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Full-Text Articles in Life Sciences
Identifying Rare Variants From Exome Scans: The Gaw17 Experience, Saurabh Ghosh, Heike Bickeboller, Julia Bailey, Joan E. Bailey-Wilson, Rita Cantor, Robert Culverhouse, Warwick Daw, Anita L. Destefano, Corinne D. Engelman, Anthony Hinrichs, Jeanine Houwing-Duistermaat, Inke R. Konig, Jack Kent, Nan Laird, Nathan Pankratz, Andrew Paterson, Elizabeth Pugh, Brian Suarez, Yan Sun, Alun Thomas, Nathan L. Tintle, Xiaofeng Zhu, Andreas Ziegler, Jean W. Maccluer, Laura Almasy
Identifying Rare Variants From Exome Scans: The Gaw17 Experience, Saurabh Ghosh, Heike Bickeboller, Julia Bailey, Joan E. Bailey-Wilson, Rita Cantor, Robert Culverhouse, Warwick Daw, Anita L. Destefano, Corinne D. Engelman, Anthony Hinrichs, Jeanine Houwing-Duistermaat, Inke R. Konig, Jack Kent, Nan Laird, Nathan Pankratz, Andrew Paterson, Elizabeth Pugh, Brian Suarez, Yan Sun, Alun Thomas, Nathan L. Tintle, Xiaofeng Zhu, Andreas Ziegler, Jean W. Maccluer, Laura Almasy
Faculty Work Comprehensive List
Genetic Analysis Workshop 17 (GAW17) provided a platform for evaluating existing statistical genetic methods and for developing novel methods to analyze rare variants that modulate complex traits. In this article, we present an overview of the 1000 Genomes Project exome data and simulated phenotype data that were distributed to GAW17 participants for analyses, the different issues addressed by the participants, and the process of preparation of manuscripts resulting from the discussions during the workshop
Inflated Type I Error Rates When Using Aggregation Methods To Analyze Rare Variants In The 1000 Genomes Project Exon Sequencing Data In Unrelated Individuals: Summary Results From Group 7 At Genetic Analysis Workshop 17, Nathan L. Tintle, Hugues Aschard, Inchi Hu, Nora Nock, Haitian Wang, Elizabeth Pugh
Inflated Type I Error Rates When Using Aggregation Methods To Analyze Rare Variants In The 1000 Genomes Project Exon Sequencing Data In Unrelated Individuals: Summary Results From Group 7 At Genetic Analysis Workshop 17, Nathan L. Tintle, Hugues Aschard, Inchi Hu, Nora Nock, Haitian Wang, Elizabeth Pugh
Faculty Work Comprehensive List
As part of Genetic Analysis Workshop 17 (GAW17), our group considered the application of novel and standard approaches to the analysis of genotype-phenotype association in next-generation sequencing data. Our group identified a major issue in the analysis of the GAW17 next-generation sequencing data: type I error and false-positive report probability rates higher than those expected based on empirical type I error levels (as high as 90%). Two main causes emerged: population stratification and long-range correlation (gametic phase disequilibrium) between rare variants. Population stratification was expected because of the diverse sample. Correlation between rare variants was attributable to both random causes …
Identification Of Genetic Association Of Multiple Rare Variants Using Collapsing Methods, Yan V. Sun, Yun Ju Sung, Nathan L. Tintle, Andreas Ziegler
Identification Of Genetic Association Of Multiple Rare Variants Using Collapsing Methods, Yan V. Sun, Yun Ju Sung, Nathan L. Tintle, Andreas Ziegler
Faculty Work Comprehensive List
Next-generation sequencing technology allows investigation of both common and rare variants in humans. Exomes are sequenced on the population level or in families to further study the genetics of human diseases. Genetic Analysis Workshop 17 (GAW17) provided exomic data from the 1000 Genomes Project and simulated phenotypes. These data enabled evaluations of existing and newly developed statistical methods for rare variant sequence analysis for which standard statistical methods fail because of the rareness of the alleles. Various alternative approaches have been proposed that overcome the rareness problem by combining multiple rare variants within a gene. These approaches are termed collapsing …