Smoothing of the bivariate LOD score for non-normal quantitative traits

Alfonso Buil, Thomas D. Dyer, Laura Almasy, John Blangero

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Variance component analysis provides an efficient method for performing linkage analysis for quantitative traits. However, type I error of variance components-based likelihood ratio testing may be affected when phenotypic data are non-normally distributed (especially with high values of kurtosis). This results in inflated LOD scores when the normality assumption does not hold. Even though different solutions have been proposed to deal with this problem with univariate phenotypes, little work has been done in the multivariate case. We present an empirical approach to adjust the inflated LOD scores obtained from a bivariate phenotype that violates the assumption of normality. Using the Collaborative Study on the Genetics of Alcoholism data available for the Genetic Analysis Workshop 14, we show how bivariate linkage analysis with leptokurtotic traits gives an inflated type I error. We perform a novel correction that achieves acceptable levels of type I error.

Original languageEnglish (US)
Article numberS111
JournalBMC genetics
Volume6
Issue numberSUPPL.1
DOIs
StatePublished - Dec 30 2005
Externally publishedYes

ASJC Scopus subject areas

  • Genetics
  • Genetics(clinical)

Fingerprint

Dive into the research topics of 'Smoothing of the bivariate LOD score for non-normal quantitative traits'. Together they form a unique fingerprint.

Cite this