Assessing shared genetic and causal links between vascular calcification, Alzheimer's disease, and cognitive traits

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Here, we investigate the shared genetic and causal relationships between vascular calcification—coronary artery calcification (CAC) and abdominal aortic calcification (AAC)—and Alzheimer’s disease (AD), as well as cognitive traits, by analysing large-scale genome-wide association studies summary statistics. We observed a nominally significant positive genome-wide genetic correlation between CAC and AD, which became non-significant after excluding the APOE region. CAC and AAC demonstrate significant negative correlations with cognitive performance and educational attainment. Mendelian randomisation revealed no causal association between CAC or AAC and AD or cognitive traits, except for a bidirectional borderline significance of AAC with fluid intelligence scores. Pairwise-GWAS analysis identified no shared causal SNPs (posterior probability of association [PPA]3 0.9), particularly on chromosome 19 with ‘mBAT-combo’ analyses revealing significant genes in shared regions, including APOE, TOMM40, NECTIN2 , and APOC1 . Moreover, we identified suggestively significant loci (PPA4 > 0.5) on chromosomes 1, 6, 7, 9 and 19, highlighting pleiotropic genes, including NAV1, IPO9, PHACTR1, UFL1, FHL5 , and FOCAD . Current findings reveal limited genome-wide genetic correlation and no significant causal associations of CAC and AAC with AD or cognitive traits. However, significant pleiotropic loci and genes underscore shared genetic susceptibility of CAC and AAC with AD and cognitive traits, identifying targets for further investigation. Biological sciences/Genetics Biological sciences/Genetics/Genetic association study Biological sciences/Genetics/Genomics Health sciences/Diseases/Neurological disorders/Dementia Health sciences/Diseases/Neurological disorders/Neurodegenerative diseases/Alzheimers disease Health sciences/Diseases/Cardiovascular diseases/Vascular diseases/Calcification Alzheimer's disease abdominal aortic calcification coronary artery calcification cognitive traits Mendelian randomisation vascular calcification Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Alzheimer's disease (AD) is a neurodegenerative disorder characterised by memory loss and progressive cognitive decline [ 1 – 3 ]. The growing burden of the disorder has become a major global public health concern, with over 10 million new dementia cases diagnosed annually—AD being the most prevalent form [ 1 – 4 ]. Although the exact causes of AD are not well understood, there is an increasing appreciation of the likely role of vascular pathology in its onset and progression [ 5 – 7 ]. Emerging evidence, for instance, highlights a link between vascular calcification and both AD and cognitive impairment [ 8 – 10 ]. Vascular calcification, including coronary artery calcification (CAC) and abdominal aortic calcification (AAC), refers to the abnormal deposition of calcium-phosphate complexes within the walls of arteries, often serving as a marker of subclinical atherosclerosis [ 11 ]. Notably, CAC is widely recognised as a critical indicator for predicting future atherosclerotic cardiovascular disease risk [ 12 , 13 ]. Atherosclerosis, the thickening and hardening of arterial walls due to cellular accumulation and calcification, contributes to the narrowing of blood vessels and is associated with both cardiovascular diseases and neurodegenerative conditions, including dementia [ 6 , 14 ]. Recent studies have identified a potential interaction between atherosclerosis and AD, potentially leading to additive effects on cognitive decline [ 7 ]. Additionally, an observational study found that older women with AAC had an increased risk of developing all-cause late-life dementia [ 9 ]. The association between vascular calcification and cognitive impairment has also been observed in multiple populations. For instance, a Chinese-based study reported a link between CAC and cognitive decline [ 10 ], aligning with findings from a Dutch cohort and other international studies [ 8 , 15 – 17 ]. These and similar findings suggest some forms of association or co-occurring relationships between CAC or AAC and AD or cognitive impairment. Such relationships may accelerate disease progression and contribute to adverse outcomes, including more complex management plans [ 18 ]. On the other hand, co-occurring relationships may play a role in the pathogenesis of these disorders through shared genetic predisposition, common biological mechanisms, or direct causal relationships. Thus, understanding the nature of the relationships and their underlying mechanisms could advance our knowledge of the aetiology of the disorders and provide opportunities for developing preventative measures or therapies. Despite growing evidence of the associations between vascular calcification and AD or cognitive impairment, conflicting findings remain. For example, a systematic review and meta-analysis found limited evidence linking CAC scores with dementia risk [ 19 ]. Similarly, a scoping review reported mixed findings on the association between vascular calcification and AD or cognitive decline [ 20 ]. Given these inconsistent findings and the inherent limitations of traditional observational studies—such as measurement error, reverse causation and residual confounding [ 21 ]—some questions remain unanswered. First, it is not completely clear whether CAC or AAC is associated with AD or cognition, or whether previous observational studies that drew these conclusions were biased or otherwise falsely positive. Second, it is unclear whether any observed relationships between these forms of vascular calcification and AD or cognition are causal or reflect shared genetic susceptibility. An unbiased genetic study utilising large-scale genome-wide association studies (GWAS) is less prone to the many limitations of traditional observational studies and thus provides an alternative study approach [ 22 – 25 ]. Such approaches offer robust avenues for assessing the genetic architecture of CAC or AAC with AD and cognitive traits, with the potential of advancing insights into their likely biological mechanisms and identifying novel therapeutic targets [ 22 – 25 ]. Hence, the current study systematically assesses the potential shared genetic and causal relationship of CAC and AAC with AD, cognitive performance, common executive function (cEF), and other cognitive-related traits. cEF is a unified cognitive factor that represents the shared variance in executive control processes, derived from tasks like response inhibition, interference control, working memory updating, and set-shifting [ 26 ]. We analysed several large-scale genetic data employing well-regarded statistical genetics methods, including linkage disequilibrium score regression (LDSC) to examine genome-wide cross-traits genetic correlations. We also used the Mendelian randomisation (MR) method [ 27 – 30 ] to investigate whether CAC or AAC is causally associated with AD or cognitive traits, and vice versa. We utilised the pairwise GWAS (GWAS-PW) colocalisation method to further investigate the relationship between CAC/AAC and AD/cognitive traits for insights into shared genetic variants and loci. Lastly, we performed gene-level analyses using a recently developed powerful gene-based association analysis method [ 31 , 32 ] to identify pleiotropic genes shared by CAC or AAC and AD or cognitive traits. The approach used in our study can contribute to advancing knowledge of the connections between vascular calcification, AD, and cognitive traits. The study approach is especially important given the inconsistent evidence from traditional observational studies. By building evidence-based knowledge, we can improve our understanding of the causes of AD and identify new potential targets for further investigation. Results Overview of study design This study aimed to explore the genetic relationships of vascular calcification (CAC and AAC) with AD and cognitive traits. We employed four analytical approaches to achieve our objectives. First, we performed cross-traits genome-wide genetic correlation between CAC/AAC and AD/cognitive traits using the LDSC method. Given the strong evidence of the Apolipoprotein E ( APOE ) region’s significant impact on AD, we performed a genetic correlation analysis excluding the region to determine if it drives the observed relationship between CAC or AAC and AD. We applied a Bonferroni-corrected significance threshold of 8.33 × 10⁻ 3 for genetic correlations involving AD and five cognitive traits, with P-values below 0.05 considered nominally significant. Second, we assessed potential causal relationships through the bidirectional MR analysis method with sensitivity analyses and rigorous assessment of horizontal pleiotropy and heterogeneity. Third, we applied the GWAS-PW method to identify shared causal variants and pleiotropic loci for a pair of CAC or AAC and AD or cognitive traits, testing 1,703 genomic regions. The GWAS-PW method scans the genome to pinpoint pleiotropic loci by estimating the posterior probability that a genetic variant is causal for a pair of traits, or a genomic locus is associated with the two traits through distinct variants [ 33 ]. Lastly, we conducted gene-based association analyses to identify pleiotropic genes associated with CAC/AAC and AD/cognitive traits, prioritising pleiotropic loci identified in the GWAS-PW analysis. We used a recently developed and powerful gene-based analysis method, harnessing the strengths of three models of gene-based association analyses: fastBAT, mBAT, and mBAT-combo [ 31 , 32 ]. All analyses used well-powered genome-wide association studies (GWAS) summary data from individuals of European ancestry, with no evidence of significant sample overlap between the pairs of traits assessed. The Methods section provides further details on the specific methods and data analysed. Supplementary Table 1 provides additional information and links to the GWAS data sources or associated publications. Results of genome-wide genetic correlation analyses Table 1 presents the results of our genome-wide genetic correlation analyses. First, we observed a nominally significant positive genetic correlation (rg = 0.10, P = 3.14 × 10 − 2 ) between CAC and AD GWAS (from Jansen et al. [ 34 ]), but when excluding the APOE region, the signal weakens and becomes non-significant (P = 5.33 × 10 − 2 , Table 1 ). We found no evidence of a significant genome-wide correlation between CAC and another AD GWAS (Lambert et al. [ 35 ]). Conversely, we observed a negative correlation between CAC and cognitive traits, including cognitive performance (rg = -0.11, P = 1.59 × 10 − 6 ), fluid intelligence scores (rg = -0.11, P = 1.87 × 10 − 5 ), intelligence (rg = -0.11, P = 5.14 × 10 − 6 ), and educational attainment (rg = -0.10, P = 1.08 × 10 − 6 ), all surpassing the Bonferroni threshold. The correlation assessment between CAC and common executive function was not statistically significant (Table 1 ). Lastly, the genetic correlation between AAC and CAC was high (rg = 0.95, P = 4.70 × 10 − 3 ), surpassing the Bonferroni-corrected significance threshold, indicating a strong shared genetic basis between the two vascular calcification traits. Table 1 genome-wide genetic correlation between AAC, CAC, AD and Cognitive traits Trait 1 Trait 2 Rg se p CAC AD GWAS (Jansen et al) 0.1 0.05 3.14 × 10 − 2 AD GWAS (Jansen et al.) excluding APOE region 0.1 0.05 5.33 × 10 − 2 AD GWAS (Lambert et al) 0.01 0.04 8.06 × 10 − 1 AD GWAS (Lambert et al.) excluding APOE region 0 0.05 9.17 × 10 − 1 Cognitive performance -0.11 0.02 1.59 × 10 − 6 Fluid intelligence scores -0.11 0.03 1.87 × 10 − 5 Intelligence (Jansen et al 2018) -0.11 0.02 5.14 × 10 − 6 common Executive function -0.04 0.02 7.55 × 10 − 2 Educational attainment -0.1 0.02 1.08 × 10 − 6 Abdominal aortic calcification 0.95 0.34 4.70 × 10 − 3 AAC AD GWAS (Jansen et al) 0.08 0.07 2.32 × 10 − 1 AD GWAS (Jansen et al.) excluding APOE region 0.07 0.07 3.49 × 10 − 1 AD GWAS (Lambert et al) 0.01 0.07 9.28 × 10 − 1 AD GWAS (Lambert et al.) excluding APOE region -0.01 0.07 9.19 × 10 − 1 Cognitive performance -0.1 0.03 5.49 × 10 − 3 Fluid intelligence scores -0.07 0.03 5.24 × 10 − 2 Intelligence (Jansen et al 2018) -0.06 0.04 1.12 × 10 − 1 common Executive function -0.04 0.04 2.53 × 10 − 1 Educational attainment -0.13 0.03 6.39 × 10 − 5 AAC: abdominal aortic calcification, AD: Alzheimer’s disease, CAC: coronary artery calcification, GWAS: genome-wide association studies, APOE : Apolipoprotein E, rg: genetic correlation estimates, se: standard error, p: p-value. Second, we found no evidence of a significant genetic correlation between AAC and AD across different GWAS datasets, including the Jansen et al. GWAS (rg = 0.08, P = 2.32 × 10 − 1 ) and the Lambert et al. GWAS (rg = 0.01, P = 9.28 × 10 − 1 ). Excluding the APOE regions did not change these outcomes (Table 1 ). However, we found significant negative genetic correlations of AAC with cognitive performance (rg = -0.10, P = 5.49 × 10 − 3 ) and educational attainment (rg = -0.13, P = 6.39 × 10 − 5 ), surpassing nominal and Bonferroni significance thresholds, respectively. The correlations with fluid intelligence (rg = -0.07, P = 5.24 × 10 − 2 ), intelligence (rg = -0.06, P = 1.12 × 10 − 1 ), and executive function (rg = -0.04, P = 2.53 × 10 − 1 ) were not statistically significant. Results of MR-based causal association assessment We conducted a two-sample Mendelian randomisation (2SMR) analysis to gain insights into the potential causal relationships between CAC or AAC and AD and cognitive traits. We employed multiple MR approaches and performed a bidirectional analysis to gain a comprehensive understanding of these associations. To enhance the reliability of our results, we carefully selected suitable instrumental variables (IVs) and addressed concerns related to horizontal pleiotropy and heterogeneity. Figure 1 presents a detailed overview of our MR study design. The main MR results from the IVW model indicated no significant causal effect of CAC on AD (OR: 1.00, 95% CI: 0.99–1.01, P: 0.56) or AAC on AD (OR: 1.00, 95% CI: 0.98–1.03, P: 0.78) [Figures 2 and 3 , respectively]. Similarly, CAC or AAC had no significant causal effect on cognitive traits (Figs. 2 and 3 ). The only exception was the causal association of AAC on fluid intelligence scores. This effect was borderline nominally significant in the IVW model and nominally significant in both the weighted median and MR-PRESSO analyses (Fig. 3 and Supplementary Table 2). However, the results did not survive the correction for multiple testing (Fig. 3 ). In the reverse analyses, our findings showed no significant causal effect of AD on CAC (OR: 1.29, 95% CI: 0.67–2.46, P: 0.44) or AAC (OR: 1.00, 95% CI: 0.86–1.16, P: 0.99) as summarised in Figs. 4 and 5 , respectively. Likewise, none of the cognitive traits had a significant causal effect on CAC or AAC, except the borderline nominally significance of fluid intelligence scores’ effect on AAC (Fig. 5 ), which did not survive correction for multiple testing. Current findings were consistent across other MR models, such as the weighted median and MR-Egger (Supplementary Tables 2–5). The MR-PRESSO method also found no significant relationship between AD and cognitive traits with CAC or AAC (except between fluid intelligence scores and AAC). Indeed, there were no outputs in the corrected analysis, suggesting the absence of outliers IVs. Supplementary Tables 2–5 provide comprehensive results for our MR analysis. MR-Egger intercept and heterogeneity tests indicate these findings were not biased by pleiotropic instruments (Table 2 , and more comprehensively in Supplementary Table 6). Detailed information on IVs utilised for analysis is presented in Supplementary Tables 7–30. Table 2 MR sensitivity analyses of AD and cognitive traits with vascular calcification Exposure Outcome Heterogeneity tests Horizontal pleiotropy tests Method Cochran's Q P value Method Intercept P value Coronary artery calcification vs AD and cognitive traits CAC Alzheimer's disease IVW 0.72 Egger intercept -0.0013 0.72 cExecutive function 0.94 -0.00042 0.59 Cognitive performance 0.5 0.0013 0.43 Educational attainment 0.81 0.00041 0.61 Fluid intelligence scores 0.65 -0.0026 0.63 Intelligence 0.39 0.00032 0.86 Abdominal aortic calcification vs AD and cognitive traits AAC Alzheimer's disease IVW 0.88 Egger intercept -0.0014 0.55 cExecutive function 0.71 -0.0019 0.11 Cognitive performance 0.19 -0.0069 0.024 Educational attainment 0.56 0.0013 0.47 Fluid intelligence scores 0.69 -0.0057 0.5 Intelligence 0.72 -0.0027 0.35 AD and cognitive traits vs coronary artery calcification Alzheimer's disease CAC IVW 0.26 Egger intercept -0.021 0.11 cExecutive function 0.96 0.018 0.21 Cognitive performance 0.95 0.0097 0.89 Educational attainment 1 0.0022 0.7 Fluid intelligence scores 0.96 -0.019 0.45 Intelligence 0.86 -0.0014 0.88 AD and cognitive traits vs abdominal aortic calcification Alzheimer's disease AAC IVW 0.99 Egger intercept -0.00019 0.95 cExecutive function 0.99 0.0047 0.55 Cognitive performance 0.95 0.0036 0.26 Educational attainment 1 -0.00096 0.63 Fluid intelligence scores 0.78 0.0065 0.88 Intelligence 0.98 -0.00017 0.95 AAC: abdominal aortic calcification, CAC: coronary artery calcification, CI: confidence interval, IVW, inverse variance weighted, MR: Mendelian randomisation, MR-PRESSO: Mendelian randomisation pleiotropy residual sum and outlier, OR: odds ratio, P: P-value, cExecutive function: common executive function. Addressing horizontal pleiotropy in MR analysis Here, we present findings demonstrating how seemingly non-significant MR pleiotropy tests can lead to erroneous claims of causal associations—emphasising the importance of addressing pleiotropy and heterogeneity for valid causal estimates. For example, our initial analysis showed a significant causal effect of AD on CAC (IVW model, OR: 3.06, 95% CI: 1.60–5.85, P: 7.51 × 10⁻⁴, Supplementary Table 31). This result was supported by a non-significant pleiotropy test (Egger intercept: -0.020, P: 0.122) and corroborated by other MR methods, including MR Egger, the weighted median, and the weighted mode (Supplementary Table 31). Additionally, the crude estimate from MR-PRESSO aligned with the IVW result, supporting a significant causal effect of AD on CAC. The corrected MR-PRESSO results indicated a nominally significant causal influence of AD on CAC (OR: 2.11, 95% CI: 1.20–3.73, P: 1.68 × 10⁻²). Using the same approach, we found a significant causal effect of AD on AAC (Supplementary Table 31). However, these results do not reflect true causal relationships, as there was evidence of significant heterogeneity (Supplementary Table 31). Upon examining the effect of the selected instruments on the outcome variables, we found that heterogeneity arose from the association of IVs with the outcomes, violating the third assumption of MR, which requires influence on the outcome only through the exposure pathway. IVs associated with the outcome variable—even at a nominal level—can directly affect the outcome, violating the exclusion restriction assumption. When we addressed the heterogeneity in our study by excluding pleiotropic SNPs, the results, which were once significant, became non-significant. Based on our experience with recent MR publications, we speculate that such false positive results occur frequently, leading to erroneous causal claims or conclusions. The