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Jeng XJ. Estimating the proportion of signal variables under arbitrary covariance dependence. Electron J Stat 2023. [DOI: 10.1214/23-ejs2119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
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2
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Wang T, Ionita-Laza I, Wei Y. Integrated Quantile RAnk Test (iQRAT) for gene-level associations. Ann Appl Stat 2022. [DOI: 10.1214/21-aoas1548] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Tianying Wang
- Center for Statistical Science & Department of Industrial Engineering, Tsinghua University
| | | | - Ying Wei
- Department of Biostatistics, Columbia University
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3
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Jeng XJ, Rhyne J, Zhang T, Tzeng JY. Effective SNP ranking improves the performance of eQTL mapping. Genet Epidemiol 2020; 44:611-619. [PMID: 32216117 DOI: 10.1002/gepi.22293] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Revised: 02/21/2020] [Accepted: 03/11/2020] [Indexed: 11/06/2022]
Abstract
Genome-wide expression quantitative trait loci (eQTLs) mapping explores the relationship between gene expression and DNA variants, such as single-nucleotide polymorphism (SNPs), to understand genetic basis of human diseases. Due to the large number of genes and SNPs that need to be assessed, current methods for eQTL mapping often suffer from low detection power, especially for identifying trans-eQTLs. In this paper, we propose the idea of performing SNP ranking based on the higher criticism statistic, a summary statistic developed in large-scale signal detection. We illustrate how the HC-based SNP ranking can effectively prioritize eQTL signals over noise, greatly reduce the burden of joint modeling, and improve the power for eQTL mapping. Numerical results in simulation studies demonstrate the superior performance of our method compared to existing methods. The proposed method is also evaluated in HapMap eQTL data analysis and the results are compared to a database of known eQTLs.
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Affiliation(s)
- X Jessie Jeng
- Department of Statistics, North Carolina State University, Raleigh, North Carolina
| | - Jacob Rhyne
- Department of Statistics, North Carolina State University, Raleigh, North Carolina
| | - Teng Zhang
- Department of Statistics, North Carolina State University, Raleigh, North Carolina
| | - Jung-Ying Tzeng
- Department of Statistics, North Carolina State University, Raleigh, North Carolina.,Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina.,Department of Statistics, National Cheng-Kung University, Tainan, Taiwan.,Division of Biostatistics, Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan
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Marceau West R, Lu W, Rotroff DM, Kuenemann MA, Chang SM, Wu MC, Wagner MJ, Buse JB, Motsinger-Reif AA, Fourches D, Tzeng JY. Identifying individual risk rare variants using protein structure guided local tests (POINT). PLoS Comput Biol 2019; 15:e1006722. [PMID: 30779729 PMCID: PMC6396946 DOI: 10.1371/journal.pcbi.1006722] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2018] [Revised: 03/01/2019] [Accepted: 12/17/2018] [Indexed: 01/08/2023] Open
Abstract
Rare variants are of increasing interest to genetic association studies because of their etiological contributions to human complex diseases. Due to the rarity of the mutant events, rare variants are routinely analyzed on an aggregate level. While aggregation analyses improve the detection of global-level signal, they are not able to pinpoint causal variants within a variant set. To perform inference on a localized level, additional information, e.g., biological annotation, is often needed to boost the information content of a rare variant. Following the observation that important variants are likely to cluster together on functional domains, we propose a protein structure guided local test (POINT) to provide variant-specific association information using structure-guided aggregation of signal. Constructed under a kernel machine framework, POINT performs local association testing by borrowing information from neighboring variants in the 3-dimensional protein space in a data-adaptive fashion. Besides merely providing a list of promising variants, POINT assigns each variant a p-value to permit variant ranking and prioritization. We assess the selection performance of POINT using simulations and illustrate how it can be used to prioritize individual rare variants in PCSK9, ANGPTL4 and CETP in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) clinical trial data.
