Genome‐wide pleiotropy analysis of longitudinal blood pressure and harmonized cognitive performance measures

Moonil Kang; Ting Fang Alvin Ang; Sherral A Devine; Richard Sherva; Shubhabrata Mukherjee; Emily H Trittschuh; Phoebe Scollard; Michael Lee; Seo‐Eun Choi; Brandon Klinedinst; Connie Nakano; Logan C Dumitrescu; Timothy J Hohman; Michael L Cuccaro; Andrew J Saykin; Walter A Kukull; David A Bennett; Li‐San Wang; Richard P Mayeux; Jonathan L Haines; Margaret A Pericak‐Vance; Gerard D Schellenberg; Paul K Crane; Rhoda Au; Kathryn L Lunetta; Jesse Mez; Lindsay A Farrer

doi:10.1002/alz.70681

. 2025 Sep 15;21(9):e70681. doi: 10.1002/alz.70681

Genome‐wide pleiotropy analysis of longitudinal blood pressure and harmonized cognitive performance measures

Moonil Kang ¹, Ting Fang Alvin Ang ^2,^3,⁴, Sherral A Devine ^2,³, Richard Sherva ¹, Shubhabrata Mukherjee ⁵, Emily H Trittschuh ^6,⁷, Phoebe Scollard ⁵, Michael Lee ⁵, Seo‐Eun Choi ⁵, Brandon Klinedinst ⁵, Connie Nakano ⁵, Logan C Dumitrescu ^8,⁹, Timothy J Hohman ^8,⁹, Michael L Cuccaro ¹⁰, Andrew J Saykin ^11,^12,¹³, Walter A Kukull ¹⁴, David A Bennett ¹⁵, Li‐San Wang ¹⁶, Richard P Mayeux ¹⁷, Jonathan L Haines ¹⁸, Margaret A Pericak‐Vance ¹⁰, Gerard D Schellenberg ¹⁶, Paul K Crane ⁵, Rhoda Au ^2,^3,^4,^19,^20,²¹, Kathryn L Lunetta ^3,²², Jesse Mez ^3,^19,²¹, Lindsay A Farrer ^1,^3,^19,^20,^21,^22,^23,^✉

^¹Department of Medicine (Biomedical Genetics), Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts, USA

^²Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts, USA

^³Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts, USA

^⁴Slone Epidemiology Center, Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts, USA

^⁵Department of Medicine, University of Washington School of Medicine, Seattle, Washington, USA

^⁶Geriatric Research, Education, and Clinical Center, Veterans Affairs Puget Sound Health Care System, Seattle, Washington, USA

^⁷Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle, Washington, USA

^⁸Vanderbilt Memory & Alzheimer's Center, Vanderbilt University Medical Center, Nashville, Tennessee, USA

^⁹Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, USA

^¹⁰John P. Hussman Institute for Human Genomics, Miller School of Medicine, Miami, Florida, USA

^¹¹Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, Indiana, USA

^¹²Department of Radiology and Imaging Services, Indiana University School of Medicine, Indianapolis, Indiana, USA

^¹³Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA

^¹⁴Department of Epidemiology, University of Washington, Seattle, Washington, USA

^¹⁵Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois, USA

^¹⁶Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA

^¹⁷Department of Neurology, Columbia University School of Medicine, New York, New York, USA

^¹⁸Cleveland Institute for Computational Biology, Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, USA

^¹⁹Boston University Alzheimer's Disease Research Center, Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts, USA

^²⁰Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts, USA

^²¹Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts, USA

^²²Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA

^²³Department of Ophthalmology, Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts, USA

Correspondence , Lindsay A. Farrer, Biomedical Genetics E200, Boston University Chobanian & Avedisian School of Medicine, 72 East Concord Street, Boston, MA 02118, USA. Email: farrer@bu.edu

^✉

Corresponding author.

PMCID: PMC12434708 PMID: 40951946

Abstract

INTRODUCTION

Identifying pleiotropy for blood pressure (BP) and cognitive performance measures may indicate mechanistic links between hypertension and Alzheimer's disease (AD).

METHODS

We performed a pleiotropy genome‐wide association study (GWAS) for paired measures of systolic, diastolic, pulse, and mean arterial pressure with memory, executive function, and language scores using 116,075 exam data from 25,726 participants in clinic‐based and prospective cohorts. Significant findings were evaluated by Bayesian colocalization and differential gene expression in brain tissue from pathologically confirmed AD cases with and without clinical symptoms.

RESULTS

Genome‐wide significant pleiotropy for BP and cognitive performance with JPH2, GATA3, PAX2, LOC105371656, and SUFU in the total sample; RTN4, ULK2, SORBS2, and LOC100128993 in prospective cohorts; and ADAMTS3 and LINC02946 in clinic‐based cohorts. Six pleiotropic loci influence cognition directly, and six genes were differentially expressed between pathologically confirmed AD cases with and without antemortem cognitive impairment.

DISCUSSION

Our results provide insight into mechanisms underlying hypertension and AD.

Highlights

Genome‐wide significant pleiotropy in blood pressure (BP) and cognitive performance measures were identified with 11 novel loci: JPH2, GATA3, PAX2, LOC105371656, SUFU in the total sample; RTN4, ULK2, SORBS2, LOC100128993 in prospective cohorts; and ADAMTS3, LINC02946 in clinic‐based cohorts.
SUFU, RTN4, SORBS2, ADAMTS3, and GATA3 affected cognition directly rather than through BP.
ACTR1A, HIF1AN, ADAMTS3, RTN4, SORBS2, and SUFU at pleiotropic loci were differentially expressed among controls and pathologically confirmed AD cases with and without clinical symptoms.

Keywords: Alzheimer's disease, blood pressure, cognitive domain score, differential gene expression, genome‐wide association study, longitudinal study, pathway analysis, pleiotropy

1. INTRODUCTION

Hypertension is a well‐established risk factor for dementia, ¹ particularly Alzheimer's disease (AD) and vascular dementia—the two most common types of dementia which usually co‐exist and collectively account for 85% of dementia cases. ² Accumulating evidence from large‐scale longitudinal studies consistently suggests that elevated blood pressure (BP) in midlife (45−65 years) is associated with a higher risk of late‐life dementia ³ , ⁴ and a steeper cognitive decline. ⁵ However, while the association of late‐life high BP with incident AD and AD‐related dementias (ADRD) and cognitive decline is controversial in older adults, ⁶ multiple studies report the benefit of lowering BP in elderly hypertensive patients ⁷ , ⁸ and the association of abnormally lower diastolic BP (DBP) in late‐life with a higher risk of cognitive impairment and AD/ADRD. ⁹ , ¹⁰ Data from the Framingham Heart Study (FHS) indicate that patterns of age‐related systolic BP (SBP) and DBP changes differ in older adults. Compared to SBP which linearly rises across the life course, DBP increases continuously until 50−60 years but decreases after age 60, corresponding to a late‐life increase in pulse pressure (PP). ¹¹

Although multiple genome‐wide association studies (GWAS) have identified more than 1000 loci for various BP traits ¹² , ¹³ , ¹⁴ and 80 loci associated with the risk of AD/ADRD, ¹⁵ , ¹⁶ , ¹⁷ , ¹⁸ only a few studies have investigated their shared genetic architecture ¹⁹ , ²⁰ that has been implied by neuropathological evidence showing a link between high BP and AD, ²¹ , ²² , ²³ although the mechanisms are poorly understood and controversial. ²⁴ , ²⁵ Here, we investigated pleiotropy, occurring when a single gene or variant affects two or more phenotypes, ²⁶ for BP traits and measures of cognitive performance on a genome‐wide scale in several large clinic‐based and prospective cohorts. Identifying pleiotropy for BP and cognitive performance measures may provide more insight into the genetic basis and mechanistic links between high BP and AD/ADRD. In addition, based on recent evidence suggesting that BP and biomarkers of vascular integrity may impact cognitive resilience through tau pathology, ²⁷ , ²⁸ we compared expression of genes located within top‐ranked loci from the GWAS in brain tissue obtained from individuals with pathologically confirmed AD who were cognitively impaired or normal (i.e., cognitively resilient) prior to death to test the hypothesis that some of these pleiotropic genes may influence propensity to cognitive resilience in addition to or independent of mechanisms leading to AD hallmark amyloid‐β (Aβ) and tau pathologies.

2. METHODS

2.1. Participant ascertainment and assessment

RESEARCH IN CONTEXT

Systematic review: The authors reviewed the literature reported in traditional (e.g., PubMed, abstracts published in conferences) as well as preprinted (e.g., medRxiv) sources on genetic association studies of blood pressure (BP) and Alzheimer's disease (AD) and functional studies of top‐ranked pleiotropic genes.
Interpretation: We identified genome‐wide significant (GWS) pleiotropy in BP and cognitive performance measures with apolipoprotein E (APOE) and 11 novel loci. In the total sample, pleiotropy was identified with JPH2, GATA3, PAX2, LOC105371656, and SUFU. In prospective cohorts, pleiotropy was found with RTN4, ULK2, SORBS2, and LOC100128993. In clinic‐based cohorts, ADAMTS3 and LINC02946 were identified. Six GWS pleiotropic loci influenced cognition directly or through mechanisms unrelated to BP, and six genes at pleiotropic loci were differentially expressed between controls and pathologically confirmed AD cases with and without clinical symptoms.
Future directions: Follow‐up studies should confirm the pleiotropy genome‐wide association study (GWAS) findings in larger cohorts. Future research could also investigate the role of shared mechanisms between BP and cognitive measures in AD progression or development, as well as the connection of ADAMTS3, RTN4, SORBS2, and SUFU to cognitive resilience.

