Genome-wide meta-analysis identifies eight new susceptibility loci for cutaneous squamous cell carcinoma

Cutaneous squamous cell carcinoma (SCC) is one of the most common cancers in the United States. Previous genome-wide association studies (GWAS) have identified 14 single nucleotide polymorphisms (SNPs) associated with cutaneous SCC. Here, we report the largest cutaneous SCC meta-analysis to date, representing six international cohorts and totaling 19,149 SCC cases and 680,049 controls. We discover eight novel loci associated with SCC, confirm all previously associated loci, and perform fine mapping of causal variants. The novel SNPs occur within skin-specific regulatory elements and implicate loci involved in cancer development, immune regulation, and keratinocyte differentiation in SCC susceptibility

Working toward precision medicine: Predicting phenotypes from exomes in the Critical Assessment of Genome Interpretation (CAGI) challenges

Precision medicine aims to predict a patient's disease risk and best therapeutic options by using that individual's genetic sequencing data. The Critical Assessment of Genome Interpretation (CAGI) is a community experiment consisting of genotype–phenotype prediction challenges; participants build models, undergo assessment, and share key findings. For CAGI 4, three challenges involved using exome-sequencing data: Crohn's disease, bipolar disorder, and warfarin dosing. Previous CAGI challenges included prior versions of the Crohn's disease challenge. Here, we discuss the range of techniques used for phenotype prediction as well as the methods used for assessing predictive models. Additionally, we outline some of the difficulties associated with making predictions and evaluating them. The lessons learned from the exome challenges can be applied to both research and clinical efforts to improve phenotype prediction from genotype. In addition, these challenges serve as a vehicle for sharing clinical and research exome data in a secure manner with scientists who have a broad range of expertise, contributing to a collaborative effort to advance our understanding of genotype–phenotype relationships

Machine learning for genetic prediction of psychiatric disorders: a systematic review

Machine learning methods have been employed to make predictions in psychiatry from genotypes, with the potential to bring improved prediction of outcomes in psychiatric genetics; however, their current performance is unclear. We aim to systematically review machine learning methods for predicting psychiatric disorders from genetics alone and evaluate their discrimination, bias and implementation. Medline, PsycInfo, Web of Science and Scopus were searched for terms relating to genetics, psychiatric disorders and machine learning, including neural networks, random forests, support vector machines and boosting, on 10 September 2019. Following PRISMA guidelines, articles were screened for inclusion independently by two authors, extracted, and assessed for risk of bias. Overall, 63 full texts were assessed from a pool of 652 abstracts. Data were extracted for 77 models of schizophrenia, bipolar, autism or anorexia across 13 studies. Performance of machine learning methods was highly varied (0.48–0.95 AUC) and differed between schizophrenia (0.54–0.95 AUC), bipolar (0.48–0.65 AUC), autism (0.52–0.81 AUC) and anorexia (0.62–0.69 AUC). This is likely due to the high risk of bias identified in the study designs and analysis for reported results. Choices for predictor selection, hyperparameter search and validation methodology, and viewing of the test set during training were common causes of high risk of bias in analysis. Key steps in model development and validation were frequently not performed or unreported. Comparison of discrimination across studies was constrained by heterogeneity of predictors, outcome and measurement, in addition to sample overlap within and across studies. Given widespread high risk of bias and the small number of studies identified, it is important to ensure established analysis methods are adopted. We emphasise best practices in methodology and reporting for improving future studies

Pharmacogenomics study of thiazide diuretics and QT interval in multi-ethnic populations: the cohorts for heart and aging research in genomic epidemiology

Thiazide diuretics, commonly used antihypertensives, may cause QT interval (QT) prolongation, a risk factor for highly fatal and difficult to predict ventricular arrhythmias. We examined whether common single-nucleotide polymorphisms (SNPs) modified the association between thiazide use and QT or its component parts (QRS interval, JT interval) by performing ancestry-specific, transethnic and cross-phenotype genome-wide analyses of European (66%), African American (15%) and Hispanic (19%) populations (N = 78 199), leveraging longitudinal data, incorporating corrected standard errors to account for underestimation of interaction estimate variances and evaluating evidence for pathway enrichment. Although no loci achieved genome-wide significance (P < 5 x 10(-8)), we found suggestive evidence (P < 5 x 10(-6)) for SNPs modifying the thiazide-QT association at 22 loci, including ion transport loci (for example, NELL1, KCNQ3). The biologic plausibility of our suggestive results and simulations demonstrating modest power to detect interaction effects at genome-wide significant levels indicate that larger studies and innovative statistical methods are warranted in future efforts evaluating thiazide-SNP interactions

Recommendations for the use of pediatric data in artificial intelligence and machine learning ACCEPT-AI

Abstract ACCEPT-AI is a framework of recommendations for the safe inclusion of pediatric data in artificial intelligence and machine learning (AI/ML) research. It has been built on fundamental ethical principles of pediatric and AI research and incorporates age, consent, assent, communication, equity, protection of data, and technological considerations. ACCEPT-AI has been designed to guide researchers, clinicians, regulators, and policymakers and can be utilized as an independent tool, or adjunctively to existing AI/ML guidelines

Targeted Exon Capture and Sequencing in Sporadic Amyotrophic Lateral Sclerosis

<div><p>Amyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative disease that results in progressive degeneration of motor neurons, ultimately leading to paralysis and death. Approximately 10% of ALS cases are familial, with the remaining 90% of cases being sporadic. Genetic studies in familial cases of ALS have been extremely informative in determining the causative mutations behind ALS, especially as the same mutations identified in familial ALS can also cause sporadic disease. However, the cause of ALS in approximately 30% of familial cases and in the majority of sporadic cases remains unknown. Sporadic ALS cases represent an underutilized resource for genetic information about ALS; therefore, we undertook a targeted sequencing approach of 169 known and candidate ALS disease genes in 242 sporadic ALS cases and 129 matched controls to try to identify novel variants linked to ALS. We found a significant enrichment in novel and rare variants in cases versus controls, indicating that we are likely identifying disease associated mutations. This study highlights the utility of next generation sequencing techniques combined with functional studies and rare variant analysis tools to provide insight into the genetic etiology of a heterogeneous sporadic disease.</p></div

Comparison with Irish population.

<p>Genes are sorted into two categories, depending on whether they where implicated in the Mendelian form of the disease (equivalent to the “known ALS” category in this paper) or are low penetrance/tentative ALS genes (“associated”), as used in the original publication <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004704#pgen.1004704-Kenna2" target="_blank">[35]</a>.</p><p>Comparison with Irish population.</p