Genomic loci influence patterns of structural covariance in the human brain.

Normal and pathologic neurobiological processes influence brain morphology in coordinated ways that give rise to patterns of structural covariance (PSC) across brain regions and individuals during brain aging and diseases. The genetic underpinnings of these patterns remain largely unknown. We apply a stochastic multivariate factorization method to a diverse population of 50,699 individuals (12 studies and 130 sites) and derive data-driven, multi-scale PSCs of regional brain size. PSCs were significantly correlated with 915 genomic loci in the discovery set, 617 of which are newly identified, and 72% were independently replicated. Key pathways influencing PSCs involve reelin signaling, apoptosis, neurogenesis, and appendage development, while pathways of breast cancer indicate potential interplays between brain metastasis and PSCs associated with neurodegeneration and dementia. Using support vector machines, multi-scale PSCs effectively derive imaging signatures of several brain diseases. Our results elucidate genetic and biological underpinnings that influence structural covariance patterns in the human brain.

Integrative analysis of multi-omics and imaging data with incorporation of biological information via structural Bayesian factor analysis.

MOTIVATION: With the rapid development of modern technologies, massive data are available for the systematic study of Alzheimer's disease (AD). Though many existing AD studies mainly focus on single-modality omics data, multi-omics datasets can provide a more comprehensive understanding of AD. To bridge this gap, we proposed a novel structural Bayesian factor analysis framework (SBFA) to extract the information shared by multi-omics data through the aggregation of genotyping data, gene expression data, neuroimaging phenotypes and prior biological network knowledge. Our approach can extract common information shared by different modalities and encourage biologically related features to be selected, guiding future AD research in a biologically meaningful way. METHOD: Our SBFA model decomposes the mean parameters of the data into a sparse factor loading matrix and a factor matrix, where the factor matrix represents the common information extracted from multi-omics and imaging data. Our framework is designed to incorporate prior biological network information. Our simulation study demonstrated that our proposed SBFA framework could achieve the best performance compared with the other state-of-the-art factor-analysis-based integrative analysis methods. RESULTS: We apply our proposed SBFA model together with several state-of-the-art factor analysis models to extract the latent common information from genotyping, gene expression and brain imaging data simultaneously from the ADNI biobank database. The latent information is then used to predict the functional activities questionnaire score, an important measurement for diagnosis of AD quantifying subjects' abilities in daily life. Our SBFA model shows the best prediction performance compared with the other factor analysis models. AVAILABILITY: Code are publicly available at https://github.com/JingxuanBao/SBFA. CONTACT: qlong@upenn.edu.

Multiparameter cortical surface morphology in former amateur contact sport athletes.

The lifetime effects of repetitive head impacts have captured considerable public and scientific interest over the past decade, yet a knowledge gap persists in our understanding of midlife neurological well-being, particularly in amateur level athletes. This study aimed to identify the effects of lifetime exposure to sports-related head impacts on brain morphology in retired, amateur athletes. This cross-sectional study comprised of 37 former amateur contact sports athletes and 21 age- and sex-matched noncontact athletes. High-resolution anatomical, T1 scans were analyzed for the cortical morphology, including cortical thickness, sulcal depth, and sulcal curvature, and cognitive function was assessed using the Dementia Rating Scale-2. Despite no group differences in cognitive functions, the contact group exhibited significant cortical thinning particularly in the bilateral frontotemporal regions and medial brain regions, such as the cingulate cortex and precuneus, compared to the noncontact group. Deepened sulcal depth and increased sulcal curvature across all four lobes of the brain were also notable in the contact group. These data suggest that brain morphology of middle-aged former amateur contact athletes differs from that of noncontact athletes and that lifetime exposure to repetitive head impacts may be associated with neuroanatomical changes.

Deep learning-based identification of genetic variants: application to Alzheimer's disease classification.

