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Schizophrenia is a highly polygenic disorder with many common genetic variants contributing to the disease risk. These genetic variants are identified by genome-wide association studies (GWAS) and used to calculate a combined genetic risk, called a polygenic risk score (PRS), for each individual. The PRS approach is being increasingly used to determine whether PRS could be used as a predictive tool in determining the clinical trajectory of schizophrenia patients. Here, we provide a general overview of evidence relating to the applicability of PRS in relation to schizophrenia symptoms. Studies illustrate that schizophrenia PRS could be used to predict certain core clinical correlates of schizophrenia, including a more severe course of illness, negative symptoms, brain activation patterns, and cognitive deficits. However, there is no strong support for association between PRS and positive symptoms, treatment-resistance status, and brain structural changes. Overall, studies are showing the potential clinical utility of PRS to aid in more precise patient stratification and in predicting schizophrenia-related symptoms with a promising future for widespread clinical implementation.

Schizophrenia is a severe mental illness characterized by negative symptoms (e.g. blunted affect, social withdrawal), positive symptoms (e.g. hallucinations, delusions), and cognitive impairments.1 The prevalence of schizophrenia is around 1% worldwide, with an annual incidence rate of 15–20 per 100 000.2 Although the etiology of schizophrenia is still not fully understood, studies have illustrated that both genetic and environmental factors could be associated with the risk of developing this disease. Environmental factors such as prenatal exposure to infections, season of birth, childhood trauma, migration, and cannabis use have all been associated with schizophrenia risk to varying degrees.3 It should be noted that among the known environmental risk factors, none of them confer more than 1–5% relative risk for developing schizophrenia, which is far less than having a family history of the illness, highlighting the contribution of genetics in the pathology of the disease.3 

Genetic investigations of schizophrenia, captured in part by twin studies, suggest a heritability estimate of 70–80% for schizophrenia liability.4 In five recent twin studies from Europe and Japan, schizophrenia concordance rates have been reported to be between 41 and 65% in monozygotic twins and 0–28% in dizygotic twins.5 In addition, a meta-analysis of 12 twin studies illustrated a substantial additive genetic effect on the liability of schizophrenia, with a heritability estimate of 81%.6 However, it should be noted that heritability estimates are prone to overestimating the genetic contribution to the pathology of the disease. This is due to the overall contribution of both genetic and environmental factors in the potential etiology of the disease in monozygotic and dizygotic twins. Nonetheless, twin studies documented higher concordance rates of schizophrenia in monozygotic twins compared to dizygotic twins.

In addition to twin studies, adoption studies have also provided similar lines of evidence for a genetic basis of schizophrenia. During the 1960s, Heston and colleagues7 performed one of the first investigations on the offspring of mothers with schizophrenia, which was later expanded in the 1960s and 1970s by Rosenthal and Kety in their Denmark adoption studies.8–11 In these studies, Rosenthal and Kety investigated children with a parent with schizophrenia who were adopted away at birth and adopted children who developed schizophrenia. The results of these analyses aimed toward inherited biological factors, rather than environmental factors, being a key component in the development of schizophrenia.8–11 Although the Danish adoption studies were influential in demonstrating the importance of genetics in schizophrenia’s development, they might have failed to capture the rearing family environment. To account for the potential gene–environment interaction, an independent adoption study in Finland confirmed that while biological factors indeed confer the majority of risk for developing the disease, environmental factors may also contribute to this risk.12 Overall, adoption studies documented that schizophrenia is more frequent in adopted-away children of parents with schizophrenia compared to their control adoptees.

In addition to twin and adoption studies, family studies of patients with schizophrenia also provided insight into the genetics behind this disease. The earliest known genetic family study on schizophrenia was carried out by Ernst Rüdin in Emil Kraepelin’s clinic (one of the first people to diagnose the condition of schizophrenia) in 1916.13 Since then, many other investigations from different areas around the world have confirmed the higher incidence rate of schizophrenia in relatives of schizophrenia patients compared to the general population.13–16 To summarize, evidence from all the abovementioned genetics studies highlights the contribution of multiple genes on the mode of inheritance for schizophrenia, meaning schizophrenia susceptibility does not simply follow Mendelian genetics.

