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WISARD: workbench for integrated superfast association studies for related datasets

Cited 3 time in Web of Science Cited 3 time in Scopus
Authors
Lee, Sungyoung; Choi, Sungkyoung; Qiao, Dandi; Cho, Michael; Silverman, Edwin K; Park, Taesung; Won, Sungho
Issue Date
2018-04-20
Publisher
BioMed Central
Citation
BMC Medical Genomics, 11(Suppl 2):39
Keywords
Family-based designGenome-wide association analysesNext generation sequencingMulti-threaded analysesRelated samples
Abstract
Background
A Mendelian transmission produces phenotypic and genetic relatedness between family members, giving family-based analytical methods an important role in genetic epidemiological studies—from heritability estimations to genetic association analyses. With the advance in genotyping technologies, whole-genome sequence data can be utilized for genetic epidemiological studies, and family-based samples may become more useful for detecting de novo mutations. However, genetic analyses employing family-based samples usually suffer from the complexity of the computational/statistical algorithms, and certain types of family designs, such as incorporating data from extended families, have rarely been used.

Results
We present a Workbench for Integrated Superfast Association studies for Related Data (WISARD) programmed in C/C++. WISARD enables the fast and a comprehensive analysis of SNP-chip and next-generation sequencing data on extended families, with applications from designing genetic studies to summarizing analysis results. In addition, WISARD can automatically be run in a fully multithreaded manner, and the integration of R software for visualization makes it more accessible to non-experts.

Conclusions
Comparison with existing toolsets showed that WISARD is computationally suitable for integrated analysis of related subjects, and demonstrated that WISARD outperforms existing toolsets. WISARD has also been successfully utilized to analyze the large-scale massive sequencing dataset of chronic obstructive pulmonary disease data (COPD), and we identified multiple genes associated with COPD, which demonstrates its practical value.
ISSN
1755-8794
Language
English
URI
https://hdl.handle.net/10371/141243
DOI
https://doi.org/10.1186/s12920-018-0345-y
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College of Natural Sciences (자연과학대학)Dept. of Statistics (통계학과)Journal Papers (저널논문_통계학과)
College of Natural Sciences (자연과학대학)Program in Bioinformatics (협동과정-생물정보학전공)Journal Papers (저널논문_협동과정-생물정보학전공)
Graduate School of Public Health (보건대학원)Dept. of Public Health (보건학과)Journal Papers (저널논문_보건학과)
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