1
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Woodward AA, Urbanowicz RJ, Naj AC, Moore JH. Genetic heterogeneity: Challenges, impacts, and methods through an associative lens. Genet Epidemiol 2022; 46:555-571. [PMID: 35924480 PMCID: PMC9669229 DOI: 10.1002/gepi.22497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 07/06/2022] [Accepted: 07/19/2022] [Indexed: 01/07/2023]
Abstract
Genetic heterogeneity describes the occurrence of the same or similar phenotypes through different genetic mechanisms in different individuals. Robustly characterizing and accounting for genetic heterogeneity is crucial to pursuing the goals of precision medicine, for discovering novel disease biomarkers, and for identifying targets for treatments. Failure to account for genetic heterogeneity may lead to missed associations and incorrect inferences. Thus, it is critical to review the impact of genetic heterogeneity on the design and analysis of population level genetic studies, aspects that are often overlooked in the literature. In this review, we first contextualize our approach to genetic heterogeneity by proposing a high-level categorization of heterogeneity into "feature," "outcome," and "associative" heterogeneity, drawing on perspectives from epidemiology and machine learning to illustrate distinctions between them. We highlight the unique nature of genetic heterogeneity as a heterogeneous pattern of association that warrants specific methodological considerations. We then focus on the challenges that preclude effective detection and characterization of genetic heterogeneity across a variety of epidemiological contexts. Finally, we discuss systems heterogeneity as an integrated approach to using genetic and other high-dimensional multi-omic data in complex disease research.
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Affiliation(s)
- Alexa A. Woodward
- Department of Biostatistics, Epidemiology and InformaticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Ryan J. Urbanowicz
- Department of Computational BiomedicineCedars‐Sinai Medical CenterLos AngelesCaliforniaUSA
| | - Adam C. Naj
- Department of Biostatistics, Epidemiology and InformaticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Jason H. Moore
- Department of Computational BiomedicineCedars‐Sinai Medical CenterLos AngelesCaliforniaUSA
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2
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Chiu CY, Wang S, Zhang B, Luo Y, Simpson C, Zhang W, Wilson AF, Bailey-Wilson JE, Agron E, Chew EY, Zhang J, Xiong M, Fan R. Gene-level association analysis of ordinal traits with functional ordinal logistic regressions. Genet Epidemiol 2022; 46:234-255. [PMID: 35438198 DOI: 10.1002/gepi.22451] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 11/01/2021] [Accepted: 01/20/2022] [Indexed: 11/11/2022]
Abstract
In this paper, we develop functional ordinal logistic regression (FOLR) models to perform gene-based analysis of ordinal traits. In the proposed FOLR models, genetic variant data are viewed as stochastic functions of physical positions and the genetic effects are treated as a function of physical positions. The FOLR models are built upon functional data analysis which can be revised to analyze the ordinal traits and high dimension genetic data. The proposed methods are capable of dealing with dense genotype data which is usually encountered in analyzing the next-generation sequencing data. The methods are flexible and can analyze three types of genetic data: (1) rare variants only, (2) common variants only, and (3) a combination of rare and common variants. Simulation studies show that the likelihood ratio test statistics of the FOLR models control type I errors well and have good power performance. The proposed methods achieve the goals of analyzing ordinal traits directly, reducing high dimensionality of dense genetic variants, being computationally manageable, facilitating model convergence, properly controlling type I errors, and maintaining high power levels. The FOLR models are applied to analyze Age-Related Eye Disease Study data, in which two genes are found to strongly associate with four ordinal traits.
