The gut microbiome is a significant risk factor for future chronic lung disease.

BACKGROUND: The gut-lung axis is generally recognized, but there are few large studies of the gut microbiome and incident respiratory disease in adults. OBJECTIVE: We sought to investigate the association and predictive capacity of the gut microbiome for incident asthma and chronic obstructive pulmonary disease (COPD). METHODS: Shallow metagenomic sequencing was performed for stool samples from a prospective, population-based cohort (FINRISK02; N = 7115 adults) with linked national administrative health register-derived classifications for incident asthma and COPD up to 15 years after baseline. Generalized linear models and Cox regressions were used to assess associations of microbial taxa and diversity with disease occurrence. Predictive models were constructed using machine learning with extreme gradient boosting. Models considered taxa abundances individually and in combination with other risk factors, including sex, age, body mass index, and smoking status. RESULTS: A total of 695 and 392 statistically significant associations were found between baseline taxonomic groups and incident asthma and COPD, respectively. Gradient boosting decision trees of baseline gut microbiome abundance predicted incident asthma and COPD in the validation data sets with mean area under the curves of 0.608 and 0.780, respectively. Cox analysis showed that the baseline gut microbiome achieved higher predictive performance than individual conventional risk factors, with C-indices of 0.623 for asthma and 0.817 for COPD. The integration of the gut microbiome and conventional risk factors further improved prediction capacities. CONCLUSIONS: The gut microbiome is a significant risk factor for incident asthma and incident COPD and is largely independent of conventional risk factors.

Multivariate Genome-wide Association Analysis of a Cytokine Network Reveals Variants with Widespread Immune, Haematological, and Cardiometabolic Pleiotropy.

Cytokines are essential regulatory components of the immune system, and their aberrant levels have been linked to many disease states. Despite increasing evidence that cytokines operate in concert, many of the physiological interactions between cytokines, and the shared genetic architecture that underlies them, remain unknown. Here, we aimed to identify and characterize genetic variants with pleiotropic effects on cytokines. Using three population-based cohorts (n = 9,263), we performed multivariate genome-wide association studies (GWAS) for a correlation network of 11 circulating cytokines, then combined our results in meta-analysis. We identified a total of eight loci significantly associated with the cytokine network, of which two (PDGFRB and ABO) had not been detected previously. In addition, conditional analyses revealed a further four secondary signals at three known cytokine loci. Integration, through the use of Bayesian colocalization analysis, of publicly available GWAS summary statistics with the cytokine network associations revealed shared causal variants between the eight cytokine loci and other traits; in particular, cytokine network variants at the ABO, SERPINE2, and ZFPM2 loci showed pleiotropic effects on the production of immune-related proteins, on metabolic traits such as lipoprotein and lipid levels, on blood-cell-related traits such as platelet count, and on disease traits such as coronary artery disease and type 2 diabetes.

The intersect of genetics, environment, and microbiota in asthma-perspectives and challenges.

In asthma, a significant portion of the interaction between genetics and environment occurs through microbiota. The proposed mechanisms behind this interaction are complex and at times contradictory. This review covers recent developments in our understanding of this interaction: the "microbial hypothesis" and the "farm effect"; the role of endotoxin and genetic variation in pattern recognition systems; the interaction with allergen exposure; the additional involvement of host gut and airway microbiota; the role of viral respiratory infections in interaction with the 17q21 and CDHR3 genetic loci; and the importance of in utero and early-life timing of exposures. We propose a unified framework for understanding how all these phenomena interact to drive asthma pathogenesis. Finally, we point out some future challenges for continued research in this field, in particular the need for multiomic integration, as well as the potential utility of asthma endotyping.

Developmental patterns in the nasopharyngeal microbiome during infancy are associated with asthma risk.

