bioRxivEvolutionary BiologyPreprint
Triangulating evidence to detect signatures of stabilizing selection acting on protein levels in humans
Mihaela Diana Zanoaga, Théo CavinatoiD, Thibault LatrilleiD, Orsolya LiskaiD, Tabea SchoeleriD, Balázs PappiD, Zoltán KutalikiD
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Abstract
Stabilizing selection is thought to maintain quantitative traits near evolutionary optima, yet the extent to which it constrains human molecular phenotypes remains largely unknown. Here, we investigate signatures of stabilizing selection on the circulating levels of 2'923 plasma proteins in up to 50'000 UK Biobank participants by integrating complementary evidence across evolutionary timescales. We combine genetic signatures of selection accumulated over generations with associations between protein abundance and contemporary fitness-related outcomes. Genetic evidence was assessed using the frequency-dependent architecture of common variants with LDpred2 and a newly developed likelihood-based framework leveraging rare loss-of-function variant burdens. Contemporary evidence was obtained through quadratic associations between protein abundance and three fitness proxies: reproductive success, somatic health, and parental longevity. Across all approaches, negative selection coefficients predominated. Among significant estimates, 100% of common-variant and 92.4% of rare-variant estimates were consistent with stabilizing selection, compared with 99.8% for somatic health, 97.7% for parental longevity, and 89.3% for reproductive success. Despite capturing distinct aspects of selection, the approaches showed partial convergence. Rare-variant-based estimates correlated with health-score-derived coefficients (rho = 0.36, p = 5.2 x 10^-6) and independent measures of evolutionary constraint, including protein heritability, loss-of-function intolerance, and cross-species transcript evolutionary rates. Together, our findings suggest that stabilizing selection is widespread across the human plasma proteome and highlight the value of integrating genetic architecture with fitness-related phenotypes to understand evolutionary constraints on molecular variation.
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