Genome-wide association study of hippocampal blood-oxygen-level-dependent-cerebral blood flow correlation in Chinese Han population

Hui Xue; Xiaojun Xu; Zhihan Yan; Jingliang Cheng; Longjiang Zhang; Wenzhen Zhu; Guangbin Cui; Quan Zhang; Shijun Qiu; Zhenwei Yao; Wen Qin; Feng Liu; Meng Liang; Jilian Fu; Qiang Xu; Jiayuan Xu; Yingying Xie; Peng Zhang; Wei Li; Caihong Wang; Wen Shen; Xiaochu Zhang; Kai Xu; Xi-Nian Zuo; Zhaoxiang Ye; Yongqiang Yu; Junfang Xian; Chunshui Yu; the CHIMGEN Consortium

doi:10.1016/j.isci.2023.108005

. 2023 Sep 21;26(10):108005. doi: 10.1016/j.isci.2023.108005

Genome-wide association study of hippocampal blood-oxygen-level-dependent-cerebral blood flow correlation in Chinese Han population

Hui Xue ^1,²³, Xiaojun Xu ^2,²³, Zhihan Yan ^3,²³, Jingliang Cheng ⁴, Longjiang Zhang ⁵, Wenzhen Zhu ⁶, Guangbin Cui ⁷, Quan Zhang ⁸, Shijun Qiu ⁹, Zhenwei Yao ¹⁰, Wen Qin ¹, Feng Liu ¹, Meng Liang ¹¹, Jilian Fu ¹, Qiang Xu ¹, Jiayuan Xu ¹, Yingying Xie ¹, Peng Zhang ¹², Wei Li ¹², Caihong Wang ⁴, Wen Shen ¹³, Xiaochu Zhang ¹⁴, Kai Xu ¹⁵, Xi-Nian Zuo ^16,¹⁷, Zhaoxiang Ye ¹⁸, Yongqiang Yu ^19,^∗, Junfang Xian ^20,^∗∗, Chunshui Yu ^1,^21,^24,^∗∗∗; the CHIMGEN Consortium²²

¹Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China

²Department of Radiology, The Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou 310009, China

³Department of Radiology, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou 325027, China

⁴Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China

⁵Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210002, China

⁶Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China

⁷Functional and Molecular Imaging Key Lab of Shaanxi Province & Department of Radiology, Tangdu Hospital, Air Force Medical University, Xi’an 710038, China

⁸Department of Radiology, Characteristic Medical Center of Chinese People’s Armed Police Force, Tianjin 300162, China

⁹Department of Medical Imaging, the First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou 510405, China

¹⁰Department of Radiology, Huashan Hospital, Fudan University, Shanghai 200040, China

¹¹School of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin 300203, China

¹²Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China

¹³Department of Radiology, Tianjin First Center Hospital, Tianjin 300192, China

¹⁴Division of Life Science and Medicine, University of Science & Technology of China, Hefei 230027, China

¹⁵Department of Radiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou 221006, China

¹⁶Developmental Population Neuroscience Research Center at IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China

¹⁷Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China

¹⁸Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin 300060, China

¹⁹Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China

²⁰Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China

²¹CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China

^∗

Corresponding author yuyongqiang@ahmu.edu.cn

^∗∗

Corresponding author cjr.xianjunfang@vip.163.com

^∗∗∗

Corresponding author chunshuiyu@tmu.edu.cn

²²

Further details can be found in the supplemental information

²³

These authors contributed equally

²⁴

Lead contact

PMCID: PMC10562876 PMID: 37822511

Summary

Correlation between blood-oxygen-level-dependent (BOLD) and cerebral blood flow (CBF) has been used as an index of neurovascular coupling. Hippocampal BOLD-CBF correlation is associated with neurocognition, and the reduced correlation is associated with neuropsychiatric disorders. We conducted the first genome-wide association study of the hippocampal BOLD-CBF correlation in 4,832 Chinese Han subjects. The hippocampal BOLD-CBF correlation had an estimated heritability of 16.2–23.9% and showed reliable genome-wide significant association with a locus at 3q28, in which many variants have been linked to neuroimaging and cerebrospinal fluid markers of Alzheimer’s disease. Gene-based association analyses showed four significant genes (GMNC, CRTC2, DENND4B, and GATAD2B) and revealed enrichment for mast cell calcium mobilization, microglial cell proliferation, and ubiquitin-related proteolysis pathways that regulate different cellular components of the neurovascular unit. This is the first unbiased identification of the association of hippocampal BOLD-CBF correlation, providing fresh insights into the genetic architecture of hippocampal neurovascular coupling.

Subject areas: Medical imaging, Neurogenetics, Neuroanatomy, Association analysis, Genomics

Graphical abstract

Highlights

•
The genetic architecture of hippocampal BOLD-CBF correlation remains unknown
•
The heritability of hippocampal BOLD-CBF correlation was estimated 16.2–23.9%
•
2 independent significant loci were revealed by two-stage GWAS
•
Gene-based association analyses showed 4 significant genes

Medical imaging; Neurogenetics; Neuroanatomy; Association analysis; Genomics

Introduction

About a half million of studies have applied the blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) to investigate brain function in healthy individuals and patients with neuropsychiatric disorders. BOLD-fMRI depends on the neurobiological mechanism of neurovascular coupling (NVC) whereby local cerebral blood flow (CBF) rapidly increases following regional neural activity.¹^,² NVC depends on the concerted action of cellular components of neurovascular unit (NVU), including neurons and astrocytes from brain parenchyma, as well as pericytes, smooth muscle cells and endothelial cells from blood vessels.³ NVC is a critical mechanism for any brain structure to maintain its normal function and neurovascular uncoupling has been associated with neuropsychiatric disorders, such as Alzheimer’s disease (AD), stroke, and autism spectrum disorder.⁴^,⁵

With in vivo brain magnetic resonance imaging (MRI) techniques, BOLD-CBF correlation has been used as a proxy to reflect NVC. Regional BOLD-CBF correlation varies across individuals and is correlated with age, sex, and executive function.⁶ In addition, alterations in regional BOLD-CBF correlations have been observed in many neuropsychiatric disorders, such as AD,⁷ schizophrenia,⁸ subcortical ischemic vascular disease,⁹ and generalized anxiety disorder.¹⁰ These results indicate that BOLD-CBF correlation may be an individual trait, however, there are still lacking genome-wide association studies (GWASs) on regional BOLD-CBF correlation.

The hippocampus is critically important for human memory, navigation, and other cognitive functions,¹¹^,¹² and is implicated in common brain disorders, such as AD and schizophrenia.¹³^,¹⁴ A unique ability of the hippocampus is to generate new neurons throughout life and neurogenesis is regulated by the hippocampal NVC.¹⁵ The newborn neurons can be integrated into the existing neural circuitry of cognitive control, and their dysfunction has been associated with cognitive impairments and dementia.¹⁶ The hippocampus shows much lower NVC than neocortical regions due to microvascular differences, which could explain why the hippocampus is more vulnerable than other regions in brain disorders with reduced energy supply.¹⁷ For instance, the hippocampal NVC reduction is considered as a potential biomarker of AD¹⁸^,¹⁹; the peak latency of the hemodynamic response function (HRF-PL) of hippocampus is associated with stress response and risk for depression²⁰; and hippocampal BOLD-CBF correlation is altered in patients with schizophrenia⁶ and diabetes mellitus.²¹ Considering the critical role of the hippocampal NVC, there is an urgent need to investigate the genetic architecture of the hippocampal BOLD-CBF correlation.

