Breast cancer risk associated with germline likely pathogenic/pathogenic variants (PV) varies by gene, often by penetrance (high >50% or moderate 20–50%), and specific locus.
Germline PVs in BRCA1 ...and BRCA2 play important roles in the development of breast and ovarian cancer in particular, as well as in other cancers such as pancreatic and prostate cancers and melanoma. Recent studies suggest that other cancer susceptibility genes, including ATM, CHEK2, PALB2, RAD51C and RAD51D confer differential risks of breast and other specific cancers.
In the era of multigene panel testing, advances in next-generation sequencing technologies have notably reduced costs in the United States (US) and enabled sequencing of BRCA1/2 concomitantly with additional genes. The use of multigene-panel testing is beginning to expand in Europe as well.
Further research into the clinical implications of variants in moderate penetrance genes, particularly in unaffected carriers, is needed for appropriate counselling and risk management with data-driven plans for surveillance and/or risk reduction. For individuals at high risk without any pathogenic or likely pathogenic variant in cancer susceptibility genes or some carriers of pathogenic variants in moderate-risk genes such as ATM and CHEK2, polygenic risk scores offer promise to help stratify breast cancer risk and guide appropriate risk management options.
Cancer patients whose tumours are driven by the loss of function of both copies of a predisposition gene may benefit from therapies targeting the biological alterations induced by the dysfunctional gene e.g. poly ADP ribose polymerase (PARP) inhibitors and other novel pathway agents in cancers with DNA repair deficiencies. A better understanding of mechanisms by which germline variants drive various malignancies may lead to improvements in both therapeutic and preventive management options.
•The interpretation of genetic testing results requires careful attention.•ATM, CHEK2, RAD51D and BARD1 correlated with breast and other cancers risk.•European and American guidelines discrepancies.•Support European healthcare providers in interpreting and managing female carriers.
This research elucidates the correlation between solar radiation insolation, polygenic risk score (PRS), and systemic lupus erythematosus (SLE) diagnosis, utilizing genomic, environmental, and ...clinical data.
We included 1,800 SLE participants and 1,800 controls from the Taiwan Precision Medicine Initiative, genotyped via the Affymetrix Genome-Wide TWB 2.0 SNP Array. The study employed a SLE-PRS tailored for individuals of Taiwanese ancestry, comprising 27 single nucleotide polymorphisms (SNPs). QGIS computed solar radiation insolation from participants' residences. We employed logistic regression to investigate the associations between SLE-PRS, solar insolation susceptibility, and SLE. Additive and multiplicative interactions were utilized to assess the interactions between solar insolation and SLE-PRS regarding the risk of SLE.
SLE patients showed decreased solar insolation (p < 0.001). The highest decile of SLE-PRS exhibited a statistically significant lower solar insolation 1, 3, 6, and 12 months prior to diagnosis as compared to the lowest decile. Specifically, there were significant differences observed at 1 and 12 months (p = 0.025 and p = 0.004, respectively). It suggests that higher SLE-PRS correlated with reduced solar insolation tolerance. We observed an increase in SLE risk across ascending SLE-PRS percentiles exclusively in the high solar insolation group, not in the low solar insolation group. However, the interaction effect of SLE-PRS and solar insolation on SLE risk is not statistically significant. Compared to the lowest decile, the highest SLE-PRS decile showed a 10.98-fold increase in SLE risk (95 % CI, 3.773–31.952, p < 0.001). High SLE-PRS scores in conjunction with high solar insolation contribute to SLE incidence.
Our study unveils the intertwined nature of UV insolation and polygenic risks in SLE. Future studies should explore the preventative potential of robust solar radiation protection for high-risk individuals before the disease onset.
