Psychiatric genetics has evolved from candidate-gene studies to whole-genome sequencing efforts. With hundreds of disease-associated loci now identified, functional interpretation of the associated loci becomes the critical next step toward translational applications. The article discusses achievements, challenges, and opportunities in psychiatric genomics associated with complexity and heterogeneity. Brain expression quantitative trait loci, single-cell ribonucleic acid-sequence, and functional genomics technologies are highlighted. It also covers newly developed techniques with improved spatiotemporal resolution, quality and sensitivity, coupled with advanced analytical methods and artificial intelligence. The power of collaborative research and inclusion of diverse populations will ensure a bright future for precision psychiatry.
Key points
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Genetic studies identified hundreds of genetic variants associated with mental illnesses, with a significant overlap across disorders challenging traditional classifications and calling for functional interpretation.
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Mapping of expression quantitative trait loci in brain and neural cells has revealed functional elements regulating gene expression. Some of these regulations influence psychiatric pathology.
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Single-cell and other new technologies and cellular models have revolutionized functional studies of genetic variants and their associated genes in the human brain.
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Major databases and websites of functional genomics are cataloged here for reference.
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The complexity of psychiatric disorders calls for data from large cohorts and diverse populations, interdisciplinary and international collaboration, and novel technologies.
| AI | artificial intelligence |
| ASD | autism spectrum disorder |
| BD | bipolar disorder |
| DNA | deoxyribonucleic acid |
| eQTL | expression quantitative trait loci |
| GTex | genotype-tissue expression |
| GWAS | genome-wide association studies |
| JTI | joint-tissue imputation |
| LD | linkage-disequilibrium |
| MDD | major depressive disorder |
| ML | machine learning |
| MPRA | massive parallel reporter assay |
| MR | Mendelian randomization |
| PGC | psychiatric genomics consortium |
| RNA | ribonucleic acid |
| scRNA-seq | single-cell RNA sequencing |
| SCZ | schizophrenia |
| SNPs | single nucleotide polymorphisms |
| snRNA-seq | single-nuclei RNA sequencing |
| STARR-seq | self-transcribing active regulatory region sequencing |
| TWAS | transcriptome-wide association studies |
Introduction
At the dawn of the 21st century, psychiatric genomics, at the confluence of genetics, genomics, neuroscience, and psychiatry, evolved dramatically. It has been instrumental in redefining our understanding of psychiatric conditions, moving from subjective symptom-based diagnostics toward a more biologically informed approach. This article aims to provide a comprehensive overview of the strides made in psychiatric genomics, charting its evolution from the early days of candidate-gene studies to the contemporary era of whole-genome sequencing and genome-wide association studies (GWAS), while also anticipating the next phase of research linking genes to biology through functional genomics and single-cell sequencing.
This article summarizes the state-of-the-art in psychiatric genomics as of 2025, highlighting the significant methodological advances, key findings, challenges, and solutions. We describe how integrating genomic data with clinical psychiatry has transformed our understanding of psychiatric disorders. Acknowledging that psychiatric genomics is a vast and complex field, we start with key definitions and an orientation that may be helpful as you navigate this and subsequent articles in this volume.
First, consider the general structure of a eukaryotic cell, as shown in Fig. 1 . In the cell’s nucleus, there are pairs of chromosomes (which vary in number and type by species), 1 set of which we have inherited from each of our parents. Each chromosome is essentially a very long unbroken double-helical chain of deoxyribonucleic acid (DNA) molecules with a complementarity that binds adenine nucleotides to thymine nucleotides and cytosine nucleotides to guanine nucleotides. Because of this base-pairing, DNA has an inherent recipe for faithful replication. Most of our genome sequence is the same across individuals, but we have key differences too. Most of these individual differences are single nucleotide differences (called single nucleotide polymorphisms, or SNPs), though there are other types of variation as well. The different versions of an SNP are called alleles, and a combination of 2 alleles possessed by an individual (1 inherited from each parent) constitutes their genotype at a given locus. The long stretches of DNA are densely packed and wound around proteins called histones; collectively, this DNA/histone complex is the nucleosome. Histones can modulate the chromatin states and associated gene expression by various chemical modifications such as methylation and acetylation. When a cell is ready for action , transcription factors in the nucleus cause the target DNA to unwind from its associated histone and the 2 DNA strands to unpair from each other, so that new single-stranded ribonucleic acid (RNA) transcripts (complementary to 1 of the 2 unwound DNA stretches) can be written . These transcripts are then translated from the language of nucleic acids to that of amino acids, which are then assembled into polypeptide chains. When those polypeptide chains get long enough (>50 amino acids), the chain can fold, loop, bend, and wind, taking on a secondary structure, which we then call a protein. This pipeline constitutes the bare bones of the central dogma of molecular biology. Although this simplification omits a vast array of complex steps in the journey from DNA to protein, it may suffice as an orientation to appreciate the other material in this and subsequent articles in this volume ( Box 1 ).