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Affiliation(s)
- Chi-Yang Chiu
- Division of Biostatistics, Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, Tennessee, USA.,Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Baltimore, Maryland, USA
| | - Shuqi Wang
- Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, District of Columbia, USA
| | - Bingsong Zhang
- Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, District of Columbia, USA
| | - Yutong Luo
- Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, District of Columbia, USA
| | - Claire Simpson
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, Tennessee, USA
| | - Wei Zhang
- Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China
| | - Alexander F Wilson
- Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Baltimore, Maryland, USA
| | - Joan E Bailey-Wilson
- Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Baltimore, Maryland, USA
| | - Elvira Agron
- National Eye Institute, National Institute of Health, Bethesda, Maryland, USA
| | - Emily Y Chew
- National Eye Institute, National Institute of Health, Bethesda, Maryland, USA
| | - Jun Zhang
- Department of Computer Science and Engineering Technology, University of Maryland Eastern Shore, Princess Anne, Maryland, USA
| | - Momiao Xiong
- Human Genetics Center, University of Texas-Houston, Houston, Texas, USA
| | - Ruzong Fan
- Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Baltimore, Maryland, USA.,Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, District of Columbia, USA
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3
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Zhang B, Chiu CY, Yuan F, Sang T, Cook RJ, Wilson AF, Bailey-Wilson JE, Chew EY, Xiong M, Fan R. Gene-based analysis of bi-variate survival traits via functional regressions with applications to eye diseases. Genet Epidemiol 2021; 45:455-470. [PMID: 33645812 DOI: 10.1002/gepi.22381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Revised: 01/15/2021] [Accepted: 02/08/2021] [Indexed: 11/12/2022]
Abstract
Genetic studies of two related survival outcomes of a pleiotropic gene are commonly encountered but statistical models to analyze them are rarely developed. To analyze sequencing data, we propose mixed effect Cox proportional hazard models by functional regressions to perform gene-based joint association analysis of two survival traits motivated by our ongoing real studies. These models extend fixed effect Cox models of univariate survival traits by incorporating variations and correlation of multivariate survival traits into the models. The associations between genetic variants and two survival traits are tested by likelihood ratio test statistics. Extensive simulation studies suggest that type I error rates are well controlled and power performances are stable. The proposed models are applied to analyze bivariate survival traits of left and right eyes in the age-related macular degeneration progression.
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Affiliation(s)
- Bingsong Zhang
- Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, District of Columbia, USA
| | - Chi-Yang Chiu
- Division of Biostatistics, Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, Tennessee, USA.,Computational and Statistical Genomics Branch, National Human Genome, Research Institute, National Institutes of Health (NIH), Baltimore, Maryland, USA
| | - Fang Yuan
- Department of Biochemistry and Molecular Biology, School of Basic Medicine, Kunming Medical University, Kunming, People's Republic of China
| | - Tian Sang
- Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, District of Columbia, USA.,School of Mathematics, Physics and Statistics, Shanghai University of Engineering Science, Shanghai, China
| | - Richard J Cook
- Department of Statistics and Actuarial Science, Waterloo, Ontario, Canada
| | - Alexander F Wilson
- Computational and Statistical Genomics Branch, National Human Genome, Research Institute, National Institutes of Health (NIH), Baltimore, Maryland, USA
| | - Joan E Bailey-Wilson
- Computational and Statistical Genomics Branch, National Human Genome, Research Institute, National Institutes of Health (NIH), Baltimore, Maryland, USA
| | - Emily Y Chew
- Division of Epidemiology and Clinical Applications, National Eye Institute, NIH, Bethesda, Maryland, USA
| | - Momiao Xiong
- Human Genetics Center, University of Texas-Houston, Houston, Texas, USA
| | - Ruzong Fan
- Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, District of Columbia, USA.,Computational and Statistical Genomics Branch, National Human Genome, Research Institute, National Institutes of Health (NIH), Baltimore, Maryland, USA
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4
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Jiang Y, Chiu CY, Yan Q, Chen W, Gorin MB, Conley YP, Lakhal-Chaieb ML, Cook RJ, Amos CI, Wilson AF, Bailey-Wilson JE, McMahon FJ, Vazquez AI, Yuan A, Zhong X, Xiong M, Weeks DE, Fan R. Gene-Based Association Testing of Dichotomous Traits With Generalized Functional Linear Mixed Models Using Extended Pedigrees: Applications to Age-Related Macular Degeneration. J Am Stat Assoc 2020; 116:531-545. [PMID: 34321704 PMCID: PMC8315575 DOI: 10.1080/01621459.2020.1799809] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2017] [Revised: 07/09/2020] [Accepted: 07/17/2020] [Indexed: 10/23/2022]
