Evidence map›Paper›PMID 26915271›Full record

ReviewAdvances in genetics2016

Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases.

Yan V Sun, Yi-Juan Hu

Registry-linked trialAbstract readReview
In one paragraph

Review in Advances in genetics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06297928 (Metabolomics and Bariatric Surgery in Patients With Metabolically Unhealthy Obesity), which is not on this map. Cited by 226 papers, 7 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
226citing papers in PubMed, 7 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.

NCT06297928 completednot on this mapstarted 2024, after this paper: background citation

Metabolomics and Bariatric Surgery in Patients With Metabolically Unhealthy Obesity: Advancing Precision Medicine for the Treatment of Obesity

TypeobservationalSponsorParc de Salut MarRan2024 to 2025Enrolled145ConditionsBariatric Surgery Candidate, Obesity, Morbid, Obesity, Metabolically BenignArmsbaratric surgery
3 · Its place in the literature

Who cites it

226 citing papers in PubMed, 7 syntheses or guidelines pooled it.

  1. Pooled it
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  4. Metabolomic signatures after bariatric surgery - a systematic review.Reviews in endocrine & metabolic disorders · 2022
    Pooled it
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  20. New Personalized Medicine Model for Medication Management.Journal of personalized medicine · 2026
    Review

166 more citing papers are in PubMed but not listed here.

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

2 authors.

Yan V SunDepartment of Epidemiology, Rollins School of Public Health, Atlanta, GA, United States; Department of Biomedical Informatics, School of Medicine, Atlanta, GA, United States.
Yi-Juan HuDepartment of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, United States.

Funding

Pilot Project ProgramP30ES019776 · NIEHS · EMORY UNIVERSITY · PI William Michael Caudle · 2013 to 2026
$22.6M
Intergenerational Impact of Genetic and Psychological Factors on Blood PressureR01NR013520 · NINR · YALE UNIVERSITY · PI TAYLOR, JACQUELYN Y · 2014 to 2019
$3.9M
Association Tests of Rare Variants Using Sequence Reads without Calling GenotypesR01GM116065 · NIGMS · EMORY UNIVERSITY · PI HU, YIJUAN · 2015 to 2019
$1.5M
Identification of genetic and metabolomic markers influencing dystoniaR21NS096455 · NINDS · EMORY UNIVERSITY · PI JINNAH, HYDER A, SUN, YAN · 2016 to 2017
$429k
Epigenome-wide association study of asthma in populations of African descentR03AI111396 · NIAID · EMORY UNIVERSITY · PI HU, YIJUAN · 2014 to 2015
$174k
NIAID NIH HHS R03 AI111396NIAID NIH HHS R03AI111396NIEHS NIH HHS P30 ES019776NIEHS NIH HHS P30ES019776NIGMS NIH HHS R01 GM116065NIGMS NIH HHS R01GM116065NINDS NIH HHS R21 NS096455NINR NIH HHS R01 NR013520
6 · The paper itself

Abstract

Complex and dynamic networks of molecules are involved in human diseases. High-throughput technologies enable omics studies interrogating thousands to millions of makers with similar biochemical properties (eg, transcriptomics for RNA transcripts). However, a single layer of "omics" can only provide limited insights into the biological mechanisms of a disease. In the case of genome-wide association studies, although thousands of single nucleotide polymorphisms have been identified for complex diseases and traits, the functional implications and mechanisms of the associated loci are largely unknown. Additionally, the genomic variants alone are not able to explain the changing disease risk across the life span. DNA, RNA, protein, and metabolite often have complementary roles to jointly perform a certain biological function. Such complementary effects and synergistic interactions between omic layers in the life course can only be captured by integrative study of multiple molecular layers. Building upon the success in single-omics discovery research, population studies started adopting the multi-omics approach to better understanding the molecular function and disease etiology. Multi-omics approaches integrate data obtained from different omic levels to understand their interrelation and combined influence on the disease processes. Here, we summarize major omics approaches available in population research, and review integrative approaches and methodologies interrogating multiple omic layers, which enhance the gene discovery and functional analysis of human diseases. We seek to provide analytical recommendations for different types of multi-omics data and study designs to guide the emerging multi-omic research, and to suggest improvement of the existing analytical methods.

Indexed as

Environmental ExposureEpigenomicsMetabolomicsProteomicsTranscriptomeData Interpretation, StatisticalDiseaseGenetic MarkersGenome-Wide Association StudyHumansPhenotypePolymorphism, Single NucleotideQuantitative Trait LociGenetic MarkersDNA methylationEpigenomeGene expressionGenomic epidemiologyGWASIntegrative genomicsMetabolomeProteomeQuantitative trait lociTranscriptome

Identifiers

PMID26915271
PMCPMC5742494

What OpenQuestion holds

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Read underepoch 390

Registered trials

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.