Novel genomic risk loci and improved prediction for treatment-resistant schizophrenia are revealed by leveraging polygenic overlap with body-mass index

doi:10.21203/rs.3.rs-2198111/v1

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Novel genomic risk loci and improved prediction for treatment-resistant schizophrenia are revealed by leveraging polygenic overlap with body-mass index

https://doi.org/10.21203/rs.3.rs-2198111/v1

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Treatment resistant schizophrenia (TRS) is characterized by repeated treatment failure with antipsychotics. A recent genome-wide association study (GWAS) of TRS showed a polygenic architecture, but no significant loci were identified. Clozapine is shown to be the superior drug in terms of clinical effect in TRS; at the same time it has a serious side effect profile, including weight gain. Here, we sought to increase power for genetic discovery and improve polygenic prediction of TRS, by leveraging genetic overlap with Body Mass Index (BMI). We analysed GWAS summary statistics for TRS and BMI applying the conditional false discovery rate (cFDR) framework. We observed cross-trait polygenic enrichment for TRS conditioned on associations with BMI. Leveraging this cross-trait enrichment, we identified 2 novel loci for TRS at cFDR < 0.01, suggesting a role of MAP2K1 and ZDBF2. Further, polygenic prediction based on the cFDR analysis explained more variance in TRS when compared to the standard TRS GWAS. These findings highlight putative molecular pathways which may distinguish TRS patients from treatment responsive patients. Moreover, these findings confirm that shared genetic mechanisms influence both TRS and BMI and provide new insights into the biological underpinnings of metabolic dysfunction and antipsychotic treatment.

Biological sciences/Genetics

Health sciences/Diseases/Psychiatric disorders/Schizophrenia

Use of antipsychotic drugs in patients with schizophrenia (SCZ) is standard treatment and often long-term. However repeated treatment failure with inadequate improvement in symptoms occurs in approximately 30% of patients treated with antipsychotics. This clinical picture is termed treatment-resistant schizophrenia (TRS)¹. The biological mechanisms underlying TRS remain unclear, although disease heterogeneity, pharmacokinetics and/or genetic diversity, are likely of major relevance². Initial genetic studies attempting to better understand the molecular mechanisms of TRS produced conflicting results, due in part to differences in the patient groups studied as well as the use of the candidate gene approach^{3, 4}. Most recently, a genome-wide association study (GWAS) of TRS confirmed that it is polygenic in nature (estimated SNP-based heritability of 1–6%), despite not identifying any genome-wide significant loci⁵. Obtaining a better understanding of the underlying mechanisms and early identification of TRS remain critical priorities in SCZ research.

Clozapine (CLZ) is superior to other antipsychotics in terms of clinical effect and is the only licensed treatment for TRS^{6, 7}. Despite this, the mechanism of action underlying the superior efficacy of CLZ remains unknown⁸. Despite being effective in ~ 60% of TRS cases however, treatment may be accompanied by severe adverse effects such as agranulocytosis, seizures and myocarditis, limiting its use as a first line SCZ drug^{6, 9}. Treatment-induced weight gain and accompanying dyslipidemia and glucose dysregulation are also major adverse effects which may limit adherence and success of antipsychotic treatment^{7, 10, 11}. A recent review investigated the effects of 18 different antipsychotics on metabolic function and identified large heterogeneity in their effect on body weight, body mass index (BMI), cholesterol and glucose concentrations, with CLZ associated with the largest degree of metabolic dysfunction¹². Improvements in symptom severity were associated with increases in body weight, BMI, low-density lipoprotein (LDL) and total cholesterol, highlighting that the most efficacious treatment options, such as CLZ, also result in the greatest metabolic dysfunction¹². Moreover, higher BMI is commonly observed in TRS patients taking CLZ when compared to patients with SCZ responding to antipsychotics other than CLZ^{12, 13}. Although this relationship between antipsychotic treatment and metabolic dysfunction is well described^{7, 10, 11}, it remains unknown whether metabolic disturbances are associated with antipsychotic efficacy due to overlapping molecular mechanisms¹².

