Genetics and Genomics Commons^™

Associations Of Circulating Very-Long-Chain Saturated Fatty Acids And Incident Type 2 Diabetes: A Pooled Analysis Of Prospective Cohort Studies, Amanda M. Fretts, Fumiaki Imamura, Matti Marklund, Renata Micha, Jason H. Y. Wu, Rachel A. Murphy, Kuo-Liong Chien, Barbara Mcknight, Nathan L. Tintle, Nita G. Forouhi, Waqas T. Qureshi, Jyrki K. Virtanen, Kerry Wong, Alexis C. Wood, Maria Lankinen, Kalina Rajaobelina, Tamara B. Harris, Luc Djousse, Bill Harris, Nick J. Wareham, Lyn M. Steffen, Markku Laakso, Jenna Veenstra, Cecilia Samieri, Ingeborg A. Brouwer, Chaoyu Ian Yu, Albert Koulman, Brian T. Steffen, Catherine Helmer, Nona Sotoodehnia, David Siscovick, Vilmunder Gudnason, Interact Consortium, Lynne Wagenknecht, Sari Voutilainen, Michael Y. Tsai, Matti Uusitupa, Anya Kalsbeek, Claudine Berr, Dariush Mozaffarian, Rozenn N. Lemaitre

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Background: Saturated fatty acids (SFAs) of different chain lengths have unique metabolic and biological effects, and a small number of recent studies suggest that higher circulating concentrations of the very-long-chain SFAs (VLSFAs) arachidic acid (20:0), behenic acid (22:0), and lignoceric acid (24:0) are associated with a lower risk of diabetes. Confirmation of these findings in a large and diverse population is needed.

Objective: We investigated the associations of circulating VLSFAs 20:0, 22:0, and 24:0 with incident type 2 diabetes in prospective studies.

Methods: Twelve studies that are part of the Fatty Acids and Outcomes Research Consortium participated in the analysis. …

Leveraging Summary Statistics To Make Inferences About Complex Phenotypes In Large Biobanks, Angela Gasdaska, Derek Friend, Rachel Chen, Jason Westra, Matthew Zawistowski, William Lindsey, Nathan L. Tintle

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As genetic sequencing becomes less expensive and data sets linking genetic data and medical records (e.g., Biobanks) become larger and more common, issues of data privacy and computational challenges become more necessary to address in order to realize the benefits of these datasets. One possibility for alleviating these issues is through the use of already-computed summary statistics (e.g., slopes and standard errors from a regression model of a phenotype on a genotype). If groups share summary statistics from their analyses of biobanks, many of the privacy issues and computational challenges concerning the access of these data could be bypassed. In …

Implementing And Evaluating A Gaussian Mixture Framework For Identifying Gene Function From Tnseq Data, Kevin Li, Rachel Chen, William Lindsey, Aaron Best, Matthew Dejongh, Christopher Henry, Nathan L. Tintle

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The rapid acceleration of microbial genome sequencing increases opportunities to understand bacterial gene function. Unfortunately, only a small proportion of genes have been studied. Recently, TnSeq has been proposed as a cost-effective, highly reliable approach to predict gene functions as a response to changes in a cell's fitness before-after genomic changes. However, major questions remain about how to best determine whether an observed quantitative change in fitness represents a meaningful change. To address the limitation, we develop a Gaussian mixture model framework for classifying gene function from TnSeq experiments. In order to implement the mixture model, we present the Expectation-Maximization …

Application Of Novel And Existing Methods To Identify Genes With Evidence Of Epigenetic Association: Results From Gaw20, Angga M. Fuady, Samantha Lent, Chloé Sarnowski, Nathan L. Tintle

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Background: The rise in popularity and accessibility of DNA methylation data to evaluate epigenetic associations with disease has led to numerous methodological questions. As part of GAW20, our working group of 8 research groups focused on gene searching methods.

