Abstract
Genome-wide association studies have identified hundreds of robust genetic associations underlying psychiatric disorders and provided important biological insights into disease onset and progression. There is optimism that genetic findings will pave the way to precision psychiatry by facilitating the development of more effective treatments and the identification of groups of patients that these treatments should be targeted toward. However, there are several challenges that must be addressed before genetic findings can be translated into the clinic. In this Perspective, we highlight ten challenges for the field of psychiatric genetics, focused on the robust and generalizable detection of genetic risk factors, improved definition and assessment of psychopathology and achieving better clinical indicators. We discuss recent advancements in the field that will improve the explanatory and predictive power of genetic data and ultimately contribute to improving the management and treatment of patients with a psychiatric disorder.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
References
Pardinas, A. F. et al. Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection. Nat. Genet. 50, 381–389 (2018).
Stahl, E. A. et al. Genome-wide association study identifies 30 loci associated with bipolar disorder. Nat. Genet. 51, 793–803 (2019).
Levey, D. F. et al. Bi-ancestral depression GWAS in the Million Veteran Program and meta-analysis in >1.2 million individuals highlight new therapeutic directions. Nat. Neurosci. 24, 954–963 (2021).
Sullivan, P. F. et al. Psychiatric genomics: an update and an agenda. Am. J. Psychiatry 175, 15–27 (2018).
Rees, E. & Owen, M. J. Translating insights from neuropsychiatric genetics and genomics for precision psychiatry. Genome Med. 12, 43 (2020).
Hindorff, L. A. et al. Prioritizing diversity in human genomics research. Nat. Rev. Genet. 19, 175–185 (2018).
Popejoy, A. B. & Fullerton, S. M. Genomics is failing on diversity. Nature 538, 161–164 (2016).
Peterson, R. E. et al. Genome-wide association studies in ancestrally diverse populations: opportunities, methods, pitfalls, and recommendations. Cell 179, 589–603 (2019).
Nagai, A. et al. Overview of the BioBank Japan Project: study design and profile. J. Epidemiol. 27, S2–S8 (2017).
Chen, Z. et al. China Kadoorie Biobank of 0.5 million people: survey methods, baseline characteristics and long-term follow-up. Int. J. Epidemiol. 40, 1652–1666 (2011).
Manrai, A. K. et al. Genetic misdiagnoses and the potential for health disparities. N. Engl. J. Med. 375, 655–665 (2016).
Lee, P. H. et al. Genomic relationships, novel loci, and pleiotropic mechanisms across eight psychiatric disorders. Cell 179, 1469–1482 (2019).
Wojcik, G. L. et al. Genetic analyses of diverse populations improves discovery for complex traits. Nature 570, 514–518 (2019).
Lam, M. et al. Comparative genetic architectures of schizophrenia in East Asian and European populations. Nat. Genet. 51, 1670–1678 (2019).
Giannakopoulou, O. et al. The genetic architecture of depression in individuals of East Asian ancestry: a genome-wide association study. JAMA Psychiatry 78, 1258–1269 (2021).
Atkinson, E. G. et al. Tractor uses local ancestry to enable the inclusion of admixed individuals in GWAS and to boost power. Nat. Genet. 53, 195–204 (2021).
Wang, Y. et al. Theoretical and empirical quantification of the accuracy of polygenic scores in ancestry divergent populations. Nat. Commun. 11, 3865 (2020).
Brown, B. C., Asian Genetic Epidemiology Network Type 2 Diabetes Consortium, Ye, C. J., Price, A. L. & Zaitlen, N. Transethnic genetic-correlation estimates from summary statistics. Am. J. Hum. Genet. 99, 76–88 (2016).
Sullivan, P. F. & Kendler, K. S. The state of the science in psychiatric genomics. Psychol. Med. 51, 2145–2147 (2021).
Manolio, T. A. et al. Finding the missing heritability of complex diseases. Nature 461, 747–753 (2009).
Levy, R. J., Xu, B., Gogos, J. A. & Karayiorgou, M. Copy number variation and psychiatric disease risk. Methods Mol. Biol. 838, 97–113 (2012).
