The Allelic Landscape of Human Blood Cell Trait Variation and Links to Common Complex Disease

Many common variants have been associated with hematological traits, but identification of causal genes and pathways has proven challenging. We performed a genome-wide association analysis in the UK Biobank and INTERVAL studies, testing 29.5 million genetic variants for association with 36 red cell, white cell, and platelet properties in 173,480 European-ancestry participants. This effort yielded hundreds of low frequency (<5%) and rare (<1%) variants with a strong impact on blood cell phenotypes. Our data highlight general properties of the allelic architecture of complex traits, including the proportion of the heritable component of each blood trait explained by the polygenic signal across different genome regulatory domains. Finally, through Mendelian randomization, we provide evidence of shared genetic pathways linking blood cell indices with complex pathologies, including autoimmune diseases, schizophrenia, and coronary heart disease and evidence suggesting previously reported population associations between blood cell indices and cardiovascular disease may be non-causal.We thank members of the Cambridge BioResource Scientific Advisory Board and Management Committee for their support of our study and the National Institute for Health Research Cambridge Biomedical Research Centre for funding. K.D. is funded as a HSST trainee by NHS Health Education England. M.F. is funded from the BLUEPRINT Grant Code HEALTH-F5-2011-282510 and the BHF Cambridge Centre of Excellence [RE/13/6/30180]. J.R.S. is funded by a MRC CASE Industrial studentship, co-funded by Pfizer. J.D. is a British Heart Foundation Professor, European Research Council Senior Investigator, and National Institute for Health Research (NIHR) Senior Investigator. S.M., S.T, M.H, K.M. and L.D. are supported by the NIHR BioResource-Rare Diseases, which is funded by NIHR. Research in the Ouwehand laboratory is supported by program grants from the NIHR to W.H.O., the European Commission (HEALTH-F2-2012-279233), the British Heart Foundation (BHF) to W.J.A. and D.R. under numbers RP-PG-0310-1002 and RG/09/12/28096 and Bristol Myers-Squibb; the laboratory also receives funding from NHSBT. W.H.O is a NIHR Senior Investigator. The INTERVAL academic coordinating centre receives core support from the UK Medical Research Council (G0800270), the BHF (SP/09/002), the NIHR and Cambridge Biomedical Research Centre, as well as grants from the European Research Council (268834), the European Commission Framework Programme 7 (HEALTH-F2-2012-279233), Merck and Pfizer. DJR and DA were supported by the NIHR Programme ‘Erythropoiesis in Health and Disease’ (Ref. NIHR-RP-PG-0310-1004). N.S. is supported by the Wellcome Trust (Grant Codes WT098051 and WT091310), the EU FP7 (EPIGENESYS Grant Code 257082 and BLUEPRINT Grant Code HEALTH-F5-2011-282510). The INTERVAL study is funded by NHSBT and has been supported by the NIHR-BTRU in Donor Health and Genomics at the University of Cambridge in partnership with NHSBT. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, the Department of Health of England or NHSBT. D.G. is supported by a “la Caixa”-Severo Ochoa pre-doctoral fellowship

Genome-wide analysis of 53,400 people with irritable bowel syndrome highlights shared genetic pathways with mood and anxiety disorders

Funder: Kennedy Trust Rheumatology Research Prize StudentshipFunder: DFG Cluster of Excellence “Precision Medicine in Chronic In-flammation” (PMI; ID: EXC2167)Funder: EC | EC Seventh Framework Programm | FP7 Ideas: European Research Council (FP7-IDEAS-ERC - Specific Programme: “Ideas” Implementing the Seventh Framework Programme of the European Community for Research, Technological Development and Demonstration Activities (2007 to 2013)); doi: https://doi.org/10.13039/100011199; Grant(s): 715772Funder: NWO-VIDI grant 016.178.056, the Netherlands Heart Foundation CVON grant 2018-27, and NWO Gravitation grant ExposomeNLFunder: Li Ka Shing Foundation (Li Ka Shing Foundation Limited); doi: https://doi.org/10.13039/100007421Abstract: Irritable bowel syndrome (IBS) results from disordered brain–gut interactions. Identifying susceptibility genes could highlight the underlying pathophysiological mechanisms. We designed a digestive health questionnaire for UK Biobank and combined identified cases with IBS with independent cohorts. We conducted a genome-wide association study with 53,400 cases and 433,201 controls and replicated significant associations in a 23andMe panel (205,252 cases and 1,384,055 controls). Our study identified and confirmed six genetic susceptibility loci for IBS. Implicated genes included NCAM1, CADM2, PHF2/FAM120A, DOCK9, CKAP2/TPTE2P3 and BAG6. The first four are associated with mood and anxiety disorders, expressed in the nervous system, or both. Mirroring this, we also found strong genome-wide correlation between the risk of IBS and anxiety, neuroticism and depression (rg > 0.5). Additional analyses suggested this arises due to shared pathogenic pathways rather than, for example, anxiety causing abdominal symptoms. Implicated mechanisms require further exploration to help understand the altered brain–gut interactions underlying IBS