BACKGROUND: Studies indicate that the nasal microbiome may correlate strongly with the presence or future risk of childhood asthma. OBJECTIVES: In this study, we tested whether developmental trajectories of the nasopharyngeal microbiome in early life and the composition of the microbiome during illnesses were related to risk of childhood asthma. METHODS: Children participating in the Childhood Origins of Asthma study (N = 285) provided nasopharyngeal mucus samples in the first 2 years of life, during routine healthy study visits (at 2, 4, 6, 9, 12, 18, and 24 months of age), and during episodes of respiratory illnesses, all of which were analyzed for respiratory viruses and bacteria. We identified developmental trajectories of early-life microbiome composition, as well as predominant bacteria during respiratory illnesses, and we correlated these with presence of asthma at 6, 8, 11, 13, and 18 years of age. RESULTS: Of the 4 microbiome trajectories identified, a Staphylococcus-dominant microbiome in the first 6 months of life was associated with increased risk of recurrent wheezing by age 3 years and asthma that persisted throughout childhood. In addition, this trajectory was associated with the early onset of allergic sensitization. During wheezing illnesses, detection of rhinoviruses and predominance of Moraxella were associated with asthma that persisted throughout later childhood. CONCLUSION: In infancy, the developmental composition of the microbiome during healthy periods and the predominant microbes during acute wheezing illnesses are both associated with the subsequent risk of developing persistent childhood asthma.

Vitamin D over the first decade and susceptibility to childhood allergy and asthma.

BACKGROUND: Vitamin D (25(OH)D) deficiency has been implicated as a possible risk factor for asthma development, but studies at selected time points measuring 25(OH)D levels during childhood have yielded conflicting findings. Prospective studies tracking 25(OH)D levels during the initiation phase of asthma in early childhood have not been reported. OBJECTIVE: We sought to elucidate relationships between 25(OH)D levels from birth to age 10 years and susceptibility to allergic sensitization, respiratory tract infections, and asthma. METHODS: Asthma-, allergy-, and respiratory tract infection-associated phenotypes (including pathogen identification) were characterized in a high-risk birth cohort. Plasma 25(OH)D concentrations were quantified at birth and at clinical follow-ups at the ages of 0.5, 1, 2, 3, 4, 5, and 10 years, and relationships with clinical outcomes were examined. RESULTS: Cross-sectional analyses demonstrated inverse associations between 25(OH)D concentrations and the risk for concurrent sensitization at age 0.5, 2, and 3 years, and mixed-effects regression demonstrated inverse longitudinal associations of 25(OH)D levels with both sensitization and eczema. Multivariate regression modeling suggested that the number of 25(OH)D-deficient follow-ups was positively associated with risk for asthma/wheeze, eczema, and sensitization at 10 years; adjustment for sensitization (particularly by 2 years) in the asthma/wheeze models reduced 25(OH)D associations with these latter outcomes. 25(OH)D levels were also inversely associated with early nasopharyngeal colonization with Streptococcus species and age of first febrile lower respiratory illness, both of which are known asthma risk factors. CONCLUSION: 25(OH)D deficiency in early childhood is associated with increased risk for persistent asthma, potentially through modulating susceptibility to early allergic sensitization, upper respiratory tract colonization with bacterial pathogens, or both. These relationships are only evident if 25(OH)D status is monitored prospectively and longitudinally.

The lower airway microbiota in early cystic fibrosis lung disease: a longitudinal analysis.

