Abstract
Polygenic scores (PGSs) have emerged as a standard approach to predict phenotypes from genotype data in a wide array of applications from socio-genomics to personalized medicine. Traditional PGSs assume genotype data to be error-free, ignoring possible errors and uncertainties introduced from genotyping, sequencing, and/or imputation. In this work, we investigate the effects of genotyping error due to low coverage sequencing on PGS estimation. We leverage SNP array and low-coverage whole-genome sequencing data (lcWGS, median coverage 0.04×) of 802 individuals from the Dana-Farber PROFILE cohort to show that PGS error correlates with sequencing depth (p = 1.2 × 10−7). We develop a probabilistic approach that incorporates genotype error in PGS estimation to produce well-calibrated PGS credible intervals and show that the probabilistic approach increases classification accuracy by up to 6% as compared to traditional PGSs that ignore genotyping error. Finally, we use simulations to explore the combined effect of genotyping and effect size errors and their implication on PGS-based risk-stratification. Our results illustrate the importance of considering genotyping error as a source of PGS error especially for cohorts with varying genotyping technologies and/or low-coverage sequencing.
Original language | English (US) |
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Pages (from-to) | 1319-1329 |
Number of pages | 11 |
Journal | American journal of human genetics |
Volume | 110 |
Issue number | 8 |
DOIs | |
State | Published - Aug 3 2023 |
Externally published | Yes |
Keywords
- effect sizes
- genotype error
- lcWGS
- PGS
- PGS error
- risk stratification
- uncertainty
ASJC Scopus subject areas
- Genetics
- Genetics(clinical)