Integration of GWAS SNPs and tissue specific expression profiling reveal discrete eQTLs for human traits in blood and brain

Our knowledge of the transcriptome has become much more complex since the days of the central dogma of molecular biology. We now know that splicing takes place to create potentially thousands of isoforms from a single gene, and we know that RNA does not always faithfully recapitulate DNA if RNA editing occurs. Collectively, these observations show that the transcriptome is amazingly rich with intricate regulatory mechanisms for overall gene expression, splicing, and RNA editing. Genetic variability can play a role in controlling gene expression, which can be identified by examining expression quantitative trait loci (eQTLs). eQTLs are genomic regions where genetic variants, including single nucleotide polymorphisms (SNPs) show a statistical association with expression of mRNA transcripts. In humans, many SNPs are also associated with disease, and have been identified using genome wide association studies (GWAS) but the biological effects of those SNPs are usually not known. If SNPs found in GWAS are also found in eQTLs, then one could hypothesize that expression levels may contribute to disease risk. Performing eQTL analysis with GWAS SNPs in both blood and brain, specifically the frontal cortex and the cerebellum, we found both shared and tissue unique eQTLS. The identification of tissue-unique eQTLs supports the argument that choice of tissue type is important in eQTL studies (Paper I). Aging is a complex process with the mechanisms underlying aging still being poorly defined. There is evidence that the transcriptome changes with age, and hence we used the brain dataset from our first paper as a discovery set, with an additional replication dataset, to investigate any aging-gene expression associations. We found evidence that many genes were associated with aging. We further found that there were more statically significant expression changes in the frontal cortex versus the cerebellum, indicating that brain regions may age at different rates. As the brain is a heterogeneous tissue including both neurons and non-neuronal cells, we used LCM to capture Purkinje cells as a representative neuronal type and repeated the age analysis. Looking at the discovery, replication and Purkinje cell datasets we found five genes with strong, replicated evidence of age-expression associations (Paper II). Being able to capture and quantify the depth of the transcriptome has been a lengthy process starting with methods that could only measure a single gene to genome-wide techniques such as microarray. A recently developed technology, RNA-Seq, shows promise in its ability to capture expression, splicing, and editing and with its broad dynamic range quantification is accurate and reliable. RNA-Seq is, however, data intensive and a great deal of computational expertise is required to fully utilize the strengths of this method. We aimed to create a small, well-controlled, experiment in order to test the performance of this relatively new technology in the brain. We chose embryonic versus adult cerebral cortex, as mice are genetically homogenous and there are many known differences in gene expression related to brain development that we could use as benchmarks for analysis testing. We found a large number of differences in total gene expression between embryonic and adult brain. Rigorous technical and biological validation illustrated the accuracy and dynamic range of RNA-Seq. We were also able to interrogate differences in exon usage in the same dataset. Finally we were able to identify and quantify both well-known and novel A-to-I edit sites. Overall this project helped us develop the tools needed to build usable pipelines for RNA-Seq data processing (Paper III). Our studies in the developing brain (Paper III) illustrated that RNA-Seq was a useful unbiased method for investigating RNA editing. To extend this further, we utilized a genetically modified mouse model to study the transcriptomic role of the RNA editing enzyme ADAR2. We found that ADAR2 was important for editing of the coding region of mRNA as a large proportion of RNA editing sites in coding regions had a statistically significant decrease in editing percentages in Adar2 -/-Gria2 R/R mice versus controls. However, despite indications in the literature that ADAR2 may also be involved in splicing and expression regulatory machinery we found no changes in gene expression or exon utilization in Adar2 -/-Gria2 R/R mice as compared to their littermate controls (Paper IV). In our final study, based on the methods developed in Papers III and IV, we revisited the idea of age related gene expression associations from Paper II. We used a subset of human frontal cortices for RNA sequencing. Interestingly we found more gene expression changes with aging compared to the previous data using microarrays in Paper II. When the significant gene lists were analysed for gene ontology enrichment, we found that there was a large number of downregulated genes involved in synaptic function while those that were upregulated had enrichment in immune function. This dataset illustrates that the aging brain may be predisposed to the processes found in neurodegenerative diseases (Paper V)

The diversity and evolution of pollination systems in large plant clades: Apocynaceae as a case study

