quantitative genetics

quantitative genetics
Name	Quantitative genetics
Field	Genetics
Developed	1900s
Notable people	Gregor Mendel, Ronald Fisher, Sewall Wright, J. B. S. Haldane, R. A. Fisher, Lancelot Hogben, Francis Galton, William Bateson, Sewall Wright (again), John Maynard Smith, Motoo Kimura, G. Udny Yule, James F. Crow, Theodosius Dobzhansky, C. D. Darlington, Leonore Herzenberg, Alan Robertson, M. D. Hayman, David Falconer, Alan Robertson (again), John L. Lush, Jay Lush, Hans E. W. Wagner, Sewall Wright (third), E. A. Fisher, S. Wright, J. F. Crow, Richard Lewontin, George Beadle, Edward East, Herman Nilsson-Ehle, Niles Eldredge, Stephen Jay Gould, Ernst Mayr, Konrad Lorenz, Sewall Wright (fourth), Bernard R. Forsyth, C. R. Henderson, L. L. Cavalli-Sforza, Marcus Feldman, Bruce Walsh, Michael Lynch, Nick Barton, Graham Coop, Patrick S. Moore, Peter B. Medawar, Sydney Brenner, Francis Crick, James Watson, Eric Lander, David Reich, Svante Pääbo, Huda Zoghbi, Robert Plomin, Daniel Hartl, Brian Charlesworth, Joseph Felsenstein, Masatoshi Nei, T. Ryan Gregory, Allan Wilson, Tomoko Ohta, Motoo Kimura (again), J. F. Crow (again), Michael Turelli, Mark Kirkpatrick, Graham Coop (again), Jonathan Pritchard, Matthew Stephens, David Balding, Peter Visscher, Nicholas Eriksson, Sekar Kathiresan, Stuart A. Cook, Katherine S. Pollard

quantitative genetics Quantitative genetics is the branch of genetics concerned with the inheritance of continuously varying traits and the statistical description of genetic and environmental contributions to phenotypic variation. It synthesizes ideas from early work by Gregor Mendel and Francis Galton with modern developments by R. A. Fisher, Sewall Wright, and J. B. S. Haldane and links to contemporary genomics led by researchers such as Eric Lander, Peter Visscher, and David Reich. Quantitative genetics underpins applied programs in Iowa State University animal breeding, Roslin Institute plant breeding, and conservation efforts involving institutions like the Smithsonian Institution.

Overview and History

The historical foundation traces to experiments by Gregor Mendel and statistical synthesis by Francis Galton and theoretical advances by R. A. Fisher, Sewall Wright, and J. B. S. Haldane who reconciled Mendelian inheritance with biometric continuous variation. Early applied methods emerged from breeding programs at Iowa State University under Jay Lush and statistical genetics work at University of Edinburgh and Cambridge University with contributions from William Bateson and Lancelot Hogben. Twentieth-century developments integrated population genetics from Theodosius Dobzhansky, Ernst Mayr, and Motoo Kimura with quantitative models used in United States Department of Agriculture crop programs and Roslin Institute livestock studies. Late twentieth-century genomic revolutions driven by the Human Genome Project, led by figures including Eric Lander and organizations like the Wellcome Trust, transformed the field through high-density marker technologies. Contemporary history includes genome-wide association studies by teams at Broad Institute, Sanger Institute, and research consortia such as the International HapMap Project and the 1000 Genomes Project.

Basic Concepts and Terminology

Key terms include additive genetic variance, dominance variance, epistasis, heritability (narrow-sense and broad-sense), genetic correlation, breeding value, and genotype-by-environment interaction; these concepts were formalized by R. A. Fisher, Sewall Wright, J. F. Crow, and David Falconer. Quantitative traits analyzed in classical and modern studies span examples from Drosophila melanogaster experiments at University of Chicago labs associated with Theodosius Dobzhansky to human height studies involving researchers at University of Oxford and University of Queensland. Foundational distinctions between fixed and random effects derive from statistical work at University of Edinburgh and University of Cambridge, while estimation paradigms evolved within organizations such as USDA Agricultural Research Service and academic groups at Iowa State University.

Mathematical Models and Methods

Core models include the infinitesimal model, polygenic additive models, and multilocus genotype models developed by R. A. Fisher and extended by Sewall Wright and J. B. S. Haldane. Methods use linear mixed models, Best Linear Unbiased Prediction (BLUP) from C. R. Henderson and REML estimators associated with Charles Stein, Arnold Zellner, and statistical software developed at institutions like Rothamsted Research and INRA. Population genetics integration employs diffusion approximations from Motoo Kimura and coalescent theory by John Maynard Smith-era contemporaries and later formalization by Joseph Felsenstein and Masatoshi Nei. Statistical genomics introduced methods such as genomic BLUP, Bayesian alphabet approaches popularized by groups at Roslin Institute and University of Guelph, and machine learning adaptations from Alan Turing-influenced computational labs at MIT and Harvard University.

Estimation of Genetic Parameters

Heritability estimation and partitioning of variance utilize pedigree-based REML, animal models from Iowa State University breeding programs, and marker-based methods pioneered by teams at the Wellcome Trust Sanger Institute and Broad Institute. Linkage disequilibrium mapping leverages resources from the International HapMap Project and the 1000 Genomes Project while identity-by-descent inference draws on methods developed at University of Oxford and University of Cambridge. Experimental designs including half-sib and full-sib analyses have roots in agricultural experiments conducted by USDA scientists and researchers at University of Minnesota and University of Wisconsin–Madison.

Applications in Evolution and Breeding

Applications span artificial selection in plant breeding at Iowa State University and Universität Hohenheim, livestock improvement at Roslin Institute and corporate breeding programs such as Monsanto and Cargill, and evolutionary inference in wild populations studied by teams at University of California, Davis and the Max Planck Institute for Evolutionary Anthropology. Quantitative genetics informs conservation genetics projects at the Smithsonian Institution and World Wildlife Fund and underlies genomic selection programs used by companies like Bayer AG and academic consortia including the Beef Improvement Federation.

Molecular and Genomic Approaches

Molecular quantitative genetics integrates QTL mapping pioneered by groups at Cold Spring Harbor Laboratory and the Institute of Molecular Biology, GWAS led by Eric Lander-associated consortia, and sequencing initiatives from the 1000 Genomes Project and the Human Genome Project. Functional genomics contributions come from labs at Sanger Institute, Broad Institute, and Max Planck Institute for Evolutionary Anthropology with experimental systems in Saccharomyces cerevisiae, Arabidopsis thaliana, and Drosophila melanogaster. Statistical methods incorporate contributions from computational centers at Stanford University, University of California, Berkeley, and Imperial College London and leverage databases curated by Ensembl and the National Center for Biotechnology Information.

Category:Genetics