Gene: the basic unit of heredity stored in cells as a stretch of DNA that encodes a functional element; for example, a gene may define an area of the DNA that is transcribed and then translated into a protein (a coding gene), or an area that is transcribed but not translated (creating a noncoding RNA)
Genetics: the study of DNA sequence variation and its association with traits
Phenotype: a trait whose variability in the population is commonly influenced by genetic factor(s)
Quantitative Trait Locus (QTL): A DNA sequence variation related to a quantifiable (rather than binary) trait. An expression QTL (eQTL) is DNA sequence variation that facilitates or inhibits the transcription of a gene, where the amount and type of gene product expressed is the quantitative trait of interest.
Genome: the full complement of DNA base sequences present in the cells of an organism
Genomics: the study of the structure and function of the genome
Epigenetics: the study of chemical modifications to the genome not involving changes in its DNA base sequence; for example, methylation of cytosine DNA bases, or acetylation of chromosomal histones
Epigenomics: the study of the structure and function of the epigenome
Functional genomics: the study of the functions of genes and their transcribed products
Transcriptomics: the study of all RNA transcripts expressed in a cell or tissue
Proteomics: the study of all translated proteins expressed in a cell or tissue
Now, with this primer under our belt, we summarize the state of the science of psychiatric genomics in 2024. We briefly describe the latest developments in the use of GWAS and genome-sequencing to identify genetic variants that account for heritable variation in a range of psychiatric disorders; this will also be covered in more detail in subsequent chapters focused on specific disorders. Beyond the genetic progress, we will also discuss methodological innovations that are helping researchers move beyond the assessment of a static genome and into the dynamic sphere of genome biology to answer the key question of psychiatric genomics, that is, How do genes and environments interact to change biology and make individuals susceptible to expressing symptoms of psychiatric illness ?
Genetics
Psychiatric genomics has advanced markedly since completion of the first draft of the human genome in 2001. The Human Genome Project mapped the entire human genome and laid the foundation for genomic medicine, with the initial discovery of genetic variants associated with mental health disorders and the development of advanced technologies that have enabled the exploration of the genome in its entirety. These advancements have facilitated a deeper understanding of the complex interplay between genetic, environmental, and lifestyle factors in the etiology of psychiatric conditions.
Not only did the completion of the draft of the human genome allow us to understand its sequence and structure, but it also ushered in a wave of methodological developments that have been vital in relating genetic variation to complex phenotypes; that is, those traits, like psychiatric symptoms and disorders, that are genetically influenced but not caused by a single genetic mutation. From over 100 years of twin studies, we know that all psychiatric disorders studied to date have at least some genetic contributions to their susceptibility. The estimated degree of genetic influence can vary widely, from as low as 30% of total susceptibility for some anxiety disorders to more than 80% for major mental illnesses like schizophrenia (SCZ) and bipolar disorder (BD). ,
Several decades of genetic association analyses (relating specific alleles to psychiatric phenotypes), particularly GWAS, have begun to explain considerable proportions of the heritable variance in a growing number of mental illnesses. Individual SNPs tend to have very small effects on overall susceptibility for a given disorder, and no SNP is either necessary or sufficient to cause a mental illness. In aggregate, however, large numbers of SNPs can begin to account for major portions of the heritability estimated from twin studies. The most common aggregate index of genetic association with a complex psychiatric trait is the polygenic risk score , representing a weighted sum of the combined effects of hundreds to thousands of alleles. For example, the best-performing polygenic risk score from the latest GWAS of SCZ explained 24% of the estimated 80% heritability of SCZ determined from twin studies. A similar approach has been applied to other disorders, including BD, major depressive disorder (MDD), attention-deficit hyperactivity disorder, autism spectrum disorder (ASD), eating disorders, and substance use disorders. Further details on each of these achievements are provided in subsequent papers in this volume.
A major achievement in psychiatric genetics over the past 2 decades has been the discovery of molecular evidence for shared genetic contributions to most psychiatric disorders. In fact, the degree of risk-gene sharing among major psychiatric disorders is far greater than would be expected by chance, which indicates that many psychiatric disorders share an underlying heritable component. Put another way, many genetic variants increase risk for psychiatric illness in a broad sense, rather than simply putting individuals at risk for a particular diagnosis. In fact, for some pairs of disorders, like SCZ and BD, it is difficult to identify any genetic risk factors that have specificity, as virtually all variants found to increase the risk for one of these disorders also increase the risk for the other. Thus, in a sense, psychiatric genetics research has provided ample evidence refuting the Kraepelinian dichotomy that considered these disorders as entirely separate entities. Molecular genetic evidence would suggest that these diagnoses have more in common at the genetic level than what sets them apart. This has major implications not only for our understanding of these illnesses but also for their nosology and differential diagnosis, as well as their treatment, management, and prevention. This also leaves a major challenge for the field of psychiatric genetics in the coming years: identifying unique genetic contributions to specific mental disorders, and/or using genetic data to help inform a revision to the current nosology.
The subsequent papers in this volume will provide in-depth detail on what is known about the genetic contributions to specific psychiatric diagnoses and dimensions, so we paint here with broad strokes. We introduce methods that have taken us from a point 2 decades ago, when we had literally zero reliable risk genes for psychiatric disorders, to the present where hundreds of genes are reliably associated with disease susceptibility. Further, large proportions of the variance in who becomes affected with a psychiatric disorder are knowable from genetic screening. We have a long way to go before these methods can be extended to reliable use in clinical practice, but speaking as researchers in this field over this entire duration, the progress has been nothing less than astounding.
Genomics
The field has moved toward a more integrated approach, combining genomic data with epigenetic, transcriptomic, and proteomic insights to unravel the multifaceted nature of psychiatric disorders. The advanced genomic techniques and an enhanced understanding of psychiatric disorders pave the way for groundbreaking discoveries and innovative therapeutic approaches.
One of the most notable developments in recent years has been the emergence of functional genomics studies in the brain. These studies have enabled researchers to move beyond identifying genetic variants to understanding how these variations affect brain function and, consequently, behavior and mental health. Identifying genetic variants that regulate the expression levels of transcripts (expression quantitative trait loci, or eQTLs) in the human brain represents one of the most important achievements.
Another groundbreaking advancement has been the application of single-cell transcriptomics in psychiatry. This technique allows for the examination of gene expression at the level of individual cells, providing unprecedented resolution and insights into the cellular heterogeneity of the brain. The application of single-cell transcriptomics has been particularly transformative in unraveling the complexities of psychiatric disorders, shedding light on the specific cell types and states that contribute to these conditions.
This article will also delve into functional studies of disease-associated genetic variants of candidate genes, discussing innovative techniques such as massive parallel reporter assay (MPRA), spatial transcriptomics, self-transcribing active regulatory region sequencing (STARR-seq), cell-village, and perturb-Seq. These high-throughput functional genomics tools have revolutionized our ability to study the function of genetic variants, offering new vistas in the understanding and treatment of psychiatric disorders.
In addition to these scientific advancements, this article will address the translational use of genomic findings in clinical practice. Integrating genomic data into clinical psychiatry holds immense promise for personalized medicine, offering tailored approaches to treatment based on an individual’s genetic makeup.
Functional genomics studies in brain
Functional genomics, with its focus on deciphering the roles, relationships, and actions of genes and their products, stands at the forefront of neuropsychiatric research, particularly in the context of understanding the complexities of brain function and its deviations in psychiatric disorders. Given that the brain is considered the primary organ involved in the pathology of mental illnesses, understanding how genes are regulated and function individually and collectively is the foundation of all mechanistic studies of psychiatric disorders. Quickly evolving technologies have made meaningful functional research possible and numerous informative resources have been developed to facilitate such discoveries.
Goals of Functional Genomics in Understanding Brain Function and Psychiatric Disorders
The primary objectives of functional genomics in studying the brain and psychiatric disorders are manifold, aiming to bridge the gap between genetic information and functional outcomes. A critical goal is to elucidate how specific genetic variants, both common and rare, contribute to the risk of psychiatric disorders by affecting synaptic function, neural circuitry, and brain development. Functional genomics seeks to understand the molecular and developmental mechanisms through which genes influence brain function, including gene expression regulation, posttranscriptional modifications, and protein interactions. With the current advanced sequencing and gene-editing techniques such as clustered regularly interspaced short palindromic repeats (CRISPR), gene expression regulation is the most highly studied area to date.