Singh, T. et al. Rare coding variants in ten genes confer substantial risk for schizophrenia. Nature 604, 509–516 (2022).
Wainschtein, P. et al. Assessing the contribution of rare variants to complex trait heritability from whole-genome sequence data. Nat. Genet. 54, 263–273 (2022).
Uffelmann, E. & Posthuma, D. Emerging methods and resources for biological interrogation of neuropsychiatric polygenic signal. Biol. Psychiatry 89, 41–53 (2021).
Yang, J. et al. Genetic variance estimation with imputed variants finds negligible missing heritability for human height and body mass index. Nat. Genet. 47, 1114–1120 (2015).
Visscher, P. M. et al. 10 years of GWAS discovery: biology, function, and translation. Am. J. Hum. Genet. 101, 5–22 (2017).
Halvorsen, M. et al. Increased burden of ultra-rare structural variants localizing to boundaries of topologically associated domains in schizophrenia. Nat. Commun. 11, 1842 (2020).
Karczewski, K. J. et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 581, 434–443 (2020).
Satterstrom, F. K. et al. Large-scale exome sequencing study implicates both developmental and functional changes in the neurobiology of autism. Cell 180, 568–584 (2020).
Wilfert, A. B. et al. Recent ultra-rare inherited variants implicate new autism candidate risk genes. Nat. Genet. 53, 1125–1134 (2021).
Satterstrom, F. K. et al. Autism spectrum disorder and attention deficit hyperactivity disorder have a similar burden of rare protein-truncating variants. Nat. Neurosci. 22, 1961–1965 (2019).
Sul, J. H. et al. Contribution of common and rare variants to bipolar disorder susceptibility in extended pedigrees from population isolates. Transl. Psychiatry 10, 74 (2020).
Backman, J. D. et al. Exome sequencing and analysis of 454,787 UK Biobank participants. Nature 599, 628–634 (2021).
Sanders, S. J. et al. Whole genome sequencing in psychiatric disorders: the WGSPD consortium. Nat. Neurosci. 20, 1661–1668 (2017).
Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018).
Fry, A. et al. Comparison of sociodemographic and health-related characteristics of UK Biobank participants with those of the general population. Am. J. Epidemiol. 186, 1026–1034 (2017).
Munafo, M. R., Tilling, K., Taylor, A. E., Evans, D. M. & Davey Smith, G. Collider scope: when selection bias can substantially influence observed associations. Int. J. Epidemiol. 47, 226–235 (2018).
Pirastu, N. et al. Genetic analyses identify widespread sex-differential participation bias. Nat. Genet. 53, 663–671 (2021).
Tyrrell, J. et al. Genetic predictors of participation in optional components of UK Biobank. Nat. Commun. 12, 886 (2021).
Grotzinger, A. D. et al. Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits. Nat. Hum. Behav. 3, 513–525 (2019).
Selzam, S. et al. Comparing within- and between-family polygenic score prediction. Am. J. Hum. Genet. 105, 351–363 (2019).
Howe, L. J. et al. Within-sibship GWAS improve estimates of direct genetic effects. Genet. Epidemiol. 45, 801 (2021).
Hyde, C. L. et al. Identification of 15 genetic loci associated with risk of major depression in individuals of European descent. Nat. Genet. 48, 1031–1036 (2016).
Sanchez-Roige, S. & Palmer, A. A. Emerging phenotyping strategies will advance our understanding of psychiatric genetics. Nat. Neurosci. 23, 475–480 (2020).
Howard, D. M. et al. Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nat. Neurosci. 22, 343–352 (2019).
Mitchell, B. L. et al. Polygenic risk scores derived from varying definitions of depression and risk of depression. JAMA Psychiatry 78, 1152–1160 (2021).
Cai, N. et al. Minimal phenotyping yields genome-wide association signals of low specificity for major depression. Nat. Genet. 52, 437–447 (2020).
Clements, C. C. et al. Genome-wide association study of patients with a severe major depressive episode treated with electroconvulsive therapy. Mol. Psychiatry 26, 2429–2439 (2021).
