Search

To compare the original synthetic control (OSC) method with alternative approaches (Generalised (GSC), Micro (MSC), and Bayesian (BSC) synthetic control methods) and re-evaluate the impact of a significant restructuring of urgent and emergency care in North-East England, which included the opening of the UK's first purpose-built specialist emergency care hospital.Simulations and data from Secondary Uses Service data, a single comprehensive repository for patient-level health care data in England.Hospital use of individuals exposed and unexposed to the restructuring is compared. We estimate the impact using OSC, MSC, BSC and GSC applied at the general practice level. We contrast the estimation methods' performance in a Monte Carlo simulation study.Hospital activity data from Secondary Uses Service for patients aged over 18 years registered at a general practice in England from April 2011 to March 2019.None of the methods dominated across all simulation scenarios. GSC was generally preferred. In contrast to an earlier evaluation which used OSC, GSC reported a smaller impact of the opening of the hospital on Accident and Emergency (AE) department (also known as emergency department or casualty) visits and no evidence for any impact on the proportion of AE patients seen within 4 hours.The simulation study highlights cases where the considered methods may lead to biased estimates in health policy evaluations. GSC was found to be the most reliable method of those considered. Considering more disaggregated data over a longer time span and applying GSC indicates that the specialist emergency care hospitals in Northumbria had less impact on AE visits and waiting times than suggested by the original evaluation which applied OSC to more aggregated data. This article is protected by copyright. All rights reserved.

Background The COVID-19 pandemic has led to an ongoing increase in the use of remote consultations in general practice in England. Though the evidence is limited, there are concerns that the increase in remote consultations could lead to more antibiotic prescribing. Methods We used patient-level primary care data from the Clinical Practice Research Datalink to estimate the association between consultation mode (remote vs face-to-face) and antibiotic prescribing in England for acute respiratory infections (ARI) between April 2021 - March 2022. We used targeted maximum likelihood estimation, a causal machine learning method with adjustment for patient-, clinician- and practice-level factors. Findings There were 45,997 ARI consultations (34,555 unique patients), of which 28,127 were remote and 17,870 face-to-face. For children, 48% of consultations were remote whereas for adults 66% were remote. For children, 42% of remote and 43% face-to-face consultations led to an antibiotic prescription; the equivalent in adults was 52% of remote and 42% face-to-face. Adults with a remote consultation had 23% (Odds Ratio (OR) 1.23 95% Confidence Interval (CI): 1.18-1.29) higher chance of being prescribed antibiotics compared to if they had been seen face-to-face. We found no significant association between consultation mode and antibiotic prescribing in children (OR 1.04 95% CI 0.98-1.11). Interpretation This study uses rich patient-level data and robust statistical methods and represents an important contribution to the evidence base on antibiotic prescribing in post-COVID primary care. The higher rates of antibiotic prescribing in remote consultations for adults are cause for concern. We see no significant difference in antibiotic prescribing between consultation mode for children. These findings should inform antimicrobial stewardship activities for health care professionals and policy makers. Future research should examine differences in guideline-compliance between remote and face-to-face consultations to understand the factors driving antibiotic prescribing in different consultation modes. Funding No external funding. Keywords general practice; England; antibiotics; remote consultations; telehealth; telemedicine; TMLE; causal inference; machine learning; acute respiratory infections; antimicrobial resistance; covid; ARTI; ARI; antibiotic prescribing; primary care

BACKGROUND The COVID-19 pandemic has had a significant impact on primary care service delivery with an increased use of remote consultations. With general practice delivering record numbers of appointments and rising concerns around access, funding, and staffing in the UK National Health Service, we assessed contemporary trends in consultation rate and modes (ie, face-to-face versus remote). OBJECTIVE This paper describes trends in consultation rates in general practice in England for key demographics before and during the COVID-19 pandemic. We explore the use of remote and face-to-face consultations with regard to socioeconomic deprivation to understand the possible effect of changes in consultation modes on health inequalities. METHODS We did a retrospective analysis of 9,429,919 consultations by general practitioners, nurses, or other health care professionals between March 2018 and February 2022 for patients registered at 397 general practices in England. We used routine electronic health records from Clinical Practice Research Datalink Aurum with linkage to national data sets. Negative binomial models were used to predict consultation rates and modes (ie, remote versus face-to-face) by age, sex, and socioeconomic deprivation over time. RESULTS Overall consultation rates increased by 15% from 4.92 in 2018-2019 to 5.66 in 2021-2022 with some fluctuation during the start of the COVID-19 pandemic. The breakdown into face-to-face and remote consultations shows that the pandemic precipitated a rapid increase in remote consultations across all groups, but the extent varies by age. Consultation rates increased with increasing levels of deprivation. Socioeconomic differences in consultation rates, adjusted for sex and age, halved during the pandemic (from 0.36 to 0.18, indicating more consultations in the most deprived), effectively narrowing relative differences between deprivation quintiles. This trend remains when stratified by sex, but the difference across deprivation quintiles is smaller for men. The most deprived saw a relatively larger increase in remote and decrease in face-to-face consultation rates compared to the least deprived. CONCLUSIONS The substantial increases in consultation rates observed in this study imply an increased pressure on general practice. The narrowing of consultation rates between deprivation quintiles is cause for concern, given ample evidence that health needs are greater in more deprived areas.