This study included non‐Hispanic white participants aged 60 or older from five longitudinal cohort studies. Three cohorts, including the FHS ²⁹ , ³⁰ , ³¹ , the Adult Changes in Thought (ACT) Study, ³² and the Religious Orders Study/Rush Memory and Aging Project (ROSMAP), ³³ recruited cognitively normal participants and followed them over time. The National Institute on Aging (NIA)‐sponsored Alzheimer's Disease Research Centers, which collect a uniform set of phenotypic data archived by the National Alzheimer's Coordinating Center (NACC), ³⁴ , ³⁵ and the Alzheimer's Disease Neuroimaging Initiative (ADNI) ³⁶ , ³⁷ are clinic‐based cohorts. Individuals in these cohorts included in this study were cognitively normal or met the criteria for mild cognitive impairment (MCI) or AD at the most recent examination. All participants underwent an examination at enrollment and each follow‐up visit, including basic anthropometry, BP measurements, medications, and medical history. For those taking antihypertensive medications at the time point when BP was measured, we added 10 and 5 mmHg to the measured SBP and DBP, respectively, as previously recommended. ³⁸ These corrected SBP and DBP were used to calculate PP (SBP minus DBP) and mean arterial pressure (MAP; DBP plus one third of PP) ³⁹ . Body mass index (BMI) was calculated in the unit of kg/m² as one's weight divided by the square of height at each examination. Further information regarding the ascertainment, evaluation, and diagnostic procedures in each cohort was described elsewhere. ³² , ³³ , ³⁴ , ³⁷ , ⁴⁰ , ⁴¹

2.2. Cognitive domain scores

As described previously, ⁴² , ⁴³ , ⁴⁴ cognitive domain scores for executive function, language, and memory were calculated from tests from unique cognitive batteries administered across the five cohort studies and co‐calibrated to be on the same scale across cohorts. Briefly, an expert panel of neuropsychologists and behavioral neurologists classified neuropsychological (NP) test items into one of the cognitive domains. Anchor items, identical NP test items found in multiple batteries were used to put composite scores on the same scale across studies. Co‐calibrated composite scores for each cognitive domain were generated using confirmatory factor analysis in Mplus ⁴⁵ with loadings for anchor items forced to be equal across studies. Co‐calibrated cognitive scores with a standard error (SE) > 0.6 and those obtained solely from the Mini‐Mental State Examination, which shows a ceiling effect, ⁴⁴ were excluded. We also excluded data collected at ages < 60 that were available only for FHS participants to mitigate the concern that the genetic architecture of BP and/or cognitive performance may differ at younger ages.

2.3. Genotype quality control, generating principal components, and genetic relationship matrix

Genotype quality control (QC) procedures were applied to the Trans‐Omics for Precision Medicine‐imputed genome‐wide single nucleotide polymorphism (SNP) data, ¹⁸ , ⁴⁶ which were aligned to the Genome Research Consortium human build 38. After excluding variants with poor imputation quality (r ²< 0.3) or minor allele frequency (MAF) < 0.01, roughly 8.7 million variants remained for each individual cohort. We performed linkage disequilibrium (LD) pruning for genotyped variants (MAF > 0.05 and call rate > 99%) with an LD threshold of 0.1, and principal components (PCs) of the population structure for each sample within each cohort study were derived from the LD‐pruned variants using GENESIS. ⁴⁷ A kinship matrix for family‐based samples was estimated for FHS participants using self‐reported pedigree information and the R package kinship2. ⁴⁸ An empirical genetic relationship matrix (GRM) was derived for other individuals using established procedures. ⁴⁹ , ⁵⁰ , ⁵¹

2.4. GWAS for BP and cognitive domain measures

We performed GWAS for SBP, DBP, MAP, and PP in each dataset separately using the GMMAT ⁵² and MAGEE ⁵³ software. The association of each SNP with BP traits over time was evaluated in the following generalized linear mixed model:

\begin{matrix} BP & = & α_{A} age + α_{B} sex + α_{C} BMI + β_{G} SNP + β_{G \times Age} (SNP \times age) \\ + \sum_{i = 1}^{5} α_{i} P C_{i} + GRM \end{matrix}

In the model, α_A , α_B , and α_C represent the effects of age, sex, and BMI, respectively, and α_i denotes the effect of the first five PCs. β_G and β_G×Age indicates the SNP and SNP×age interaction effects, respectively. GRM was also included in the model as a random effect. We utilized the GWAS results for executive function, language, and memory that were generated previously in each dataset by applying similar models including the SNP and SNP×age interaction terms, and covariates for age, sex, educational level (less than high school, high school, some college, or college graduate), GRM, and the first five PCs. ⁵⁴ The SNP×age interaction term accounts for the inclusion of data collected from individuals at multiple time points and allows for the possibility of associations that are age‐dependent. In models for all traits, age was centered by subtracting the median age for all observations for all individuals in the dataset from the age at each exam to alleviate the multicollinearity and improve the interpretability of the SNP×age interaction model coefficients. ⁵⁵ A random age slope and intercept were incorporated in the model to account for repeated measures. Results from each dataset were combined by meta‐analysis using the inverse variance‐weighted method in METAL. ⁵⁶ We also calculated a p‐value for the joint test of the null hypothesis that the SNP and SNP×age interaction effects are both zero by combining Z‐scores for the SNP's main effect and SNP×age interaction. To reduce systematic inflation caused by jointly testing the SNP's main and SNP×age interaction effects, ⁵⁷ , ⁵⁸ we applied a joint meta‐analysis approach that takes account of the covariance between their coefficients. ⁵⁹ Results were also combined in clinic‐based cohorts (NACC and ADNI) and prospective cohorts (FHS, ACT, and ROSMAP) separately to allow for unique associations due to the disparity in age and AD ascertainment between these groups (Figure S1). ⁵⁴ p‐values were further corrected by applying the genomic inflation factor (λ) estimated for each GWAS and were considered genome‐wide significant (GWS) if the corrected p‐value was less than 5×10⁻⁸.

2.5. Genetic correlations and genome‐wide pleiotropy analyses

Genetic correlations among the BP and cognitive domain measures were estimated by cross‐trait LD score regression ⁶⁰ , ⁶¹ using meta‐analyzed GWAS summary statistics for each trait and LD scores derived previously from European ancestry samples in the 1000 Genomes Project Phase 3 data. We performed genome‐wide pleiotropy analyses for paired outcomes of BP and cognitive domain traits by combining summarized GWAS results for the individual traits in the total sample and separately in the clinic‐based and prospective cohorts (Figure S1). Using the R package PLACO, ⁶² we examined the composite null hypothesis that a maximum of one phenotype is associated with a given variant such that rejecting this hypothesis implies that the variant affects both phenotypes and is thus pleiotropic. ²⁶ The PLACO test statistic is the product of the Z‐scores for a given variant estimated from GWAS for each individual phenotype and follows a mixture distribution that allows for the variant to be associated with at most one phenotype. ⁶² Potential type I error rate inflation due to Z‐scores derived from GWAS for BP and cognitive measures with overlapping samples between two phenotypes ⁶³ was corrected based on the Pearson correlation among Z‐scores for variants with no associations with any trait (P > 1×10⁻⁴), as recommended. ⁶² Variants with extreme effects (squared Z‐score > 80) for any trait, which could indicate spurious signals of pleiotropy, ⁶⁰ , ⁶⁴ were excluded.

2.6. Differentiation of horizontal pleiotropy from mediated pleiotropy

For top‐ranked GWS pleiotropic loci, we conducted Bayesian colocalization analyses using the R package COLOC ⁶⁵ to differentiate horizontal pleiotropy (i.e., SNP's direct effect on cognition or its effect through mechanisms that bypass BP) from mediated pleiotropy (i.e., SNP's effect on cognition through BP). Colocalization was tested for SNPs within 2 Mb windows of the top‐ranked GWS variant of pleiotropy for each locus based on GWAS summary statistics for individual BP and cognitive performance measures. Posterior probabilities were estimated for the following hypotheses: H₃ (i.e., association with BP and cognitive performance measures, two independent causal SNPs) and H₄ (i.e., association with BP and cognitive performance measures, one shared causal SNP). Horizontal pleiotropy was considered to exist at the allelic level for a locus with a posterior probability for H₄> 0.5 and was considered to exist at the locus level for a locus with a sum of posterior probabilities (PP _sum) for H₃ and H₄> 0.5.

2.7. Differential gene expression analyses

We compared the expression of genes located at top‐ranked pleiotropic loci among cognitively normal controls (i.e., neither clinically nor pathologically diagnosed with AD), pathologically confirmed asymptomatic AD cases (i.e., cognitively normal prior to death and thus considered to be resilient), and pathologically confirmed symptomatic AD cases (i.e., cognitively impaired or demented prior to death). Genes containing or adjacent (closer than 100 kb) to variants showing GWS evidence of pleiotropy in the SNP main effect, SNP interaction with age, or their joint effects in the total, clinic‐based, or prospective cohort samples were targeted. We obtained bulk RNA‐sequencing data measured in the dorsolateral prefrontal cortex (DLPFC) from ROSMAP (195 controls, 172 asymptomatic AD, and 200 symptomatic AD cases), ⁶⁶ Boston University Alzheimer's Disease Research Center (BUADRC) (35 controls, 20 asymptomatic AD, and 30 symptomatic AD cases), ⁶⁷ and FHS (73 controls, 12 asymptomatic AD, and 42 symptomatic AD cases) ⁶⁸ participants. Results were adjusted for a false discovery rate (FDR) by applying the Benjamini–Hochberg method ⁶⁹ and considered significant if the FDR was less than a threshold of 0.05.

2.8. Pathway enrichment analyses

We conducted pathway enrichment analyses, each of which was seeded with genes containing variants showing evidence of pleiotropy (P < 1×10⁻⁴) in the total sample, clinic‐based cohorts, or prospective cohorts using the Ingenuity Pathway Analysis software (QIAGEN Inc.). ⁷⁰ The Benjamini–Hochberg method ⁶⁹ was applied to adjust each canonical pathway's enrichment p‐value in each sample stratum for each pair of traits. Pathways were considered significant if the FDR was less than a threshold of 0.05.