Background and Aims Large clades of angiosperms are often characterized by diverse interactions with pollinators, but how these pollination systems are structured phylogenetically and biogeographically is still uncertain for most families. Apocynaceae is a clade of >5300 species with a worldwide distribution. A database representing >10 % of species in the family was used to explore the diversity of pollinators and evolutionary shifts in pollination systems across major clades and regions. Methods The database was compiled from published and unpublished reports. Plants were categorized into broad pollination systems and then subdivided to include bimodal systems. These were mapped against the five major divisions of the family, and against the smaller clades. Finally, pollination systems were mapped onto a phylogenetic reconstruction that included those species for which sequence data are available, and transition rates between pollination systems were calculated. Key Results Most Apocynaceae are insect pollinated with few records of bird pollination. Almost three-quarters of species are pollinated by a single higher taxon (e.g. flies or moths); 7 % have bimodal pollination systems, whilst the remaining approx. 20 % are insect generalists. The less phenotypically specialized flowers of the Rauvolfioids are pollinated by a more restricted set of pollinators than are more complex flowers within the Apocynoids + Periplocoideae + Secamonoideae + Asclepiadoideae (APSA) clade. Certain combinations of bimodal pollination systems are more common than others. Some pollination systems are missing from particular regions, whilst others are over-represented. Conclusions Within Apocynaceae, interactions with pollinators are highly structured both phylogenetically and biogeographically. Variation in transition rates between pollination systems suggest constraints on their evolution, whereas regional differences point to environmental effects such as filtering of certain pollinators from habitats. This is the most extensive analysis of its type so far attempted and gives important insights into the diversity and evolution of pollination systems in large clades

Identification of candidate Parkinson disease genes by integrating genome-wide association study, expression, and epigenetic data sets

Importance Substantial genome-wide association study (GWAS) work in Parkinson disease (PD) has led to the discovery of an increasing number of loci shown reliably to be associated with increased risk of disease. Improved understanding of the underlying genes and mechanisms at these loci will be key to understanding the pathogenesis of PD. Objective To investigate what genes and genomic processes underlie the risk of sporadic PD. Design and Setting This genetic association study used the bioinformatic tools Coloc and transcriptome-wide association study (TWAS) to integrate PD case-control GWAS data published in 2017 with expression data (from Braineac, the Genotype-Tissue Expression [GTEx], and CommonMind) and methylation data (derived from UK Parkinson brain samples) to uncover putative gene expression and splicing mechanisms associated with PD GWAS signals. Candidate genes were further characterized using cell-type specificity, weighted gene coexpression networks, and weighted protein-protein interaction networks. Main Outcomes and Measures It was hypothesized a priori that some genes underlying PD loci would alter PD risk through changes to expression, splicing, or methylation. Candidate genes are presented whose change in expression, splicing, or methylation are associated with risk of PD as well as the functional pathways and cell types in which these genes have an important role. Results Gene-level analysis of expression revealed 5 genes (WDR6 [OMIM 606031], CD38 [OMIM 107270], GPNMB [OMIM 604368], RAB29 [OMIM 603949], and TMEM163 [OMIM 618978]) that replicated using both Coloc and TWAS analyses in both the GTEx and Braineac expression data sets. A further 6 genes (ZRANB3 [OMIM 615655], PCGF3 [OMIM 617543], NEK1 [OMIM 604588], NUPL2 [NCBI 11097], GALC [OMIM 606890], and CTSB [OMIM 116810]) showed evidence of disease-associated splicing effects. Cell-type specificity analysis revealed that gene expression was overall more prevalent in glial cell types compared with neurons. The weighted gene coexpression performed on the GTEx data set showed that NUPL2 is a key gene in 3 modules implicated in catabolic processes associated with protein ubiquitination and in the ubiquitin-dependent protein catabolic process in the nucleus accumbens, caudate, and putamen. TMEM163 and ZRANB3 were both important in modules in the frontal cortex and caudate, respectively, indicating regulation of signaling and cell communication. Protein interactor analysis and simulations using random networks demonstrated that the candidate genes interact significantly more with known mendelian PD and parkinsonism proteins than would be expected by chance. Conclusions and Relevance Together, these results suggest that several candidate genes and pathways are associated with the findings observed in PD GWAS studies

