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WISARD: workbench for integrated superfast association studies for related datasets.

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.

journal_name

BMC Med Genomics

journal_title

BMC medical genomics

authors

Lee S,Choi S,Qiao D,Cho M,Silverman EK,Park T,Won S

doi

10.1186/s12920-018-0345-y

subject

Has Abstract

pub_date

2018-04-20 00:00:00

pages

39

issue

Suppl 2

issn

1755-8794

pii

10.1186/s12920-018-0345-y

journal_volume

11

pub_type

杂志文章

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