BackgroundThe COVID-19 pandemic has had a significant impact on primary care service delivery. With general practice delivering record numbers of appointments and rising concerns around access, funding and staffing in the UK National Health Service, we assessed contemporary trends in consultation rate and mode (face-to-face versus remote).MethodsWe did a retrospective analysis of 9,429,919 consultations by GP, nurse or other health care professional between March 2018 and February 2022 for patients registered at 397 English general practices. We used routine electronic health records from Clinical Practice Research Datalink Aurum with linkage to national datasets. Negative binomial models were used to predict consultation rates and modes (remote versus face-to-face) by age, sex, and socio-economic deprivation.FindingsOverall consultation rates increased by 15% from 4.92 in 2018-19 to 5.66 in 2021-22 with some fluctuation during the start of the pandemic. Consultation rates increased with deprivation. The breakdown into face-to-face and remote consultations shows that the pandemic precipitated a rapid increase in remote consultations across all groups but varies by age. Socioeconomic differences in consultation rate, adjusted for sex and age, halved during the pandemic (from 0.36 to 0.18 more consultations in the most deprived). The most deprived saw a relatively larger increase in remote and decrease in face-to-face consultations rates.InterpretationSubstantial increases in consultation rates imply increased pressure on general practice. The narrowing of consultation rates between deprivation quintiles is cause for concern, given ample evidence that health needs are greater in more deprived areas.FundingNo external funding.Research in contextEvidence before this studyPressure on general practice has increased over recent years and there is consensus that the COVID-19 pandemic added to this. There is also consensus that the way general practice is delivering care has changed with increased use of remote consultation but there no estimates of the full extent of this and uptake by different groups. A seminal paper - Clinical workload in UK primary care: a retrospective analysis of 100 million consultations in England, 2007–14 – found an increase in consultation rates over the study period and in increased reliance on telephone contacts even before the pandemic.Added value of this studyThis study reports recent data for general practice consultation rates overall and by delivery mode. Our findings show that overall consultation rates were higher in 2021-22 than prior to the pandemic and that there has been a shift from face-to-face to remote consultations. However, the increase in overall consultations rates varies between index of multiple deprivation quintiles when adjusting for age and sex. These findings are based on close to 10 million consultations and 2 million person-years of observation from a validated data base of routinely collected electronic clinical records (the Clinical Practice Research Datalink Aurum).Implications of all the available evidenceOur analysis shows that general practice is busier than ever. We provide details on the use of remote versus face-to-face consultations by different patient groups over time. The narrowing of the difference between consultation rates of deprivation quintiles implies increasing health inequality in the population as existing differences in health needs are therefore not fully reflected in the consultation rates. The relatively larger increase in remote consultation rates and drop in face-to-face consultations for the most deprived provides detail on what type of consultations different patient groups receive but also raises additional questions.

Background The COVID-19 pandemic has had a significant impact on primary care service delivery with an increased use of remote consultations. With general practice delivering record numbers of appointments and rising concerns around access, funding, and staffing in the UK National Health Service, we assessed contemporary trends in consultation rate and modes (ie, face-to-face versus remote). Objective This paper describes trends in consultation rates in general practice in England for key demographics before and during the COVID-19 pandemic. We explore the use of remote and face-to-face consultations with regard to socioeconomic deprivation to understand the possible effect of changes in consultation modes on health inequalities. Methods We did a retrospective analysis of 9,429,919 consultations by general practitioners, nurses, or other health care professionals between March 2018 and February 2022 for patients registered at 397 general practices in England. We used routine electronic health records from Clinical Practice Research Datalink Aurum with linkage to national data sets. Negative binomial models were used to predict consultation rates and modes (ie, remote versus face-to-face) by age, sex, and socioeconomic deprivation over time. Results Overall consultation rates increased by 15% from 4.92 in 2018-2019 to 5.66 in 2021-2022 with some fluctuation during the start of the COVID-19 pandemic. The breakdown into face-to-face and remote consultations shows that the pandemic precipitated a rapid increase in remote consultations across all groups, but the extent varies by age. Consultation rates increased with increasing levels of deprivation. Socioeconomic differences in consultation rates, adjusted for sex and age, halved during the pandemic (from 0.36 to 0.18, indicating more consultations in the most deprived), effectively narrowing relative differences between deprivation quintiles. This trend remains when stratified by sex, but the difference across deprivation quintiles is smaller for men. The most deprived saw a relatively larger increase in remote and decrease in face-to-face consultation rates compared to the least deprived. Conclusions The substantial increases in consultation rates observed in this study imply an increased pressure on general practice. The narrowing of consultation rates between deprivation quintiles is cause for concern, given ample evidence that health needs are greater in more deprived areas.

