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

BMC Proceedings

Proceedings

Shaw-Hwa Lo¹  Ying Liu¹  ChienHsun Huang¹  Tian Zheng¹  Inchi Hu² 

[1] Department of Statistics, Columbia University, 1255 Amsterdam Avenue, 10027, New York, NY, USA;ISOM, Hong Kong University of Science and Technology, Kowloon, Hong Kong;

关键词: Similarity Score;    Rare Variant;    Whole Genome Sequencing;    Area Under Curve;    Genetic Analysis Workshop;   

DOI  :  10.1186/1753-6561-8-S1-S47

来源: Springer

【 摘 要 】

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.

【 授权许可】

Unknown   
© Liu et al.; licensee BioMed Central Ltd. 2014. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.