RATIONALE: In infants and young children with cystic fibrosis, lower airway infection and inflammation are associated with adverse respiratory outcomes. However, the role of lower airway microbiota in the pathogenesis of early cystic fibrosis lung disease remains uncertain. OBJECTIVES: To assess the development of the lower airway microbiota over time in infants and young children with cystic fibrosis, and to explore its association with airway inflammation and pulmonary function at age 6 years. METHODS: Serial, semi-annual bronchoscopies and bronchoalveolar lavage (BAL) procedures were performed in infants newly diagnosed with cystic fibrosis following newborn screening. Quantitative microbiological cultures and inflammatory marker (interleukin 8 and neutrophil elastase) measurements were undertaken contemporaneously. 16S ribosomal RNA gene sequencing was conducted on stored BAL samples. Spirometry results recorded at 6 years of age were extracted from medical records. MEASUREMENTS AND MAIN RESULTS: Ninety-five BAL samples provided 16S ribosomal RNA gene data. These were collected from 48 subjects aged 1.2-78.3 months, including longitudinal samples from 27 subjects and 13 before age 6 months. The lower airway microbiota varied, but diversity decreased with advancing age. Detection of recognised cystic fibrosis bacterial pathogens was associated with reduced microbial diversity and greater lower airway inflammation. There was no association between the lower airway microbiota and pulmonary function at age 6 years. CONCLUSIONS: In infants with cystic fibrosis, the lower airway microbiota is dynamic. Dominance of the microbiota by recognised cystic fibrosis bacterial pathogens is associated with increased lower airway inflammation, however early microbial diversity is not associated with pulmonary function at 6 years of age.

Deep whole-genome sequencing of 100 southeast Asian Malays.

Whole-genome sequencing across multiple samples in a population provides an unprecedented opportunity for comprehensively characterizing the polymorphic variants in the population. Although the 1000 Genomes Project (1KGP) has offered brief insights into the value of population-level sequencing, the low coverage has compromised the ability to confidently detect rare and low-frequency variants. In addition, the composition of populations in the 1KGP is not complete, despite the fact that the study design has been extended to more than 2,500 samples from more than 20 population groups. The Malays are one of the Austronesian groups predominantly present in Southeast Asia and Oceania, and the Singapore Sequencing Malay Project (SSMP) aims to perform deep whole-genome sequencing of 100 healthy Malays. By sequencing at a minimum of 30× coverage, we have illustrated the higher sensitivity at detecting low-frequency and rare variants and the ability to investigate the presence of hotspots of functional mutations. Compared to the low-pass sequencing in the 1KGP, the deeper coverage allows more functional variants to be identified for each person. A comparison of the fidelity of genotype imputation of Malays indicated that a population-specific reference panel, such as the SSMP, outperforms a cosmopolitan panel with larger number of individuals for common SNPs. For lower-frequency (<5%) markers, a larger number of individuals might have to be whole-genome sequenced so that the accuracy currently afforded by the 1KGP can be achieved. The SSMP data are expected to be the benchmark for evaluating the value of deep population-level sequencing versus low-pass sequencing, especially in populations that are poorly represented in population-genetics studies.

The changing impacts of social determinants on youth mental health in Australia.

AIMS: Most lifetime mental health disorders begin by age 25 years, and the prevalence among young people has been increasing over recent years. We sought to understand what impact, if any, social determinants have had on this increase through the analysis of an Australian longitudinal dataset (with data from 2007 to 2021). METHODS: The analysis focused on five social determinants: loneliness and lack of social support, family relationships, participation in education and employment, receipt of government benefits and relative socio-economic status. We analysed cross-sectional changes in self-reported psychological distress between 2007 and 2021 (using the Kessler-10 item; K10 scores) and examined the effects of these five social determinants on psychological distress using weighted linear regression models. RESULTS: We identified a significant increase in psychological distress among Australians from 2007 to 2021, with the sharpest rise among those aged 15 to 25 years, who saw more than doubling in the percentage of high and very high K10. This period also saw an increase in the prevalence of social determinants such as loneliness and lack of social support, as well as poor family relationships, particularly in 2021 post COVID-19 pandemic. Regression models suggest loneliness and lack of social support had the most pronounced and increasing impact on psychological distress, followed by poor family relationships. DISCUSSION: The observed significant and steady increases in psychological distress and related social determinant factors, particularly loneliness and lack of social support among young people, highlight the urgent need for comprehensive actions. Coordinated research and community-based initiatives are needed to deliver intrapersonal, interpersonal and socially-focused interventions with a holistic approach to support psychosocial wellbeing. Policymakers must adopt a comprehensive shift in political commitment and a whole-of-government approach to address these challenges.

