Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes

Type 2 diabetes (T2D) is a very common disease in humans. Here we conduct a meta-analysis of genome-wide association studies (GWAS) with ~16 million genetic variants in 62,892 T2D cases and 596,424 controls of European ancestry. We identify 139 common and 4 rare variants associated with T2D, 42 of which (39 common and 3 rare variants) are independent of the known variants. Integration of the gene expression data from blood (n = 14,115 and 2765) with the GWAS results identifies 33 putative functional genes for T2D, 3 of which were targeted by approved drugs. A further integration of DNA methylation (n = 1980) and epigenomic annotation data highlight 3 genes (CAMK1D, TP53INP1, and ATP5G1) with plausible regulatory mechanisms, whereby a genetic variant exerts an effect on T2D through epigenetic regulation of gene expression. Our study uncovers additional loci, proposes putative genetic regulatory mechanisms for T2D, and provides evidence of purifying selection for T2D-associated variants

Global Biobank Meta-analysis Initiative:Powering genetic discovery across human disease

Biobanks facilitate genome-wide association studies (GWASs), which have mapped genomic loci across a range of human diseases and traits. However, most biobanks are primarily composed of individuals of European ancestry. We introduce the Global Biobank Meta-analysis Initiative (GBMI)—a collaborative network of 23 biobanks from 4 continents representing more than 2.2 million consented individuals with genetic data linked to electronic health records. GBMI meta-analyzes summary statistics from GWASs generated using harmonized genotypes and phenotypes from member biobanks for 14 exemplar diseases and endpoints. This strategy validates that GWASs conducted in diverse biobanks can be integrated despite heterogeneity in case definitions, recruitment strategies, and baseline characteristics. This collaborative effort improves GWAS power for diseases, benefits understudied diseases, and improves risk prediction while also enabling the nomination of disease genes and drug candidates by incorporating gene and protein expression data and providing insight into the underlying biology of human diseases and traits.</p

Refining Attention-Deficit/Hyperactivity Disorder and Autism Spectrum Disorder Genetic Loci by Integrating Summary Data From Genome-wide Association, Gene Expression, and DNA Methylation Studies

Background: Recent genome-wide association studies (GWASs) identified the first genetic loci associated with attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). The next step is to use these results to increase our understanding of the biological mechanisms involved. Most of the identified variants likely influence gene regulation. The aim of the current study is to shed light on the mechanisms underlying the genetic signals and prioritize genes by integrating GWAS results with gene expression and DNA methylation (DNAm) levels. Methods: We applied summary-data–based Mendelian randomization to integrate ADHD and ASD GWAS data with fetal brain expression and methylation quantitative trait loci, given the early onset of these disorders. We also analyzed expression and methylation quantitative trait loci datasets of adult brain and blood, as these provide increased statistical power. We subsequently used summary-data–based Mendelian randomization to investigate if the same variant influences both DNAm and gene expression levels. Results: We identified multiple gene expression and DNAm levels in fetal brain at chromosomes 1 and 17 that were associated with ADHD and ASD, respectively, through pleiotropy at shared genetic variants. The analyses in brain and blood showed additional associated gene expression and DNAm levels at the same and additional loci, likely because of increased statistical power. Several of the associated genes have not been identified in ADHD and ASD GWASs before. Conclusions: Our findings identified the genetic variants associated with ADHD and ASD that likely act through gene regulation. This facilitates prioritization of candidate genes for functional follow-up studies

The genome of the water strider Gerris buenoi reveals expansions of gene repertoires associated with adaptations to life on the water.

BACKGROUND: Having conquered water surfaces worldwide, the semi-aquatic bugs occupy ponds, streams, lakes, mangroves, and even open oceans. The diversity of this group has inspired a range of scientific studies from ecology and evolution to developmental genetics and hydrodynamics of fluid locomotion. However, the lack of a representative water strider genome hinders our ability to more thoroughly investigate the molecular mechanisms underlying the processes of adaptation and diversification within this group. RESULTS: Here we report the sequencing and manual annotation of the Gerris buenoi (G. buenoi) genome; the first water strider genome to be sequenced thus far. The size of the G. buenoi genome is approximately 1,000 Mb, and this sequencing effort has recovered 20,949 predicted protein-coding genes. Manual annotation uncovered a number of local (tandem and proximal) gene duplications and expansions of gene families known for their importance in a variety of processes associated with morphological and physiological adaptations to a water surface lifestyle. These expansions may affect key processes associated with growth, vision, desiccation resistance, detoxification, olfaction and epigenetic regulation. Strikingly, the G. buenoi genome contains three insulin receptors, suggesting key changes in the rewiring and function of the insulin pathway. Other genomic changes affecting with opsin genes may be associated with wavelength sensitivity shifts in opsins, which is likely to be key in facilitating specific adaptations in vision for diverse water habitats. CONCLUSIONS: Our findings suggest that local gene duplications might have played an important role during the evolution of water striders. Along with these findings, the sequencing of the G. buenoi genome now provides us the opportunity to pursue exciting research opportunities to further understand the genomic underpinnings of traits associated with the extreme body plan and life history of water striders