Results: Although the methods were varied, we identified 3 main themes within our group. First, many groups tackled the question of how best to use pedigree information in downstream analyses, finding that (a) the use of kinship matrices is common practice, (b) ascertainment corrections may be necessary, and (c) pedigree information may be useful for identifying parent-of-origin effects. Second, many groups …

Epigenome Wide Association Study Of Snp–Cpg Interactions On Changes In Triglyceride Levels After Pharmaceutical Intervention: A Gaw20 Analysis, Jenna Veenstra, Anya Kalsbeek, Karissa Koster, Nathan Ryder, Abbey Bos, Jordan Huisman, Lucas Vander Berg, Jason Vander Woude, Nathan L. Tintle

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In the search for an understanding of how genetic variation contributes to the heritability of common human disease, the potential role of epigenetic factors, such as methylation, is being explored with increasing frequency. Although standard analyses test for associations between methylation levels at individual cytosine-phosphateguanine (CpG) sites and phenotypes of interest, some investigators have begun testing for methylation and how methylation may modulate the effects of genetic polymorphisms on phenotypes. In our analysis, we used both a genome-wide and candidate gene approach to investigate potential single-nucleotide polymorphism (SNP)–CpG interactions on changes in triglyceride levels. Although we were able to identify …

Evaluating The Performance Of Gene-Based Tests Of Genetic Association When Testing For Association Between Methylation And Change In Triglyceride Levels At Gaw20, Jason Vander Woude, Jordan Huisman, Lucas Vander Berg, Jenna Veenstra, Abbey Bos, Anya Kalsbeek, Karissa Koster, Nathan Ryder, Nathan L. Tintle

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Although methylation data continues to rise in popularity, much is still unknown about how to best analyze methylation data in genome-wide analysis contexts. Given continuing interest in gene-based tests for next-generation sequencing data, we evaluated the performance of novel gene-based test statistics on simulated data from GAW20. Our analysis suggests that most of the gene-based tests are detecting real signals and maintaining the Type I error rate. The minimum pvalue and threshold-based tests performed well compared to single-marker tests in many cases, especially when the number of variants was relatively large with few true causal variants in the set.

A Genome-Wide Association Study Of Red-Blood Cell Fatty Acids And Ratios Incorporating Dietary Covariates: Framingham Heart Study Offspring Cohort, Anya Kalsbeek, Jenna Veenstra, Jason Westra, Craig Disselkoen, Kristin Koch, Katelyn A. Mckenzie, Jacob O'Bott, Jason Vander Woude, Karen Fischer, Greg C. Shearer, William S. Harris, Nathan L. Tintle

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Recent analyses have suggested a strong heritable component to circulating fatty acid (FA) levels; however, only a limited number of genes have been identified which associate with FA levels. In order to expand upon a previous genome wide association study done on participants in the Framingham Heart Study Offspring Cohort and FA levels, we used data from 2,400 of these individuals for whom red blood cell FA profiles, dietary information and genotypes are available, and then conducted a genome-wide evaluation of potential genetic variants associated with 22 FAs and 15 FA ratios, after adjusting for relevant dietary covariates. Our analysis …

Analyzing Metabolomics Data For Association With Genotypes Using Two-Component Gaussian Mixture Distributions, Jason Westra, Nicholas Hartman, Bethany Lake, Gregory Shearer, Nathan L. Tintle

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Standard approaches to evaluate the impact of single nucleotide polymorphisms (SNP) on quantitative phenotypes use linear models. However, these normal-based approaches may not optimally model phenotypes which are better represented by Gaussian mixture distributions (e.g., some metabolomics data). We develop a likelihood ratio test on the mixing proportions of two-component Gaussian mixture distributions and consider more restrictive models to increase power in light of a priori biological knowledge. Data were simulated to validate the improved power of the likelihood ratio test and the restricted likelihood ratio test over a linear model and a log transformed linear model. Then, using real …

Improvements To Bayesian Gene Activity State Estimation From Genome-Wide Transcriptomics Data, Craig Disselkoen, Nathan Hekman, Brian Gilbert, Sydney Benson, Matthew Anderson, Matthew Dejongh, Aaron Best, Nathan L. Tintle