Integration of polygenic and gut metagenomic risk prediction for common diseases.

Multiomics has shown promise in noninvasive risk profiling and early detection of various common diseases. In the present study, in a prospective population-based cohort with ~18 years of e-health record follow-up, we investigated the incremental and combined value of genomic and gut metagenomic risk assessment compared with conventional risk factors for predicting incident coronary artery disease (CAD), type 2 diabetes (T2D), Alzheimer disease and prostate cancer. We found that polygenic risk scores (PRSs) improved prediction over conventional risk factors for all diseases. Gut microbiome scores improved predictive capacity over baseline age for CAD, T2D and prostate cancer. Integrated risk models of PRSs, gut microbiome scores and conventional risk factors achieved the highest predictive performance for all diseases studied compared with models based on conventional risk factors alone. The present study demonstrates that integrated PRSs and gut metagenomic risk models improve the predictive value over conventional risk factors for common chronic diseases.

Inequitable access to mental healthcare for socially excluded adolescents.

BACKGROUND: Adolescence is a critical period for mental health and social exclusion, a key social determinant of mental health. Early intervention approaches are key to mitigating the impact of mental ill-health during adolescence, however social exclusion can create additional barriers to accessing care. AIM: We aimed to better understand help-seeking experiences of adolescents facing co-occurring social exclusion and mental ill-health, including sources of support, barriers and preferences for service provision. METHOD: Cross-sectional data were analysed, from the 2022 Mission Australia Youth Survey (N = 18,800). Adolescents aged 15 to 19 years were recruited from around Australia, through schools, community organisations and digital platforms. Indices of four domains of social exclusion (housing, finances, relational and education/employment) were created using existing Youth Survey variables, and supplemented with demographic characteristics, psychological distress and help-seeking behaviours (perceived need, mental health supports, barriers to access and preferences). Relationships between social exclusion domains, mental health concerns and help-seeking behaviours were explored using logistic regression models. RESULTS: A total of 9,743 young people reported having needed mental health support, yet only 58.1% reportedly sought support (n = 5,565). Social exclusion domains were associated with different help-seeking behaviours: housing challenges with higher help-seeking (OR = 1.28; 95% CI [1.15, 1.42]); relational difficulties and edu-employment issues with lower (OR = 0.75; 95% CI [0.68, 0.83] and OR = 0.82; 95% CI [0.75, 0.89]). Stigma, confidentiality concerns, cost and not knowing where to seek help were common barriers to help-seeking; those experiencing social exclusion more likely to report these. Participants reported a strong preference for face-to-face support. CONCLUSIONS: This study highlights the additional needs and challenges faced by adolescents dealing with both social exclusion and mental ill-health. With greater barriers to help-seeking, concerted efforts are needed to reduce stigma, improve mental health literacy and increase access to trusted information sources. Further initiatives should focus on structural factors that socially exclude young people and exacerbate inequitable access to mental healthcare.

Changes in Rates of Special Considerations in Higher Education Applications Pre- and During the COVID-19 Pandemic in Victoria, Australia.

BACKGROUND AND AIMS: Since the onset of the COVID-19 pandemic, a significant rise in mental ill health has been observed globally in young people, particularly those in their final years of secondary school. Students' negative experiences coincide with a critical transitional period which can disrupt milestones in social and educational development. This study aimed to use innovative population-level data to map the impact of the pandemic on students entering higher education. METHODS: Pre-pandemic (2019/2020) and pandemic (2020/2021) tertiary education application data were obtained from the Victorian Tertiary Admissions Centre. Prevalence of applications for special consideration related to mental ill health were compared between cohorts across various geographical areas and applicant demographic subgroups. Relative risk regression models were used to understand the role of different risk factors. RESULTS: Rates of mental health-related special consideration applications increased by 38% among all applications (pre-pandemic: 7.8%, n = 56 916; pandemic: 10.8%, n = 58 260). Highest increases were observed among students in areas with both extended and close-quarter lockdown experiences, and areas impacted by 2019/2020 black summer bushfires. The increases were higher among Year 12 students and students with other special consideration needs (e.g., physical condition, learning disability). Slightly higher increases were observed in areas with higher socio-economic status, which may potentially be related to inequality in mental health service access. CONCLUSION: As consequences of mental health difficulties and academic disruption in youth can be long lasting, it is critical to establish a mental health support framework both in and outside of higher education to facilitate young people's recovery from the pandemic.

