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The avian origin A/H7N9 influenza virus causes high admission rates (>99%) and mortality (>30%), with ultimately favourable outcomes ranging from rapid recovery to prolonged hospitalization. Using a multicolour assay for monitoring adaptive and innate immunity, here we dissect the kinetic emergence of different effector mechanisms across the spectrum of H7N9 disease and recovery. We find that a diversity of response mechanisms contribute to resolution and survival. Patients discharged within 2–3 weeks have early prominent H7N9-specific CD8+ T-cell responses, while individuals with prolonged hospital stays have late recruitment of CD8+/CD4+ T cells and antibodies simultaneously (recovery by week 4), augmented even later by prominent NK cell responses (recovery >30 days). In contrast, those who succumbed have minimal influenza-specific immunity and little evidence of T-cell activation. Our study illustrates the importance of robust CD8+ T-cell memory for protection against severe influenza disease caused by newly emerging influenza A viruses., H7N9 avian influenza viruses can cause severe human disease. Here, the authors analyse blood samples from hospitalized H7N9 patients and show that a diversity of immune mechanisms seem to influence disease length and outcome.

Integrative analyses of high-throughput omics data have elucidated the aetiology and pathogenesis for complex traits and diseases, and the linking of omics information to electronic health records promises new insights into human health and disease. Recent nuclear magnetic resonance (NMR) spectroscopy biomarker profiling has implicated glycoprotein acetyls (GlycA) as a biomarker for cardiovascular risk and all-cause mortality. To elucidate biological processes contributing to GlycA- associated mortality risk, we leveraged human omics data from three population- based cohorts together with nation-wide Finnish hospital and mortality records. Elevated GlycA was associated with myriad infection-related inflammatory processes. Within individuals, elevated GlycA levels were stable over long time periods, up to a decade, and chronically elevated GlycA was also associated with modest elevation of numerous cytokines. Individuals with elevated GlycA also showed increased expression of a transcriptional sub-network, the Neutrophil Degranulation Module (NDM), suggesting an increased activity of microbe-driven immune response. Subsequent analysis of nation-wide hospitalisation and death records was consistent with a microbial basis for GlycA-associated mortality, with each standard deviation increase in GlycA raising an individual's future risk of hospitalization and death from non-localized infection by 40% and 136%, respectively. These results show that, beyond its established role in acute-phase response, elevated GlycA is more broadly a biomarker for low-grade chronic inflammation and increased neutrophil activity. Further, increased risk of susceptibility to severe microbial-infection events in healthy individuals suggests this inflammation is a contributor to mortality risk. Taken together, this study demonstrates the power of an integrative approach that combines omics data and health records to delineate the biological processes underlying a newly discovered biomarker, providing a model strategy for future systems medicine studies.

Article first published online 3 April 2015., Background: The intestinal microbiota is involved in the pathogenesis of inflammatory bowel disease. A reduction in the diversity of the intestinal microbiota as well as specific taxonomic and functional shifts have been reported in Crohn's disease and may play a central role in the inflammatory process. The aim was to systematically review recent developments in the structural and functional changes observed in the gastrointestinal microbiome in patients with Crohn's Disease. Results: Seventy-two abstracts were included in this review. The effects of host genetics, disease phenotype, and inflammatory bowel disease treatment on the gastrointestinal microbiome in Crohn's disease were reviewed, and taxonomic shifts in patients with early and established disease were described. The relative abundance of Bacteroidetes is increased and Firmicutes decreased in Crohn's disease compared with healthy controls. Enterobacteriaceae, specifically Eschericia coli, is enriched in Crohn's disease. Faecalibacterium prausnitzii is found at lower abundance in Crohn's disease and in those with postoperative recurrence. Observed functional changes include major shifts in oxidative stress pathways, a decrease in butanoate and propanoate metabolism gene expression, lower levels of butyrate, and other short-chain fatty acids, decreased carbohydrate metabolism, and decreased amino acid biosynthesis. Conclusions: Changes in microbial composition and function have been described, although a causative role remains to be established. Larger, prospective, and longitudinal studies are required with deep interrogation of the microbiome if causality is to be determined, and refined microbial manipulation is to emerge as a focused therapy.

There is a pressing need to reduce the high global disease burden of rheumatic heart disease (RHD) and its harbinger, acute rheumatic fever (ARF). ARF is a classical example of an autoimmune syndrome and is of particular immunological interest because it follows a known antecedent infection with group A streptococcus (GAS). However, the poorly understood immunopathology of these post-infectious diseases means that, compared to much progress in other immune-mediated diseases, we still lack useful biomarkers, new therapies or an effective vaccine in ARF and RHD. Here, we summarise recent literature on the complex interaction between GAS and the human host that culminates in ARF and the subsequent development of RHD. We contrast ARF with other post-infectious streptococcal immune syndromes - post-streptococcal glomerulonephritis (PSGN) and the still controversial paediatric autoimmune neuropsychiatric disorders associated with streptococcal infections (PANDAS), in order to highlight the potential significance of variations in the host immune response to GAS. We discuss a model for the pathogenesis of ARF and RHD in terms of current immunological concepts and the potential for application of in depth "omics" technologies to these ancient scourges.

The nasopharynx (NP) is a reservoir for microbes associated with acute respiratory illnesses (ARI). The development of asthma is initiated during infancy, driven by airway inflammation associated with infections. Here, we report viral and bacterial community profiling of NP aspirates across a birth cohort, capturing all lower respiratory illnesses during their first year. Most infants were initially colonized with Staphylococcus or Corynebacterium before stable colonization with Alloiococcus or Moraxella, with transient incursions of Streptococcus, Moraxella or Haemophilus marking virus-associated ARIs. Our data identify the NP microbiome as a determinant for infection spread to the lower airways, severity of accompanying inflammatory symptoms, and risk for future asthma development. Early asymptomatic colonization with Streptococcus was a strong asthma predictor, and antibiotic usage disrupted asymptomatic colonization patterns.

The human intestinal type IIb Na+-Picotransporter (hNaPi-IIb) gene promoter lacks a TATA box and has a high GC content in the 5′-flanking region. To understand the mechanism of hNaPi-IIb gene transcription, the current study was performed to characterize the minimal promoter region and transcriptional factor(s) necessary to activate gene expression in human intestinal cells (Caco-2). With the use of progressively shorter promoter constructs, a minimal promoter extending from bp −58 to +15 was identified and shown to direct high levels of hNaPi-IIb cotransporter expression in Caco-2 cells. Gel mobility shift assays (GMSAs) indicated that two regions could be bound by nuclear proteins from Caco-2 cells: region A at bp −26/−23 and region B at bp −44/−35. The introduction of mutations in region A abolished promoter activity, whereas mutations in region B had no effect. Deletion mutants of the same regions showed identical results. Furthermore, DNase I footprinting experiments confirmed the observation made by GMSAs. Additional studies, which used a specific nuclear factor 1 (NF1) antiserum, demonstrated that NF1 protein(s) binds to the minimal promoter at region A. These results indicated that the NF1 protein(s) is required to activate the basal transcription of hNaPi-IIb gene under normal growth conditions. This study has thus identified a new target gene in the small intestinal epithelium that is directly regulated by NF1 transcriptional factor(s).