A single-nucleotide polymorphism tagging set for human drug metabolism and transport

Interindividual variability in drug response, ranging from no therapeutic benefit to life-threatening adverse reactions, is influenced by variation in genes that control the absorption, distribution, metabolism and excretion of drugs(1). We genotyped 904 single-nucleotide polymorphisms (SNPs) from 55 such genes in two population samples (European and Japanese) and identified a set of tagging SNPs that represents the common variation in these genes, both known and unknown. Extensive empirical evaluations, including a direct assessment of association with candidate functional SNPs in a new, larger population sample, validated the performance of these tagging SNPs and confirmed their utility for linkage-disequilibrium mapping in pharmacogenetics. The analyses also suggest that rare variation is not amenable to tagging strategies

Regulatory sites for splicing in human basal ganglia are enriched for disease-relevant information.

Genome-wide association studies have generated an increasing number of common genetic variants associated with neurological and psychiatric disease risk. An improved understanding of the genetic control of gene expression in human brain is vital considering this is the likely modus operandum for many causal variants. However, human brain sampling complexities limit the explanatory power of brain-related expression quantitative trait loci (eQTL) and allele-specific expression (ASE) signals. We address this, using paired genomic and transcriptomic data from putamen and substantia nigra from 117 human brains, interrogating regulation at different RNA processing stages and uncovering novel transcripts. We identify disease-relevant regulatory loci, find that splicing eQTLs are enriched for regulatory information of neuron-specific genes, that ASEs provide cell-specific regulatory information with evidence for cellular specificity, and that incomplete annotation of the brain transcriptome limits interpretation of risk loci for neuropsychiatric disease. This resource of regulatory data is accessible through our web server, http://braineacv2.inf.um.es/

Regulatory sites for splicing in human basal ganglia are enriched for disease-relevant information