Deep learning is a promising tool that uses nonlinear transformations to extract features from high-dimensional data. Deep learning is challenging in genome-wide association studies (GWAS) with high-dimensional genomic data. Here we propose a novel three-step approach (SWAT-CNN) for identification of genetic variants using deep learning to identify phenotype-related single nucleotide polymorphisms (SNPs) that can be applied to develop accurate disease classification models. In the first step, we divided the whole genome into nonoverlapping fragments of an optimal size and then ran convolutional neural network (CNN) on each fragment to select phenotype-associated fragments. In the second step, using a Sliding Window Association Test (SWAT), we ran CNN on the selected fragments to calculate phenotype influence scores (PIS) and identify phenotype-associated SNPs based on PIS. In the third step, we ran CNN on all identified SNPs to develop a classification model. We tested our approach using GWAS data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) including (N = 981; cognitively normal older adults (CN) = 650 and AD = 331). Our approach identified the well-known APOE region as the most significant genetic locus for AD. Our classification model achieved an area under the curve (AUC) of 0.82, which was compatible with traditional machine learning approaches, random forest and XGBoost. SWAT-CNN, a novel deep learning-based genome-wide approach, identified AD-associated SNPs and a classification model for AD and may hold promise for a range of biomedical applications.

Paravascular fluid dynamics reveal arterial stiffness assessed using dynamic diffusion-weighted imaging.

Paravascular cerebrospinal fluid (pCSF) surrounding the cerebral arteries within the glymphatic system is pulsatile and moves in synchrony with the pressure waves of the vessel wall. Whether such pulsatile pCSF can infer pulse wave propagation-a property tightly related to arterial stiffness-is unknown and has never been explored. Our recently developed imaging technique, dynamic diffusion-weighted imaging (dynDWI), captures the pulsatile pCSF dynamics in vivo and can explore this question. In this work, we evaluated the time shifts between pCSF waves and finger pulse waves, where pCSF waves were measured by dynDWI and finger pulse waves were measured by the scanner's built-in finger pulse oximeter. We hypothesized that the time shifts reflect brain-finger pulse wave travel time and are sensitive to arterial stiffness. We applied the framework to 36 participants aged 18-82 years to study the age effect of travel time, as well as its associations with cognitive function within the older participants (N = 15, age > 60 years). Our results revealed a strong and consistent correlation between pCSF pulse and finger pulse (mean CorrCoeff = 0.66), supporting arterial pulsation as a major driver for pCSF dynamics. The time delay between pCSF and finger pulses (TimeDelay) was significantly lower (i.e., faster pulse propagation) with advanced age (Pearson's r = -0.44, p = 0.007). Shorter TimeDelay was further associated with worse cognitive function in the older participants. Overall, our study demonstrated pCSF as a viable pathway for measuring intracranial pulses and encouraged future studies to investigate its relevance with cerebrovascular functions.

Relationship of Hoarding and Depression Symptoms in Older Adults.

Hoarding disorder (HD) is a debilitating neuropsychiatric condition that affects 2%-6% of the population and increases in incidence with age. Major depressive disorder (MDD) co-occurs with HD in approximately 50% of cases and leads to increased functional impairment and disability. However, only one study to date has examined the rate and trajectory of hoarding symptoms in older individuals with a lifetime history of MDD, including those with current active depression (late-life depression; LLD). We therefore sought to characterize this potentially distinct phenotype. We determined the incidence of HD in two separate cohorts of participants with LLD (n = 73) or lifetime history of MDD (n = 580) and examined the reliability and stability of hoarding symptoms using the Saving Inventory-Revised (SI-R) and Hoarding Rating Scale-Self Report (HRS), as well as the co-variance of hoarding and depression scores over time. HD was present in 12% to 33% of participants with MDD, with higher rates found in those with active depressive symptoms. Hoarding severity was stable across timepoints in both samples (all correlations >0.75), and fewer than 30% of participants in each sample experienced significant changes in severity between any two timepoints. Change in depression symptoms over time did not co-vary with change in hoarding symptoms. These findings indicate that hoarding is a more common comorbidity in LLD than previously suggested, and should be considered in screening and management of LLD. Future studies should further characterize the interaction of these conditions and their impact on outcomes, particularly functional impairment in this vulnerable population.

Anxiety in late-life depression is associated with poorer performance across multiple cognitive domains.