With more advancements in the field of molecular genetics, researchers started to employ a candidate gene approach to study schizophrenia. This approach uses a case-control study design to explore the association of certain susceptibility genes with the disease. Candidate genes are selected usually due to their functionality (e.g. genes involved in dopamine or serotonin neurotransmission). To date, over 1000 candidate genes, with relatively small effect sizes, have been studied. The most studied candidate genes include dopamine D2 receptor (DRD2, 11q23.2), neuregulin (NRG1, 8p12–21), catechol-O-methyltransferase (COMT, 22q11.21), dopamine D3 receptor (DRD3, 3q13.31), dysbindin (DTNBP1, 6p22.3), disrupted-in-schizophrenia 1 (DISC1, 1q42.1), and the serotonin 2A receptor (HTR2A, 13q14.2).17–22 Although candidate gene studies in schizophrenia might unravel interesting associations with the disease process based on pre-existing hypotheses, there are multiple limitations. These include small sample sizes, and therefore low statistical power, and lack of independent replication of significant results.

Nevertheless, the candidate gene approach could elucidate valuable knowledge regarding pathophysiology and potentially lead to the identification of novel drug targets. One notable example is the SLC18A2 gene, encoding the vesicular monoamine transporter 2 (VMAT2), and its implication in tardive dyskinesia. Tardive dyskinesia is a motor side-effect which is developed in some schizophrenia patients treated chronically with atypical antipsychotics. In 2013, Zai et al.23 conducted an association study between nine polymorphisms in the SLC18A2 gene and found multiple significant variants to be associated with tardive dyskinesia. Their findings of a tardive dyskinesia-risk variant that was also associated with increased expression of VMAT2 were in line with the development of multiple drugs, such as valbenazine24 and deutetrabenazine,25 which target VMAT2 and are currently being used as a treatment option for tardive dyskinesia.

Moreover, advancements in high-throughput technologies, such as genotyping microarrays, have enabled us to simultaneously examine thousands of genes (up to 30 000) in addition to the above-mentioned candidate genes, increasing the efficiency of the genotyping output. These advancements could potentially lead to the identification of novel candidate genes or pathways that might be used for the development of novel drugs and predict drug responsiveness, as well as potentially uncover premorbid conditions and predict disease trajectories.

Given that schizophrenia is a complex and polygenic disorder, there are potentially thousands of genetic variants associated with schizophrenia with each conferring a relatively small effect.26 Unlike hypothesis-driven candidate gene studies, which examine a small number of genetic variants, hypothesis-free genome-wide association studies (GWAS) test millions of genetic variants, leading to uncovering a wide range of schizophrenia-associated common genetic variants. The common genetic variants identified through GWAS typically have relatively small effect sizes (genotypic relative risks of less than 1.5).27 Thus, GWAS requires very large sample sizes (hundreds of thousands) of cases and controls to detect risk variants with adequate statistical power. Due to this barrier of small sample size, early schizophrenia GWAS failed to identify genome-wide-significant variants.28–31 However, in the last 15 years, large-scale international collaborations have made it possible to accumulate large datasets,32 moving from early efforts identifying one significant genome-wide locus in 497 cases28 to identifying 287 loci in upwards of 70 000 cases in the most recent schizophrenia GWAS.32 

