We obtained GWAS summary statistics for AD from a meta-analysis of 46 AD GWAS datasets (21,982 cases and 41,944 controls) by the International Genomics of Alzheimer’s Project (IGAP) (Lambert et al., 2013; Kunkle et al., 2019). A total of 9,456,058 common variants and 2,024,574 rare variants passed the quality control (Kunkle et al., 2019). We obtained GWAS summary statistics for EA and cognitive performance from a meta-analysis of 71 cohort-level results by the Social Science Genetic Association Consortium (SSGAC), containing 1,131,881 and 257,828 individuals, respectively (Lee et al., 2018). GWAS summary statistics for intelligence were obtained from the Center for Neurogenomics and Cognitive Research (CNCR) CTGlab (Sniekers et al., 2017). Sniekers et al. (2017) performed a meta-analysis of several intelligence GWAS, including UK Biobank web-based measure (N = 17,862), UK Biobank touchscreen measure (N = 36,257), Childhood Intelligence Consortium (CHIC) (N = 12,441), and five additional cohorts (N = 11,748). We obtained childhood intelligence GWAS from CHIC (Benyamin et al., 2014). CHIC assessed the intelligence of 17,989 children of European origin aged 6–18 years and performed the single-nucleotide polymorphism (SNP) classification of GWAS, including six discovery (N = 12,441) and three replication (N = 5,548) cohorts (Benyamin et al., 2014). Details of the participants were shown in the original studies (Lambert et al., 2013; Sniekers et al., 2017; Lee et al., 2018; Kunkle et al., 2019).

We removed the rare variants (MAF <0.01) and performed the exchange of the reference genome (hg18/hg19) required in part of the study. We only focused on autosomal chromosomes and excluded the HLA region in this study. All participants were of European descent, and informed consent was obtained from all the participants in each study (Sniekers et al., 2017; Lee et al., 2018; Kunkle et al., 2019). The study protocols were approved by the corresponding institutional review boards.

We performed a cross-trait linkage disequilibrium score (LDSC) regression to evaluate the genetic correlations of AD with the four cognition-related phenotypes (Bulik-Sullivan B. et al., 2015; Qiu et al., 2023). We used precomputed LD scores derived from HapMap3 reference panels, which contained more than one million European participants from UK Biobank (Bulik-Sullivan B. et al., 2015; Bulik-Sullivan B. K. et al., 2015). LDSC regression first calculated the average LD between SNPs across the genome and then regression of the GWAS summary statistics based on baseline LD scores (Bulik-Sullivan B. et al., 2015). The slope of the regression line represented an estimate of heritability for a trait or disease. Notably, LDSC also corrected for confounding factors such as sample overlap and population stratification, which might affect heritability estimates. More details about the LDSC algorithm have been reported in previous studies (Bulik-Sullivan B. et al., 2015). The statistically significant association after multiple testing is defined to be p < 0.05/4 = 0.0125.

We divided the gene components into pre-specified LD-independent segments (1,703 segments) and calculated the local genetic correlation of each segment separately. Herein, we performed two powerful computing tools: Heritability Estimation from Summary Statistics (HESS) and pairwise analysis of GWAS (GWAS-PW) (Pickrell et al., 2016; Shi et al., 2017). HESS was used for estimating and visualizing the local SNP-heritability and genetic correlations and calculating genetic covariance to measure the similarity between a pair of traits driven by genetic variants (Shi et al., 2017). GWAS-PW was used for evaluating the local correlations and associated SNPs of each segment, under a Bayesian colocalization framework (Pickrell et al., 2016; Shi et al., 2017). The statistically significant association for HESS is defined to be p < 0.05/1703 = 2.94E-05 after correcting for multiple testing. The statistically significant association for GWAS-PW is defined to be posterior probability 3 (PPA_3) > 0.9.

