Power comparisons between similarity-based multilocus association methods, logistics regression, and score tests for haplotypes

Wan Yu Lin, Daniel J. Schaid

Research output: Contribution to journalArticlepeer-review

26 Scopus citations

Abstract

Recently, a genomic distance-based regression for multilocus associations was proposed (Wessel and Schork [2006] Am. J. Hum. Genet. 79:792-806) in which either locus or haplotype scoring can be used to measure genetic distance. Although it allows various measures of genomic similarity and simultaneous analyses of multiple phenotypes, its power relative to other methods for case-control analyses is not well known. We compare the power of traditional methods with this new distance-based approach, for both locus-scoring and haplotype-scoring strategies. We discuss the relative power of these association methods with respect to five properties: (1) the marker informativity; (2) the number of markers; (3) the causal allele frequency; (4) the preponderance of the most common high-risk haplotype; (5) the correlation between the causal single-nucleotide polymorphism (SNP) and its flanking markers. We found that locus-based logistic regression and the global score test for haplotypes suffered from power loss when many markers were included in the analyses, due to many degrees of freedom. In contrast, the distance-based approach was not as vulnerable to more markers or more haplotypes. A genotype counting measure was more sensitive to the marker informativity and the correlation between the causal SNP and its flanking markers. After examining the impact of the five properties on power, we found that on average, the genomic distance-based regression that uses a matching measure for diplotypes was the most powerful and robust method among the seven methods we compared.

Original languageEnglish (US)
Pages (from-to)183-197
Number of pages15
JournalGenetic epidemiology
Volume33
Issue number3
DOIs
StatePublished - 2009

Keywords

  • Canonical correlation
  • Genomic distance
  • Genotyping errors
  • Linkage disequilibrium

ASJC Scopus subject areas

  • Epidemiology
  • Genetics(clinical)

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