1
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Long JP, Zhu H, Do KA, Ha MJ. Estimating causal effects with hidden confounding using instrumental variables and environments. Electron J Stat 2023; 17:2849-2879. [PMID: 38957485 PMCID: PMC11219021 DOI: 10.1214/23-ejs2160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/04/2024]
Abstract
Recent works have proposed regression models which are invariant across data collection environments [24, 20, 11, 16, 8]. These estimators often have a causal interpretation under conditions on the environments and type of invariance imposed. One recent example, the Causal Dantzig (CD), is consistent under hidden confounding and represents an alternative to classical instrumental variable estimators such as Two Stage Least Squares (TSLS). In this work we derive the CD as a generalized method of moments (GMM) estimator. The GMM representation leads to several practical results, including 1) creation of the Generalized Causal Dantzig (GCD) estimator which can be applied to problems with continuous environments where the CD cannot be fit 2) a Hybrid (GCD-TSLS combination) estimator which has properties superior to GCD or TSLS alone 3) straightforward asymptotic results for all methods using GMM theory. We compare the CD, GCD, TSLS, and Hybrid estimators in simulations and an application to a Flow Cytometry data set. The newly proposed GCD and Hybrid estimators have superior performance to existing methods in many settings.
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Affiliation(s)
- James P Long
- Department of Biostatistics, University of Texas MD Anderson Cancer Center
| | - Hongxu Zhu
- Department of Biostatistics, University of Texas, School of Public Health
| | - Kim-Anh Do
- Department of Biostatistics, University of Texas MD Anderson Cancer Center
| | - Min Jin Ha
- Department of Biostatistics, Graduate School of Public Health, Yonsei University
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2
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Seng LL, Liu CT, Wang J, Li J. Instrumental variable model average with applications in Mendelian randomization. Stat Med 2023; 42:3547-3567. [PMID: 37476915 DOI: 10.1002/sim.9819] [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: 12/02/2022] [Revised: 04/20/2023] [Accepted: 05/29/2023] [Indexed: 07/22/2023]
Abstract
Mendelian randomization is a technique used to examine the causal effect of a modifiable exposure on a trait using an observational study by utilizing genetic variants. The use of many instruments can help to improve the estimation precision but may suffer bias when the instruments are weakly associated with the exposure. To overcome the difficulty of high-dimensionality, we propose a model average estimator which involves using different subsets of instruments (single nucleotide polymorphisms, SNPs) to predict the exposure in the first stage, followed by weighting the submodels' predictions using penalization by common penalty functions such as least absolute shrinkage and selection operator (LASSO), smoothly clipped absolute deviation (SCAD) and minimax concave penalty (MCP). The model averaged predictions are then used as a genetically predicted exposure to obtain the estimation of the causal effect on the response in the second stage. The novelty of our model average estimator also lies in that it allows the number of submodels and the submodels' sizes to grow with the sample size. The practical performance of the estimator is examined in a series of numerical studies. We apply the proposed method on a real genetic dataset investigating the relationship between stature and blood pressure.
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Affiliation(s)
- Loraine Liping Seng
- Department of Statistics and Data Science, National University of Singapore, Singapore
- Duke-NUS Graduate Medical School, National University of Singapore, Singapore
| | - Ching-Ti Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA
- Department of Statistics, National Cheng Kung University, Tainan, Taiwan
| | - Jingli Wang
- School of Statistics and Data Science, Nankai University, China
| | - Jialiang Li
- Department of Statistics and Data Science, National University of Singapore, Singapore
- Duke-NUS Graduate Medical School, National University of Singapore, Singapore
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3
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Xue H, Shen X, Pan W. Causal Inference in Transcriptome-Wide Association Studies with Invalid Instruments and GWAS Summary Data. J Am Stat Assoc 2023; 118:1525-1537. [PMID: 37808547 PMCID: PMC10557939 DOI: 10.1080/01621459.2023.2183127] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 02/14/2023] [Indexed: 02/24/2023]
Abstract
Transcriptome-wide association studies (TWAS) have recently emerged as a popular tool to discover (putative) causal genes by integrating an outcome GWAS dataset with another gene expression/transcriptome GWAS (called eQTL) dataset. In our motivating and target application, we'd like to identify causal genes for low-density lipoprotein cholesterol (LDL), which is crucial for developing new treatments for hyperlipidemia and cardiovascular diseases. The statistical principle underlying TWAS is (two-sample) two-stage least squares (2SLS) using multiple correlated SNPs as instrumental variables (IVs); it is closely related to typical (two-sample) Mendelian randomization (MR) using independent SNPs as IVs, which is expected to be impractical and lower-powered for TWAS (and some other) applications. However, often some of the SNPs used may not be valid IVs, e.g. due to the widespread pleiotropy of their direct effects on the outcome not mediated through the gene of interest, leading to false conclusions by TWAS (or MR). Building on recent advances in sparse regression, we propose a robust and efficient inferential method to account for both hidden confounding and some invalid IVs via two-stage constrained maximum likelihood (2ScML), an extension of 2SLS. We first develop the proposed method with individual-level data, then extend it both theoretically and computationally to GWAS summary data for the most popular two-sample TWAS design, to which almost all existing robust IV regression methods are however not applicable. We show that the proposed method achieves asymptotically valid statistical inference on causal effects, demonstrating its wider applicability and superior finite-sample performance over the standard 2SLS/TWAS (and MR). We apply the methods to identify putative causal genes for LDL by integrating large-scale lipid GWAS summary data with eQTL data.
