A weighted empirical Bayes risk prediction model using multiple traits

DOI10.1515/SAGMB-2019-0056zbMath1461.92074OpenAlexW3083057855WikidataQ98950850 ScholiaQ98950850MaRDI QIDQ830640

Gengxin Li, Cen Wu, Lin Hou, Xiaoyu Liu

Publication date: 7 May 2021

Published in: Statistical Applications in Genetics and Molecular Biology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1515/sagmb-2019-0056

zbMATH Keywords

empirical Bayesrare variantsrisk predictionnext-generation sequencing

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Protein sequences, DNA sequences (92D20) Genetics and epigenetics (92D10)

Uses Software

• SNPeffect

Cites Work

• On the maximum of bivariate normal random variables
• Bias-reduced estimators and confidence intervals for odds ratios in genome-wide association studies
• Empirical Bayes Estimates for Large-Scale Prediction Problems
• Ridge Regression: Biased Estimation for Nonorthogonal Problems
• Random forests
• An empirical Bayes risk prediction model using multiple traits for sequencing data

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