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Hongyu Zhao, PhD

Ira V. Hiscock Professor of Biostatistics, Professor of Genetics and Professor of Statistics and Data Science; Affiliated Faculty, Yale Institute for Global Health

Contact Information

Hongyu Zhao, PhD

Office Location

Mailing Address

  • Biostatistics

    PO Box 208034, 60 College Street

    New Haven, CT 06520-8034

    United States

Research Summary

Our research is driven by the need to analyze and interpret large and complex data sets in biomedical research. For example, in genome wide association studies involving thousands to hundreds of thousands of individuals, millions of DNA variants are analyzed for each person. Such data offer researchers the opportunity to identify genes and variants affecting disease susceptibility and develop risk prediction models to facilitate disease prevention, monitoring, and treatment. There are many statistical challenges arising from the analysis of such data, including the very high dimensionality of the markers, the relatively weak signals, and the need to incorporate prior knowledge and other data sets in analysis. Other examples include the analyses of next generation sequence data, single cell data, image data, microbiome data, wearable device data, and electronic medical records, which present even greater statistical and computational challenges. Our group has been developing statistical methods to address these challenges, such as empirical Bayes methods to borrow information across different data sets, different generalizations of Gaussian graphical models for network inference, Markov random field models for spatial and temporal modeling, and general machine learning methods for high dimensional data.

Specialized Terms: Statistical genomics and proteomics; Bioinformatics; Data integration; High dimensional data; Network and graphical models; Disease risk prediction; Microbiome; Cancer genomics; Single cell analysis; Imaging genetics; Wearable device; Electronic medical records

Extensive Research Description

  • Genome Wide Association Studies: We are developing statistical methods to integrate diverse data types and prior biological knowledge to identify genes and variants for common diseases and risk prediction models. We also develop methods to infer the genetic architecture of complex diseases and for risk predictions.
  • Single Cell Analysis: We are developing statistically robust and computationally efficient methods for single cell data with the objectives of inferring genetic regulation and signaling at the single cell level, and the identifications of cellular changes across different conditions.
  • Network Modeling: We are developing statistical methods to model biological networks under the general framework of Gaussian and other graphical models. Specific networks we are working on include gene expression regulatory networks, signaling networks, and eQTL networks.
  • Imaging Genetics: We focus on the analysis of data from several consortia to infer the impacts of genetic factors on imaging traits, as well as their associations with complex diseases.
  • Wearable Device: We are developing methods to extract signals from wearable devices and then combine them with genetics data to infer the genetic basis of activity and sleeping traits.
  • Cancer Genomics: We are developing statistical and computational methods to analyze cancer genomics data, e.g. microarrays and next generation sequencing, to identify cancer subtypes, driver mutations, biomarkers, and appropriate treatments for cancer patients.
  • Microbiome Analysis: We are developing modeling and analysis approaches for microbiome data generated from next generation sequencing data.
  • Proteomics: Our current focus is on targeted proteomics, such as Multiple Reaction Monitoring.


Coauthors

Research Interests

Genetics; Public Health; Computational Biology; Statistics; Genomics; Proteomics; Biostatistics; Single-Cell Analysis; Microbiota; Wearable Electronic Devices

