Search

Background: Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. Results: To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry metaanalysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3–5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. Conclusions: Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk., United States Department of Health & Human Services National Institutes of Health (NIH) - USA NIH National Heart Lung & Blood Institute (NHLBI) R01HL127564 R01HL142711, Wellcome Trust 201543/B/16/Z 202802/Z/16/Z, European Commission HEALTH-F2-2013-601456 608765 786833, TriPartite Immunometabolism Consortium [TrIC]-Novo Nordisk Foundation NNF15CC0018486, American Diabetes Association 1-19-ICTS-068, Academy of Finland 312062 Finnish Foundation for Cardiovascular Research, Sigrid Juselius Foundation, Finnish innovation fund Sitra (EW) Finska Lakaresallskapet, American Heart Association 15POST24470131 17POST33650016, University of Bristol NIHR Biomedical Research Centre BRC-1215-2001, MRC & WT 217065/Z/19/Z, UK Research & Innovation (UKRI), Medical Research Council UK (MRC) MC_UU_00011, CRUK Integrative Cancer Epidemiology Programme C18281/A19169, Medical Research Council UK (MRC) MC_UU_00011/1, UK National Institute for Health Research Academic Clinical Fellowship, American Heart Association 18CDA34110116, Miguel Servet contract from the ISCIII Spanish Health Institute CP17/00142, European Social Fund (ESF), Westlake Education Foundation, British Heart Foundation FS/14/66/3129 Z01HG200362 R01HL142302 R01HL105756

Purpose To investigate whether exposure to systemic antibiotics influences the risk of developing type 2 diabetes and overweight/obesity. Methods The study sample comprised 2209 (110 with incident diabetes) participants from the population-based Cardiovascular Risk in Young Finns Study (YFS) aged 24–39 years in 2001. The exposure was national linked register data on purchased antibiotic courses between 1993 and 2001. Clinical examinations including BMI were conducted in 2001, 2007 and 2011. Participants with prevalent diabetes in 2001 were excluded. Data on type 2 diabetes was also obtained from two national registers until 2017. Data from four population-based National FINRISK studies were used for replication (N = 24,674, 1866 with incident diabetes). Results Prior antibiotic exposure (> 5 versus 0–1 antibiotic courses) was associated with subsequent type 2 diabetes in both YFS (OR 2.29; 95%CI 1.33–3.96) and FINRISK (HR 1.73; 95%CI 1.51–1.99). An increased risk for type 2 diabetes was observed in YFS (OR 1.043; 95%CI 1.013–1.074) and FINRISK (HR 1.022; 95%CI 1.016–1.029) per course. Exposure to antibiotics increased the risk of overweight/obesity (BMI > 25 kg/m2) after a 10-year follow-up in YFS (OR 1.043; 95%CI 1.019–1.068) and in FINRISK (OR 1.023; 95%CI 1.018–1.029) at baseline per antibiotic course. Adjustments for confounders from early life in YFS and at baseline in FINRISK, including BMI, socioeconomic status, smoking, insulin, blood pressure, and physical activity, did not appreciably alter the findings. Conclusion Our results show that exposure to antibiotics was associated with increased risk for future type 2 diabetes and overweight/obesity and support judicious antibiotic prescribing.

Objectives Little is known about the length of working life, even though it is a key indicator for policy-makers. In this paper, we study how the length of working life at age 50 has developed in the United States from a cohort perspective. Methods We use a large longitudinal sample of U.S. Social Security register data that covers close to 1.7 million individuals of the cohorts born from 1920 to 1965. For all of these cohorts, we study the employment trajectories and working life expectancy (WLE) at age 50 by gender and nativity (native-born/foreign-born). For the cohorts with employment trajectories that are only incompletely observed, we borrow information from older cohorts to predict their WLE. Results The length of working life has been increasing for the native-born males and females, and the younger cohorts worked longer than the older cohorts. However, WLE might soon peak, and then stall. The gap in WLE between the native-born and the foreign-born has increased over time, although latter group might be able to catch up in the coming years. Discussion Our findings show that studying employment from a cohort perspective reveals crucial information about patterns of working life. The future development of the length of working life should be a major concern for policy-makers.

STUDY QUESTION Does the risk of low birth weight and premature birth increase with age among mothers who conceive through medically assisted reproduction (MAR)? SUMMARY ANSWER Among MAR mothers, the risk of poorer birth outcomes does not increase with maternal age at birth except at very advanced maternal ages (40+). WHAT IS KNOWN ALREADY The use of MAR treatments has been increasing over the last few decades and is especially diffused among women who conceive at older ages. Although advanced maternal age is a well-known risk factor for adverse birth outcomes in natural pregnancies, only a few studies have directly analysed the maternal age gradient in birth outcomes for MAR mothers. STUDY DESIGN, SIZE, DURATION The base dataset was a 20% random sample of households with at least one child aged 0–14 at the end of 2000, drawn from the Finnish population register and other administrative registers. This study included children who were born in 1995–2000, because the information on whether a child was conceived through MAR or naturally was available only from 1995 onwards. PARTICIPANTS/MATERIALS, SETTING, METHODS The outcome measures were whether the child had low birth weight (LBW, MAIN RESULTS AND THE ROLE OF CHANCE A total of 56 026 children, 2624 of whom were conceived through MAR treatments, were included in the study. Among the mothers who used MAR to conceive, maternal age was not associated with an increased risk of LBW (the overall prevalence was 12.6%) at ages 25–39. For example, compared to the risk of LBW at ages 30–34, the risk was 0.22 percentage points lower (95% CI: −3.2, 2.8) at ages 25–29 and was 1.34 percentage points lower (95% CI: −4.5, 1.0) at ages 35–39. The risk of LBW was increased only at maternal ages ≥40 (six percentage points, 95% CI: 0.2, 12). Adjustment for maternal characteristics only marginally attenuated these associations. In contrast, among the mothers who conceived naturally, the results showed a clear age gradient. For example, compared to the risk of LBW (the overall prevalence was 3.3%) at maternal ages 30–34, the risk was 1.1 percentage points higher (95% CI: 0.6, 1.6) at ages 35–39 and was 1.5 percentage points higher (95% CI: 0.5, 2.6) at ages ≥40. The results were similar for preterm births. LIMITATIONS, REASON FOR CAUTION A limited number of confounders were included in the study because of the administrative nature of the data used. Our ability to reliably distinguish mothers based on MAR treatment type was also limited. WIDER IMPLICATIONS OF THE FINDINGS This is the first study to analyse the maternal age gradient in the risk of adverse birth outcomes among children conceived through MAR using data from a nationally representative sample and controlling for important maternal health and socio-economic characteristics. This topic is of considerable importance in light of the widespread and increasing use of MAR treatments. STUDY FUNDING/COMPETING INTEREST(S) Funding for this project was provided by the European Research Council (grant no. 803959 MARTE to Alice Goisis and grant no. 336475 COSTPOST to Mikko Myrskylä). E.S. reports personal fees from Theramex, personal fees from Merck Serono, personal fees from Health Reimbursement Arrangement, non-financial support from Merck Serono and grants from Ferring, grants from Theramex, outside the submitted work. The remaining authors have no competing interests. TRIAL REGISTRTION NUMBER N/A

