Differences in polygenic score distributions in European ancestry populations: implications for breast cancer risk prediction.

The 313-variant polygenic risk score (PRS313) provides a promising tool for breast cancer risk prediction. However, evaluation of the PRS313 across different European populations which could influence risk estimation has not been performed. Here, we explored the distribution of PRS313 across European populations using genotype data from 94,072 females without breast cancer, of European-ancestry from 21 countries participating in the Breast Cancer Association Consortium (BCAC) and 225,105 female participants from the UK Biobank. The mean PRS313 differed markedly across European countries, being highest in south-eastern Europe and lowest in north-western Europe. Using the overall European PRS313 distribution to categorise individuals leads to overestimation and underestimation of risk in some individuals from south-eastern and north-western countries, respectively. Adjustment for principal components explained most of the observed heterogeneity in mean PRS. Country-specific PRS distributions may be used to calibrate risk categories in individuals from different countries.

Tissue and plasma proteomic profiling indicates AHSG as a potential biomarker for ascending thoracic aortic aneurysms.

BACKGROUND: Thoracic Aortic Aneurysms (TAAs) develop asymptomatically and are characterized by dilatation of the aorta. This is considered a life-threatening vascular disorder due to the risk of aortic dissection and rupture. There is an urgent need to identify blood-borne biomarkers for the early detection of TAA. The goal of the present study was to identify potential protein biomarkers associated with TAAs, using proteomic analysis of aortic tissue and plasma samples. METHODS: Extracted proteins from 14 aneurysmal and 12 non-aneurysmal thoracic aortic tissue specimens as well as plasma samples from six TAA patients collected pre-and postoperatively and six healthy controls (HC), were analyzed by liquid chromatography-tandem mass spectrometry. Proteomic data were further processed and following filtering criteria, one protein was selected for verification and validation in a larger cohort of patients and controls using a targeted quantitative proteomic approach and enzyme-linked immunosorbent assay, respectively. RESULTS: A total of 1593 and 363 differentially expressed proteins were identified in tissue and plasma samples, respectively. Pathway enrichment analysis on the differentially expressed proteins revealed a number of dysregulated molecular pathways that might be implicated in aneurysm pathology including complement and coagulation cascades, focal adhesion, and extracellular matrix receptor interaction pathways. Alpha-2-HS glycoprotein (AHSG) was selected for further verification in 36 TAA and 21 HC plasma samples using targeted quantitative proteomic approach. The results showed a significantly decreased concentration of AHSG (p = 0.0002) in the preoperative plasma samples compared with HC samples. Further analyses using a larger validation dataset revealed that AHSG protein levels were significantly lower (p = 0.03) compared with HC. Logistic regression analysis on the validation dataset revealed males, advanced age, hypertension and hyperlipidaemia as significant risk factors for TAA. CONCLUSION: AHSG concentrations distinguish plasma samples derived from TAA patients and controls. The findings of this study suggest that AHSG may be a potential biomarker for TAA that could lead to better diagnostic capabilities.

Combination of a 15-SNP Polygenic Risk Score and Classical Risk Factors for the Prediction of Breast Cancer Risk in Cypriot Women.

The PRS combines multiplicatively the effects of common low-risk single nucleotide polymorphisms (SNPs) and has the potential to be used for the estimation of an individual's risk for a trait or disease. PRS has been successfully implemented for the prediction of breast cancer risk. The combination of PRS with classical breast cancer risk factors provides a more comprehensive risk estimation and could, thus, improve risk stratification and personalized preventative strategies. In this study, we assessed the predictive performance of the combined effect of PRS15 with classical breast-cancer risk factors in Cypriot women using 1109 cases and 1177 controls from the MASTOS study. The PRS15 was significantly associated with an increased breast cancer risk in Cypriot women OR (95% CI) 1.66 (1.25-2.19). The integrated risk model obtained an AUC (95% CI) 0.70 (0.67-0.72) and had the ability to stratify women according to their disease status at the extreme deciles. These results provide evidence that the combination of PRS with classical risk factors may be used in the future for the stratification of Cypriot women based on their disease risk, and support its potential clinical utility for targeted preventative actions and population screening.

Prediction of Parkinson's Disease Risk Based on Genetic Profile and Established Risk Factors.

BACKGROUND: Parkinson's disease (PD) is a neurodegenerative disorder, and literature suggests that genetics and lifestyle/environmental factors may play a key role in the triggering of the disease. This study aimed to evaluate the predictive performance of a 12-Single Nucleotide Polymorphisms (SNPs) polygenic risk score (PRS) in combination with already established PD-environmental/lifestyle factors. METHODS: Genotypic and lifestyle/environmental data on 235 PD-patients and 464 controls were obtained from a previous study carried out in the Cypriot population. A PRS was calculated for each individual. Univariate logistic-regression analysis was used to assess the association of PRS and each risk factor with PD-status. Stepwise-regression analysis was used to select the best predictive model for PD combining genetic and lifestyle/environmental factors. RESULTS: The 12-SNPs PRS was significantly increased in PD-cases compared to controls. Furthermore, univariate analyses showed that age, head injury, family history, depression, and Body Mass Index (BMI) were significantly associated with PD-status. Stepwise-regression suggested that a model which includes PRS and seven other independent lifestyle/environmental factors is the most predictive of PD in our population. CONCLUSIONS: These results suggest an association between both genetic and environmental factors and PD, and highlight the potential for the use of PRS in combination with the classical risk factors for risk prediction of PD.