A systematic SNP selection approach to identify mechanisms underlying disease aetiology: linking height to post-menopausal breast and colorectal cancer risk

The Relationship Between the hOGG1 rs1052133 Polymorphism and the Occurrence of Nasopharyngeal Carcinoma: A Systematic Review and Meta-Analysis

Objectives: Exploring the relationship between the hOGG1 rs1052133 polymorphism and the occurrence of nasopharyngeal carcinoma (NPC). Methods: PubMed, Web of Science, Scopus, CNKI, Wanfangdata, and VIP were used to search for studies and the NOS evaluation scale was used to evaluate the quality. All studies were grouped according to different genotypes. The Cochrane's Q test and I2 test were used for heterogeneity evaluations. If heterogeneity was small, the fixed effects model was used, and conversely, the random effects model was used. Publication bias was also detected. P < .05 in all results indicated statistically significant. Results: We ultimately included 6 studies with 2021 NPC patients in the study group and 2375 healthy populations in the control group. After meta-analysis, it was found that the total OR value of the "Ser/Cys (CG) vs Ser/Ser (CC)" group was 1.00 (95% CI: 0.85-1.18) and the "Cys/Cys (GG) vs Ser/Ser (CC)" group was 1.06 (95% CI: 0.87-1.28). These results were not statistically significant (P > .05). Furthermore, the integrated total OR values of each group were not statistically significant with or without the smoking history, even in other genotype models (Allele, Dominant, Recessive, and Additive) (P > .05). Conclusion: There is no clear correlation between the hOGG1 rs1052133 polymorphism and the occurrence of NPC, even with or without the smoking history.

Empirical Investigation of Genomic Clusters Associated With Height and the Risk of Postmenopausal Breast and Colorectal Cancer in the Netherlands Cohort Study

We empirically investigated genomic clusters associated with both height and postmenopausal breast cancer (BC) or colorectal cancer (CRC) (or both) in the Netherlands Cohort Study to unravel shared underlying mechanisms between height and these cancers. The Netherlands Cohort Study (1986-2006) includes 120,852 participants (case-cohort study: nsubcohort = 5,000; 20.3 years of follow-up). Variants in clusters on chromosomes 2, 4, 5, 6 (2 clusters), 10, and 20 were genotyped using toenail DNA. Cluster-specific genetic risk scores were modeled in relation to height and postmenopausal BC and CRC risk using age-adjusted linear regression and multivariable-adjusted Cox regression, respectively. Only the chromosome 10 cluster risk score was associated with all 3 phenotypes in the same sex (women); that is, it was associated with increased height (βcontinuous = 0.34, P = 0.014), increased risk of hormone-receptor-positive BC (for estrogen-receptor-positive BC, hazard ratio (HRcontinuous score) = 1.10 (95% confidence interval (CI): 1.02, 1.20); for progesterone-receptor-positive BC, HRcontinuous score = 1.15 (95% CI: 1.04, 1.26)), and increased risk of distal colon (HRcontinuous score = 1.13, 95% CI: 1.01, 1.27) and rectal (HRcontinuous score = 1.14, 95% CI: 0.99, 1.30) cancer. The chromosome 10 cluster variants were all annotated to the zinc finger MIZ-type containing 1 gene (ZMIZ1), which is involved in androgen receptor activity. This suggests that hormone-related growth mechanisms could influence both height and postmenopausal BC and CRC.

Positive association between actinic keratosis and internal malignancies: a nationwide population-based cohort study

Little is known about the comorbidities in actinic keratosis patients. To evaluate the association of actinic keratosis with certain malignancies. All patients with actinic keratosis (n = 61,438) and age- and sex-matched control subjects (n = 307,190) at a 5:1 ratio were enrolled using data from the Korean National Health Insurance Service between the years 2007 and 2014. In subjects with actinic keratosis, overall cancer incidence was higher than that for controls after income level, habitat, diabetes, hypertension, and dyslipidemia were adjusted (Hazard Ratio [HR] = 1.43 [95% confidence interval 1.38–1.47]). The positive association of specific cancers were observed in the following order: skin cancer (HR = 3.43 [2.47–4.75]), oral cavity and pharyngeal cancer (HR = 1.99 [1.57–2.52]), lymphoma (HR = 1.59 [1.28–1.96]), leukemia (HR = 1.35 [1.03–1.77]), prostate cancer (HR = 1.35 [1.21–1.51]), renal cancer (HR = 1.29 [1.02–1.63]), liver cancer (HR = 1.21 [1.09–1.35]), thyroid cancer (HR = 1.20 [1.05–1.38]), and gastric cancer (HR = 1.13 [1.03–1.23]). Although further research on pathologic mechanism is needed, the implications of a positive correlation between actinic keratosis and internal organ malignancies has great significance.

