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
2012
Sparse principal component analysis by choice of norm
Qi 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.Peer-Reviewed Original ResearchHigh-dimensional situationsSparse principal component analysisReal gene expression dataEfficient iterative algorithmHigh-dimensional dataSparse principal component analysis methodEigenvalue problemOptimization problemIterative methodChoice of normDimensional situationTheoretical resultsTraditional eigenvalue problemIterative algorithmStrict convexityLinear combinationSingle-component modelExpensive computationSparse linear combinationDimensional dataUsual normExistence of correlationsGene expression dataPractical applicationsCompetitive results
1999
On a Randomization Procedure in Linkage Analysis
Zhao 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.Peer-Reviewed Original ResearchConceptsEfficient simulation procedureObserved test statisticSimulation-based methodTheoretical resultsTest statisticNovel simulation methodSimulation methodReal dataSimulation procedureUninformative markersTheoretical workStatistical testsPedigree structureGenomewide significance levelRandomization procedureDiabetes dataStatisticsA more powerful method to evaluate p‐values in GENEHUNTER
Zhao 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.Peer-Reviewed Original Research