MR study is an effective method for the analysis of causal inference in epidemiology (Hemani et al., 2018). MR analysis uses independent genome-wide significant SNPs (P < 5E-08, r ² < 0.3) as instrumental variables (IVs) to estimate the causal estimates of exposure on the outcome (Hemani et al., 2018; Qiu et al., 2021; Hu et al., 2022; Qiu et al., 2022). The genotypes of SNP instruments are established at birth and may not be altered by confounding factors. Here, we selected 262, 116, and 146 independent genome-wide significant SNPs from GWAS summary statistics for EA, cognitive performance, and intelligence, respectively, as instruments to perform two-sample MR (Hemani et al., 2018). We implemented MR-Egger and MR Pleiotropy RESidual Sum and Outlier (MR-PRESSO) as sensitivity analyses (Bowden et al., 2016; Hemani et al., 2018; Verbanck et al., 2018). The TwoSampleMR (version 0.5.6) and MR-PRESSO (version 1.0) R packages were used for MR analyses. p < 0.05/3 = 0.0167 was considered significant enrichment after multiple testing.

The association across EA, cognitive performance, childhood intelligence, and intelligence may interfere with the effect of one of these cognition-related phenotypes on AD. Here, we performed multi-trait-based conditional and joint (mtCOJO) analysis to adjust for pleiotropic SNPs among the cognition-related phenotypes and then implemented generalized summary-data-based Mendelian randomization (GSMR) analysis using conditional GWAS data (Zhu et al., 2018).

The mtCOJO method is performed as described in Zhu et al. (2018). If we adjust for the three covariates (cognitive performance, childhood intelligence, and intelligence) when estimating the influence of an SNP on EA, it is to use the causal estimates of cognitive performance, childhood intelligence, and intelligence on EA calculated by GSMR analysis as the condition in a GWAS conditional analysis (Zhu et al., 2018). In other words, in exploring the causal relationship between EA and AD, we removed the effects of three covariates from exposure. As a result, we obtained conditional GWAS data for each phenotype after adjusting for pleiotropy.

Leveraging conditional GWAS data, we investigated the causality between cognition-related phenotypes and AD using GSMR analysis (Hemani et al., 2018). Based on the MR framework, GSMR performs summary-based Mendelian randomization (SMR) analysis on each SNP instrument separately, considers the sampling variance of each SNP and LD between SNPs, and integrates the causal estimation of all SNP instruments through the generalized least squares method (Zhu et al., 2016; Zhu et al., 2018). Thus, GSMR excluded the estimation biases that might arise from pleiotropy (Zhu et al., 2018). We then carried out an instrument selector called heterogeneity in dependent instruments (HEIDI)-outlier to distinguish causality from pleiotropy (Zhu et al., 2018). Finally, we used 1000 Genomes Phase III as LD reference panels to clump the SNPs and selected independent genome-wide significant SNPs of conditional GWAS (LD: r < 0.1, p < 5E-08).

We performed genomic controls to adjust for the expansion and deflation of the empirical null distribution in GWAS due to population stratification and overcorrection of test statistics for polygenic traits (Yang et al., 2011; Andreassen et al., 2013). To assess the pleiotropic enrichment and shared risk loci of cognition-related phenotypes associated with AD, we generated conditional quantile–quantile (Q–Q) plots and computed the conditional false discovery rate (condFDR) statistics. Conditional Q–Q plots were generated using the log₁₀(p) value of all SNP loci for the main trait (e.g., AD) and the log₁₀(p) values of SNP loci across several different thresholds for the conditional trait (e.g., EA) (Andreassen et al., 2013; Andreassen et al., 2014; Desikan et al., 2015). The thresholds of condFDR included p < 1, p < 0.1, p < 0.01, p < 0.001, and p < 0.0001. Details of these statistical methods have been described in previous publications (Andreassen et al., 2013; Andreassen et al., 2014; Desikan et al., 2015).

The unconditional FDR (uFDR) refers to the probability that an SNP locus is associated with a trait as a false positive and is expressed as the ratio of the observed p-value to the observed quantile under the null hypothesis (Andreassen et al., 2013; Andreassen et al., 2014; Desikan et al., 2015). The condFDR is an extension of uFDR defined as the probability that an SNP locus is not associated with a main trait i (e.g., AD) if the p-value in both traits is less than a preset significance threshold and vice versa (Andreassen et al., 2013; Andreassen et al., 2014; Desikan et al., 2015). The conjFDR minimizes the effect of a single trait driving the shared association signal (Andreassen et al., 2013; Andreassen et al., 2014; Desikan et al., 2015). When the conjFDR value of this SNP is less than 0.01, it is generally considered to be significantly associated with both traits. The conjFDR method is implemented using the R package “GWAScFDR.”

