Haplotype block structure and its applications to association studies: power and study designs.
Recent studies have shown that the human genome has a haplotype block structure, such that it can be divided into discrete blocks of limited haplotype diversity. In each block, a small fraction of single-nucleotide polymorphisms (SNPs), referred to as "tag SNPs," can be used to distinguish a large fraction of the haplotypes. These tag SNPs can potentially be extremely useful for association studies, in that it may not be necessary to genotype all SNPs; however, this depends on how much power is lost. Here we develop a simulation study to quantitatively assess the power loss for a variety of study designs, including case-control designs and case-parental control designs. First, a number of data sets containing case-parental or case-control samples are generated on the basis of a disease model. Second, a small fraction of case and control individuals in each data set are genotyped at all the loci, and a dynamic programming algorithm is used to determine the haplotype blocks and the tag SNPs based on the genotypes of the sampled individuals. Third, the statistical power of tests was evaluated on the basis of three kinds of data: (1) all of the SNPs and the corresponding haplotypes, (2) the tag SNPs and the corresponding haplotypes, and (3) the same number of randomly chosen SNPs as the number of tag SNPs and the corresponding haplotypes. We study the power of different association tests with a variety of disease models and block-partitioning criteria. Our study indicates that the genotyping efforts can be significantly reduced by the tag SNPs, without much loss of power. Depending on the specific haplotype block-partitioning algorithm and the disease model, when the identified tag SNPs are only 25% of all the SNPs, the power is reduced by only 4%, on average, compared with a power loss of approximately 12% when the same number of randomly chosen SNPs is used in a two-locus haplotype analysis. When the identified tag SNPs are approximately 14% of all the SNPs, the power is reduced by approximately 9%, compared with a power loss of approximately 21% when the same number of randomly chosen SNPs is used in a two-locus haplotype analysis. Our study also indicates that haplotype-based analysis can be much more powerful than marker-by-marker analysis.
Zhang
Kui
K
Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles 90089, USA.
Calabrese
Peter
P
Nordborg
Magnus
M
Sun
Fengzhu
F
D000465:Algorithms; D016022:Case-Control Studies; D002874:Chromosome Mapping / Q000379:methods* / Q000706:statistics & numerical data; D003198:Computer Simulation; D005819:Genetic Markers / Q000235:genetics; D006239:Haplotypes / Q000235:genetics*; D006801:Humans; D015810:Linkage Disequilibrium / Q000235:genetics; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D011995:Recombination, Genetic / Q000235:genetics; D012107:Research Design
D005819:Genetic Markers