Public Health Interests

Cancer; Genetics, Genomics, Epigenetics; Global Health; Infectious Diseases

Selected Publications

  • Low-Rank Regression Models for Multiple Binary Responses and their Applications to Cancer Cell-Line Encyclopedia DataPark S, Lee E, Zhao H. Low-Rank Regression Models for Multiple Binary Responses and their Applications to Cancer Cell-Line Encyclopedia Data Journal Of The American Statistical Association 2022, 1-15. DOI: 10.1080/01621459.2022.2105704.
  • Variance estimation and confidence intervals from genome-wide association studies through high-dimensional misspecified mixed model analysisDao C, Jiang J, Paul D, Zhao H. Variance estimation and confidence intervals from genome-wide association studies through high-dimensional misspecified mixed model analysis Journal Of Statistical Planning And Inference 2022, 220: 15-23. DOI: 10.1016/j.jspi.2022.01.003.
  • A general kernel boosting framework integrating pathways for predictive modeling based on genomic dataZeng L, Yu Z, Zhang Y, Zhao H. A general kernel boosting framework integrating pathways for predictive modeling based on genomic data 2022, 1-8. DOI: 10.1145/3535508.3545526.
  • Advanced Genetic Study of Problematic Alcohol Use in > 1 Million Subjects From Multiple PopulationsZhou H, Kember R, Deak J, Dao C, Galimberti M, Levey D, Overstreet C, Xu K, Zhao H, Justice A, Stein M, Kranzler H, Gelernter J. Advanced Genetic Study of Problematic Alcohol Use in > 1 Million Subjects From Multiple Populations Biological Psychiatry 2022, 91: s68. DOI: 10.1016/j.biopsych.2022.02.189.
  • A Zero-Inflated Logistic Normal Multinomial Model for Extracting Microbial CompositionsZeng Y, Pang D, Zhao H, Wang T. A Zero-Inflated Logistic Normal Multinomial Model for Extracting Microbial Compositions Journal Of The American Statistical Association 2022, 1-14. DOI: 10.1080/01621459.2022.2044827.
  • MYOCARDIAL IMPACT OF CLONAL HEMATOPOIESIS OF INDETERMINATE POTENTIAL (CHIP) SECRETOMES IN CARDIO-ONCOLOGY PATIENTSKwan J, Vanoudenhove J, Vanoudenhove J, Halder S, Halder S, Biancon G, Jain K, Jain K, Chakraborty R, Lustberg M, Lustberg M, Pusztai L, Campbell S, Campbell S, Zhao H, Halene S, Hwa J. MYOCARDIAL IMPACT OF CLONAL HEMATOPOIESIS OF INDETERMINATE POTENTIAL (CHIP) SECRETOMES IN CARDIO-ONCOLOGY PATIENTS Journal Of The American College Of Cardiology 2022, 79: 1912. DOI: 10.1016/s0735-1097(22)02903-5.
  • Abstract P3-07-01: Young women with breast cancer and high risk family history but no high penetrance germline mutations have a higher load of rare high functional impact germline variants in cancer relevant genesRozenblit M, Qing T, Ye Y, Zhao H, Hofstatter E, Singh V, Reisenbichler E, Murray M, Pusztai L. Abstract P3-07-01: Young women with breast cancer and high risk family history but no high penetrance germline mutations have a higher load of rare high functional impact germline variants in cancer relevant genes Cancer Research 2022, 82: p3-07-01-p3-07-01. DOI: 10.1158/1538-7445.sabcs21-p3-07-01.
  • Abstract WP74: An Automated, Electronic Health Record-based Algorithm To Classify Ischemic Stroke EtiologySharma R, Lee H, Schwamm L, Kamel H, Sansing L, Kim J, Zhao H, Krumholz H, Sharma R. Abstract WP74: An Automated, Electronic Health Record-based Algorithm To Classify Ischemic Stroke Etiology Stroke 2022, 53: awp74-awp74. DOI: 10.1161/str.53.suppl_1.wp74.
  • Characterizing Spatiotemporal Transcriptome of the Human Brain Via Low-Rank Tensor DecompositionLiu T, Yuan M, Zhao H. Characterizing Spatiotemporal Transcriptome of the Human Brain Via Low-Rank Tensor Decomposition Statistics In Biosciences 2022, 14: 485-513. DOI: 10.1007/s12561-021-09331-5.
  • Nonparametric Functional Graphical Modeling Through Functional Additive Regression OperatorLee K, Li L, Li B, Zhao H. Nonparametric Functional Graphical Modeling Through Functional Additive Regression Operator Journal Of The American Statistical Association 2022, 1-15. DOI: 10.1080/01621459.2021.2006667.
  • Abstract 11594: Deep Learning of the Retina Enables Phenome- and Genome- Wide Analyses of the MicrovasculatureZekavat S, Raghu V, Trinder M, Ye Y, Pampana A, Urbut S, O'Regan D, Zhao H, Ellinor P, Segre A, Elze T, Wiggs J, Martone J, Adelman R, Zebardast N, Del Priore L, Wang J, Natarajan P. Abstract 11594: Deep Learning of the Retina Enables Phenome- and Genome- Wide Analyses of the Microvasculature Circulation 2021, 144: a11594-a11594. DOI: 10.1161/circ.144.suppl_1.11594.
