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Liyanage JSS, Estepp JH, Srivastava K, Li Y, Mori M, Kang G. GMEPS: a fast and efficient likelihood approach for genome-wide mediation analysis under extreme phenotype sequencing. Stat Appl Genet Mol Biol 2022; 21:sagmb-2021-0071. [PMID: 35266368 DOI: 10.1515/sagmb-2021-0071] [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: 09/14/2021] [Accepted: 02/17/2022] [Indexed: 11/15/2022]
Abstract
Due to many advantages such as higher statistical power of detecting the association of genetic variants in human disorders and cost saving, extreme phenotype sequencing (EPS) is a rapidly emerging study design in epidemiological and clinical studies investigating how genetic variations associate with complex phenotypes. However, the investigation of the mediation effect of genetic variants on phenotypes is strictly restrictive under the EPS design because existing methods cannot well accommodate the non-random extreme tails sampling process incurred by the EPS design. In this paper, we propose a likelihood approach for testing the mediation effect of genetic variants through continuous and binary mediators on a continuous phenotype under the EPS design (GMEPS). Besides implementing in EPS design, it can also be utilized as a general mediation analysis procedure. Extensive simulations and two real data applications of a genome-wide association study of benign ethnic neutropenia under EPS design and a candidate-gene study of neurocognitive performance in patients with sickle cell disease under random sampling design demonstrate the superiority of GMEPS under the EPS design over widely used mediation analysis procedures, while demonstrating compatible capabilities under the general random sampling framework.
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Affiliation(s)
- Janaka S S Liyanage
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis 38105, TN, USA
| | - Jeremie H Estepp
- Departments of Global Pediatric Medicine and Hematology, St. Jude Children's Research Hospital, Memphis 38105, TN, USA
| | - Kumar Srivastava
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis 38105, TN, USA
| | - Yun Li
- Department of Biostatistics, Department of Genetics, Department of Computer Science, The University of North Carolina at Chapel Hill, Chapel Hill 27599, NC, USA
| | - Motomi Mori
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis 38105, TN, USA
| | - Guolian Kang
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis 38105, TN, USA
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2
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Bi W, Li Y, Smeltzer MP, Gao G, Zhao S, Kang G. STEPS: an efficient prospective likelihood approach to genetic association analyses of secondary traits in extreme phenotype sequencing. Biostatistics 2020; 21:33-49. [PMID: 30007308 PMCID: PMC8559722 DOI: 10.1093/biostatistics/kxy030] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2017] [Revised: 05/16/2018] [Accepted: 06/02/2018] [Indexed: 11/13/2022] Open
Abstract
It has been well acknowledged that methods for secondary trait (ST) association analyses under a case-control design (ST$_{\text{CC}}$) should carefully consider the sampling process to avoid biased risk estimates. A similar situation also exists in the extreme phenotype sequencing (EPS) designs, which is to select subjects with extreme values of continuous primary phenotype for sequencing. EPS designs are commonly used in modern epidemiological and clinical studies such as the well-known National Heart, Lung, and Blood Institute Exome Sequencing Project. Although naïve generalized regression or ST$_{\text{CC}}$ method could be applied, their validity is questionable due to difference in statistical designs. Herein, we propose a general prospective likelihood framework to perform association testing for binary and continuous STs under EPS designs (STEPS), which can also incorporate covariates and interaction terms. We provide a computationally efficient and robust algorithm to obtain the maximum likelihood estimates. We also present two empirical mathematical formulas for power/sample size calculations to facilitate planning of binary/continuous STs association analyses under EPS designs. Extensive simulations and application to a genome-wide association study of benign ethnic neutropenia under an EPS design demonstrate the superiority of STEPS over all its alternatives above.
