This timely book illustrates the value of bioinformatics, not simply as a set of tools but rather as a science increasingly essential to navigate and manage the host of information generated by genomics and the availability of completely sequenced genomes. Bioinformatics can be used at all stages of genetics research: to improve study design, to assist in candidate gene identification, to aid data interpretation and management and to shed light on the molecular pathology of disease-causing mutations. Written specifically for geneticists, this book explains the relevance of bioinformatics showing how it may be used to enhance genetic data mining and markedly improve genetic analysis.

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.

The heritability of a quantitative trait is a very important parameter to quantify the genetic variation present in a population. Although traditional techniques for estimating heritability require accurate information of the genetic relationship among individuals, pedigree structure is generally lacking in natural population. Nowadays, the development of DNA markers is making possible to reconstruct pedigree accurately with sufficient markers. These reconstructed pedigrees have then been used with restricted maximum likelihood under a general linear mixed model to estimate heritability. In this thesis, we use markers and phenotypic observations jointly to estimate the pedigree and heritability simultaneously. We develop a MCMC sampling method of moving through the sibship configuration space and space of parameters of quantitative trait, and finding the configuration and optimal parameter values that maximizes the full joint likelihood or posterior distribution of proposed family structure and genetic variance components. Using this method, we estimate of heritabilities of 318 abalone at different time points separately and independently. Both MLE and Bayes estimate are superior to two-step method using insufficient markers (two microsatellite markers). We also give the discussion about the choices of prior distributions of parameters in the model. At the end, we extend our method to incorporate with observations at multiple time points, but we don't obtain any significant improvements.

We study statistical methods toestimate the heritability of a biological trait,which is the proportion of variations of thistrait that can be explained by genetic factors.First, we propose to study the heritability ofquantitative traits using high-dimensionalsparse linear mixed models. We investigate thetheoretical properties of the maximumlikelihood estimator for the heritability and weshow that it is a consistent estimator and that itsatisfies a central limit theorem with a closedformexpression for the asymptotic variance.This result, supported by an extendednumerical study, shows that the variance of ourestimator is strongly affected by the ratiobetween the number of observations and thesize of the random genetic effects. Moreprecisely, when the number of observations issmall compared to the size of the geneticeffects (which is often the case in geneticstudies), the variance of our estimator is verylarge. This motivated the development of avariable selection method in order to capturethe genetic variants which are involved themost in the phenotypic variations and providemore accurate heritability estimations. Wepropose then a variable selection methodadapted to high dimensional settings and weshow that, depending on the number of geneticvariants actually involved in the phenotypicvariations, called causal variants, it was a goodidea to include or not a variable selection stepbefore estimating heritability.The last part of this thesis is dedicated toheritability estimation for binary data, in orderto study the proportion of genetic factorsinvolved in complex diseases. We propose tostudy the theoretical properties of the methoddeveloped by Golan et al. (2014) for casecontroldata, which is very efficient in practice.Our main result is the proof of the consistencyof their heritability estimator.

Professors Lynch and Walsh bring together the diverse array of theoretical and empirical applications of quantitative genetics in a work that is comprehensive and accessible to anyone with a rudimentary understanding of statistics and genetics.