Hongyu Zhao, PhD
Research & Publications
Biography
News
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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
- Cell-type-specific co-expression inference from single cell RNA-sequencing dataSu C, Xu Z, Shan X, Cai B, Zhao H, Zhang J. Cell-type-specific co-expression inference from single cell RNA-sequencing data. Nature Communications 2023, 14: 4846. PMID: 37563115, PMCID: PMC10415381, DOI: 10.1038/s41467-023-40503-7.
- A statistical framework to identify cell types whose genetically regulated proportions are associated with complex diseasesLiu W, Deng W, Chen M, Dong Z, Zhu B, Yu Z, Tang D, Sauler M, Lin C, Wain L, Cho M, Kaminski N, Zhao H. A statistical framework to identify cell types whose genetically regulated proportions are associated with complex diseases. PLOS Genetics 2023, 19: e1010825. PMID: 37523391, PMCID: PMC10414598, DOI: 10.1371/journal.pgen.1010825.
- Shared Genetic Architecture of Blood Eosinophil Counts and Asthma in UK BiobankLi B, Wang Y, Wang Z, Li X, Kay S, Chupp G, Zhao H, Gomez J. Shared Genetic Architecture of Blood Eosinophil Counts and Asthma in UK Biobank. ERJ Open Research 2023, 00291-2023. DOI: 10.1183/23120541.00291-2023.
- Shared genetic architecture of blood eosinophil counts and asthma in UK BiobankLi B, Wang Y, Wang Z, Li X, Kay S, Chupp G, Zhao H, Gomez J. Shared genetic architecture of blood eosinophil counts and asthma in UK Biobank. ERJ Open Research 2023, 9: 00291-2023. PMID: 37650091, PMCID: PMC10463033, DOI: 10.1183/23120541.00291-2023.
- eQTL studies: from bulk tissues to single cells.Zhang J, Zhao H. eQTL studies: from bulk tissues to single cells. Journal Of Genetics And Genomics 2023 PMID: 37207929, DOI: 10.1016/j.jgg.2023.05.003.
- Multi-trait genome-wide association analyses leveraging alcohol use disorder findings identify novel loci for smoking behaviors in the Million Veteran ProgramCheng Y, Dao C, Zhou H, Li B, Kember R, Toikumo S, Zhao H, Gelernter J, Kranzler H, Justice A, Xu K. Multi-trait genome-wide association analyses leveraging alcohol use disorder findings identify novel loci for smoking behaviors in the Million Veteran Program. Translational Psychiatry 2023, 13: 148. PMID: 37147289, PMCID: PMC10162964, DOI: 10.1038/s41398-023-02409-2.
- Single Cell Genomic Analysis Reveals Cell Type-Specific Molecular Signatures in the Human PTSD Prefrontal CortexGirgenti M, Zhang J, Skarica M, Hwang A, Xu K, Young K, Zhao H, Sestan N, Krystal J. Single Cell Genomic Analysis Reveals Cell Type-Specific Molecular Signatures in the Human PTSD Prefrontal Cortex. Biological Psychiatry 2023, 93: s11-s12. DOI: 10.1016/j.biopsych.2023.02.049.
- Tuning Parameters for Polygenic Risk Score Methods Using GWAS Summary Statistics from Training DataJiang W, Chen L, Girgenti M, Zhao H. Tuning Parameters for Polygenic Risk Score Methods Using GWAS Summary Statistics from Training Data. 2023 DOI: 10.21203/rs.3.rs-2939390/v1.
- Predicting Breast Cancer Risk for Women Veterans of African Ancestry in the Million Veteran ProgramLuoh S, Minnier J, Zhao H, Gao L. Predicting Breast Cancer Risk for Women Veterans of African Ancestry in the Million Veteran Program. Health Equity 2023, 7: 303-306. PMID: 37284538, PMCID: PMC10240329, DOI: 10.1089/heq.2023.0011.
- Author Correction: mbDenoise: microbiome data denoising using zero-inflated probabilistic principal components analysisZeng Y, Li J, Wei C, Zhao H, Wang T. Author Correction: mbDenoise: microbiome data denoising using zero-inflated probabilistic principal components analysis. Genome Biology 2023, 24: 84. PMID: 37085916, PMCID: PMC10120140, DOI: 10.1186/s13059-023-02940-x.
- Early breast cancer risk detection: a novel framework leveraging polygenic risk scores and machine learningTao L, Ye Y, Zhao H. Early breast cancer risk detection: a novel framework leveraging polygenic risk scores and machine learning. Journal Of Medical Genetics 2023, 60: 960-964. PMID: 37055164, DOI: 10.1136/jmg-2022-108582.
- Estimation on risk of spontaneous abortions by genomic disorders from a meta‐analysis of microarray results on large case series of pregnancy lossesPeng G, Zhou Q, Chai H, Wen J, Zhao H, Taylor H, Jiang Y, Li P. Estimation on risk of spontaneous abortions by genomic disorders from a meta‐analysis of microarray results on large case series of pregnancy losses. Molecular Genetics & Genomic Medicine 2023, 11: e2181. PMID: 37013615, PMCID: PMC10422064, DOI: 10.1002/mgg3.2181.
- Abstract 6762: Tumor immune microenvironment &genomic features of non-small cell lung carcinomas in patients with HIV (PWH)Desai S, Ranjan K, Salahuddin S, Yusuf R, Gu J, Tang D, Kong Y, Rajendran B, Zhao H, Kluger Y, Goldberg S, Emu B, Schalper K. Abstract 6762: Tumor immune microenvironment &genomic features of non-small cell lung carcinomas in patients with HIV (PWH). Cancer Research 2023, 83: 6762-6762. DOI: 10.1158/1538-7445.am2023-6762.
- A genome-wide association study of frailty identifies significant genetic correlation with neuropsychiatric, cardiovascular, and inflammation pathwaysYe Y, Noche R, Szejko N, Both C, Acosta J, Leasure A, Brown S, Sheth K, Gill T, Zhao H, Falcone G. A genome-wide association study of frailty identifies significant genetic correlation with neuropsychiatric, cardiovascular, and inflammation pathways. GeroScience 2023, 1-13. PMID: 36928559, DOI: 10.1007/s11357-023-00771-z.
- Live imaging reveals chromatin compaction transitions and dynamic transcriptional bursting during stem cell differentiation in vivoMay D, Yun S, Gonzalez D, Park S, Chen Y, Lathrop E, Cai B, Xin T, Zhao H, Wang S, Gonzalez L, Cockburn K, Greco V. Live imaging reveals chromatin compaction transitions and dynamic transcriptional bursting during stem cell differentiation in vivo. ELife 2023, 12: e83444. PMID: 36880644, PMCID: PMC10027315, DOI: 10.7554/elife.83444.
- OTTERS: a powerful TWAS framework leveraging summary-level reference dataDai Q, Zhou G, Zhao H, Võsa U, Franke L, Battle A, Teumer A, Lehtimäki T, Raitakari O, Esko T, Epstein M, Yang J. OTTERS: a powerful TWAS framework leveraging summary-level reference data. Nature Communications 2023, 14: 1271. PMID: 36882394, PMCID: PMC9992663, DOI: 10.1038/s41467-023-36862-w.
- NLRP6 deficiency expands a novel CD103+ B cell population that confers immune tolerance in NOD micePearson J, Peng J, Huang J, Yu X, Tai N, Hu Y, Sha S, Flavell R, Zhao H, Wong F, Wen L. NLRP6 deficiency expands a novel CD103+ B cell population that confers immune tolerance in NOD mice. Frontiers In Immunology 2023, 14: 1147925. PMID: 36911699, PMCID: PMC9995752, DOI: 10.3389/fimmu.2023.1147925.
- eQTL studies: from bulk tissues to single cellsZhang J, Zhao H. eQTL studies: from bulk tissues to single cells. Journal Of Genetics And Genomics 2023, 4 PMID: 36866231, PMCID: PMC9980190, DOI: 10.1016/j.jgg.2023.05.003.
- Whole-Exome Sequencing Analyses Support a Role of Vitamin D Metabolism in Ischemic StrokeXie Y, Acosta J, Ye Y, Demarais Z, Conlon C, Chen M, Zhao H, Falcone G. Whole-Exome Sequencing Analyses Support a Role of Vitamin D Metabolism in Ischemic Stroke. Stroke 2023, 54: 800-809. PMID: 36762557, PMCID: PMC10467223, DOI: 10.1161/strokeaha.122.040883.
- HBV-infected hepatocellular carcinoma can be robustly classified into three clinically relevant subgroups by a novel analytical protocolCheng Z, Li L, Zhang Y, Ren Y, Gu J, Wang X, Zhao H, Lu H. HBV-infected hepatocellular carcinoma can be robustly classified into three clinically relevant subgroups by a novel analytical protocol. Briefings In Bioinformatics 2023, 24: bbac601. PMID: 36736372, DOI: 10.1093/bib/bbac601.