Legionella pneumophila is the most common cause of the severe respiratory infection known as Legionnaires’ disease. However, the microorganism is typically a symbiont of free-living amoeba, and our understanding of the bacterial factors that determine human pathogenicity is limited. Here we carried out a population genomic study of 902 L. pneumophila isolates from human clinical and environmental samples to examine their genetic diversity, global distribution and the basis for human pathogenicity. We find that the capacity for human disease is representative of the breadth of species diversity although some clones are more commonly associated with clinical infections. We identified a single gene (lag-1) to be most strongly associated with clinical isolates. lag-1, which encodes an O-acetyltransferase for lipopolysaccharide modification, has been distributed horizontally across all major phylogenetic clades of L. pneumophila by frequent recent recombination events. The gene confers resistance to complement-mediated killing in human serum by inhibiting deposition of classical pathway molecules on the bacterial surface. Furthermore, acquisition of lag-1 inhibits complement-dependent phagocytosis by human neutrophils, and promoted survival in a mouse model of pulmonary legionellosis. Thus, our results reveal L. pneumophila genetic traits linked to disease and provide a molecular basis for resistance to complement-mediated killing., The bacterium Legionella pneumophila can cause severe respiratory infection, but is typically a symbiont of free-living amoeba. Here, the authors analyse the genomes of 902 clinical and environmental isolates, and identify a bacterial gene that is strongly associated with human infection and confers resistance to complement-mediated killing.

The mechanisms underlying how monozygotic (or identical) twins arise are yet to be determined. Here, the authors investigate this in an epigenome-wide association study, showing that monozygotic twinning has a characteristic DNA methylation signature in adult somatic tissues. Monozygotic (MZ) twins and higher-order multiples arise when a zygote splits during pre-implantation stages of development. The mechanisms underpinning this event have remained a mystery. Because MZ twinning rarely runs in families, the leading hypothesis is that it occurs at random. Here, we show that MZ twinning is strongly associated with a stable DNA methylation signature in adult somatic tissues. This signature spans regions near telomeres and centromeres, Polycomb-repressed regions and heterochromatin, genes involved in cell-adhesion, WNT signaling, cell fate, and putative human metastable epialleles. Our study also demonstrates a never-anticipated corollary: because identical twins keep a lifelong molecular signature, we can retrospectively diagnose if a person was conceived as monozygotic twin., Netherlands Organization for Scientific Research (NWO); Biobanking and Biomolecular Research Infrastructure (BBMRI–NL); NWO Large Scale infrastructures; X-Omics

Understanding the mortality impact of COVID-19 requires not only counting the dead, but analyzing how premature the deaths are. We calculate years of life lost (YLL) across 81 countries due to COVID-19 attributable deaths, and also conduct an analysis based on estimated excess deaths. We find that over 20.5 million years of life have been lost to COVID-19 globally. As of January 6, 2021, YLL in heavily affected countries are 2–9 times the average seasonal influenza; three quarters of the YLL result from deaths in ages below 75 and almost a third from deaths below 55; and men have lost 45% more life years than women. The results confirm the large mortality impact of COVID-19 among the elderly. They also call for heightened awareness in devising policies that protect vulnerable demographics losing the largest number of life-years. The research funding included grant LCF/PR/GN12/50250002 from La Caixa Foundation for Héctor Pifarré i Arolas and Guillem López Casasnovas. Enrique Acosta is supported by the Social Sciences and Humanities Research Council and the Fonds de recherche du Québec - Société et culture. Catia Nicodemo is supported by the University of Oxford’s COVID-19 Research Response Fund, Number 0009139. The views expressed are those of the author and not necessarily those of the University of Oxford.

Objective Growth-based determination of pubertal onset timing would be cheap and practical. We aimed to determine this timing based on pubertal growth markers. Secondary aims were to estimate the differences in growth between cohorts and identify the role of overweight in onset timing. Design This multicohort study includes data from three Finnish cohorts—the Type 1 Diabetes Prediction and Prevention (DIPP, N = 2,825) Study, the Special Turku Coronary Risk Factor Intervention Project (STRIP, N = 711), and the Boy cohort (N = 66). Children were monitored for growth and Tanner staging (except in DIPP). Methods The growth data were analyzed using a Super-Imposition by Translation And Rotation growth curve model, and pubertal onset analyses were run using a time-to-pubertal onset model. Results The time-to-pubertal onset model used age at peak height velocity (aPHV), peak height velocity (PHV), and overweight status as covariates, with interaction between aPHV and overweight status for girls, and succeeded in determining the onset timing. Cross-validation showed a good agreement (71.0% for girls, 77.0% for boys) between the observed and predicted onset timings. Children in STRIP were taller overall (girls: 1.7 [95% CI: 0.9, 2.5] cm, boys: 1.0 [0.3, 2.2] cm) and had higher PHV values (girls: 0.13 [0.02, 0.25] cm/year, boys: 0.35 [0.21, 0.49] cm/year) than those in DIPP. Boys in the Boy cohort were taller (2.3 [0.3, 4.2] cm) compared with DIPP. Overweight girls showed pubertal onset at 1.0 [0.7, 1.4] year earlier compared with other girls. In boys, there was no such difference. Conclusions The novel modeling approach provides an opportunity to evaluate the Tanner breast/genital stage–based pubertal onset timing in cohort studies including longitudinal data on growth but lacking pubertal follow-up.