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Affiliation(s)
- Wenjian Bi
- Department of Biostatistics, St. Jude Children’s Research
Hospital, Memphis, TN 38105, USA
| | - Yun Li
- Department of Genetics, University of North Carolina, Chapel
Hill, NC 27599, USA
- Department of Biostatistics, University of North Carolina, Chapel
Hill, NC 27599, USA
- Department of Computer Science, University of North Carolina,
Chapel Hill, NC 27599, USA
| | - Matthew P Smeltzer
- Division of Epidemiology, Biostatistics, and Environmental Health, School of
Public Health, University of Memphis, Memphis, TN 38152, USA
| | - Guimin Gao
- Department of Public Health Sciences, University of Chicago,
Chicago, IL 60637, USA
| | - Shengli Zhao
- School of Statistics, Qufu Normal University, Qufu 273165, PR
China
| | - Guolian Kang
- Department of Biostatistics, St. Jude Children’s Research
Hospital, Memphis, TN 38105, USA
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3
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Zhang H, Bi W, Cui Y, Chen H, Chen J, Zhao Y, Kang G. Extreme-value sampling design is cost-beneficial only with a valid statistical approach for exposure-secondary outcome association analyses. Stat Methods Med Res 2019; 29:466-480. [PMID: 30945605 DOI: 10.1177/0962280219839093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
In epidemiology cohort studies, exposure data are collected in sub-studies based on a primary outcome (PO) of interest, as with the extreme-value sampling design (EVSD), to investigate their correlation. Secondary outcomes (SOs) data are also readily available, enabling researchers to assess the correlations between the exposure and the SOs. However, when the EVSD is used, the data for SOs are not representative samples of a general population; thus, many commonly used statistical methods, such as the generalized linear model (GLM), are not valid. A prospective likelihood method has been developed to associate SOs with single-nucleotide polymorphisms under an extreme phenotype sequencing design. In this paper, we describe the application of the prospective likelihood method (STEVSD) to exposure-SO association analysis under an EVSD. We undertook extensive simulations to assess the performance of the STEVSD method in associating binary and continuous exposures with SOs, comparing it to the simple GLM method that ignores the EVSD. To demonstrate the cost-benefit of the STEVSD method, we also mimicked the design of two new retrospective studies, as would be done in actual practice, based on the PO of interest, which was the same as the SO in the EVSD study. We then analyzed these data by using the GLM method and compared its power to that of the STEVSD method. We demonstrated the usefulness of the STEVSD method by applying it to a benign ethnic neutropenia dataset. Our results indicate that the STEVSD method can control type I error well, whereas the GLM method cannot do so owing to its ignorance of EVSD, and that the STEVSD method is cost-effective because it has statistical power similar to that of two new retrospective studies that require collecting new exposure data for selected individuals.
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Affiliation(s)
- Hang Zhang
- Key Laboratory of Systems and Control, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, PR China.,School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, PR China
| | - Wenjian Bi
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Yuehua Cui
- Department of Statistics and Probability, Michigan State University, East Lansing, MI, USA
| | - Honglei Chen
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, USA
| | - Jinbo Chen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Yanlong Zhao
- Key Laboratory of Systems and Control, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, PR China.,School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, PR China
| | - Guolian Kang
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN, USA
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Bi W, Kang G, Pounds SB. Statistical selection of biological models for genome-wide association analyses. Methods 2018; 145:67-75. [PMID: 29803781 DOI: 10.1016/j.ymeth.2018.05.019] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2018] [Revised: 04/13/2018] [Accepted: 05/22/2018] [Indexed: 01/23/2023] Open
Abstract
Genome-wide association studies have discovered many biologically important associations of genes with phenotypes. Typically, genome-wide association analyses formally test the association of each genetic feature (SNP, CNV, etc) with the phenotype of interest and summarize the results with multiplicity-adjusted p-values. However, very small p-values only provide evidence against the null hypothesis of no association without indicating which biological model best explains the observed data. Correctly identifying a specific biological model may improve the scientific interpretation and can be used to more effectively select and design a follow-up validation study. Thus, statistical methodology to identify the correct biological model for a particular genotype-phenotype association can be very useful to investigators. Here, we propose a general statistical method to summarize how accurately each of five biological models (null, additive, dominant, recessive, co-dominant) represents the data observed for each variant in a GWAS study. We show that the new method stringently controls the false discovery rate and asymptotically selects the correct biological model. Simulations of two-stage discovery-validation studies show that the new method has these properties and that its validation power is similar to or exceeds that of simple methods that use the same statistical model for all SNPs. Example analyses of three data sets also highlight these advantages of the new method. An R package is freely available at www.stjuderesearch.org/site/depts/biostats/maew.