- Identification of Novel, Replicable Genetic Risk Loci for Suicidal Thoughts and Behaviors Among US Military VeteransKimbrel N, Ashley-Koch A, Qin X, Lindquist J, Garrett M, Dennis M, Hair L, Huffman J, Jacobson D, Madduri R, Trafton J, Coon H, Docherty A, Mullins N, Ruderfer D, Harvey P, McMahon B, Oslin D, Beckham J, Hauser E, Hauser M, Agarwal K, Ashley-Koch A, Aslan M, Beckham J, Begoli E, Bhattacharya T, Brown B, Calhoun P, Cheung K, Choudhury S, Cliff A, Cohn J, Crivelli S, Cuellar-Hengartner L, Deangelis H, Dennis M, Dhaubhadel S, Finley P, Ganguly K, Garvin M, Gelernter J, Hair L, Harvey P, Hauser E, Hauser M, Hengartner N, Jacobson D, Jones P, Kainer D, Kaplan A, Katz I, Kember R, Kimbrel N, Kirby A, Ko J, Kolade B, Lagergren J, Lane M, Levey D, Levin D, Lindquist J, Liu X, Madduri R, Manore C, Martins S, McCarthy J, McDevitt-Cashman M, McMahon B, Miller I, Morrow D, Oslin D, Pavicic-Venegas M, Pestian J, Pyarajan S, Qin X, Rajeevan N, Ramsey C, Ribeiro R, Rodriguez A, Romero J, Santel D, Schaefferkoetter N, Shi Y, Stein M, Sullivan K, Sun N, Tamang S, Townsend A, Trafton J, Walker A, Wang X, Wangia-Anderson V, Yang R, Yoon H, Yoo S, Zamora-Resendiz R, Zhao H, Docherty A, Mullins N, Coleman J, Shabalin A, Kang J, Murnyak B, Wendt F, Adams M, Campos A, DiBlasi E, Fullerton J, Kranzler H, Bakian A, Monson E, Rentería M, Andreassen O, Bulik C, Edenberg H, Kessler R, Mann J, Nurnberger J, Pistis G, Streit F, Ursano R, Awasthi S, Bergen A, Berrettini W, Bohus M, Brandt H, Chang X, Chen H, Chen W, Christensen E, Crawford S, Crow S, Duriez P, Edwards A, Fernández-Aranda F, Fichter M, Galfalvy H, Gallinger S, Gandal M, Gorwood P, Guo Y, Hafferty J, Hakonarson H, Halmi K, Hishimoto A, Jain S, Jamain S, Jiménez-Murcia S, Johnson C, Kaplan A, Kaye W, Keel P, Kennedy J, Kim M, Klump K, Levey D, Li D, Liao S, Lieb K, Lilenfeld L, Lori A, Magistretti P, Marshall C, Mitchell J, Myers R, Okazaki S, Otsuka I, Pinto D, Powers A, Ramoz N, Ripke S, Roepke S, Rozanov V, Scherer S, Schmahl C, Sokolowski M, Starnawska A, Strober M, Su M, Thornton L, Treasure J, Ware E, Watson H, Witt S, Woodside D, Yilmaz Z, Zillich L, Agerbo E, Børglum A, Breen G, Demontis D, Erlangsen A, Esko T, Gelernter J, Glatt S, Hougaard D, Hwu H, Kuo P, Lewis C, Li Q, Liu C, Martin N, McIntosh A, Medland S, Mors O, Nordentoft M, Nurnberger J, Olsen C, Porteous D, Smith D, Stahl E, Stein M, Wasserman D, Werge T, Whiteman D, Willour V, Coon H, Ruderfer D, Dedert E, Elbogen E, Fairbank J, Hurley R, Kilts J, Martindale S, Marx C, McDonald S, Moore S, Morey R, Naylor J, Rowland J, Shura R, Swinkels C, Tupler L, Van Voorhees E, Yoash-Gantz R, Gaziano J, Muralidhar S, Ramoni R, Chang K, O’Donnell C, Tsao P, Breeling J, Hauser E, Sun Y, Huang G, Casas J, Moser J, Whitbourne S, Brewer J, Conner T, Argyres D, Stephens B, Brophy M, Humphries D, Selva L, Do N, Shayan S, Cho K, Churby L, Wilson P, McArdle R, Dellitalia L, Mattocks K, Harley J, Whittle J, Jacono F, Wells J, Gutierrez S, Gibson G, Hammer K, Kaminsky L, Villareal G, Kinlay S, Xu J, Hamner M, Mathew R, Bhushan S, Iruvanti P, Godschalk M, Ballas Z, Ivins D, Mastorides S, Moorman J, Gappy S, Klein J, Ratcliffe N, Florez H, Okusaga O, Murdoch M, Sriram P, Yeh S, Tandon N, Jhala D, Liangpunsakul S, Oursler K, Whooley M, Ahuja S, Constans J, Meyer P, Greco J, Rauchman M, Servatius R, Gaddy M, Wallbom A, Morgan T, Stapley T, Sherman S, Ross G, Strollo P, Boyko E, Meyer L, Gupta S, Huq M, Fayad J, Hung A, Lichy J, Hurley R, Robey B, Striker R. Identification of Novel, Replicable Genetic Risk Loci for Suicidal Thoughts and Behaviors Among US Military Veterans. JAMA Psychiatry 2023, 80: 135-145. PMID: 36515925, PMCID: PMC9857322, DOI: 10.1001/jamapsychiatry.2022.3896.
- Massively parallel knock-in engineering of human T cellsDai X, Park J, Du Y, Na Z, Lam S, Chow R, Renauer P, Gu J, Xin S, Chu Z, Liao C, Clark P, Zhao H, Slavoff S, Chen S. Massively parallel knock-in engineering of human T cells. Nature Biotechnology 2023, 41: 1239-1255. PMID: 36702900, DOI: 10.1038/s41587-022-01639-x.
- TP53-mediated clonal hematopoiesis confers increased risk for incident atherosclerotic diseaseZekavat S, Viana-Huete V, Matesanz N, Jorshery S, Zuriaga M, Uddin M, Trinder M, Paruchuri K, Zorita V, Ferrer-Pérez A, Amorós-Pérez M, Kunderfranco P, Carriero R, Greco C, Aroca-Crevillen A, Hidalgo A, Damrauer S, Ballantyne C, Niroula A, Gibson C, Pirruccello J, Griffin G, Ebert B, Libby P, Fuster V, Zhao H, Ghassemi M, Natarajan P, Bick A, Fuster J, Klarin D. TP53-mediated clonal hematopoiesis confers increased risk for incident atherosclerotic disease. Nature Cardiovascular Research 2023, 2: 144-158. PMID: 36949957, PMCID: PMC10026701, DOI: 10.1038/s44161-022-00206-6.
- A novel Bayesian framework for harmonizing information across tissues and studies to increase cell type deconvolution accuracy.Deng W, Li B, Wang J, Jiang W, Yan X, Li N, Vukmirovic M, Kaminski N, Wang J, Zhao H. A novel Bayesian framework for harmonizing information across tissues and studies to increase cell type deconvolution accuracy. Briefings In Bioinformatics 2023, 24 PMID: 36631398, PMCID: PMC9851324, DOI: 10.1093/bib/bbac616.
- Targeting ATAD3A-PINK1-mitophagy axis overcomes chemoimmunotherapy resistance by redirecting PD-L1 to mitochondriaXie X, Yang Y, Wang Q, Liu H, Fang X, Li C, Jiang Y, Wang S, Zhao H, Miao J, Ding S, Liu X, Yao X, Yang W, Jiang J, Shao Z, Jin G, Bian X. Targeting ATAD3A-PINK1-mitophagy axis overcomes chemoimmunotherapy resistance by redirecting PD-L1 to mitochondria. Cell Research 2023, 33: 215-228. PMID: 36627348, PMCID: PMC9977947, DOI: 10.1038/s41422-022-00766-z.
- Association of Gulf War Illness with Characteristics in Deployed vs. Non-Deployed Gulf War Era Veterans in the Cooperative Studies Program 2006/Million Veteran Program 029 Cohort: A Cross-Sectional AnalysisDuong L, Djotsa A, Vahey J, Steele L, Quaden R, Harrington K, Ahmed S, Polimanti R, Streja E, Gaziano J, Concato J, Zhao H, Radhakrishnan K, Hauser E, Helmer D, Aslan M, Gifford E. Association of Gulf War Illness with Characteristics in Deployed vs. Non-Deployed Gulf War Era Veterans in the Cooperative Studies Program 2006/Million Veteran Program 029 Cohort: A Cross-Sectional Analysis. International Journal Of Environmental Research And Public Health 2022, 20: 258. PMID: 36612580, PMCID: PMC9819371, DOI: 10.3390/ijerph20010258.
- 492. Dectin-1 Stimulation Promotes a Distinct Inflammatory Signature in the Setting of Aging and HIV-infectionZapata H, Kumar A, Wang J, Shaw A, Zhou H, Radcliffe C, Barakat L, Wyk B, Allore H, Zhao H, Tsang S, Manager D, Mohanty S. 492. Dectin-1 Stimulation Promotes a Distinct Inflammatory Signature in the Setting of Aging and HIV-infection. Open Forum Infectious Diseases 2022, 9: ofac492.550. PMCID: PMC9751937, DOI: 10.1093/ofid/ofac492.550.
- Benchmarking automated cell type annotation tools for single-cell ATAC-seq dataWang Y, Sun X, Zhao H. Benchmarking automated cell type annotation tools for single-cell ATAC-seq data. Frontiers In Genetics 2022, 13: 1063233. PMID: 36583014, PMCID: PMC9792779, DOI: 10.3389/fgene.2022.1063233.
- An unbiased kinship estimation method for genetic data analysisJiang W, Zhang X, Li S, Song S, Zhao H. An unbiased kinship estimation method for genetic data analysis. BMC Bioinformatics 2022, 23: 525. PMID: 36474154, PMCID: PMC9727941, DOI: 10.1186/s12859-022-05082-2.
- SDPRX: A statistical method for cross-population prediction of complex traitsZhou G, Chen T, Zhao H. SDPRX: A statistical method for cross-population prediction of complex traits. American Journal Of Human Genetics 2022, 110: 13-22. PMID: 36460009, PMCID: PMC9892700, DOI: 10.1016/j.ajhg.2022.11.007.
- Cell Cycle and Senescence Regulation by Podocyte Histone Deacetylase 1 and 2Rangel P, Cross E, Liu C, Pedigo C, Tian X, Gutiérrez-Calabrés E, Nagata S, Priyadarshini A, Lerner G, Bunda P, Perincheri S, Gu J, Zhao H, Wang Y, Inoue K, Ishibe S. Cell Cycle and Senescence Regulation by Podocyte Histone Deacetylase 1 and 2. Journal Of The American Society Of Nephrology 2022, 34: 433-450. PMID: 36414418, PMCID: PMC10103311, DOI: 10.1681/asn.2022050598.
- Outcomes Stratification of Head and Neck Cancer Using Pre- and Post-treatment DNA Methylation From 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 From Peripheral Blood. International Journal Of Radiation Oncology • Biology • Physics 2022, 115: 1217-1228. PMID: 36410685, DOI: 10.1016/j.ijrobp.2022.11.009.
- Fibromuscular Dysplasia and Abdominal Aortic Aneurysms Are Dimorphic Sex-Specific Diseases With Shared Complex Genetic ArchitectureKatz A, Yang M, Levin M, Tcheandjieu C, Mathis M, Hunker K, Blackburn S, Eliason J, Coleman D, Fendrikova-Mahlay N, Gornik H, Karmakar M, Hill H, Xu C, Zawistowski M, Brummett C, Zoellner S, Zhou X, O’Donnell C, Douglas J, Assimes T, Tsao P, Li J, Damrauer S, Stanley J, Ganesh S, Gaziano J, Muralidhar S, Ramoni R, Beckham J, Chang K, O’Donnell C, Tsao P, Breeling J, Huang G, Casas J, Muralidhar S, Moser J, Whitbourne S, Brewer J, Aslan M, Connor T, Argyres D, Tsao P, Gaziano J, Stephens B, Brophy M, Humphries D, Selva L, Do N, Shayan S, Cho K, Churby L, O’Donnell C, O’Donnell C, Pyarajan S, Tsao P, Cho K, DuVall S, Pyarajan S, Hauser E, Sun Y, Zhao H, Wilson P, McArdle R, Dellitalia L, Mattocks K, Harley J, Whittle J, Jacono F, Beckham J, Wells J, Gutierrez S, Gibson G, Hammer K, Kaminsky L, Villareal G, Kinlay S, Xu J, Hamner M, Mathew R, Bhushan S, Iruvanti P, Godschalk M, Ballas Z, Ivins D, Mastorides S, Moorman J, Gappy S, Klein J, Ratcliffe N, Florez H, Okusaga O, Murdoch M, Sriram P, Yeh S, Tandon N, Jhala D, Aguayo S, Cohen D, Sharma S, Liangpunsakul S, Oursler K, Whooley M, Ahuja S, Constans J, Meyer P, Greco J, Rauchman M, Servatius R, Gaddy M, Wallbom A, Morgan T, Stapley T, Sherman S, Ross G, Tsao P, Strollo P, Boyko E, Meyer L, Gupta S, Huq M, Fayad J, Hung A, Lichy J, Hurley R, Robey B, Striker R. Fibromuscular Dysplasia and Abdominal Aortic Aneurysms Are Dimorphic Sex-Specific Diseases With Shared Complex Genetic Architecture. Circulation Genomic And Precision Medicine 2022, 15: e003496. PMID: 36374587, PMCID: PMC9772208, DOI: 10.1161/circgen.121.003496.