Enterococcus faecalis is a commensal and nosocomial pathogen, which is also ubiquitous in animals and insects, representing a classical generalist microorganism. Here, we study E. faecalis isolates ranging from the pre-antibiotic era in 1936 up to 2018, covering a large set of host species including wild birds, mammals, healthy humans, and hospitalised patients. We sequence the bacterial genomes using short- and long-read techniques, and identify multiple extant hospital-associated lineages, with last common ancestors dating back as far as the 19th century. We find a population cohesively connected through homologous recombination, a metabolic flexibility despite a small genome size, and a stable large core genome. Our findings indicate that the apparent hospital adaptations found in hospital-associated E. faecalis lineages likely predate the “modern hospital” era, suggesting selection in another niche, and underlining the generalist nature of this nosocomial pathogen., Enterococcus faecalis is a commensal microorganism of animals, insects and humans, but also a nosocomial pathogen. Here, the authors analyse genomic sequences from E. faecalis isolates from animals and humans, and find that the last common ancestors of multiple hospital-associated lineages date to the pre-antibiotic era.

Background The US rural disadvantage in life expectancy (LE) relative to urban areas has grown over time. We measured the contribution of cardiovascular disease (CVD), drug-overdose deaths (DODs) and other major causes of death to LE trends in rural and urban counties and the rural–urban LE gap. Methods Counterfactual life tables and cause-of-death decompositions were constructed using data on all US deaths in 1999–2019 (N = 51 998 560) from the Centers for Disease Control and Prevention. Results During 1999–2009, rural and urban counties experienced robust LE gains, but urban LE increased by 1.19 years more in women and 0.86 years more in men compared with rural LE. During 2010–2019, rural counties experienced absolute declines in LE (women −0.20, men −0.30 years), whereas urban counties experienced modest increases (women 0.55, men 0.29 years). Counterfactual analysis showed that slowed CVD-mortality declines, particularly in ages 65+ years, were the main reason why rural LE stopped increasing after 2010. However, slow progress in CVD-mortality influenced LE trends more in urban areas. If CVD-mortality had continued to decline at its pre-2010 pace, the rural–urban LE gap would have grown even more post 2010. DODs and other causes of death also contributed to the LE trends and differences in each period, but their impact in comparison to that of CVD was relatively small. Conclusions Rural disadvantage in LE continues to grow, but at a slower pace than pre 2010. This slowdown is more attributable to adverse trends in CVD and DOD mortality in urban areas than improvements in rural areas.

Background From 2015–2016 through 2017–2018, injectable, trivalent inactivated influenza vaccines (IIV3) and a nasal spray, tetravalent live-attenuated influenza vaccine (LAIV4) were used in parallel in Finland. To understand how well vaccination with each vaccine type protected children against influenza under real-life conditions, vaccine effectiveness in 2-year-olds was estimated for all 3 seasons. Methods Each season, a nationwide register-based cohort study was conducted. The study population comprised 60 088, 60 860, and 60 345 children in 2015–2016, 2016–2017, and 2017–2018, respectively. Laboratory-confirmed influenza was the study outcome. Seasonal influenza vaccination with either LAIV4 or IIV3 was the time-dependent exposure of interest. Vaccine effectiveness was defined as 1 minus the hazard ratio comparing vaccinated with unvaccinated children. Results From 2015–2016 through 2017–2018, the effectiveness of LAIV4 against influenza of any virus type was estimated at 54.2% (95% confidence interval, 32.2–69.0%), 20.3% (−12.7%, 43.6%), and 30.5% (10.9–45.9%); the corresponding effectiveness of IIV3 was 77.2% (48.9–89.8%), 24.5% (−29.8%, 56.1%), and −20.1% (−61.5%, 10.7%). Neither influenza vaccine clearly excelled in protecting children. The LAIV4 effectiveness against type B was greater than against type A and greater than the IIV3 effectiveness against type B. Conclusions To understand how influenza vaccines could be improved, vaccine effectiveness must be analyzed by vaccine and virus type. Effectiveness estimates also expressing overall protection levels are needed to guide individual and programmatic decision-making processes. Supported by this analysis, the vaccination program in Finland now recommends LAIV4 and injectable, tetravalent inactivated influenza vaccines replacing IIV3.

This article reconsiders the role of social origin in health selection by examining whether parental education moderates the association between early health and educational attainment and whether health problems mediate the intergenerational transmission of education. We used longitudinal register data on Finns born in 1986–1991 (n = 352,899). We measured the completion of secondary and tertiary education until age 27 and used data on hospital care and medication reimbursements to assess chronic somatic conditions, frequent infections, and mental disorders at ages 10–16. We employed linear probability models to estimate the associations between different types of health problems and educational outcomes and to examine moderation by parental education, both overall in the population and comparing siblings with and without health problems. Finally, we performed a mediation analysis with g-computation to simulate whether a hypothetical eradication of health problems would weaken the association between parental and offspring education. All types of health problems reduced the likelihood of secondary education, but mental disorders were associated with the largest reductions. Among those with secondary education, there was further evidence of selection to tertiary education. High parental education buffered against the negative impact of mental disorders on completing secondary education but exacerbated it in the case of tertiary education. The simulated eradication of health problems slightly reduced disparities by parental education in secondary education (up to 10%) but increased disparities in tertiary education (up to 2%). Adolescent health problems and parental education are strong but chiefly independent predictors of educational attainment. Electronic supplementary material The online version of this article (10.1007/s13524-020-00919-y) contains supplementary material, which is available to authorized users.

Recombination is an essential part of meiosis as it facilitates novel combinations of homologous chromosomes, following their successive segregation in offspring. Founder population size, demographic changes (eg. population bottlenecks or rapid expansion) can lead to variation in recombination rates across different populations. Previous research has shown that using population-specific reference panels has a significant effect on downstream population genomic analysis like haplotype phasing, genotype imputation and association, especially in the context of population isolates. Here, we developed a high-resolution recombination rate mapping at 10kb and 50kb scale using high-coverage (20-30x) whole-genome sequencing (WGS) data of 55 family trios from Finland and compared it to recombination rates of non-Finnish Europeans (NFE). We then tested the downstream effects of the population-specific recombination rates in statistical phasing and genotype imputation in Finns as compared to the same analyses performed by using the NFE-based recombination rates. Finnish recombination rates have a moderately high correlation (Spearman rho =0.67-0.79) with non-Finnish Europeans, although on average (across all autosomal chromosomes), Finnish rates (2.268±0.4209 cM/Mb) are 12-14% lower than NFE (2.641±0.5032 cM/Mb). Population-specific effective population sizes were found to have no significant effect in haplotype phasing accuracy (switch error rates, SER″2%) and average imputation concordance rates (with reference panels in phasing: rates were 97-98% for common, 92-96% for low frequency and 78-90% for rare variants) irrespective of the recombination map used. Similarly, we found no effect of population-specific (Finnish) recombination maps in phasing with comparable switch error rates (SER) across autosomes when compared to HapMap based maps. Our results suggest that downstream population genomic analyses like haplotype phasing and genotype imputation mostly depend on population-specific contexts like appropriate reference panels and their sample size, but not on population-specific recombination maps or effective population sizes. Currently, available HapMap recombination maps seem robust for population-specific phasing and imputation pipelines, even in the context of relatively isolated populations like Finland.

