Ning Sun, PhD
Senior Research Scientist in BiostatisticsCards
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Senior Research Scientist in Biostatistics
Biography
Dr. Sun is a Research Scientist in the Department of Biostatistics. She received her B.S. in Civil and Environmental Engineering from Tsinghua University in 1991 and Ph.D. in Environmental Engineering from the University of California at Los Angeles in 1998. Prior to her position at Yale School of Public Health, she was a Postdoctoral Associate and Associate Research Scientist in Chemical Engineering at Yale between 1998 and 2000. Dr. Sun has extensive experiences in environmental engineering focusing on modeling contaminant transport in heterogeneous porous media, and she developed a mathematical and computational approach for colloid surface interactions in Chemical Engineering. As a bioinformatician and biostatistician, Dr. Sun is experienced in statistical methods for transcriptome analysis using both gene expression microarray data and next generation sequencing data, regulatory network modeling, data integration methods, functional genomics, and genome wide association studies. Dr. Sun has developed a number of novel statistical methods for more efficient data integration for the purpose of inferring gene expression networks and disease gene identifications.
Appointments
Biostatistics
Senior Research ScientistPrimary
Other Departments & Organizations
Education & Training
- PhD
- University of California at Los Angeles (1998)
- MS
- University of California at Los Angeles (1994)
- BS
- Tsinghua University (1991)
Research
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Overview
Medical Research Interests
Public Health Interests
ORCID
0000-0002-4123-5362
Research at a Glance
Yale Co-Authors
Publications Timeline
Research Interests
Hongyu Zhao, PhD
Mihaela Aslan, PhD
John Concato, MD, FACP, MS, MPH
Nallakkandi Rajeevan, PhD
Joel Gelernter, MD
Susan Tausta
Genomics
Publications
2020
Genotyping Array Design and Data Quality Control in the Million Veteran Program
Hunter-Zinck H, Shi Y, Li M, Gorman BR, Ji SG, Sun N, Webster T, Liem A, Hsieh P, Devineni P, Karnam P, Gong X, Radhakrishnan L, Schmidt J, Assimes TL, Huang J, Pan C, Humphries D, Brophy M, Moser J, Muralidhar S, Huang GD, Przygodzki R, Concato J, Gaziano JM, Gelernter J, O’Donnell C, Hauser ER, Zhao H, O’Leary T, Program V, Tsao PS, Pyarajan S. Genotyping Array Design and Data Quality Control in the Million Veteran Program. American Journal Of Human Genetics 2020, 106: 535-548. PMID: 32243820, PMCID: PMC7118558, DOI: 10.1016/j.ajhg.2020.03.004.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsMillion Veteran ProgramGenome-wide association studiesGenome-wide scanHigh-quality genotypesArray-based genotypingWhole-genome sequencingNon-European individualsAssociation studiesGenetic markersOmics assaysAxiom arrayDownstream analysisVeteran ProgramCommon variantsGenetic associationAfrican American ancestryAmerican ancestryMVP cohortRare variantsSingle assayDiversityFurther data releasesLarge biobanksPromising resourceQuality control
2019
Genome‐wide association study of cognitive performance in U.S. veterans with schizophrenia or bipolar disorder
Harvey PD, Sun N, Bigdeli TB, Fanous AH, Aslan M, Malhotra AK, Lu Q, Hu Y, Li B, Chen Q, Mane S, Miller P, Rajeevan N, Sayward F, Cheung K, Li Y, Greenwood TA, Gur RE, Braff DL, on the Genetics of Schizophrenia C, Brophy M, Pyarajan S, O'Leary TJ, Gleason T, Przygodszki R, Muralidhar S, Gaziano JM, Concato J, Zhao H, Siever LJ. Genome‐wide association study of cognitive performance in U.S. veterans with schizophrenia or bipolar disorder. American Journal Of Medical Genetics Part B Neuropsychiatric Genetics 2019, 183: 181-194. PMID: 31872970, DOI: 10.1002/ajmg.b.32775.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsMeSH KeywordsAdultAgedAllelesBipolar DisorderCognitionCognition DisordersFemaleGenome-Wide Association StudyGenotypeHumansMaleMiddle AgedNeuropsychological TestsOligonucleotide Array Sequence AnalysisPolymorphism, Single NucleotideSchizophreniaUnited StatesUnited States Department of Veterans AffairsVeteransGENOMICS OF SUICIDAL IDEATION AND BEHAVIOR IN VETERANS WITH SEVERE MENTAL ILLNESS
