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Lea K. Davis

Bio: Lea K. Davis is an academic researcher from Vanderbilt University Medical Center. The author has contributed to research in topics: Genome-wide association study & Medicine. The author has an hindex of 34, co-authored 138 publications receiving 7193 citations. Previous affiliations of Lea K. Davis include Meharry Medical College & Semel Institute for Neuroscience and Human Behavior.


Papers
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Journal ArticleDOI
TL;DR: A large genome-wide association study of clinically diagnosed AD and AD-by-proxy identifies new loci and functional pathways that contribute to AD risk and adds novel insights into the neurobiology of AD.
Abstract: Alzheimer's disease (AD) is highly heritable and recent studies have identified over 20 disease-associated genomic loci. Yet these only explain a small proportion of the genetic variance, indicating that undiscovered loci remain. Here, we performed a large genome-wide association study of clinically diagnosed AD and AD-by-proxy (71,880 cases, 383,378 controls). AD-by-proxy, based on parental diagnoses, showed strong genetic correlation with AD (rg = 0.81). Meta-analysis identified 29 risk loci, implicating 215 potential causative genes. Associated genes are strongly expressed in immune-related tissues and cell types (spleen, liver, and microglia). Gene-set analyses indicate biological mechanisms involved in lipid-related processes and degradation of amyloid precursor proteins. We show strong genetic correlations with multiple health-related outcomes, and Mendelian randomization results suggest a protective effect of cognitive ability on AD risk. These results are a step forward in identifying the genetic factors that contribute to AD risk and add novel insights into the neurobiology of AD.

1,460 citations

Journal ArticleDOI
28 May 2009-Nature
TL;DR: Several new susceptibility genes encoding neuronal cell-adhesion molecules, including NLGN1 and ASTN2, were enriched with CNVs in ASD cases compared to controls, and duplications 55 kilobases upstream of complementary DNA AK123120 indicate that these two important gene networks expressed within the central nervous system may contribute to the genetic susceptibility of ASD.
Abstract: Several lines of evidence point to genetic involvement in autism spectrum disorders (ASDs), neurodevelopmental and neuropsychiatric disorders characterized by impaired verbal communication and social interaction. The clinical and genetic complexities of the condition make it difficult to identify susceptibility factors, but two related studies now present robust evidence for a genetic involvement. The first, a genome-wide association study, identifies six single-nucleotide polymorphisms strongly associated with autism. These variants lie between two genes encoding neuronal cell-adhesion molecules (cadherins 9 and 10), suggesting possible involvement in ASD pathogenesis. The second study used copy number variation screens to identify genetic variants in two major gene pathways in children with ASDs. The changes are in the ubiquitin pathway, which has previously been associated with neurological disease, and in genes for neuronal cell-adhesion molecules.

1,331 citations

Journal ArticleDOI
09 Jun 2011-Neuron
TL;DR: A genome-wide analysis of rare copy-number variation in 1124 autism spectrum disorder families, each comprised of a single proband, unaffected parents, and, in most kindreds, an unaffected sibling, finds significant association of ASD with de novo duplications of 7q11.23, where the reciprocal deletion causes Williams-Beuren syndrome.

1,198 citations

Journal ArticleDOI
Phil Lee, Verneri Anttila, Hyejung Won1, Yen-Chen Anne Feng1  +603 moreInstitutions (10)
12 Dec 2019-Cell
TL;DR: Genetic influences on psychiatric disorders transcend diagnostic boundaries, suggesting substantial pleiotropy of contributing loci within genes that show heightened expression in the brain throughout the lifespan, beginning prenatally in the second trimester, and play prominent roles in neurodevelopmental processes.

781 citations

Journal ArticleDOI
TL;DR: A GWAS of lifetime cannabis use reveals new risk loci, shows that cannabis use has genetic overlap with smoking and alcohol use, and indicates that the likelihood of initiating cannabis use is causally influenced by schizophrenia.
Abstract: Cannabis use is a heritable trait that has been associated with adverse mental health outcomes. In the largest genome-wide association study (GWAS) for lifetime cannabis use to date (N = 184,765), we identified eight genome-wide significant independent single nucleotide polymorphisms in six regions. All measured genetic variants combined explained 11% of the variance. Gene-based tests revealed 35 significant genes in 16 regions, and S-PrediXcan analyses showed that 21 genes had different expression levels for cannabis users versus nonusers. The strongest finding across the different analyses was CADM2, which has been associated with substance use and risk-taking. Significant genetic correlations were found with 14 of 25 tested substance use and mental health-related traits, including smoking, alcohol use, schizophrenia and risk-taking. Mendelian randomization analysis showed evidence for a causal positive influence of schizophrenia risk on cannabis use. Overall, our study provides new insights into the etiology of cannabis use and its relation with mental health.