Genetics of osteoarthritis Zhai, Guangju; Huang, Jingyi
Best practice & research. Clinical rheumatology,
07/2024
Journal Article
Peer reviewed
Open access
Osteoarthritis (OA) is the most common form of arthritis with well recognized multifactorial nature. While several environmental factors such as older age, obesity and previous joint injury are ...strongly associated with its development, a genetic influence on OA has been recognized for over 80 years. Identification of genes associated with OA has received considerable attention over the last two decades, aided by the rapidly evolving genotyping and sequencing technologies. More than 300 genomic loci have been identified to be associated with OA at different joints. These findings are likely to help our better understanding of the pathogenesis of OA and lead to important therapeutic and diagnostic advances in this most common disabling rheumatic disorder. This article will review the data that support the role of genetic factors in common idiopathic OA.
Genome-wide polygenic risk scores (GW-PRSs) have been reported to have better predictive ability than PRSs based on genome-wide significance thresholds across numerous traits. We compared the ...predictive ability of several GW-PRS approaches to a recently developed PRS of 269 established prostate cancer-risk variants from multi-ancestry GWASs and fine-mapping studies (PRS269). GW-PRS models were trained with a large and diverse prostate cancer GWAS of 107,247 cases and 127,006 controls that we previously used to develop the multi-ancestry PRS269. Resulting models were independently tested in 1,586 cases and 1,047 controls of African ancestry from the California Uganda Study and 8,046 cases and 191,825 controls of European ancestry from the UK Biobank and further validated in 13,643 cases and 210,214 controls of European ancestry and 6,353 cases and 53,362 controls of African ancestry from the Million Veteran Program. In the testing data, the best performing GW-PRS approach had AUCs of 0.656 (95% CI = 0.635–0.677) in African and 0.844 (95% CI = 0.840–0.848) in European ancestry men and corresponding prostate cancer ORs of 1.83 (95% CI = 1.67–2.00) and 2.19 (95% CI = 2.14–2.25), respectively, for each SD unit increase in the GW-PRS. Compared to the GW-PRS, in African and European ancestry men, the PRS269 had larger or similar AUCs (AUC = 0.679, 95% CI = 0.659–0.700 and AUC = 0.845, 95% CI = 0.841–0.849, respectively) and comparable prostate cancer ORs (OR = 2.05, 95% CI = 1.87–2.26 and OR = 2.21, 95% CI = 2.16–2.26, respectively). Findings were similar in the validation studies. This investigation suggests that current GW-PRS approaches may not improve the ability to predict prostate cancer risk compared to the PRS269 developed from multi-ancestry GWASs and fine-mapping.
Our findings suggest that in men of African and European ancestry, genome-wide polygenic risk scores including >1 million variants are not more predictive of prostate cancer risk than a carefully constructed polygenic risk score limited to variants with statistical evidence of an association with prostate cancer risk.
Aim: Clinical risk scores for coronary artery disease (CAD) are used in clinical practice to select patients for diagnostic testing and therapy. Several studies have proposed that polygenic risk ...scores (PRSs) can improve the prediction of CAD, but the scores need to be validated in clinical populations with accurately characterized phenotypes. We assessed the predictive power of the three most promising PRSs for the prediction of coronary atherosclerosis and obstructive CAD.Methods: This study was conducted on 943 symptomatic patients with suspected CAD for whom the phenotype was accurately characterized using anatomic and functional imaging. Previously published genome-wide polygenic scores were generated to compare a genetic model based on PRSs with a model based on clinical data. The test and PRS cohorts were predominantly Caucasian of northern European ancestry.Results: All three PRSs predicted coronary atherosclerosis and obstructive CAD statistically significantly. The predictive accuracy of the models combining clinical data and different PRSs varied between 0.778 and 0.805 in terms of the area under the receiver operating characteristic (AUROC), being close to the model including only clinical variables (AUROC 0.769). The difference between the clinical model and combined clinical + PRS model was not significant for PRS1 (p=0.627) and PRS3 (p=0.061). Only PRS2 slightly improved the predictive power of the model (p=0.04). The likelihood ratios showed the very weak diagnostic power of all PRSs.Conclusion: The addition of PRSs to conventional risk factors did not clinically significantly improve the predictive accuracy for either coronary atherosclerosis or obstructive CAD, showing that current PRSs are not justified for routine clinical use in CAD.
