A Unifying Framework for Imputing Summary Statistics in Genome-Wide Association Studies-Reference-Cited by-同舟云学术

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A Unifying Framework for Imputing Summary Statistics in Genome-Wide Association Studies

Published:2020-03-01 Issue:3 Volume:27 Page:418-428
ISSN:1557-8666
Container-title:Journal of Computational Biology
language:en
Short-container-title:Journal of Computational Biology

Author:

Wu Yue¹,Eskin Eleazar¹²³,Sankararaman Sriram¹²³

Affiliation:

1. Department of Computer Science, University of California, Los Angeles, Los Angeles.

2. Department of Human Genetics, and University of California, Los Angeles, Los Angeles.

3. Department of Computational Medicine, University of California, Los Angeles, Los Angeles.

Publisher

Mary Ann Liebert Inc

Subject

Computational Theory and Mathematics,Computational Mathematics,Genetics,Molecular Biology,Modelling and Simulation

Link

https://www.liebertpub.com/doi/pdf/10.1089/cmb.2019.0449

Reference27 articles.

1. Rapid and Accurate Haplotype Phasing and Missing-Data Inference for Whole-Genome Association Studies By Use of Localized Haplotype Clustering

2. A genome-wide association study identifies KIAA0350 as a type 1 diabetes gene

3. Rapid and Accurate Multiple Testing Correction and Power Estimation for Millions of Correlated Markers

4. Identification of causal genes for complex traits

5. Identifying Causal Variants at Loci with Multiple Signals of Association

Cited by 2 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. Animal-SNPAtlas: a comprehensive SNP database for multiple animals;Nucleic Acids Research;2022-10-27

2. Plant-ImputeDB: an integrated multiple plant reference panel database for genotype imputation;Nucleic Acids Research;2020-11-02

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