Improved heritability estimation from genome-wide SNPs.

By Doug Speed, Gibran Hemani, Michael R. Johnson, David J. Balding. American Journal of Human Genetics, 2012

765 citations recorded by the source corpus.

Estimation of narrow-sense heritability, h(2), from genome-wide SNPs genotyped in unrelated individuals has recently attracted interest and offers several advantages over traditional pedigree-based methods. With the use of this approach, it has been estimated that over half the heritability of human height can be attributed to the ~300,000 SNPs on a genome-wide genotyping array. In comparison, only 5%-10% can be explained by SNPs reaching genome-wide significance. We investigated via simulation the validity of several key assumptions underpinning the mixed-model analysis used in SNP-based h(2) estimation. Although we found that the method is reasonably robust to violations of four key assumptions, it can be highly sensitive to uneven linkage disequilibrium (LD) between SNPs: contributions to h(2) are overestimated from causal variants in regions of high LD and are underestimated in regions of low LD. The overall direction of the bias can be up or down depending on the genetic architecture of the trait, but it can be substantial in realistic scenarios. We propose a modified kinship matrix in which SNPs are weighted according to local LD. We show that this correction greatly reduces the bias and increases the precision of h(2) estimates. We demonstrate the impact of our method on the first seven diseases studied by the Wellcome Trust Case Control Consortium. Our LD adjustment revises downward the h(2) estimate for immune-related diseases, as expected because of high LD in the major-histocompatibility region, but increases it for some nonimmune diseases. To calculate our revised kinship matrix, we developed LDAK, software for computing LD-adjusted kinships.

Subfields: Genetics

View Paper: https://www.semanticscholar.org/paper/a7d2c5f78bccfceec26e7cd6a5c81bc2923ece4c

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In Genetics

Citations in the corpus

Genetics and Intelligence Differences: Five Special Findings (2014, neutral): “Other methods 37 and modifications 38, 39 are also emerging 40, 41 .”
LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis (2016, background): “Table S1, displays descriptive information for each of the GWAS in LD Hub, including, trait name, consortium name, ethnicity, gender, number of cases and controls, sample size, PubMed ID, year of publication, and other relevant information.”
An Atlas of Genetic Correlations across Human Diseases and Traits (2015, background): “It is probably reasonable to treat h 2 per SNP as a constant function of MAF for SNPs with MAF above 5%, but we have very little information about h 2 per SNP for SNPs with MAF below 5%.”
Identifying Causal Variants at Loci with Multiple Signals of Association (2014, background): “This assumption is the basis of many recent approaches to estimate heritability (Yang et al. 2011a,b; Speed et al. 2012; Kostem and Eskin 2013) and to correct for population structure in GWAS (Kang et al. 2008; Lippert et al. 2011; Listgarten et al. 2012; Segura et al. 2012; Zhou and Stephens 2012).”
Fine-mapping from summary data with the “Sum of Single Effects” model (2021, background): “, [42, 43]), and implies that effect sizes for SNPs with low minor allele frequencies (MAFs) tend to be larger than those with large MAFs.”
Evidence for Weak Selective Constraint on Human Gene Expression (2018, background): “This likely reflects the spatial distribution of causal regulatory variants; we and others find that eQTLs are enriched near TSSs (Figure S2; Stranger et al. 2007; Veyrieras et al. 2008).”