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Key Points Investigation of disease-causing variants such as F508del is resolving the mechanisms underlying cystic fibrosis transmembrane conductance regulator (CFTR) folding and will inform rational design of compounds to correct the folding of mutant CFTR. New tissue culture methods will facilitate the evaluation of molecular targeted therapy for a wide array of CFTR genotypes, and new animal models should enable assessment of treatment at the earliest stages of the disease. Analyses of affected twin and sibling pairs have quantified the contribution of genetic and non-genetic modifiers to variation in key features of cystic fibrosis. Candidate and genome-wide approaches have identified biologically plausible gene modifiers of lung disease severity, neonatal intestinal obstruction and diabetes in cystic fibrosis. Annotation of variants in CFTR will increase the utility of genetic testing in newborn screening, carrier testing and diagnostic settings. Assignment of variants as disease-causing will validate efforts to target variants for molecular therapies. Small-molecule therapy for cystic fibrosis has been successful for patients carrying a subset of CFTR variants. Grouping of variants according to responses in cell-based assays (that is, theratypes) could expedite treatment of affected individuals with rare CFTR genotypes. Mendelian conditions, which are caused by dysfunction of a single gene, illustrate how the availability of the human genome sequence and tools for interrogating individual genomes can provide insights into disease. In this Review, cystic fibrosis is presented as an example of how genetics can continuously inform clinical research and practice. The availability of the human genome sequence and tools for interrogating individual genomes provide an unprecedented opportunity to apply genetics to medicine. Mendelian conditions, which are caused by dysfunction of a single gene, offer powerful examples that illustrate how genetics can provide insights into disease. Cystic fibrosis, one of the more common lethal autosomal recessive Mendelian disorders, is presented here as an example. Recent progress in elucidating disease mechanism and causes of phenotypic variation, as well as in the development of treatments, demonstrates that genetics continues to play an important part in cystic fibrosis research 25 years after the discovery of the disease-causing gene.

Matthew Nelson and colleagues investigate how well genetic evidence for disease susceptibility predicts drug mechanisms. They find a correlation between gene products that are successful drug targets and genetic loci associated with the disease treated by the drug and predict that selecting genetically supported targets could increase the success rate of drugs in clinical development. Over a quarter of drugs that enter clinical development fail because they are ineffective. Growing insight into genes that influence human disease may affect how drug targets and indications are selected. However, there is little guidance about how much weight should be given to genetic evidence in making these key decisions. To answer this question, we investigated how well the current archive of genetic evidence predicts drug mechanisms. We found that, among well-studied indications, the proportion of drug mechanisms with direct genetic support increases significantly across the drug development pipeline, from 2.0% at the preclinical stage to 8.2% among mechanisms for approved drugs, and varies dramatically among disease areas. We estimate that selecting genetically supported targets could double the success rate in clinical development. Therefore, using the growing wealth of human genetic data to select the best targets and indications should have a measurable impact on the successful development of new drugs.

Genome-wide association studies (GWAS) have laid the foundation for investigations into the biology of complex traits, drug development and clinical guidelines. However, the majority of discovery efforts are based on data from populations of European ancestry 1 – 3 . In light of the differential genetic architecture that is known to exist between populations, bias in representation can exacerbate existing disease and healthcare disparities. Critical variants may be missed if they have a low frequency or are completely absent in European populations, especially as the field shifts its attention towards rare variants, which are more likely to be population-specific 4 – 10 . Additionally, effect sizes and their derived risk prediction scores derived in one population may not accurately extrapolate to other populations 11 , 12 . Here we demonstrate the value of diverse, multi-ethnic participants in large-scale genomic studies. The Population Architecture using Genomics and Epidemiology (PAGE) study conducted a GWAS of 26 clinical and behavioural phenotypes in 49,839 non-European individuals. Using strategies tailored for analysis of multi-ethnic and admixed populations, we describe a framework for analysing diverse populations, identify 27 novel loci and 38 secondary signals at known loci, as well as replicate 1,444 GWAS catalogue associations across these traits. Our data show evidence of effect-size heterogeneity across ancestries for published GWAS associations, substantial benefits for fine-mapping using diverse cohorts and insights into clinical implications. In the United States—where minority populations have a disproportionately higher burden of chronic conditions 13 —the lack of representation of diverse populations in genetic research will result in inequitable access to precision medicine for those with the highest burden of disease. We strongly advocate for continued, large genome-wide efforts in diverse populations to maximize genetic discovery and reduce health disparities. Genetic analyses of ancestrally diverse populations show evidence of heterogeneity across ancestries and provide insights into clinical implications, highlighting the importance of including ancestrally diverse populations to maximize genetic discovery and reduce health disparities.