Draft genome of the red harvester ant \u3ci\u3ePogonomyrmex barbatus\u3c/i\u3e

We report the draft genome sequence of the red harvester ant, Pogonomyrmex barbatus. The genome was sequenced using 454 pyrosequencing, and the current assembly and annotation were completed in less than 1 y. Analyses of conserved gene groups (more than 1,200 manually annotated genes to date) suggest a high-quality assembly and annotation comparable to recently sequenced insect genomes using Sanger sequencing. The red harvester ant is a model for studying reproductive division of labor, phenotypic plasticity, and sociogenomics. Although the genome of P. barbatus is similar to other sequenced hymenopterans (Apis mellifera and Nasonia vitripennis) in GC content and compositional organization, and possesses a complete CpG methylation toolkit, its predicted genomic CpG content differs markedly from the other hymenopterans. Gene networks involved in generating key differences betweenthe queenandworker castes (e.g.,wingsandovaries) show signatures of increased methylation and suggest that ants and bees may have independently co-opted the same gene regulatory mechanisms for reproductive division of labor. Gene family expansions (e.g., 344 functional odorant receptors) and pseudogene accumulation in chemoreception and P450 genes compared with A. mellifera and N. vitripennis are consistent with major life-history changes during the adaptive radiation of Pogonomyrmex spp., perhaps inparallel with the development of the North American deserts