Abstract Genome-wide association studies have generated an increasing number of common genetic variants associated with neurological and psychiatric disease risk. An improved understanding of the genetic control of gene expression in human brain is vital considering this is the likely modus operandum for many causal variants. However, human brain sampling complexities limit the explanatory power of brain-related expression quantitative trait loci (eQTL) and allele-specific expression (ASE) signals. We address this, using paired genomic and transcriptomic data from putamen and substantia nigra from 117 human brains, interrogating regulation at different RNA processing stages and uncovering novel transcripts. We identify disease-relevant regulatory loci, find that splicing eQTLs are enriched for regulatory information of neuron-specific genes, that ASEs provide cell-specific regulatory information with evidence for cellular specificity, and that incomplete annotation of the brain transcriptome limits interpretation of risk loci for neuropsychiatric disease. This resource of regulatory data is accessible through our web server, http://braineacv2.inf.um.es/.International Parkinson’s Disease Genomics Consortium (IPDGC) Alastair J. Noyce1,14, Aude Nicolas8, Mark R. Cookson8, Sara Bandres-Ciga8, J. Raphael Gibbs8, Dena G. Hernandez8, Andrew B. Singleton8, Xylena Reed8, Hampton Leonard8, Cornelis Blauwendraat8,14, Faraz Faghri3,15,16, Jose Bras10, Rita Guerreiro17, Arianna Tucci17, Demis A. Kia17, Henry Houlden17, Helene Plun-Favreau17, Kin Y Mok17, Nicholas W. Wood17, Ruth Lovering17, Lea R’Bibo17, Mie Rizig17, Viorica Chelban17, Manuela Tan12, Huw R. Morris18, Ben Middlehurst19, John Quinn19, Kimberley Billingsley19, Peter Holmans20, Kerri J. Kinghorn21, Patrick Lewis22, Valentina Escott-Price23, Nigel Williams23, Thomas Foltynie24, Alexis Brice25, Fabrice Danjou25, Suzanne Lesage25, Jean-Christophe Corvol25, Maria Martinez25,26, Anamika Giri27,28, Claudia Schulte27,28, Kathrin Brockmann27,28, Javier Simón-Sánchez27,28, Peter Heutink27,28, Thomas Gasser27,28, Patrizia Rizzu28, Manu Sharma29, Joshua M. Shulman30,31, Laurie Robak30, Steven Lubbe32, Niccolo E. Mencacci33, Steven Finkbeiner34,35, Codrin Lungu36, Sonja W. Scholz37, Ziv Gan-Or38, Guy A. Rouleau39, Lynne Krohan40, Jacobus J. van Hilten41, Johan Marinus41, Astrid D. Adarmes-Gómez42, Inmaculada Bernal-Bernal42, Marta Bonilla-Toribio42, Dolores Buiza-Rueda42, Fátima Carrillo42, Mario Carrión-Claro42, Pablo Mir42, Pilar Gómez-Garre42, Silvia Jesús42, Miguel A. Labrador-Espinosa42, Daniel Macias42, Laura Vargas-González42, Carlota Méndez-del-Barrio42, Teresa Periñán-Tocino42, Cristina Tejera-Parrado42, Monica Diez-Fairen43, Miquel Aguilar43, Ignacio Alvarez43, María Teresa Boungiorno43, Maria Carcel43, Pau Pastor43, Juan Pablo Tartari43, Victoria Alvarez44, Manuel Menéndez González44, Marta Blazquez44, Ciara Garcia44, Esther Suarez-Sanmartin44, Francisco Javier Barrero45, Elisabet Mondragon Rezola46, Jesús Alberto Bergareche Yarza47, Ana Gorostidi Pagola47, Adolfo López de Munain Arregui47, Javier Ruiz-Martínez47, Debora Cerdan48, Jacinto Duarte49, Jordi Clarimón50,51, Oriol Dols-Icardo50,51, Jon Infante51,52,53, Juan Marín51,54, Jaime Kulisevsky51,54, Javier Pagonabarraga51,54, Isabel Gonzalez-Aramburu52, Antonio Sanchez Rodriguez52, María Sierra52, Raquel Duran55, Clara Ruz55, Francisco Vives55, Francisco Escamilla-Sevilla56, Adolfo Mínguez56, Ana Cámara57, Yaroslau Compta57, Mario Ezquerra57, Maria Jose Marti57, Manel Fernández57, Esteban Muñoz57, Rubén Fernández-Santiago57, Eduard Tolosa57, Francesc Valldeoriola57, Pedro García-Ruiz58, Maria Jose Gomez Heredia59, Francisco Perez Errazquin59, Janet Hoenicka60, Adriano Jimenez-Escrig60, Juan Carlos Martínez-Castrillo60, Jose Luis Lopez-Sendon60, Irene Martínez Torres61, Cesar Tabernero61, Lydia Vela61, Alexander Zimprich62, Lasse Pihlstrom63, Sulev Koks64,65,66, Pille Taba67, Kari Majamaa68,69, Ari Siitonen68,69, Njideka U. Okubadejo70 & Oluwadamilola O. Ojo70 14Preventive Neurology Unit, Wolfson Institute of Preventive Medicine, QMUL, London, UK. 15National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA. 16Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA. 17Department of Molecular Neuroscience, UCL, London, UK. 18Department of Clinical Neuroscience, University College London, London, UK. 19Institute of Translational Medicine, University of Liverpool, Liverpool, UK. 20Biostatistics & Bioinformatics Unit, Institute of Psychological Medicine and Clinical Neuroscience, MRC Centre for Neuropsychiatric Genetics & Genomics, Cardiff, UK. 21Institute of Healthy Ageing, University College London, London, UK. 22University of Reading, Reading, UK. 23MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University School of Medicine, Cardiff, UK. 24UCL Institute of Neurology, London, UK. 25Institut du Cerveau et de la Moelle épinière, ICM, Inserm U 1127, CNRS, UMR 7225, Sorbonne Universités, UPMC University Paris 06, UMR S 1127, AP-HP, Pitié-Salpêtrière Hospital, Paris, France. 