Sum statistics for the joint detection of multiple disease loci in case-control association studies with SNP markers.

In complex traits, multiple disease loci presumably interact to produce the disease. For this reason, even with high-resolution single nucleotide polymorphism (SNP) marker maps, it has been difficult to map susceptibility loci by conventional locus-by-locus methods. Fine mapping strategies are needed that allow for the simultaneous detection of interacting disease loci while handling large numbers of densely spaced markers. For this purpose, sum statistics were recently proposed as a first-stage analysis method for case-control association studies with SNPs. Via sums of single-marker statistics, information over multiple disease-associated markers is combined and, with a global significance value alpha, a small set of "interesting" markers is selected for further analysis. Here, the statistical properties of such approaches are examined by computer simulation. It is shown that sum statistics can often be successfully applied when marker-by-marker approaches fail to detect association. Compared with Bonferroni or False Discovery Rate (FDR) procedures, sum statistics have greater power, and more disease loci can be detected. However, in studies with tightly linked markers, simple sum statistics can be suboptimal, since the intermarker correlation is ignored. A method is presented that takes the correlation structure among marker loci into account when marker statistics are combined.

Wille A ，Hoh J ，Ott J 《GENETIC EPIDEMIOLOGY》

Further investigation of linkage disequilibrium SNPs and their ability to identify associated susceptibility loci.

North BV ，Curtis D ，Martin ER ，Lai EH ，Roses AD ，Sham PC ... -  《ANNALS OF HUMAN GENETICS》

The power of genome-wide association studies of complex disease genes: statistical limitations of indirect approaches using SNP markers.

Ohashi J ，Tokunaga K 《JOURNAL OF HUMAN GENETICS》

Detecting susceptibility genes in case-control studies using set association.

Kim S ，Zhang K ，Sun F 《BMC GENETICS》

Statistical multilocus methods for disequilibrium analysis in complex traits.

Hundreds of thousands of SNP markers are being generated with the purpose of carrying out case-control association studies for complex traits, which are thought to be due to multiple underlying susceptibility genes. The number of markers is typically much larger than the number of observations so that joint analysis of marker genotypes and their interactions is not feasible. We discuss a two-stage approach to first select a small subset of markers and then model the effects of the selected markers on disease. Examples of two procedures for marker selection are given with subsequent modeling of main and interaction effects. The approaches are applied to a data set with 89 SNPs in lieu of a genome screen with many more markers.

Ott J ，Hoh J 《-》