Efficient testing and effect size estimation for set‐based genetic association inference via semiparametric multilevel mixture modeling-Reference-Cited by-同舟云学术

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Efficient testing and effect size estimation for set‐based genetic association inference via semiparametric multilevel mixture modeling

Published:2022-05-11 Issue:6 Volume:64 Page:1142-1152
ISSN:0323-3847
Container-title:Biometrical Journal
language:en
Short-container-title:Biometrical J

Author:

Sugasawa Shonosuke¹²^ORCID,Noma Hisashi²³^ORCID

Affiliation:

1. Center for Spatial Information Science The University of Tokyo Chiba Japan

2. Research Center for Medical and Health Data Science The Institute of Statistical Mathematics Tokyo Japan

3. Department of Data Science The Institute of Statistical Mathematics Tokyo Japan

Funder

Japan Society for the Promotion of Science

Japan Agency for Medical Research and Development

Core Research for Evolutional Science and Technology

Publisher

Wiley

Subject

Statistics, Probability and Uncertainty,General Medicine,Statistics and Probability

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/bimj.202100234

Reference35 articles.

1. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

2. Bayesian optimal discovery procedure for simultaneous significance testing

3. Group association test using a hidden Markov model

4. Genome-wide rare variant analysis for thousands of phenotypes in over 70,000 exomes from two cohorts

5. Multiple Rare Alleles Contribute to Low Plasma Levels of HDL Cholesterol

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