Unsupervised learning for medical data: A review of probabilistic factorization methods

We review popular unsupervised learning methods for the analysis of high-dimensional data encountered in, for example, genomics, medical imaging, cohort studies, and biobanks. We show that four commonly used methods, principal component analysis, K-means clustering, nonnegative matrix factorization, and latent Dirichlet allocation, can be written as probabilistic models underpinned by a low-rank matrix factorization. In addition to highlighting their similarities, this formulation clarifies the various assumptions and restrictions of each approach, which eases identifying the appropriate method for specific applications for applied medical researchers. We also touch upon the most important aspects of inference and model selection for the application of these methods to health data.

Genetic association studies in critically ill patients: protocol for a systematic review

INTRODUCTION

Patients in the intensive care unit (ICU) are highly heterogeneous in characteristics, their clinical course, and outcomes. Genetic variability may partly explain the variability and similarity in disease courses observed among critically ill patients and may identify clusters of subgroups. The aim of this study is to conduct a systematic review of all genetic association studies of critically ill patients with their outcomes.

METHODS AND ANALYSIS

This systematic review will be conducted and reported according to the HuGE Review Handbook V1.0. We will search PubMed, Embase, and the Cochrane Library for relevant studies. All types of genetic association studies that included acutely admitted medical and surgical adult ICU patients will be considered for this review. All studies will be selected according to predefined selection criteria, evaluated and assessed for risk of bias independently by two reviewers. Risk of bias will be assessed according to the HuGE Review Handbook V1.0 with some modifications reflecting recent insights. We will provide an overview of all included studies by reporting the characteristics of the study designs, the patients included in the studies, the genetic variables, and the outcomes evaluated.

ETHICS AND DISSEMINATION

We will use data from peer-reviewed published articles, and hence, there is no requirement for ethics approval. The results of this systematic review will be disseminated through publication in a peer-reviewed scientific journal.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42021209744.

Structural and non-coding variants increase the diagnostic yield of clinical whole genome sequencing for rare diseases

BACKGROUND

Whole genome sequencing is increasingly being used for the diagnosis of patients with rare diseases. However, the diagnostic yields of many studies, particularly those conducted in a healthcare setting, are often disappointingly low, at 25-30%. This is in part because although entire genomes are sequenced, analysis is often confined to in silico gene panels or coding regions of the genome.

METHODS

We undertook WGS on a cohort of 122 unrelated rare disease patients and their relatives (300 genomes) who had been pre-screened by gene panels or arrays. Patients were recruited from a broad spectrum of clinical specialties. We applied a bioinformatics pipeline that would allow comprehensive analysis of all variant types. We combined established bioinformatics tools for phenotypic and genomic analysis with our novel algorithms (SVRare, ALTSPLICE and GREEN-DB) to detect and annotate structural, splice site and non-coding variants.

RESULTS

Our diagnostic yield was 43/122 cases (35%), although 47/122 cases (39%) were considered solved when considering novel candidate genes with supporting functional data into account. Structural, splice site and deep intronic variants contributed to 20/47 (43%) of our solved cases. Five genes that are novel, or were novel at the time of discovery, were identified, whilst a further three genes are putative novel disease genes with evidence of causality. We identified variants of uncertain significance in a further fourteen candidate genes. The phenotypic spectrum associated with RMND1 was expanded to include polymicrogyria. Two patients with secondary findings in FBN1 and KCNQ1 were confirmed to have previously unidentified Marfan and long QT syndromes, respectively, and were referred for further clinical interventions. Clinical diagnoses were changed in six patients and treatment adjustments made for eight individuals, which for five patients was considered life-saving.

CONCLUSIONS

Genome sequencing is increasingly being considered as a first-line genetic test in routine clinical settings and can make a substantial contribution to rapidly identifying a causal aetiology for many patients, shortening their diagnostic odyssey. We have demonstrated that structural, splice site and intronic variants make a significant contribution to diagnostic yield and that comprehensive analysis of the entire genome is essential to maximise the value of clinical genome sequencing.

Topic modeling identifies novel genetic loci associated with multimorbidities in UK Biobank

Many diseases show patterns of co-occurrence, possibly driven by systemic dysregulation of underlying processes affecting multiple traits. We have developed a method (treeLFA) for identifying such multimorbidities from routine health-care data, which combines topic modeling with an informative prior derived from medical ontology. We apply treeLFA to UK Biobank data and identify a variety of topics representing multimorbidity clusters, including a healthy topic. We find that loci identified using topic weights as traits in a genome-wide association study (GWAS) analysis, which we validated with a range of approaches, only partially overlap with loci from GWASs on constituent single diseases. We also show that treeLFA improves upon existing methods like latent Dirichlet allocation in various ways. Overall, our findings indicate that topic models can characterize multimorbidity patterns and that genetic analysis of these patterns can provide insight into the etiology of complex traits that cannot be determined from the analysis of constituent traits alone.

Lifestyle factors related to prevalent chronic disease multimorbidity: A population-based cross-sectional study

BACKGROUND

Multimorbidity is associated with poor quality of life, polypharmacy, health care costs and mortality, with those affected potentially benefitting from a healthy lifestyle. We assessed a comprehensive set of lifestyle factors in relation to multimorbidity with major chronic diseases.

METHODS

This cross-sectional study utilised baseline data for adults from the prospective Lifelines Cohort in the north of the Netherlands (N = 79,345). We defined multimorbidity as the co-existence of two or more chronic diseases (i.e. cardiovascular disease, cancer, respiratory disease, type 2 diabetes) and evaluated factors in six lifestyle domains (nutrition, physical (in)activity, substance abuse, sleep, stress, relationships) among groups by the number of chronic diseases (≥2, 1, 0). Multinomial logistic regression models were created, adjusted for appropriate confounders, and odds ratios (OR) with 95% confidence intervals (95%CI) were reported.

RESULTS

3,712 participants had multimorbidity (4.7%, age 53.5 ± 12.5 years), and this group tended to have less healthy lifestyles. Compared to those without chronic diseases, those with multimorbidity reported physical inactivity more often (OR, 1.15; 95%CI, 1.06-1.25; not significant for one condition), chronic stress (OR, 2.14; 95%CI, 1.92-2.38) and inadequate sleep (OR, 1.70; 95%CI, 1.41-2.06); as expected, they more often watched television (OR, 1.70; 95%CI, 1.42-2.04) and currently smoked (OR, 1.91; 95%CI, 1.73-2.11), but they also had lower alcohol intakes (OR, 0.66; 95%CI, 0.59-0.74).

CONCLUSIONS

Chronic stress and poor sleep, in addition to physical inactivity and smoking, are lifestyle factors of great concern in patients with multimorbidity.

Timely referral for device-aided therapy in Parkinson's disease. Development of a screening tool

BACKGROUND

Timely referral of Parkinson's disease (PD) patients to specialized centers for treatment with device-aided therapies (DAT) is suboptimal.

OBJECTIVE

To develop a screening tool for timely referral for DAT in PD and to compare the tool with the published 5-2-1 criteria.

METHODS

A cross-sectional, observational study was performed in 8 hospitals in the catchment area of a specialized movement disorder center in the Northern part of the Netherlands. The target population comprised PD patients not yet on DAT visiting the outpatient clinic of participating hospitals. The primary outcome was apparent eligibility for referral for DAT based on consensus by a panel of 5 experts in the field of DAT. Multivariable logistic regression modelling was used to develop a screening tool for eligibility for referral for DAT. Potential predictors were patient and disease characteristics as observed by attending neurologists.

