Poor sleep and shift work associate with increased blood pressure and inflammation in UK Biobank participants.

Disrupted circadian rhythms have been linked to an increased risk of hypertension and cardiovascular disease. However, many studies show inconsistent findings and are not sufficiently powered for targeted subgroup analyses. Using the UK Biobank cohort, we evaluate the association between circadian rhythm-disrupting behaviours, blood pressure (SBP, DBP) and inflammatory markers in >350,000 adults with European white British ancestry. The independent U-shaped relationship between sleep length and SBP/DBP is most prominent with a low inflammatory status. Poor sleep quality and permanent night shift work are also positively associated with SBP/DBP. Although fully adjusting for BMI in the linear regression model attenuated effect sizes, these associations remain significant. Two-sample Mendelian Randomisation (MR) analyses support a potential causal effect of long sleep, short sleep, chronotype, daytime napping and sleep duration on SBP/DBP. Thus, in the current study, we present a positive association between circadian rhythm-disrupting behaviours and SBP/DBP regulation in males and females that is largely independent of age.

New-onset atrial fibrillation prediction: the HARMS2-AF risk score.

AIMS: Lifestyle risk factors are a modifiable target in atrial fibrillation (AF) management. The relative contribution of individual lifestyle risk factors to AF development has not been described. Development and validation of an AF lifestyle risk score to identify individuals at risk of AF in the general population are the aims of the study. METHODS AND RESULTS: The UK Biobank (UKB) and Framingham Heart Study (FHS) are large prospective cohorts with outcomes measured >10 years. Incident AF was based on International Classification of Diseases version 10 coding. Prior AF was excluded. Cox proportional hazards regression identified independent AF predictors, which were evaluated in a multivariable model. A weighted score was developed in the UKB and externally validated in the FHS. Kaplan-Meier estimates ascertained the risk of AF development. Among 314 280 UKB participants, AF incidence was 5.7%, with median time to AF 7.6 years (interquartile range 4.5-10.2). Hypertension, age, body mass index, male sex, sleep apnoea, smoking, and alcohol were predictive variables (all P < 0.001); physical inactivity [hazard ratio (HR) 1.01, 95% confidence interval (CI) 0.96-1.05, P = 0.80] and diabetes (HR 1.03, 95% CI 0.97-1.09, P = 0·38) were not significant. The HARMS2-AF score had similar predictive performance [area under the curve (AUC) 0.782] to the unweighted model (AUC 0.802) in the UKB. External validation in the FHS (AF incidence 6.0% of 7171 participants) demonstrated an AUC of 0.757 (95% CI 0.735-0.779). A higher HARMS2-AF score (≥5 points) was associated with a heightened AF risk (score 5-9: HR 12.79; score 10-14: HR 38.70). The HARMS2-AF risk model outperformed the Framingham-AF (AUC 0.568) and ARIC (AUC 0.713) risk models (both P < 0.001) and was comparable to the CHARGE-AF risk score (AUC 0.754, P = 0.73). CONCLUSION: The HARMS2-AF score is a novel lifestyle risk score which may help identify individuals at risk of AF in the general community and assist population screening.

FT-GPI, a highly sensitive and accurate predictor of GPI-anchored proteins, reveals the composition and evolution of the GPI proteome in Plasmodium species.

BACKGROUND: Protozoan parasites are known to attach specific and diverse group of proteins to their plasma membrane via a GPI anchor. In malaria parasites, GPI-anchored proteins (GPI-APs) have been shown to play an important role in host-pathogen interactions and a key function in host cell invasion and immune evasion. Because of their immunogenic properties, some of these proteins have been considered as malaria vaccine candidates. However, identification of all possible GPI-APs encoded by these parasites remains challenging due to their sequence diversity and limitations of the tools used for their characterization. METHODS: The FT-GPI software was developed to detect GPI-APs based on the presence of a hydrophobic helix at both ends of the premature peptide. FT-GPI was implemented in C ++and applied to study the GPI-proteome of 46 isolates of the order Haemosporida. Using the GPI proteome of Plasmodium falciparum strain 3D7 and Plasmodium vivax strain Sal-1, a heuristic method was defined to select the most sensitive and specific FT-GPI software parameters. RESULTS: FT-GPI enabled revision of the GPI-proteome of P. falciparum and P. vivax, including the identification of novel GPI-APs. Orthology- and synteny-based analyses showed that 19 of the 37 GPI-APs found in the order Haemosporida are conserved among Plasmodium species. Our analyses suggest that gene duplication and deletion events may have contributed significantly to the evolution of the GPI proteome, and its composition correlates with speciation. CONCLUSION: FT-GPI-based prediction is a useful tool for mining GPI-APs and gaining further insights into their evolution and sequence diversity. This resource may also help identify new protein candidates for the development of vaccines for malaria and other parasitic diseases.

The impact of coffee subtypes on incident cardiovascular disease, arrhythmias, and mortality: long-term outcomes from the UK Biobank.

