Abstract Among candidate neurodegenerative/neuropsychiatric risk-predictive biomarkers, platelet count, mean platelet volume and platelet distribution width have been associated with the risk of major depressive disorder (MDD), Alzheimer’s disease (AD) and Parkinson’s disease (PD) through epidemiological and genomic studies, suggesting partial co-heritability. We exploited these relationships for a multi-trait association analysis, using publicly available summary statistics of genome-wide association studies (GWASs) of all traits reported above. Gene-based enrichment tests were carried out, as well as a network analysis of significantly enriched genes. We analyzed 4,540,326 single nucleotide polymorphisms shared among the analyzed GWASs, observing 149 genome-wide significant multi-trait LD-independent associations (p < 5 × 10^−8) for AD, 70 for PD and 139 for MDD. Among these, 27 novel associations were detected for AD, 34 for PD and 40 for MDD. Out of 18,781 genes with annotated variants within ±10 kb, 62 genes were enriched for associations with AD, 70 with PD and 125 with MDD (p < 2.7 × 10^−6). Of these, seven genes were novel susceptibility loci for AD (EPPK1, TTLL1, PACSIN2, TPM4, PIF1, ZNF689, AZGP1P1), two for PD (SLC26A1, EFNA3) and two for MDD (HSPH1, TRMT61A). The resulting network showed a significant excess of interactions (enrichment p = 1.0 × 10^−16). The novel genes that were identified are involved in the organization of cytoskeletal architecture (EPPK1, TTLL1, PACSIN2, TPM4), telomere shortening (PIF1), the regulation of cellular aging (ZNF689, AZGP1P1) and neurodevelopment (EFNA3), thus, providing novel insights into the shared underlying biology of brain disorders and platelet parameters. Keywords: Alzheimer’s disease, Parkinson’s disease, major depressive disorder, genomics, multi-trait associations, platelets 1. Introduction Platelets have represented, for decades, an interesting setting to investigate the biological underpinnings of neuropsychiatric and neurodegenerative disorders since they are considered “circulating mirrors of neurons” [[36]1]. Indeed, despite their different embryonic origin, platelets and neurons share common characteristics in subcellular organization and in protein composition. There are proteins typically expressed in both neurons and circulating platelets, and they were found to regulate processes such as platelet activation, hemostasis and thrombosis [[37]2]. For example, Reelin- neuronal protein that regulates cell migration, synaptic plasticity and memory formation- is also expressed in blood and is actively released following platelet activation [[38]3,[39]4]. Amyloid Aβ peptides, which accumulate in senile plaques in dementia, and the amyloid precursor protein (APP), are expressed in megakaryocytes, stored in platelet α-granules and released upon platelet activation [[40]1]. Released APP is able to participate in hemostasis and can trigger platelet activation, adhesion and aggregation through a number of different pathways [[41]5]. Soluble APP inhibits the activity of the blood coagulation factors IXa, XIa and Xa, and, to a lesser extent, of factor VIIa–tissue factor complex [[42]6]. It plays a role in the coagulation cascade, modulating hemostasis following vascular injury. Brain derived neurotrophic factor (BDNF)-a secretory protein regulating the development and function of neural circuits-is expressed in both central and peripheral nervous systems but also in human megakaryocyte α-granules together with platelet factor 4 (PF4), where they are stored and released by platelets at the site of injury during platelet aggregation [[43]5,[44]7]. Similarly, serotonin—a neurotransmitter with important roles in controlling behavior and sociality—is stored in platelet-dense granules, where it is released upon activation to act as a weak agonist [[45]3,[46]4]. Epidemiological and genetic evidence also supports the existence of a bridge between platelet traits and neurodegenerative/neuropsychiatric disorders, such as Alzheimer’s disease (AD), Parkinson’s disease (PD) and major depressive disorder (MDD), which often suffer from a lack of risk-predictive biomarkers [[47]5]. Among candidate circulating biomarkers, platelet parameters such as platelet count (Plt), mean platelet volume (MPV) and platelet distribution width (PDW) have been associated with the risk of MDD, AD and PD [[48]7]. Some epidemiological studies consistently reported a