A genome-wide scan statistic framework for whole-genome sequence data analysis

Zihuai He; Bin Xu; Joseph Buxbaum; Iuliana Ionita-Laza

doi:10.1038/s41467-019-11023-0

Nature Communications (Jul 2019)

A genome-wide scan statistic framework for whole-genome sequence data analysis

Zihuai He,
Bin Xu,
Joseph Buxbaum,
Iuliana Ionita-Laza

Affiliations

Zihuai He: Department of Biostatistics, Columbia University
Bin Xu: Department of Psychiatry, Columbia University
Joseph Buxbaum: Departments of Psychiatry, Neuroscience, and Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai
Iuliana Ionita-Laza: Department of Biostatistics, Columbia University

DOI: https://doi.org/10.1038/s41467-019-11023-0
Journal volume & issue: Vol. 10, no. 1
pp. 1 – 11

Abstract

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Whole-genome sequencing data reveals a large number of variants for testing their associations with phenotypic traits and diseases. Here, the authors develop WGScan, a statistical method for detecting the existence and estimating the locations of the association signal at genome-wide scale.

Published in Nature Communications

ISSN: 2041-1723 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/ncomms/

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