example in our study, thus, underscores the importance of rigorously addressing heterogeneity in 2SM studies to avoid false claims of causality or misleading conclusions. Shared genomic loci between CAC or AAC and AD or cognitive traits To advance our understanding of the relationship between CAC and AAC with AD and cognitive traits, we applied the GWAS-PW method towards identifying potential pleiotropic loci or variants (see Methods). Our analysis found that none of the tested 1,703 genomic regions had a posterior probability of association 3 (PPA3—the model where a shared locus with the same causal variant influences both traits) greater than 0.5, indicating no evidence of causal SNPs associated with both CAC/AAC and AD/cognitive traits. However, we identified pleiotropic loci shared by CAC or AAC and AD or cognitive traits (Tables 3 and 4 ), particularly on chromosome 19, suggesting a shared locus but with separate causal variants influencing the pair of traits. The PPA4 estimates were greater than 0.9 in these regions, indicating strong evidence or a high likelihood of pleiotropy, with the regions associated with both traits through distinct SNPs. For example, our findings revealed that the locus at chr19:44,744,370–46,102,289 (hg19) is pleiotropic for CAC and AD, with separate top SNPs (Table 3 , PPA4 = 1). Similarly, the locus at chr19:44,744,108–46,102,684 (hg19) showed strong evidence of association with AAC and AD involving distinct SNPs (Table 3 ). Using the mBAT-combo method, we identified genes within these regions, many of which exhibited significant associations (Bonferroni adjusted P gene < 1.06 × 10⁻³) with CAC or AAC and AD (or at least a nominal significance) (Table 3 , PPA4 = 1), and were also identified by at least an additional gene-based method, either fastBAT or mBAT. These genes include BCAM, TOMM40, NECTIN2, APOE, APOC1, CBLC, APOC4, APOC2, APOC4-APOC2, EXOC3L2 , and CLPTM1 (Table 3 ). Table 3 Shared genomic loci of CAC and AAC with AD CAC/ AAC AD Chr: BP PPA 4 Shared genes Gene CAC/AAC AD Gene P gene * Top SNP Top SNP P Gene P gene * Top SNP Top SNP P CAC AD 19: 44744370 – 46102289 1.00 BCAM 7.08 × 10 − 9 rs118147862 1.40 × 10 − 10 0 rs41289512 1.5 × 10 − 278 TOMM40 3.15 × 10 − 8 rs41290120 1.57 × 10 − 11 0 rs12972156 0 NECTIN2 4.29 × 10 − 8 rs41290120 1.57 × 10 − 11 0 rs12972156 0 APOE 2.69 × 10 − 7 rs41290120 1.57 × 10 − 11 0 rs12972156 0 APOC1 8.13 × 10 − 7 rs41290120 1.57 × 10 − 11 0 rs12972156 0 CBLC 1.01 × 10 − 5 rs118147862 1.40 × 10 − 10 3.67 × 10 − 264 rs41289512 1.5 × 10 − 278 APOC4 1.99 × 10 − 5 rs7412 4.61 × 10 − 10 0 rs2075650 0 APOC2 2.16 × 10 − 5 rs7412 4.61 × 10 − 10 0 rs10119 0 APOC4-APOC2 2.97 × 10 − 5 rs7412 4.61 × 10 − 10 0 rs2075650 0 EXOC3L2 4.56 × 10 − 5 rs12461144 7.03 × 10 − 5 1.95 × 10 − 66 rs10415850 2.17 × 10 − 33 TRAPPC6A 5.08 × 10 − 5 rs12461144 7.03 × 10 − 5 4.30 × 10 − 74 rs28469095 1.07 × 10 − 38 BLOC1S3 8.06 × 10 − 5 rs12461144 7.03 × 10 − 5 1.17 × 10 − 65 rs28469095 1.07 × 10 − 38 NKPD1 3.92 × 10 − 4 rs10421247 1.04 × 10 − 4 1.06 × 10 − 79 rs28469095 1.07 × 10 − 38 CLPTM1 6.14 × 10 − 4 rs7412 4.61 × 10 − 10 0 rs769449 0 PPP1R37 1.59 × 10 − 3 rs10421247 1.04 × 10 − 4 5.47 × 10 − 84 rs28469095 1.07 × 10 − 38 BCL3 3.23 × 10 − 3 rs148933445 1.26 × 10 − 7 7.93 × 10 − 127 rs2965169 9.24 × 10 − 58 MARK4 5.66 × 10 − 3 rs12461144 7.03 × 10 − 5 3.44 × 10 − 98 rs28469095 1.07 × 10 − 38 CEACAM16 8.13 × 10 − 3 rs62117204 1.58 × 10 − 6 7.75 × 10 − 121 rs2965169 9.24 × 10 − 58 AAC AD 19: 44744108 – 46102684 1.00 TOMM40 2.36 × 10 − 11 rs1065853 3.07 × 10 − 13 0 rs12972156 0 NECTIN2 2.44 × 10 − 10 rs1065853 3.07 × 10 − 13 0 rs12972156 0 APOE 2.55 × 10 − 9 rs1065853 3.07 × 10 − 13 0 rs12972156 0 APOC1 3.97 × 10 − 9 rs1065853 3.07 × 10 − 13 0 rs12972156 0 APOC2 6.12 × 10 − 9 rs1065853 3.07 × 10 − 13 0 rs10119 0 APOC4 1.43 × 10 − 8 rs1065853 3.07 × 10 − 13 0 rs2075650 0 APOC4-APOC2 1.55 × 10 − 8 rs1065853 3.07 × 10 − 13 0 rs2075650 0 CLPTM1 2.55 × 10 − 8 rs1065853 3.07 × 10 − 13 0 rs769449 0 BCAM 1.06 × 10 − 5 rs4803760 3.00 × 10 − 7 0 rs41289512 1.46 × 10 − 278 CBLC 4.26 × 10 − 4 rs4803760 3.00 × 10 − 7 3.67 × 10 − 264 rs41289512 1.46 × 10 − 278 AAC: abdominal aortic calcification, CAC: coronary artery calcification, AD: Alzheimer’s disease, SNP: single nucleotide polymorphism, P: p-value. PPA4: Posterior Probability of Association for Model 4—the probability that a genetic locus is associated with both traits, signifying pleiotropy, but independent variants are associated with each trait. For instance, a PPA4 value > 0.90 indicates a high certainty that the locus is pleiotropic for both traits. * Gene-based analysis was computed using the mBAT-combo approach, with results consistent with at least one additional gene-based method, either mBAT or fastBAT. Table 4 highlights the shared loci between CAC/AAC and cognitive traits and the pleiotropic genes associated with each trait pair. Notably, CAC and cEF share a region on chromosome 19: 44744370 − 46102547 (hg19, PPA4 = 1), where the implicated gene ( PHLDB3 ) is only nominally significant for both traits. This locus was identified exclusively by the mBAT model. Similar to AD, regions in chromosome 19 were significantly associated with CAC or AAC and some cognitive traits, including educational attainment, cEF, and fluid intelligence scores, as presented in Table 4 . Table 4 Shared genomic loci of CAC and AAC with cognitive traits CAC/ AAC CT Chr: BP PPA4 Shared genes CAC/AAC CT Gene P* Top SNP Top SNP P Gene-P* Top SNP Top SNP P CAC cEF 19: 44744370–46102547 1.00 **PHLDB3 4.38 × 10 − 2 rs62115754 2.28 × 10 − 3 3.88 × 10 − 2 rs11668385 1.10 × 10 − 2 CAC EA 19: 44744370–46102547 0.99 TOMM40 3.15 × 10 − 8 rs41290120 1.57 × 10 − 11 7.56 × 10 − 3 rs405509 1.07 × 10 − 5 NECTIN2 4.29 × 10 − 8 rs41290120 1.57 × 10 − 11 2.05 × 10 − 2 rs405509 1.07 × 10 − 5 APOE 2.69 × 10 − 7 rs41290120 1.57 × 10 − 11 7.88 × 10 − 3 rs405509 1.07 × 10 − 5 APOC1 8.13 × 10 − 7 rs41290120 1.57 × 10 − 11 6.22 × 10 − 3 rs405509 1.07 × 10 − 5 APOC4 1.99 × 10 − 5 rs7412 4.61 × 10 − 10 5.83 × 10 − 3 rs405509 1.07 × 10 − 5 APOC2 2.16 × 10 − 5 rs7412 4.61 × 10 − 10 1.01 × 10 − 3 rs405509 1.07 × 10 − 5 APOC4-APOC2 2.97 × 10 − 5 rs7412 4.61 × 10 − 10 5.72 × 10 − 3 rs405509 1.07 × 10 − 5 EXOC3L2 4.56 × 10 − 5 rs12461144 7.03 × 10 − 5 8.51 × 10 − 5 rs386569 8.22 × 10 − 6 TRAPPC6A 5.08 × 10 − 5 rs12461144 7.03 × 10 − 5 3.77 × 10 − 2 rs12974200 3.56 × 10 − 3 BLOC1S3 8.06 × 10 − 5 rs12461144 7.03 × 10 − 5 1.50 × 10 − 3 rs151165225 3.27 × 10 − 5 CLPTM1 6.14 × 10 − 4 rs7412 4.61 × 10 − 10 4.07 × 10 − 2 rs405509 1.07 × 10 − 5 PPP1R37 1.59 × 10 − 3 rs10421247 1.04 × 10 − 4 1.94 × 10 − 2 rs139290129 5.95 × 10 − 4 MARK4 5.66 × 10 − 3 rs12461144 7.03 × 10 − 5 7.59 × 10 − 7 rs10402747 1.71 × 10 − 8 AAC cEF 19: 44744147–46101600 1.00 TOMM40 2.36 × 10 − 11 rs1065853 3.07 × 10 − 13 1.57 × 10 − 15 rs429358 9.52 × 10 − 20 NECTIN2 2.44 × 10 − 10 rs1065853 3.07 × 10 − 13 1.08 × 10 − 14 rs429358 9.52 × 10 − 20 APOE 2.55 × 10 − 9 rs1065853 3.07 × 10 − 13 1.68 × 10 − 16 rs429358 9.52 × 10 − 20 APOC1 3.97 × 10 − 9 rs1065853 3.07 × 10 − 13 1.18 × 10 − 16 rs429358 9.52 × 10 − 20 APOC2 6.12 × 10 − 9 rs1065853 3.07 × 10 − 13 3.02 × 10 − 16 rs429358 9.52 × 10 − 20 APOC4 1.43 × 10 − 8 rs1065853 3.07 × 10 − 13 1.02 × 10 − 15 rs429358 9.52 × 10 − 20 APOC4-APOC2 1.55 × 10 − 8 rs1065853 3.07 × 10 − 13 7.48 × 10 − 16 rs429358 9.52 × 10 − 20 CLPTM1 2.55 × 10 − 8 rs1065853 3.07 × 10 − 13 2.71 × 10 − 15 rs429358 9.52 × 10 − 20 BCAM 1.06 × 10 − 5 rs4803760 3.00 × 10 − 7 2.63 × 10 − 4 rs4803764 4.24 × 10 − 4 CBLC 4.26 × 10 − 4 rs4803760 3.00 × 10 − 7 1.78 × 10 − 6 rs12162222 6.16 × 10 − 4 AAC EA 19: 44744147–46101600 0.96 TOMM40 2.36 × 10 − 11 rs1065853 3.07 × 10 − 13 7.56 × 10 − 3 rs405509 1.07 × 10 − 5 NECTIN2 2.44 × 10 − 10 rs1065853 3.07 × 10 − 13 2.05 × 10 − 2 rs405509 1.07 × 10 − 5 APOE 2.55 × 10 − 9 rs1065853 3.07 × 10 − 13 7.88 × 10 − 3 rs405509 1.07 × 10 − 5 APOC1 3.97 × 10 − 9 rs1065853 3.07 × 10 − 13 6.22 × 10 − 3 rs405509 1.07 × 10 − 5 APOC2 6.12 × 10 − 9 rs1065853 3.07 × 10 − 13 1.01 × 10 − 2 rs405509 1.07 × 10 − 5 APOC4 1.43 × 10 − 8 rs1065853 3.07 × 10 − 13 5.83 × 10 − 3 rs405509 1.07 × 10 − 5 APOC4-APOC2 1.55 × 10 − 8 rs1065853 3.07 × 10 − 13 5.72 × 10 − 3 rs405509 1.07 × 10 − 5 CLPTM1 2.55 × 10 − 8 rs1065853 3.07 × 10 − 13 4.07 × 10 − 2 rs405509 1.07 × 10 − 5 AAC FIS 19: 44744147–46101600 0.96 TOMM40 2.36 × 10 − 11 rs1065853 3.07 × 10 − 13 1.54 × 10 − 3 rs11668861 1.23 × 10 − 3 NECTIN2 2.44 × 10 − 10 rs1065853 3.07 × 10 − 13 2.19 × 10 − 3 rs8113311 7.09 × 10 − 4 APOE 2.55 × 10 − 9 rs1065853 3.07 × 10 − 13 3.20 × 10 − 3 rs11668861 1.23 × 10 − 3 APOC1 3.97 × 10 − 9 rs1065853 3.07 × 10 − 13 6.93 × 10 − 3 rs11668861 1.23 × 10 − 3 BCAM 1.06 × 10 − 5 rs4803760 3.00 × 10 − 7 3.39 × 10 − 3 rs8113311 7.09 × 10 − 4 CBLC 4.26 × 10 − 4 rs4803760 3.00 × 10 − 7 2.41 × 10 − 2 rs8113311 7.09 × 10 − 4 AAC: abdominal aortic calcification, CAC: coronary artery calcification, cEF: common executive function, CT: cognitive traits, EA: educational attainment, FIS: fluid intelligence scores, SNP: single nucleotide polymorphism, P: p-value. PPA4: Posterior Probability of Association for Model 4—the probability that a genetic locus is associated with both traits, signifying pleiotropy, but independent variants are associated with each trait. For instance, a PPA4 value > 0.90 indicates a high certainty that the locus is pleiotropic for both traits. * Gene-based analysis was computed using the mBAT-combo approach, with results consistent with at least one additional gene-based method, either mBAT or fastBAT. **gene identified by mBAT gene-based model only. Lastly, we identified additional loci with only suggestive association in the GWAS-PW analysis implicating regions in chromosomes one, six, seven and 19 and their corresponding significant genes for the pair of traits assessed. Supplementary Table 32 provides information about these loci and the likely pleiotropic genes implicated. Discussion Using well-established methods, we present findings from analyses assessing the potential shared genetic architecture and causal relationships between CAC and AAC and AD or cognitive traits. Our genome-wide genetic correlation analysis revealed a nominally significant association between CAC and AD. However, we did not replicate this result in another AD dataset. Although differences in study power may explain some variability, the nominal significance initially observed disappeared after excluding the APOE region, suggesting that the excluded region primarily drove the correlation. Furthermore, there was no evidence of a significant genome-wide correlation between AAC and AD. Conversely, we found a significant negative genetic correlation between CAC and several cognitive traits, surviving correction for multiple testing, including cognitive performance, educational attainment, and intelligence scores. AAC also demonstrated a significant negative correlation with cognitive performance and educational attainment. These significant negative genetic correlations suggest that a higher genetic predisposition to CAC and AAC is associated with lower cognitive abilities, potentially aligning with studies that reported the association of vascular calcification with cognitive decline [ 8 , 15 – 17 ]. Conversely, given it is a genetic correlation assessment, the inverse relationship could also suggest that genetic predisposition to a higher performance on the selected cognitive traits is associated with a reduced risk of vascular calcification. It is crucial to note that genetic correlation does not imply causation due to possibilities for alternative explanations, including pleiotropy or shared genetic susceptibility. Additionally, for traits like educational attainment, non-genetic factors—such as social status, socioeconomic conditions, and environmental influences—may further complicate the relationship. Thus, while our findings indicate an inverse association between CAC/AAC and certain cognitive traits, alternative explanations and interactions between genetics, cognition, environment, and various life outcomes may also play a role. We performed bidirectional MR analyses to examine the potential causal associations of CAC and AAC with AD and cognitive traits and gain further insights into the nature of the relationships. Our comprehensive and rigorous analyses indicate that CAC or AAC is not causally associated with AD or cognitive traits, regardless of the direction of analysis—whether CAC or AAC was exposure or outcome variables. The only exception was the causal effect of AAC on fluid intelligence scores and vice versa, which was only borderline nominally significant in the IVW model. These results did not survive correction for multiple testing, making them less convincing. Our findings were consistent across several MR models, and tests for heterogeneity or pleiotropy did not indicate potential bias. Vascular calcification, specifically CAC, is a well-established tool for improved risk prediction of subsequent atherosclerotic cardiovascular disease [ 12 , 13 ]. Atherosclerosis (characterised by vascular calcification) is associated with the risk of AD [ 6 ]. Indeed, a recent study highlighted an additive interaction effect between atherosclerosis and AD on cognitive functions [ 7 ]. Hence, it is reasonable to hypothesise that vascular calcification might be related to AD or cognitive traits. Several conventional observational studies support this hypothesis, suggesting a positive association between vascular calcification and AD or cognitive decline [ 9 , 10 , 15 – 17 , 37 ], although mixed results have also been reported [ 20 ]. Our genetic-based assessment did not confirm convincing significant causal associations. Current results provide new insights into the interplay between these phenotypes and improve our understanding beyond what is known through traditional observational evidence. For example, a recent observational study we contributed to reported an association between AAC and an increased risk of all-cause late-life dementia among elderly women [ 9 ]. While this previous study highlighted a potential link [ 9 ], our current genetic-based research does not support causality. Unlike the genetic-based approach, traditional observational studies are often limited by many factors such as unmeasured confounders and biases from lifestyle or environment, which may clarify our findings. Further, it should be noted that the present study focuses on AD, while the observational study [ 9 ] did not differentiate between the various types of dementia and was limited to late-life onset (after 80 years) in women only. Hence, there is a need for further investigation into specific dementia subtypes to better understand the relationship between vascular calcification, dementia, and cognitive decline. Our study emphasises the critical importance of rigorously addressing pleiotropy and heterogeneity to enhance the reliability of MR findings. Despite the challenges in proving some of them, it is essential to uphold the core assumptions of MR to infer valid causal estimates [ 29 ]. Adhering to best practices is vital to prevent estimating spurious causal associations, including implementing various MR approaches and conducting sensitivity testing [ 27 – 29 , 38 ]. Notably, heterogeneity tests can reveal potential biases in MR studies, as illustrated in our results. For example, there was evidence for heterogeneity between AD (as exposure) and CAC (as outcome) based on Cochran's Q P value (Supplementary Table 31) in our illustrated example. This evidence signals a potential violation of MR assumptions, and ignoring such would have resulted in false positives in our study. This observation, thus, underscores the need for further assessment, such as checking that the IVs are valid. We often found cases where authors overlook such significant heterogeneity tests in recent MR studies, potentially leading to claims of causality that may not be true. The limited genome-wide genetic correlation and non-causal associations results in our analyses do not preclude shared genetic predisposition through specific regions in the genome. Hence, we progressed our study to investigate the potential shared variants or loci between CAC/AAC and AD or cognitive traits using the GWAS-PW method. Our findings revealed that none of the 1,703 tested genomic regions exhibited a PPA3 greater than 0.5, indicating a lack of evidence for causal SNPs influencing CAC or AAC and AD or cognitive traits. However, we identified loci on chromosome 19 demonstrating a high likelihood of pleiotropy (PPA4 > 0.9) with distinct causal variants affecting CAC/AAC and AD/cognitive traits. These findings implicate pleiotropic genetic underpinnings, primarily driven by the identified loci, particularly regions in chromosome 19—partly highlighting consistency between our genetic correlation and GWAS-PW results. Within the implicated loci on chromosome 19, we identified several pleiotropic genes, including BCAM, TOMM40, NECTIN2, APOE, APOC1, CBLC, APOC4, APOC2, APOC4-APOC2, EXOC3L2 , and CLPTM1 . These genes exhibited significant associations with both CAC or AAC and AD or cognitive traits, indicating that they play roles in their genetic architecture, and potentially the co-occurring state of CAC/AAC with AD/cognitive traits. Furthermore, our analysis revealed shared loci between CAC/AAC and cognitive traits, as detailed in Table 4 , including a significant region on chr19: 44744370–46102547 associated with CAC and cEF via the gene PHLDB3. Although this gene was only nominally significant (the only significant gene for both traits) and identified only by the mBAT model of the gene-based analysis, the region has PPA4 = 1, indicating a high probability of being pleiotropic (via distinct variants) for the pair of traits. In conclusion, our study provides new insights into the complex genetic relationships of CAC and AAC with AD and cognitive traits. While we observed some genetic correlations, particularly a negative association between CAC/CAC and cognitive traits, our findings highlight the absence of causal relationships in the MR analyses. Further assessment using the GWAS-PW method suggests that the observed associations do not indicate the presence of shared causal SNPs but reflect shared genetic susceptibilities influenced by distinct variants in the implicated loci. Identifying pleiotropic loci and significant genes associated with vascular calcification, AD, and cognitive traits further supports this position. The identified shared genes and loci provide essential targets for further investigation in AD, cognitive traits, and vascular calcification. Strengths and limitations A key strength of this study is its use of a genetic approach, which is less susceptible to confounding from environmental and lifestyle factors, thus enhancing the reliability of our findings beyond what is possible through the conventional observational study approach. To our knowledge, this is the first study to investigate the relationship between vascular calcification, AD, and cognitive traits using statistical genetic methods. However, several limitations should be considered when interpreting our results. Firstly, the data utilised are exclusively from the European population, limiting the generalisability of our findings to other ancestries. Secondly, due to a lack of sufficient genome-wide significant instruments for CAC and AAC, we relaxed the threshold for IVs selection in our MR analysis to the suggestive level. This observation may indicate less powerful GWAS results for CAC