Quantitative genetics of disease traits

John James authored two key papers on the theory of risk to relatives for binary disease traits and the relationship between parameters on the observed binary scale and an unobserved scale of liability (James Annals of Human Genetics, 1971; 35: 47; Reich, James and Morris Annals of Human Genetics, 1972; 36: 163). These two papers are John James' most cited papers (198 and 328 citations, November 2014). They have been influential in human genetics and have recently gained renewed popularity because of their relevance to the estimation of quantitative genetics parameters for disease traits using SNP data. In this review, we summarize the two early papers and put them into context. We show recent extensions of the theory for ascertained case-control data and review recent applications in human genetics

THE USE OF GENETICS PRINCIPLES IN RESEARCH EVALUATION: AN EXAMPLE WITH SOYBEANS

This paper explores the potential use of quantitative genetics principles in evaluating economic returns to plant breeding research. Basic factors affecting genetic progress are described along with possibilities for quantifying them in relation to research expenditures. An example with soybeans illustrates how this information can be incorporated into ex ante research evaluation.Research and Development/Tech Change/Emerging Technologies,

Accounting for genetic interactions improves modeling of individual quantitative trait phenotypes in yeast.

Experiments in model organisms report abundant genetic interactions underlying biologically important traits, whereas quantitative genetics theory predicts, and data support, the notion that most genetic variance in populations is additive. Here we describe networks of capacitating genetic interactions that contribute to quantitative trait variation in a large yeast intercross population. The additive variance explained by individual loci in a network is highly dependent on the allele frequencies of the interacting loci. Modeling of phenotypes for multilocus genotype classes in the epistatic networks is often improved by accounting for the interactions. We discuss the implications of these results for attempts to dissect genetic architectures and to predict individual phenotypes and long-term responses to selection

Theory and Practice in Quantitative Genetics

With the rapid advances in molecular biology, the near completion of the human genome, the development of appropriate statistical genetic methods and the availability of the necessary computing power, the identification of quantitative trait loci has now become a realistic prospect for quantitative geneticists. We briefly describe the theoretical biometrical foundations underlying quantitative genetics. These theoretical underpinnings are translated into mathematical equations that allow the assessment of the contribution of observed (using DNA samples) and unobserved (using known genetic relationships) genetic variation to population variance in quantitative traits. Several statistical models for quantitative genetic analyses are described, such as models for the classical twin design, multivariate and longitudinal genetic analyses, extended twin analyses, and linkage and association analyses. For each, we show how the theoretical biometrical model can be translated into algebraic equations that may be used to generate scripts for statistical genetic software packages, such as Mx, Lisrel, SOLAR, or MERLIN. For using the former program a web-library (available from http://www.psy.vu.nl/mxbib) has been developed of freely available scripts that can be used to conduct all genetic analyses described in this paper

Multivariate Survival Mixed Models for Genetic Analysis of Longevity Traits

A class of multivariate mixed survival models for continuous and discrete time with a complex covariance structure is introduced in a context of quantitative genetic applications. The methods introduced can be used in many applications in quantitative genetics although the discussion presented concentrates on longevity studies. The framework presented allows to combine models based on continuous time with models based on discrete time in a joint analysis. The continuous time models are approximations of the frailty model in which the hazard function will be assumed to be piece-wise constant. The discrete time models used are multivariate variants of the discrete relative risk models. These models allow for regular parametric likelihood-based inference by exploring a coincidence of their likelihood functions and the likelihood functions of suitably defined multivariate generalized linear mixed models. The models include a dispersion parameter, which is essential for obtaining a decomposition of the variance of the trait of interest as a sum of parcels representing the additive genetic effects, environmental effects and unspecified sources of variability; as required in quantitative genetic applications. The methods presented are implemented in such a way that large and complex quantitative genetic data can be analyzed.Comment: 36 pages, 2 figures, 3 table

Signal and Noise in Correlation Matrix

Using random matrix technique we determine an exact relation between the eigenvalue spectrum of the covariance matrix and of its estimator. This relation can be used in practice to compute eigenvalue invariants of the covariance (correlation) matrix. Results can be applied in various problems where one experimentally estimates correlations in a system with many degrees of freedom, like in statistical physics, lattice measurements of field theory, genetics, quantitative finance and other applications of multivariate statistics.Comment: 17 pages, 3 figures, corrected typos, revtex style changed to elsar

Rare variants contribute disproportionately to quantitative trait variation in yeast.

How variants with different frequencies contribute to trait variation is a central question in genetics. We use a unique model system to disentangle the contributions of common and rare variants to quantitative traits. We generated ~14,000 progeny from crosses among 16 diverse yeast strains and identified thousands of quantitative trait loci (QTLs) for 38 traits. We combined our results with sequencing data for 1011 yeast isolates to show that rare variants make a disproportionate contribution to trait variation. Evolutionary analyses revealed that this contribution is driven by rare variants that arose recently, and that negative selection has shaped the relationship between variant frequency and effect size. We leveraged the structure of the crosses to resolve hundreds of QTLs to single genes. These results refine our understanding of trait variation at the population level and suggest that studies of rare variants are a fertile ground for discovery of genetic effects

Quantitative genetics of the Desert Locust's larval growth: Rate and life-history strategy : PS3M358 Ecology

Schistocerca gregaria, the desert locust, which is distributed from north-west Africa to south-east Asia, is one of the most known and studied locust species. In order to prevent local human populations from the threat of pullulations, we need to know more about population dynamics of the desert locust, especially for the solitarious populations which are less studied than gregarious ones. Predicting the evolution of population dynamics needs a good knowledge of life-history traits that influence population demography. Growth is one of these traits, since it is directly linked to the onset of sexual maturity, then reproduction. Moreover, in order to investigate the potential of selection on individual growth, one needs to evaluate its heritability by disentangling genetic and additive variation in traits phenotype from non-genetic variance due to other (environmental) factors. Here we present a work done on a first generation lab population of S. gregaria sampled in the field. We recorded individual larval growth of 15 full-sib families, based on body weight and morphology in controlled conditions. We describe larval growth by analyzing growth rate as well as several key variables involved in life-history strategy (body weight and size at emergence, growth rate until adult emergence, maximum body weight, age at adult emergence, larval strategy). Thanks to the known relatedness structure of our sample, we also calculate the heritability of those traits and make predictions about the ability of S. gregaria populations to respond to selection on larval growth