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Reevaluation of SNP heritability in complex human traits
Doug Speed1, Na Cai2,3,
1UCL Genetics Institute, University College London, London, UK.
Nature Genetics
|May 23, 2017
Summary
This study developed a new model for estimating SNP heritability, finding it higher than previous methods. The improved model better accounts for genetic factors influencing traits across the genome.
Area of Science:
- Genetics
- Bioinformatics
- Statistical Genomics
Background:
- SNP heritability estimation relies on unverified assumptions about heritability distribution.
- Current models may underestimate the genetic contribution to phenotypic variance.
Purpose of the Study:
- To develop and validate an empirically derived model for SNP heritability estimation.
- To improve the accuracy of heritability estimates by considering MAF, LD, and genotype certainty.
Main Methods:
- Analysis of imputed genetic data across numerous human traits.
- Empirical derivation of a novel heritability model.
- Comparison of new estimates with established software (GCTA, GCTA-LDMS).
Main Results:
- The improved model yielded SNP heritability estimates 43% higher than GCTA and 25% higher than GCTA-LDMS on average.
- The estimated contribution of DNase I hypersensitivity sites to heritability decreased significantly from 79% to 24% with the new model.
Conclusions:
- Current assumptions in heritability estimation are inadequate.
- The novel heritability model provides more accurate estimates of genetic contributions to human traits.
- Re-evaluation of regulatory element contributions to heritability is necessary.
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