Harvey 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.Peer-Reviewed Original ResearchS93. GENOMICS OF SUICIDAL IDEATION AND BEHAVIOR IN VETERANS WITH SCHIZOPHRENIA AND BIPOLAR ILLNESS
Harvey P, Aslan M, Sun N, Zhao H, Concato J. S93. GENOMICS OF SUICIDAL IDEATION AND BEHAVIOR IN VETERANS WITH SCHIZOPHRENIA AND BIPOLAR ILLNESS. Schizophrenia Bulletin 2019, 45: s342-s342. PMCID: PMC6455790, DOI: 10.1093/schbul/sbz020.638.Peer-Reviewed Original ResearchM25 GENETICS OF COGNITIVE FUNCTION IN SCHIZOPHRENIA AND BIPOLAR DISORDER
Harvey P, Sun N, Lu Q, Hu Y, Li B, Chen Q, Aslan M, Radhakrishnan K, Cheung K, Li Y, Sayward F, Rajeevan N, Zhao H, Gaziano M, Concato J. M25 GENETICS OF COGNITIVE FUNCTION IN SCHIZOPHRENIA AND BIPOLAR DISORDER. European Neuropsychopharmacology 2019, 29: s967-s968. DOI: 10.1016/j.euroneuro.2017.08.332.Peer-Reviewed Original ResearchCitations
2016
Arabidopsis SAURs are critical for differential light regulation of the development of various organs
Sun N, Wang J, Gao Z, Dong J, He H, Terzaghi W, Wei N, Deng XW, Chen H. Arabidopsis SAURs are critical for differential light regulation of the development of various organs. Proceedings Of The National Academy Of Sciences Of The United States Of America 2016, 113: 6071-6076. PMID: 27118848, PMCID: PMC4889401, DOI: 10.1073/pnas.1604782113.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsPhytochrome-interacting factorsAuxin levelsType 2C protein phosphatasesElongation of hypocotylsExpansion of cotyledonsSmall auxinArabidopsis seedlingsProtein phosphataseRNA genesHypocotyl elongationCotyledon expansionTranscriptomic analysisLight regulationDifferential regulationCell enlargementHypocotylsSAURPhosphatase activityCotyledonsDifferential growthGenesMutation analysisExpressionRegulationDeetiolation
2014
Putting things in order
Sun N, Zhao H. Putting things in order. Proceedings Of The National Academy Of Sciences Of The United States Of America 2014, 111: 16236-16237. PMID: 25380598, PMCID: PMC4246324, DOI: 10.1073/pnas.1418862111.Peer-Reviewed Original ResearchCitationsAltmetric
2009
A transcriptome atlas of rice cell types uncovers cellular, functional and developmental hierarchies
Jiao Y, Lori Tausta S, Gandotra N, Sun N, Liu T, Clay NK, Ceserani T, Chen M, Ma L, Holford M, Zhang HY, Zhao H, Deng XW, Nelson T. A transcriptome atlas of rice cell types uncovers cellular, functional and developmental hierarchies. Nature Genetics 2009, 41: 258-263. PMID: 19122662, DOI: 10.1038/ng.282.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and Concepts
2006
Bayesian error analysis model for reconstructing transcriptional regulatory networks
Sun N, Carroll RJ, Zhao H. Bayesian error analysis model for reconstructing transcriptional regulatory networks. Proceedings Of The National Academy Of Sciences Of The United States Of America 2006, 103: 7988-7993. PMID: 16702552, PMCID: PMC1472417, DOI: 10.1073/pnas.0600164103.Peer-Reviewed Original ResearchCitationsMeSH Keywords and ConceptsConceptsTranscriptional regulatory networksGene expression dataTranscription regulationRegulatory networksExpression dataProtein-DNA binding dataDNA sequence dataFundamental biological processesYeast cell cycleHigh-throughput technologiesMicroarray gene expression dataBiological experimentsSequence dataGenomic dataBiological processesCell cycleClear biological interpretationThroughput technologiesBiological interpretationMarkov chain Monte CarloBayesian hierarchical model frameworkBiochemical reactionsRegulationLinear system modelHierarchical model framework
2004
Genomic approaches in dissecting complex biological pathways
Sun N, Zhao H. Genomic approaches in dissecting complex biological pathways. Pharmacogenomics 2004, 5: 163-179. PMID: 15016608, DOI: 10.1517/phgs.5.2.163.27488.Peer-Reviewed Original ResearchCitationsMeSH Keywords and ConceptsConceptsNetwork modelScale-free network modelDifferent genomic dataBoolean network modelDifferential equationsScale-free propertyMathematical modelBayesian network modelLarge-scale genomic dataGeneral network propertiesLarge-scale dataData integration methodIntegration methodGenomic dataLocal propertiesComputational methodsNetwork motifsData typesNetwork propertiesRich informationPrior knowledgeDifferent modelsRigorous comparisonEquationsMajor methodologies
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