Atrial fibrillation genetic risk differentiates cardioembolic stroke from other stroke subtypes

AbstractObjectiveWe sought to assess whether genetic risk factors for atrial fibrillation can explain cardioembolic stroke risk.MethodsWe evaluated genetic correlations between a prior genetic study of AF and AF in the presence of cardioembolic stroke using genome-wide genotypes from the Stroke Genetics Network (N = 3,190 AF cases, 3,000 cardioembolic stroke cases, and 28,026 referents). We tested whether a previously-validated AF polygenic risk score (PRS) associated with cardioembolic and other stroke subtypes after accounting for AF clinical risk factors.ResultsWe observed strong correlation between previously reported genetic risk for AF, AF in the presence of stroke, and cardioembolic stroke (Pearson’s r=0.77 and 0.76, respectively, across SNPs with p &lt; 4.4 × 10−4 in the prior AF meta-analysis). An AF PRS, adjusted for clinical AF risk factors, was associated with cardioembolic stroke (odds ratio (OR) per standard deviation (sd) = 1.40, p = 1.45×10−48), explaining ∼20% of the heritable component of cardioembolic stroke risk. The AF PRS was also associated with stroke of undetermined cause (OR per sd = 1.07, p = 0.004), but no other primary stroke subtypes (all p &gt; 0.1).ConclusionsGenetic risk for AF is associated with cardioembolic stroke, independent of clinical risk factors. Studies are warranted to determine whether AF genetic risk can serve as a biomarker for strokes caused by AF.</jats:sec

NxRepair: error correction in de novo sequence assembly using Nextera mate pairs

Scaffolding errors and incorrect repeat disambiguation during de novo assembly can result in large scale misassemblies in draft genomes. Nextera mate pair sequencing data provide additional information to resolve assembly ambiguities during scaffolding. Here, we introduce NxRepair, an open source toolkit for error correction in de novo assemblies that uses Nextera mate pair libraries to identify and correct large-scale errors. We show that NxRepair can identify and correct large scaffolding errors, without use of a reference sequence, resulting in quantitative improvements in the assembly quality. NxRepair can be downloaded from GitHub or PyPI, the Python Package Index; a tutorial and user documentation are also available

Viime: Visualization and Integration of Metabolomics Experiments

Metabolomics involves the comprehensive measurement of metabolites from a biological system. The resulting metabolite profiles are influenced by genetics, lifestyle, biological stresses, disease, diet and the environment and therefore provides a more holistic biological readout of the pathological condition of the organism (Beger et al., 2016; Wishart, 2016). The challenge for metabolomics is that no single analytical platform can provide a truly comprehensive coverage of the metabolome. The most commonly used platforms are based on mass-spectrometry (MS) and nuclear magnetic resonance (NMR). Investigators are increasingly using both methods to increase the metabolite coverage. The challenge for this type of multi-platform approach is that the data structure may be very different in these two platforms. For example, NMR data may be reported as a list of spectral features, e.g., bins or peaks with arbitrary intensity units or more directly with named metabolites reported in concentration units ranging from micromolar to millimolar. Some MS approaches can also provide data in the form of identified metabolite concentrations, but given the superior sensitivity of MS, the concentrations can be several orders of magnitude lower than for NMR. Other MS approaches yield data in the form of arbitrary response units where the dynamic range can be more than 6 orders of magnitude. Importantly, the variability and reproducibility of the data may differ across platforms. Given the diversity of data structures (i.e., magnitude and dynamic range) integrating the data from multiple platforms can be challenging. This often leads investigators to analyze the datasets separately, which prevents the observation of potentially interesting relationships and correlations between metabolites detected on different platforms. Viime (VIsualization and Integration of Metabolomics Experiments) is an open-source, web-based application designed to integrate metabolomics data from multiple platforms

miRNA-1 promotes acute myeloid leukemia cell pathogenesis through metabolic regulation