RESULTS

In total, 259 consecutive PD patients were included, of whom 17 were deemed eligible for referral for DAT (point prevalence: 6.6%). Presence of response fluctuations and troublesome dyskinesias were the strongest independent predictors of being considered eligible. Both variables were included in the final model, as well as levodopa equivalent daily dose. Decision curve analysis revealed the new model outperforms the 5-2-1 criteria. A simple chart was constructed to provide guidance for referral. Discrimination of this simplified scoring system proved excellent (AUC after bootstrapping: 0.97).

CONCLUSIONS

Awaiting external validation, the developed screening tool already appears promising for timely referral and subsequent treatment with DAT in patients with PD.

Respiratory Syncytial Virus, Human Metapneumovirus, and Parainfluenza Virus Infections in Lung Transplant Recipients: A Systematic Review of Outcomes and Treatment Strategies

Background

Respiratory syncytial virus (RSV), parainfluenza virus (PIV), and human metapneumovirus (hMPV) are increasingly associated with chronic lung allograft dysfunction (CLAD) in lung transplant recipients (LTR). This systematic review primarily aimed to assess outcomes of RSV/PIV/hMPV infections in LTR and secondarily to assess evidence regarding the efficacy of ribavirin.

Methods

Relevant databases were queried and study outcomes extracted using a standardized method and summarized.

Results

Nineteen retrospective and 12 prospective studies were included (total 1060 cases). Pooled 30-day mortality was low (0–3%), but CLAD progression 180–360 days postinfection was substantial (pooled incidences 19–24%) and probably associated with severe infection. Ribavirin trended toward effectiveness for CLAD prevention in exploratory meta-analysis (odds ratio [OR] 0.61, [0.27–1.18]), although results were highly variable between studies.

Conclusions

RSV/PIV/hMPV infection was followed by a high CLAD incidence. Treatment options, including ribavirin, are limited. There is an urgent need for high-quality studies to provide better treatment options for these infections.

Benchmarking small-variant genotyping in polyploids

Genotyping from sequencing is the basis of emerging strategies in the molecular breeding of polyploid plants. However, compared with the situation for diploids, in which genotyping accuracies are confidently determined with comprehensive benchmarks, polyploids have been neglected; there are no benchmarks measuring genotyping error rates for small variants using real sequencing reads. We previously introduced a variant calling method, Octopus, that accurately calls germline variants in diploids and somatic mutations in tumors. Here, we evaluate Octopus and other popular tools on whole-genome tetraploid and hexaploid data sets created using in silico mixtures of diploid Genome in a Bottle (GIAB) samples. We find that genotyping errors are abundant for typical sequencing depths but that Octopus makes 25% fewer errors than other methods on average. We supplement our benchmarks with concordance analysis in real autotriploid banana data sets.

Recurrence and survival after laparoscopy versus laparotomy without lymphadenectomy in early-stage endometrial cancer: Long-term outcomes of a randomised trial

BACKGROUND

Laparoscopic hysterectomy is accepted worldwide as the standard treatment option for early-stage endometrial cancer. However, there are limited data on long-term survival, particularly when no lymphadenectomy is performed. We compared the survival outcomes of total laparoscopic hysterectomy (TLH) and total abdominal hysterectomy (TAH), both without lymphadenectomy, for early-stage endometrial cancer up to 5 years postoperatively.

METHODS

Follow-up of a multi-centre, randomised controlled trial comparing TLH and TAH, without routine lymphadenectomy, for women with stage I endometrial cancer. Enrolment was between 2007 and 2009 by 2:1 randomisation to TLH or TAH. Outcomes were disease-free survival (DFS), overall survival (OS), disease-specific survival (DSS), and primary site of recurrence. Multivariable Cox regression analyses were adjusted for age, stage, grade, and radiotherapy with adjusted hazard ratios (aHR) and 95% confidence intervals (95%CI) reported. To test for significance, non-inferiority margins were defined.

RESULTS

In total, 279 women underwent a surgical procedure, of whom 263 (94%) had follow-up data. For the TLH (n = 175) and TAH (n = 88) groups, DFS (90.3% vs 84.1%; aHR[recurrence], 0.69; 95%CI, 0.31-1.52), OS (89.2% vs 82.8%; aHR[death], 0.60; 95%CI, 0.30-1.19), and DSS (95.0% vs 89.8%; aHR[death], 0.62; 95%CI, 0.23-1.70) were reported at 5 years. At a 10% significance level, and with a non-inferiority margin of 0.20, the null hypothesis of inferiority was rejected for all three outcomes. There were no port-site or wound metastases, and local recurrence rates were comparable.

CONCLUSION

Disease recurrence and 5-year survival rates were comparable between the TLH and TAH groups and comparable to studies with lymphadenectomy, supporting the widespread use of TLH without lymphadenectomy as the primary treatment for early-stage, low-grade endometrial cancer.

Neuropeptide S receptor 1 is a nonhormonal treatment target in endometriosis

Endometriosis is a common chronic inflammatory condition causing pelvic pain and infertility in women, with limited treatment options and 50% heritability. We leveraged genetic analyses in two species with spontaneous endometriosis, humans and the rhesus macaque, to uncover treatment targets. We sequenced DNA from 32 human families contributing to a genetic linkage signal on chromosome 7p13-15 and observed significant overrepresentation of predicted deleterious low-frequency coding variants in NPSR1, the gene encoding neuropeptide S receptor 1, in cases (predominantly stage III/IV) versus controls (P = 7.8 × 10^-4). Significant linkage to the region orthologous to human 7p13-15 was replicated in a pedigree of 849 rhesus macaques (P = 0.0095). Targeted association analyses in 3194 surgically confirmed, unrelated cases and 7060 controls revealed that a common insertion/deletion variant, rs142885915, was significantly associated with stage III/IV endometriosis (P = 5.2 × 10^-5; odds ratio, 1.23; 95% CI, 1.09 to 1.39). Immunohistochemistry, qRT-PCR, and flow cytometry experiments demonstrated that NPSR1 was expressed in glandular epithelium from eutopic and ectopic endometrium, and on monocytes in peritoneal fluid. The NPSR1 inhibitor SHA 68R blocked NPSR1-mediated signaling, proinflammatory TNF-α release, and monocyte chemotaxis in vitro (P < 0.01), and led to a significant reduction of inflammatory cell infiltrate and abdominal pain (P < 0.05) in a mouse model of peritoneal inflammation as well as in a mouse model of endometriosis. We conclude that the NPSR1/NPS system is a genetically validated, nonhormonal target for the treatment of endometriosis with likely increased relevance to stage III/IV disease.

A unified haplotype-based method for accurate and comprehensive variant calling

Almost all haplotype-based variant callers were designed specifically for detecting common germline variation in diploid populations, and give suboptimal results in other scenarios. Here we present Octopus, a variant caller that uses a polymorphic Bayesian genotyping model capable of modeling sequencing data from a range of experimental designs within a unified haplotype-aware framework. Octopus combines sequencing reads and prior information to phase-called genotypes of arbitrary ploidy, including those with somatic mutations. We show that Octopus accurately calls germline variants in individuals, including single nucleotide variants, indels and small complex replacements such as microinversions. Using a synthetic tumor data set derived from clean sequencing data from a sample with known germline haplotypes and observed mutations in a large cohort of tumor samples, we show that Octopus is more sensitive to low-frequency somatic variation, yet calls considerably fewer false positives than other methods. Octopus also outputs realigned evidence BAM files to aid validation and interpretation.

