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Delgado DA, Chernoff M, Huang L, Tong L, Chen L, Jasmine F, Shinkle J, Cole SA, Haack K, Kent J, Umans J, Best LG, Nelson H, Griend DV, Graziano J, Kibriya MG, Navas-Acien A, Karagas MR, Ahsan H, Pierce BL. Rare, Protein-Altering Variants in AS3MT and Arsenic Metabolism Efficiency: A Multi-Population Association Study. ENVIRONMENTAL HEALTH PERSPECTIVES 2021; 129:47007. [PMID: 33826413 PMCID: PMC8041273 DOI: 10.1289/ehp8152] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 01/15/2021] [Accepted: 03/15/2021] [Indexed: 05/03/2023]
Abstract
BACKGROUND Common genetic variation in the arsenic methyltransferase (AS3MT) gene region is known to be associated with arsenic metabolism efficiency (AME), measured as the percentage of dimethylarsinic acid (DMA%) in the urine. Rare, protein-altering variants in AS3MT could have even larger effects on AME, but their contribution to AME has not been investigated. OBJECTIVES We estimated the impact of rare, protein-coding variation in AS3MT on AME using a multi-population approach to facilitate the discovery of population-specific and shared causal rare variants. METHODS We generated targeted DNA sequencing data for the coding regions of AS3MT for three arsenic-exposed cohorts with existing data on arsenic species measured in urine: Health Effects of Arsenic Longitudinal Study (HEALS, n = 2,434 ), Strong Heart Study (SHS, n = 868 ), and New Hampshire Skin Cancer Study (NHSCS, n = 666 ). We assessed the collective effects of rare (allele frequency < 1 % ), protein-altering AS3MT variants on DMA%, using multiple approaches, including a test of the association between rare allele carrier status (yes/no) and DMA% using linear regression (adjusted for common variants in 10q24.32 region, age, sex, and population structure). RESULTS We identified 23 carriers of rare-protein-altering AS3MT variant across all cohorts (13 in HEALS and 5 in both SHS and NHSCS), including 6 carriers of predicted loss-of-function variants. DMA% was 6-10% lower in carriers compared with noncarriers in HEALS [β = - 9.4 (95% CI: - 13.9 , - 4.8 )], SHS [β = - 6.9 (95% CI: - 13.6 , - 0.2 )], and NHSCS [β = - 8.7 (95% CI: - 15.6 , - 2.2 )]. In meta-analyses across cohorts, DMA% was 8.7% lower in carriers [β = - 8.7 (95% CI: - 11.9 , - 5.4 )]. DISCUSSION Rare, protein-altering variants in AS3MT were associated with lower mean DMA%, an indicator of reduced AME. Although a small percentage of the population (0.5-0.7%) carry these variants, they are associated with a 6-10% decrease in DMA% that is consistent across multiple ancestral and environmental backgrounds. https://doi.org/10.1289/EHP8152.
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Affiliation(s)
- Dayana A. Delgado
- Department of Public Health Sciences, University of Chicago (UChicago), Chicago, Illinois, USA
| | - Meytal Chernoff
- Department of Public Health Sciences, University of Chicago (UChicago), Chicago, Illinois, USA
| | - Lei Huang
- Center for Research Informatics, UChicago, Chicago, Illinois, USA
| | - Lin Tong
- Department of Public Health Sciences, University of Chicago (UChicago), Chicago, Illinois, USA
| | - Lin Chen
- Department of Public Health Sciences, University of Chicago (UChicago), Chicago, Illinois, USA
| | - Farzana Jasmine
- Department of Public Health Sciences, University of Chicago (UChicago), Chicago, Illinois, USA
| | - Justin Shinkle
- Department of Public Health Sciences, University of Chicago (UChicago), Chicago, Illinois, USA
| | - Shelley A. Cole
- Texas Biomedical Research Institute, San Antonio, Texas, USA
| | - Karin Haack
- Texas Biomedical Research Institute, San Antonio, Texas, USA
| | - Jack Kent
- Texas Biomedical Research Institute, San Antonio, Texas, USA
| | - Jason Umans
- Georgetown-Howard Universities Center for Clinical and Translational Science, Washington, DC, USA
| | - Lyle G. Best
- Missouri Breaks Industries Research, Inc., Timber Lake, South Dakota, USA
| | - Heather Nelson
- School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA
| | - Donald Vander Griend
- Department of Pathology, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Joseph Graziano
- Mailman School of Public Health, Columbia University, New York City, New York, USA
| | - Muhammad G. Kibriya
- Department of Public Health Sciences, University of Chicago (UChicago), Chicago, Illinois, USA
| | - Ana Navas-Acien
- Mailman School of Public Health, Columbia University, New York City, New York, USA
| | - Margaret R. Karagas
- Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire, USA
| | - Habibul Ahsan
- Department of Public Health Sciences, University of Chicago (UChicago), Chicago, Illinois, USA
- Department of Human Genetics, UChicago, Chicago, Illinois, USA
- Comprehensive Cancer Center, UChicago, Chicago, Illinois, USA
- Department of Medicine, UChicago, Chicago, Illinois, USA
| | - Brandon L. Pierce
- Department of Public Health Sciences, University of Chicago (UChicago), Chicago, Illinois, USA
- Department of Human Genetics, UChicago, Chicago, Illinois, USA
- Comprehensive Cancer Center, UChicago, Chicago, Illinois, USA
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Lee S, Kim S, Kim Y, Oh B, Hwang H, Park T. Pathway analysis of rare variants for the clustered phenotypes by using hierarchical structured components analysis. BMC Med Genomics 2019; 12:100. [PMID: 31296220 PMCID: PMC6624181 DOI: 10.1186/s12920-019-0517-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