Brainstorm, C. et al. Analysis of shared heritability in common disorders of the brain. Science 360, eaap8757 (2018).
Grotzinger, A. D. et al. Genetic architecture of 11 major psychiatric disorders at biobehavioral, functional genomic and molecular genetic levels of analysis. Nat. Genet. 54, 548–559 (2022).
Gerring, Z. F., Thorp, J. G., Gamazon, E. R. & Derks, E. M. A local genetic correlation analysis provides biological insights into the shared genetic architecture of psychiatric and substance use phenotypes. Biol. Psychiatry https://doi.org/10.1016/j.biopsych.2022.03.001 (2022).
Ruderfer, D. M. et al. Genomic dissection of bipolar disorder and schizophrenia, including 28 subphenotypes. Cell 173, 1705–1715 (2018).
Thorp, J. G. et al. Symptom-level modelling unravels the shared genetic architecture of anxiety and depression. Nat. Hum. Behav. 5, 1432–1442 (2021).
Gandal, M. J. et al. Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap. Science 359, 693–697 (2018).
Gerring, Z. F., Thorp, J. G., Gamazon, E. R. & Derks, E. M. An analysis of genetically regulated gene expression and the role of co-expression networks across 16 psychiatric and substance use phenotypes. Eur. J. Hum. Genet. 30, 560–566 (2022).
Caspi, A. & Moffitt, T. E. All for one and one for all: mental disorders in one dimension. Am. J. Psychiatry 175, 831–844 (2018).
Lee, P. H., Feng, Y.-C. A. & Smoller, J. W. Pleiotropy and cross-disorder genetics among psychiatric disorders. Biol. Psychiatry 89, 20–31 (2021).
Smoller, J. W. et al. Psychiatric genetics and the structure of psychopathology. Mol. Psychiatry 24, 409–420 (2018).
Waldman, I. D., Poore, H. E., Luningham, J. M. & Yang, J. Testing structural models of psychopathology at the genomic level. World Psychiatry 19, 350–359 (2020).
Insel, Thomas et al. Research Domain Criteria (RDoC): toward a new classification framework for research on mental disorders. Am. J. Psychiatry 167, 748–751 (2010).
Waszczuk, M. A. et al. Redefining phenotypes to advance psychiatric genetics: implications from hierarchical taxonomy of psychopathology. J. Abnorm. Psychol. 129, 143–161 (2020).
Michelini, G., Palumbo, I. M., DeYoung, C. G., Latzman, R. D. & Kotov, R. Linking RDoC and HiTOP: a new interface for advancing psychiatric nosology and neuroscience. Clin. Psychol. Rev. 86, 102025 (2021).
Weinberger, D. R., Glick, I. D. & Klein, D. F. Whither Research Domain Criteria (RDoC)?: the good, the bad, and the ugly. JAMA Psychiatry 72, 1161–1162 (2015).
Wittchen, H.-U. & Beesdo-Baum, K. ‘Throwing out the baby with the bathwater’? Conceptual and methodological limitations of the HiTOP approach. World Psychiatry 17, 298–299 (2018).
Hodgson, K., McGuffin, P. & Lewis, C. M. Advancing psychiatric genetics through dissecting heterogeneity. Hum. Mol. Genet. 26, R160–R165 (2017).
Cai, N., Choi, K. W. & Fried, E. I. Reviewing the genetics of heterogeneity in depression: operationalizations, manifestations and etiologies. Hum. Mol. Genet. 29, R10–R18 (2020).
American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders (DSM-5®) (American Psychiatric Publishing, 2013).
Fried, E. I., Coomans, F. & Lorenzo-Luaces, L. The 341 737 ways of qualifying for the melancholic specifier. Lancet Psychiatry 7, 479–480 (2020).
Nguyen, T.-D. et al. Genetic heterogeneity and subtypes of major depression. Mol. Psychiatry 27, 1667–1675 (2022).