Given our limited understanding of the underlying genetics of TRS based on the conventional GWAS approach⁵, additional statical approaches may be employed to boost statistical power for genetic discovery. The conditional false discovery rate (cFDR) framework increases discovery and improves polygenic prediction in underpowered GWAS by leveraging genetic overlap with a second, more powerful GWAS^14–17. Considering the relationship between antipsychotic treatment and weight gain, we hypothesized that TRS and measures of metabolic function (such as BMI) may have a shared genetic basis, despite a low genome-wide genetic correlation (rg = 0.048). In contrast to the TRS GWAS which included only 10,501 TRS cases⁵, the latest GWAS of BMI includes almost 800,000 individuals and identified approximately 100 genome-wide significant loci¹⁸. Therefore, we sought to utilize the cFDR framework to increase power for genetic discovery and improve polygenic prediction of TRS, by conditioning on BMI.

GWAS Sample Description

Publicly available GWAS summary statistics for TRS, BMI and SCZ were obtained. The TRS sample comprised 10,501 TRS and 20,325 non-TRS patients⁵, while the latest GWAS of BMI included 795,640 individuals¹⁸. Moreover, a second GWAS of BMI in 28,681 children at eight years old was also obtained for replication analysis¹⁹. The SCZ GWAS sample comprised 53,386 cases and 77,258 controls²⁰ and was only used in the polygenic score analysis. All GWAS data used in these analyses were from individuals of European ancestry. An overview of the study design and samples is shown in Fig. 1. The Norwegian Institutional Review Board for the South-East Norway Region has evaluated the current protocol and found that no additional institutional review board approval was needed because no individual data were used. More detailed descriptions are available in the Supplementary methods and original publications^{5, 18–20}.

Conditional/Conjunctional False Discovery Rate

We applied cFDR to boost discovery of genetic variants associated with TRS. Firstly, conditional QQ-plots were constructed by comparing enrichment of association in all variants in the primary trait (TRS) with 3 subsets of variants defined by their strength of association (p < 0.1, p < 0.01 and p < 0.001) with the secondary trait (BMI). Successive left-ward deflection with increasing strength of association indicates cross-trait enrichment. Shift in enrichment conditional on the secondary trait can be directly interpreted according to the Bayesian definition of the true discovery rate (TDR = 1-FDR), whereby a larger shift is consistent with a smaller FDR. This means cFDR values can be computed for each variant by comparing enrichment of all variants with a subset of variants which are as strongly or more strongly associated with the secondary trait. The cFDR value can therefore be interpreted as the probability that a given SNP is not associated with the primary trait given that the SNP is more strongly or as strongly associated with both phenotypes than observed in the original GWAS. Look-up plots can be constructed which provide cFDR values given the p-values in the primary and secondary traits. A conjunctional FDR statistic is subsequently computed by repeating the analysis having switched the primary and secondary trait. The maximum of the two cFDR statistics represents the probability that a given SNP is not associated with the primary or secondary trait given that the SNP is more strongly or as strongly associated with both phenotypes than observed in the original GWAS. We used n = 100 iterations of random pruning with an LD threshold r² = 0.1 to define LD-blocks throughout the genome. Genomic inflation was corrected for by a conservative genomic control procedure utilizing intergenic variants which lack true associations relative to other functional regions²¹. An FDR level of 0.01 per pair-wise comparison was set for cFDR, corresponding to 1 false positive per 100 reported associations. We excluded SNPs around the extended MHC region and chromosome 8p23.1 (genome build 19 locations chr6:25119106–33854733 and chr8:7200000–12500000, respectively) before fitting the FDR model, since their intricate regional LD may bias cFDR estimation²². All code used to perform the described analyses is available online (https://github.com/precimed/pleiofdr). More details about the cFDR methods can be found in the original publications^{14, 15} and subsequent review¹⁶.

