Title of article :
Methods for Population-Based eQTL Analysis in Human Genetics
Author/Authors :
Tian, Lu University of North Carolina at Charlotte - College of Computing and Informatics - Department of Bioinformatics and Genomics, USA , Quitadamo, Andrew University of North Carolina at Charlotte - College of Computing and Informatics - Department of Bioinformatics and Genomics, USA , Lin, Frederick University of North Carolina at Charlotte - College of Computing and Informatics - Department of Bioinformatics and Genomics, USA , Shi, Xinghua University of North Carolina at Charlotte - College of Computing and Informatics - Department of Bioinformatics and Genomics, USA
Abstract :
Gene expression is a critical process in biological system that is influenced and modulated by many factors including genetic variation. Expression Quantitative Trait Loci (eQTL) analysis provides a powerful way to understand how genetic variants affect gene expression. For genome wide eQTL analysis, the number of genetic variants and that of genes are large and thus the search space is tremendous. Therefore, eQTL analysis brings about computational and statistical challenges. In this paper, we provide a comprehensive review of recent advances in methods for eQTL analysis in population-based studies. We first present traditional pairwise association methods, which are widely used in human genetics. To account for expression heterogeneity, we investigate the methods for correcting confounding factors. Next, we discuss newly developed statistical learning methods including Lasso-based models. In the conclusion, we provide an overview of future method development in analyzing eQTL associations. Although we focus on human genetics in this review, the methods are applicable to many other organisms.
Keywords :
expression Quantitative Trait Loci (eQTL) analysis , confounding factors , sparse learning models , Lasso
Journal title :
Tsinghua Science and Technology
Journal title :
Tsinghua Science and Technology
