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Updated: Aug 15, 2026
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In Vivo Modeling of the Morbid Human Genome using Danio rerio
Published on: August 24, 2013
Correlations between causal effect sizes of proximal SNPs vary with functional annotations and implicate stabilizing
Martin Jinye Zhang1,2,3, Arun Durvasula4,5,6,7, Colby Chiang8
1Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA. martinzh@andrew.cmu.edu.
Nature Genetics
|August 14, 2026
Summary
Causal disease effects of nearby single-nucleotide polymorphisms (SNPs) are often correlated, not independent. Our new method, LDSPEC, reveals these correlations, impacting heritability estimates and highlighting the role of linkage masking in evolution.
Area of Science:
- Genetics
- Statistical Genetics
- Population Genetics
Background:
- Causal disease effect sizes of proximal single-nucleotide polymorphisms (SNPs) are typically assumed to be independent.
- This assumption may not hold true, potentially affecting genetic analyses and interpretations.
Purpose of the Study:
- To introduce a novel method, linkage disequilibrium SNP-pair effect correlation regression (LDSPEC), for estimating correlations between causal disease effect sizes of proximal SNPs.
- To investigate the extent and characteristics of these correlations in large-scale human genetic data.
Main Methods:
- Development and application of the LDSPEC regression method.
- Analysis of 70 UK Biobank diseases and traits (N≈305,646) to estimate SNP-pair effect correlations.
- Forward simulations incorporating stabilizing selection to explore evolutionary implications.
Main Results:
- LDSPEC produced robust estimates in simulations.
- Significant non-zero SNP-pair effect correlations were detected, decaying with distance and varying with allele frequency and linkage disequilibrium.
- SNP pairs with shared functions exhibited stronger correlations over longer genomic distances.
- SNP heritability estimates were reduced compared to the sum of causal effect variances, particularly for certain functional annotations.
Conclusions:
- The independence assumption for proximal SNP causal effects is often violated.
- Detected SNP-pair effect correlations influence heritability estimates and suggest the importance of linkage masking in evolutionary processes.
- Findings have implications for understanding genetic architecture of diseases and evolutionary dynamics.
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