A large GWAS in 2014 (34 241 cases; 45 604 controls) implicated novel candidate genes and pathways in glutamatergic neurotransmission with therapeutic relevance to schizophrenia, including glutamatergic receptors (e.g. GRIN2A and GRM3) as potential drug targets. In this study, an important finding was the genome-wide significant association of polymorphisms in the DRD2 gene coding for the dopamine D2 receptor, the drug target for most antipsychotic drugs.33 In addition, several common variants have been identified in the major histocompatibility complex (MHC), Transcription Factor 4 (TCF4) gene (involved in neuronal development)34 and Neurogranin (NRGN) gene (involved in modulating synaptic plasticity through calcium regulation).35 These studies point to the importance of dysfunctional brain development and cognitive processes in the pathophysiology of schizophrenia.30 Building on these results, the most recent and largest schizophrenia GWAS (76 755 cases; 243 649 controls) has identified 287 distinct loci to be associated with schizophrenia susceptibility, with the MHC region remaining the strongest observed signal.32 Using fine-mapping approaches, 120 genes (106 protein coding) were identified, which partly explained the observed associations. Among the 120 fine-mapped genes, 15 genes had synaptic annotations. Some of the implicated genes encode receptors and ion channels, such as the voltage-gated chloride and calcium channels (chloride voltage-gated channel 3 [CLCN3], calcium voltage-gated channel subunit alpha1 C [CACNA1C]), ligand-gated N-methyl-d-aspartate (NMDA) receptor subunit [GRIN2A], and metabotropic receptors (glutamate metabotropic receptor 1 [GRM1], gamma-aminobutyric acid type B receptor subunit 2 [GABBR2]). Moreover, genes encoding proteins that are involved in synaptic differentiation and organization include PAK6 (p21 (RAC1) activated kinase 6), GPM6A (glycoprotein M6A), and PTPRD (protein tyrosine phosphatase receptor type D). Also, with the help of functional genomic data, several fundamental processes were implicated, including neuronal function, synaptic organization, synaptic differentiation, and neurotransmission. On a molecular level, studies have also identified variants that could impact the differential gene expression of schizophrenia-related genes, with transcription factor binding showing a modest power in predicting these regulatory variants.36 

Despite the success of GWAS in recent years in identifying many common variants, these variants only explain close to 24% of the variance for schizophrenia heritability, and much less is explained by rare variants (2%).32,37 In addition, schizophrenia GWAS are mostly performed in samples of predominantly European ancestry, an approach which poses potential issues in generalizing the results to other ancestral populations. For example, GWAS in Asian populations38 fail to capture the MHC–schizophrenia association, which is the strongest signal observed in studies on Europeans. This difference in results could potentially be explained by a difference in the minor-allele frequency of key variants or ancestry-specific patterns of linkage disequilibrium.26 Consequently, it could be advantageous to compare and combine GWAS findings from different ancestries for a better understanding of schizophrenia pathobiology.

Within the past decade, GWAS has uncovered genetic variants related to schizophrenia susceptibility, each having a relatively small effect on disease risk. However, in a polygenic disorder such as schizophrenia, a single genetic variant does not provide much value in determining disease risk. Consequently, it becomes imperative to consider the collective impact of all identified risk variants, representing one’s genetic loading for a specific disease, to accurately assess individuals at heightened risk of developing the disorder. Polygenic risk score (PRS) is a method that captures the combined set of genetic risk variants carried by an individual. The risk variants included in PRS calculations are determined based on the latest and largest GWAS results. The number of risk alleles at each variant (0, 1, or 2) is weighted by their effect sizes and combined for each individual, resulting in a single score of individualized genetic loading for a disease.39 The summing of all risk variants requires the assumption that the disease or trait of interest has an additive genetic architecture where the risk variants are independent of each other. Although summing all of the risk variants to count for disease risk might seem simplistic, the largest meta-analysis of the heritability of complex traits in twin studies has supported the use of a simple additive model in the majority of examined traits.40  Figure 1.1 demonstrates the overall workflow of calculating PRS scores from GWAS studies and its potential implications.

Figure 1.1

The workflow for calculating polygenic risk scores (PRS) based on genome-wide association studies (GWAS) and its potential implication in healthcare. Created with BioRender.com.

Figure 1.1

The workflow for calculating polygenic risk scores (PRS) based on genome-wide association studies (GWAS) and its potential implication in healthcare. Created with BioRender.com.

