Heritability, the proportion of variation in a particular trait that is attributable to genetic factors, is a fundamental parameter in genetics. First introduced by Sewall Wright and Ronald Fisher nearly a century ago, it is key to the response to selection in evolutionary biology and agriculture, and to the prediction of disease risk in medicine.
Heritability is not necessarily constant in a population. Changes in the method of measurement, environmental change and the effects of migration, selection and inbreeding all can alter heritability.
The use of high-density genetic marker technologies allows novel estimation methods of heritability, for example, estimation in unpedigreed populations and estimation within families — free of assumptions about variation between families.
The estimation of heritability for new phenotypes — those that can be measured with recently developed technologies — provides knowledge about the nature of between-individual differences in core biological processes. For example, amounts of gene expression, brain scanning measurements, the length of telomeres and biochemical compounds measured by mass spectrometry show substantial heritability.
Heritabilities are often surprisingly large and at present there is no consensus theory to explain why heritabilities have the values they do. Fortunately, the incredible pace of gene–phenotype discoveries in many species will allow new insights to these questions in the near future.
Heritability allows a comparison of the relative importance of genes and environment to the variation of traits within and across populations. The concept of heritability and its definition as an estimable, dimensionless population parameter was introduced by Sewall Wright and Ronald Fisher nearly a century ago. Despite continuous misunderstandings and controversies over its use and application, heritability remains key to the response to selection in evolutionary biology and agriculture, and to the prediction of disease risk in medicine. Recent reports of substantial heritability for gene expression and new estimation methods using marker data highlight the relevance of heritability in the genomics era.
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The authors are supported by the Australian National Health and Medical Research Council (grants 389892, 442915 and 443011) and the Australian Research Council (grant DP0770096). We thank I. Deary and the referees for their many comments on earlier versions of the manuscript.
- Linear mixed model
A statistical model in which the dependent variable is a linear function of both fixed and random independent variables. Fixed effects are constant following the taking of repeated samples, whereas random effects are a sample from a distribution of effects.
- Sampling variance
The variation of a parameter estimate across repeated samples due to finite sample size.
- Bayesian estimation
An estimation method that combines prior information and observed data to draw statistical inference.
The impossibility of separating the effect of two or more causal factors on an observed variable.
- Assortative mating
The tendency of mates to resemble each other in phenotype.
- Truncation selection
Selection of individuals with trait values equal to or greater than some threshold as parents of the next generation.
- Stabilizing selection
Selection, either natural or artificial, of individuals with trait values in the middle of the distribution as parents of the next generation.
- Gametic disequilibrium
The non-random association of alleles at different loci (also termed linkage disequilibrium).
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Visscher, P., Hill, W. & Wray, N. Heritability in the genomics era — concepts and misconceptions. Nat Rev Genet 9, 255–266 (2008). https://doi.org/10.1038/nrg2322
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