The identification of causal relationships between specific genes and social, behavioral, and health outcomes is challenging due to environmental confounding from population stratification and dynastic genetic effects. Existing methods to eliminate environmental confounding leverage random genetic variation resulting from recombination and require within-family dyadic genetic data (i.e., parent-child and/or sibling pairs), meaning they can only be applied in relatively small and selected samples. We introduce the model and provide derivations showing that it-under plausible assumptions-provides consistent (and, in certain cases, unbiased) estimates of genetic effects using just a single individual's genotype. Then, leveraging distinct samples of fully and partially genotyped sibling pairs in the Wisconsin Longitudinal Study, we use polygenic indices and phenotypic data for 24 different traits to empirically validate the phenotype differences model. Finally, we utilize the model to test the effects of 40 polygenic indices on lifespan. After a 10% false discovery rate correction, we find that polygenic indices for three traits-body mass index, self-rated health, chronic obstructive pulmonary disease-have a statistically significant effect on an individual's lifespan.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11626128 | PMC |
| http://dx.doi.org/10.1073/pnas.2405725121 | DOI Listing |
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