Quantitative Trait Linkage Analysis of S. Cerevisiae Gene Expression Data

• Activity Number: 28
• Type: Contributed
• Date/Time: Sunday, August 3, 2003 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): Xinping Cui*+ and David A. Elashoff and Caroline E. Uhlik and Kathleen Hayashibara and Esha Ray and John H. McCusker and Joseph L. DeRisi and David O. Siegmund and Patrick O. Brown
• Companies: University of California, Los Angeles and University of California, Los Angeles and Stanford University and Applied Biosystems and Stanford University and Duke University and University of California and Stanford University and Stanford University
• Keywords: microarray ; linkage mapping ; normalization ; cluster analysis ; multiple test

Abstract

The combination of genomic mismatch scanning and cDNA microarray provides the possibility of parallelly mapping loci responsible for heritable differences in gene expression on a genome-wide scale. However, such study suffered from an enormous quantity of data with significant amount of noise. In addition, multiple testing problems become a serious concern. To build accurate linkage maps, we developed a comprehensive statistical framework including: (1) data normalization, (2) missing data imputation, (3) identification of heritable genes using modified ANOVA model, (4) cluster analysis of heritable genes, and (5) novel linkage analysis of these clusters in which multiple tests have been accounted for. This protocol was applied to map the genetic origins of the heritable molecular phenotype from a cross of two unrelated yeast strains. We found 12 clusters linked to HO locus on chromosome 4 (p=0.0001) and four clusters linked to both HO locus and MDS allele of CST13 on chromosome 2 (p=0.0002). Locating the same loci for both macrophenotype and gene expression pattern strongly suggests this systematic analysis can be generally applied to understand the genetic basis of phenotypic diversity.