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Affiliation(s)
- Haoran Xue
- School of Statistics, University of Minnesota, Minneapolis, Minnesota 55455
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota 55455
| | - Xiaotong Shen
- School of Statistics, University of Minnesota, Minneapolis, Minnesota 55455
| | - Wei Pan
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota 55455
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4
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Statistical analysis of Markov switching vector autoregression models with endogenous explanatory variables. J MULTIVARIATE ANAL 2023. [DOI: 10.1016/j.jmva.2023.105164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2023]
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5
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Yu D, Wang L, Kong D, Zhu H. Mapping the Genetic-Imaging-Clinical Pathway with Applications to Alzheimer’s Disease. J Am Stat Assoc 2022; 117:1656-1668. [PMID: 37009529 PMCID: PMC10062702 DOI: 10.1080/01621459.2022.2087658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Alzheimer's disease is a progressive form of dementia that results in problems with memory, thinking, and behavior. It often starts with abnormal aggregation and deposition of β amyloid and tau, followed by neuronal damage such as atrophy of the hippocampi, leading to Alzheimers Disease (AD). The aim of this paper is to map the genetic-imaging-clinical pathway for AD in order to delineate the genetically-regulated brain changes that drive disease progression based on the Alzheimers Disease Neuroimaging Initiative (ADNI) dataset. We develop a novel two-step approach to delineate the association between high-dimensional 2D hippocampal surface exposures and the Alzheimers Disease Assessment Scale (ADAS) cognitive score, while taking into account the ultra-high dimensional clinical and genetic covariates at baseline. Analysis results suggest that the radial distance of each pixel of both hippocampi is negatively associated with the severity of behavioral deficits conditional on observed clinical and genetic covariates. These associations are stronger in Cornu Ammonis region 1 (CA1) and subiculum subregions compared to Cornu Ammonis region 2 (CA2) and Cornu Ammonis region 3 (CA3) subregions. 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)
- Dengdeng Yu
- Department of Mathematics, University of Texas at Arlington
| | - Linbo Wang
- Department of Statistical Sciences, University of Toronto
| | - Dehan Kong
- Department of Statistical Sciences, University of Toronto
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina, Chapel Hill for the Alzheimer’s Disease Neuroimaging Initiative*
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6
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Guo Z, Ćevid D, Bühlmann P. Doubly debiased lasso: High-dimensional inference under hidden confounding. Ann Stat 2022; 50:1320-1347. [DOI: 10.1214/21-aos2152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Zijian Guo
- Department of Statistics, Rutgers University
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7
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Amini M. Genomic data analysis using a two stage expectation propagation algorithm for analysis of sparse Bayesian high-dimensional instrumental variables regression. COMMUN STAT-SIMUL C 2022. [DOI: 10.1080/03610918.2022.2075896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Affiliation(s)
- Morteza Amini
- Department of Statistics, School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran
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8
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Boehm FJ, Zhou X. Statistical methods for Mendelian randomization in genome-wide association studies: A review. Comput Struct Biotechnol J 2022; 20:2338-2351. [PMID: 35615025 PMCID: PMC9123217 DOI: 10.1016/j.csbj.2022.05.015] [Citation(s) in RCA: 101] [Impact Index Per Article: 50.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 05/08/2022] [Accepted: 05/09/2022] [Indexed: 11/15/2022] Open
Abstract
Genome-wide association studies have yielded thousands of associations for many common diseases and disease-related complex traits. The identified associations made it possible to identify the causal risk factors underlying diseases and investigate the causal relationships among complex traits through Mendelian randomization. Mendelian randomization is a form of instrumental variable analysis that uses SNP associations from genome-wide association studies as instruments to study and uncover causal relationships between complex traits. By leveraging SNP genotypes as instrumental variables, or proxies, for the exposure complex trait, investigators can tease out causal effects from observational data, provided that necessary assumptions are satisfied. We discuss below the development of Mendelian randomization methods in parallel with the growth of genome-wide association studies. We argue that the recent availability of GWAS summary statistics for diverse complex traits has motivated new Mendelian randomization methods with relaxed causality assumptions and that this area continues to offer opportunities for robust biological discoveries.
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Affiliation(s)
- Frederick J. Boehm
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
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9
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Yue M, Li J, Sun B. Conditional sparse boosting for high-dimensional instrumental variable estimation. J STAT COMPUT SIM 2022. [DOI: 10.1080/00949655.2022.2056739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Mu Yue
- Singapore University of Technology and Design, Singapore, Singapore
| | - Jialiang Li
- National University of Singapore, Duke University-NUS Graduate Medical School, Singapore Eye Research Institute, Singapore, Singapore
| | - Baoluo Sun
- National University of Singapore, Singapore, Singapore
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10
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Mishra AK, Müller CL. Negative binomial factor regression with application to microbiome data analysis. Stat Med 2022; 41:2786-2803. [PMID: 35466418 PMCID: PMC9325477 DOI: 10.1002/sim.9384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 02/28/2022] [Accepted: 03/07/2022] [Indexed: 11/17/2022]
Abstract
The human microbiome provides essential physiological functions and helps maintain host homeostasis via the formation of intricate ecological host‐microbiome relationships. While it is well established that the lifestyle of the host, dietary preferences, demographic background, and health status can influence microbial community composition and dynamics, robust generalizable associations between specific host‐associated factors and specific microbial taxa have remained largely elusive. Here, we propose factor regression models that allow the estimation of structured parsimonious associations between host‐related features and amplicon‐derived microbial taxa. To account for the overdispersed nature of the amplicon sequencing count data, we propose negative binomial reduced rank regression (NB‐RRR) and negative binomial co‐sparse factor regression (NB‐FAR). While NB‐RRR encodes the underlying dependency among the microbial abundances as outcomes and the host‐associated features as predictors through a rank‐constrained coefficient matrix, NB‐FAR uses a sparse singular value decomposition of the coefficient matrix. The latter approach avoids the notoriously difficult joint parameter estimation by extracting sparse unit‐rank components of the coefficient matrix sequentially, effectively delivering interpretable bi‐clusters of taxa and host‐associated factors. To solve the nonconvex optimization problems associated with these factor regression models, we present a novel iterative block‐wise majorization procedure. Extensive simulation studies and an application to the microbial abundance data from the American Gut Project (AGP) demonstrate the efficacy of the proposed procedure. In the AGP data, we identify several factors that strongly link dietary habits and host life style to specific microbial families.