A Hybrid Supervised Approach to Human Population Identification Using Genomics Data

Single nucleotide polymorphisms (SNPs) are one type of genetic variations and each SNP represents a difference in a single DNA building block, namely a nucleotide. Previous research demonstrated that SNPs can be used to identify the correct source population of an individual. In addition, variations in the DNA sequences have an influence on human diseases. In this regard, SNPs studies are helpful for personalized medicine and treatment. In the literature, unsupervised clustering methods especially principal component analysis (PCA) have been popular for studying population structure. In this study, we investigate supervised approaches, particularly the LASSO multinomial regression classification method, for recognizing individuals' origin genetic population. Then, we introduce PCA-LASSO as an extension of LASSO method that benefits from advantageous characteristics of both PCA and LASSO regression. The experimental results obtained on the 1,000 genome project dataset show PCA-LASSO's significantly high accuracy in prediction of individual's origin population.

Genetic Variation and Hot Flashes: A Systematic Review

CONTEXT

Approximately 70% of women report experiencing vasomotor symptoms (VMS, hot flashes and/or night sweats). The etiology of VMS is not clearly understood but may include genetic factors.

EVIDENCE ACQUISITION

We searched PubMed and Embase in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidance. We included studies on associations between genetic variation and VMS. We excluded studies focused on medication interventions or prevention or treatment of breast cancer.

EVIDENCE SYNTHESIS

Of 202 unique citations, 18 citations met the inclusion criteria. Study sample sizes ranged from 51 to 17 695. Eleven of the 18 studies had fewer than 500 participants; 2 studies had 1000 or more. Overall, statistically significant associations with VMS were found for variants in 14 of the 26 genes assessed in candidate gene studies. The cytochrome P450 family 1 subfamily A member 1 (CYP1B1) gene was the focus of the largest number (n = 7) of studies, but strength and statistical significance of associations of CYP1B1 variants with VMS were inconsistent. A genome-wide association study reported statistically significant associations between 14 single-nucleotide variants in the tachykinin receptor 3 gene and VMS. Heterogeneity across trials regarding VMS measurement methods and effect measures precluded quantitative meta-analysis; there were few studies of each specific genetic variant.

CONCLUSIONS

Genetic variants are associated with VMS. The associations are not limited to variations in sex-steroid metabolism genes. However, studies were few and future studies are needed to confirm and extend these findings.

Identification of infectious disease-associated host genes using machine learning techniques

Background

With the global spread of multidrug resistance in pathogenic microbes, infectious diseases emerge as a key public health concern of the recent time. Identification of host genes associated with infectious diseases will improve our understanding about the mechanisms behind their development and help to identify novel therapeutic targets.

Results

We developed a machine learning techniques-based classification approach to identify infectious disease-associated host genes by integrating sequence and protein interaction network features. Among different methods, Deep Neural Networks (DNN) model with 16 selected features for pseudo-amino acid composition (PAAC) and network properties achieved the highest accuracy of 86.33% with sensitivity of 85.61% and specificity of 86.57%. The DNN classifier also attained an accuracy of 83.33% on a blind dataset and a sensitivity of 83.1% on an independent dataset. Furthermore, to predict unknown infectious disease-associated host genes, we applied the proposed DNN model to all reviewed proteins from the database. Seventy-six out of 100 highly-predicted infectious disease-associated genes from our study were also found in experimentally-verified human-pathogen protein-protein interactions (PPIs). Finally, we validated the highly-predicted infectious disease-associated genes by disease and gene ontology enrichment analysis and found that many of them are shared by one or more of the other diseases, such as cancer, metabolic and immune related diseases.

Conclusions

To the best of our knowledge, this is the first computational method to identify infectious disease-associated host genes. The proposed method will help large-scale prediction of host genes associated with infectious-diseases. However, our results indicated that for small datasets, advanced DNN-based method does not offer significant advantage over the simpler supervised machine learning techniques, such as Support Vector Machine (SVM) or Random Forest (RF) for the prediction of infectious disease-associated host genes. Significant overlap of infectious disease with cancer and metabolic disease on disease and gene ontology enrichment analysis suggests that these diseases perturb the functions of the same cellular signaling pathways and may be treated by drugs that tend to reverse these perturbations. Moreover, identification of novel candidate genes associated with infectious diseases would help us to explain disease pathogenesis further and develop novel therapeutics.