and AAC, which could affect the non-significant causal associations observed in our study. Future research needs to explore these relationships further as more robust CAC and AAC GWAS data become available. Thirdly, although sample overlap can complicate analyses such as genetic correlation, MR, and GWAS-PW, our preliminary evaluation indicates no evidence of significant sample overlap between CAC/AAC and AD/cognitive traits, thus ruling out bias from this factor in our study. Finally, our findings of limited genetic overlap and no causal association of CAC and AAC with AD or cognitive traits do not eliminate the possibility of associations due to shared risk factors. Therefore, comprehensive prospective studies would be beneficial for further elucidating the nature of these associations. Moreover, we recommend an assessment of the relationships based on the various types of dementia. Materials and Methods Data sources We leveraged large-scale genome-wide association study (GWAS) summary data from publicly available repositories. We sourced GWAS summary data for CAC from a recent study [39], which included 28,655 individuals. For AAC, we used data from another recent publication [40] with a sample size of 31,786. Additionally, we drew on one of the largest publicly available AD GWAS datasets, which comprised 71,880 cases and 383,378 controls, encompassing both clinically diagnosed AD and AD by proxy [34]. For cognitive traits, we included summary data from five studies: cEF (n = 427,037) [26], cognitive performance (n = 257,828) [41], intelligence (n = 269,867) [42], fluid intelligence scores (n = 125,935) [43], and educational attainment (n = 766,345) [41]. cEF refers to a unified cognitive factor that captures the shared variance across multiple executive function (EF) tasks, including response inhibition, interference control, working memory updating, and set-shifting [26]. In the original publication, the authors conducted a GWAS analysis to derive a cEF factor score that represents the common executive control elements across various cognitive-related tasks [26]. Participants in the data underlying our study were individuals of European ancestry. Detailed descriptions of the datasets, including participant demographics, study settings, measurements, and quality control procedures, are available in the referenced publications. Additional cohort-specific information and data download links can be found in Supplementary Table 1. Cross-trait genome-wide genetic correlation analyses We conducted cross-trait genetic correlation analyses between CAC and each of AD and cognitive traits using the LDSC method. We similarly assessed the genetic correlation of AAC with AD and cognitive traits. The LDSC method estimates trait heritability and genetic correlations by regressing GWAS test statistics (e.g., Z-scores) on LD scores for each SNP [44]. The method can distinguish true genetic signals from confounding factors and provide robust estimates of genetic relationships between traits [44]. Initially, we estimated genetic correlations with an unconstrained genetic covariance intercept to assess the potential proportion of sample overlap between traits, similar to the practice in previous studies [24, 25, 44-47]. Our findings indicated that the genetic covariance intercepts were not significantly different from zero, suggesting no substantial sample overlap between AAC or CAC and AD or cognitive traits. Consequently, we proceeded with genetic covariance intercept-constrained analyses. In all LDSC analyses, pre-computed LD scores from the 1000 Genomes European reference panel were applied, excluding SNPs that did not intersect with the reference panel or had a MAF < 1%. To explore the potential impact of the APOE region (especially on AD), we conducted analyses both with and without these regions. A Bonferroni-corrected significance threshold of 8.33 × 10⁻ 3 was applied for genetic correlations involving AD and five cognitive traits, with P-values below 0.05 considered nominally significant. Causal relationship assessment using Mendelian randomisation We used a two-sample 2SMR analysis to explore the potential causality of CAC or AAC with AD and various cognitive traits. Several MR methods were utilised, and we conducted a bidirectional assessment for a clear insight into the likely causal relationships of these traits. To ensure the robustness of our findings, we carefully selected appropriate instrumental variables (IVs) for the analysis and rigorously addressed potential issues around horizontal pleiotropy and heterogeneity. The procedural outline of our study is depicted in Figure 1. Selection of instrumental variables for MR analysis Briefly, we selected IVs at the genome-wide significance (GWS) level (P < 5 × 10⁻⁸) from the relevant GWAS summary data used in our study. This stringent selection criterion ensures that the IVs are strongly associated with the exposure variables, with an F-statistic greater than 10 [27], thereby minimising the risk of weak instrument bias and fulfilling the first assumption of MR. Due to the limited availability of GWS IVs for CAC and AAC as exposure variables, we relaxed the selection threshold to the genome-wide suggestive level (P < 1 × 10⁻⁵). This adjustment was made with the understanding that using a smaller number of SNPs (< 10) as IVs may introduce potential bias into our study. Although the second MR assumption—that IVs are not associated with confounders—is challenging or nearly impossible to fully validate, we rigorously evaluated our IVs and performed linkage disequilibrium clumping at a stringent threshold ( r 2 < 0.001, Figure 1) to enhance the independence of the selected instruments. Importantly, we ensured that our IVs were not linked to the outcome variables (P < 0.05), thereby adhering to the third MR assumption. As highlighted in the sub-sections for the main MR and sensitivity analyses, we carried out other specific tests, to ensure our IVs are robust. Performing MR analyses We used the inverse-variance weighted (IVW) as the main MR model in the current study. The IVW method assumes the absence of horizontal pleiotropy, and the model is reliable so long this assumption holds. To address potential heterogeneity among the causal estimates derived from different variants, we employed multiplicative random effects of the IVW model. Also, to complement our IVW estimates, we used additional MR methods, including the weighted median (which can yield valid estimates with up to 50% invalid IVs) and the MR-Egger (which can provide valid estimates by correcting for pleiotropy) [27, 28]. We consider MR results with P < 0.05 as nominally significant and implemented a Bonferroni correction to mitigate the risk of false positives due to multiple tests across the various outcomes. Based on this adjustment, we set the significance threshold at P < 0.008 (0.05/6), corresponding to the analysis of six outcome variables (where applicable). We utilised the R statistical packages and the Unix environment for data management and analyses and used the 2SMR software (version 0.5.6), and MR-PRESSO for the MR analyses (implemented on the R packages [version 4.2.1]). MR sensitivity analyses Following the practice in related studies [27, 48-52], we conducted further tests to assess the reliability of our results. These tests include Cochran's Q statistics to evaluate the heterogeneity of SNP effects, individual MR analyses, 'leave-one-out' analyses to determine the impact of each IV on the overall results, and examining the funnel plot for symmetry. We used the MR-Egger intercept to check for potential violations of the assumption of no unbalanced pleiotropy. Significant deviations of the MR-Egger intercept from zero indicate a possible violation of this assumption. Additionally, we implemented the 'MR-pleiotropy residual sum and outlier' (MR-PRESSO) method, known for identifying and addressing pleiotropy by excluding outliers [53]. Importantly, as an additional step, we meticulously reviewed our analyses and excluded IVs associated with the outcome variables at a significance level of P < 0.05. This process involved scrutinising our LD clumped (clumping performed at r 2 < 0.001) and properly harmonised data and excluding IVs with P outcome-variable < 0.05, followed by MR analysis on the remaining instruments. Assessing shared genetic risk loci using the pairwise GWAS approach We conducted colocalisation analysis using the GWAS-PW method, a tool designed to scan the genome for regions that likely share a causal variant or pleiotropic loci between traits [33]. This program applies a Bayesian statistical model to estimate the probability (or PPA) and we modelled four potential scenarios: (1) a region contains a variant associated only with trait 1 (PPA1); (2) a variant associated only with trait 2 (PPA2); (3) a variant associated with both traits 1 and 2 (PPA3); or (4) independent variants are associated with each trait (PPA4) but the region is shared by both traits [33]. In this study, we first conducted an analysis using CAC as trait 1 against AD and cognitive traits as trait 2. In the second analysis, we used AAC as trait 1 and again compared it with AD and cognitive traits as trait 2. We applied GWAS-PW to assess potential shared causal variants and loci between CAC and AAC with AD and cognitive traits. The summary statistics for CAC and AAC were aligned with AD and cognitive trait data by rsID and alleles, ensuring consistent effect and non-effect alleles across traits. Standardised Z-scores and variances for each SNP were then used as input to the GWAS-PW model [33]. The analysis covered 1,703 predefined independent genetic regions based on LD patterns from the 1000 Genomes Project European reference data. There is no evidence for a significant overlap of samples between CAC or AAC and AD or cognitive traits, ruling out potential confounding or the need for adjustments in our analysis. Our focus was on PPA3 and PPA4 results, thus, we considered regions with PPA3 > 0.9 to have a significant shared causal variant between the two traits (e.g., CAC and AD), while those with PPA3 > 0.5 were deemed suggestive. Similarly, we interpreted Loci with PPA4 > 0.9 as harbouring distinct causal variants for each trait, influencing both independently while PPA4 > 0.5 was considered a suggestive association. The identification of risk variants or loci by GWAS-PW was further refined using three gene-based association analysis methods, including fastBAT, mBAT and the ‘mBAT-combo’ [31, 32] to aggregate variant signals within genes and assess their overall contribution. Briefly, we aimed to detect shared genes in regions with strong evidence for pleiotropy based on PPA4 > 0.9 (and at the suggestive level, PPA4 > 0.5), and we used the gene-based method to ensure the mapped genes are associated with the pairs of traits assessed. The mBAT-combo method combines mBAT and fastBAT statistics and is superior to traditional sum-χ² approaches, especially for identifying genes with masking effects [32]. The method has proven more powerful in simulations and real-world data [32]; hence, we prioritise using the method in the current study. SNPs were mapped within 50kb of gene boundaries for this analysis. Declarations Ethics declarations This study was conducted based on a secondary analysis of pre-existing, completely de-identified genetic data. Ethical approval was obtained for each of the primary studies that published the data used, as detailed in the original associated studies. No additional ethical clearance was necessary for this current investigation. Data Availability The GWAS data used in this study were sourced from publicly accessible repositories and research groups or consortia, as specified in the data sources section. Supplementary Table S1 contains further details about these datasets, including links to their sources. The published article and its supplementary materials include all data generated during this study. Acknowledgments: We gratefully acknowledge all the international research groups and consortia that generated the GWAS data analysed in this study. This research work has been supported by the Western Australian Future Health Research and Innovation Fund. Author contributions statement E.O.A. and S.M.L. contributed to the conceptualisation of the study. E.O.A. designed and performed data curation and formal analysis. E.O.A. wrote the original draft, and both E.O.A. and S.M.L. participated in reviewing and editing the manuscript. S.M.L. and E.O.A. acquired funding. All authors have read and agreed to the published version of the manuscript. Additional information Competing interests: The authors declare no competing interests. References Knopman, D.S., et al., Alzheimer disease. Nature Reviews Disease Primers, 2021. 7 (1): p. 33. Nandi, A., et al., Cost of care for Alzheimer’s disease and related dementias in the United States: 2016 to 2060. npj Aging, 2024. 10 (1): p. 13. Nandi, A., et al., Global and regional projections of the economic burden of Alzheimer's disease and related dementias from 2019 to 2050: a value of statistical life approach. EClinicalMedicine, 2022. 51 . International, A.s.D. Dementia statistics . 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Skrivankova, V.W., et al., Strengthening the reporting of observational studies in epidemiology using Mendelian randomization: the STROBE-MR statement. Jama, 2021. 326 (16): p. 1614-1621. Bakshi, A., et al., Fast set-based association analysis using summary data from GWAS identifies novel gene loci for human complex traits. Scientific Reports, 2016. 6 (1): p. 32894. Li, A., et al., mBAT-combo: A more powerful test to detect gene-trait associations from GWAS data. The American Journal of Human Genetics, 2023. 110 (1): p. 30-43. Pickrell, J.K., et al., Detection and interpretation of shared genetic influences on 42 human traits. Nat Genet, 2016. 48 (7): p. 709-17. Jansen, I.E., et al., Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk. Nature genetics, 2019. 51 (3): p. 404-413. Lambert, J.C., et al., Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease. Nat Genet, 2013. 45 (12): p. 1452-8. Carter, A.R. &Anderson, E.L., Correct illustration of assumptions in Mendelian randomization. International Journal of Epidemiology, 2024. 53 (2): p. dyae050. Chu, Z., Cheng, L., &Tong, Q., Carotid artery calcification score and its association with cognitive impairment. Clinical Interventions in Aging, 2019. 14 (null): p. 167-177. Haycock, P.C., et al., Best (but oft-forgotten) practices: the design, analysis, and interpretation of Mendelian randomization studies1. The American Journal of Clinical Nutrition, 2016. 103 (4): p. 965-978. Kavousi, M., et al., Multi-ancestry genome-wide study identifies effector genes and druggable pathways for coronary artery calcification. Nat Genet, 2023. 55 (10): p. 1651-1664. Sethi, A., et al., Calcification of the abdominal aorta is an under-appreciated cardiovascular disease risk factor in the general population. Frontiers in Cardiovascular Medicine, 2022. 9 . Lee, J.J., et al., Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nature Genetics, 2018. 50 (8): p. 1112-1121. Savage, J.E., et al., Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence. Nature Genetics, 2018. 50 (7): p. 912-919. Watanabe, K., et al., A global overview of pleiotropy and genetic architecture in complex traits. Nature Genetics, 2019. 51 (9): p. 1339-1348. Bulik-Sullivan, B.K., et al., LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nature genetics, 2015. 47 (3): p. 291-295. Adewuyi, E.O., et al., Genetic analysis of endometriosis and depression identifies shared loci and implicates causal links with gastric mucosa abnormality. Human genetics, 2021. 140 : p. 529-552. Adewuyi, E.O., et al., Shared molecular genetic mechanisms underlie endometriosis and migraine comorbidity. Genes, 2020. 11 (3): p. 268. Tasnim, S., Wilson, S.G., Walsh, J.P., &Nyholt, D.R., Cross-Trait Genetic Analyses Indicate Pleiotropy and Complex Causal Relationships between Headache and Thyroid Function Traits. Genes, 2022. 14 (1): p. 16. Adewuyi, E.O., Mehta, D., &Nyholt, D., Genetic overlap analysis of endometriosis and asthma identifies shared loci implicating sex hormones and thyroid signalling pathways. Human Reproduction, 2022. 37 (2): p. 366-383. Akosile, W. &Adewuyi, E., Genetic correlation and causality assessment between post-traumatic stress disorder and coronary artery disease-related traits. Gene, 2022. 842 : p. 146802. Islam, M.R., Nyholt, D.R., &The International Headache Genetics, C., Cross-trait analyses identify shared genetics between migraine, headache, and glycemic traits, and a causal relationship with fasting proinsulin. Human Genetics, 2023. Tasnim, S., Wilson, S.G., Walsh, J.P., &Nyholt, D.R., Shared genetics and causal relationships between migraine and thyroid function traits. Cephalalgia, 2023. 43 (2): p. 03331024221139253. Adewuyi, E.O., O’Brien, E.K., Porter, T., &Laws, S.M., Relationship of Cognition and Alzheimer’s Disease with Gastrointestinal Tract Disorders: A Large-Scale Genetic Overlap and Mendelian Randomisation Analysis. International Journal of Molecular Sciences, 2022. 23 (24): p. 16199. Verbanck, M., Chen, C.-y., Neale, B., &Do, R., Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nature genetics, 2018. 50 (5): p. 693-698. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5275152","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":371701037,"identity":"41e584f8-f3b2-46ee-a03e-66b18bc23637","order_by":0,"name":"Emmanuel O Adewuyi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYJACCRDBxt4A5jA2EK+F5wCpWhgkEojUws/A/PDGx7bDiX2Sjw9+5mGwkd1wgPmZBD4tkg1sxpYzgVrapNOSpXkY0ow3HGAzw6vF4ACDmTTvNpCWHDNmHobDiRuAIni12B9g/yb9F6RF8vw3oJb/QC3s3/DbwsBjJs0I0iLBwwbUcgCohQe/LRKHeYote/+lG7fxpBlLzjFINp4JFLHAp4W/vX3jjR9nrGXntx9++OFNhZ1s33GgCD4tDMwQyrEB6k64CEFgT6S6UTAKRsEoGIkAADuDRVSXhhEgAAAAAElFTkSuQmCC","orcid":"","institution":"Edith Cowan University","correspondingAuthor":true,"prefix":"","firstName":"Emmanuel","middleName":"O","lastName":"Adewuyi","suffix":""},{"id":371701040,"identity":"6df72bec-4d09-4213-a035-932bcefd4075","order_by":1,"name":"Simon M Laws","email":"","orcid":"","institution":"Edith Cowan University","correspondingAuthor":false,"prefix":"","firstName":"Simon","middleName":"M","lastName":"Laws","suffix":""}],"badges":[],"createdAt":"2024-10-16 10:53:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5275152/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5275152/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.3390/biomedicines13030618","type":"published","date":"2025-03-03T00:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":68140265,"identity":"ca9938f7-589f-4380-abe6-dd0f92a0182c","added_by":"auto","created_at":"2024-11-04 04:43:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":126958,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSchematic representation of MR and study design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe figure provides an overview of the Mendelian Randomisation (MR) analysis approach, emphasising its application and core assumptions in examining the potential causal relationship between exposure and outcome variables. It highlights the three fundamental assumptions of MR: (1) the genetic variants (SNPs) used as instrumental variables must be robustly associated with the exposure, (2) these SNPs should not be associated with confounding factors [we note the direction of the arrow as recently illustrated [36]], and (3) they must influence the outcome exclusively through the exposure. The figure also details the clumping parameters employed to ensure the independence and relevance of the genetic instruments. The analysis involves two rounds: first using CAC and AAC as exposures against AD and cognitive traits as outcomes, and the reverse analysis where AD and cognitive traits serve as exposures, with CAC and AAC as the outcome variables.