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Affiliation(s)
- Aditya K. Mishra
- Center for Computational Mathematics, Flatiron Institute Simons Foundation New York New York USA
| | - Christian L. Müller
- Center for Computational Mathematics, Flatiron Institute Simons Foundation New York New York USA
- Department of Statistics LMU München Munich Germany
- Institute of Computational Biology Helmholtz Zentrum München Munich Germany
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11
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Miao W, Hu W, Ogburn EL, Zhou X. Identifying effects of multiple treatments in the presence of unmeasured confounding. J Am Stat Assoc 2022. [DOI: 10.1080/01621459.2021.2023551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Wang Miao
- Department of Probability and Statistics, Peking University, Beijing, PRC
| | - Wenjie Hu
- Department of Probability and Statistics, Peking University, Beijing, PRC
| | - Elizabeth L. Ogburn
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Xiaohua Zhou
- Department of Biostatistics and Beijing International Center for Mathematical Research, Peking University, Beijing, PRC
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12
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Fan Q, Yu W. Adaptive k-class estimation in high-dimensional linear models. COMMUN STAT-SIMUL C 2021. [DOI: 10.1080/03610918.2019.1634206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Affiliation(s)
- Qingliang Fan
- The Wang Yanan Institute of Studies in Economics (WISE) and School of Economics, Xiamen University, Xiamen, China
| | - Wanlu Yu
- Jinshan College of Fujian Agriculture and Forestry University, Fujian, China
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13
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Wu C, Zhu J, King A, Tong X, Lu Q, Park JY, Wang L, Gao G, Deng HW, Yang Y, Knudsen KE, Rebbeck TR, Long J, Zheng W, Pan W, Conti DV, Haiman CA, Wu L. Novel strategy for disease risk prediction incorporating predicted gene expression and DNA methylation data: a multi-phased study of prostate cancer. Cancer Commun (Lond) 2021; 41:1387-1397. [PMID: 34520132 PMCID: PMC8696216 DOI: 10.1002/cac2.12205] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 06/10/2021] [Accepted: 07/26/2021] [Indexed: 12/15/2022] Open
Abstract
Background DNA methylation and gene expression are known to play important roles in the etiology of human diseases such as prostate cancer (PCa). However, it has not yet been possible to incorporate information of DNA methylation and gene expression into polygenic risk scores (PRSs). Here, we aimed to develop and validate an improved PRS for PCa risk by incorporating genetically predicted gene expression and DNA methylation, and other genomic information using an integrative method. Methods Using data from the PRACTICAL consortium, we derived multiple sets of genetic scores, including those based on available single‐nucleotide polymorphisms through widely used methods of pruning and thresholding, LDpred, LDpred‐funt, AnnoPred, and EBPRS, as well as PRS constructed using the genetically predicted gene expression and DNA methylation through a revised pruning and thresholding strategy. In the tuning step, using the UK Biobank data (1458 prevalent cases and 1467 controls), we selected PRSs with the best performance. Using an independent set of data from the UK Biobank, we developed an integrative PRS combining information from individual scores. Furthermore, in the testing step, we tested the performance of the integrative PRS in another independent set of UK Biobank data of incident cases and controls. Results Our constructed PRS had improved performance (C statistics: 76.1%) over PRSs constructed by individual benchmark methods (from 69.6% to 74.7%). Furthermore, our new PRS had much higher risk assessment power than family history. The overall net reclassification improvement was 69.0% by adding PRS to the baseline model compared with 12.5% by adding family history. Conclusions We developed and validated a new PRS which may improve the utility in predicting the risk of developing PCa. Our innovative method can also be applied to other human diseases to improve risk prediction across multiple outcomes.
