Ronald Fisher needed to develop elaborate models of genetic effects in order to set the foundations of Quantitative Genetics in his 1918 paper “The correlation between relatives on the supposition of Mendelian inheritance”. Since then, many significant implementations have been made to model genetic effects. However, at the verge of one century after Fisher’s kick-off, models of genetic effects keep on being discussed and implemented. Indeed, the relatively recent advent of QTL analyses challenged the state of the art of this field by providing researchers the opportunity to obtain and analyze estimates of genetic effects from real data. In this context, the development of this field was not exempt of some polemics, like the debate about the convenience of the functional and the statistical epistasis approaches. This research topic is meant to provide recent developments in models and estimation of genetic effects and to enrich the discussion about how and why models of genetic effects must be further developed and applied. The articles in this Research Topic shall thus extend, refine and/or provide a refresh look at Fisher’s original models of genetic effects and their application to genetic effects estimation and to improve our understanding of evolutionary processes and breeding programs.

The main objectives of this study were: (1) to compare quadratic and likelihood estimators of dispersion parameters in a sire plus maternal grandsire model (S-M) with respect to estimated bias (EB) and estimated mean squared error (EMSE) when the data are affected by selection, (2) to estimate direct and maternal (co)variance components (CVC) using an animal model (MAM) and genetic grouping for weaning weight in beef cattle, (3) to extend the theory of genetic grouping in models with maternal effects allowing for differential criteria to be used when assigning groups for direct and maternal effects, and (4) to present a Bayesian approach to estimation of CVC in MAM. To achieve objective (1) some designs based on S-M were compared with respect to the variance of estimates of heritability. Also, simulated data under S-M and either: (1) random, (2) translation invariant, or (3) location-variant selection were employed. Although perhaps the model and design did not permit to reveal large differences in EB and EMSE among estimators, likelihood based methods tended to outperform quadratic ones with respect to EMSE and, to a lesser extent, with respect to EB. This was specially so under non-random selection. Records from 935 Angus calves were used to estimate CVC in MAM by restricted maximum likelihood to attain objective (2). Estimates of CVC and of functions thereof did not differ very much in models which included or excluded genetic groups. Estimates of heritability for direct and maternal effects were smaller than those reported previously. The estimate of the additive correlation between direct and maternal effects was $-$0.31. Genetic and environmental trends for direct effects were positive whereas corresponding trends for maternal effects were close to zero. The Bayesian approach for making inferences about CVC in MAM used inverted-Wishart and inverted chi-square prior distributions. The joint posterior density of CVC was obtained in closed form and three methods to achieve further marginalization were discussed.

In this book, Neven Sesardic defends the view that it is both possible and useful to measure the separate contributions of heredity and environment to the explanation of human psychological differences. He critically examines the view - very widely accepted by scientists, social scientists and philosophers of science - that heritability estimates have no causal implications and are devoid of any interest. In a series of clearly written chapters he introduces the reader to the problems and subjects the arguments to close philosophical scrutiny. His conclusion is that anti-heritability arguments are based on conceptual confusions and misunderstandings of behavioural genetics. His book is a fresh and compelling intervention in a very contentious debate.

A comprehensive analysis of the genetic, ecological and phylogenetic aspects of social behaviour, by experts in the field.

DNA copies inherited from the same ancestral copy by related individuals are said to be identical by descent (IBD). IBD gives rise to genetic similarities between related individuals. In quantitative genetics, two fundamental problems are heritability estimation and gene mapping for genetic traits. IBD plays a critical role in the study of both problems. When working with population-based samples where pedigree information is unavailable, it is essential to estimate IBD accurately from genetic marker data using pedigree-free methods. The estimated IBD can then be used in heritability estimation and gene mapping using random effects models. For pedigree-free IBD estimation, we showed that it is important to use the fact that DNA is inherited in segments as opposed to independent loci. As the single nucleotide polymorphism (SNP) marker panels become increasingly dense, the impact of allelic association (or linkage disequilibrium, LD) on accuracy of IBD estimation also grows. Through simulation studies, we demonstrated that adjusting for LD in the marker panel can lead to improved IBD estimation accuracy. For heritability estimation and gene mapping using random effects models, a difficult task is to specify the correlation structures of the random genetic effects, which are typically functions of IBD sharing over the putative causal genomic region. We provided formulas for the asymptotic bias and sampling error of heritability estimates, when the genetic correlation structures are potentially mis-specified. Mis-specification of the genetic correlation structures can occur due to inaccurate IBD estimation or mis-identification of the causal genome. We showed that such mis-specification can lead to substantial downward bias in heritability estimation, or loss of power in gene mapping.

This book gathers the expertise of 30 evolutionary biologists from around the globe to highlight how applying the field of quantitative genetics - the analysis of the genetic basis of complex traits - aids in the study of wild populations.