Improving Imputation Accuracy by Inferring Causal Variants in Genetic Studies-Reference-Cited by-同舟云学术

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Improving Imputation Accuracy by Inferring Causal Variants in Genetic Studies

Published:2019-11-01 Issue:11 Volume:26 Page:1203-1213
ISSN:1557-8666
Container-title:Journal of Computational Biology
language:en
Short-container-title:Journal of Computational Biology

Author:

Wu Yue¹,Hormozdiari Farhad¹²³,Joo Jong Wha J.⁴,Eskin Eleazar¹⁵

Affiliation:

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

2. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.

3. Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts.

4. Department of Computer Science and Engineering, Dongguk University, Seoul, South Korea.

5. Department of Human Genetics, University of California Los Angeles, Los Angeles, California.

Publisher

Mary Ann Liebert Inc

Subject

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

Link

https://www.liebertpub.com/doi/pdf/10.1089/cmb.2018.0139

Reference31 articles.

1. Missing data imputation and haplotype phase inference for genome-wide association studies

2. Genomic Control for Association Studies

3. A map of human genome variation from population-scale sequencing

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

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

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