In this study, we conducted the first GWAS on the hippocampal BOLD-CBF correlation in 4,832 Chinese Han participants from the Chinese Imaging Genetics (CHIMGEN) cohort.²² Here, the across-voxel correlation between reginal homogeneity (ReHo) derived from the resting-state fMRI data and CBF measured by the arterial spin labeling (ASL) MRI data within hippocampal voxels was used as a proxy to represent hippocampal NVC. The study design is illustrated in Figure S1.

Results

Subjects

This study included 4,832 Chinese Han healthy young subjects with qualified genomic, resting-state fMRI, and ASL data from 17 centers of the CHIMGEN study. Since the scanner effect which was variability caused by MRI data acquired by different scanners is a major bias in multicenter neuroimaging studies causing reduced reliability of neuroimaging measurements,²³ we divided subjects into the discovery and replication samples based on the types of MRI scanners. Of the 4,832 subjects, 4,406 subjects (2,778 females; age: 23.6 ± 2.4 years) were defined as the discovery sample since their MRI data were acquired by the same type (GE Discovery MR750) of scanners with the same scanning parameters, in which the bias from the scanner effect was expected to be small. We defined other 426 subjects (283 females; age: 24.0 ± 2.4 years) whose MRI data were acquired by different types of scanners as the replication sample. The mixed replication sample can test whether the discovered results are applicable for MRI data acquired by different types of MRI scanners. The demographic data of the included subjects are shown in Table S1 and the distribution of these subjects across centers is presented in Table S2.

Hippocampal blood-oxygen-level-dependent-cerebral blood flow correlation calculation

In the main analysis, the hippocampal BOLD-CBF correlation of each subject was measured by ReHo-CBF correlation, the across-voxel correlation coefficient between ReHo and CBF, in bilateral hippocampal voxels defined by the automated anatomical atlas (AAL)²⁴ in the standard space. Here, ReHo was used to measure spontaneous neuronal activity and was calculated based the preprocessed fMRI data without global signal regression (GSR). The distribution of the average ReHo-CBF correlations across AAL regions are presented in Figure S2, revealing that ReHo-CBF correlation was disparate in different brain region and was consistent with previous study.⁶ Since MRI data were acquired from different scanners, we used the Combat approach²⁵ with sex and age as covariates to alleviate the scanner effects on these brain imaging measures and found that Combat harmonization could reduce between-scanner variations of hippocampal ReHo and CBF (Figure S3). After harmonization, the age, sex, and site explained 0.038% (β = −0.019, p = 0.18), 1.14% (β = −0.13, p = 1.43 × 10⁻¹³), and 0.11% (β = −0.033, p = 0.021) variance of ReHo-CBF correlation, respectively. The calculation process of the hippocampal BOLD-CBF correlation is illustrated in Figure 1.

Calculation of the hippocampal BOLD-CBF correlation representing neurovascular coupling

(A–D) For each subject, the voxel-wise CBF map is calculated from ASL data and the voxel-wise ReHo map is computed from resting-state fMRI data without GSR. After ComBat harmonization, the harmonized CBF and ReHo maps are used to calculate the hippocampal BOLD-CBF correlation. The hippocampal voxels are extracted from the AAL and the hippocampal NVC is measured by BOLD-CBF correlation defined as the across-voxel correlation coefficient between ReHo and CBF in all hippocampal voxels. The hippocampal NVC is also calculated by other four approaches: (A) using ALFF to measure spontaneous neuronal activity; (B) using fALFF to measure spontaneous neuronal activity; (C) conducting GSR during the fMRI data preprocessing; (D) using the BNA to extract the hippocampal voxels. Abbreviations: AAL, automated anatomical atlas; ALFF, amplitude of low-frequency fluctuation; ASL, arterial spin labeling; BOLD, blood-oxygen-level-dependent; BNA, Brainnetome atlas; CBF, cerebral blood flow; fALFF, fractional ALFF; fMRI, functional magnetic resonance imaging; GSR, global signal regression; NVC, neurovascular coupling; ReHo, regional homogeneity.

Since there is no standard pipeline to calculate the hippocampal NVC based on MRI data, we also used other four approaches to compute the measure to verify our main findings in these analyses. To test the impact of the selection of fMRI measure to represent spontaneous neuronal activity, we also used the amplitude of low-frequency fluctuation (ALFF) of BOLD signals and fractional ALFF (fALFF) to represent spontaneous neuronal activity and defined the hippocampal BOLD-CBF correlation as ALFF-CBF correlation and fALFF-CBF correlation. To test the impact of selecting brain atlas to extract the hippocampal voxels, we also extracted the hippocampal voxels from the Brainnetome atlas (BNA)²⁶ and recalculated the hippocampal ReHo-CBF correlation. To test the impact of GSR during the fMRI data preprocessing, we regressed out global BOLD signals and recalculated ReHo-CBF correlation (ReHo-CBF-GSR). The hippocampal BOLD-CBF correlations estimated by the five methods were highly correlated (r values ranged from 0.84 to 0.98) (Figure S4). We also tested the intra-class correlation coefficients (ICC) of these measures based on MRI data from eight participants who received MR scans at two different time points. We found that the ICC was 0.914 (p = 0.015), 0.986 (p = 0.0029), and 0.973 (p = 0.007) for the ReHo-CBF, ALFF-CBF, and fALFF-CBF correlations, respectively.

In 100 subjects randomly selected from the discovery sample, we also tested the associations of ReHo-CBF correlation with the previously proposed resting-state fMRI NVC markers,²⁷ three shape parameters of the hemodynamic response function (HRF) including the response height (RH), time to peak (TTP), and full width at half maximum (FWHM). Across AAL regions, the correlations of ReHo-CBF coupling with the three shape parameters showed broad distributions (Figure S5), ranged from −0.62 to 0.68 for RH, from −0.37 to 0.25 for TTP, and from −0.34 to 0.25 for FWHM, indicating that the relations between ReHo-CBF coupling and HRF shape parameters are different across brain regions. For the bilateral hippocampal region, we found correlations of the ReHo-CBF coupling with TTP (r = −0.47, p = 6.07 × 10⁻⁷) and FWHM (r = −0.28, p = 4.4 × 10⁻⁴), but not with RH (r = −0.17, p = 0.097), indicating that ReHo-CBF coupling is partially associated with the latency and duration of HRF. In the 4,832 subjects, we also tested the correlation of the ReHo-CBF coupling with the absolute and relative volumes for the bilateral hippocampi and found that the ReHo-CBF coupling was not correlated with the absolute hippocampal volume (r = −0.0016, p > 0.05) but showed a weak correlation with relative hippocampal volume (r = 0.052, p = 3.22 × 10⁻⁴).

Heritability of the hippocampal blood-oxygen-level-dependent-cerebral blood flow correlation

We used the GREML-LDMS method²⁸ to estimate the single-nucleotide polymorphism (SNP) heritability (h²) for the hippocampal BOLD-CBF correlation in the 4,832 unrelated individuals. In these subjects, the imputed 6,830,145 autosomal variants explained 20.7% (standard error (s.e.) = 7.6%) of the variance for the hippocampal BOLD-CBF correlation and the SNP heritability was significantly different from zero at the nominal 5% significance level (p = 6.66 × 10⁻³). When we used other four approaches to calculate the hippocampal BOLD-CBF correlation, the estimated h² values (16.2–23.9%, p = 0.001–0.024) for the hippocampal BOLD-CBF correlation were still significant (Table S3).