positive association between MPV and MDD [[49]5,[50]7,[51]8,[52]9,[53]10], as well as between depressive symptoms and PDW, while the evidence of an association between Plt and MDD status is less consistent [[54]7]. Similarly, increased MPV was also reported for PD-although other studies observed an inverse correlation with the staging of the disease [[55]7,[56]11]-and with AD and cognitive performance, although not always consistently across studies and types of dementia [[57]7]. PDW has been instead more consistently (inversely) associated not only with AD risk [[58]5] but also with mild cognitive impairment and vascular dementia [[59]12]. More recently, genomic studies investigated the genetic correlation-or single nucleotide polymorphism (SNP)-based co-heritability-of platelet traits and brain disorders, as well as potential causality links through Mendelian randomization (MR) approaches. In a large genome-wide association study (GWAS) of blood cell measures, Astle et al. performed a multivariable MR analysis on platelet parameters and MDD risk, which revealed no significant causal effect of the formers on the latter, although MPV and PDW showed marginally significant effects [[60]5]. Later on, Wray et al. investigated genetic correlations between MDD risk and Plt and MPV, reporting no significant genetic correlations between depression and platelet parameters [[61]13], which we instead observed for PDW and MDD risk [[62]14]. Our group later detected a significant genomic overlap between PDW and PD risk through linkage disequilibrium (LD) score regression, which was confirmed also by a polygenic score analysis, as well as a trend of significance for genetic correlations between PDW and AD risk [[63]5]. Despite these promising genetic findings, the variants and genes at the basis of the genomic overlap between platelet parameters and brain disorders remain largely under-investigated. A first attempt was made to carry out a multi-trait genetic association analysis between PD age at onset and MPV, which revealed novel associations in interesting candidate genes such as KALRN (Kalirin RhoGEF Kinase), encoding a PINK1 interactor previously implicated in schizophrenia, AD and PD [[64]5]. Here, we aimed to identify novel genetic associations with AD, PD and MDD using a multi-trait association analysis (MTAG) approach [[65]15], exploiting their reciprocal genetic correlations and evidence of genomic overlap previously identified among themselves and with three common platelet parameters, namely, Plt, MPV and PDW. Such an approach provided higher power to detect novel susceptibility variants for neuropsychiatric and neurodegenerative disorders, as well as overlapping genes and pathways enriched for these associations. This allowed us to untangle the resulting molecular networks at a more fine-grained resolution, revealing potential molecular targets for future treatments of these disorders. 2. Materials and Methods We used MTAG [[66]15] to perform a multi-trait association analysis of three neuropsychiatric/neurodegenerative disorders-AD, PD and MDD-and three common platelet parameters that have been associated with their risk, namely, Plt, MPV and PDW [[67]7]. MTAG is a generalization of standard, inverse-variance-weighted meta-analysis between two or more genetically correlated traits, which generates trait-specific associations for each genetic variant shared among the source GWAS studies, using linkage disequilibrium (LD) score regression to account for potential sample overlap [[68]15]. The summary statistics of AD (71,880 cases, 383,378 controls and 13,309,438 SNPs) [[69]16], PD (37,688 cases, 18,618 UK Biobank proxy-cases, 1,417,791 controls and 10,081,487 SNPs) [[70]17], MDD (246,363 cases, 561,190 controls and 7,880,531 SNPs) [[71]18], Plt (38,561,936 SNPs), MPV (41,254,093 SNPs) and PDW (41,253,708 SNPs; all with N = 408,112 participants of European ancestry) [[72]19] were obtained from published GWAS summary statistics data (see URLs). To carry out MTAG analysis, we first pre-processed and quality-controlled summary statistics from each study involved: SNP (rs) ids were retrieved, and Z-score was computed by log (OR)/SE when these were not available; MAF threshold was set at 0.01; indels were removed and SNPs mapping to the same position of other variants and/or showing conflicting alleles among different studies were dropped. After variant filtering, the number of variants left for