What you need to know Interventions to transform the delivery of health and social care are being implemented widely, such as those linked to Accountable Care Organizations in the United States,1 or to integrated care systems in the UK.2 Assessing the impact of these health interventions enables healthcare teams to learn and to improve services, and can inform future policy.3 However, some healthcare interventions are implemented without high quality evaluation, in ways that require onerous data collection, or may not be evaluated at all.4 A range of routinely collected administrative and clinically generated healthcare data could be used to evaluate the impact of interventions to improve care. However, there is a lack of guidance as to where relevant routine data can be found or accessed and how they can be linked to other data. A diverse array of methodological literature can also make it hard to understand which methods to apply to analyse the data. This article provides an introduction to help clinicians, commissioners, and other healthcare professionals wishing to commission, interpret, or perform an impact evaluation of a health intervention. We highlight what to consider and discuss key concepts relating to design, analysis, implementation, and interpretation. A health intervention is a combination of activities or strategies designed to assess, improve, maintain, promote, or modify health among individuals or an entire population. Interventions can include educational or care programmes, policy changes, environmental improvements, or health promotion campaigns. Interventions that include multiple independent or interacting components are referred to as complex.5 The impact of any intervention is likely …

We conducted a genome-wide association study of host resistance to severePlasmodium falciparummalaria in over 17,000 individuals from 11 malaria-endemic countries, undertaking a wide ranging analysis which identifies five replicable associations with genome-wide levels of evidence. Our findings include a newly implicated variant on chromosome 6 associated with risk of cerebral malaria, and the discovery of an erythroid-specific transcription start site underlying the association inATP2B4. Previously reported HLA associations cannot be replicated in this dataset. We estimate substantial heritability of severe malaria (h2~ 23%), of which around 10% is explained by the currently identified associations. Our dataset will provide a major building block for future research on the genetic determinants of disease in these diverse human populations.

Background Prognostic scores and models of illness severity are useful both clinically and for research. The aim of this study was to develop two prognostic models for the prediction of long-term (6 months) and 28-day mortality of postoperative critically ill patients with faecal peritonitis (FP). Methods Patients admitted to intensive care units with faecal peritonitis and recruited to the European GenOSept study were divided into a derivation and a geographical validation subset; patients subsequently recruited to the UK GAinS study were used for temporal validation. Using all 50 clinical and laboratory variables available on day 1 of critical care admission, Cox proportional hazards regression was fitted to select variables for inclusion in two prognostic models, using stepwise selection and nonparametric bootstrapping sampling techniques. Using Area under the receiver operating characteristic curve (AuROC) analysis, the performance of the models was compared to SOFA and APACHE II. Results Five variables (age, SOFA score, lowest temperature, highest heart rate, haematocrit) were entered into the prognostic models. The discriminatory performance of the 6-month prognostic model yielded an AuROC 0.81 (95% CI 0.76–0.86), 0.73 (95% CI 0.69–0.78) and 0.76 (95% CI 0.69–0.83) for the derivation, geographic and temporal external validation cohorts, respectively. The 28-day prognostic tool yielded an AuROC 0.82 (95% CI 0.77–0.88), 0.75 (95% CI 0.69–0.80) and 0.79 (95% CI 0.71–0.87) for the same cohorts. These AuROCs appeared consistently superior to those obtained with the SOFA and APACHE II scores alone. Conclusions The two prognostic models developed for 6-month and 28-day mortality prediction in critically ill septic patients with FP, in the postoperative phase, enhanced the day one SOFA score’s predictive utility by adding a few key variables: age, lowest recorded temperature, highest recorded heart rate and haematocrit. External validation of their predictive capability in larger cohorts is needed, before introduction of the proposed scores into clinical practice to inform decision making and the design of clinical trials. Electronic supplementary material The online version of this article (doi:10.1186/s13613-017-0314-1) contains supplementary material, which is available to authorized users.

Many human genetic associations with resistance to malaria have been reported, but few have been reliably replicated. We collected data on 11,890 cases of severe malaria due to Plasmodium falciparum and 17,441 controls from 12 locations in Africa, Asia and Oceania. We tested 55 SNPs in 27 loci previously reported to associate with severe malaria. There was evidence of association at P < 1 × 10(-4) with the HBB, ABO, ATP2B4, G6PD and CD40LG loci, but previously reported associations at 22 other loci did not replicate in the multicenter analysis. The large sample size made it possible to identify authentic genetic effects that are heterogeneous across populations or phenotypes, with a striking example being the main African form of G6PD deficiency, which reduced the risk of cerebral malaria but increased the risk of severe malarial anemia. The finding that G6PD deficiency has opposing effects on different fatal complications of P. falciparum infection indicates that the evolutionary origins of this common human genetic disorder are more complex than previously supposed.