3. RESULTS

3.1. Basic characteristics

Data available for analyses were obtained from 116,075 longitudinal examinations of 25,726 participants with a mean age of 76.5 years, more than half of whom were female (55.4%) and college graduates (59.5%) (Tables 1 and S1). Even though FHS participants were much younger (p < 0.001) than those in the other cohorts (70.5 vs. 76.5−81.9 years), the mean SBP and PP were significantly higher (p < 0.001) in FHS participants compared to individuals in NACC, ADNI, and ROSMAP (139.9 vs. 136.2−138.9 mmHg for SBP; 63.5 vs. 61.6−62.2 mmHg for PP), which may be because FHS included untreated hypertensive participants who were recruited longer ago. Participants in the clinic‐based cohorts (NACC and ADNI) were slightly younger at the last visit than those in the prospective cohorts (FHS, ACT, and ROSMAP). Compared to prospective cohorts, individuals in the clinic‐based cohorts had a significantly higher proportion of MCI or AD cases (p < 0.001), lower scores for executive function and memory, and higher scores for language (p < 0.001) at the last visit. ⁵⁴

TABLE 1.

Characteristics of the study sample.

	Total sample	Clinic‐based cohorts			Prospective cohorts
Parameter	Total sample	NACC	ADNI	Combined	FHS	ACT	ROSMAP	Combined
Observations, n	116,075	59,862	6,824	66,686	19,820	13,692	15,877	49,389
Participants, n	25,726	14,360	1,335	15,695	4,976	3,009	2,046	10,031
Age, years (mean ± SD)	76.5 ± 8.5	76.5 ± 8.2	76.8 ± 7.0	76.5 ± 8.1	70.5 ± 7.7	78.8 ± 7.0	81.9 ± 7.2	76.5 ± 8.9
Sex, female, n (%)	14,256 (55.4)	7,854 (54.7)	583 (43.7)	8,437 (53.8)	2,703 (54.3)	1,686 (56.0)	1,430 (69.9)	5,819 (58.0)
Educational level, n (%) ^†
Under high school degree	974 (4.2)	302 (2.3)	41 (3.0)	343 (2.4)	305 (8.5)	240 (7.9)	86 (4.2)	631 (7.2)
High school degree	3,987 (17.3)	1,958 (15.1)	160 (11.7)	2,118 (14.8)	927 (25.7)	620 (20.4)	322 (15.5)	1,869 (21.5)
Some college	4,387 (19.0)	2,212 (17.0)	259 (18.9)	2,471 (17.2)	905 (25.1)	688 (22.7)	323 (15.6)	1,916 (22.0)
Over college graduate	13,717 (59.5)	8,513 (65.6)	907 (66.3)	9,420 (65.6)	1,470 (40.8)	1,487 (49.0)	1,340 (64.7)	4,297 (49.3)
BMI, kg/m² (mean ± SD)	26.7 ± 4.7	26.5 ± 4.7	26.5 ± 4.5	26.5 ± 4.6	27.3 ± 4.8	26.8 ± 4.6	27.0 ± 5.1	27.1 ± 4.9
BP, mmHg (mean ± SD)
SBP	139.2 ± 20.0	138.9 ± 19.5	136.2 ± 18.0	138.7 ± 19.4	139.9 ± 20.8	142.7 ± 21.8	137.6 ± 19.4	139.9 ± 20.7
DBP	76.2 ± 10.6	76.7 ± 10.4	74.6 ± 10.0	76.5 ± 10.4	76.4 ± 10.5	75.0 ± 10.8	75.5 ± 10.9	75.7 ± 10.8
MAP	97.2 ± 12.0	97.5 ± 11.8	95.2 ± 11.0	97.2 ± 11.7	97.5 ± 12.2	97.6 ± 12.9	96.2 ± 12.2	97.1 ± 12.4
PP	63.0 ± 16.8	62.2 ± 16.5	61.6 ± 15.4	62.2 ± 16.4	63.5 ± 17.8	67.7 ± 17.8	62.0 ± 15.8	64.2 ± 17.3
Cognitive score (mean ± SD) ^†
Executive function	0.197 ± 0.752	0.163 ± 0.827	0.318 ± 0.746	0.182 ± 0.819	−0.007 ± 0.688	0.189 ± 0.499	0.343 ± 0.698	0.217 ± 0.650
Language	0.353 ± 0.808	0.472 ± 0.869	0.392 ± 0.700	0.463 ± 0.851	0.229 ± 0.662	0.166 ± 0.422	0.154 ± 0.805	0.173 ± 0.695
Memory	0.337 ± 0.890	0.372 ± 1.007	0.146 ± 0.873	0.346 ± 0.995	0.286 ± 0.636	0.512 ± 0.549	0.194 ± 0.836	0.324 ± 0.721
Cognitive status, n (%) ^†
MCI	2,247 (9.7)	820 (6.3)	444 (32.5)	1,264 (8.8)	287 (8.0)	NA	696 (33.6)	983 (11.3)
AD	9,272 (40.2)	7,090 (54.6)	486 (35.6)	7,576 (52.8)	445 (12.3)	485 (16.0)	766 (37.0)	1,696 (19.5)
Dementia (other than AD)	627 (2.7)	184 (1.4)	NA	184 (1.3)	135 (3.7)	283 (9.3)	25 (1.2)	443 (5.1)

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Abbreviations: ACT, Adult Changes in Thought; AD, Alzheimer's disease; ADNI, Alzheimer's Disease Neuroimaging Initiative; BP, blood pressure; BMI, body mass index; DBP, diastolic blood pressure; FHS, Framingham Heart Study; GWAS, genome‐wide association study; MAP, mean arterial pressure; MCI, mild cognitive impairment; NACC, National Alzheimer's Coordinating Center; PP, pulse pressure; SBP, systolic blood pressure;

^{^†}

Characteristics were based on individuals included in the GWAS for cognitive domain scores.

3.2. Phenotypic and genetic correlations

As shown in Figure S2, phenotypic and genetic correlations (r) were moderate to high for all pairs of the four BP measures (phenotypic r = 0.51−0.88, genetic r = 0.43−0.91), except for the DBP and PP (phenotypic r = 0.05, genetic r = 0.03), and for all pairs of the three cognitive domains (phenotypic r = 0.72−0.78, genetic r = 0.55−0.73). Predictably, SBP, MAP, and PP were negatively correlated with cognitive domain scores consistently, even though they had weak phenotypic and genetic correlations ranging from −0.16 to −0.04 and −0.07 to −0.01, respectively.

3.3. Genetic associations with individual BP and cognitive domain scores

We identified six GWS BP loci, including SLC7A1, ULK4, HOTTIP, IGFBP3, PIK3CG, and MCTP2 (Table S2a), with little evidence of genomic inflation (λ = 1.017−1.076) in the joint testing of the SNP and SNP×age interaction effects across all sample strata (Figure S3). GWS associations of top‐ranked SNPs in each BP locus were primarily due to the effect of the SNP rather than the SNP×age interaction and were supported by several adjacent variants in high LD (Figure S4 and Table S3). Previously, we identified GWS associations of individual cognitive domain scores with ULK2, CDK14, LINC02712, PURG, and several established AD loci (BIN1, CR1, MS4A6A, and GRN) (Table S2b) in addition to GWS associations of many SNPs in the APOE region with all cognitive domains. ⁵⁴

3.4. Pleiotropy for BP and cognitive domain scores

We identified GWS pleiotropic associations with SNPs in JPH2 and ULK2 emerging from the total sample and prospective cohorts, respectively (Tables 2 and S4), with no evidence of genomic inflation (λ = 0.868−0.968) in the joint testing of the main SNP and SNP×age interaction effects on pleiotropy between BP and cognitive domain scores across all strata of the sample (Figure S5). The JPH2 association observed between SBP paired with language and rs6031436 (P _Joint= 6.09×10⁻⁹) in the total sample (Table 2) was supported by multiple GWS SNPs in the same locus (Figure 1A and Table S5). This JPH2 SNP also showed a GWS pleiotropy for PP paired with language (P _Joint= 3.25×10⁻⁸) in the total sample (Table 2), which was supported by multiple adjacent variants in high LD (Figure 1B and Table S5). In the prospective cohorts, a GWS pleiotropy was observed with ULK2 SNP rs157398 (P _Joint= 2.85×10⁻⁸) for the pair of DBP and executive function (Table 2), which was supported by many neighboring GWS or suggestive variants (Figure 2A and Table S5). In addition, many SNPs in the APOE region showed GWS pleiotropy for all the pairs of BP and cognitive performance measures across all strata of analyses, particularly in the clinic‐based cohorts (Table S5).

TABLE 2.

GWS pleiotropy between BP and cognitive performance measures (excluding the APOE region).