Thorp, J. G. et al. Genetic heterogeneity in self-reported depressive symptoms identified through genetic analyses of the PHQ-9. Psychol. Med. 50, 2385–2396 (2020).
Milaneschi, Y. et al. Genetic association of major depression with atypical features and obesity-related immunometabolic dysregulations. JAMA Psychiatry 74, 1214–1225 (2017).
Sanchez-Roige, S. et al. Genome-wide association study meta-analysis of the Alcohol Use Disorders Identification Test (AUDIT) in two population-based cohorts. Am. J. Psychiatry 176, 107–118 (2019).
Dahl, A. et al. Reverse GWAS: using genetics to identify and model phenotypic subtypes. PLoS Genet. 15, e1008009 (2019).
Han, B. et al. A method to decipher pleiotropy by detecting underlying heterogeneity driven by hidden subgroups applied to autoimmune and neuropsychiatric diseases. Nat. Genet. 48, 803–810 (2016).
Hukku, A. et al. Probabilistic colocalization of genetic variants from complex and molecular traits: promise and limitations. Am. J. Hum. Genet. 108, 25–35 (2021).
Hernandez, L. M. et al. Transcriptomic insight into the polygenic mechanisms underlying psychiatric disorders. Biol. Psychiatry 89, 54–64 (2021).
Gamazon, E. R., Zwinderman, A. H., Cox, N. J., Denys, D. & Derks, E. M. Multi-tissue transcriptome analyses identify genetic mechanisms underlying neuropsychiatric traits. Nat. Genet. 51, 933–940 (2019).
Mountjoy, E. et al. An open approach to systematically prioritize causal variants and genes at all published human GWAS trait-associated loci. Nat. Genet. 53, 1527–1533 (2021).
Gamazon, E. R. et al. A gene-based association method for mapping traits using reference transcriptome data. Nat. Genet. 47, 1091–1098 (2015).
Gusev, A. et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 48, 245–252 (2016).
Zhang, F. et al. OSCA: a tool for omic-data-based complex trait analysis. Genome Biol. 20, 107 (2019).
Sey, N. Y. A. et al. A computational tool (H-MAGMA) for improved prediction of brain-disorder risk genes by incorporating brain chromatin interaction profiles. Nat. Neurosci. 23, 583–593 (2020).
Meng, X. H. et al. Integration of summary data from GWAS and eQTL studies identified novel causal BMD genes with functional predictions. Bone 113, 41–48 (2018).
Hormozdiari, F. et al. Colocalization of GWAS and eQTL signals detects target genes. Am. J. Hum. Genet. 99, 1245–1260 (2016).
Caligiuri, S. P. & Kenny, P. J. The promise of genome editing for modeling psychiatric disorders. Neuropsychopharmacology 43, 223–224 (2018).
Begley, C. G. et al. Drug repurposing: misconceptions, challenges, and opportunities for academic researchers. Sci. Transl. Med. 13, eabd5524 (2021).
Sertkaya, A., Birkenbach, A., Berlind, A. & Eyraud, J. Examination of Clinical Trial Costs and Barriers for Drug Development (US Department of Health and Human Services, 2014).
Nelson, M. R. et al. The support of human genetic evidence for approved drug indications. Nat. Genet. 47, 856–860 (2015).
Pushpakom, S. et al. Drug repurposing: progress, challenges and recommendations. Nat. Rev. Drug Discov. 18, 41–58 (2018).
Gaspar, H. A. et al. Using genetic drug-target networks to develop new drug hypotheses for major depressive disorder. Transl. Psychiatry 9, 117 (2019).
Reay, W. R., Atkins, J. R., Carr, V. J., Green, M. J. & Cairns, M. J. Pharmacological enrichment of polygenic risk for precision medicine in complex disorders. Sci. Rep. 10, 879 (2020).
Subramanian, A. et al. A next generation connectivity map: L1000 platform and the first 1,000,000 profiles. Cell 171, 1437–1452 (2017).
Gerring, Z. F., Gamazon, E. R., White, A. & Derks, E. M. Integrative network-based analysis reveals gene networks and novel drug repositioning candidates for Alzheimer disease. Neurol. Genet. 7, e622 (2021).