INTRODUCTION Malaria parasites cause human disease by invading and replicating inside red blood cells. In the case of Plasmodium falciparum , this can lead to severe forms of malaria that are a major cause of childhood mortality in Africa. This species of parasite enters the red blood cell through interactions with surface proteins including the glycophorins GYPA and GYPB, which determine the polymorphic MNS blood group system. In a recent genome-wide association study, we identified alleles associated with protection against severe malaria near the cluster of genes encoding these invasion receptors. RATIONALE Investigation of genetic variants at this locus and their relation to severe malaria is challenging because of the high sequence similarity between the neighboring glycophorin genes and the relative lack of available sequence data capturing the genetic diversity of sub-Saharan Africa. To better assess whether variation in the glycophorin genes could explain the signal of association, we generated additional sequence data from sub-Saharan African populations and developed an analytical approach to characterize structural variation at this complex locus. RESULTS Using 765 newly sequenced human genomes from 10 African ethnic groups along with data from the 1000 Genomes Project, we generated a reference panel of haplotypes across the glycophorin region. In addition to single-nucleotide polymorphisms and short indels, we assayed large copy number variants (CNVs) using sequencing read depth and uncovered extensive structural diversity. By imputing from this reference panel into 4579 severe malaria cases and 5310 controls from three African populations, we found that a complex CNV, here called DUP4, is associated with resistance to severe malaria and fully explains the previously reported signal of association. In our sample, DUP4 is present only in east Africa, and this localization, as well as the extent of similarity between DUP4 haplotypes, suggests that it has recently increased in frequency, presumably under natural selection due to malaria. To evaluate the potential functional consequences of this structural variant, we analyzed high-coverage sequence-read data from multiple individuals to generate a model of the DUP4 chromosome structure. The DUP4 haplotype contains five glycophorin genes, including two hybrid genes that juxtapose the extracellular domain of GYPB with the transmembrane and intracellular domains of GYPA. Noting that these predicted hybrids are characteristic of the Dantu antigen in the MNS blood group system, we sequenced a Dantu positive individual and confirmed that DUP4 is the molecular basis of the Dantu NE blood group variant. CONCLUSION Although a role for GYPA and GYPB in parasite invasion is well known, a direct link between glycophorin polymorphisms and clinical susceptibility to malaria has been elusive. Here we have provided a systematic catalog of CNVs, describing structural diversity that may have functional importance at this locus. Our results identify a specific variant that encodes hybrid glycophorin proteins and is associated with protection against severe malaria. This discovery calls for further work to determine how this particular molecular rearrangement affects parasite invasion and the red blood cell response and may lead us toward new parasite vulnerabilities that can be utilized in future interventions against this deadly disease.

This protocol describes how to perform basic statistical analysis in a population-based genetic association case-control study. The steps described involve the (i) appropriate selection of measures of association and relevance of disease models; (ii) appropriate selection of tests of association; (iii) visualization and interpretation of results; (iv) consideration of appropriate methods to control for multiple testing; and (v) replication strategies. Assuming no previous experience with software such as PLINK, R or Haploview, we describe how to use these popular tools for handling single-nucleotide polymorphism data in order to carry out tests of association and visualize and interpret results. This protocol assumes that data quality assessment and control has been performed, as described in a previous protocol, so that samples and markers deemed to have the potential to introduce bias to the study have been identified and removed. Study design, marker selection and quality control of case-control studies have also been discussed in earlier protocols. The protocol should take ~1 h to complete.

This protocol details the data quality assessment and control steps that are typically carried out during case-control association studies. The steps described involve the identification and removal of DNA samples and markers that introduce bias to the study. These critical steps are paramount to the success of a case-control study and are necessary before statistically testing for association. We describe how to use PLINK, a tool for handling SNP data, to carry out assessments of failure rate per-individual and per-SNP and to assess the degree of relatedness between individuals. We also detail other quality control procedures, including the use of SMARTPCA for the identification of ancestral outliers. These platforms were selected because they are user-friendly, widely used, and computationally efficient. Steps needed to detect and establish a disease association using case-control data are not discussed, as these are provided in a further protocol in the series. Issues concerning the study design and marker selection in case-control studies have been discussed in our earlier protocols. The protocol should take approximately 8 hours to complete.

The first Crohn's disease (CD) susceptibility gene identified was CARD15, which is a member of the emerging NOD-like receptor (NLR) family. These function as intracellular cystosolic pattern recognition receptors (PRRs) and play a central role in the innate immune response. We studied other members of the NLR family using a gene-wide haplotype tagging approach in a well-characterised collection of 547 CD patients and 465 controls. Four single nucleotide polymorphisms (SNPs) in NLRP3 had P values < 0.05 and are in high linkage disequilibrium (LD) with each other (r(2) > 0.90 for all four SNPs). rs4925648 and rs10925019 were the most strongly associated with CD susceptibility (P = 0.001, odds ratio (OR) 1.62, 95% CI 1.2-2.18; and P = 6.5 x 10(-4), OR 1.65, 95% CI 1.23-2.19, respectively). rs1363758 located in NLRP11 was associated with CD susceptibility [P = 0.002 (1.64, 1.19-2.25)], which was weakly confirmed in an independent case-cohort collection on joint analysis [P = 0.05, (1.28, 1-1.64)]. On sub-phenotype analysis, an interesting association between NLRP1 and skin extra-intestinal manifestations and colonic, inflammatory CD was identified. None of these results was replicated in the Wellcome Trust Case Control Consortium study and therefore need replication in a further large cohort.

