Evidence map›Paper›PMID 38472646›Full record

ArticleGenetic epidemiology2024

Causation and familial confounding as explanations for the associations of polygenic risk scores with breast cancer: Evidence from innovative ICE FALCON and ICE CRISTAL analyses.

Shuai Li, Gillian S Dite, Robert J MacInnis, Minh Bui, Tuong L Nguyen, Vivienne F C Esser, Zhoufeng Ye, James G Dowty, Enes Makalic, Joohon Sung and 3 more

Open access · hybridAbstract read
In one paragraph

Article in Genetic epidemiology, 2024. 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
2.2field-weighted citation impact, top 13% 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, 4 citations in OpenAlex.

  1. Article
  2. Article
  3. A cautionary note on the recently proposed ICE Falcon method.International journal of epidemiology · 2024
    Article
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

13 authors at 3 institutions in 4 countries.

Shuai LiCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia.ORCID 0000-0002-8696-8594
Gillian S DiteCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia.
Robert J MacInnisCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia.
Minh BuiCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia.
Tuong L NguyenCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia.
Vivienne F C EsserCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia.
Zhoufeng YeCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia.ORCID 0000-0002-0884-4246
James G DowtyCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia.
Enes MakalicCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia.
Joohon SungDivision of Genome and Health Big Data, Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul, Korea.
Graham G GilesCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia.
Melissa C SoutheyPrecision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia.
John L HopperCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia.
The University of Melbourne · AURoyal Children's Hospital · AUSeoul National University · KR

Funding

Core-001U01CA164920 · NCI · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI Irene Andrulis, Sarah Violet Colonna · 2018 to 2026
$17.0M
Breast Cancer Family Registry CohortUM1CA164920 · NCI · CANCER PREVENTION INSTIT OF CALIFORNIA · PI ANDRULIS, IRENE, BUYS, SAUNDRA S · 2012 to 2017
$12.7M
Cancer Council VictoriaCancer Council Victoria and VicHealthNational Breast Cancer FoundationNational Health and Medical Research Council GNT2024867National Research Foundation of Korea (NRF) 2020R1A2C2101041National Research Foundation of Korea (NRF) 2023R1A2C2002925NCI NIH HHS U01 CA164920NCI NIH HHS UM1 CA164920New South Wales Cancer Council, the Victorian Health Promotion FoundationVictorian Breast Cancer Research Consortium, Cancer Australia
6 · The paper itself

Abstract

A polygenic risk score (PRS) combines the associations of multiple genetic variants that could be due to direct causal effects, indirect genetic effects, or other sources of familial confounding. We have developed new approaches to assess evidence for and against causation by using family data for pairs of relatives (Inference about Causation from Examination of FAmiliaL CONfounding [ICE FALCON]) or measures of family history (Inference about Causation from Examining Changes in Regression coefficients and Innovative STatistical AnaLyses [ICE CRISTAL]). Inference is made from the changes in regression coefficients of relatives' PRSs or PRS and family history before and after adjusting for each other. We applied these approaches to two breast cancer PRSs and multiple studies and found that (a) for breast cancer diagnosed at a young age, for example, <50 years, there was no evidence that the PRSs were causal, while (b) for breast cancer diagnosed at later ages, there was consistent evidence for causation explaining increasing amounts of the PRS-disease association. The genetic variants in the PRS might be in linkage disequilibrium with truly causal variants and not causal themselves. These PRSs cause minimal heritability of breast cancer at younger ages. There is also evidence for nongenetic factors shared by first-degree relatives that explain breast cancer familial aggregation. Familial associations are not necessarily due to genes, and genetic associations are not necessarily causal.

Indexed as

Breast NeoplasmsGenetic Predisposition to DiseaseMultifactorial InheritanceAdultCausalityFamilyFemaleGenetic Risk ScoreGenome-Wide Association StudyHumansMiddle AgedPolymorphism, Single NucleotideRisk Factorsbreast cancercausationfamilial confoundingfamily dataICE CRISTALICE FALCONsiblings

Identifiers

PMID38472646
PMCPMC11588973
OpenAlexW4392754722

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.