Article Text
Abstract
Background: Many genome-wide scans aimed at complex traits have been statistically underpowered due to small sample size. Combining data from several genome-wide screens with comparable quantitative phenotype data should improve statistical power for the localisation of genomic regions contributing to these traits.
Objective: To perform a genome-wide screen for loci affecting adult stature by combined analysis of four previously performed genome-wide scans.
Methods: We developed a web based computer tool, Cartographer, for combining genetic marker maps which positions genetic markers accurately using the July 2003 release of the human genome sequence and the deCODE genetic map. Using Cartographer, we combined the primary genotype data from four genome-wide scans and performed variance components (VC) linkage analyses for human stature on the pooled dataset of 1417 individuals from 277 families and performed VC analyses for males and females separately.
Results: We found significant linkage to stature on 1p21 (multipoint LOD score 4.25) and suggestive linkages on 9p24 and 18q21 (multipoint LOD scores 2.57 and 2.39, respectively) in males-only analyses. We also found suggestive linkage to 4q35 and 22q13 (multipoint LOD scores 2.18 and 2.85, respectively) when we analysed both females and males and to 13q12 (multipoint LOD score 2.66) in females-only analyses.
Conclusions: We strengthened the evidence for linkage to previously reported quantitative trait loci (QTL) for stature and also found significant evidence of a novel male-specific QTL on 1p21. Further investigation of several interesting candidate genes in this region will help towards characterisation of this first sex-specific locus affecting human stature.
- FCHL, familial combined hyperlipidaemia
- HDL-C, high-density lipoprotein cholesterol
- HME, hereditary multiple exostosis
- QTL, quantitative trait locus
- VC, variance components
- body height
- genome scan
- linkage mapping
- quantitative trait loci
- stature
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Footnotes
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This work was funded by the Center for Excellence in Disease Genetics of the Academy of Finland and Biocentrum Helsinki (SS, TH, ES, KS, AM, HEL, LP, and MP), NIH grant R01HL 70150-0IA1 (LP), EC GenomElltwin project grant QLG2-CT-2002-01254 (LP, MP, and SS), the Helsinki Biomedical Graduate School (SS), and the Emil Aaltonen Foundation (TH). LCG, TT, and the Botnia Study are principally supported by the Sigrid Juselius Foundation, the Academy of Finland, the Finnish Diabetes Research Foundation, The Folkhalsan Research Foundation, the EC (BM4-CT95-0662, GIFT), the Swedish Medical Research Council, the JDF Wallenberg Foundation, Novo Nordisk Foundation, Whitehead Institute, and Millennium Pharmaceuticals.
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Competing interests: none declared