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Affiliation(s)
- Wenjian Bi
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Guolian Kang
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Stanley B Pounds
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN 38105, USA.
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Kang G, Bi W, Zhang H, Pounds S, Cheng C, Shete S, Zou F, Zhao Y, Zhang JF, Yue W. A Robust and Powerful Set-Valued Approach to Rare Variant Association Analyses of Secondary Traits in Case-Control Sequencing Studies. Genetics 2017; 205:1049-1062. [PMID: 28040743 PMCID: PMC5340322 DOI: 10.1534/genetics.116.192377] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2016] [Accepted: 12/29/2016] [Indexed: 12/16/2022] Open
Abstract
In many case-control designs of genome-wide association (GWAS) or next generation sequencing (NGS) studies, extensive data on secondary traits that may correlate and share the common genetic variants with the primary disease are available. Investigating these secondary traits can provide critical insights into the disease etiology or pathology, and enhance the GWAS or NGS results. Methods based on logistic regression (LG) were developed for this purpose. However, for the identification of rare variants (RVs), certain inadequacies in the LG models and algorithmic instability can cause severely inflated type I error, and significant loss of power, when the two traits are correlated and the RV is associated with the disease, especially at stringent significance levels. To address this issue, we propose a novel set-valued (SV) method that models a binary trait by dichotomization of an underlying continuous variable, and incorporate this into the genetic association model as a critical component. Extensive simulations and an analysis of seven secondary traits in a GWAS of benign ethnic neutropenia show that the SV method consistently controls type I error well at stringent significance levels, has larger power than the LG-based methods, and is robust in performance to effect pattern of the genetic variant (risk or protective), rare or common variants, rare or common diseases, and trait distributions. Because of the SV method's striking and profound advantage, we strongly recommend the SV method be employed instead of the LG-based methods for secondary traits analyses in case-control sequencing studies.
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Affiliation(s)
- Guolian Kang
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, Tennessee 38105
| | - Wenjian Bi
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, Tennessee 38105
| | - Hang Zhang
- Key Laboratory of Systems and Control, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, People's Republic of China
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - Stanley Pounds
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, Tennessee 38105
| | - Cheng Cheng
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, Tennessee 38105
| | - Sanjay Shete
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030
| | - Fei Zou
- Department of Biostatistics, The University of North Carolina at Chapel Hill, North Carolina 27599
| | - Yanlong Zhao
- Key Laboratory of Systems and Control, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, People's Republic of China
| | - Ji-Feng Zhang
- Key Laboratory of Systems and Control, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, People's Republic of China
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - Weihua Yue
- Institute of Mental Health, Key Laboratory of Mental Health, Ministry of Health & National Clinical Research Center for Mental Disorders, Sixth Hospital, Peking University, Beijing 100191, People's Republic of China
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Larson NB, McDonnell S, Albright LC, Teerlink C, Stanford J, Ostrander EA, Isaacs WB, Xu J, Cooney KA, Lange E, Schleutker J, Carpten JD, Powell I, Bailey-Wilson J, Cussenot O, Cancel-Tassin G, Giles G, MacInnis R, Maier C, Whittemore AS, Hsieh CL, Wiklund F, Catolona WJ, Foulkes W, Mandal D, Eeles R, Kote-Jarai Z, Ackerman MJ, Olson TM, Klein CJ, Thibodeau SN, Schaid DJ. Post hoc Analysis for Detecting Individual Rare Variant Risk Associations Using Probit Regression Bayesian Variable Selection Methods in Case-Control Sequencing Studies. Genet Epidemiol 2016; 40:461-9. [PMID: 27312771 PMCID: PMC5063501 DOI: 10.1002/gepi.21983] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2015] [Revised: 04/22/2016] [Accepted: 04/27/2016] [Indexed: 12/27/2022]
Abstract
Rare variants (RVs) have been shown to be significant contributors to complex disease risk. By definition, these variants have very low minor allele frequencies and traditional single-marker methods for statistical analysis are underpowered for typical sequencing study sample sizes. Multimarker burden-type approaches attempt to identify aggregation of RVs across case-control status by analyzing relatively small partitions of the genome, such as genes. However, it is generally the case that the aggregative measure would be a mixture of causal and neutral variants, and these omnibus tests do not directly provide any indication of which RVs may be driving a given association. Recently, Bayesian variable selection approaches have been proposed to identify RV associations from a large set of RVs under consideration. Although these approaches have been shown to be powerful at detecting associations at the RV level, there are often computational limitations on the total quantity of RVs under consideration and compromises are necessary for large-scale application. Here, we propose a computationally efficient alternative formulation of this method using a probit regression approach specifically capable of simultaneously analyzing hundreds to thousands of RVs. We evaluate our approach to detect causal variation on simulated data and examine sensitivity and specificity in instances of high RV dimensionality as well as apply it to pathway-level RV analysis results from a prostate cancer (PC) risk case-control sequencing study. Finally, we discuss potential extensions and future directions of this work.