Locus Definitions and Annotations

Genetic loci were defined based on association summary statistics produced with cFDR following the protocol implemented in FUMA with default parameters²³. Briefly, independent significant genetic variants were identified as variants with cFDR < 0.01 and linkage disequilibrium (LD) r² < 0.6 with each other. A subset of these independent significant variants with LD r² < 0.1 were then selected as lead variants. Moreover, for each independent significant variant all candidate variants were identified as variants with cFDR < 0.1 and LD r² ≥ 0.6. For a given lead variant the borders of the genomic locus were defined as minimum/maximum positional coordinates over all corresponding candidate variants. Finally, loci were merged if they were separated by less than 250kb.

Genes were mapped to each identified locus using the Open Targets Genetics platform (https://genetics.opentargets.org/)²⁴. This platform aggregates human GWAS and functional genomics data including gene expression, protein abundance, chromatin interaction and conformation data from a wide range of cell types and tissues to make robust connections between GWAS-associated loci, variants and likely causal genes. To assign likely causal genes for a given variant, a disease-agnostic Variant to Gene (V2G) analysis pipeline provides a single aggregated V2G score for each variant-gene prediction. For each locus we considered the top 3 genes with the highest V2G score. Finally, we queried SNPs for known expression quantitative trait loci (eQTLs) across multiple tissues using the GTEx portal (GTEx v8)²⁵, in peripheral blood using the eQTLGen²⁶ and in different brain tissues using the BRAINEAC portal²⁷.

Polygenic Score Analysis and SNP Annotations

Polygenic scores (PGSs) were constructed using PRSice-2²⁸ from summary statistics from TRS⁵, BMI¹⁸ and SCZ²⁰. Using the pleioPGS approach¹⁷, we leveraged our multivariate analysis by comparing standard GWAS-ranked lead SNPs with cFDR-based ranking, using the same weights derived from the original TRS GWAS. We calculated the scores for specific numbers of lead SNPs rather than for significance thresholds to allow for direct comparison between the approaches, at equal numbers of SNPs in each set. We compared the top 100 (approximate number of SNPs p < 1E-5 in the original TRS GWAS) to 105,000 SNPs (number of independent SNPs p < 1 in the original TRS GWAS). Sex, age and 10 principal components were included as covariates. Functional consequences of the SNPs contributing to the PGSs explaining the most variability in TRS status were annotated using FUMA²³.

The initial PGS target sample (TDM cohort) included patients phenotyped with TRS (n = 984) or non-TRS (n = 749) from the therapeutic drug monitoring (TDM) service at Center for Psychopharmacology in Diakonhjemmet Hospital, Oslo, Norway during the period January 2005 and August 2020 (Fig. 1). The TRS cohort was defined based on the use/prescription of CLZ, the main drug indicated in TRS²⁹, verified by detectable serum concentration of CLZ and/or available CLZ dose information in TDM registries. All non-TRS patients had only used non-CLZ antipsychotic drug(s) and had never been prescribed CLZ as confirmed by their longitudinal TDM profiles. In Norway, TDM analyses are reimbursed by the governmental health service, and it is used as a tool for clinical follow-up in psychiatry. The analyses are typically requested as a routine control to monitor e.g. adherence and drug-drug interactions, or to assess adverse effects or lack of effect, in relation to a therapeutic concentration reference range. All results from the analyses are interpreted by the TDM laboratory against the therapeutic reference range as basis for the reports sent to the physicians.

The validation PGS target sample (TOP cohort) included 310 patients with SCZ with reported antipsychotic treatment history and RNA microarray data from peripheral blood (Fig. 1, Supplementary Methods). The recruitment procedure and clinical evaluation for this study sample are described in detail in previous reports^30–32. Briefly, patients were defined as being TRS (n = 89) based on two or more failed trials of antipsychotic treatment, each of at least six weeks duration and with therapeutic dosage. At least one of the antipsychotics had to be a second-generation antipsychotic. All other patients with SCZ (n = 221) were considered non-TRS.

All participants gave written informed consent and the study was approved by the Norwegian Regional Committee for Medical Research Ethics and the Norwegian Data Inspectorate. All procedures and methods were carried out in accordance with relevant guidelines and regulations.