The recently cloned NaPi-IIb cotransporter is an apical membrane protein that is involved in the absorption of phosphate in the intestine. To expedite functional and structural studies, the human intestinal NaPi-IIb cotransporter was stably expressed in hamster fibroblast (PS120) cells. The hNaPi-IIb cDNA stably transfected cells exhibited a 1.8-fold higher sodium-dependent phosphate uptake than vector DNA transfected cells, and had a K(m) for Pi of approximately 106 microM and a K(m) for Na(+) of approximately 34 mM. The hNaPi-IIb cotransporter was also expressed in Xenopus oocytes and it exhibited a K(m) for Pi of approximately 113 microM and a K(m) for Na(+) of approximately 65 mM. The hNaPi-IIb cotransporter expressed in both PS120 cells and oocytes was inhibited by high external pH. Furthermore, the phosphate uptake mediated by the hNaPi-IIb cotransporter was inhibited by 5 mM phosphonoformic acid (PFA), 1 mM arsenate and 100 nM phorbol myristate acetate (PMA). These results demonstrate that the human intestinal NaPi-IIb cotransporter is functional when expressed in hamster fibroblasts, and that this model system may be useful in the future to identify NaPi-IIb cotransporter-specific inhibitors.

Rapid molecular typing of bacterial pathogens is critical for public health epidemiology, surveillance and infection control, yet routine use of whole genome sequencing (WGS) for these purposes poses significant challenges. Here we present SRST2, a read mapping-based tool for fast and accurate detection of genes, alleles and multi-locus sequence types (MLST) from WGS data. Using >900 genomes from common pathogens, we show SRST2 is highly accurate and outperforms assembly-based methods in terms of both gene detection and allele assignment. We include validation of SRST2 within a public health laboratory, and demonstrate its use for microbial genome surveillance in the hospital setting. In the face of rising threats of antimicrobial resistance and emerging virulence among bacterial pathogens, SRST2 represents a powerful tool for rapidly extracting clinically useful information from raw WGS data. Source code is available from http://katholt.github.io/srst2/. Electronic supplementary material The online version of this article (doi:10.1186/s13073-014-0090-6) contains supplementary material, which is available to authorized users.

Exome sequencing studies in complex diseases are challenged by the allelic heterogeneity, large number and modest effect sizes of associated variants on disease risk and the presence of large numbers of neutral variants, even in phenotypically relevant genes. Isolated populations with recent bottlenecks offer advantages for studying rare variants in complex diseases as they have deleterious variants that are present at higher frequencies as well as a substantial reduction in rare neutral variation. To explore the potential of the Finnish founder population for studying low-frequency (0.5–5%) variants in complex diseases, we compared exome sequence data on 3,000 Finns to the same number of non-Finnish Europeans and discovered that, despite having fewer variable sites overall, the average Finn has more low-frequency loss-of-function variants and complete gene knockouts. We then used several well-characterized Finnish population cohorts to study the phenotypic effects of 83 enriched loss-of-function variants across 60 phenotypes in 36,262 Finns. Using a deep set of quantitative traits collected on these cohorts, we show 5 associations (p, National Institutes of Health (U.S.) (NIH RC2 HL-102925)

Coronary artery disease (CAD) is a complex disease driven by myriad interactions of genetics and environmental factors. Traditionally, studies have analyzed only one disease factor at a time, providing useful but limited understanding of the underlying etiology. Recent advances in cost-effective and high-throughput technologies, such as single nucleotide polymorphism (SNP) genotyping, exome/genome sequencing, gene expression microarrays and metabolomics assays have enabled the collection of millions of data points in many thousands of individuals. In order to make sense of such 'omics' data, effective analytical methods are needed. We review and highlight some of the main results in this area, focusing on integrative approaches that consider multiple modalities simultaneously. Such analyses have the potential to uncover the genetic basis of CAD, produce genomic risk scores (GRS) for disease prediction, disentangle the complex interactions underlying disease, and predict response to treatment.

Epistasis has long been thought to contribute to the genetic aetiology of complex diseases, yet few robust epistatic interactions in humans have been detected. We have conducted exhaustive genome-wide scans for pairwise epistasis in five independent celiac disease (CD) case-control studies, using a rapid model-free approach to examine over 500 billion SNP pairs in total. We found 20 significant epistatic signals within the HLA region which achieved stringent replication criteria across multiple studies and were independent of known CD risk HLA haplotypes. The strongest independent CD epistatic signal corresponded to genes in the HLA class III region, in particular PRRC2A and GPANK1/C6orf47, which are known to contain variants for non-Hodgkin's lymphoma and early menopause, co-morbidities of celiac disease. Replicable evidence for epistatic variants outside the MHC was not observed. Both within and between European populations, we observed striking consistency of epistatic models and epistatic model distribution. Within the UK population, models of CD based on both epistatic and additive single-SNP effects increased explained CD variance by approximately 1% over those of single SNPs. Models of only epistatic pairs or additive single-SNPs showed similar levels of CD variance explained, indicating the existence of a substantial overlap of additive and epistatic components. Our findings have implications for the determination of genetic architecture and, by extension, the use of human genetics for validation of therapeutic targets.