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Affiliation(s)
- Nicholas B. Larson
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN
| | - Shannon McDonnell
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN
| | - Lisa Cannon Albright
- Dept. Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT
| | - Craig Teerlink
- Dept. Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT
| | | | | | | | - Jianfeng Xu
- NorthShore University Health System Research Institute, Chicago, IL
| | - Kathleen A. Cooney
- Depts. of Internal Medicine and Urology, University of Michigan Medical School, Ann Arbor, MI
| | - Ethan Lange
- Dept. of Genetics, University of North Carolina, Chapel Hill, NC
| | - Johanna Schleutker
- Dept. of Medical Biochemistry and Genetics, Institute of Biomedicine, University of Turku, Finland
| | - John D. Carpten
- Integrated Cancer Genomics Division, The Translational Genomics Research Institute, Phoenix, AZ
| | | | - Joan Bailey-Wilson
- Statistical Genetics Section, National Human Genome Research Institute, Bethesda, MD
| | | | | | - Graham Giles
- Cancer Epidemiology Centre, Cancer Council Victoria, and Centre for Epidemiology and Biostatistics, School of Population and Global Health, University of Melbourne, Melbourne, Australia
| | - Robert MacInnis
- Cancer Epidemiology Centre, Cancer Council Victoria, and Centre for Epidemiology and Biostatistics, School of Population and Global Health, University of Melbourne, Melbourne, Australia
| | | | | | - Chih-Lin Hsieh
- Dept. of Urology, University of Southern California, Los Angeles, CA
| | - Fredrik Wiklund
- Dept. of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | | | - William Foulkes
- Depts. Of Oncology and Human Genetics, Montreal General Hospital, Montreal QC, Canada
| | - Diptasri Mandal
- Dept. of Genetics, LSU Health Sciences Center, New Orleans, LA
| | - Rosalind Eeles
- Genetics and Epidemiology, Institute of Cancer Research, Sutton Surrey, UK
| | - Zsofia Kote-Jarai
- Genetics and Epidemiology, Institute of Cancer Research, Sutton Surrey, UK
| | | | - Timothy M. Olson
- Dept. of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, MN
| | | | | | - Daniel J. Schaid
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN
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7
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Evaluation of a two-step iterative resampling procedure for internal validation of genome-wide association studies. J Hum Genet 2015; 60:729-38. [PMID: 26377241 DOI: 10.1038/jhg.2015.110] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2015] [Revised: 06/14/2015] [Accepted: 08/09/2015] [Indexed: 12/31/2022]
Abstract
Genome-wide association studies (GWAS) have successfully identified many common genetic variants associated with complex diseases over the past decade. The 'gold standard' method for validating the top single nucleotide polymorphisms (SNPs) identified in GWAS is to independently replicate the findings in similar or diverse large-scale external cohorts. However, for rare diseases, it can be difficult to find an external validation cohort within a reasonable timeframe. In such situations, resampling methods, such as the two-step iterative resampling (TSIR) approach have been used to identify SNPs associated with the outcome of interest. However, the TSIR approach involves choosing several parameters in each step, which can influence the performance of the approach. In this paper, we undertook extensive simulation studies to assess the effect of choice of different parameters on the type I error and power for both binary and continuous phenotypes and also compared the TSIR approach with the traditional one-stage (OS) and two-stage (TS) GWAS analysis. We illustrate the usefulness of the TSIR approach by applying it to a GWAS of childhood cancer survivors. Our results indicate that the TSIR approach with an at least 70:30 split and a cutoff of discovering and replicating SNPs at least 20 times in 100 replications provides conservative type I error control and has near 'optimal' power for internally validated SNPs. Its performance is comparable with the TS GWAS for which an external validation cohort is available with only slight reduction in power in some situations. It has almost the same power as OS GWAS with conservative type I error which leads to fewer false positive findings. TSIR is a powerful and efficient method for identifying and internally validating SNPs for GWAS when independent cohorts for external validation may not be available.
