A comprehensive evaluation of polygenic score and genotype imputation performances of human SNP arrays in diverse populations

Abstract

Regardless of the overwhelming use of next-generation sequencing technologies, microarray-based genotyping combined with the imputation of untyped variants remains a cost-effective means to interrogate genetic variations across the human genome. This technology is widely used in genome-wide association studies (GWAS) at bio-bank scales, and more recently, in polygenic score (PGS) analysis to predict and stratify disease risk. Over the last decade, human genotyping arrays have undergone a tremendous growth in both number and content making a comprehensive evaluation of their performances became more important. Here, we performed a comprehensive performance assessment for 23 available human genotyping arrays in 6 ancestry groups using diverse public and in-house datasets. The analyses focus on performance estimation of derived imputation (in terms of accuracy and coverage) and PGS (in terms of concordance to PGS estimated from whole-genome sequencing data) in three different traits and diseases. We found that the arrays with a higher number of SNPs are not necessarily the ones with higher imputation performance, but the arrays that are well-optimized for the targeted population could provide very good imputation performance. In addition, PGS estimated by imputed SNP array data is highly correlated to PGS estimated by whole-genome sequencing data in most cases. When optimal arrays are used, the correlations of PGS between two types of data are higher than 0.97, but interestingly, arrays with high density can result in lower PGS performance. Our results suggest the importance of properly selecting a suitable genotyping array for PGS applications. Finally, we developed a web tool that provides interactive analyses of tag SNP contents and imputation performance based on population and genomic regions of interest. This study would act as a practical guide for researchers to design their genotyping arrays-based studies. The tool is available at: https://genome.vinbigdata.org/tools/saa/.

Employing emerging technologies such as motion capture to study the complex interplay between genotype and power-related performance traits

“…This challenge led to emergence of the bioinformatics field, and it is now essential to have a biostatistician or an expert in data interpretation as a collaborator. Furthermore, artificial intelligence has the capacity to discover novel approaches to analyze biomarker and genomic data, equipping us with cutting-edge tools for predicting genes or epigenetic factors associated with performance traits and to address challenges associated with evaluating and understanding vast quantities of genomic and biomechanical data ( Nguyen et al, 2022 ). Moreover, the large quantity of data being generated fundamentally changes the validation process, which now necessitates both discovery and hypothesis testing.…”

Recent advances in polygenic scores: translation, equitability, methods and FAIR tools

“…Typically PGS have been developed in cohorts of genotyped individuals using a limited set of directly measured variants on a genotyping array, which has been imputed to higher genome coverage using reference panels [ 139 ]. Recent studies have shown that the choice of imputation panel and strategy can affect PGS accuracy [ 140 ], and the choice of genotyping array can be particularly important for underrepresented populations [ 141 ]. Ideally, the clinical use of PGS should combine common and rare variants [ 139 ], even if the improvements to risk-stratification at the population level may be limited [ 142 ].…”

The predictive capacity of polygenic risk scores for disease risk is only moderately influenced by imputation panels tailored to the target population

“…Moreover, rare SNPs (<1% MAF) are more sensitive to the choice of the imputation process ( Lencz et al 2018 , Shi et al 2018 ), but PRSs rely mainly on common SNPs. While some works examined factors in the imputation process that impact the quality of PRS, such as the genotyping platform, phasing, and imputation methods ( Chen et al 2020 , Thanh Nguyen et al 2022 ), there has been no systematic assessment of how different ethnic compositions of the imputation panel affect the accuracy of PRS risk prediction.…”