Evidence map›Paper›PMID 35924480›Full record

ReviewGenetic epidemiology2022

Genetic heterogeneity: Challenges, impacts, and methods through an associative lens.

Alexa A Woodward, Ryan J Urbanowicz, Adam C Naj, Jason H Moore

Abstract readReview
In one paragraph

Review in Genetic epidemiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
31citing 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

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

  1. Pooled it
  2. Review
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  4. Article
  5. Article
  6. Review
  7. Survival-LCS: Rule-Based Survival Analysis without Proportional Hazard Assumptions.ACM transactions on evolutionary learning and optimization · 2026
    Article
  8. Article
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  12. Article
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  16. Review
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  20. Oocyte and dietary supplements: a mini review.Frontiers in cell and developmental biology · 2025
    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

4 authors.

Alexa A WoodwardDepartment of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Ryan J UrbanowiczDepartment of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, California, USA.
Adam C NajDepartment of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Jason H MooreDepartment of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, California, USA.

Funding

Training Program in Computational GenomicsT32HG000046 · NHGRI · UNIVERSITY OF PENNSYLVANIA · PI JUNHYONG KIM, Mingyao Li · 1999 to 2026
$9.5M
Bioinformatics Strategies for Genome-Wide Association StudiesR01LM010098 · NLM · UNIVERSITY OF PENNSYLVANIA · PI MOORE, JASON H., WILLIAMS, SCOTT MATTHEW · 2009 to 2023
$5.1M
Primaquine metabolism and treatment of P. vivax in MadagascarR01AI148469 · NIAID · CASE WESTERN RESERVE UNIVERSITY · PI WILLIAMS, SCOTT MATTHEW, ZIMMERMAN, PETER A. · 2020 to 2023
$2.6M
Biomedical Computing and Informatics Strategies for Precision MedicineR01LM012601 · NLM · UNIVERSITY OF PENNSYLVANIA · PI HUANG, XIUZHEN, MOORE, JASON H. · 2017 to 2020
$1.8M
Artificial Intelligence Strategies for Alzheimer's Disease ResearchR01AG066833 · NIA · UNIVERSITY OF PENNSYLVANIA · PI MOORE, JASON H., RITCHIE, MARYLYN D · 2021 to 2021
$1.6M
Rule-based machine learning to address heterogeneity in high-dimensional survival dataF31LM013583 · NLM · UNIVERSITY OF PENNSYLVANIA · PI WOODWARD, ALEXA ABIGAIL · 2021 to 2022
$71k
NHGRI NIH HHS T32 HG000046NIA NIH HHS R01 AG066833NLM NIH HHS F31 LM013583NLM NIH HHS R01 LM010098NLM NIH HHS R01 LM012601
6 · The paper itself

Abstract

Genetic heterogeneity describes the occurrence of the same or similar phenotypes through different genetic mechanisms in different individuals. Robustly characterizing and accounting for genetic heterogeneity is crucial to pursuing the goals of precision medicine, for discovering novel disease biomarkers, and for identifying targets for treatments. Failure to account for genetic heterogeneity may lead to missed associations and incorrect inferences. Thus, it is critical to review the impact of genetic heterogeneity on the design and analysis of population level genetic studies, aspects that are often overlooked in the literature. In this review, we first contextualize our approach to genetic heterogeneity by proposing a high-level categorization of heterogeneity into "feature," "outcome," and "associative" heterogeneity, drawing on perspectives from epidemiology and machine learning to illustrate distinctions between them. We highlight the unique nature of genetic heterogeneity as a heterogeneous pattern of association that warrants specific methodological considerations. We then focus on the challenges that preclude effective detection and characterization of genetic heterogeneity across a variety of epidemiological contexts. Finally, we discuss systems heterogeneity as an integrated approach to using genetic and other high-dimensional multi-omic data in complex disease research.

Indexed as

Genetic HeterogeneityPrecision MedicineHumansMachine LearningPhenotypecomplex diseasegenetic heterogeneityGWASprecision medicine

Identifiers

PMID35924480
PMCPMC9669229

What Socratic holds

Textmetadata
LicenceCC BY-NC
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 Socratic graph.