OBJECTIVE: Anxiety is a common comorbid feature of late-life depression (LLD) and is associated with poorer global cognitive functioning independent of depression severity. However, little is known about whether comorbid anxiety is associated with a domain-specific pattern of cognitive dysfunction. We therefore examined group differences (LLD with and without comorbid anxiety) in cognitive functioning performance across multiple domains. METHOD: Older adults with major depressive disorder (N = 228, ages 65-91) were evaluated for anxiety and depression severity, and cognitive functioning (learning, memory, language, processing speed, executive functioning, working memory, and visuospatial functioning). Ordinary least squares regression adjusting for age, sex, education, and concurrent depression severity examined anxiety group differences in performance on tests of cognitive functioning. RESULTS: Significant group differences emerged for confrontation naming and visuospatial functioning, as well as for verbal fluency, working memory, and inhibition with lower performance for LLD with comorbid anxiety compared to LLD only, controlling for depression severity. CONCLUSIONS: Performance patterns identified among older adults with LLD and comorbid anxiety resemble neuropsychological profiles typically seen in neurodegenerative diseases of aging. These findings have potential implications for etiological considerations in the interpretation of neuropsychological profiles.

Preference matrix guided sparse canonical correlation analysis for mining brain imaging genetic associations in Alzheimer's disease.

Investigating the relationship between genetic variation and phenotypic traits is a key issue in quantitative genetics. Specifically for Alzheimer's disease, the association between genetic markers and quantitative traits remains vague while, once identified, will provide valuable guidance for the study and development of genetics-based treatment approaches. Currently, to analyze the association of two modalities, sparse canonical correlation analysis (SCCA) is commonly used to compute one sparse linear combination of the variable features for each modality, giving a pair of linear combination vectors in total that maximizes the cross-correlation between the analyzed modalities. One drawback of the plain SCCA model is that the existing findings and knowledge cannot be integrated into the model as priors to help extract interesting correlations as well as identify biologically meaningful genetic and phenotypic markers. To bridge this gap, we introduce preference matrix guided SCCA (PM-SCCA) that not only takes priors encoded as a preference matrix but also maintains computational simplicity. A simulation study and a real-data experiment are conducted to investigate the effectiveness of the model. Both experiments demonstrate that the proposed PM-SCCA model can capture not only genotype-phenotype correlation but also relevant features effectively.

Anxiety in late-life depression: Associations with brain volume, amyloid beta, white matter lesions, cognition, and functional ability.

OBJECTIVES: Late-life depression (LLD) is common and frequently co-occurs with neurodegenerative diseases of aging. Little is known about how heterogeneity within LLD relates to factors typically associated with neurodegeneration. Varying levels of anxiety are one source of heterogeneity in LLD. We examined associations between anxiety symptom severity and factors associated with neurodegeneration, including regional brain volumes, amyloid beta (Aß) deposition, white matter disease, cognitive dysfunction, and functional ability in LLD. PARTICIPANTS AND MEASUREMENTS: Older adults with major depression (N = 121, Ages 65-91) were evaluated for anxiety severity and the following: brain volume (orbitofrontal cortex [OFC], insula), cortical Aß standardized uptake value ratio (SUVR), white matter hyperintensity (WMH) volume, global cognition, and functional ability. Separate linear regression analyses adjusting for age, sex, and concurrent depression severity were conducted to examine associations between anxiety and each of these factors. A global regression analysis was then conducted to examine the relative associations of these variables with anxiety severity. RESULTS: Greater anxiety severity was associated with lower OFC volume (ß = -68.25, t = -2.18, p = .031) and greater cognitive dysfunction (ß = 0.23, t = 2.46, p = .016). Anxiety severity was not associated with insula volume, Aß SUVR, WMH, or functional ability. When examining the relative associations of cognitive functioning and OFC volume with anxiety in a global model, cognitive dysfunction (ß = 0.24, t = 2.62, p = .010), but not OFC volume, remained significantly associated with anxiety. CONCLUSIONS: Among multiple factors typically associated with neurodegeneration, cognitive dysfunction stands out as a key factor associated with anxiety severity in LLD which has implications for cognitive and psychiatric interventions.

Association of Heart Failure With Cognitive Decline and Development of Mild Cognitive Impairment and Dementia.

