Genotype imputation and variability in polygenic risk score estimation
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RESEARCH
Open Access
Genotype imputation and variability in polygenic risk score estimation Shang-Fu Chen1,2, Raquel Dias1,2, Doug Evans1,2, Elias L. Salfati1,2, Shuchen Liu1,2, Nathan E. Wineinger1,2 and Ali Torkamani1,2*
Abstract Background: Polygenic risk scores (PRSs) are a summarization of an individual’s genetic risk for a disease or trait. These scores are being generated in research and commercial settings to study how they may be used to guide healthcare decisions. PRSs should be updated as genetic knowledgebases improve; however, no guidelines exist for their generation or updating. Methods: Here, we characterize the variability introduced in PRS calculation by a common computational process used in their generation—genotype imputation. We evaluated PRS variability when performing genotype imputation using 3 different pre-phasing tools (Beagle, Eagle, SHAPEIT) and 2 different imputation tools (Beagle, Minimac4), relative to a WGS-based gold standard. Fourteen different PRSs spanning different disease architectures and PRS generation approaches were evaluated. Results: We find that genotype imputation can introduce variability in calculated PRSs at the individual level without any change to the underlying genetic model. The degree of variability introduced by genotype imputation differs across algorithms, where pre-phasing algorithms with stochastic elements introduce the greatest degree of score variability. In most cases, PRS variability due to imputation is minor (< 5 percentile rank change) and does not influence the interpretation of the score. PRS percentile fluctuations are also reduced in the more informative tails of the PRS distribution. However, in rare instances, PRS instability at the individual level can result in singular PRS calculations that differ substantially from a whole genome sequence-based gold standard score. Conclusions: Our study highlights some challenges in applying population genetics tools to individual-level genetic analysis including return of results. Rare individual-level variability events are masked by a high degree of overall score reproducibility at the population level. In order to avoid PRS result fluctuations during updates, we suggest that deterministic imputation processes or the average of multiple iterations of stochastic imputation processes be used to generate and deliver PRS results. Keywords: Genotype phasing, Genotype imputation, Polygenic risk score, PRS, Coronary artery disease, Polygenic score, Genetic risk score, Genome-wide score
* Correspondence: [email protected] 1 Scripps Research Translational Institute, Scripps Research, La Jolla, CA 92037, USA 2 Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, CA 92037, USA © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the sou
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