Pathway analysis for genome-wide association study of lung cancer in Han Chinese population

Searching for improvements in predicting human eye colour from DNA

Increasing understanding of human genome variability allows for better use of the predictive potential of DNA. An obvious direct application is the prediction of the physical phenotypes. Significant success has been achieved, especially in predicting pigmentation characteristics, but the inference of some phenotypes is still challenging. In search of further improvements in predicting human eye colour, we conducted whole-exome (enriched in regulome) sequencing of 150 Polish samples to discover new markers. For this, we adopted quantitative characterization of eye colour phenotypes using high-resolution photographic images of the iris in combination with DIAT software analysis. An independent set of 849 samples was used for subsequent predictive modelling. Newly identified candidates and 114 additional literature-based selected SNPs, previously associated with pigmentation, and advanced machine learning algorithms were used. Whole-exome sequencing analysis found 27 previously unreported candidate SNP markers for eye colour. The highest overall prediction accuracies were achieved with LASSO-regularized and BIC-based selected regression models. A new candidate variant, rs2253104, located in the ARFIP2 gene and identified with the HyperLasso method, revealed predictive potential and was included in the best-performing regression models. Advanced machine learning approaches showed a significant increase in sensitivity of intermediate eye colour prediction (up to 39%) compared to 0% obtained for the original IrisPlex model. We identified a new potential predictor of eye colour and evaluated several widely used advanced machine learning algorithms in predictive analysis of this trait. Our results provide useful hints for developing future predictive models for eye colour in forensic and anthropological studies.

Association of education & lifestyle factors with the perception of genetic knowledge on the development of lung cancer

BACKGROUND & OBJECTIVES

The perception of genetic knowledge is useful for improving the heath behaviour change against developing cancers. However, no studies have investigated the perception of genetic knowledge on the development of lung cancer. The aim of this study was to examine demographic and lifestyle factors of the perception of genetic knowledge on the development of lung cancer.

METHODS

Data on 2,295 US adults (739 had the perception of genetic knowledge) were taken from the 2003 Health Information National Trends Survey. Multiple logistic regression models were used to evaluate potential factors of the perception of genetic knowledge of lung cancer.

RESULTS

Participants aged ≥65 yr were more likely to have the perception of genetic knowledge than those aged 18-44 yr (OR=1.77, 95% CI=1.27-2.46). Higher education was associated with a greater perception of genetic knowledge (OR=1.47, 95% CI=1.16-1.87). Subjects with correct smoking attitude were more than three times more likely to have the perception of genetic knowledge (OR=3.15, 95% CI=2.10-4.72). Subjects with exercise were at an increased likelihood of having the perception of genetic knowledge than those without exercise (OR=1.63, 95% CI=1.24-2.13).

INTERPRETATION & CONCLUSIONS

Positive associations were observed between education and lifestyle factors and the perception of genetic knowledge on the development of lung cancer among US adults. Strategies developed to improve the perception of genetic knowledge of lung cancer may target on individuals who are young, less educated, and lack correct smoking attitude or exercise.

Pathway analysis of genome-wide association study and transcriptome data highlights new biological pathways in colorectal cancer

Colorectal cancer (CRC) is a common malignancy that meets the definition of a complex disease. Genome-wide association study (GWAS) has identified several loci of weak predictive value in CRC, however, these do not fully explain the occurrence risk. Recently, gene set analysis has allowed enhanced interpretation of GWAS data in CRC, identifying a number of metabolic pathways as important for disease pathogenesis. Whether there are other important pathways involved in CRC, however, remains unclear. We present a systems analysis of KEGG pathways in CRC using (1) a human CRC GWAS dataset and (2) a human whole transcriptome CRC case-control expression dataset. Analysis of the GWAS dataset revealed significantly enriched KEGG pathways related to metabolism, immune system and diseases, cellular processes, environmental information processing, genetic information processing, and neurodegenerative diseases. Altered gene expression was confirmed in these pathways using the transcriptome dataset. Taken together, these findings not only confirm previous work in this area, but also highlight new biological pathways whose deregulation is critical for CRC. These results contribute to our understanding of disease-causing mechanisms and will prove useful for future genetic and functional studies in CRC.

Association between interleukin-8 -251A/T polymorphism and risk of lung cancer: a meta-analysis

This study is to evaluate the association between IL-8 -251A/T polymorphism and lung cancer risk in diverse populations. We performed a meta-analysis of six case-control studies that included 3,265 lung-cancer cases and 3,607 case-free controls. Overall, results showed that the IL-8 -251A/T polymorphism was not associated with a significantly increased risk of lung cancer in all genetic models. However, stratified by ethnicity, a significantly increased risk was found among Asians. In conclusion, IL-8 -251A/T polymorphism is associated with lung cancer susceptibility in Asians and the -251 A allele may increase risk of lung cancer in Asians.

Genetic susceptibility to lung cancer and co-morbidities

Lung cancer is a leading cause of cancer death and disease burden in many countries. Understanding of the biological pathways involved in lung cancer aetiology is required to identify key biomolecules that could be of significant clinical value, either as predictive, prognostic or diagnostic markers, or as targets for the development of novel therapies to treat this disease, in addition to smoking avoidance strategies. Genome-wide association studies (GWAS) have enabled significant progress in the past 5 years in investigating genetic susceptibility to lung cancer. Large scale, multi-cohort GWAS of mainly Caucasian, smoking, populations have identified strong associations for lung cancer mapped to chromosomal regions 15q [nicotinic acetylcholine receptor (nAChR) subunits: CHRNA3, CHRNA5], 5p (TERT-CLPTM1L locus) and 6p (BAT3-MSH5). Some studies in Asian populations of smokers have found similar risk loci, whereas GWAS in never smoking Asian females have identified associations in other chromosomal regions, e.g., 3q (TP63), that are distinct from smoking-related lung cancer risk loci. GWAS of smoking behaviour have identified risk loci for smoking quantity at 15q (similar genes to lung cancer susceptibility: CHRNA3, CHRNA5) and 19q (CYP2A6). Other genes have been mapped for smoking initiation and smoking cessation. In chronic obstructive pulmonary disease (COPD), which is a known risk factor for lung cancer, GWAS in large cohorts have also found CHRNA3 and CHRNA5 single nucleotide polymorphisms (SNPs) mapping at 15q as risk loci, as well as other regions at 4q31 (HHIP), 4q24 (FAM13A) and 5q (HTR4). The overlap in risk loci between lung cancer, smoking behaviour and COPD may be due to the effects of nicotine addiction; however, more work needs to be undertaken to explore the potential direct effects of nicotine and its metabolites in gene-environment interaction in these phenotypes. Goals of future genetic susceptibility studies of lung cancer should focus on refining the strongest risk loci in a wide range of populations with lung cancer, and integrating other clinical and biomarker information, in order to achieve the aim of personalised therapy for lung cancer.