openaire: EC/H2020/742158/EU//SCARABEE Covariance-based discovery of polymorphisms under co-selective pressure or epistasis has received considerable recent attention in population genomics. Both statistical modeling of the population level covariation of alleles across the chromosome and model-free testing of dependencies between pairs of polymorphisms have been shown to successfully uncover patterns of selection in bacterial populations. Here we introduce a model-free method, SpydrPick, whose computational efficiency enables analysis at the scale of pan-genomes of many bacteria. SpydrPick incorporates an efficient correction for population structure, which adjusts for the phylogenetic signal in the data without requiring an explicit phylogenetic tree. We also introduce a new type of visualization of the results similar to the Manhattan plots used in genome-wide association studies, which enables rapid exploration of the identified signals of co-evolution. Simulations demonstrate the usefulness of our method and give some insight to when this type of analysis is most likely to be successful. Application of the method to large population genomic datasets of two major human pathogens, Streptococcus pneumoniae and Neisseria meningitidis, revealed both previously identified and novel putative targets of co-selection related to virulence and antibiotic resistance, highlighting the potential of this approach to drive molecular discoveries, even in the absence of phenotypic data.

Background The first few weeks’ post-imprisonment are associated with high mortality, particularly among individuals with a history of substance use. Excess risk may vary by societal context due to a range of penal systems and substance use patterns. Using data on Finnish individuals who had sought treatment for substance use, we studied the association between criminal sanctions with cause-specific mortality. Methods The database contained 10887 individuals who had sought treatment between 1990 and 2009. Their treatment data were combined with register data on imprisonments and community sanctions and weekly mortality between 1992 and 2015. Mortality was analysed using discrete-time survival models. We controlled for age and sociodemographic factors, and analysed whether education, type of substance used and the type of latest sentence modified the associations. Findings Mortality was high in the first two weeks after sanctions (all-cause odds ratio [OR] 2.61, 95% confidence interval [CI] 1.67-4.07; drug-related deaths OR 8.52, 95% CI 4.64-15.7). Excess risk declined over time (OR after 12 weeks: 1.19, 95% CI 1.07-1.31). Most of the excess risk was attributable to external causes. Mortality was low during imprisonment, but not during community sanctions. The patterns were similar by level of education, substance use and the type of latest sentence. Conclusions Community sanctions were not associated with mortality among people with substance use disorders. Mortality was low during imprisonment, but high post-release. Criminal sanctions should be better utilised as intervention touchpoints and follow-up resources should target prisoners with substance use treatment history to reduce post-release mortality.

Identification of intracellular pathways that play key roles in cancer progression and drug resistance is a prerequisite for developing targeted cancer treatments. The era of personalized medicine calls for computational methods that can function with one sample or very small set of samples. Developing such methods is challenging because standard statistical approaches pose several limiting assumptions, such as number of samples, that prevent their application when n approaches to one. We have developed a novel pathway analysis method called PerPAS to estimate pathway activity at a single sample level by integrating pathway topology and transcriptomics data. In addition, PerPAS is able to identify altered pathways between cancer and control samples as well as to identify key nodes that contribute to the pathway activity. In our case study using breast cancer data, we show that PerPAS can identify highly altered pathways that are associated with patient survival. PerPAS identified four pathways that were associated with patient survival and were successfully validated in three independent breast cancer cohorts. In comparison to two other pathway analysis methods that function at a single sample level, PerPAS had superior performance in both synthetic and breast cancer expression datasets. PerPAS is a free R package (http://csbi.ltdk.helsinki.fi/pub/czliu/perpas/).

Coupling dense genotype data with new computational methods offers unprecedented opportunities for individual-level ancestry estimation once geographically precisely defined reference data sets become available. We study such a reference data set for Finland containing 2376 such individuals from the FINRISK Study survey of 1997 both of whose parents were born close to each other. This sampling strategy focuses on the population structure present in Finland before the 1950s. By using the recent haplotype-based methods ChromoPainter (CP) and FineSTRUCTURE (FS) we reveal a highly geographically clustered genetic structure in Finland and report its connections to the settlement history as well as to the current dialectal regions of the Finnish language. The main genetic division within Finland shows striking concordance with the 1323 borderline of the treaty of Nöteborg. In general, we detect genetic substructure throughout the country, which reflects stronger regional genetic differences in Finland compared to, for example, the UK, which in a similar analysis was dominated by a single unstructured population. We expect that similar population genetic reference data sets will become available for many more populations in the near future with important applications, for example, in forensic genetics and in genetic association studies. With this in mind, we report those extensions of the CP + FS approach that we found most useful in our analyses of the Finnish data.

The control of the human body sway by the central nervous system, muscles, and conscious brain is of interest since body sway carries information about the physiological status of a person. Several models have been proposed to describe body sway in an upright standing position, however, due to the statistical intractability of the more realistic models, no formal parameter inference has previously been conducted and the expressive power of such models for real human subjects remains unknown. Using the latest advances in Bayesian statistical inference for intractable models, we fitted a nonlinear control model to posturographic measurements, and we showed that it can accurately predict the sway characteristics of both simulated and real subjects. Our method provides a full statistical characterization of the uncertainty related to all model parameters as quantified by posterior probability density functions, which is useful for comparisons across subjects and test settings. The ability to infer intractable control models from sensor data opens new possibilities for monitoring and predicting body status in health applications.

