Investigating the association of the effect of genetically proxied PCSK9i with mood disorders using cis-pQTLs: A drug-target Mendelian randomization study

Alisha Aman; Eric A. W. Slob; Joey Ward; Naveed Sattar; Rona J. Strawbridge

doi:10.1371/journal.pone.0310396

Abstract

PCSK9-inhibitors (PCSK9i) are new drugs recently approved to lower LDL-cholesterol levels. However, due to the lack of long-term clinical data, the potential adverse effects of long-term use are still unknown. The PCSK9 genetic locus has been recently implicated in mood disorders and hence we wanted to assess if the effect of PCSK9i that block the PCSK9 protein can lead to an increase in the incidence of mood disorders. We used genetically-reduced PCSK9 protein levels (pQTLs) in plasma, serum, cerebrospinal fluid as a proxy for the effect of PCSK9i. We performed Mendelian randomization analyses using PCSK9 levels as exposure and mood disorder traits major depressive disorder, mood instability, and neuroticism score as outcomes. We find no association of PCSK9 levels with mood disorder traits in serum, plasma, and cerebrospinal fluid. We can conclude that genetically proxied on-target effect of pharmacological PCSK9 inhibition is unlikely to contribute to mood disorders.

Citation: Aman A, Slob EAW, Ward J, Sattar N, Strawbridge RJ (2024) Investigating the association of the effect of genetically proxied PCSK9i with mood disorders using cis-pQTLs: A drug-target Mendelian randomization study. PLoS ONE 19(9): e0310396. https://doi.org/10.1371/journal.pone.0310396

Editor: Paolo Magni, Università degli Studi di Milano, ITALY

Received: October 12, 2023; Accepted: September 1, 2024; Published: September 26, 2024

Copyright: © 2024 Aman et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are wuthin the manuscript and its supplementary tables.

Funding: The author(s) received no specific funding for this work.

Competing interests: Naveed Sattar reports consulting and/or speaker fees from Abbott Laboratories, Amgen, AstraZeneca, Boehringer Ingelheim, Eli Lilly, Hanmi Pharmaceuticals, Janssen, Merck Sharp & Dohme, Novartis, Novo Nordisk, Pfizer, Roche Diagnostics, and Sanofi; and grant support paid to his institution from AstraZeneca, Boehringer Ingelheim, Novartis, and Roche Diagnostics outside the submitted work. All other authors declare no potential conflicts. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Introduction

PCSK9 inhibitors (PCSK9i) are a new class of drugs prescribed to reduce uncontrolled levels of low-density lipoprotein cholesterol (LDL-C), especially where the gold standard treatment of statins and ezetimibe (NPC1L1 inhibitor) fails to control LDL-C levels [1]. As PCSK9i were only approved in 2015–16 [2], there is no long-term clinical data in which to assess possible adverse drug reactions (ADRs). As ADRS leads to increased morbidity and mortality and a higher burden on the healthcare system [3], it is important to explore these as early as possible. For statins, a drug that blocks HMGCR (3-hydroxy-3-methyl-glutaryl-coenzyme A reductase), it took several decades of use for enough evidence to be gathered to conclusively demonstrate an increased type 2 diabetes incidence with its long-term use [4].

In contrast, genetic analyses make it possible to predict ADRs in the absence of clinical data by using techniques such as Mendelian randomization (MR), which investigates causal relationships between exposures (proxied with genetic data) and outcomes [5, 6]. Genetic variants that reduce gene expression (eQTLs) or protein levels (pQTL) [7] can thus be used a proxy for a drug that reduces levels of, or blocks the function of a gene or its product.

Previously, we uncovered associations between genetic variants in the PCSK9 locus and mood disorder traits [8]. Mood disorders include the traits mood swings or instability, neuroticism, anxiety, mania, and major depressive disorder (MDD). The global burden of such conditions is significant, with the global number of disability-adjusted life years (DALYS) attributed to it being 125.3 million as of 2019, contributing to 4.9% of the proportion of global DALYS [9]. If the usage of PCSK9i indeed increases to mood disorders, the burden to both the patients and health services will increase. Although PCSK9i do not cross the blood-brain barrier under normal conditions, various disease states can modify the permeability of the blood-brain barrier [10]. Hence, it is essential that this association be further explored.

PCSK9i work as either single interfering RNA (siRNA) to block mRNA and prevent its translation [11], or are monoclonal antibodies that bind to the protein and block the function of the protein itself [11]. Previously, we have demonstrated that PCSK9 gene expression data showed no association with mood disorder traits [12], suggesting that siRNA PCSK9i usage are unlikely to lead to the mood disorder traits mood instability, neuroticism, or MDD. However, expanding the assessment to PCSK9i which directly target protein levels is needed to assess the possibility of ADRs from this alternative modality.