  • Outcomes Stratification of Head and Neck Cancer Using Pre- and Post-Treatment DNA Methylation in Peripheral BloodQian D, Ulrich B, Peng G, Zhao H, Conneely K, Miller A, Bruner D, Eldridge R, Wommack E, Higgins K, Shin D, Saba N, Smith A, Burtness B, Park H, Stokes W, Beitler J, Xiao C. Outcomes Stratification of Head and Neck Cancer Using Pre- and Post-Treatment DNA Methylation in Peripheral Blood International Journal Of Radiation Oncology • Biology • Physics 2021, 111: s143-s144. DOI: 10.1016/j.ijrobp.2021.07.323.
  • 67. ADVANCED GENETIC STUDY OF PROBLEMATIC ALCOHOL USE IN MULTIPLE POPULATIONS IN > 1 MILLION SUBJECTSZhou H, Kember R, Deak J, Dao C, Galimberti M, Levey D, Overstreet C, Xu K, Zhao H, Justice A, Stein M, Kranzler H, Gelernter J. 67. ADVANCED GENETIC STUDY OF PROBLEMATIC ALCOHOL USE IN MULTIPLE POPULATIONS IN > 1 MILLION SUBJECTS European Neuropsychopharmacology 2021, 51: e77. DOI: 10.1016/j.euroneuro.2021.07.156.
  • Conditional Functional Graphical ModelsLee K, Ji D, Li L, Constable T, Zhao H. Conditional Functional Graphical Models Journal Of The American Statistical Association 2021, 1-15. DOI: 10.1080/01621459.2021.1924178.
  • Genomic and Phenomic Correlates of Suicidality Among US Veterans With Schizophrenia or Bipolar DisorderBigdeli T, Peterson R, Khankari N, Rajeevan N, Swann A, Sayward F, Meyers J, Li Y, Nielsen D, Wilkinson A, Graham D, O'Leary T, Zhao H, Cheung K, Mane S, Miller P, Brophy M, Program M, Przygodszki R, Siever L, Pyarajan S, Gleason T, Muralidhar S, Gaziano J, Concato J, Huang G, Fanous A, Aslan M, Kosten T, Harvey P. Genomic and Phenomic Correlates of Suicidality Among US Veterans With Schizophrenia or Bipolar Disorder Biological Psychiatry 2021, 89: s237-s238. DOI: 10.1016/j.biopsych.2021.02.596.
  • Integrative Functional Genomic Analysis of Human PTSD Molecular Pathology and RiskGirgenti M, Skarica M, Zhang J, Wang J, Friedman M, Zhao H, Krystal J. Integrative Functional Genomic Analysis of Human PTSD Molecular Pathology and Risk Biological Psychiatry 2021, 89: s12. DOI: 10.1016/j.biopsych.2021.02.050.
  • Statistical Methods for Analyzing Tree-Structured Microbiome DataWang T, Zhao H. Statistical Methods for Analyzing Tree-Structured Microbiome Data 2021, 193-220. DOI: 10.1007/978-3-030-73351-3_8.
  • eP037 Timing of newborn blood collection alters screening performance for metabolic disordersPeng G, Tang Y, Gandotra N, Cowan T, Zhao H, Scharfe C. eP037 Timing of newborn blood collection alters screening performance for metabolic disorders Molecular Genetics And Metabolism 2021, 132: s26. DOI: 10.1016/s1096-7192(21)00124-4.
  • Model-Based Microbiome Data Ordination: A Variational Approximation ApproachZeng Y, Zhao H, Wang T. Model-Based Microbiome Data Ordination: A Variational Approximation Approach Journal Of Computational And Graphical Statistics 2021, 30: 1036-1048. DOI: 10.1080/10618600.2021.1882467.
  • Transcriptome wide association studies: general framework and methodsXie Y, Shan N, Zhao H, Hou L. Transcriptome wide association studies: general framework and methods Quantitative Biology 2021, 0: 0. DOI: 10.15302/j-qb-020-0228.
  • 9: Evaluation of Aerosolized Epoprostenol in COVID-19 ARDS PatientsAmmar M, Gu S, Jiang W, Zhao H, Ammar A, Johnson J, Owusu K, Deshpande R, Siner J, Hwa J. 9: Evaluation of Aerosolized Epoprostenol in COVID-19 ARDS Patients Critical Care Medicine 2020, 49: 5-5. DOI: 10.1097/01.ccm.0000726064.02960.52.
  • A Set of Efficient Methods to Generate High-Dimensional Binary Data With Specified Correlation StructuresJiang W, Song S, Hou L, Zhao H. A Set of Efficient Methods to Generate High-Dimensional Binary Data With Specified Correlation Structures The American Statistician 2020, 75: 310-322. DOI: 10.1080/00031305.2020.1816213.
  • Insightful Data ScienceZhao H. Insightful Data Science Harvard Data Science Review 2020 DOI: 10.1162/99608f92.34d1d59e.
  • Genome-Wide Association Studies of Schizophrenia and Bipolar Disorder in a Diverse Cohort of US VeteransBigdeli T, Fanous A, Li Y, Rajeevan N, Sayward F, Investigators G, Investigators G, Zhao H, Brophy M, Pyarajan S, O'Leary T, Gleason T, Przygodszki R, Muralidhar S, Gaziano J, Program M, Concato J, Siever L, Aslan M, Harvey P. Genome-Wide Association Studies of Schizophrenia and Bipolar Disorder in a Diverse Cohort of US Veterans Biological Psychiatry 2020, 87: s178. DOI: 10.1016/j.biopsych.2020.02.468.
  • Transcriptomic Organization of Human Posttraumatic Stress DisorderGirgenti M, Wang J, Ji D, Cruz D, Group T, Program M, Stein M, Gelernter J, Young K, Huber B, Williamson D, Friedman M, Krystal J, Krystal J, Zhao H, Duman R. Transcriptomic Organization of Human Posttraumatic Stress Disorder Biological Psychiatry 2020, 87: s122. DOI: 10.1016/j.biopsych.2020.02.330.
