Towards a patient-centred classification of genetic disease severity
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Using twin-pairs to assess potential bias in polygenic prediction of externalising behaviours across development
Prediction from polygenic scores may be confounded by sources of passive gene-environment correlation (rGE; e.g. population stratification, assortative mating, and environmentally mediated effects of parental genotype on child phenotype). Using genomic data from 10 000 twin pairs, we asked whether polygenic scores from the most recent externalising genome-wide association study predict conduct problems, ADHD symptomology and callous-unemotional traits, and whether these predictions are biased by rGE. We ran regression models including within-family and between-family polygenic scores, to separate the direct genetic influence on a trait from environmental influences that correlate with genes (indirect genetic effects). Findings suggested that this externalising polygenic score is a good index of direct genetic influence on conduct and ADHD-related symptoms across development, with minimal bias from rGE, although the polygenic score predicted less variance in CU traits. Post-hoc analyses showed some indirect genetic effects acting on a common factor indexing stability of conduct problems across time and contexts.
Rapid brain tumor classification from sparse epigenomic data
Although the intraoperative molecular diagnosis of the approximately 100 known brain tumor entities described to date has been a goal of neuropathology for the past decade, achieving this within a clinically relevant timeframe of under 1 h after biopsy collection remains elusive. Advances in third-generation sequencing have brought this goal closer, but established machine learning techniques rely on computationally intensive methods, making them impractical for live diagnostic workflows in clinical applications. Here we present MethyLYZR, a naive Bayesian framework enabling fully tractable, live classification of cancer epigenomes. For evaluation, we used nanopore sequencing to classify over 200 brain tumor samples, including 10 sequenced in a clinical setting next to the operating room, achieving highly accurate results within 15 min of sequencing. MethyLYZR can be run in parallel with an ongoing nanopore experiment with negligible computational overhead. Therefore, the only limiting factors for even faster time to results are DNA extraction time and the nanopore sequencer’s maximum parallel throughput. Although more evidence from prospective studies is needed, our study suggests the potential applicability of MethyLYZR for live molecular classification of nervous system malignancies using nanopore sequencing not only for the neurosurgical intraoperative use case but also for other oncologic indications and the classification of tumors from cell-free DNA in liquid biopsies.
Commentary: Why is genetic testing underutilized worldwide? The case for hereditary breast cancer
It is thirty years since the BRCA1 and BRCA2 genes were discovered and genetic testing for BRCA1 and BRCA2 was introduced. Despite increasing awareness of the genetic basis of cancer and our evolving knowledge of effective means of prevention, screening, and treatment for hereditary breast and ovarian cancers, genetic testing is underutilized, and most mutation carriers remain unidentified. In this commentary, we explore possible reasons for why this might be so. Our focus is on factors that may influence or deter a patient from pursuing testing, rather than discussing the implications of receiving a positive test result. Issues of concern include an inadequate number of genetic counselors, restrictive (and conflicting) eligibility criteria for testing, the cost of the test, health insurance coverage, fear of future insurance discrimination, privacy issues, lack of familiarity with the testing process in primary care and gaps in both patient and provider knowledge about the impact and the value of testing. We discuss how these factors may lead to the underutilization of genetic testing in North America and throughout the world and discuss alternative models of genetic healthcare delivery. We have invited leaders in cancer genetic from around the world to tell us what they think are the barriers to testing in their host countries.
Evolution, genetic diversity, and health
Human genetic diversity in today’s world has been shaped by evolutionary history, demographic shifts and environmental exposures, influencing complex traits, disease susceptibility and drug responses. Capturing this diversity is essential for advancing precision medicine and promoting equitable healthcare. Despite the great progress achieved with initiatives such as the human Pangenome and large biobanks that aim for a better representation of human diversity, important challenges remain. In this Perspective, we discuss the importance of diversity in clinical genomics through an evolutionary lens. We highlight progress and challenges and outline key clinical applications of diverse genetic data. We argue that diversifying both datasets and methodologies—integrating ancestral and environmental factors—is crucial for fully understanding the genetic basis of human health and disease.
Associations between common genetic variants and income provide insights about the socio-economic health gradient
We conducted a genome-wide association study on income among individuals of European descent (N = 668,288) to investigate the relationship between socio-economic status and health disparities. We identified 162 genomic loci associated with a common genetic factor underlying various income measures, all with small effect sizes (the Income Factor). Our polygenic index captures 1–5% of income variance, with only one fourth due to direct genetic effects. A phenome-wide association study using this index showed reduced risks for diseases including hypertension, obesity, type 2 diabetes, depression, asthma and back pain. The Income Factor had a substantial genetic correlation (0.92, s.e. = 0.006) with educational attainment. Accounting for the genetic overlap of educational attainment with income revealed that the remaining genetic signal was linked to better mental health but reduced physical health and increased risky behaviours such as drinking and smoking. These findings highlight the complex genetic influences on income and health.
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