Evidence map›Paper›PMID 29562348›Full record

ArticleNucleic acids research2018

Efficient pathway enrichment and network analysis of GWAS summary data using GSA-SNP2.

Sora Yoon, Hai C T Nguyen, Yun J Yoo, Jinhwan Kim, Bukyung Baik, Sounkou Kim, Jin Kim, Sangsoo Kim, Dougu Nam

Abstract read
In one paragraph

Article in Nucleic acids research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
52citing papers in PubMed, 2 pooled it
–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

52 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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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

9 authors.

Sora YoonSchool of Life Sciences, Ulsan National Institute of Science and Technology, Ulsan 44919, Republic of Korea.
Hai C T NguyenSchool of Life Sciences, Ulsan National Institute of Science and Technology, Ulsan 44919, Republic of Korea.
Yun J YooInterdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, South Korea.
Jinhwan KimSchool of Life Sciences, Ulsan National Institute of Science and Technology, Ulsan 44919, Republic of Korea.
Bukyung BaikSchool of Life Sciences, Ulsan National Institute of Science and Technology, Ulsan 44919, Republic of Korea.
Sounkou KimSchool of Life Sciences, Ulsan National Institute of Science and Technology, Ulsan 44919, Republic of Korea.
Jin KimSK Telecom, Seoul 04539, Republic of Korea.
Sangsoo KimSchool of Systems Biomedical Science, Soongsil University, Seoul 06978, Republic of Korea.
Dougu NamSchool of Life Sciences, Ulsan National Institute of Science and Technology, Ulsan 44919, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pathway-based analysis in genome-wide association study (GWAS) is being widely used to uncover novel multi-genic functional associations. Many of these pathway-based methods have been used to test the enrichment of the associated genes in the pathways, but exhibited low powers and were highly affected by free parameters. We present the novel method and software GSA-SNP2 for pathway enrichment analysis of GWAS P-value data. GSA-SNP2 provides high power, decent type I error control and fast computation by incorporating the random set model and SNP-count adjusted gene score. In a comparative study using simulated and real GWAS data, GSA-SNP2 exhibited high power and best prioritized gold standard positive pathways compared with six existing enrichment-based methods and two self-contained methods (alternative pathway analysis approach). Based on these results, the difference between pathway analysis approaches was investigated and the effects of the gene correlation structures on the pathway enrichment analysis were also discussed. In addition, GSA-SNP2 is able to visualize protein interaction networks within and across the significant pathways so that the user can prioritize the core subnetworks for further studies. GSA-SNP2 is freely available at https://sourceforge.net/projects/gsasnp2.

Indexed as

SoftwareAsian PeopleBody HeightDatabases, GeneticDiabetes Mellitus, Type 2Genome-Wide Association StudyHumansPolymorphism, Single NucleotideProgramming LanguagesProtein Interaction Maps

Identifiers

PMID29562348
PMCPMC6007455

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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.