\u003c/p\u003e\n\u003cp\u003eAAC: abdominal aortic calcification, AD: Alzheimer’s disease, CAC: coronary artery calcification, EA: effect allele, IVs: instrumental variables, IVW: inverse variance weighted, MR: Mendelian randomisation, MR-PRESSO: Mendelian Randomization Pleiotropy RESidual Sum and Outlier, NEA: non-effect allele, SNP: single nucleotide polymorphism.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5275152/v1/2e3cfa1a5b8880b5cb591c7b.png"},{"id":68140264,"identity":"87def56a-9417-4c83-be68-0f722e42cceb","added_by":"auto","created_at":"2024-11-04 04:43:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":97283,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCausal effect of CAC against AD and cognitive traits\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAD: Alzheimer’s disease, CAC: coronary artery calcification, CI: confidence interval, IVW: inverse variance weighted, nIV: number of instrumental variables, OR: Odds ratio, P: P-value, cExecutive function: common executive function.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5275152/v1/b61de72a6aa401deb781d2f5.png"},{"id":68140380,"identity":"d6215b17-8b6e-415b-8a64-dfe0169298fe","added_by":"auto","created_at":"2024-11-04 04:51:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":103067,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCausal effect of AAC against AD and cognitive traits\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAAC: Abdominal aortic calcification, AD: Alzheimer’s disease, CI: confidence interval, IVW: inverse variance weighted, nIV: number of instrumental variables, OR: Odds ratio, P: P-value, cExecutive function: common executive function.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5275152/v1/57512aa1be6f7dc4e2f65137.png"},{"id":68140262,"identity":"ef9be03b-8f58-456c-bf62-7843697e0525","added_by":"auto","created_at":"2024-11-04 04:43:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":117643,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCausal effect of AD and cognitive traits against CAC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAD: Alzheimer’s disease, CAC: coronary artery calcification, CI: confidence interval, IVW: inverse variance weighted, nIV: number of instrumental variables, OR: Odds ratio, P: P-value, cExecutive function: common executive function.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5275152/v1/722f60c0855c42057e816eca.png"},{"id":68140263,"identity":"adc8894e-e7da-4d7b-9ec7-452770cec850","added_by":"auto","created_at":"2024-11-04 04:43:55","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":118473,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCausal effect of AD and cognitive traits against AAC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAAC: Abdominal aortic calcification, AD: Alzheimer’s disease, CI: confidence interval, IVW: inverse variance weighted, nIV: number of instrumental variables, OR: Odds ratio, P: P-value, cExecutive function: common executive function.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5275152/v1/e3a30f6634a511deaeeb4d98.png"},{"id":78696848,"identity":"f12364be-fcdc-41ae-9114-0116918ce71e","added_by":"auto","created_at":"2025-03-17 17:35:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2282853,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5275152/v1/789604df-c1d6-48f1-b16f-27ef058e3551.pdf"},{"id":68140267,"identity":"6002a34f-57a5-428f-b6b7-a9754a76cd2e","added_by":"auto","created_at":"2024-11-04 04:43:55","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":319022,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5275152/v1/9ffc67437ec63196e8f61855.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing shared genetic and causal links between vascular calcification, Alzheimer's disease, and cognitive traits","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAlzheimer's disease (AD) is a neurodegenerative disorder characterised by memory loss and progressive cognitive decline [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The growing burden of the disorder has become a major global public health concern, with over 10\u0026nbsp;million new dementia cases diagnosed annually\u0026mdash;AD being the most prevalent form [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Although the exact causes of AD are not well understood, there is an increasing appreciation of the likely role of vascular pathology in its onset and progression [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Emerging evidence, for instance, highlights a link between vascular calcification and both AD and cognitive impairment [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Vascular calcification, including coronary artery calcification (CAC) and abdominal aortic calcification (AAC), refers to the abnormal deposition of calcium-phosphate complexes within the walls of arteries, often serving as a marker of subclinical atherosclerosis [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Notably, CAC is widely recognised as a critical indicator for predicting future atherosclerotic cardiovascular disease risk [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Atherosclerosis, the thickening and hardening of arterial walls due to cellular accumulation and calcification, contributes to the narrowing of blood vessels and is associated with both cardiovascular diseases and neurodegenerative conditions, including dementia [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecent studies have identified a potential interaction between atherosclerosis and AD, potentially leading to additive effects on cognitive decline [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Additionally, an observational study found that older women with AAC had an increased risk of developing all-cause late-life dementia [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The association between vascular calcification and cognitive impairment has also been observed in multiple populations. For instance, a Chinese-based study reported a link between CAC and cognitive decline [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], aligning with findings from a Dutch cohort and other international studies [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. These and similar findings suggest some forms of association or co-occurring relationships between CAC or AAC and AD or cognitive impairment. Such relationships may accelerate disease progression and contribute to adverse outcomes, including more complex management plans [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. On the other hand, co-occurring relationships may play a role in the pathogenesis of these disorders through shared genetic predisposition, common biological mechanisms, or direct causal relationships. Thus, understanding the nature of the relationships and their underlying mechanisms could advance our knowledge of the aetiology of the disorders and provide opportunities for developing preventative measures or therapies.\u003c/p\u003e \u003cp\u003eDespite growing evidence of the associations between vascular calcification and AD or cognitive impairment, conflicting findings remain. For example, a systematic review and meta-analysis found limited evidence linking CAC scores with dementia risk [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Similarly, a scoping review reported mixed findings on the association between vascular calcification and AD or cognitive decline [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Given these inconsistent findings and the inherent limitations of traditional observational studies\u0026mdash;such as measurement error, reverse causation and residual confounding [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u0026mdash;some questions remain unanswered. First, it is not completely clear whether CAC or AAC is associated with AD or cognition, or whether previous observational studies that drew these conclusions were biased or otherwise falsely positive. Second, it is unclear whether any observed relationships between these forms of vascular calcification and AD or cognition are causal or reflect shared genetic susceptibility. An unbiased genetic study utilising large-scale genome-wide association studies (GWAS) is less prone to the many limitations of traditional observational studies and thus provides an alternative study approach [\u003cspan additionalcitationids=\"CR23 CR24\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Such approaches offer robust avenues for assessing the genetic architecture of CAC or AAC with AD and cognitive traits, with the potential of advancing insights into their likely biological mechanisms and identifying novel therapeutic targets [\u003cspan additionalcitationids=\"CR23 CR24\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHence, the current study systematically assesses the potential shared genetic and causal relationship of CAC and AAC with AD, cognitive performance, common executive function (cEF), and other cognitive-related traits. cEF is a unified cognitive factor that represents the shared variance in executive control processes, derived from tasks like response inhibition, interference control, working memory updating, and set-shifting [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. We analysed several large-scale genetic data employing well-regarded statistical genetics methods, including linkage disequilibrium score regression (LDSC) to examine genome-wide cross-traits genetic correlations. We also used the Mendelian randomisation (MR) method [\u003cspan additionalcitationids=\"CR28 CR29\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] to investigate whether CAC or AAC is causally associated with AD or cognitive traits, and vice versa. We utilised the pairwise GWAS (GWAS-PW) colocalisation method to further investigate the relationship between CAC/AAC and AD/cognitive traits for insights into shared genetic variants and loci. Lastly, we performed gene-level analyses using a recently developed powerful gene-based association analysis method [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] to identify pleiotropic genes shared by CAC or AAC and AD or cognitive traits. The approach used in our study can contribute to advancing knowledge of the connections between vascular calcification, AD, and cognitive traits. The study approach is especially important given the inconsistent evidence from traditional observational studies. By building evidence-based knowledge, we can improve our understanding of the causes of AD and identify new potential targets for further investigation.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eOverview of study design\u003c/h2\u003e\n \u003cp\u003eThis study aimed to explore the genetic relationships of vascular calcification (CAC and AAC) with AD and cognitive traits. We employed four analytical approaches to achieve our objectives. First, we performed cross-traits genome-wide genetic correlation between CAC/AAC and AD/cognitive traits using the LDSC method. Given the strong evidence of the Apolipoprotein E (\u003cem\u003eAPOE\u003c/em\u003e) region\u0026rsquo;s significant impact on AD, we performed a genetic correlation analysis excluding the region to determine if it drives the observed relationship between CAC or AAC and AD. We applied a Bonferroni-corrected significance threshold of 8.33 \u0026times; 10⁻\u003csup\u003e3\u003c/sup\u003e for genetic correlations involving AD and five cognitive traits, with P-values below 0.05 considered nominally significant. Second, we assessed potential causal relationships through the bidirectional MR analysis method with sensitivity analyses and rigorous assessment of horizontal pleiotropy and heterogeneity. Third, we applied the GWAS-PW method to identify shared causal variants and pleiotropic loci for a pair of CAC or AAC and AD or cognitive traits, testing 1,703 genomic regions.\u003c/p\u003e\n \u003cp\u003eThe GWAS-PW method scans the genome to pinpoint pleiotropic loci by estimating the posterior probability that a genetic variant is causal for a pair of traits, or a genomic locus is associated with the two traits through distinct variants [\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e]. Lastly, we conducted gene-based association analyses to identify pleiotropic genes associated with CAC/AAC and AD/cognitive traits, prioritising pleiotropic loci identified in the GWAS-PW analysis. We used a recently developed and powerful gene-based analysis method, harnessing the strengths of three models of gene-based association analyses: fastBAT, mBAT, and mBAT-combo [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e]. All analyses used well-powered genome-wide association studies (GWAS) summary data from individuals of European ancestry, with no evidence of significant sample overlap between the pairs of traits assessed. The Methods section provides further details on the specific methods and data analysed. Supplementary Table\u0026nbsp;1 provides additional information and links to the GWAS data sources or associated publications.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eResults of genome-wide genetic correlation analyses\u003c/h3\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents the results of our genome-wide genetic correlation analyses. First, we observed a nominally significant positive genetic correlation (rg\u0026thinsp;=\u0026thinsp;0.10, P\u0026thinsp;=\u0026thinsp;3.14 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) between CAC and AD GWAS (from Jansen et al. [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e]), but when excluding the \u003cem\u003eAPOE\u003c/em\u003e region, the signal weakens and becomes non-significant (P\u0026thinsp;=\u0026thinsp;5.33 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e, Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). We found no evidence of a significant genome-wide correlation between CAC and another AD GWAS (Lambert et al. [\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e]). Conversely, we observed a negative correlation between CAC and cognitive traits, including cognitive performance (rg = -0.11, P\u0026thinsp;=\u0026thinsp;1.59 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e), fluid intelligence scores (rg = -0.11, P\u0026thinsp;=\u0026thinsp;1.87 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e), intelligence (rg = -0.11, P\u0026thinsp;=\u0026thinsp;5.14 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e), and educational attainment (rg = -0.10, P\u0026thinsp;=\u0026thinsp;1.08 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e), all surpassing the Bonferroni threshold. The correlation assessment between CAC and common executive function was not statistically significant (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Lastly, the genetic correlation between AAC and CAC was high (rg\u0026thinsp;=\u0026thinsp;0.95, P\u0026thinsp;=\u0026thinsp;4.70 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e), surpassing the Bonferroni-corrected significance threshold, indicating a strong shared genetic basis between the two vascular calcification traits.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003egenome-wide genetic correlation between AAC, CAC, AD and Cognitive traits\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTrait 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTrait 2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRg\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ese\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"10\"\u003e\n \u003cp\u003eCAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAD GWAS (Jansen et al)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.14 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAD GWAS (Jansen et al.) excluding APOE region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.33 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAD GWAS (Lambert et al)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.06 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAD GWAS (Lambert et al.) excluding APOE region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.17 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCognitive performance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.59 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFluid intelligence scores\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.87 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntelligence (Jansen et al 2018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.14 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecommon Executive function\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.55 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducational attainment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.08 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbdominal aortic calcification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.70 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"9\"\u003e\n \u003cp\u003eAAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAD GWAS (Jansen et al)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.32 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAD GWAS (Jansen et al.) excluding \u003cem\u003eAPOE\u003c/em\u003e region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.49 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAD GWAS (Lambert et al)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.28 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAD GWAS (Lambert et al.) excluding \u003cem\u003eAPOE\u003c/em\u003e region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.19 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCognitive performance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.49 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFluid intelligence scores\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.24 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntelligence (Jansen et al 2018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.12 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecommon Executive function\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.53 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducational attainment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.39 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAAC: abdominal aortic calcification, AD: Alzheimer\u0026rsquo;s disease, CAC: coronary artery calcification, GWAS: genome-wide association studies, \u003cem\u003eAPOE\u003c/em\u003e: Apolipoprotein E, rg: genetic correlation estimates, se: standard error, p: p-value.