BACKGROUND: Incidence of cognitive impairment and its consequences have not been fully examined in heart failure (HF). OBJECTIVE: The aim of this study was to examine associations of HF with cognitive decline, frequencies and risks of, and time-to-develop mild cognitive impairment (MCI) or dementia during 15-year follow-up. METHODS: For this retrospective cohort study, data were retrieved from the National Alzheimer's Coordinating Center. Cognitive decline was assessed using the Uniform Data Set neuropsychological battery. Development of MCI and dementia was assessed using clinically diagnosed cognitive status. RESULTS: Compared with participants without HF (n = 12 904), participants with HF (n = 256) had more decline in attention, executive function, and memory while controlling for covariates including apolipoprotein E4. Participants with HF developed MCI or dementia more frequently (44.9% vs 34.4%), developed dementia faster from normal cognition, and had a lower risk of dementia from MCI after controlling for covariates (hazard ratio, 0.71) than participants without HF. CONCLUSIONS: Heart failure was associated with accelerated cognitive decline.

Transcriptional risk scores in Alzheimer's disease: From pathology to cognition.

INTRODUCTION: Our previously developed blood-based transcriptional risk scores (TRS) showed associations with diagnosis and neuroimaging biomarkers for Alzheimer's disease (AD). Here, we developed brain-based TRS. METHODS: We integrated AD genome-wide association study summary and expression quantitative trait locus data to prioritize target genes using Mendelian randomization. We calculated TRS using brain transcriptome data of two independent cohorts (N = 878) and performed association analysis of TRS with diagnosis, amyloidopathy, tauopathy, and cognition. We compared AD classification performance of TRS with polygenic risk scores (PRS). RESULTS: Higher TRS values were significantly associated with AD, amyloidopathy, tauopathy, worse cognition, and faster cognitive decline, which were replicated in an independent cohort. The AD classification performance of PRS was increased with the inclusion of TRS up to 16% with the area under the curve value of 0.850. DISCUSSION: Our results suggest brain-based TRS improves the AD classification of PRS and may be a potential AD biomarker. HIGHLIGHTS: Transcriptional risk score (TRS) is developed using brain RNA-Seq data. Higher TRS values are shown in Alzheimer's disease (AD). TRS improves the AD classification power of PRS up to 16%. TRS is associated with AD pathology presence. TRS is associated with worse cognitive performance and faster cognitive decline.

Associations Between Social Network Characteristics and Brain Structure Among Older Adults.

INTRODUCTION: Social connectedness is associated with slower cognitive decline among older adults. Recent research suggests that distinct aspects of social networks may have differential effects on cognitive resilience, but few studies analyze brain structure. METHODS: This study includes 117 cognitively impaired and 59 unimpaired older adults. The effects of social network characteristics (bridging/bonding) on brain regions of interests were analyzed using linear regressions and voxel-wise multiple linear regressions of gray matter density. RESULTS: Increased social bridging was associated with greater bilateral amygdala volume and insular thickness, and left frontal lobe thickness, putamen, and thalamic volumes. Increased social bonding was associated with greater bilateral medial orbitofrontal and caudal anterior cingulate thickness, as well as right frontal lobe thickness, putamen, and amygdala volumes. DISCUSSION: The associations between social connectedness and brain structure vary depending on the types of social enrichment accessible through social networks, suggesting that psychosocial interventions could mitigate neurodegeneration. HIGHLIGHTS: Distinct forms of social capital are uniquely linked to gray matter density (GMD). Bridging is associated with preserved GMD in limbic system structures. Bonding is associated with preserved GMD in frontal lobe regions. Bridging is associated with increased brain reserve in sensory processing regions. Bonding is associated with increased brain reserve in regions of stress modulation.

Integrating amyloid imaging and genetics for early risk stratification of Alzheimer's disease.

INTRODUCTION: Alzheimer's disease (AD) initiates years prior to symptoms, underscoring the importance of early detection. While amyloid accumulation starts early, individuals with substantial amyloid burden may remain cognitively normal, implying that amyloid alone is not sufficient for early risk assessment. METHODS: Given the genetic susceptibility of AD, a multi-factorial pseudotime approach was proposed to integrate amyloid imaging and genotype data for estimating a risk score. Validation involved association with cognitive decline and survival analysis across risk-stratified groups, focusing on patients with mild cognitive impairment (MCI). RESULTS: Our risk score outperformed amyloid composite standardized uptake value ratio in correlation with cognitive scores. MCI subjects with lower pseudotime risk score showed substantial delayed onset of AD and slower cognitive decline. Moreover, pseudotime risk score demonstrated strong capability in risk stratification within traditionally defined subgroups such as early MCI, apolipoprotein E (APOE) ε4+ MCI, APOE ε4- MCI, and amyloid+ MCI. DISCUSSION: Our risk score holds great potential to improve the precision of early risk assessment. HIGHLIGHTS: Accurate early risk assessment is critical for the success of clinical trials. A new risk score was built from integrating amyloid imaging and genetic data. Our risk score demonstrated improved capability in early risk stratification.