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Affiliation(s)
- Chong Wu
- Department of Statistics, Florida State University, Tallahassee, FL, 32304, USA
| | - Jingjing Zhu
- Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI, 96813, USA
| | - Austin King
- Department of Statistics, Florida State University, Tallahassee, FL, 32304, USA
| | - Xiaoran Tong
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, 48824, USA
| | - Qing Lu
- Department of Biostatistics, University of Florida, Gainesville, FL, 32603, USA
| | - Jong Y Park
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
| | - Liang Wang
- Department of Tumor Biology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
| | - Guimin Gao
- Department of Public Health Sciences, University of Chicago, Chicago, IL, 60637, USA
| | - Hong-Wen Deng
- Center of Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, Tulane University, New Orleans, LA, 70112, USA
| | - Yaohua Yang
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, 37203, USA
| | - Karen E Knudsen
- Department of Cancer Biology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, 19107, USA
| | - Timothy R Rebbeck
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA.,Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, 02115, USA
| | - Jirong Long
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, 37203, USA
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, 37203, USA
| | - Wei Pan
- Division of Biostatistics, University of Minnesota, Minneapolis, MN, 55455, USA
| | - David V Conti
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA, 90033, USA
| | - Christopher A Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA, 90033, USA
| | - Lang Wu
- Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI, 96813, USA
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14
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Zhao P, Wang J, Tang X, Yang W. Double penalized regularization estimation for partially linear instrumental variable models with ultrahigh dimensional instrumental variables. COMMUN STAT-SIMUL C 2021. [DOI: 10.1080/03610918.2021.1965166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Peixin Zhao
- College of Mathematics and Statistics, Chongqing Technology and Business University, Chongqing, China
- Chongqing Key Laboratory of Social Economic and Applied Statistics, Chongqing, China
| | - Junqi Wang
- College of Mathematics and Statistics, Chongqing Technology and Business University, Chongqing, China
| | - Xinrong Tang
- Department of Assets Management, Chongqing Technology and Business University, Chongqing, China
| | - Weiming Yang
- College of Mathematics and Statistics, Chongqing Technology and Business University, Chongqing, China
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15
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Yang T, Wei P, Pan W. Integrative analysis of multi-omics data for discovering low-frequency variants associated with low-density lipoprotein cholesterol levels. Bioinformatics 2021; 36:5223-5228. [PMID: 33070182 DOI: 10.1093/bioinformatics/btaa898] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 09/26/2020] [Accepted: 10/06/2020] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION The abundance of omics data has facilitated integrative analyses of single and multiple molecular layers with genome-wide association studies focusing on common variants. Built on its successes, we propose a general analysis framework to leverage multi-omics data with sequencing data to improve the statistical power of discovering new associations and understanding of the disease susceptibility due to low-frequency variants. The proposed test features its robustness to model misspecification, high power across a wide range of scenarios and the potential of offering insights into the underlying genetic architecture and disease mechanisms. RESULTS Using the Framingham Heart Study data, we show that low-frequency variants are predictive of DNA methylation, even after conditioning on the nearby common variants. In addition, DNA methylation and gene expression provide complementary information to functional genomics. In the Avon Longitudinal Study of Parents and Children with a sample size of 1497, one gene CLPTM1 is identified to be associated with low-density lipoprotein cholesterol levels by the proposed powerful adaptive gene-based test integrating information from gene expression, methylation and enhancer-promoter interactions. It is further replicated in the TwinsUK study with 1706 samples. The signal is driven by both low-frequency and common variants. AVAILABILITY AND IMPLEMENTATION Models are available at https://github.com/ytzhong/DNAm. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Tianzhong Yang
- Department of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA
| | - Peng Wei
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Wei Pan
- Department of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA
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16
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Dummy endogenous treatment effect estimation using high‐dimensional instrumental variables. CAN J STAT 2021. [DOI: 10.1002/cjs.11648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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17
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18
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Knutson KA, Deng Y, Pan W. Implicating causal brain imaging endophenotypes in Alzheimer's disease using multivariable IWAS and GWAS summary data. Neuroimage 2020; 223:117347. [PMID: 32898681 PMCID: PMC7778364 DOI: 10.1016/j.neuroimage.2020.117347] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2020] [Revised: 08/24/2020] [Accepted: 08/28/2020] [Indexed: 02/06/2023] Open
Abstract
Recent evidence suggests the existence of many undiscovered heritable brain phenotypes involved in Alzheimer's Disease (AD) pathogenesis. This finding necessitates methods for the discovery of causal brain changes in AD that integrate Magnetic Resonance Imaging measures and genotypic data. However, existing approaches for causal inference in this setting, such as the univariate Imaging Wide Association Study (UV-IWAS), suffer from inconsistent effect estimation and inflated Type I errors in the presence of genetic pleiotropy, the phenomenon in which a variant affects multiple causal intermediate risk phenotypes. In this study, we implement a multivariate extension to the IWAS model, namely MV-IWAS, to consistently estimate and test for the causal effects of multiple brain imaging endophenotypes from the Alzheimer's Disease Neuroimaging Initiative (ADNI) in the presence of pleiotropic and possibly correlated SNPs. We further extend MV-IWAS to incorporate variant-specific direct effects on AD, analogous to the existing Egger regression Mendelian Randomization approach, which allows for testing of remaining pleiotropy after adjusting for multiple intermediate pathways. We propose a convenient approach for implementing MV-IWAS that solely relies on publicly available GWAS summary data and a reference panel. Through simulations with either individual-level or summary data, we demonstrate the well controlled Type I errors and superior power of MV-IWAS over UV-IWAS in the presence of pleiotropic SNPs. We apply the summary statistic based tests to 1578 heritable imaging derived phenotypes (IDPs) from the UK Biobank. MV-IWAS detected numerous IDPs as possible false positives by UV-IWAS while uncovering many additional causal neuroimaging phenotypes in AD which are strongly supported by the existing literature.
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Affiliation(s)
- Katherine A Knutson
- Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota United States
| | - Yangqing Deng
- Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota United States
| | - Wei Pan
- Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota United States.