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Bi W, Kang G, Zhao Y, Cui Y, Yan S, Li Y, Cheng C, Pounds SB, Borowitz MJ, Relling MV, Yang JJ, Liu Z, Pui CH, Hunger SP, Hartford CM, Leung W, Zhang JF. SVSI: fast and powerful set-valued system identification approach to identifying rare variants in sequencing studies for ordered categorical traits. Ann Hum Genet 2015; 79:294-309. [PMID: 25959545 DOI: 10.1111/ahg.12117] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2014] [Accepted: 02/23/2015] [Indexed: 11/29/2022]
Abstract
In genetic association studies of an ordered categorical phenotype, it is usual to either regroup multiple categories of the phenotype into two categories and then apply the logistic regression (LG), or apply ordered logistic (oLG), or ordered probit (oPRB) regression, which accounts for the ordinal nature of the phenotype. However, they may lose statistical power or may not control type I error due to their model assumption and/or instable parameter estimation algorithm when the genetic variant is rare or sample size is limited. To solve this problem, we propose a set-valued (SV) system model to identify genetic variants associated with an ordinal categorical phenotype. We couple this model with a SV system identification algorithm to identify all the key system parameters. Simulations and two real data analyses show that SV and LG accurately controlled the Type I error rate even at a significance level of 10(-6) but not oLG and oPRB in some cases. LG had significantly less power than the other three methods due to disregarding of the ordinal nature of the phenotype, and SV had similar or greater power than oLG and oPRB. We argue that SV should be employed in genetic association studies for ordered categorical phenotype.
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Affiliation(s)
- Wenjian Bi
- Key Laboratory of Systems and Control, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, P.R.C
| | - Guolian Kang
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, Tennessee 38105, U.S.A
| | - Yanlong Zhao
- Key Laboratory of Systems and Control, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, P.R.C
| | - Yuehua Cui
- Department of Statistics and Probability, Michigan State University, East Lansing, Michigan 48824, U.S.A
| | - Song Yan
- Department of Genetics, Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, U.S.A
| | - Yun Li
- Department of Genetics, Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, U.S.A.,Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, U.S.A
| | - Cheng Cheng
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, Tennessee 38105, U.S.A
| | - Stanley B Pounds
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, Tennessee 38105, U.S.A
| | | | - Mary V Relling
- Department of Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, Tennessee 38105, U.S.A
| | - Jun J Yang
- Department of Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, Tennessee 38105, U.S.A
| | - Zhifa Liu
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, Tennessee 38105, U.S.A
| | - Ching-Hon Pui
- Department of Oncology, St. Jude Children's Research Hospital, Memphis, Tennessee 38105, U.S.A.,Department of Pediatrics, University of Tennessee Health Science Center, Memphis, Tennessee, 38163, U.S.A
| | - Stephen P Hunger
- University of Colorado School of Medicine and Children's Hospital Colorado, Aurora, Colorado 80045, U.S.A
| | - Christine M Hartford
- Department of Bone Marrow Transplantation and Cellular Therapy, St. Jude Children's Research Hospital, Memphis, Tennessee 38105, U.S.A
| | - Wing Leung
- Department of Bone Marrow Transplantation and Cellular Therapy, St. Jude Children's Research Hospital, Memphis, Tennessee 38105, U.S.A.,Department of Pediatrics, University of Tennessee Health Science Center, Memphis, Tennessee, 38163, U.S.A
| | - Ji-Feng Zhang
- Key Laboratory of Systems and Control, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, P.R.C
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