Studying the phenotypic manifestations of increased genetic liability for schizophrenia can increase our understanding of this disorder. Specifically, information from alleles identified in ...genome-wide association studies can be collapsed into a polygenic risk score (PRS) to explore how genetic risk is manifest within different samples. In this systematic review, we provide a comprehensive assessment of studies examining associations between schizophrenia PRS (SZ-PRS) and several phenotypic measures. We searched EMBASE, Medline and PsycINFO (from August 2009–14th March 2016) plus references of included studies, following PRISMA guidelines. Study inclusion was based on predetermined criteria and data were extracted independently and in duplicate. Overall, SZ-PRS was associated with increased risk for psychiatric disorders such as depression and bipolar disorder, lower performance IQ and negative symptoms. SZ-PRS explained up to 6% of genetic variation in psychiatric phenotypes, compared to <0.7% in measures of cognition. Future gains from using the PRS approach may be greater if used for examining phenotypes that are more closely related to biological substrates, for scores based on gene-pathways, and where PRSs are used to stratify individuals for study of treatment response. As it was difficult to interpret findings across studies due to insufficient information provided by many studies, we propose a framework to guide robust reporting of PRS associations in the future.
The interplay between genetic and lifestyle factors in the development of bipolar disorder (BD) remains unclear.
A cohort study was carried out on 365,517 participants from the UK Biobank. Lifestyle ...scores, based on smoking, physical activity, diet, alcohol consumption, sedentary behavior, sleep duration, and social contact, were grouped as favorable (scores 6–7), intermediate (scores 4–5), or unfavorable (scores 0–3). The BD polygenic risk score (PRS) was also categorized into high, intermediate, and low-risk groups using PRS tertiles. Cox regression models determined hazard ratios (HRs) and 95 % confidence intervals (CIs) for BD.
During the 12.9-year follow-up, 529 individuals developed BD. Comparing those with favorable lifestyles to those with unfavorable participants, the HR of developing BD was 3.28 (95 % CI, 2.76–3.89). Similarly, individuals with a high PRS had a risk of 3.20 (95 % CI, 2.83–3.63) compared to those with a low PRS. Notably, individuals with both a high PRS and an unfavorable lifestyle had a significantly higher risk of BD (HR = 6.31, 95 % CI, 4.14–9.63) compared to those with a low PRS and a favorable lifestyle. Additionally, the interaction between PRS and lifestyle contributed an additional risk, with a relative excess risk of 1.74 (95 % CI, 0.40–3.07) and an attributable proportion due to the interaction of 0.37 (95 % CI, 0.16–0.58).
Our findings suggest that genetic liability for BD, measured as PRS, and lifestyle have an additive effect on the risk of developing BD. A favorable lifestyle was associated with a reduced risk of developing BD.
•High polygenic risk score and unfavorable lifestyles both increase risk of bipolar disorder.•The coexistence of high polygenic risk score and unfavorable lifestyle has an additive interaction on the risk of developing bipolar disorder.•Irrespective of polygenic risk score, adopting a favorable lifestyle was consistently linked to a decreased risk of bipolar disorder.
In the past decade, precision genomics based medicine has emerged to provide tailored and effective healthcare for patients depending upon their genetic features. Genome Wide Association Studies have ...also identified population based risk genetic variants for common and complex diseases. In order to meet the full promise of precision medicine, research is attempting to leverage our increasing genomic understanding and further develop personalized medical healthcare through ever more accurate disease risk prediction models. Polygenic risk scoring and machine learning are two primary approaches for disease risk prediction. Despite recent improvements, the results of polygenic risk scoring remain limited due to the approaches that are currently used. By contrast, machine learning algorithms have increased predictive abilities for complex disease risk. This increase in predictive abilities results from the ability of machine learning algorithms to handle multi-dimensional data. Here, we provide an overview of polygenic risk scoring and machine learning in complex disease risk prediction. We highlight recent machine learning application developments and describe how machine learning approaches can lead to improved complex disease prediction, which will help to incorporate genetic features into future personalized healthcare. Finally, we discuss how the future application of machine learning prediction models might help manage complex disease by providing tissue-specific targets for customized, preventive interventions.
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