A dual-clustering framework for association screening with whole genome sequencing data and longitudinal traits

• Published in: BMC proceedings Vol. 8; no. Suppl 1; p. S47
• Main Authors: Liu, Ying, Huang, ChienHsun, Hu, Inchi, Lo, Shaw-Hwa, Zheng, Tian
• Format: Journal Article
• Language: English
• Published: London BioMed Central 17.06.2014
• Subjects: Biomedical research; Biomedicine; Blood pressure; Diabetes; Genomes; Genomics; Genotype & phenotype; Medicine; Medicine & Public Health; Proceedings; Statistical methods; Studies; Variance analysis; Rare Variant; Genetic Analysis Workshop; Area Under Curve; Similarity Score; Whole Genome Sequencing
• ISSN: 1753-6561; 1753-6561
• DOI: 10.1186/1753-6561-8-S1-S47

Current sequencing technology enables generation of whole genome sequencing data sets that contain a high density of rare variants, each of which is carried by, at most, 5% of the sampled subjects. Such variants are involved in the etiology of most common diseases in humans. These diseases can be studied by relevant longitudinal phenotype traits. Tests for association between such genotype information and longitudinal traits allow the study of the function of rare variants in complex human disorders. In this paper, we propose an association-screening framework that highlights the genotypic differences observed on rare variants and the longitudinal nature of phenotypes. In particular, both variants within a gene and longitudinal phenotypes are used to create partitions of subjects. Association between the 2 sets of constructed partitions is then evaluated. We apply the proposed strategy to the simulated data from the Genetic Analysis Workshop 18 and compare the obtained results with those from sequence kernel association test using the receiver operating characteristic curves.