Genetic linkage analysis of longitudinal hypertension phenotypes using three summary measures

BACKGROUND: Longitudinal data often have multiple (repeated) measures recorded along a time trajectory. For example, the two cohorts from the Framingham Heart Study (GAW13 Problem 1) contain 21 and 5 repeated measures for hypertension phenotypes as well as epidemiological risk factors, respectively. Direct modelling of a large number of serially and biologically correlated traits in the context of linkage analysis can be prohibitively complex. Alternatively, we may consider using univariate transformation for linkage analysis of longitudinal repeated measures. RESULTS: We evaluated the utility of three conventional summary measures (mean, slope, and principal components) for genetic linkage analysis of longitudinal phenotypes by analyzing the chromosome 10 data of the Framingham Heart Study. Except for the temporal slope, all of the summary methods and the multivariate analysis identified the previously reported region, marker GATA64A09, for systolic blood pressure or high blood pressure. Further analysis revealed that this region may harbor gene(s) affecting human blood pressure at multiple stages of life. CONCLUSION: We conclude that mean and principal components are feasible alternatives for genetic linkage analysis of longitudinal phenotypes, but the slope might have a separate genetic basis from that of the original longitudinal phenotypes

Multivariate sib-pair linkage analysis of longitudinal phenotypes by three step-wise analysis approaches

BACKGROUND: Current statistical methods for sib-pair linkage analysis of complex diseases include linear models, generalized linear models, and novel data mining techniques. The purpose of this study was to further investigate the utility and properties of a novel pattern recognition technique (step-wise discriminant analysis) using the chromosome 10 linkage data from the Framingham Heart Study and by comparing it with step-wise logistic regression and linear regression. RESULTS: The three step-wise approaches were compared in terms of statistical significance and gene localization. Step-wise discriminant linkage analysis approach performed best; next was step-wise logistic regression; and step-wise linear regression was the least efficient because it ignored the categorical nature of disease phenotypes. Nevertheless, all three methods successfully identified the previously reported chromosomal region linked to human hypertension, marker GATA64A09. We also explored the possibility of using the discriminant analysis to detect gene × gene and gene × environment interactions. There was evidence to suggest the existence of gene × environment interactions between markers GATA64A09 or GATA115E01 and hypertension treatment and gene × gene interactions between markers GATA64A09 and GATA115E01. Finally, we answered the theoretical question "Is a trichotomous phenotype more efficient than a binary?" Unlike logistic regression, discriminant sib-pair linkage analysis might have more power to detect linkage to a binary phenotype than a trichotomous one. CONCLUSION: We confirmed our previous speculation that step-wise discriminant analysis is useful for genetic mapping of complex diseases. This analysis also supported the possibility of the pattern recognition technique for investigating gene × gene or gene × environment interactions

Premature Myocardial Infarction Novel Susceptibility Locus on Chromosome 1P34-36 Identified by Genomewide Linkage Analysis

The most frequent causes of death and disability in the Western world are atherosclerotic coronary artery disease (CAD) and acute myocardial infarction (MI). This common disease is thought to have a polygenic basis with a complex interaction with environmental factors. Here, we report results of a genomewide search for susceptibility genes for MI in a well-characterized U.S. cohort consisting of 1,613 individuals in 428 multiplex families with familial premature CAD and MI: 712 with MI, 974 with CAD, and average age of onset of 44.4±9.7 years. Genotyping was performed at the National Heart, Lung, and Blood Institute Mammalian Genotyping Facility through use of 408 markers that span the entire human genome every 10 cM. Linkage analysis was performed with the modified Haseman-Elston regression model through use of the SIBPAL program. Three genomewide scans were conducted: single-point, multipoint, and multipoint performed on of white pedigrees only (92% of the cohort). One novel significant susceptibility locus was detected for MI on chromosomal region 1p34-36, with a multipoint allele-sharing P value of <10(−12) (LOD=11.68). Validation by use of a permutation test yielded a pointwise empirical P value of .00011 at this locus, which corresponds to a genomewide significance of P<.05. For the less restrictive phenotype of CAD, no genetic locus was detected, suggesting that CAD and MI may not share all susceptibility genes. The present study thus identifies a novel genetic-susceptibility locus for MI and provides a framework for the ultimate cloning of a gene for the complex disease MI

Multivariate sib-pair linkage analysis of longitudinal phenotypes by three step-wise analysis approaches-0

<p><b>Copyright information:</b></p><p>Taken from "Multivariate sib-pair linkage analysis of longitudinal phenotypes by three step-wise analysis approaches"</p><p>http://www.biomedcentral.com/1471-2156/4/s1/S68</p><p>BMC Genetics 2003;4(Suppl 1):S68-S68.</p><p>Published online 31 Dec 2003</p><p>PMCID:PMC1866506.</p><p></p