Abstract
Genetics plays a role in age-related macular degeneration (AMD), a common cause of blindness in the elderly. There is a need for powerful methods for carrying out region-based association tests between a dichotomous trait like AMD and genetic variants on family data. Here, we apply our new generalized functional linear mixed models (GFLMM) developed to test for gene-based association in a set of AMD families. Using common and rare variants, we observe significant association with two known AMD genes: CFH and ARMS2. Using rare variants, we find suggestive signals in four genes: ASAH1, CLEC6A, TMEM63C, and SGSM1. Intriguingly, ASAH1 is down-regulated in AMD aqueous humor, and ASAH1 deficiency leads to retinal inflammation and increased vulnerability to oxidative stress. These findings were made possible by our GFLMM which model the effect of a major gene as a fixed mean, the polygenic contributions as a random variation, and the correlation of pedigree members by kinship coefficients. Simulations indicate that the GFLMM likelihood ratio tests (LRTs) accurately control the Type I error rates. The LRTs have similar or higher power than existing retrospective kernel and burden statistics. Our GFLMM-based statistics provide a new tool for conducting family-based genetic studies of complex diseases. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
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Affiliation(s)
- Yingda Jiang
- Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA
| | - Chi-Yang Chiu
- Division of Biostatistics, Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, TN
- Computational and Statistical Genomics Branch, National Human Genome Research Institute, NIH, Baltimore, MD
| | - Qi Yan
- Division of Pulmonary Medicine, Allergy and Immunology, Children’s Hospital of Pittsburgh at The University of Pittsburgh, Pittsburgh, PA
| | - Wei Chen
- Division of Pulmonary Medicine, Allergy and Immunology, Children’s Hospital of Pittsburgh at The University of Pittsburgh, Pittsburgh, PA
| | - Michael B. Gorin
- Department of Ophthalmology, David Geffen School of Medicine, UCLA Stein Eye Institute, Los Angeles, CA
| | - Yvette P. Conley
- Department of Health Promotion and Development, University of Pittsburgh, Pittsburgh, PA
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA
| | | | - Richard J. Cook
- Department of Statistics and Actuarial Science, Waterloo, ON, Canada
| | | | - Alexander F. Wilson
- Computational and Statistical Genomics Branch, National Human Genome Research Institute, NIH, Baltimore, MD
| | - Joan E. Bailey-Wilson
- Computational and Statistical Genomics Branch, National Human Genome Research Institute, NIH, Baltimore, MD
| | - Francis J. McMahon
- Human Genetics Branch and Genetic Basis of Mood and Anxiety Disorders Section, National Institute of Mental Health, NIH, Bethesda, MD
| | - Ana I. Vazquez
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI
| | - Ao Yuan
- Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, DC
| | - Xiaogang Zhong
- Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, DC
| | - Momiao Xiong
- Human Genetics Center, University of Texas, Houston, TX
| | - Daniel E. Weeks
- Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA
| | - Ruzong Fan
- Computational and Statistical Genomics Branch, National Human Genome Research Institute, NIH, Baltimore, MD
- Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, DC
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5
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Wei Y, Liu Y, Sun T, Chen W, Ding Y. Gene-based association analysis for bivariate time-to-event data through functional regression with copula models. Biometrics 2019; 76:619-629. [PMID: 31625595 DOI: 10.1111/biom.13165] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Accepted: 10/08/2019] [Indexed: 11/28/2022]
Abstract
Several gene-based association tests for time-to-event traits have been proposed recently to detect whether a gene region (containing multiple variants), as a set, is associated with the survival outcome. However, for bivariate survival outcomes, to the best of our knowledge, there is no statistical method that can be directly applied for gene-based association analysis. Motivated by a genetic study to discover the gene regions associated with the progression of a bilateral eye disease, age-related macular degeneration (AMD), we implement a novel functional regression (FR) method under the copula framework. Specifically, the effects of variants within a gene region are modeled through a functional linear model, which then contributes to the marginal survival functions within the copula. Generalized score test statistics are derived to test for the association between bivariate survival traits and the genetic region. Extensive simulation studies are conducted to evaluate the type I error control and power performance of the proposed approach, with comparisons to several existing methods for a single survival trait, as well as the marginal Cox FR model using the robust sandwich estimator for bivariate survival traits. Finally, we apply our method to a large AMD study, the Age-related Eye Disease Study, and to identify the gene regions that are associated with AMD progression.