Recent advances in the scale and diversity of population genomic datasets for bacteria now provide the potential for genome-wide patterns of co-evolution to be studied at the resolution of individual bases. Here we describe a new statistical method, genomeDCA, which uses recent advances in computational structural biology to identify the polymorphic loci under the strongest co-evolutionary pressures. We apply genomeDCA to two large population data sets representing the major human pathogens Streptococcus pneumoniae (pneumococcus) and Streptococcus pyogenes (group A Streptococcus). For pneumococcus we identified 5,199 putative epistatic interactions between 1,936 sites. Over three-quarters of the links were between sites within the pbp2x, pbp1a and pbp2b genes, the sequences of which are critical in determining non-susceptibility to beta-lactam antibiotics. A network-based analysis found these genes were also coupled to that encoding dihydrofolate reductase, changes to which underlie trimethoprim resistance. Distinct from these antibiotic resistance genes, a large network component of 384 protein coding sequences encompassed many genes critical in basic cellular functions, while another distinct component included genes associated with virulence. The group A Streptococcus (GAS) data set population represents a clonal population with relatively little genetic variation and a high level of linkage disequilibrium across the genome. Despite this, we were able to pinpoint two RNA pseudouridine synthases, which were each strongly linked to a separate set of loci across the chromosome, representing biologically plausible targets of co-selection. The population genomic analysis method applied here identifies statistically significantly co-evolving locus pairs, potentially arising from fitness selection interdependence reflecting underlying protein-protein interactions, or genes whose product activities contribute to the same phenotype. This discovery approach greatly enhances the future potential of epistasis analysis for systems biology, and can complement genome-wide association studies as a means of formulating hypotheses for targeted experimental work., Author summary Epistatic interactions between polymorphisms in DNA are recognized as important drivers of evolution in numerous organisms. Study of epistasis in bacteria has been hampered by the lack of densely sampled population genomic data, suitable statistical models and inference algorithms sufficiently powered for extremely high-dimensional parameter spaces. We introduce the first model-based method for genome-wide epistasis analysis and use two of the largest available bacterial population genome data sets on Streptococcus pneumoniae (the pneumococcus) and Streptococcus pyogenes (group A Streptococcus) to demonstrate its potential for biological discovery. Our approach reveals interacting networks of resistance, virulence and core machinery genes in the pneumococcus, which highlights putative candidates for novel drug targets. We also discover a number of plausible targets of co-selection in S. pyogenes linked to RNA pseudouridine synthases. Our method significantly enhances the future potential of epistasis analysis for systems biology, and can complement genome-wide association studies as a means of formulating hypotheses for targeted experimental work.

In assessments of child sexual abuse (CSA) allegations, informative background information is often overlooked or not used properly. We therefore created and tested an instrument that uses accessible background information to calculate the probability of a child being a CSA victim that can be used as a starting point in the following investigation. Studying 903 demographic and socioeconomic variables from over 11,000 Finnish children, we identified 42 features related to CSA. Using Bayesian logic to calculate the probability of abuse, our instrument—the Finnish Investigative Instrument of Child Sexual Abuse (FICSA)—has two separate profiles for boys and girls. A cross-validation procedure suggested excellent diagnostic utility (area under the curve [AUC] = 0.97 for boys and AUC = 0.88 for girls). We conclude that the presented method can be useful in forensic assessments of CSA allegations by adding a reliable statistical approach to considering background information, and to support clinical decision making and guide investigative efforts.

Open access funding provided by Max Planck Society. The ability to work at older ages depends on health and education. Both accumulate starting very early in life. We assess how childhood disadvantages combine with education to affect working and health trajectories. Applying multistate period life tables to data from the Health and Retirement Study (HRS) for the period 2008-2014, we estimate how the residual life expectancy at age 50 is distributed in number of years of work and disability, by number of childhood disadvantages, gender, and race/ethnicity. Our findings indicate that number of childhood disadvantages is negatively associated with work and positively with disability, irrespective of gender and race/ethnicity. Childhood disadvantages intersect with low education resulting in shorter lives, and redistributing life years from work to disability. Among the highly educated, health and work differences between groups of childhood disadvantage are small. Combining multistate models and inverse probability weighting, we show that the return of high education is greater among the most disadvantaged. Publisher PDF

Human infection with the gastrointestinal pathogen Campylobacter jejuni is dependent upon the opportunity for zoonotic transmission and the ability of strains to colonize the human host. Certain lineages of this diverse organism are more common in human infection but the factors underlying this overrepresentation are not fully understood. We analyzed 601 isolate genomes from agricultural animals and human clinical cases, including isolates from the multihost (ecological generalist) ST-21 and ST-45 clonal complexes (CCs). Combined nucleotide and amino acid sequence analysis identified 12 human-only amino acid KPAX clusters among polyphyletic lineages within the common disease causing CC21 group isolates, with no such clusters among CC45 isolates. Isolate sequence types within human-only CC21 group KPAX clusters have been sampled from other hosts, including poultry, so rather than representing unsampled reservoir hosts, the increase in relative frequency in human infection potentially reflects a genetic bottleneck at the point of human infection. Consistent with this, sequence enrichment analysis identified nucleotide variation in genes with putative functions related to human colonization and pathogenesis, in human-only clusters. Furthermore, the tight clustering and polyphyly of human-only lineage clusters within a single CC suggest the repeated evolution of human association through acquisition of genetic elements within this complex. Taken together, combined nucleotide and amino acid analysis of large isolate collections may provide clues about human niche tropism and the nature of the forces that promote the emergence of clinically important C. jejuni lineages.

Summary Recent studies have shown that the traditional position weight matrix model is often insufficient for modeling transcription factor binding sites, as intra-motif dependencies play a significant role for an accurate description of binding motifs. Here, we present the Java application InMoDe, a collection of tools for learning, leveraging and visualizing such dependencies of putative higher order. The distinguishing feature of InMoDe is a robust model selection from a class of parsimonious models, taking into account dependencies only if justified by the data while choosing for simplicity otherwise. Availability and Implementation InMoDe is implemented in Java and is available as command line application, as application with a graphical user-interface, and as an integration into Galaxy on the project website at http://www.jstacs.de/index.php/InMoDe.