Multi Locus View: an extensible web-based tool for the analysis of genomic data

Tracking and understanding data quality, analysis and reproducibility are critical concerns in the biological sciences. This is especially true in genomics where next generation sequencing (NGS) based technologies such as ChIP-seq, RNA-seq and ATAC-seq are generating a flood of genome-scale data. However, such data are usually processed with automated tools and pipelines, generating tabular outputs and static visualisations. Interpretation is normally made at a high level without the ability to visualise the underlying data in detail. Conventional genome browsers are limited to browsing single locations and do not allow for interactions with the dataset as a whole. Multi Locus View (MLV), a web-based tool, has been developed to allow users to fluidly interact with genomics datasets at multiple scales. The user is able to browse the raw data, cluster, and combine the data with other analysis and annotate the data. User datasets can then be shared with other users or made public for quick assessment from the academic community. MLV is publically available at https://mlv.molbiol.ox.ac.uk .

Short and long-read genome sequencing methodologies for somatic variant detection; genomic analysis of a patient with diffuse large B-cell lymphoma

Recent advances in throughput and accuracy mean that the Oxford Nanopore Technologies PromethION platform is a now a viable solution for genome sequencing. Much of the validation of bioinformatic tools for this long-read data has focussed on calling germline variants (including structural variants). Somatic variants are outnumbered many-fold by germline variants and their detection is further complicated by the effects of tumour purity/subclonality. Here, we evaluate the extent to which Nanopore sequencing enables detection and analysis of somatic variation. We do this through sequencing tumour and germline genomes for a patient with diffuse B-cell lymphoma and comparing results with 150 bp short-read sequencing of the same samples. Calling germline single nucleotide variants (SNVs) from specific chromosomes of the long-read data achieved good specificity and sensitivity. However, results of somatic SNV calling highlight the need for the development of specialised joint calling algorithms. We find the comparative genome-wide performance of different tools varies significantly between structural variant types, and suggest long reads are especially advantageous for calling large somatic deletions and duplications. Finally, we highlight the utility of long reads for phasing clinically relevant variants, confirming that a somatic 1.6 Mb deletion and a p.(Arg249Met) mutation involving TP53 are oriented in trans.

Demographic inference from multiple whole genomes using a particle filter for continuous Markov jump processes

Demographic events shape a population's genetic diversity, a process described by the coalescent-with-recombination model that relates demography and genetics by an unobserved sequence of genealogies along the genome. As the space of genealogies over genomes is large and complex, inference under this model is challenging. Formulating the coalescent-with-recombination model as a continuous-time and -space Markov jump process, we develop a particle filter for such processes, and use waypoints that under appropriate conditions allow the problem to be reduced to the discrete-time case. To improve inference, we generalise the Auxiliary Particle Filter for discrete-time models, and use Variational Bayes to model the uncertainty in parameter estimates for rare events, avoiding biases seen with Expectation Maximization. Using real and simulated genomes, we show that past population sizes can be accurately inferred over a larger range of epochs than was previously possible, opening the possibility of jointly analyzing multiple genomes under complex demographic models. Code is available at https://github.com/luntergroup/smcsmc.

DeepC: predicting 3D genome folding using megabase-scale transfer learning

Predicting the impact of noncoding genetic variation requires interpreting it in the context of three-dimensional genome architecture. We have developed deepC, a transfer-learning-based deep neural network that accurately predicts genome folding from megabase-scale DNA sequence. DeepC predicts domain boundaries at high resolution, learns the sequence determinants of genome folding and predicts the impact of both large-scale structural and single base-pair variations.

An equivariant Bayesian convolutional network predicts recombination hotspots and accurately resolves binding motifs

MOTIVATION

Convolutional neural networks (CNNs) have been tremendously successful in many contexts, particularly where training data are abundant and signal-to-noise ratios are large. However, when predicting noisily observed phenotypes from DNA sequence, each training instance is only weakly informative, and the amount of training data is often fundamentally limited, emphasizing the need for methods that make optimal use of training data and any structure inherent in the process.

RESULTS

Here we show how to combine equivariant networks, a general mathematical framework for handling exact symmetries in CNNs, with Bayesian dropout, a version of Monte Carlo dropout suggested by a reinterpretation of dropout as a variational Bayesian approximation, to develop a model that exhibits exact reverse-complement symmetry and is more resistant to overtraining. We find that this model combines improved prediction consistency with better predictive accuracy compared to standard CNN implementations and state-of-art motif finders. We use our network to predict recombination hotspots from sequence, and identify binding motifs for the recombination-initiation protein PRDM9 previously unobserved in this data, which were recently validated by high-resolution assays. The network achieves a predictive accuracy comparable to that attainable by a direct assay of the H3K4me3 histone mark, a proxy for PRDM9 binding.

AVAILABILITY AND IMPLEMENTATION

https://github.com/luntergroup/EquivariantNetworks.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Inferring B cell specificity for vaccines using a Bayesian mixture model

Background

Vaccines have greatly reduced the burden of infectious disease, ranking in their impact on global health second only after clean water. Most vaccines confer protection by the production of antibodies with binding affinity for the antigen, which is the main effector function of B cells. This results in short term changes in the B cell receptor (BCR) repertoire when an immune response is launched, and long term changes when immunity is conferred. Analysis of antibodies in serum is usually used to evaluate vaccine response, however this is limited and therefore the investigation of the BCR repertoire provides far more detail for the analysis of vaccine response.

Results

Here, we introduce a novel Bayesian model to describe the observed distribution of BCR sequences and the pattern of sharing across time and between individuals, with the goal to identify vaccine-specific BCRs. We use data from two studies to assess the model and estimate that we can identify vaccine-specific BCRs with 69% sensitivity.

Conclusion

Our results demonstrate that statistical modelling can capture patterns associated with vaccine response and identify vaccine specific B cells in a range of different data sets. Additionally, the B cells we identify as vaccine specific show greater levels of sequence similarity than expected, suggesting that there are additional signals of vaccine response, not currently considered, which could improve the identification of vaccine specific B cells.

Haplotype matching in large cohorts using the Li and Stephens model

Motivation

The Li and Stephens model, which approximates the coalescent describing the pattern of variation in a population, underpins a range of key tools and results in genetics. Although highly efficient compared to the coalescent, standard implementations of this model still cannot deal with the very large reference cohorts that are starting to become available, and practical implementations use heuristics to achieve reasonable runtimes.

Results

Here I describe a new, exact algorithm (‘fastLS’) that implements the Li and Stephens model and achieves runtimes independent of the size of the reference cohort. Key to achieving this runtime is the use of the Burrows-Wheeler transform, allowing the algorithm to efficiently identify partial haplotype matches across a cohort. I show that the proposed data structure is very similar to, and generalizes, Durbin’s positional Burrows-Wheeler transform.

Efficient inference in state-space models through adaptive learning in online Monte Carlo expectation maximization

Expectation maximization (EM) is a technique for estimating maximum-likelihood parameters of a latent variable model given observed data by alternating between taking expectations of sufficient statistics, and maximizing the expected log likelihood. For situations where sufficient statistics are intractable, stochastic approximation EM (SAEM) is often used, which uses Monte Carlo techniques to approximate the expected log likelihood. Two common implementations of SAEM, Batch EM (BEM) and online EM (OEM), are parameterized by a "learning rate", and their efficiency depend strongly on this parameter. We propose an extension to the OEM algorithm, termed Introspective Online Expectation Maximization (IOEM), which removes the need for specifying this parameter by adapting the learning rate to trends in the parameter updates. We show that our algorithm matches the efficiency of the optimal BEM and OEM algorithms in multiple models, and that the efficiency of IOEM can exceed that of BEM/OEM methods with optimal learning rates when the model has many parameters. Finally we use IOEM to fit two models to a financial time series. A Python implementation is available at https://github.com/luntergroup/IOEM.git.