\u003c/p\u003e\n\u003cp\u003eSecond, we found no evidence of a significant genetic correlation between AAC and AD across different GWAS datasets, including the Jansen et al. GWAS (rg\u0026thinsp;=\u0026thinsp;0.08, P\u0026thinsp;=\u0026thinsp;2.32 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and the Lambert et al. GWAS (rg\u0026thinsp;=\u0026thinsp;0.01, P\u0026thinsp;=\u0026thinsp;9.28 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). Excluding the \u003cem\u003eAPOE\u003c/em\u003e regions did not change these outcomes (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). However, we found significant negative genetic correlations of AAC with cognitive performance (rg = -0.10, P\u0026thinsp;=\u0026thinsp;5.49 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e) and educational attainment (rg = -0.13, P\u0026thinsp;=\u0026thinsp;6.39 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e), surpassing nominal and Bonferroni significance thresholds, respectively. The correlations with fluid intelligence (rg = -0.07, P\u0026thinsp;=\u0026thinsp;5.24 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e), intelligence (rg = -0.06, P\u0026thinsp;=\u0026thinsp;1.12 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), and executive function (rg = -0.04, P\u0026thinsp;=\u0026thinsp;2.53 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) were not statistically significant.\u003c/p\u003e\n\u003ch3\u003eResults of MR-based causal association assessment\u003c/h3\u003e\n\u003cp\u003eWe conducted a two-sample Mendelian randomisation (2SMR) analysis to gain insights into the potential causal relationships between CAC or AAC and AD and cognitive traits. We employed multiple MR approaches and performed a bidirectional analysis to gain a comprehensive understanding of these associations. To enhance the reliability of our results, we carefully selected suitable instrumental variables (IVs) and addressed concerns related to horizontal pleiotropy and heterogeneity. Figure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents a detailed overview of our MR study design.\u003c/p\u003e\n\u003cp\u003eThe main MR results from the IVW model indicated no significant causal effect of CAC on AD (OR: 1.00, 95% CI: 0.99\u0026ndash;1.01, P: 0.56) or AAC on AD (OR: 1.00, 95% CI: 0.98\u0026ndash;1.03, P: 0.78) [Figures \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, respectively]. Similarly, CAC or AAC had no significant causal effect on cognitive traits (Figs. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The only exception was the causal association of AAC on fluid intelligence scores. This effect was borderline nominally significant in the IVW model and nominally significant in both the weighted median and MR-PRESSO analyses (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and Supplementary Table 2). However, the results did not survive the correction for multiple testing (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eIn the reverse analyses, our findings showed no significant causal effect of AD on CAC (OR: 1.29, 95% CI: 0.67\u0026ndash;2.46, P: 0.44) or AAC (OR: 1.00, 95% CI: 0.86\u0026ndash;1.16, P: 0.99) as summarised in Figs. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, respectively. Likewise, none of the cognitive traits had a significant causal effect on CAC or AAC, except the borderline nominally significance of fluid intelligence scores\u0026rsquo; effect on AAC (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e), which did not survive correction for multiple testing. Current findings were consistent across other MR models, such as the weighted median and MR-Egger (Supplementary Tables 2\u0026ndash;5).\u003c/p\u003e\n\u003cp\u003eThe MR-PRESSO method also found no significant relationship between AD and cognitive traits with CAC or AAC (except between fluid intelligence scores and AAC). Indeed, there were no outputs in the corrected analysis, suggesting the absence of outliers IVs. Supplementary Tables 2\u0026ndash;5 provide comprehensive results for our MR analysis. MR-Egger intercept and heterogeneity tests indicate these findings were not biased by pleiotropic instruments (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, and more comprehensively in Supplementary Table 6). Detailed information on IVs utilised for analysis is presented in Supplementary Tables 7\u0026ndash;30.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMR sensitivity analyses of AD and cognitive traits with vascular calcification\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eExposure\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHeterogeneity tests\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eHorizontal pleiotropy tests\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMethod\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCochran\u0026apos;s Q\u003c/strong\u003e \u003cstrong\u003eP\u003c/strong\u003e \u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMethod\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntercept\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoronary artery calcification vs AD and cognitive traits\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eCAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlzheimer\u0026apos;s disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eEgger intercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecExecutive function\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCognitive performance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducational attainment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFluid intelligence scores\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntelligence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbdominal aortic calcification vs AD and cognitive traits\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eAAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlzheimer\u0026apos;s disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eEgger intercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecExecutive function\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCognitive performance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducational attainment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFluid intelligence scores\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntelligence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eAD and cognitive traits vs coronary artery calcification\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlzheimer\u0026apos;s disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eCAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eEgger intercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecExecutive function\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCognitive performance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducational attainment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFluid intelligence scores\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntelligence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eAD and cognitive traits vs abdominal aortic calcification\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlzheimer\u0026apos;s disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eAAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eEgger intercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecExecutive function\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCognitive performance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducational attainment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFluid intelligence scores\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntelligence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAAC: abdominal aortic calcification, CAC: coronary artery calcification, CI: confidence interval, IVW, inverse variance weighted, MR: Mendelian randomisation, MR-PRESSO: Mendelian randomisation pleiotropy residual sum and outlier, OR: odds ratio, P: P-value, cExecutive function: common executive function.\u003c/p\u003e\n\u003ch3\u003eAddressing horizontal pleiotropy in MR analysis\u003c/h3\u003e\n\u003cp\u003eHere, we present findings demonstrating how seemingly non-significant MR pleiotropy tests can lead to erroneous claims of causal associations\u0026mdash;emphasising the importance of addressing pleiotropy and heterogeneity for valid causal estimates. For example, our initial analysis showed a significant causal effect of AD on CAC (IVW model, OR: 3.06, 95% CI: 1.60\u0026ndash;5.85, P: 7.51 \u0026times; 10⁻⁴, Supplementary Table\u0026nbsp;31). This result was supported by a non-significant pleiotropy test (Egger intercept: -0.020, P: 0.122) and corroborated by other MR methods, including MR Egger, the weighted median, and the weighted mode (Supplementary Table\u0026nbsp;31). Additionally, the crude estimate from MR-PRESSO aligned with the IVW result, supporting a significant causal effect of AD on CAC. The corrected MR-PRESSO results indicated a nominally significant causal influence of AD on CAC (OR: 2.11, 95% CI: 1.20\u0026ndash;3.73, P: 1.68 \u0026times; 10⁻\u0026sup2;). Using the same approach, we found a significant causal effect of AD on AAC (Supplementary Table\u0026nbsp;31).\u003c/p\u003e\n\u003cp\u003eHowever, these results do not reflect true causal relationships, as there was evidence of significant heterogeneity (Supplementary Table\u0026nbsp;31). Upon examining the effect of the selected instruments on the outcome variables, we found that heterogeneity arose from the association of IVs with the outcomes, violating the third assumption of MR, which requires influence on the outcome only through the exposure pathway. IVs associated with the outcome variable\u0026mdash;even at a nominal level\u0026mdash;can directly affect the outcome, violating the exclusion restriction assumption. When we addressed the heterogeneity in our study by excluding pleiotropic SNPs, the results, which were once significant, became non-significant.\u003c/p\u003e\n\u003cp\u003eBased on our experience with recent MR publications, we speculate that such false positive results occur frequently, leading to erroneous causal claims or conclusions. The example in our study, thus, underscores the importance of rigorously addressing heterogeneity in 2SM studies to avoid false claims of causality or misleading conclusions.\u003c/p\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eShared genomic loci between CAC or AAC and AD or cognitive traits\u003c/h2\u003e\n \u003cp\u003eTo advance our understanding of the relationship between CAC and AAC with AD and cognitive traits, we applied the GWAS-PW method towards identifying potential pleiotropic loci or variants (see Methods). Our analysis found that none of the tested 1,703 genomic regions had a posterior probability of association 3 (PPA3\u0026mdash;the model where a shared locus with the same causal variant influences both traits) greater than 0.5, indicating no evidence of causal SNPs associated with both CAC/AAC and AD/cognitive traits. However, we identified pleiotropic loci shared by CAC or AAC and AD or cognitive traits (Tables \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e), particularly on chromosome 19, suggesting a shared locus but with separate causal variants influencing the pair of traits. The PPA4 estimates were greater than 0.9 in these regions, indicating strong evidence or a high likelihood of pleiotropy, with the regions associated with both traits through distinct SNPs.\u003c/p\u003e\n \u003cp\u003eFor example, our findings revealed that the locus at chr19:44,744,370\u0026ndash;46,102,289 (hg19) is pleiotropic for CAC and AD, with separate top SNPs (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, PPA4\u0026thinsp;=\u0026thinsp;1). Similarly, the locus at chr19:44,744,108\u0026ndash;46,102,684 (hg19) showed strong evidence of association with AAC and AD involving distinct SNPs (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Using the mBAT-combo method, we identified genes within these regions, many of which exhibited significant associations (Bonferroni adjusted P\u003csub\u003egene\u003c/sub\u003e \u0026lt; 1.06 \u0026times; 10⁻\u0026sup3;) with CAC or AAC and AD (or at least a nominal significance) (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, PPA4\u0026thinsp;=\u0026thinsp;1), and were also identified by at least an additional gene-based method, either fastBAT or mBAT. These genes include \u003cem\u003eBCAM, TOMM40, NECTIN2, APOE, APOC1, CBLC, APOC4, APOC2, APOC4-APOC2, EXOC3L2\u003c/em\u003e, and \u003cem\u003eCLPTM1\u003c/em\u003e (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eShared genomic loci of CAC and AAC with AD\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"11\"\u003e\u003c/colgroup\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCAC/\u003c/p\u003e\n \u003cp\u003eAAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eChr: BP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePPA\u003c/p\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eShared genes\u003c/p\u003e\n \u003cp\u003eGene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eCAC/AAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eAD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGene P\u003csub\u003egene\u003c/sub\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTop SNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTop SNP P\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGene P\u003csub\u003egene\u003c/sub\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTop SNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTop SNP P\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"18\"\u003e\n \u003cp\u003eCAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"18\"\u003e\n \u003cp\u003eAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"18\"\u003e\n \u003cp\u003e19:\u003c/p\u003e\n \u003cp\u003e44744370 \u0026ndash;\u003c/p\u003e\n \u003cp\u003e46102289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"18\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBCAM\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.08 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers118147862\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.40 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers41289512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;278\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eTOMM40\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.15 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers41290120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.57 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;11\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers12972156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eNECTIN2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.29 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers41290120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.57 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;11\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers12972156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAPOE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.69 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers41290120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.57 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;11\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers12972156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAPOC1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.13 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers41290120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.57 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;11\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers12972156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCBLC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers118147862\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.40 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.67 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;264\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers41289512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;278\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAPOC4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.99 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers7412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.61 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers2075650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAPOC2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.16 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers7412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.61 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers10119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAPOC4-APOC2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.97 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers7412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.61 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers2075650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eEXOC3L2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.56 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers12461144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.03 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.95 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;66\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers10415850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.17 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;33\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eTRAPPC6A\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.08 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers12461144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.03 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.30 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;74\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers28469095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;38\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBLOC1S3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.06 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers12461144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.03 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.17 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;65\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers28469095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;38\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eNKPD1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.92 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers10421247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;79\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers28469095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;38\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCLPTM1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.14 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers7412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.61 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers769449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePPP1R37\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.59 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers10421247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.47 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;84\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers28469095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;38\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBCL3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.23 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers148933445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.26 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.93 