analysis was 8,632,257 for AD; 6,582,074 for PD; 7,183,400 for MDD; 8,933,201 for Plt; 8,933,763 for MPV; and 8,934,170 for PDW. Of these, 4,540,326 variants were in common among the different studies and, therefore, underwent MTAG analysis. Using Functional Mapping and Annotation (FUMA) platform [[73]20], we first identified LD-independent genome-wide significant SNPs within each study (p < 5.0 × 10^−8; pairwise r^2 < 0.6 in a 1 Mb window). Then, we selected novel genome-wide significant SNP associations with the analyzed disorders, defined as associations not detected as genome-wide significant in the original study, nor in previous GWASs of the same disorder, based on the GWAS catalog and literature search until 30 April 2022 ([74]Table 1). Table 1. Novel genes associated with neuropsychiatric/neurodegenerative disorders based on single variant associations. CHR Pos REF ALT rsID p nSNPs Gene Disorder 8 144,992,361 C T rs7822511 8.82 × 10^−11 139 EPPK1 AD 22 43,414,330 A G rs3091364 3.82 × 10^−10 16 TTLL1 AD 22 43,279,611 A G rs4822218 3.91 × 10^−10 9 PACSIN2 AD 19 16,211,630 A G rs59508494 1.49 × 10^−9 3 TPM4 AD 15 65,170,949 C T rs2013555 6.37 × 10^−9 72 PIF1 AD 16 30,902,353 A G rs80095680 3.62 × 10^−8 89 ZNF689 AD 7 99,581,469 C T rs11761882 4.64 × 10^−8 34 AZGP1P1 AD 4 975,238 C T rs73211813 2.99 × 10^−10 45 SLC26A1 PD 1 155,053,719 C T rs1462855 4.17 × 10^−8 36 EFNA3 PD 14 104,000,183 C T rs2756127 8.20 × 10^−11 108 TRMT61A MDD 13 31,733,057 A G rs41292151 5.34 × 10^−9 23 HSPH1 MDD [75]Open in a new tab To have further biological insights into the underlying biology of common variance in different neuropsychiatric/neurodegenerative disorders and platelet parameters, associations underwent gene, gene ontology and pathway enrichment analysis through MAGMA v1.08 [[76]21], within the FUMA platform [[77]20]. This was carried out for all the protein-coding genes to which at least one SNP was annotated within a ±10 kb interval, namely, 18,781 genes. A Bonferroni correction for multiple testing was applied accordingly, based on the number of genes tested (α = 2.7 × 10^−6) ([78]Table 2). Table 2. Novel genes associated with (a) AD, (b) PD and (c) MDD based on gene-based enrichment analysis. (a) SYMBOL CHR START STOP NSNPS ZSTAT p PVRIG 7 99,805,864 99,829,113 43 5.5752 1.24 × 10^−8 FAM57B 16 30,025,748 30074299 42 5.1135 1.58 × 10^−7 NDUFS2 1 1.61 × 10^8 1.61 × 10^8 43 5.0689 2.00 × 10^−7 C16orf92 16 30,024,655 30,049,057 27 4.8555 6.01 × 10^−7 KLC3 19 45,826,692 45,864,778 46 4.6901 1.37 × 10^−6 B4GALT3 1 1.61 × 10^8 1.61 × 10^8 18 4.6514 1.65 × 10^−6 ZNF688 16 30,570,667 30,594,055 5 4.6452 1.70 × 10^−6 DEDD 1 1.61 × 10^8 1.61 × 10^8 22 4.6293 1.83 × 10^−6 (b) SYMBOL CHR START STOP NSNPS ZSTAT p DPM3 1 1.55 × 10^8 1.55 × 10^8 17 7.3804 7.89 × 10^−14 SLC26A1 4 962,861 997,228 60 6.3491 1.08 × 10^−10 FBXL19 16 30,924,376 30,970,104 26 6.2787 1.71 × 10^−10 SMIM15 5 60,443,536 60,468,301 38 6.1459 3.98 × 10^−10 ERCC8 5 60,159,658 60,250,900 133 5.727 5.11 × 10^−9 FAM200B 4 15,673,285 15,717,188 66 5.2295 8.50 × 10^−8 CTF1 16 30,897,928 30,924,881 13 5.2123 9.32 × 10^−8 ADAM15 1 1.55 × 10^8 1.55 × 10^8 24 5.1236 1.50 × 10^−7 PRSS8 16 31,132,756 31,157,083 17 5.0704 1.99 × 10^−7 NCOR1 17 15,922,471 16,131,499 206 5.0238 2.53 × 10^−7 VKORC1 16 31,092,163 31,117,301 10 4.9064 4.64 × 10^−7 ZNF668 16 31,062,164 31,095,641 19 4.8085 7.61 × 10^−7 PRSS36 16 31,140,246 31,171,415 19 4.7714 9.15 × 10^−7 ZNF668 16 31,062,813 31,083,451 14 4.7305 1.12 × 10^−6 SRCAP 16 30,699,530 30,765,602 18 4.6567 1.61 × 10^−6 (c) SYMBOL CHR START STOP NSNPS ZSTAT p ZNF165 6 28,038,753 28,067,341 45 7.314 1.30 × 10^−13 BTN2A2 6 26,373,324 26,405,102 88 6.2064 2.71 × 10^−10 OR2W1 6 29,001,990 29023,017 5 5.9545 1.30 × 10^−9 OR12D3 6 29,331,200 29,353,068 6 5.9392 1.43 × 10^−9 TRMT61A 14 1.04 × 10^8 1.04 × 10^8 44 5.9312 1.50 × 10^−9 OR2J1 6 29,058,386 29,079,658 5 5.6346 8.77 × 10^−9 HMGN4 6 26,528,633 26,556,482 30 5.5889 1.14 × 10^−8 OR2B3 6 29,043,985 29,065,090 4 5.2266 8.63 × 10^−8 BTN3A3 6 26,430,700 26,463,643 54 4.9893 3.03 × 10^−7 ZNF197 3 44,616,380 44,699,963 70 4.8584 5.92 × 10^−7 ZNF35 3 44,680,219 44,712,283 18 4.8361 6.62 × 10^−7 TRIM27 6 28,860,779 28,901,766 11 4.7826 8.65 × 10^−7 OR2B6 6 27,915,019 27,935,960 14 4.7288 1.13 × 10^−6 DLST 14 75,338,594 75,380,448 62 4.6985 1.31 × 10^−6 RHOBTB1 10 62,619,196 62,771,198 238 4.6824 1.42 × 10^−6 ZNF660 3 44,609,715 44,651,186 46 4.6731 1.48 × 10^−6 RBM4B 