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Affiliation(s)
- Yue Wei
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Yi Liu
- Department of Biostatistics and Data Sciences, Boehringer Ingelheim, Ridgefield, Connecticut
| | - Tao Sun
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Wei Chen
- Department of Pediatrics, Children's Hospital of Pittsburgh, Pittsburgh, Pennsylvania
| | - Ying Ding
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, Pennsylvania
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6
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Chiu CY, Zhang B, Wang S, Shao J, Lakhal-Chaieb ML, Cook RJ, Wilson AF, Bailey-Wilson JE, Xiong M, Fan R. Gene-based association analysis of survival traits via functional regression-based mixed effect cox models for related samples. Genet Epidemiol 2019; 43:952-965. [PMID: 31502722 DOI: 10.1002/gepi.22254] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2019] [Revised: 06/26/2019] [Accepted: 07/16/2019] [Indexed: 01/09/2023]
Abstract
The importance to integrate survival analysis into genetics and genomics is widely recognized, but only a small number of statisticians have produced relevant work toward this study direction. For unrelated population data, functional regression (FR) models have been developed to test for association between a quantitative/dichotomous/survival trait and genetic variants in a gene region. In major gene association analysis, these models have higher power than sequence kernel association tests. In this paper, we extend this approach to analyze censored traits for family data or related samples using FR based mixed effect Cox models (FamCoxME). The FamCoxME model effect of major gene as fixed mean via functional data analysis techniques, the local gene or polygene variations or both as random, and the correlation of pedigree members by kinship coefficients or genetic relationship matrix or both. The association between the censored trait and the major gene is tested by likelihood ratio tests (FamCoxME FR LRT). Simulation results indicate that the LRT control the type I error rates accurately/conservatively and have good power levels when both local gene or polygene variations are modeled. The proposed methods were applied to analyze a breast cancer data set from the Consortium of Investigators of Modifiers of BRCA1 and BRCA2 (CIMBA). The FamCoxME provides a new tool for gene-based analysis of family-based studies or related samples.
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Affiliation(s)
- Chi-Yang Chiu
- Division of Biostatistics, Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, Tennessee
| | - Bingsong Zhang
- Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, District of Columbia
| | - Shuqi Wang
- Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, District of Columbia
| | - Jingyi Shao
- Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, District of Columbia
| | | | - Richard J Cook
- Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada
| | - Alexander F Wilson
- Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland
| | - Joan E Bailey-Wilson
- Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland
| | - Momiao Xiong
- Department of Biostatistics, Human Genetics Center, University of Texas-Houston, Houston, Texas
| | - Ruzong Fan
- Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, District of Columbia
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7
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Chiu CY, Yuan F, Zhang BS, Yuan A, Li X, Fang HB, Lange K, Weeks DE, Wilson AF, Bailey-Wilson JE, Musolf AM, Stambolian D, Lakhal-Chaieb ML, Cook RJ, McMahon FJ, Amos CI, Xiong M, Fan R. Linear mixed models for association analysis of quantitative traits with next-generation sequencing data. Genet Epidemiol 2019; 43:189-206. [PMID: 30537345 PMCID: PMC6375753 DOI: 10.1002/gepi.22177] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2018] [Revised: 08/27/2018] [Accepted: 09/26/2018] [Indexed: 01/01/2023]
Abstract
We develop linear mixed models (LMMs) and functional linear mixed models (FLMMs) for gene-based tests of association between a quantitative trait and genetic variants on pedigrees. The effects of a major gene are modeled as a fixed effect, the contributions of polygenes are modeled as a random effect, and the correlations of pedigree members are modeled via inbreeding/kinship coefficients. F -statistics and χ 2 likelihood ratio test (LRT) statistics based on the LMMs and FLMMs are constructed to test for association. We show empirically that the F -distributed statistics provide a good control of the type I error rate. The F -test statistics of the LMMs have similar or higher power than the FLMMs, kernel-based famSKAT (family-based sequence kernel association test), and burden test famBT (family-based burden test). The F -statistics of the FLMMs perform well when analyzing a combination of rare and common variants. For small samples, the LRT statistics of the FLMMs control the type I error rate well at the nominal levels α = 0.01 and 0.05 . For moderate/large samples, the LRT statistics of the FLMMs control the type I error rates well. The LRT statistics of the LMMs can lead to inflated type I error rates. The proposed models are useful in whole genome and whole exome association studies of complex traits.
