Year: 2018
Type: article
Abstract: Random-effects models are a popular tool for analysing total narrow-sense heritability for quantitative phenotypes, on the basis of large-scale SNP data. Recently, there have been disputes over the validity of conclusions that may be drawn from such analysis. We derive some of the fundamental statistical properties of heritability estimates arising from these models, showing that the bias will generally be small. We show that that the score function may be manipulated into a form that facilitates intelligible interpretations of the results. We go on to use this score function to explore the behavior of the model when certain key assumptions of the model are not satisfied - shared environment, measurement error, and genetic effects that are confined to a small subset of sites. The variance and bias depend crucially on the variance of certain functionals of the singular values of the genotype matrix. A useful baseline is the singular value distribution associated with genotypes that are completely independent - that is, with no linkage and no relatedness - for a given number of individuals and sites. We calculate the corresponding variance and bias for this setting.
Source: Electronic Journal of Statistics
DOI: 10.1214/17-ejs1386
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $675
Awards: CAREER AWARD PROGRAM IN DEMOGRAPHY OF AGING, Genomic Medicine and Statistics., SOCGEN: Combining Social Science and Molecular Genetic Research to Examine Inequality and the Life Course, Principled extensions of GWAS to multivariate phenotypes: uncovering causal genetic structure in the presence of unobserved confounders., Center on the Economics and Demography of Aging
OpenAlex ID: W2952644534