11 66,422,469 66,455,392 19 4.6717 1.49 × 10^−6 ITGB6 2 1.61 × 10^8 1.61 × 10^8 312 4.6636 1.55 × 10^−6 RBM14-RBM4 11 66,374,097 66,423,940 32 4.5834 2.29 × 10^−6 [79]Open in a new tab Legend: CHR—chromosome; NSNPS—number of SNPs in the gene; START/STOP—start/stop position (bp) of the variant in the genome (GRCh37/hg19 coordinates); ZSTAT—enrichment Z-score statistics; and P—p-value. To estimate protein–protein interactions (PPIs) among the genes enriched for associations, we used the search tool for the retrieval of interacting genes/proteins in db-STRING v11.5 [[80]22]. We analyzed genes significantly enriched for associations with AD, PD and MDD, first separately and then merged into a single list, to compute “global” interactions among all the genes significantly enriched for any of the three disorders. The obtained network included both direct (physical) and indirect (functional) associations, specifically evidence of interaction from curated databases; evidence experimentally determined; gene neighborhood; gene fusions; gene co-occurrence; joint gene mentioning based on text mining in published articles; gene co-expression; and protein homology. We set a minimum interaction score of >0.7 so that only high-confidence interactions between proteins were included in the analysis. The observed excess of interactions compared to the expected number of edges among nodes, average node degree (i.e., the average number of edges per node in the graph) and local clustering coefficient (i.e., a measure of the extent to which nodes in the graph tend to cluster together) were taken as measures of network density and clustering levels. 3. Results In the multi-trait association analysis of three different brain disorders (AD, PD, MDD) and three different platelet parameters (Plt, MPV and PDW), we analyzed 4,540,326 variants shared across the different source studies that passed QC (see Methods section). Given the main aim of the manuscript, we report in detail below the results of the associations with AD, PD and MDD, along with the overlap with platelet parameters in terms of single variant and the gene enrichment of associations. The full results of the MTAG analysis of platelet traits are reported in [81]Supplementary Materials ([82]Figures S1 and S2) and online (see Data Availability Statement). MTAG revealed 358 genome-wide significant multi-trait associations (p < 5 × 10^−8; pairwise r^2 < 0.6; [83]Figure 1): 149 for AD (top hit at rs1081105, within the APOE gene, p = 2.36 × 10^−180), 70 for PD (top hit at rs356219, within the SNCA gene, p = 6.01 × 10^−41) and 139 for MDD (top hit at rs2232429, within the ZSCAN12 gene, p = 6.41 × 10^−19). Among these, we observed 27 novel associations with AD (top hit at rs2232429, within the ITGB5 gene, p = 6.41 × 10^−19), 34 with PD (top hit at rs1372518 within the SNCA gene, p = 3.33 × 10^−28) and 40 with MDD (top hit at rs200965 in a transcription factor binding site on 6p22.1, p = 4.07 × 10^−15). Of these SNPs, seven were mapped in novel susceptibility genes for AD (EPPK1, TTLL1, PACSIN2, TPM4, PIF1, ZNF689 and AZGP1P1), two for PD (SLC26A1 and EFNA3) and two for MDD (HSPH1 and TRMT61A). The analysis of 18,781 genes tested in the gene-based enrichment analysis revealed 62, 70 and 125 genes with a significant enrichment of associations with AD, PD and MDD, surviving correction for multiple gene testing (p < 2.7 × 10^−6, [84]Figure 2). Of these, eight genes represented novel associations with AD (PVRIG, FAM57B, NDFUS2, C16orf92, KLC3, B4GALT3, ZNF688 and DEDD), 14 with PD (DPM3, SLC26A1, FBXL19, SMIM15, ERCC8, FAM200B, CTF1, ADAM15, PRSS8, NCOR1, VKORC1, ZNF668, PRSS36 and SRCAP) and 38 with MDD (ZNF165, BTN2A2, OR2W1, OR12D3, TRMT61A, OR2J1, HMGN4, OR2B3, BTN3A3, ZNF197, ZNF35, TRIM27, OR2B6, DLST, RHOBTB1, ZNF660, RBM4B, ITGB6, RBM14-RBM4, HIST1H4K, HIST1H2AK, HIST1H2BL, HIST1H3H, HIST1H2AL, HIST1H1B, HIST1H3I, HIST1H4J, HIST1H2BM, HIST1H2AI, HIST1H2AJ, HIST1H2AM, HIST1H3J, HIST1H2BN, HIST1H2AG, HIST1H2BO, HIST1H4L, HIST1H2BJ and HIST1H4I). None of these genes were overlapping across the three disorders. However, when we checked the overlap between each neurodegenerative/neuropsychiatric disorder and platelet parameters in terms of single variant associations, we found 24 SNPs associated with AD and 3 associated with PD, which were also associated with 1 or more platelet parameters. Of these, 12 AD- and 3 PD-associated SNPs represented novel