Human Molecular Genetics Advance Access originally published online on November 17, 2004
Human Molecular Genetics 2005 14(1):145-153; doi:10.1093/hmg/ddi019
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Human Molecular Genetics, Vol. 14, No. 1 © Oxford University Press 2005; all rights reserved
The optimal measure of linkage disequilibrium reduces error in association mapping of affection status
1Human Genetics Division, University of Southampton, Southampton General Hospital, Southampton SO16 6YD, UK and 2Discovery Genetics, GlaxoSmithKline, Stevenage SG1 2NY, UK
* To whom correspondence should be addressed at: Human Genetics Division, Southampton General Hospital, University of Southampton, School of Medicine, Duthie Building (MP808), Southampton SO16 6YD, UK. Tel: +44 2380796538; Fax: +44 238080794264; Email: n.maniatis{at}soton.ac.uk
Received July 7, 2004; Revised September 17, 2004; Accepted November 5, 2004
We have developed a simple yet powerful approach for disease gene association mapping by linkage disequilibrium (LD). This method is unique because it applies a model with evolutionary theory that incorporates a parameter for the location of the causal polymorphism. The method exploits LD maps, which assign a location in LD units (LDU) for each marker. This approach is based on single marker tests within a composite likelihood framework, which avoids the heavy Bonferroni correction through multiple testing. As a proof of principle, we tested an 890 kb region flanking the CYP2D6 gene associated with poor drug-metabolizing activity in order to refine the localization of a causal mutation. Previous LD mapping studies using single markers and haplotypes have identified a 390 kb significant region associated with the poor drug-metabolizing phenotype on chromosome 22. None of the 27 Single nucleotide polymorphisms was within the gene. Using a metric LDU map, the commonest functional polymorphism within the gene was located at 14.9 kb from its true location, surrounded within a 95% confidence interval of 172 kb. The kb map had a relative efficiency of 33% compared with the LDU map. Our findings indicate that the support interval and location error are smaller than any published results. Despite the low resolution and the strong LD in the region, our results provide evidence of the substantial utility of LDU maps for disease gene association mapping. These tests are robust to large numbers of markers and are applicable to haplotypes, diplotypes, whole-genome association or candidate region studies.
The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors.
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