Practical application of genomic-based risk stratification to clinical diagnosis is appealing yet performance varies widely depending on the disease and genomic risk score (GRS) method. Celiac disease (CD), a common immune-mediated illness, is strongly genetically determined and requires specific HLA haplotypes. HLA testing can exclude diagnosis but has low specificity, providing little information suitable for clinical risk stratification. Using six European cohorts, we provide a proof-of-concept that statistical learning approaches which simultaneously model all SNPs can generate robust and highly accurate predictive models of CD based on genome-wide SNP profiles. The high predictive capacity replicated both in cross-validation within each cohort (AUC of 0.87–0.89) and in independent replication across cohorts (AUC of 0.86–0.9), despite differences in ethnicity. The models explained 30–35% of disease variance and up to ∼43% of heritability. The GRS's utility was assessed in different clinically relevant settings. Comparable to HLA typing, the GRS can be used to identify individuals without CD with ≥99.6% negative predictive value however, unlike HLA typing, fine-scale stratification of individuals into categories of higher-risk for CD can identify those that would benefit from more invasive and costly definitive testing. The GRS is flexible and its performance can be adapted to the clinical situation by adjusting the threshold cut-off. Despite explaining a minority of disease heritability, our findings indicate a genomic risk score provides clinically relevant information to improve upon current diagnostic pathways for CD and support further studies evaluating the clinical utility of this approach in CD and other complex diseases., Author Summary Celiac disease (CD) is a common immune-mediated illness, affecting approximately 1% of the population in Western countries but the diagnostic process remains sub-optimal. The development of CD is strongly dependent on specific human leukocyte antigen (HLA) genes, and HLA testing to identify CD susceptibility is now commonly undertaken in clinical practice. The clinical utility of HLA typing is to exclude CD when the CD susceptibility HLA types are absent, but notably, most people who possess HLA types imparting susceptibility for CD never develop CD. Therefore, while genetic testing in CD can overcome several limitations of the current diagnostic tools, the utility of HLA typing to identify those individuals at increased-risk of CD is limited. Using large datasets assaying single nucleotide polymorphisms (SNPs), we have developed genomic risk scores (GRS) based on multiple SNPs that can more accurately predict CD risk across several populations in “real world” clinical settings. The GRS can generate predictions that optimize CD risk stratification and diagnosis, potentially reducing the number of unnecessary follow-up investigations. The medical and economic impact of improving CD diagnosis is likely to be significant, and our findings support further studies into the role of personalized GRS's for other strongly heritable human diseases.

Principal component analysis (PCA) is routinely used to analyze genome-wide single-nucleotide polymorphism (SNP) data, for detecting population structure and potential outliers. However, the size of SNP datasets has increased immensely in recent years and PCA of large datasets has become a time consuming task. We have developed flashpca, a highly efficient PCA implementation based on randomized algorithms, which delivers identical accuracy in extracting the top principal components compared with existing tools, in substantially less time. We demonstrate the utility of flashpca on both HapMap3 and on a large Immunochip dataset. For the latter, flashpca performed PCA of 15,000 individuals up to 125 times faster than existing tools, with identical results, and PCA of 150,000 individuals using flashpca completed in 4 hours. The increasing size of SNP datasets will make tools such as flashpca essential as traditional approaches will not adequately scale. This approach will also help to scale other applications that leverage PCA or eigen-decomposition to substantially larger datasets.

To identify the susceptibility gene in hand osteoarthritis (OA) the authors used a two-stage approach genomewide association study using two discovery samples (the TwinsUK cohort and the Rotterdam discovery subset; a total of 1804 subjects) and four replication samples (the Chingford Study, the Chuvasha Skeletal Aging Study, the Rotterdam replication subset and the Genetics, Arthrosis, and Progression (GARP) Study; a total of 3266 people). Five single-nucleotide polymorphisms (SNPs) had a likelihood of association with hand OA in the discovery stage and one of them (rs716508), was successfully confirmed in the replication stage (meta-analysis p = 1.81×10 -5). The C allele conferred a reduced risk of 33% to 41% using a case-control definition. The SNP is located in intron 1 of the A2BP1 gene. This study also found that the same allele of the SNP significantly reduced bone density at both the hip and spine (p

Background A central goal of genomics is to predict phenotypic variation from genetic variation. Fitting predictive models to genome-wide and whole genome single nucleotide polymorphism (SNP) profiles allows us to estimate the predictive power of the SNPs and potentially develop diagnostic models for disease. However, many current datasets cannot be analysed with standard tools due to their large size. Results We introduce SparSNP, a tool for fitting lasso linear models for massive SNP datasets quickly and with very low memory requirements. In analysis on a large celiac disease case/control dataset, we show that SparSNP runs substantially faster than four other state-of-the-art tools for fitting large scale penalised models. SparSNP was one of only two tools that could successfully fit models to the entire celiac disease dataset, and it did so with superior performance. Compared with the other tools, the models generated by SparSNP had better than or equal to predictive performance in cross-validation. Conclusions Genomic datasets are rapidly increasing in size, rendering existing approaches to model fitting impractical due to their prohibitive time or memory requirements. This study shows that SparSNP is an essential addition to the genomic analysis toolkit. SparSNP is available at http://www.genomics.csse.unimelb.edu.au/SparSNP

Association testing of multiple correlated phenotypes offers better power than univariate analysis of single traits. We analyzed 6,600 individuals from two population-based cohorts with both genome-wide SNP data and serum metabolomic profiles. From the observed correlation structure of 130 metabolites measured by nuclear magnetic resonance, we identified 11 metabolic networks and performed a multivariate genome-wide association analysis. We identified 34 genomic loci at genome-wide significance, of which 7 are novel. In comparison to univariate tests, multivariate association analysis identified nearly twice as many significant associations in total. Multi-tissue gene expression studies identified variants in our top loci, SERPINA1 and AQP9, as eQTLs and showed that SERPINA1 and AQP9 expression in human blood was associated with metabolites from their corresponding metabolic networks. Finally, liver expression of AQP9 was associated with atherosclerotic lesion area in mice, and in human arterial tissue both SERPINA1 and AQP9 were shown to be upregulated (6.3-fold and 4.6-fold, respectively) in atherosclerotic plaques. Our study illustrates the power of multi-phenotype GWAS and highlights candidate genes for atherosclerosis.

Background Multi-locus sequence typing (MLST) has become the gold standard for population analyses of bacterial pathogens. This method focuses on the sequences of a small number of loci (usually seven) to divide the population and is simple, robust and facilitates comparison of results between laboratories and over time. Over the last decade, researchers and population health specialists have invested substantial effort in building up public MLST databases for nearly 100 different bacterial species, and these databases contain a wealth of important information linked to MLST sequence types such as time and place of isolation, host or niche, serotype and even clinical or drug resistance profiles. Recent advances in sequencing technology mean it is increasingly feasible to perform bacterial population analysis at the whole genome level. This offers massive gains in resolving power and genetic profiling compared to MLST, and will eventually replace MLST for bacterial typing and population analysis. However given the wealth of data currently available in MLST databases, it is crucial to maintain backwards compatibility with MLST schemes so that new genome analyses can be understood in their proper historical context. Results We present a software tool, SRST, for quick and accurate retrieval of sequence types from short read sets, using inputs easily downloaded from public databases. SRST uses read mapping and an allele assignment score incorporating sequence coverage and variability, to determine the most likely allele at each MLST locus. Analysis of over 3,500 loci in more than 500 publicly accessible Illumina read sets showed SRST to be highly accurate at allele assignment. SRST output is compatible with common analysis tools such as eBURST, Clonal Frame or PhyloViz, allowing easy comparison between novel genome data and MLST data. Alignment, fastq and pileup files can also be generated for novel alleles. Conclusions SRST is a novel software tool for accurate assignment of sequence types using short read data. Several uses for the tool are demonstrated, including quality control for high-throughput sequencing projects, plasmid MLST and analysis of genomic data during outbreak investigation. SRST is open-source, requires Python, BWA and SamTools, and is available from http://srst.sourceforge.net.

