A unified method for rare variant analysis of gene‐environment interactions-Reference-Cited by-同舟云学术

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A unified method for rare variant analysis of gene‐environment interactions

Published:2019-12-04 Issue:6 Volume:39 Page:801-813
ISSN:0277-6715
Container-title:Statistics in Medicine
language:en
Short-container-title:Statistics in Medicine

Author:

Lim Elise¹^ORCID,Chen Han²³,Dupuis Josée¹,Liu Ching‐Ti¹^ORCID

Affiliation:

1. Department of BiostatisticsBoston University Boston Massachusetts

2. Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public HealthThe University of Texas Health Science Center at Houston Houston Texas

3. Center for Precision Health, School of Public Health and School of Biomedical InformaticsThe University of Texas Health Science Center at Houston Houston Texas

Funder

National Institutes of Health

Publisher

Wiley

Subject

Statistics and Probability,Epidemiology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.8446

Reference33 articles.

1. Enfermedades complejas y análisis genéticos por el método GWAS. Ventajas y limitaciones

2. Meta-analysis of genome-wide association studies of adult height in East Asians identifies 17 novel loci

3. Finding Disease Variants in Mendelian Disorders By Using Sequence Data: Methods and Applications

4. Rare-Variant Association Analysis: Study Designs and Statistical Tests

5. Pathway analysis of complex diseases for GWAS, extending to consider rare variants, multi-omics and interactions

Cited by 9 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Gene–environment interactions in human health;Nature Reviews Genetics;2024-05-28

2. Cauchy combination methods for the detection of gene–environment interactions for rare variants related to quantitative phenotypes;Heredity;2023-07-22

3. A Varying Coefficient Model to Jointly Test Genetic and Gene–Environment Interaction Effects;Behavior Genetics;2023-01-09

4. A tree‐based gene–environment interaction analysis with rare features;Statistical Analysis and Data Mining: The ASA Data Science Journal;2022-03

5. A review of regression and classification techniques for analysis of common and rare variants and gene-environmental factors;Neurocomputing;2021-12

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