					Individual traits					Pleiotropy
Variant (chr:position)	A1/A2 (MAF)	Nearest gene	Traits	Sample ^†	β _G (SE)	P _G	β _G×Age (SE)	P _G×Age	P _Joint	P _{G, Placo}	P _{G×Age, Placo}	P _{Joint, Placo}
rs6031436 (20:44175941)	G/C (0.138)	JPH2	SBP	Total	1.070 (0.197)	7.74×10⁻⁷	0.035 (0.023)	1.38×10⁻¹	6.42×10⁻⁷	6.54×10⁻⁹	6.43×10⁻³	6.09×10⁻⁹
			Language	Total	−0.030 (0.008)	3.41×10⁻⁴	−0.002 (0.001)	1.33×10⁻²	1.52×10⁻⁴	6.54×10⁻⁹	6.43×10⁻³	6.09×10⁻⁹
			PP	Total	0.714 (0.160)	4.24×10⁻⁵	0.042 (0.018)	2.25×10⁻²	2.37×10⁻⁵	1.45×10⁻⁷	7.20×10⁻⁴	3.25×10⁻⁸
			Language	Total	−0.030 (0.008)	3.41×10⁻⁴	−0.002 (0.001)	1.33×10⁻²	1.52×10⁻⁴	1.45×10⁻⁷	7.20×10⁻⁴	3.25×10⁻⁸
rs263419 (10:8076130)	T/A (0.183)	GATA3	SBP	Total	0.163 (0.176)	3.99×10⁻¹	0.078 (0.021)	1.44×10⁻⁴	1.01×10⁻³	6.43×10⁻¹	1.42×10⁻⁸	1.01×10⁻⁷
rs263419 (10:8076130)	T/A (0.183)	GATA3	Language	Total	−0.002 (0.007)	7.95×10⁻¹	−0.003 (0.001)	6.86×10⁻⁵	4.98×10⁻⁴	6.43×10⁻¹	1.42×10⁻⁸	1.01×10⁻⁷
rs73050834 (1:187487328)	T/C (0.086)	LOC105371656	MAP	Total	0.176 (0.146)	2.67×10⁻¹	−0.077 (0.018)	1.01×10⁻⁵	3.43×10⁻⁵	1.71×10⁻¹	1.75×10⁻⁸	9.76×10⁻⁸
rs73050834 (1:187487328)	T/C (0.086)	LOC105371656	Language	Total	0.010 (0.009)	3.07×10⁻¹	0.004 (0.001)	6.73×10⁻⁴	3.59×10⁻³	1.71×10⁻¹	1.75×10⁻⁸	9.76×10⁻⁸
rs7902306 (10:102530705)	T/C (0.286)	SUFU	DBP	Total	−0.287 (0.079)	6.57×10⁻⁴	0.002 (0.009)	8.16×10⁻¹	1.40×10⁻³	2.10×10⁻⁸	5.51×10⁻¹	1.51×10⁻⁷
rs7902306 (10:102530705)	T/C (0.286)	SUFU	Language	Total	−0.029 (0.006)	3.16×10⁻⁶	−0.001 (0.001)	2.33×10⁻¹	4.65×10⁻⁶	2.10×10⁻⁸	5.51×10⁻¹	1.51×10⁻⁷
rs117854720 (10:100654739)	T/C (0.017)	PAX2 HIF1AN	MAP	Total	0.069 (0.320)	8.43×10⁻¹	0.164 (0.038)	1.78×10⁻⁵	1.63×10⁻⁴	8.91×10⁻¹	4.22×10⁻⁸	2.60×10⁻⁷
rs117854720 (10:100654739)	T/C (0.017)	PAX2 HIF1AN	Exec.func	Total	0.004 (0.021)	8.47×10⁻¹	−0.008 (0.002)	1.10×10⁻³	4.78×10⁻³	8.91×10⁻¹	4.22×10⁻⁸	2.60×10⁻⁷
rs10518102 (4:72403157)	T/G (0.049)	ADAMTS3	PP	Clinic	1.439 (0.325)	3.50×10⁻⁵	0.024 (0.041)	5.53×10⁻¹	8.69×10⁻⁵	2.37×10⁻⁸	4.79×10⁻¹	1.53×10⁻⁷
rs10518102 (4:72403157)	T/G (0.049)	ADAMTS3	Language	Clinic	0.086 (0.020)	2.05×10⁻⁴	0.002 (0.002)	5.31×10⁻¹	1.48×10⁻⁴	2.37×10⁻⁸	4.79×10⁻¹	1.53×10⁻⁷
rs10201413 (2:105593893)	A/C (0.175)	LINC02946	SBP	Clinic	−0.342 (0.225)	1.72×10⁻¹	0.116 (0.030)	8.58×10⁻⁵	1.75×10⁻⁴	6.34×10⁻¹	3.47×10⁻⁸	2.43×10⁻⁷
			Memory	Clinic	−0.002 (0.013)	8.89×10⁻¹	0.006 (0.002)	3.86×10⁻⁴	2.39×10⁻³	6.34×10⁻¹	3.47×10⁻⁸	2.43×10⁻⁷
			PP	Clinic	−0.087 (0.187)	6.62×10⁻¹	0.091 (0.023)	5.76×10⁻⁵	3.32×10⁻⁴	8.43×10⁻¹	4.25×10⁻⁸	2.97×10⁻⁷
			Memory	Clinic	−0.002 (0.013)	8.89×10⁻¹	0.006 (0.002)	3.86×10⁻⁴	2.39×10⁻³	8.43×10⁻¹	4.25×10⁻⁸	2.97×10⁻⁷
rs157398 (17:19826593)	A/G (0.019)	ULK2	DBP	Prospective	1.404 (0.409)	1.01×10⁻³	0.032 (0.043)	4.64×10⁻¹	2.83×10⁻³	1.77×10⁻⁸	1.39×10⁻²	2.85×10⁻⁸
rs157398 (17:19826593)	A/G (0.019)	ULK2	Exec.func	Prospective	0.119 (0.023)	1.13×10⁻⁶	0.010 (0.002)	1.42×10⁻⁵	5.12×10⁻⁹	1.77×10⁻⁸	1.39×10⁻²	2.85×10⁻⁸
rs6707036 (2:55060191)	G/A (0.335)	RTN4	SBP	Prospective	0.298 (0.246)	2.44×10⁻¹	0.104 (0.024)	1.88×10⁻⁵	9.44×10⁻⁵	4.93×10⁻²	1.49×10⁻⁸	6.45×10⁻⁸
rs6707036 (2:55060191)	G/A (0.335)	RTN4	Language	Prospective	−0.014 (0.007)	5.31×10⁻²	−0.003 (0.001)	2.34×10⁻⁴	5.16×10⁻⁴	4.93×10⁻²	1.49×10⁻⁸	6.45×10⁻⁸
rs2168164 (4:185951760)	A/C (0.216)	SORBS2	PP	Prospective	−0.792 (0.219)	1.04×10⁻³	−0.023 (0.022)	3.06×10⁻¹	2.00×10⁻³	2.33×10⁻⁸	4.78×10⁻²	1.38×10⁻⁷
rs2168164 (4:185951760)	A/C (0.216)	SORBS2	Memory	Prospective	−0.045 (0.008)	2.11×10⁻⁶	−0.002 (0.001)	3.66×10⁻²	1.33×10⁻⁶	2.33×10⁻⁸	4.78×10⁻²	1.38×10⁻⁷
rs57127265 (8:19209874)	G/A (0.044)	LOC100128993	DBP	Prospective	−0.373 (0.279)	2.01×10⁻¹	−0.113 (0.030)	1.46×10⁻⁴	6.02×10⁻⁴	1.20×10⁻¹	2.81×10⁻⁸	1.93×10⁻⁷
rs57127265 (8:19209874)	G/A (0.044)	LOC100128993	Memory	Prospective	−0.021 (0.016)	2.64×10⁻¹	−0.008 (0.002)	5.85×10⁻⁵	5.88×10⁻⁴	1.20×10⁻¹	2.81×10⁻⁸	1.93×10⁻⁷

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Abbreviations: APOE, apolipoprotein E; BP, blood pressure; DBP, diastolic blood pressure; MAF, minor allele frequency; MAP, mean arterial pressure; PP, pulse pressure; SBP, systolic blood pressure.

^{^†}

GWAS for BP and cognitive performance measures were conducted separately in the total sample, clinic‐based, and prospective cohorts.

LocusZoom plots for top‐ranked pleiotropic loci emerging from the total sample. (A) rs6031436 and *JPH2* variants showing pleiotropy between SBP and language. (B) rs6031436 and *JPH2* variants showing pleiotropy between PP and language. (C) rs263419 and *GATA3* variants showing pleiotropy between SBP and language. (D) rs7902306 and *SUFU* variants showing pleiotropy between DBP and language. A purple diamond indicates the top‐ranked SNP at each locus. SNPs are color‐coded according to their LD (r ²) with the top‐ranked SNP in the region. Horizontal dotted lines represent the GWS threshold of P = 5×10⁻⁸. Vertical blue lines indicate locations of the high recombination rate among SNPs at the chromosomal position. Approximate location, transcription direction, and coding portions (exons represented by vertical bars) of genes are shown below the diagram. cM, centimorgan; DBP, diastolic blood pressure; GWS, genome‐wide significant; LD, linkage disequilibrium; Mb, megabase; PP, pulse pressure; SBP, systolic blood pressure; SNP, single nucleotide polymorphism.

LocusZoom plots for top‐ranked pleiotropic loci emerging from the clinic‐based or prospective cohort samples. (A) rs157398 and *ULK2* variants showing pleiotropy between DBP and executive function in the prospective cohorts. (B) rs10518102 and *ADAMTS3* variants showing pleiotropy between PP and language in the clinic‐based cohorts. (C) rs6707036 and *RTN4* variants showing pleiotropy between SBP and language in the prospective cohorts. (D) rs2168164 and *SORBS2* variants showing pleiotropy between PP and memory in the prospective cohorts. A purple diamond indicates the top‐ranked SNP at each locus. SNPs are color‐coded according to their LD (r ²) with the top‐ranked SNP in the region. Horizontal dotted lines represent the GWS threshold of P = 5×10⁻⁸. Vertical blue lines indicate locations of the high recombination rate among SNPs at the chromosomal position. Approximate location, transcription direction, and coding portions (exons represented by vertical bars) of genes are shown below the diagram. cM, centimorgan; DBP, diastolic blood pressure; GWS, genome‐wide significant; Mb, megabase; PP, pulse pressure; SBP, systolic blood pressure; SNP, single nucleotide polymorphism.

