GAW14 Workshop Summary
Dissection of heterogeneous phenotypes for quantitative trait mapping
Article first published online: 8 DEC 2005
© 2005 Wiley-Liss, Inc.
Supplement: Summarizing Analyses Comparing Microsatellite and SNP Marker Loci for Genome-Wide Scans
Volume 29, Issue S1, pages S41–S47, 2005
How to Cite
Bickeböller, H., Bailey, J. N., Papanicolaou, G. J., Rosenberger, A. and Viel, K. R. (2005), Dissection of heterogeneous phenotypes for quantitative trait mapping. Genet. Epidemiol., 29: S41–S47. doi: 10.1002/gepi.20109
- Issue published online: 8 DEC 2005
- Article first published online: 8 DEC 2005
- Bundesministerium für Bildung und Forschung (BMBF). Grant Numbers: German National Genome Research Network 01GR0462, 01GS0422
- multidimensional traits;
- recombination rate;
- chip technology;
- genome-wide scan
We discuss analyses of Genetic Analysis Workshop 14 data from the Collaborative Study on the Genetics of Alcoholism (COGA) as well as from a simulated complex disease, Kofendrerd personality disorder (KPD), with both genetic and phenotypic heterogeneity. Both data sets included numerous related phenotypes in addition to disease definitions. All analyses either chose from the given selection of phenotypes or defined new ones, including traits that may not have been related to alcoholism or KPD. Some contributors evaluated the genetic components of the trait. Many investigated genome-wide linkage and/or association, using microsatellites and/or single-nucleotide polymorphism (SNP) chip data. Here we will focus on methodological issues that the investigators faced. Their results depended on phenotype selection, whether continuous or discrete, the covariates included, and ethnicity of the study population. For SNP chip data, members of our group detected no difference in results for Affymetrix or Illumina chips, although higher marker density for association studies appeared to be advantageous. Overall, there were some observations that different chromosomal segments, i.e., physical locations on the p-arm, q-arm, or middle segment, might lead to possible differences in type I error rates. This finding and others highlight the importance of empirical determination of P-values to determine significance. Genet. Epidemiol. 29(Suppl. 1):S41–S47, 2005. © 2005 Wiley-Liss, Inc.