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Chu BB, Ko S, Zhou JJ, Jensen A, Zhou H, Sinsheimer JS, Lange K. Multivariate genome-wide association analysis by iterative hard thresholding. Bioinformatics 2023; 39:btad193. [PMID: 37067496 PMCID: PMC10133532 DOI: 10.1093/bioinformatics/btad193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 04/07/2023] [Accepted: 04/13/2023] [Indexed: 04/18/2023] Open
Abstract
MOTIVATION In a genome-wide association study, analyzing multiple correlated traits simultaneously is potentially superior to analyzing the traits one by one. Standard methods for multivariate genome-wide association study operate marker-by-marker and are computationally intensive. RESULTS We present a sparsity constrained regression algorithm for multivariate genome-wide association study based on iterative hard thresholding and implement it in a convenient Julia package MendelIHT.jl. In simulation studies with up to 100 quantitative traits, iterative hard thresholding exhibits similar true positive rates, smaller false positive rates, and faster execution times than GEMMA's linear mixed models and mv-PLINK's canonical correlation analysis. On UK Biobank data with 470 228 variants, MendelIHT completed a three-trait joint analysis (n=185 656) in 20 h and an 18-trait joint analysis (n=104 264) in 53 h with an 80 GB memory footprint. In short, MendelIHT enables geneticists to fit a single regression model that simultaneously considers the effect of all SNPs and dozens of traits. AVAILABILITY AND IMPLEMENTATION Software, documentation, and scripts to reproduce our results are available from https://github.com/OpenMendel/MendelIHT.jl.
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Affiliation(s)
- Benjamin B Chu
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095-1554, United States
| | - Seyoon Ko
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095-1554, United States
- Department of Biostatistics, Fielding School of Public Health at UCLA, Los Angeles, CA 90095-1554, United States
| | - Jin J Zhou
- Department of Biostatistics, Fielding School of Public Health at UCLA, Los Angeles, CA 90095-1554, United States
- Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095-1554, United States
| | - Aubrey Jensen
- Department of Biostatistics, Fielding School of Public Health at UCLA, Los Angeles, CA 90095-1554, United States
| | - Hua Zhou
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095-1554, United States
- Department of Biostatistics, Fielding School of Public Health at UCLA, Los Angeles, CA 90095-1554, United States
| | - Janet S Sinsheimer
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095-1554, United States
- Department of Biostatistics, Fielding School of Public Health at UCLA, Los Angeles, CA 90095-1554, United States
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095-1554, United States
| | - Kenneth Lange
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095-1554, United States
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095-1554, United States
- Department of Statistics at UCLA, Los Angeles, CA 90095-1554, United States
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Chen J, Bie R, Qin Y, Li Y, Ma S. Lq-based robust analytics on ultrahigh and high dimensional data. Stat Med 2022; 41:5220-5241. [PMID: 36098057 DOI: 10.1002/sim.9563] [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: 08/27/2021] [Revised: 06/02/2022] [Accepted: 08/02/2022] [Indexed: 11/10/2022]
Abstract
Ultrahigh and high dimensional data are common in regression analysis for various fields, such as omics data, finance, and biological engineering. In addition to the problem of dimension, the data might also be contaminated. There are two main types of contamination: outliers and model misspecification. We develop an unique method that takes into account the ultrahigh or high dimensional issues and both types of contamination. In this article, we propose a framework for feature screening and selection based on the minimum Lq-likelihood estimation (MLqE), which accounts for the model misspecification contamination issue and has also been shown to be robust to outliers. In numerical analysis, we explore the robustness of this framework under different outliers and model misspecification scenarios. To examine the performance of this framework, we conduct real data analysis using the skin cutaneous melanoma data. When comparing with traditional screening and feature selection methods, the proposed method shows superiority in both variable identification effectiveness and parameter estimation accuracy.
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Affiliation(s)
- Jiachen Chen
- Department of Biostatistics, Boston University, Boston, MA, USA
| | - Ruofan Bie
- Department of Biostatistics, Brown University, Providence, RI, USA
| | - Yichen Qin
- Department of Operations, Business Analytics and Information Systems, University of Cincinnati, Cincinnati, OH, USA
| | - Yang Li
- Center for Applied Statistics and School of Statistics, Renmin University of China, Beijing, China.,RSS and China-Re Life Joint Lab on Public Health and Risk Management, Renmin University of China, Beijing, China
| | - Shuangge Ma
- Department of Biostatistics, Boston University, Boston, MA, USA.,RSS and China-Re Life Joint Lab on Public Health and Risk Management, Renmin University of China, Beijing, China
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