Models And Estimation Of Genetic Effects

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.

Parameters, statistics, and expected values; A little about matrix algebra; Quantifying the simple mendelian model; A short summation on population genetics; Genes identical by descent-the basis of genetic likeness. Genetic values and genetic covariances; The selection index; Determining the coeficients for selection index equations; Sire evaluation, example of application of selection index; Probability statements about true value. Superiority of selected groups; Selection index flow chart for single traits; Selection with more than one trait measured; Using records on all traits of relatives; Selection index for categorical data. Selection for embedded traits: maternal effects; Selection when traits influenced by grandmaternal and maternal effects;Fetal effects model(sire of fetus effect).

This book reevaluates the health risks of ionizing radiation in light of data that have become available since the 1980 report on this subject was published. The data include new, much more reliable dose estimates for the A-bomb survivors, the results of an additional 14 years of follow-up of the survivors for cancer mortality, recent results of follow-up studies of persons irradiated for medical purposes, and results of relevant experiments with laboratory animals and cultured cells. It analyzes the data in terms of risk estimates for specific organs in relation to dose and time after exposure, and compares radiation effects between Japanese and Western populations.

Presents the Terminology and Methods of Mendelian Randomization for Epidemiological StudiesMendelian randomization uses genetic instrumental variables to make inferences about causal effects based on observational data. It, therefore, can be a reliable way of assessing the causal nature of risk factors, such as biomarkers, for a wide range of disea

Developments in statistics and computing as well as their application to genetic improvement of livestock gained momentum over the last 20 years. This text reviews and consolidates the statistical foundations of animal breeding. This text will prove useful as a reference source to animal breeders, quantitative geneticists and statisticians working in these areas. It will also serve as a text in graduate courses in animal breeding methodology with prerequisite courses in linear models, statistical inference and quantitative genetics.

Quantitative traits-be they morphological or physiological characters, aspects of behavior, or genome-level features such as the amount of RNA or protein expression for a specific gene-usually show considerable variation within and among populations. Quantitative genetics, also referred to as the genetics of complex traits, is the study of such characters and is based on mathematical models of evolution in which many genes influence the trait and in which non-genetic factors may also be important. Evolution and Selection of Quantitative Traits presents a holistic treatment of the subject, showing the interplay between theory and data with extensive discussions on statistical issues relating to the estimation of the biologically relevant parameters for these models. Quantitative genetics is viewed as the bridge between complex mathematical models of trait evolution and real-world data, and the authors have clearly framed their treatment as such. This is the second volume in a planned trilogy that summarizes the modern field of quantitative genetics, informed by empirical observations from wide-ranging fields (agriculture, evolution, ecology, and human biology) as well as population genetics, statistical theory, mathematical modeling, genetics, and genomics. Whilst volume 1 (1998) dealt with the genetics of such traits, the main focus of volume 2 is on their evolution, with a special emphasis on detecting selection (ranging from the use of genomic and historical data through to ecological field data) and examining its consequences.