Consistent and idiosyncratic pleiotropy in shaping genetic correlations.

Authors

Haoran Cai, Kerry Geiler-Samerotte, David L Des Marais

Published in

Molecular biology and evolution. Aug 22, 2026. Epub Aug 22, 2026.

Abstract

Pleiotropy, the phenomenon where a single mutation influences multiple phenotypic traits, creates genetic correlations that can constrain evolutionary trajectories. Yet genetic correlations differ in their persistence: some remain stable over long evolutionary timescales, whereas others change rapidly across generations or environments. One explanation is that similar values of genetic correlation, rG, can arise from different pleiotropic architectures: broadly aligned effects across many loci, or disproportionate covariance contributions from a few large effect loci. Motivated by the distinction between vertical and horizontal pleiotropy, here, we develop a bivariate marker effect framework for recombinant mapping populations that separates candidate large covariance contributors from the polygenic background correlation, rD. We define rD as the correlation among marker effects after trimming markers with unusually large covariance contributions. rD is a trait-pair summary of how consistently small and moderate effect markers align across the genome; high rD is expected when many perturbations propagate through shared developmental, physiological, causal, or geometric structure. Applying this framework to high-dimensional yeast single-cell morphology, we show that trait pairs with similar rG can differ substantially in rD, and that a small number of candidate outlier regions can strongly influence some marker effect correlations. We then test whether rD predicts the environmental stability of genetic correlations under geldanamycin-mediated Hsp90 perturbation. Trait pairs with stronger rD show smaller absolute changes in rG. These results suggest that genetic correlations supported by a strong polygenic marker effect background are more environmentally stable than correlations shaped primarily by a few large covariance contributors.

PMID:
42628966
Bibliographic data and abstract were imported from PubMed on 22 Aug 2026.

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