Inverse Association Between Height-Increasing Alleles and Extreme Longevity in Japanese Women

Growth hormone (GH)/insulin-like growth factor-1 (IGF-1)/insulin signaling is one of the most plausible biological pathways regulating aging and longevity. Previous studies have demonstrated that several single nucleotide polymorphisms (SNPs) in the GH/IGF-1/insulin signaling-associated genes influence both longevity and adult height, suggesting the possibility of a shared genetic architecture between longevity and height. We therefore examined the relationship between 30 height-associated SNPs and extreme longevity in a Japanese population consisting of 428 centenarians and 4,026 younger controls. We confirmed that height-increasing genetic scores (HGSs) constructed based on 30 SNPs were significantly associated with height in the controls (p = 6.95 × 10-23). HGS was significantly and inversely associated with extreme longevity in women (p = .011), but not in men, although no SNPs were significantly associated with extreme longevity after Bonferroni correction. The odds ratio for extreme longevity in the lowest HGS group (≤27) and the second lowest HGS group (28-30) relative to the highest HGS group (≥37) was 1.71 (p = .056) and 1.69 (p = .034), respectively, for women. In conclusion, the present study demonstrated an inverse association between height-increasing alleles with extreme longevity in Japanese women, providing novel insight into the genetic architecture of longevity and aging.

Genetic variants involved in extracellular matrix homeostasis play a role in the susceptibility to frozen shoulder: A case-control study

Frozen shoulder is a condition of loss of active and passive motion as result of inflammatory contracture and fibrosis of the joint capsule. We hypothesize that genetic variants in genes involved in these processes such as genes that play a role in extracellular matrix homeostasis (collagens, glycoproteins, genes involved in TGFβ signaling, and metalloproteinases and its inhibitors) may contribute to the susceptibility to frozen shoulder. We evaluated eighteen SNPs of genes involved in extracellular matrix homeostasis in 186 cases (N_females  = 114; N_males  = 72) of frozen shoulder and 600 age-matched controls (N_females  = 308; N_males  = 292). Multivariate logistic regressions were carried out with age, gender, genetic ancestry, and common comorbidities as covariates. Carriers of the C allele of MMP13 rs2252070 and G/G MMP9 (rs17576 A>G/rs17577 G>A) haplotype may have an increased risk of frozen shoulder (p = 0.002, OR = 1.64, 95%CI = 1.20-2.26, and p = 0.046, OR = 1.40, 95%CI = 1.01-1.95, respectively), especially in females (p = 0.005, OR = 1.91, 95%CI = 1.22-2.99, and p = 0.046, OR = 1.59, 95%CI = 1.01-2.51, respectively). In females, the G allele of MMP9 rs17576 tended to contribute to the susceptibility to the studied disease (p = 0.05, OR = 1.51, 95%CI = 0.97-2.33). In contrast, the presence of the C allele of TGFB1 rs1800470 seems to be associated with a reduced risk (p = 0.04, OR = 0.47, 95%CI = 0.23-0.96) while the GG-genotype of TGFBR1 rs1590 was associated with increased risk (p = 0.027, OR = 4.11, 95%CI = 1.17-14.38) to frozen shoulder development in males. Thus, we identified genetic variants that were independent risk factors that can aid in the risk assessment of frozen shoulder reinforcing the involvement of MMP and TGFβ signaling in disease development. © 2019 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res.

Size matters: height, cell number and a person's risk of cancer

The multistage model of carcinogenesis predicts cancer risk will increase with tissue size, since more cells provide more targets for oncogenic somatic mutation. However, this increase is not seen among mammal species of different sizes (Peto's paradox), a paradox argued to be due to larger species evolving added cancer suppression. If this explanation is correct, the cell number effect is still expected within species. Consistent with this, the hazard ratio for overall cancer risk per 10 cm increase in human height (HR₁₀) is about 1.1, indicating a 10% increase in cancer risk per 10 cm; however, an alternative explanation invokes an indirect effect of height, with factors that increase cancer risk independently increasing adult height. The data from four large-scale surveillance projects on 23 cancer categories were tested against quantitative predictions of the cell-number hypothesis, predictions that were accurately supported. For overall cancer risk the HR₁₀ predicted versus observed was 1.13 versus 1.12 for women and 1.11 versus 1.09 for men, suggesting that cell number variation provides a null hypothesis for assessing height effects. Melanoma showed an unexpectedly strong relationship to height, indicating an additional effect, perhaps due to an increasing cell division rate mediated through increasing IGF-I with height. Similarly, only about one-third of the higher incidence of non-reproductive cancers in men versus women can be explained by cell number. The cancer risks of obesity are not correlated with effects of height, consistent with different primary causation. The direct effect of height on cancer risk suggests caution in identifying height-related SNPs as cancer causing.