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eQTL Mapping for Functional Classes of Saccharomyces cerevisiae Genes with Multivariate Sparse Partial Least Squares Regression

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Handbook of Statistical Bioinformatics

Part of the book series: Springer Handbooks of Computational Statistics ((SHCS))

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Abstract

The availability of high-throughput genotyping technologies and microarray assays has enabled investigation of genetic variations that influence levels of gene expression. Expression Quantitative Trait Loci (eQTL) mapping methods have been successfully used to identify the genetic basis of gene expression which in turn led to identification of candidate genes and construction of regulatory networks. One challenging statistical aspect of eQTL mapping is the existence of thousands of traits. We have recently proposed a multivariate sparse partial least squares framework for mapping multiple quantitative traits and identifying genetic variations that affect the expression of a group of genes. In this book chapter, we provide a comprehensive illustration of this methodology with a Saccharomyces cerevisiae linkage study. Data from this study involves segregants from a cross between two Saccharomyces cerevisiae strains. Our application focuses on elucidating genomic markers that affect expression of functional yeast gene classes. We illustrate identification of eQTL regions affecting whole functional classes of genes as well as eQTL regions influencing individual genes.

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Acknowledgements

This research was supported by NSF grant DMS 0804597 to SK.

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Authors and Affiliations

  1. Department of Statistics and Department of Biostatistics and Medical Informatics, University of Wisconsin, 53706, Madison, WI, USA

    Sündüz Keleş

  2. Department of Statistics, University of Wisconsin, 53706, Madison, WI, USA

    Dongjun Chung

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  1. Dongjun Chung
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  2. Sündüz Keleş
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Correspondence to Sündüz Keleş .

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Editors and Affiliations

  1. , Institute of Statistics, National Chiao Tung University, Ta Hsueh Road 1001, Hsinchu, 30050, Taiwan, Taiwan R.O.C.

    Henry Horng-Shing Lu

  2. , Department of Empirical Inference, MPI for Intelligent Systems, Spemannstraße 38, Tübingen, 72076, Germany

    Bernhard Schölkopf

  3. School of Medicine, Dept. Epidemiology & Public Health, Yale University, College Street 60, New Haven, 06520, Connecticut, USA

    Hongyu Zhao

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Chung, D., Keleş, S. (2011). eQTL Mapping for Functional Classes of Saccharomyces cerevisiae Genes with Multivariate Sparse Partial Least Squares Regression. In: Lu, HS., Schölkopf, B., Zhao, H. (eds) Handbook of Statistical Bioinformatics. Springer Handbooks of Computational Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16345-6_13

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