associations never detected before ([85]Table 3a,b). As for overlapping gene enrichments with any of the platelet parameters analyzed, we identified 29 genes matching with AD, 14 with PD and 15 with MDD. Of these, 6 were novel susceptibility loci for AD, 4 for PD and 12 for MDD ([86]Table 4a–c). Figure 1. [87]Figure 1 [88]Figure 1 [89]Open in a new tab Manhattan plots of multi-trait associations with (a) AD, (b) PD and (c) MDD. The x-axis shows chromosomal position, and the y-axis shows association p-values on a −log10 scale. Red dashed line represents the statistical significance thresholds (α = 5 × 10^−8). Figure 2. [90]Figure 2 [91]Figure 2 [92]Open in a new tab Manhattan plots of gene-based enrichments of multi-trait associations with (a) AD, (b) PD and (c) MDD. The x-axis shows chromosomal position, and the y-axis shows association p-values on a −log10 scale. Red dashed line represents the statistical significance thresholds (α = 2.7 × 10^−6). Table 3. Single variant association overlap of (a) AD and (b) PD with platelet parameters. (a) SNPs Overlap between SNP Gene Chr:position Function Previously Associated with AD Previously Associated with Platelet Parameters AD and all platelet parameters rs6727023 EHD3 2:31475960 Upstream rs62118504 EXOC3L2 19:45734751 Intronic [[93]23] rs2143926 22:43185180 rs4822218 PACSIN2 22:43279611 Intronic rs2267487 PACSIN2 22:43411389 Upstream AD, MPV and PDW rs4575098 B4GALT3 1:161155392 Upstream [[94]23] rs585021 ITGB5 3:124482869 Intronic rs11620465 LRCH1 13:47250344 Intronic rs3091364 22:43414330 AD, MPV and Plt rs12461065 PPP1R37 19:45605308 Intronic [[95]23] rs12461144 EXOC3L2 19:45723706 Intronic [[96]23] rs123187 19:45830947 [[97]23] AD, PDW and Plt rs9357551 6:47606029 [[98]24] Plt [[99]25] AD and MPV rs10934680 KALRN 3:124440780 Downstream rs11669338 NECTIN2 19:45382984 Downstream [[100]24] rs138235833 19:45415285 rs620807 19:45706952 [[101]24] rs4803806 19:45708947 AD and PDW rs858502 CASTOR3 7:99843353 Intronic [[102]24] rs7113976 11:85869737 [[103]24] rs283810 NECTIN2 19:45388241 Downstream [[104]24] rs1160983 TOMM40 19:45397229 Synonymous variant rs584007 APOC1 19:45416478 Upstream [[105]24] rs117648021 EIF3L 22:38274632 Intronic (b) SNPs Overlap between SNP Gene Chr:position Function Previously Associated with PD Previously Associated with Platelet Parameters PD, MPV and PDW rs10847839 HIP1R 12:122838013 Intronic PD and MPV rs17689966 CRHR1 17:45833089 Intronic rs9899833 MAPT 17:45915577 Intronic [106]Open in a new tab No overlaps were found between SNPs associated with MDD and those associated with platelet parameters. Legend: AD—Alzheimer’s disease; PD—Parkinson’s disease; Plt—platelet count; MPV—mean platelet volume; and PDW—platelet distribution width. Table 4. Gene enrichment overlap of (a) AD, (b) PD and (c) MDD with platelet parameters. (a) Genes Overlap between Gene Previously Associated with AD Previously Associated with Platelet Parameters AD and all platelet parameters AC005779.2 AC006126.3 CD2AP [[107]26] MPV [[108]26] and Plt [[109]26] CKM [[110]16] EHD3 MPV [[111]27], PDW [[112]19] and Plt [[113]27] EXOC3L2 [[114]24] KLC3 [115]L47234.1 MARK4 [[116]24] MPV [[117]25], PDW [[118]27] and Plt [[119]25] PVR [[120]24] MPV [[121]27] AD, MPV and PDW ADAMTS4 [[122]24] [123]AL590714.1 APOA2 B4GALT3 DEDD [[124]24] NDUFS2 TOMM40L [[125]28] AD, MPV and Plt GEMIN7 [[126]24] AD, PDW and Plt GATS [[127]29] AZGP1 [[128]30] PILRA [[129]24] AD and MPV BLOC1S3 [[130]24] MPV [[131]25], PDW [[132]27] and Plt [[133]27] PPP1R37 [[134]24] PVRL2 [[135]31] AD and PDW APOC1 [[136]23] PDW [[137]19] APOE [[138]24] PDW [[139]19] and Plt [[140]27] GPC2 [[141]32] PVRIG SLC24A4 [[142]24] PDW [[143]19] STAG3 [[144]33] TOMM40 [[145]24] AD and Plt IGSF23 [[146]24] PICALM [[147]24] (b) Genes Overlap between Gene Previously Associated with PD Previously Associated with Platelet Parameters PD and MPV WNT3 [[148]34] [149]AC008498.1 SPPL2C [[150]34] ARHGAP27 [[151]34] CRHR1 [[152]34] MAPT [[153]34] Plt [[154]25] ELOVL7 [[155]34] MPV [[156]19] ERCC8 SMIM15 PLEKHM1 [[157]34] KANSL1 [[158]34] NSF [[159]34] NDUFAF2 [[160]34] STH [[161]35] PD and PDW SRCAP MPV [[162]19] (c) Genes Overlap between Gene Previously Associated with AD Previously Associated with Platelet Parameters MDD and MPV OR2J3 [[163]36] TRIM27 OR5V1 [[164]37] C11orf31 OR2B3 NRD1 OR12D3 HIST1H2BK OR2J1 OR2W1 HIST1H4K HIST1H2AK MDD and PDW MARK3 [[165]37] PDW [[166]38] TRMT61A MDD, PDW and Plt HIST1H3B [167]Open