- Genome-Wide Investigation of Maximum Habitual Alcohol Intake in US Veterans in Relation to Alcohol Consumption Traits and Alcohol Use DisorderDeak JD, Levey DF, Wendt FR, Zhou H, Galimberti M, Kranzler HR, Gaziano JM, Stein MB, Polimanti R, Gelernter J, Muralidhar S, Moser J, Deen J, Gaziano J, Beckham J, Chang K, Tsao P, Luoh S, Casas J, Churby L, Whitbourne S, Brewer J, Brophy M, Selva L, Shayan S, Cho K, Pyarajan S, DuVall S, Connor T, Argyres D, Aslan M, Stephens B, Concato J, Gelernter J, Gleason T, Huang G, Koenen K, Marx C, Radhakrishnan K, Schork N, Stein M, Zhao H, Kaufman J, Nunez Y, Pietrzak R, Beck D, Cissell S, Crutchfield P, Lance W, Cheung K, Li Y, Sun N, Chen Q, Rajeevan N, Sayward F, Gagnon D, Harrington K, Quaden R, O'Leary T, Ramoni R. Genome-Wide Investigation of Maximum Habitual Alcohol Intake in US Veterans in Relation to Alcohol Consumption Traits and Alcohol Use Disorder. JAMA Network Open 2022, 5: e2238880. PMID: 36301540, PMCID: PMC9614582, DOI: 10.1001/jamanetworkopen.2022.38880.
- 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.
- Sex-specific genetic association between psychiatric disorders and cognition, behavior and brain imaging in children and adultsGui Y, Zhou X, Wang Z, Zhang Y, Wang Z, Zhou G, Zhao Y, Liu M, Lu H, Zhao H. Sex-specific genetic association between psychiatric disorders and cognition, behavior and brain imaging in children and adults. Translational Psychiatry 2022, 12: 347. PMID: 36028495, PMCID: PMC9418275, DOI: 10.1038/s41398-022-02041-6.
- The Relationship of Attention-Deficit/Hyperactivity Disorder With Posttraumatic Stress Disorder: A Two-Sample Mendelian Randomization and Population-Based Sibling Comparison StudyWendt FR, Garcia-Argibay M, Cabrera-Mendoza B, Valdimarsdóttir UA, Gelernter J, Stein MB, Nivard MG, Maihofer AX, Consortium P, Maihofer A, Choi K, Coleman J, Daskalakis N, Denckla C, Ketema E, Morey R, Polimanti R, Ratanatharathorn A, Torres K, Wingo A, Zai C, Aiello A, Almli L, Amstadter A, Andersen S, Andreassen O, Arbisi P, Ashley-Koch A, Austin S, Avdibegovic E, Borglum A, Babic D, Bækvad-Hansen M, Baker D, Beckham J, Bierut L, Bisson J, Boks M, Bolger E, Bradley B, Brashear M, Breen G, Bryant R, Bustamante A, Bybjerg-Grauholm J, Calabrese J, Caldas-de-Almeida J, Chen C, Dale A, Dalvie S, Deckert J, Delahanty D, Dennis M, Disner S, Domschke K, Duncan L, Kulenovic A, Erbes C, Evans A, Farrer L, Feeny N, Flory J, Forbes D, Franz C, Galea S, Garrett M, Gautam A, Gelaye B, Gelernter J, Geuze E, Gillespie C, Uka A, Gordon S, Guffanti G, Hammamieh R, Hauser M, Heath A, Hemmings S, Hougaard D, Jakovljevic M, Jett M, Johnson E, Jones I, Jovanovic T, Qin X, Karstoft K, Kaufman M, Kessler R, Khan A, Kimbrel N, King A, Koen N, Kranzler H, Kremen W, Lawford B, Lebois L, Lewis C, Liberzon I, Linnstaedt S, Logue M, Lori A, Lugonja B, Luykx J, Lyons M, Maples-Keller J, Marmar C, Martin N, Maurer D, Mavissakalian M, McFarlane A, McGlinchey R, McLaughlin K, McLean S, Mehta D, Mellor R, Michopoulos V, Milberg W, Miller M, Morris C, Mors O, Mortensen P, Nelson E, Nordentoft M, Norman S, O’Donnell M, Orcutt H, Panizzon M, Peters E, Peterson A, Peverill M, Pietrzak R, Polusny M, Rice J, Risbrough V, Roberts A, Rothbaum A, Rothbaum B, Roy-Byrne P, Ruggiero K, Rung A, Rutten B, Saccone N, Sanchez S, Schijven D, Seedat S, Seligowski A, Seng J, Sheerin C, Silove D, Smith A, Smoller J, Sponheim S, Stein D, Stevens J, Teicher M, Thompson W, Trapido E, Uddin M, Ursano R, van den Heuvel L, Van Hooff M, Vermetten E, Vinkers C, Voisey J, Wang Y, Wang Z, Werge T, Williams M, Williamson D, Winternitz S, Wolf C, Wolf E, Yehuda R, Young K, Young R, Zhao H, Zoellner L, Haas M, Lasseter H, Provost A, Salem R, Sebat J, Shaffer R, Wu T, Ripke S, Daly M, Ressler K, Koenen K, Stein M, Nievergelt C, Nievergelt C, Larsson H, Mattheisen M, Polimanti R, Meier S. The Relationship of Attention-Deficit/Hyperactivity Disorder With Posttraumatic Stress Disorder: A Two-Sample Mendelian Randomization and Population-Based Sibling Comparison Study. Biological Psychiatry 2022, 93: 362-369. PMID: 36335070, PMCID: PMC10496427, DOI: 10.1016/j.biopsych.2022.08.012.
- 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.
- Additive Conditional Independence for Large and Complex Biological StructuresLee K, Li B, Zhao H. Additive Conditional Independence for Large and Complex Biological Structures. 2022, 153-171. DOI: 10.1007/978-3-662-65902-1_8.
- Association of Kidney Comorbidities and Acute Kidney Failure With Unfavorable Outcomes After COVID-19 in Individuals With the Sickle Cell TraitVerma A, Huffman JE, Gao L, Minnier J, Wu WC, Cho K, Ho YL, Gorman BR, Pyarajan S, Rajeevan N, Garcon H, Joseph J, McGeary JE, Suzuki A, Reaven PD, Wan ES, Lynch JA, Petersen JM, Meigs JB, Freiberg MS, Gatsby E, Lynch KE, Zekavat SM, Natarajan P, Dalal S, Jhala DN, Arjomandi M, Bonomo RA, Thompson TK, Pathak GA, Zhou JJ, Donskey CJ, Madduri RK, Wells QS, Gelernter J, Huang RDL, Polimanti R, Chang KM, Liao KP, Tsao PS, Sun YV, Wilson PWF, O’Donnell C, Hung AM, Gaziano JM, Hauger RL, Iyengar SK, Luoh SW, Muralidhar S, Beckham J, Moser J, Thomann L, Garcon H, Kosik N, Damrauer S, Assimes T, Roussos P, Striker R, Tuteja S, DuVall S, Lynch K, Gatsby E, Ramoni R, Breeling J, Huang G, Whitbourne S, Brewer J, Aslan M, Connor T, Argyres D, Stephens B, Brophy M, Humphries D, Selva L, Do N, Shayan S, Churby L, Hauser E, Zhao H, Wilson P, McArdle R, Dellitalia L, Mattocks K, Harley J, Whittle J, Jacono F, Wells J, Gutierrez S, Gibson G, Hammer K, Kaminsky L, Villareal G, Kinlay S, Xu J, Hamner M, Mathew R, Bhushan S, Iruvanti P, Godschalk M, Ballas Z, Ivins D, Mastorides S, Moorman J, Gappy S, Klein J, Ratcliffe N, Florez H, Okusaga O, Murdoch M, Sriram P, Yeh S, Tandon N, Jhala D, Aguayo S, Cohen D, Sharma S, Liangpunsakul S, Oursler K, Whooley M, Ahuja S, Constans J, Meyer P, Greco J, Rauchman M, Servatius R, Gaddy M, Wallbom A, Morgan T, Stapley T, Sherman S, Ross G, Tsao P, Strollo P, Boyko E, Meyer L, Gupta S, Huq M, Fayad J, Hung A, Lichy J, Hurley R, Robey B. Association of Kidney Comorbidities and Acute Kidney Failure With Unfavorable Outcomes After COVID-19 in Individuals With the Sickle Cell Trait. JAMA Internal Medicine 2022, 182: 796-804. PMID: 35759254, PMCID: PMC9237798, DOI: 10.1001/jamainternmed.2022.2141.
- Mendelian randomization for causal inference accounting for pleiotropy and sample structure using genome-wide summary statisticsHu X, Zhao J, Lin Z, Wang Y, Peng H, Zhao H, Wan X, Yang C. Mendelian randomization for causal inference accounting for pleiotropy and sample structure using genome-wide summary statistics. Proceedings Of The National Academy Of Sciences Of The United States Of America 2022, 119: e2106858119. PMID: 35787050, PMCID: PMC9282238, DOI: 10.1073/pnas.2106858119.
- Genetically‐Proxied Levels of Vitamin D and Risk of Intracerebral HemorrhageSzejko N, Acosta JN, Both CP, Leasure A, Matouk C, Sansing L, Gill TM, Hongyu Z, Sheth K, Falcone GJ. Genetically‐Proxied Levels of Vitamin D and Risk of Intracerebral Hemorrhage. Journal Of The American Heart Association 2022, 11: e024141. PMID: 35730641, PMCID: PMC9333362, DOI: 10.1161/jaha.121.024141.
- Glaucoma Genetic Risk Scores in the Million Veteran ProgramWaksmunski A, Kinzy T, Cruz L, Nealon C, Halladay C, Simpson P, Canania R, Anthony S, Roncone D, Rogers L, Leber J, Dougherty J, Greenberg P, Sullivan J, Wu W, Iyengar S, Crawford D, Peachey N, Bailey J, Gaziano J, Ramoni R, Breeling J, Chang K, Huang G, Muralidhar S, O’Donnell C, Tsao P, Muralidhar S, Moser J, Whitbourne S, Brewer J, Concato J, Warren S, Argyres D, Tsao P, Stephens B, Brophy M, Humphries D, Do N, Shayan S, Nguyen X, O’Donnell C, Pyarajan S, Cho K, Pyarajan S, Hauser E, Sun Y, Zhao H, Wilson P, McArdle R, Dellitalia L, Harley J, Whittle J, Beckham J, Wells J, Gutierrez S, Gibson G, Kaminsky L, Villareal G, Kinlay S, Xu J, Hamner M, Haddock K, Bhushan S, Iruvanti P, Godschalk M, Ballas Z, Buford M, Mastorides S, Klein J, Ratcliffe N, Florez H, Swann A, Murdoch M, Sriram P, Yeh S, Washburn R, Jhala D, Aguayo S, Cohen D, Sharma S, Callaghan J, Oursler K, Whooley M, Ahuja S, Gutierrez A, Schifman R, Greco J, Rauchman M, Servatius R, Oehlert M, Wallbom A, Fernando R, Morgan T, Stapley T, Sherman S, Anderson G, Tsao P, Sonel E, Boyko E, Meyer L, Gupta S, Fayad J, Hung A, Lichy J, Hurley R, Robey B, Striker R. Glaucoma Genetic Risk Scores in the Million Veteran Program. Ophthalmology 2022, 129: 1263-1274. PMID: 35718050, PMCID: PMC9997524, DOI: 10.1016/j.ophtha.2022.06.012.
- SCADIE: simultaneous estimation of cell type proportions and cell type-specific gene expressions using SCAD-based iterative estimating procedureTang D, Park S, Zhao H. SCADIE: simultaneous estimation of cell type proportions and cell type-specific gene expressions using SCAD-based iterative estimating procedure. Genome Biology 2022, 23: 129. PMID: 35706040, PMCID: PMC9199219, DOI: 10.1186/s13059-022-02688-w.
