Adding genetic scores to risk models in colorectal cancer

Gargallo-Puyuelo, C J; Lanas, Ángel; Garcı́a-González, Maria Asunción

doi:10.18632/oncotarget.27110

Cited by 2 publications

(2 citation statements)

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“…A study by Gargallo et al [ 14 ] showed that rs10795668 is associated with the susceptibility to colorectal adenomas and modified by the presence of a family history of CRC. The authors demonstrate that the low contribution of identified genetic variants associated with CRC does provide clinically relevant information by itself, but can increase the risk of CRC in a combination of risk-associated alleles in a polygenic model [ 14 , 15 ].…”

Section: Discussionmentioning

confidence: 99%

Association of four genetic variants with colorectal cancer in Kazakhstan population

et al. 2021

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The study was conducted to search for polymorphisms located in the 10th chromosome associated with colorectal adenocarcinoma in representatives of the Kazakhstan population. Study was performed with 282 colorectal cancer (CRC) patients and 159 controls. Genotyping of SNPs was performed by QuantStudio 12K Flex PCR. For four significant SNPs inheritance model analysis was performed. Increasing risk of CRC was noted for rs10795668 in log-additive model (OR = 1.45, 95% CI: 1.05-1.99, p = 0.023); for rs1035209 in log-additive model (OR = 1.79, 95% CI: 1.18-2.72, p = 0.003); for rs11190164 in log-additive model (OR = 1.67, 95% CI: 1.17-2.38, p = 0.004). Decreasing risk of CRC was noted for rs10506868 in logadditive model (OR = 0.56, 95% CI: 0.37-0.85, p = 0.006). We detected SNPs that are associated with CRC risk in the Kazakhstan population.

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Section: Discussionmentioning

confidence: 99%

Association of four genetic variants with colorectal cancer in Kazakhstan population

et al. 2021

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“…16 Furthermore, the combination of the polygenic risk scores and family history resulted in more accurate risk stratification than consideration of either of these factors alone, 16 and multiple studies support that adding polygenic risk scores to CRC risk prediction models significantly improved the discriminatory accuracy of these models as measured by the area under the curve. 16,17 Thus far, only a fraction of all CRC risk loci has been identified, so it is expected that the predictive power of the polygenic risk scores will further improve as more genetic risk variants are discovered and machine learning approaches are applied to very large genetic studies to refine genome-wide genetic risk scores. 16 As the identified genetic risk variants tend to show similar risks in advanced adenoma it is likely that the polygenic risk score can be important for the prediction of both advanced adenoma and CRC.…”

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confidence: 99%