  • Statistical Methods in Genome-Wide Association StudiesSun N, Zhao H. Statistical Methods in Genome-Wide Association Studies Annual Review Of Biomedical Data Science 2020, 3: 1-24. DOI: 10.1146/annurev-biodatasci-030320-041026.
  • Automated yeast cells segmentation and counting using a parallel U-Net based two-stage frameworkKong Y, Li H, Ren Y, Genchev G, Wang X, Zhao H, Xie Z, Lu H. Automated yeast cells segmentation and counting using a parallel U-Net based two-stage framework Optics Continuum 2020, 3: 982. DOI: 10.1364/osac.388082.
  • Modeling Species Specific Gene Expression Across Multiple Regions in the BrainDiao L, Zhu Y, Sestan N, Zhao H. Modeling Species Specific Gene Expression Across Multiple Regions in the Brain 2020, 3-22. DOI: 10.1007/978-3-030-33416-1_1.
  • GENOMICS OF SUICIDAL IDEATION AND BEHAVIOR IN VETERANS WITH SEVERE MENTAL ILLNESSHarvey P, Bigdeli T, Fanous A, Aslan M, Sun N, Zhao H. GENOMICS OF SUICIDAL IDEATION AND BEHAVIOR IN VETERANS WITH SEVERE MENTAL ILLNESS European Neuropsychopharmacology 2019, 29: s34. DOI: 10.1016/j.euroneuro.2019.07.069.
  • Abstract #4314 Pilot study of combined aerobic resistance exercise on fatigue and inflammation for patients with head and neck cancerXiao C, Beitler J, Higgins K, Zhu Y, Zhao H, Bruner D, Miller A, Gary R. Abstract #4314 Pilot study of combined aerobic resistance exercise on fatigue and inflammation for patients with head and neck cancer Brain Behavior And Immunity 2019, 81: 29. DOI: 10.1016/j.bbi.2019.08.099.
  • Improving Genetic Association Analysis through Integration of Functional Annotations of the Human GenomeLu Q, Zhao H. Improving Genetic Association Analysis through Integration of Functional Annotations of the Human Genome 2019, 679-30. DOI: 10.1002/9781119487845.ch24.
  • O2‐10‐03: LEVERAGING TISSUE SPECIFIC GENE EXPRESSION REGULATION TO IDENTIFY GENES ASSOCIATED WITH ALZHEIMER'S DISEASELiu W, Li M, Zhang W, Zhou G, Wu X, Wang J, Zhao H. O2‐10‐03: LEVERAGING TISSUE SPECIFIC GENE EXPRESSION REGULATION TO IDENTIFY GENES ASSOCIATED WITH ALZHEIMER'S DISEASE Alzheimer's & Dementia 2019, 15: p564-p565. DOI: 10.1016/j.jalz.2019.06.4507.
  • Phylogeny-based tumor subclone identification using a Bayesian feature allocation modelZeng L, Warren J, Zhao H. Phylogeny-based tumor subclone identification using a Bayesian feature allocation model The Annals Of Applied Statistics 2019, 13: 1212-1241. DOI: 10.1214/18-aoas1223.
  • Sparse principal component analysis with missing observationsPark S, Zhao H. Sparse principal component analysis with missing observations The Annals Of Applied Statistics 2019, 13: 1016-1042. DOI: 10.1214/18-aoas1220.
  • The Prognostic Value of Monocyte Count in Idiopathic Pulmonary Fibrosis: A Multi-Omic Cohort StudyScott M, Quinn K, Li Q, Carroll R, Chen S, Carns M, Aren K, Sun J, Koloms K, Lee J, Kropski J, Zhao H, Herzog E, Martinez F, Moore B, Hinchcliff M, Denny J, Kaminski N, Herazo-Maya J, Shah N, Khatri P. The Prognostic Value of Monocyte Count in Idiopathic Pulmonary Fibrosis: A Multi-Omic Cohort Study 2019, a7342-a7342. DOI: 10.1164/ajrccm-conference.2019.199.1_meetingabstracts.a7342.
  • S93. GENOMICS OF SUICIDAL IDEATION AND BEHAVIOR IN VETERANS WITH SCHIZOPHRENIA AND BIPOLAR ILLNESSHarvey P, Aslan M, Sun N, Zhao H, Concato J. S93. GENOMICS OF SUICIDAL IDEATION AND BEHAVIOR IN VETERANS WITH SCHIZOPHRENIA AND BIPOLAR ILLNESS Schizophrenia Bulletin 2019, 45: s342-s342. PMCID: PMC6455790, DOI: 10.1093/schbul/sbz020.638.
  • M25 GENETICS OF COGNITIVE FUNCTION IN SCHIZOPHRENIA AND BIPOLAR DISORDERHarvey P, Harvey P, Sun N, Lu Q, Lu Q, Hu Y, Hu Y, Li B, Chen Q, Chen Q, Aslan M, Radhakrishnan K, Radhakrishnan K, Cheung K, Li Y, Li Y, Sayward F, Rajeevan N, Zhao H, Gaziano M, Gaziano M, Concato J, Concato J. M25 GENETICS OF COGNITIVE FUNCTION IN SCHIZOPHRENIA AND BIPOLAR DISORDER European Neuropsychopharmacology 2019, 29: s967-s968. DOI: 10.1016/j.euroneuro.2017.08.332.
  • STUDIES ON ALCOHOL DEPENDENCE GENETICS FROM YALE-PENN AND THE MVPGelernter J, Sun N, Polimanti R, Zhou H, Levey D, Harrington K, Gaziano M, Pyarajan S, Aslan M, Sayward F, Li B, Farrer L, Zhao H, Concato J, Stein M. STUDIES ON ALCOHOL DEPENDENCE GENETICS FROM YALE-PENN AND THE MVP European Neuropsychopharmacology 2019, 29: s1035-s1036. DOI: 10.1016/j.euroneuro.2018.07.032.
  • RESULTS FROM THE USVA MVP PTSD COOPERATIVE STUDY GWAS: PCL TRAITSGelernter J, Sun N, Polimanti R, Zhou H, Pietrzak R, Levey D, Harrington K, Gaziano M, Pyarajan S, Radhakrishnan K, Hu Y, Li B, Zhao H, Concato J, Stein M. RESULTS FROM THE USVA MVP PTSD COOPERATIVE STUDY GWAS: PCL TRAITS European Neuropsychopharmacology 2019, 29: s1055-s1056. DOI: 10.1016/j.euroneuro.2018.07.071.
