IRT for CRC
  • Developing an Integrated Risk Prediction Tool for Colorectal Cancer
  • Cohort used for the study
  • Clinical risk factors
    • Creating the clinical dataframe
    • Creating the clinical risk model
    • Analyzing the results
  • Polygenic risk scores
    • PRSice
    • Preparing to use PRSice
    • Obtaining polygenic risk scores
    • Analyzing the results
    • Plink2
  • Integrated risk score
    • Obtaining integrated risk scores
    • Analyzing the results
  • Working with real patient data
    • Genetic data from Tempus Labs
      • Preparing the data and obtaining polygenic risk scores
      • Interpreting the scores
    • Clinical data from Gastroenterology Atlanta
      • Obtaining integrated scores and understanding the results
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  • Results
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  1. Integrated risk score

Obtaining integrated risk scores

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Last updated 1 year ago

Command

The integrated risk scores were computed using generalized linear models (GLM) in R.

model <- glm(PHENO ~ PRS + Age + Sex + height + weight + physical_activity + meat + smoking + alcohol + father_cancer + mother_cancer + sibling_cancer + polyps + crohns_disease + ulcerative_colitis, data = merged_df, family = binomial)
summary(model)

Results

When using the polygenic risk scores obtained from the PGS000074 scoring file

When using the polygenic risk scores obtained from the PGS000785 scoring file

Observation

The last column represents the p-value of the risk factor. When the p-value is <0.05, it means that the null hypothesis is to be rejected. When the p-value is >0.05, it means that there is no observed effect of the covariate on the hypothesis.

From this, covariates that significantly affect the risk of colorectal cancer include PRS, sex (male), body weight, meat consumption, physical activity, meat and alcohol consumption, family history of cancer, and history of polyps and ulcerative colitis.

Since the AIC value is lesser for PGS000785 (and the previously obtained AUC values are more), it can be inferred that the scoring file PGS000785 provides a better fit.