To further assess whether the shared risk loci of AD and cognition-related phenotypes could regulate gene expression, we performed the expression quantitative trait loci (eQTL) analysis in whole blood and 13 brain tissues (brain amygdala, anterior cingulate cortex, caudate basal ganglia, cerebellar hemisphere, cerebellum, cortex, hippocampus, hypothalamus, frontal cortex, nucleus accumbens basal ganglia, putamen basal ganglia, spinal cord cervical, and substantia nigra) from the Genotype-Tissue Expression (GTEx) (GTEx Consortium et al., 2017). 1E-06 was used as the threshold for FDR <0.05. To explore the functional locations and pathways of shared risk loci, we performed enrichment analysis in Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) using the clusterProfiler package (Yu et al., 2012). p < 0.05 was considered significant enrichment after multiple testing.

GWAS identifies susceptibility loci for complex traits, but whether these loci influence the phenotypes through gene expression remains unknown. TWAS were used as an association test to identify the expression of putative risk genes in complex traits (Gusev et al., 2016). Here, we selected dorsolateral prefrontal cortex (DLPFC) RNA-seq datasets from the CommonMind Consortium (CMC) as the expression reference weights and imputed expression into GWAS summary statistics using Fusion software (Fromer et al., 2016; Gusev et al., 2016; Senthil et al., 2017). For genes showing significant association in the TWAS test (p < 0.05/No. of genes), we further performed colocalization analysis to scan for shared causal genes (Giambartolomei et al., 2014; Wallace, 2020). Colocalization used the Bayesian statistical test to calculate the posterior probability of five hypotheses (H0–H5), and the posterior probability PPH4 > 0.75 was interpreted as colocalization generally. The common significance of TWAS and colocalization analysis ensured the accuracy of the association test (Gusev et al., 2016).

In order to confirm the credibility of the causal genes identified by TWAS/colocalization analysis, we carried out the fine-mapping of causal gene sets (FOCUS) (Mancuso et al., 2019). FOCUS assigned the probability of associated signals to the risk genes identified by TWAS, which could be used for gene prioritization in functional analysis (Mancuso et al., 2019). The overlapping signals of TWAS, colocalization, and fine-mapping might be credible causal genes. We prioritized the mapping in brain tissues and showed the predicted expression correlation of each gene within the risk region.

We performed a cross-trait LDSC regression and observed negative genetic correlations of AD with EA (r_g = −0.22, p = 5.07E-05), cognitive performance (r_g = −0.27, p = 2.44E-05), and intelligence (r_g = −0.30, p = 3.00E-04) (Table 1). Nevertheless, we found non-significant genetic correlations of childhood intelligence and AD (r_g = −0.02, p = 0.60) (Table 1).

In order to observe the similarity between two traits driven by genetic variants in specific regions of the genome, we calculated the local genetic covariance of AD with EA, cognitive performance, and intelligence in 1,703 regions with independent LD. As a result, both EA (P _AD&EA = 1.62E-09) and intelligence (P _{AD&intelligence} = 4.33E-06) were associated with AD in the chromosome 19: 44.7M–46.1M region (Figure 2, Supplementary Tables S1–3). The local genetic overlap of AD and EA was also shown in the chromosome 14: 9.12M–9.31M region using GWAS-PW analysis (Supplementary Tables S4–6).

Univariable MR analysis showed causal relationships between the genetically predicted cognition-related phenotypes and AD (Figure 3). Every one standard deviation (SD) increase in EA, cognitive performance, and intelligence was associated with 30%, 26%, and 27% lower risk of AD (EA: odds ratio (OR), 0.70; 95% confidence interval (CI), 0.60 to 0.81, p = 2.28E-06; cognitive performance: OR, 0.74; 95% CI, 0.63 to 0.87, p = 0.00023; and intelligence: OR, 0.73; 95% CI, 0.62 to 0.87, p = 0.00028), respectively. MR-PRESSO and MR-Egger found no evidence of directional pleiotropy.

