Evidence map›Paper›PMID 39812506›Full record

ArticleGenetic epidemiology2025

General Kernel Machine Methods for Multi-Omics Integration and Genome-Wide Association Testing With Related Individuals.

Amarise Little, Ni Zhao, Anna Mikhaylova, Angela Zhang, Wodan Ling, Florian Thibord, Andrew D Johnson, Laura M Raffield, Joanne E Curran, John Blangero and 20 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Genetic epidemiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Integrative Analysis of Multimodal Omics Data.Annual review of statistics and its application · 2026
    Article
  2. Review
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

30 authors.

Amarise LittleDepartment of Biostatistics, University of Washington, Seattle, Washington, USA.ORCID http://orcid.org/0009-0000-2618-7916
Ni ZhaoDepartment of Biostatistics, Johns Hopkins University, Baltimore, Maryland, USA.ORCID http://orcid.org/0000-0002-7762-3949
Anna MikhaylovaDepartment of Biostatistics, University of Washington, Seattle, Washington, USA.
Angela ZhangDepartment of Biostatistics, University of Washington, Seattle, Washington, USA.
Wodan LingDepartment of Population Health Sciences, Division of Biostatistics, Weill Cornell Medicine, New York, New York, USA.
Florian ThibordNational Heart, Lung, and Blood Institute, Boston University's Framingham Heart Study, Framingham, Massachusetts, USA.
Andrew D JohnsonNational Heart, Lung, and Blood Institute, Boston University's Framingham Heart Study, Framingham, Massachusetts, USA.
Laura M RaffieldDepartment of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Joanne E CurranDepartment of Human Genetics, University of Texas Rio Grande Valley School of Medicine, Brownsville, Texas, USA.
John BlangeroDepartment of Human Genetics, University of Texas Rio Grande Valley School of Medicine, Brownsville, Texas, USA.
Jeffrey R O'ConnellDepartment of Medicine, University of Maryland, Baltimore, Maryland, USA.
Huichun XuDepartment of Medicine, University of Maryland, Baltimore, Maryland, USA.
Jerome I RotterDepartment of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, California, USA.
Stephen S RichCenter for Public Health Genomics, University of Virginia, Charlottesville, Virginia, USA.ORCID http://orcid.org/0000-0003-3872-7793
Kenneth M RiceDepartment of Biostatistics, University of Washington, Seattle, Washington, USA.
Ming-Huei ChenNational Heart, Lung, and Blood Institute, Boston University's Framingham Heart Study, Framingham, Massachusetts, USA.
Alexander ReinerPublic Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA.
Charles KooperbergPublic Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA.
Thao VuDepartment of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA.
Lifang HouDepartment of Preventive Medicine, Northwestern Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
Myriam FornageBrown Foundation Institute for Molecular Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas, USA.
Ruth J F LoosThe Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Eimear KennyThe Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Rasika MathiasDepartment of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
Lewis BeckerDepartment of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
Albert V SmithDepartment of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan, USA.
Eric BoerwinkleDepartment of Epidemiology, School of Public Health, University of Texas Health Science Center at Houston, Houston, Texas, USA.
Bing YuDepartment of Epidemiology, School of Public Health, University of Texas Health Science Center at Houston, Houston, Texas, USA.
Timothy ThorntonDepartment of Biostatistics, University of Washington, Seattle, Washington, USA.
Michael C WuDepartment of Biostatistics, University of Washington, Seattle, Washington, USA.