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An important question in many biological applications, is to estimate or classify gene activity states (active or inactive) based on genome-wide transcriptomics data. Recently, we proposed a Bayesian method, titled MultiMM, which showed superior results compared to existing methods. In short, MultiMM performed better than existing methods on both simulated and real gene expression data, confirming well-known biological results and yielding better agreement with fluxomics data. Despite these promising results, MultiMM has numerous limitations. First, MultiMM leverages co-regulatory models to improve activity state estimates, but information about co-regulation is incorporated in a manner that assumes that networks are known with …

Illustrating, Quantifying, And Correcting For Bias In Post-Hoc Analysis Of Gene-Based Rare Variant Tests Of Association, Kelsey E. Grinde, Jaron Arbet, Alden Green, Michael O'Connell, Alessandra Valcarcel, Jason Westra, Nathan L. Tintle

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To date, gene-based rare variant testing approaches have focused on aggregating information across sets of variants to maximize statistical power in identifying genes showing significant association with diseases. Beyond identifying genes that are associated with diseases, the identification of causal variant(s) in those genes and estimation of their effect is crucial for planning replication studies and characterizing the genetic architecture of the locus. However, we illustrate that straightforward single-marker association statistics can suffer from substantial bias introduced by conditioning on gene-based test significance, due to the phenomenon often referred to as “winner's curse.” We illustrate the ramifications of this bias …

Powerful Method For Including Genotype Uncertainty In Tests Of Hardy-Weinberg Equilibrium, Andrew Beck, Alexander Luedtke, Keli Liu, Nathan L. Tintle

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The use of posterior probabilities to summarize genotype uncertainty is pervasive across genotype, sequencing and imputation platforms. Prior work in many contexts has shown the utility of incorporating genotype uncertainty (posterior probabilities) in downstream statistical tests. Typical approaches to incorporating genotype uncertainty when testing Hardy-Weinberg equilibrium tend to lack calibration in the type I error rate, especially as genotype uncertainty increases. We propose a new approach in the spirit of genomic control that properly calibrates the type I error rate, while yielding improved power to detect deviations from Hardy-Weinberg Equilibrium. We demonstrate the improved performance of our method on both …

Improved Performance Of Gene Set Analysis On Genome-Wide Transcriptomics Data When Using Gene Activity State Estimates, Thomas Kamp, Micah Adams, Craig Disselkoen, Nathan L. Tintle

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Gene set analysis methods continue to be a popular and powerful method of evaluating genome-wide transcriptomics data. These approach require a priori grouping of genes into biologically meaningful sets, and then conducting downstream analyses at the set (instead of gene) level of analysis. Gene set analysis methods have been shown to yield more powerful statistical conclusions than single-gene analyses due to both reduced multiple testing penalties and potentially larger observed effects due to the aggregation of effects across multiple genes in the set. Traditionally, gene set analysis methods have been applied directly to normalized, log-transformed, transcriptomics data. Recently, efforts have …

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 …

Perseverance: Psychospiritual And Genetic Perspectives, Tony N. Jelsma, Arielle Johnston, Bruce Vermeer

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Perseverance constitutes a quality that motivates humankind to press onward usually in the face of significant adversity and resistance. Perseverance is also important in the Christian life. The apostle Paul, using athletic training metaphors, frequently urges his readers to persevere in the faith, even describing his own life as a fight and a race (2 Tim.4:7). Yet, certain groups of people seem to possess a greater measure of perseverance than others have. We are therefore led to ask, “Can our ability to persevere be, in God’s providence, at least partly genetically influenced?”

General Approach For Combining Diverse Rare Variant Association Tests Provides Improved Robustness Across A Wider Range Of Genetic Architectures, Brian Greco, Allison Hainline, Jaron Arbet, Kelsey Grinde, Alejandra Benitez, Nathan L. Tintle

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The widespread availability of genome sequencing data made possible by way of next-generation technologies has yielded a flood of different gene-based rare variant association tests. Most of these tests have been published because they have superior power for particular genetic architectures. However, for applied researchers it is challenging to know which test to choose in practice when little is known a priori about genetic architecture. Recently, tests have been proposed which combine two particular individual tests (one burden and one variance components) to minimize power loss while improving robustness to a wider range of genetic architectures. In our analysis we …

Playing God, Jeff Ploegstra

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"Technically speaking, animals and plants that we have selectively bred are genetically modified organisms (GMOs)."

