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Related Concept Videos

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Several factors can increase the risk of cancer in an individual. About 50% of cancer cases can be prevented by adopting a healthy lifestyle, regular exercise, eating healthy, and following a modest cancer prevention diet. Epidemiological studies have consistently shown that populations with vegetable and fruit-rich diets have reduced the incidence of cancer. On the other hand, populations who have a diet rich in animal fat, red meat, junk food, or high calories are predisposed to cancer.
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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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Cause and Effect01:53

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While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?
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Genes usually encode proteins necessary for the proper functioning of a healthy cell. Mutations can often cause changes to the gene expression pattern, thereby altering the phenotype.
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  1. Home
  3. 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.
  1. Home
  3. 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.

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Causation and familial confounding as explanations for the associations of polygenic risk scores with breast cancer:

Shuai Li1,2,3,4, Gillian S Dite1,5, Robert J MacInnis1,6

  • 1Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia.

Genetic Epidemiology
|March 13, 2024

View abstract on PubMed

Summary
This summary is machine-generated.
Keywords:
ICE CRISTALICE FALCONbreast cancercausationfamilial confoundingfamily datasiblings

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Polygenic risk scores (PRS) show no causal link for early-onset breast cancer. However, evidence suggests PRS causation increases for later-onset breast cancer, indicating complex genetic and environmental factors influence disease risk.

Area of Science:

  • Genetics
  • Epidemiology
  • Biostatistics

Background:

  • Polygenic risk scores (PRS) aggregate genetic variants but may reflect familial confounding rather than direct causation.
  • Distinguishing genetic causation from other familial influences on disease risk is a significant challenge in genetic epidemiology.

Purpose of the Study:

  • To develop and apply novel statistical methods to assess the causal inference of polygenic risk scores for breast cancer.
  • To investigate the age-dependent causal contribution of PRS to breast cancer risk using family data and family history.

Main Methods:

  • Developed Inference about Causation from Examination of FAmiliaL CONfounding (ICE FALCON) using relative pair data.
  • Developed Inference about Causation from Examining Changes in Regression coefficients and Innovative STatistical AnaLyses (ICE CRISTAL) using family history.
  • Applied ICE FALCON and ICE CRISTAL to breast cancer PRS across multiple studies, adjusting for familial factors.

    • Main Results:

    • No evidence of PRS causation was found for breast cancer diagnosed at younger ages (<50 years).
    • Consistent evidence for PRS causation was observed for breast cancer diagnosed at later ages, with increasing contribution.
    • Identified potential non-genetic familial factors contributing to breast cancer aggregation, suggesting familial associations are not solely genetic.

    Conclusions:

    • The causal interpretation of PRS for breast cancer varies with age of diagnosis.
    • PRS may capture associations with causal variants via linkage disequilibrium, not necessarily being causal themselves.
    • Familial aggregation of breast cancer involves both genetic and shared non-genetic environmental factors.