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
15843988
TITLE
The importance of modelling heterogeneity in complex disease: application to NIMH Schizophrenia Genetics Initiative data.
ABSTRACT
As for other complex diseases, linkage analyses of schizophrenia (SZ) have produced evidence for numerous chromosomal regions, with inconsistent results reported across studies. The presence of locus heterogeneity appears likely and may reduce the power of linkage analyses if homogeneity is assumed. In addition, when multiple heterogeneous datasets are pooled, inter-sample variation in the proportion of linked families (alpha) may diminish the power of the pooled sample to detect susceptibility loci, in spite of the larger sample size obtained. We compare the significance of linkage findings obtained using allele-sharing LOD scores (LOD(exp))-which assume homogeneity-and heterogeneity LOD scores (HLOD) in European American and African American NIMH SZ families. We also pool these two samples and evaluate the relative power of the LOD(exp) and two different heterogeneity statistics. One of these (HLOD-P) estimates the heterogeneity parameter alpha only in aggregate data, while the second (HLOD-S) determines alpha separately for each sample. In separate and combined data, we show consistently improved performance of HLOD scores over LOD(exp). Notably, genome-wide significant evidence for linkage is obtained at chromosome 10p in the European American sample using a recessive HLOD score. When the two samples are combined, linkage at the 10p locus also achieves genome-wide significance under HLOD-S, but not HLOD-P. Using HLOD-S, improved evidence for linkage was also obtained for a previously reported region on chromosome 15q. In linkage analyses of complex disease, power may be maximised by routinely modelling locus heterogeneity within individual datasets, even when multiple datasets are combined to form larger samples.
DATE PUBLISHED
2005 Jul
HISTORY
PUBSTATUS PUBSTATUSDATE
received 2004/11/22
accepted 2005/01/18
aheadofprint 2005/04/21
pubmed 2005/04/22 09:00
medline 2005/10/12 09:00
entrez 2005/04/22 09:00
AUTHORS
NAME COLLECTIVENAME LASTNAME FORENAME INITIALS AFFILIATION AFFILIATIONINFO
Holliday E Holliday Elizabeth E Queensland Centre for Mental Health Research, Level 3, Dawson House, The Park, Centre for Mental Health, Wacol, QLD 4076, Australia. lizh@qcmhr.uq.edu.au
Mowry B Mowry Bryan B
Chant D Chant David D
Nyholt D Nyholt Dale D
INVESTIGATORS
JOURNAL
VOLUME: 117
ISSUE: 2-3
TITLE: Human genetics
ISOABBREVIATION: Hum. Genet.
YEAR: 2005
MONTH: Jul
DAY:
MEDLINEDATE:
SEASON:
CITEDMEDIUM: Print
ISSN: 0340-6717
ISSNTYPE: Print
MEDLINE JOURNAL
MEDLINETA: Hum Genet
COUNTRY: Germany
ISSNLINKING: 0340-6717
NLMUNIQUEID: 7613873
PUBLICATION TYPE
PUBLICATIONTYPE TEXT
Journal Article
Multicenter Study
COMMENTS AND CORRECTIONS
GRANTS
GENERAL NOTE
KEYWORDS
MESH HEADINGS
DESCRIPTORNAME QUALIFIERNAME
Chromosomes, Human, Pair 10 genetics
Chromosomes, Human, Pair 15 genetics
Databases, Genetic genetics
Genetic Predisposition to Disease genetics
Genotype genetics
Humans genetics
Lod Score genetics
Schizophrenia genetics
SUPPLEMENTARY MESH
GENE SYMBOLS
CHEMICALS
OTHER ID's