Evidence map›Paper›PMID 37349888›Full record

ArticleInternational journal of epidemiology2023

Variance of age-specific log incidence decomposition (VALID): a unifying model of measured and unmeasured genetic and non-genetic risks.

John L Hopper, James G Dowty, Tuong L Nguyen, Shuai Li, Gillian S Dite, Robert J MacInnis, Enes Makalic, Daniel F Schmidt, Minh Bui, Jennifer Stone and 5 more

Open access · hybridAbstract readTwin Study
In one paragraph

Article in International journal of epidemiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.4field-weighted citation impact, top 16% 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

3 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
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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

15 authors at 4 institutions in 2 countries.

John L HopperCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia.
James G DowtyCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia.
Tuong L NguyenCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia.ORCID 0000-0002-6597-8363
Shuai LiCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia.ORCID 0000-0002-8696-8594
Gillian S DiteCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia.ORCID 0000-0002-2448-2548
Robert J MacInnisCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia.
Enes MakalicCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia.ORCID 0000-0003-3017-0871
Daniel F SchmidtCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia.
Minh BuiCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia.
Jennifer StoneSchool of Population and Global Health, University of Western Australia, Perth, WA, Australia.ORCID 0000-0001-5077-0124
Joohon SungDivision of Genome and Health Big Data, Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul, Korea.ORCID 0000-0001-9948-0160
Mark A JenkinsCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia.ORCID 0000-0002-8964-6160
Graham G GilesCancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia.
Melissa C SoutheyCancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia.
John D MathewsCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia.ORCID 0000-0001-9029-7140
The University of Melbourne · AUCancer Council Victoria · AUSeoul National University · KRThe University of Western Australia · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe extent to which known and unknown factors explain how much people of the same age differ in disease risk is fundamental to epidemiology. Risk factors can be correlated in relatives, so familial aspects of risk (genetic and non-genetic) must be considered. DEVELOPMENT: We present a unifying model (VALID) for variance in risk, with risk defined as log(incidence) or logit(cumulative incidence). Consider a normally distributed risk score with incidence increasing exponentially as the risk increases. VALID's building block is variance in risk, Δ2, where Δ = log(OPERA) is the difference in mean between cases and controls and OPERA is the odds ratio per standard deviation. A risk score correlated r between a pair of relatives generates a familial odds ratio of exp(rΔ2). Familial risk ratios, therefore, can be converted into variance components of risk, extending Fisher's classic decomposition of familial variation to binary traits. Under VALID, there is a natural upper limit to variance in risk caused by genetic factors, determined by the familial odds ratio for genetically identical twin pairs, but not to variation caused by non-genetic factors. APPLICATION: For female breast cancer, VALID quantified how much variance in risk is explained-at different ages-by known and unknown major genes and polygenes, non-genomic risk factors correlated in relatives, and known individual-specific factors.

conclusionVALID has shown that, while substantial genetic risk factors have been discovered, much is unknown about genetic and familial aspects of breast cancer risk especially for young women, and little is known about individual-specific variance in risk.

Indexed as

Breast NeoplasmsGenetic Predisposition to DiseaseAge FactorsFemaleHumansIncidenceRisk FactorsBreast cancerfamilial causefamilial odds ratiofamilial risk ratiogenetic causegenomic causemajor genenon-familial causepolygenic risk scorevariance components

Identifiers

PMID37349888
PMCPMC10655167
OpenAlexW4381716386

What OpenQuestion holds

Textmetadata
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.