Evidence map›Paper›PMID 32466134›Full record

SynthesisGenes2020

Association Analysis and Meta-Analysis of Multi-Allelic Variants for Large-Scale Sequence Data.

Yu Jiang, Sai Chen, Xingyan Wang, Mengzhen Liu, William G Iacono, John K Hewitt, John E Hokanson, Kenneth Krauter, Markku Laakso, Kevin W Li and 10 more

Abstract readMeta-Analysis
In one paragraph

Synthesis in Genes, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 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

12 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  7. Case reports in genetics · 2025
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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

20 authors.

Yu JiangDepartment of Public Health Sciences, Penn State College of Medicine, Hershey, PA 17033, USA.
Sai ChenIllumina Inc., 5200 Illuminay Way, San Diego, CA 92122, USA.
Xingyan WangDepartment of Public Health Sciences, Penn State College of Medicine, Hershey, PA 17033, USA.
Mengzhen LiuDepartment of Psychology, University of Minnesota, Minneapolis, MN 55454, USA.
William G IaconoDepartment of Psychiatry, University of Minnesota, Minneapolis, MN 55454, USA.
John K HewittInstitute for Behavioral Genetics, University of Colorado Boulder, Aurora, CO 80045, USA.ORCID 0000-0001-9415-2723
John E HokansonDepartment of Epidemiology, School of Public Health, University of Colorado Denver, Aurora, CO 80045, USA.
Kenneth KrauterInstitute for Behavioral Genetics, University of Colorado Boulder, Aurora, CO 80045, USA.
Markku LaaksoDepartment of Medicine, University of Eastern Finland and Kuopio University Hospital, 70211 Kuopio, Finland.
Kevin W LiCenter of Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.
Sharon M LutzDepartment of Biostatistics and Informatics, University of Colorado, Anschutz Medical Campus, Aurora, CO 80045, USA.
Matthew McGueDepartment of Psychology, University of Minnesota, Minneapolis, MN 55454, USA.
Anita PanditCenter of Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.
Gregory J M ZajacCenter of Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.
Michael BoehnkeCenter of Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.
Goncalo R AbecasisCenter of Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.
Scott I VriezeDepartment of Psychology, University of Minnesota, Minneapolis, MN 55454, USA.ORCID 0000-0003-3861-7930
Bibo JiangDepartment of Public Health Sciences, Penn State College of Medicine, Hershey, PA 17033, USA.
Xiaowei ZhanDepartment of Clinical Science, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
Dajiang J LiuDepartment of Public Health Sciences, Penn State College of Medicine, Hershey, PA 17033, USA.

Funding

Genetic Epidemiology of COPDU01HL089897 · NHLBI · NATIONAL JEWISH HEALTH · PI CRAPO, JAMES D · 2007 to 2021
$56.9M
Metabolomic signatures of empysema and COPD progression in the COPDGene cohortR01HL089897 · NHLBI · NATIONAL JEWISH HEALTH · PI CRAPO, JAMES D · 2012 to 2016
$31.1M
Regional Pilot And Feasibility Study Grants ProgramP30DK020572 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Scott Soleimanpour · 2013 to 2026
$24.3M
Twin StudyP60DA011015 · NIDA · UNIVERSITY OF COLORADO DENVER · PI HEWITT, JOHN K. · 1997 to 2013
$22.5M
University of Michigan Training Program in Genomic ScienceT32HG000040 · NHGRI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Sebastian Zoellner · 1995 to 2026
$16.4M
Twin Family Study of Vulnerability to Substance AbuseR37DA005147 · NIDA · UNIVERSITY OF MINNESOTA · PI IACONO, WILLIAM G. · 2009 to 2018
$6.4M
DESIGN AND ANALYSIS OF HUMAN GENE MAPPING STUDIESR01HG000376 · NHGRI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI BOEHNKE, MICHAEL L · 1993 to 2016
$5.5M
TWIN/FAMILY STUDY OF VULNERABILITY TO SUBSTANCE ABUSER01DA005147 · NIDA · UNIVERSITY OF MINNESOTA TWIN CITIES · PI IACONO, WILLIAM G · 1987 to 2007
$5.5M
Adult consequences of youth substance use: Twin study enriched for SUD riskR01DA036216 · NIDA · UNIVERSITY OF MINNESOTA · PI IACONO, WILLIAM G. · 2013 to 2017
$3.2M
Genetic Determinants of COPD in Diverse Ethnic GroupsR01HL084323 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI SILVERMAN, EDWIN K · 2007 to 2010
$2.6M
Design and Analysis of Human Gene Mapping StudiesR01HG009976 · NHGRI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI BOEHNKE, MICHAEL L · 2018 to 2022
$2.5M
Genetic Association Meta-Analyses of Smoking and Drinking for the Sequencing AgeR01DA037904 · NIDA · UNIVERSITY OF MINNESOTA · PI VRIEZE, SCOTT IAN · 2015 to 2019
$2.4M
NHGRI NIH HHS R01 HG008983NHGRI NIH HHS R01 HG009976NHGRI NIH HHS T32 HG000040NHLBI NIH HHS K01 HL125858NHLBI NIH HHS R01 HL084323NHLBI NIH HHS R01 HL089897NHLBI NIH HHS U01 HL089897NIDA NIH HHS P60 DA011015NIDA NIH HHS R01 DA005147NIDA NIH HHS R01 DA036216NIDA NIH HHS R01 DA037904NIDA NIH HHS R37 DA005147NIDDK NIH HHS P30 DK020572NIGMS NIH HHS R01 GM126479NIH HHS DA036216NIH HHS DA05147NIH HHS K01HL125858NIH HHS R01HG000376NIH HHS R01HG008983
6 · The paper itself

Abstract

There is great interest in understanding the impact of rare variants in human diseases using large sequence datasets. In deep sequence datasets of >10,000 samples, ~10% of the variant sites are observed to be multi-allelic. Many of the multi-allelic variants have been shown to be functional and disease-relevant. Proper analysis of multi-allelic variants is critical to the success of a sequencing study, but existing methods do not properly handle multi-allelic variants and can produce highly misleading association results. We discuss practical issues and methods to encode multi-allelic sites, conduct single-variant and gene-level association analyses, and perform meta-analysis for multi-allelic variants. We evaluated these methods through extensive simulations and the study of a large meta-analysis of ~18,000 samples on the cigarettes-per-day phenotype. We showed that our joint modeling approach provided an unbiased estimate of genetic effects, greatly improved the power of single-variant association tests among methods that can properly estimate allele effects, and enhanced gene-level tests over existing approaches. Software packages implementing these methods are available online.

Indexed as

Genetic Predisposition to DiseaseAllelesCigarette SmokingData Interpretation, StatisticalFemaleGenetic VariationGenome-Wide Association StudyHumansMalePhenotypePolymorphism, Single NucleotideRare DiseasesGWASmeta-analysismulti-allelic variantssmoking

Identifiers

PMID32466134
PMCPMC7288273

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.