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Wang X, Liu M, Nogues IE, Chen T, Xiong X, Bonzel CL, Zhang H, Hong C, Xia Y, Dahal K, Costa L, Cui J, Gaziano JM, Kim SC, Ho YL, Cho K, Cai T, Liao KP. Heterogeneous associations between interleukin-6 receptor variants and phenotypes across ancestries and implications for therapy. Sci Rep 2024; 14:8021. [PMID: 38580710 PMCID: PMC10997791 DOI: 10.1038/s41598-024-54063-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 02/08/2024] [Indexed: 04/07/2024] Open
Abstract
The Phenome-Wide Association Study (PheWAS) is increasingly used to broadly screen for potential treatment effects, e.g., IL6R variant as a proxy for IL6R antagonists. This approach offers an opportunity to address the limited power in clinical trials to study differential treatment effects across patient subgroups. However, limited methods exist to efficiently test for differences across subgroups in the thousands of multiple comparisons generated as part of a PheWAS. In this study, we developed an approach that maximizes the power to test for heterogeneous genotype-phenotype associations and applied this approach to an IL6R PheWAS among individuals of African (AFR) and European (EUR) ancestries. We identified 29 traits with differences in IL6R variant-phenotype associations, including a lower risk of type 2 diabetes in AFR (OR 0.96) vs EUR (OR 1.0, p-value for heterogeneity = 8.5 × 10-3), and higher white blood cell count (p-value for heterogeneity = 8.5 × 10-131). These data suggest a more salutary effect of IL6R blockade for T2D among individuals of AFR vs EUR ancestry and provide data to inform ongoing clinical trials targeting IL6 for an expanding number of conditions. Moreover, the method to test for heterogeneity of associations can be applied broadly to other large-scale genotype-phenotype screens in diverse populations.
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Affiliation(s)
- Xuan Wang
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT, USA
| | - Molei Liu
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA
| | | | - Tony Chen
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Xin Xiong
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Clara-Lea Bonzel
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Harrison Zhang
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, 60 Fenwood Road, Boston, MA, 02115, USA
| | - Chuan Hong
- Department of Biostatistics, Duke University, Durham, NC, USA
| | - Yin Xia
- Department of Statistics and Data Science, Fudan University, Shanghai, China
| | - Kumar Dahal
- Department of Biostatistics, Duke University, Durham, NC, USA
| | - Lauren Costa
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston, MA, USA
| | - Jing Cui
- Department of Biostatistics, Duke University, Durham, NC, USA
| | - J Michael Gaziano
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston, MA, USA
- Division of Aging, Brigham and Women's Hospital, Boston, MA, USA
| | - Seoyoung C Kim
- Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital, Boston, MA, USA
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston, MA, USA
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston, MA, USA
- Division of Aging, Brigham and Women's Hospital, Boston, MA, USA
| | - Tianxi Cai
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
| | - Katherine P Liao
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, 60 Fenwood Road, Boston, MA, 02115, USA.
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston, MA, USA.
- Rheumatology Section, VA Boston Healthcare System, Boston, USA.
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Li S, Yao Y, Zhang CH. Comments on "A Scale-Free Approach for False Discovery Rate Control in Generalized Linear Models". J Am Stat Assoc 2023; 118:1586-1589. [PMID: 38404948 PMCID: PMC10888134 DOI: 10.1080/01621459.2023.2224412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 06/02/2023] [Indexed: 02/27/2024]
Affiliation(s)
- Sai Li
- Associate Professor, Institute of Statistics and Big Data, Renmin University of China, China
| | - Yisha Yao
- Postdoctoral Associate, Department of Biostatistics, Yale University, New Haven, Connecticut
| | - Cun-Hui Zhang
- Distinguished Professor, Department of Statistics, Rutgers University, Piscataway, NJ
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Ma R, Cai TT, Li H. Global and Simultaneous Hypothesis Testing for High-Dimensional Logistic Regression Models. J Am Stat Assoc 2021; 116:984-998. [PMID: 34421157 DOI: 10.1080/01621459.2019.1699421] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
High-dimensional logistic regression is widely used in analyzing data with binary outcomes. In this paper, global testing and large-scale multiple testing for the regression coefficients are considered in both single- and two-regression settings. A test statistic for testing the global null hypothesis is constructed using a generalized low-dimensional projection for bias correction and its asymptotic null distribution is derived. A lower bound for the global testing is established, which shows that the proposed test is asymptotically minimax optimal over some sparsity range. For testing the individual coefficients simultaneously, multiple testing procedures are proposed and shown to control the false discovery rate (FDR) and falsely discovered variables (FDV) asymptotically. Simulation studies are carried out to examine the numerical performance of the proposed tests and their superiority over existing methods. The testing procedures are also illustrated by analyzing a data set of a metabolomics study that investigates the association between fecal metabolites and pediatric Crohn's disease and the effects of treatment on such associations.
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Affiliation(s)
- Rong Ma
- University of Pennsylvania Perelman School of Medicine, Philadelphia, 19104 United States.,The Wharton School - Univ of Pennsylvania, Philadelphia, 19104 United States.,University of Pennsylvania School of Medicine, 215 Blockley Hall, Philadelphia, 19104 United States
| | - T Tony Cai
- University of Pennsylvania Perelman School of Medicine, Philadelphia, 19104 United States.,The Wharton School - Univ of Pennsylvania, Philadelphia, 19104 United States.,University of Pennsylvania School of Medicine, 215 Blockley Hall, Philadelphia, 19104 United States
| | - Hongzhe Li
- University of Pennsylvania Perelman School of Medicine, Philadelphia, 19104 United States.,The Wharton School - Univ of Pennsylvania, Philadelphia, 19104 United States.,University of Pennsylvania School of Medicine, 215 Blockley Hall, Philadelphia, 19104 United States
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