Acute myeloid leukemia (AML) is a heterogeneous and deadly disease characterized by uncontrolled expansion of malignant blasts. Altered metabolism and dysregulated microRNA (miRNA) expression profiles are both characteristic of AML. However, there is a paucity of studies exploring how changes in the metabolic state of the leukemic cells regulate miRNA expression leading to altered cellular behavior. Here, we blocked pyruvate entry into mitochondria by deleting the Mitochondria Pyruvate Carrier (MPC1) gene in human AML cell lines, which decreased Oxidative Phosphorylation (OXPHOS). This metabolic shift also led to increased expression of miR-1 in the human AML cell lines tested. AML patient sample datasets showed that higher miR-1 expression correlates with reduced survival. Transcriptional and metabolic profiling of miR-1 overexpressing AML cells revealed that miR-1 increased OXPHOS, along with key metabolites that fuel the TCA cycle such as glutamine and fumaric acid. Inhibition of glutaminolysis decreased OXPHOS in miR-1 overexpressing MV4-11 cells, highlighting that miR-1 promotes OXPHOS through glutaminolysis. Finally, overexpression of miR-1 in AML cells exacerbated disease in a mouse xenograft model. Together, our work expands current knowledge within the field by uncovering novel connections between AML cell metabolism and miRNA expression that facilitates disease progression. Further, our work points to miR-1 as a potential new therapeutic target that may be used to disrupt AML cell metabolism and thus pathogenesis in the clinic

Mitochondrial Pyruvate Carrier 1 Promotes Peripheral T Cell Homeostasis through Metabolic Regulation of Thymic Development

Metabolic pathways regulate T&nbsp;cell development and function, but many remain understudied. Recently, the mitochondrial pyruvate carrier (MPC) was identified as the transporter that mediates pyruvate entry into mitochondria, promoting pyruvate oxidation. Here we find that deleting Mpc1, an obligate MPC subunit, in the hematopoietic system results in a specific reduction in peripheral αβ T&nbsp;cell numbers. MPC1-deficient T&nbsp;cells have defective thymic development at the β-selection, intermediate single positive (ISP)-to-double-positive (DP), and positive selection steps. We find that early thymocytes deficient in MPC1 display alterations to multiple pathways involved in T&nbsp;cell development. This results in preferred escape of more activated T&nbsp;cells. Finally, mice with hematopoietic deletion of Mpc1 are more susceptible to experimental autoimmune encephalomyelitis. Altogether, our study demonstrates that pyruvate oxidation by T&nbsp;cell precursors is necessary for optimal αβ T&nbsp;cell development and that its deficiency results in reduced but activated peripheral T&nbsp;cell populations

An ensemble penalized regression method for multi-ancestry polygenic risk prediction

Abstract Great efforts are being made to develop advanced polygenic risk scores (PRS) to improve the prediction of complex traits and diseases. However, most existing PRS are primarily trained on European ancestry populations, limiting their transferability to non-European populations. In this article, we propose a novel method for generating multi-ancestry Polygenic Risk scOres based on enSemble of PEnalized Regression models (PROSPER). PROSPER integrates genome-wide association studies (GWAS) summary statistics from diverse populations to develop ancestry-specific PRS with improved predictive power for minority populations. The method uses a combination of $${{{{{{\mathscr{L}}}}}}}_{1}$$ L 1 (lasso) and $${{{{{{\mathscr{L}}}}}}}_{2}$$ L 2 (ridge) penalty functions, a parsimonious specification of the penalty parameters across populations, and an ensemble step to combine PRS generated across different penalty parameters. We evaluate the performance of PROSPER and other existing methods on large-scale simulated and real datasets, including those from 23andMe Inc., the Global Lipids Genetics Consortium, and All of Us. Results show that PROSPER can substantially improve multi-ancestry polygenic prediction compared to alternative methods across a wide variety of genetic architectures. In real data analyses, for example, PROSPER increased out-of-sample prediction R2 for continuous traits by an average of 70% compared to a state-of-the-art Bayesian method (PRS-CSx) in the African ancestry population. Further, PROSPER is computationally highly scalable for the analysis of large SNP contents and many diverse populations