Evidence map›Paper›PMID 29986644›Full record

ArticleBMC genomics2018

Stepwise approach to SNP-set analysis illustrated with the Metabochip and colorectal cancer in Japanese Americans of the Multiethnic Cohort.

John Cologne, Lenora Loo, Yurii B Shvetsov, Munechika Misumi, Philip Lin, Christopher A Haiman, Lynne R Wilkens, Loïc Le Marchand

Open access · goldAbstract read
In one paragraph

Article in BMC genomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.8field-weighted citation impact, top 27% of its field
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

5 citing papers in PubMed, 8 citations in OpenAlex.

  1. Review
  2. Cell cycle (Georgetown, Tex.) · 2023
    Article
  3. Review
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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

8 authors at 3 institutions in 2 countries.

John CologneDepartment of Statistics, Radiation Effects Research Foundation, Hiroshima, 732-0815, Japan. jcologne@rerf.jp.ORCID http://orcid.org/0000-0003-1540-6639
Lenora LooEpidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, 96813, USA.
Yurii B ShvetsovEpidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, 96813, USA.
Munechika MisumiDepartment of Statistics, Radiation Effects Research Foundation, Hiroshima, 732-0815, Japan.
Philip LinEpidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, 96813, USA.
Christopher A HaimanDepartment of Preventive Medicine and Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA.
Lynne R WilkensBiostatistics and Informatics Shared Resource, University of Hawaii Cancer Center, Honolulu, HI, 96813, USA.
Loïc Le MarchandEpidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, 96813, USA.
University of Hawaiʻi at Mānoa · USRadiation Effects Research Foundation · JPUniversity of Southern California · US

Funding

University of Hawaii Cancer Center CCSGP30CA071789 · NCI · UNIVERSITY OF HAWAII AT MANOA · PI GERTRAUD MASKARINEC · 1996 to 2026
$56.2M
Understanding Population Differences in Cancer: The MEC StudyU01CA164973 · NCI · UNIVERSITY OF HAWAII AT MANOA · PI HAIMAN, CHRISTOPHER ALAN, LE MARCHAND, LOIC · 2015 to 2025
$37.4M
Epidemiology of Putative Causal Variants in the Multiethnic CohortU01HG004802 · NHGRI · UNIVERSITY OF HAWAII AT MANOA · PI LE MARCHAND, LOIC · 2008 to 2012
$8.4M
National Institutes of Health U01 CA164973National Institutes of Health U01 HG004802NCI NIH HHS P30 CA071789NCI NIH HHS U01 CA164973NHGRI NIH HHS U01 HG004802
6 · The paper itself

Abstract

backgroundCommon variants have explained less than the amount of heritability expected for complex diseases, which has led to interest in less-common variants and more powerful approaches to the analysis of whole-genome scans. Because of low frequency (low statistical power), less-common variants are best analyzed using SNP-set methods such as gene-set or pathway-based analyses. However, there is as yet no clear consensus regarding how to focus in on potential risk variants following set-based analyses. We used a stepwise, telescoping approach to analyze common- and rare-variant data from the Illumina Metabochip array to assess genomic association with colorectal cancer (CRC) in the Japanese sub-population of the Multiethnic Cohort (676 cases, 7180 controls). We started with pathway analysis of SNPs that are in genes and pathways having known mechanistic roles in colorectal cancer, then focused on genes within the pathways that evidenced association with CRC, and finally assessed individual SNPs within the genes that evidenced association. Pathway SNPs downloaded from the dbSNP database were cross-matched with Metabochip SNPs and analyzed using the logistic kernel machine regression approach (logistic SNP-set kernel-machine association test, or sequence kernel association test; SKAT) and related methods.

resultsThe TGF-β and WNT pathways were associated with all CRC, and the WNT pathway was associated with colon cancer. Individual genes demonstrating the strongest associations were TGFBR2 in the TGF-β pathway and SMAD7 (which is involved in both the TGF-β and WNT pathways). As partial validation of our approach, a known CRC risk variant in SMAD7 (in both the TGF-β and WNT pathways: rs11874392) was associated with CRC risk in our data. We also detected two novel candidate CRC risk variants (rs13075948 and rs17025857) in TGFBR2, a gene known to be associated with CRC risk.

conclusionsA stepwise, telescoping approach identified some potentially novel risk variants associated with colorectal cancer, so it may be a useful method for following up on results of set-based SNP analyses. Further work is required to assess the statistical characteristics of the approach, and additional applications should aid in better clarifying its utility.

Indexed as

Polymorphism, Single NucleotideAsianCohort StudiesColorectal NeoplasmsGenome-Wide Association StudyGenotypeHumansJapanReceptor, Transforming Growth Factor-beta Type IIRiskSignal TransductionSmad7 ProteinTransforming Growth Factor betaReceptor, Transforming Growth Factor-beta Type IISmad7 ProteinSMAD7 protein, humanTGFBR2 protein, humanTransforming Growth Factor betaColorectal cancerGenome-wide association studyMetabochip arrayMultiethnic CohortSNP set analysis

Identifiers

PMID29986644
PMCPMC6038257
OpenAlexW2829808564

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.