This paper describes the data repository for the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) initial study cohort. The Cam-CAN Stage 2 repository contains multi-modal (MRI, MEG, and cognitive-behavioural) data from a large (approximately N = 700), cross-sectional adult lifespan (18–87 years old) population-based sample. The study is designed to characterise age-related changes in cognition and brain structure and function, and to uncover the neurocognitive mechanisms that support healthy cognitive ageing. The database contains raw and preprocessed structural MRI, functional MRI (active tasks and resting state), and MEG data (active tasks and resting state), as well as derived scores from cognitive behavioural experiments spanning five broad domains (attention, emotion, action, language, and memory), and demographic and neuropsychological data. The dataset thus provides a depth of neurocognitive phenotyping that is currently unparalleled, enabling integrative analyses of age-related changes in brain structure, brain function, and cognition, and providing a testbed for novel analyses of multi-modal neuroimaging data., Highlights • Cross-sectional uniform adult-lifespan population-based data • Multimodal MRI, fMRI and MEG neuroimaging data • Unprecedented depth of cognitive phenotyping • Age-related differences in brain structure, function, and cognition

Various l 1-penalised estimation methods such as graphical lasso and CLIME are widely used for sparse precision matrix estimation and learning of undirected network structure from data. Many of these methods have been shown to be consistent under various quantitative assumptions about the underlying true covariance matrix. Intuitively, these conditions are related to situations where the penalty term will dominate the optimisation. We explore the consistency of l 1-based methods for a class of bipartite graphs motivated by the structure of models commonly used for gene regulatory networks. We show that all l 1-based methods fail dramatically for models with nearly linear dependencies between the variables. We also study the consistency on models derived from real gene expression data and note that the assumptions needed for consistency never hold even for modest sized gene networks and l 1-based methods also become unreliable in practice for larger networks. Our results demonstrate that l 1-penalised undirected network structure learning methods are unable to reliably learn many sparse bipartite graph structures, which arise often in gene expression data. Users of such methods should be aware of the consistency criteria of the methods and check if they are likely to be met in their application of interest.

Background Pneumococcus kills over one million children annually and over 90 % of these deaths occur in low-income countries especially in Sub-Saharan Africa (SSA) where HIV exacerbates the disease burden. In SSA, serotype 1 pneumococci particularly the endemic ST217 clone, causes majority of the pneumococcal disease burden. To understand the evolution of the virulent ST217 clone, we analysed ST217 whole genomes from isolates sampled from African and Asian countries. Methods We analysed 226 whole genome sequences from the ST217 lineage sampled from 9 African and 4 Asian countries. We constructed a whole genome alignment and used it for phylogenetic and coalescent analyses. We also screened the genomes to determine presence of antibiotic resistance conferring genes. Results Population structure analysis grouped the ST217 isolates into five sequence clusters (SCs), which were highly associated with different geographical regions and showed limited intracontinental and intercontinental spread. The SCs showed lower than expected genomic sequence, which suggested strong purifying selection and small population sizes caused by bottlenecks. Recombination rates varied between the SCs but were lower than in other successful clones such as PMEN1. African isolates showed higher prevalence of antibiotic resistance genes than Asian isolates. Interestingly, certain West African isolates harbored a defective chloramphenicol and tetracycline resistance-conferring element (Tn5253) with a deletion in the loci encoding the chloramphenicol resistance gene (cat pC194), which caused lower chloramphenicol than tetracycline resistance. Furthermore, certain genes that promote colonisation were absent in the isolates, which may contribute to serotype 1’s rarity in carriage and consequently its lower recombination rates. Conclusions The high phylogeographic diversity of the ST217 clone shows that this clone has been in circulation globally for a long time, which allowed its diversification and adaptation in different geographical regions. Such geographic adaptation reflects local variations in selection pressures in different locales. Further studies will be required to fully understand the biological mechanisms which makes the ST217 clone highly invasive but unable to successfully colonise the human nasopharynx for long durations which results in lower recombination rates. Electronic supplementary material The online version of this article (doi:10.1186/s12879-016-1987-z) contains supplementary material, which is available to authorized users.

The threat of the new pandemic influenza A(H1N1)pdm09 imposed a heavy burden on the public health system in Finland in 2009-2010. An extensive vaccination campaign was set up in the middle of the first pandemic season. However, the true number of infected individuals remains uncertain as the surveillance missed a large portion of mild infections. We constructed a transmission model to simulate the spread of influenza in the Finnish population. We used the model to analyse the two first years (2009-2011) of A(H1N1)pdm09 in Finland. Using data from the national surveillance of influenza and data on close person-to-person (social) contacts in the population, we estimated that 6% (90% credible interval 5.1 – 6.7%) of the population was infected with A(H1N1)pdm09 in the first pandemic season (2009/2010) and an additional 3% (2.5 – 3.5%) in the second season (2010/2011). Vaccination had a substantial impact in mitigating the second season. The dynamic approach allowed us to discover how the proportion of detected cases changed over the course of the epidemic. The role of time-varying reproduction number, capturing the effects of weather and changes in behaviour, was important in shaping the epidemic., Author Summary In 2009, the threat of the new pandemic influenza A(H1N1)pdm09 (referenced in media as ‘swine flu’) created a heavy burden to the public health systems wordwide. In Finland, an extensive vaccination campaign was set up in the middle of the first pandemic season 2009/2010. However, the true number of infected individuals remains uncertain as the surveillance missed a large portion of mild infections. We built a probabilistic model of influenza transmission that accounts for observation bias and the possible impact of the changing weather and population behaviour. We used the model to simulate the spread of influenza in Finland during the two first years (2009-2011) of A(H1N1)pdm09 in Finland. Using data from the national surveillance of influenza and data on social contacts in the population, we estimated that 9% of the population was infected with A(H1N1)pdm09 during the studied period. Vaccination had a substantial impact in mitigating the second season.

Background Campylobacter jejuni is a major zoonotic pathogen, causing gastroenteritis in humans. Invasion is an important pathogenesis trait by which C. jejuni causes disease. Here we report the genomic analysis of 134 strains to identify traits unique to hyperinvasive isolates. Methods A total of 134 C. jejuni genomes were used to create a phylogenetic tree to position the hyperinvasive strains. Comparative genomics lead to the identification of mosaic capsule regions. A pan genome approach led to the discovery of unique loci, or loci with unique alleles, to the hyperinvasive strains. Results Phylogenetic analysis showed that the hyper-invasive phenotype is a generalist trait. Despite the fact that hyperinvasive strains are only distantly related based on the whole genome phylogeny, they all possess genes within the capsule region with high identity to capsule genes from C. jejuni subsp. doylei and C. lari. In addition there were genes unique to the hyper-invasive strains with identity to non-C. jejuni genes, as well as allelic variants of mainly pathogenesis related genes already known in the other C. jejuni. In particular, the sequence of flagella genes, flgD-E and flgL were highly conserved amongst the hyper-invasive strains and divergent from sequences in other C. jejuni. A novel cytolethal distending toxin (cdt) operon was also identified as present in all hyper-invasive strains in addition to the classic cdt operon present in other C. jejuni. Conclusions Overall, the hyper-invasive phenotype is strongly linked to the presence of orthologous genes from other Campylobacter species in their genomes, notably within the capsule region, in addition to the observed association with unique allelic variants in flagellar genes and the secondary cdt operon which is unlikely under random sharing of accessory alleles in separate lineages. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-2087-y) contains supplementary material, which is available to authorized users.

