The technological and computational breakthroughs in the years since the sequencing of the human genome have provided an unprecedented opportunity to understand the etiology of complex human diseases. Notably, the diminishing cost of next-generation sequencing means that it is now possible for researchers to obtain complete genome sequence information on many thousands of individuals, with widespread access to that data via large repositories of electronic health records (EHRs; e.g,. biobanks). However, major statistical questions remain about biobank-era analysis strategies in order to study the contribution of genetic variation to common diseases. In particular, foundational statistical questions exist in the areas of: (a) a recognition of the need to minimize computational complexity and respect data privacy concerns, (b) random and non-random missing data, (c) data uncertainty and errors (both phenotypic and genotypic), and (d) the role of multi-marker (variant-set) tests, which aggregate evidence from many individual variants into a single test statistic. Research by our group as part of our existing award (R15-HG0006915 (2011-present)) began by developing a framework for evaluating the performance of existing variant-set tests. We then utilized this framework to provide a clear understanding of test performance in a variety of circumstances, developed novel robust and powerful tests, evaluated method performance in light of genotype uncertainty, developed methods to characterize underlying genetic architecture and demonstrated the utility of these methods to understand the genetics of fatty acids and high blood pressure. Recently, we proposed a novel method for utilizing summary statistics from simple one variant ? one phenotype linear models to draw inferences about complex phenotypes (in this case, the linear combination of phenotypes) as a first step to provide computationally efficient, biobank era-ready statistical methods for assessing genotype-phenotype association. Moving forward, our research will generalize this initial method to be applicable to any complex phenotype. Additionally, we will continue to build on a strong history of exploration of uncertainty, by considering the impact of random and non-random errors and uncertainty on genotype-phenotype association tests in the biobank era, and extension of these methods to multi-marker test settings. Methods we develop will be tested on both simulated and real data via the CHARGE consortium. Additionally, the work we will perform addresses the three main goals of NIH?s R15 program: (a) to conduct meritorious research that will (b) strengthen the research environment of the liberal arts college where the research will be conducted, while (c) exposing undergraduate students to statistical genetics research. With this last goal in mind, the fourth aim of our proposal is to provide research experiences to undergraduate students when conducting aims 1, 2 and 3.

Public Health Relevance

The number of genetic association studies seeking to identify genetic variants that predispose to human diseases continues to grow. Furthermore, the environment for conducting these studies is rapidly changing due to declining sequencing and genotyping costs, new statistical technologies (e.g. imputation) and increasing understanding of the human genome. The proposed research will provide design and analysis strategies for genetic association studies in order to accelerate the pace of research towards the goal of a complete understanding of the genetic architecture of common human diseases.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Academic Research Enhancement Awards (AREA) (R15)
Project #
2R15HG006915-03
Application #
9813293
Study Section
Genetic Variation and Evolution Study Section (GVE)
Program Officer
Ramos, Erin
Project Start
2012-09-20
Project End
2022-07-31
Budget Start
2019-09-15
Budget End
2022-07-31
Support Year
3
Fiscal Year
2019
Total Cost
Indirect Cost
Name
Dordt College
Department
Biostatistics & Other Math Sci
Type
Schools of Arts and Sciences
DUNS #
078009222
City
Sioux Center
State
IA
Country
United States
Zip Code
51250
Fuady, Angga M; Lent, Samantha; Sarnowski, Chloé et al. (2018) Application of novel and existing methods to identify genes with evidence of epigenetic association: results from GAW20. BMC Genet 19:72
Veenstra, Jenna; Kalsbeek, Anya; Koster, Karissa et al. (2018) Epigenome wide association study of SNP-CpG interactions on changes in triglyceride levels after pharmaceutical intervention: a GAW20 analysis. BMC Proc 12:58
Kalsbeek, Anya; Veenstra, Jenna; Westra, Jason et al. (2018) A genome-wide association study of red-blood cell fatty acids and ratios incorporating dietary covariates: Framingham Heart Study Offspring Cohort. PLoS One 13:e0194882
Westra, Jason; Hartman, Nicholas; Lake, Bethany et al. (2018) Analyzing metabolomics data for association with genotypes using two-component Gaussian mixture distributions. Pac Symp Biocomput 23:496-506
Tintle, Nathan L; Fardo, David W; de Andrade, Mariza et al. (2018) GAW20: methods and strategies for the new frontiers of epigenetics and pharmacogenomics. BMC Proc 12:26
Vander Woude, Jason; Huisman, Jordan; Vander Berg, Lucas et al. (2018) Evaluating the performance of gene-based tests of genetic association when testing for association between methylation and change in triglyceride levels at GAW20. BMC Proc 12:50
Beck, Andrew; Luedtke, Alexander; Liu, Keli et al. (2017) A POWERFUL METHOD FOR INCLUDING GENOTYPE UNCERTAINTY IN TESTS OF HARDY-WEINBERG EQUILIBRIUM. Pac Symp Biocomput 22:368-379
Grinde, Kelsey E; Arbet, Jaron; Green, Alden et al. (2017) Illustrating, Quantifying, and Correcting for Bias in Post-hoc Analysis of Gene-Based Rare Variant Tests of Association. Front Genet 8:117
Veenstra, Jenna; Kalsbeek, Anya; Westra, Jason et al. (2017) Genome-Wide Interaction Study of Omega-3 PUFAs and Other Fatty Acids on Inflammatory Biomarkers of Cardiovascular Health in the Framingham Heart Study. Nutrients 9:
Kamp, Thomas; Adams, Micah; Disselkoen, Craig et al. (2017) IMPROVED PERFORMANCE OF GENE SET ANALYSIS ON GENOME-WIDE TRANSCRIPTOMICS DATA WHEN USING GENE ACTIVITY STATE ESTIMATES. Pac Symp Biocomput 22:449-460

Showing the most recent 10 out of 30 publications