Repertoire-wide phylogenetic models of B cell molecular evolution reveal evolutionary signatures of aging and vaccination

In order to produce effective antibodies, B cells undergo rapid somatic hypermutation (SHM) and selection for binding affinity to antigen via a process called affinity maturation. The similarities between this process and evolution by natural selection have led many groups to use phylogenetic methods to characterize the development of immunological memory, vaccination, and other processes that depend on affinity maturation. However, these applications are limited by the fact that most phylogenetic models are designed to be applied to individual lineages comprising genetically diverse sequences, while B cell repertoires often consist of hundreds to thousands of separate low-diversity lineages. Further, several features of affinity maturation violate important assumptions in standard phylogenetic models. Here, we introduce a hierarchical phylogenetic framework that integrates information from all lineages in a repertoire to more precisely estimate model parameters while simultaneously incorporating the unique features of SHM. We demonstrate the power of this repertoire-wide approach by characterizing previously undescribed phenomena in affinity maturation. First, we find evidence consistent with age-related changes in SHM hot-spot targeting. Second, we identify a consistent relationship between increased tree length and signs of increased negative selection, apparent in the repertoires of recently vaccinated subjects and those without any known recent infections or vaccinations. This suggests that B cell lineages shift toward negative selection over time as a general feature of affinity maturation. Our study provides a framework for undertaking repertoire-wide phylogenetic testing of SHM hypotheses and provides a means of characterizing dynamics of mutation and selection during affinity maturation.

Sequencing of human genomes with nanopore technology

Whole-genome sequencing (WGS) is becoming widely used in clinical medicine in diagnostic contexts and to inform treatment choice. Here we evaluate the potential of the Oxford Nanopore Technologies (ONT) MinION long-read sequencer for routine WGS by sequencing the reference sample NA12878 and the genome of an individual with ataxia-pancytopenia syndrome and severe immune dysregulation. We develop and apply a novel reference panel-free analytical method to infer and then exploit phase information which improves single-nucleotide variant (SNV) calling performance from otherwise modest levels. In the clinical sample, we identify and directly phase two non-synonymous de novo variants in SAMD9L, (OMIM #159550) inferring that they lie on the same paternal haplotype. Whilst consensus SNV-calling error rates from ONT data remain substantially higher than those from short-read methods, we demonstrate the substantial benefits of analytical innovation. Ongoing improvements to base-calling and SNV-calling methodology must continue for nanopore sequencing to establish itself as a primary method for clinical WGS.

A high throughput screen for active human transposable elements

Background

Transposable elements (TEs) are mobile genetic sequences that randomly propagate within their host’s genome. This mobility has the potential to affect gene transcription and cause disease. However, TEs are technically challenging to identify, which complicates efforts to assess the impact of TE insertions on disease. Here we present a targeted sequencing protocol and computational pipeline to identify polymorphic and novel TE insertions using next-generation sequencing: TE-NGS. The method simultaneously targets the three subfamilies that are responsible for the majority of recent TE activity (L1HS, AluYa5/8, and AluYb8/9) thereby obviating the need for multiple experiments and reducing the amount of input material required.

Results

Here we describe the laboratory protocol and detection algorithm, and a benchmark experiment for the reference genome NA12878. We demonstrate a substantial enrichment for on-target fragments, and high sensitivity and precision to both reference and NA12878-specific insertions. We report 17 previously unreported loci for this individual which are supported by orthogonal long-read evidence, and we identify 1470 polymorphic and novel TEs in 12 additional samples that were previously undocumented in databases of insertion polymorphisms.

Conclusions

We anticipate that future applications of TE-NGS alongside exome sequencing of patients with sporadic disease will reduce the number of unresolved cases, and improve estimates of the contribution of TEs to human genetic disease.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4485-4) contains supplementary material, which is available to authorized users.

Accurate clinical detection of exon copy number variants in a targeted NGS panel using DECoN

Background: Targeted next generation sequencing (NGS) panels are increasingly being used in clinical genomics to increase capacity, throughput and affordability of gene testing. Identifying whole exon deletions or duplications (termed exon copy number variants, ‘exon CNVs’) in exon-targeted NGS panels has proved challenging, particularly for single exon CNVs. 

Methods: We developed a tool for the Detection of Exon Copy Number variants (DECoN), which is optimised for analysis of exon-targeted NGS panels in the clinical setting. We evaluated DECoN performance using 96 samples with independently validated exon CNV data. We performed simulations to evaluate DECoN detection performance of single exon CNVs and to evaluate performance using different coverage levels and sample numbers. Finally, we implemented DECoN in a clinical laboratory that tests BRCA1 and BRCA2 with the TruSight Cancer Panel (TSCP). We used DECoN to analyse 1,919 samples, validating exon CNV detections by multiplex ligation-dependent probe amplification (MLPA). 

Results: In the evaluation set, DECoN achieved 100% sensitivity and 99% specificity for BRCA exon CNVs, including identification of 8 single exon CNVs. DECoN also identified 14/15 exon CNVs in 8 other genes. Simulations of all possible BRCA single exon CNVs gave a mean sensitivity of 98% for deletions and 95% for duplications. DECoN performance remained excellent with different levels of coverage and sample numbers; sensitivity and specificity was >98% with the typical NGS run parameters. In the clinical pipeline, DECoN automatically analyses pools of 48 samples at a time, taking 24 minutes per pool, on average. DECoN detected 24 BRCA exon CNVs, of which 23 were confirmed by MLPA, giving a false discovery rate of 4%. Specificity was 99.7%. 

Conclusions: DECoN is a fast, accurate, exon CNV detection tool readily implementable in research and clinical NGS pipelines. It has high sensitivity and specificity and acceptable false discovery rate. DECoN is freely available at www.icr.ac.uk/decon.

B-cell repertoire dynamics after sequential hepatitis B vaccination and evidence for cross-reactive B-cell activation

BACKGROUND

A diverse B-cell repertoire is essential for recognition and response to infectious and vaccine antigens. High-throughput sequencing of B-cell receptor (BCR) genes can now be used to study the B-cell repertoire at great depth and may shed more light on B-cell responses than conventional immunological methods. Here, we use high-throughput BCR sequencing to provide novel insight into B-cell dynamics following a primary course of hepatitis B vaccination.

METHODS

Nine vaccine-naïve participants were administered three doses of hepatitis B vaccine (months 0, 1, and 2 or 7). High-throughput Illumina sequencing of the total BCR repertoire was combined with targeted sequencing of sorted vaccine antigen-enriched B cells to analyze the longitudinal response of both the total and vaccine-specific repertoire after each vaccine. ELISpot was used to determine vaccine-specific cell numbers following each vaccine.

RESULTS

Deconvoluting the vaccine-specific from total BCR repertoire showed that vaccine-specific sequence clusters comprised <0.1 % of total sequence clusters, and had certain stereotypic features. The vaccine-specific BCR sequence clusters were expanded after each of the three vaccine doses, despite no vaccine-specific B cells being detected by ELISpot after the first vaccine dose. These vaccine-specific BCR clusters detected after the first vaccine dose had distinct properties compared to those detected after subsequent doses; they were more mutated, present at low frequency even prior to vaccination, and appeared to be derived from more mature B cells.