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;127\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers2965169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.24 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;58\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eMARK4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.66 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers12461144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.03 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.44 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;98\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers28469095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;38\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCEACAM16\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.13 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers62117204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.58 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.75 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;121\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers2965169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.24 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;58\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"10\"\u003e\n \u003cp\u003eAAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"10\"\u003e\n \u003cp\u003eAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"10\"\u003e\n \u003cp\u003e19: 44744108 \u0026ndash;\u003c/p\u003e\n \u003cp\u003e46102684\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"10\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eTOMM40\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.36 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;11\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers1065853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.07 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;13\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers12972156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eNECTIN2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.44 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers1065853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.07 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;13\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers12972156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAPOE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.55 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers1065853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.07 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;13\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers12972156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAPOC1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.97 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers1065853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.07 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;13\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers12972156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAPOC2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.12 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers1065853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.07 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;13\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers10119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAPOC4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.43 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers1065853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.07 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;13\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers2075650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAPOC4-APOC2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.55 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers1065853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.07 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;13\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers2075650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCLPTM1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.55 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers1065853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.07 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;13\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers769449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBCAM\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers4803760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.00 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers41289512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.46 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;278\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCBLC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.26 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers4803760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.00 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.67 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;264\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers41289512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.46 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;278\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eAAC: abdominal aortic calcification, CAC: coronary artery calcification, AD: Alzheimer\u0026rsquo;s disease, SNP: single nucleotide polymorphism, P: p-value. PPA4: Posterior Probability of Association for Model 4\u0026mdash;the probability that a genetic locus is associated with both traits, signifying pleiotropy, but independent variants are associated with each trait. For instance, a PPA4 value\u0026thinsp;\u0026gt;\u0026thinsp;0.90 indicates a high certainty that the locus is pleiotropic for both traits. * Gene-based analysis was computed using the mBAT-combo approach, with results consistent with at least one additional gene-based method, either mBAT or fastBAT.\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e highlights the shared loci between CAC/AAC and cognitive traits and the pleiotropic genes associated with each trait pair. Notably, CAC and cEF share a region on chromosome 19: 44744370 \u0026minus;\u0026thinsp;46102547 (hg19, PPA4\u0026thinsp;=\u0026thinsp;1), where the implicated gene (\u003cem\u003ePHLDB3\u003c/em\u003e) is only nominally significant for both traits. This locus was identified exclusively by the mBAT model. Similar to AD, regions in chromosome 19 were significantly associated with CAC or AAC and some cognitive traits, including educational attainment, cEF, and fluid intelligence scores, as presented in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eShared genomic loci of CAC and AAC with cognitive traits\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"11\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCAC/ AAC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCT\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eChr: BP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePPA4\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eShared genes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eCAC/AAC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eCT\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGene P*\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTop SNP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTop SNP P\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGene-P*\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTop SNP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTop SNP P\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecEF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19: 44744370\u0026ndash;46102547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e**PHLDB3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.38 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers62115754\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.28 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.88 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers11668385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.10 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"13\"\u003e\n \u003cp\u003eCAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"13\"\u003e\n \u003cp\u003eEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"13\"\u003e\n \u003cp\u003e19:\u003c/p\u003e\n \u003cp\u003e44744370\u0026ndash;46102547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"13\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eTOMM40\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.15 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers41290120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.57 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;11\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.56 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers405509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.07 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eNECTIN2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.29 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers41290120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.57 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;11\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.05 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers405509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.07 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAPOE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.69 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers41290120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.57 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;11\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.88 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers405509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.07 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAPOC1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.13 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers41290120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.57 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;11\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.22 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers405509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.07 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAPOC4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.99 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers7412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.61 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.83 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers405509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.07 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAPOC2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.16 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers7412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.61 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.01 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers405509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.07 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAPOC4-APOC2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.97 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers7412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.61 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.72 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers405509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.07 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eEXOC3L2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.56 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers12461144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.03 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.51 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers386569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.22 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eTRAPPC6A\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.08 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers12461144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.03 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.77 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers12974200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.56 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBLOC1S3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.06 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers12461144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.03 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.50 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers151165225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.27 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCLPTM1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.14 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers7412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.61 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.07 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers405509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.07 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePPP1R37\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.59 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers10421247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.04 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.94 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers139290129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.95 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eMARK4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.66 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers12461144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.03 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.59 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers10402747\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.71 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"10\"\u003e\n \u003cp\u003eAAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"10\"\u003e\n \u003cp\u003ecEF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"10\"\u003e\n \u003cp\u003e19: 44744147\u0026ndash;46101600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"10\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eTOMM40\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.36 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;11\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers1065853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.07 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;13\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.57 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;15\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers429358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.52 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;20\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eNECTIN2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.44 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers1065853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.07 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;13\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.08 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;14\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers429358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.52 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;20\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAPOE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.55 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers1065853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.07 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;13\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.68 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;16\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers429358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.52 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;20\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAPOC1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.97 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers1065853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.07 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;13\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.18 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;16\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers429358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.52 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;20\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAPOC2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.12 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers1065853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.07 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;13\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.02 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;16\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers429358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.52 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;20\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAPOC4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.43 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers1065853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.07 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;13\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.02 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;15\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers429358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.52 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;20\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAPOC4-APOC2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.55 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers1065853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.07 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;13\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.48 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;16\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers429358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.52 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;20\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCLPTM1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.55 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers1065853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.07 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;13\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.71 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;15\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers429358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.52 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;20\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBCAM\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.06 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers4803760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.00 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.63 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers4803764\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.24 