- Hemodynamic differences between women and men with elevated blood pressure in China: A non-invasive assessment of 45,082 adults using impedance cardiographyCaraballo C, Mahajan S, Gu J, Lu Y, Spatz ES, Dreyer RP, Zhang M, Sun N, Ren Y, Zheng X, Zhao H, Lu H, J. Z, Krumholz HM. Hemodynamic differences between women and men with elevated blood pressure in China: A non-invasive assessment of 45,082 adults using impedance cardiography. PLOS ONE 2022, 17: e0269777. PMID: 35700163, PMCID: PMC9197037, DOI: 10.1371/journal.pone.0269777.
- Network assisted analysis of de novo variants using protein-protein interaction information identified 46 candidate genes for congenital heart diseaseXie Y, Jiang W, Dong W, Li H, Jin SC, Brueckner M, Zhao H. Network assisted analysis of de novo variants using protein-protein interaction information identified 46 candidate genes for congenital heart disease. PLOS Genetics 2022, 18: e1010252. PMID: 35671298, PMCID: PMC9205499, DOI: 10.1371/journal.pgen.1010252.
- phyloMDA: an R package for phylogeny-aware microbiome data analysisLiu T, Zhou C, Wang H, Zhao H, Wang T. phyloMDA: an R package for phylogeny-aware microbiome data analysis. BMC Bioinformatics 2022, 23: 213. PMID: 35668363, PMCID: PMC9169257, DOI: 10.1186/s12859-022-04744-5.
- Pan-Cancer Single-Cell Analysis Reveals the Core Factors and Pathway in Specific Cancer Stem Cells of Upper Gastrointestinal CancerLi L, Zhang Y, Ren Y, Cheng Z, Zhang Y, Wang X, Zhao H, Lu H. Pan-Cancer Single-Cell Analysis Reveals the Core Factors and Pathway in Specific Cancer Stem Cells of Upper Gastrointestinal Cancer. Frontiers In Bioengineering And Biotechnology 2022, 10: 849798. PMID: 35646860, PMCID: PMC9136039, DOI: 10.3389/fbioe.2022.849798.
- 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.
- Leveraging LD eigenvalue regression to improve the estimation of SNP heritability and confounding inflationSong S, Jiang W, Zhang Y, Hou L, Zhao H. Leveraging LD eigenvalue regression to improve the estimation of SNP heritability and confounding inflation. American Journal Of Human Genetics 2022, 109: 802-811. PMID: 35421325, PMCID: PMC9118121, DOI: 10.1016/j.ajhg.2022.03.013.
- A Manifold Proximal Linear Method for Sparse Spectral Clustering with Application to Single-Cell RNA Sequencing Data AnalysisWang Z, Liu B, Chen S, Ma S, Xue L, Zhao H. A Manifold Proximal Linear Method for Sparse Spectral Clustering with Application to Single-Cell RNA Sequencing Data Analysis. INFORMS Journal On Optimization 2022, 4: 200-214. DOI: 10.1287/ijoo.2021.0064.
- 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, Halder S, Biancon G, Jain K, Chakraborty R, Lustberg M, Pusztai L, 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.
- 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. PMID: 37089275, PMCID: PMC10121196, DOI: 10.1016/j.jspi.2022.01.003.
- Single-cell multi-omics reveals dyssynchrony of the innate and adaptive immune system in progressive COVID-19Unterman A, Sumida TS, Nouri N, Yan X, Zhao AY, Gasque V, Schupp JC, Asashima H, Liu Y, Cosme C, Deng W, Chen M, Raredon MSB, Hoehn KB, Wang G, Wang Z, DeIuliis G, Ravindra NG, Li N, Castaldi C, Wong P, Fournier J, Bermejo S, Sharma L, Casanovas-Massana A, Vogels CBF, Wyllie AL, Grubaugh ND, Melillo A, Meng H, Stein Y, Minasyan M, Mohanty S, Ruff WE, Cohen I, Raddassi K, Niklason L, Ko A, Montgomery R, Farhadian S, Iwasaki A, Shaw A, van Dijk D, Zhao H, Kleinstein S, Hafler D, Kaminski N, Dela Cruz C. Single-cell multi-omics reveals dyssynchrony of the innate and adaptive immune system in progressive COVID-19. Nature Communications 2022, 13: 440. PMID: 35064122, PMCID: PMC8782894, DOI: 10.1038/s41467-021-27716-4.
- 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, 118: 1718-1732. 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.
- M-DATA: A statistical approach to jointly analyzing de novo mutations for multiple traitsXie Y, Li M, Dong W, Jiang W, Zhao H. M-DATA: A statistical approach to jointly analyzing de novo mutations for multiple traits. PLOS Genetics 2021, 17: e1009849. PMID: 34735430, PMCID: PMC8568192, DOI: 10.1371/journal.pgen.1009849.
- 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.
- Dissecting the Shared Genetic Architecture of Suicide Attempt, Psychiatric Disorders, and Known Risk FactorsMullins N, Kang J, Campos AI, Coleman JRI, Edwards AC, Galfalvy H, Levey DF, Lori A, Shabalin A, Starnawska A, Su MH, Watson HJ, Adams M, Awasthi S, Gandal M, Hafferty JD, Hishimoto A, Kim M, Okazaki S, Otsuka I, Ripke S, Ware EB, Bergen AW, Berrettini WH, Bohus M, Brandt H, Chang X, Chen WJ, Chen HC, Crawford S, Crow S, DiBlasi E, Duriez P, Fernández-Aranda F, Fichter MM, Gallinger S, Glatt SJ, Gorwood P, Guo Y, Hakonarson H, Halmi KA, Hwu HG, Jain S, Jamain S, Jiménez-Murcia S, Johnson C, Kaplan AS, Kaye WH, Keel PK, Kennedy JL, Klump KL, Li D, Liao SC, Lieb K, Lilenfeld L, Liu CM, Magistretti PJ, Marshall CR, Mitchell JE, Monson ET, Myers RM, Pinto D, Powers A, Ramoz N, Roepke S, Rozanov V, Scherer SW, Schmahl C, Sokolowski M, Strober M, Thornton LM, Treasure J, Tsuang MT, Witt SH, Woodside DB, Yilmaz Z, Zillich L, Adolfsson R, Agartz I, Air TM, Alda M, Alfredsson L, Andreassen OA, Anjorin A, Appadurai V, Artigas M, Van der Auwera S, Azevedo MH, Bass N, Bau CHD, Baune BT, Bellivier F, Berger K, Biernacka JM, Bigdeli TB, Binder EB, Boehnke M, Boks MP, Bosch R, Braff DL, Bryant R, Budde M, Byrne EM, Cahn W, Casas M, Castelao E, Cervilla JA, Chaumette B, Cichon S, Corvin A, Craddock N, Craig D, Degenhardt F, Djurovic S, Edenberg HJ, Fanous AH, Foo JC, Forstner AJ, Frye M, Fullerton JM, Gatt JM, Gejman PV, Giegling I, Grabe HJ, Green MJ, Grevet EH, Grigoroiu-Serbanescu M, Gutierrez B, Guzman-Parra J, Hamilton SP, Hamshere ML, Hartmann A, Hauser J, Heilmann-Heimbach S, Hoffmann P, Ising M, Jones I, Jones LA, Jonsson L, Kahn RS, Kelsoe JR, Kendler KS, Kloiber S, Koenen KC, Kogevinas M, Konte B, Krebs MO, Landén M, Lawrence J, Leboyer M, Lee PH, Levinson DF, Liao C, Lissowska J, Lucae S, Mayoral F, McElroy SL, McGrath P, McGuffin P, McQuillin A, Medland SE, Mehta D, Melle I, Milaneschi Y, Mitchell PB, Molina E, Morken G, Mortensen PB, Müller-Myhsok B, Nievergelt C, Nimgaonkar V, Nöthen MM, O’Donovan M, Ophoff RA, Owen MJ, Pato C, Pato MT, Penninx BWJH, Pimm J, Pistis G, Potash JB, Power RA, Preisig M, Quested D, Ramos-Quiroga JA, Reif A, Ribasés M, Richarte V, Rietschel M, Rivera M, Roberts A, Roberts G, Rouleau GA, Rovaris DL, Rujescu D, Sánchez-Mora C, Sanders AR, Schofield PR, Schulze TG, Scott LJ, Serretti A, Shi J, Shyn SI, Sirignano L, Sklar P, Smeland OB, Smoller JW, Sonuga-Barke EJS, Spalletta G, Strauss JS, Świątkowska B, Trzaskowski M, Turecki G, Vilar-Ribó L, Vincent JB, Völzke H, Walters JTR, Weickert C, Weickert TW, Weissman MM, Williams LM, Wray NR, Zai CC, Ashley-Koch AE, Beckham JC, Hauser ER, Hauser MA, Kimbrel NA, Lindquist JH, McMahon B, Oslin DW, Qin X, Agerbo E, Børglum A, Breen G, Erlangsen A, Esko T, Gelernter J, Hougaard D, Kessler R, Kranzler H, Li Y, Martin N, McIntosh A, Mors O, Nordentoft M, Olsen C, Porteous D, Ursano R, Wasserman D, Werge T, Whiteman D, Bulik C, Coon H, Demontis D, Docherty A, Kuo P, Lewis G, Mann J, Rentería M, Smith D, Stahl E, Stein D, Streit F, Willour V, Ruderfer D, Wray N, Ripke S, Mattheisen M, Trzaskowski M, Byrne E, Abdellaoui A, Adams M, Agerbo E, Air T, Andlauer T, Bacanu S, Bækvad-Hansen M, Beekman A, Bigdeli T, Binder E, Bryois J, Buttenschøn H, Bybjerg-Grauholm J, Cai N, Castelao E, Christensen J, Clarke T, Coleman J, Colodro-Conde L, Couvy-Duchesne B, Craddock N, Crawford G, Davies G, Degenhardt F, Derks E, Direk N, Dolan C, Dunn E, Eley T, Escott-Price V, Kiadeh F, Finucane H, Foo J, Forstner A, Frank J, Gaspar H, Gill M, Goes F, Gordon S, Weinsheimer S, Wellmann J, Willemsen G, Witt S, Wu Y, Xi H, Yang J, Zhang F, Arolt V, Baune B, Berger K, Boomsma D, Cichon S, Dannlowski U, de Geus E, Depaulo J, Domenici E, Domschke K, Esko T, Grabe H, Hamilton S, Grove J, Hall L, Hansen C, Hansen T, Herms S, Hickie I, Hoffmann P, Homuth G, Horn C, Hottenga J, Hougaard D, Howard D, Ising M, Jansen R, Jones I, Jones L, Jorgenson E, Knowles J, Kohane I, Kraft J, 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- Leveraging functional annotation to identify genes associated with complex diseasesLiu W, Li M, Zhang W, Zhou G, Wu X, Wang J, Lu Q, Zhao H. Leveraging functional annotation to identify genes associated with complex diseases. PLOS Computational Biology 2020, 16: e1008315. PMID: 33137096, PMCID: PMC7660930, DOI: 10.1371/journal.pcbi.1008315.
