Genome-wide association studies have identified more than 90 susceptibility loci for breast cancer. However, the missing heritability is evident, and the contributions of coding variants to breast cancer susceptibility have not yet been systematically evaluated. Here, we present a large-scale whole-exome association study for breast cancer consisting of 24,162 individuals (10,055 cases and 14,107 controls). In addition to replicating known susceptibility loci (e.g., , and), we identify two novel missense variants in (rs13047478, = 4.52 × 10) and (rs3810151, = 7.60 × 10) and one new noncoding variant at 7q21.11 ( < 5 × 10). and possessed functional roles in the control of breast cancer cell growth, and the two coding variants were found to be the eQTL for several nearby genes. rs13047478 was significantly ( < 5.00 × 10) associated with the expression of genes and in breast mammary tissues. rs3810151 was found to be significantly associated with the expression of genes ( = 8.39 × 10) and ( = 3.77 × 10) in human blood samples. and, together with these eQTL genes, were differentially expressed in breast tumors versus normal breast. Our study reveals additional loci and novel genes for genetic predisposition to breast cancer and highlights a polygenic basis of disease development. Large-scale genetic screening identifies novel missense variants and a noncoding variant as predisposing factors for breast cancer. .

Smoking is a complex behavior with a heritability as high as 50%. Given such a large genetic contribution, it provides an opportunity to prevent those individuals who are susceptible to smoking dependence from ever starting to smoke by predicting their inherited predisposition with their genomic profiles. Although previous studies have identified many susceptibility variants for smoking, they have limited power to predict smoking behavior. We applied the support vector machine (SVM) and random forest (RF) methods to build prediction models for smoking behavior. We first used 1,431 smokers and 1,503 non-smokers of African origin for model building with a 10-fold cross-validation and then tested the prediction models on an independent dataset consisting of 213 smokers and 224 non-smokers. The SVM model with 500 top single nucleotide polymorphisms (SNPs) selected using logistic regression (p<0.01) as the feature selection method achieved an area under the curve (AUC) of 0.691, 0.721, and 0.720 for the training, test, and independent test samples, respectively. The RF model with 500 top SNPs selected using logistic regression (p<0.01) achieved AUCs of 0.671, 0.665, and 0.667 for the training, test, and independent test samples, respectively. Finally, we used the combined logistic (p<0.01) and LASSO (l=10 −3) regression to select features and the SVM algorithm for model building. The SVM model with 500 top SNPs achieved AUCs of 0.756, 0.776, and 0.897 for the training, test, and independent test samples, respectively. We conclude that machine learning methods are promising means to build predictive models for smoking.

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ObjectiveThis study aimed to compare the variability of HPV16/18/52/58 subtype infections in patients with different cervical lesions, to explore the guiding significance of persistent positive HPV subtypes 52 and 58 in the stratified management of cervical lesions, and to determine the appropriate management model.MethodThis study was conducted through a retrospective analysis of 244,218 patients who underwent HPV testing at the Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University from September 2014 to December 2020 to examine the distribution of different types of HPV infection. From March 2015 to September 2017, 3,014 patients with known HPV underwent colposcopy to analyze high-risk HPV infection for different cervical lesions. Meanwhile, from September 2014 to December 2020, 1,616 patients positive for HPV16/18/52/58 alone with normal TCT who underwent colposcopy in our hospital were retrospectively analyzed for the occurrence of cervical and vulvovaginal lesions, with colposcopic biopsy pathology results serving as the gold standard for statistical analysis.ResultAnalysis of 244,218 patients who had HPV tested revealed that the top 3 high-risk HPV types were HPV52, HPV58, and HPV16. Further analysis of 3,014 patients showed that 78.04% of patients referred for colposcopy had HPV16/18/52/58 alone. Among high-grade squamous intraepithelial lesions (HSIL) and cervical cancer, the most common is HPV16, followed by HPV58 and then HPV52 (p &lt; 0.05). A total of 1,616 patients with normal TCT who were referred for colposcopy due to HPV16/18/52/58 infection were further analyzed. Based on pathological findings in lesions of HSIL and CC, HPV16 is the most common, followed by HPV58 and then HPV18 (p &lt; 0.05). In the 1,616 patients analyzed, high-grade vulvovaginal lesions were detected, with HPV58 being the most common, followed by HPV16 and then HPV52 (p &lt; 0.05).Conclusion1. In patients with positive HPV58 alone and normal TCT, the indications for colposcopy may be relaxed, with particular attention paid to the possibility of vulvar and vaginal lesions.2. Patients with a positive HPV type 52 alone and normal TCT may be considered for a follow-up review and, if necessary, a colposcopy.3. The development of a more suitable HPV vaccine for the Asian population, such as HPV16/18/52/58, may better protect women’s health.