The challenge of comparing two or more genomes that have undergone recombination and substantial amounts of segmental loss and gain has recently been addressed for small numbers of genomes. However, datasets of hundreds of genomes are now common and their sizes will only increase in the future. Multiple sequence alignment of hundreds of genomes remains an intractable problem due to quadratic increases in compute time and memory footprint. To date, most alignment algorithms are designed for commodity clusters without parallelism. Hence, we propose the design of a multiple sequence alignment algorithm on massively parallel, distributed memory supercomputers to enable research into comparative genomics on large data sets. Following the methodology of the sequential progressiveMauve algorithm, we design data structures including sequences and sorted k-mer lists on the IBM Blue Gene/P supercomputer (BG/P). Preliminary results show that we can reduce the memory footprint so that we can potentially align over 250 bacterial genomes on a single BG/P compute node. We verify our results on a dataset of E.coli, Shigella and S.pneumoniae genomes. Our implementation returns results matching those of the original algorithm but in 1/2 the time and with 1/4 the memory footprint for scaffold building. In this study, we have laid the basis for multiple sequence alignment of large-scale datasets on a massively parallel, distributed memory supercomputer, thus enabling comparison of hundreds instead of a few genome sequences within reasonable time.

Following the widespread use of genome-wide association studies to elucidate the genetic architectures of complex phenotypes, there has been a push to augment existing observational studies with additional layers of molecular information. The resulting high-dimensional data have led the emergence of research in integrative systems biology. Here, we examine recent progress in characterizing biological networks as well as the corresponding conceptual and analytical challenges. Using examples from metabolomics, we contend that integrative systems biology should prompt a re-examination of conventional phenotypic measures where heterogeneous or correlated phenotypes can be fine-mapped. Although still in its infancy, it is apparent that the large-scale characterization of molecular systems will transform our understanding of phenotype, biology and pathogenesis.

Publisher Summary This chapter begins with the bias and error in genotype calling that are obvious for large database. Any systematic bias in calling can reduce both the power and coverage of a study. This is especially true for case-control studies where informative missingness, differential patterns of missing genotype calls between cases and controls can lead to an artificial inflation in the number of “significant associations.” To identify potential informative missingness or other areas of bias, quantile-quantile plots of observed and expected test statistics are typically generated and the degree of inflation is determined and the genotype calling algorithm directly targets this inflation. There are different strategies one can take to reduce bias. When there are cluster shifts between cases and controls (or indeed for any kind of batch processing), calling the cases and controls separately can alleviate test statistic inflation. Genotype imputation can offer a powerful solution to informative missingness as well as increase the power and coverage of a study. The chapter further concerns the genotyping platforms, normalization algorithms, genotype calling from a single array, the illuminus algorithm, and various other genotype calling algorithms.

Recent genome-wide (GW) scans have identified several independent loci affecting human stature, but their contribution through the different skeletal components of height is still poorly understood. We carried out a genome-wide scan in 12,611 participants, followed by replication in an additional 7,187 individuals, and identified 17 genomic regions with GW-significant association with height. Of these, two are entirely novel (rs11809207 in CATSPER4, combined P-value = 6.1×10−8 and rs910316 in TMED10, P-value = 1.4×10−7) and two had previously been described with weak statistical support (rs10472828 in NPR3, P-value = 3×10−7 and rs849141 in JAZF1, P-value = 3.2×10−11). One locus (rs1182188 at GNA12) identifies the first height eQTL. We also assessed the contribution of height loci to the upper- (trunk) and lower-body (hip axis and femur) skeletal components of height. We find evidence for several loci associated with trunk length (including rs6570507 in GPR126, P-value = 4×10−5 and rs6817306 in LCORL, P-value = 4×10−4), hip axis length (including rs6830062 at LCORL, P-value = 4.8×10−4 and rs4911494 at UQCC, P-value = 1.9×10−4), and femur length (including rs710841 at PRKG2, P-value = 2.4×10−5 and rs10946808 at HIST1H1D, P-value = 6.4×10−6). Finally, we used conditional analyses to explore a possible differential contribution of the height loci to these different skeletal size measurements. In addition to validating four novel loci controlling adult stature, our study represents the first effort to assess the contribution of genetic loci to three skeletal components of height. Further statistical tests in larger numbers of individuals will be required to verify if the height loci affect height preferentially through these subcomponents of height., Author Summary The first genetic association studies of adult height have confirmed a role of many common variants in influencing human height, but to date, the genetic basis of differences between different skeletal components of height have not been addressed. Here, we take advantage of recent technical and methodological advances to examine the role of common genetic variants on both height and skeletal components of height. By examining nearly 20,000 individuals from the UK and the Netherlands, we provide statistically significant evidence that 17 genomic regions are associated with height, including four novel regions. We also examine, for the first time, the association of these 17 regions with skeletal size measurements of spine, femur, and hip axis length, a measurement of hip geometry known to influence the risk of osteoporotic fractures. We find that some height loci are also associated with these skeletal components, although further statistical tests will be required to verify if these genetic variants act differentially on the individual skeletal measurements. The knowledge generated by this and other studies will not only inform the genetics of human quantitative variation, but will also lead to the potential discovery of many medically important polymorphisms.

To identify previously unknown genetic loci associated with fasting glucose concentrations, we examined the leading association signals in ten genome-wide association scans involving a total of 36,610 individuals of European descent. Variants in the gene encoding melatonin receptor 1B (MTNR1B) were consistently associated with fasting glucose across all ten studies. The strongest signal was observed at rs10830963, where each G allele (frequency 0.30 in HapMap CEU) was associated with an increase of 0.07 (95% CI = 0.06-0.08) mmol/l in fasting glucose levels (P = 3.2 × 10-50) and reduced beta-cell function as measured by homeostasis model assessment (HOMA-B, P = 1.1 × 10 -15). The same allele was associated with an increased risk of type 2 diabetes (odds ratio = 1.09 (1.05-1.12), per G allele P = 3.3 × 10 -7) in a meta-analysis of 13 case-control studies totaling 18,236 cases and 64,453 controls. Our analyses also confirm previous associations of fasting glucose with variants at the G6PC2 (rs560887, P = 1.1 × 10 -57) and GCK (rs4607517, P = 1.0 × 10-25) loci.