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In the context of schizophrenia, the predictive ability of disease risk is higher than in other psychiatric disorders such as depression. Based on the most recent schizophrenia GWAS study, PRS scores explain 7.3% of the variance in disease liability (single nucleotide polymorphisms [SNPs] with p < 0.05), where this predictive power drops to 2.4% when only including the genome-wide significant SNPs. The predictive power of schizophrenia PRS has not reached clinical implementation yet, but in the context of research, PRS could substantially differentiate individuals based on their genetic predisposition to disease. For instance, individuals in the highest centile of schizophrenia PRS have an odds ratio of 39 (95% confidence interval (CI) 29–53) compared to the lowest centile, and 5.6 (CI 4.9–6.5) when compared to the remaining 99% of individuals.32 

Elucidating the associations between schizophrenia susceptibility genes and clinical manifestations of the disease could represent a pivotal advancement in comprehending the genetic underpinnings of psychosis. With an increasing sample size of discovery cohorts, the variance observed in schizophrenia case-control status explained by PRS has improved.33 However, significant heterogeneity in the course of illness and clinical manifestation of schizophrenia remains to be explained. To examine the potential of PRS as a tool for predicting symptom severity, cognition, and any diagnostic change during the course of illness, Jonas with colleagues conducted a study that clinically examined 249 schizophrenia patients and 205 demographically matched controls. Patients were assessed six times over a 20 year period and symptoms were rated using the schedule for assessment of positive and negative symptoms. Patients had significantly higher schizophrenia PRS upon first admission than controls. Upon follow-up assessments and within the psychosis cohort, schizophrenia PRS was a significant predictor of greater illness severity (β = −0.28, p ≤ 0.01), more severe negative symptoms (β = 0.21, p = 0.02), and worse cognitive symptoms (β = −0.35, p < 0.01). In addition, schizophrenia PRS strongly predicted a diagnostic shift from affective to non-affective psychosis during the 20 year follow-up study of psychiatrist assessments. Schizophrenia PRS was not associated with positive symptoms (e.g. hallucinations/delusions), disorganization, inexpressivity, or depression.41 Currently, the majority of medications and treatment options for schizophrenia are centred on treating positive symptoms. Thus, the relationship between PRS and positive symptoms is of high interest and should be further investigated. One potential way to examine this relationship is to capture certain endophenotypes for positive symptoms relating to treatment outcomes (e.g. how well one responds to medication). Using machine learning methods, an accumulation of PRS combined with other physiological and environmental data could be used to categorize different treatment responders, which might provide valuable knowledge in the genetic underpinnings of the positive symptom dimension and treatment outcome. Overall, a more in-depth understanding of different genetic factors influencing symptom dimensions through PRS could assist in developing targeted and novel schizophrenia treatments, especially for negative and cognitive symptoms.

Approximately 20–30% of patients with schizophrenia are considered to be treatment-resistant, meaning they have not adequately responded to two prior antipsychotic treatments (each for at least 4–6 weeks) at the appropriate dosage.42 Treatment-resistant schizophrenia (TRS) is associated with severe functional impairment (e.g. worse social functioning) and poor quality of life. Clozapine is the only drug currently prescribed to TRS patients, but it has side effects such as potentially life-threatening agranulocytosis (the number of neutrophils in the blood decreases). Therefore, the use of PRS to better understand the underlying pathophysiology of TRS is highly desirable.43 Early efforts to utilize schizophrenia PRS in relation to TRS demonstrated promising results in two independent studies, showing higher schizophrenia PRS scores in TRS patients.44,45 However, attempts to replicate the positive results between schizophrenia PRS and TRS failed, although the effects were in the expected direction.46–49 The lack of statistically significant results in these studies could be partly due to inadequate sample size or/and ambiguous non-standardized definitions of TRS. Although no significant positive results were replicated in the independent studies, a meta-analysis of all association studies, increasing number of analyzed samples and, hence, statistical power, demonstrated a significant result (OR = 1.090, p = 0.0027) between schizophrenia PRS and TRS.48 This outcome clearly illustrates that schizophrenia PRS partly explains the genetic susceptibility to TRS. Although there is preliminary evidence that PRS could be beneficial as a predictor for TRS, and potentially useful in reducing the initiation time for prescribing clozapine, further evidence is needed to support the clinical usefulness of schizophrenia PRS in predicting TRS status.