- A large-scale genome-wide association study meta-analysis of cannabis use disorderJohnson EC, Demontis D, Thorgeirsson TE, Walters RK, Polimanti R, Hatoum AS, Sanchez-Roige S, Paul SE, Wendt FR, Clarke TK, Lai D, Reginsson GW, Zhou H, He J, Baranger DAA, Gudbjartsson DF, Wedow R, Adkins DE, Adkins AE, Alexander J, Bacanu SA, Bigdeli TB, Boden J, Brown SA, Bucholz KK, Bybjerg-Grauholm J, Corley RP, Degenhardt L, Dick DM, Domingue BW, Fox L, Goate AM, Gordon SD, Hack LM, Hancock DB, Hartz SM, Hickie IB, Hougaard DM, Krauter K, Lind PA, McClintick JN, McQueen MB, Meyers JL, Montgomery GW, Mors O, Mortensen PB, Nordentoft M, Pearson JF, Peterson RE, Reynolds MD, Rice JP, Runarsdottir V, Saccone NL, Sherva R, Silberg JL, Tarter RE, Tyrfingsson T, Wall TL, Webb BT, Werge T, Wetherill L, Wright MJ, Zellers S, Adams MJ, Bierut LJ, Boardman JD, Copeland WE, Farrer LA, Foroud TM, Gillespie NA, Grucza RA, Harris KM, Heath AC, Hesselbrock V, Hewitt JK, Hopfer CJ, Horwood J, Iacono WG, Johnson EO, Kendler KS, Kennedy MA, Kranzler HR, Madden PAF, Maes HH, Maher BS, Martin NG, McGue M, McIntosh AM, Medland SE, Nelson EC, Porjesz B, Riley BP, Stallings MC, Vanyukov MM, Vrieze S, Workgroup P, Walters R, Polimanti R, Johnson E, McClintick J, Hatoum A, He J, Wendt F, Zhou H, Adams M, Adkins A, Aliev F, Bacanu S, Batzler A, Bertelsen S, Biernacka J, Bigdeli T, Chen L, Clarke T, Chou Y, Degenhardt F, Docherty A, Edwards A, Fontanillas P, Foo J, Fox L, Frank J, Giegling I, Gordon S, Hack L, Hartmann A, Hartz S, Heilmann-Heimbach S, Herms S, Hodgkinson C, Hoffman P, Hottenga J, Kennedy M, Alanne-Kinnunen M, Konte B, Lahti J, Lahti-Pulkkinen M, Lai D, Ligthart L, Loukola A, Maher B, Mbarek H, McIntosh A, McQueen M, Meyers J, Milaneschi Y, Palviainen T, Pearson J, Peterson R, Ripatti S, Ryu E, Saccone N, Salvatore J, Sanchez-Roige S, Schwandt M, Sherva R, Streit F, Strohmaier J, Thomas N, Wang J, Webb B, Wedow R, Wetherill L, Wills A, Boardman J, Chen D, Choi D, Copeland W, Culverhouse R, Dahmen N, Degenhardt L, Domingue B, Elson S, Frye M, Gäbel W, Hayward C, Ising M, Keyes M, Kiefer F, Kramer J, Kuperman S, Lucae S, Lynskey M, Maier W, Mann K, Männistö S, Müller-Myhsok B, Murray A, Nurnberger J, Palotie A, Preuss U, Räikkönen K, Reynolds M, Ridinger M, Scherbaum N, Schuckit M, Soyka M, Treutlein J, Witt S, Wodarz N, Zill P, Adkins D, Boden J, Boomsma D, Bierut L, Brown S, Bucholz K, Cichon S, Costello E, de Wit H, Diazgranados N, Dick D, Eriksson J, Farrer L, Foroud T, Gillespie N, Goate A, Goldman D, Grucza R, Hancock D, Harris K, Heath A, Hesselbrock V, Hewitt J, Hopfer C, Horwood J, Iacono W, Johnson E, Kaprio J, Karpyak V, Kendler K, Kranzler H, Krauter K, Lichtenstein P, Lind P, McGue M, MacKillop J, Madden P, Maes H, Magnusson P, Martin N, Medland S, Montgomery G, Nelson E, Nöthen M, Palmer A, Pederson N, Penninx B, Porjesz B, Rice J, Rietschel M, Riley B, Rose R, Rujescu D, Shen P, Silberg J, Stallings M, Tarter R, Vanyukov M, Vrieze S, Wall T, Whitfield J, Zhao H, Neale B, Gelernter J, Edenberg H, Agrawal A, Davis L, Bogdan R, Gelernter J, Edenberg H, Stefansson K, Børglum A, Agrawal A. A large-scale genome-wide association study meta-analysis of cannabis use disorder. The Lancet Psychiatry 2020, 7: 1032-1045. PMID: 33096046, PMCID: PMC7674631, DOI: 10.1016/s2215-0366(20)30339-4.
- Genome-wide association study of smoking trajectory and meta-analysis of smoking status in 842,000 individualsXu K, Li B, McGinnis KA, Vickers-Smith R, Dao C, Sun N, Kember RL, Zhou H, Becker WC, Gelernter J, Kranzler HR, Zhao H, Justice AC. Genome-wide association study of smoking trajectory and meta-analysis of smoking status in 842,000 individuals. Nature Communications 2020, 11: 5302. PMID: 33082346, PMCID: PMC7598939, DOI: 10.1038/s41467-020-18489-3.
- Automated feature extraction from population wearable device data identified novel loci associated with sleep and circadian rhythmsLi X, Zhao H. Automated feature extraction from population wearable device data identified novel loci associated with sleep and circadian rhythms. PLOS Genetics 2020, 16: e1009089. PMID: 33075057, PMCID: PMC7595622, DOI: 10.1371/journal.pgen.1009089.
- 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.
- Genetic Architecture of Abdominal Aortic Aneurysm in the Million Veteran ProgramKlarin D, Verma S, Judy R, Dikilitas O, Wolford B, Paranjpe I, Levin M, Pan C, Tcheandjieu C, Spin J, Lynch J, Assimes T, Åldstedt Nyrønning L, Mattsson E, Edwards T, Denny J, Larson E, Lee M, Carrell D, Zhang Y, Jarvik G, Gharavi A, Harley J, Mentch F, Pacheco J, Hakonarson H, Skogholt A, Thomas L, Gabrielsen M, Hveem K, Nielsen J, Zhou W, Fritsche L, Huang J, Natarajan P, Sun Y, DuVall S, Rader D, Cho K, Chang K, Wilson P, O’Donnell C, Kathiresan S, Scali S, Berceli S, Willer C, Jones G, Bown M, Nadkarni G, Kullo I, Ritchie M, Damrauer S, Tsao P, Gaziano J, Ramoni R, Beckham J, Breeling J, Chang K, Huang G, Muralidhar S, O’Donnell C, Casas Romero J, Tsao P, Muralidhar S, Moser J, Whitbourne S, Brewer J, Concato J, Warren S, Argyres D, Tsao P, Gaziano J, Stephens B, Brophy M, Humphries D, Do N, Shayan S, Nguyen X, O’Donnell C, Pyarajan S, Tsao P, Cho K, Pyarajan S, Hauser E, Sun Y, Zhao H, Wilson P, McArdle R, Dellitalia L, Harley J, Zablocki C, Whittle J, Beckham J, Wells J, Gutierrez S, Gibson G, Kaminsky L, Villareal G, Kinlay S, Xu J, Hamner M, Haddock K, Bhushan S, Iruvanti P, Godschalk M, Ballas Z, Buford M, Mastorides S, Klein J, Ratcliffe N, Florez H, Swann A, Murdoch M, Sriram P, Yeh S, Washburn R, Jhala D, Aguayo S, Cohen D, Sharma S, Callaghan J, Oursler K, Whooley M, Ahuja S, Gutierrez A, Schifman R, Greco J, Rauchman M, Servatius R, Oehlert M, Wallbom A, Fernando R, Morgan T, Stapley T, Sherman S, Anderson G, Tsao P, Sonel E, Boyko E, Meyer L, Gupta S, Fayad J, Hung A, Lichy J, Hurley R, Robey B, Striker R. Genetic Architecture of Abdominal Aortic Aneurysm in the Million Veteran Program. Circulation 2020, 142: 1633-1646. PMID: 32981348, PMCID: PMC7580856, DOI: 10.1161/circulationaha.120.047544.
- A comprehensive genetic and epidemiological association analysis of vitamin D with common diseases/traits in the UK BiobankYe Y, Yang H, Wang Y, Zhao H. A comprehensive genetic and epidemiological association analysis of vitamin D with common diseases/traits in the UK Biobank. Genetic Epidemiology 2020, 45: 24-35. PMID: 32918767, DOI: 10.1002/gepi.22357.
- 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, 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, Zhao H, Duman R. Transcriptomic Organization of Human Posttraumatic Stress Disorder. Biological Psychiatry 2020, 87: s122. DOI: 10.1016/j.biopsych.2020.02.330.
- Comparative Analysis of Genetic Diversity on Venous Thromboembolism (VTE) Between Caucasian and Chinese Han Population: A Population Based Genetic Family Studies and Case-Control StudiesZhang Z, Zhou G, Zhai Z, Pang W, Zhang Y, Shu S, Zhao H, Zhang X, Wang C. Comparative Analysis of Genetic Diversity on Venous Thromboembolism (VTE) Between Caucasian and Chinese Han Population: A Population Based Genetic Family Studies and Case-Control Studies. 2020, a7236-a7236. DOI: 10.1164/ajrccm-conference.2020.201.1_meetingabstracts.a7236.
- 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.
- Relationship of Age With the Hemodynamic Parameters in Individuals With Elevated Blood PressureMahajan S, Gu J, Caraballo C, Lu Y, Spatz ES, Zhao H, Zhang M, Sun N, Zheng X, Lu H, Yuan H, J. Z, Krumholz HM. Relationship of Age With the Hemodynamic Parameters in Individuals With Elevated Blood Pressure. Journal Of The American Geriatrics Society 2020, 68: 1520-1528. PMID: 32212398, DOI: 10.1111/jgs.16411.
- 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.
- Integrating Multidimensional Data for Clustering Analysis With Applications to Cancer Patient DataPark S, Xu H, Zhao H. Integrating Multidimensional Data for Clustering Analysis With Applications to Cancer Patient Data. Journal Of The American Statistical Association 2020, 116: 14-26. PMID: 36339813, PMCID: PMC9634961, DOI: 10.1080/01621459.2020.1730853.
