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Articles 1 - 10 of 10
Full-Text Articles in Medicine and Health Sciences
A Multistep Approach To Single Nucleotide Polymorphism–Set Analysis: An Evaluation Of Power And Type I Error Of Gene-Based Tests Of Association After Pathway-Based Association Tests, Alessandra Valcarcel, Kelsey Griinde, Kaitlyn Cook, Alden Green, Nathan L. Tintle
A Multistep Approach To Single Nucleotide Polymorphism–Set Analysis: An Evaluation Of Power And Type I Error Of Gene-Based Tests Of Association After Pathway-Based Association Tests, Alessandra Valcarcel, Kelsey Griinde, Kaitlyn Cook, Alden Green, Nathan L. Tintle
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The aggregation of functionally associated variants given a priori biological information can aid in the discovery of rare variants associated with complex diseases. Many methods exist that aggregate rare variants into a set and compute a single p value summarizing association between the set of rare variants and a phenotype of interest. These methods are often called gene-based, rare variant tests of association because the variants in the set are often all contained within the same gene. A reasonable extension of these approaches involves aggregating variants across an even larger set of variants (eg, all variants contained in genes within …
Novel Approach To Identify Optimal Metabotypes Of Elongase And Desaturase Activities In Prevention Of Acute Coronary Syndrome, Nathan L. Tintle, John W. Newman, Gregory C. Shearer
Novel Approach To Identify Optimal Metabotypes Of Elongase And Desaturase Activities In Prevention Of Acute Coronary Syndrome, Nathan L. Tintle, John W. Newman, Gregory C. Shearer
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Both metabolomic and genomic approaches are valuable for risk analysis, however typical approaches evaluating differences in means do not model the changes well. Gene polymorphisms that alter function would appear as distinct populations, or metabotypes, from the predominant one, in which case risk is revealed as changed mixing proportions between control and case samples. Here we validate a model accounting for mixed populations using biomarkers of fatty acid metabolism derived from a case/control study of acute coronary syndrome subjects in which both metabolomic and genomic approaches have been used previously. We first used simulated data to show improved power and …
Genome-Wide Association Study Of Saturated, Mono- And Polyunsaturated Red Blood Cell Fatty Acids In The Framingham Heart Offspring Study, Nathan L. Tintle, James V. Pottala, Sean Lacey, Vasan Ramachandran, Jason Westra, Ally Rogers, Jake Clark, Ben Olthoff, Martin Larson, William Harris, Gregory C. Shearer
Genome-Wide Association Study Of Saturated, Mono- And Polyunsaturated Red Blood Cell Fatty Acids In The Framingham Heart Offspring Study, Nathan L. Tintle, James V. Pottala, Sean Lacey, Vasan Ramachandran, Jason Westra, Ally Rogers, Jake Clark, Ben Olthoff, Martin Larson, William Harris, Gregory C. Shearer
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Most genome-wide association studies have explored relationships between genetic variants and plasma phospholipid fatty acid proportions, but few have examined apparent genetic influences on the membrane fatty acid profile of red blood cells (RBC). Using RBC fatty acid data from the Framingham Offspring Study, we analyzed over 2.5 million single nucleotide polymorphisms (SNPs) for association with 14 RBC fatty acids identifying 191 different SNPs associated with at least 1 fatty acid. Significant associations (p<1×10−8) were located within five distinct 1 MB regions. Of particular interest were novel associations between (1) arachidonic acid and PCOLCE2 (regulates apoA-I maturation …
General Approaches For Combining Multiple Rare Variant Associate Tests Provide Improved Power Across A Wider Range Of Genetic Architecture, Nathan L. Tintle, Brian Greco, Allison Hainline, Keli Liu, Jaron Arbet, Alejandra Benitez, Kelsey Grinde
General Approaches For Combining Multiple Rare Variant Associate Tests Provide Improved Power Across A Wider Range Of Genetic Architecture, Nathan L. Tintle, Brian Greco, Allison Hainline, Keli Liu, Jaron Arbet, Alejandra Benitez, Kelsey Grinde
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In the wake of the widespread availability of genome sequencing data made possible by way of nextgeneration technologies, a flood of gene‐based rare variant tests have been proposed. Most methods claim superior power against particular genetic architectures. However, an important practical issue remains for the applied researcher—namely, which test should be used for a particular association study which may consider multiple genes and/or multiple phenotypes. Recently, tests have been proposed which combine individual tests to minimize power loss while improving the robustness to a wide range of genetic architectures. In our analysis, we propose an expansion of these approaches, by …
Evaluation Of The Power And Type 1 Error Of Recently Proposed Family-Based Tests Of Assocations For Rare Variants, Allison Hainline, Carolina Alvarez, Alexander Luedtke, Brian Greco, Andrew Beck, Nathan L. Tintle
Evaluation Of The Power And Type 1 Error Of Recently Proposed Family-Based Tests Of Assocations For Rare Variants, Allison Hainline, Carolina Alvarez, Alexander Luedtke, Brian Greco, Andrew Beck, Nathan L. Tintle
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Until very recently, few methods existed to analyze rare-variant association with binary phenotypes in complex pedigrees. We consider a set of recently proposed methods applied to the simulated and real hypertension phenotype as part of the Genetic Analysis Workshop 18. Minimal power of the methods is observed for genes containing variants with weak effects on the phenotype. Application of the methods to the real hypertension phenotype yielded no genes meeting a strict Bonferroni cutoff of significance. Some prior literature connects 3 of the 5 most associated genes (p <1 × 10−4) to hypertension or related phenotypes. Further methodological development is needed to extend these methods to handle covariates, and to explore more powerful test alternatives.