A major component driving cross-country fertility differences in the developed world is differences in the probability of having additional children among those who have one. Why do people stop at having only one child? We hypothesize that the experience of the transition to parenthood is an important determinant of further fertility. Analyzing longitudinal data from Germany, we find that the experience during the transition to parenthood, as measured by changes in subjective well-being, predicts further parity progression. A drop in well-being surrounding first birth predicts a decreased likelihood of having another child. The association is particularly strong for older parents and those with higher education: these characteristics may be related to the ability or willingness to revise fertility plans based on prior experiences. Parents’ experience with the first birth is an important and understudied factor in determining completed family size, and policy-makers concerned about low fertility should pay attention to factors that influence the well-being of new parents.

MOTIVATION: Estimation of bacterial community composition from high-throughput sequenced 16S rRNA gene amplicons is a key task in microbial ecology. Since the sequence data from each sample typically consist of a large number of reads and are adversely impacted by different levels of biological and technical noise, accurate analysis of such large datasets is challenging. \ud \ud RESULTS: There has been a recent surge of interest in using compressed sensing inspired and convex-optimization based methods to solve the estimation problem for bacterial community composition. These methods typically rely on summarizing the sequence data by frequencies of low-order k-mers and matching this information statistically with a taxonomically structured database. Here we show that the accuracy of the resulting community composition estimates can be substantially improved by aggregating the reads from a sample with an unsupervised machine learning approach prior to the estimation phase. The aggregation of reads is a pre-processing approach where we use a standard K-means clustering algorithm that partitions a large set of reads into subsets with reasonable computational cost to provide several vectors of first order statistics instead of only single statistical summarization in terms of k-mer frequencies. The output of the clustering is then processed further to obtain the final estimate for each sample. The resulting method is called Aggregation of Reads by K-means (ARK), and it is based on a statistical argument via mixture density formulation. ARK is found to improve the fidelity and robustness of several recently introduced methods, with only a modest increase in computational complexity. \ud \ud AVAILABILITY: An open source, platform-independent implementation of the method in the Julia programming language is freely available at https://github.com/dkoslicki/ARK. A Matlab implementation is available at http://www.ee.kth.se/ctsoftware.

Streptococcus pneumoniae is a significant human pathogen and a leading cause of infant mortality in developing countries. Considerable global variation in the pneumococcal carriage prevalence has been observed and the ecological factors contributing to it are not yet fully understood. We use data from a cohort of infants in Asia to study the effects of climatic conditions on both acquisition and clearance rates of the bacterium, finding significantly higher transmissibility during the cooler and drier months. Conversely, the length of a colonization period is unaffected by the season. Independent carriage data from studies conducted on the African and North American continents suggest similar effects of the climate on the prevalence of this bacterium, which further validates the obtained results. Further studies could be important to replicate the findings and explain the mechanistic role of cooler and dry air in the physiological response to nasopharyngeal acquisition of the pneumococcus.

Human mobility has been empirically observed to exhibit Levy flight characteristics and behaviour with power-law distributed jump size. The fundamental mechanisms behind this behaviour has not yet been fully explained. In this paper, we analyze urban human mobility and we propose to explain the Levy walk behaviour observed in human mobility patterns by decomposing them into different classes according to the different transportation modes, such as Walk/Run, Bicycle, Train/Subway or Car/Taxi/Bus. Our analysis is based on two real-life GPS datasets containing approximately 10 and 20 million GPS samples with transportation mode information. We show that human mobility can be modelled as a mixture of different transportation modes, and that these single movement patterns can be approximated by a lognormal distribution rather than a power-law distribution. Then, we demonstrate that the mixture of the decomposed lognormal flight distributions associated with each modality is a power-law distribution, providing an explanation to the emergence of Levy Walk patterns that characterize human mobility patterns.

Escherichia coli is a highly diverse group of pathogens ranging from commensal of the intestinal tract, through to intestinal pathogen, and extraintestinal pathogen. Here, we present data on the population diversity of E. coli, using Bayesian analysis to identify 13 distinct clusters within the population from multilocus sequence typing data, which map onto a whole-genome-derived phylogeny based on 62 genome sequences. Bayesian analysis of recombination within the core genome identified reduction in detectable core genome recombination as one moves from the commensals, through the intestinal pathogens down to the multidrug-resistant extraintestinal pathogenic clone E. coli ST131. Our data show that the emergence of a multidrug-resistant, extraintestinal pathogenic lineage of E. coli is marked by substantial reduction in detectable core genome recombination, resulting in a lineage which is phylogenetically distinct and sexually isolated in terms of core genome recombination.

Background:Staphylococcus aureus exhibits tropisms to many distinct animal hosts. While spillover events can occur wherever there is an interface between host species, changes in host tropism only occur with the establishment of sustained transmission in the new host species, leading to clonal expansion. Although the genomic variation underpinning adaptation in S. aureus genotypes infecting bovids and poultry has been well characterized the frequency of switches from one host to another remains obscure. We sought to identify sustained switches in host tropism in the S. aureus population, both anthroponotic and zoonotic, and their distribution over the species phylogeny. Methodologies/Results:We have used a sample of 3042 isolates, representing 696 distinct MLST genotypes, from a well-established database (www.mlst.net). Using an empirical parsimony approach (AdaptML) we have investigated the distribution of switches in host association between both human and non-human (henceforth referred to as animal) hosts. We reconstructed a credible description of past events in the form of a phylogenetic tree; the nodes and leaves of which are statistically associated with either human or animal habitats, estimated from extant host-association and the degree of sequence divergence between genotypes. We identified 15 likely historical switching events; 13 anthroponoses and two zoonoses. Importantly, we identified two human-associated clade candidates (CC25 and CC59) that have arisen from animal-associated ancestors; this demonstrates that a human-specific lineage can emerge from an animal host. We also highlight novel rabbit-associated genotypes arising from a human ancestor. Conclusions: S. aureus is an organism with the capacity to switch into and adapt to novel hosts, even after long periods of isolation in a single host species. Based on this evidence, animal-adapted S. aureus lineages exhibiting resistance to antibiotics must be considered a major threat to public health, as they can adapt to the human population.

