Semiparametric methods for genome-wide linkage analysis of human gene expression data.

@article{citeulike:2803513, abstract = {ABSTRACT : With the availability of high-throughput microarray technologies, investigators can simultaneously measure the expression levels of many thousands of genes in a short period. Although there are rich statistical methods for analyzing microarray data in the literature, limited work has been done in mapping expression quantitative trait loci (eQTL) that influence the variation in levels of gene expression. Most existing eQTL mapping methods assume that the expression phenotypes follow a normal distribution and violation of the normality assumption may lead to inflated type I error and reduced power. QTL analysis of expression data involves the mapping of many expression phenotypes at thousands or hundreds of thousands of marker loci across the whole genome. An appropriate procedure to adjust for multiple testing is essential for guarding against an abundance of false positive results. In this study, we applied a semiparametric quantitative trait loci (SQTL) mapping method to human gene expression data. The SQTL mapping method is rank-based and therefore robust to non-normality and outliers. Furthermore, we apply an efficient Monte Carlo procedure to account for multiple testing and assess the genome-wide significance level. Particularly, we apply the SQTL mapping method and the Monte-Carlo approach to the gene expression data provided by Genetic Analysis Workshop 15.}, address = {Department of Statistics, George Mason University, 4400 University Drive, MS 4A7, Fairfax, Virginia 22030, USA. gdiao@gmu.edu.}, author = {Diao, G. and Lin, D. }, citeulike-article-id = {2803513}, issn = {1753-6561}, journal = {BMC proceedings}, posted-at = {2008-05-16 02:35:08}, priority = {2}, title = {Semiparametric methods for genome-wide linkage analysis of human gene expression data.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/18466586}, volume = {1 Suppl 1}, year = {2007} }

TY - JOUR ID - citeulike:2803513 L3 - citeulike-article-id:2803513 TI - Semiparametric methods for genome-wide linkage analysis of human gene expression data. JF - BMC proceedings VL - 1 Suppl 1 AD - Department of Statistics, George Mason University, 4400 University Drive, MS 4A7, Fairfax, Virginia 22030, USA. gdiao@gmu.edu. SN - 1753-6561 N2 - ABSTRACT : With the availability of high-throughput microarray technologies, investigators can simultaneously measure the expression levels of many thousands of genes in a short period. Although there are rich statistical methods for analyzing microarray data in the literature, limited work has been done in mapping expression quantitative trait loci (eQTL) that influence the variation in levels of gene expression. Most existing eQTL mapping methods assume that the expression phenotypes follow a normal distribution and violation of the normality assumption may lead to inflated type I error and reduced power. QTL analysis of expression data involves the mapping of many expression phenotypes at thousands or hundreds of thousands of marker loci across the whole genome. An appropriate procedure to adjust for multiple testing is essential for guarding against an abundance of false positive results. In this study, we applied a semiparametric quantitative trait loci (SQTL) mapping method to human gene expression data. The SQTL mapping method is rank-based and therefore robust to non-normality and outliers. Furthermore, we apply an efficient Monte Carlo procedure to account for multiple testing and assess the genome-wide significance level. Particularly, we apply the SQTL mapping method and the Monte-Carlo approach to the gene expression data provided by Genetic Analysis Workshop 15. AU - Diao, G AU - Lin, D PY - 2007/// UR - http://view.ncbi.nlm.nih.gov/pubmed/18466586 ER -