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Affiliation(s)
- Chi-Yang Chiu
- Division of Biostatistics, Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, Tennessee
- Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health (NIH), Bethesda, Maryland
| | - Fang Yuan
- Department of Biochemistry and Molecular Biology, School of Basic Medicine, Kunming Medical University, Kunming, Yunnan, China
| | - Bing-Song Zhang
- Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, District of Columbia
| | - Ao Yuan
- Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, District of Columbia
| | - Xin Li
- Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, District of Columbia
| | - Hong-Bin Fang
- Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, District of Columbia
| | - Kenneth Lange
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, California
| | - Daniel E Weeks
- Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Alexander F Wilson
- Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health (NIH), Bethesda, Maryland
| | - Joan E Bailey-Wilson
- Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health (NIH), Bethesda, Maryland
| | - Anthony M Musolf
- Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health (NIH), Bethesda, Maryland
| | - Dwight Stambolian
- Department of Genetics, University of Pennsylvania, Philadelphia, Pennsylvania
| | | | - Richard J Cook
- Department of Statistics and Actuarial Science, Waterloo, Ontario, Quebec, Canada
| | - Francis J McMahon
- Human Genetics Branch and Genetic Basis of Mood and Anxiety Disorders Section, University of Waterloo, National Institute of Mental Health, NIH, Bethesda, Maryland
| | | | - Momiao Xiong
- Human Genetics Center, University of Texas-Houston, Houston, Texas
| | - Ruzong Fan
- Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health (NIH), Bethesda, Maryland
- Department of Biochemistry and Molecular Biology, School of Basic Medicine, Kunming Medical University, Kunming, Yunnan, China
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8
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Abstract
While genome-wide association studies have been very successful in identifying associations of common genetic variants with many different traits, the rarer frequency spectrum of the genome has not yet been comprehensively explored. Technological developments increasingly lift restrictions to access rare genetic variation. Dense reference panels enable improved genotype imputation for rarer variants in studies using DNA microarrays. Moreover, the decreasing cost of next generation sequencing makes whole exome and genome sequencing increasingly affordable for large samples. Large-scale efforts based on sequencing, such as ExAC, 100,000 Genomes, and TopMed, are likely to significantly advance this field.The main challenge in evaluating complex trait associations of rare variants is statistical power. The choice of population should be considered carefully because allele frequencies and linkage disequilibrium structure differ between populations. Genetically isolated populations can have favorable genomic characteristics for the study of rare variants.One strategy to increase power is to assess the combined effect of multiple rare variants within a region, known as aggregate testing. A range of methods have been developed for this. Model performance depends on the genetic architecture of the region of interest.
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Affiliation(s)
- Karoline Kuchenbaecker
- Wellcome Trust Sanger Institute, Cambridge, UK. .,University College London, London, UK.
| | - Emil Vincent Rosenbaum Appel
- Novo Nordisk Foundation Center for Basic Metabolic Research, Section for Metabolic Genetics, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
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9
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Meta-analysis of quantitative pleiotropic traits for next-generation sequencing with multivariate functional linear models. Eur J Hum Genet 2016; 25:350-359. [PMID: 28000696 DOI: 10.1038/ejhg.2016.170] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2016] [Revised: 07/26/2016] [Accepted: 09/27/2016] [Indexed: 11/09/2022] Open
Abstract
To analyze next-generation sequencing data, multivariate functional linear models are developed for a meta-analysis of multiple studies to connect genetic variant data to multiple quantitative traits adjusting for covariates. The goal is to take the advantage of both meta-analysis and pleiotropic analysis in order to improve power and to carry out a unified association analysis of multiple studies and multiple traits of complex disorders. Three types of approximate F -distributions based on Pillai-Bartlett trace, Hotelling-Lawley trace, and Wilks's Lambda are introduced to test for association between multiple quantitative traits and multiple genetic variants. Simulation analysis is performed to evaluate false-positive rates and power of the proposed tests. The proposed methods are applied to analyze lipid traits in eight European cohorts. It is shown that it is more advantageous to perform multivariate analysis than univariate analysis in general, and it is more advantageous to perform meta-analysis of multiple studies instead of analyzing the individual studies separately. The proposed models require individual observations. The value of the current paper can be seen at least for two reasons: (a) the proposed methods can be applied to studies that have individual genotype data; (b) the proposed methods can be used as a criterion for future work that uses summary statistics to build test statistics to meta-analyze the data.
