Population genetics: Separating nurture from nature in estimating heritability

Dissecting the genetic architecture of quantitative traits using genome-wide identity-by-descent sharing

Additive and dominance genetic variances underlying the expression of quantitative traits are important quantities for predicting short-term responses to selection, but they are notoriously challenging to estimate in most non-model wild populations. Specifically, large-sized or panmictic populations may be characterized by low variance in genetic relatedness among individuals which, in turn, can prevent accurate estimation of quantitative genetic parameters. We used estimates of genome-wide identity-by-descent (IBD) sharing from autosomal SNP loci to estimate quantitative genetic parameters for ecologically important traits in nine-spined sticklebacks (Pungitius pungitius) from a large, outbred population. Using empirical and simulated datasets, with varying sample sizes and pedigree complexity, we assessed the performance of different crossing schemes in estimating additive genetic variance and heritability for all traits. We found that low variance in relatedness characteristic of wild outbred populations with high migration rate can impair the estimation of quantitative genetic parameters and bias heritability estimates downwards. On the other hand, the use of a half-sib/full-sib design allowed precise estimation of genetic variance components and revealed significant additive variance and heritability for all measured traits, with negligible dominance contributions. Genome-partitioning and QTL mapping analyses revealed that most traits had a polygenic basis and were controlled by genes at multiple chromosomes. Furthermore, different QTL contributed to variation in the same traits in different populations suggesting heterogeneous underpinnings of parallel evolution at the phenotypic level. Our results provide important guidelines for future studies aimed at estimating adaptive potential in the wild, particularly for those conducted in outbred large-sized populations.

Bayesian inference of genetic parameters based on conditional decompositions of multivariate normal distributions

It is widely recognized that the mixed linear model is an important tool for parameter estimation in the analysis of complex pedigrees, which includes both pedigree and genomic information, and where mutually dependent genetic factors are often assumed to follow multivariate normal distributions of high dimension. We have developed a Bayesian statistical method based on the decomposition of the multivariate normal prior distribution into products of conditional univariate distributions. This procedure permits computationally demanding genetic evaluations of complex pedigrees, within the user-friendly computer package WinBUGS. To demonstrate and evaluate the flexibility of the method, we analyzed two example pedigrees: a large noninbred pedigree of Scots pine (Pinus sylvestris L.) that includes additive and dominance polygenic relationships and a simulated pedigree where genomic relationships have been calculated on the basis of a dense marker map. The analysis showed that our method was fast and provided accurate estimates and that it should therefore be a helpful tool for estimating genetic parameters of complex pedigrees quickly and reliably.

Whole genome approaches to quantitative genetics

Apart from parent-offspring pairs and clones, relative pairs vary in the proportion of the genome that they share identical by descent. In the past, quantitative geneticists have used the expected value of sharing genes by descent to estimate genetic parameters and predict breeding values. With the possibility to genotype individuals for many markers across the genome it is now possible to empirically estimate the actual relationship between relatives. We review some of the theory underlying the variation in genetic identity, show applications to estimating genetic variance for height in humans and discuss other applications.

Estimation of additive genetic and environmental sources of quantitative trait variation using data on married couples and their siblings

Twin studies have been used to understand the sources of genetic and environmental variation in body height, body weight and other common human quantitative traits. However, it is rather unclear whether these two sources of variation could be really separated in practice. Here, we consider a special study design where phenotype data from married couples and their siblings have been collected. The marital status gives information about the shared environment, while siblings give information about both genetic and environmental variation. To dissect sources of variation and to allow some deviations and pedigree errors in the data, we model such data using a robust polygenic model with finite genome length assumption. As a summary, we provide the estimates for age-dependent proportions of total variation which are due to polygenic and environmental effects. Here, these estimates are provided for body height, weight, systolic blood pressure and total serum cholesterol measured from subjects of the Indian Migration Study.

Heritability in the genomics era--concepts and misconceptions

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.

Familial resemblance and heritability

Familial resemblance arises when family members are more similar than unrelated pairs of individuals, and may be estimated in terms of correlations or covariances among family members. Multifactorial heritability (or generalized heritability) quantifies the strength of the familial resemblance and represents the percentage of variance that is due to all additive familial effects including additive genetic and those of the familial environment. However, the traditional concept of heritability, which may be more appropriately called the genetic heritability, represents only the percentage of phenotypic variance due to additive genetic effects. Resolving the various sources of familial resemblance entails other issues. For example, there may be major gene effects that are largely or entirely nonadditive, temporal or developmental trends, and gene-gene (epistasis) and gene-environment interactions. The design of a family study determines which of these sources are resolvable. For example, in nuclear families consisting of parents and offspring, the genetic and familial environmental effects are not resolvable because these relatives share both genes and environments. However, extended pedigree and twin and adoption designs allow separation of the heritable effects and, possibly, more complex etiologies, including interactions. Various factors affect the estimation and interpretability of heritabilities, for example, assumptions regarding linearity and additivity, assortative mating, and the underlying distribution of the data. Nonnormality of the data can lead to errors in hypothesis testing, although it yields reasonably unbiased estimates. Fortunately, these and other complications can be directly modeled in many of the sophisticated software packages available today in genetic epidemiology.