- Shared genetic risk between eating disorder‐ and substance‐use‐related phenotypes: Evidence from genome‐wide association studiesMunn‐Chernoff M, Johnson EC, Chou Y, Coleman JRI, Thornton LM, Walters RK, Yilmaz Z, Baker JH, Hübel C, Gordon S, Medland SE, Watson HJ, Gaspar HA, Bryois J, Hinney A, Leppä VM, Mattheisen M, Ripke S, Yao S, Giusti‐Rodríguez P, Hanscombe KB, Adan RAH, Alfredsson L, Ando T, Andreassen OA, Berrettini WH, Boehm I, Boni C, Perica V, Buehren K, Burghardt R, Cassina M, Cichon S, Clementi M, Cone RD, Courtet P, Crow S, Crowley JJ, Danner UN, Davis OSP, Zwaan M, Dedoussis G, Degortes D, DeSocio JE, Dick DM, Dikeos D, Dina C, Dmitrzak‐Weglarz M, Docampo E, Duncan LE, Egberts K, Ehrlich S, Escaramís G, Esko T, Estivill X, Farmer A, Favaro A, Fernández‐Aranda F, Fichter MM, Fischer K, Föcker M, Foretova L, Forstner AJ, Forzan M, Franklin CS, Gallinger S, Giegling I, Giuranna J, Gonidakis F, Gorwood P, Mayora M, Guillaume S, Guo Y, Hakonarson H, Hatzikotoulas K, Hauser J, Hebebrand J, Helder SG, Herms S, Herpertz‐Dahlmann B, Herzog W, Huckins LM, Hudson JI, Imgart H, Inoko H, Janout V, Jiménez‐Murcia S, Julià A, Kalsi G, Kaminská D, Karhunen L, Karwautz A, Kas MJH, Kennedy JL, Keski‐Rahkonen A, Kiezebrink K, Kim Y, Klump KL, Knudsen GPS, La Via MC, Le Hellard S, Levitan RD, Li D, Lilenfeld L, Lin BD, Lissowska J, Luykx J, Magistretti PJ, Maj M, Mannik K, Marsal S, Marshall CR, Mattingsdal M, McDevitt S, McGuffin P, Metspalu A, Meulenbelt I, Micali N, Mitchell K, Monteleone AM, Monteleone P, Nacmias B, Navratilova M, Ntalla I, O'Toole JK, Ophoff RA, Padyukov L, Palotie A, Pantel J, Papezova H, Pinto D, Rabionet R, Raevuori A, Ramoz N, Reichborn‐Kjennerud T, Ricca V, Ripatti S, Ritschel F, Roberts M, Rotondo A, Rujescu D, Rybakowski F, Santonastaso P, Scherag A, Scherer SW, Schmidt U, Schork NJ, Schosser A, Seitz J, Slachtova L, Slagboom PE, Landt M, Slopien A, Sorbi S, Świątkowska B, Szatkiewicz JP, Tachmazidou I, Tenconi E, Tortorella A, Tozzi F, Treasure J, Tsitsika A, Tyszkiewicz‐Nwafor M, Tziouvas K, Elburg A, Furth E, Wagner G, Walton E, Widen E, Zeggini E, Zerwas S, Zipfel S, Bergen AW, Boden JM, Brandt H, Crawford S, Halmi KA, Horwood LJ, Johnson C, Kaplan AS, Kaye WH, Mitchell J, Olsen CM, Pearson JF, Pedersen NL, Strober M, Werge T, Whiteman DC, Woodside DB, Grove J, Henders AK, Larsen JT, Parker R, Petersen LV, Jordan J, Kennedy MA, Birgegård A, Lichtenstein P, Norring C, Landén M, Mortensen PB, Polimanti R, McClintick JN, Adkins AE, Aliev F, Bacanu S, Batzler A, Bertelsen S, Biernacka JM, Bigdeli TB, Chen L, Clarke T, Degenhardt F, Docherty AR, Edwards AC, Foo JC, Fox L, Frank J, Hack LM, Hartmann AM, Hartz SM, Heilmann‐Heimbach S, Hodgkinson C, Hoffmann P, Hottenga J, Konte B, Lahti J, Lahti‐Pulkkinen M, Lai D, Ligthart L, Loukola A, Maher BS, Mbarek H, McIntosh AM, McQueen MB, Meyers JL, Milaneschi Y, Palviainen T, Peterson RE, Ryu E, Saccone NL, Salvatore JE, Sanchez‐Roige S, Schwandt M, Sherva R, Streit F, Strohmaier J, Thomas N, Wang J, Webb BT, Wedow R, Wetherill L, Wills AG, Zhou H, Boardman JD, Chen D, Choi D, Copeland WE, Culverhouse RC, Dahmen N, Degenhardt L, Domingue BW, Frye MA, Gäebel W, Hayward C, Ising M, Keyes M, Kiefer F, Koller G, Kramer J, Kuperman S, Lucae S, Lynskey MT, Maier W, Mann K, Männistö S, Müller‐Myhsok B, Murray AD, Nurnberger JI, Preuss U, Räikkönen K, Reynolds MD, Ridinger M, Scherbaum N, Schuckit MA, Soyka M, Treutlein J, Witt SH, Wodarz N, Zill P, Adkins DE, Boomsma DI, Bierut LJ, Brown SA, Bucholz KK, Costello EJ, Wit H, Diazgranados N, Eriksson JG, Farrer LA, Foroud TM, Gillespie NA, Goate AM, Goldman D, Grucza RA, Hancock DB, Harris KM, Hesselbrock V, Hewitt JK, Hopfer CJ, Iacono WG, Johnson EO, Karpyak VM, Kendler KS, Kranzler HR, Krauter K, Lind PA, McGue M, MacKillop J, Madden PAF, Maes HH, Magnusson PKE, Nelson EC, Nöthen MM, Palmer AA, Penninx BWJH, Porjesz B, Rice JP, Rietschel M, Riley BP, Rose RJ, Shen P, Silberg J, Stallings MC, Tarter RE, Vanyukov MM, Vrieze S, Wall TL, Whitfield JB, Zhao H, Neale BM, Wade TD, Heath AC, Montgomery GW, Martin NG, Sullivan PF, Kaprio J, Breen G, Gelernter J, Edenberg HJ, Bulik CM, Agrawal A. Shared genetic risk between eating disorder‐ and substance‐use‐related phenotypes: Evidence from genome‐wide association studies. Addiction Biology 2020, 26: e12880. PMID: 32064741, PMCID: PMC7429266, DOI: 10.1111/adb.12880.
- Leveraging effect size distributions to improve polygenic risk scores derived from summary statistics of genome-wide association studiesSong S, Jiang W, Hou L, Zhao H. Leveraging effect size distributions to improve polygenic risk scores derived from summary statistics of genome-wide association studies. PLOS Computational Biology 2020, 16: e1007565. PMID: 32045423, PMCID: PMC7039528, DOI: 10.1371/journal.pcbi.1007565.
- Molecular genetic overlap between posttraumatic stress disorder and sleep phenotypes.Lind M, Brick L, Gehrman P, Duncan L, Gelaye B, Maihofer A, Nievergelt C, Nugent N, Stein M, Amstadter A, Aiello A, Almli L, Amstadter A, Andersen S, Andreassen O, Arbisi P, Ashley-Koch A, Atkinson E, Austin S, Avdibegovic E, Babić D, Bækvad-Hansen M, Baker D, Beckham J, Bierut L, Bisson J, Boks M, Bolger E, Børglum A, Bradley B, Brashear M, Breen G, Bryant R, Bustamante A, Bybjerg-Grauholm J, Calabrese J, Caldas-de-Almeida J, Chen C, Coleman J, Dale A, Dalvie S, Daly M, Daskalakis N, Deckert J, Delahanty D, Dennis M, Disner S, Domschke K, Duncan L, Dzubur-Kulenovic A, Erbes C, Evans A, Farrer L, Feeny N, Flory J, Forbes D, Franz C, Galea S, Garrett M, Gelaye B, Gelernter J, Geuze E, Gillespie C, Uka A, Gordon S, Guffanti G, Haas M, Hammamieh R, Hauser M, Heath A, Hemmings S, Hougaard D, Jakovljevic M, Jett M, Johnson E, Jones I, Jovanovic T, Junglen A, Karstoft K, Kaufman M, Kessler R, Khan A, Kimbrel N, King A, Koen N, Koenen K, Kranzler H, Kremen W, Lawford B, Lebois L, Lewis C, Liberzon I, Linnstaedt S, Logue M, Lori A, Lugonja B, Luykx J, Lyons M, Maihofer A, Maples-Keller J, Marmar C, Martin N, Maurer D, Mavissakalian M, McFarlane A, McGlinchey R, McLaughlin K, McLean S, McLeay S, Mehta D, Milberg W, Miller M, Morey R, Morris C, Mors O, Mortensen P, Nelson E, Nievergelt C, Nordentoft M, Norman S, O’Donnell M, Orcutt H, Panizzon M, Peters E, Peterson A, Peverill M, Pietrzak R, Polusny M, Qin X, Ratanatharathorn A, Ressler K, Rice J, Risbrough V, Roberts A, Rothbaum A, Rothbaum B, Roy-Byrne P, Ruggiero K, Rung A, Rutten B, Saccone N, Sanchez S, Schijven D, Seedat S, Seligowski A, Seng J, Sheerin C, Silove D, Smith A, Smoller J, Sponheim S, Stein D, Stein M, Stevens J, Teicher M, Thompson W, Torres K, Trapido E, Uddin M, Ursano R, van den Heuvel L, van Hooff M, Vermetten E, Vinkers C, Voisey J, Wang Y, Wang Z, Werge T, Williams M, Williamson D, Winternitz S, Wolf C, Wolf E, Wolff J, Yehuda R, Young K, McD Young R, Zhao H, Zoellner L. Molecular genetic overlap between posttraumatic stress disorder and sleep phenotypes. Sleep 2019, 43 PMID: 31802129, PMCID: PMC7157187, DOI: 10.1093/sleep/zsz257.
- NITUMID: Nonnegative matrix factorization-based Immune-TUmor MIcroenvironment DeconvolutionTang D, Park S, Zhao H. NITUMID: Nonnegative matrix factorization-based Immune-TUmor MIcroenvironment Deconvolution. Bioinformatics 2019, 36: 1344-1350. PMID: 31593244, PMCID: PMC8215918, DOI: 10.1093/bioinformatics/btz748.
- International meta-analysis of PTSD genome-wide association studies identifies sex- and ancestry-specific genetic risk lociNievergelt CM, Maihofer AX, Klengel T, Atkinson EG, Chen CY, Choi KW, Coleman JRI, Dalvie S, Duncan LE, Gelernter J, Levey DF, Logue MW, Polimanti R, Provost AC, Ratanatharathorn A, Stein MB, Torres K, Aiello AE, Almli LM, Amstadter AB, Andersen SB, Andreassen OA, Arbisi PA, Ashley-Koch AE, Austin SB, Avdibegovic E, Babić D, Bækvad-Hansen M, Baker DG, Beckham JC, Bierut LJ, Bisson JI, Boks MP, Bolger EA, Børglum AD, Bradley B, Brashear M, Breen G, Bryant RA, Bustamante AC, Bybjerg-Grauholm J, Calabrese JR, Caldas- de- Almeida J, Dale AM, Daly MJ, Daskalakis NP, Deckert J, Delahanty DL, Dennis MF, Disner SG, Domschke K, Dzubur-Kulenovic A, Erbes CR, Evans A, Farrer LA, Feeny NC, Flory JD, Forbes D, Franz CE, Galea S, Garrett ME, Gelaye B, Geuze E, Gillespie C, Uka AG, Gordon SD, Guffanti G, Hammamieh R, Harnal S, Hauser MA, Heath AC, Hemmings SMJ, Hougaard DM, Jakovljevic M, Jett M, Johnson EO, Jones I, Jovanovic T, Qin XJ, Junglen AG, Karstoft KI, Kaufman ML, Kessler RC, Khan A, Kimbrel NA, King AP, Koen N, Kranzler HR, Kremen WS, Lawford BR, Lebois LAM, Lewis CE, Linnstaedt SD, Lori A, Lugonja B, Luykx JJ, Lyons MJ, Maples-Keller J, Marmar C, Martin AR, Martin NG, Maurer D, Mavissakalian MR, McFarlane A, McGlinchey RE, McLaughlin KA, McLean SA, McLeay S, Mehta D, Milberg WP, Miller MW, Morey RA, Morris CP, Mors O, Mortensen PB, Neale BM, Nelson EC, Nordentoft M, Norman SB, O’Donnell M, Orcutt HK, Panizzon MS, Peters ES, Peterson AL, Peverill M, Pietrzak RH, Polusny MA, Rice JP, Ripke S, Risbrough VB, Roberts AL, Rothbaum AO, Rothbaum BO, Roy-Byrne P, Ruggiero K, Rung A, Rutten BPF, Saccone NL, Sanchez SE, Schijven D, Seedat S, Seligowski AV, Seng JS, Sheerin CM, Silove D, Smith AK, Smoller JW, Sponheim SR, Stein DJ, Stevens JS, Sumner JA, Teicher MH, Thompson WK, Trapido E, Uddin M, Ursano RJ, van den Heuvel LL, Van Hooff M, Vermetten E, Vinkers CH, Voisey J, Wang Y, Wang Z, Werge T, Williams MA, Williamson DE, Winternitz S, Wolf C, Wolf EJ, Wolff JD, Yehuda R, Young RM, Young KA, Zhao H, Zoellner LA, Liberzon I, Ressler KJ, Haas M, Koenen KC. International meta-analysis of PTSD genome-wide association studies identifies sex- and ancestry-specific genetic risk loci. Nature Communications 2019, 10: 4558. PMID: 31594949, PMCID: PMC6783435, DOI: 10.1038/s41467-019-12576-w.
