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Risk prediction models hold great promise to reduce the impact of cancer in society through advanced warning of risk and improved preventative modalities. These models are evolving and becoming more complex, increasingly integrating genetic screening data and polygenic risk scores as well as calculating risk for multiple types of a disease. However, unclear regulatory compliance requirements applicable to these models raise significant legal uncertainty and new questions about the regulation of medical devices. This paper aims to address these novel regulatory questions by presenting an initial assessment of the legal status likely applicable to risk prediction models in Canada, using the CanRisk tool for breast and ovarian cancer as an exemplar. Legal analysis is supplemented with qualitative perspectives from expert stakeholders regarding the accessibility and compliance challenges of the Canadian regulatory framework. While the paper focuses on the Canadian context, it also refers to European and U.S. regulations in this domain to contrast them. Legal analysis and stakeholder perspectives highlight the need to clarify and update the Canadian regulatory framework for Software as a Medical Device as it applies to risk prediction models. Findings demonstrate how normative guidance perceived as convoluted, contradictory or overly burdensome can discourage innovation, compliance, and ultimately, implementation. This contribution aims to initiate discussion about a more optimal legal framework for risk prediction models as they continue to evolve and are increasingly integrated into landscape for public health. [ABSTRACT FROM AUTHOR]

Reproducibility is not only essential for the integrity of scientific research but is also a prerequisite for model validation and refinement for the future application of predictive algorithms. However, reproducible research is becoming increasingly challenging, particularly in high-dimensional genomic data analyses with complex statistical or algorithmic techniques. Given that there are no mandatory requirements in most biomedical and statistical journals to provide the original data, analytical source code, or other relevant materials for publication, accessibility to these supplements naturally suggests a greater credibility of the published work. In this study, we performed a reproducibility assessment of the notable paper by Gerstung et al. (Nat Genet 49:332–340, 2017) by rerunning the analysis using their original code and data, which are publicly accessible. Despite an open science setting, it was challenging to reproduce the entire research project; reasons included: incomplete data and documentation, suboptimal code readability, coding errors, limited portability of intensive computing performed on a specific platform, and an R computing environment that could no longer be re-established. We learn that the availability of code and data does not guarantee transparency and reproducibility of a study; paradoxically, the source code is still liable to error and obsolescence, essentially due to methodological and computational complexity, a lack of reproducibility checking at submission, and updates for software and operating environment. The complex code may also hide problematic methodological aspects of the proposed research. Building on the experience gained, we discuss the best programming and software engineering practices that could have been employed to improve reproducibility, and propose practical criteria for the conduct and reporting of reproducibility studies for future researchers. [ABSTRACT FROM AUTHOR]

Migraine—a painful, throbbing headache disorder—is the most common complex brain disorder, yet its molecular mechanisms remain unclear. Genome-wide association studies (GWAS) have proven successful in identifying migraine risk loci; however, much work remains to identify the causal variants and genes. In this paper, we compared three transcriptome-wide association study (TWAS) imputation models—MASHR, elastic net, and SMultiXcan—to characterise established genome-wide significant (GWS) migraine GWAS risk loci, and to identify putative novel migraine risk gene loci. We compared the standard TWAS approach of analysing 49 GTEx tissues with Bonferroni correction for testing all genes present across all tissues (Bonferroni), to TWAS in five tissues estimated to be relevant to migraine, and TWAS with Bonferroni correction that took into account the correlation between eQTLs within each tissue (Bonferroni-matSpD). Elastic net models performed in all 49 GTEx tissues using Bonferroni-matSpD characterised the highest number of established migraine GWAS risk loci (n = 20) with GWS TWAS genes having colocalisation (PP4 > 0.5) with an eQTL. SMultiXcan in all 49 GTEx tissues identified the highest number of putative novel migraine risk genes (n = 28) with GWS differential expression at 20 non-GWS GWAS loci. Nine of these putative novel migraine risk genes were later found to be at and in linkage disequilibrium with true (GWS) migraine risk loci in a recent, more powerful migraine GWAS. Across all TWAS approaches, a total of 62 putative novel migraine risk genes were identified at 32 independent genomic loci. Of these 32 loci, 21 were true risk loci in the recent, more powerful migraine GWAS. Our results provide important guidance on the selection, use, and utility of imputation-based TWAS approaches to characterise established GWAS risk loci and identify novel risk gene loci. [ABSTRACT FROM AUTHOR]

Precision medicine aims to tailor medical treatment to match individual characteristics and to stratify individuals to concentrate benefits and avoid harm. It has recently been joined by precision public health—the application of precision medicine at population scale to decrease morbidity and optimise population health. Newborn preventive genomic sequencing (NPGS) provides a helpful case study to consider how we should approach ethical questions in precision public health. In this paper, I use NPGS as a case in point to argue that both precision medicine and precision public health need public health ethics. I make this argument in two parts. First, I claim that discussions of ethics in precision medicine and NPGS tend to focus on predominantly individualistic concepts from medical ethics such as autonomy and empowerment. This highlights some deficiencies, including overlooking that choice is subject to constraints and that an individual's place in the world might impact their capacity to 'be responsible'. Second, I make the case for using a public health ethics approach when considering ethics and NPGS, and thus precision public health more broadly. I discuss how precision public health needs to be construed as a collective enterprise and not just as an aggregation of individual interests. I also show how analysing collective values and interests through concepts such as solidarity can enrich ethical discussion of NPGS and highlight previously overlooked issues. With this approach, bioethics can contribute to more just and more appropriate applications of precision medicine and precision public health, including NPGS. [ABSTRACT FROM AUTHOR]