2022
Variance estimation and confidence intervals from genome-wide association studies through high-dimensional misspecified mixed model analysis
Dao 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.Peer-Reviewed Original Research
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
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