Genetic Epidemiology, Translational Neurogenomics, Psychiatric Genetics and Statistical Genetics Laboratories investigate the pattern of disease in families, particularly identical and non-identical twins, to assess the relative importance of genes and environment in a variety of important health problems.
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PMID
18949033
TITLE
Predicting unobserved phenotypes for complex traits from whole-genome SNP data.
ABSTRACT
Genome-wide association studies (GWAS) for quantitative traits and disease in humans and other species have shown that there are many loci that contribute to the observed resemblance between relatives. GWAS to date have mostly focussed on discovery of genes or regulatory regions habouring causative polymorphisms, using single SNP analyses and setting stringent type-I error rates. Genome-wide marker data can also be used to predict genetic values and therefore predict phenotypes. Here, we propose a Bayesian method that utilises all marker data simultaneously to predict phenotypes. We apply the method to three traits: coat colour, %CD8 cells, and mean cell haemoglobin, measured in a heterogeneous stock mouse population. We find that a model that contains both additive and dominance effects, estimated from genome-wide marker data, is successful in predicting unobserved phenotypes and is significantly better than a prediction based upon the phenotypes of close relatives. Correlations between predicted and actual phenotypes were in the range of 0.4 to 0.9 when half of the number of families was used to estimate effects and the other half for prediction. Posterior probabilities of SNPs being associated with coat colour were high for regions that are known to contain loci for this trait. The prediction of phenotypes using large samples, high-density SNP data, and appropriate statistical methodology is feasible and can be applied in human medicine, forensics, or artificial selection programs.
DATE PUBLISHED
2008 Oct
HISTORY
PUBSTATUS PUBSTATUSDATE
received 2008/04/25
accepted 2008/09/18
epublish 2008/10/24
pubmed 2008/10/25 09:00
medline 2008/12/23 09:00
entrez 2008/10/25 09:00
AUTHORS
NAME COLLECTIVENAME LASTNAME FORENAME INITIALS AFFILIATION AFFILIATIONINFO
Lee SH Lee Sang Hong SH School of Environmental and Rural Science, University of New England, Armidale, NSW, Australia.
van der Werf JH van der Werf Julius H J JH
Hayes BJ Hayes Ben J BJ
Goddard ME Goddard Michael E ME
Visscher PM Visscher Peter M PM
INVESTIGATORS
JOURNAL
VOLUME: 4
ISSUE: 10
TITLE: PLoS genetics
ISOABBREVIATION: PLoS Genet.
YEAR: 2008
MONTH: Oct
DAY:
MEDLINEDATE:
SEASON:
CITEDMEDIUM: Internet
ISSN: 1553-7404
ISSNTYPE: Electronic
MEDLINE JOURNAL
MEDLINETA: PLoS Genet
COUNTRY: United States
ISSNLINKING: 1553-7390
NLMUNIQUEID: 101239074
PUBLICATION TYPE
PUBLICATIONTYPE TEXT
Journal Article
Research Support, Non-U.S. Gov't
COMMENTS AND CORRECTIONS
REFTYPE REFSOURCE REFPMID NOTE
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GRANTS
GENERAL NOTE
KEYWORDS
MESH HEADINGS
DESCRIPTORNAME QUALIFIERNAME
Animals
Bayes Theorem
Genome
Mice
Models, Genetic
Models, Statistical
Phenotype
Polymorphism, Single Nucleotide
Quantitative Trait Loci
SUPPLEMENTARY MESH
GENE SYMBOLS
CHEMICALS
OTHER ID's
OTHERID SOURCE
PMC2565502 NLM
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