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19
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Grant AJ, Burgess S. An efficient and robust approach to Mendelian randomization with measured pleiotropic effects in a high-dimensional setting. Biostatistics 2020; 23:609-625. [PMID: 33155035 PMCID: PMC9007434 DOI: 10.1093/biostatistics/kxaa045] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Revised: 09/14/2020] [Accepted: 09/26/2020] [Indexed: 01/21/2023] Open
Abstract
Valid estimation of a causal effect using instrumental variables requires that all of the instruments are independent of the outcome conditional on the risk factor of interest and any confounders. In Mendelian randomization studies with large numbers of genetic variants used as instruments, it is unlikely that this condition will be met. Any given genetic variant could be associated with a large number of traits, all of which represent potential pathways to the outcome which bypass the risk factor of interest. Such pleiotropy can be accounted for using standard multivariable Mendelian randomization with all possible pleiotropic traits included as covariates. However, the estimator obtained in this way will be inefficient if some of the covariates do not truly sit on pleiotropic pathways to the outcome. We present a method that uses regularization to identify which out of a set of potential covariates need to be accounted for in a Mendelian randomization analysis in order to produce an efficient and robust estimator of a causal effect. The method can be used in the case where individual-level data are not available and the analysis must rely on summary-level data only. It can be used where there are any number of potential pleiotropic covariates up to the number of genetic variants less one. We show the results of simulation studies that demonstrate the performance of the proposed regularization method in realistic settings. We also illustrate the method in an applied example which looks at the causal effect of urate plasma concentration on coronary heart disease.
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Affiliation(s)
- Andrew J Grant
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK and Cardiovascular Epidemiology Unit, University of Cambridge, Cambridge, UK
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On the Asymptotic Distribution of Ridge Regression Estimators Using Training and Test Samples. ECONOMETRICS 2020. [DOI: 10.3390/econometrics8040039] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The asymptotic distribution of the linear instrumental variables (IV) estimator with empirically selected ridge regression penalty is characterized. The regularization tuning parameter is selected by splitting the observed data into training and test samples and becomes an estimated parameter that jointly converges with the parameters of interest. The asymptotic distribution is a nonstandard mixture distribution. Monte Carlo simulations show the asymptotic distribution captures the characteristics of the sampling distributions and when this ridge estimator performs better than two-stage least squares. An empirical application on returns to education data is presented.
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Liu J, Han M, Yue Z, Dong C, Wen P, Zhao G, Wu L, Xia J, Bin Y. Prediction of Radiosensitivity in Head and Neck Squamous Cell Carcinoma Based on Multiple Omics Data. Front Genet 2020; 11:960. [PMID: 33014019 PMCID: PMC7461877 DOI: 10.3389/fgene.2020.00960] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Accepted: 07/31/2020] [Indexed: 12/22/2022] Open
Abstract
Head and neck squamous cell carcinoma (HNSCC) is a malignant tumor. Radiotherapy (RT) is an important treatment for HNSCC, but not all patients derive survival benefit from RT due to the individual differences on radiosensitivity. A prediction model of radiosensitivity based on multiple omics data might solve this problem. Compared with single omics data, multiple omics data can illuminate more systematical associations between complex molecular characteristics and cancer phenotypes. In this study, we obtained 122 differential expression genes by analyzing the gene expression data of HNSCC patients with RT (N = 287) and without RT (N = 189) downloaded from The Cancer Genome Atlas. Then, HNSCC patients with RT were randomly divided into a training set (N = 149) and a test set (N = 138). Finally, we combined multiple omics data of 122 differential genes with clinical outcomes on the training set to establish a 12-gene signature by two-stage regularization and multivariable Cox regression models. Using the median score of the 12-gene signature on the training set as the cutoff value, the patients were divided into the high- and low-score groups. The analysis revealed that patients in the low-score group had higher radiosensitivity and would benefit from RT. Furthermore, we developed a nomogram to predict the overall survival of HNSCC patients with RT. We compared the prognostic value of 12-gene signature with those of the gene signatures based on single omics data. It suggested that the 12-gene signature based on multiple omics data achieved the best ability for predicting radiosensitivity. In conclusion, the proposed 12-gene signature is a promising biomarker for estimating the RT options in HNSCC patients.