Taubes, A. et al. Experimental and real-world evidence supporting the computational repurposing of bumetanide for APOE4-related Alzheimer’s disease. Nat. Aging 1, 932–947 (2021).
Pingault, J. B. et al. Using genetic data to strengthen causal inference in observational research. Nat. Rev. Genet. 19, 566–580 (2018).
Ohlsson, H. & Kendler, K. S. Applying causal inference methods in psychiatric epidemiology: a review. JAMA Psychiatry 77, 637–644 (2020).
Davey Smith, G. & Hemani, G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum. Mol. Genet. 23, R89–R98 (2014).
Skrivankova, V. W. et al. Strengthening the reporting of observational studies in epidemiology using Mendelian randomization: the STROBE-MR Statement. JAMA 326, 1614–1621 (2021).
Wootton, R. E., Jones, H. J. & Sallis, H. M. Mendelian randomisation for psychiatry: how does it work, and what can it tell us?. Mol. Psychiatry 47, 1672–1679 (2021).
Watanabe, K. et al. A global overview of pleiotropy and genetic architecture in complex traits. Nat. Genet. 51, 1339–1348 (2019).
McAdams, T. A., Rijsdijk, F. V., Zavos, H. M. S. & Pingault, J. B. Twins and causal inference: leveraging nature’s experiment. Cold Spring Harb. Perspect. Med. 11, a039552 (2021).
Choi, K. W. et al. An exposure-wide and Mendelian randomization approach to identifying modifiable factors for the prevention of depression. Am. J. Psychiatry 177, 944–954 (2020).
Zhou, E. A. Global Biobank Meta-analysis Initiative: powering genetic discovery across human diseases. Preprint at medRxiv https://doi.org/10.1101/2021.11.19.21266436 (2021).
Dudbridge, F. Power and predictive accuracy of polygenic risk scores. PLoS Genet. 9, e1003348 (2013).
Lewis, C. M. & Vassos, E. Polygenic risk scores: from research tools to clinical instruments. Genome Med. 12, 44 (2020).
Fullerton, J. M. & Nurnberger, J. I. Polygenic risk scores in psychiatry: will they be useful for clinicians? F1000Res 8, https://doi.org/10.12688/f1000research.18491.1 (2019).
Garcia-Gonzalez, J. et al. Pharmacogenetics of antidepressant response: a polygenic approach. Prog. Neuropsychopharmacol. Biol. Psychiatry 75, 128–134 (2017).
Ward, J. et al. Polygenic risk scores for major depressive disorder and neuroticism as predictors of antidepressant response: meta-analysis of three treatment cohorts. PLoS ONE 13, e0203896 (2018).
Natarajan, P. et al. Polygenic risk score identifies subgroup with higher burden of atherosclerosis and greater relative benefit from statin therapy in the primary prevention setting. Circulation 135, 2091–2101 (2017).
Gaziano, J. M. et al. Million Veteran Program: a mega-biobank to study genetic influences on health and disease. J. Clin. Epidemiol. 70, 214–223 (2016).
Acknowledgements
E.M.D. and Z.F.G. are supported by the NIA, NIH (AG068026). J.G.T. is supported by a University of Queensland Research Training Program scholarship. We acknowledge the biomedical illustrator M. Kersting Flynn and the graphic designer J.M. Suarez for assisting with creation of the figures.
Author information
Authors and Affiliations
Contributions
E.M.D., J.G.T. and Z.F.G. conceived and wrote the manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
Supplementary Information
Supplementary Note
Supplementary Tables
Supplementary Tables 1–3.
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Derks, E.M., Thorp, J.G. & Gerring, Z.F. Ten challenges for clinical translation in psychiatric genetics. Nat Genet 54, 1457–1465 (2022). https://doi.org/10.1038/s41588-022-01174-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41588-022-01174-0
This article is cited by
-
Incorporating genetic similarity of auxiliary samples into eGene identification under the transfer learning framework
Journal of Translational Medicine (2024)
