Understanding and predicting complex traits: knowledge from cattle

Kemper, Kathryn E. and Goddard, Mike E. (2012) Understanding and predicting complex traits: knowledge from cattle. Human Molecular Genetics, 21 R1: R45-R51. doi:10.1093/hmg/dds332

Author Kemper, Kathryn E.
Goddard, Mike E.
Title Understanding and predicting complex traits: knowledge from cattle
Journal name Human Molecular Genetics   Check publisher's open access policy
ISSN 0964-6906
Publication date 2012-10-15
Year available 2012
Sub-type Article (original research)
DOI 10.1093/hmg/dds332
Open Access Status Not yet assessed
Volume 21
Issue R1
Start page R45
End page R51
Total pages 7
Place of publication Oxford, United Kingdom
Publisher Oxford University Press
Language eng
Abstract The genetic architecture of complex traits in cattle includes very large numbers of loci affecting any given trait. Most of these loci have small effects but occasionally there are loci with moderate-to-large effects segregating due to recent selection for the mutant allele. Genomic markers capture most but not all of the additive genetic variance for traits, probably because there are causal mutations with low allele frequency and therefore in incomplete linkage disequilibrium with the markers. The prediction of genetic value from genomic markers can achieve high accuracy by using statistical models that include all markers and assuming that marker effects are random variables drawn from a specified prior distribution. Recent effective population size is in the order of 100 within cattle breeds and ~;2500 animals with genotypes and phenotypes are sufficient to predict the genetic value of animals with an accuracy of 0.65. Recent effective population size for humans is much larger, in the order of 10 000-15 000, and more than 145 000 records would be required to reach a similar accuracy for people. However, our calculations assume that genomic markers capture all the genetic variance. This may be possible in the future as causal polymorphisms are genotyped using genome sequence data.
Keyword Biochemistry & Molecular Biology
Genetics & Heredity
Biochemistry & Molecular Biology
Genetics & Heredity
Q-Index Code C1
Q-Index Status Provisional Code
Grant ID DP1093502
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: Institute for Molecular Bioscience - Publications
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Citation counts: TR Web of Science Citation Count  Cited 33 times in Thomson Reuters Web of Science Article | Citations
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