in a new tab Legend: AD—Alzheimer’s disease; PD—Parkinson’s disease; MDD—major depressive disorder; Plt—platelet count; MPV—mean platelet volume; and PDW—platelet distribution width. The molecular network resulting from the gene enrichment test, as produced by STRING v11.5 analysis, showed more significant interactions than expected for AD (enrichment p = 1.0 × 10^−16; 57 nodes, 57 edges vs. 2 expected, average node degree 2.00 and average local clustering coefficient 0.36), PD (enrichment p = 8.46 × 10^−9; 63 nodes, 18 edges vs. 3 expected, average node degree 0.57 and average local clustering coefficient 0.23) and MDD (enrichment p = 1.0 × 10^−16; 120 nodes, 264 edges vs. 49 expected, average node degree 4.4 and average local clustering coefficient 0.47) ([168]Figure S3a–c; [169]Table S1). Similarly, we observed evidence of an interaction also when the genes enriched for the three disorders were analyzed together (enrichment p = 1.0 × 10^−16; 240 nodes, 281 edges vs. 61 expected, average node degree 2.34 and average local clustering coefficient 0.3) ([170]Figure 3; [171]Table S1). Figure 3. [172]Figure 3 [173]Open in a new tab Protein–protein interaction network of genes significantly enriched for associations with AD, PD and MDD. The reported network, including both direct (physical) and indirect (functional) associations, was based on the STRING v11.5 database [[174]22]. Only high-confidence interactions between proteins are reported (interaction score > 0.7), while disconnected nodes in the network are hidden. Each node represents all the proteins produced by a single protein-coding gene locus, while edges represent protein–protein associations. Line color indicates the type of interaction evidence: light blue— from curated databases; purple—experimentally determined; green—gene neighborhood; red—gene fusions; blue—gene co-occurrence; yellow—text mining; black—co-expression; and violet—protein homology. Gene-set analysis also revealed significant enrichment for the three disorders, the most associated gene ontology (GO) term was negative regulation of amyloid precursor protein catabolic process (13 genes, β(SE) = 2.23(0.26); enrichment p after Bonferroni correction = 2.7 × 10^−13) for AD, IgG binding (9 genes, β(SE) = 0.03(0.31); P[bonf] = 0.0056) for PD and GABAergic synapse (64 genes, β(SE) = 0.74(0.13); P[bonf] = 9.5 × 10^−5) for MDD ([175]Table S2; see URLs to access the full list of pathways tested and the genes driving these enrichments). 4. Discussion We report the first multi-trait association analysis of structural platelet parameters routinely assessed in blood tests and three of the most common neurodegenerative/neuropsychiatric disorders, identifying novel candidate susceptibility genes for AD, PD and MDD. The most significant associations were detected in some of the most implicated genes in neurodegenerative/neuropsychiatric disorders, namely, APOE (apolipoprotein E, with AD) [[176]39], SNCA (alpha synuclein, with PD) [[177]40] and ZSCAN12 (zinc finger and SCAN domain-containing 12, with MDD) [[178]41]. APOE is a protein associated with lipid particles that mainly functions in lipoprotein-mediated lipid transport between organs via plasma and interstitial fluids [[179]42]. Alpha synuclein is involved in synaptic activities such as the regulation of synaptic vesicle trafficking and subsequent neurotransmitter release [[180]43,[181]44]; moreover, it modulates DNA repair processes, including the repair of double-strand breaks [[182]45]. ZSCAN12 encodes a Zinc finger and SCAN domain-containing protein involved in transcriptional regulation. Still, MTAG analysis also revealed novel genes showing significant multi-trait associations, which, to our knowledge, were never associated with these disorders before. Among genes associated with AD, EPPK1, TTLL1, PACSIN2 and TPM4 play a role in the organization of cytoskeletal architecture, which has been identified as an important component in the development of neurodegenerative disorders [[183]46,[184]47,[185]48,[186]49]. PIF1 prevents telomere elongation by inhibiting the action of telomerase, while ZNF689 and AZGP1P1 are transcription factors involved in cell viability and apoptosis, and both molecular functions can affect cellular aging and the development of age-related disorders such as AD and PD [[187]24]. Moreover, TTLL1 [[188]19], PACSIN2 [[189]26,[190]40], TPM4 [[191]27] and PIF1 [[192]19] were previously associated