In this study, we used genetically predicted protein levels of PCSK9 to proxy the pharmacological effect of PCSK9i, and MR analyses to explore the possibility that protein-targeting PCSK9i might increase the risk of mood disorder traits in the long-term. We had used pQTL (genetic variants modulating PCSK9 protein levels) from serum, plasma, brain, and cerebrospinal fluid (CSF) to proxy the effect of PCSK9i. Despite PCSK9 being produced primarily in the liver, protein levels in the plasma and serum provide an overall effect of the protein level [13], whereas protein levels from brain and CSF can assess any localized effects.

Results

PCSK9 protein levels in plasma or serum are not associated with mood disorder traits

AGES cohort (serum).

In both relaxed and stringent threshold of pQTL, we found no association between genetically predicted reduction in PCSK9 levels and MDD (relaxed-P_SMR-multi = 0.410, stringent-P_SMR-multi = 0.290), or mood instability (relaxed-P_SMR-multi = 0.004, stringent-P_SMR-multi = 0.07) in either of the sets. However, a significant association was observed of reduced PCSK9 levels with reduction in neuroticism score in the relaxed set (relaxed-P_SMR-multi = 0.002) (data in S3 Table) but not the stringent set (stringent-P_SMR-multi = 0.010) (Fig 1, Table 1, data in S4 Table). There was a significant association between reduced PCSK9 levels in the serum and reduced LDL-C levels (positive control) in both the stringent (P_SMR-multi = 1.27x10^-77) and relaxed (P_SMR-multi = 3.69x10^-68) sets. The p-value for the HEIDI test in both the relaxed and stringent set for all mood disorder trait outcomes are above the P_HEIDI≥0.01 threshold, indicating that the observed effect sizes are not due to heterogeneity.

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Fig 1. Association of reduced PCSK9 protein levels in the plasma and serum with mood disorder traits with stringent p-value threshold.

SMR multi-SNP analyses was performed using cis-pQTLs as instruments to assess association of PCSK9 levels (exposure) with mood disorder traits (MDD, mood instability, and neuroticism score) as outcomes. On the X-axis are the outcomes (mood disorder traits and LDL-C as a positive control). The Y-axis depicts the -log10(p-value) for the SMR associations using multiple SNP. Three datasets were used for PCSK9 protein levels: ARIC (plasma), AGES (serum), and PCSK9 meta-GWAS (serum and plasma combined). The direction of the arrow represents the direction of effect with respect to reduction of PCSK9 levels–triangle pointing up: reduction of PCSK9 levels increases the outcome levels/risk, triangle pointing down: reduction of PCSK9 levels reduces the outcome levels/risk. The red line signifies the Bonferroni corrected p-value threshold of 0.0031.

https://doi.org/10.1371/journal.pone.0310396.g001

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Table 1. SMR results for PCSk9 protein levels as exposure and various outcomes.

https://doi.org/10.1371/journal.pone.0310396.t001

ARIC cohort (plasma).

We found no association between PCSK9 reduced levels, and MDD (relaxed-P_SMR = 0.190, stringent-P_SMR = 0.106) and mood instability (relaxed-P_SMR-multi = 0.012, stringent-P_SMR-multi = 0.090) in either the relaxed or stringent set. However, the association between reduced PCSK9 levels and increased neuroticism score (P_SMR-multi = 0.001) was significant in the relaxed set (data in S2 Table), but not in the stringent set (P_SMR-multi = 0.022) (Fig 1 and Table 1, data in S3 Table). The association of reduced PCSK9 levels in the plasma with reduced LDL-C levels was significant in both the stringent (P_SMR-multi = 1.07x10^-59) and relaxed (P_SMR-multi = 1.58x10^-62) set. The p-value for the HEIDI test in both the relaxed and stringent set for all mood disorder trait outcomes are above the P_HEIDI≥0.01 threshold, indicating that the observed effect sizes are not due to heterogeneity.

PCSK9 meta-analyses cohort (serum + plasma).

There was no association between reduced PCSK9 protein levels and MDD (stringent-P_SMR-multi = 0.209, relaxed-P_SMR-multi = 0.253), mood instability (relaxed-P_SMR-multi = 0.019, stringent-P_SMR-multi = 0.012), or neuroticism score (relaxed-P_SMR-multi = 0.047, stringent-P_SMR-multi = 0.039) (Fig 1, Table 1, data in S2 and S4 Tables). Reduced PCSK9 levels were significantly associated with reduced LDL-C levels (relaxed-P_SMR-multi = 8.514x10^-60, stringent-P_SMR-multi = 6.29x10^-62) in both sets. The p-value for the HEIDI test in both the relaxed and stringent set for all mood disorder trait outcomes are above the P_HEIDI≥0.01 threshold, indicating that the observed effect sizes are not due to heterogeneity.

