Evidence map›Paper›PMID 29697360›Full record

ArticleBMC medical genomics2018

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

Sungyoung Lee, Sungkyoung Choi, Dandi Qiao, Michael Cho, Edwin K Silverman, Taesung Park, Sungho Won

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical genomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Sungyoung LeeInterdisciplinary Program in Bioinformatics, Seoul National University, Seoul, South Korea.
Sungkyoung ChoiDepartment of Pharmacology, Yonsei University College of Medicine, Seoul, South Korea.
Dandi QiaoChanning Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Michael ChoChanning Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Edwin K SilvermanChanning Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Taesung ParkInterdisciplinary Program in Bioinformatics, Seoul National University, Seoul, South Korea. tspark@stats.snu.ac.kr.
Sungho WonInterdisciplinary Program in Bioinformatics, Seoul National University, Seoul, South Korea. won1@snu.ac.kr.

Funding

Identifying Genetic Determinants of Severe, Early-Onset COPDR01HL113264 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI CHO, MICHAEL H., SILVERMAN, EDWIN K · 2012 to 2016
$6.0M
Network-based Methods for Understanding Genetic Determinants and Epistasis in COPDK01HL129039 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI QIAO, DANDI · 2015 to 2019
$844k
NHLBI NIH HHS K01 HL129039NHLBI NIH HHS R01 HL113264
6 · The paper itself

Abstract

backgroundA 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.

resultsWe 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.

conclusionsComparison 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.

Indexed as

Programming LanguagesAlgorithmsGenome-Wide Association StudyHigh-Throughput Nucleotide SequencingHumansPolymorphism, Single NucleotidePulmonary Disease, Chronic ObstructiveSequence Analysis, DNAFamily-based designGenome-wide association analysesMulti-threaded analysesNext generation sequencingRelated samples

Identifiers

PMID29697360
PMCPMC5918457

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.