Adult height is a model polygenic trait, but there has been limited success in identifying the genes underlying its normal variation. To identify genetic variants influencing adult human height, we used genome-wide association data from 13,665 individuals and genotyped 39 variants in an additional 16,482 samples. We identified 20 variants associated with adult height (P < 5 x 10(-7), with 10 reaching P < 1 x 10(-10)). Combined, the 20 SNPs explain approximately 3% of height variation, with a approximately 5 cm difference between the 6.2% of people with 17 or fewer 'tall' alleles compared to the 5.5% with 27 or more 'tall' alleles. The loci we identified implicate genes in Hedgehog signaling (IHH, HHIP, PTCH1), extracellular matrix (EFEMP1, ADAMTSL3, ACAN) and cancer (CDK6, HMGA2, DLEU7) pathways, and provide new insights into human growth and developmental processes. Finally, our results provide insights into the genetic architecture of a classic quantitative trait.

Genome-wide association studies (GWAS) have enabled a considerable portion of the human genome to be scanned for genetic variants associated with disease etiology. Such large-scale investigations depend on DNA samples of high biological integrity and quality. As clinical DNA is often available in limited quantities, in vitro reproduction of quality template DNA using whole-genome amplification is necessary. The most widely used technique is multiple displacement amplification with φ29 polymerase1 (φ29MDA). Earlier studies do not offer a detailed map of how robust genome-wide panels of 300,000 to 1 million single-nucleotide polymorphisms (SNPs) will perform with φ29MDA2-3. We performed a meta-analysis of 6,541 DNA samples to assess the extent of information lost (Supplementary Table 1 online), and investigated genotype imputation for recovering the statistical power and genomic coverage of GWAS. As previously seen with array-CGH3, we observed that φ29MDA led to differential rates of hybridization compared to genomic DNA, especially in telomeric regions, on both Affymetrix and Illumina arrays (Fig. 1a and Supplementary Fig. 1 online). This correlated to the G+C content of the SNP oligonucleotide probes (Supplementary Fig. 2 online) and to the presence of segmental duplications (Supplementary Table 2 online). In the context of a GWAS, this results in a proportion of SNPs with lower signal strength and increased variability, often resulting in overlapping or heavily scattered genotype clusters for both the allelic signal and strength-contrast scales (Supplementary Fig. 3 online). This increases the uncertainty when assigning genotypes and lowers call rates. The average call rates for SNPs on the Affymetrix array for two British cohorts, with φ29MDA amplified (OBC) and genomic (58C) DNA, were 96.3% and 98.7% respectively. The corresponding call rates for the Illumina arrays for a φ29MDA-amplified cohort (ML) and 58C were 95.9% and 98.5%. Furthermore, missing data were distributed nonrandomly across the genome resulting in significant decreases in genomic coverage when we applied GWAS SNP quality control. By excluding SNPs with call rates < 95.0%, the Affymetrix 500K array experienced a 6.5% drop in coverage from 60.6% to 54.1% when measured on the HapMap CEU population at an r2 threshold of 0.8. The Illumina 650Y, a chip with high tag-SNP content, had an 8.1% decrease (81.3% to 73.2%; Supplementary Table 3 and Supplementary Fig. 4 online). Figure 1 Missingness and imputation of amplified DNA on chromosome 16. (a) The relative performance of amplified DNA to genomic DNA, as quantified by the ratio of hybridization strengths and the standardized difference in mean hybridization strength. Each plot ... Most low call rate SNPs contain individuals with signal intensities found in overlapping genotype clusters. Although the use of custom calling algorithms can potentially alleviate this4, genotype imputation provides a highly promising solution for analyzing regions with sufficient linkage disequilibrium by statistically inferring missing genotypes with high accuracy5 (Supplementary Fig. 5 online). Expanding this strategy to a genome-wide scale, we imputed the regions of data loss for the OBC cohort, and assessed genome coverage and data recovery. Using a probability threshold of 0.90, imputation of all missing genotypes for OBC samples recovered an additional 2.4% of the original φ29MDA dataset, giving an overall call rate of 98.7% across all SNPs. This is comparable to the performance for the 58C. Typically, one can expect to recover 60% of a SNP’s missing genotypes if the initial call rate is >75.0% (Fig. 1b). Imputation rescued 328 (14.9%) samples and 80,613 (16.7%) SNPs. The recovery of SNPs increased genome coverage for the Affymetrix 500K (measured by the HapMap CEU population at a pairwise r2 > 0.8) from 54.1% to 59.7% (with benchmark coverage at 60.6%), while the recovered samples allowed greater power in a GWAS. Although variation in between SNPs across different populations and the SNP content of genotyping arrays may affect the performance of imputation, the statistical inference of missing genotypes presents a powerful solution for genetic studies constrained by severely limited quantities of DNA.

Udgivelsesdato: 2008-Nov-6 Here we present the first diploid genome sequence of an Asian individual. The genome was sequenced to 36-fold average coverage using massively parallel sequencing technology. We aligned the short reads onto the NCBI human reference genome to 99.97% coverage, and guided by the reference genome, we used uniquely mapped reads to assemble a high-quality consensus sequence for 92% of the Asian individual's genome. We identified approximately 3 million single-nucleotide polymorphisms (SNPs) inside this region, of which 13.6% were not in the dbSNP database. Genotyping analysis showed that SNP identification had high accuracy and consistency, indicating the high sequence quality of this assembly. We also carried out heterozygote phasing and haplotype prediction against HapMap CHB and JPT haplotypes (Chinese and Japanese, respectively), sequence comparison with the two available individual genomes (J. D. Watson and J. C. Venter), and structural variation identification. These variations were considered for their potential biological impact. Our sequence data and analyses demonstrate the potential usefulness of next-generation sequencing technologies for personal genomics.

We report results of a nonsynonymous SNP scan for ulcerative colitis and identify a previously unknown susceptibility locus at ECM1. We also show that several risk loci are common to ulcerative colitis and Crohn's disease (IL23R, IL12B, HLA, NKX2-3 and MST1), whereas autophagy genes ATG16L1 and IRGM, along with NOD2 (also known as CARD15), are specific for Crohn's disease. These data provide the first detailed illustration of the genetic relationship between these common inflammatory bowel diseases.