Genome-wide association studies of the hippocampal reginal homogeneity-cerebral blood flow correlation

A two-stage GWAS was conducted to find genetic associations with the hippocampal ReHo-CBF correlation, while controlling for age, age,² sex, age × sex, age² × sex, fMRI frame-wise displacement (FD), and the first ten genetic principal components. We used genome-wide significance (p < 5 × 10⁻⁸) as the discovery criterion and nominal significance (p < 0.05) as the replication criterion due to the small sample size. In the discovery stage (n = 4,406), we found two genome-wide significant loci associated with the hippocampal ReHo-CBF correlation with an inflation factor (λ_GC) of 1.005 indicating the well control of population substructure. One locus was at 3q28 with a lead SNP of rs13323015 (p = 4.82 × 10⁻⁸) and the other locus was at 12p11.22 with a lead SNP of rs10843101 (p = 5.25 × 10⁻⁹). In the replication stage (n = 426), the association of rs13323015 with the hippocampal ReHo-CBF correlation was replicated (consistent direction, p = 5.79 × 10⁻³), but we failed to replicate the association of rs10843101 with the hippocampal ReHo-CBF correlation (beta values in the same direction, p = 0.396). It was likely because limited sample size in the replication stage was difficult to have enough power to replicate the result, but it was found that association with lead SNP rs10843101 had the same direction of beta value, which was suggestive. We also conducted a GWAS meta-analysis to make full use of the available data to generate GWAS results for the gene-based association and enrichment analyses. We found that the two loci identified in the discovery stage also showed genome-wide significant associations (p = 1.79 × 10⁻⁹ for the lead SNP rs13323015 at 3q28 and p = 5.65 × 10⁻⁹ for the lead SNP rs10843101 at 12p11.22) (Table S4) with the hippocampal ReHo-CBF correlation and quantile–quantile plots did not indicate genomic inflation (λ_GC = 1.003) (Figure 2).

Genome-wide significant associations for the hippocampal BOLD-CBF correlation

(A) Manhattan plots show genome-wide significant associations (red line: p = 5 × 10⁻⁸) in the discovery stage for the hippocampal BOLD-CBF correlation measured by five methods. Each point represents a single genetic variant plotted according to its genomic position (x axis) and its -log10(P) value for the association (y axis).

(B) The comparisons of the hippocampal BOLD-CBF correlation among the genotypic groups of the lead variant (rs13323015 at 3q28) in replication stage and the y axis means the residuals of normalized hippocampal BOLD-CBF correlation while controlling for covariates used in genome-wide associations.

(C) Manhattan plots show genome-wide significant associations (red line: p = 5 × 10⁻⁸) in the meta-analysis for the hippocampal BOLD-CBF correlation measured by the five methods. Abbreviations: ALFF, amplitude of low-frequency fluctuation; BOLD, blood-oxygen-level-dependent; BNA, Brainnetome atlas; CBF, cerebral blood flow; CHR, chromosome; fALFF, fractional ALFF; GSR, global signal regression; NVC, neurovascular coupling; ReHo, regional homogeneity.

Robustness of genome-wide association studies results

We also used other four approaches to calculate hippocampal BOLD-CBF correlation of these subjects and re-performed two-stage GWASs with the same covariates as the main analysis to confirm GWAS findings of the hippocampal ReHo-CBF correlation. In all the four analyses, the genome-wide significant association between rs13323015 at 3q28 and hippocampal BOLD-CBF correlation was confirmed in the discovery stage (n = 4,406; p = 1.40–4.25 × 10⁻⁸), replication stage (n = 426; consistent direction of effects, p = 0.0025–0.0094), and meta-analysis of the two stages (n = 4,832; p = 5.34–8.04 × 10⁻¹⁰) (Table S4). However, the association between rs10843101 at 12p11.22 and hippocampal BOLD-CBF correlation was not confirmed in the analyses (discovery stage: p = 0.50–1.87 × 10⁻⁷, replication stage: p = 0.28–0.75, meta-analysis: p = 0.41–3.30 × 10⁻⁷, Table S4). Although a few lead SNPs at 12p11.22 showed genome-wide significant association (p < 5 × 10⁻⁸) with hippocampal BOLD-CBF correlation in the discovery stage and/or meta-analysis, none of the tested associations was confirmed in the replication stage (all p > 0.05) (Table S5). Moreover, none of other SNPs outside the two loci reached genome-wide significant (p < 5 × 10⁻⁸) in any of these four GWASs (Figure 2). QQ-plots of the four GWASs showed good control of population substructure in both discovery and meta-analysis (λ_GC = 0.997–1.016, Figure S6). These results indicate that the association of 3q28 with hippocampal BOLD-CBF correlation is reproducible and the association of 12p11.22 with hippocampal BOLD-CBF correlation needs to be further confirmed in larger samples. We also used the CompCor approach²⁹ to remove white matter (WM) and cerebrospinal fluid (CSF) signals and then recalculated the ReHo maps, based on which we recalculated hippocampal ReHo-CBF correlation. We found significant correlation (r = 0.95, p < 0.001) between ReHo-CBF correlations in the hippocampal voxels derived from the two fMRI preprocessing approaches. We also re-performed GWAS in the 4,832 participants and found the same two loci with same lead SNPs of rs13081641 (β = 0.139, p = 1.16 × 10⁻⁹) and rs11049363 (β = 0.1952, p = 9.06 × 10⁻⁹, Figure S7). In addition, we used the same steps to conduct GWAS for the ReHo-CBF correlation of the bilateral precentral gyri defined by the AAL atlas in the 4,832 participants, however, we did not find any significant association at p < 5 × 10⁻⁸ (Figure S8). Compared with previous GWASs of the bilateral hippocampal volume,³⁰^,³¹ there was not overlapping SNP between hippocampal ReHo-CBF correlation and hippocampal volume in mono-ancestry, but we found an overlapping SNP rs14474594 (3:190642839:A:G) in 3q28 near to GMNC (hippocampal ReHo-CBF correlation: β = 0.13, p = 2.34 × 10⁻⁸; right hippocampal volume: β = 0.038, p = 2.18 × 10⁻⁸; left hippocampal volume: β = 0.042, p = 5.04 × 10⁻¹⁰) between hippocampal ReHo-CBF correlation and bilateral hippocampal volume in cross-ancestry.³⁰

Single- and multi-trait genome-wide association studies

In the 4,832 subjects, we also conducted GWAS for the hippocampal CBF and ReHo, respectively. Although we failed to identify genome-wide significant association for hippocampal ReHo (Figure S9A), we found a significant locus at 3q28 (rs78054167: β = −0.14, p = 1.38 × 10⁻⁹) for hippocampal CBF (Figure S9B). However, the effect was in the opposite direction compared to that for the ReHo-CBF correlation. We used three multivariate methods to conduct genetic association analyses by integrating the three neuroimaging phenotypes. Although the multi-trait analysis of GWAS (MTAG)³² failed to find any significant genome-wide associations at p < 5 × 10⁻⁸ (Figure S9C), both CGWAS and MinGWAS³³ replicated our results (rs74712405 at 3q28: p = 2.26 × 10⁻¹⁰ in CGWAS, p = 3.20 × 10⁻⁹ in MinGWAS and rs11049363 at 12p11.22: p = 2.81 × 10⁻⁸ in CGWAS, p = 1.16 × 10⁻⁸ in MinGWAS, Figure S9D) but failed to find any new genetic associations.