Posting about the benefits and dangers of genetic modification from In All Things - an online hub committed to the claim that the life, death, and resurrection of Jesus Christ has implications for the entire world.

http://inallthings.org/playing-god/

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

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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

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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

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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

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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

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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

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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 …

Evaluating The Impact Of Genotype Errors On Rare Variant Tests Of Association, Kaitlyn Cook, Alejandra Benitez, Casey Fu, Nathan L. Tintle

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The new class of rare variant tests has usually been evaluated assuming perfect genotype information. In reality, rare variant genotypes may be incorrect, and so rare variant tests should be robust to imperfect data. Errors and uncertainty in SNP genotyping are already known to dramatically impact statistical power for single marker tests on common variants and, in some cases, inflate the type I error rate. Recent results show that uncertainty in genotype calls derived from sequencing reads are dependent on several factors, including read depth, calling algorithm, number of alleles present in the sample, and the frequency at which an …

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

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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. …

Assessing Methods For Assigning Snps To Genes In Gene-Based Tests Of Association Using Common Variants, Ashley Petersen, Carolina Alvarez, Scott Declaire, Nathan L. Tintle

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Gene-based tests of association are frequently applied to common SNPs (MAF>5%) as an alternative to single-marker tests. In this analysis we conduct a variety of simulation studies applied to five popular gene-based tests investigating general trends related to their performance in realistic situations. In particular, we focus on the impact of non-causal SNPs and a variety of LD structures on the behavior of these tests. Ultimately, we find that non-causal SNPs can significantly impact the power of all gene-based tests. On average, we find that the “noise” from 6–12 non-causal SNPs will cancel out the “signal” of one causal …

Geometric Framework For Evaluating Rare Variant Tests Of Association, Keli Liu, Shannon Fast, Matthew Zawistowski, Nathan L. Tintle

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The wave of next-generation sequencing data has arrived. However, many questions still remain about how to best analyze sequence data, particularly the contribution of rare genetic variants to human disease. Numerous statistical methods have been proposed to aggregate association signals across multiple rare variant sites in an effort to increase statistical power; however, the precise relation between the tests is often not well understood. We present a geometric representation for rare variant data in which rare allele counts in case and control samples are treated as vectors in Euclidean space. The geometric framework facilitates a rigorous classification of existing rare …

Optimal Methods For Using Posterior Probabilities In Association Testing, Keli Liu, Alexander Luedtke, Nathan L. Tintle

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Objective: The use of haplotypes to impute the genotypes of unmeasured single nucleotide variants continues to rise in popularity. Simulation results suggest that the use of the dosage as a one-dimensional summary statistic of imputation posterior probabilities may be optimal both in terms of statistical power and computational efficiency; however, little theoretical understanding is available to explain and unify these simulation results. In our analysis, we provide a theoretical foundation for the use of the dosage as a one-dimensional summary statistic of genotype posterior probabilities from any technology. Methods: We analytically evaluate the dosage, mode and the more general set …

Assessing The Impact Of Differential Genotyping Errors On Rare Variant Tests Of Association, Morgan Mayer-Jochimsen, Shannon Fast, Nathan L. Tintle

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Genotyping errors are well-known to impact the power and type I error rate in single marker tests of association. Genotyping errors that happen according to the same process in cases and controls are known as non-differential genotyping errors, whereas genotyping errors that occur with different processes in the cases and controls are known as differential genotype errors. For single marker tests, non-differential genotyping errors reduce power, while differential genotyping errors increase the type I error rate. However, little is known about the behavior of the new generation of rare variant tests of association in the presence of genotyping errors. In …