CONCLUSIONS

These results demonstrate the high-sensitivity of our vaccine-specific BCR analysis approach and suggest an alternative view of the B-cell response to novel antigens. In the response to the first vaccine dose, many vaccine-specific BCR clusters appeared to largely derive from previously activated cross-reactive B cells that have low affinity for the vaccine antigen, and subsequent doses were required to yield higher affinity B cells.

The Diversity and Molecular Evolution of B-Cell Receptors during Infection

B-cell receptors (BCRs) are membrane-bound immunoglobulins that recognize and bind foreign proteins (antigens). BCRs are formed through random somatic changes of germline DNA, creating a vast repertoire of unique sequences that enable individuals to recognize a diverse range of antigens. After encountering antigen for the first time, BCRs undergo a process of affinity maturation, whereby cycles of rapid somatic mutation and selection lead to improved antigen binding. This constitutes an accelerated evolutionary process that takes place over days or weeks. Next-generation sequencing of the gene regions that determine BCR binding has begun to reveal the diversity and dynamics of BCR repertoires in unprecedented detail. Although this new type of sequence data has the potential to revolutionize our understanding of infection dynamics, quantitative analysis is complicated by the unique biology and high diversity of BCR sequences. Models and concepts from molecular evolution and phylogenetics that have been applied successfully to rapidly evolving pathogen populations are increasingly being adopted to study BCR diversity and divergence within individuals. However, BCR dynamics may violate key assumptions of many standard evolutionary methods, as they do not descend from a single ancestor, and experience biased mutation. Here, we review the application of evolutionary models to BCR repertoires and discuss the issues we believe need be addressed for this interdisciplinary field to flourish.

Analysis of B Cell Repertoire Dynamics Following Hepatitis B Vaccination in Humans, and Enrichment of Vaccine-specific Antibody Sequences

Generating a diverse B cell immunoglobulin repertoire is essential for protection against infection. The repertoire in humans can now be comprehensively measured by high-throughput sequencing. Using hepatitis B vaccination as a model, we determined how the total immunoglobulin sequence repertoire changes following antigen exposure in humans, and compared this to sequences from vaccine-specific sorted cells. Clonal sequence expansions were seen 7 days after vaccination, which correlated with vaccine-specific plasma cell numbers. These expansions caused an increase in mutation, and a decrease in diversity and complementarity-determining region 3 sequence length in the repertoire. We also saw an increase in sequence convergence between participants 14 and 21 days after vaccination, coinciding with an increase of vaccine-specific memory cells. These features allowed development of a model for in silico enrichment of vaccine-specific sequences from the total repertoire. Identifying antigen-specific sequences from total repertoire data could aid our understanding B cell driven immunity, and be used for disease diagnostics and vaccine evaluation.

BCR repertoire sequencing: different patterns of B-cell activation after two Meningococcal vaccines

Next-generation sequencing was used to investigate the B-cell receptor heavy chain transcript repertoire of different B-cell subsets (naive, marginal zone (MZ), immunoglobulin M (IgM) memory and IgG memory) at baseline, and of plasma cells (PCs) 7 days following administration of serogroup ACWY meningococcal polysaccharide and protein-polysaccharide conjugate vaccines. Baseline B-cell subsets could be distinguished from each other using a small number of repertoire properties (clonality, mutation from germline and complementarity-determining region 3 (CDR3) length) that were conserved between individuals. However, analyzing the CDR3 amino-acid sequence (which is particularly important for antigen binding) of the baseline subsets showed few sequences shared between individuals. In contrast, day 7 PCs demonstrated nearly 10-fold greater sequence sharing between individuals than the baseline subsets, consistent with the PCs being induced by the vaccine antigen and sharing specificity for a more limited range of epitopes. By annotating PC sequences based on IgG subclass usage and mutation, and also comparing them with the sequences of the baseline cell subsets, we were able to identify different signatures after the polysaccharide and conjugate vaccines. PCs produced after conjugate vaccination were predominantly IgG1, and most related to IgG memory cells. In contrast, after polysaccharide vaccination, the PCs were predominantly IgG2, less mutated and were equally likely to be related to MZ, IgM memory or IgG memory cells. High-throughput B-cell repertoire sequencing thus provides a unique insight into patterns of B-cell activation not possible from more conventional measures of immunogenicity.

In-Depth Assessment of Within-Individual and Inter-Individual Variation in the B Cell Receptor Repertoire

High-throughput sequencing of the B cell receptor (BCR) repertoire can provide rapid characterization of the B cell response in a wide variety of applications in health, after vaccination and in infectious, inflammatory and immune-driven disease, and is starting to yield clinical applications. However, the interpretation of repertoire data is compromised by a lack of studies to assess the intra and inter-individual variation in the BCR repertoire over time in healthy individuals. We applied a standardized isotype-specific BCR repertoire deep sequencing protocol to a single highly sampled participant, and then evaluated the method in 9 further participants to comprehensively describe such variation. We assessed total repertoire metrics of mutation, diversity, VJ gene usage and isotype subclass usage as well as tracking specific BCR sequence clusters. There was good assay reproducibility (both in PCR amplification and biological replicates), but we detected striking fluctuations in the repertoire over time that we hypothesize may be due to subclinical immune activation. Repertoire properties were unique for each individual, which could partly be explained by a decrease in IgG2 with age, and genetic differences at the immunoglobulin locus. There was a small repertoire of public clusters (0.5, 0.3, and 1.4% of total IgA, IgG, and IgM clusters, respectively), which was enriched for expanded clusters containing sequences with suspected specificity toward antigens that should have been historically encountered by all participants through prior immunization or infection. We thus provide baseline BCR repertoire information that can be used to inform future study design, and aid in interpretation of results from these studies. Furthermore, our results indicate that BCR repertoire studies could be used to track changes in the public repertoire in and between populations that might relate to population immunity against infectious diseases, and identify the characteristics of inflammatory and immunological diseases.

CSN and CAVA: variant annotation tools for rapid, robust next-generation sequencing analysis in the clinical setting

BACKGROUND

Next-generation sequencing (NGS) offers unprecedented opportunities to expand clinical genomics. It also presents challenges with respect to integration with data from other sequencing methods and historical data. Provision of consistent, clinically applicable variant annotation of NGS data has proved difficult, particularly of indels, an important variant class in clinical genomics. Annotation in relation to a reference genome sequence, the DNA strand of coding transcripts and potential alternative variant representations has not been well addressed. Here we present tools that address these challenges to provide rapid, standardized, clinically appropriate annotation of NGS data in line with existing clinical standards.

METHODS

We developed a clinical sequencing nomenclature (CSN), a fixed variant annotation consistent with the principles of the Human Genome Variation Society (HGVS) guidelines, optimized for automated variant annotation of NGS data. To deliver high-throughput CSN annotation we created CAVA (Clinical Annotation of VAriants), a fast, lightweight tool designed for easy incorporation into NGS pipelines. CAVA allows transcript specification, appropriately accommodates the strand of a gene transcript and flags variants with alternative annotations to facilitate clinical interpretation and comparison with other datasets. We evaluated CAVA in exome data and a clinical BRCA1/BRCA2 gene testing pipeline.