Statistical challenges associated with detecting copy number variations with next-generation sequencing.

MOTIVATION: Analysing next-generation sequencing (NGS) data for copy number variations (CNVs) detection is a relatively new and challenging field, with no accepted standard protocols or quality control measures so far. There are by now several algorithms developed for each of the four broad methods for CNV detection using NGS, namely the depth of coverage (DOC), read-pair, split-read and assembly-based methods. However, because of the complexity of the genome and the short read lengths from NGS technology, there are still many challenges associated with the analysis of NGS data for CNVs, no matter which method or algorithm is used. RESULTS: In this review, we describe and discuss areas of potential biases in CNV detection for each of the four methods. In particular, we focus on issues pertaining to (i) mappability, (ii) GC-content bias, (iii) quality control measures of reads and (iv) difficulty in identifying duplications. To gain insights to some of the issues discussed, we also download real data from the 1000 Genomes Project and analyse its DOC data. We show examples of how reads in repeated regions can affect CNV detection, demonstrate current GC-correction algorithms, investigate sensitivity of DOC algorithm before and after quality control of reads and discuss reasons for which duplications are harder to detect than deletions.

Multi-platform segmentation for joint detection of copy number variants.

MOTIVATION: With the expansion of whole-genome studies, there is rapid evolution of genotyping platforms. This leads to practical issues such as upgrading of genotyping equipment which often results in research groups having data from different platforms for the same samples. While having more data can potentially yield more accurate copy-number estimates, combining such data is not straightforward as different platforms show different degrees of attenuation of the true copy-number or different noise characteristics and marker panels. Currently, there is still a relative lack of procedures for combining information from different platforms. RESULTS: We develop a method, called MPSS, based on a correlated random-effect model for the unobserved patterns and extend the robust smooth segmentation approach to the multiple-platform scenario. We also propose an objective criterion for discrete segmentation required for downstream analyses. For each identified segment, the software reports a P-value to indicate the likelihood of the segment being a true CNV. From the analyses of real and simulated data, we show that MPSS has better operating characteristics when compared to single-platform methods, and have substantially higher sensitivity compared to an existing multiplatform method. AVAILABILITY: The methods are implemented in an R package MPSS, and the source is available from http://www.meb.ki.se/~yudpaw.

Regions of homozygosity in three Southeast Asian populations.

The genomes of outbred populations were first shown in 2006 to contain regions of homozygosity (ROHs) of several megabases. Further studies have also investigated the characteristics of ROHs in healthy individuals in various populations but there are no studies on Singapore populations to date. This study aims to identify and investigate the characteristics of ROHs in three Singapore populations. A total of 268 samples (96 Chinese, 89 Malays and 83 Indians) are genotyped on Illumina Human 1 M Beadchip and Affymetrix Genome-Wide Human SNP Array 6.0. We use the PennCNV algorithm to detect ROHs. We report an abundance of ROHs (≥500 kb), with an average of more than one hundred regions per individual. On average, the Indian population has the lowest number of ROHs and smallest total length of ROHs per individual compared with the Chinese and Malay populations. We further investigate the relationship between the occurrence of ROHs and haplotype frequency, regional linkage disequilibrium (LD) and positive selection. Based on the results of this data set, we find that the frequency of occurrence of ROHs is positively associated with haplotype frequency and regional LD. The majority of regions detected for recent positive selection and regions with differential LD between populations overlap with the ROH loci. When we consider both the location of the ROHs and the allelic form of the ROHs, we are able to separate the populations by principal component analysis, demonstrating that ROHs contain information on population structure and the demographic history of a population.