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Affiliation(s)
- Jie Liu
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, Hefei, China
| | - Mengmeng Han
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, Hefei, China
| | - Zhenyu Yue
- Anhui Provincial Engineering Laboratory of Beidou Precision Agricultural Information, Anhui Agricultural University, Hefei, China.,School of Information and Computer, Anhui Agricultural University, Hefei, China
| | - Chao Dong
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, Hefei, China
| | - Pengbo Wen
- Key Laboratory of High Magnetic Field and Ion Beam Physical Biology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China.,Department of Bioinformatics, School of Medical Informatics, Xuzhou Medical University, Xuzhou, China
| | - Guoping Zhao
- Key Laboratory of High Magnetic Field and Ion Beam Physical Biology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China
| | - Lijun Wu
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, Hefei, China.,Key Laboratory of High Magnetic Field and Ion Beam Physical Biology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China
| | - Junfeng Xia
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, Hefei, China
| | - Yannan Bin
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, Hefei, China
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Xu X, Li X, Zhang J. Regularization methods for high-dimensional sparse control function models. J Stat Plan Inference 2020. [DOI: 10.1016/j.jspi.2019.09.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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23
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Liu C, Zhao P, Yang Y. Regularization statistical inferences for partially linear models with high dimensional endogenous covariates. J Korean Stat Soc 2020. [DOI: 10.1007/s42952-020-00067-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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Tang X, Zhao P, Yang Y, Yang W. Adjusted empirical likelihood inferences for varying coefficient partially non linear models with endogenous covariates. COMMUN STAT-THEOR M 2020. [DOI: 10.1080/03610926.2020.1747078] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Affiliation(s)
- Xinrong Tang
- Department of Assets Management, Chongqing Technology and Business University, Chongqing, China
| | - Peixin Zhao
- College of Mathematics and Statistics, Chongqing Technology and Business University, Chongqing, China
- Chongqing Key Laboratory of Social Economy and Applied Statistics, Chongqing, China
| | - Yiping Yang
- College of Mathematics and Statistics, Chongqing Technology and Business University, Chongqing, China
| | - Weiming Yang
- College of Mathematics and Statistics, Chongqing Technology and Business University, Chongqing, China
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Gao B, Liu X, Li H, Cui Y. Integrative analysis of genetical genomics data incorporating network structures. Biometrics 2019; 75:1063-1075. [PMID: 31009063 PMCID: PMC6810723 DOI: 10.1111/biom.13072] [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: 11/03/2016] [Revised: 03/15/2019] [Accepted: 03/28/2019] [Indexed: 12/18/2022]
Abstract
In a living organism, tens of thousands of genes are expressed and interact with each other to achieve necessary cellular functions. Gene regulatory networks contain information on regulatory mechanisms and the functions of gene expressions. Thus, incorporating network structures, discerned either through biological experiments or statistical estimations, could potentially increase the selection and estimation accuracy of genes associated with a phenotype of interest. Here, we considered a gene selection problem using gene expression data and the graphical structures found in gene networks. Because gene expression measurements are intermediate phenotypes between a trait and its associated genes, we adopted an instrumental variable regression approach. We treated genetic variants as instrumental variables to address the endogeneity issue. We proposed a two-step estimation procedure. In the first step, we applied the LASSO algorithm to estimate the effects of genetic variants on gene expression measurements. In the second step, the projected expression measurements obtained from the first step were treated as input variables. A graph-constrained regularization method was adopted to improve the efficiency of gene selection and estimation. We theoretically showed the selection consistency of the estimation method and derived the L ∞ bound of the estimates. Simulation and real data analyses were conducted to demonstrate the effectiveness of our method and to compare it with its counterparts.
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Affiliation(s)
- Bin Gao
- Department of Statistics and Probability, Michigan State University, East Lansing, Michigan
- Quantitative Sciences, Janssen Research & Development, LLC, Spring House, Pennsylvania
| | - Xu Liu
- School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, China
| | - Hongzhe Li
- Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Yuehua Cui
- Department of Statistics and Probability, Michigan State University, East Lansing, Michigan
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Hu X, Xie W, Wu C, Xu S. A directed learning strategy integrating multiple omic data improves genomic prediction. PLANT BIOTECHNOLOGY JOURNAL 2019; 17:2011-2020. [PMID: 30950198 PMCID: PMC6737184 DOI: 10.1111/pbi.13117] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2018] [Revised: 02/04/2019] [Accepted: 03/13/2019] [Indexed: 05/05/2023]
Abstract
Genomic prediction (GP) aims to construct a statistical model for predicting phenotypes using genome-wide markers and is a promising strategy for accelerating molecular plant breeding. However, current progress of phenotype prediction using genomic data alone has reached a bottleneck, and previous studies on transcriptomic and metabolomic predictions ignored genomic information. Here, we designed a novel strategy of GP called multilayered least absolute shrinkage and selection operator (MLLASSO) by integrating multiple omic data into a single model that iteratively learns three layers of genetic features (GFs) supervised by observed transcriptome and metabolome. Significantly, MLLASSO learns higher order information of gene interactions, which enables us to achieve a significant improvement of predictability of yield in rice from 0.1588 (GP alone) to 0.2451 (MLLASSO). In the prediction of the first two layers, some genes were found to be genetically predictable genes (GPGs) as their expressions were accurately predicted with genetic markers. Interestingly, we made three dramatic discoveries for the GPGs: (i) GPGs are good predictors for highly complex traits like yield; (ii) GPGs are mostly eQTL genes (cis or trans); and (iii) trait-related transcriptional factor families are enriched in GPGs. These findings support the notion that learned GFs not only are good predictors for traits but also have specific biological implications regarding regulation of gene expressions. To differentiate the new method from conventional GP models, we called MLLASSO a directed learning strategy supervised by intermediate omic data. This new prediction model appears to be more reliable and more robust than conventional GP models.
