2023
Tuning Parameters for Polygenic Risk Score Methods Using GWAS Summary Statistics from Training Data
Jiang 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.Peer-Reviewed Original Research
2021
Comparison of methods for estimating genetic correlation between complex traits using GWAS summary statistics
Zhang Y, Cheng Y, Jiang W, Ye Y, Lu Q, Zhao H. Comparison of methods for estimating genetic correlation between complex traits using GWAS summary statistics. Briefings In Bioinformatics 2021, 22: bbaa442. PMID: 33497438, PMCID: PMC8425307, DOI: 10.1093/bib/bbaa442.Peer-Reviewed Original ResearchConceptsReal data applicationData applicationsCorrelation estimation methodsGWAS summary statisticsSample overlapIndividual-level genotype dataSummary statisticsComputational efficiencyGenetic correlation estimationLD estimationEstimation methodCorrelation estimationComprehensive simulationsMajor technical challengeStatisticsTechnical challengesEasy accessBenchmark studyComparison of methodsEstimationInformative metricsPost-GWAS analysisSimulationsArchitectureApplications
2020
Leveraging effect size distributions to improve polygenic risk scores derived from summary statistics of genome-wide association studies
Song 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.Peer-Reviewed Original ResearchConceptsEffect size distributionClass of methodsReal data applicationOnly summary statisticsTheoretical resultsSummary statisticsExtensive simulation resultsLD informationSimulation resultsData applicationsFirst methodImportant problemOptimal propertiesGenetic risk predictionAccurate predictionPrediction accuracyStandard PRSStatisticsPrediction method
2018
Empirical Bayes Estimation and Prediction Using Summary-Level Information From External Big Data Sources Adjusting for Violations of Transportability
Estes J, Mukherjee B, Taylor J. Empirical Bayes Estimation and Prediction Using Summary-Level Information From External Big Data Sources Adjusting for Violations of Transportability. Statistics In Biosciences 2018, 10: 568-586. PMID: 31123532, PMCID: PMC6529204, DOI: 10.1007/s12561-018-9217-4.Peer-Reviewed Original ResearchEmpirical Bayes estimatorsSummary-level informationConstrained maximum likelihoodBayes estimatorsEmpirical Bayes shrinkage estimatorsSimulation studyBayes shrinkage estimatorShrinkage estimatorsLikelihood estimationCovariate distributionsConditional probability distributionData applicationsTrade biasMaximum likelihoodProbability distributionLoss of efficiencyCancer Prevention TrialIndividual-level dataEstimationProstate Cancer Prevention TrialPrevention trialsInternational population
2016
On high-dimensional misspecified mixed model analysis in genome-wide association study
Jiang 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.Peer-Reviewed Original ResearchREML estimatorsAsymptotic resultsAsymptotic conditional varianceReal data applicationRandom effectsMaximum likelihood estimatorExtensive simulation studyAsymptotic analysisConvergence rateLikelihood estimatorLinear mixed modelsEstimatorSimulation studyConvergenceData applicationsTrue varianceConditional varianceImportant genetic implicationsCertain limits
2015
Estimating DNA Methylation Levels by Joint Modeling of Multiple Methylation Profiles From Microarray Data
Wang T, Chen M, Zhao H. Estimating DNA Methylation Levels by Joint Modeling of Multiple Methylation Profiles From Microarray Data. Biometrics 2015, 72: 354-363. PMID: 26433612, PMCID: PMC4820364, DOI: 10.1111/biom.12422.Peer-Reviewed Original Research
2014
“Big Data” and the Electronic Health Record
Ross M, Wei W, Ohno-Machado L. “Big Data” and the Electronic Health Record. Yearbook Of Medical Informatics 2014, 23: 97-104. PMID: 25123728, PMCID: PMC4287068, DOI: 10.15265/iy-2014-0003.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsBig dataEHR systemsElectronic health record systemsHealth record systemsData miningElectronic health recordsData applicationsActionable knowledgeMassive numberAdditional keywordsNew keywordsSecondary useInformatics professionalsHealth recordsRecord systemKeywordsLarge amountPrivacyNext stepMiningSecurityEHRSystemImplementationData
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