Genome-wide association studies (GWAS) have mapped thousands of common trait-influencing variants yet the overwhelming majority of trait loci have yet to be discovered. The goal of this proposal is to develop and apply statistical approaches that move beyond the standard GWAS paradigm to map additional trait-influencing variation within the human genome. Most of our proposed tools are based on a flexible high-dimensional framework called kernel machine regression, which we have had past success employing for powerful gene mapping of complex traits in GWAS and next-generation sequencing (NGS) studies. We believe the inherent flexibility of the kernel framework makes it ideal for exploring new paradigms in gene mapping of complex human traits.
Aim 1 proposes novel kernel methods for integrated analysis of both single-nucleotide variation data (derived from GWAS and/or NGS) and genomic data (such as gene-expression and methylation patterns) that we believe will provide improved power for trait mapping.
Aim 2 proposes novel kernel methods for large scale gene-gene interaction analysis across the genome, as well as a computational approach that enables efficient adjustment for multiple testing when applying such exhaustive testing procedures.
Aim 3 establishes novel kernel methods for association mapping of SNVs on the X chromosome. The flexible nature of kernel machines makes it ideal for modeling potential sex-specific effects on this chromosome and the methods further can accommodate random X inactivation.
Aim 4 proposes novel kernel approach for robust analysis of rare trait-influencing variation within families; such family-based designs are generally not considered in current rare-variant procedures. We will evaluate these methods on large-scale datasets that we are actively involved in and will implement the methods in user-friendly software for public distribution (Aim 5).

Public Health Relevance

The goal of this project is to develop novel and powerful statistical tools for identifying genetic loci acting independently or in conjunction with other genetic/environmental factors to influence complex human diseases or disease-related quantitative traits. Application of the proposed methods to applied datasets should improve our understanding of the genetic origins of complex traits and enhance existing risk-prediction models of complex disease.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Research Project (R01)
Project #
5R01HG007508-03
Application #
8894057
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Brooks, Lisa
Project Start
2013-09-04
Project End
2017-06-30
Budget Start
2015-07-01
Budget End
2017-06-30
Support Year
3
Fiscal Year
2015
Total Cost
Indirect Cost
Name
Emory University
Department
Genetics
Type
Schools of Medicine
DUNS #
066469933
City
Atlanta
State
GA
Country
United States
Zip Code
30322
Fischer, S Taylor; Jiang, Yunxuan; Broadaway, K Alaine et al. (2018) Powerful and robust cross-phenotype association test for case-parent trios. Genet Epidemiol 42:447-458
Rudra, Pratyaydipta; Broadaway, K Alaine; Ware, Erin B et al. (2018) Testing cross-phenotype effects of rare variants in longitudinal studies of complex traits. Genet Epidemiol 42:320-332
Zhao, Ni; Zhan, Xiang; Huang, Yen-Tsung et al. (2018) Kernel machine methods for integrative analysis of genome-wide methylation and genotyping studies. Genet Epidemiol 42:156-167
Zhan, Xiang; Wu, Michael C (2018) Reader Reaction: A note on testing and estimation in marker-set association study using semiparametric quantile regression kernel machine. Biometrics 74:764-766
Plantinga, Anna; Zhan, Xiang; Zhao, Ni et al. (2017) MiRKAT-S: a community-level test of association between the microbiota and survival times. Microbiome 5:17
Zhan, Xiang; Plantinga, Anna; Zhao, Ni et al. (2017) A fast small-sample kernel independence test for microbiome community-level association analysis. Biometrics 73:1453-1463
Zhan, Xiang; Zhao, Ni; Plantinga, Anna et al. (2017) Powerful Genetic Association Analysis for Common or Rare Variants with High-Dimensional Structured Traits. Genetics 206:1779-1790
Broadaway, K Alaine; Cutler, David J; Duncan, Richard et al. (2016) A Statistical Approach for Testing Cross-Phenotype Effects of Rare Variants. Am J Hum Genet 98:525-540
He, Qianchuan; Cai, Tianxi; Liu, Yang et al. (2016) Prioritizing individual genetic variants after kernel machine testing using variable selection. Genet Epidemiol 40:722-731
Zhan, Xiang; Girirajan, Santhosh; Zhao, Ni et al. (2016) A novel copy number variants kernel association test with application to autism spectrum disorders studies. Bioinformatics 32:3603-3610

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