Statistical Analysis

Statistical analyses were performed in R using the base statistical package³³. The TOP cohort described above was used to assess differences in MAP2K1 mRNA levels that were investigated between the TRS and non-TRS patient groups, between rs3087660 genotype groups, and between rs3087660 genotype groups within the TRS and non-TRS patient groups (Fig. 1). Analyses were performed using linear regression, adjusting for age and sex. A p-value threshold of p < 0.05 was applied.

Genetic Overlap and Discovery

The conditional QQ-plot suggests the presence of enrichment for TRS given BMI (Fig. 2A), shown by the incremental incidence of association with TRS (leftward deflection) as a function of the significance of association with BMI. We leveraged this cross-phenotype polygenic enrichment using cFDR analyses and re-ranked TRS SNPs conditionally on their association with BMI. At cFDR < 0.01 we identified 2 loci (rs7560232 chr2:207,121,329 − 207,207,137 and rs3087660 chr15:66,624,854 − 66,797,492) associated with TRS after conditioning on BMI (Fig. 2B, Table 1, Supplementary Table 1). Investigation of these loci in the GWAS catalogue³⁴ showed that the locus on chromosome 2 has been previously associated with changes in volume of the cerebellar vermal lobules³⁵, lower self-rated health³⁶ and age-related hearing impairment³⁷, while the locus on chromosome 15 is only previously associated with BMI³⁸. The top three genes with the highest V2G score for the locus on chromosome 2 include ZDBF2, CMKLR2 and ADAM23, and for the locus on chromosome 15 include SNAPC5, TIPIN and MAP2K1 (Fig. 2B, Table 1). As a replication analysis, we similarly re-ranked TRS SNPs conditionally on their association with BMI in an independent BMI GWAS in children at eight years old¹⁹ (Supplementary Fig. 1 and Supplementary Table 2) and confirmed the association of the locus on chr15 (cFDR = 9.8E-03; rs11631065 chr15:66,603,388 − 66,797,492) with TRS.

Table 1

Novel loci for treatment resistant schizophrenia (TRS) after conditioning on body-mass index (BMI)
Chr	Range	Lead SNP	A1/A2^#	Mapped Genes	Functional category	p-value TRS	OR TRS	p-value BMI	Beta BMI	cFDR
2	207,121,329 − 207,207,137	rs7560232	G/A	ZDBF2, CMKLR2, ADAM23	Intergenic	1.9E-06	0.898	1.1E-09	0.011	1.6E-03
15*	66,624,854 − 66,797,492	rs3087660	G/A	SNAPC5, TIPIN, MAP2K1	5’ UTR (ZWILCH), Intronic (RPL4)	3.1E-06	0.899	1.3E-10	0.011	2.7E-03
Lead SNPs in independent genomic loci associated with TRS after conditioning on BMI at cFDR < 0.01, after merging regions < 250 kb apart into a single locus. The table presents chromosomal position (Chr), mapped genes and functional category, as well as p-values and effect sizes (odds ratios (OR) and betas) from the original summary statistics on TRS⁵ and BMI¹⁸. The effect sizes are given with reference to allele 2 (A1). For more details and a list of all candidate variants in these loci, see Supplementary Table 1. ^#A1 is effect allele. *Locus replicated with analysis using independent BMI GWAS data, see Supplementary Table 2.

