Authors
Martin Jinye Zhang, Arun Durvasula, Colby Chiang, Evan M Koch, Benjamin J Strober, Huwenbo Shi, Alison R Barton, Samuel S Kim, Omer Weissbrod, Po-Ru Loh, Steven Gazal, Shamil Sunyaev, Alkes L Price
Published in
Nature genetics. Aug 13, 2026. Epub Aug 13, 2026.
Abstract
Causal disease effect sizes of proximal single-nucleotide polymorphisms (SNPs) are widely assumed to be independent but could be correlated. Here we introduce a new method, linkage disequilibrium SNP-pair effect correlation regression (LDSPEC), to estimate the correlation of causal disease effect sizes of derived alleles between proximal SNPs; LDSPEC produced robust estimates in simulations. Analyzing 70 UK Biobank diseases and traits (average N = 305,646), we detected significantly non-zero SNP-pair effect correlations (for example, -0.37 ± 0.09 for low-frequency positive linkage disequilibrium 0-100-bp SNP pairs) that decayed with distance and varied with allele frequency and linkage disequilibrium between SNPs. SNP pairs with shared functions had stronger effect correlations that spanned longer genomic distances. Consequently, SNP heritability estimates were smaller than estimates of the sum of causal effect size variances across SNPs, particularly for certain functional annotations. We recapitulated our findings via forward simulations involving stabilizing selection, implicating the action of linkage masking, whereby haplotypes containing linked SNPs with opposite effects on disease have reduced effects on fitness and escape negative selection.
PMID:
42595902
Bibliographic data and abstract were imported from PubMed on 14 Aug 2026.
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