Functional annotation for genome-wide association studies significant loci

In the main GWAS meta-analysis (ReHo-CBF), we found 183 genome-wide significant SNPs (p < 5.0 × 10⁻⁸) at 3q28 (lead SNP: rs74712405) and 12p11.22 (lead SNP: rs11049363) (Table S6; Figure S10). Most of the 152 genome-wide significant SNPs at 3q28 were mapped to an intergenic region between GMNC (Geminin Coiled-Coil Domain Containing) and OSTN (Osteocrin), of which 21 significant SNPs at 3q28 were associated with the expression of GMNC in the hippocampus from the Braineac dataset (p = 0.03–0.045, Table S7) and GMNC is an essential gene for neurogenesis after birth.³⁴ A portion of significant SNPs at 3q28 have been associated with neuroimaging markers (e.g., hippocampal CA1-body volume, fornix microstructure, and ventricle volume) and biomarkers (e.g., cerebrospinal P-tau level) of AD. The 31 significant SNPs of the suggestive locus (12p11.22) were mostly in the intergenic region between PTHLH (Parathyroid Hormone Like Hormone) and CCDC91 (Coiled-Coil Domain Containing 91) and partly in the intronic region of CCDC91. Several SNPs have been associated with cortical surface area and thickness (Table S6). The HaploReg database showed that rs28380759 in strong linkage disequilibrium (LD) (r² = 0.81) with significant SNP rs10843111 was overlapped with the H3K4me3 promoter peaks in the hippocampus, indicative of transcriptionally active. Furthermore, rs10843111 was associated with the expression of CCDC91 in the hippocampus (p = 9.23 × 10⁻¹⁶). Therefore, our results indicate a potential role of GMNC and CCDC91 in regulating hippocampal ReHo-CBF correlation, however, further confirmation is needed.

Gene-based association analysis of the hippocampal blood-oxygen-level-dependent-cerebral blood flow correlation

We performed gene-based association analysis to identify genes associated with the hippocampal ReHo-CBF correlation in the 4,832 subjects and a Bonferroni method was used to adjust for the number of genes (n = 18,000) with a significant threshold of p = 2.78 × 10⁻⁶. We found that GMNC was the most significant gene associated with the hippocampal ReHo-CBF correlation (p = 4.65 × 10⁻¹¹). Moreover, six additional genes including CRTC2 (CREB Regulated Transcription Coactivator 2), DENND4B (DENN Domain Containing 4B), CREB3L4 (CAMP Responsive Element Binding Protein 3 Like 4), GATAD2B (GATA Zinc Finger Domain Containing 2B), SLC39A1 (Solute Carrier Family 39 Member 1) on chromosome 1, and IL43 (Interleukin 34) on chromosome 16, were also significantly associated with the hippocampal ReHo-CBF correlation (p < 2.78 × 10⁻⁶). Based on the hippocampal BOLD-CBF correlation obtained from four additional methods, we re-performed gene-based association analyses with the same covariates and confirmed four genes that were consistently significant (p < 2.78 × 10⁻⁶) in these robustness analyses, including GMNC, CRTC2, DENND4B, and GATAD2B (Figure 3; Table S8).

Genes associated with the hippocampal BOLD-CBF correlation in gene-based association analyses

Manhattan (left) and quantile-quantile (QQ) (right) plots show genes with significant associations with the hippocampal BOLD-CBF correlation in the gene-based association analyses. The red line indicates a Bonferroni-corrected threshold (p = 2.78 × 10⁻⁶) for gene-based analysis. Abbreviations: ALFF, amplitude of low-frequency fluctuation; BOLD, blood-oxygen-level-dependent; BNA, Brainnetome atlas; CBF, cerebral blood flow; CHR, chromosome; fALFF, fractional ALFF; GSR, global signal regression; NVC, neurovascular coupling; ReHo, regional homogeneity.

Enrichment analyses

To gain more biological insight of the hippocampal BOLD-CBF correlation, we performed a series of enrichment analyses based on the main (ReHo-CBF) gene-based association results (18, 000 background genes). The tibial artery (p = 0.0029), coronary artery (p = 0.0031), and aorta artery (p = 0.0096) were the top-ranked three items in the tissue-specific enrichment analysis (Figure 4A), of which only tibial artery was confirmed in all the four robustness analyses (p < 0.05, Table S9). The radial glia-like cells (RGCs) in the subventricular zone (SVZ-RGCs, p = 0.0035), RGCs in the dentate gyrus (DG-RGCs, p = 0.0261), and oligodendrocytes (p = 0.0306) showed nominally significant enrichment in the cell-specific enrichment analysis (Figure 4B), of which DG-RGCs were also nominally significant (p = 0.0066–0.0484) in three robustness analyses (Table S10). In the pathway enrichment analysis, we found five pathways with significant enrichment (p < 4.88 × 10⁻⁶, Bonferroni correction for the 10,226 pathways), including the pathways of the role of LAT2/NTAL/LAB on calcium mobilization, the negative regulation of activity of TFAP2 (AP-2) family of transcription factors, the microglial cell proliferation, the endosomal sorting complex required for transport (ESCRT) system, and the ubiquitin dependent protein catabolic process via the multivesicular body sorting pathway (Figure 4C; Table S11). Of these pathways, the LAT2/NTAL/LAB on calcium mobilization, microglial cell proliferation, and ESCRT system were confirmed in all robustness analyses (p < 0.05). In addition, we found 38 pathways with nominal significance (p < 0.05) in all the five pathway enrichment analyses (Table S12).

Discussion

This is the first GWAS to explore the genetic architecture of hippocampal BOLD-CBF correlation with an estimated heritability of 16.2–23.9%. We identified two genome-wide significant loci (3q28 and 12p11.22) associated with hippocampal BOLD-CBF correlation in 4,406 Chinese Han healthy individuals, of which the 3q28 was replicated in an independent sample of 426 subjects and confirmed by all the four robustness analyses. In gene-based association analyses, we discovered and confirmed four genes (GMNC, DENND4B, CRTC2, and GATAD2B) showing consistent associations with hippocampal BOLD-CBF correlation. The related genes enriched for vasculature, DG-RGCs, and biological pathways associated with calcium mobilization, microglial cell proliferation, and ESCRT system. These findings provide novel biological insight into the individual differences in hippocampal BOLD-CBF correlation.

The most important finding of this study was the discovery of a reliable association between the locus at 3q28 with the lead SNP of rs74712405 and the hippocampal BOLD-CBF correlation. Several significant SNPs in the locus have been associated with the biomarkers (such as cerebrospinal fluid P-tau level) and neuroimaging markers (e.g., cortical thickness and surface area, hippocampal and ventricular volumes, and white matter integrity) of AD.³⁵^,³⁶^,³⁷^,³⁸^,³⁹^,⁴⁰^,⁴¹^,⁴²^,⁴³^,⁴⁴^,⁴⁵ For example, rs150434736 has been associated with the hippocampal CA1 volume³⁸; and rs150434736, rs141962260 and rs12727448 were associated with the white matter microstructure in the fornix³⁸ that is the major output fibers of the hippocampus and the fractional anisotropy reduction in the fornix is an important feature of AD, even at an early stage of the disorder.⁴⁶ In terms of genes, we provided converging evidence for the association of GMNC with the hippocampal NVC, including significant SNPs at 3q28 were around GMNC; significant SNPs at 3q28 were associated with GMNC expression in the hippocampal tissue; and GMNC showed the strongest correlation with the hippocampal BOLD-CBF correlation in gene-based association analysis. GMNC also called GEMC1 encode Geminin coiled-coil domain-containing protein 1, which is essential for DNA replication⁴⁷ and regulates the balance between neural stem cell generation and ependymal cell differentiation in the postnatal brain.³⁴ We also found a suggestive locus at 12p11.22, which was mapped to CCDC91. CCDC91 encoding p56, an accessory protein of the Golgi associated, γ-adaptin ear containing, Arf binding proteins (GGAs), is involved in transportation from Golgi to lysosome⁴⁸ and regulates the delivery of lysosomal enzymes in large neurons such as the hippocampal pyramidal neurons.⁴⁹