with platelet parameters, suggesting a possible pleiotropic effect of these genes. EFNA3, a novel gene resulting in an association with PD, encodes a member of the ephrin family, previously implicated in mediating developmental events, especially in the central nervous system [[193]50]. A gene-based enrichment analysis also revealed novel genes associated with AD, PD and MDD. Interestingly, among these genes are several encode transcription factors that may be involved in development, maintenance and survival of neurons and olfactory receptors [[194]51]. Indeed, olfactory dysfunction, which is thought to be due to the loss of synaptic function, has been linked with most neurodegenerative, neuropsychiatric and communication disorders [[195]52]. Moreover, among these genes, there are also some histone complex proteins, in line with some recent studies revealing associations between histone methylation/acetylation and AD [[196]53] and implicating several histone deacetylases in the pathogenesis of PD [[197]54]. These findings suggest the pleiotropic influence of several genes on the risk of neurodegenerative and neuropsychiatric disorders, which were not previously detected through classical univariate GWAS analyses. Of note, we found several overlaps between genes and SNPs multi-trait associations with the brain disorders and platelet parameters analyzed. Among genes enriched for association, we identified clusters of genes encoding products involved in mitochondrial function (e.g., NDUFS2, NDUFAF2 and TOMM40L), cytoskeleton remodeling (CD2AP and KLC3, as discussed above) and histone proteins (HIST1H2BK, HIST1H4K, HIST1H2AK and HIST1H3B, as explained below). Indeed, NDUFS2 and NDUFAF2 encode for a subunit and for a chaperone involved in the assembly of complex I, located on the inner mitochondrial membrane, while TOMM40L is involved in mitochondrial transmembrane translocation. Several studies suggest that platelet mitochondrial dysfunction may be involved in neurodegenerative diseases such as AD and PD [[198]55,[199]56,[200]57]. Still, further studies are needed to clarify the variant association overlap between platelet parameters and MDD, which we were not able to identify here, possibly due to the genetic and phenotypic heterogeneity of depression. Protein–protein interaction analysis revealed a significant excess of interactions among enriched genes for the brain disorders tested both separately and jointly, suggesting that their gene products are highly likely to be linked in a global molecular network. In particular, this highlighted some local networks of interests, such as the one among the histone proteins complex-HIST1H2BI, HIST1H2BF and HIST1H2BJ-which may play a role in the onset of neurodegenerative diseases due to the alteration of methylation patterns [[201]58]. Similarly, the apolipoproteins APOE, APOA2, APOC1 and APOC4 have been repeatedly implicated in triglyceride and cholesterol transport and metabolism [[202]59,[203]60], as well as in neurodegenerative [[204]61] and cardiovascular risk [[205]62], while the local network among EPHA1, EPHB2, EFNA1, EFNA3 and EFNA4, highlights the importance of the interactions between ephrins and ephrin receptors in the etiology of several neurodegenerative and neuroinflammatory disorders, suggesting potential links with (cellular) immunity [[206]63]. Gene-set analysis revealed significant enrichments of GO terms involved in the regulation, formation and catabolic processes of amyloid beta and in the negative regulation of metalloendopeptidase activity for AD, supporting the hypothesis that metallopeptidases are implicated in the pathogenesis of several central nervous system diseases such as multiple sclerosis and AD [[207]64]. For PD, significant enrichments of IgG binding is interesting in light of a higher fraction of IgG, a different IgG glycosylation profile [[208]65], and of increased IgG (but not IgM) binding in dopaminergic neurons of PD cases vs. controls [[209]66]. Moreover, serum IgG levels in PD patients are negatively associated with mood/cognition scores [[210]67], in line with a potential pleiotropic role of humoral immunity at the interface among the mood, cognitive and motor control domains. Similarly, the significant enrichment of GABAergic synapse GO term in MDD analysis corroborates the hypothesis that the alteration in GABAergic receptors may play a role in long-term depression [[211]68]. Overall, we provide