A Litmus Test for Confounding in Polygenic Scores DOI Creative Commons
Samuel Pattillo Smith, Olivia S. Smith, Hakhamanesh Mostafavi

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 4, 2025

Abstract Polygenic scores (PGSs) are being rapidly adopted for trait prediction in the clinic and beyond. PGSs often thought of as capturing direct genetic effect one’s genotype on their phenotype. However, because constructed from population-level associations, they influenced by factors other than effects, including stratification, assortative mating, dynastic effects (“SAD effects”). Our interpretation application may hinge relative impact SAD since be environmentally or culturally mediated. We developed a method that estimates proportion variance PGS (in given sample) is driven covariance. leverage comparison interest based standard GWAS with sibling GWAS—which largely immune to effects—to quantify contribution each type interest. method, Partitioning Genetic Scores Using Siblings (PGSUS, pron. “Pegasus”), breaks down components further axes ancestry, allowing nuanced effects. In particular, PGSUS can detect stratification along major ancestry well “isotropic” respect ancestry. Applying PGSUS, we found evidence using large meta-analyses height educational attainment range UK Biobank. some instances, appears stratified axis one sample but not another (for example, comparisons samples different countries, ancient DNA vs. contemporary samples). Finally, show approaches adjustment population structure GWASs have distinct advantages mitigation ancestry-axis-specific isotropic PGS. study illustrates how family-based designs combined population-based guide genomic predictors.

Language: Английский

A Litmus Test for Confounding in Polygenic Scores DOI Creative Commons
Samuel Pattillo Smith, Olivia S. Smith, Hakhamanesh Mostafavi

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 4, 2025

Abstract Polygenic scores (PGSs) are being rapidly adopted for trait prediction in the clinic and beyond. PGSs often thought of as capturing direct genetic effect one’s genotype on their phenotype. However, because constructed from population-level associations, they influenced by factors other than effects, including stratification, assortative mating, dynastic effects (“SAD effects”). Our interpretation application may hinge relative impact SAD since be environmentally or culturally mediated. We developed a method that estimates proportion variance PGS (in given sample) is driven covariance. leverage comparison interest based standard GWAS with sibling GWAS—which largely immune to effects—to quantify contribution each type interest. method, Partitioning Genetic Scores Using Siblings (PGSUS, pron. “Pegasus”), breaks down components further axes ancestry, allowing nuanced effects. In particular, PGSUS can detect stratification along major ancestry well “isotropic” respect ancestry. Applying PGSUS, we found evidence using large meta-analyses height educational attainment range UK Biobank. some instances, appears stratified axis one sample but not another (for example, comparisons samples different countries, ancient DNA vs. contemporary samples). Finally, show approaches adjustment population structure GWASs have distinct advantages mitigation ancestry-axis-specific isotropic PGS. study illustrates how family-based designs combined population-based guide genomic predictors.

Language: Английский

Citations

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