Wachter, K. W. | bioRxiv Science

bioRxiv2017

On negative heritability and negative estimates of heritability

AO_SCPLOWBSTRACTC_SCPLOWWe consider the problem of interpreting negative maximum likelihood estimates of heritability that sometimes arise from popular statistical models of additive genetic variation. These may result from random noise acting on estimates of genuinely positive heritability, but we argue that they may also arise from misspecification of the standard additive mechanism that is supposed to justify the statistical procedure. Researchers should be open to the possibility that negative heritability estimates could reflect a real physical feature of the biological process from which the data were sampled.

genetics↗

bioRxiv2016

Statistical properties of simple random-effects models for genetic heritability

Random-effects models are a popular tool for analysing total narrow-sense heritability for simple 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 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 -- as well as to elucidate the meaning of negative heritability estimates that may arise.\n\nThe 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.\n\nMSC 2010 subject classifications: Primary 92D10; secondary 62P10; 62F10; 60B20.

genetics↗