- 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.
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- Author Correction: Genome-wide association study of alcohol consumption and use disorder in 274,424 individuals from multiple populationsKranzler HR, Zhou H, Kember RL, Smith RV, Justice AC, Damrauer S, Tsao PS, Klarin D, Baras A, Reid J, Overton J, Rader DJ, Cheng Z, Tate JP, Becker WC, Concato J, Xu K, Polimanti R, Zhao H, Gelernter J. Author Correction: Genome-wide association study of alcohol consumption and use disorder in 274,424 individuals from multiple populations. Nature Communications 2019, 10: 4050. PMID: 31481659, PMCID: PMC6722074, DOI: 10.1038/s41467-019-11916-0.
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- 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.
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- Network Clustering Analysis Using Mixture Exponential-Family Random Graph Models and Its Application in Genetic Interaction DataWang Y, Fang H, Yang D, Zhao H, Deng M. Network Clustering Analysis Using Mixture Exponential-Family Random Graph Models and Its Application in Genetic Interaction Data. IEEE/ACM Transactions On Computational Biology And Bioinformatics 2017, 16: 1743-1752. PMID: 28858811, DOI: 10.1109/tcbb.2017.2743711.
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- 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.
- Joint modeling of genetically correlated diseases and functional annotations increases accuracy of polygenic risk predictionHu Y, Lu Q, Liu W, Zhang Y, Li M, Zhao H. Joint modeling of genetically correlated diseases and functional annotations increases accuracy of polygenic risk prediction. PLOS Genetics 2017, 13: e1006836. PMID: 28598966, PMCID: PMC5482506, DOI: 10.1371/journal.pgen.1006836.
- Leveraging functional annotations in genetic risk prediction for human complex diseasesHu Y, Lu Q, Powles R, Yao X, Yang C, Fang F, Xu X, Zhao H. Leveraging functional annotations in genetic risk prediction for human complex diseases. PLOS Computational Biology 2017, 13: e1005589. PMID: 28594818, PMCID: PMC5481142, DOI: 10.1371/journal.pcbi.1005589.
- Structured subcomposition selection in regression and its application to microbiome data analysisWang T, Zhao H. Structured subcomposition selection in regression and its application to microbiome data analysis. The Annals Of Applied Statistics 2017, 11: 771-791. DOI: 10.1214/16-aoas1017.
- 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.
- 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. PMID: 37388623, PMCID: PMC10309155, 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.
- A Dirichlet‐tree multinomial regression model for associating dietary nutrients with gut microorganismsWang T, Zhao H. A Dirichlet‐tree multinomial regression model for associating dietary nutrients with gut microorganisms. Biometrics 2017, 73: 792-801. PMID: 28112797, PMCID: PMC5587402, DOI: 10.1111/biom.12654.
- On joint estimation of Gaussian graphical models for spatial and temporal dataLin Z, Wang T, Yang C, Zhao H. On joint estimation of Gaussian graphical models for spatial and temporal data. Biometrics 2017, 73: 769-779. PMID: 28099997, PMCID: PMC5515703, DOI: 10.1111/biom.12650.
- 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.
- Graphical model selection with latent variablesWu C, Zhao H, Fang H, Deng M. Graphical model selection with latent variables. Electronic Journal Of Statistics 2017, 11: 3485-3521. DOI: 10.1214/17-ejs1331.
- 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.
- On an additive partial correlation operator and nonparametric estimation of graphical modelsLee KY, Li B, Zhao H. On an additive partial correlation operator and nonparametric estimation of graphical models. Biometrika 2016, 103: 513-530. PMID: 29422689, PMCID: PMC5793672, DOI: 10.1093/biomet/asw028.
- 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.
- Integrative Tissue-Specific Functional Annotations in the Human Genome Provide Novel Insights on Many Complex Traits and Improve Signal Prioritization in Genome Wide Association StudiesLu Q, Powles RL, Wang Q, He BJ, Zhao H. Integrative Tissue-Specific Functional Annotations in the Human Genome Provide Novel Insights on Many Complex Traits and Improve Signal Prioritization in Genome Wide Association Studies. PLOS Genetics 2016, 12: e1005947. PMID: 27058395, PMCID: PMC4825932, DOI: 10.1371/journal.pgen.1005947.
- CCor: A whole genome network‐based similarity measure between two genesHu Y, Zhao H. CCor: A whole genome network‐based similarity measure between two genes. Biometrics 2016, 72: 1216-1225. PMID: 26953524, PMCID: PMC5016231, DOI: 10.1111/biom.12508.
- 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.
- eQTL mappingChen M, Yang C, Li C, Zhao H. eQTL mapping. 2015, 208-228. DOI: 10.1017/cbo9781107337459.016.
- Pervasive pleiotropy between psychiatric disorders and immune disorders revealed by integrative analysis of multiple GWASWang Q, Yang C, Gelernter J, Zhao H. Pervasive pleiotropy between psychiatric disorders and immune disorders revealed by integrative analysis of multiple GWAS. Human Genetics 2015, 134: 1195-1209. PMID: 26340901, PMCID: PMC4630076, DOI: 10.1007/s00439-015-1596-8.
- DATA INTEGRATION METHODS IN GENOME WIDE ASSOCIATION STUDIESSUN N, ZHAO H. DATA INTEGRATION METHODS IN GENOME WIDE ASSOCIATION STUDIES. 2015, 961-976. DOI: 10.1142/9789814583312_0026.
- A Statistical Framework to Predict Functional Non-Coding Regions in the Human Genome Through Integrated Analysis of Annotation DataLu Q, Hu Y, Sun J, Cheng Y, Cheung KH, Zhao H. A Statistical Framework to Predict Functional Non-Coding Regions in the Human Genome Through Integrated Analysis of Annotation Data. Scientific Reports 2015, 5: 10576. PMID: 26015273, PMCID: PMC4444969, DOI: 10.1038/srep10576.
- Gene-based and pathway-based genome-wide association study of alcohol dependenceLingjun Z, ZHANG CK, SAYWARD FG, CHEUNG KH, Kesheng W, KRYSTAL JH, Hongyu Z, Xingguang L. Gene-based and pathway-based genome-wide association study of alcohol dependence. General Psychiatry 2015, 27: 111-118. PMID: 26120261, PMCID: PMC4466852, DOI: 10.11919/j.issn.1002-0829.215031.
- A Markov random field-based approach to characterizing human brain development using spatial–temporal transcriptome dataLin Z, Sanders SJ, Li M, Sestan N, State MW, Zhao H. A Markov random field-based approach to characterizing human brain development using spatial–temporal transcriptome data. The Annals Of Applied Statistics 2015, 9: 429-451. PMID: 26877824, PMCID: PMC4751044, DOI: 10.1214/14-aoas802.
- 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.
- GPA: A Statistical Approach to Prioritizing GWAS Results by Integrating Pleiotropy and AnnotationChung D, Yang C, Li C, Gelernter J, Zhao H. GPA: A Statistical Approach to Prioritizing GWAS Results by Integrating Pleiotropy and Annotation. PLOS Genetics 2014, 10: e1004787. PMID: 25393678, PMCID: PMC4230845, DOI: 10.1371/journal.pgen.1004787.
- Statistical Analysis of Biomarkers from -Omics TechnologiesPang H, Zhao H. Statistical Analysis of Biomarkers from -Omics Technologies. 2014, 435-452. DOI: 10.1201/b17716-23.
- 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.
- Graphical Modeling of Biological Pathways in Genome-wide Association StudiesChen M, Cho J, Zhao H. Graphical Modeling of Biological Pathways in Genome-wide Association Studies. 2014, 294-317. DOI: 10.1093/acprof:oso/9780198709022.003.0012.
- On an Additive Semigraphoid Model for Statistical Networks With Application to Pathway AnalysisLi B, Chun H, Zhao H. On an Additive Semigraphoid Model for Statistical Networks With Application to Pathway Analysis. Journal Of The American Statistical Association 2014, 109: 1188-1204. PMID: 26401064, PMCID: PMC4577248, DOI: 10.1080/01621459.2014.882842.
- 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.
- Guilt by rewiring: gene prioritization through network rewiring in Genome Wide Association StudiesHou L, Chen M, Zhang CK, Cho J, Zhao H. Guilt by rewiring: gene prioritization through network rewiring in Genome Wide Association Studies. Human Molecular Genetics 2013, 23: 2780-2790. PMID: 24381306, PMCID: PMC3990172, DOI: 10.1093/hmg/ddt668.
- Improving genetic risk prediction by leveraging pleiotropyLi C, Yang C, Gelernter J, Zhao H. Improving genetic risk prediction by leveraging pleiotropy. Human Genetics 2013, 133: 639-650. PMID: 24337655, PMCID: PMC3988249, DOI: 10.1007/s00439-013-1401-5.
- 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.
- Application of Bayesian Sparse Factor Analysis Models in BioinformaticsMa H, Zhao H. Application of Bayesian Sparse Factor Analysis Models in Bioinformatics. 2013, 350-365. DOI: 10.1017/cbo9781139226448.018.
- 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.
- Sparse principal component analysis by choice of normQi X, Luo R, Zhao H. Sparse principal component analysis by choice of norm. Journal Of Multivariate Analysis 2012, 114: 127-160. PMID: 23524453, PMCID: PMC3601508, DOI: 10.1016/j.jmva.2012.07.004.
- 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.
- iFad: an integrative factor analysis model for drug-pathway association inference†Ma H, Zhao H. iFad: an integrative factor analysis model for drug-pathway association inference†. Bioinformatics 2012, 28: 1911-1918. PMID: 22581178, PMCID: PMC3389771, DOI: 10.1093/bioinformatics/bts285.
- 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.
- Sparse Estimation of Conditional Graphical Models With Application to Gene NetworksLi B, Chun H, Zhao H. Sparse Estimation of Conditional Graphical Models With Application to Gene Networks. Journal Of The American Statistical Association 2012, 107: 152-167. PMID: 24574574, PMCID: PMC3932550, DOI: 10.1080/01621459.2011.644498.
- 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.
- Bayesian hierarchical modeling for signaling pathway inference from single cell interventional dataLuo R, Zhao H. Bayesian hierarchical modeling for signaling pathway inference from single cell interventional data. The Annals Of Applied Statistics 2011, 5: 725-745. PMID: 22162986, PMCID: PMC3233205, DOI: 10.1214/10-aoas425.
- 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.