Purpose The purpose of this document is to provide updated guidance for the genetic evaluation of cardiomyopathy and for an approach to manage secondary findings from cardiomyopathy genes. The genetic bases of the primary cardiomyopathies (dilated, hypertrophic, arrhythmogenic right ventricular, and restrictive) have been established, and each is medically actionable; in most cases established treatments or interventions are available to improve survival, reduce morbidity, and enhance quality of life. Methods A writing group of cardiologists and genetics professionals updated guidance, first published in 2009 for the Heart Failure Society of America (HFSA), in a collaboration with the American College of Medical Genetics and Genomics (ACMG). Each recommendation was assigned to teams of individuals by expertise, literature was reviewed, and recommendations were decided by consensus of the writing group. Recommendations for family history, phenotype screening of at-risk family members, referral to expert centers as needed, genetic counseling, and cardiovascular therapies, informed in part by phenotype, are presented in the HFSA document. Results A genetic evaluation of cardiomyopathy is indicated with a cardiomyopathy diagnosis, which includes genetic testing. Guidance is also provided for clinical approaches to secondary findings from cardiomyopathy genes. This is relevant as cardiomyopathy is the phenotype associated with 27% of the genes on the ACMG list for return of secondary findings. Recommendations herein are considered expert opinion per current ACMG policy as no systematic approach to literature review was conducted. Conclusion Genetic testing is indicated for cardiomyopathy to assist in patient care and management of at-risk family members.

Historically, medical geneticists and genetic counselors have provided the majority of genetic services. Advances in technology, reduction in testing costs, and increased public awareness have led to a growing demand for genetic services in both clinical and direct-to-consumer spaces. Recent and anticipated changes in the workforce of genetic counselors and medical geneticists require a reexamination of the way we educate health-care providers and the means by which we provide access to genetic services. The time is ripe for rapid growth of genetic and genomic services, but to capitalize on these opportunities, we need to consider a variety of educational mechanisms to reach providers both within and beyond the traditional genetic counseling and medical genetics sectors, including nurses, physician assistants, and nongenetics physicians. This article summarizes the educational efforts underway in each of these professions.

Purpose and scopeThe aim of this Position Statement is to provide recommendations for Canadian medical geneticists, clinical laboratory geneticists, genetic counsellors and other physicians regarding the use of genome-wide sequencing of germline DNA in the context of clinical genetic diagnosis. This statement has been developed to facilitate the clinical translation and development of best practices for clinical genome-wide sequencing for genetic diagnosis of monogenic diseases in Canada; it does not address the clinical application of this technology in other fields such as molecular investigation of cancer or for population screening of healthy individuals.Methods of statement developmentTwo multidisciplinary groups consisting of medical geneticists, clinical laboratory geneticists, genetic counsellors, ethicists, lawyers and genetic researchers were assembled to review existing literature and guidelines on genome-wide sequencing for clinical genetic diagnosis in the context of monogenic diseases, and to make recommendations relevant to the Canadian context. The statement was circulated for comment to the Canadian College of Medical Geneticists (CCMG) membership-at-large and, following incorporation of feedback, approved by the CCMG Board of Directors. The CCMG is a Canadian organisation responsible for certifying medical geneticists and clinical laboratory geneticists, and for establishing professional and ethical standards for clinical genetics services in Canada.Results and conclusionsRecommendations include (1) clinical genome-wide sequencing is an appropriate approach in the diagnostic assessment of a patient for whom there is suspicion of a significant monogenic disease that is associated with a high degree of genetic heterogeneity, or where specific genetic tests have failed to provide a diagnosis; (2) until the benefits of reporting incidental findings are established, we do not endorse the intentional clinical analysis of disease-associated genes other than those linked to the primary indication; and (3) clinicians should provide genetic counselling and obtain informed consent prior to undertaking clinical genome-wide sequencing. Counselling should include discussion of the limitations of testing, likelihood and implications of diagnosis and incidental findings, and the potential need for further analysis to facilitate clinical interpretation, including studies performed in a research setting. These recommendations will be routinely re-evaluated as knowledge of diagnostic and clinical utility of clinical genome-wide sequencing improves. While the document was developed to direct practice in Canada, the applicability of the statement is broader and will be of interest to clinicians and health jurisdictions internationally.