PCSK9 levels in the brain and CSF are not associated with mood disorder traits

We found that reduced CSF PCSK9 levels were not significantly associated with MDD (relaxed-P_SMR-multi = 0.816, stringent-P_SMR-multi = 0.600), and mood instability (relaxed-P_SMR-multi = 0.022, stringent-P_SMR-multi = 0.014) in either the relaxed or stringent set (Fig 2, Table 1, data in S3 Table). A significant association was found between reduced CSF PCSK9 levels and reduced LDL-C levels in both the relaxed (P_SMR-multi = 3.75x10^-18) and stringent (P_SMR-multi = 7.41x10^-16) set. However, the power of the MR analyses between PCSK9 levels in CSF and neuroticism score was very low at 0.41, and hence was not reported. The p-value for the HEIDI test in both the relaxed and stringent set for all mood disorder trait outcomes, except for mood instability, are above the P_HEIDI≥0.01 threshold, indicating that the observed effect sizes are not due to heterogeneity.

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Fig 2. Association of reduced PCSK9 protein levels in the CSF with mood disorder traits.

SMR multi-SNP analyses was performed using cis-pQTLs as instruments to assess association of PCSK9 levels (exposure) with mood disorder traits MDD, and mood instability as outcomes. The x-axis represents the three mood disorder traits and LDL-C as positive control. The y-axis depicts the -log10(p-value) for the SMR associations using multiple SNP. The shape represents the direction of effect with respect to reduction of PCSK9 levels: triangle pointing up: reduction of PCSK9 levels increases the outcome levels/risk, triangle pointing down: reduction of PCSK9 levels reduces the outcome levels/risk. Red line signifies the Bonferroni corrected p-value threshold of 0.0031.

https://doi.org/10.1371/journal.pone.0310396.g002

Concordance of results using traditional MR methods

We performed a traditional two sample MR IVW test to ascertain if the SMR results are concordant. We found that in the serum, plasma, and CSF i.e. all tissues tested, there PCSK9 protein levels were not significantly associated with the mood disorder traits of mood instability, neuroticism score, and MDD. LDL-C levels were used as a positive control and we found that genetically proxied reduction of PCSK9 protein levels were significantly associated with lower levels of LDL-C (Table 2).

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Table 2. Associations between PCSK9 protein levels and various mood disorder traits using two sample MR (IVW) test.

https://doi.org/10.1371/journal.pone.0310396.t002

Discussion

We specifically assessed whether long term PCSK9i use might increase the risk of mood disorder traits by using genetically reduced protein levels as a proxy for the effect of PCSK9i. We also investigated PCSK9 levels in CSF to reflect a more localized effect of PCSK9i.

We found no significant associations of reduced PCSK9 levels (serum and plasma) with increased mood disorder traits using the stringent set of pQTLS, suggesting that the variants strongly and specifically associated with PCSK9 protein levels do not influence neuroticism. The increased statistical power when using the relaxed set of pQTLS also comes with the trade-off of reduced specificity of the genetic instruments (i.e. these variants might be acting through other pathways in addition to the PCSK9 protein levels). Therefore, the associations of the ARIC and AGES relaxed set of pQTLS with (an effect on) neuroticism score, when viewed alongside the results of the stringent set of pQTLS is not convincing. That the same is not observed with the meta-analyses could be due to the larger sample size in the meta-analyses, which provides relaxed pQTL associations closer to the “true” effects (in this case closer to null) on PCSK9 protein levels and reduces the impact of study-specific outliers. Overall, we found that PCSK9 levels using pQTLs derived in the plasma, serum, and in combined plasma and serum tissues were not associated with mood-disorder traits MDD, mood instability, and neuroticism score.

This work follows on from a previous genetic study that explored the impact of PCSK9 genetic variants on a variety of cardiometabolic and psychiatric-related phenotypes [8]. That study found associations with cardiovascular phenotypes, in line with known functions of PCSK9 effects. The findings on mood traits, that could not be explained by current understanding of PCSK9 functions, required further investigation, hence this study. A strength of our study is the very specific hypothesis tested, alongside “positive control” analyses of known effects. We cannot exclude the possibility of effects on other organs that were not explored.

Other strengths of this study include the concordance of our results across multiple tissues and cohorts and replicating the SMR analyses using a traditional MR method. Limitations of our study includes the lack of PCSK9 pQTLs in the brain, as no cis-pQTLs were detected for PCSK9 at either the relaxed or stringent p-value threshold. Hence, as PCSK9 is expressed in the brain, therefore it would be useful to investigate this further through PCSK9 pQTLs in the brain. Similarly, we were unable to analyse the impact of CSF PCSK9 levels and neuroticism score. Analyses are limited to European ancestry, however it is important to determine whether the effect is consistent across different ancestries. For future directions, we aim to also to incorporate pQTLs from different tissues if and when they become available.