Funding

Large Scale Sequencing and Analysis of GenomesU54HG003067 · NHGRI · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI GABRIEL, STACEY, LANDER, ERIC S · 2004 to 2015
$568.6M
The Human Genome Sequencing CenterU54HG003273 · NHGRI · BAYLOR COLLEGE OF MEDICINE · PI GIBBS, RICHARD A · 2004 to 2015
$341.3M
UCLA Clinical Translational Science InstituteUL1TR001881 · NCATS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI ARLEEN F. BROWN, ARASH NAEIM · 2016 to 2026
$118.1M
Supplement Proposal: Accelerated Genome Aggregation and Joint Variant Calling EffortUM1HG008853 · NHGRI · WASHINGTON UNIVERSITY · PI HALL, IRA M, MILBRANDT, JEFFREY D · 2016 to 2020
$76.2M
Institute for Clinical and Translational ResearchUL1TR001079 · NCATS · JOHNS HOPKINS UNIVERSITY · PI FORD, DANIEL ERNEST · 2013 to 2017
$60.1M
Genetic Epidemiology of COPDU01HL089897 · NHLBI · NATIONAL JEWISH HEALTH · PI CRAPO, JAMES D · 2007 to 2021
$56.9M
Transgenic & Knock-out MouseP30DK063491 · NIDDK · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI PETER J TONTONOZ · 2003 to 2026
$40.4M
ZINC AND CA EDTA THERAPY OF PLUMBISMM01RR000052 · NCRR · JOHNS HOPKINS UNIVERSITY · PI BELCHER, HAROLYN MILLICENT EDITH · 1985 to 2007
$37.3M
Wake Forest Clinical and Translational Science AwardUL1TR001420 · NCATS · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI ARD, JAMY D, FOLEY, KRISTIE L · 2015 to 2023
$32.3M
Metabolomic signatures of empysema and COPD progression in the COPDGene cohortR01HL089897 · NHLBI · NATIONAL JEWISH HEALTH · PI CRAPO, JAMES D · 2012 to 2016
$31.1M
PEDIGREE ANALYSIS OF LIPOPROTEIN PHENOTYPESP01HL045522 · NHLBI · TEXAS BIOMEDICAL RESEARCH INSTITUTE · PI ALMASY, LAURA A. · 1991 to 2012
$27.6M
Clinical and Translational Science AwardUL1TR000040 · NCATS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI GINSBERG, HENRY N · 2012 to 2015
$26.2M
NCATS NIH HHS UL1 TR000040NCATS NIH HHS UL1 TR001079NCATS NIH HHS UL1 TR001420NCATS NIH HHS UL1 TR001881NCRR NIH HHS M01 RR000052NHGRI NIH HHS U01 HG007417NHGRI NIH HHS U54 HG003067NHGRI NIH HHS U54 HG003273NHGRI NIH HHS UM1 HG008853NHLBI NIH HHS HHSN268201500015CNHLBI NIH HHS N01 HC025195NHLBI NIH HHS N01 HC065233NHLBI NIH HHS N01 HC065234NHLBI NIH HHS N01 HC065235NHLBI NIH HHS N01 HC065236NHLBI NIH HHS N01 HC065237NHLBI NIH HHS N01 HC095159NHLBI NIH HHS N01 HC095160NHLBI NIH HHS N01 HC095161NHLBI NIH HHS N01 HC095162NHLBI NIH HHS N01 HC095163NHLBI NIH HHS N01 HC095164NHLBI NIH HHS N01 HC095165NHLBI NIH HHS N01 HC095166NHLBI NIH HHS N01 HC095167NHLBI NIH HHS N01 HC095168NHLBI NIH HHS N01 HC095169NHLBI NIH HHS P01 HL045522NHLBI NIH HHS R01 HL080295NHLBI NIH HHS R01 HL087698NHLBI NIH HHS R01 HL089856NHLBI NIH HHS R01 HL089897NHLBI NIH HHS R01 HL092577NHLBI NIH HHS R01 HL105756NHLBI NIH HHS R01 HL110068NHLBI NIH HHS R01 HL112064NHLBI NIH HHS R01 HL113323NHLBI NIH HHS R01 HL117626NHLBI NIH HHS R01 HL118356NHLBI NIH HHS R01 HL120393NHLBI NIH HHS R01 HL121007NHLBI NIH HHS R01 HL172803NHLBI NIH HHS U01 HL072515NHLBI NIH HHS U01 HL072518NHLBI NIH HHS U01 HL080295NHLBI NIH HHS U01 HL089897NHLBI NIH HHS U01 HL130114NIA NIH HHS R01 AG018728NIA NIH HHS R01 AG023629NIBIB NIH HHS R01 EB015611NIDDK NIH HHS P30 DK063491NIDDK NIH HHS U01 DK085524NIMH NIH HHS R01 MH078111NIMH NIH HHS R01 MH078143NIMH NIH HHS R01 MH083824U.S. Department of Health and Human Services, National Institute on Minority Health and Health Disparities, National Institutes of Health, National Human Genome Research Institute, National Center for Research Resources, COPD Foundation, National Heart, Lung, and Blood Institute, National Science Foundation, National Institute on Aging, and National Institute of Neurological Disorders and Stroke.
6 · The paper itself

Abstract

Integrating multi-omics data may help researchers understand the genetic underpinnings of complex traits and diseases. However, the best ways to integrate multi-omics data and use them to address pressing scientific questions remain a challenge. One important and topical problem is how to assess the aggregate effect of multiple genomic data types (e.g. genotypes and gene expression levels) on a phenotype, particularly while accommodating routine issues, such as having related subjects' data in analyses. In this paper, we extend an existing composite kernel machine regression model to integrate two multi-omics data types, while accommodating for general correlation structures amongst outcomes. Due to the kernel machine regression framework, our methods allow for the integration of high-dimensional omics data with small, nonlinear, and interactive effects, and accommodation of general study designs. Here, we focus on scientific questions that aim to assess the association between a functional grouping (such as a gene or a pathway) and a quantitative trait of interest. We use a kernel machine regression to integrate the two multi-omics data types, as they may relate to the trait, and perform a global test of association. We demonstrate the advantage of this approach over single data type association tests via simulation. Finally, we apply this method to a large, multi-ethnic data set to investigate how predicted gene expression and rare genetic variation may be related to two platelet traits.

Indexed as

Genome-Wide Association StudyGenomicsAlgorithmsGenotypeHumansMachine LearningModels, GeneticMultiomicsPhenotypePolymorphism, Single NucleotideQuantitative Trait LociRegression Analysisassociation testingcomposite kernelgeneral linear regressionintegrative analysiskernel machine regressionmulti‐omicsvariance component test

Identifiers

PMID39812506
PMCPMC13170581

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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.