RESULTS

CAVA generated CSN calls for 10,313,034 variants in the ExAC database in 13.44 hours, and annotated the ICR1000 exome series in 6.5 hours. Evaluation of 731 different indels from a single individual revealed 92 % had alternative representations in left aligned and right aligned data. Annotation of left aligned data, as performed by many annotation tools, would thus give clinically discrepant annotation for the 339 (46 %) indels in genes transcribed from the forward DNA strand. By contrast, CAVA provides the correct clinical annotation for all indels. CAVA also flagged the 370 indels with alternative representations of a different functional class, which may profoundly influence clinical interpretation. CAVA annotation of 50 BRCA1/BRCA2 gene mutations from a clinical pipeline gave 100 % concordance with Sanger data; only 8/25 BRCA2 mutations were correctly clinically annotated by other tools.

CONCLUSIONS

CAVA is a freely available tool that provides rapid, robust, high-throughput clinical annotation of NGS data, using a standardized clinical sequencing nomenclature.

Factors influencing success of clinical genome sequencing across a broad spectrum of disorders

To assess factors influencing the success of whole-genome sequencing for mainstream clinical diagnosis, we sequenced 217 individuals from 156 independent cases or families across a broad spectrum of disorders in whom previous screening had identified no pathogenic variants. We quantified the number of candidate variants identified using different strategies for variant calling, filtering, annotation and prioritization. We found that jointly calling variants across samples, filtering against both local and external databases, deploying multiple annotation tools and using familial transmission above biological plausibility contributed to accuracy. Overall, we identified disease-causing variants in 21% of cases, with the proportion increasing to 34% (23/68) for mendelian disorders and 57% (8/14) in family trios. We also discovered 32 potentially clinically actionable variants in 18 genes unrelated to the referral disorder, although only 4 were ultimately considered reportable. Our results demonstrate the value of genome sequencing for routine clinical diagnosis but also highlight many outstanding challenges.

High-throughput DNA Sequencing Identifies Novel CtIP (RBBP8) Variants in Muscle-invasive Bladder Cancer Patients

BACKGROUND

Germline mutations in DNA damage signalling and repair genes predispose individuals to cancer. Rare germline variants may also increase cancer risk and be predictive of outcomes following cancer treatments, but require high-throughput sequencing (HTS) for detection in large cohorts.

OBJECTIVE

To use a dual indexing system on a HTS platform to detect novel variants in CtIP (RBBP8) which may be associated with clinical outcomes following radiotherapy treatment for bladder cancer.

METHODS

All exons and flanking introns of CtIP were amplified from germline DNA from bladder cancer patients using seven primer pairs by automated long-range PCR. Amplicons were pooled, fragmented and ligated to adaptor sequences. One of 96 tag sequences was introduced at each end by PCR. Sequencing was performed on a single flow cell of an Illumina MiSeq. Reads were mapped by Stampy and variants called by Platypus. For phasing experiments, target regions were amplified and cloned for Sanger sequencing.

RESULTS

Of 201 samples, 160 were successfully amplified. Eleven CtIP variants were called, within the exons and 15 bp adjacent intronic DNA, including eight known variants from the 1000 Genomes project, plus three previously unreported variants now confirmed by Sanger sequencing. In two individuals, phasing experiments showed two variants of interest to be on separate alleles, likely to result in stronger impairment of gene function.

CONCLUSIONS

We have demonstrated proof of principle for dual indexing on 160 samples on one MiSeq flow cell sequencing surface, and show that for the CtIP gene multiplexing of up to 720 samples would provide sufficient coverage to achieve >98% detection power for rare germline variation, reducing HTS costs substantially.

scrm: efficiently simulating long sequences using the approximated coalescent with recombination

Motivation: Coalescent-based simulation software for genomic sequences allows the efficient in silico generation of short- and medium-sized genetic sequences. However, the simulation of genome-size datasets as produced by next-generation sequencing is currently only possible using fairly crude approximations.

Results: We present the sequential coalescent with recombination model (SCRM), a new method that efficiently and accurately approximates the coalescent with recombination, closing the gap between current approximations and the exact model. We present an efficient implementation and show that it can simulate genomic-scale datasets with an essentially correct linkage structure.

Availability and implementation: The open source implementation scrm is freely available at https://scrm.github.io under the conditions of the GPLv3 license.

Contact:staab@bio.lmu.de or gerton.lunter@well.ox.ac.uk.

Supplementary information:Supplementary data are available at Bioinformatics online.

Identification of antigen-specific B cell receptor sequences using public repertoire analysis

High-throughput sequencing allows detailed study of the BCR repertoire postimmunization, but it remains unclear to what extent the de novo identification of Ag-specific sequences from the total BCR repertoire is possible. A conjugate vaccine containing Haemophilus influenzae type b (Hib) and group C meningococcal polysaccharides, as well as tetanus toxoid (TT), was used to investigate the BCR repertoire of adult humans following immunization and to test the hypothesis that public or convergent repertoire analysis could identify Ag-specific sequences. A number of Ag-specific BCR sequences have been reported for Hib and TT, which made a vaccine containing these two Ags an ideal immunological stimulus. Analysis of identical CDR3 amino acid sequences that were shared by individuals in the postvaccine repertoire identified a number of known Hib-specific sequences but only one previously described TT sequence. The extension of this analysis to nonidentical, but highly similar, CDR3 amino acid sequences revealed a number of other TT-related sequences. The anti-Hib avidity index postvaccination strongly correlated with the relative frequency of Hib-specific sequences, indicating that the postvaccination public BCR repertoire may be related to more conventional measures of immunogenicity correlating with disease protection. Analysis of public BCR repertoire provided evidence of convergent BCR evolution in individuals exposed to the same Ags. If this finding is confirmed, the public repertoire could be used for rapid and direct identification of protective Ag-specific BCR sequences from peripheral blood.

Integrating mapping-, assembly- and haplotype-based approaches for calling variants in clinical sequencing applications

High-throughput DNA sequencing technology has transformed genetic research and is starting to make an impact on clinical practice. However, analyzing high-throughput sequencing data remains challenging, particularly in clinical settings where accuracy and turnaround times are critical. We present a new approach to this problem, implemented in a software package called Platypus. Platypus achieves high sensitivity and specificity for SNPs, indels and complex polymorphisms by using local de novo assembly to generate candidate variants, followed by local realignment and probabilistic haplotype estimation. It is an order of magnitude faster than existing tools and generates calls from raw aligned read data without preprocessing. We demonstrate the performance of Platypus in clinically relevant experimental designs by comparing with SAMtools and GATK on whole-genome and exome-capture data, by identifying de novo variation in 15 parent-offspring trios with high sensitivity and specificity, and by estimating human leukocyte antigen genotypes directly from variant calls.

Improved workflows for high throughput library preparation using the transposome-based Nextera system

BACKGROUND

The Nextera protocol, which utilises a transposome based approach to create libraries for Illumina sequencing, requires pure DNA template, an accurate assessment of input concentration and a column clean-up that limits its applicability for high-throughput sample preparation. We addressed the identified limitations to develop a robust workflow that supports both rapid and high-throughput projects also reducing reagent costs.

RESULTS

We show that an initial bead-based normalisation step can remove the need for quantification and improves sample purity. A 75% cost reduction was achieved with a low-volume modified protocol which was tested over genomes with different GC content to demonstrate its robustness. Finally we developed a custom set of index tags and primers which increase the number of samples that can simultaneously be sequenced on a single lane of an Illumina instrument.

CONCLUSIONS

We addressed the bottlenecks of Nextera library construction to produce a modified protocol which harnesses the full power of the Nextera kit and allows the reproducible construction of libraries on a high-throughput scale reducing the associated cost of the kit.