Similarity between two individuals in the combination of genetic markers along their chromosomes indicates shared ancestry and can be used to identify historical connections between different population groups due to admixture. We use a genome-wide, haplotype-based, analysis to characterise the structure of genetic diversity and gene-flow in a collection of 48 sub-Saharan African groups. We show that coastal populations experienced an influx of Eurasian haplotypes over the last 7000 years, and that Eastern and Southern Niger-Congo speaking groups share ancestry with Central West Africans as a result of recent population expansions. In fact, most sub-Saharan populations share ancestry with groups from outside of their current geographic region as a result of gene-flow within the last 4000 years. Our in-depth analysis provides insight into haplotype sharing across different ethno-linguistic groups and the recent movement of alleles into new environments, both of which are relevant to studies of genetic epidemiology. DOI: http://dx.doi.org/10.7554/eLife.15266.001, eLife digest Our genomes contain a record of historical events. This is because when groups of people are separated for generations, the DNA sequence in the two groups’ genomes will change in different ways. Looking at the differences in the genomes of people from the same population can help researchers to understand and reconstruct the historical interactions that brought their ancestors together. The mixing of two populations that were previously separate is known as admixture. Africa as a continent has few written records of its history. This means that it is somewhat unknown which important movements of people in the past generated the populations found in modern-day Africa. Busby et al. have now attempted to use DNA to look into this and reconstruct the last 4000 years of genetic history in African populations. As has been shown in other regions of the world, the new analysis showed that all African populations are the result of historical admixture events. However, Busby et al. could characterize these events to unprecedented level of detail. For example, multiple ethnic groups from The Gambia and Mali all show signs of sharing the same set of ancestors from West Africa, Europe and Asia who mixed around 2000 years ago. Evidence of a migration of people from Central West Africa, known as the Bantu expansion, could also be detected, and was shown to carry genes to the south and east. An important next step will be to now look at the consequences of the observed gene-flow, and ask if it has contributed to spreading beneficial, or detrimental, mutations around Africa. DOI: http://dx.doi.org/10.7554/eLife.15266.002

Introduction: Faecal peritonitis (FP) is a common cause of sepsis and admission to the intensive care unit (ICU). The Genetics of Sepsis and Septic Shock in Europe (GenOSept) project is investigating the influence of genetic variation on the host response and outcomes in a large cohort of patients with sepsis admitted to ICUs across Europe. Here we report an epidemiological survey of the subset of patients with FP. Objectives: To define the clinical characteristics, outcomes and risk factors for mortality in patients with FP admitted to ICUs across Europe. Methods: Data was extracted from electronic case report forms. Phenotypic data was recorded using a detailed, quality-assured clinical database. The primary outcome measure was 6-month mortality. Patients were followed for 6 months. Kaplan-Meier analysis was used to determine mortality rates. Cox proportional hazards regression analysis was employed to identify independent risk factors for mortality. Results: Data for 977 FP patients admitted to 102 centres across 16 countries between 29 September 2005 and 5 January 2011 was extracted. The median age was 69.2 years (IQR 58.3-77.1), with a male preponderance (54.3 %). The most common causes of FP were perforated diverticular disease (32.1 %) and surgical anastomotic breakdown (31.1 %). The ICU mortality rate at 28 days was 19.1 %, increasing to 31.6 % at 6 months. The cause of FP, pre-existing co-morbidities and time from estimated onset of symptoms to surgery did not impact on survival. The strongest independent risk factors associated with an increased rate of death at 6 months included age, higher APACHE II score, acute renal and cardiovascular dysfunction within 1 week of admission to ICU, hypothermia, lower haematocrit and bradycardia on day 1 of ICU stay. Conclusions: In this large cohort of patients admitted to European ICUs with FP the 6 month mortality was 31.6 %. The most consistent predictors of mortality across all time points were increased age, development of acute renal dysfunction during the first week of admission, lower haematocrit and hypothermia on day 1 of ICU admission. © 2013 Springer-Verlag Berlin Heidelberg and ESICM.

Background Genome-wide association studies (GWAS) of single nucleotide polymorphisms (SNPs) have been successful in identifying loci contributing genetic effects to a wide range of complex human diseases and quantitative traits. The traditional approach to GWAS analysis is to consider each phenotype separately, despite the fact that many diseases and quantitative traits are correlated with each other, and often measured in the same sample of individuals. Multivariate analyses of correlated phenotypes have been demonstrated, by simulation, to increase power to detect association with SNPs, and thus may enable improved detection of novel loci contributing to diseases and quantitative traits. Results We have developed the SCOPA software to enable GWAS analysis of multiple correlated phenotypes. The software implements “reverse regression” methodology, which treats the genotype of an individual at a SNP as the outcome and the phenotypes as predictors in a general linear model. SCOPA can be applied to quantitative traits and categorical phenotypes, and can accommodate imputed genotypes under a dosage model. The accompanying META-SCOPA software enables meta-analysis of association summary statistics from SCOPA across GWAS. Application of SCOPA to two GWAS of high-and low-density lipoprotein cholesterol, triglycerides and body mass index, and subsequent meta-analysis with META-SCOPA, highlighted stronger association signals than univariate phenotype analysis at established lipid and obesity loci. The META-SCOPA meta-analysis also revealed a novel signal of association at genome-wide significance for triglycerides mapping to GPC5 (lead SNP rs71427535, p = 1.1x10−8), which has not been reported in previous large-scale GWAS of lipid traits. Conclusions The SCOPA and META-SCOPA software enable discovery and dissection of multiple phenotype association signals through implementation of a powerful reverse regression approach.