AIMS: Epidemiological studies report the beneficial effects of habitual coffee consumption on incident arrhythmia, cardiovascular disease (CVD), and mortality. However, the impact of different coffee preparations on cardiovascular outcomes and survival is largely unknown. The aim of this study was to evaluate associations between coffee subtypes on incident outcomes, utilizing the UK Biobank. METHODS AND RESULTS: Coffee subtypes were defined as decaffeinated, ground, and instant, then divided into 0, <1, 1, 2-3, 4-5, and >5 cups/day, and compared with non-drinkers. Cardiovascular disease included coronary heart disease, cardiac failure, and ischaemic stroke. Cox regression modelling with hazard ratios (HRs) assessed associations with incident arrhythmia, CVD, and mortality. Outcomes were determined through ICD codes and death records. A total of 449 563 participants (median 58 years, 55.3% females) were followed over 12.5 ± 0.7 years. Ground and instant coffee consumption was associated with a significant reduction in arrhythmia at 1-5 cups/day but not for decaffeinated coffee. The lowest risk was 4-5 cups/day for ground coffee [HR 0.83, confidence interval (CI) 0.76-0.91, P < 0.0001] and 2-3 cups/day for instant coffee (HR 0.88, CI 0.85-0.92, P < 0.0001). All coffee subtypes were associated with a reduction in incident CVD (the lowest risk was 2-3 cups/day for decaffeinated, P = 0.0093; ground, P < 0.0001; and instant coffee, P < 0.0001) vs. non-drinkers. All-cause mortality was significantly reduced for all coffee subtypes, with the greatest risk reduction seen with 2-3 cups/day for decaffeinated (HR 0.86, CI 0.81-0.91, P < 0.0001); ground (HR 0.73, CI 0.69-0.78, P < 0.0001); and instant coffee (HR 0.89, CI 0.86-0.93, P < 0.0001). CONCLUSION: Decaffeinated, ground, and instant coffee, particularly at 2-3 cups/day, were associated with significant reductions in incident CVD and mortality. Ground and instant but not decaffeinated coffee was associated with reduced arrhythmia.

Genomic risk scores for juvenile idiopathic arthritis and its subtypes.

OBJECTIVES: Juvenile idiopathic arthritis (JIA) is an autoimmune disease and a common cause of chronic disability in children. Diagnosis of JIA is based purely on clinical symptoms, which can be variable, leading to diagnosis and treatment delays. Despite JIA having substantial heritability, the construction of genomic risk scores (GRSs) to aid or expedite diagnosis has not been assessed. Here, we generate GRSs for JIA and its subtypes and evaluate their performance. METHODS: We examined three case/control cohorts (UK, US-based and Australia) with genome-wide single nucleotide polymorphism (SNP) genotypes. We trained GRSs for JIA and its subtypes using lasso-penalised linear models in cross-validation on the UK cohort, and externally tested it in the other cohorts. RESULTS: The JIA GRS alone achieved cross-validated area under the receiver operating characteristic curve (AUC)=0.670 in the UK cohort and externally-validated AUCs of 0.657 and 0.671 in the US-based and Australian cohorts, respectively. In logistic regression of case/control status, the corresponding odds ratios (ORs) per standard deviation (SD) of GRS were 1.831 (1.685 to 1.991) and 2.008 (1.731 to 2.345), and were unattenuated by adjustment for sex or the top 10 genetic principal components. Extending our analysis to JIA subtypes revealed that the enthesitis-related JIA had both the longest time-to-referral and the subtype GRS with the strongest predictive capacity overall across data sets: AUCs 0.82 in UK; 0.84 in Australian; and 0.70 in US-based. The particularly common oligoarthritis JIA also had a GRS that outperformed those for JIA overall, with AUCs of 0.72, 0.74 and 0.77, respectively. CONCLUSIONS: A GRS for JIA has potential to augment clinical JIA diagnosis protocols, prioritising higher-risk individuals for follow-up and treatment. Consistent with JIA heterogeneity, subtype-specific GRSs showed particularly high performance for enthesitis-related and oligoarthritis JIA.

CSAM: Compressed SAM format.

MOTIVATION: Next generation sequencing machines produce vast amounts of genomic data. For the data to be useful, it is essential that it can be stored and manipulated efficiently. This work responds to the combined challenge of compressing genomic data, while providing fast access to regions of interest, without necessitating decompression of whole files. RESULTS: We describe CSAM (Compressed SAM format), a compression approach offering lossless and lossy compression for SAM files. The structures and techniques proposed are suitable for representing SAM files, as well as supporting fast access to the compressed information. They generate more compact lossless representations than BAM, which is currently the preferred lossless compressed SAM-equivalent format; and are self-contained, that is, they do not depend on any external resources to compress or decompress SAM files. AVAILABILITY AND IMPLEMENTATION: An implementation is available at https://github.com/rcanovas/libCSAM CONTACT: canovas-ba@lirmm.frSupplementary Information: Supplementary data is available at Bioinformatics online.

Lossy compression of quality scores in genomic data.

MOTIVATION: Next-generation sequencing technologies are revolutionizing medicine. Data from sequencing technologies are typically represented as a string of bases, an associated sequence of per-base quality scores and other metadata, and in aggregate can require a large amount of space. The quality scores show how accurate the bases are with respect to the sequencing process, that is, how confident the sequencer is of having called them correctly, and are the largest component in datasets in which they are retained. Previous research has examined how to store sequences of bases effectively; here we add to that knowledge by examining methods for compressing quality scores. The quality values originate in a continuous domain, and so if a fidelity criterion is introduced, it is possible to introduce flexibility in the way these values are represented, allowing lossy compression over the quality score data. RESULTS: We present existing compression options for quality score data, and then introduce two new lossy techniques. Experiments measuring the trade-off between compression ratio and information loss are reported, including quantifying the effect of lossy representations on a downstream application that carries out single nucleotide polymorphism and insert/deletion detection. The new methods are demonstrably superior to other techniques when assessed against the spectrum of possible trade-offs between storage required and fidelity of representation. AVAILABILITY AND IMPLEMENTATION: An implementation of the methods described here is available at https://github.com/rcanovas/libCSAM. CONTACT: rcanovas@student.unimelb.edu.au SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.