GWS pleiotropy for nine additional loci was identified with the main SNP effect or SNP×age interaction terms (Tables 2 and S4). In the total sample, pleiotropy for the SNP×age interaction effect was observed with GATA3 SNP rs263419 (Figure 1C) for SBP paired with language (P _G×Age= 1.42×10⁻⁸), LOC105371656 SNP rs73050834 (Figure S6a) for MAP paired with language (P _G×Age= 1.75×10⁻⁸), and rs117854720 located between HIF1AN and PAX2 (Figure S6b) for MAP paired with executive function (P _G×Age= 4.22×10⁻⁸) (Tables 2 and S4). The main effect of SUFU SNP rs7902306 was associated with the paired outcomes of DBP and language (P _G= 2.10×10⁻⁸), a finding that was supported by several variants in high LD (Figure 1D). In the clinic‐based cohorts, pleiotropy for the main SNP effect was observed with ADAMTS3 SNP rs10518102 (Figure 2B) for PP paired with language (P _G= 2.37×10⁻⁸), and the SNP×age interaction effect of LINC02946 SNP rs10201413 was associated with the paired outcomes of SBP and memory (P _G×Age= 3.47×10⁻⁸) and PP and memory (P _G×Age= 4.25×10⁻⁸) (Figures S6c and S6d). In the prospective cohorts, pleiotropy for the SNP×age interaction effect was observed with RTN4 SNP rs6707036 (Figure 2C) for SBP paired with language (P _G×Age= 1.49×10⁻⁸) and LOC100128993 SNP rs57127265 (Figure S6e) for DBP paired with memory (P _G×Age= 2.81×10⁻⁸). The main effect of SORBS2 SNP rs2168164 was also associated with the paired outcomes of PP and memory (P _G= 2.33×10⁻⁸) (Figure 2D). Bayesian colocalization analyses revealed that the influence on cognition of six of the 11 GWS pleiotropic loci, including ADAMTS3 (PP _sum= 0.93), SUFU (PP _sum= 0.72), SORBS2 (PP _sum= 0.61), RTN4 (PP _sum= 0.57), LOC100128993 (PP _sum= 0.56), and JPH2 (PP _sum= 0.54), was direct or through mechanisms unrelated to BP (Table 3).

TABLE 3.

Differentiation of the directional pleiotropy from the mediated pleiotropy for GWS loci.

Locus	Sentinel SNP (chr:position)	Sample	Traits	Number of SNPs ^a	Posterior probability ^b
Locus	Sentinel SNP (chr:position)	Sample	Traits	Number of SNPs ^a	H₃	H₄
JPH2	rs6031436 (20:44175941)	Total	SBP	5,761	0.06	0.48
			Language	5,761	0.06	0.48
			PP	5,761	0.04	0.15
			Language	5,761	0.04	0.15
GATA3	rs263419 (10:8076130)	Total	SBP	8,300	0.04	0.31
GATA3	rs263419 (10:8076130)	Total	Language	8,300	0.04	0.31
LOC105371656	rs73050834 (1:187487328)	Total	MAP	7,344	0.03	0.17
LOC105371656	rs73050834 (1:187487328)	Total	Language	7,344	0.03	0.17
SUFU	rs7902306 (10:102530705)	Total	DBP	4,012	0.25	0.47
SUFU	rs7902306 (10:102530705)	Total	Language	4,012	0.25	0.47
PAX2 HIF1AN	rs117854720 (10:100654739)	Total	MAP	5,111	0.02	0.25
PAX2 HIF1AN	rs117854720 (10:100654739)	Total	Exec.func	5,111	0.02	0.25
ADAMTS3	rs10518102 (4:72403157)	Clinic	PP	5,385	0.14	0.79
ADAMTS3	rs10518102 (4:72403157)	Clinic	Language	5,385	0.14	0.79
LINC02946	rs10201413 (2:105593893)	Clinic	SBP	7,037	0.02	0.04
			Memory	7,037	0.02	0.04
			PP	7,037	0.03	0.06
			Memory	7,037	0.03	0.06
ULK2	rs157398 (17:19826593)	Prospective	DBP	3,734	0.06	0.24
ULK2	rs157398 (17:19826593)	Prospective	Exec.func	3,734	0.06	0.24
RTN4	rs6707036 (2:55060191)	Prospective	SBP	7,090	0.49	0.08
RTN4	rs6707036 (2:55060191)	Prospective	Language	7,090	0.49	0.08
SORBS2	rs2168164 (4:185951760)	Prospective	PP	8,306	0.20	0.41
SORBS2	rs2168164 (4:185951760)	Prospective	Memory	8,306	0.20	0.41
LOC100128993	rs57127265 (8:19209874)	Prospective	DBP	9,716	0.06	0.50
LOC100128993	rs57127265 (8:19209874)	Prospective	Memory	9,716	0.06	0.50

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Abbreviations: DBP, diastolic blood pressure; GWS, genome‐wide significant; MAP, mean arterial pressure; PP, pulse pressure; SBP, systolic blood pressure; SNP, single nucleotide polymorphism.

^{^a}

Colocalization analysis included SNPs within 2 Mb windows of the sentinel SNP for each locus.

^{^b}

Posterior probability of colocalization for H₃ (association with BP and cognitive performance measures, two independent causal SNPs) and H₄ (association with BP and cognitive performance measures, one shared causal SNP); Horizontal pleiotropy may exist at the allelic level for loci with a posterior probability for H₄ > 0.5, and it may exist at the locus level for loci with a sum of posterior probabilities for H₃ and H₄ > 0.5.

3.5. Differentially expressed top‐ranked pleiotropic genes

Among genes containing or closer than 100 kb to top‐ranked GWS pleiotropic SNPs, six genes (ACTR1A, HIF1AN, ADAMTS3, RTN4, SORBS2, and SUFU) were significantly differentially expressed in DLPFC from pathologically confirmed AD cases with and without clinical symptoms prior to death compared to that from cognitively and pathologically normal controls (Figure 3 and Table S6). ACTR1A, which contains many high LD variants of the top‐ranked SUFU SNP rs7902306, had significantly lower expression levels (FDR = 1.27×10⁻⁴) in symptomatic AD cases than controls (Figure 3A). Expression of HIF1AN, located near the top‐ranked PAX2 SNP rs117854720, was significantly lower in symptomatic AD cases compared to controls (FDR = 4.26×10⁻³) (Figure 3B). ADAMTS3 and RTN4 expression was significantly reduced in symptomatic AD cases compared to controls (FDR < 1.31×10⁻⁷) and resilient AD cases (FDR < 9.78×10⁻³) (Figure 3C,D). Expression of SORBS2 was also significantly lower (FDR = 1.39×10⁻⁴) in symptomatic AD cases compared to controls (Figure 3E). SUFU was significantly overexpressed in symptomatic AD cases compared to controls (FDR = 3.91×10⁻²) (Figure 3F). None of the top‐ranked or suggestive pleiotropic variants were associated with the expression of any of the differentially expressed genes.

Pleiotropic genes significantly differentially expressed in the DLPFC in pathologically diagnosed AD cases with and without clinical symptoms prior to death and cognitively normal controls. Genes shown are significantly differentially expressed in the DLPFC among pathologically confirmed symptomatic (SymAD) and asymptomatic (AsymAD) AD cases, and cognitively normal controls. Differential expression levels in the DLPFC were estimated and meta‐analyzed using the ROSMAP, BUADRC, and FHS datasets. (A) *ACTR1A*, (B) *HIF1AN*, (C) *ADAMTS3*, (D) *RTN4*, (E) *SORBS2*, and (F) *SUFU*. Units on the Y‐axis represent log‐transformed (log₂) expression of genes. *FDR < 0.05, **FDR < 0.01, ***FDR < 0.001. AD, Alzheimer's disease; BUADRC, Boston University Alzheimer's Disease Research Center; DLPFC, Dorsolateral Prefrontal Cortex; FDR, false discovery rate; FHS, Framingham Heart Study; ROSMAP, Religious Orders Study/Rush Memory and Aging Project.

3.6. Pathways enriched for top‐ranked pleiotropic genes

We identified many canonical pathways significantly enriched for genes containing top‐ranked pleiotropic variants (P < 1×10⁻⁴) (Table S7), primarily derived from analyses seeded with genes affecting memory paired with SBP or DBP (Table 4). Multiple pathways closely connected to G protein‐coupled receptors (GPCRs) and their downstream signaling pathways were significantly enriched for genes with pleiotropy between SBP and memory (FDR < 1.20×10⁻³), including orexin signaling, apelin endothelial signaling, P2Y purinergic receptor signaling, protein kinase A (PKA) signaling, γ‐aminobutyric acid (GABA)‐related (e.g., GABAergic) receptor signaling, glutaminergic receptor signaling, Gα12/13 signaling, and signaling by rho family GTPases. We also found significant pathways linked to the renin–angiotensin system (RAS) and cardiac hypertrophy (FDR < 1.20×10⁻³), such as the role of nuclear factor of activated T cells (NFAT) in cardiac hypertrophy, renin–angiotensin signaling, cell junction organization, cardiac hypertrophy signaling, and gap junction signaling. Several pathways related to pro‐inflammatory responses of microglia and mast cells were significantly enriched (FDR < 8.91×10⁻⁴), including oxytocin signaling, docosahexaenoic acid signaling, leptin signaling, eicosanoid signaling, relaxin signaling, and interleukin‐1 signaling. Other pathways significantly enriched for top‐ranked pleiotropic gene were related to insulin resistance (e.g., apelin pancreas signaling, type II diabetes mellitus signaling, white adipose tissue browning pathway, and insulin receptor signaling) or neuronal development signaling, such as synaptogenesis signaling, gonadotropin‐releasing hormone (GnRH) signaling, and endocannabinoid developing neuron pathway. Eleven of 24 (45.8%) of pathways with FDR < 0.001 were previously identified to be significantly enriched for genes containing top‐ranked variants that are associated with individual cognitive domains or pleiotropy for paired cognitive domains (Table 4). ⁵⁴

TABLE 4.