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCBLC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.26 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers4803760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.00 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.78 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers12162222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.16 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"8\"\u003e\n \u003cp\u003eAAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"8\"\u003e\n \u003cp\u003eEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"8\"\u003e\n \u003cp\u003e19: 44744147\u0026ndash;46101600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"8\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eTOMM40\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.36 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;11\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers1065853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.07 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;13\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.56 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers405509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.07 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eNECTIN2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.44 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers1065853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.07 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;13\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.05 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers405509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.07 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAPOE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.55 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers1065853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.07 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;13\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.88 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers405509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.07 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAPOC1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.97 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers1065853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.07 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;13\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.22 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers405509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.07 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAPOC2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.12 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers1065853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.07 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;13\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.01 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers405509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.07 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAPOC4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.43 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers1065853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.07 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;13\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.83 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers405509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.07 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAPOC4-APOC2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.55 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers1065853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.07 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;13\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.72 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers405509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.07 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCLPTM1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.55 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers1065853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.07 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;13\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.07 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers405509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.07 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eAAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eFIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003e19: 44744147\u0026ndash;46101600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"6\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eTOMM40\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.36 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;11\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers1065853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.07 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;13\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.54 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers11668861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.23 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eNECTIN2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.44 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers1065853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.07 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;13\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.19 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers8113311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.09 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAPOE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.55 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers1065853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.07 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;13\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.20 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers11668861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.23 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAPOC1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.97 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers1065853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.07 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;13\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.93 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers11668861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.23 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBCAM\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.06 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers4803760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.00 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.39 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers8113311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.09 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCBLC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.26 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers4803760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.00 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.41 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers8113311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.09 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eAAC: abdominal aortic calcification, CAC: coronary artery calcification, cEF: common executive function, CT: cognitive traits, EA: educational attainment, FIS: fluid intelligence scores, SNP: single nucleotide polymorphism, P: p-value. PPA4: Posterior Probability of Association for Model 4\u0026mdash;the probability that a genetic locus is associated with both traits, signifying pleiotropy, but independent variants are associated with each trait. For instance, a PPA4 value\u0026thinsp;\u0026gt;\u0026thinsp;0.90 indicates a high certainty that the locus is pleiotropic for both traits. * Gene-based analysis was computed using the mBAT-combo approach, with results consistent with at least one additional gene-based method, either mBAT or fastBAT. **gene identified by mBAT gene-based model only.\u003c/p\u003e\n \u003cp\u003eLastly, we identified additional loci with only suggestive association in the GWAS-PW analysis implicating regions in chromosomes one, six, seven and 19 and their corresponding significant genes for the pair of traits assessed. Supplementary Table\u0026nbsp;32 provides information about these loci and the likely pleiotropic genes implicated.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eUsing well-established methods, we present findings from analyses assessing the potential shared genetic architecture and causal relationships between CAC and AAC and AD or cognitive traits. Our genome-wide genetic correlation analysis revealed a nominally significant association between CAC and AD. However, we did not replicate this result in another AD dataset. Although differences in study power may explain some variability, the nominal significance initially observed disappeared after excluding the \u003cem\u003eAPOE\u003c/em\u003e region, suggesting that the excluded region primarily drove the correlation. Furthermore, there was no evidence of a significant genome-wide correlation between AAC and AD.\u003c/p\u003e \u003cp\u003eConversely, we found a significant negative genetic correlation between CAC and several cognitive traits, surviving correction for multiple testing, including cognitive performance, educational attainment, and intelligence scores. AAC also demonstrated a significant negative correlation with cognitive performance and educational attainment. These significant negative genetic correlations suggest that a higher genetic predisposition to CAC and AAC is associated with lower cognitive abilities, potentially aligning with studies that reported the association of vascular calcification with cognitive decline [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Conversely, given it is a genetic correlation assessment, the inverse relationship could also suggest that genetic predisposition to a higher performance on the selected cognitive traits is associated with a reduced risk of vascular calcification. It is crucial to note that genetic correlation does not imply causation due to possibilities for alternative explanations, including pleiotropy or shared genetic susceptibility. Additionally, for traits like educational attainment, non-genetic factors\u0026mdash;such as social status, socioeconomic conditions, and environmental influences\u0026mdash;may further complicate the relationship. Thus, while our findings indicate an inverse association between CAC/AAC and certain cognitive traits, alternative explanations and interactions between genetics, cognition, environment, and various life outcomes may also play a role.\u003c/p\u003e \u003cp\u003eWe performed bidirectional MR analyses to examine the potential causal associations of CAC and AAC with AD and cognitive traits and gain further insights into the nature of the relationships. Our comprehensive and rigorous analyses indicate that CAC or AAC is not causally associated with AD or cognitive traits, regardless of the direction of analysis\u0026mdash;whether CAC or AAC was exposure or outcome variables. The only exception was the causal effect of AAC on fluid intelligence scores and vice versa, which was only borderline nominally significant in the IVW model. These results did not survive correction for multiple testing, making them less convincing. Our findings were consistent across several MR models, and tests for heterogeneity or pleiotropy did not indicate potential bias.\u003c/p\u003e \u003cp\u003eVascular calcification, specifically CAC, is a well-established tool for improved risk prediction of subsequent atherosclerotic cardiovascular disease [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Atherosclerosis (characterised by vascular calcification) is associated with the risk of AD [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Indeed, a recent study highlighted an additive interaction effect between atherosclerosis and AD on cognitive functions [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Hence, it is reasonable to hypothesise that vascular calcification might be related to AD or cognitive traits. Several conventional observational studies support this hypothesis, suggesting a positive association between vascular calcification and AD or cognitive decline [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], although mixed results have also been reported [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Our genetic-based assessment did not confirm convincing significant causal associations. Current results provide new insights into the interplay between these phenotypes and improve our understanding beyond what is known through traditional observational evidence.\u003c/p\u003e \u003cp\u003eFor example, a recent observational study we contributed to reported an association between AAC and an increased risk of all-cause late-life dementia among elderly women [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. While this previous study highlighted a potential link [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], our current genetic-based research does not support causality. Unlike the genetic-based approach, traditional observational studies are often limited by many factors such as unmeasured confounders and biases from lifestyle or environment, which may clarify our findings. Further, it should be noted that the present study focuses on AD, while the observational study [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] did not differentiate between the various types of dementia and was limited to late-life onset (after 80 years) in women only. Hence, there is a need for further investigation into specific dementia subtypes to better understand the relationship between vascular calcification, dementia, and cognitive decline.\u003c/p\u003e \u003cp\u003eOur study emphasises the critical importance of rigorously addressing pleiotropy and heterogeneity to enhance the reliability of MR findings. Despite the challenges in proving some of them, it is essential to uphold the core assumptions of MR to infer valid causal estimates [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Adhering to best practices is vital to prevent estimating spurious causal associations, including implementing various MR approaches and conducting sensitivity testing [\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Notably, heterogeneity tests can reveal potential biases in MR studies, as illustrated in our results. For example, there was evidence for heterogeneity between AD (as exposure) and CAC (as outcome) based on Cochran's Q P value (Supplementary Table\u0026nbsp;31) in our illustrated example. This evidence signals a potential violation of MR assumptions, and ignoring such would have resulted in false positives in our study. This observation, thus, underscores the need for further assessment, such as checking that the IVs are valid. We often found cases where authors overlook such significant heterogeneity tests in recent MR studies, potentially leading to claims of causality that may not be true.\u003c/p\u003e \u003cp\u003eThe limited genome-wide genetic correlation and non-causal associations results in our analyses do not preclude shared genetic predisposition through specific regions in the genome. Hence, we progressed our study to investigate the potential shared variants or loci between CAC/AAC and AD or cognitive traits using the GWAS-PW method. Our findings revealed that none of the 1,703 tested genomic regions exhibited a PPA3 greater than 0.5, indicating a lack of evidence for causal SNPs influencing CAC or AAC and AD or cognitive traits. However, we identified loci on chromosome 19 demonstrating a high likelihood of pleiotropy (PPA4\u0026thinsp;\u0026gt;\u0026thinsp;0.9) with distinct causal variants affecting CAC/AAC and AD/cognitive traits. These findings implicate pleiotropic genetic underpinnings, primarily driven by the identified loci, particularly regions in chromosome 19\u0026mdash;partly highlighting consistency between our genetic correlation and GWAS-PW results.\u003c/p\u003e \u003cp\u003eWithin the implicated loci on chromosome 19, we identified several pleiotropic genes, including \u003cem\u003eBCAM, TOMM40, NECTIN2, APOE, APOC1, CBLC, APOC4, APOC2, APOC4-APOC2, EXOC3L2\u003c/em\u003e, and \u003cem\u003eCLPTM1\u003c/em\u003e. These genes exhibited significant associations with both CAC or AAC and AD or cognitive traits, indicating that they play roles in their genetic architecture, and potentially the co-occurring state of CAC/AAC with AD/cognitive traits. Furthermore, our analysis revealed shared loci between CAC/AAC and cognitive traits, as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, including a significant region on chr19: 44744370\u0026ndash;46102547 associated with CAC and cEF via the gene \u003cem\u003ePHLDB3.\u003c/em\u003e Although this gene was only nominally significant (the only significant gene for both traits) and identified only by the mBAT model of the gene-based analysis, the region has PPA4\u0026thinsp;=\u0026thinsp;1, indicating a high probability of being pleiotropic (via distinct variants) for the pair of traits.\u003c/p\u003e \u003cp\u003eIn conclusion, our study provides new insights into the complex genetic relationships of CAC and AAC with AD and cognitive traits. While we observed some genetic correlations, particularly a negative association between CAC/CAC and cognitive traits, our findings highlight the absence of causal relationships in the MR analyses. Further assessment using the GWAS-PW method suggests that the observed associations do not indicate the presence of shared causal SNPs but reflect shared genetic susceptibilities influenced by distinct variants in the implicated loci. Identifying pleiotropic loci and significant genes associated with vascular calcification, AD, and cognitive traits further supports this position. The identified shared genes and loci provide essential targets for further investigation in AD, cognitive traits, and vascular calcification.\u003c/p\u003e\n\u003ch3\u003eStrengths and limitations\u003c/h3\u003e\n\u003cp\u003eA key strength of this study is its use of a genetic approach, which is less susceptible to confounding from environmental and lifestyle factors, thus enhancing the reliability of our findings beyond what is possible through the conventional observational study approach. To our knowledge, this is the first study to investigate the relationship between vascular calcification, AD, and cognitive traits using statistical genetic methods. However, several limitations should be considered when interpreting our results. Firstly, the data utilised are exclusively from the European population, limiting the generalisability of our findings to other ancestries. Secondly, due to a lack of sufficient genome-wide significant instruments for CAC and AAC, we relaxed the threshold for IVs selection in our MR analysis to the suggestive level. This observation may indicate less powerful GWAS results for CAC and AAC, which could affect the non-significant causal associations observed in our study. Future research needs to explore these relationships further as more robust CAC and AAC GWAS data become available. Thirdly, although sample overlap can complicate analyses such as genetic correlation, MR, and GWAS-PW, our preliminary evaluation indicates no evidence of significant sample overlap between CAC/AAC and AD/cognitive traits, thus ruling out bias from this factor in our study. Finally, our findings of limited genetic overlap and no causal association of CAC and AAC with AD or cognitive traits do not eliminate the possibility of associations due to shared risk factors. Therefore, comprehensive prospective studies would be beneficial for further elucidating the nature of these associations. Moreover, we recommend an assessment of the relationships based on the various types of dementia.