Background Y. enterocolitica biotype (BT) 1A strains are often isolated from human clinical samples but their contribution to disease has remained a controversial topic. Variation and the population structure among the clinical Y. enterocolitica BT 1A isolates have been poorly characterized. We used multi-locus sequence typing (MLST), 16S rRNA gene sequencing, PCR for ystA and ystB, lipopolysaccharide analysis, phage typing, human serum complement killing assay and analysis of the symptoms of the patients to characterize 298 clinical Y. enterocolitica BT 1A isolates in order to evaluate their relatedness and pathogenic potential. Results A subset of 71 BT 1A strains, selected based on their varying LPS patterns, were subjected to detailed genetic analyses. The MLST on seven house-keeping genes (adk, argA, aroA, glnA, gyrB, thrA, trpE) conducted on 43 of the strains discriminated them into 39 MLST-types. By Bayesian analysis of the population structure (BAPS) the strains clustered conclusively into two distinct lineages, i.e. Genetic groups 1 and 2. The strains of Genetic group 1 were more closely related (97% similarity) to the pathogenic bio/serotype 4/O:3 strains than Genetic group 2 strains (95% similarity). Further comparison of the 16S rRNA genes of the BT 1A strains indicated that altogether 17 of the 71 strains belong to Genetic group 2. On the 16S rRNA analysis, these 17 strains were only 98% similar to the previously identified subspecies of Y. enterocolitica. The strains of Genetic group 2 were uniform in their pathogenecity-related properties: they lacked the ystB gene, belonged to the same LPS subtype or were of rough type, were all resistant to the five tested yersiniophages, were largely resistant to serum complement and did not ferment fucose. The 54 strains in Genetic group 1 showed much more variation in these properties. The most commonly detected LPS types were similar to the LPS types of reference strains with serotypes O:6,30 and O:6,31 (37%), O:7,8 (19%) and O:5 (15%). Conclusions The results of the present study strengthen the assertion that strains classified as Y. enterocolitica BT 1A represent more than one subspecies. Especially the BT 1A strains in our Genetic group 2 commonly showed resistance to human serum complement killing, which may indicate pathogenic potential for these strains. However, their virulence mechanisms remain unknown.

Enterococcus faecium has recently emerged as an important multiresistant nosocomial pathogen. Defining population structure in this species is required to provide insight into the existence, distribution, and dynamics of specific multiresistant or pathogenic lineages in particular environments, like the hospital. Here, we probe the population structure of E. faecium using Bayesian-based population genetic modeling implemented in Bayesian Analysis of Population Structure (BAPS) software. The analysis involved 1,720 isolates belonging to 519 sequence types (STs) (491 for E. faecium and 28 for Enterococcus faecalis). E. faecium isolates grouped into 13 BAPS (sub)groups, but the large majority (80%) of nosocomial isolates clustered in two subgroups (2-1 and 3-3). Phylogenetic and eBURST analysis of BAPS groups 2 and 3 confirmed the existence of three separate hospital lineages (17, 18, and 78), highlighting different evolutionary trajectories for BAPS 2-1 (lineage 78) and 3-3 (lineage 17 and lineage 18) isolates. Phylogenomic analysis of 29 E. faecium isolates showed agreement between BAPS assignment of STs and their relative positions in the phylogenetic tree. Odds ratio calculation confirmed the significant association between hospital isolates with BAPS 3-3 and lineages 17, 18, and 78. Admixture analysis showed a scarce number of recombination events between the different BAPS groups. For the E. faecium hospital population, we propose an evolutionary model in which strains with a high propensity to colonize and infect hospitalized patients arise through horizontal gene transfer. Once adapted to the distinct hospital niche, this subpopulation becomes isolated, and recombination with other populations declines., IMPORTANCE Multiresistant Enterococcus faecium has become one of the most important nosocomial pathogens, causing increasing numbers of nosocomial infections worldwide. Here, we used Bayesian population genetic analysis to identify groups of related E. faecium strains and show a significant association of hospital and farm animal isolates to different genetic groups. We also found that hospital isolates could be divided into three lineages originating from sequence types (STs) 17, 18, and 78. We propose that, driven by the selective pressure in hospitals, the three hospital lineages have arisen through horizontal gene transfer, but once adapted to the distinct pathogenic niche, this population has become isolated and recombination with other populations declines. Elucidation of the population structure is a prerequisite for effective control of multiresistant E. faecium since it provides insight into the processes that have led to the progressive change of E. faecium from an innocent commensal to a multiresistant hospital-adapted pathogen.

Background Worldwide effort on sampling and characterization of molecular variation within a large number of human and animal pathogens has lead to the emergence of multi-locus sequence typing (MLST) databases as an important tool for studying the epidemiology and evolution of pathogens. Many of these databases are currently harboring several thousands of multi-locus DNA sequence types (STs) enriched with metadata over traits such as serotype, antibiotic resistance, host organism etc of the isolates. Curators of the databases have thus the possibility of dividing the pathogen populations into subsets representing different evolutionary lineages, geographically associated groups, or other subpopulations, which are defined in terms of molecular similarities and dissimilarities residing within a database. When combined with the existing metadata, such subsets may provide invaluable information for assessing the position of a new set of isolates in relation to the whole pathogen population. Results To enable users of MLST schemes to query the databases with sets of new bacterial isolates and to automatically analyze their relation to existing curated sequences, we introduce here a Bayesian model-based method for semi-supervised classification of MLST data. Our method can use an MLST database as a training set and assign simultaneously any set of query sequences into the earlier discovered lineages/populations, while also allowing some or all of these sequences to form previously undiscovered genetically distinct groups. This tool provides probabilistic quantification of the classification uncertainty and is highly efficient computationally, thus enabling rapid analyses of large databases and sets of query sequences. The latter feature is a necessary prerequisite for an automated access through the MLST web interface. We demonstrate the versatility of our approach by anayzing both real and synthesized data from MLST databases. The introduced method for semi-supervised classification of sets of query STs is freely available for Windows, Mac OS X and Linux operative systems in BAPS 5.4 software which is downloadable at http://web.abo.fi/fak/mnf/mate/jc/software/baps.html. The query functionality is also directly available for the Staphylococcus aureus database at http://www.mlst.net and shortly will be available for other species databases hosted at this web portal. Conclusions We have introduced a model-based tool for automated semi-supervised classification of new pathogen samples that can be integrated into the web interface of the MLST databases. In particular, when combined with the existing metadata, the semi-supervised labeling may provide invaluable information for assessing the position of a new set of query strains in relation to the particular pathogen population represented by the curated database. Such information will be useful both for clinical and basic research purposes.