PUBLISHED, PMID: 17558408, We tested 310,605 single-nucleotide polymorphisms for association in 778 celiac disease cases and 1422 controls. Outside the HLA, the most significant finding (rs13119723, P=2.0 ? 10?7, empirical genome-wide significance P=0.045) was in the KIAA1109/Tenr/IL2/IL21 linkage disequilibrium block. Association was independently confirmed in two further collections (strongest at rs6822844, 24kB 5' of IL21, meta-analysis P=1.3 ? 10?14, OR 0.63), suggesting genetic variation in this region predisposes to celiac disease., We acknowledge funding from Coeliac UK; the Coeliac Disease Consortium (an innovative cluster approved by the Netherlands Genomics Initiative and partly funded by the Dutch Government, grant BSIK03009); the Netherlands Genomics Initiative (grant 050-72-425); the Netherlands Organization for Scientific Research (grant 901-04-219); the Science Foundation Ireland and the Wellcome Trust (GR068094MA Clinician Scientist Fellowship to D.A.vH; New Blood Fellowship to R.M.; support for the work of R.McG and P.D.). The authors acknowledge use of DNA from the British 1958 Birth Cohort collection, funded by the UK Medical Research Council grant G0000934 and the Wellcome Trust grant 068545/Z/02.

Motivation: Large-scale genotyping relies on the use of unsupervised automated calling algorithms to assign genotypes to hybridization data. A number of such calling algorithms have been recently established for the Affymetrix GeneChip genotyping technology. Here, we present a fast and accurate genotype calling algorithm for the Illumina BeadArray genotyping platforms. As the technology moves towards assaying millions of genetic polymorphisms simultaneously, there is a need for an integrated and easy-to-use software for calling genotypes. Results: We have introduced a model-based genotype calling algorithm which does not rely on having prior training data or require computationally intensive procedures. The algorithm can assign genotypes to hybridization data from thousands of individuals simultaneously and pools information across multiple individuals to improve the calling. The method can accommodate variations in hybridization intensities which result in dramatic shifts of the position of the genotype clouds by identifying the optimal coordinates to initialize the algorithm. By incorporating the process of perturbation analysis, we can obtain a quality metric measuring the stability of the assigned genotype calls. We show that this quality metric can be used to identify SNPs with low call rates and accuracy. Availability: The C++ executable for the algorithm described here is available by request from the authors. Contact: teo@well.ox.ac.uk or tgc@well.ox.ac.uk

P708 What causes recurrence of Crohn's Disease after intestinal resection? A prospective evaluation of microbiota, smoking and anti-TNF therapy. Results from the POCER study E. Wright*1, M. Kamm1, 2, J. Wagner3, S.M. Teo4, P. De Cruz1, A. Hamilton1, K. Ritchie1, M. Inouye4, C. Kirkwood3 1St Vincent's Hospital & University of Melbourne, Gastroenterology, Melbourne, Australia, 2Imperial College London, Medicine, London, United Kingdom, 3Murdoch Children's Research Institute, Enteric Virus Group, Melbourne, Australia, 4The University of Melbourne, Pathology, Melbourne, Australia

One of the general paradigms for ab initio protein structure prediction involves sampling the conformational space such that a large set of decoy (candidate) structures are generated and then selecting native-like conformations from those decoys using various scoring functions. In this study, based on a physical/geometric approach first suggested by Banavar and colleagues, we formulate a knowledge-based scoring function, which uses the radii of curvature formed among triplets of residues in a protein conformation. By analyzing its performance on various decoy sets, we determine a good set of parameters—the distance cutoff and the number of distance bins—to use for configuring such a function. Furthermore, we investigate the effect of using various approaches for compiling the prior distribution on the performance of the knowledge-based function. Possible extensions to the current form of the residue triplet scoring function are discussed.

The current experiments were designed to study the effect of beta-estradiol on type IIb sodium-coupled phosphate (NaPi-IIb) cotransporter gene expression. Uptake studies with intestinal brush-border membrane vesicles (BBMV) showed that estrogen treatment increased sodium-dependent phosphate absorption by approximately 45% in rat intestine. Northern blot analysis indicated that NaPi-IIb mRNA expression was increased by approximately 50% after estrogen treatment. Western blot analysis also detected an increase in BBMV NaPi-IIb protein expression in estrogen-treated rats. In human intestinal Caco-2 cells, NaPi-IIb mRNA abundance was increased approximately 60% after estrogen treatment, and this increase could be abolished by inhibition of gene transcription. Transfection studies with human NaPi-IIb promoter reporter constructs showed that the promoter was responsive to estrogen treatment. These studies demonstrate for the first time that estrogen stimulates intestinal sodium-dependent phosphate absorption in female rats. This stimulation is associated with increased NaPi-IIb mRNA and protein expression. Thus the effect of estrogen on intestinal Pi absorption may be partially due to activation of NaPi-IIb gene transcription.

The type IIb sodium-phosphate (NaPi-IIb) cotransporter mediates intestinal phosphate absorption. Previous work in our laboratory has shown that EGF inhibited NaPi-IIb cotransporter expression through transcriptional regulation. To understand this regulation, progressively shorter human NaPi-IIb promoter constructs were used to define the EGF response region, and gel mobility shift assays (GMSAs) were used to characterize DNA-protein interactions. Promoter analysis determined that the EGF response region was located between −784 and −729 base pair (bp) of the promoter. GMSAs and overexpression studies revealed an interaction between this promoter region and c-myb transcription factor. Inhibition of EGF receptor activation restored promoter function. Further studies suggested that MAPK, PKC, and/or PKA pathways are involved in this regulation. In conclusion, these studies suggest that EGF decreases human NaPi-IIb gene expression by modifying the c-myb protein such that it inhibits transcriptional activation. We further conclude that this downregulation of promoter function is mediated by EGF-activated PKC/PKA and MAPK pathways. This is the first study that demonstrates involvement of c-myb in the regulation of intestinal nutrient absorption.

Recent advances in our understanding of the genomics of the human metabolome have shed light on the pathways involved in metabolic and cardiovascular disease. Such studies crucially depend on the interpretation of complex molecular spectra. A recent study by Suhre and colleagues provides a way to identify potentially clinically relevant biomarkers without a priori information, such as reference spectra, thus aiding the discovery of additional spectral features and corresponding genomic loci associated with metabolism and disease.

Genetic factors explain a majority of risk variance for age-related macular degeneration (AMD). While genome-wide association studies (GWAS) for late AMD implicate genes in complement, inflammatory and lipid pathways, the genetic architecture of early AMD has been relatively under studied. We conducted a GWAS meta-analysis of early AMD, including 4,089 individuals with prevalent signs of early AMD (soft drusen and/or retinal pigment epithelial changes) and 20,453 individuals without these signs. For various published late AMD risk loci, we also compared effect sizes between early and late AMD using an additional 484 individuals with prevalent late AMD. GWAS meta-analysis confirmed previously reported association of variants at the complement factor H (CFH) (peak P = 1.5×10(-31)) and age-related maculopathy susceptibility 2 (ARMS2) (P = 4.3×10(-24)) loci, and suggested Apolipoprotein E (ApoE) polymorphisms (rs2075650; P = 1.1×10(-6)) associated with early AMD. Other possible loci that did not reach GWAS significance included variants in the zinc finger protein gene GLI3 (rs2049622; P = 8.9×10(-6)) and upstream of GLI2 (rs6721654; P = 6.5×10(-6)), encoding retinal Sonic hedgehog signalling regulators, and in the tyrosinase (TYR) gene (rs621313; P = 3.5×10(-6)), involved in melanin biosynthesis. For a range of published, late AMD risk loci, estimated effect sizes were significantly lower for early than late AMD. This study confirms the involvement of multiple established AMD risk variants in early AMD, but suggests weaker genetic effects on the risk of early AMD relative to late AMD. Several biological processes were suggested to be potentially specific for early AMD, including pathways regulating RPE cell melanin content and signalling pathways potentially involved in retinal regeneration, generating hypotheses for further investigation.