The analyses were conducted in an effort expand on our previous work which studied the effect of targeting RNA as a mode of PCKS9 inhibition on mood-disorder traits. For this study, we focused on PCSK9 protein blocking instead by using PCSK9 pQTLs. The results for both our studies are consistent: long-term effect of PCSK9 inhibition, whether at the mRNA or the protein level, are unlikely to have a negative impact on mood-disorders. However, this is a genetically proxied study using MR that estimates the lifetime causal effect of an exposure (in this case PCSK9 inhibition) with an outcome (in this case mood disorder traits). It is not a clinical study, but a genetic one which was be used to infer potential ADRs that may arise due to long-term reduction in the levels of PCSK9.

In summary, here we have demonstrated that long-term use of PCSK9is, proxied genetically, which target PCSK9 protein levels are unlikely to increase the risk of mood disorder traits, consistent with our previous work using PCSK9 gene expression to mimic siRNA PCSK9is.

Methods

Genetic data for exposure

Summary statistics for genetic variants associated with levels of PCSK9 (cis-pQTLs, ±1Mb flanking the gene) in European ancestry population were extracted from AGES (n = 5368, serum) [14], ARIC (n = 7,213, plasma) [15], a meta-analysis of PCSK9 levels (n = 12,721, plasma and serum combined) [13], and brain (n = 458) and CSF (n = 971) samples [16]. Characteristics of cohorts were presented in the data in S1 Table. Cis-pQTLs were filtered based on the p-value for association with PCSK9 protein levels at a threshold of P_pQTL≤1x10^-5 (relaxed set) and P_pQTL≤5x10^-8 (stringent set). We explored the relaxed threshold, as too few pQTLS would mean low statistical power. Increasing the number of pQTLs in the analyses by using the relaxed set enables verification that null associations are true null (rather than lack of power). Cis-pQTLs were used as the instrumental variables for the MR analyses.

Genetic data for outcomes

The mood disorder traits assessed in this study were mood instability (an emotional state defined as rapid oscillations of intense affect leading to behavioural consequences [17]), neuroticism score (a score fir the trait of neuroticism characterised by an experience of negative effects, irritability, anxiety and other negative mood effects [18]), and MDD (major depressive disorder: a complex psychiatric condition with a clinical presentation of depressed mood, loss of interest, changes in appetite or weight, and increased likelihood of suicidal ideation [19]). Summary statistics were obtained from European ancestry genome-wide association studies of mood instability (n = 451,619), neuroticism score (n = 374,323), and MDD (n = 217,584) [20–22]. We also used circulating LDL-C levels as a positive control, as reduction in PCSK9 levels leads to a reduction in circulating levels of LDL-C [23]. Data was extracted from the IEU open GWAS project database using the ieugwasr R package version 0.1.5 [21].

Power calculations

Power calculations were conducted using mRnd web version (accessed 15/06/2023) [24]. We used the observed estimate for the effect of PCSK9 protein levels on neuroticism score, MDD, and LDL-C. Where these were not available, we used reported estimates of PCSK9 genetic risk score on the above outcomes instead [25, 26]. No reported estimates were available for PCSK9 protein levels on mood instability and hence we could not calculate the power for this outcome. We assumed the type-I error rate (α) as 0.05. The parameters used for the power calculations are available in the data in S2 Table.

Statistical analyses

Causal associations between PCSK9i, proxied by reduced PCSK9 protein levels, and mood disorder traits was assessed using two-sample MR. Specifically, we used Summary-based MR (SMR) v1.3.1 [27], which is a modified two sample MR method that integrates QTL and GWAS summary statistics to assess if the intermediate phenotype modulated by the QTLs is causally associated with the trait. We also performed the Heterogeneity in dependent instruments (HEIDI) test to test the null hypothesis that the SMR estimate is due to directional pleiotropy instead of true causality, rejecting the hypothesis with a P_HEIDI<0.01.

The parameters used for SMR analyses were: P_pQTL≤5x10^-8 (stringent set) and P_pQTL≤1x10^-5 (relaxed set), linkage disequilibrium (LD) pruning threshold of r²<0.2, and exclusion of pQTLs in very high LD (r²>0.9) or minimal LD (r²<0.05) with the lowest P-value pQTL for the HEIDI test. The analyses and results from the brain tissues were excluded due to lack of power. The significance threshold was corrected for multiple testing and set at P-value <0.0031 (Bonferroni corrected for 4 outcomes in 4 cohorts). As the effects of pQTLs are very small, only the significance, not the effect size of the association was used to infer causality in this study.

We also repeated the analyses using the traditional MR methods to compare the SMR results. Inverse variance weighted (IVW) was used as the main MR method with sensitivity analyses: weighted median MR, MR Egger to assess pleiotropy and Cochrane Q test to assess heterogeneity. Instruments for the exposure, PCSK9 protein levels, used were cis-pQTLs from three different cohorts: ARIC, AGES, and PCSK9-Meta GWAS with P_pQTL≤5x10^-8. These instruments were clumped at an LD threshold of r²<0.2 using the European superpopulation 1000Genomes Reference panel. The exposure summary statistics were then harmonised against the outcomes, mood instability, neuroticism score, MDD, and LDL-C levels, and then used to conduct the MR analyses. TwoSampleMR R package [28] was used to conduct the analyses.