In gene-based association analysis, we additionally identified three genes (CRTC2, DENND4B, GATAD2B) consistently associated with hippocampal BOLD-CBF correlation in all the five analyses. DENND4B is a GDP-GTP exchange factor (GEF) targeting to a tubular membrane compartment adjacent to the Golgi and activating RAB10⁵⁰ and RAB GTPases control membrane transport in neurons and astrocytes and are associated with cognitive functions and brain disorders including AD.⁵¹ GATAD2B encodes a protein involved in chromatin modification and transcription regulation, which is required in neurons for normal cognitive performance and synapse development.⁵² CRTC2 encodes a member of the family of the cAMP-response element binding (CREB)-regulated transcription coactivators (CRTC), and the cAMP pathway can stabilize endothelial barrier and maintain vascular physiology. CRTC2 was highly expressed in endothelial cells and related to angiogenesis acting as a protective molecule for the integrity of endothelium under ischemic condition.⁵³

The significant enrichment of genes associated with the hippocampal BOLD-CBF correlation for vascular tissue may reflect that NVC is regulated by vascular component of the NVU. The significant enrichment for RGCs in subventricular zone and dentate gyrus, the only two regions with neurogenesis in adult brain,⁵⁴ indicates that the adulthood neurogenesis may be regulated by hippocampal BOLD-CBF correlation. We also identified three biological pathways associated with the genetic regulation of the hippocampal BOLD-CBF correlation in both main and robustness analyses. For example, the association of the hippocampal BOLD-CBF correlation with the regulatory pathway of mast cell calcium mobilization is consistent with the blood flow regulation of mast cells by releasing vasoactive substance⁵⁵ and the preferential location of mast cells in mature vessels surrounding by astrocytes,⁵⁶ two core components of the NVU. Microglial and endothelial cells are bidirectionally and permanently communicated with each other and excessive microglia response impairs endothelial cells, neurons, and astrocytes in the pathogenesis of many neurological disorders including AD,⁵⁷ supporting the link of the hippocampal NVC to the microglial cell proliferation. The ubiquitin-proteasome system of intracellular proteolysis is central to the regulation of cellular homeostasis including neurons, astrocytes, endothelial cells, all of which are the main components of the NVU. These mechanisms may underlie the associations of this system with brain functions (such as memory) and disorders (such as AD).⁵⁸^,⁵⁹

In conclusion, this study identified one reliable locus (3q28) and four reliable genes (GMNC, CRTC2, DENND4B, and GATAD2B) associated with hippocampal BOLD-CBF correlation. Enrichment analyses indicate that the hippocampal BOLD-CBF correlation is regulated by several biological processes such as calcium mobilization by mast cells, ubiquitin-related proteolysis, and microglial cell proliferation, which impact the NVU components of neurons, astrocytes, and endothelial cells separately or in combination. These findings may improve our understanding of the genetic architecture underlying the hippocampal BOLD-CBF correlation and provide potential therapeutic targets for brain disorders with severe neurovascular uncoupling.

Limitation of the study

There are several limitations that should be mentioned in this study. First, there are lacking putative neuroimaging markers for in vivo assessment of NVC, although several proxies of NVC have been used previously.²⁷ The large variation between BOLD-CBF correlation and HRF shape parameters across brain regions further indicate that these neuroimaging markers may reflect different aspects of NVC. Second, although we conducted internal validation and sensitivity analyses, we cannot conduct external validation due to the lack of qualified neuroimaging datasets. Third, the sample size used in this study was small and only has power to detect common variants with very strong associations.

STAR★Methods

Key resources table

REAGENT or RESOURCE	SOURCE	IDENTIFIER
Deposited data

GWAS Summary statistics	This study	https://doi.org/10.6084/m9.figshare.23551605.v1
Individual level genotype, imputed markers and MRI data	The CHIMGEN study	http://chimgen.tmu.edu.cn/

Software and algorithms

Matlab 2017b	Mathworks	https://login.mathworks.com/embedded-login/landing.html?cid=getmatlab&s_tid=gn_getml
R 4.1.3	R Foundation	https://cran.r-project.org/bin/linux/
Python 3.11.0	Python Software Foundation	https://www.python.org/
rsHRF Matlab toolbox	Wu et al.²⁷	https://github.com/compneuro-da/rsHRF
GCTA-LDMS	Yang et al.²⁸	https://yanglab.westlake.edu.cn/software/gcta/#GREMLinWGSorimputeddata
PLINK v2.0	Chang et al.⁶⁰	https://www.cog-genomics.org/plink/2.0/
METAL	Willer et al.⁶¹	http://csg.sph.umich.edu/abecasis/Metal/
LocusZoom	Pruim et al.⁶²	http://locuszoom.org/
ANNOVAR	Wang et al.⁶³	https://annovar.openbioinformatics.org/en/latest/
MTAG	Turley et al.³²	https://github.com/JonJala/mtag
CGWAS	Xiong et al.³³	https://github.com/TianTTL/CGWAS
MAGMA v1.09	de Leeuw et al.⁶⁴	https://ctg.cncr.nl/software/magma

Open in a new tab

Resource availability

Lead contact

Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Chunshui Yu (chunshuiyu@tmu.edu.cn).

Materials availability

This study did not generate new unique reagents.

Experimental model and study participant details

This study reports the results of a GWAS conducted on 48,32 Chinese Han subjects. The procedure including recruitment and quality control of data are described in the method details section. The demographic characteristics are reported in Table S1. The study was approved by the Ethics Committee of each site, and all participants signed the written informed consent.

Method details

Subjects

All subjects were recruited from the CHIMGEN study, which has collected genomic and neuroimaging data from 7,306 healthy Chinese Han individuals aged 18 to 30 from 32 centers in 21 cities.²² The protocols of the CHIMGEN study were approved by the ethics committee of each site, and written informed consent was obtained from each subject. The inclusion and exclusion criteria of the CHIMGEN subjects are provided in Table S13. After excluding subjects without qualified genomic and neuroimaging (ASL and fMRI) data, 4,832 subjects from 17 research centers were finally included in this study.

Genotyping and imputation

Subjects were genotyped on the Illumina Infinium ASA-750K (Asian Screening Array). We excluded SNPs with a call rate <95% or minor allele frequency (MAF) < 0.001 and SNPs significantly deviated from Hardy-Weinberg equilibrium (p < 1 × 10⁻⁶). We excluded subjects with genotyping call rate <97%, excess heterozygosity (> mean ±5 standard deviations), or sex mismatch, and duplicated or related subjects (identity by decent >0.1875) with lower genotyping call rate. Principal component analysis (PCA) was used to evaluate genetic population substructure. Subjects with significant deviation from the population was removed and the top 10 principal components (PCs) were used as the covariate in the GWASs. After these quality control procedures, a total of 7,163 subjects and 549,309 variants (Figure S11) were included in imputation. Pre-phasing was performed using SHAPEIT⁶⁵ in chunks of 5000 kb with 250 kb overlap between chunks. Genotype imputation of autosomal SNPs was conducted with the IMPUTE2⁶⁶ with a merged panel of the 1000 Genomes Project (1KGP)⁶⁷ and the SG10K project.⁶⁸ After excluding variants with MAF <0.01 and information score (info) < 0.9, 6,830,145 autosomal SNPs were finally included in the GWASs.

