Combinations of single nucleotide polymorphisms identified in genome‐wide association studies determine risk for colorectal cancer

Abstract

Genome‐wide association studies (GWASs) have identified single nucleotide polymorphisms (SNPs) associated with colorectal cancer (CRC) risk, but whether these SNPs have additive effects on the risk of CRC remains unclear. We performed a systematic analysis of GWAS‐identified SNPs using GWAS datasets from China (2,248 patients and 3,173 controls) and Europe (4,461 patients and 4,140 controls). We analyzed 58 independent variants from DNA samples from Chinese populations and found 19 SNPs that were significantly associated with CRC risk. We identified two genetic risk scores (GRSs) based on 58 and 19 SNPs, which were significantly associated with an increased risk of CRC. A decision curve analysis showed higher predictive power for the 58 SNPs. Using all the 58 SNPs to assess 5‐year absolute risk (AR), we found that, at a cutoff of 0.4% (two times the median AR) and 0.6% (three times the median AR), approximately 32.76 and 16.45% of Chinese individuals were grouped as high risk for developing CRC, respectively. Risk stratification analysis further indicated that the population in the top 30% risk group accounted for 46.71% of the CRC cases. In addition, the 58 SNPs could explain approximately 1.13% of the phenotypic variance in Chinese populations. Similar findings were found in European populations. Combinations of SNPs identified in GWASs may therefore be useful for identifying individuals at high risk for CRC.

Polygenic risk prediction models for colorectal cancer: a systematic review

“… Model were compared among each other respect to NRI (95%CI; p -value) and IDI (95%CI; p -value): the simple-count-GRS vs. logistic regression weighted OR-GRS showed an NRI of − 0.082 (− 0.159, − 0.007; p value: 0.033) and an IDI of − 0.002 (− 0.004, − 2.33E− 04; 0.028); the simple-count-GRS vs. explained variance weighted OR-GRS showed an NRI of 0.017 (− 0.055, 0.090; 0.638) and an IDI of 2.80E− 04 (− 0.001, 0.001; 0.567); logistic regression weighted-GRS vs. explained variance weighted OR-GRS showed an NRI − 0.077 (− 0.153, − 0.001; 0.046) and an IDI of − 5.54E− 04 (− 0.001, − 3.17E− 05; 0.038). In addition, a model including only smoking factors was with a model including smoking factors and simple count GRS (SC-GRS), with an increased AUC, NRI and IDI in combined model of 0.084, 0.317 (0.225, 0.408) and 0.031 (0.023, 0.039) Xin J, 2019 [ 52 ] Case-control Chinese Chinese studies: 2248 cases and 3173 controls; GECCO study: 4461 cases and 4140 controls Chinese studies: 19 SNPs vs. 58 SNPs; GECCO study: 19 SNPs vs. 75 SNPs Weighted GRS using weights derived from the same study Gender, age, first principal component Chinese studies: 19 SNPs model of 0.597 (0.581–0.613), 58 SNPs model of 0.623 (0.604–0.642); GECCO study: 19 SNPs model of 0.575 (0.563–0.587), 58 SNPs model of 0.585 (0.573–0.597) Yeh CC, 2007 [ 53 ] Case-control Taiwanese 727 cases and 736 controls 10 SNPs Age, education, physical activity, coffee consumption, cigarette consumption, alcohol use, staple consumption, meat, vegetable/fruit and fish/shrimp intake. Zhang L, 2017 [ 54 ] Case-control Chinese 369 cases and 929 controls 4 SNPs Age, BMI, physical activity, emotion status, mental stress, cholesterol, drinking and smoking, vegetables and seafood consumption CRC colorectal cancer, SNP single nucleotide polymorphism, ERS environmental risk score, GRS genetic risk score, TRS traditional risk score, PRS polygenic risk score, ct-DNA circulating tumor-DNA, RR relative risk, HR hazard ratio, OR odds ratio, GWAS genome-wide association study, BMI body mass index, FH family history, NSAID nonsteroidal anti-inflammatory drug …”

“…In order to include genetic factors into prediction models, different methodologies were investigated across the included studies. In particular, 26 (78.79%) studies used a GRS, 11 (42.31%) of which used a weighted GRS [ 31 , 33 – 35 , 40 , 42 – 46 , 52 ], other 6 (23.08%) studies used an unweighted GRS [ 22 , 24 , 26 – 29 ]. Instead, a total of 9 studies (34.62%) used both unweighted and weighted methods to develop risk scores [ 23 , 25 , 30 , 32 , 36 , 37 , 49 – 51 ].…”

KCNB1 gene polymorphisms and related indel as predictor biomarkers of treatment response for colorectal cancer – toward a personalized medicine

“…Increasing evidence demonstrated that ion channel genes contribute to the progression of various carcinomas, and the proliferation of several cell types. 35–40 We explored the correlation between rs3331, rs1051295, and rs11468831 variants and the response to chemotherapy among CRC patients.…”

Explaining the Genetic Causality for Complex Phenotype via Deep Association Kernel Learning

“…The genotyping data of GWAS of colorectal cancer and LC were derived from previous studies. 45,46 DAK is available from Github: https://github.com/fbaothu/DAK. Other tools used in this work can be downloaded from: Plink: http://zzz.bwh.harvard.edu/plink/; HAPGEN 2: https://mathgen.stats.…”