Early prediction of incident liver disease using conventional risk factors and gut-microbiome-augmented gradient boosting.

The gut microbiome has shown promise as a predictive biomarker for various diseases. However, the potential of gut microbiota for prospective risk prediction of liver disease has not been assessed. Here, we utilized shallow shotgun metagenomic sequencing of a large population-based cohort (N > 7,000) with ∼15 years of follow-up in combination with machine learning to investigate the predictive capacity of gut microbial predictors individually and in conjunction with conventional risk factors for incident liver disease. Separately, conventional and microbial factors showed comparable predictive capacity. However, microbiome augmentation of conventional risk factors using machine learning significantly improved the performance. Similarly, disease-free survival analysis showed significantly improved stratification using microbiome-augmented models. Investigation of predictive microbial signatures revealed previously unknown taxa for liver disease, as well as those previously associated with hepatic function and disease. This study supports the potential clinical validity of gut metagenomic sequencing to complement conventional risk factors for prediction of liver diseases.

Regions of homozygosity and their impact on complex diseases and traits.

Regions of homozygosity (ROHs) are more abundant in the human genome than previously thought. These regions are without heterozygosity, i.e. all the genetic variations within the regions have two identical alleles. At present there are no standardized criteria for defining the ROHs resulting in the different studies using their own criteria in the analysis of homozygosity. Compared to the era of genotyping microsatellite markers, the advent of high-density single nucleotide polymorphism genotyping arrays has provided an unparalleled opportunity to comprehensively detect these regions in the whole genome in different populations. Several studies have identified ROHs which were associated with complex phenotypes such as schizophrenia, late-onset of Alzheimer's disease and height. Collectively, these studies have conclusively shown the abundance of ROHs larger than 1 Mb in outbred populations. The homozygosity association approach holds great promise in identifying genetic susceptibility loci harboring recessive variants for complex diseases and traits.

A population-based study of copy number variants and regions of homozygosity in healthy Swedish individuals.

The abundance of copy number variants (CNVs) and regions of homozygosity (ROHs) have been well documented in previous studies. In addition, their roles in complex diseases and traits have since been increasingly appreciated. However, only a limited amount of CNV and ROH data is currently available for the Swedish population. We conducted a population-based study to detect and characterize CNVs and ROHs in 87 randomly selected healthy Swedish individuals using the Affymetrix SNP Array 6.0. More than 600 CNV loci were detected in the population using two different CNV-detection algorithms (PennCNV and Birdsuite). A total of 196 loci were consistently identified by both algorithms, suggesting their reliability. Numerous disease-associated and pharmacogenetics-related genes were found to be overlapping with common CNV loci such as CFHR1/R3, LCE3B/3C, UGT2B17 and GSTT1. Correlation analysis between copy number polymorphisms (CNPs) and genome-wide association studies-identified single-nucleotide polymorphisms also indicates the potential roles of several CNPs as causal variants for diseases and traits such as body mass index, Crohn's disease and multiple sclerosis. In addition, we also identified a total of 14 815 ROHs 500 kb or 2814 ROHs 1M in the Swedish individuals with an average of 170 and 32 regions detected per individual respectively. Approximately 141 Mb or 4.92% of the genome is homozygous in each individual of the Swedish population. This is the first population-based study to investigate the population characteristics of CNVs and ROHs in the Swedish population. This study found many CNV loci that warrant further investigation, and also highlighted the abundance and importance of investigating ROHs for their associations with complex diseases and traits.

Copy number polymorphisms in new HapMap III and Singapore populations.