It has been hypothesized that multivariate analysis and systematic detection of epistatic interactions between explanatory genotyping variables may help resolve the problem of "missing heritability" currently observed in genome-wide association studies (GWAS). However, even the simplest bivariate analysis is still held back by significant statistical and computational challenges that are often addressed by reducing the set of analysed markers. Theoretically, it has been shown that combinations of loci may exist that show weak or no effects individually, but show significant (even complete) explanatory power over phenotype when combined. Reducing the set of analysed SNPs before bivariate analysis could easily omit such critical loci. We have developed an exhaustive bivariate GWAS analysis methodology that yields a manageable subset of candidate marker pairs for subsequent analysis using other, often more computationally expensive techniques. Our model-free filtering approach is based on classification using ROC curve analysis, an alternative to much slower regression-based modelling techniques. Exhaustive analysis of studies containing approximately 450,000 SNPs and 5,000 samples requires only 2 hours using a desktop CPU or 13 minutes using a GPU (Graphics Processing Unit). We validate our methodology with analysis of simulated datasets as well as the seven Wellcome Trust Case-Control Consortium datasets that represent a wide range of real life GWAS challenges. We have identified SNP pairs that have considerably stronger association with disease than their individual component SNPs that often show negligible effect univariately. When compared against previously reported results in the literature, our methods re-detect most significant SNP-pairs and additionally detect many pairs absent from the literature that show strong association with disease. The high overlap suggests that our fast analysis could substitute for some slower alternatives. We demonstrate that the proposed methodology is robust, fast and capable of exhaustive search for epistatic interactions using a standard desktop computer. First, our implementation is significantly faster than timings for comparable algorithms reported in the literature, especially as our method allows simultaneous use of multiple statistical filters with low computing time overhead. Second, for some diseases, we have identified hundreds of SNP pairs that pass formal multiple test (Bonferroni) correction and could form a rich source of hypotheses for follow-up analysis. A web-based version of the software used for this analysis is available at http://bioinformatics.research.nicta.com.au/gwis .

Nuclear magnetic resonance assays allow for measurement of a wide range of metabolic phenotypes. We report here the results of a GWAS on 8,330 Finnish individuals genotyped and imputed at 7.7 million SNPs for a range of 216 serum metabolic phenotypes assessed by NMR of serum samples. We identified significant associations (P < 2.31 - 10 g-10) at 31 loci, including 11 for which there have not been previous reports of associations to a metabolic trait or disorder. Analyses of Finnish twin pairs suggested that the metabolic measures reported here show higher heritability than comparable conventional metabolic phenotypes. In accordance with our expectations, SNPs at the 31 loci associated with individual metabolites account for a greater proportion of the genetic component of trait variance (up to 40%) than is typically observed for conventional serum metabolic phenotypes. The identification of such associations may provide substantial insight into cardiometabolic disorders. © 2012 Nature America, Inc. All rights reserved.

The lipid–leukocyte (LL) module is associated with, and reactive to, a wide variety of serum metabolites. The LL module appears to be a link between metabolism, adiposity, and inflammation. Serum metabolite concentrations themselves determine the connectedness of LL module., Comprehensive characterization of human tissues promises novel insights into the biological architecture of human diseases and traits. We assessed metabonomic, transcriptomic, and genomic variation for a large population-based cohort from the capital region of Finland. Network analyses identified a set of highly correlated genes, the lipid–leukocyte (LL) module, as having a prominent role in over 80 serum metabolites (of 134 measures quantified), including lipoprotein subclasses, lipids, and amino acids. Concurrent association with immune response markers suggested the LL module as a possible link between inflammation, metabolism, and adiposity. Further, genomic variation was used to generate a directed network and infer LL module's largely reactive nature to metabolites. Finally, gene co-expression in circulating leukocytes was shown to be dependent on serum metabolite concentrations, providing evidence for the hypothesis that the coherence of molecular networks themselves is conditional on environmental factors. These findings show the importance and opportunity of systematic molecular investigation of human population samples. To facilitate and encourage this investigation, the metabonomic, transcriptomic, and genomic data used in this study have been made available as a resource for the research community.

Objective— Genetic studies might provide new insights into the biological mechanisms underlying lipid metabolism and risk of CAD. We therefore conducted a genome-wide association study to identify novel genetic determinants of low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides. Methods and Results— We combined genome-wide association data from 8 studies, comprising up to 17 723 participants with information on circulating lipid concentrations. We did independent replication studies in up to 37 774 participants from 8 populations and also in a population of Indian Asian descent. We also assessed the association between single-nucleotide polymorphisms (SNPs) at lipid loci and risk of CAD in up to 9 633 cases and 38 684 controls. We identified 4 novel genetic loci that showed reproducible associations with lipids (probability values, 1.6×10 −8 to 3.1×10 −10 ). These include a potentially functional SNP in the SLC39A8 gene for HDL-C, an SNP near the MYLIP/GMPR and PPP1R3B genes for LDL-C, and at the AFF1 gene for triglycerides. SNPs showing strong statistical association with 1 or more lipid traits at the CELSR2 , APOB , APOE-C1-C4-C2 cluster, LPL , ZNF259-APOA5-A4-C3-A1 cluster and TRIB1 loci were also associated with CAD risk (probability values, 1.1×10 −3 to 1.2×10 −9 ). Conclusion— We have identified 4 novel loci associated with circulating lipids. We also show that in addition to those that are largely associated with LDL-C, genetic loci mainly associated with circulating triglycerides and HDL-C are also associated with risk of CAD. These findings potentially provide new insights into the biological mechanisms underlying lipid metabolism and CAD risk.

Recent advances in genome-wide association studies have stimulated interest in the genomic prediction of disease risk, potentially enabling individual-level risk estimates for early intervention and improved diagnostic procedures. Here, we review recent findings and approaches to genomic prediction model construction and performance, then contrast the potential benefits of such models in two complex human diseases, aiding diagnosis in celiac disease and prospective risk prediction for cardiovascular disease. Early indications are that optimal application of genomic risk scores will differ substantially for each disease depending on underlying genetic architecture as well as current clinical and public health practice. As costs decline, genomic profiles become common, and popular understanding of risk and its communication improves, genomic risk will become increasingly useful for the individual and the clinician.

