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A high quality benchmark for small variants encompassing 88 to 90% of the reference genome has been developed for seven Genome in a Bottle (GIAB) reference samples. However a reliable benchmark for large indels and structural variants (SVs) is more challenging. In this study, we manually curated 1235 SVs, which can ultimately be used to evaluate SV callers or train machine learning models. We developed a crowdsourcing app-SVCurator-to help GIAB curators manually review large indels and SVs within the human genome, and report their genotype and size accuracy. SVCurator displays images from short, long, and linked read sequencing data from the GIAB Ashkenazi Jewish Trio son [NIST RM 8391/HG002]. We asked curators to assign labels describing SV type (deletion or insertion), size accuracy, and genotype for 1235 putative insertions and deletions sampled from different size bins between 20 and 892,149 bp. 'Expert' curators were 93% concordant with each other, and 37 of the 61 curators had at least 78% concordance with a set of 'expert' curators. The curators were least concordant for complex SVs and SVs that had inaccurate breakpoints or size predictions. After filtering events with low concordance among curators, we produced high confidence labels for 935 events. The SVCurator crowdsourced labels were 94.5% concordant with the heuristic-based draft benchmark SV callset from GIAB. We found that curators can successfully evaluate putative SVs when given evidence from multiple sequencing technologies.

Legionella pneumophila is an opportunistic pathogen of humans where the source of infection is usually from contaminated man-made water systems. When an outbreak of Legionnaires' disease caused by L. pneumophila occurs, it is necessary to discover the source of infection. A seven allele sequence-based typing scheme (SBT) has been very successful in providing the means to attribute outbreaks of L. pneumophila to a particular source or sources. Particular sequence types described by this scheme are known to exhibit specific phenotypes. For instance some types are seen often in clinical cases but are rarely isolated from the environment and vice versa. Of those causing human disease some types are thought to be more likely to cause more severe disease. It is possible that the genetic basis for these differences are vertically inherited and associated with particular genetic lineages within the population. In order to provide a framework within which to test this hypothesis and others relating to the population biology of L. pneumophila, a set of genomes covering the known diversity of the organism is required. Firstly, this study describes a means to group L. pneumophila strains into pragmatic clusters, using a methodology that takes into consideration the genetic forces operating on the population. These clusters can be used as a standardised nomenclature, so those wishing to describe a group of strains can do so. Secondly, the clusters generated from the first part of the study were used to select strains rationally for whole genome sequencing (WGS). The data generated was used to compare phylogenies derived from SBT and WGS. In general the SBT sequence type (ST) accurately reflects the whole genome-based genotype. Where there are exceptions and recombination has resulted in the ST no longer reflecting the genetic lineage described by the whole genome sequence, the clustering technique employed detects these sequence types as being admixed, indicating their mixed inheritance. We conclude that SBT is usually a good proxy for the genetic lineage described by the whole genome, and therefore utility of SBT is still suitable until the technology and economics of high throughput sequencing reach the point where routine WGS of L. pneumophila isolates for outbreak investigation is feasible.

AbstractObjectiveTo compare prevalent and incident morbidity and mortality between those with the HFE p.C282Y genetic variant (responsible for most hereditary haemochromatosis type 1) and those with no p.C282Y mutations, in a large UK community sample of European descent.DesignCohort study.Setting22 centres across England, Scotland, and Wales in UK Biobank (2006-10).Participants451 243 volunteers of European descent aged 40 to 70 years, with a mean follow-up of seven years (maximum 9.4 years) through hospital inpatient diagnoses and death certification.Main outcome measureOdds ratios and Cox hazard ratios of disease rates between participants with and without the haemochromatosis mutations, adjusted for age, genotyping array type, and genetic principal components. The sexes were analysed separately as morbidity due to iron excess occurs later in women.ResultsOf 2890 participants homozygous for p.C282Y (0.6%, or 1 in 156), haemochromatosis was diagnosed in 21.7% (95% confidence interval 19.5% to 24.1%, 281/1294) of men and 9.8% (8.4% to 11.2%, 156/1596) of women by end of follow-up. p.C282Y homozygous men aged 40 to 70 had a higher prevalence of diagnosed haemochromatosis (odds ratio 411.1, 95% confidence interval 299.0 to 565.3, P<0.001), liver disease (4.30, 2.97 to 6.18, P<0.001), rheumatoid arthritis (2.23, 1.51 to 3.31, P<0.001), osteoarthritis (2.01, 1.71 to 2.36, P<0.001), and diabetes mellitus (1.53, 1.16 to 1.98, P=0.002), versus no p.C282Y mutations (n=175 539). During the seven year follow-up, 15.7% of homozygous men developed at least one incident associated condition versus 5.0% (P<0.001) with no p.C282Y mutations (women 10.1% v 3.4%, P<0.001). Haemochromatosis diagnoses were more common in p.C282Y/p.H63D heterozygotes, but excess morbidity was modest.ConclusionsIn a large community sample, HFE p.C282Y homozygosity was associated with substantial prevalent and incident clinically diagnosed morbidity in both men and women. As p.C282Y associated iron overload is preventable and treatable if intervention starts early, these findings justify re-examination of options for expanded early case ascertainment and screening.