BACKGROUNDS Recent large-scale genetic studies often involve clustered phenotypes such as repeated measurements. Compared to a series of univariate analyses of single phenotypes, an analysis of clustered phenotypes can be useful for substantially increasing statistical power to detect more genetic associations. Moreover, for the analysis of rare variants, incorporation of biological information can boost weak effects of the rare variants. RESULTS Through simulation studies, we showed that the proposed method outperforms other method currently available for pathway-level analysis of clustered phenotypes. Moreover, a real data analysis using a large-scale whole exome sequencing dataset of 995 samples with metabolic syndrome-related phenotypes successfully identified the glyoxylate and dicarboxylate metabolism pathway that could not be identified by the univariate analyses of single phenotypes and other existing method. CONCLUSION In this paper, we introduced a novel pathway-level association test by combining hierarchical structured components analysis and penalized generalized estimating equations. The proposed method analyzes all pathways in a single unified model while considering their correlations. C/C++ implementation of PHARAOH-GEE is publicly available at http://statgen.snu.ac.kr/software/pharaoh-gee/ .
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Affiliation(s)
- Sungyoung Lee
- Center for Precision Medicine, Seoul National University Hospital, Seoul, Korea
| | - Sunmee Kim
- Department of Psychology, McGill University, Montreal, Canada
| | - Yongkang Kim
- Department of Statistics, Seoul National University, Seoul, Korea
| | - Bermseok Oh
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, Korea
| | - Heungsun Hwang
- Department of Psychology, McGill University, Montreal, Canada
| | - Taesung Park
- Department of Statistics, Seoul National University, Seoul, Korea.
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea.
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Larson NB, Chen J, Schaid DJ. A review of kernel methods for genetic association studies. Genet Epidemiol 2019; 43:122-136. [PMID: 30604442 DOI: 10.1002/gepi.22180] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2018] [Revised: 11/09/2018] [Accepted: 11/26/2018] [Indexed: 12/17/2022]
Abstract
Evaluating the association of multiple genetic variants with a trait of interest by use of kernel-based methods has made a significant impact on how genetic association analyses are conducted. An advantage of kernel methods is that they tend to be robust when the genetic variants have effects that are a mixture of positive and negative effects, as well as when there is a small fraction of causal variants. Another advantage is that kernel methods fit within the framework of mixed models, providing flexible ways to adjust for additional covariates that influence traits. Herein, we review the basic ideas behind the use of kernel methods for genetic association analysis as well as recent methodological advancements for different types of traits, multivariate traits, pedigree data, and longitudinal data. Finally, we discuss opportunities for future research.
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Affiliation(s)
- Nicholas B Larson
- Department of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota
| | - Jun Chen
- Department of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota
| | - Daniel J Schaid
- Department of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota
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Powerful Genetic Association Analysis for Common or Rare Variants with High-Dimensional Structured Traits. Genetics 2017. [PMID: 28642271 DOI: 10.1534/genetics.116.199646] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
Many genetic association studies collect a wide range of complex traits. As these traits may be correlated and share a common genetic mechanism, joint analysis can be statistically more powerful and biologically more meaningful. However, most existing tests for multiple traits cannot be used for high-dimensional and possibly structured traits, such as network-structured transcriptomic pathway expressions. To overcome potential limitations, in this article we propose the dual kernel-based association test (DKAT) for testing the association between multiple traits and multiple genetic variants, both common and rare. In DKAT, two individual kernels are used to describe the phenotypic and genotypic similarity, respectively, between pairwise subjects. Using kernels allows for capturing structure while accommodating dimensionality. Then, the association between traits and genetic variants is summarized by a coefficient which measures the association between two kernel matrices. Finally, DKAT evaluates the hypothesis of nonassociation with an analytical P-value calculation without any computationally expensive resampling procedures. By collapsing information in both traits and genetic variants using kernels, the proposed DKAT is shown to have a correct type-I error rate and higher power than other existing methods in both simulation studies and application to a study of genetic regulation of pathway gene expressions.
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