Abstract
BackgroundCurrent 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.ResultsThe 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.ConclusionWe 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.
Highlights
Current statistical methods for sib-pair linkage analysis of complex diseases include linear models, generalized linear models, and novel data mining techniques
The discriminant analysis proposed by us recently is in essence anti-traditional in that the positions of the components in the genetic model are reversed, i.e., we believe that the variation in marker identity by descent (IBD) among the sib-pairs is due to the classification of the phenotypes of a sib pair, for example, concordant affected, discordant, and concordant unaffected
We further investigated its properties and performance by applying it to the chromosome 10 data from the Framingham Heart Study and by comparing it with step-wise logistic regression and linear regression
Summary
Current statistical methods for sib-pair linkage analysis of complex diseases include linear models, generalized linear models, and novel data mining techniques. The discriminant analysis proposed by us recently is in essence anti-traditional in that the positions of the components in the genetic model are reversed, i.e., we believe that the variation in marker identity by descent (IBD) among the sib-pairs is due to the classification of the phenotypes of a sib pair, for example, concordant affected, discordant, and concordant unaffected This novel multivariate approach has several unique characteristics in the context of linkage analysis. No distribution assumption for the grouping variable is assumed because it is considered to be fixed (constant) in the discriminant analysis, while a multivariate normal distribution is often imposed on the feature variables within a group It can have very distinct statistical properties from the conventional (generalized) linear models, for example, there is not a balanced design for sib-pair linkage analysis and the statistical power for linkage analysis of a binary disease can be higher than the corresponding ordinal traits [2,3]. We further investigated its properties and performance by applying it to the chromosome 10 data from the Framingham Heart Study and by comparing it with step-wise logistic regression and linear regression
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