Additional assessment of the variant-gene relationships in the GTEx database showed significant associations between the lead SNP rs3087660 on chromosome 15 and MAP2K1 gene expression in the cerebellum (p = 6.6E-5) and adipose subcutaneous tissues (p = 7.8E-7) (Supplementary Figs. 2 and 3). Similarly, the rs3087660 G allele was associated with increased MAP2K1 expression in peripheral blood in the eQTLGen database (p = 1.5E-11). Although not significant, a similar pattern is also observed in the cerebellum in the BRAINEAC database (p = 0.15) (Supplementary Fig. 4). We further assessed the associations between rs3087660 genotype and MAP2K1 gene expression in the TOP cohort of 310 patients with SCZ, including 89 TRS and 221 non-TRS patients. MAP2K1 gene expression was significantly (p = 0.010) lower in the TRS group compared to the non-TRS group (Supplementary Fig. 5A and Supplementary Table 3). Like our observations in the GTEx database, greater MAP2K1 gene expression was observed in patients with the rs3087660 GG genotype when compared to those with the GA and AA genotypes, although these differences between genotype groups were not significant (Supplementary Fig. 5B and Supplementary Table 3). Interestingly, when MAP2K1 gene expression was compared between rs3087660 genotype groups, in TRS and non-TRS patients separately, the expected dose effect was greater in the non-TRS group than in the TRS group (Supplementary Fig. 5C and Supplementary Table 3).

Polygenic Prediction

We compared the top 100–105,000 SNPs using original TRS GWAS p-value ranking and cFDR-based ranking (Supplementary Fig. 6), hypothesizing that the boosted power from our conditional analysis would select more informative variants than standard GWAS, resulting in improved PGS performance. The greatest variance explained by any of the tested PGS was captured within the first 500 SNPs (Fig. 3). The maximum variance explained by the original TRS PGS (liability r² = 4.96%, p = 0.013) was achieved using the top 500 independent SNPs. Interestingly, cFDR-based ranking of TRS conditioned on BMI achieved a similar explained variance (liability r² = 4.77%, p = 0.017) using only the top 100 independent SNPs, while the maximum variance explained (liability r² = 5.62%, p = 0.011) required only the top 300 independent SNPs and outperformed the original TRS PGS by 1.13 fold. PGSs for BMI and SCZ were also plotted for comparison.

Assessment of the functional consequences of the SNPs included in the PGSs showed enrichment of intronic SNPs (enrichment = 1.29, p = 3.74E-130) and depletion of intergenic (enrichment = 0.846, p = 8.94E-60) and ncRNA intronic (enrichment = 0.828, p = 6.86E-13) SNPs in the cFDR-based PGS relative to the reference. In contrast, intergenic SNPs (enrichment = 1.06, p = 9.62E-11) and ncRNA intronic (enrichment = 1.09, p = 2.41E-4) SNPs were enriched, while intronic SNPs were depleted (enrichment = 0.908, p = 3.93E-14) in the original TRS PGS relative to the reference panel (Supplementary Fig. 7).

We sought to validate these results in the smaller TOP cohort of SCZ patients used to investigate the differential gene expression of MAP2K1 as described above. Although the greatest maximum variance explained was observed for the original TRS PGS (liability r² = 0.70%, p = 0.205, 700 SNPs) and not with the cFDR-based ranking of TRS conditioned on BMI (liability r² = 0.47%, p = 0.299, 600 SNPs), we did observe greater explained variance for the cFDR-based ranking for all SNP thresholds below 700 SNPs (Supplementary Fig. 8).

Here we applied the cFDR method to boost discovery of genetic variants associated with TRS, and identified two novel loci associated with TRS after conditioning on BMI. These results provide evidence for a genetic overlap between TRS and BMI. Identified specific loci may provide insights into the shared genetic mechanisms influencing TRS and BMI and inform on their biological underpinnings. In addition, we showed that the polygenic prediction of TRS is improved by leveraging the identified genetic overlap with BMI.

