Abstract 881: Benchmarking genome-wide polygenic risk score development techniques in colorectal cancer risk prediction

Abstract Background In this study, we aim to uncover the relationship between estrogen levels and the genetic polymorphism of estrogen metabolism-related enzymes with breast cancer (BC) and establish a risk prediction model based on polygenic risk score. Methods Unrelated BC patients and healthy subjects were recruited for analysis of the estrogen levels and the single nucleotide polymorphisms (SNPs) of estrogen metabolism-related enzymes. The polygenic risk score (PRS) was used to explore the combined effect of multiple genes which was calculated using a Bayesian approach. The independent sample t test was used to evaluate the difference between PRS scores of BC and healthy subjects. Discriminatory accuracy of the models was compared using the area under the receiver operating characteristic curve (ROC). Results The estrogen homeostasis profile was disturbed in BC patients, with parent estrogens (E1, E2) and carcinogenic catechol estrogens (2/4-OHE1, 2-OHE2, 4-OHE2) significantly accumulated in the serum of BC patients. Then,we established PRS model to evaluate the role of multiple genes SNPs. The PRS model 1 (M1) was established from 6 GWAS-identified high risk genes SNPs. On the basis of M1, we added 7 estrogen metabolism enzyme genes SNPs to establish PRS model 2 (M2). The independent sample t test results show that there is no difference between BC and healthy subjects in M1 (P = 0.17), however, there is significant difference between BC and healthy subjects in M2 (P = 4.9*10− 5). The ROC curve results also show that the accuracy of M2 (AUC = 62.18%) in breast cancer risk identification was better than M1 (AUC = 54.56%). Conclusion Estrogens and the related metabolic enzymes gene polymorphisms are closely related to BC. The model constructed by adding estrogen metabolic enzyme genes SNPs has a good ability in breast cancer risk prediction, and the accuracy is greatly improved comparing PRS model only includes GWAS-identified genes SNPs.

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Breast cancer risk prediction using a clinical risk model and polygenic risk score

Breast Cancer Research and Treatment ◽

10.1007/s10549-016-3953-2 ◽

2016 ◽

Vol 159 (3) ◽

pp. 513-525 ◽

Cited By ~ 53

Author(s):

Yiwey Shieh ◽

Donglei Hu ◽

Lin Ma ◽

Scott Huntsman ◽

Charlotte C. Gard ◽

...

Keyword(s):

Breast Cancer ◽

Breast Cancer Risk ◽

Cancer Risk ◽

Risk Prediction ◽

Risk Score ◽

Risk Model ◽

Polygenic Risk Score ◽

Polygenic Risk ◽

Clinical Risk

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Novel Common Genetic Susceptibility Loci for Colorectal Cancer

JNCI Journal of the National Cancer Institute ◽

10.1093/jnci/djy099 ◽

2018 ◽

Vol 111 (2) ◽

pp. 146-157 ◽

Cited By ~ 37

Author(s):

Stephanie L Schmit ◽

Christopher K Edlund ◽

Fredrick R Schumacher ◽

Jian Gong ◽

Tabitha A Harrison ◽

...

Keyword(s):

Colorectal Cancer ◽

Relative Risk ◽

Risk Score ◽

European Ancestry ◽

Polygenic Risk Score ◽

Susceptibility Loci ◽

Polygenic Risk ◽

Control Subjects ◽

Genome Wide ◽

Familial Relative Risk

Abstract Background Previous genome-wide association studies (GWAS) have identified 42 loci (P < 5 × 10−8) associated with risk of colorectal cancer (CRC). Expanded consortium efforts facilitating the discovery of additional susceptibility loci may capture unexplained familial risk. Methods We conducted a GWAS in European descent CRC cases and control subjects using a discovery–replication design, followed by examination of novel findings in a multiethnic sample (cumulative n = 163 315). In the discovery stage (36 948 case subjects/30 864 control subjects), we identified genetic variants with a minor allele frequency of 1% or greater associated with risk of CRC using logistic regression followed by a fixed-effects inverse variance weighted meta-analysis. All novel independent variants reaching genome-wide statistical significance (two-sided P < 5 × 10−8) were tested for replication in separate European ancestry samples (12 952 case subjects/48 383 control subjects). Next, we examined the generalizability of discovered variants in East Asians, African Americans, and Hispanics (12 085 case subjects/22 083 control subjects). Finally, we examined the contributions of novel risk variants to familial relative risk and examined the prediction capabilities of a polygenic risk score. All statistical tests were two-sided. Results The discovery GWAS identified 11 variants associated with CRC at P < 5 × 10−8, of which nine (at 4q22.2/5p15.33/5p13.1/6p21.31/6p12.1/10q11.23/12q24.21/16q24.1/20q13.13) independently replicated at a P value of less than .05. Multiethnic follow-up supported the generalizability of discovery findings. These results demonstrated a 14.7% increase in familial relative risk explained by common risk alleles from 10.3% (95% confidence interval [CI] = 7.9% to 13.7%; known variants) to 11.9% (95% CI = 9.2% to 15.5%; known and novel variants). A polygenic risk score identified 4.3% of the population at an odds ratio for developing CRC of at least 2.0. Conclusions This study provides insight into the architecture of common genetic variation contributing to CRC etiology and improves risk prediction for individualized screening.

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