  • Initial Results From An Opioid Dependence Whole Exome Sequencing StudyGelernter J, Sherva R, Zhao H, Kranzler H, Farrer L. Initial Results From An Opioid Dependence Whole Exome Sequencing Study European Neuropsychopharmacology 2019, 29: s732. DOI: 10.1016/j.euroneuro.2017.06.056.
  • M31 GENOME-WIDE ASSOCIATION STUDY OF COMORBID ALCOHOL DEPENDENCE AND MAJOR DEPRESSIONZhou H, Polimanti R, Yang B, Wang Q, Han S, Sherva R, Nunez Y, Zhao H, Farrer L, Kranzler H, Gelernter J. M31 GENOME-WIDE ASSOCIATION STUDY OF COMORBID ALCOHOL DEPENDENCE AND MAJOR DEPRESSION European Neuropsychopharmacology 2019, 29: s971. DOI: 10.1016/j.euroneuro.2017.08.338.
  • SU137DIFFERENCES DETWEEN AUDIT-C AND AUD PHENOTYPES REVEALED BY GENOME-WIDE ANALYSISKember R, Zhou H, Justice A, Smith R, Tate J, Becker W, Concato J, Fiellin D, Xu K, Zhao H, Gelernter J, Kranzler H. SU137DIFFERENCES DETWEEN AUDIT-C AND AUD PHENOTYPES REVEALED BY GENOME-WIDE ANALYSIS European Neuropsychopharmacology 2019, 29: s1339-s1340. DOI: 10.1016/j.euroneuro.2018.08.501.
  • O3‐03‐06: CROSS‐TISSUE TRANSCRIPTOME‐WIDE ASSOCIATION META‐ANALYSIS IDENTIFIES NOVEL RISK GENES FOR LATE‐ONSET ALZHEIMER'S DISEASELu Q, Hu Y, Li M, Weng H, Wang J, Zekavat S, Yu Z, Li B, Muchnik S, Shi Y, Kunkle B, Mukherjee S, Natarajan P, Crane P, Zhao H. O3‐03‐06: CROSS‐TISSUE TRANSCRIPTOME‐WIDE ASSOCIATION META‐ANALYSIS IDENTIFIES NOVEL RISK GENES FOR LATE‐ONSET ALZHEIMER'S DISEASE Alzheimer's & Dementia 2018, 14: p1017-p1018. DOI: 10.1016/j.jalz.2018.06.2787.
  • F123. Genome-Wide Epigenetic Signatures of Major Depressive Disorder in WomenMontalvo-Ortiz J, Kranzler H, Zhao H, Zhang H, Gelernter J. F123. Genome-Wide Epigenetic Signatures of Major Depressive Disorder in Women Biological Psychiatry 2018, 83: s285. DOI: 10.1016/j.biopsych.2018.02.736.
  • 20. Child Abuse and Epigenetic Mechanisms of Disease RiskMontalvo-Ortiz J, Gelernter J, Wymbs N, Althoff R, Hudziak J, Zhao H, Kaufman J. 20. Child Abuse and Epigenetic Mechanisms of Disease Risk Biological Psychiatry 2018, 83: s8. DOI: 10.1016/j.biopsych.2018.02.037.
  • 159. GWAS of PTSD Re-experiencing Symptoms in the VA Million Veteran ProgramStein M, Gelernter J, Zhao H, Sun N, Pietrzak R, Harrington K, Cho K, Honerlaw J, Quaden R, Gaziano J, Concato J, MVP M. 159. GWAS of PTSD Re-experiencing Symptoms in the VA Million Veteran Program Biological Psychiatry 2018, 83: s64-s65. DOI: 10.1016/j.biopsych.2018.02.177.
  • Pivotal variable detection of the covariance matrix and its application to high-dimensional factor modelsZhao J, Zhao H, Zhu L. Pivotal variable detection of the covariance matrix and its application to high-dimensional factor models Statistics And Computing 2017, 28: 775-793. DOI: 10.1007/s11222-017-9762-6.
  • Constructing Predictive Microbial Signatures at Multiple Taxonomic LevelsWang T, Zhao H. Constructing Predictive Microbial Signatures at Multiple Taxonomic Levels Journal Of The American Statistical Association 2017, 112: 1022-1031. DOI: 10.1080/01621459.2016.1270213.
  • Abstract 1326: A pooled genome-wide association study of pancreatic cancer susceptibility loci in American JewsStreicher S, Klein A, Olson S, Kurtz R, DeWan A, Zhao H, Risch H. Abstract 1326: A pooled genome-wide association study of pancreatic cancer susceptibility loci in American Jews Cancer Research 2017, 77: 1326-1326. DOI: 10.1158/1538-7445.am2017-1326.
  • [O1–03–05]: ANNOTATION‐STRATIFIED GENETIC CORRELATION ANALYSIS IDENTIFIES SHARED AND DISTINCT GENETIC ARCHITECTURE OF LATE‐ONSET ALZHEIMER's DISEASE AND AMYOTROPHIC LATERAL SCLEROSISLu Q, Mukherjee S, Crane P, Zhao H. [O1–03–05]: ANNOTATION‐STRATIFIED GENETIC CORRELATION ANALYSIS IDENTIFIES SHARED AND DISTINCT GENETIC ARCHITECTURE OF LATE‐ONSET ALZHEIMER's DISEASE AND AMYOTROPHIC LATERAL SCLEROSIS Alzheimer's & Dementia 2017, 13: p193-p193. DOI: 10.1016/j.jalz.2017.07.049.
  • Dissecting Pathway Disturbances Using Network Topology and Multi-platform Genomics DataZhang Y, Linder M, Shojaie A, Ouyang Z, Shen R, Baggerly K, Baladandayuthapani V, Zhao H. Dissecting Pathway Disturbances Using Network Topology and Multi-platform Genomics Data Statistics In Biosciences 2017, 10: 86-106. DOI: 10.1007/s12561-017-9193-0.
  • Abstract 24Timberlake A, Choi J, Zaidi S, Lu Q, Nelson-Williams C, Brooks E, Bilguvar K, Tikhonova I, Mane S, Yang J, Sawh-Martinez R, Persing S, Zellner E, Loring E, Chuang C, Galm A, Hashim P, Steinbacher D, DiLuna M, Duncan C, Pelphrey K, Zhao H, Persing J, Lifton R. Abstract 24 Plastic & Reconstructive Surgery Global Open 2017, 5: 19-20. PMCID: PMC5417878, DOI: 10.1097/01.gox.0000516545.88372.7b.