Using the cFDR approach, we identified two novel loci (rs7560232 on chromosome 2 and rs3087660 on chromosome 15) associated with TRS after conditioning on BMI, one (rs3087660) of which replicated using an independent BMI GWAS¹⁹. The top three genes mapped to this locus (rs3087660) include SNAPC5, TIPIN and MAP2K1. Of these, MAP2K1 is of particular interest since it forms part of the MAPK/ERK pathway activated by antipsychotics³⁹. Activation of this pathway results in the phosphorylation of proteins involved in several biological processes including, transcriptional and translational regulation, cellular excitability, dendritic organization, long-term potentiation and depression, neuronal survival, synaptogenesis and neurotransmitter release⁴⁰, and ERK activation specifically contributes to synaptic plasticity and connectivity^{41, 42}. The MAP2K1 gene was recently shown to be downregulated in peripheral blood of SCZ patients prior to treatment, when compared to healthy controls, and upregulated after treatment with antipsychotics⁴³. Moreover, a strong positive correlation between demethylation of the MAP2K1 gene promoter region and lifetime antipsychotic use was identified in frontal cortex postmortem tissue samples collected from patients with SCZ⁴⁴. Investigation of the rs3087660-MAP2K1 relationship in the GTEx database suggests that this variant may influence MAP2K1 gene expression, particularly in the cerebellum and adipose subcutaneous tissues where the rs3087660 G allele increases expression. Comparable findings were also confirmed in peripheral blood and the cerebellum in the eQTLGen and BRAINEAC databases, respectively. Further evaluation of this relationship in our naturalistic TOP cohort of TRS and non-TRS SCZ patients showed a similar pattern in peripheral blood samples, where patients with the GG genotype had greater MAP2K1 gene expression when compared to patients with the GA or AA genotypes. Moreover, stratifying this cohort by TRS status suggests that this effect may be greater in non-TRS patients than in TRS patients. Our cFDR findings show that the rs3087660 G allele is associated with reduced risk of TRS. It is tempting to speculate that the increase in MAP2K1 activity expected after treatment with conventional antipsychotics may be facilitated by the presence of the rs3087660 G in non-TRS patients, and that MAP2K1 activity is reduced in TRS patients regardless of rs3087660 genotype. Interestingly, although CLZ is also shown to increase MAP2K1 activity ⁴⁵, the mechanism is distinct from other antipsychotics⁴⁶. Thus, response to CLZ in TRS patients may, in part, be due to the specific and unique manner in which CLZ influences MAP2K1 gene expression. These results provide a novel target mechanism that should be explored to better understand the mechanism of action underlying the superior efficacy of CLZ.

Genes mapped to the other identified locus (rs7560232) include ZDBF2, CMKLR2 and ADAM23. Of interest is the ZDBF2 gene that is mostly expressed in the nucleus accumbens⁴⁷. The ZDBF2 gene has been shown to be downregulated in SCZ^{48, 49}, but it is not known how ZDBF2 expression relates to TRS. Moreover, this locus has previously been implicated in other complex health-related traits including volume of the cerebellar vermal lobules³⁵, lower self-rated health³⁶ and age-related hearing impairment³⁷. Interestingly, the effects of both lead SNPs (rs3087660 and rs7560232), as well as all other candidate SNPs within these loci in LD with the lead SNPs, are in line with the observed relationship between antipsychotic treatment and metabolic dysfunction^{7, 10, 11}, that these variants reduce risk of TRS while increasing risk of higher BMI. This suggests that similar underlying biological processes contribute to antipsychotic response and metabolic dysfunction, such as increased BMI, and supports the concept that metabolic disturbances may be a component underlying antipsychotic drug efficacy and not the result of off-target actions¹². This hypothesis, which needs to be investigated in more detail in future studies, is in line with the fact that CLZ exhibits the best clinical response in TRS but is also associated with the highest BMI increase¹².

By leveraging the identified genetic overlap between TRS and BMI, and re-ranking genetic variants according to the TRS|BMI cFDR values, we also show a 1.13 fold improvement in TRS polygenic prediction in the TDM cohort. This pleioPGS approach outperformed the standard GWAS-based ranking, utilizing less SNPs in the PGS, despite using the same SNP weightings. Annotation of the variants contributing to the PGSs with greatest variance explained showed enrichment of intronic SNPs and depletion of intergenic and ncRNA intronic SNPs within those contributing to the cFDR PGS, while SNPs contributing to the original TRS PGS showed the opposite pattern with enrichment of intergenic and ncRNA intronic SNPs and depletion of intronic SNPs. Recent evidence suggests that intronic regions are enriched for regulatory elements of expression of genes involved in tissue-specific functions, while housekeeping genes are more often controlled by intergenic enhancers⁵⁰. This suggests that the pleioPGS method may be prioritizing tissue-specific regulatory variants more likely to be involved in TRS aetiology and therefore more predictive of the outcome.

