Genetic Epidemiology, Psychiatric Genetics, Asthma 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
16724003
TITLE
A simple method to localise pleiotropic susceptibility loci using univariate linkage analyses of correlated traits.
ABSTRACT
Univariate linkage analysis is used routinely to localise genes for human complex traits. Often, many traits are analysed but the significance of linkage for each trait is not corrected for multiple trait testing, which increases the experiment-wise type-I error rate. In addition, univariate analyses do not realise the full power provided by multivariate data sets. Multivariate linkage is the ideal solution but it is computationally intensive, so genome-wide analysis and evaluation of empirical significance are often prohibitive. We describe two simple methods that efficiently alleviate these caveats by combining P-values from multiple univariate linkage analyses. The first method estimates empirical pointwise and genome-wide significance between one trait and one marker when multiple traits have been tested. It is as robust as an appropriate Bonferroni adjustment, with the advantage that no assumptions are required about the number of independent tests performed. The second method estimates the significance of linkage between multiple traits and one marker and, therefore, it can be used to localise regions that harbour pleiotropic quantitative trait loci (QTL). We show that this method has greater power than individual univariate analyses to detect a pleiotropic QTL across different situations. In addition, when traits are moderately correlated and the QTL influences all traits, it can outperform formal multivariate VC analysis. This approach is computationally feasible for any number of traits and was not affected by the residual correlation between traits. We illustrate the utility of our approach with a genome scan of three asthma traits measured in families with a twin proband.
DATE PUBLISHED
2006 Aug
HISTORY
PUBSTATUS PUBSTATUSDATE
aheadofprint 2006/05/24
pubmed 2006/05/26 09:00
medline 2006/12/09 09:00
entrez 2006/05/26 09:00
AUTHORS
NAME COLLECTIVENAME LASTNAME FORENAME INITIALS AFFILIATION AFFILIATIONINFO
Ferreira MA Ferreira Manuel A R MA Queensland Institute of Medical Research, Royal Brisbane Hospital, Brisbane, Australia. manuelF@qimr.edu.au
Visscher PM Visscher Peter M PM
Martin NG Martin Nicholas G NG
Duffy DL Duffy David L DL
INVESTIGATORS
JOURNAL
VOLUME: 14
ISSUE: 8
TITLE: European journal of human genetics : EJHG
ISOABBREVIATION: Eur. J. Hum. Genet.
YEAR: 2006
MONTH: Aug
DAY:
MEDLINEDATE:
SEASON:
CITEDMEDIUM: Print
ISSN: 1018-4813
ISSNTYPE: Print
MEDLINE JOURNAL
MEDLINETA: Eur J Hum Genet
COUNTRY: England
ISSNLINKING: 1018-4813
NLMUNIQUEID: 9302235
PUBLICATION TYPE
PUBLICATIONTYPE TEXT
Comparative Study
Evaluation Studies
Journal Article
Research Support, Non-U.S. Gov't
COMMENTS AND CORRECTIONS
GRANTS
GENERAL NOTE
KEYWORDS
MESH HEADINGS
DESCRIPTORNAME QUALIFIERNAME
Asthma genetics
Chromosome Mapping genetics
Forced Expiratory Volume genetics
Genetic Linkage genetics
Genetic Markers genetics
Genetic Predisposition to Disease genetics
Genome, Human genetics
Humans genetics
Multivariate Analysis genetics
Quantitative Trait Loci genetics
Regression Analysis genetics
SUPPLEMENTARY MESH
GENE SYMBOLS
CHEMICALS
REGISTRYNUMBER NAMEOFSUBSTANCE
0 Genetic Markers
OTHER ID's