IntroductionMesenteric fibrosis can be a severe complication of small intestinal neuroendocrine tumours (SINETs) leading to significant complications and overall shorter patient survival. Development of new treatments is limited by a poor understanding of mesenteric fibrosis and lack of reliable SINET models.MethodsGOT1 cells (SINET cell line) and primary fibroblasts, isolated from normal small intestine, primary tumour, normal mesentery, and mesenteric metastases from SINET patients, were cultured together in 3D hydrogels generated using the extracellular matrix of decellularized healthy small intestine from humans. Co-cultures were analysed using Prestoblue, histology and immunohistochemistry. Bulk RNA sequencing and DNA methylation analysis was performed on a cohort of 46 SINET patients, stratified into 4 groups (none, minimal, mild, and severe) based on the severity of mesenteric fibrosis.ResultsGOT1 cells and GOT-CAF cocultures showed good viability in human intestine ECM gels. Synaptophysin (GOT1) and αSMA (fibroblast) staining showed the distribution of cells and the localization of fibroblasts around clusters of GOT1 cells. Epigenetic and transcriptomic results were overlayed and displayed significant differences between mesenteric metastases and primary SINETs whilst also highlighting several genes with both significantly altered methylation and expression signatures. including ACOT7, SFRS4 and GNG4.ConclusionsThe model generated has shown promise in being able to mimic tumour cell-fibroblast interactions in a 3D structure using human ECM of the small intestine and thus, could be a better model than those currently available. The highlighted genes from analysis into patient tissue will be taken for further analysis to understand the pathways and progression of mesenteric fibrosis. Together, these results could accelerate the research into the poorly understood mechanisms of mesenteric fibrosis and SINETs.

Loss of muscle mass and function, termed sarcopenia, occurs commonly with advancing age. This loss of strength can have a profound impacts on an individual’s life expectancy and quality of life. Population genetic studies can provide information on underlying biological mechanisms, but little was known about the genetic contributions to sarcopenia.By using data from multi-national community based studies of 256,523 individuals of European ancestry aged 60 years or older I have identified 15 genomic risk loci for muscle weakness with age. I have shown that the genetic contributions to muscle weakness in later life have novel characteristics not seen in studies of muscle strength at younger ages. I have also shown that for a section of the older population meeting the criteria for sarcopenia, there is a substantial auto-immune component separate from diagnosed autoimmune conditions, such as Rheumatoid arthritis. Analysis of sex-specific cohorts has highlighted that the underlying genetics contributing to muscle weakness with age differ between the sexes. Additional research on the shared pathways between age-related traits and muscle weakness with age has shown that diabetes, rheumatoid arthritis and life courses traits, for example birth weight, share at least some of the same biological pathways. Biological pathways implicated included transcription regulation, processing of misfolded proteins, cell growth and development.In conclusion I have identified several common genetic variants associated with sarcopenia in humans, which has highlighted an autoimmune component and several shared casual pathways with traits ranging from life-course and growth traits through to later life conditions such as Rheumatoid arthritis and diabetes. These findings should inform efforts to prevent and treat muscle loss with advancing age, and may more personalised approaches to intervention.

Abstract Dynapenia or muscle weakness is a core feature of sarcopenia and frailty, and leads to significant functional impairment in older adults. We aimed to identify genetic variants associated with dynapenia in older adults, to shed light on the underlying mechanisms. A large-scale meta-analysis of >250,000 volunteers of European descent from 16 cohorts in the CHARGE and GEFOS consortia aged 60+ years at assessment is underway, using maximum hand grip strength to define dynapenia. Preliminary analysis in UK Biobank found that dynapenia is associated with nine genomic loci at genome wide significance (p<5x10-8), with differences between men and women, suggesting sex-specific routes to dynapenia. These results suggest that there are specific genetic determinants of low strength in older adults, and these differ from the determinants of normal-range strength differences. The full meta-analysis of all cohorts will extend these findings. Understanding the biological mechanisms may lead to geroscience interventions.

Background A striking effect of old age is the involuntary loss of muscle mass and strength leading to sarcopenia and reduced physiological functions. However, effects of heavy-load exercise in older adults on diseases and functions as predicted by changes in muscle gene expression have been inadequately studied. Methods Thigh muscle global transcriptional activity (transcriptome) was analyzed in cohorts of older and younger adults before and after 12–13 weeks heavy-load strength exercise using Affymetrix microarrays. Three age groups, similarly trained, were compared: younger adults (age 24 ± 4 years), older adults of average age 70 years (Oslo cohort) and above 80 years (old BSU cohort). To increase statistical strength, one of the older cohorts was used for validation. Ingenuity Pathway analysis (IPA) was used to identify predicted biological effects of a gene set that changed expression after exercise, and Principal Component Analysis (PCA) was used to visualize differences in muscle gene expressen between cohorts and individual participants as well as overall changes upon exercise. Results Younger adults, showed few transcriptome changes, but a marked, significant impact was observed in persons of average age 70 years and even more so in persons above 80 years. The 249 transcripts positively or negatively altered in both cohorts of older adults (q-value < 0.1) were submitted to gene set enrichment analysis using IPA. The transcripts predicted increase in several aspects of “vascularization and muscle contractions”, whereas functions associated with negative health effects were reduced, e.g., “Glucose metabolism disorder” and “Disorder of blood pressure”. Several genes that changed expression after intervention were confirmed at the genome level by containing single nucleotide variants associated with handgrip strength and muscle expression levels, e.g., CYP4B1 ( p  = 9.2E-20), NOTCH4 ( p  = 9.7E-8), and FZD4 ( p  = 5.3E-7). PCA of the 249 genes indicated a differential pattern of muscle gene expression in young and elderly. However, after exercise the expression patterns in both young and old BSU cohorts were changed in the same direction for the vast majority of participants. Conclusions The positive impact of heavy-load strength training on the transcriptome increased markedly with age. The identified molecular changes translate to improved vascularization and muscular strength, suggesting highly beneficial health effects for older adults.