Evaluating The Concordance Between Sequencing, Imputation And Microarray Genotype Calls In The Gaw18 Data, Ally Rogers, Andrew Beck, Nathan L. Tintle
Evaluating The Concordance Between Sequencing, Imputation And Microarray Genotype Calls In The Gaw18 Data, Ally Rogers, Andrew Beck, Nathan L. Tintle
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Genotype errors are well known to increase type I errors and/or decrease power in related tests of genotypephenotype association, depending on whether the genotype error mechanism is associated with the phenotype. These relationships hold for both single and multimarker tests of genotype-phenotype association. To assess the potential for genotype errors in Genetic Analysis Workshop 18 (GAW18) data, where no gold standard genotype calls are available, we explored concordance rates between sequencing, imputation, and microarray genotype calls. Our analysis shows that missing data rates for sequenced individuals are high and that there is a modest amount of called genotype discordance between …
Genetic Analysis Workshop 18: Methods And Strategies For Analyzing Human Sequence And Phenotype Data In Members Of Extended Pedigrees, Heike Bickeboller, Julia N. Bailey, Joseph Beyene, Rita M. Cantor, Heather J. Cordell, Robert C. Culverhouse, Corinne D. Engelman, David W. Fardo, Saurabh Ghosh, Inke R. Konig, Justo Lorenzo Bermejo, Phillip E. Melton, Stephanie A. Santorico, Glen A. Satten, Lei Sun, Nathan L. Tintle, Andreas Ziegler, Jean W. Maccluer, Laura Almasy
Genetic Analysis Workshop 18: Methods And Strategies For Analyzing Human Sequence And Phenotype Data In Members Of Extended Pedigrees, Heike Bickeboller, Julia N. Bailey, Joseph Beyene, Rita M. Cantor, Heather J. Cordell, Robert C. Culverhouse, Corinne D. Engelman, David W. Fardo, Saurabh Ghosh, Inke R. Konig, Justo Lorenzo Bermejo, Phillip E. Melton, Stephanie A. Santorico, Glen A. Satten, Lei Sun, Nathan L. Tintle, Andreas Ziegler, Jean W. Maccluer, Laura Almasy
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Genetic Analysis Workshop 18 provided a platform for developing and evaluating statistical methods to analyze whole-genome sequence data from a pedigree-based sample. In this article we present an overview of the data sets and the contributions that analyzed these data. The family data, donated by the Type 2 Diabetes Genetic Exploration by Next-Generation Sequencing in Ethnic Samples Consortium, included sequence-level genotypes based on sequencing and imputation, genome-wide association genotypes from prior genotyping arrays, and phenotypes from longitudinal assessments. The contributions from individual research groups were extensively discussed before, during, and after the workshop in theme-based discussion groups before being submitted …
Application Of Family-Based Tests Of Association For Rare Variants To Pathways, Brian Greco, Alexander Luedtke, Allison Hainline, Carolina Alvarez, Andrew Beck, Nathan L. Tintle
Application Of Family-Based Tests Of Association For Rare Variants To Pathways, Brian Greco, Alexander Luedtke, Allison Hainline, Carolina Alvarez, Andrew Beck, Nathan L. Tintle
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Pathway analysis approaches for sequence data typically either operate in a single stage (all variants within all genes in the pathway are combined into a single, very large set of variants that can then be analyzed using standard “gene-based” test statistics) or in 2-stages (gene-based p values are computed for all genes in the pathway, and then the gene-based p values are combined into a single pathway p value). To date, little consideration has been given to the performance of gene-based tests (typically designed for a smaller number of single-nucleotide variants [SNVs]) when the number of SNVs in the gene …
Value Of Mendelian Laws Of Segregation In Families: Data Quality Control, Imputation, And Beyond, Elizabeth M. Blue, Lei Sun, Nathan L. Tintle, Ellen M. Wijsman
Value Of Mendelian Laws Of Segregation In Families: Data Quality Control, Imputation, And Beyond, Elizabeth M. Blue, Lei Sun, Nathan L. Tintle, Ellen M. Wijsman
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When analyzing family data, we dream of perfectly informative data, even whole-genome sequences (WGSs) for all family members. Reality intervenes, and we find that next-generation sequencing (NGS) data have errors and are often too expensive or impossible to collect on everyone. The Genetic Analysis Workshop 18 working groups on quality control and dropping WGSs through families using a genome-wide association framework focused on finding, correcting, and using errors within the available sequence and family data, developing methods to infer and analyze missing sequence data among relatives, and testing for linkage and association with simulated blood pressure. We found that single-nucleotide …
Pathway Analysis Approaches For Rare And Common Variants: Insights From Genetic Analysis Workshop 18, Stella Aslibekyan, Marcio Almeida, Nathan L. Tintle
Pathway Analysis Approaches For Rare And Common Variants: Insights From Genetic Analysis Workshop 18, Stella Aslibekyan, Marcio Almeida, Nathan L. Tintle
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Pathway analysis, broadly defined as a group of methods incorporating a priori biological information from public databases, has emerged as a promising approach for analyzing high-dimensional genomic data. As part of Genetic Analysis Workshop 18, seven research groups applied pathway analysis techniques to whole-genome sequence data from the San Antonio Family Study. Overall, the groups found that the potential of pathway analysis to improve detection of causal variants by lowering the multiple-testing burden and incorporating biologic insight remains largely unrealized. Specifically, there is a lack of best practices at each stage of the pathway approach: annotation, analysis, interpretation, and follow-up. …