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Updated: Jul 8, 2025

In Vivo Modeling of the Morbid Human Genome using Danio rerio
Published on: August 24, 2013
Pervasive correlations between causal disease effects of proximal SNPs vary with functional annotations and implicate
Martin Jinye Zhang1,2,3, Arun Durvasula2,3,4, Colby Chiang5
1Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.
The study reveals that linked genetic variants (SNPs) influencing disease risk are not independent, challenging long-held assumptions. This finding impacts how we estimate heritability and understand disease genetics.
Area of Science:
- Human Genetics
- Complex Traits
- Statistical Genomics
Background:
- Human disease and complex trait genetics are widely studied.
- Causal effect sizes of proximal single nucleotide polymorphisms (SNPs) are assumed independent.
- Little is known about the correlation of causal effect sizes between nearby SNPs.
Conclusions:
- Proximal SNPs' causal effects are correlated, not independent, impacting genetic architecture understanding.
- Linkage masking, driven by linked SNPs with opposing effects, may explain reduced fitness effects and escape from negative selection.
- Current heritability estimation methods may be overestimated due to the assumption of SNP independence.
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