Background Complex diseases are characterized by multiple subtle perturbations to biological processes. New omics platforms can detect these perturbations, but translating the diverse molecular and statistical information into testable mechanistic hypotheses is challenging. Therefore, we set out to create a public tool that integrates these data across multiple datasets, platforms, study designs and species in order to detect the most promising targets for further mechanistic studies. Results We developed Mergeomics, a computational pipeline consisting of independent modules that 1) leverage multi-omics association data to identify biological processes that are perturbed in disease, and 2) overlay the disease-associated processes onto molecular interaction networks to pinpoint hubs as potential key regulators. Unlike existing tools that are mostly dedicated to specific data type or settings, the Mergeomics pipeline accepts and integrates datasets across platforms, data types and species. We optimized and evaluated the performance of Mergeomics using simulation and multiple independent datasets, and benchmarked the results against alternative methods. We also demonstrate the versatility of Mergeomics in two case studies that include genome-wide, epigenome-wide and transcriptome-wide datasets from human and mouse studies of total cholesterol and fasting glucose. In both cases, the Mergeomics pipeline provided statistical and contextual evidence to prioritize further investigations in the wet lab. The software implementation of Mergeomics is freely available as a Bioconductor R package. Conclusion Mergeomics is a flexible and robust computational pipeline for multidimensional data integration. It outperforms existing tools, and is easily applicable to datasets from different studies, species and omics data types for the study of complex traits. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-3198-9) contains supplementary material, which is available to authorized users.

Previous genome-wide association studies (GWAS) of stroke, the second leading cause of death, have been conducted in populations of predominantly European ancestry.1,2 We undertook cross-ancestry GWAS meta-analyses of stroke and its subtypes in 110,182 stroke patients (33% non-European) and 1,503,898 control individuals of five ancestries from population- and clinic-based studies, nearly doubling the number of cases in previous stroke GWAS. We identified association signals at 89 independent loci, of which 61 were novel. Effect sizes were overall highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis using a novel machine-learning approach,3 transcriptome and proteome-wide association analyses revealed putative causal genes (e.g. SH3PXD2A and FURIN) and variants (e.g. at GRK5 and NOS3). Using a novel three-pronged approach,4 we provided genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWAS with vascular risk factor GWAS (iPGS) showed strong prediction of ischemic stroke risk in European and, for the first time, East-Asian populations.5,6 The iPGS performed better than stroke PGS alone and better than previous best iPGS, in Europeans and East-Asians. Transferability of European-specific iPGS to East-Asians was limited. Stroke genetic risk scores were predictive of ischemic stroke independent of clinical risk factors in 52,600 clinical trial participants with cardiometabolic disease and performed considerably better than previous scores, both in Europeans and East-Asians. Altogether our results provide critical insight to inform biology, reveal potential drug targets for intervention, and provide genetic risk prediction tools across ancestries for targeted prevention.

Most published GWAS do not examine SNP interactions due to the high computational complexity of computing p-values for the interaction terms. Our aim is to utilize supercomputing resources to apply complex statistical techniques to the world's accumulating GWAS, epidemiology, survival and pathology data to uncover more information about genetic and environmental risk, biology and aetiology. We performed the Bayesian Posterior Probability test on a pseudo data set with 500,000 single nucleotide polymorphism and 100 samples as proof of principle. We carried out strong scaling simulations on 2 to 4,096 processing cores with factor 2 increments in partition size. On two processing cores, the run time is 317h, i.e. almost two weeks, compared to less than 10 minutes on 4,096 processing cores. The speedup factor is 2,020 that is very close to the theoretical value of 2,048. This work demonstrates the feasibility of performing exhaustive higher order analysis of GWAS studies using independence testing for contingency tables. We are now in a position to employ supercomputers with hundreds of thousands of threads for higher order analysis of GWAS data using complex statistics.

Genome-wide association studies (GWAS) are a common approach for systematic discovery of single nucleotide polymorphisms (SNPs) which are associated with a given disease. Univariate analysis approaches commonly employed may miss important SNP associations that only appear through multivariate analysis in complex diseases. However, multivariate SNP analysis is currently limited by its inherent computational complexity. In this work, we present a computational framework that harnesses supercomputers. Based on our results, we estimate a three-way interaction analysis on 1.1 million SNP GWAS data requiring over 5.8 years on the full "Avoca" IBM Blue Gene/Q installation at the Victorian Life Sciences Computation Initiative. This is hundreds of times faster than estimates for other CPU based methods and four times faster than runtimes estimated for GPU methods, indicating how the improvement in the level of hardware applied to interaction analysis may alter the types of analysis that can be performed. Furthermore, the same analysis would take under 3 months on the currently largest IBM Blue Gene/Q supercomputer "Sequoia" at the Lawrence Livermore National Laboratory assuming linear scaling is maintained as our results suggest. Given that the implementation used in this study can be further optimised, this runtime means it is becoming feasible to carry out exhaustive analysis of higher order interaction studies on large modern GWAS.

Background: Atrial fibrillation (AF) is an important heart rhythm disorder in aging populations. The gut microbiome composition has been previously related to cardiovascular disease risk factors. Whether the gut microbial profile is also associated with the risk of AF remains unknown. Methods: We examined the associations of prevalent and incident AF with gut microbiota in the FINRISK 2002 study, a random population sample of 6763 individuals. We replicated our findings in an independent case–control cohort of 138 individuals in Hamburg, Germany. Findings: Multivariable-adjusted regression models revealed that prevalent AF (N = 116) was associated with nine microbial genera. Incident AF (N = 539) over a median follow-up of 15 years was associated with eight microbial genera with false discovery rate (FDR)-corrected P < 0.05. Both prevalent and incident AF were associated with the genera Enorma and Bifidobacterium (FDR-corrected P < 0.001). AF was not significantly associated with bacterial diversity measures. Seventy-five percent of top genera (Enorma, Paraprevotella, Odoribacter, Collinsella, Barnesiella, Alistipes) in Cox regression analyses showed a consistent direction of shifted abundance in an independent AF case–control cohort that was used for replication. Interpretation: Our findings establish the basis for the use of microbiome profiles in AF risk prediction. However, extensive research is still warranted before microbiome sequencing can be used for prevention and targeted treatment of AF. Funding: This study was funded by European Research Council, German Ministry of Research and Education, Academy of Finland, Finnish Medical Foundation, and the Finnish Foundation for Cardiovascular Research, the Emil Aaltonen Foundation, and the Paavo Nurmi Foundation.