Estimating the polygenicity (proportion of causally associated single nucleotide polymorphisms (SNPs)) and discoverability (effect size variance) of causal SNPs for human traits is currently of ...considerable interest. SNP-heritability is proportional to the product of these quantities. We present a basic model, using detailed linkage disequilibrium structure from a reference panel of 11 million SNPs, to estimate these quantities from genome-wide association studies (GWAS) summary statistics. We apply the model to diverse phenotypes and validate the implementation with simulations. We find model polygenicities (as a fraction of the reference panel) ranging from ≃ 2 × 10-5 to ≃ 4 × 10-3, with discoverabilities similarly ranging over two orders of magnitude. A power analysis allows us to estimate the proportions of phenotypic variance explained additively by causal SNPs reaching genome-wide significance at current sample sizes, and map out sample sizes required to explain larger portions of additive SNP heritability. The model also allows for estimating residual inflation (or deflation from over-correcting of z-scores), and assessing compatibility of replication and discovery GWAS summary statistics.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Summary
Background
Several new genes and clinical subtypes have been identified since the publication in 2014 of the report of the last International Consensus Meeting on Epidermolysis Bullosa (EB).
...Objectives
We sought to reclassify disorders with skin fragility, with a focus on EB, based on new clinical and molecular data.
Methods
This was a consensus expert review.
Results
In this latest consensus report, we introduce the concept of genetic disorders with skin fragility, of which classical EB represents the prototype. Other disorders with skin fragility, where blisters are a minor part of the clinical picture or are not seen because skin cleavage is very superficial, are classified as separate categories. These include peeling skin disorders, erosive disorders, hyperkeratotic disorders, and connective tissue disorders with skin fragility. Because of the common manifestation of skin fragility, these ‘EB‐related’ disorders should be considered under the EB umbrella in terms of medical and socioeconomic provision of care.
Conclusions
The proposed classification scheme should be of value both to clinicians and researchers, emphasizing both clinical and genetic features of EB.
What is already known about this topic?
Epidermolysis bullosa (EB) is a group of genetic disorders with skin blistering.
The last updated recommendations on diagnosis and classification were published in 2014.
What does this study add?
We introduce the concept of genetic disorders with skin fragility, of which classical EB represents the prototype.
Clinical and genetic aspects, genotype–phenotype correlations, disease‐modifying factors and natural history of EB are reviewed.
Other disorders with skin fragility, e.g. peeling skin disorders, erosive disorders, hyperkeratotic disorders, and connective tissue disorders with skin fragility are classified as separate categories; these ‘EB‐related’ disorders should be considered under the EB umbrella in terms of medical and socioeconomic provision of care.
Linked Comment: Pope. Br J Dermatol 2020; 183:603.
Linked Comment: Pope. Br J Dermatol 2020; 183:603.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
More accurate and precise phenotyping strategies are necessary to empower high-resolution linkage mapping and genome-wide association studies and for training genomic selection models in plant ...improvement. Within this framework, the objective of modern phenotyping is to increase the accuracy, precision and throughput of phenotypic estimation at all levels of biological organization while reducing costs and minimizing labor through automation, remote sensing, improved data integration and experimental design. Much like the efforts to optimize genotyping during the 1980s and 1990s, designing effective phenotyping initiatives today requires multi-faceted collaborations between biologists, computer scientists, statisticians and engineers. Robust phenotyping systems are needed to characterize the full suite of genetic factors that contribute to quantitative phenotypic variation across cells, organs and tissues, developmental stages, years, environments, species and research programs. Next-generation phenotyping generates significantly more data than previously and requires novel data management, access and storage systems, increased use of ontologies to facilitate data integration, and new statistical tools for enhancing experimental design and extracting biologically meaningful signal from environmental and experimental noise. To ensure relevance, the implementation of efficient and informative phenotyping experiments also requires familiarity with diverse germplasm resources, population structures, and target populations of environments. Today, phenotyping is quickly emerging as the major operational bottleneck limiting the power of genetic analysis and genomic prediction. The challenge for the next generation of quantitative geneticists and plant breeders is not only to understand the genetic basis of complex trait variation, but also to use that knowledge to efficiently synthesize twenty-first century crop varieties.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
At different stages of any research project, molecular biologists need to choose - often somewhat arbitrarily, even after careful statistical data analysis - which genes or proteins to investigate ...further experimentally and which to leave out because of limited resources. Computational methods that integrate complex, heterogeneous data sets - such as expression data, sequence information, functional annotation and the biomedical literature - allow prioritizing genes for future study in a more informed way. Such methods can substantially increase the yield of downstream studies and are becoming invaluable to researchers.