Imaging data acquisition

All MRI data used in this study were acquired by three types of 3.0-Tesla MRI scanners (GE Discovery MR750, GE Discovery MR750w, and GE Signa HDxt). Foam padding was used to minimize head motion and earplugs were used to reduce scanner noise. All subjects were instructed to keep eyes closed, to move as little as possible, and to stay awake during scanning. The resting-state fMRI data were acquired using a gradient-echo single-short echo planar imaging sequence and were used to assess spontaneous neuronal activity; the resting-sate ASL data were acquired using a three-dimensional pseudo-continuous ASL sequence with a spiral fast spin-echo acquisition and were used to assess regional CBF; and the three-dimensional T1-weighted imaging (T1WI) data were acquired by a brain volume sequence and were used for estimating parameters for spatial normalization and calculating hippocampal volume. The specific MRI scanning parameters are provided Tables S14–S16.

Estimating parameters for spatial normalization

CAT12 (version r1364, http://dbm.neuro.uni-jena.de/cat) was used to preprocess T1WI images to estimate spatial normalization parameters. After the image inhomogeneity correction, we segmented the images into gray matter (GM), WM and CSF based on an adaptive Maximum A Posterior technique.⁶⁹ With SPM12 (http://www.fil.ion.ucl.ac.uk/spm), the Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra (DARTEL) algorithm⁷⁰ was applied to create the population-specific tissue probability templates for GM, WM, and CSF in the Montreal Neurological Institute (MNI) space based on the 5,743 CHIMGEN participants. The segmented T1WI images were spatially normalized to the population-specific templates using the DARTEL algorithm and were resampled into a cubic voxel of 1.5 mm. The estimated transformation parameters were also used for spatial normalization of fMRI and ASL images. Modulation was performed on the normalized GM images to preserve the absolute GM volume (GMV), from which we calculated the bilateral hippocampal volume defined by the AAL template.

CBF calculation

The ASL sequence can simultaneously generate the CBF, ASL, and proton density (PD) images. Since the signal to noise ratio (SNR) and contrast of CBF images were too low to perform skull stripping and spatial normalization, we generated the merged images to estimate the spatial normalization parameters of the CBF images. Based on the ASL images with good contrast but poor SNR and the PD images with good SNR but poor contrast, we merged the two sets of images to obtain a set of merged images with both high SNR and contrast using the following equation: merged images = (ASL images × PD images)/the mean value of all voxels in the ASL images. The merged images were just used to improve spatial normalization. Specifically, non-brain tissues in the merged images were stripped using the brain extraction tool. Individual skull-stripped images were aligned to the T1WI images using a boundary-based registration (BBR) function⁷¹ and then were non-linearly normalized to the MNI space with the parameters estimated for T1WI images. Based on deformation parameter derived from the merged images, the CBF images of each subject were written into the MNI space and resampled to 3-mm isotropic voxels, and then spatially smoothed with a Gaussian kernel of 6-mm FWHM.

fMRI data preprocessing and metric calculation

The SPM12 was used to preprocess the fMRI data. The first 5 volumes of each subject were discarded to avoid unstable signals. The remaining volumes were corrected for the acquisition time delay between slices so that the collection times of all voxels were consistent within a repetition time. Then, we checked the head motion of each subject to ensure that it met the requirement (translation < 2mm and rotation < 2°) and realignment was performed to correct for head motion by rigid body transformation. The same method for the normalization of the CBF images from the individual space to the MNI space was used to normalize fMRI images into the MNI space and the normalized images were resampled to 3-mm isotropic voxels. We calculated frame-wise displacement (FD) indexing volume-to-volume changes in head position. For each volume with FD > 0.5 mm, we defined this volume and one forward and two backward volumes as the imperfect volumes. We regressed out nuisance covariates including the average fMRI signals of WM (90% probability) and CSF (70% probability) defined by default masks in SPM12, 24 head motion parameters, imperfect volumes and linear trend of volumes. Finally, the fMRI data were band-pass filtered using a frequency range of 0.01 to 0.08 Hz. Since there are different signal sources in WM and CSF that cannot be reflected by the mean fMRI signals, we also used the CompCor approach²⁹ to estimate the first 10 principal components of the combined mask of WM and CSF, which were used to replace the mean fMRI signals of WM and CSF in the regression of nuisance covariates.

From the preprocessed fMRI data, we calculated three metrics (ReHo, ALFF, and fALFF) to assess spontaneous neuronal activity. ReHo was defined as the Kendall’s coefficient of concordance (KCC) of the fMRI time series of this voxel with its nearest 26 voxels within the GM mask⁷² ALFF was defined as the average square root of power spectrum in a low-frequency range (0.01–0.08 Hz),⁷³ and fALFF was the ratio of power spectrum of low-frequency to that of the entire frequency range to reduce the noise.⁷⁴ A 6-mm FWHM Gaussian kernel was used to smooth these maps. All preprocessing details are provided in Table S17.

Quality assessment for spatial normalization

Since distortion and signal wipeouts of the CBF and fMRI images may affect the quality of spatial normalization, we provided the following proof for proper preprocessing. First, we showed the T1WI, merged, and fMRI images (Figure S12) of a randomly selected subject to confirm that image quality was acceptable for spatial normalization. For this subject, we also showed successful co-registration of the merged and fMRI images with T1WI images (Figure S12). Second, we presented the segmented group-averaged GM images of 100 subjects and the 6th generation DARTEL template in MNI space (Figure S13), which indicates that segmentation and spatial normalization were successful. Third, we provided an overlay display of the group-averaged GM images with the bilateral hippocampal mask in MNI space (Figure S14), indicating good match in the hippocampi between the normalized images and the masks. Finally, we provided a parallel display for the group-averaged spatially normalized T1WI, CBF, and fMRI images (Figure S15) of the subjects, indicating satisfactory spatial normalization of CBF and fMRI images.

Calculation of hippocampal BOLD-CBF correlation

After reslicing the 1-mm³ AAL mask²⁴ of the hippocampus into a 3-mm³ hippocampal mask by the function of “reslice” in SPM12, we obtained a hippocampal mask of 552 voxels in MNI space. Before calculating hippocampal BOLD-CBF correlation, we used Combat²⁵ to harmonize the CBF, ReHo, ALFF and fALFF images that were calculated based on MRI data acquired by different scanners while adding age and sex as covariates to protect the variance of these two biological indicators. The harmonized CBF, ReHo, ALFF and fALFF images of each subject were normalized into z-scores to make them comparable across subjects. The hippocampal BOLD-CBF correlation was defined as the voxel-wise spatial correlation of ReHo, ALFF, and fALFF with CBF in this subject, which reflects the voxel-level consistency between spontaneous neuronal activity and blood flow in the hippocampus during rest. A rank-based inverse normal transformation was applied to these coupling measures. In this study, the ReHo-CBF correlation was used for discovery and the ALFF-CBF and fALFF-CBF correlations were used for robustness analyses. We also calculated ReHo-CBF correlation in each AAL region for the 4,832 participants. After removing WM and CSF signals with the CompCor approach,²⁹ we recalculated ReHo and hippocampal ReHo-CBF correlation. Pearson correlation was then used to test the consistency of hippocampal ReHo-CBF correlation calculated by the different approaches of nuisance regress. In eight participants with MRI data acquired at two different time points, we also calculated these coupling measures (ReHo-CBF, ALFF-CBF, and fALFF-CBF) and computed the intra-class correlation coefficient (ICC) of each measure. In the 4,832 subjects, we also tested the correlation of the ReHo-CBF correlation with the absolute and relative volumes for the bilateral hippocampi calculated by FreeSurfer v7.0 (https://surfer.nmr.mgh.harvard.edu/) using Pearson correlation.