GAT: a simulation framework for testing the association of genomic intervals

MOTIVATION

A common question in genomic analysis is whether two sets of genomic intervals overlap significantly. This question arises, for example, when interpreting ChIP-Seq or RNA-Seq data in functional terms. Because genome organization is complex, answering this question is non-trivial.

SUMMARY

We present Genomic Association Test (GAT), a tool for estimating the significance of overlap between multiple sets of genomic intervals. GAT implements a null model that the two sets of intervals are placed independently of one another, but allows each set's density to depend on external variables, for example, isochore structure or chromosome identity. GAT estimates statistical significance based on simulation and controls for multiple tests using the false discovery rate.

AVAILABILITY

GAT's source code, documentation and tutorials are available at http://code.google.com/p/genomic-association-tester.

The origin, evolution, and functional impact of short insertion-deletion variants identified in 179 human genomes

Short insertions and deletions (indels) are the second most abundant form of human genetic variation, but our understanding of their origins and functional effects lags behind that of other types of variants. Using population-scale sequencing, we have identified a high-quality set of 1.6 million indels from 179 individuals representing three diverse human populations. We show that rates of indel mutagenesis are highly heterogeneous, with 43%–48% of indels occurring in 4.03% of the genome, whereas in the remaining 96% their prevalence is 16 times lower than SNPs. Polymerase slippage can explain upwards of three-fourths of all indels, with the remainder being mostly simple deletions in complex sequence. However, insertions do occur and are significantly associated with pseudo-palindromic sequence features compatible with the fork stalling and template switching (FoSTeS) mechanism more commonly associated with large structural variations. We introduce a quantitative model of polymerase slippage, which enables us to identify indel-hypermutagenic protein-coding genes, some of which are associated with recurrent mutations leading to disease. Accounting for mutational rate heterogeneity due to sequence context, we find that indels across functional sequence are generally subject to stronger purifying selection than SNPs. We find that indel length modulates selection strength, and that indels affecting multiple functionally constrained nucleotides undergo stronger purifying selection. We further find that indels are enriched in associations with gene expression and find evidence for a contribution of nonsense-mediated decay. Finally, we show that indels can be integrated in existing genome-wide association studies (GWAS); although we do not find direct evidence that potentially causal protein-coding indels are enriched with associations to known disease-associated SNPs, our findings suggest that the causal variant underlying some of these associations may be indels.

Unlocking the bottleneck in forward genetics using whole-genome sequencing and identity by descent to isolate causative mutations

Forward genetics screens with N-ethyl-N-nitrosourea (ENU) provide a powerful way to illuminate gene function and generate mouse models of human disease; however, the identification of causative mutations remains a limiting step. Current strategies depend on conventional mapping, so the propagation of affected mice requires non-lethal screens; accurate tracking of phenotypes through pedigrees is complex and uncertain; out-crossing can introduce unexpected modifiers; and Sanger sequencing of candidate genes is inefficient. Here we show how these problems can be efficiently overcome using whole-genome sequencing (WGS) to detect the ENU mutations and then identify regions that are identical by descent (IBD) in multiple affected mice. In this strategy, we use a modification of the Lander-Green algorithm to isolate causative recessive and dominant mutations, even at low coverage, on a pure strain background. Analysis of the IBD regions also allows us to calculate the ENU mutation rate (1.54 mutations per Mb) and to model future strategies for genetic screens in mice. The introduction of this approach will accelerate the discovery of causal variants, permit broader and more informative lethal screens to be used, reduce animal costs, and herald a new era for ENU mutagenesis.

A new isolation with migration model along complete genomes infers very different divergence processes among closely related great ape species

We present a hidden Markov model (HMM) for inferring gradual isolation between two populations during speciation, modelled as a time interval with restricted gene flow. The HMM describes the history of adjacent nucleotides in two genomic sequences, such that the nucleotides can be separated by recombination, can migrate between populations, or can coalesce at variable time points, all dependent on the parameters of the model, which are the effective population sizes, splitting times, recombination rate, and migration rate. We show by extensive simulations that the HMM can accurately infer all parameters except the recombination rate, which is biased downwards. Inference is robust to variation in the mutation rate and the recombination rate over the sequence and also robust to unknown phase of genomes unless they are very closely related. We provide a test for whether divergence is gradual or instantaneous, and we apply the model to three key divergence processes in great apes: (a) the bonobo and common chimpanzee, (b) the eastern and western gorilla, and (c) the Sumatran and Bornean orang-utan. We find that the bonobo and chimpanzee appear to have undergone a clear split, whereas the divergence processes of the gorilla and orang-utan species occurred over several hundred thousands years with gene flow stopping quite recently. We also apply the model to the Homo/Pan speciation event and find that the most likely scenario involves an extended period of gene flow during speciation.

Recessive mutations in SPTBN2 implicate β-III spectrin in both cognitive and motor development

β-III spectrin is present in the brain and is known to be important in the function of the cerebellum. Heterozygous mutations in SPTBN2, the gene encoding β-III spectrin, cause Spinocerebellar Ataxia Type 5 (SCA5), an adult-onset, slowly progressive, autosomal-dominant pure cerebellar ataxia. SCA5 is sometimes known as "Lincoln ataxia," because the largest known family is descended from relatives of the United States President Abraham Lincoln. Using targeted capture and next-generation sequencing, we identified a homozygous stop codon in SPTBN2 in a consanguineous family in which childhood developmental ataxia co-segregates with cognitive impairment. The cognitive impairment could result from mutations in a second gene, but further analysis using whole-genome sequencing combined with SNP array analysis did not reveal any evidence of other mutations. We also examined a mouse knockout of β-III spectrin in which ataxia and progressive degeneration of cerebellar Purkinje cells has been previously reported and found morphological abnormalities in neurons from prefrontal cortex and deficits in object recognition tasks, consistent with the human cognitive phenotype. These data provide the first evidence that β-III spectrin plays an important role in cortical brain development and cognition, in addition to its function in the cerebellum; and we conclude that cognitive impairment is an integral part of this novel recessive ataxic syndrome, Spectrin-associated Autosomal Recessive Cerebellar Ataxia type 1 (SPARCA1). In addition, the identification of SPARCA1 and normal heterozygous carriers of the stop codon in SPTBN2 provides insights into the mechanism of molecular dominance in SCA5 and demonstrates that the cell-specific repertoire of spectrin subunits underlies a novel group of disorders, the neuronal spectrinopathies, which includes SCA5, SPARCA1, and a form of West syndrome.

The bonobo genome compared with the chimpanzee and human genomes

Two African apes are the closest living relatives of humans: the chimpanzee (Pan troglodytes) and the bonobo (Pan paniscus). Although they are similar in many respects, bonobos and chimpanzees differ strikingly in key social and sexual behaviours, and for some of these traits they show more similarity with humans than with each other. Here we report the sequencing and assembly of the bonobo genome to study its evolutionary relationship with the chimpanzee and human genomes. We find that more than three per cent of the human genome is more closely related to either the bonobo or the chimpanzee genome than these are to each other. These regions allow various aspects of the ancestry of the two ape species to be reconstructed. In addition, many of the regions that overlap genes may eventually help us understand the genetic basis of phenotypes that humans share with one of the two apes to the exclusion of the other.