No ethical approval was required as all data used in the study are publicly available and anonymised.

Supporting information

S1 Table. Cohort descriptions.

https://doi.org/10.1371/journal.pone.0310396.s001

(XLSX)

S2 Table. Power calculations.

https://doi.org/10.1371/journal.pone.0310396.s002

(XLSX)

S3 Table. SMR results for eQTLs with relaxed p-value threshold of p< = 1x10e-5.

https://doi.org/10.1371/journal.pone.0310396.s003

(XLSX)

S4 Table. SMR results for eQTLs with relaxed p-value threshold of p< = 5x10e-8.

https://doi.org/10.1371/journal.pone.0310396.s004

(XLSX)

Acknowledgments

We thank all participants and staff of the studies used in these analyses.

References

1. Rifai MA, Ballantyne CM. PCSK9-targeted therapies: present and future approaches. Nature Reviews Cardiology. 2021;18(12):805–6. pmid:34663945
- View Article
- PubMed/NCBI
- Google Scholar
2. Habeichi W, Katira R. Evolocumab and Alirocumab: A Review of the Emerging Role of proprotein convertase subtilisin/Kexin Type 9 (PCSK9) Inhibitors in the Management of Hyperlipidaemia. EC Cardiology. 2018;5:621–9.
- View Article
- Google Scholar
3. Khalil H, Huang C. Adverse drug reactions in primary care: a scoping review. BMC health services research. 2020;20(1):1–13. pmid:31902367
- View Article
- PubMed/NCBI
- Google Scholar
4. Galicia-Garcia U, Jebari S, Larrea-Sebal A, Uribe KB, Siddiqi H, Ostolaza H, et al. Statin treatment-induced development of type 2 diabetes: from clinical evidence to mechanistic insights. International Journal of Molecular Sciences. 2020;21(13):4725. pmid:32630698
- View Article
- PubMed/NCBI
- Google Scholar
5. Burgess S, Thompson DJ, Rees JM, Day FR, Perry JR, Ong KK. Dissecting causal pathways using Mendelian randomization with summarized genetic data: application to age at menarche and risk of breast cancer. Genetics. 2017;207(2):481–7. pmid:28835472
- View Article
- PubMed/NCBI
- Google Scholar
6. Davey Smith G, Hemani G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Human molecular genetics. 2014;23(R1):R89–R98. pmid:25064373
- View Article
- PubMed/NCBI
- Google Scholar
7. Gallagher MD, Chen-Plotkin AS. The post-GWAS era: from association to function. The American Journal of Human Genetics. 2018;102(5):717–30. pmid:29727686
- View Article
- PubMed/NCBI
- Google Scholar
8. Hay R, Cullen B, Graham N, Lyall D, Aman A, Pell J, et al. Genetic analysis of the PCSK9 locus in psychological, psychiatric, metabolic and cardiovascular traits in UK Biobank. 2021.
- View Article
- Google Scholar
9. Collaborators GMD. Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. The Lancet Psychiatry. 2022;9(2):137–50. pmid:35026139
- View Article
- PubMed/NCBI
- Google Scholar
10. O’Connell EM, Lohoff FW. Proprotein convertase subtilisin/kexin type 9 (PCSK9) in the brain and relevance for neuropsychiatric disorders. Frontiers in neuroscience. 2020;14:609. pmid:32595449
- View Article
- PubMed/NCBI
- Google Scholar
11. Nicholls SJ. PCSK9 inhibitors and reduction in cardiovascular events: Current evidence and future perspectives. Kardiologia Polska (Polish Heart Journal). 2023. pmid:36739653
- View Article
- PubMed/NCBI
- Google Scholar
12. Aman A, Slob EA, Ward J, Cullen B, Graham N, Lyall DM, et al. Investigating the potential impact of PCSK9-inhibitors on mood disorders using eQTL-based Mendelian randomization. Plos one. 2022;17(12):e0279381. pmid:36580462
- View Article
- PubMed/NCBI
- Google Scholar
13. Pott J, Gådin JR, Theusch E, Kleber ME, Delgado GE, Kirsten H, et al. Meta-GWAS of PCSK9 levels detects two novel loci at APOB and TM6SF2. Human Molecular Genetics. 2022;31(6):999–1011. pmid:34590679
- View Article
- PubMed/NCBI
- Google Scholar
14. Gudjonsson A, Gudmundsdottir V, Axelsson GT, Gudmundsson EF, Jonsson BG, Launer LJ, et al. A genome-wide association study of serum proteins reveals shared loci with common diseases. Nature communications. 2022;13(1):480. pmid:35078996
- View Article
- PubMed/NCBI
- Google Scholar
