Evidence map›Paper›PMID 29351777›Full record

ArticleBMC medical genomics2018

Are minor alleles more likely to be risk alleles?

Takashi Kido, Weronika Sikora-Wohlfeld, Minae Kawashima, Shinichi Kikuchi, Naoyuki Kamatani, Anil Patwardhan, Richard Chen, Marina Sirota, Keiichi Kodama, Dexter Hadley and 1 more

Open access · goldAbstract read
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 35 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed, 1 pooled it
6.1field-weighted citation impact, top 3% 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

35 citing papers in PubMed, 1 synthesis or guideline pooled it, 91 citations in OpenAlex.

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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

11 authors at 6 institutions in 2 countries.

Takashi KidoRikengenesis Co., Ltd., 1-2-2 Ohsaki, Shinagawa-ku, Tokyo, 141-0032, Japan. kido.takashi@gmail.com.
Weronika Sikora-WohlfeldDivision of Systems Medicine, Department of Pediatrics, Stanford University, Stanford, CA, 94305-5208, USA.
Minae KawashimaDepartment of Human Genetics, Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Hongo, 7-3-1, Tokyo, 113-0033, Japan.
Shinichi KikuchiAI System Department, DeNA, Inc., Shibuya 2-21-1, Tokyo, 150-8510, Japan.
Naoyuki KamataniStaGen Inc., 4-11-6, Kuramae, Taito-ku, Tokyo, 111-0051, Japan.
Anil PatwardhanPersonalis, Inc., 1330 O'Brien Dr, Menlo Park, CA, 94025, USA.
Richard ChenPersonalis, Inc., 1330 O'Brien Dr, Menlo Park, CA, 94025, USA.
Marina SirotaInstitute for Computational Health Sciences, University of California, 550 16th Street, San Francisco, CA, 94158, USA.
Keiichi KodamaInstitute for Computational Health Sciences, University of California, 550 16th Street, San Francisco, CA, 94158, USA.
Dexter HadleyInstitute for Computational Health Sciences, University of California, 550 16th Street, San Francisco, CA, 94158, USA.
Atul J ButteInstitute for Computational Health Sciences, University of California, 550 16th Street, San Francisco, CA, 94158, USA.
University of California, San Francisco · USPersonalis (United States) · USShibuya (Japan) · JPStaGen (Japan) · JPStanford University · USThe University of Tokyo · JP

Funding

Spectrum Stanford Center for clinical and Translational Research and EducationUL1TR001085 · NCATS · STANFORD UNIVERSITY · PI CULLEN, MARK RICHARD, GREENBERG, HARRY BERNARD · 2013 to 2017
$37.1M
Clinically Relevant Genome Variation DatabaseU01HG007436 · NHGRI · STANFORD UNIVERSITY · PI BUSTAMANTE, CARLOS DANIEL, PLON, SHARON E. · 2013 to 2016
$10.5M
RESOURCE-BASED CENTER FOR THE ADVANCEMENT OF PRECISION MEDICINE IN RHEUMATOLOGYP30AR070155 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Mary C Nakamura · 2016 to 2026
$8.2M
Crowd-Assisted Deep Learning (CrADLe) Digital Curation to Translate Big Data into Precision MedicineU01LM012675 · NLM · UNIVERSITY OF CENTRAL FLORIDA · PI HADLEY, DEXTER D · 2017 to 2020
$2.1M
Elucidating the Role of the Genetic and Environmental Determinants of Preterm Birth Using Integrative Computational ApproachesK01LM012381 · NLM · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI SIROTA, MARINA · 2016 to 2018
$531k
NCATS NIH HHS UL1 TR001085NHGRI NIH HHS U01 HG007436NIAMS NIH HHS P30 AR070155NLM NIH HHS K01 LM012381NLM NIH HHS U01 LM012675
6 · The paper itself

Abstract

backgroundGenome-wide association studies (GWASs) have revealed relationships between over 57,000 genetic variants and diseases. However, unlike Mendelian diseases, complex diseases arise from the interplay of multiple genetic and environmental factors. Natural selection has led to a high tendency of risk alleles to be enriched in minor alleles in Mendelian diseases. Therefore, an allele that was previously advantageous or neutral may later become harmful, making it a risk allele.

methodsUsing data in the NHGRI-EBI Catalog and the VARIMED database, we investigated whether (1) GWASs more easily detect risk alleles and (2) facilitate evolutionary insights by comparing risk allele frequencies of different diseases. We conducted computer simulations of P-values for association tests when major and minor alleles were risk alleles. We compared the expected proportion of SNVs whose risk alleles were minor alleles with the observed proportion.

resultsOur statistical results revealed that risk alleles were enriched in minor alleles, especially for variants with low minor allele frequencies (MAFs < 0.1). Our computer simulations revealed that > 50% risk alleles were minor alleles because of the larger difference in the power of GWASs to differentiate between minor and major alleles, especially with low MAFs or when the number of controls exceeds the number of cases. However, the observed ratios between minor and major alleles in low MAFs (< 0.1) were much larger than the expected ratios of GWAS's power imbalance, especially for diseases whose average risk allele frequencies were low, such as myopia, sudden cardiac arrest, and systemic lupus erythematosus.

conclusionsMinor alleles are more likely to be risk alleles in the published GWASs on complex diseases. One reason is that minor alleles are more easily detected as risk alleles in GWASs. Even when correcting for the GWAS's power imbalance, minor alleles are more likely to be risk alleles, especially in some diseases whose average risk allele frequencies are low. These analyses serve as a starting point for future studies on quantifying the degree of negative natural selection in various complex diseases.

Indexed as

AllelesComputational BiologyGenome-Wide Association StudyDatabases, GeneticEvolution, MolecularGenetic Predisposition to DiseaseHumansLinkage DisequilibriumComplex diseasesGWASsMinor allelesNegative natural selectionRisk alleles

Identifiers

PMID29351777
PMCPMC5775585
OpenAlexW2788304168

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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.