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Affiliation(s)
- Xuehai Hu
- College of InformaticsAgricultural Bioinformatics Key Laboratory of Hubei ProvinceHuazhong Agricultural UniversityWuhanHubeiChina
- Department of Botany and Plant SciencesUniversity of CaliforniaRiversideCAUSA
| | - Weibo Xie
- College of InformaticsAgricultural Bioinformatics Key Laboratory of Hubei ProvinceHuazhong Agricultural UniversityWuhanHubeiChina
- Department of Botany and Plant SciencesUniversity of CaliforniaRiversideCAUSA
| | - Chengchao Wu
- College of InformaticsAgricultural Bioinformatics Key Laboratory of Hubei ProvinceHuazhong Agricultural UniversityWuhanHubeiChina
| | - Shizhong Xu
- Department of Botany and Plant SciencesUniversity of CaliforniaRiversideCAUSA
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Geng P, Tong X, Lu Q. An integrative U method for joint analysis of multi-level omic data. BMC Genet 2019; 20:40. [PMID: 30967125 PMCID: PMC6457037 DOI: 10.1186/s12863-019-0742-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2018] [Accepted: 03/20/2019] [Indexed: 11/30/2022] Open
Abstract
Background The advance of high-throughput technologies has made it cost-effective to collect diverse types of omic data in large-scale clinical and biological studies. While the collection of the vast amounts of multi-level omic data from these studies provides a great opportunity for genetic research, the high dimensionality of omic data and complex relationships among multi-level omic data bring tremendous analytic challenges. Results To address these challenges, we develop an integrative U (IU) method for the design and analysis of multi-level omic data. While non-parametric methods make less model assumptions and are flexible for analyzing different types of phenotypes and omic data, they have been less developed for association analysis of omic data. The IU method is a nonparametric method that can accommodate various types of omic and phenotype data, and consider interactive relationship among different levels of omic data. Through simulations and a real data application, we compare the IU test with commonly used variance component tests. Conclusions Results show that the proposed test attains more robust type I error performance and higher empirical power than variance component tests under various types of phenotypes and different underlying interaction effects. Electronic supplementary material The online version of this article (10.1186/s12863-019-0742-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Pei Geng
- Department of Mathematics, Illinois State University, Normal, IL, 61761, USA
| | - Xiaoran Tong
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, 48824, USA
| | - Qing Lu
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, 48824, USA.
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Windmeijer F, Farbmacher H, Davies N, Davey Smith G. On the Use of the Lasso for Instrumental Variables Estimation with Some Invalid Instruments. J Am Stat Assoc 2018; 114:1339-1350. [PMID: 31708716 PMCID: PMC6817329 DOI: 10.1080/01621459.2018.1498346] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2016] [Revised: 07/01/2018] [Indexed: 01/27/2023]
Abstract
We investigate the behavior of the Lasso for selecting invalid instruments in linear instrumental variables models for estimating causal effects of exposures on outcomes, as proposed recently by Kang et al. Invalid instruments are such that they fail the exclusion restriction and enter the model as explanatory variables. We show that for this setup, the Lasso may not consistently select the invalid instruments if these are relatively strong. We propose a median estimator that is consistent when less than 50% of the instruments are invalid, and its consistency does not depend on the relative strength of the instruments, or their correlation structure. We show that this estimator can be used for adaptive Lasso estimation, with the resulting estimator having oracle properties. The methods are applied to a Mendelian randomization study to estimate the causal effect of body mass index (BMI) on diastolic blood pressure, using data on individuals from the UK Biobank, with 96 single nucleotide polymorphisms as potential instruments for BMI. Supplementary materials for this article are available online.
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Affiliation(s)
- Frank Windmeijer
- Department of Economics, University of Bristol, Bristol, United Kingdom.,MRC Integrative Epidemiology Unit, Bristol, United Kingdom
| | - Helmut Farbmacher
- Center for the Economics of Aging, Max Planck Society Munich, Germany
| | - Neil Davies
- MRC Integrative Epidemiology Unit, Bristol, United Kingdom.,School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, Bristol, United Kingdom.,School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom
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Zhang W, Dai X, Xu S, Zhao PX. 2D association and integrative omics analysis in rice provides systems biology view in trait analysis. Commun Biol 2018; 1:153. [PMID: 30272029 PMCID: PMC6160469 DOI: 10.1038/s42003-018-0159-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2018] [Accepted: 08/30/2018] [Indexed: 12/28/2022] Open
Abstract
The interactions among genes and between genes and environment contribute significantly to the phenotypic variation of complex traits and may be possible explanations for missing heritability. However, to our knowledge no existing tool can address the two kinds of interactions. Here we propose a novel linear mixed model that considers not only the additive effects of biological markers but also the interaction effects of marker pairs. Interaction effect is demonstrated as a 2D association. Based on this linear mixed model, we developed a pipeline, namely PATOWAS. PATOWAS can be used to study transcriptome-wide and metabolome-wide associations in addition to genome-wide associations. Our case analysis with real rice recombinant inbred lines (RILs) at three omics levels demonstrates that 2D association mapping and integrative omics are able to provide a systems biology view into the analyzed traits, leading toward an answer about how genes, transcripts, proteins, and metabolites work together to produce an observable phenotype. Wenchao Zhang et al. developed a tool for analyzing traits using data generated from genome-wide, transcriptome-wide, and metabolome-wide association studies. They test their approach in rice, providing a systems biology view of identified traits.
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Affiliation(s)
- Wenchao Zhang
- Computational Biology and Bioinformatics Lab, Noble Research Institute, Ardmore, OK, 73401, USA
| | - Xinbin Dai
- Computational Biology and Bioinformatics Lab, Noble Research Institute, Ardmore, OK, 73401, USA
| | - Shizhong Xu
- Department of Botany and Plant Sciences, University of California, Riverside, CA, 92521, USA.
| | - Patrick X Zhao
- Computational Biology and Bioinformatics Lab, Noble Research Institute, Ardmore, OK, 73401, USA.
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30
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Zhang H, Zheng Y, Yoon G, Zhang Z, Gao T, Joyce B, Zhang W, Schwartz J, Vokonas P, Colicino E, Baccarelli A, Hou L, Liu L. Regularized estimation in sparse high-dimensional multivariate regression, with application to a DNA methylation study. Stat Appl Genet Mol Biol 2017; 16:159-171. [PMID: 28734115 DOI: 10.1515/sagmb-2016-0073] [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] [Indexed: 11/15/2022]
Abstract
In this article, we consider variable selection for correlated high dimensional DNA methylation markers as multivariate outcomes. A novel weighted square-root LASSO procedure is proposed to estimate the regression coefficient matrix. A key feature of this method is tuning-insensitivity, which greatly simplifies the computation by obviating cross validation for penalty parameter selection. A precision matrix obtained via the constrained ℓ1 minimization method is used to account for the within-subject correlation among multivariate outcomes. Oracle inequalities of the regularized estimators are derived. The performance of our proposed method is illustrated via extensive simulation studies. We apply our method to study the relation between smoking and high dimensional DNA methylation markers in the Normative Aging Study (NAS).