15. Zhang J, Dutta D, Köttgen A, Tin A, Schlosser P, Grams ME, et al. Plasma proteome analyses in individuals of European and African ancestry identify cis-pQTLs and models for proteome-wide association studies. Nature Genetics. 2022;54(5):593–602. pmid:35501419
- View Article
- PubMed/NCBI
- Google Scholar
16. Yang C, Farias FH, Ibanez L, Suhy A, Sadler B, Fernandez MV, et al. Genomic atlas of the proteome from brain, CSF and plasma prioritizes proteins implicated in neurological disorders. Nature neuroscience. 2021;24(9):1302–12. pmid:34239129
- View Article
- PubMed/NCBI
- Google Scholar
17. Ward J, Tunbridge EM, Sandor C, Lyall LM, Ferguson A, Strawbridge RJ, et al. The genomic basis of mood instability: identification of 46 loci in 363,705 UK Biobank participants, genetic correlation with psychiatric disorders, and association with gene expression and function. Molecular psychiatry. 2020;25(11):3091–9. pmid:31168069
- View Article
- PubMed/NCBI
- Google Scholar
18. Widiger TA, Oltmanns JR. Neuroticism is a fundamental domain of personality with enormous public health implications. World psychiatry. 2017;16(2):144. pmid:28498583
- View Article
- PubMed/NCBI
- Google Scholar
19. Cui L, Li S, Wang S, Wu X, Liu Y, Yu W, et al. Major depressive disorder: hypothesis, mechanism, prevention and treatment. Signal Transduction and Targeted Therapy. 2024;9(1):30. pmid:38331979
- View Article
- PubMed/NCBI
- Google Scholar
20. Howard DM, Adams MJ, Shirali M, Clarke T-K, Marioni RE, Davies G, et al. Genome-wide association study of depression phenotypes in UK Biobank identifies variants in excitatory synaptic pathways. Nature communications. 2018;9(1):1–10.
- View Article
- Google Scholar
21. Elsworth B, Lyon M, Alexander T, Liu Y, Matthews P, Hallett J, et al. The MRC IEU OpenGWAS data infrastructure. BioRxiv. 2020.
- View Article
- Google Scholar
22. Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS medicine. 2015;12(3):e1001779. pmid:25826379
- View Article
- PubMed/NCBI
- Google Scholar
23. Sabatine MS. PCSK9 inhibitors: clinical evidence and implementation. Nature reviews cardiology. 2019;16(3):155–65. pmid:30420622
- View Article
- PubMed/NCBI
- Google Scholar
24. Brion M-JA, Shakhbazov K, Visscher PM. Calculating statistical power in Mendelian randomization studies. International journal of epidemiology. 2013;42(5):1497–501. pmid:24159078
- View Article
- PubMed/NCBI
- Google Scholar
25. Alghamdi J, Matou-Nasri S, Alghamdi F, Alghamdi S, Alfadhel M, Padmanabhan S. Risk of neuropsychiatric adverse effects of lipid-lowering drugs: a mendelian randomization study. International Journal of Neuropsychopharmacology. 2018;21(12):1067–75. pmid:29986042
- View Article
- PubMed/NCBI
- Google Scholar
26. Smith DJ, Escott-Price V, Davies G, Bailey ME, Colodro-Conde L, Ward J, et al. Genome-wide analysis of over 106 000 individuals identifies 9 neuroticism-associated loci. Molecular psychiatry. 2016;21(6):749–57.
- View Article
- Google Scholar
27. Zhu Z, Zhang F, Hu H, Bakshi A, Robinson MR, Powell JE, et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nature genetics. 2016;48(5):481–7. pmid:27019110
- View Article
- PubMed/NCBI
- Google Scholar
28. Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, et al. The MR-Base platform supports systematic causal inference across the human phenome. elife. 2018;7:e34408. pmid:29846171
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. Rifai MA, Ballantyne CM. PCSK9-targeted therapies: present and future approaches. Nature Reviews Cardiology. 2021;18(12):805–6. pmid:34663945
View Article
PubMed/NCBI
Google Scholar

[2] View Article

[3] PubMed/NCBI

[4] Google Scholar

[ref2] 2. Habeichi W, Katira R. Evolocumab and Alirocumab: A Review of the Emerging Role of proprotein convertase subtilisin/Kexin Type 9 (PCSK9) Inhibitors in the Management of Hyperlipidaemia. EC Cardiology. 2018;5:621–9.
View Article
Google Scholar

[6] View Article

[7] Google Scholar

[ref3] 3. Khalil H, Huang C. Adverse drug reactions in primary care: a scoping review. BMC health services research. 2020;20(1):1–13. pmid:31902367
View Article
PubMed/NCBI
Google Scholar