  • T45 Genetic Factor Common To Schizophrenia And Hiv Infection Is Associated With Risky Sexual BehaviorWang Q, Polimanti R, Kranzler H, Farrer L, Zhao H, Gelernter J. T45 Genetic Factor Common To Schizophrenia And Hiv Infection Is Associated With Risky Sexual Behavior European Neuropsychopharmacology 2017, 27: s460-s461. DOI: 10.1016/j.euroneuro.2016.09.533.
  • Estimating a sparse reduction for general regression in high dimensionsWang T, Chen M, Zhao H, Zhu L. Estimating a sparse reduction for general regression in high dimensions Statistics And Computing 2016, 28: 33-46. DOI: 10.1007/s11222-016-9714-6.
  • On high-dimensional misspecified mixed model analysis in genome-wide association studyJiang J, Li C, Paul D, Yang C, Zhao H. On high-dimensional misspecified mixed model analysis in genome-wide association study The Annals Of Statistics 2016, 44: 2127-2160. DOI: 10.1214/15-aos1421.
  • O1‐03‐01: Integrative Analysis of Gwas Summary Data and Functional Annotations Highlights Signal Enrichment in Immune‐Related DNA Elements for Late‐Onset Alzheimer’s DiseaseLu Q, Mukherjee S, Crane P, Zhao H. O1‐03‐01: Integrative Analysis of Gwas Summary Data and Functional Annotations Highlights Signal Enrichment in Immune‐Related DNA Elements for Late‐Onset Alzheimer’s Disease Alzheimer's & Dementia 2016, 12: p176-p177. DOI: 10.1016/j.jalz.2016.06.304.
  • P3‐090: Integrative Analysis of GWAS Summary Data and Functional Annotations Identifies Additional Loci for Late‐Onset Alzheimer's DiseaseLu Q, Mukherjee S, Kunkle B, Crane P, Zhao H, Consortium T. P3‐090: Integrative Analysis of GWAS Summary Data and Functional Annotations Identifies Additional Loci for Late‐Onset Alzheimer's Disease Alzheimer's & Dementia 2016, 12: p854-p854. DOI: 10.1016/j.jalz.2016.06.1749.
  • PM296. SORCS2 regulates alcohol withdrawal severity and excitatory synaptic transmissionSmith A, Bolcho U, Phokaew C, Ovesen P, Yeo S, Jensen K, Diazgranados N, Zhao H, Farrer L, Goldman D, Glerup S, Kranzler H, Nykjær A, Gelernter J. PM296. SORCS2 regulates alcohol withdrawal severity and excitatory synaptic transmission The International Journal Of Neuropsychopharmacology 2016, 19: 5-6. PMCID: PMC5616232, DOI: 10.1093/ijnp/pyw041.296.
  • Speaker 4: Joel Gelernter, USASherva R, Wang Q, Kranzler H, Zhao H, Koesterer R, Herman A, Farrer L, Gelernter J. Speaker 4: Joel Gelernter, USA The International Journal Of Neuropsychopharmacology 2016, 19: 29-29. PMCID: PMC5616553, DOI: 10.1093/ijnp/pyw042.077.
  • Objective measurement and significance of PD-L1, B7-H3, B7-H4 and TILs in small cell lung cancer (SCLC).Schalper K, Carvajal-Hausdorf D, McLaughlin J, Altan M, Chiang A, Velcheti V, Kaftan E, Zhang J, Lu L, Rimm D, Han B, Lu H, Zhao H, Herbst R. Objective measurement and significance of PD-L1, B7-H3, B7-H4 and TILs in small cell lung cancer (SCLC). Journal Of Clinical Oncology 2016, 34: 8566-8566. DOI: 10.1200/jco.2016.34.15_suppl.8566.
  • Variable selection via additive conditional independenceLee K, Li B, Zhao H. Variable selection via additive conditional independence Journal Of The Royal Statistical Society Series B (Statistical Methodology) 2016, 78: 1037-1055. DOI: 10.1111/rssb.12150.
  • On high-dimensional misspecified mixed model analysis in genome-wide association studyJ. Jiang, C. Li, D. Paul, C. Yang, H. Zhao (2016) On high-dimensional misspecified mixed model analysis in genome-wide association study. Annals of Statistics, 44: 2127–2160.
  • Introduction to statistical methods in genome-wide association studiesYang C, Li C, Chung D, Chen M, Gelernter J, Zhao H. Introduction to statistical methods in genome-wide association studies 2015, 26-52. DOI: 10.1017/cbo9781107337459.005.
  • Statistical issues in binding site identification through CLIP-seqChen X, Chung D, Stefani G, Slack F, Zhao H. Statistical issues in binding site identification through CLIP-seq Statistics And Its Interface 2015, 8: 419-436. DOI: 10.4310/sii.2015.v8.n4.a2.
  • Low-Rank Modeling and Its Applications in Image AnalysisZhou X, Yang C, Zhao H, Yu W. Low-Rank Modeling and Its Applications in Image Analysis ACM Computing Surveys 2014, 47: 1-33. DOI: 10.1145/2674559.
  • Ttn as a likely causal gene for QTL of alcohol preference on mouse chromosome 2Wang L, Jiao Y, Huang Y, Bennett B, Williams R, Li D, Zhao H, Gelernter J, Kranzler H, Farrer L, Gu W. Ttn as a likely causal gene for QTL of alcohol preference on mouse chromosome 2 BMC Bioinformatics 2014, 15: p12. PMCID: PMC4196026, DOI: 10.1186/1471-2105-15-s10-p12.
  • Statistical Methods for the Analysis of Next Generation Sequencing Data from Paired Tumor-Normal SamplesChen M, Hou L, Zhao H. Statistical Methods for the Analysis of Next Generation Sequencing Data from Paired Tumor-Normal Samples 2014, 379-404. DOI: 10.1007/978-3-319-07212-8_19.
