Abstract
Assessing the effects of multiple markers in genetic association studies.
Highlights
Multi-marker tests of association can be used to maximize power to detect association at the gene or regional level
The study by Burkett et al (2013) included in this issue considered an approach for a multi-marker regional test. They demonstrate how gene genealogies estimated from haplotype data can be used to find disease-predisposing genetic variants and propose a tree-based test of association based on assessing haplotype similarity of cases versus controls
Noting that genotype correlations within an LD block asymptotically lead to a multivariate normal distribution for score test statistics, Taub et al (2013) developed a set of weights for markers to maximize power of multi-marker association tests, and found that a method previously proposed by Conneely and Boehnke (2007) is a practical and powerful method for a range of scenarios
Summary
Multi-marker tests of association can be used to maximize power to detect association at the gene or regional level. The study by Burkett et al (2013) included in this issue considered an approach for a multi-marker regional test. They demonstrate how gene genealogies estimated from haplotype data can be used to find disease-predisposing genetic variants and propose a tree-based test of association based on assessing haplotype similarity of cases versus controls.
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