Association studies can focus on candidate gene(s), a particular genomic region, or adopt a genome-wide association approach, each of which has implications for marker selection. The strategy for marker selection will affect the statistical power of the study to detect a disease association and is a crucial element of study design. The abundant single nucleotide polymorphisms (SNPs) are the markers of choice in genetic case-control association studies. The genotypes of neighboring SNPs are often highly correlated ('in linkage disequilibrium', LD) within a population, which is utilized for selecting specific 'tagSNPs' to serve as proxies for other nearby SNPs in high LD. General guidelines for SNP selection in candidate genes/regions and genome-wide studies are provided in this protocol, along with illustrative examples. Publicly available web-based resources are utilized to browse and retrieve data, and software, such as Haploview and Goldsurfer2, is applied to investigate LD and to select tagSNPs.

Association replication studies have a poor track record and, even when successful, often claim association with different markers, alleles, and phenotypes than those reported in the primary study. It is unknown whether these outcomes reflect genuine associations or false-positive results. A greater understanding of these observations is essential for genomewide association (GWA) studies, since they have the potential to identify multiple new associations that that will require external validation. Theoretically, a repeat association with precisely the same variant in an independent sample is the gold standard for replication, but testing additional variants is commonplace in replication studies. Finding different associated SNPs within the same gene or region as that originally identified is often reported as confirmatory evidence. Here, we compare the probability of replicating a gene or region under two commonly used marker-selection strategies: an “exact” approach that involves only the originally significant markers and a “local” approach that involves both the originally significant markers and others in the same region. When a region of high intermarker linkage disequilibrium is tested to replicate an initial finding that is only weak association with disease, the local approach is a good strategy. Otherwise, the most powerful and efficient strategy for replication involves testing only the initially identified variants. Association with a marker other than that originally identified can occur frequently, even in the presence of real effects in a low-powered replication study, and instances of such association increase as the number of included variants increases. Our results provide a basis for the design and interpretation of GWA replication studies and point to the importance of a clear distinction between fine mapping and replication after GWA.

Inherited genetic variation has a critical but as yet largely uncharacterized role in human disease. Here we report a public database of common variation in the human genome: more than one million single nucleotide polymorphisms (SNPs) for which accurate and complete genotypes have been obtained in 269 DNA samples from four populations, including ten 500-kilobase regions in which essentially all information about common DNA variation has been extracted. These data document the generality of recombination hotspots, a block-like structure of linkage disequilibrium and low haplotype diversity, leading to substantial correlations of SNPs with many of their neighbours. We show how the HapMap resource can guide the design and analysis of genetic association studies, shed light on structural variation and recombination, and identify loci that may have been subject to natural selection during human evolution. © 2005 Nature Publishing Group., link_to_OA_fulltext

INTRODUCTION Community acquired pneumonia (CAP) is the most common infectious reason for admission to the Intensive Care Unit (ICU). The GenOSept study was designed to determine genetic influences on sepsis outcome. Phenotypic data was recorded using a robust clinical database allowing a contemporary analysis of the clinical characteristics, microbiology, outcomes and independent risk factors in patients with severe CAP admitted to ICUs across Europe. METHODS Kaplan-Meier analysis was used to determine mortality rates. A Cox Proportional Hazards (PH) model was used to identify variables independently associated with 28-day and six-month mortality. RESULTS Data from 1166 patients admitted to 102 centres across 17 countries was extracted. Median age was 64 years, 62% were male. Mortality rate at 28 days was 17%, rising to 27% at six months. Streptococcus pneumoniae was the commonest organism isolated (28% of cases) with no organism identified in 36%. Independent risk factors associated with an increased risk of death at six months included APACHE II score (hazard ratio, HR, 1.03; confidence interval, CI, 1.01-1.05), bilateral pulmonary infiltrates (HR1.44; CI 1.11-1.87) and ventilator support (HR 3.04; CI 1.64-5.62). Haematocrit, pH and urine volume on day one were all associated with a worse outcome. CONCLUSIONS The mortality rate in patients with severe CAP admitted to European ICUs was 27% at six months. Streptococcus pneumoniae was the commonest organism isolated. In many cases the infecting organism was not identified. Ventilator support, the presence of diffuse pulmonary infiltrates, lower haematocrit, urine volume and pH on admission were independent predictors of a worse outcome.