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DOBA, IJS, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Recall by genotype (RbG) studies aim to better understand the phenotypes that correspond to genetic variants of interest, by recruiting carriers of such variants for further phenotyping. RbG ...approaches pose major ethical and legal challenges related to the disclosure of possibly unwanted genetic information. The Cooperative Health Research in South Tyrol (CHRIS) study is a longitudinal cohort study based in South Tyrol, Italy. Demand has grown for CHRIS study participants to be enrolled in RbG studies, thus making the design of a suitable ethical framework a pressing need. We here report upon the design of a pilot RbG study conducted with CHRIS study participants. By reviewing the literature and by consulting relevant stakeholders (CHRIS participants, clinical geneticists, ethics board, GPs), we identified key ethical issues in RbG approaches (e.g. complexity of the context, communication of genetic results, measures to further protect participants). The design of the pilot was based on a feasibility assessment, the selection of a suitable test case within the ProtectMove Research Unit on reduced penetrance of hereditary movement disorders, and the development of appropriate recruitment and communication strategies. An empirical study was embedded in the pilot study with the aim of understanding participants' views on RbG. Our experience with the pilot study in CHRIS allowed us to contribute to the development of best practices and policies for RbG studies by drawing recommendations: addressing the possibility of RbG in the original consent, implementing tailored communication strategies, engaging stakeholders, designing embedded empirical studies, and sharing research experiences and methodology.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
DNA methylation is an epigenetic mark that has suspected regulatory roles in a broad range of biological processes and diseases. The technology is now available for studying DNA methylation ...genome-wide, at a high resolution and in a large number of samples. This Review discusses relevant concepts, computational methods and software tools for analysing and interpreting DNA methylation data. It focuses not only on the bioinformatic challenges of large epigenome-mapping projects and epigenome-wide association studies but also highlights software tools that make genome-wide DNA methylation mapping more accessible for laboratories with limited bioinformatics experience.
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DOBA, IJS, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Abstract
Summary
PhenoScanner is a curated database of publicly available results from large-scale genetic association studies in humans. This online tool facilitates ‘phenome scans’, where genetic ...variants are cross-referenced for association with many phenotypes of different types. Here we present a major update of PhenoScanner (‘PhenoScanner V2’), including over 150 million genetic variants and more than 65 billion associations (compared to 350 million associations in PhenoScanner V1) with diseases and traits, gene expression, metabolite and protein levels, and epigenetic markers. The query options have been extended to include searches by genes, genomic regions and phenotypes, as well as for genetic variants. All variants are positionally annotated using the Variant Effect Predictor and the phenotypes are mapped to Experimental Factor Ontology terms. Linkage disequilibrium statistics from the 1000 Genomes project can be used to search for phenotype associations with proxy variants.
Availability and implementation
PhenoScanner V2 is available at www.phenoscanner.medschl.cam.ac.uk.
Genome-wide association studies (GWAS) involve testing genetic variants across the genomes of many individuals to identify genotype-phenotype associations. GWAS have revolutionized the field of ...complex disease genetics over the past decade, providing numerous compelling associations for human complex traits and diseases. Despite clear successes in identifying novel disease susceptibility genes and biological pathways and in translating these findings into clinical care, GWAS have not been without controversy. Prominent criticisms include concerns that GWAS will eventually implicate the entire genome in disease predisposition and that most association signals reflect variants and genes with no direct biological relevance to disease. In this Review, we comprehensively assess the benefits and limitations of GWAS in human populations and discuss the relevance of performing more GWAS.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Most mutations in cancer are rare, which complicates the identification of therapeutically significant mutations and thus limits the clinical impact of genomic profiling in patients with cancer. ...Here, we analyzed 24,592 cancers including 10,336 prospectively sequenced patients with advanced disease to identify mutant residues arising more frequently than expected in the absence of selection. We identified 1,165 statistically significant hotspot mutations of which 80% arose in 1 in 1,000 or fewer patients. Of 55 recurrent in-frame indels, we validated that novel
duplications induced pathway hyperactivation and conferred AKT inhibitor sensitivity. Cancer genes exhibit different rates of hotspot discovery with increasing sample size, with few approaching saturation. Consequently, 26% of all hotspots in therapeutically actionable oncogenes were novel. Upon matching a subset of affected patients directly to molecularly targeted therapy, we observed radiographic and clinical responses. Population-scale mutant allele discovery illustrates how the identification of driver mutations in cancer is far from complete.
Our systematic computational, experimental, and clinical analysis of hotspot mutations in approximately 25,000 human cancers demonstrates that the long right tail of biologically and therapeutically significant mutant alleles is still incompletely characterized. Sharing prospective genomic data will accelerate hotspot identification, thereby expanding the reach of precision oncology in patients with cancer.
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Dissections or ruptures of aortic aneurysms remain a leading cause of death in the developed world, with the majority of deaths being preventable if individuals at risk are identified and properly ...managed. Genetic variants predispose individuals to these aortic diseases. In the case of thoracic aortic aneurysm and dissections (thoracic aortic disease), genetic data can be used to identify some at-risk individuals and dictate management of the associated vascular disease. For abdominal aortic aneurysms, genetic associations have been identified, which provide insight on the molecular pathogenesis but cannot be used clinically yet to identify individuals at risk for abdominal aortic aneurysms. This compendium will discuss our current understanding of the genetic basis of thoracic aortic disease and abdominal aortic aneurysm disease. Although both diseases share several pathogenic similarities, including proteolytic elastic tissue degeneration and smooth muscle dysfunction, they also have several distinct differences, including population prevalence and modes of inheritance.
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