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10
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Chiu CY, Jung J, Wang Y, Weeks DE, Wilson AF, Bailey-Wilson JE, Amos CI, Mills JL, Boehnke M, Xiong M, Fan R. A comparison study of multivariate fixed models and Gene Association with Multiple Traits (GAMuT) for next-generation sequencing. Genet Epidemiol 2016; 41:18-34. [PMID: 27917525 DOI: 10.1002/gepi.22014] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2016] [Revised: 09/01/2016] [Accepted: 09/19/2016] [Indexed: 01/23/2023]
Abstract
In this paper, extensive simulations are performed to compare two statistical methods to analyze multiple correlated quantitative phenotypes: (1) approximate F-distributed tests of multivariate functional linear models (MFLM) and additive models of multivariate analysis of variance (MANOVA), and (2) Gene Association with Multiple Traits (GAMuT) for association testing of high-dimensional genotype data. It is shown that approximate F-distributed tests of MFLM and MANOVA have higher power and are more appropriate for major gene association analysis (i.e., scenarios in which some genetic variants have relatively large effects on the phenotypes); GAMuT has higher power and is more appropriate for analyzing polygenic effects (i.e., effects from a large number of genetic variants each of which contributes a small amount to the phenotypes). MFLM and MANOVA are very flexible and can be used to perform association analysis for (i) rare variants, (ii) common variants, and (iii) a combination of rare and common variants. Although GAMuT was designed to analyze rare variants, it can be applied to analyze a combination of rare and common variants and it performs well when (1) the number of genetic variants is large and (2) each variant contributes a small amount to the phenotypes (i.e., polygenes). MFLM and MANOVA are fixed effect models that perform well for major gene association analysis. GAMuT can be viewed as an extension of sequence kernel association tests (SKAT). Both GAMuT and SKAT are more appropriate for analyzing polygenic effects and they perform well not only in the rare variant case, but also in the case of a combination of rare and common variants. Data analyses of European cohorts and the Trinity Students Study are presented to compare the performance of the two methods.
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Affiliation(s)
- Chi-Yang Chiu
- Biostatistics and Bioinformatics Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health (NIH), Bethesda, MD, USA
| | - Jeesun Jung
- Laboratory of Epidemiology and Biometry, National Institute on Alcohol, Abuse and Alcoholism, NIH, Bethesda, MD, USA
| | - Yifan Wang
- Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, MD, USA
| | - Daniel E Weeks
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Alexander F Wilson
- Computational and Statistical Genomics Branch, National Human Genome Research Institute, NIH, Bethesda, MD, USA
| | - Joan E Bailey-Wilson
- Computational and Statistical Genomics Branch, National Human Genome Research Institute, NIH, Bethesda, MD, USA
| | - Christopher I Amos
- Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - James L Mills
- Epidemiology Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health (NIH), Bethesda, MD, USA
| | - Michael Boehnke
- Department of Biostatistics, School of Public Health, The University of Michigan, Ann Arbor, MI, USA
| | - Momiao Xiong
- Human Genetics Center, University of Texas-Houston, Houston, TX, USA
| | - Ruzong Fan
- Biostatistics and Bioinformatics Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health (NIH), Bethesda, MD, USA
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11
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Fan R, Chiu CY, Jung J, Weeks DE, Wilson AF, Bailey-Wilson JE, Amos CI, Chen Z, Mills JL, Xiong M. A Comparison Study of Fixed and Mixed Effect Models for Gene Level Association Studies of Complex Traits. Genet Epidemiol 2016; 40:702-721. [PMID: 27374056 DOI: 10.1002/gepi.21984] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2015] [Revised: 03/08/2016] [Accepted: 04/26/2016] [Indexed: 12/22/2022]
Abstract
In association studies of complex traits, fixed-effect regression models are usually used to test for association between traits and major gene loci. In recent years, variance-component tests based on mixed models were developed for region-based genetic variant association tests. In the mixed models, the association is tested by a null hypothesis of zero variance via a sequence kernel association test (SKAT), its optimal unified test (SKAT-O), and a combined sum test of rare and common variant effect (SKAT-C). Although there are some comparison studies to evaluate the performance of mixed and fixed models, there is no systematic analysis to determine when the mixed models perform better and when the fixed models perform better. Here we evaluated, based on extensive simulations, the performance of the fixed and mixed model statistics, using genetic variants located in 3, 6, 9, 12, and 15 kb simulated regions. We compared the performance of three models: (i) mixed models that lead to SKAT, SKAT-O, and SKAT-C, (ii) traditional fixed-effect additive models, and (iii) fixed-effect functional regression models. To evaluate the type I error rates of the tests of fixed models, we generated genotype data by two methods: (i) using all variants, (ii) using only rare variants. We found that the fixed-effect tests accurately control or have low false positive rates. We performed simulation analyses to compare power for two scenarios: (i) all causal variants are rare, (ii) some causal variants are rare and some are common. Either one or both of the fixed-effect models performed better than or similar to the mixed models except when (1) the region sizes are 12 and 15 kb and (2) effect sizes are small. Therefore, the assumption of mixed models could be satisfied and SKAT/SKAT-O/SKAT-C could perform better if the number of causal variants is large and each causal variant contributes a small amount to the traits (i.e., polygenes). In major gene association studies, we argue that the fixed-effect models perform better or similarly to mixed models in most cases because some variants should affect the traits relatively large. In practice, it makes sense to perform analysis by both the fixed and mixed effect models and to make a comparison, and this can be readily done using our R codes and the SKAT packages.
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Affiliation(s)
- Ruzong Fan
- Biostatistics and Bioinformatics Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver, National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Chi-Yang Chiu
- Biostatistics and Bioinformatics Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver, National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Jeesun Jung
- Laboratory of Epidemiology and Biometry, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Daniel E Weeks
- Departments of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.,Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Alexander F Wilson
- Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Joan E Bailey-Wilson
- Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Christopher I Amos
- Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire, United States of America
| | - Zhen Chen
- Biostatistics and Bioinformatics Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver, National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland, United States of America
| | - James L Mills
- Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver, National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Momiao Xiong
- Human Genetics Center, University of Texas-Houston, Houston, Texas, United States of America
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Meta-analysis of Complex Diseases at Gene Level with Generalized Functional Linear Models. Genetics 2015; 202:457-70. [PMID: 26715663 DOI: 10.1534/genetics.115.180869] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2015] [Accepted: 12/09/2015] [Indexed: 11/18/2022] Open
Abstract
We developed generalized functional linear models (GFLMs) to perform a meta-analysis of multiple case-control studies to evaluate the relationship of genetic data to dichotomous traits adjusting for covariates. Unlike the previously developed meta-analysis for sequence kernel association tests (MetaSKATs), which are based on mixed-effect models to make the contributions of major gene loci random, GFLMs are fixed models; i.e., genetic effects of multiple genetic variants are fixed. Based on GFLMs, we developed chi-squared-distributed Rao's efficient score test and likelihood-ratio test (LRT) statistics to test for an association between a complex dichotomous trait and multiple genetic variants. We then performed extensive simulations to evaluate the empirical type I error rates and power performance of the proposed tests. The Rao's efficient score test statistics of GFLMs are very conservative and have higher power than MetaSKATs when some causal variants are rare and some are common. When the causal variants are all rare [i.e., minor allele frequencies (MAF) < 0.03], the Rao's efficient score test statistics have similar or slightly lower power than MetaSKATs. The LRT statistics generate accurate type I error rates for homogeneous genetic-effect models and may inflate type I error rates for heterogeneous genetic-effect models owing to the large numbers of degrees of freedom and have similar or slightly higher power than the Rao's efficient score test statistics. GFLMs were applied to analyze genetic data of 22 gene regions of type 2 diabetes data from a meta-analysis of eight European studies and detected significant association for 18 genes (P < 3.10 × 10(-6)), tentative association for 2 genes (HHEX and HMGA2; P ≈ 10(-5)), and no association for 2 genes, while MetaSKATs detected none. In addition, the traditional additive-effect model detects association at gene HHEX. GFLMs and related tests can analyze rare or common variants or a combination of the two and can be useful in whole-genome and whole-exome association studies.
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