Edinburgh Research Explorer Genome-wide association study of depression phenotypes in UK Biobank identifies variants in excitatory synaptic pathways

Depression is a polygenic trait that causes extensive periods of disability. Previous genetic studies have identi fi ed common risk variants which have progressively increased in number with increasing sample sizes of the respective studies. Here, we conduct a genome-wide association study in 322,580 UK Biobank participants for three depression-related phenotypes: broad depression, probable major depressive disorder (MDD), and International Classi fi cation of Diseases (ICD, version 9 or 10)-coded MDD. We identify 17 independent loci that are signi fi cantly associated ( P < 5 × 10 − 8 ) across the three phenotypes. The direction of effect of these loci is consistently replicated in an independent sample, with 14 loci likely representing novel fi ndings. Gene sets are enriched in excitatory neurotransmission, mechanosensory behaviour, post synapse, neuron spine and dendrite functions. Our fi ndings suggest that broad depression is the most tractable UK Biobank phenotype for discovering genes and gene sets that further our understanding of the biological pathways underlying depression.

[1]  C. Haley,et al.  Haplotype Heritability Mapping Method Uncovers Missing Heritability of Complex Traits , 2018, Scientific Reports.

[2]  Warren W. Kretzschmar,et al.  Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression , 2017, Nature Genetics.

[3]  P. Donnelly,et al.  Genome-wide genetic data on ~500,000 UK Biobank participants , 2017, bioRxiv.

[4]  Blair H. Smith,et al.  The Stratification Of Major Depressive Disorder Into Genetic Subgroups , 2017 .

[5]  S. Mathew,et al.  Targeting glutamate signalling in depression: progress and prospects , 2017, Nature Reviews Drug Discovery.

[6]  The Gene Ontology Consortium,et al.  Expansion of the Gene Ontology knowledgebase and resources , 2016, Nucleic Acids Res..

[7]  N. Wray,et al.  Meta-analysis of genome-wide association studies of anxiety disorders , 2016, Molecular Psychiatry.

[8]  D. Hinds,et al.  Identification of 15 genetic loci associated with risk of major depression in individuals of European descent , 2016, Nature Genetics.

[9]  Tom R. Gaunt,et al.  LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis , 2016, bioRxiv.

[10]  J. Wellmann,et al.  Pain Sensitivity in Patients With Major Depression: Differential Effect of Pain Sensitivity Measures, Somatic Cofactors, and Disease Characteristics. , 2016, The journal of pain : official journal of the American Pain Society.

[11]  Joseph K. Pickrell,et al.  Genetic variants associated with subjective well-being, depressive symptoms and neuroticism identified through genome-wide analyses , 2016, Nature Genetics.

[12]  A. Dehghan,et al.  Polygenic dissection of major depression clinical heterogeneity , 2016, Molecular Psychiatry.

[13]  C. Spencer,et al.  A contribution of novel CNVs to schizophrenia from a genome-wide study of 41,321 subjects: CNV Analysis Group and the Schizophrenia Working Group of the Psychiatric Genomics Consortium , 2016, bioRxiv.

[14]  Gabor T. Marth,et al.  A global reference for human genetic variation , 2015, Nature.

[15]  Warren W. Kretzschmar,et al.  Sparse whole genome sequencing identifies two loci for major depressive disorder , 2015, Nature.

[16]  S. Thompson,et al.  An excitatory synapse hypothesis of depression , 2015, Trends in Neurosciences.

[17]  Joris M. Mooij,et al.  MAGMA: Generalized Gene-Set Analysis of GWAS Data , 2015, PLoS Comput. Biol..

[18]  R. Gao,et al.  Common mechanisms of excitatory and inhibitory imbalance in schizophrenia and autism spectrum disorders. , 2015, Current molecular medicine.

[19]  J. Hirschhorn,et al.  Biological interpretation of genome-wide association studies using predicted gene functions , 2015, Nature Communications.

[20]  Carson C Chow,et al.  Second-generation PLINK: rising to the challenge of larger and richer datasets , 2014, GigaScience.

[21]  Ronald S Duman,et al.  NEUROBIOLOGY OF STRESS, DEPRESSION, AND RAPID ACTING ANTIDEPRESSANTS: REMODELING SYNAPTIC CONNECTIONS , 2014, Depression and anxiety.

[22]  M. Daly,et al.  LD Score regression distinguishes confounding from polygenicity in genome-wide association studies , 2014, Nature Genetics.

[23]  Jonathan J. Evans,et al.  Prevalence and Characteristics of Probable Major Depression and Bipolar Disorder within UK Biobank: Cross-Sectional Study of 172,751 Participants , 2013, PloS one.

[24]  Ellen T. Gelfand,et al.  The Genotype-Tissue Expression (GTEx) project , 2013, Nature Genetics.

[25]  Peter Kraft,et al.  A Genome-Wide Association Study of Depressive Symptoms , 2013, Biological Psychiatry.

[26]  R. Huganir,et al.  Local potentiation of excitatory synapses by serotonin and its alteration in rodent models of depression , 2013, Nature Neuroscience.

[27]  D. Bentley,et al.  Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near ODZ4 , 2012, Nature Genetics.

[28]  P. Visscher,et al.  Estimating missing heritability for disease from genome-wide association studies. , 2011, American journal of human genetics.

[29]  Josyf Mychaleckyj,et al.  Robust relationship inference in genome-wide association studies , 2010, Bioinform..

[30]  Yun Li,et al.  METAL: fast and efficient meta-analysis of genomewide association scans , 2010, Bioinform..

[31]  Pablo Tamayo,et al.  Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[32]  P. Sullivan,et al.  Genetic epidemiology of major depression: review and meta-analysis. , 2000, The American journal of psychiatry.

[33]  Blair H. Smith,et al.  Genome-wide Regional Heritability Mapping Identifies a Locus Within the TOX2 Gene Associated With Major Depressive Disorder , 2017, Biological Psychiatry.

[34]  Tanya M. Teslovich,et al.  LocusZoom: regional visualization of genome-wide association scan results , 2010, Bioinform..

[35]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .