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CONNECTING QTLS TO THE G‐MATRIX OF EVOLUTIONARY QUANTITATIVE GENETICS
Author(s) -
Kelly John K.
Publication year - 2009
Publication title -
evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.84
H-Index - 199
eISSN - 1558-5646
pISSN - 0014-3820
DOI - 10.1111/j.1558-5646.2008.00590.x
Subject(s) - quantitative trait locus , biology , quantitative genetics , pleiotropy , genetic architecture , family based qtl mapping , selection (genetic algorithm) , genetics , population , evolutionary biology , context (archaeology) , allele , natural selection , inclusive composite interval mapping , genetic variation , gene mapping , phenotype , artificial intelligence , computer science , gene , chromosome , paleontology , demography , sociology
Evolutionary quantitative genetics has recently advanced in two distinct streams. Many biologists address evolutionary questions by estimating phenotypic selection and genetic (co)variances ( G matrices). Simultaneously, an increasing number of studies have applied quantitative trait locus (QTL) mapping methods to dissect variation. Both conceptual and practical difficulties have isolated these two foci of quantitative genetics. A conceptual integration follows from the recognition that QTL allele frequencies are the essential variables relating the G ‐matrix to marker‐based mapping experiments. Breeding designs initiated from randomly selected parental genotypes can be used to estimate QTL‐specific genetic (co)variances. These statistics appropriately distill allelic variation and provide an explicit population context for QTL mapping estimates. Within this framework, one can parse the G ‐matrix into a set of mutually exclusive genomic components and ask whether these parts are similar or dissimilar in their respective features, for example the magnitude of phenotypic effects and the extent and nature of pleiotropy. As these features are critical determinants of sustained response to selection, the integration of QTL mapping methods into G ‐matrix estimation can provide a concrete, genetically based experimental program to investigate the evolutionary potential of natural populations.