Methodological Advances in Functional Genomics
Recent years have seen remarkable innovations in the methodologies applied to functional genomics studies. Coupled with innovative add-ons or modifications, many kinds of sequencing technologies have been created to quantify various molecules, from DNA to RNA. Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of the cellular complexity of the brain by allowing the characterization of gene expression patterns at the resolution of individual cells, revealing novel cell types and states implicated in psychiatric diseases. , Techniques such as CRISPR-Cas9 genome editing have emerged as powerful tools for elucidating gene function in neuronal development and psychiatric disorders, enabling precise manipulation of genomic sequences in model organisms and human cells. , Additionally, the advent of brain cellular models and organoid technology offers a unique window into human brain development and disease modeling, facilitating the study of the effects of genetic variants in a controlled, yet physiologically relevant, environment. ,
Current Data Resources for Functional Genomics Studies
Advanced technologies have greatly accelerated the production of various data and provided massive, comprehensive data resources. The major data types include psychiatric GWAS results; gene-expression or protein-expression profiles in brain or brain cells; epigenomic elements in the brain; and genetic variants associated with gene and other molecule expression. Epigenomic elements include DNA methylation, histone modifications, chromatin accessibility, transcription factor binding sites, and chromatin folding and organization. Developmental dynamics and spatial variations are also captured.
The Psychiatric Genomics Consortium (PGC) has aggregated extensive genomic data on a wide range of psychiatric disorders, facilitating large-scale GWAS and meta-analyses that have identified numerous risk loci for psychiatric conditions. The Genotype-Tissue Expression (GTEx) project, which shares transcriptomic data from dozens of tissues sampled from the same individual donors, has been instrumental in providing insights into tissue-specific gene expression and regulation, including the brain. The BrainSpan Atlas of the developing human brain offers an invaluable repository of gene expression data across different stages of brain development, aiding in the identification of critical windows where genetic variants may exert their effects. These data are summarized in Table 1 .
| Database Name | Data Type(s) | URL |
|---|---|---|
| GWAS Catalog | GWAS | https://www.ebi.ac.uk/gwas/ |
| GWAS Atlas | GWAS | https://atlas.ctglab.nl/ |
| PGC | GWAS, Psychiatric genetics | https://pgc.unc.edu/for-researchers/download-results/ |
| Gene4Denovo | Human de novo mutations | http://www.genemed.tech/gene4denovo/home |
| BrainEXP | Gene expression, human brain | http://www.brainexp.org/ |
| BrainEXP-NPD | DEG, brain disorders | http://brainexpnpd.org:8088/BrainEXPNPD/index.html |
| Allen Brain Atlas | Gene expression, Anatomy, cell-type-specific | http://www.brain-map.org/ |
| Brain RNA-seq | Human and mouse brain, Gene expression, cell-type-specific | https://brainrnaseq.org/ |
| BRAINSPAN | Gene expression (developmental stages) | http://www.brainspan.org/ |
| GTEx | eQTL, Gene expression | https://www.gtexportal.org/home/ |
| Gene Expression in Cortical Organoids | Gene Expression, Cortical Organoids | http://solo.bmap.ucla.edu/shiny/GECO/ |
| Human Protein Atlas | RNA and protein expression, in tissues and cells | https://www.proteinatlas.org/ |
| ENCODE (Encyclopedia of DNA Elements) | Gene expression, eQTL, DNA methylation | https://www.encodeproject.org/ |
| EpiMap | Epigenome, tissues, and cells | https://compbio.mit.edu/epimap/ |
| MetaBrain | eQTL, Gene expression (brain-specific) | https://www.metabrain.nl/ |
| Brain xQTL Serve | H3k9Ac, DNAm, and Gene expression QTL | https://mostafavilab.stat.ubc.ca/xqtl/ |
| PsychENCODE | eQTL, brain | http://resource.psychencode.org/ |
| Brain eQTL Almanac (Braineac) | eQTL, Gene expression | http://www.braineac.org/ |
| UK Biobank | GWAS, gene expression, imaging data | https://www.ukbiobank.ac.uk/ |
| Roadmap Epigenomics Mapping Consortium | DNA methylation, Histone modification | http://www.roadmapepigenomics.org/ |
Methodological innovations and expansive data resources have begun to reveal the dynamic landscape of functional genomics in the brain, underscoring the complexity of biological processes. These data are instrumental for unraveling the biology underlying psychiatric disorders through data mining and for generating novel, testable hypotheses for experimental biologists. This wealth of information allows for investigating disease biology at a systemic level rather than focusing on individual genes, offering a more comprehensive understanding of the underlying mechanisms.
