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Estimating Realized Heritability in Panmictic Populations
M. Lstibůrek, V. Bittner, GR. Hodge, J. Picek, TFC. Mackay,
Jazyk angličtina Země Spojené státy americké
Typ dokumentu časopisecké články, práce podpořená grantem
NLK
Free Medical Journals
od 1916 do Před 6 měsíci
Freely Accessible Science Journals
od 1916 do Před 1 rokem
Europe PubMed Central
od 1916 do Před 1 rokem
ProQuest Central
od 2004-10-01 do 2020-12-31
Open Access Digital Library
od 1916-01-01
Open Access Digital Library
od 1916-01-01
Medline Complete (EBSCOhost)
od 1996-01-01 do Před 1 rokem
Health & Medicine (ProQuest)
od 2004-10-01 do 2020-12-31
Family Health Database (ProQuest)
od 2004-10-01 do 2020-12-31
Public Health Database (ProQuest)
od 2004-10-01 do 2020-12-31
- MeSH
- algoritmy MeSH
- fenotyp MeSH
- kvantitativní znak dědičný MeSH
- lidé MeSH
- modely genetické * MeSH
- počítačová simulace MeSH
- populační genetika * MeSH
- rodokmen MeSH
- selekce (genetika) MeSH
- tělesná výška MeSH
- typy dědičnosti * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
Narrow sense heritability [Formula: see text] is a key concept in quantitative genetics, as it expresses the proportion of the observed phenotypic variation that is transmissible from parents to offspring. [Formula: see text] determines the resemblance among relatives, and the rate of response to artificial and natural selection. Classical methods for estimating [Formula: see text] use random samples of individuals with known relatedness, as well as response to artificial selection, when it is called realized heritability. Here, we present a method for estimating realized [Formula: see text] based on a simple assessment of a random-mating population with no artificial manipulation of the population structure, and derive SE of the estimates. This method can be applied to arbitrary phenotypic segments of the population (for example, the top-ranking p parents and offspring), rather than random samples. It can thus be applied to nonpedigreed random mating populations, where relatedness is determined from molecular markers in the p selected parents and offspring, thus substantially saving on genotyping costs. Further, we assessed the method by stochastic simulations, and, as expected from the mathematical derivation, it provides unbiased estimates of [Formula: see text] We compared our approach to the regression and maximum-likelihood approaches utilizing Galton's dataset on human heights, and all three methods provided identical results.
Citace poskytuje Crossref.org
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