Calculation of HRF shape parameters

To make comparison, we also calculated three shape parameters of the hemodynamic response function (HRF) including the response height (RH), time to peak (TTP), and full width at half maximum (FWHM) for 100 subjects randomly selected from the discovery sample. For each subject, the rsHRF toolbox²⁷ was used to calculate the three HRF shape parameters of each AAL region. We then calculated the Pearson correlation between ReHo-CBF correlation and HRF shape parameters in each brain region across the 100 subjects. Similarly, we also calculated and tested the correlation between ReHo-CBF correlation and HRF shape parameters in the bilateral hippocampal region.

Quantification and statistical analysis

Heritability of hippocampal BOLD-CBF correlation

Based on the 6,830,145 autosomal SNPs, we estimated the SNP heritability of the hippocampal BOLD-CBF correlation in 4,832 subjects with the GREML-LDMS method.²⁸ The covariates used in the SNP heritability estimation were the same as those used in the GWAS, including the age, sex, age,² age × sex, age² × sex, first ten PCs, and FD. The SNP heritability estimation was conducted for the hippocampal BOLD-CBF correlation measures derived from all five approaches.

GWAS design and statistical methods

For the 6,830,145 autosomal SNPs, a two-stage GWAS was designed to explore genetic associations of the hippocampal ReHo-CBF correlation. The discovery and replication samples included 4,606 and 426 subjects, respectively. In each GWAS, linear regression with an additive model was conducted to investigate the association between the dosage of each variant and each imaging phenotype by Plink2⁶⁰ with age, sex, age,² age × sex, age² × sex, first ten PCs, and FD as covariates. In the discovery stage, genome-wide significance was defined as p < 5 × 10⁻⁸. In the replication stage, we replicated each genome-wide significant SNP identified in the discovery stage with a threshold of p < 0.05. Finally, we combined the GWAS results of the two samples by fixed-effects inverse-variance weighted meta-analysis using METAL.⁶¹ In this study, significant SNPs were defined as those met the following two criteria: p < 5 × 10⁻⁸ in the discovery sample and p < 0.05 in the replication sample with the same direction of the effect. Details about genetic associations were presented using regional plots generated by LocusZoom.⁶² We compared GWAS results on ReHo-CBF correlation with previous GWASs on the bilateral hippocampal volume³⁰^,³¹ to find overlapping SNP, which was in the same position and had the same alleles, meanwhile was significant in both GWASs (p < 5 × 10⁻⁸) because of the difference LD structure among different ancestry.

Robustness analyses by variation of the NVC phenotype

Four robustness analyses were conducted to confirm the identified genome-wide significant associations for hippocampal BOLD-CBF correlation assessed by the ReHo-CBF correlation. Since we don’t know which fMRI measure is the best indicator for regional spontaneous neuronal activity, we also used other two fMRI measures (ALFF and fALFF) to reflect regional spontaneous neuronal activity and calculated ALFF-CBF and fALFF-CBF correlations to represent hippocampal BOLD-CBF correlation and re-performed GWASs to verify the main results. Since the spatial range of the hippocampus is slightly varied in different brain atlases, we also extracted the hippocampal voxels based on another atlas (BNA)²⁶ and recalculated hippocampal ReHo-CBF correlation and re-performed GWASs. Since the GSR during preprocessing is matter of debate in the field of resting-state fMRI data analyses,⁷⁵ we also regressed out global signals during fMRI data preprocessing and recalculate ReHo and ReHo-CBF correlations and re-performed GWASs. Based on the hippocampal ReHo-CBF correlation calculated from fMRI data preprocessed by the CompCor approach,²⁹ we re-performed GWAS in the 4,832 participants. In addition, we also conducted GWAS for the ReHo-CBF correlation of the bilateral precentral gyri in the 4,832 participants.

Single- and multi-trait GWASs

In the 4,832 subjects, we also conducted GWAS for the hippocampal mean CBF and mean ReHo, respectively. Moreover, we used three multivariate methods (MTAG,³² CGWAS and MinGWAS³³) to conduct genetic association analyses by integrating the three phenotypes (ReHo, CBF, and ReHo-CBF correlation). The significance threshold and covariates were the same as the GWAS for ReHo-CBF correlation.

Functional annotations

To characterize variants’ functions, significant SNPs were annotated by Variant Effect Predictor (https://www.ensembl.org/info/docs/tools/vep/index.html) and ANNOVAR.⁶³ HaploReg v4⁷⁶ and RegulomeDB⁷⁷ were used to assess chromatin state and functional motifs of variants in LD (r² ≥ 0.8 in EAS of 1KGP3) with the lead SNPs. We also searched the roles of the identified significant SNPs as expression quantitative trait loci from the QTLbase⁷⁸ and checked their relations with brain gene expression in the Braineac database (http://www.braineac.org/).

Gene-based association analysis and enrichment analyses

Translation of the genome-wide association signals of SNPs into genes contributes to the identification of biological pathways and functional mechanisms of complex human traits. Therefore, we conducted gene-based association analysis based on the meta-analysis results by the MAGMA 1.09.⁶⁴ We assigned genetic variants to protein-coding genes based on their positions according to the NCBI 37.3 (hg19) build. After excluding SNPs in the major histocompatibility complex (MHC), 18,000 genes containing at least one qualified genetic variant were included gene-based association analysis. The multiple testing was adjusted for the number of genes (n = 18,000) with a Bonferroni corrected threshold of p = 2.78 × 10⁻⁶. And we also performed robustness analyses with additional four hippocampal BOLD-CBF phenotypes to confirm the main results. Next, all the 18000 genes for the gene-based association analysis were included in tissue-specific and cell-specific enrichment analyses. In tissue-specific enrichment analysis, gene expression profiles of 54 different types of tissues were derived from GTEx v8.⁷⁹ The cell-specific enrichment analysis was conducted based on the gene expression data of 39 cell types.⁸⁰^,⁸¹ Pathway enrichment involved in 10,226 pathways derived from the KEGG, REACTOME, BIOCARTA and GO Terms pathway-gene data (downloaded via the Molecular Signatures Database 7.4, http://www.gsea-msigdb.org/gsea). These enrichment analyses also conducted for four additional hippocampal BOLD-CBF correlations to test the reliability of these results.

Acknowledgments

We are grateful to all participants and researchers of CHIMGEN study. We are grateful to the ENIGMA consortia for providing GWAS summary statistics of hippocampal volumes. This work was supported by the National Key Research and Development Program of China (2018YFC1314300), and the National Natural Science Foundation of China (82030053, 81425013). The authors report no biomedical financial interests or potential conflicts of interest. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the article.

Author contributions

C.Y. and H.X. designed the study and wrote the article. H.X., X.X., and Z.Y. analyzed the data. C.Y., J.X., Y.Y. supervised this work. H.X., X.X., Z.Y., J.C., L.Z., W.Z., G.C., Q.Z., S.Q., Z.Y., W.Q., F.L., M.L., J.F., Q.X., J.X., Y.X., P.Z., W.L., C.W., W.S., X.Z., K.X., X.Z., Z.Y., Y.Y., J.X. and C.Y. acquired the data. All authors critically reviewed the final version of the article.