365 citations


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TL;DR: In the latest version 10.5 of STRING, the biggest changes are concerned with data dissemination: the web frontend has been completely redesigned to reduce dependency on outdated browser technologies, and the database can now also be queried from inside the popular Cytoscape software framework.
Abstract: A system-wide understanding of cellular function requires knowledge of all functional interactions between the expressed proteins. The STRING database aims to collect and integrate this information, by consolidating known and predicted protein-protein association data for a large number of organisms. The associations in STRING include direct (physical) interactions, as well as indirect (functional) interactions, as long as both are specific and biologically meaningful. Apart from collecting and reassessing available experimental data on protein-protein interactions, and importing known pathways and protein complexes from curated databases, interaction predictions are derived from the following sources: (i) systematic co-expression analysis, (ii) detection of shared selective signals across genomes, (iii) automated text-mining of the scientific literature and (iv) computational transfer of interaction knowledge between organisms based on gene orthology. In the latest version 10.5 of STRING, the biggest changes are concerned with data dissemination: the web frontend has been completely redesigned to reduce dependency on outdated browser technologies, and the database can now also be queried from inside the popular Cytoscape software framework. Further improvements include automated background analysis of user inputs for functional enrichments, and streamlined download options. The STRING resource is available online, at http://string-db.org/.

5,569 citations

01 Feb 2015
TL;DR: In this article, the authors describe the integrative analysis of 111 reference human epigenomes generated as part of the NIH Roadmap Epigenomics Consortium, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression.
Abstract: The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease.

4,409 citations

Journal ArticleDOI
Silvia De Rubeis1, Xin-Xin He2, Arthur P. Goldberg1, Christopher S. Poultney1, Kaitlin E. Samocha3, A. Ercument Cicek2, Yan Kou1, Li Liu2, Menachem Fromer1, Menachem Fromer3, R. Susan Walker4, Tarjinder Singh5, Lambertus Klei6, Jack A. Kosmicki3, Shih-Chen Fu1, Branko Aleksic7, Monica Biscaldi8, Patrick Bolton9, Jessica M. Brownfeld1, Jinlu Cai1, Nicholas G. Campbell10, Angel Carracedo11, Angel Carracedo12, Maria H. Chahrour3, Andreas G. Chiocchetti, Hilary Coon13, Emily L. Crawford10, Lucy Crooks5, Sarah Curran9, Geraldine Dawson14, Eftichia Duketis, Bridget A. Fernandez15, Louise Gallagher16, Evan T. Geller17, Stephen J. Guter18, R. Sean Hill19, R. Sean Hill3, Iuliana Ionita-Laza20, Patricia Jiménez González, Helena Kilpinen, Sabine M. Klauck21, Alexander Kolevzon1, Irene Lee22, Jing Lei2, Terho Lehtimäki, Chiao-Feng Lin17, Avi Ma'ayan1, Christian R. Marshall4, Alison L. McInnes23, Benjamin M. Neale24, Michael John Owen25, Norio Ozaki7, Mara Parellada26, Jeremy R. Parr27, Shaun Purcell1, Kaija Puura, Deepthi Rajagopalan4, Karola Rehnström5, Abraham Reichenberg1, Aniko Sabo28, Michael Sachse, Stephen Sanders29, Chad M. Schafer2, Martin Schulte-Rüther30, David Skuse31, David Skuse22, Christine Stevens24, Peter Szatmari32, Kristiina Tammimies4, Otto Valladares17, Annette Voran33, Li-San Wang17, Lauren A. Weiss29, A. Jeremy Willsey29, Timothy W. Yu3, Timothy W. Yu19, Ryan K. C. Yuen4, Edwin H. Cook18, Christine M. Freitag, Michael Gill16, Christina M. Hultman34, Thomas Lehner35, Aarno Palotie24, Aarno Palotie36, Aarno Palotie3, Gerard D. Schellenberg17, Pamela Sklar1, Matthew W. State29, James S. Sutcliffe10, Christopher A. Walsh19, Christopher A. Walsh3, Stephen W. Scherer4, Michael E. Zwick37, Jeffrey C. Barrett5, David J. Cutler37, Kathryn Roeder2, Bernie Devlin6, Mark J. Daly24, Mark J. Daly3, Joseph D. Buxbaum1 
13 Nov 2014-Nature
TL;DR: Using exome sequencing, it is shown that analysis of rare coding variation in 3,871 autism cases and 9,937 ancestry-matched or parental controls implicates 22 autosomal genes at a false discovery rate of < 0.05, plus a set of 107 genes strongly enriched for those likely to affect risk (FDR < 0.30).
Abstract: The genetic architecture of autism spectrum disorder involves the interplay of common and rare variants and their impact on hundreds of genes. Using exome sequencing, here we show that analysis of rare coding variation in 3,871 autism cases and 9,937 ancestry-matched or parental controls implicates 22 autosomal genes at a false discovery rate (FDR) < 0.05, plus a set of 107 autosomal genes strongly enriched for those likely to affect risk (FDR < 0.30). These 107 genes, which show unusual evolutionary constraint against mutations, incur de novo loss-of-function mutations in over 5% of autistic subjects. Many of the genes implicated encode proteins for synaptic formation, transcriptional regulation and chromatin-remodelling pathways. These include voltage-gated ion channels regulating the propagation of action potentials, pacemaking and excitability-transcription coupling, as well as histone-modifying enzymes and chromatin remodellers-most prominently those that mediate post-translational lysine methylation/demethylation modifications of histones.

2,228 citations