Accurate reconstruction of insertion-deletion histories by statistical phylogenetics

The Multiple Sequence Alignment (MSA) is a computational abstraction that represents a partial summary either of indel history, or of structural similarity. Taking the former view (indel history), it is possible to use formal automata theory to generalize the phylogenetic likelihood framework for finite substitution models (Dayhoff's probability matrices and Felsenstein's pruning algorithm) to arbitrary-length sequences. In this paper, we report results of a simulation-based benchmark of several methods for reconstruction of indel history. The methods tested include a relatively new algorithm for statistical marginalization of MSAs that sums over a stochastically-sampled ensemble of the most probable evolutionary histories. For mammalian evolutionary parameters on several different trees, the single most likely history sampled by our algorithm appears less biased than histories reconstructed by other MSA methods. The algorithm can also be used for alignment-free inference, where the MSA is explicitly summed out of the analysis. As an illustration of our method, we discuss reconstruction of the evolutionary histories of human protein-coding genes.

A fine-scale chimpanzee genetic map from population sequencing

To study the evolution of recombination rates in apes, we developed methodology to construct a fine-scale genetic map from high-throughput sequence data from 10 Western chimpanzees, Pan troglodytes verus. Compared to the human genetic map, broad-scale recombination rates tend to be conserved, but with exceptions, particularly in regions of chromosomal rearrangements and around the site of ancestral fusion in human chromosome 2. At fine scales, chimpanzee recombination is dominated by hotspots, which show no overlap with those of humans even though rates are similarly elevated around CpG islands and decreased within genes. The hotspot-specifying protein PRDM9 shows extensive variation among Western chimpanzees, and there is little evidence that any sequence motifs are enriched in hotspots. The contrasting locations of hotspots provide a natural experiment, which demonstrates the impact of recombination on base composition.

The human pancreatic islet transcriptome: expression of candidate genes for type 1 diabetes and the impact of pro-inflammatory cytokines

Type 1 diabetes (T1D) is an autoimmune disease in which pancreatic beta cells are killed by infiltrating immune cells and by cytokines released by these cells. Signaling events occurring in the pancreatic beta cells are decisive for their survival or death in diabetes. We have used RNA sequencing (RNA–seq) to identify transcripts, including splice variants, expressed in human islets of Langerhans under control conditions or following exposure to the pro-inflammatory cytokines interleukin-1β (IL-1β) and interferon-γ (IFN-γ). Based on this unique dataset, we examined whether putative candidate genes for T1D, previously identified by GWAS, are expressed in human islets. A total of 29,776 transcripts were identified as expressed in human islets. Expression of around 20% of these transcripts was modified by pro-inflammatory cytokines, including apoptosis- and inflammation-related genes. Chemokines were among the transcripts most modified by cytokines, a finding confirmed at the protein level by ELISA. Interestingly, 35% of the genes expressed in human islets undergo alternative splicing as annotated in RefSeq, and cytokines caused substantial changes in spliced transcripts. Nova1, previously considered a brain-specific regulator of mRNA splicing, is expressed in islets and its knockdown modified splicing. 25/41 of the candidate genes for T1D are expressed in islets, and cytokines modified expression of several of these transcripts. The present study doubles the number of known genes expressed in human islets and shows that cytokines modify alternative splicing in human islet cells. Importantly, it indicates that more than half of the known T1D candidate genes are expressed in human islets. This, and the production of a large number of chemokines and cytokines by cytokine-exposed islets, reinforces the concept of a dialog between pancreatic islets and the immune system in T1D. This dialog is modulated by candidate genes for the disease at both the immune system and beta cell level.

Pancreatic beta cells are destroyed by the immune system in type 1 diabetes mellitus, causing insulin dependence for life. Candidate genes for diabetes contribute to this process by acting both at the immune system and, as we suggest here, at the pancreatic beta cell level. We have utilized a novel technology, RNA sequencing, to define all transcripts expressed in human pancreatic islets under basal conditions and following exposure to cytokines, pro-inflammatory mediators that contribute to trigger diabetes. Our observations double the number of known genes present in human islets and indicate that >60% of the candidate genes for type 1 diabetes are expressed in beta cells. The data also show that pro-inflammatory cytokines modify alternative splicing in human islets, a process that may generate novel RNAs and proteins recognizable by the immune system. This, taken together with the findings that pancreatic beta cells themselves express and release many cytokines and chemokines (proteins that attract immune cells), indicates that early type 1 diabetes is characterized by a dialog between beta cells and the immune system. We suggest that candidate genes for diabetes function at least in part as “writers” for the beta cell words in this dialog.

Insights into hominid evolution from the gorilla genome sequence

Gorillas are humans’ closest living relatives after chimpanzees, and are of comparable importance for the study of human origins and evolution. Here we present the assembly and analysis of a genome sequence for the western lowland gorilla, and compare the whole genomes of all extant great ape genera. We propose a synthesis of genetic and fossil evidence consistent with placing the human-chimpanzee and human-chimpanzee-gorilla speciation events at approximately 6 and 10 million years ago (Mya). In 30% of the genome, gorilla is closer to human or chimpanzee than the latter are to each other; this is rarer around coding genes, indicating pervasive selection throughout great ape evolution, and has functional consequences in gene expression. A comparison of protein coding genes reveals approximately 500 genes showing accelerated evolution on each of the gorilla, human and chimpanzee lineages, and evidence for parallel acceleration, particularly of genes involved in hearing. We also compare the western and eastern gorilla species, estimating an average sequence divergence time 1.75 million years ago, but with evidence for more recent genetic exchange and a population bottleneck in the eastern species. The use of the genome sequence in these and future analyses will promote a deeper understanding of great ape biology and evolution.

A systematic survey of loss-of-function variants in human protein-coding genes

Genome-sequencing studies indicate that all humans carry many genetic variants predicted to cause loss of function (LoF) of protein-coding genes, suggesting unexpected redundancy in the human genome. Here we apply stringent filters to 2951 putative LoF variants obtained from 185 human genomes to determine their true prevalence and properties. We estimate that human genomes typically contain ~100 genuine LoF variants with ~20 genes completely inactivated. We identify rare and likely deleterious LoF alleles, including 26 known and 21 predicted severe disease-causing variants, as well as common LoF variants in nonessential genes. We describe functional and evolutionary differences between LoF-tolerant and recessive disease genes and a method for using these differences to prioritize candidate genes found in clinical sequencing studies.

The variant call format and VCFtools

SUMMARY

The variant call format (VCF) is a generic format for storing DNA polymorphism data such as SNPs, insertions, deletions and structural variants, together with rich annotations. VCF is usually stored in a compressed manner and can be indexed for fast data retrieval of variants from a range of positions on the reference genome. The format was developed for the 1000 Genomes Project, and has also been adopted by other projects such as UK10K, dbSNP and the NHLBI Exome Project. VCFtools is a software suite that implements various utilities for processing VCF files, including validation, merging, comparing and also provides a general Perl API.

AVAILABILITY

http://vcftools.sourceforge.net

The variant call format and VCFtools

Summary: The variant call format (VCF) is a generic format for storing DNA polymorphism data such as SNPs, insertions, deletions and structural variants, together with rich annotations. VCF is usually stored in a compressed manner and can be indexed for fast data retrieval of variants from a range of positions on the reference genome. The format was developed for the 1000 Genomes Project, and has also been adopted by other projects such as UK10K, dbSNP and the NHLBI Exome Project. VCFtools is a software suite that implements various utilities for processing VCF files, including validation, merging, comparing and also provides a general Perl API.

Availability:http://vcftools.sourceforge.net

Contact:rd@sanger.ac.uk