Copy number variations can be identified using newer genotyping arrays with higher single nucleotide polymorphisms (SNPs) density and copy number probes accompanied by newer algorithms. McCarroll et al. (2008) applied these to the HapMap II samples and identified 1316 copy number polymorphisms (CNPs). In our study, we applied the same approach to 859 samples from three Singapore populations and seven HapMap III populations. Approximately 50% of the 1291 autosomal CNPs were found to be polymorphic only in populations of non-African ancestry. Pairwise comparisons among the 10 populations showed substantial differences in the CNPs frequencies. Additionally, 698 CNPs showed significant differences with false discovery rate (FDR)<0.01 among the 10 populations and these loci overlap with known disease-associated or pharmacogenetic-related genes such as CFHR3 and CFHR1 (age related macular degeneration), GSTTI (metabolism of various carcinogenic compounds and cancers) and UGT2B17 (prostate cancer and graft-versus-host disease). The correlations between CNPs and genome-wide association studies-SNPs were investigated and several loci, which were previously unreported, that may potentially be implicated in complex diseases and traits were found; for example, childhood acute lymphoblastic leukaemia, age-related macular degeneration, breast cancer, response to antipsychotic treatment, rheumatoid arthritis and type-1 diabetes. Additionally, we also found 5014 novel copy number loci that have not been reported previously by McCarroll et al. (2008) in the 10 populations.

Integrative analysis of the plasma proteome and polygenic risk of cardiometabolic diseases.

Cardiometabolic diseases are frequently polygenic in architecture, comprising a large number of risk alleles with small effects spread across the genome1-3. Polygenic scores (PGS) aggregate these into a metric representing an individual's genetic predisposition to disease. PGS have shown promise for early risk prediction4-7 and there is an open question as to whether PGS can also be used to understand disease biology8. Here, we demonstrate that cardiometabolic disease PGS can be used to elucidate the proteins underlying disease pathogenesis. In 3,087 healthy individuals, we found that PGS for coronary artery disease, type 2 diabetes, chronic kidney disease and ischaemic stroke are associated with the levels of 49 plasma proteins. Associations were polygenic in architecture, largely independent of cis and trans protein quantitative trait loci and present for proteins without quantitative trait loci. Over a follow-up of 7.7 years, 28 of these proteins associated with future myocardial infarction or type 2 diabetes events, 16 of which were mediators between polygenic risk and incident disease. Twelve of these were druggable targets with therapeutic potential. Our results demonstrate the potential for PGS to uncover causal disease biology and targets with therapeutic potential, including those that may be missed by approaches utilizing information at a single locus.

Luminal microbiota related to Crohn's disease recurrence after surgery.

BACKGROUND: Microbial factors are likely to be involved in the recurrence of Crohn's disease (CD) after bowel resection. We investigated the luminal microbiota before and longitudinally after surgery, in relation to disease recurrence, using 16S metagenomic techniques. METHODS: In the prospective Post-Operative Crohn's Endoscopic Recurrence (POCER) study, fecal samples were obtained before surgery and 6, 12, and 18 months after surgery from 130 CD patients. Endoscopy was undertaken to detect disease recurrence, defined as Rutgeerts score ≥i2, at 6 months in two-thirds of patients and all patients at 18 months after surgery. The V2 region of the 16S rRNA gene was sequenced using Illumina MiSeq. Cluster analysis was performed at family level, assessing microbiome community differences between patients with and without recurrence. RESULTS: Six microbial cluster groups were identified. The cluster associated with maintenance of remission was enriched for the Lachnospiraceae family [adjusted OR 0.47 (0.27-0.82), P = .007]. The OTU diversity of Lachnospiraceae within this cluster was significantly greater than in all other clusters. The cluster enriched for Enterobacteriaceae was associated with an increased risk of disease recurrence [adjusted OR 6.35 (1.24-32.44), P = .026]. OTU diversity of Enterobacteriaceae within this cluster was significantly greater than in other clusters. CONCLUSIONS: Luminal bacterial communities are associated with protection from, and the occurrence of, Crohn's disease recurrence after surgery. Recurrence may relate to a higher abundance of facultatively anaerobic pathobionts from the Enterobacteriaceae family. The ecologic change of depleted Lachnospiraceae, a genus of butyrate-producing bacteria, may permit expansion of Enterobacteriaceae through luminal environmental perturbation.