  • Detection boundary and Higher Criticism approach for rare and weak genetic effectsWu Z, Sun Y, He S, Cho J, Zhao H, Jin J. Detection boundary and Higher Criticism approach for rare and weak genetic effects The Annals Of Applied Statistics 2014, 8: 824-851. DOI: 10.1214/14-aoas724.
  • Signaling through the Adaptor Molecule MyD88 in CD4+ T Cells Is Required to Overcome Suppression by Regulatory T CellsSchenten D, Nish S, Yu S, Yan X, Lee H, Brodsky I, Pasman L, Yordy B, Wunderlich F, Brüning J, Zhao H, Medzhitov R. Signaling through the Adaptor Molecule MyD88 in CD4+ T Cells Is Required to Overcome Suppression by Regulatory T Cells Immunity 2014, 40: 814. DOI: 10.1016/j.immuni.2014.04.012.
  • A Genome-Wide Association Study on Obesity and Obesity-Related TraitsWang K, Li W, Zhang C, Wang Z, Glessner J, Grant S, Zhao H, Hakonarson H, Price R. A Genome-Wide Association Study on Obesity and Obesity-Related Traits 2014, 57-69. DOI: 10.1201/b16443-4.
  • Adaptive clinical trial designs to detect interaction between treatment and a dichotomous biomarkerZhu H, Hu F, Zhao H. Adaptive clinical trial designs to detect interaction between treatment and a dichotomous biomarker Canadian Journal Of Statistics 2013, 41: 525-539. DOI: 10.1002/cjs.11184.
  • PrefaceZhao H. Preface Statistics In Biosciences 2013, 5: 1-2. DOI: 10.1007/s12561-013-9088-7.
  • 975 Novel Associations of Uncommon Crohn's Disease Risk Alleles With Higher Frequencies in the Ashkenazi Jewish PopulationHui K, Zhang W, Bowen B, Haritunians T, Silverberg M, Rioux J, Katz S, Cheifetz A, Brant S, McGovern D, Zhao H, Duerr R, Peter I, Cho J. 975 Novel Associations of Uncommon Crohn's Disease Risk Alleles With Higher Frequencies in the Ashkenazi Jewish Population Gastroenterology 2013, 144: s-178. DOI: 10.1016/s0016-5085(13)60633-2.
  • Time course RNA-seq: A potential avenue with somewhat different approach in tandem of differential analysisOh S, Zhao H, Noonan J. Time course RNA-seq: A potential avenue with somewhat different approach in tandem of differential analysis 2012, 1: 580-587. DOI: 10.1109/cisis.2012.204.
  • Abstract 178: RCBTB1 Genotypes Modulate Smoking Effect on Carotid Intima-Media Thickness: A Finding from a Genome-Wide Interaction AnalysisDong C, Wang L, Cabral D, Beecham A, Blanton S, Zhao H, Rundek T, Sacco R. Abstract 178: RCBTB1 Genotypes Modulate Smoking Effect on Carotid Intima-Media Thickness: A Finding from a Genome-Wide Interaction Analysis Arteriosclerosis Thrombosis And Vascular Biology 2012, 32 DOI: 10.1161/atvb.32.suppl_1.a178.
  • Correction: A Genome-Wide Association Study on Obesity and Obesity-Related TraitsWang K, Li W, Zhang C, Wang Z, Glessner J, Grant S, Zhao H, Hakonarson H, Price R. Correction: A Genome-Wide Association Study on Obesity and Obesity-Related Traits PLOS ONE 2012, 7: 10.1371/annotation/a34ee94e-3e6a-48bd-a19e-398a4bb88580. PMCID: PMC3293772, DOI: 10.1371/annotation/a34ee94e-3e6a-48bd-a19e-398a4bb88580.
  • Erratum: Genome-Wide Association Study of Alcohol Dependence Implicates KIAA0040 on Chromosome 1qZuo L, Gelernter J, Zhang C, Zhao H, Lu L, Kranzler H, Malison R, Li C, Wang F, Zhang X, Deng H, Krystal J, Zhang F, Luo X. Erratum: Genome-Wide Association Study of Alcohol Dependence Implicates KIAA0040 on Chromosome 1q Neuropsychopharmacology 2011, 37: 581-582. PMCID: PMC3242324, DOI: 10.1038/npp.2011.271.
  • A permutation test approach to the choice of size k for the nearest neighbors classifierLai Y, Wu B, Zhao H. A permutation test approach to the choice of size k for the nearest neighbors classifier Journal Of Applied Statistics 2011, 38: 2289-2302. DOI: 10.1080/02664763.2010.547565.
  • Reverse Engineering of Gene Regulation Networks with an Application to the DREAM4 in silico Network ChallengeChun H, Kang J, Zhang X, Deng M, Ma H, Zhao H. Reverse Engineering of Gene Regulation Networks with an Application to the DREAM4 in silico Network Challenge 2011, 461-477. DOI: 10.1007/978-3-642-16345-6_22.
  • Bayesian Methods in Genomics and Proteomics StudiesSun N, Zhao H. Bayesian Methods in Genomics and Proteomics Studies 2010, 125-136. DOI: 10.1002/9780470669716.ch6.
  • The 24-bp CHIT1 Exon 10 Polymorphism Is Associated With Airway Hyperresponsiveness In A Population Of Autobody Shop WorkersOng P, Wisnewski A, Stowe M, Holm C, He X, Zheng W, Zhang C, Zhao H, Redlich C, Chupp G. The 24-bp CHIT1 Exon 10 Polymorphism Is Associated With Airway Hyperresponsiveness In A Population Of Autobody Shop Workers 2010, a4002-a4002. DOI: 10.1164/ajrccm-conference.2010.181.1_meetingabstracts.a4002.
  • Trends and Statistical Challenges in Genomewide Association StudiesSun N, Zhao H. Trends and Statistical Challenges in Genomewide Association Studies 2010, 283-308. DOI: 10.1002/9780470567647.ch12.