To validate these findings, we repeated this PGS analysis in the independent TOP cohort. We observed the expected greater variance explained by the TRS|BMI PGS compared to the standard TRS PGS for all SNP thresholds below 700 SNPs, however the greatest variance explained was by the standard TRS PGS at 700 SNPs. Variance explained by PGS in this validation cohort (maximum variance explained = 0.70%) was also considerably less than for the initial test cohort (maximum variance explained = 5.62%). This may be due to the smaller sample size of the TOP cohort, as well as, the difference in TRS definitions used. The TDM cohort TRS definition was based on CLZ use, matching the definition used in the TRS GWAS⁵, while for the TOP cohort the TRS definition was based on two or more failed trials of antipsychotic treatment. Thus, the phenotype for the TDM cohort better matches the discovery GWAS from which PGS SNP weights were assigned.

The results of the cFDR analysis highlight how data from small GWAS might still be used to understand genetic architecture and overlap between traits. However, despite identifying novel loci for TRS, larger GWAS of TRS are still required to better understand the underlying genetic etiology. In addition, the predictive ability of the PGS remains far from being clinically relevant, and larger GWAS of TRS will also improve SNP weights for polygenic prediction. Moreover, these analyses were limited to European-ancestry participants due to available data and differences in linkage disequilibrium between ancestral groups. Larger, more diverse TRS samples, as well as new trans-ancestral methods and analyses are required to increase the generalizability of these findings.

In conclusion, we identified two novel loci associated with TRS and showed improved prediction of TRS, by leveraging genetic overlap between TRS and BMI. In addition, we identified a novel target mechanism that should be explored to better understand the mechanism of action underlying the superior efficacy of CLZ. These findings confirm that shared genetic mechanisms influence both TRS and BMI and provide new insights into the biological underpinnings of metabolic dysfunction and response to antipsychotic treatment.

Acknowledgements

We thank the research participants, employees and researchers of the PGC, GIANT and TOP for making this research possible. This work was partly performed on the TSD (Tjeneste for Sensitive Data) facilities, owned by the University of Oslo, operated and developed by the TSD service group at the University of Oslo, IT-Department (USIT). Computations were also performed on resources provided by UNINETT Sigma2—the National Infrastructure for High Performance Computing and Data Storage in Norway. We gratefully acknowledge support from the American National Institutes of Health (R01MH124875, NS057198, EB00790), the Research Council of Norway (RCN) (248980, 296030, 229129, 213837, 324252, 300309, 273291, 223273), the South-East Norway Regional Health Authority (2022-073), and KG Jebsen Stiftelsen (SKGJ-MED-021). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 847776 and 964874.

Conflict of Interest

Dr. Andreassen reported grants from Stiftelsen Kristian Gerhard Jebsen, South-East Regional Health Authority, Research Council of Norway, and European Union’s Horizon 2020 during the conduct of the study; personal fees from HealthLytix (stock options), Lundbeck (speaker’s honorarium), and Sunovion (speaker’s honorarium) outside the submitted work; and had a pending patent for systems and methods for identifying polymorphisms. Dr. Dale reported grants from the National Institutes of Health outside the submitted work; had a patent for US7324842 licensed to Siemens Healthineers; is a founder of and holds equity in Cortechs Labs and serves on its scientific advisory board; is a member of the scientific advisory board of Human Longevity; a member of the scientific advisory board of Healthlytix; and receives funding through a research agreement with GE Healthcare. No other disclosures were reported.

Code Availability

Code for cFDR and pleioFDR are publicly available at https://github.com/precimed/pleiofdr and https://github.com/norment/open-science/tree/main/2021_VanderMeer_medRxiv_pleioPGS, respectively.

Supplementary information is available at MP’s website

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Novel genomic risk loci and improved prediction for treatment-resistant schizophrenia are revealed by leveraging polygenic overlap with body-mass index

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