Genome-Wide Association and Linkage Analysis of Quantitative Traits: Comparison pf Likelihood-Ratio Test and Conditional Score Statistic

Over the past decade, genetic analysis has shifted from linkage studies, which identify broad regions containing putative trait loci, to genome-wide association studies, which detect the association of a marker with a specific phenotype. Because linkage and association analysis provide complementary information, developing a method to combine these analyses may increase the power to detect a true association. In this paper we compare a linkage score and association score test as well as a newly proposed combination of these two scores with traditional linkage and association methods. Background Improvement in genotyping technologies has led to great advances in the quest to map genes influencing complex traits. In the late 1980s came linkage studies in family samples that identified broad regions containing putative trait loci. Recently, dense single-nucleotide polymorphism (SNP) chip technology has resulted in genome-wide association analysis, where the genome is queried for association with a specific phenotype. The high number of SNPs run (from 300,000 to >1,000,000) enables relatively thorough coverage of the genome, but also greatly increases the chance of false-positive results. A low p-value in the range of 10 is often used to declare genome-wide significance and finding small to moderate associations remains difficult. One advantage of association analysis is it can be carried out in samples of unrelated individuals, which may be easier to recruit. On the other hand, family samples provide extra information about segregation of the phenotype, and both linkage and association analysis may be performed when genotype and phenotype data are available on family members. Variance-component analysis [1] is a commonly used approach for performing linkage analysis of quantitative traits. Its great flexibility to accommodate extended pedigrees is offset by increased type I error of the likelihood-ratio test (LRT) when the trait is not normally distributed [2]. An alternate approach consists of using the efficient score statistic for linkage analysis standardized by its variance-computed conditional on the