title: Multiple linear combination (MLC) regression tests for common variants adapted to linkage disequilibrium structure.
abstract: By jointly analyzing multiple variants within a gene, instead of one at a time, gene-based multiple regression can improve power, robustness, and interpretation in genetic association analysis. We investigate multiple linear combination (MLC) test statistics for analysis of common variants under realistic trait models with linkage disequilibrium (LD) based on HapMap Asian haplotypes. MLC is a directional test that exploits LD structure in a gene to construct clusters of closely correlated variants recoded such that the majority of pairwise correlations are positive. It combines variant effects within the same cluster linearly, and aggregates cluster-specific effects in a quadratic sum of squares and cross-products, producing a test statistic with reduced degrees of freedom (df) equal to the number of clusters. By simulation studies of 1000 genes from across the genome, we demonstrate that MLC is a well-powered and robust choice among existing methods across a broad range of gene structures. Compared to minimum P-value, variance-component, and principal-component methods, the mean power of MLC is never much lower than that of other methods, and can be higher, particularly with multiple causal variants. Moreover, the variation in gene-specific MLC test size and power across 1000 genes is less than that of other methods, suggesting it is a complementary approach for discovery in genome-wide analysis. The cluster construction of the MLC test statistics helps reveal within-gene LD structure, allowing interpretation of clustered variants as haplotypic effects, while multiple regression helps to distinguish direct and indirect associations.
authors.affiliation: Department of Mathematics Education, Seoul National University, Seoul, South Korea.|Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, South Korea.
authors.forename: Yun Joo
authors.initials: YJ
authors.lastname: Yoo
authors.affiliation: Department of Statistical Sciences, University of Toronto, Toronto, Canada.|Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada.
authors.forename: Lei
authors.initials: L
authors.lastname: Sun
authors.affiliation: Prosserman Centre for Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Canada.
authors.forename: Julia G
authors.initials: JG
authors.lastname: Poirier
authors.affiliation: Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada.|Program in Genetics and Genome Biology, Hospital for Sick Children Research Institute, Toronto, Canada.
authors.forename: Andrew D
authors.initials: AD
authors.lastname: Paterson
authors.affiliation: Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada.|Prosserman Centre for Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Canada.
authors.forename: Shelley B
authors.initials: SB
authors.lastname: Bull
mesh_terms: D005819:Genetic Markers / Q000235:genetics*; D006239:Haplotypes / Q000235:genetics*; D006801:Humans; D016014:Linear Models; D015810:Linkage Disequilibrium; D008957:Models, Genetic; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D040641:Quantitative Trait Loci
chemical_list: D005819:Genetic Markers
keywords: common variants; linkage disequilibrium; multibin linear combination test; multivariant test; quantitative trait