Evidence map›Paper›PMID 36206742›Full record

ArticleAmerican journal of human genetics2022

Segregation analysis of 17,425 population-based breast cancer families: Evidence for genetic susceptibility and risk prediction.

Shuai Li, Robert J MacInnis, Andrew Lee, Tu Nguyen-Dumont, Leila Dorling, Sara Carvalho, Gillian S Dite, Mitul Shah, Craig Luccarini, Qin Wang and 9 more

Open access · hybridAbstract read
In one paragraph

Article in American journal of human genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
4.3field-weighted citation impact, top 5% 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

13 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Cancer Risks Associated WithJCO precision oncology · 2024
    Article
  7. Article
  8. Article
  9. Article
  10. Population-based analysis ofJournal of medical genetics · 2023
    Article
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  12. Review
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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

19 authors at 6 institutions in 2 countries.

Shuai LiCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, 207 Bouverie Street, Carlton, VIC 3053, Australia; Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK; Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia; Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, VIC 3051, Australia. Electronic address: shuai.li@unimelb.edu.au.
Robert J MacInnisCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, 207 Bouverie Street, Carlton, VIC 3053, Australia; Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC 3004, Australia.
Andrew LeeCentre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK.
Tu Nguyen-DumontPrecision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia; Department of Clinical Pathology, The University of Melbourne, Parkville, VIC 3051, Australia.
Leila DorlingCentre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK.
Sara CarvalhoCentre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK.
Gillian S DiteCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, 207 Bouverie Street, Carlton, VIC 3053, Australia; Genetic Technologies Ltd., Fitzroy, VIC 3065, Australia.
Mitul ShahCentre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge CB1 8RN, UK.
Craig LuccariniCentre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge CB1 8RN, UK.
Qin WangCentre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK.
Roger L MilneCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, 207 Bouverie Street, Carlton, VIC 3053, Australia; Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia; Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC 3004, Australia.
Mark A JenkinsCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, 207 Bouverie Street, Carlton, VIC 3053, Australia.
Graham G GilesCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, 207 Bouverie Street, Carlton, VIC 3053, Australia; Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia; Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC 3004, Australia.
Alison M DunningCentre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge CB1 8RN, UK.
Paul D P PharoahCentre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK.
Melissa C SoutheyPrecision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia; Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC 3004, Australia; Department of Clinical Pathology, The University of Melbourne, Parkville, VIC 3051, Australia.
Douglas F EastonCentre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK.
John L HopperCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, 207 Bouverie Street, Carlton, VIC 3053, Australia.
Antonis C AntoniouCentre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK.
University of Cambridge · GBUniversity of Melbourne · AUCancer Council Victoria · AUGenetic Technologies (Australia) · AUMonash Health · AUMonash University · AU

Funding

Genes, environment and breast cancer risk: The 15 year follow-up of the Prof-SC (Diversity Supplement)R01CA159868 · NCI · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI HOPPER, JOHN L, TERRY, MARY BETH · 2011 to 2015
$9.0M
Cancer Research UK 20861NCI NIH HHS R01 CA159868
6 · The paper itself

Abstract

Rare pathogenic variants in known breast cancer-susceptibility genes and known common susceptibility variants do not fully explain the familial aggregation of breast cancer. To investigate plausible genetic models for the residual familial aggregation, we studied 17,425 families ascertained through population-based probands, 86% of whom were screened for pathogenic variants in BRCA1, BRCA2, PALB2, CHEK2, ATM, and TP53 via gene-panel sequencing. We conducted complex segregation analyses and fitted genetic models in which breast cancer incidence depended on the effects of known susceptibility genes and other unidentified major genes and a normally distributed polygenic component. The proportion of familial variance explained by the six genes was 46% at age 20-29 years and decreased steadily with age thereafter. After allowing for these genes, the best fitting model for the residual familial variance included a recessive risk component with a combined genotype frequency of 1.7% (95% CI: 0.3%-5.4%) and a penetrance to age 80 years of 69% (95% CI: 38%-95%) for homozygotes, which may reflect the combined effects of multiple variants acting in a recessive manner, and a polygenic variance of 1.27 (95% CI: 0.94%-1.65), which did not vary with age. The proportion of the residual familial variance explained by the recessive risk component was 40% at age 20-29 years and decreased with age thereafter. The model predicted age-specific familial relative risks consistent with those observed by large epidemiological studies. The findings have implications for strategies to identify new breast cancer-susceptibility genes and improve disease-risk prediction, especially at a young age.

Indexed as

Breast NeoplasmsGenetic Predisposition to DiseaseAdultAged, 80 and overCase-Control StudiesFemaleHumansMultifactorial InheritancePenetranceYoung AdultATMBOADICEABRCA1BRCA2breast cancerCHEK2familial aggregationfamilial riskmissing heritabilityPALB2segregation analysisTP53

Identifiers

PMID36206742
PMCPMC9606477
OpenAlexW4302306910

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.