Measurement scales such as the Positive and Negative Syndrome Scale (PANSS), among others, are methods used by clinicians and researchers to assess symptom dimensions in schizophrenia patients. Therefore, ratings on these scales could be useful for determining the symptom severity and for tracking the treatment progress of patients. Using ratings from symptom scales, researchers conducted studies to assess whether PRS scores could reliably predict symptom progression. A systematic review analyzing the association between schizophrenia PRS and symptom dimensions revealed that schizophrenia PRS is associated with negative symptoms, increased risk for psychiatric disorders such as bipolar disorder and depression, and could explain up to 6% of the genetic variance in psychiatric symptoms.50 In addition, more recent studies also showed that increased schizophrenia PRS was associated with higher disorganization in addition to negative symptoms.51,52 Moreover, the negative and positive symptom dimensions have been examined in both population-based studies and clinical studies. Population-based studies have reported inconsistency in terms of predicting symptoms, whereas clinical studies illustrated a more consistent ability to explain negative symptoms.50 Although the association between schizophrenia PRS and various positive and negative symptoms has been observed in independent studies, it seems that the genetic risk for schizophrenia, captured by PRS, could better explain negative symptoms or general psychopathology symptoms compared to positive symptoms. One potential reason for this could be that the large datasets from the schizophrenia genomic consortiums include more chronic patients who expressed negative symptoms after treatment of their positive symptoms by antipsychotic drugs. Therefore, the clinical samples might be enriched with individuals with negative symptoms. This could imply that the biological pathways deduced from GWAS could be more relevant for understanding and potentially developing drugs to treat negative rather than positive symptoms.53 In addition, another reason for the lack of association between PRS and positive symptoms is the challenging nature of studying patients who are not yet treated for positive symptoms. This might result in potential issues in not having a representative study sample for different positive symptoms. In addition, methods such as probabilistic learning, which takes into account probability distributions in making predictions, could assist clinicians in assessing cognitive deficits related to positive symptoms at the prodromal stage, which could help in capturing a better representation of different symptom dimensions.

Cognitive deficits are one of the core features of schizophrenia. Evidence points to a moderate cognitive deficiency, equal to a lower average intellectual quotient (IQ) by 8 points (SD = 0.5), in children and adolescents who develop schizophrenia later in life. Furthermore, adults diagnosed with schizophrenia also demonstrate a more pronounced cognitive deficit, with a meta-analysis revealing a 14-point IQ deficiency (SD = 0.90) in first-episode schizophrenia patients and a 15–21 point IQ deficiency (SD = 1.0–1.5) in more chronic patients.54 Both GWAS and twin studies point toward a negative genetic association between schizophrenia liability and cognitive ability, suggesting a genetic overlap. Schizophrenia PRS has been consistently associated with poorer cognition, mainly in population-based studies,55–57 with a recent meta-analysis showing that schizophrenia PRS is associated with global cognition in healthy individuals but not in schizophrenia patients.58 Furthermore, higher schizophrenia PRS in healthy individuals has been associated with poorer cognition, and lower PRS scores for cognition have also been associated with higher schizophrenia risk.59 Thus, although the meta-analysis did not show a direct association between schizophrenia PRS and cognitive deficit, cognition could potentially be a mediating factor in which genetic risk could exert its effect on schizophrenia risk.60 There are still inconsistent results as to which aspect of cognition is most robustly associated with schizophrenia’s liability, with general cognitive deficit (e.g. IQ) having the most support in the literature. Although evidence for the course of cognitive decline in schizophrenia is not yet fully understood, PRS could potentially assist in delineating the potential genetic basis for this phenomenon. This may result in early intervention strategies for individuals susceptible to more severe cognitive deficiencies. Future studies could examine the relationship between PRS and specific cognitive tasks.

