Statistical Physics of Fitness Landscapes Finds Risk Genes Specific to Different AD Genetic Backgrounds - Lagiesetty, Yashwanth; Samieinasab, Maryam; Al‐Ramahi, Ismael; Wilhelm, Kevin; Katsonis, Panagiotis; Asmussen, Jenn; Lee, Kwanghyuk Danny; Botas, Juan; Lichtarge, Olivier (Alzheimer's & dementia, 2023)
Background To study how genetic backgrounds modulate neurodegenerative risk requires genotype‐phenotype association methods effective in small patient cohorts. Method We developed a genome sequence analysis framework rooted in Statistical Physics, which yields a reliable measure of gene functional impact in any chosen population. When comparing two populations, such as cases and controls, shifts in gene importance identify those likely to drive phenotype differences. Results In repeated case‐control genome sequence studies of Alzheimer’s Disease (AD) focused on a single sex, APOE allele, or ethnic ancestry, we identified gene sets that met criteria for success, including prior GWAS studies, post‐mortem of AD brain tissues expression, live Drosophila experiments, and risk modeling. Conclusion Statistical Physics of fitness landscape is a new tool to characterize genes and mutations that enhance or protect from AD, powerful enough to identify similarities, complementarities, and differences in risk gene in target subgroups of about 1000 subjects.