Faecal peritonitis (FP) is a common cause of sepsis and admission to the intensive care unit (ICU). The Genetics of Sepsis and Septic Shock in Europe (GenOSept) project is investigating the influence of genetic variation on the host response and outcomes in a large cohort of patients with sepsis admitted to ICUs across Europe. Here we report an epidemiological survey of the subset of patients with FP.To define the clinical characteristics, outcomes and risk factors for mortality in patients with FP admitted to ICUs across Europe.Data was extracted from electronic case report forms. Phenotypic data was recorded using a detailed, quality-assured clinical database. The primary outcome measure was 6-month mortality. Patients were followed for 6 months. Kaplan-Meier analysis was used to determine mortality rates. Cox proportional hazards regression analysis was employed to identify independent risk factors for mortality.Data for 977 FP patients admitted to 102 centres across 16 countries between 29 September 2005 and 5 January 2011 was extracted. The median age was 69.2 years (IQR 58.3-77.1), with a male preponderance (54.3%). The most common causes of FP were perforated diverticular disease (32.1%) and surgical anastomotic breakdown (31.1%). The ICU mortality rate at 28 days was 19.1%, increasing to 31.6% at 6 months. The cause of FP, pre-existing co-morbidities and time from estimated onset of symptoms to surgery did not impact on survival. The strongest independent risk factors associated with an increased rate of death at 6 months included age, higher APACHE II score, acute renal and cardiovascular dysfunction within 1 week of admission to ICU, hypothermia, lower haematocrit and bradycardia on day 1 of ICU stay.In this large cohort of patients admitted to European ICUs with FP the 6 month mortality was 31.6%. The most consistent predictors of mortality across all time points were increased age, development of acute renal dysfunction during the first week of admission, lower haematocrit and hypothermia on day 1 of ICU admission.

Multiple rare variants either within or across genes have been hypothesised to collectively influence complex human traits. The increasing availability of high throughput sequencing technologies offers the opportunity to study the effect of rare variants on these traits. However, appropriate and computationally efficient analytical methods are required to account for collections of rare variants that display a combination of protective, deleterious and null effects on the trait. We have developed a novel method for the analysis of rare genetic variation in a gene, region or pathway that, by simply aggregating summary statistics at each variant, can: (i) test for the presence of a mixture of effects on a trait; (ii) be applied to both binary and quantitative traits in population-based and family-based data; (iii) adjust for covariates to allow for non-genetic risk factors and; (iv) incorporate imputed genetic variation. In addition, for preliminary identification of promising genes, the method can be applied to association summary statistics, available from meta-analysis of published data, for example, without the need for individual level genotype data. Through simulation, we show that our method is immune to the presence of bi-directional effects, with no apparent loss in power across a range of different mixtures, and can achieve greater power than existing approaches as long as summary statistics at each variant are robust. We apply our method to investigate association of type-1 diabetes with imputed rare variants within genes in the major histocompatibility complex using genotype data from the Wellcome Trust Case Control Consortium., Author Summary Rapid advances in sequencing technology mean that it is now possible to directly assay rare genetic variation. In addition, the availability of almost fully sequenced human genomes by the 1000 Genomes Project allows genotyping at rare variants that are not present on arrays commonly used in genome-wide association studies. Rare variants within a gene or region may act to collectively influence a complex trait. Methods for testing these rare variants should be able to account for a combination of those that serve to either increase, decrease or have no effect on the trait of interest. Here, we introduce a method for the analysis of a collection of rare genetic variants, within a gene or region, which assesses evidence for a mixture of effects. Our method simply aggregates summary statistics at each variant and, as such, can be applied to both population and family-based data, to binary or quantitative traits and to either directly genotyped or imputed data. In addition, it does not require individual level genotype or phenotype data, and can be adjusted for non-genetic risk factors. We illustrate our approach by examining imputed rare variants in the major histocompatibility complex for association with type-1 diabetes using genotype data from the Wellcome Trust case Control Consortium.

Assuming continuous, normally distributed environmental and categorical genotype variables, the authors compare 6 case-only designs for tests of association in gene-environment interaction. Novel tests modeling the environmental variable as either the response or the predictor and allowing a genetic variable with multiallelic variants are included. The authors show that tests imposing the same genotypic pattern of inheritance perform similarly regardless of whether genotype is the response variable or the predictor variable. The novel tests using the genetic variable as the response variable are advantageous because they are robust to non-normally distributed environmental exposures. Dominance deviance-deviation from additivity in the main or interaction effects-is key to test performance: When it is zero or modest, tests searching for a trend with increasing risk alleles are optimal; when it is large, tests for genotypic effects are optimal. However, the authors show that dominance deviance is attenuated when it is observed at a proxy locus, which is common in genome-wide association studies, so large dominance deviance is likely to be rare. The authors conclude that the trend test is the appropriate tool for large-scale association scans where the true gene-environment interaction model is unknown. The common practice of assuming a dominant pattern of inheritance can cause serious losses of power in the presence of any recessive, or modest dominant, effects.

Significant allele flipping, where associations for the same disease occur at opposite alleles of the same bi-allelic locus, is increasing. But when is a significant allele flip genuine? We address the statistical issues of claiming and observing genuine allele flips in actual samples. We show that unless an allele flip is genuine, the probability of observing a significant allele flip in samples ascertained similarly from a common population is negligible. We derive expressions for the expected values of commonly used measures of association, which confirm previous findings that the underlying mechanism of a genuine allele flip is variation in the haplotype frequencies and show further how this variation interacts with variation in the genetic effects to impact allele flipping. We show that for association testing at proxy SNPs, common in genome-wide association studies, variation in haplotype frequencies must coincide with a reversal in the sign of linkage disequilibrium (LD) to trigger genuine allele flips. Using HapMap data and r, rather than r2, to highlight previously unobserved effects, we show that unless genetic effects are large, variation in LD is unlikely to cause genuine allele flips in samples drawn from the same population. However, as populations diverge, it is an increasingly viable cause of a genuine allele flip for sufficiently large genetic effect and/or sample sizes. We conclude that evidence of variation in local patterns of LD, ancestral composition of study samples, and environmental exposures between study populations can provide compelling practical evidence in defense of a genuine allele flip. Genet. Epidemiol. 34: 266–274, 2010. © 2009 Wiley-Liss, Inc.