Altered brain structures have been well characterized in schizophrenia. A meta-analysis of 58 studies has provided evidence for an enlarged globus pallidus (involved in controlling motor systems and the midbrain dopamine system) and lateral ventricles, and a reduction in the volume of the hippocampus, amygdala, thalamus, and caudate nucleus, in schizophrenia patients.61 In addition, brain structural changes have also been reported in unaffected first-degree relatives of schizophrenia patients, suggesting a genetic basis for altered brain structures.62 However, findings from genetic studies using PRS to elucidate a relationship between schizophrenia and brain structural changes have been mixed. A systematic review of seven studies reported that schizophrenia PRS is not associated with brain structural changes.63 The lack of robust evidence between schizophrenia PRS and brain structural changes could be due to the potential effect of PRS on intermediate phenotypes rather than brain structure. In addition, many confounding variables relevant to schizophrenia could affect the results of these studies, leading to a lack of association. These confounders may include demographic (e.g. age, education), environmental (e.g. substance/alcohol abuse, medication, childhood trauma, traumatic brain injuries), and physiological (e.g. hydration, circadian rhythm) factors.64 

Moreover, PRS has also been examined in relation to functional MRI (fMRI), which focuses on the function of the brain by measuring its activation through blood flow rather than the structure captured by MRI. Studies examining the association of schizophrenia PRS with fMRI phenotypes reported preliminary evidence of a potential relationship between brain activation and genetic risk for schizophrenia. It is suggested that the effect of schizophrenia PRS is not localized to a single region or a specific neuronal pathway but rather influences task-dependent brain activation of the total brain networks, with frontal regions having a more prominent role independent of the task. In addition, schizophrenia PRS could be a useful measure for the identification of neuronal activation patterns that might mediate schizophrenia risk.65 Overall, there is supporting evidence for the notion that PRS could be informative in identifying specific neural activation patterns in the brain. Future research on large datasets could potentially lead to informative findings on the underlying genetic architecture of the brain networks and activation patterns in schizophrenia.

Over the past few years, increasing sample sizes combined with more advanced statistical and computational methods have improved the strength of schizophrenia PRS. The more powerful schizophrenia PRS has allowed us to explore how the genetic risk for schizophrenia manifests itself at the general population level, helping researchers to gain a better understanding of the consequences of the underlying genetic risk. In addition, another advantage of PRS is the ability to investigate the effects of PRS on individuals at different stages of the disease. This is mostly exemplified by studies investigating psychopathology and cognitive-related phenotypes of schizophrenia. In independent population-based studies, higher PRS were not associated with positive symptoms.66–68 This is likely due to the inability of the current genetic variants identified by GWAS to characterize positive symptoms. In addition, since the genetic variants identified in schizophrenia GWAS are mostly based on clinical samples, it is possible that the calculated PRS may not be applicable to population-based samples, as opposed to clinical samples. This is also evident in population-based samples that measured negative symptoms and produced inconsistent results. One study reported higher schizophrenia PRS to be associated with increased negative symptoms,67 whereas another study reported a decrease.68 The inconsistent results in population-based samples could also be due to the challenging nature of identifying and measuring negative symptoms in the general (mostly non-psychotic) population. Furthermore, evidence from clinical samples shows a more consistent trend of higher schizophrenia PRS being predictive of increased negative symptoms.67,69,70

