Linkage Analysis Using Co-Phenotypes in the BRIGHT Study Reveals Novel Potential Susceptibility Loci for Hypertension
| Autor: | Chris Wallace, Mark J. Caulfield, Mingzhan Xue, Richard Dobson, Patricia B. Munroe, John M. C. Connell, David Clayton, Morris J. Brown, John Webster, Ana Carolina B. Marçano, Johannie Gungadoo, Martin Farrall, Abiodun Onipinla, G. Mark Lathrop, Stephen Newhouse, Nilesh J. Samani, Charles A. Mein, Anna F. Dominiczak, Beverley Burke |
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| Rok vydání: | 2006 |
| Předmět: |
Score test
Genetic Markers Male Genetic Linkage Locus (genetics) Computational biology 030204 cardiovascular system & hematology Biology Genetic determinism Article 03 medical and health sciences 0302 clinical medicine Genetic linkage Covariate Genetics Humans Genetic Predisposition to Disease Genetics(clinical) Genetics (clinical) 030304 developmental biology 0303 health sciences Genetic heterogeneity Genome Human Chromosome Mapping Regression analysis Middle Aged United Kingdom Phenotype Genetic marker Hypertension Female |
| Zdroj: | BASE-Bielefeld Academic Search Engine |
| ISSN: | 0002-9297 |
| DOI: | 10.1086/506370 |
| Popis: | Identification of the genetic influences on human essential hypertension and other complex diseases has proved difficult, partly because of genetic heterogeneity. In many complex-trait resources, additional phenotypic data have been collected, allowing comorbid intermediary phenotypes to be used to characterize more genetically homogeneous subsets. The traditional approach to analyzing covariate-defined subsets has typically depended on researchers' previous expectations for definition of a comorbid subset and leads to smaller data sets, with a concomitant attrition in power. An alternative is to test for dependence between genetic sharing and covariates across the entire data set. This approach offers the advantage of exploiting the full data set and could be widely applied to complex-trait genome scans. However, existing maximum-likelihood methods can be prohibitively computationally expensive, especially since permutation is often required to determine significance. We developed a less computationally intensive score test and applied it to biometric and biochemical covariate data, from 2,044 sibling pairs with severe hypertension, collected by the British Genetics of Hypertension (BRIGHT) study. We found genomewide-significant evidence for linkage with hypertension and several related covariates. The strongest signals were with leaner-body-mass measures on chromosome 20q (maximum LOD = 4.24) and with parameters of renal function on chromosome 5p (maximum LOD = 3.71). After correction for the multiple traits and genetic locations studied, our global genomewide P value was .046. This is the first identity-by-descent regression analysis of hypertension to our knowledge, and it demonstrates the value of this approach for the incorporation of additional phenotypic information in genetic studies of complex traits. |
| Databáze: | OpenAIRE |
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