  • An Evaluation of Gene Module Concepts in the Interpretation of Gene Expression DataZhang X, Zhao H. An Evaluation of Gene Module Concepts in the Interpretation of Gene Expression Data 2010, 15: 331-349. DOI: 10.1007/978-1-84996-196-7_17.
  • Family-Based Association StudiesZhang K, Zhao H. Family-Based Association Studies 2009, 191-240. DOI: 10.1007/978-3-540-69264-5_7.
  • Protein Interaction Predictions from Diverse SourcesLiu Y, Kim I, Zhao H. Protein Interaction Predictions from Diverse Sources 2009, 1: 159-178. DOI: 10.1142/9789812837448_0007.
  • The promise of systems biology for deciphering the control of C 4 leaf development: transcriptome profiling of leaf cell typesNelson T, Tausta S, Gandotra N, Liu T, Ceserani T, Chen M, Jiao Y, Ma L, Deng X, Sun N, Holfold M, Li N, Zhao H. The promise of systems biology for deciphering the control of C 4 leaf development: transcriptome profiling of leaf cell types 2008, 317-332. DOI: 10.1142/9789812709523_0018.
  • Pathway‐Based Methods for Analyzing Microarray DataPang H, Kim I, Zhao H. Pathway‐Based Methods for Analyzing Microarray Data 2008, 355-384. DOI: 10.1002/9783527622818.ch13.
  • Is Subcellular Localization Informative for Modeling Protein-Protein Interaction Signal?Liu J, Zhao H, Tan J, Luo D, Yu W, Harner E, Shih W. Is Subcellular Localization Informative for Modeling Protein-Protein Interaction Signal? Research Letters In Signal Processing 2008, 2008: 1-5. DOI: 10.1155/2008/365152.
  • Bayesian Mass Spectra Peak Alignment from Mass Charge RatiosLiu J, Yu W, Wu B, Zhao H. Bayesian Mass Spectra Peak Alignment from Mass Charge Ratios Cancer Informatics 2008, 6: 117693510800600006. DOI: 10.4137/117693510800600006.
  • Chromosome MapsSpeed T, Zhao H. Chromosome Maps 2007, 1-39. DOI: 10.1002/9780470061619.ch1.
  • Identifying Stage-Specific Genes by Combining Information from Two Different Types of Oligonucleotide ArraysLiu Y, Sun N, Liu J, Chen L, McIntosh M, Zheng L, Zhao H. Identifying Stage-Specific Genes by Combining Information from Two Different Types of Oligonucleotide Arrays 2007, 59-74. DOI: 10.1007/978-0-387-34569-7_5.
  • MALDI-MS Data Analysis for Disease Biomarker DiscoveryYu W, Wu B, Liu J, Li X, Stone K, Williams K, Zhao H. MALDI-MS Data Analysis for Disease Biomarker Discovery 2006, 328: 199-216. DOI: 10.1385/1-59745-026-x:199.
  • Statistical Methods in ProteomicsYu W, Wu B, Huang T, Li X, Williams K, Zhao H. Statistical Methods in Proteomics 2006, 623-638. DOI: 10.1007/978-1-84628-288-1_34.
  • Serum proteins marker for early detection of ovarian cancerMor G, Visintin I, Zhao H, Schwartz P, Rutherford T, Yui L, Bray-Ward P, Ward D. Serum proteins marker for early detection of ovarian cancer Journal Of Clinical Oncology 2005, 23: 9508-9508. DOI: 10.1200/jco.2005.23.16_suppl.9508.
  • Erratum to “A statistical method to detect chromosomal regions with DNA copy number alterations using SNP-array-based CGH data” [Comput. Biol. Chem. 29 (2005) 47–54]Lai Y, Zhao H. Erratum to “A statistical method to detect chromosomal regions with DNA copy number alterations using SNP-array-based CGH data” [Comput. Biol. Chem. 29 (2005) 47–54] Computational Biology And Chemistry 2005, 29: 258. DOI: 10.1016/j.compbiolchem.2005.05.002.
  • Aligning Peaks Across Multiple Mass Spectrometry Data Sets Using A Scale-Space Based ApproachYu W, Li X, Zhao H. Aligning Peaks Across Multiple Mass Spectrometry Data Sets Using A Scale-Space Based Approach 2005, 126-127. DOI: 10.1109/csbw.2005.19.
  • Chromosome MapsSpeed T, Zhao H. Chromosome Maps 2003 DOI: 10.1002/0470022620.bbc01.
  • ON A FAMILY-BASED HAPLOTYPE PATTERN MINING METHOD FOR LINKAGE DISEQUILIBRIUM MAPPINGZHANG S, ZHANG K, LI J, ZHAO H. ON A FAMILY-BASED HAPLOTYPE PATTERN MINING METHOD FOR LINKAGE DISEQUILIBRIUM MAPPING 2001, 100-111. DOI: 10.1142/9789812799623_0010.
  • Family-based association studiesZhao H. Family-based association studies Statistical Methods In Medical Research 2000, 9: 563-587. DOI: 10.1177/096228020000900604.
  • Reply to Kong and NicolaeZhao H, Merikangas K, Kidd K. Reply to Kong and Nicolae American Journal Of Human Genetics 2000, 67: 1355-1356. DOI: 10.1016/s0002-9297(07)62968-1.
  • Stochastic modeling of the crossover process during meiosisZhao H, Speed T. Stochastic modeling of the crossover process during meiosis Communication In Statistics- Theory And Methods 1998, 27: 1557-1580. DOI: 10.1080/03610929808832177.
  • The Poisson-skip model of crossing-overLange K, Zhao H, Speed T. The Poisson-skip model of crossing-over The Annals Of Applied Probability 1997, 7: 299-313. DOI: 10.1214/aoap/1034625332.