Declaration of interests

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Published: September 21, 2023

Footnotes

Supplemental information can be found online at https://doi.org/10.1016/j.isci.2023.108005.

Contributor Information

Yongqiang Yu, Email: yuyongqiang@ahmu.edu.cn.

Junfang Xian, Email: cjr.xianjunfang@vip.163.com.

Chunshui Yu, Email: chunshuiyu@tmu.edu.cn.

Supplemental information

Document S1. Figures S1–S15 and Tables S1–S3, S11, and S14–S16

mmc1.pdf^{(3.9MB, pdf)}

Table S4. Two-stage GWAS results for hippocampal NVC, related to Figure 2

mmc2.xlsx^{(10.8KB, xlsx)}

Table S5. The statistical results of lead SNP at 12p11.22 from sensitivity GWAS anlayses for hippocampal NVC, related to Figure 2

mmc3.xlsx^{(10KB, xlsx)}

Table S6. Genome-wide significant associations for hippocampal NVC (ReHo-CBF) in main GWAS meta-analysis and functional annotations, related to Figure 2

mmc4.xlsx^{(38.9KB, xlsx)}

Table S7. SNPs at 3q28 associated with the expression of GMNC in hippocampal tissue in Brainec dataset, related to Figure 2

mmc5.xlsx^{(10.3KB, xlsx)}

Table S8. Gene-wide significant associations with hippocampal NVC eveluated by five methods, related to Figure 3

mmc6.xlsx^{(11.1KB, xlsx)}

Table S9. Tissue-specific enrichments for genes associated with hippocampal NVC evaluated by five methods, related to Figure 4

mmc7.xlsx^{(16.3KB, xlsx)}

Table S10. Cell-specific enrichment for genes associated with hippocampal NVC evaluated by five methods, related to Figure 4

mmc8.xlsx^{(14.6KB, xlsx)}

Table S12. Pathways significantly associated with hippocampal neurovascular coupling evaluated by five methods (P < 0.05), related to Figure 4

mmc9.xlsx^{(17.9KB, xlsx)}

Table S13. Inclusion and exclusion criteria for the CHIMGEN participants, related to STAR Methods

mmc10.xlsx^{(9.9KB, xlsx)}

Table S17. Preprocessing setting details of the multi-modality images, related to STAR Methods

mmc11.xlsx^{(11KB, xlsx)}

Data and code availability

•
The raw brain imaging and genotype data are available from the authors upon reasonable request and with permissions of the CHIMGEN consortia. The raw brain imaging and genotype data reported in this study cannot be deposited in a public repository because the local raw. To request access, the raw data was available with the permission of the CHIMGEN consortia by contact with lead contact. In addition, the GWAS summary for hippocampal BOLD-CBF correlation have been deposited at https://doi.org/10.6084/m9.figshare.23551605.v1 and is publicly available as of the date of publication.
•
This paper does not report original code. We made use of publicly available software and tools in this study. All relevant software and code are described in the text and can be found at the URLs or references cited.
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Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Document S1. Figures S1–S15 and Tables S1–S3, S11, and S14–S16

mmc1.pdf^{(3.9MB, pdf)}

Table S4. Two-stage GWAS results for hippocampal NVC, related to Figure 2

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Table S5. The statistical results of lead SNP at 12p11.22 from sensitivity GWAS anlayses for hippocampal NVC, related to Figure 2

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Table S6. Genome-wide significant associations for hippocampal NVC (ReHo-CBF) in main GWAS meta-analysis and functional annotations, related to Figure 2

mmc4.xlsx^{(38.9KB, xlsx)}

Table S7. SNPs at 3q28 associated with the expression of GMNC in hippocampal tissue in Brainec dataset, related to Figure 2

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Table S8. Gene-wide significant associations with hippocampal NVC eveluated by five methods, related to Figure 3

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Table S9. Tissue-specific enrichments for genes associated with hippocampal NVC evaluated by five methods, related to Figure 4

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Table S10. Cell-specific enrichment for genes associated with hippocampal NVC evaluated by five methods, related to Figure 4

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Table S12. Pathways significantly associated with hippocampal neurovascular coupling evaluated by five methods (P < 0.05), related to Figure 4

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Table S13. Inclusion and exclusion criteria for the CHIMGEN participants, related to STAR Methods

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Table S17. Preprocessing setting details of the multi-modality images, related to STAR Methods

mmc11.xlsx^{(11KB, xlsx)}

Data Availability Statement

•
The raw brain imaging and genotype data are available from the authors upon reasonable request and with permissions of the CHIMGEN consortia. The raw brain imaging and genotype data reported in this study cannot be deposited in a public repository because the local raw. To request access, the raw data was available with the permission of the CHIMGEN consortia by contact with lead contact. In addition, the GWAS summary for hippocampal BOLD-CBF correlation have been deposited at https://doi.org/10.6084/m9.figshare.23551605.v1 and is publicly available as of the date of publication.
•
This paper does not report original code. We made use of publicly available software and tools in this study. All relevant software and code are described in the text and can be found at the URLs or references cited.
•
Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request

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PERMALINK

Genome-wide association study of hippocampal blood-oxygen-level-dependent-cerebral blood flow correlation in Chinese Han population

Hui Xue

Xiaojun Xu

Zhihan Yan

Jingliang Cheng

Longjiang Zhang

Wenzhen Zhu

Guangbin Cui

Quan Zhang

Shijun Qiu

Zhenwei Yao

Wen Qin

Feng Liu

Meng Liang

Jilian Fu

Qiang Xu

Jiayuan Xu

Yingying Xie

Peng Zhang

Wei Li

Caihong Wang

Wen Shen

Xiaochu Zhang

Kai Xu

Xi-Nian Zuo

Zhaoxiang Ye

Yongqiang Yu

Junfang Xian

Chunshui Yu

Summary

Graphical abstract

Highlights

Introduction

Results

Subjects

Hippocampal blood-oxygen-level-dependent-cerebral blood flow correlation calculation

Figure 1.

Heritability of the hippocampal blood-oxygen-level-dependent-cerebral blood flow correlation

Genome-wide association studies of the hippocampal reginal homogeneity-cerebral blood flow correlation

Figure 2.

Robustness of genome-wide association studies results

Single- and multi-trait genome-wide association studies

Functional annotation for genome-wide association studies significant loci

Gene-based association analysis of the hippocampal blood-oxygen-level-dependent-cerebral blood flow correlation

Figure 3.

Enrichment analyses

Figure 4.

Discussion

Limitation of the study

STAR★Methods

Key resources table

Resource availability

Lead contact

Materials availability

Experimental model and study participant details

Method details

Subjects

Genotyping and imputation

Imaging data acquisition

Estimating parameters for spatial normalization

CBF calculation

fMRI data preprocessing and metric calculation

Quality assessment for spatial normalization

Calculation of hippocampal BOLD-CBF correlation

Calculation of HRF shape parameters

Quantification and statistical analysis

Heritability of hippocampal BOLD-CBF correlation

GWAS design and statistical methods

Robustness analyses by variation of the NVC phenotype

Single- and multi-trait GWASs

Functional annotations

Gene-based association analysis and enrichment analyses

Acknowledgments

Author contributions

Declaration of interests

Footnotes

Contributor Information

Supplemental information

Data and code availability