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
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