PAGER: A novel genotype encoding strategy for modeling deviations from additivity in complex trait association studies

作者：Philip J. Freda, Attri Ghosh, Priyanka Bhandary, Nicholas Matsumoto, Apurva S. Chitre, Jiayan Zhou, Molly A. Hall, Abraham A. Palmer, Tayo Obafemi-Ajayi, Jason H. Moore · 发表于：BioData Mining · 年份：2024 · DOI：10.1186/s13040-024-00393-x · 被引用次数：2 · 研究领域：Genetic Associations and Epidemiology、Genetic Mapping and Diversity in Plants and Animals、Genetic and phenotypic traits in livestock

BACKGROUND: The additive model of inheritance assumes that heterozygotes (Aa) are exactly intermediate in respect to homozygotes (AA and aa). While this model is commonly used in single-locus genetic association studies, significant deviations from additivity are well-documented and contribute to phenotypic variance across many traits and systems. This assumption can introduce type I and type II errors by overestimating or underestimating the effects of variants that deviate from additivity. Alternative genotype encoding strategies have been explored to account for different inheritance patterns, but they often incur significant computational or methodological costs. To address these challenges, we introduce PAGER (Phenotype Adjusted Genotype Encoding and Ranking), an efficient pre-processing method that encodes each genetic variant based on normalized mean phenotypic differences between diallelic genotype classes (AA, Aa, and aa). This approach more accurately reflects each variant's true inheritance model, improving model precision while minimizing the costs associated with alternative encoding strategies. RESULTS: Through extensive benchmarking on SNPs simulated with both binary and continuous phenotypes, we demonstrate that PAGER accurately represents various inheritance patterns (including additive, dominant, recessive, and heterosis), achieves levels of statistical power that meet or exceed other encoding strategies, and attains computation speeds up to 55 times faster ...