Association study for the relationship between a haplotype or haplotype set and multiple quantitative responses

• Published in: Computational statistics & data analysis Vol. 55; no. 6; pp. 2104 - 2113
• Main Authors: Tomita, Makoto, Hashimoto, Noboru, Tanaka, Yutaka
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.06.2011; Elsevier
• Series: Computational Statistics & Data Analysis
• Subjects: Additives; Combinatorics; Combinatorics. Ordered structures; Computer simulation; Data processing; Designs and configurations; Exact sciences and technology; General topics; Genetics; Haplotype; Likelihood ratio test; Mathematical models; Mathematics; Multivariate analysis; Multivariate analysis Quantitative responses Haplotype Likelihood ratio test; Normal distribution; Numerical analysis; Numerical analysis. Scientific computation; Numerical methods in probability and statistics; Probability and statistics; Quantitative responses; Sciences and techniques of general use; Statistics; Stochasticity; Multivariate analysis; Haplotype; Likelihood ratio test; Quantitative responses; Data analysis; Statistical distribution; Gaussian distribution; Multivariate distribution; Statistical association; Numerical method; Stochastic method; Additive model; Numerical analysis; Statistical computation; Genetics; Comparative study
• ISSN: 0167-9473; 1872-7352
• DOI: 10.1016/j.csda.2011.01.002

Though there have been several works on the analysis of the association between genotype and phenotype, little can be found for the association analysis between a haplotype or haplotype sets and multivariate quantitative responses. For example, QTLmarc is available for the analysis of multivariate responses, but it cannot be applied to the case of stochastic diplotype configurations and complex genetic models. The present paper proposes a method of association analysis between diplotype configuration and multivariate quantitative responses assuming the dominant, recessive and additive models. A comparative study is performed between the proposed method and QTLmarc by applying the two methods to numerical examples and small size simulated data sets with actual genotype information taken from the data set of the Hapmap project and artificial quantitative phenotype data which follow multivariate normal distributions. The results show that the proposed method is superior to QTLmarc in finding the assumed association.