Lung adenocarcinoma promotion by air pollutants

This research was conducted using the UK Biobank Resource under application number 82693. This work was supported by the Mark Foundation ASPIRE I Award (grant 21-029-ASP), the Lung Cancer Research Foundation Grant on Disparities in Lung Cancer, Advanced Grant (PROTEUS, grant agreement no. 835297), CRUK EDD (EDDPMA-Nov21\100034) and a Rosetrees Out-of-round Award (OoR2020\100009). W.H. is funded by an ERC Advanced Grant (PROTEUS, grant agreement no. 835297), CRUK EDD (EDDPMA-Nov21\100034), The Mark Foundation (grant 21-029-ASP) and has been supported by Rosetrees. E.L.L. receives funding from the NovoNordisk Foundation (ID 16584), The Mark Foundation (grant 21-029-ASP) and has been supported by Rosetrees. C.E.W. is supported by a RESPIRE4 fellowship from the European Respiratory Society and Marie-Sklodowska-Curie Actions. C.L. is supported by the Agency for Science, Technology & Research, Singapore and the Cancer Research UK City of London Centre and the City of London Centre Clinical Academic Training Programme. M.A. is supported by the City of London Centre Clinical Academic Training Programme (Year 3, SEBSTF-2021\100007). K.C. is supported by the Research Unit of Intelligence Diagnosis and Treatment in Early Non-small Cell Lung Cancer, the Chinese Academy of Medical Sciences (2021RU002), the National Natural Science Foundation of China (no. 82072566) and Peking University People’s Hospital Research and Development Funds (RS2019-01). T.K. receives grant support from JSPS Overseas Research Fellowships Program (202060447). S.-H.L. is supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (no. 2020R1A2C3006535), the National Cancer Center Grant (NCC1911269-3) and a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number HR20C0025). L.H.S. receives grant support from the Berta Kamprad Foundation, the Swedish Cancer Society and the Swedish Research Council. R.M. and S.L. acknowledge funding from the Terry Fox Research Institute. N.M. is a Sir Henry Dale Fellow, jointly funded by the Wellcome Trust and the Royal Society (grant number 211179/Z/18/Z) and receives funding from Cancer Research UK, the Rosetrees and the NIHR BRC at University College London Hospitals and the CRUK University College London Experimental Cancer Medicine Centre. J. DeGregori, M.G., Y.E.M., D.T.M. and R.L.K. receive funding from the American Association for Cancer Research/Johnson&Johnson (18-90-52-DEGR), and J. DeGregori is supported by the Courtenay C. and Lucy Patten Davis Endowed Chair in Lung Cancer Research and a Merit Award from the Veteran’s Administration (1 I01 BX004495). M.G., Y.E.M., D.T.M. and R.L.K. were supported by the National Cancer Institute (NCI) RO1 CA219893. E.J.E.J. was supported by a NCI Ruth L. Kirschstein National Research Service Award T32-CA190216 and the Blumenthal Fellowship from the Linda Crnic Institute for Down Syndrome. C.I.T. acknowledges funding from UC Anschutz (LHNC T32CA174648). The work at the University of Colorado was also supported by NCI Cancer Center Support Grant P30CA046934. K. Litchfield is funded by the UK Medical Research Council (MR/P014712/1 and MR/V033077/1), the Rosetrees Trust and the Cotswold Trust (A2437) and Cancer Research UK (C69256/A30194). M.J.-H. is a CRUK Career Establishment Awardee has received funding from Cancer Research UK, IASLC International Lung Cancer Foundation, the National Institute for Health Research, the Rosetrees Trust, UKI NETs and the NIHR University College London Hospitals Biomedical Research Centre. C.S. is a Royal Society Napier Research Professor (RSRP\R\210001). His work is supported by the Francis Crick Institute that receives its core funding from Cancer Research UK (CC2041), the UK Medical Research Council (CC2041), and the Wellcome Trust (CC2041). For the purpose of Open Access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. C.S. is funded by Cancer Research UK (TRACERx (C11496/A17786), PEACE (C416/A21999) and CRUK Cancer Immunotherapy Catalyst Network); Cancer Research UK Lung Cancer Centre of Excellence (C11496/A30025); the Rosetrees Trust, Butterfield and Stoneygate Trusts; NovoNordisk Foundation (ID16584); Royal Society Professorship Enhancement Award (RP/EA/180007); National Institute for Health Research (NIHR) University College London Hospitals Biomedical Research Centre; the Cancer Research UK-University College London Centre; Experimental Cancer Medicine Centre; the Breast Cancer Research Foundation (US) (BCRF-22-157); Cancer Research UK Early Detection an Diagnosis Primer Award (grant EDDPMA-Nov21/100034); and The Mark Foundation for Cancer Research Aspire Award (grant 21-029-ASP). This work was supported by a Stand Up To Cancer‐LUNGevity-American Lung Association Lung Cancer Interception Dream Team Translational Research Grant (grant number: SU2C-AACR-DT23-17 to S.M. Dubinett and A.E. Spira). Stand Up To Cancer is a division of the Entertainment Industry Foundation. Research grants are administered by the American Association for Cancer Research, the Scientific Partner of SU2C. C.S. is in receipt of an ERC Advanced Grant (PROTEUS) from the European Research Council under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 835297). We acknowledge the PEACE Consortium (PEACE Consortium members are named below) for their expertise and support in putting together the healthy tissue sample cohorts. We thank the clinical and administrative team of the PEACE study for their assistance in data curation (S. Shepherd, Z. Tippu, B. Shum, C. Lewis, M. O’Flaherty, A. Lucanas, E. Carlyle, L. Holt, F. Williams); nursing and biospecimen coordinators for their assistance in sample curation (K. Edmonds, L. Grostate, K. Lingard, D. Kelly, J. Korteweg, L. Terry, J. Biano, A. Murra, K. Kelly, K. Peat, N. Hunter); A. H. -K. Cheung for assistance in pathology review; J. Asklin and C. Forsberg for logistical and technical assistance; staff at the Chang Gung Memorial Hospital for providing Chang Gung Research Database (CGRD) data; staff who provided support at the Flow Cytometry Unit, the Experimental Histopathology Unit, the Advanced Light Microscopy Facility, the Advanced Sequencing Facility and the Biological Resources Unit, especially N. Chisholm and Jay O’Brien, at the Francis Crick Institute; A. Yuen, A. Azhar, K. Lau, C. Schwartz, A. Lee and C. Rider for their logistical support for the human exposure study; and staff at the Centre d’expertise et de services Génome Québec for their sequencing services and support. Data for this study are based on patient-level information collected by the NHS, as part of the care and support of cancer patients. The data are collated, maintained and quality assured by the National Cancer Registration and Analysis Service, which is part of NHS England (NHSE). We extend our thanks to the skilled Cancer Registration Officers (CROs) within the National Disease Registration Service, who abstracted and registered the English tumour and molecular testing data.Peer reviewedPostprin

Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood

Understanding the difference in genetic regulation of gene expression between brain and blood is important for discovering genes for brain-related traits and disorders. Here, we estimate the correlation of genetic effects at the top-associated cis-expression or -DNA methylation (DNAm) quantitative trait loci (cis-eQTLs or cis-mQTLs) between brain and blood (r b ). Using publicly available data, we find that genetic effects at the top cis-eQTLs or mQTLs are highly correlated between independent brain and blood samples (r b = 0.70 for cis-eQTLs and r ^ b = 0.78 for cis-mQTLs). Using meta-analyzed brain cis-eQTL/mQTL data (n = 526 to 1194), we identify 61 genes and 167 DNAm sites associated with four brain-related phenotypes, most of which are a subset of the discoveries (97 genes and 295 DNAm sites) using data from blood with larger sample sizes (n = 1980 to 14,115). Our results demonstrate the gain of power in gene discovery for brain-related phenotypes using blood cis-eQTL/mQTL data with large sample sizes. © 2018 The Author(s).Peer reviewe

The genome sequence of the leaf-cutter ant Atta cephalotes reveals insights into its obligate symbiotic lifestyle.

Leaf-cutter ants are one of the most important herbivorous insects in the Neotropics, harvesting vast quantities of fresh leaf material. The ants use leaves to cultivate a fungus that serves as the colony's primary food source. This obligate ant-fungus mutualism is one of the few occurrences of farming by non-humans and likely facilitated the formation of their massive colonies. Mature leaf-cutter ant colonies contain millions of workers ranging in size from small garden tenders to large soldiers, resulting in one of the most complex polymorphic caste systems within ants. To begin uncovering the genomic underpinnings of this system, we sequenced the genome of Atta cephalotes using 454 pyrosequencing. One prediction from this ant's lifestyle is that it has undergone genetic modifications that reflect its obligate dependence on the fungus for nutrients. Analysis of this genome sequence is consistent with this hypothesis, as we find evidence for reductions in genes related to nutrient acquisition. These include extensive reductions in serine proteases (which are likely unnecessary because proteolysis is not a primary mechanism used to process nutrients obtained from the fungus), a loss of genes involved in arginine biosynthesis (suggesting that this amino acid is obtained from the fungus), and the absence of a hexamerin (which sequesters amino acids during larval development in other insects). Following recent reports of genome sequences from other insects that engage in symbioses with beneficial microbes, the A. cephalotes genome provides new insights into the symbiotic lifestyle of this ant and advances our understanding of host-microbe symbioses

The Genome Sequence of the Leaf-Cutter Ant Atta cephalotes Reveals Insights into Its Obligate Symbiotic Lifestyle

Leaf-cutter ants are one of the most important herbivorous insects in the Neotropics, harvesting vast quantities of fresh leaf material. The ants use leaves to cultivate a fungus that serves as the colony's primary food source. This obligate ant-fungus mutualism is one of the few occurrences of farming by non-humans and likely facilitated the formation of their massive colonies. Mature leaf-cutter ant colonies contain millions of workers ranging in size from small garden tenders to large soldiers, resulting in one of the most complex polymorphic caste systems within ants. To begin uncovering the genomic underpinnings of this system, we sequenced the genome of Atta cephalotes using 454 pyrosequencing. One prediction from this ant's lifestyle is that it has undergone genetic modifications that reflect its obligate dependence on the fungus for nutrients. Analysis of this genome sequence is consistent with this hypothesis, as we find evidence for reductions in genes related to nutrient acquisition. These include extensive reductions in serine proteases (which are likely unnecessary because proteolysis is not a primary mechanism used to process nutrients obtained from the fungus), a loss of genes involved in arginine biosynthesis (suggesting that this amino acid is obtained from the fungus), and the absence of a hexamerin (which sequesters amino acids during larval development in other insects). Following recent reports of genome sequences from other insects that engage in symbioses with beneficial microbes, the A. cephalotes genome provides new insights into the symbiotic lifestyle of this ant and advances our understanding of host-microbe symbioses