- Incorporating Biological Pathways via a Markov Random Field Model in Genome-Wide Association StudiesChen M, Cho J, Zhao H. Incorporating Biological Pathways via a Markov Random Field Model in Genome-Wide Association Studies. PLOS Genetics 2011, 7: e1001353. PMID: 21490723, PMCID: PMC3072362, DOI: 10.1371/journal.pgen.1001353.
- Controlling population structure in human genetic association studies with samples of unrelated individualsAllison D, Limdi N, Liu N, Patki A, Zhao H. Controlling population structure in human genetic association studies with samples of unrelated individuals. Statistics And Its Interface 2011, 4: 317-326. PMID: 22308192, PMCID: PMC3269890, DOI: 10.4310/sii.2011.v4.n3.a6.
- 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.
- Asymptotic efficiency and finite-sample properties of the generalized profiling estimation of parameters in ordinary differential equationsQi X, Zhao H. Asymptotic efficiency and finite-sample properties of the generalized profiling estimation of parameters in ordinary differential equations. The Annals Of Statistics 2010, 38: 435-481. DOI: 10.1214/09-aos724.
- 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.
- MicroarraysSpeed T, Zhao H. Microarrays. Statistical Methods In Medical Research 2009, 18: 531-532. PMID: 20048382, DOI: 10.1177/0962280209352042.
- Family-Based Association StudiesZhang K, Zhao H. Family-Based Association Studies. 2009, 191-240. DOI: 10.1007/978-3-540-69264-5_7.
- A misclassification model for inferring transcriptional regulatory networksGoldstein D, Guerra R, Zhao H, Sun N. A misclassification model for inferring transcriptional regulatory networks. 2009, 20092645: 243-258. DOI: 10.1201/9781420010626.ch15.
- 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.
- A Misclassification Model for Inferring Transcriptional Regulatory NetworksVannucci M, Sun N, Zhao H. A Misclassification Model for Inferring Transcriptional Regulatory Networks. 2006, 347-365. DOI: 10.1017/cbo9780511584589.019.
- 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.
- Integrating mRNA Decay Information into Co-Regulation StudyChen L, Zhao H. Integrating mRNA Decay Information into Co-Regulation Study. Journal Of Computer Science And Technology 2005, 20: 434-438. DOI: 10.1007/s11390-005-0434-1.
- 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.
- Response to Dr. Kopke's comments on haplotypes at the OPRM1 locusLuo X, Gelernter J, Zhao H, Kranzler HR. Response to Dr. Kopke's comments on haplotypes at the OPRM1 locus. American Journal Of Medical Genetics Part B Neuropsychiatric Genetics 2005, 135B: 102-102. PMID: 15806579, DOI: 10.1002/ajmg.b.30060.
- 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.
- DNA-protein binding and gene expression patternsZhao H, Wu B, Sun N. DNA-protein binding and gene expression patterns. 2003, 259-274. DOI: 10.1214/lnms/1215091147.
- Light Control of Arabidopsis Development Entails Coordinated Regulation of Genome Expression and Cellular PathwaysMa L, Li J, Qu L, Hager J, Chen Z, Zhao H, Deng X. Light Control of Arabidopsis Development Entails Coordinated Regulation of Genome Expression and Cellular Pathways. The Plant Cell 2001, 13: 2589-2607. PMID: 11752374, PMCID: PMC139475, DOI: 10.1105/tpc.010229.
- 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.
- Light Control of Arabidopsis Development Entails Coordinated Regulation of Genome Expression and Cellular PathwaysMa L, Li J, Qu L, Hager J, Chen Z, Zhao H, Deng X. Light Control of Arabidopsis Development Entails Coordinated Regulation of Genome Expression and Cellular Pathways. The Plant Cell 2001, 13: 2589. DOI: 10.2307/3871521.
- Multipoint Genetic Mapping with Trisomy DataLi J, Sherman S, Lamb N, Zhao H. Multipoint Genetic Mapping with Trisomy Data. American Journal Of Human Genetics 2001, 69: 1255-1265. PMID: 11704925, PMCID: PMC1235537, DOI: 10.1086/324578.
- Comparisons of Two Methods for Haplotype Reconstruction and Haplotype Frequency Estimation from Population DataZhang S, Pakstis A, Kidd K, Zhao H. Comparisons of Two Methods for Haplotype Reconstruction and Haplotype Frequency Estimation from Population Data. American Journal Of Human Genetics 2001, 69: 906-912. PMID: 11536083, PMCID: PMC1226079, DOI: 10.1086/323622.
- Quantitative Similarity-Based Association Tests Using Population SamplesZhang S, Zhao H. Quantitative Similarity-Based Association Tests Using Population Samples. American Journal Of Human Genetics 2001, 69: 601-614. PMID: 11479834, PMCID: PMC1235489, DOI: 10.1086/323037.
- A stochastic modeling of early HIV-1 population dynamicsKamina A, Makuch R, Zhao H. A stochastic modeling of early HIV-1 population dynamics. Mathematical Biosciences 2001, 170: 187-198. PMID: 11292498, DOI: 10.1016/s0025-5564(00)00069-9.
- On Relationship Inference Using Gamete Identity by Descent DataZhao H, Liang F. On Relationship Inference Using Gamete Identity by Descent Data. Journal Of Computational Biology 2001, 8: 191-200. PMID: 11454305, DOI: 10.1089/106652701300312940.
- Test of Association for Quantitative Traits in General Pedigrees: The Quantitative Pedigree Disequilibrium TestZhang S, Zhang K, Li J, Sun F, Zhao H. Test of Association for Quantitative Traits in General Pedigrees: The Quantitative Pedigree Disequilibrium Test. Genetic Epidemiology 2001, 21: s370-s375. PMID: 11793701, DOI: 10.1002/gepi.2001.21.s1.s370.
- The Power of Transmission Disequilibrium Tests for Quantitative TraitsLi J, Wang D, Dong J, Jiang R, Zhang K, Zhang S, Zhao H, Sun F. The Power of Transmission Disequilibrium Tests for Quantitative Traits. Genetic Epidemiology 2001, 21: s632-s637. PMID: 11793752, DOI: 10.1002/gepi.2001.21.s1.s632.
- Family-based association studiesZhao H. Family-based association studies. Statistical Methods In Medical Research 2000, 9: 563-587. DOI: 10.1177/096228020000900604.
- Assessing reliability of gene clusters from gene expression dataZhang K, Zhao H. Assessing reliability of gene clusters from gene expression data. Functional & Integrative Genomics 2000, 1: 156-173. PMID: 11793234, DOI: 10.1007/s101420000019.
- 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.
- Transmission/disequilibrium tests for quantitative traitsSUN F, FLANDERS W, YANG Q, ZHAO H. Transmission/disequilibrium tests for quantitative traits. Annals Of Human Genetics 2000, 64: 555-565. DOI: 10.1017/s000348000000840x.
- Transmission/Disequilibrium Tests Using Multiple Tightly Linked MarkersZhao H, Zhang S, Merikangas K, Trixler M, Wildenauer D, Sun F, Kidd K. Transmission/Disequilibrium Tests Using Multiple Tightly Linked Markers. American Journal Of Human Genetics 2000, 67: 936-946. PMID: 10968775, PMCID: PMC1287895, DOI: 10.1086/303073.
- Multipoint Genetic Mapping with Uniparental Disomy DataZhao H, Li J, Robinson W. Multipoint Genetic Mapping with Uniparental Disomy Data. American Journal Of Human Genetics 2000, 67: 851-861. PMID: 10958760, PMCID: PMC1287890, DOI: 10.1086/303072.
- Haplotypes and Linkage Disequilibrium at the Phenylalanine Hydroxylase Locus, PAH, in a Global Representation of PopulationsKidd J, Pakstis A, Zhao H, Lu R, Okonofua F, Odunsi A, Grigorenko E, Tamir B, Friedlaender J, Schulz L, Parnas J, Kidd K. Haplotypes and Linkage Disequilibrium at the Phenylalanine Hydroxylase Locus, PAH, in a Global Representation of Populations. American Journal Of Human Genetics 2000, 66: 1882-1899. PMID: 10788337, PMCID: PMC1378054, DOI: 10.1086/302952.
- Linkage disequilibrium mapping in populations of variable size using the decay of haplotype sharing and a stepwise‐mutation modelZhang S, Zhao H. Linkage disequilibrium mapping in populations of variable size using the decay of haplotype sharing and a stepwise‐mutation model. Genetic Epidemiology 2000, 19: s99-s105. PMID: 11055377, DOI: 10.1002/1098-2272(2000)19:1+<::aid-gepi15>3.0.co;2-1.
- On a Randomization Procedure in Linkage AnalysisZhao H, Merikangas K, Kidd K. On a Randomization Procedure in Linkage Analysis. American Journal Of Human Genetics 1999, 65: 1449-1456. PMID: 10521312, PMCID: PMC1288298, DOI: 10.1086/302607.
- The Interpretation of the Parameters in the Transmission/Disequilibrium TestZhao H. The Interpretation of the Parameters in the Transmission/Disequilibrium Test. American Journal Of Human Genetics 1999, 64: 326-328. PMID: 9915979, PMCID: PMC1377738, DOI: 10.1086/302208.
- A more powerful method to evaluate p‐values in GENEHUNTERZhao H, Sheffield L, Pakstis A, Knauert M, Kidd K. A more powerful method to evaluate p‐values in GENEHUNTER. Genetic Epidemiology 1999, 17: s415-s420. PMID: 10597472, DOI: 10.1002/gepi.1370170770.
- Statistical Analysis of Ordered TetradsZhao H, Speed T. Statistical Analysis of Ordered Tetrads. Genetics 1998, 150: 459-472. PMID: 9725861, PMCID: PMC1460316, DOI: 10.1093/genetics/150.1.459.
- Statistical Analysis of Half-TetradsZhao H, Speed T. Statistical Analysis of Half-Tetrads. Genetics 1998, 150: 473-485. PMID: 9725862, PMCID: PMC1460320, DOI: 10.1093/genetics/150.1.473.
- A global survey of haplotype frequencies and linkage disequilibrium at the DRD2 locusKidd K, Morar B, Castiglione C, Zhao H, Pakstis A, Speed W, Bonne-Tamir B, Lu R, Goldman D, Lee C, Nam Y, Grandy D, Jenkins T, Kidd J. A global survey of haplotype frequencies and linkage disequilibrium at the DRD2 locus. Human Genetics 1998, 103: 211-227. PMID: 9760208, DOI: 10.1007/s004390050809.
- 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.
- Strategies to Identify Genes for Complex DiseasesZhang H, Zhao H, Merikangas K. Strategies to Identify Genes for Complex Diseases. Annals Of Medicine 1997, 29: 493-498. PMID: 9562515, DOI: 10.3109/07853899709007473.
- The Effects of Genotyping Errors and Interference on Estimation of Genetic DistanceGoldstein D, Zhao H, Speed T. The Effects of Genotyping Errors and Interference on Estimation of Genetic Distance. Human Heredity 1997, 47: 86-100. PMID: 9097090, DOI: 10.1159/000154396.
- On Genetic Map FunctionsZhao H, Speed T. On Genetic Map Functions. Genetics 1996, 142: 1369-1377. PMID: 8846913, PMCID: PMC1207133, DOI: 10.1093/genetics/142.4.1369.
- Relative Efficiencies of χ2 Models of Recombination for Exclusion Mapping and Gene OrderingGoldstein D, Zhao H, Speed T. Relative Efficiencies of χ2 Models of Recombination for Exclusion Mapping and Gene Ordering. Genomics 1995, 27: 265-273. PMID: 7557991, DOI: 10.1006/geno.1995.1041.