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31
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Shortreed SM, Ertefaie A. Outcome-adaptive lasso: Variable selection for causal inference. Biometrics 2017; 73:1111-1122. [PMID: 28273693 PMCID: PMC5591052 DOI: 10.1111/biom.12679] [Citation(s) in RCA: 80] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2016] [Revised: 02/01/2017] [Accepted: 02/01/2017] [Indexed: 11/28/2022]
Abstract
Methodological advancements, including propensity score methods, have resulted in improved unbiased estimation of treatment effects from observational data. Traditionally, a "throw in the kitchen sink" approach has been used to select covariates for inclusion into the propensity score, but recent work shows including unnecessary covariates can impact both the bias and statistical efficiency of propensity score estimators. In particular, the inclusion of covariates that impact exposure but not the outcome, can inflate standard errors without improving bias, while the inclusion of covariates associated with the outcome but unrelated to exposure can improve precision. We propose the outcome-adaptive lasso for selecting appropriate covariates for inclusion in propensity score models to account for confounding bias and maintaining statistical efficiency. This proposed approach can perform variable selection in the presence of a large number of spurious covariates, that is, covariates unrelated to outcome or exposure. We present theoretical and simulation results indicating that the outcome-adaptive lasso selects the propensity score model that includes all true confounders and predictors of outcome, while excluding other covariates. We illustrate covariate selection using the outcome-adaptive lasso, including comparison to alternative approaches, using simulated data and in a survey of patients using opioid therapy to manage chronic pain.
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Affiliation(s)
- Susan M Shortreed
- Biostatistics Unit, Group Health Research Institute, Department of Biostatistics, University of Washington,
| | - Ashkan Ertefaie
- Department of Biostatistics and Computational Biology, University of Rochester Department of Statistics, The Wharton School, University of Pennsylvania Center for Pharmacoepidemiology Research and Training, University of Pennsylvania,
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Abstract
Background Properly adjusting for unmeasured confounders is critical for health studies in order to achieve valid testing and estimation of the exposure's causal effect on outcomes. The instrumental variable (IV) method has long been used in econometrics to estimate causal effects while accommodating the effect of unmeasured confounders. Mendelian randomization (MR), which uses genetic variants as the instrumental variables, is an application of the instrumental variable method to biomedical research fields, and has become popular in recent years. One often-used estimator of causal effects for instrumental variables and Mendelian randomization is the two-stage least square estimator (TSLS). The validity of TSLS relies on the accurate prediction of exposure based on IVs in its first stage. Results In this note, we propose to model the link between exposure and genetic IVs using the least-squares kernel machine (LSKM). Some simulation studies are used to evaluate the feasibility of LSKM in TSLS setting. Conclusions Our results show that LSKM based on genotype score or genotype can be used effectively in TSLS. It may provide higher power when the association between exposure and genetic IVs is nonlinear.
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Wang F, Meyer NJ, Walley KR, Russell JA, Feng R. Causal Genetic Inference Using Haplotypes as Instrumental Variables. Genet Epidemiol 2015; 40:35-44. [PMID: 26625855 DOI: 10.1002/gepi.21940] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2015] [Revised: 08/15/2015] [Accepted: 09/19/2015] [Indexed: 02/03/2023]
Abstract
In genomic studies with both genotypes and gene or protein expression profile available, causal effects of gene or protein on clinical outcomes can be inferred through using genetic variants as instrumental variables (IVs). The goal of introducing IV is to remove the effects of unobserved factors that may confound the relationship between the biomarkers and the outcome. A valid inference under the IV framework requires pairwise associations and pathway exclusivity. Among these assumptions, the IV expression association needs to be strong for the casual effect estimates to be unbiased. However, a small number of single nucleotide polymorphisms (SNPs) often provide limited explanation of the variability in the gene or protein expression and can only serve as weak IVs. In this study, we propose to replace SNPs with haplotypes as IVs to increase the variant-expression association and thus improve the casual effect inference of the expression. In the classical two-stage procedure, we developed a haplotype regression model combined with a model selection procedure to identify optimal instruments. The performance of the new method was evaluated through simulations and compared with the IV approaches using observed multiple SNPs. Our results showed the gain of power to detect a causal effect of gene or protein on the outcome using haplotypes compared with using only observed SNPs, under either complete or missing genotype scenarios. We applied our proposed method to a study of the effect of interleukin-1 beta (IL-1β) protein expression on the 90-day survival following sepsis and found that overly expressed IL-1β is likely to increase mortality.
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Affiliation(s)
- Fan Wang
- Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Nuala J Meyer
- Center for Translational Lung Biology, Pulmonary, Allergy, and Critical Care Division, Perelman School of Medicine University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Keith R Walley
- Center for Heart Lung Innovation, University of British Columbia, Vancouver, British Columbia, Canada
| | - James A Russell
- Center for Heart Lung Innovation, University of British Columbia, Vancouver, British Columbia, Canada
| | - Rui Feng
- Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
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