Pathways significantly enriched for top‐ranked pleiotropic genes (FDR < 0.001).

BP trait	Cognitive domain	Ingenuity canonical pathway	No. of seed genes	FDR ^a
SBP	Memory	Orexin Signaling Pathway	299	3.16×10⁻⁵
		Role of NFAT in Cardiac Hypertrophy		5.62×10⁻⁵
		Apelin Endothelial Signaling Pathway		1.66×10⁻⁴
		Molecular Mechanisms of Cancer		1.86×10⁻⁴
		Colorectal Cancer Metastasis Signaling		1.86×10⁻⁴
		Renin–Angiotensin Signaling		1.86×10⁻⁴
		Cell junction organization		1.86×10⁻⁴
		Oxytocin Signaling Pathway		1.86×10⁻⁴
		Cardiac Hypertrophy Signaling (Enhanced)		2.19×10⁻⁴
		Docosahexaenoic Acid (DHA) Signaling		2.19×10⁻⁴
		Apelin Pancreas Signaling Pathway		2.19×10⁻⁴
		P2Y Purinergic Receptor Signaling Pathway		2.19×10⁻⁴
		Leptin Signaling in Obesity		3.16×10⁻⁴
		Protein Kinase A Signaling		3.16×10⁻⁴
		Synaptogenesis Signaling Pathway		3.31×10⁻⁴
		Endocannabinoid Cancer Inhibition Pathway		3.72×10⁻⁴
		GnRH Signaling		4.27×10⁻⁴
		Eicosanoid Signaling		4.27×10⁻⁴
		Type II Diabetes Mellitus Signaling		4.37×10⁻⁴
		Relaxin Signaling		4.57×10⁻⁴
		Endocannabinoid Developing Neuron Pathway		7.24×10⁻⁴
		IL‐1 Signaling		8.91×10⁻⁴
		Gα12/13 Signaling		9.33×10⁻⁴
DBP	Memory	Insulin Receptor Signaling	281	3.72×10⁻⁴
DBP	Memory	Synaptogenesis Signaling Pathway	281	3.72×10⁻⁴

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Abbreviations: BP, blood pressure; DBP, diastolic blood pressure; FDR, false discovery rate; GnRH, gonadotropin‐releasing hormone; IL, interleukin; NFAT, nuclear factor of activated T cells.

^{^a}

Benjamini‐Hochberg procedure was applied in multiple hypothesis testing to control a false discovery rate.

Pathways previously shown to be significantly enriched for genes containing top‐ranked variants associated with individual cognitive domain scores or pleiotropy for paired cognitive domains. ⁵⁴

4. DISCUSSION

4.1. Variants in multiple genes contribute to both BP and cognitive decline

Genome‐wide pleiotropy analyses for four BP measures paired with performance scores for three cognitive domains identified GWS pleiotropy with APOE and 11 novel loci. Among the novel loci, the effect on cognition of six (ADAMTS3, SUFU, SORBS2, RTN4, LOC100128993, and JPH2) was direct or through mechanisms unrelated to BP, and six genes (ACTR1A, HIF1AN, ADAMTS3, RTN4, SORBS2, and SUFU) containing or closest to the top‐ranked GWS pleiotropic SNPs were significantly differentially expressed in DLPFC from pathologically confirmed AD cases with antemortem AD clinical symptoms compared to that from cognitively normal controls and cognitively resilient pathologically confirmed AD cases. We also identified many pathways implicated in high BP and AD/ADRD, which were significantly enriched for genes seeded with top‐ranked pleiotropic variants. Our findings were based on time‐varying analyses considering repeated measures of BP and cognitive domain scores and their changes over time. Using a model with terms for the SNP main effect and SNP interaction with age, we investigated pleiotropic associations in the total, clinic‐based, and prospective cohort samples separately, which enabled us to include data collected at multiple time points and find associations that are age‐dependent.

4.2. Roles of pleiotropic loci in AD/ADRD

We identified four loci functionally relevant to processes implicated in AD/ADRD. ADAMTS3 encodes a major proteolytic enzyme that breaks down and inactivates reelin in the cerebral cortex and hippocampus, ⁷¹ and reelin plays a crucial role in resilience to AD. ⁷² Increased RTN4 expression inhibits the cleavage of amyloid precursor protein (APP) by blocking the access of β‐site APP cleaving enzyme 1 (BACE1) to APP and thus decreases Aβ production. ⁷³ Nogo‐A, also known as RTN4‐A, promotes Aβ secretion via the Nogo‐66 receptor/ROCK‐dependent BACE1 pathway, leading to the onset and development of AD/ADRD. ⁷⁴ Nogo‐B, another member of the RTN4 family, regulates endothelial sphingolipid biosynthesis and promotes endothelial dysfunction and high BP. ⁷⁵ ULK2‐dependent mitophagy activation is required for synaptic toxicity in cortical neurons and hippocampal CA1 neurons, triggered by AMP‐activated protein kinase overactivated by Aβ42 oligomers. ⁷⁶ We also identified a GWS association of DBP with ULK4. The fusion of a duplication of several ULK4 exons with a partial duplication of neighboring gene CHRNA7 ⁷⁷ forms CHRFAM7A, a negative regulator of alpha7 nicotinic acetylcholine receptor (α7nAChR) which has a high affinity for Aβ and plays a role in Aβ‐induced neuroinflammation. ⁷⁸ CHRFAM7A reduces Aβ uptake via α7nAChR and acts as an immune switch that shifts the α7nAChR from anti‐inflammatory to pro‐inflammatory, particularly in microglia‐like cells. ⁷⁸ HIF1AN encodes an inhibitor of hypoxia‐inducible factor‐1α (HIF‐1α) whose activation leads to excessive Aβ deposition and tau hyperphosphorylation by abnormally cleaving APP and inhibiting Aβ degradation. ⁷⁹ HIF‐1α also controls BP homeostasis, and the loss of HIF‐1α in vascular smooth muscle (VSM) cells leads to hypertension in vivo. ⁸⁰

GATA3 and SUFU are functionally relevant to the immune system (e.g., pro‐inflammatory signaling activation and inflammatory responses) and may enhance neuroinflammatory responses. GATA3 has been associated with female‐specific cognitive resilience to AD pathology ⁸¹ and encodes a transcription factor that controls T helper 2 cells which produce Aβ auto‐antibodies, alleviate Aβ deposition, and are therefore protective against AD. ⁸² GATA3 activates Tie2 promotor and regulates angiopoietin‐1/Tie2 signaling, inhibition of which may result in endothelial dysfunction. ⁸³ SUFU encodes a negative regulator of the sonic hedgehog signaling pathway modulating immune responses. ⁸⁴ ACTR1A located about 100 bp upstream of SUFU is linked to retrograde axonal transport, ⁸⁵ dysregulation of which is known to be an early event in AD/ADRD. ⁸⁶ SUFU and ACTR1A also have been linked to BP in a large trans‐ethnic GWAS. ¹³ JPH2 plays a critical role in maintaining the effective flux of calcium ions, ⁸⁷ dysregulation of which is related to neuroinflammatory signaling in neurons, ⁸⁸ microglia, ⁸⁹ and astrocytes. ⁹⁰ Downregulated JPH2 expression is also linked to hypertrophic cardiomyopathy ⁹¹ and increases the risk of AD/ADRD by about 50%. ⁹² LINC02946 encodes a long intergenic non‐protein coding RNA that is, not expressed in brain; however, it is located 150 kb upstream of the most proximate protein‐coding gene, NCK2, a recently identified AD GWAS locus. ⁹³ Roles of SORBS2, LOC105371656, and LOC100128993 in mechanisms of AD/ADRD or neuroinflammation are unclear at this time.

4.3. Links of abnormal BP and AD to endothelial dysfunction and neuroinflammation

Analyses of gene networks derived from top‐ranked pleiotropic genes identified pathways involved in GPCR signaling, RAS, and pro‐inflammatory responses. GPCRs have a key role in regulating BACE1 and are involved in AD/ADRD pathogenesis. ⁹⁴ GPCRs expressed in endothelial and VSM cells also contribute to maintaining vascular homeostasis and play a critical role in BP regulation. ⁹⁵ Orexin and its two GPCRs (OX1R and OX2R) regulate the sleep‐wake cycle and are closely linked to Aβ deposition, memory deficiency, and AD/ADRD, mediated by sleep deterioration. ⁹⁶ , ⁹⁷ Orexin is also involved in regulating cardiovascular responses, which may lead to increased SBP. ⁹⁸ RAS contributes to endothelial dysfunction that exacerbates hypertensive conditions and leads to neuroinflammatory responses. ²² , ⁹⁹ , ¹⁰⁰ Moreover, excessive shear stress or high BP‐induced endothelial dysfunction accelerates the infiltration of peripheral immune cells, originating outside the central nervous system, into the brain by disturbing the blood–brain barrier and activating pro‐inflammatory signaling, ²² , ¹⁰⁰ which leads to neuroinflammatory responses implicated in AD ¹⁰¹ , ¹⁰² and results in the accumulation of neurotoxic Aβ in the brain. ¹⁰³ Pro‐inflammatory responses of microglia may trigger neuroinflammation and promote AD/ADRD pathogenesis, ¹⁰⁴ , ¹⁰⁵ and pro‐inflammatory cytokines accumulated in vessels and kidneys induce vascular and renal damage, promoting a progressive increase in BP. ¹⁰⁶ Enriched pathways emerging from both this study and our previous cognitive GWAS ⁵⁴ are also supported by studies linking them to the modulation of BP and cardiac function, e.g., apelin endothelial signaling, ¹⁰⁷ PKA signaling, ¹⁰⁸ NFAT and cardiac hypertrophy signaling, ¹⁰⁹ oxytocin signaling, ¹¹⁰ interleukin‐1 signaling, ¹⁰⁶ GnRH signaling, ¹¹¹ and type II diabetes mellitus signaling. ¹¹²