Single-cell transcriptomics in psychiatry
The advent of single-cell or single-nuclei RNA sequencing (sc/snRNA-seq) has ushered in a new era in the study of psychiatric disorders, offering an unprecedented resolution to explore the complexities of the brain at the level of individual cells. Since scRNA-seq and snRNA-seq are highly similar for the major topics to be discussed in this article, we use scRNA-seq to represent both here. This section delves into the basics of scRNA-seq, its applications in psychiatric research, and the challenges faced in its implementation, providing a comprehensive overview of how this cutting-edge technology is reshaping our understanding of psychiatric conditions.
Basics of Single-Cell RNA Sequencing
scRNA-seq is a revolutionary technique that allows for gene expression profiling in individual cells, providing a detailed map of cellular diversity within tissues. Unlike traditional RNA sequencing that offers a bulk average of gene expression across thousands of cells, scRNA-seq uncovers the heterogeneity and intricate molecular processes occurring within single cells. This high-resolution approach identifies distinct cell types and states, revealing the cellular composition of tissues and the transcriptional dynamics underpinning biological processes and disease states.
Applications of Single-Cell Ribonucleic Acid Sequencing in Psychiatry
The application of scRNA-seq in psychiatry has provided novel insights into the cellular and molecular architecture of the brain, shedding light on the pathophysiology of psychiatric disorders. By dissecting the transcriptomic profiles of individual brain cells, researchers have identified specific cell types and molecular pathways implicated in conditions such as SCZ, ASD, and BD. This fine-grained analysis has revealed the cellular heterogeneity of psychiatric disorders, highlighting the role of specific neuronal and nonneuronal populations in disease mechanisms. Furthermore, scRNA-seq has been instrumental in understanding the neurobiological effects of psychiatric medications, facilitating the discovery of biomarkers for disease diagnosis and treatment response.
Up to 2024, a few psychiatric disorders have been studied for mostly snRNA-seq in postmortem human brains ( Table 2 ). ScRNA-seq experiments were also done on cultured organoids of ASD (with 745,000 cells) ; of Prader-Willi syndrome patient arcuate organoids (with 12,505 cells) ; cerebral organoids from monozygotic twins discordant for psychosis (with 2782 cells) ; and methamphetamine-treated cerebral organoid (with 20,758 cells).
| Author & Year | Disorder | Sample Size | # of Cells |
|---|---|---|---|
| Velmeshev et al, 2019 | ASD | 15 ASD, 16 HC, 8 epilepsy, 7 HC | 104,559 cells, 17 cell types |
| Grubman et al, 2019 | AD | 6 AD, 6 HC | 13,214 cells, 6 cell types |
| Del-Aguila et al, 2019 | AD | 3 AD | 26,331 cells, 13 clusters |
| Mathys et al, 2019 | AD | 24 AD, 24 HC | 75,060 cells after filtering, 8 major cell types |
| Nagy et al, 2020 | MDD | 17 MDD, 17 HC | 80,000 cells, 26 cell types |
| Lau et al, 2020 | AD | 12 AD, 9 HC | 169,496 nuclei, 6 major cell types |
| Leng et al, 2021 | AD | 7 AD, 3 HC | 42,528 cells from the entorhinal cortex and 63,608 cells from the superior frontal gyrus, 7 major cell types |
| Qian et al, 2023 | 6 CNS diseases (AD, PD, epilepsy, etc) | Total 302 | ∼1 million cells, 8 cell types |
| Soreq et al, 2023 | AD | 2 AD and 2 HC | 25,000, 10 cell types |
| Chatzinakos et al, 2023 | PTSD, MDD | 11 PTSD, 10 MDD, 11 HC, replicates 5 per group | 575,000 cells, 8 major cell types |
| Mathys et al, 2023 | AD | 427 AD of different stages | 2.3 million, 54 cell types |
| Emani et al, 2024 | SCZ, ASD, BD, AD, MDD/PTSD | 77 SCZ, 52 ASD, 34 BD, 33 AD, 10 MDD/PTSD | 2.8 million nuclie, 28 cell types |
| Fröhlich et al, 2024 | SCZ, SAZ, BD, or MDD | 87 + 32individuals, patients and HC | ∼800,000 nuclei, 21 identified cell types and 7 major cell-type clusters |
| Xie et al, 2024 | MDD | 17 MDD, 17 HC | 6014 (OL) of 4 stages |
| Liu et al, 2024 | AD | 24 AD and 24 HC. Replication: 11 AD and 7 HC | 125,939 cells, 8 major cell types |
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