Cognitively defined Alzheimer's dementia subgroups have distinct atrophy patterns.

INTRODUCTION: We sought to determine structural magnetic resonance imaging (MRI) characteristics across subgroups defined based on relative cognitive domain impairments using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and to compare cognitively defined to imaging-defined subgroups. METHODS: We used data from 584 people with Alzheimer's disease (AD) (461 amyloid positive, 123 unknown amyloid status) and 118 amyloid-negative controls. We used voxel-based morphometry to compare gray matter volume (GMV) for each group compared to controls and to AD-Memory. RESULTS: There was pronounced bilateral lower medial temporal lobe atrophy with relative cortical sparing for AD-Memory, lower left hemisphere GMV for AD-Language, anterior lower GMV for AD-Executive, and posterior lower GMV for AD-Visuospatial. Formal asymmetry comparisons showed substantially more asymmetry in the AD-Language group than any other group (p = 1.15 × 10-10 ). For overlap between imaging-defined and cognitively defined subgroups, AD-Memory matched up with an imaging-defined limbic predominant group. DISCUSSION: MRI findings differ across cognitively defined AD subgroups.

Plasma miRNAs across the Alzheimer's disease continuum: Relationship to central biomarkers.

INTRODUCTION: MicroRNAs (miRNAs) play important roles in gene expression regulation and Alzheimer's disease (AD) pathogenesis. METHODS: We investigated the association between baseline plasma miRNAs and central AD biomarkers from the Alzheimer's Disease Neuroimaging Initiative (ADNI; N = 803): amyloid, tau, and neurodegeneration (A/T/N). Differentially expressed miRNAs and their targets were identified, followed by pathway enrichment analysis. Machine learning approaches were applied to investigate the role of miRNAs as blood biomarkers. RESULTS: We identified nine, two, and eight miRNAs significantly associated with A/T/N positivity, respectively. We identified 271 genes targeted by amyloid-related miRNAs with estrogen signaling receptor-mediated signaling among the enriched pathways. Additionally, 220 genes targeted by neurodegeneration-related miRNAs showed enrichment in pathways including the insulin growth factor 1 pathway. The classification performance of demographic information for A/T/N positivity was increased up to 9% with the inclusion of miRNAs. DISCUSSION: Plasma miRNAs were associated with central A/T/N biomarkers, highlighting their potential as blood biomarkers. HIGHLIGHTS: We performed association analysis of microRNAs (miRNAs) with amyloid/tau/neurodegeneration (A/T/N) biomarker positivity. We identified dysregulated miRNAs for A/T/N biomarker positivity. We identified Alzheimer's disease biomarker-specific/common pathways related to miRNAs. miRNAs improved the classification for A/T/N positivity by up to 9%. Our study highlights the potential of miRNAs as blood biomarkers.

A cross-sectional study of α-synuclein seed amplification assay in Alzheimer's disease neuroimaging initiative: Prevalence and associations with Alzheimer's disease biomarkers and cognitive function.

INTRODUCTION: Alzheimer's disease (AD) pathology is defined by ß-amyloid (Aß) plaques and neurofibrillary tau, but Lewy bodies (LBs; 𝛼-synuclein aggregates) are a common co-pathology for which effective biomarkers are needed. METHODS: A validated α-synuclein Seed Amplification Assay (SAA) was used on recent cerebrospinal fluid (CSF) samples from 1638 Alzheimer's Disease Neuroimaging Initiative (ADNI) participants, 78 with LB-pathology confirmation at autopsy. We compared SAA outcomes with neuropathology, Aß and tau biomarkers, risk-factors, genetics, and cognitive trajectories. RESULTS: SAA showed 79% sensitivity and 97% specificity for LB pathology, with superior performance in identifying neocortical (100%) compared to limbic (57%) and amygdala-predominant (60%) LB-pathology. SAA+ rate was 22%, increasing with disease stage and age. Higher Aß burden but lower CSF p-tau181 associated with higher SAA+ rates, especially in dementia. SAA+ affected cognitive impairment in MCI and Early-AD who were already AD biomarker positive. DISCUSSION: SAA is a sensitive, specific marker for LB-pathology. Its increase in prevalence with age and AD stages, and its association with AD biomarkers, highlights the clinical importance of α-synuclein co-pathology in understanding AD's nature and progression. HIGHLIGHTS: SAA shows 79% sensitivity, 97% specificity for LB-pathology detection in AD. SAA positivity prevalence increases with disease stage and age. Higher Aß burden, lower CSF p-tau181 linked with higher SAA+ rates in dementia. SAA+ impacts cognitive impairment in early disease stages. Study underpins need for wider LB-pathology screening in AD treatment.