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eData sources\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe leveraged large-scale genome-wide association study (GWAS) summary data from publicly available repositories. We sourced GWAS summary data for CAC from a recent study\u0026nbsp;[39], which included 28,655 individuals. For AAC, we used data from another recent publication\u0026nbsp;[40]\u0026nbsp;with a sample size of 31,786. Additionally, we drew on one of the largest publicly available AD GWAS datasets, which comprised 71,880 cases and 383,378 controls, encompassing both clinically diagnosed AD and AD by proxy\u0026nbsp;[34]. For cognitive traits, we included summary data from five studies: cEF (n = 427,037)\u0026nbsp;[26], cognitive performance (n = 257,828)\u0026nbsp;[41], intelligence (n = 269,867)\u0026nbsp;[42], fluid intelligence scores (n = 125,935)\u0026nbsp;[43], and educational attainment (n = 766,345)\u0026nbsp;[41]. cEF refers to a unified cognitive factor that captures the shared variance across multiple executive function (EF) tasks, including response inhibition, interference control, working memory updating, and set-shifting\u0026nbsp;[26]. In the original publication, the authors conducted a GWAS analysis to derive a cEF factor score that represents the common executive control elements across various cognitive-related tasks\u0026nbsp;[26]. Participants in the data underlying our study were individuals of European ancestry. Detailed descriptions of the datasets, including participant demographics, study settings, measurements, and quality control procedures, are available in the referenced publications. Additional cohort-specific information and data download links can be found in Supplementary Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCross-trait genome-wide genetic correlation analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted cross-trait genetic correlation analyses between CAC and each of AD and cognitive traits using the LDSC method. We similarly assessed the genetic correlation of AAC with AD and cognitive traits. The LDSC method estimates trait heritability and genetic correlations by regressing GWAS test statistics (e.g., Z-scores) on LD scores for each SNP\u0026nbsp;[44]. The method can distinguish true genetic signals from confounding factors and provide robust estimates of genetic relationships between traits\u0026nbsp;[44].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInitially, we estimated genetic correlations with an unconstrained genetic covariance intercept to assess the potential proportion of sample overlap between traits, similar to the practice in previous studies\u0026nbsp;[24, 25, 44-47]. Our findings indicated that the genetic covariance intercepts were not significantly different from zero, suggesting no substantial sample overlap between AAC or CAC and AD or cognitive traits. Consequently, we proceeded with genetic covariance intercept-constrained analyses. In all LDSC analyses, pre-computed LD scores from the 1000 Genomes European reference panel were applied, excluding SNPs that did not intersect with the reference panel or had a MAF \u0026lt; 1%. To explore the potential impact of the APOE region (especially on AD), we conducted analyses both with and without these regions. A Bonferroni-corrected significance threshold of 8.33 \u0026times; 10⁻\u003csup\u003e3\u003c/sup\u003e was applied for genetic correlations involving AD and five cognitive traits, with P-values below 0.05 considered nominally significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCausal relationship assessment using Mendelian randomisation\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used a two-sample 2SMR analysis to explore the potential causality of CAC or AAC with AD and various cognitive traits. Several MR methods were utilised, and we conducted a bidirectional assessment for a clear insight into the likely causal relationships of these traits. To ensure the robustness of our findings, we carefully selected appropriate instrumental variables (IVs) for the analysis and rigorously addressed potential issues around horizontal pleiotropy and heterogeneity. The procedural outline of our study is depicted in Figure 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSelection of instrumental variables for MR analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBriefly, we selected IVs at the genome-wide significance (GWS) level (P \u0026lt; 5 \u0026times; 10⁻⁸) from the relevant GWAS summary data used in our study. This stringent selection criterion ensures that the IVs are strongly associated with the exposure variables, with an F-statistic greater than 10\u0026nbsp;[27], thereby minimising the risk of weak instrument bias and fulfilling the first assumption of MR. Due to the limited availability of GWS IVs for CAC and AAC as exposure variables, we relaxed the selection threshold to the genome-wide suggestive level (P \u0026lt; 1 \u0026times; 10⁻⁵). This adjustment was made with the understanding that using a smaller number of SNPs (\u0026lt; 10) as IVs may introduce potential bias into our study. Although the second MR assumption\u0026mdash;that IVs are not associated with confounders\u0026mdash;is challenging or nearly impossible to fully validate, we rigorously evaluated our IVs and performed linkage disequilibrium clumping at a stringent threshold (\u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e \u003cem\u003e\u0026lt;\u0026nbsp;\u003c/em\u003e0.001, Figure 1) to enhance the independence of the selected instruments. Importantly, we ensured that our IVs were not linked to the outcome variables (P \u0026lt; 0.05), thereby adhering to the third MR assumption. As highlighted in the sub-sections for the main MR and sensitivity analyses, we carried out other specific tests, to ensure our IVs are robust.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePerforming MR analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used the inverse-variance weighted (IVW) as the main MR model in the current study. The IVW method assumes the absence of horizontal pleiotropy, and the model is reliable so long this assumption holds. To address potential heterogeneity among the causal estimates derived from different variants, we employed multiplicative random effects of the IVW model. Also, to complement our IVW estimates, we used additional MR methods, including the weighted median (which can yield valid estimates with up to 50% invalid IVs) and the MR-Egger (which can provide valid estimates by correcting for pleiotropy)\u0026nbsp;[27, 28]. We consider MR results with P \u0026lt; 0.05 as nominally significant and implemented a Bonferroni correction to mitigate the risk of false positives due to multiple tests across the various outcomes. Based on this adjustment, we set the significance threshold at P \u0026lt; 0.008 (0.05/6), corresponding to the analysis of six outcome variables (where applicable). We utilised the R statistical packages and the Unix environment for data management and analyses and used the 2SMR software (version 0.5.6), and MR-PRESSO for the MR analyses (implemented on the R packages [version 4.2.1]).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMR sensitivity analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFollowing the practice in related studies\u0026nbsp;[27, 48-52], we conducted further tests to assess the reliability of our results. These tests include Cochran\u0026apos;s Q statistics to evaluate the heterogeneity of SNP effects, individual MR analyses, \u0026apos;leave-one-out\u0026apos; analyses to determine the impact of each IV on the overall results, and examining the funnel plot for symmetry. We used the MR-Egger intercept to check for potential violations of the assumption of no unbalanced pleiotropy. Significant deviations of the MR-Egger intercept from zero indicate a possible violation of this assumption. Additionally, we implemented the \u0026apos;MR-pleiotropy residual sum and outlier\u0026apos; (MR-PRESSO) method, known for identifying and addressing pleiotropy by excluding outliers\u0026nbsp;[53].\u0026nbsp;Importantly, as an additional step, we meticulously reviewed our analyses and excluded IVs associated with the outcome variables at a significance level of P \u0026lt; 0.05. This process involved scrutinising our LD clumped (clumping performed at r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and properly harmonised data and excluding IVs with P\u003csub\u003eoutcome-variable\u003c/sub\u003e \u0026lt; 0.05, followed by MR analysis on the remaining instruments.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssessing shared genetic risk loci using the pairwise GWAS approach\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted colocalisation analysis using the GWAS-PW method, a tool designed to scan the genome for regions that likely share a causal variant or pleiotropic loci between traits\u0026nbsp;[33]. This program applies a Bayesian statistical model to estimate the probability (or PPA) and we modelled four potential scenarios: (1) a region contains a variant associated only with trait 1 (PPA1); (2) a variant associated only with trait 2 \u0026nbsp; (PPA2); (3) a variant associated with both traits 1 and 2 (PPA3); or (4) independent variants are associated with each trait (PPA4) but the region is shared by both traits\u0026nbsp;[33]. In this study, we first conducted an analysis using CAC as trait 1 against AD and cognitive traits as trait 2. In the second analysis, we used AAC as trait 1 and again compared it with AD and cognitive traits as trait 2.\u003c/p\u003e\n\u003cp\u003eWe applied GWAS-PW to assess potential shared causal variants and loci between CAC and AAC with AD and cognitive traits. The summary statistics for CAC and AAC were aligned with AD and cognitive trait data by rsID and alleles, ensuring consistent effect and non-effect alleles across traits. Standardised Z-scores and variances for each SNP were then used as input to the GWAS-PW model\u0026nbsp;[33]. The analysis covered 1,703 predefined independent genetic regions based on LD patterns from the 1000 Genomes Project European reference data. There is no evidence for a significant overlap of samples between CAC or AAC and AD or cognitive traits, ruling out potential confounding or the need for adjustments in our analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur focus was on PPA3 and PPA4 results, thus, we considered regions with PPA3 \u0026gt; 0.9 to have a significant shared causal variant between the two traits (e.g., CAC and AD), while those with PPA3 \u0026gt; 0.5 were deemed suggestive. Similarly, we interpreted Loci with PPA4 \u0026gt; 0.9 as harbouring distinct causal variants for each trait, influencing both independently while PPA4 \u0026gt; 0.5 was considered a suggestive association. The identification of risk variants or loci by GWAS-PW was further refined using three gene-based association analysis methods, including fastBAT, mBAT and the \u0026lsquo;mBAT-combo\u0026rsquo; [31, 32] to aggregate variant signals within genes and assess their overall contribution. Briefly, we aimed to detect shared genes in regions with strong evidence for pleiotropy based on PPA4 \u0026gt; 0.9 (and at the suggestive level, PPA4 \u0026gt; 0.5), and we used the gene-based method to ensure the mapped genes are associated with the pairs of traits assessed. The mBAT-combo method combines mBAT and fastBAT statistics and is superior to traditional sum-\u0026chi;\u0026sup2; approaches, especially for identifying genes with masking effects [32]. The method has proven more powerful in simulations and real-world data [32]; hence, we prioritise using the method in the current study. SNPs were mapped within 50kb of gene boundaries for this analysis.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted based on a secondary analysis of pre-existing, completely de-identified genetic data. Ethical approval was obtained for each of the primary studies that published the data used, as detailed in the original associated studies. No additional ethical clearance was necessary for this current investigation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe GWAS data used in this study were sourced from publicly accessible repositories and research groups or consortia, as specified in the data sources section. Supplementary Table S1 contains further details about these datasets, including links to their sources. The published article and its supplementary materials include all data generated during this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe gratefully acknowledge all the international research groups and consortia that generated the GWAS data analysed in this study.\u0026nbsp;This research work has been supported by the Western Australian Future Health Research and Innovation Fund.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eE.O.A. and S.M.L. contributed to the conceptualisation of the study. E.O.A. designed and performed data curation and formal analysis. E.O.A. wrote the original draft, and both E.O.A. and S.M.L. participated in reviewing and editing the manuscript. S.M.L. and E.O.A. acquired funding. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eKnopman, D.S., et al., \u003cem\u003eAlzheimer disease.\u003c/em\u003e Nature Reviews Disease Primers, 2021. \u003cstrong\u003e7\u003c/strong\u003e(1): p. 33.\u003c/li\u003e\n \u003cli\u003eNandi, A., et al., \u003cem\u003eCost of care for Alzheimer\u0026rsquo;s disease and related dementias in the United States: 2016 to 2060.\u003c/em\u003e npj Aging, 2024. \u003cstrong\u003e10\u003c/strong\u003e(1): p. 13.\u003c/li\u003e\n \u003cli\u003eNandi, A., et al., \u003cem\u003eGlobal and regional projections of the economic burden of Alzheimer\u0026apos;s disease and related dementias from 2019 to 2050: a value of statistical life approach.\u003c/em\u003e EClinicalMedicine, 2022. \u003cstrong\u003e51\u003c/strong\u003e.\u003c/li\u003e\n \u003cli\u003eInternational, A.s.D. \u003cem\u003eDementia statistics\u003c/em\u003e. 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\u003cstrong\u003e43\u003c/strong\u003e(2): p. 03331024221139253.\u003c/li\u003e\n \u003cli\u003eAdewuyi, E.O., O\u0026rsquo;Brien, E.K., Porter, T., \u0026amp;Laws, S.M., \u003cem\u003eRelationship of Cognition and Alzheimer\u0026amp;rsquo;s Disease with Gastrointestinal Tract Disorders: A Large-Scale Genetic Overlap and Mendelian Randomisation Analysis.\u003c/em\u003e International Journal of Molecular Sciences, 2022. \u003cstrong\u003e23\u003c/strong\u003e(24): p. 16199.\u003c/li\u003e\n \u003cli\u003eVerbanck, M., Chen, C.-y., Neale, B., \u0026amp;Do, R., \u003cem\u003eDetection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases.\u003c/em\u003e Nature genetics, 2018. \u003cstrong\u003e50\u003c/strong\u003e(5): p. 693-698.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Alzheimer's disease, abdominal aortic calcification, coronary artery calcification, cognitive traits, Mendelian randomisation, vascular calcification","lastPublishedDoi":"10.21203/rs.3.rs-5275152/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5275152/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eObservational studies suggest a link between vascular calcification and dementia or cognitive decline, but the evidence is conflicting, and the underlying mechanisms are unclear. Here, we investigate the shared genetic and causal relationships between vascular calcification—coronary artery calcification (CAC) and abdominal aortic calcification (AAC)—and Alzheimer’s disease (AD), as well as cognitive traits, by analysing large-scale genome-wide association studies summary statistics. We observed a nominally significant positive genome-wide genetic correlation between CAC and AD, which became non-significant after excluding the \u003cem\u003eAPOE\u003c/em\u003e region. CAC and AAC demonstrate significant negative correlations with cognitive performance and educational attainment. Mendelian randomisation revealed no causal association between CAC or AAC and AD or cognitive traits, except for a bidirectional borderline significance of AAC with fluid intelligence scores. Pairwise-GWAS analysis identified no shared causal SNPs (posterior probability of association [PPA]3 \u0026lt; 0.5). However, we found pleiotropic loci (PPA4 \u0026gt; 0.9), particularly on chromosome 19 with ‘mBAT-combo’ analyses revealing significant genes in shared regions, including \u003cem\u003eAPOE, TOMM40, NECTIN2\u003c/em\u003e, and \u003cem\u003eAPOC1\u003c/em\u003e. Moreover, we identified suggestively significant loci (PPA4 \u0026gt; 0.5) on chromosomes 1, 6, 7, 9 and 19, highlighting pleiotropic genes, including \u003cem\u003eNAV1, IPO9, PHACTR1, UFL1, FHL5\u003c/em\u003e, and \u003cem\u003eFOCAD\u003c/em\u003e. Current findings reveal limited genome-wide genetic correlation and no significant causal associations of CAC and AAC with AD or cognitive traits. However, significant pleiotropic loci and genes underscore shared genetic susceptibility of CAC and AAC with AD and cognitive traits, identifying targets for further investigation.\u003c/p\u003e","manuscriptTitle":"Assessing shared genetic and causal links between vascular calcification, Alzheimer's disease, and cognitive traits","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-04 04:43:50","doi":"10.21203/rs.3.rs-5275152/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"23164e68-4426-4a82-8526-685a663a0b2f","owner":[],"postedDate":"November 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":39556203,"name":"Biological sciences/Genetics"},{"id":39556204,"name":"Biological sciences/Genetics/Genetic association study"},{"id":39556205,"name":"Biological sciences/Genetics/Genomics"},{"id":39556206,"name":"Health sciences/Diseases/Neurological disorders/Dementia"},{"id":39556207,"name":"Health sciences/Diseases/Neurological disorders/Neurodegenerative diseases/Alzheimers disease"},{"id":39556208,"name":"Health sciences/Diseases/Cardiovascular diseases/Vascular diseases/Calcification"}],"tags":[],"updatedAt":"2025-03-17T17:34:57+00:00","versionOfRecord":{"articleIdentity":"rs-5275152","link":"https://doi.org/10.3390/biomedicines13030618","journal":{"identity":"biomedicines","isVorOnly":true,"title":"Biomedicines"},"publishedOn":"2025-03-03 00:00:00","publishedOnDateReadable":"March 3rd, 2025"},"versionCreatedAt":"2024-11-04 04:43:50","video":"","vorDoi":"10.3390/biomedicines13030618","vorDoiUrl":"https://doi.org/10.3390/biomedicines13030618","workflowStages":[]},"version":"v1","identity":"rs-5275152","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5275152","identity":"rs-5275152","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}