In terms of cognition, schizophrenia PRS has been shown to be reliably associated with poorer cognition in population-based samples.55,56 In addition, a longitudinal study demonstrated that higher schizophrenia PRS predisposes individuals to worse cognition, with a one-standard-deviation increase in PRS being predictive of a 5-point decrease in IQ.41 Moreover, preliminary evidence has supported the use of schizophrenia PRS to investigate patterns of brain activation using fMRI. This approach might help in understanding the neuronal activation network mediating vulnerability to schizophrenia.64 In contrast, schizophrenia PRS has not been consistently shown to be associated with structural changes in the brain using MRI.63 This might be due to the variation in methodology between studies or that an intermediate phenotype, other than the volume of brain structures, might be a better proxy for future research. Furthermore, there is emerging evidence for an association between treatment resistance and schizophrenia PRS. Lastly, although not consistent, schizophrenia PRS has also been implicated with other psychiatric traits including suicidal behaviour, and non-psychiatric traits such as cardiovascular disease, diabetes, and rheumatoid arthritis.71 These inconsistent results could be due to inadequate sample sizes and differences in methodologies, such as variations in how the phenotypes or traits in each study are defined. Additionally, more advanced PRS methods are currently being developed, which incorporate transcriptomic data in addition to GWAS results, which could improve the reliability and robustness of the results. Moreover, certain inconsistencies between the results of different studies calculating PRS may be attributed to the use of earlier GWAS summary statistics for PRS construction, as opposed to utilizing more recent GWAS data with larger sample sizes. An increase in GWAS sample sizes will yield PRS scores with higher reliability and robustness, thereby enhancing the accuracy of the results.

Given the growing literature on schizophrenia PRS and its potential clinical utility, the actual reliability of using PRS as a predictive measure in clinical practice should also be examined. In particular, a question that should be answered is whether the PRS calculated based on academic research is applicable in a clinical setting or not. Zheutlin et al. demonstrated that schizophrenia PRS could detect risk for schizophrenia diagnosis based on data from the electronic health records from four separate sources including over 106 000 individuals.72 Although this study was able to detect schizophrenia risk based on health records, the PRS effect sizes were modest (the area under the curve, which is a common metric for evaluating predictive performance, was modest in this study, 0.60–0.71, with other reported schizophrenia research samples reporting an area under the curve of 0.59–0.81). Nonetheless, this study could be a proof of concept for the future direction of clinical application of PRS approaches in schizophrenia. Moreover, as PRS is showing potential for being implemented in the clinical setting, Landi et al. examined the prognostic value of PRS compared to the current standard of care in psychiatry, especially for adults with psychosis. The authors assessed whether schizophrenia PRS improves the prediction of poor clinical outcomes in over 8500 adults with schizophrenia. The results showed no improvement in the predictive models when incorporating PRS compared to standard psychiatric interviews conducted by health professionals.73 Nonetheless, as schizophrenia PRS becomes more powerful with increasing sample sizes of future schizophrenia GWAS, it may become a part of a predictive tool, which could also include sociodemographic, clinical, and environmental factors, in predicting disease risk and treatment/functional outcomes. This holistic approach could be implemented with the help of artificial intelligence and machine learning algorithms.74 A combination of clinical, environmental, and sociodemographic data, alongside genomic and transcriptomic data, could be used to have a more robust diagnostic and predictive tool for detecting schizophrenia risk in an individualized and personalized manner. Overall, schizophrenia PRS could be used to study certain core clinical correlates of schizophrenia, such as a more severe course of illness and cognitive deficits. However, the mechanism by which PRS influences the progression of schizophrenia still remains unclear. Therefore, future work could benefit from the incorporation of pathway-based PRS approaches to elucidate the functional mechanism of common genetic variance on schizophrenia liability.75 Also, the incorporation of rare genetic variants, copy number variants, and mitochondrial genetic variants related to the risk of schizophrenia could improve the reliability and strength of PRS calculations.

GWAS

Genome-wide association study

MHC

Major histocompatibility complex

NMDA

Ligand-gated N-methyl-d-aspartate

PRS

Polygenic risk score

SNP

Single nucleotide polymorphisms

TRS

Treatment-resistance schizophrenia

PANSS

Positive and negative syndrome scale

IQ

Intellectual quotient

MRI

Magnetic resonance imaging

fMRI

Functional magnetic resonance imaging

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