Background Legionella pneumophila is an opportunistic pathogen of humans where the source of infection is usually from contaminated man-made water systems. When an outbreak of Legionnaires’ disease caused by L. pneumophila occurs, it is necessary to discover the source of infection. A seven allele sequence-based typing scheme (SBT) has been very successful in providing the means to attribute outbreaks of L. pneumophila to a particular source or sources. Particular sequence types described by this scheme are known to exhibit specific phenotypes. For instance some types are seen often in clinical cases but are rarely isolated from the environment and vice versa. Of those causing human disease some types are thought to be more likely to cause more severe disease. It is possible that the genetic basis for these differences are vertically inherited and associated with particular genetic lineages within the population. In order to provide a framework within which to test this hypothesis and others relating to the population biology of L. pneumophila, a set of genomes covering the known diversity of the organism is required. Results Firstly, this study describes a means to group L. pneumophila strains into pragmatic clusters, using a methodology that takes into consideration the genetic forces operating on the population. These clusters can be used as a standardised nomenclature, so those wishing to describe a group of strains can do so. Secondly, the clusters generated from the first part of the study were used to select strains rationally for whole genome sequencing (WGS). The data generated was used to compare phylogenies derived from SBT and WGS. In general the SBT sequence type (ST) accurately reflects the whole genome-based genotype. Where there are exceptions and recombination has resulted in the ST no longer reflecting the genetic lineage described by the whole genome sequence, the clustering technique employed detects these sequence types as being admixed, indicating their mixed inheritance. Conclusions We conclude that SBT is usually a good proxy for the genetic lineage described by the whole genome, and therefore utility of SBT is still suitable until the technology and economics of high throughput sequencing reach the point where routine WGS of L. pneumophila isolates for outbreak investigation is feasible.

Copy number variants (CNV) are a major cause of disease, with over 30,000 reported in the DECIPHER database. To use read depth data from targeted Next Generation Sequencing (NGS) panels to identify CNVs with the highest degree of sensitivity, it is necessary to account for biases inherent in the data. GC content and ambiguous mapping due to repetitive sequence elements and pseudogenes are the principal components of technical variability. In addition, the algorithms used favour the detection of multi-exon CNVs, and rely on suitably matched normal dosage samples for comparison. We developed a calling strategy that subdivides target intervals, and uses pools of historical control samples to overcome these limitations in a clinical diagnostic laboratory. We compared our enhanced strategy with an unmodified pipeline using the R software package ExomeDepth, using a cohort of 109 heterozygous CNVs (91 deletions, 18 duplications in 26 genes), including 25 single exon CNVs. The unmodified pipeline detected 104/109 CNVs, giving a sensitivity of 89.62% to 98.49% at the 95% confidence interval. The detection of all 109 CNVs by our enhanced method demonstrates 95% confidence the sensitivity is ≥96.67%, allowing NGS read depth analysis to be used for CNV detection in a clinical diagnostic setting.

A high quality benchmark for small variants encompassing 88 to 90% of the reference genome has been developed for seven Genome in a Bottle (GIAB) reference samples. However a reliable benchmark for large indels and structural variants (SVs) is yet to be defined. In this study, we manually curated 1235 SVs which can ultimately be used to evaluate SV callers or train machine learning models. We developed a crowdsourcing app - SVCurator - to help curators manually review large indels and SVs within the human genome, and report their genotype and size accuracy. SVCurator is a Python Flask-based web platform that displays images from short, long, and linked read sequencing data from the GIAB Ashkenazi Jewish Trio son [NIST RM 8391/HG002]. We asked curators to assign labels describing SV type (deletion or insertion), size accuracy, and genotype for 1235 putative insertions and deletions sampled from different size bins between 20 and 892,149 bp. The crowdsourced results were highly concordant with 37 out of the 61 curators having at least 78% concordance with a set of expert curators, where there was 93% concordance amongst expert curators. This produced high confidence labels for 935 events. When compared to the heuristic-based draft benchmark SV callset from GIAB, the SVCurator crowdsourced labels were 94.5% concordant with the benchmark set. We found that curators can successfully evaluate putative SVs when given evidence from multiple sequencing technologies.