insights into the shared underlying biology of these disorders and related platelet parameters, proposing novel molecular targets for the risk prediction and treatment of these disorders. Strengths and Limitations The strengths of this study include the novelty of the analysis performed; indeed, to our knowledge, this represents the first attempt to identify the shared genomic underpinnings of platelet parameters and three of the most common neurodegenerative/neuropsychiatric disorders through a comprehensive approach, including not only multi-trait association analysis but also gene-/gene-set enrichment and molecular network analyses. Moreover, our analyses are based on large GWASs, with an important amount of genetic data, which confers robustness to our observations. Last, our focus on novel associations allowed identifying proteins and biological pathways, which should be functionally validated in the future. This study presents some limitations. First, currently only partial evidence of genetic correlation among the disorders and platelet parameters tested exists, which may have hampered the power of the analyses. Second, in multi-trait association approaches, association significance is often driven by the largest source GWAS involved in the MTAG analysis, which may have biased the analyses towards the largest studies. Still, this represents a useful approach to identifying the pleiotropic variants and genes influencing multiple traits and/or disorders, which were already proven successful with multiple correlated phenotypes [[212]15]. Third, functional studies are warranted to explain the role of the novel susceptibility genes identified here, both in neurodegenerative/neuropsychiatric risk and platelet variability. However, although there are some limitations, these studies may reveal potential molecular targets for future treatments of three of the most common neurodegenerative/neuropsychiatric disorders. Supplementary Materials The following supporting information can be downloaded at: [213]https://www.mdpi.com/article/10.3390/cells12020245/s1, Figure S1: Manhattan plots of multi-trait associations with (a) MPV, (b) PDW and (c) Plt; Figure S2: Manhattan plots of gene-based enrichments of multi-trait associations with (a) MPV, (b) PDW and (c) Plt; Figure S3: Protein–protein interaction network of genes significantly enriched for associations with (a) AD, (b) PD and (c) MDD; Table S1: Network statistics of protein–protein interaction networks of genes significantly enriched for associations with AD, PD and MDD; Table S2: Significant gene-set enrichments of associations with (a) AD, (b) PD and (c) MDD. [214]Click here for additional data file.^ (1.2MB, zip) Author Contributions A.G., L.I. and G.d.G. conceived this study. A.T. and M.S.Q. performed analyses and data curation under the supervision of A.G., L.I., G.d.G., M.B.D. and C.C. provided critical feedback on the study. All co-authors contributed to manuscript writing and critical review. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Data Availability Statement AD summary statistics: [215]https://ctg.cncr.nl/software/summary_statistics (accessed on 10 January 2019); PD summary statistics: [216]https://drive.google.com/file/d/1FZ9UL99LAqyWnyNBxxlx6qOUlfAnublN/ view?usp=sharing (accessed on 26 March 2020); MDD Summary statistics: [217]https://datashare.ed.ac.uk/handle/10283/3203 (accessed on 30 January 2019); Plt summary statistics: [218]http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST900 02001-GCST90003000/GCST90002402/ (accessed on 30 January 2022); MPV summary statistics: [219]http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST900 02001-GCST90003000/GCST90002395/ (accessed on 30 January 2022); PDW summary statistics: [220]http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST900 02001-GCST90003000/GCST90002401/ (accessed on 30 January 2022). Conflicts of Interest The authors declare no conflict of interest. Funding Statement This work was funded by Ricerca Corrente 2022-24 (Italian Ministry of Health) and partly supported by Progetto Platone-Platform for Integrated Health Life (Italian Ministry of Economic Development; PON I&C 2014–2020 call, project code: F/080032/01/X35) and by INAIL-Bric 2019 ID 47 project (Italian National Institute for Insurance against Accidents at Work; 2019 call, project CUP code: F24I19000630008). Footnotes Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. References