4.4. Pleiotropic loci may contribute to cognitive resilience

Expression of five genes (ACTR1A, HIF1AN, ADAMTS3, RTN4, and SORBS2) containing GWS pleiotropic SNPs was highest in controls, intermediate in cognitively resilient AD cases, and lowest in symptomatic AD cases, suggesting that they may impact the development of AD pathology rather than cognitive impairment. We also found significantly different expressions of ADAMTS3 and RTN4 between AD cases with and without clinical symptoms, suggesting their involvement in cognitive resilience, noting that ADAMTS3 was previously linked to RELN which has been implicated in resilience to AD. ⁷¹ , ⁷²

4.5. Effects of some pleiotropic loci are age‐dependent

We applied models with terms for the main SNP and SNP×age interaction effects and jointly tested them to increase the power to detect genetic associations and pleiotropy between BP and cognitive performance measures. However, the interpretation of results emerging from the joint test combining the main SNP and SNP×age interaction effects is not straightforward. Among GWS loci showing pleiotropy between BP and cognitive measures, five loci (JPH2, ULK2, SUFU, ADAMTS3, and SORBS2) had GWS main SNP effects, suggesting these loci may affect mechanisms that influence BP and cognitive performance rather than changes in these measures over time. In contrast, other loci (GATA3, LOC105371656, PAX2, LINC02946, RTN4, and LOC100128993) showing significant SNP×age interaction effects may affect changes in BP or cognitive measures over time. Considering there were no significant SNP×age interactions in the GWAS of individual BP traits, pairing BP (which typically increases with age) with cognitive performance (which typically decreases with age) may have afforded greater power to detect loci whose effects are age‐dependent. Associations with ADAMTS3 and LINC02946 were observed only in the clinic‐based cohorts in which the onset of AD symptoms on average occurred at a younger age than in the prospective cohorts. Conversely, associations with ULK2, RTN4, SORBS2, and LOC100128993 were observed only in the prospective cohorts, implying these loci influence AD onset at a later age or may be associated with age‐related cognitive decline that is not specific to AD/ADRD processes.

4.6. Study limitations

Our study has several limitations. Except for FHS participants, individuals included in this study were much older at the time of enrollment than those in most BP GWAS. Hence, associations with loci whose effects on BP start in young or middle adulthood may have been missed, and nonlinear age effects remaining may have influenced our findings. To mitigate this concern, we limited participants in the BP GWAS to those with ages ≥ 60. Second, despite including several large longitudinal cohorts, the sample was considerably smaller compared to samples included in BP or AD GWAS; thus, the power for testing the main SNP and SNP×age interaction effects were reduced. Third, a large portion of the participants were examined only once, and those individuals did not contribute to analyses of changes over time. Fourth, the interpretation of some of our findings is complicated because they may implicate multiple mechanisms affecting BP and cognitive performance, including their changes over time. Fifth, the high overlap of participants included in the BP and cognitive domain GWAS (85.5%, 86.0%, and 84.8% for the total, clinic‐based, and prospective cohorts, respectively) may have increased type I error, even though we corrected for it as suggested by the PLACO developers and validated our findings in additional analyses. Sixth, while several studies have investigated the effects of angiotensin‐converting enzyme inhibitors (ACEi) and beta‐blockers (BB) on Aβ metabolism, ¹¹³ which may introduce confounding into the association between BP and AD/ADRD, this study did not consider the impact of antihypertensive medications on cognitive domain scores due to a lack of information about the specific types of medications. In our study, the use of antihypertensive drugs was associated with increased the memory score across all sample strata (p < 0.05), particularly in clinic‐based cohorts; however, this effect is controversial. Some studies have indicated that ACEi or BB may decrease AD risk by lowering BP or through mechanisms that bypass BP, ²⁴ but others have reported that ACEi increases AD risk by lowering the ACE expression, ²⁵ underlying AD pathophysiology linked to the accumulation of Aβ. ¹¹⁴ Although top‐ranked GWS pleiotropic genes were evaluated by differential gene expression analyses in DLPFC from cognitively normal controls and AD cases with and without clinical symptoms, further experimental validation across AD‐related brain subregions is required to confirm the functional relevance of our findings, including their spatial expression patterns and mechanistic roles in the pathophysiology of AD/ADRD. Another concern is the lack of correction for conducting 36 genome‐wide pleiotropy analyses (i.e., 12 pairs of BP and cognitive measures in the total, clinic‐based, and prospective cohort samples). Correction for 36 analyses would elevate the GWS threshold to P < 1.39×10⁻⁹, but this corrected threshold may be too conservative because there are strong correlations among the three cognitive domain scores, among the four BP measures, and between the separately ascertained cohorts and total sample. Finally, our findings are specific to non‐Hispanic white participants and should be replicated in other population groups to generalize them more broadly.

5. CONCLUSIONS

Our findings provide additional insight into the underlying mechanisms of hypertension and AD/ADRD, particularly those involving neuroinflammation. Ongoing efforts to harmonize BP and cognitive performance measures across several cohorts would likely yield additional datasets for discovering new, replicating, and generalizing associations with loci influencing measures of both BP and cognitive performance.

CONFLICT OF INTEREST STATEMENT

L.A.F. received support from NIH grants and an honorarium for serving as a journal editor. T.H. serves on the advisory board for Vivid Genomics, deputy editor for Alzheimer's & Dementia: Translational Research & Clinical Interventions, and senior associate editor for Alzheimer's & Dementia. L.‐S.W. received honoraria for several lectures. None of the other authors have conflicts of interest to disclose. T.J.H. serves as a consultant for Circular Genomics and on the editorial board of multiple journals, owns stock in Vivid Genomics, and received travel support from the Alzheimer's Association. G.D.S. received an honorarium for serving on an external advisory board. AJS serves on advisory boards for Siemens Medical Solutions, Eisai Pharmaceuticals and Novo Nordisk, on a monitoring board and external advisory committees for NIH, and as Editor‐in‐Chief for Brain, Imaging and Behavior. A.J.S. also received equipment from Avid Radiopharmaceuticals and Gates Ventures, and in‐kind contribution of proteomics assays from Sanofi. R.A. received grants from NIH, the Alzheimer's Disease Data Initiative, Gates Ventures, American Heart Association and Chosun University; consulting fees from Novo Nordisk, Signant Health and GSK; and equipment and materials from Eli Lilly/Avid, Robert Thomas, OpenAI and Linus Health. J.M. received honoraria from the Concussion Legacy Foundation and Imperial College London.

CONSENT STATEMENT

This study did not require informed consent because research participant data were obtained from public data repositories.

Supporting information

Supporting Information

ALZ-21-e70681-s001.xlsx^{(649.9KB, xlsx)}

Supporting Information

ALZ-21-e70681-s003.docx^{(5.3MB, docx)}

Supporting Information

ALZ-21-e70681-s002.pdf^{(1.2MB, pdf)}

ACKNOWLEDGMENTS

This work was supported in part by NIH grants U19‐AG068753, R01‐AG048927, U54‐AG052427, U01‐AG058654, U01‐AG032984, RF1‐AG057519, U01‐AG062602, P30‐AG072878, U24‐AG074855, U01‐AG068057, U01‐AG082665, U01‐AG081230 and R01‐AG059716. Biological samples and associated phenotypic data used in primary data analyses were stored at study investigator institutions and at the National Cell Repository for Alzheimer's Disease (NCRAD, U24‐AG021886) at Indiana University funded by the National Institute on Aging (NIA). Associated phenotypic data used in primary and secondary data analyses were provided by the study investigators, the NIA funded Alzheimer's Disease Centers (ADCs), the National Alzheimer's Coordinating Center (NACC), U01‐AG016976), the National Institute on Aging Genetics of Alzheimer's Disease Data Storage Site (NIAGADS, U24‐AG041689) at the University of Pennsylvania, funded by NIA, and the Religious Orders Study/Rush Memory and Aging Project (ROSMAP, P30‐AG10161, P30‐AG72975, R01‐AG15819, R01‐AG17917, U01‐AG46152, and U01‐AG61356). Phenotypic data were harmonized by the Alzheimer's Disease Sequencing Project Phenotype Harmonization Consortium (ADSP‐PHC, U24‐AG074855, U01‐AG068057 and R01‐AG059716), funded by NIA. This research was supported in part by the Intramural Research Program of the National Institutes of Health (NIH), National Library of Medicine. Contributors to the genetic and phenotypic data included the study investigators on projects that were individually funded by NIA and other NIH institutes, and by private United States organizations, foreign governmental organizations, or nongovernmental organizations.

We also acknowledge the following investigators who assembled and characterized participants of cohorts included in this study:

Adult Changes in Thought: James D. Bowen, Paul K. Crane, Gail P. Jarvik, C. Dirk Keene, Eric B. Larson, W. William Lee, Wayne C. McCormick, Susan M. McCurry, Shubhabrata Mukherjee, Katie Rose Richmire.

Chicago Health and Aging Project: Philip L. De Jager, Denis A. Evans.

Estudio Familiar de la Influencia Genetica en Alzheimer: Sandra Barral, Rafael Lantigua, Richard Mayeux, Martin Medrano, Dolly Reyes‐Dumeyer, Badri Vardarajan.

Framingham Heart Study: Ting Fang Alvin Ang, Hugo J. Aparicio, Rhoda Au, Sanford Auerbach, Alexa S. Beiser, Anita DeStefano, Sherral Devine, Lindsay A. Farrer, Jesse Mez, Jose Raphael Romero, Sudha Seshadri.

Genetic Differences: Duane Beekly, James Bowen, Walter A. Kukull, Eric B. Larson, Wayne McCormick, Gerard D. Schellenberg, Linda Teri.