[9] View Article

[10] PubMed/NCBI

[11] Google Scholar

[ref4] 4. Galicia-Garcia U, Jebari S, Larrea-Sebal A, Uribe KB, Siddiqi H, Ostolaza H, et al. Statin treatment-induced development of type 2 diabetes: from clinical evidence to mechanistic insights. International Journal of Molecular Sciences. 2020;21(13):4725. pmid:32630698
View Article
PubMed/NCBI
Google Scholar

[13] View Article

[14] PubMed/NCBI

[15] Google Scholar

[ref5] 5. Burgess S, Thompson DJ, Rees JM, Day FR, Perry JR, Ong KK. Dissecting causal pathways using Mendelian randomization with summarized genetic data: application to age at menarche and risk of breast cancer. Genetics. 2017;207(2):481–7. pmid:28835472
View Article
PubMed/NCBI
Google Scholar

[17] View Article

[18] PubMed/NCBI

[19] Google Scholar

[ref6] 6. Davey Smith G, Hemani G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Human molecular genetics. 2014;23(R1):R89–R98. pmid:25064373
View Article
PubMed/NCBI
Google Scholar

[21] View Article

[22] PubMed/NCBI

[23] Google Scholar

[ref7] 7. Gallagher MD, Chen-Plotkin AS. The post-GWAS era: from association to function. The American Journal of Human Genetics. 2018;102(5):717–30. pmid:29727686
View Article
PubMed/NCBI
Google Scholar

[25] View Article

[26] PubMed/NCBI

[27] Google Scholar

[ref8] 8. Hay R, Cullen B, Graham N, Lyall D, Aman A, Pell J, et al. Genetic analysis of the PCSK9 locus in psychological, psychiatric, metabolic and cardiovascular traits in UK Biobank. 2021.
View Article
Google Scholar

[29] View Article

[30] Google Scholar

[ref9] 9. Collaborators GMD. Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. The Lancet Psychiatry. 2022;9(2):137–50. pmid:35026139
View Article
PubMed/NCBI
Google Scholar

[32] View Article

[33] PubMed/NCBI

[34] Google Scholar

[ref10] 10. O’Connell EM, Lohoff FW. Proprotein convertase subtilisin/kexin type 9 (PCSK9) in the brain and relevance for neuropsychiatric disorders. Frontiers in neuroscience. 2020;14:609. pmid:32595449
View Article
PubMed/NCBI
Google Scholar

[36] View Article

[37] PubMed/NCBI

[38] Google Scholar

[ref11] 11. Nicholls SJ. PCSK9 inhibitors and reduction in cardiovascular events: Current evidence and future perspectives. Kardiologia Polska (Polish Heart Journal). 2023. pmid:36739653
View Article
PubMed/NCBI
Google Scholar

[40] View Article

[41] PubMed/NCBI

[42] Google Scholar

[ref12] 12. Aman A, Slob EA, Ward J, Cullen B, Graham N, Lyall DM, et al. Investigating the potential impact of PCSK9-inhibitors on mood disorders using eQTL-based Mendelian randomization. Plos one. 2022;17(12):e0279381. pmid:36580462
View Article
PubMed/NCBI
Google Scholar

[44] View Article

[45] PubMed/NCBI

[46] Google Scholar

[ref13] 13. Pott J, Gådin JR, Theusch E, Kleber ME, Delgado GE, Kirsten H, et al. Meta-GWAS of PCSK9 levels detects two novel loci at APOB and TM6SF2. Human Molecular Genetics. 2022;31(6):999–1011. pmid:34590679
View Article
PubMed/NCBI
Google Scholar

[48] View Article

[49] PubMed/NCBI

[50] Google Scholar

[ref14] 14. Gudjonsson A, Gudmundsdottir V, Axelsson GT, Gudmundsson EF, Jonsson BG, Launer LJ, et al. A genome-wide association study of serum proteins reveals shared loci with common diseases. Nature communications. 2022;13(1):480. pmid:35078996
View Article
PubMed/NCBI
Google Scholar

[52] View Article

[53] PubMed/NCBI

[54] Google Scholar

[ref15] 15. Zhang J, Dutta D, Köttgen A, Tin A, Schlosser P, Grams ME, et al. Plasma proteome analyses in individuals of European and African ancestry identify cis-pQTLs and models for proteome-wide association studies. Nature Genetics. 2022;54(5):593–602. pmid:35501419
View Article
PubMed/NCBI
Google Scholar

[56] View Article

[57] PubMed/NCBI

[58] Google Scholar

[ref16] 16. Yang C, Farias FH, Ibanez L, Suhy A, Sadler B, Fernandez MV, et al. Genomic atlas of the proteome from brain, CSF and plasma prioritizes proteins implicated in neurological disorders. Nature neuroscience. 2021;24(9):1302–12. pmid:34239129
View Article
PubMed/NCBI
Google Scholar