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Affiliation(s)
- Rachel Marceau West
- Department of Statistics, North Carolina State University, Raleigh, North Carolina, United States of America
| | - Wenbin Lu
- Department of Statistics, North Carolina State University, Raleigh, North Carolina, United States of America
| | - Daniel M. Rotroff
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
| | - Melaine A. Kuenemann
- Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, United States of America
| | - Sheng-Mao Chang
- Department of Statistics, National Cheng-Kung University, Tainan, Taiwan
| | - Michael C. Wu
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Michael J. Wagner
- Center for Pharmacogenomics and Individualized Therapy, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - John B. Buse
- Department of Medicine, University of North Carolina School of Medicine, Chapel Hill, North Carolina, United States of America
| | - Alison A. Motsinger-Reif
- Department of Statistics, North Carolina State University, Raleigh, North Carolina, United States of America
- Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, United States of America
| | - Denis Fourches
- Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, United States of America
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina, United States of America
| | - Jung-Ying Tzeng
- Department of Statistics, North Carolina State University, Raleigh, North Carolina, United States of America
- Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, United States of America
- Department of Statistics, National Cheng-Kung University, Tainan, Taiwan
- Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan
- * E-mail:
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Guo Y, Zhou Y. A modified association test for rare and common variants based on affected sib-pair design. J Theor Biol 2019; 467:1-6. [PMID: 30707975 DOI: 10.1016/j.jtbi.2019.01.014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Accepted: 01/08/2019] [Indexed: 11/18/2022]
Abstract
Current genome-wide association analysis has identified a great number of rare and common variants associated with common complex traits, however, more effective approaches for detecting associations between rare and common variants with common diseases are still demanded. Approaches for detecting rare variant association analysis will compromise the power when detecting the effects of rare and common variants simultaneously. In this paper, we extend an existing method of testing for rare variant association based on affected sib pairs (TOW-sib) and propose a variable weight test for rare and common variants association based on affected sib pairs (abbreviated as VW-TOWsib). The VW-TOWsib can be used to achieve the purpose of detecting the association of rare and common variants with complex diseases. Simulation results in various scenarios show that our proposed method is more powerful than existing methods for detecting effects of rare and common variants. At the same time, the VW-TOWsib also performs well as a method for rare variant association analysis.
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Affiliation(s)
- Yixing Guo
- Department of Statistics, School of Mathematical Sciences, Heilongjiang University and Heilongjiang Provincial Key Laboratory of the Theory and Computation of Complex Systems, Harbin 150080, China
| | - Ying Zhou
- Department of Statistics, School of Mathematical Sciences, Heilongjiang University and Heilongjiang Provincial Key Laboratory of the Theory and Computation of Complex Systems, Harbin 150080, China.
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Abstract
Biomedical data science has experienced an explosion of new data over the past decade. Abundant genetic and genomic data are increasingly available in large, diverse data sets due to the maturation of modern molecular technologies. Along with these molecular data, dense, rich phenotypic data are also available on comprehensive clinical data sets from health care provider organizations, clinical trials, population health registries, and epidemiologic studies. The methods and approaches for interrogating these large genetic/genomic and clinical data sets continue to evolve rapidly, as our understanding of the questions and challenges continue to emerge. In this review, the state-of-the-art methodologies for genetic/genomic analysis along with complex phenomics will be discussed. This field is changing and adapting to the novel data types made available, as well as technological advances in computation and machine learning. Thus, I will also discuss the future challenges in this exciting and innovative space. The promises of precision medicine rely heavily on the ability to marry complex genetic/genomic data with clinical phenotypes in meaningful ways.
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Affiliation(s)
- Marylyn D. Ritchie
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
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Taylor BD, Zheng X, Darville T, Zhong W, Konganti K, Abiodun-Ojo O, Ness RB, O'Connell CM, Haggerty CL. Whole-Exome Sequencing to Identify Novel Biological Pathways Associated With Infertility After Pelvic Inflammatory Disease. Sex Transm Dis 2017; 44:35-41. [PMID: 27898568 PMCID: PMC5145761 DOI: 10.1097/olq.0000000000000533] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
BACKGROUND Ideal management of sexually transmitted infections (STI) may require risk markers for pathology or vaccine development. Previously, we identified common genetic variants associated with chlamydial pelvic inflammatory disease (PID) and reduced fecundity. As this explains only a proportion of the long-term morbidity risk, we used whole-exome sequencing to identify biological pathways that may be associated with STI-related infertility. METHODS We obtained stored DNA from 43 non-Hispanic black women with PID from the PID Evaluation and Clinical Health Study. Infertility was assessed at a mean of 84 months. Principal component analysis revealed no population stratification. Potential covariates did not significantly differ between groups. Sequencing kernel association test was used to examine associations between aggregates of variants on a single gene and infertility. The results from the sequencing kernel association test were used to choose "focus genes" (P < 0.01; n = 150) for subsequent Ingenuity Pathway Analysis to identify "gene sets" that are enriched in biologically relevant pathways. RESULTS Pathway analysis revealed that focus genes were enriched in canonical pathways including, IL-1 signaling, P2Y purinergic receptor signaling, and bone morphogenic protein signaling. CONCLUSIONS Focus genes were enriched in pathways that impact innate and adaptive immunity, protein kinase A activity, cellular growth, and DNA repair. These may alter host resistance or immunopathology after infection. Targeted sequencing of biological pathways identified in this study may provide insight into STI-related infertility.
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Affiliation(s)
- Brandie D Taylor
- From the *Department of Epidemiology and Biostatistics, Texas A&M University, College Station, TX; †Department of Pediatrics, ‡Department of Biostatistics, University of North Carolina Chapel Hill, Chapel Hill, NC; §Institute for Genome Sciences and Society, Texas A&M University, College Station, TX; ¶University of Texas School of Public Health, Houston, TX; and ∥Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA
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