Owing to the potential causal relationship between EA and AD and the possible limitations of multivariable MR, we further performed mtCOJO analysis to adjust for pleiotropic SNPs among the cognition-related phenotypes and then implemented GSMR analysis using conditional GWAS (Zhu et al., 2018). After conditioning, genetically predicted EA was significantly associated with AD risk (OR = 0.74, 95% CI: 0.58–0.94, p = 0.013) (Supplementary Table S7). However, there was a non-significant causal relationship between cognitive performance or intelligence and AD. The number of SNP instruments for childhood intelligence was not sufficient to perform MR.

To describe the pleiotropic enrichment between cognition-related phenotypes and AD, we generated conditional Q–Q plots for conditioning the cognition-related phenotypes on AD (Andreassen et al., 2013; Andreassen et al., 2014; Desikan et al., 2015). A significant upward deflection of the conditional Q–Q plot was observed for EA, cognitive performance, and intelligence as conditional traits, suggesting a significant pleiotropic enrichment of AD with the cognition-related phenotypes (Figure 4).

In order to identify the overlapping loci of cognition-related phenotypes and AD, we applied a conjFDR statistical framework based on GWAS. At conjFDR <0.01, we identified 4, 5, and 4 shared risk loci for AD and EA, cognitive performance, and intelligence, respectively (Figure 5; Table 2). Among these shared loci, APOE, PICALM, and HBEGF were susceptibility loci reported in previous GWAS for AD (Naj et al., 2014; Rosenberg et al., 2016; Jun et al., 2017). VAC14, EFL1, CKM, SKA2, and NECTIN2 were novel risk loci. HBEGF was identified in the shared risk loci for both AD and all cognition-related phenotypes. HBEGF encodes a growth factor called heparin-binding epidermal growth factor-like growth factor (HB-EGF), which binds to APP, the transmembrane glycoprotein central to AD, and acts synergistically with EGF to promote ERK signaling and neuritogenesis (da Rocha et al., 2021).

We further evaluated the cis-expression of these loci in GTEx and found that they regulated the expression of WDR55, RP11-394B2.1, AP3B2, SAXO2, ADAMTS7P1, UBE2Q2P2, GOLGA2P10, GOLGA6L9, MARK4, KLC3, TRIM37, RAD51C, TEX14, AC099850.1, SKA2, PRR11, and NECTIN2 in whole blood and various brain tissues (Supplementary Table S8). In addition, we enriched these risk loci in GO and KEGG. The shared risk loci of EA and AD were mainly enriched in the formation and regulation of Aβ and the binding of tau protein or lipoprotein receptor (Supplementary Figure S1). The shared risk loci of cognitive performance and AD were mainly enriched in keratinocyte migration and glycoprotein biosynthetic processes (Supplementary Figure S2).

Shared loci identified by conjFDR (closest to risk SNPs at physical distance) may still be affected by limitations of GWAS (Zhu et al., 2016). In order to identify the credible causal genes of these phenotypes, we conducted TWAS, colocalization, and fine-mapping. We selected brain tissue RNA-seq datasets as expression reference weights and performed the expression imputation to GWAS on each chromosome in turn (Gusev et al., 2016). As a result, a total of 18 AD genes, 439 EA genes, 292 cognitive performance genes, and 276 intelligence genes showed strong associations with the brain (Supplementary Tables S9–12). TSPAN14, FAM180B, GOLGA6L9, and MTCH2 showed significant correlation signals in both AD and cognitive performance. TSPAN14, FAM180B, and GOLGA6L9 showed significant correlation signals in both AD and intelligence. Almost all causal genes identified by TWAS were replicated in colocalization (Supplementary Tables S9–12). Fine-mapping prioritized three credible causal genes (TSPAN14, FAM180B, and GOLGA6L9), which were selected in the credible set the most times in AD and cognition-related phenotypes (Supplementary Table S13).