GWAS approaches have contributed a great deal to the progress in understanding of psychiatric disorders in the past decade1. Genetic findings have contributed to novel insights into disease classification and biological architecture of psychiatric disorders3. For psychiatric conditions such as SCZ5, BIP7, MDD8 and depressive phenotypes9, ADHD10, ASD11, PTSD13, and ANX14,15, GWAS has yielded the discovery of numerous novel genetic loci associated with these conditions. Downstream analysis of these loci can aid in the discovery and development of novel therapeutic targets and diagnostic markers2,3. Further analysis through heritability, genetic correlation, and genetic overlap estimations have provided valueable insights into how psychiatric disorders relate to each other on a molecular biological level16.
Psychiatric disorders are a prime target for large-scale genetic analysis due to their complex and polygenic nature3. Compared to candidate gene, and molecular biological approaches, the largest strides in psychiatric research has been made through international collaborative efforts incorporating a multimodal approach including genetics, clinical characterisation, and brain imaging3,17,18. In the past decade alone, the number of known genetic loci associated with SCZ has grown from 2219,20 in the early 2010s to 1085 in 2014 to 287 in 202221. Similar strides have been made for other psychiatric disorders such as MDD8,9,22, BIP6,7,23, and ASD11 by the PGC consortium alone. Functional mapping, using for instance FUMA25 and the reactome pathway database26,27, allows to map the discovered genetic loci to genes and study their involvement in known biological processes.
Large-scale genetic studies using GWAS have identified a number of biologically relevant pathways for a number of psychiatric disorders28. A higher number of genes linked to a particular trait or disorder allows for more elaborate and detailed investigation of the biological pathways associated with a particular trait27. For example, in SCZ, using the GWAS summary statistics, researchers have reliably linked the discovered genetic loci to biological processes involved in neurotransmission29. Although these findings are at the moment not suitable for diagnostic application, they can aid in the prediction of treatment response and outcome severity30.
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Howard DM, Adams MJ, Clarke TK, Hafferty JD, Gibson J, Shirali M, et al. Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nat Neurosci [Internet]. 2019/02/04 ed. 2019 Mar;22(3):343–52. Available from:
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Wray NR, Ripke S, Mattheisen M, Trzaskowski M, Byrne EM, Abdellaoui A, et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nature Genetics [Internet]. 2018 May 1;50(5):668–81. Available from:
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Demontis D, Walters RK, Martin J, Mattheisen M, Als TD, Agerbo E, et al. Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nature Genetics [Internet]. 2019 Jan 1;51(1):63–75. Available from:
https://doi.org/10.1038/s41588-018-0269-7
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Grove J, Ripke S, Als TD, Mattheisen M, Walters RK, Won H, et al. Identification of common genetic risk variants for autism spectrum disorder. Nature Genetics [Internet]. 2019 Mar 1;51(3):431–44. Available from:
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Duncan LE, Ratanatharathorn A, Aiello AE, Almli LM, Amstadter AB, Ashley-Koch AE, et al. Largest
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N=20 070) yields genetic overlap with schizophrenia and sex differences in heritability. Molecular Psychiatry [Internet]. 2018 Mar 1;23(3):666–73. Available from:
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Stein MB, Levey DF, Cheng Z, Wendt FR, Harrington K, Pathak GA, et al. Genome-wide association analyses of post-traumatic stress disorder and its symptom subdomains in the
Million Veteran Program. Nature Genetics [Internet]. 2021 Feb 1;53(2):174–84. Available from:
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Otowa T, Hek K, Lee M, Byrne EM, Mirza SS, Nivard MG, et al. Meta-analysis of genome-wide association studies of anxiety disorders. Molecular Psychiatry [Internet]. 2016 Oct 1;21(10):1391–9. Available from:
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Watson HJ, Yilmaz Z, Thornton LM, Hübel C, Coleman JRI, Gaspar HA, et al. Genome-wide association study identifies eight risk loci and implicates metabo-psychiatric origins for anorexia nervosa. Nature Genetics [Internet]. 2019 Aug 1;51(8):1207–14. Available from:
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Ripke S, Sanders AR, Kendler KS, Levinson DF, Sklar P, Holmans PA, et al. Genome-wide association study identifies five new schizophrenia loci. Nature Genetics [Internet]. 2011 Oct 1;43(10):969–76. Available from:
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Ripke S, O’Dushlaine C, Chambert K, Moran JL, Kähler AK, Akterin S, et al. Genome-wide association analysis identifies 13 new risk loci for schizophrenia. Nature Genetics [Internet]. 2013 Oct 1;45(10):1150–9. Available from:
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Trubetskoy V, Pardiñas AF, Qi T, Panagiotaropoulou G, Awasthi S, Bigdeli TB, et al. Mapping genomic loci implicates genes and synaptic biology in schizophrenia. Nature [Internet]. 2022 Apr 1;604(7906):502–8. Available from:
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Sklar P, Ripke S, Scott LJ, Andreassen OA, Cichon S, Craddock N, et al. Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near
ODZ4. Nature Genetics [Internet]. 2011 Oct 1;43(10):977–83. Available from:
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