Genomics and genome-wide association studies: An integrative approach to expression QTL mapping

@article{citeulike:3013833, abstract = {Expression QTL mapping by integrating genome-wide gene expression and genotype data is a promising approach to identifying functional genetic variation, but is hampered by the large number of multiple comparisons inherent in such studies. A novel approach to addressing multiple testing problems in genome-wide family-based association studies is screening candidate markers using heritability or conditional power. We apply these methods in settings in which microarray gene expression data are used as phenotypes, screening for SNPs near the expressed genes. We perform association analyses for phenotypes using a univariate approach. We also perform simulations on trios with large numbers of causal SNPs to determine the optimal number of markers to use in a screen. We demonstrate that our family-based screening approach performs well in the analysis of integrative genomic datasets and that screening using either heritability or conditional power produces similar, though not identical, results.}, author = {Degnan, James H. and Lasky-Su, Jessica and Raby, Benjamin A. and Xu, Mousheng and Molony, Cliona and Schadt, Eric E. and Lange, Christoph }, citeulike-article-id = {3013833}, doi = {http://dx.doi.org/10.1016/j.ygeno.2008.05.012}, journal = {Genomics}, posted-at = {2008-07-17 09:12:20}, priority = {2}, title = {Genomics and genome-wide association studies: An integrative approach to expression QTL mapping}, url = {http://dx.doi.org/10.1016/j.ygeno.2008.05.012}, volume = {In Press, Corrected Proof} }

TY - JOUR ID - citeulike:3013833 L3 - citeulike-article-id:3013833 TI - Genomics and genome-wide association studies: An integrative approach to expression QTL mapping JF - Genomics VL - In Press, Corrected Proof N2 - Expression QTL mapping by integrating genome-wide gene expression and genotype data is a promising approach to identifying functional genetic variation, but is hampered by the large number of multiple comparisons inherent in such studies. A novel approach to addressing multiple testing problems in genome-wide family-based association studies is screening candidate markers using heritability or conditional power. We apply these methods in settings in which microarray gene expression data are used as phenotypes, screening for SNPs near the expressed genes. We perform association analyses for phenotypes using a univariate approach. We also perform simulations on trios with large numbers of causal SNPs to determine the optimal number of markers to use in a screen. We demonstrate that our family-based screening approach performs well in the analysis of integrative genomic datasets and that screening using either heritability or conditional power produces similar, though not identical, results. AU - Degnan, James AU - Lasky-Su, Jessica AU - Raby, Benjamin AU - Xu, Mousheng AU - Molony, Cliona AU - Schadt, Eric AU - Lange, Christoph UR - http://dx.doi.org/10.1016/j.ygeno.2008.05.012 ER -