjBMI.SNPadjSMK (WHR: waist and hip ratio; WHRadjBMI: WHR adjusted by BMI; WHRadjBMI.SNPadjPA: with physical activity level as a covariate; WHRadjBMI.SNPadjSMK: with smoking status as a covariate; WCadjBMI.SNPadjPA: with physical activity level as a covariate; WCadjBMI.SNPadjSMK: with smoking status as a covariate). Female-Specific: Using female-specific GWAS summary statistics as input. Male-Specific: using male-specific GWAS summary statistics as input. Sex-agnostic: using sex-agnostic GWAS summary statistics as input. Combined-sex: the combination of female-specific PRS and male-specific PRS.

FIGURE 3

Flow charts that provide a synthesized guide for practitioners to choose among these methods. (A) When the sample sizes are balanced between sexes. (B) When the sample sizes are unbalanced between sexes.