To identify genetic factors influencing quantitative traits of biomedical importance, we conducted a genome-wide association study in 8,842 samples from population-based cohorts recruited in Korea. For height and body mass index, most variants detected overlapped those reported in European samples. For the other traits examined, replication of promising GWAS signals in 7,861 independent Korean samples identified six previously unknown loci. For pulse rate, signals reaching genome-wide significance mapped to chromosomes 1q32 (rs12731740, P = 2.9 x 10(-9)) and 6q22 (rs12110693, P = 1.6 x 10(-9)), with the latter approximately 400 kb from the coding sequence of GJA1. For systolic blood pressure, the most compelling association involved chromosome 12q21 and variants near the ATP2B1 gene (rs17249754, P = 1.3 x 10(-7)). For waist-hip ratio, variants on chromosome 12q24 (rs2074356, P = 7.8 x 10(-12)) showed convincing associations, although no regional transcript has strong biological candidacy. Finally, we identified two loci influencing bone mineral density at multiple sites. On chromosome 7q31, rs7776725 (within the FAM3C gene) was associated with bone density at the radius (P = 1.0 x 10(-11)), tibia (P = 1.6 x 10(-6)) and heel (P = 1.9 x 10(-10)). On chromosome 7p14, rs1721400 (mapping close to SFRP4, a frizzled protein gene) showed consistent associations at the same three sites (P = 2.2 x 10(-3), P = 1.4 x 10(-7) and P = 6.0 x 10(-4), respectively). This large-scale GWA analysis of well-characterized Korean population-based samples highlights previously unknown biological pathways.

Interest is increasing in epistasis as a possible source of the unexplained variance missed by genome-wide association studies. The Genetic Analysis Workshop 16 Group 9 participants evaluated a wide variety of classical and novel analytical methods for detecting epistasis, in both the statistical and machine learning paradigms, applied to both real and simulated data. Because the magnitude of epistasis is clearly relative to scale of penetrance, and therefore to some extent, to the choice of model framework, it is not surprising that strong interactions under one model might be minimized or even disappear entirely under a different modeling framework.

Effective population size (Ne) determines the amount of genetic variation, genetic drift, and linkage disequilibrium (LD) in populations. Here, we present the first genome-wide estimates of human effective population size from LD data. Chromosome-specific effective population size was estimated for all autosomes and the X chromosome from estimated LD between SNP pairs Ne are lower than previously published estimates based on heterozygosity, possibly because they represent one or more bottlenecks in human population size that occurred ∼10,000 to 200,000 years ago.

Parent-offspring trios are widely collected for disease gene-mapping studies and are being extensively genotyped as part of the International HapMap Project. With dense maps of markers on trios, the effects of LD and linkage can be separated, allowing estimation of recombination rates in a model-free setting. Here we define a model-free multipoint method on the basis of dense sequence polymorphism data from parent-offspring trios to estimate intermarker recombination rates. We use simulations to show that this method has up to 92% power to detect recombination hotspots of intensity 25 times background over a region of size 10 kb typed at density 1 marker per 2.5 kb and almost 100% power to detect large hotspots of intensity >125 times background over regions of size 10 kb typed with just 1 marker per 5 kb (α = 0.05). We found strong agreement at megabase scales between estimates from our method applied to HapMap trio data and estimates from the genetic map. At finer scales, using Centre d'Etude du Polymorphisme Humain (CEPH) pedigree data across a 10-Mb region of chromosome 20, a comparison of population recombination rate estimates obtained from our method with estimates obtained using a coalescent-based approximate-likelihood method implemented in PHASE 2.0 shows detection of the same coldspots and most hotspots: The Spearman rank correlation between the estimates from our method and those from PHASE is 0.58 (p < 2.2−16).

Patients admitted to intensive care following surgery for faecal peritonitis present particular challenges in terms of clinical management and risk assessment that require close collaboration between surgical and intensive care teams [1]. We aimed at establishing whether dynamic assessment of trends in selected variables may be associated with outcomes, and therefore inform medical decision-making.

We compare and contrast case-only designs for detecting gene × gene (G × G) interaction in rheumatoid arthritis (RA) using the genome-wide data provided by Genetic Analysis Workshop 16 Problem 1. Logistic as well as novel multinomial and proportional odds models that do not depend on the specification of additive or dominant models for susceptibility loci were applied to the case-only sample. We identified 519 significant interactions (p < 1 × 10-4 in at least one test). All methods detected unique significant interactions; 169 were common to more than one model and only 21 were common to all models. Results emphasize that categorization of the genetic variables and choice of regression model are critical and hugely influential in the identification of G × G. Porportional odds and multinomial methods provide new tools for identification of G × G interactions.