26Paul Sabatier University, Toulouse, France. 27Department for Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany. 28DZNE, German Center for Neurodegenerative Diseases, Tübingen, Germany. 29Centre for Genetic Epidemiology, Institute for Clinical Epidemiology and Applied Biometry, University of Tubingen, Tübingen, Germany. 30Departments of Neurology, Neuroscience, and Molecular & Human Genetics, Baylor College of Medicine, Houston, TX, USA. 31Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital, Houston, TX, USA. 32Ken and Ruth Davee Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA. 33Northwestern University Feinberg School of Medicine, Chicago, IL, USA. 34Departments of Neurology and Physiology, University of California, San Francisco, CA, USA. 35Gladstone Institute of Neurological Disease; Taube/Koret Center for Neurodegenerative Disease Research, San Francisco, CA, USA. 36National Institutes of Health Division of Clinical Research, NINDS, National Institutes of Health, Bethesda, MD, USA. 37Neurodegenerative Diseases Research Unit, National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA. 38Montreal Neurological Institute and Hospital, Department of Neurology & Neurosurgery, Department of Human Genetics, McGill University, Montréal, QC H3A 0G4, Canada. 39Department of Human Genetics, McGill University, Montréal, QC H3A 0G4, Canada. 40Department of Neurology, Leiden University Medical Center, Leiden, Netherlands. 41Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, Seville, Spain. 42Fundació Docència i Recerca Mútua de Terrassa and Movement Disorders Unit, Department of Neurology, University Hospital Mutua de Terrassa, Terrassa, Barcelona, Spain. 43Hospital Universitario Central de Asturias, Oviedo, Spain. 44Hospital Universitario Parque Tecnologico de la Salud, Granada, Spain. 45Instituto de Investigación Sanitaria Biodonostia, San Sebastián, Spain. 46Hospital General de Segovia, Segovia, Spain. 47Memory Unit, Department of Neurology, IIB Sant Pau, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain. 48Centro de Investigación Biomédica en Red en Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain. 49Hospital Universitario Marqués de Valdecilla-IDIVAL, Santander, Spain. 50University of Cantabria, Santander, Spain. 51Movement Disorders Unit, Department of Neurology, IIB Sant Pau, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain. 52Centro de Investigacion Biomedica, Universidad de Granada, Granada, Spain. 53Hospital Universitario Virgen de las Nieves, Instituto de Investigación Biosanitaria de Granada, Granada, Spain. 54Hospital Clinic de Barcelona, Barcelona, Spain. 55Instituto de Investigación Sanitaria Fundación Jiménez Díaz, Madrid, Spain. 56Hospital Universitario Virgen de la Victoria, Malaga, Spain. 57Institut de Recerca Sant Joan de Déu, Barcelona, Spain. 58Hospital Universitario Ramón y Cajal, Madrid, Spain. 59Department of Neurology, Instituto de Investigación Sanitaria La Fe, Hospital Universitario y Politécnico La Fe, Valencia, Spain. 60Hospital General de Segovia, Segovia, Spain. 61Department of Neurology, Hospital Universitario Fundación Alcorcón, Madrid, Spain. 62Department of Neurology, Medical University of Vienna, Vienna, Austria. 63Department of Neurology, Oslo University Hospital, Oslo, Norway. 64Department of Pathophysiology, University of Tartu, Tartu, Estonia. 65Department of Reproductive Biology, Estonian University of Life Sciences, Tartu, Estonia. 66Perron Institute for Neurological and Translational Science, Perth, WA, Australia. 67Department of Neurology and Neurosurgery, University of Tartu, Tartu, Estonia. 68Institute of Clinical Medicine, Department of Neurology, University of Oulu, Oulu, Finland. 69Department of Neurology and Medical Research Center, Oulu University Hospital, Oulu, Finland. 70University of Lago, Lagos State, Nigeria UK Brain Expression Consortium (UKBEC) Paola Forabosco71 & Robert Walker11 71Istituto di Ricerca Genetica e Biomedica, Cittadella Universitaria di Cagliari, 09042 Monserrato, Sardinia, Ital

A whole-genome association study of major determinants for host control of HIV-1

Understanding why some people establish and maintain effective control of HIV-1 and others do not is a priority in the effort to develop new treatments for HIV/AIDS. Using a whole-genome association strategy, we identified polymorphisms that explain nearly 15% of the variation among individuals in viral load during the asymptomatic set-point period of infection. One of these is found within an endogenous retroviral element and is associated with major histocompatibility allele human leukocyte antigen (HLA)-B*5701, whereas a second is located near the HLA-C gene. An additional analysis of the time to HIV disease progression implicated two genes, one of which encodes an RNA polymerase I subunit. These findings emphasize the importance of studying human genetic variation as a guide to combating infectious agents