[60] View Article

[61] PubMed/NCBI

[62] Google Scholar

[ref17] 17. Ward J, Tunbridge EM, Sandor C, Lyall LM, Ferguson A, Strawbridge RJ, et al. The genomic basis of mood instability: identification of 46 loci in 363,705 UK Biobank participants, genetic correlation with psychiatric disorders, and association with gene expression and function. Molecular psychiatry. 2020;25(11):3091–9. pmid:31168069
View Article
PubMed/NCBI
Google Scholar

[64] View Article

[65] PubMed/NCBI

[66] Google Scholar

[ref18] 18. Widiger TA, Oltmanns JR. Neuroticism is a fundamental domain of personality with enormous public health implications. World psychiatry. 2017;16(2):144. pmid:28498583
View Article
PubMed/NCBI
Google Scholar

[68] View Article

[69] PubMed/NCBI

[70] Google Scholar

[ref19] 19. Cui L, Li S, Wang S, Wu X, Liu Y, Yu W, et al. Major depressive disorder: hypothesis, mechanism, prevention and treatment. Signal Transduction and Targeted Therapy. 2024;9(1):30. pmid:38331979
View Article
PubMed/NCBI
Google Scholar

[72] View Article

[73] PubMed/NCBI

[74] Google Scholar

[ref20] 20. Howard DM, Adams MJ, Shirali M, Clarke T-K, Marioni RE, Davies G, et al. Genome-wide association study of depression phenotypes in UK Biobank identifies variants in excitatory synaptic pathways. Nature communications. 2018;9(1):1–10.
View Article
Google Scholar

[76] View Article

[77] Google Scholar

[ref21] 21. Elsworth B, Lyon M, Alexander T, Liu Y, Matthews P, Hallett J, et al. The MRC IEU OpenGWAS data infrastructure. BioRxiv. 2020.
View Article
Google Scholar

[79] View Article

[80] Google Scholar

[ref22] 22. Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS medicine. 2015;12(3):e1001779. pmid:25826379
View Article
PubMed/NCBI
Google Scholar

[82] View Article

[83] PubMed/NCBI

[84] Google Scholar

[ref23] 23. Sabatine MS. PCSK9 inhibitors: clinical evidence and implementation. Nature reviews cardiology. 2019;16(3):155–65. pmid:30420622
View Article
PubMed/NCBI
Google Scholar

[86] View Article

[87] PubMed/NCBI

[88] Google Scholar

[ref24] 24. Brion M-JA, Shakhbazov K, Visscher PM. Calculating statistical power in Mendelian randomization studies. International journal of epidemiology. 2013;42(5):1497–501. pmid:24159078
View Article
PubMed/NCBI
Google Scholar

[90] View Article

[91] PubMed/NCBI

[92] Google Scholar

[ref25] 25. Alghamdi J, Matou-Nasri S, Alghamdi F, Alghamdi S, Alfadhel M, Padmanabhan S. Risk of neuropsychiatric adverse effects of lipid-lowering drugs: a mendelian randomization study. International Journal of Neuropsychopharmacology. 2018;21(12):1067–75. pmid:29986042
View Article
PubMed/NCBI
Google Scholar

[94] View Article

[95] PubMed/NCBI

[96] Google Scholar

[ref26] 26. Smith DJ, Escott-Price V, Davies G, Bailey ME, Colodro-Conde L, Ward J, et al. Genome-wide analysis of over 106 000 individuals identifies 9 neuroticism-associated loci. Molecular psychiatry. 2016;21(6):749–57.
View Article
Google Scholar

[98] View Article

[99] Google Scholar

[ref27] 27. Zhu Z, Zhang F, Hu H, Bakshi A, Robinson MR, Powell JE, et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nature genetics. 2016;48(5):481–7. pmid:27019110
View Article
PubMed/NCBI
Google Scholar

[101] View Article

[102] PubMed/NCBI

[103] Google Scholar

[ref28] 28. Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, et al. The MR-Base platform supports systematic causal inference across the human phenome. elife. 2018;7:e34408. pmid:29846171
View Article
PubMed/NCBI
Google Scholar

[105] View Article

[106] PubMed/NCBI

[107] Google Scholar

Figures

Abstract

Introduction

Results

PCSK9 protein levels in plasma or serum are not associated with mood disorder traits

AGES cohort (serum).

ARIC cohort (plasma).

PCSK9 meta-analyses cohort (serum + plasma).

PCSK9 levels in the brain and CSF are not associated with mood disorder traits

Concordance of results using traditional MR methods

Discussion

Methods

Genetic data for exposure

Genetic data for outcomes

Power calculations

Statistical analyses

Supporting information

S1 Table. Cohort descriptions.

S2 Table. Power calculations.

S3 Table. SMR results for eQTLs with relaxed p-value threshold of p< = 1x10e-5.

S4 Table. SMR results for eQTLs with relaxed p-value threshold of p< = 5x10e-8.

Acknowledgments

References