Mayo Clinic: Minerva M. Carrasquillo, Dennis W. Dickson, Nilufer Ertekin‐Taner, Neill R. Graff‐Radford, Joseph E. Parisi, Ronald C. Petersen, Steven G. Younkin.

Mayo PD: Gary W. Beecham, Dennis W. Dickson, Ranjan Duara, Nilufer Ertekin‐Taner, Tatiana M. Foroud, Neill R. Graff‐Radford, Richard B. Lipton, Joseph E. Parisi, Ronald C. Petersen, Bill Scott, Jeffery M. Vance.

Memory and Aging Project: David A. Bennett, Philip L. De Jager.

Multi‐Institutional Research in Alzheimer's Genetic Epidemiology Study: Sanford Auerbach, Helena Chui, Jaeyoon Chung, L. Adrienne Cupples, Charles DeCarli, Ranjan Duara, Martin Farlow, Lindsay A. Farrer, Robert Friedland, Rodney C.P. Go, Robert C. Green, Patrick Griffith, John Growdon, Gyungah R. Jun, Walter Kukull, Alexander Kurz, Mark Logue, Kathryn L. Lunetta, Thomas Obisesan, Helen Petrovitch, Marwan Sabbagh, A. Dessa Sadovnick, Magda Tsolaki.

National Cell Repository for Alzheimer's Disease: Kelley M. Faber, Tatiana M. Foroud.

National Institute on Aging (NIA) Late Onset Alzheimer's Disease Family Study: David A. Bennett, Sarah Bertelsen, Thomas D. Bird, Bradley F. Boeve, Carlos Cruchaga, Kelley Faber, Martin Farlow, Tatiana M. Foroud, Alison M. Goate, Neill R. Graff‐Radford, Richard Mayeux, Ruth Ottman, Dolly Reyes‐Dumeyer, Roger Rosenberg, Daniel Schaid, Robert A. Sweet, Giuseppe Tosto, Debby Tsuang, Badri Vardarajan.

NIA Alzheimer Disease Centers: Erin Abner, Marilyn S. Albert, Roger L. Albin, Liana G. Apostolova, Sanjay Asthana, Craig S. Atwood, Lisa L. Barnes, Thomas G. Beach, David A. Bennett, Eileen H. Bigio, Thomas D. Bird, Deborah Blacker, Adam Boxer, James B. Brewer, James R. Burke, Jeffrey M. Burns, Joseph D. Buxbaum, Nigel J. Cairns, Chuanhai Cao, Cynthia M. Carlsson, Richard J. Caselli, Helena C. Chui, Carlos Cruchaga, Mony de Leon, Charles DeCarli, Malcolm Dick, Dennis W. Dickson, Nilufer Ertekin‐Taner, David W. Fardo, Martin R. Farlow, Lindsay A. Farrer, Steven Ferris, Tatiana M. Foroud, Matthew P. Frosch, Douglas R. Galasko, Marla Gearing, David S. Geldmacher, Daniel H. Geschwind, Bernardino Ghetti, Carey Gleason, Alison M. Goate, Teresa Gomez‐Isla, Thomas Grabowski, Neill R. Graff‐Radford, John H. Growdon, Lawrence S. Honig, Ryan M. Huebinger, Matthew J. Huentelman, Christine M. Hulette, Bradley T. Hyman, Suman Jayadev, Lee‐Way Jin, Sterling Johnson, M. Ilyas Kamboh, Anna Karydas, Jeffrey A. Kaye, C. Dirk Keene, Ronald Kim, Neil W. Kowall, Joel H. Kramer, Frank M. LaFerla, James J. Lah, Allan I. Levey, Ge Li, Andrew P. Lieberman, Oscar L. Lopez, Constantine G. Lyketsos, Daniel C. Marson, Ann C. McKee, Marsel Mesulam, Jesse Mez, Bruce L. Miller, Carol A. Miller, Abhay Moghekar, John C. Morris, John M. Olichney, Joseph E. Parisi, Henry L. Paulson, Elaine Peskind, Ronald C. Petersen, Aimee Pierce, Wayne W. Poon, Luigi Puglielli, Joseph F. Quinn, Ashok Raj, Murray Raskind, Eric M. Reiman, Barry Reisberg, Robert A. Rissman, Erik D. Roberson, Howard J. Rosen, Roger N. Rosenberg, Martin Sadowski, Mark A. Sager, David P. Salmon, Mary Sano, Andrew J. Saykin, Julie A. Schneider, Lon S. Schneider, William W. Seeley, Scott Small, Amanda G. Smith, Robert A. Stern, Russell H. Swerdlow, Rudolph E. Tanzi, Sarah E. Tomaszewski Farias, John Q. Trojanowski, Juan C. Troncoso, Debby W. Tsuang, Vivianna M. Van Deerlin, Linda J. Van Eldik, Harry V. Vinters, Jean Paul Vonsattel, Jen Chyong Wang, Sandra Weintraub, Kathleen A. Welsh‐Bohmer, Shawn Westaway, Thomas S. Wingo, Thomas Wisniewski, David A. Wolk, Randall L. Woltjer, Steven G. Younkin, Lei Yu, Chang‐En Yu.

Religious Orders Study: David A. Bennett, Philip L. De Jager.

Texas Alzheimer's Research and Care Consortium: Perrie Adams, Alyssa Aguirre, Lisa Alvarez, Gayle Ayres, Robert C. Barber, John Bertelson, Sarah Brisebois, Scott Chasse, Munro Culum, Eveleen Darby, John C. DeToledo, Thomas J. Fairchild, James R. Hall, John Hart, Michelle Hernandez, Ryan Huebinger, Leigh Johnson, Kim Johnson, Aisha Khaleeq, Janice Knebl, Laura J. Lacritz, Douglas Mains, Paul Massman, Trung Nguyen, Sid O'Bryant, Marcia Ory, Raymond Palmer, Valory Pavlik, David Paydarfar, Victoria Perez, Marsha Polk, Mary Quiceno, Joan S. Reisch, Monica Rodriguear, Roger Rosenberg, Donald R. Royall, Janet Smith, Alan Stevens, Jeffrey L. Tilson, April Wiechmann, Kirk C. Wilhelmsen, Benjamin Williams, Henrick Wilms, Martin Woon.

University of Miami: Larry D. Adams, Gary W. Beecham, Regina M. Carney, Katrina Celis, Michael L. Cuccaro, Kara L. Hamilton‐Nelson, James Jaworski, Brian W. Kunkle, Eden R. Martin, Margaret A. Pericak‐Vance, Farid Rajabli, Michael Schmidt, Jeffery M Vance.

University of Toronto: Ekaterina Rogaeva, Peter St. George‐Hyslop.

University of Washington Families: Thomas D. Bird, Olena Korvatska, Wendy Raskind, Chang‐En Yu.

Vanderbilt University: John H. Dougherty, Harry E. Gwirtsman, Jonathan L. Haines, Angela Jefferson.

Washington Heights‐Inwood Columbia Aging Project: Adam Brickman, Rafael Lantigua, Jennifer Manly, Richard Mayeux, Christiane Reitz, Nicole Schupf, Yaakov Stern, Giuseppe Tosto, Badri Vardarajan.

Kang M, Ang TFA, Devine SA, et al. Genome‐wide pleiotropy analysis of longitudinal blood pressure and harmonized cognitive performance measures. Alzheimer's Dement. 2025;21:e70681. 10.1002/alz.70681

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PERMALINK

Genome‐wide pleiotropy analysis of longitudinal blood pressure and harmonized cognitive performance measures

Moonil Kang

Ting Fang Alvin Ang

Sherral A Devine

Richard Sherva

Shubhabrata Mukherjee

Emily H Trittschuh

Phoebe Scollard

Michael Lee

Seo‐Eun Choi

Brandon Klinedinst

Connie Nakano

Logan C Dumitrescu

Timothy J Hohman

Michael L Cuccaro

Andrew J Saykin

Walter A Kukull

David A Bennett

Li‐San Wang

Richard P Mayeux

Jonathan L Haines

Margaret A Pericak‐Vance

Gerard D Schellenberg

Paul K Crane

Rhoda Au

Kathryn L Lunetta

Jesse Mez

Lindsay A Farrer

Abstract

INTRODUCTION

METHODS

RESULTS

DISCUSSION

Highlights

1. INTRODUCTION

2. METHODS

2.1. Participant ascertainment and assessment

RESEARCH IN CONTEXT

2.2. Cognitive domain scores

2.3. Genotype quality control, generating principal components, and genetic relationship matrix

2.4. GWAS for BP and cognitive domain measures

2.5. Genetic correlations and genome‐wide pleiotropy analyses

2.6. Differentiation of horizontal pleiotropy from mediated pleiotropy

2.7. Differential gene expression analyses

2.8. Pathway enrichment analyses

3. RESULTS

3.1. Basic characteristics

TABLE 1.

3.2. Phenotypic and genetic correlations

3.3. Genetic associations with individual BP and cognitive domain scores

3.4. Pleiotropy for BP and cognitive domain scores

TABLE 2.

FIGURE 1.

FIGURE 2.

TABLE 3.

3.5. Differentially expressed top‐ranked pleiotropic genes

FIGURE 3.

3.6. Pathways enriched for top‐ranked pleiotropic genes

TABLE 4.

4. DISCUSSION

4.1. Variants in multiple genes contribute to both BP and cognitive decline

4.2. Roles of pleiotropic loci in AD/ADRD

4.3. Links of abnormal BP and AD to endothelial dysfunction and neuroinflammation

4.4. Pleiotropic loci may contribute to cognitive resilience

4.5. Effects of some pleiotropic loci are age‐dependent

4.6. Study limitations

5. CONCLUSIONS

CONFLICT OF INTEREST STATEMENT

CONSENT STATEMENT

Supporting information

ACKNOWLEDGMENTS

REFERENCES

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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