Genetics of complex diseases Genome-wide association studies have identified hundreds of genetic variants associated with complex human diseases, but most confer quite small increments of risk. There seems to be a large component of heritability somehow evading detection. Possible explanations for this 'missing heritability' include great numbers of small-effect variants yet to be found, rare structural or epigenetic variation not detected by current genotyping technology and hard-to-detect gene–gene and gene–environment interactions. Teri Manolio and colleagues examine the research strategies most likely to distinguish between these and other possible explanations. Genome-wide association studies have identified hundreds of genetic variants associated with complex human diseases and traits, and have provided valuable insights into their genetic architecture. Most variants identified so far confer relatively small increments in risk, and explain only a small proportion of familial clustering, leading many to question how the remaining, ‘missing’ heritability can be explained. Here we examine potential sources of missing heritability and propose research strategies, including and extending beyond current genome-wide association approaches, to illuminate the genetics of complex diseases and enhance its potential to enable effective disease prevention or treatment.

Mark Daly and colleagues present a statistical framework to evaluate the role of de novo mutations in human disease by calibrating a model of de novo mutation rates at the individual gene level. The mutation probabilities defined by their model and list of constrained genes can be used to help identify genetic variants that have a significant role in disease. Spontaneously arising ( de novo ) mutations have an important role in medical genetics. For diseases with extensive locus heterogeneity, such as autism spectrum disorders (ASDs), the signal from de novo mutations is distributed across many genes, making it difficult to distinguish disease-relevant mutations from background variation. Here we provide a statistical framework for the analysis of excesses in de novo mutation per gene and gene set by calibrating a model of de novo mutation. We applied this framework to de novo mutations collected from 1,078 ASD family trios, and, whereas we affirmed a significant role for loss-of-function mutations, we found no excess of de novo loss-of-function mutations in cases with IQ above 100, suggesting that the role of de novo mutations in ASDs might reside in fundamental neurodevelopmental processes. We also used our model to identify ∼1,000 genes that are significantly lacking in functional coding variation in non-ASD samples and are enriched for de novo loss-of-function mutations identified in ASD cases.

Artificial intelligence (AI) is the development of computer systems that are able to perform tasks that normally require human intelligence. Advances in AI software and hardware, especially deep learning algorithms and the graphics processing units (GPUs) that power their training, have led to a recent and rapidly increasing interest in medical AI applications. In clinical diagnostics, AI-based computer vision approaches are poised to revolutionize image-based diagnostics, while other AI subtypes have begun to show similar promise in various diagnostic modalities. In some areas, such as clinical genomics, a specific type of AI algorithm known as deep learning is used to process large and complex genomic datasets. In this review, we first summarize the main classes of problems that AI systems are well suited to solve and describe the clinical diagnostic tasks that benefit from these solutions. Next, we focus on emerging methods for specific tasks in clinical genomics, including variant calling, genome annotation and variant classification, and phenotype-to-genotype correspondence. Finally, we end with a discussion on the future potential of AI in individualized medicine applications, especially for risk prediction in common complex diseases, and the challenges, limitations, and biases that must be carefully addressed for the successful deployment of AI in medical applications, particularly those utilizing human genetics and genomics data.

Current methods for inferring population structure from genetic data do not provide formal significance tests for population differentiation. We discuss an approach to studying population structure (principal components analysis) that was first applied to genetic data by Cavalli-Sforza and colleagues. We place the method on a solid statistical footing, using results from modern statistics to develop formal significance tests. We also uncover a general \"phase change\" phenomenon about the ability to detect structure in genetic data, which emerges from the statistical theory we use, and has an important implication for the ability to discover structure in genetic data: for a fixed but large dataset size, divergence between two populations (as measured, for example, by a statistic like FST) below a threshold is essentially undetectable, but a little above threshold, detection will be easy. This means that we can predict the dataset size needed to detect structure.