Design and analysis of genetic association studies to finely map a locus identified by linkage analysis: assessment of the extent to which an association can account for the linkage.
Association studies are often used to finely map quantitative trait loci identified by linkage analysis. Once a polymorphism associated with the trait has been identified, it may be useful to conduct linkage analyses which adjust for this polymorphism to determine the extent to which the association accounts for the linkage signal. However, methods for conducting statistical significance tests for an observed reduction in the linkage signal are not well developed. In the present study we develop methods for assessment of the statistical significance of an observed reduction in the variance due to the linked locus, with variance components or with Haseman-Elston linkage methods. Simulations indicate that these methods have appropriate levels of type I error and that, like other association statistics, their power depends on the magnitude of linkage disequilibrium between functional and marker alleles and on the extent of similarity between the frequency of the functional allele and the frequency of the associated marker allele. These methods can help determine which association results are likely due to strong linkage disequilibrium with functional alleles and, thus, can facilitate the selection of small chromosomal regions for more extensive study.
Hanson
R L
RL
Diabetes Epidemiology and Clinical Research Section, National Institute of Diabetes and Digestive and Kidney Diseases, 1550 East Indian School Road, Phoenix, Arizona 85014, USA. rhanson@phx.niddk.nih.gov
Knowler
W C
WC
D000483:Alleles; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005787:Gene Frequency; D008040:Genetic Linkage; D005819:Genetic Markers; D006801:Humans; D015810:Linkage Disequilibrium; D008126:Lod Score; D020161:Physical Chromosome Mapping / Q000379:methods*; D011110:Polymorphism, Genetic; D019655:Quantitative Trait, Heritable; D012044:Regression Analysis; D012107:Research Design
D005819:Genetic Markers