The Alzheimer's Disease Neuroimaging Initiative in the era of Alzheimer's disease treatment: A review of ADNI studies from 2021 to 2022.

The Alzheimer's Disease Neuroimaging Initiative (ADNI) aims to improve Alzheimer's disease (AD) clinical trials. Since 2006, ADNI has shared clinical, neuroimaging, and cognitive data, and biofluid samples. We used conventional search methods to identify 1459 publications from 2021 to 2022 using ADNI data/samples and reviewed 291 impactful studies. This review details how ADNI studies improved disease progression understanding and clinical trial efficiency. Advances in subject selection, detection of treatment effects, harmonization, and modeling improved clinical trials and plasma biomarkers like phosphorylated tau showed promise for clinical use. Biomarkers of amyloid beta, tau, neurodegeneration, inflammation, and others were prognostic with individualized prediction algorithms available online. Studies supported the amyloid cascade, emphasized the importance of neuroinflammation, and detailed widespread heterogeneity in disease, linked to genetic and vascular risk, co-pathologies, sex, and resilience. Biological subtypes were consistently observed. Generalizability of ADNI results is limited by lack of cohort diversity, an issue ADNI-4 aims to address by enrolling a diverse cohort.

The plasma miRNAome in ADNI: Signatures to aid the detection of at-risk individuals.

INTRODUCTION: MicroRNAs are short non-coding RNAs that control proteostasis at the systems level and are emerging as potential prognostic and diagnostic biomarkers for Alzheimer's disease (AD). METHODS: We performed small RNA sequencing on plasma samples from 847 Alzheimer's Disease Neuroimaging Initiative (ADNI) participants. RESULTS: We identified microRNA signatures that correlate with AD diagnoses and help predict the conversion from mild cognitive impairment (MCI) to AD. DISCUSSION: Our data demonstrate that plasma microRNA signatures can be used to not only diagnose MCI, but also, critically, predict the conversion from MCI to AD. Moreover, combined with neuropsychological testing, plasma microRNAome evaluation helps predict MCI to AD conversion. These findings are of considerable public interest because they provide a path toward reducing indiscriminate utilization of costly and invasive testing by defining the at-risk segment of the aging population. HIGHLIGHTS: We provide the first analysis of the plasma microRNAome for the ADNI study. The levels of several microRNAs can be used as biomarkers for the prediction of conversion from MCI to AD. Adding the evaluation of plasma microRNA levels to neuropsychological testing in a clinical setting increases the accuracy of MCI to AD conversion prediction.

Sex-specific genetic architecture of late-life memory performance.

BACKGROUND: Women demonstrate a memory advantage when cognitively healthy yet lose this advantage to men in Alzheimer's disease. However, the genetic underpinnings of this sex difference in memory performance remain unclear. METHODS: We conducted the largest sex-aware genetic study on late-life memory to date (Nmales  = 11,942; Nfemales  = 15,641). Leveraging harmonized memory composite scores from four cohorts of cognitive aging and AD, we performed sex-stratified and sex-interaction genome-wide association studies in 24,216 non-Hispanic White and 3367 non-Hispanic Black participants. RESULTS: We identified three sex-specific loci (rs67099044-CBLN2, rs719070-SCHIP1/IQCJ-SCHIP), including an X-chromosome locus (rs5935633-EGL6/TCEANC/OFD1), that associated with memory. Additionally, we identified heparan sulfate signaling as a sex-specific pathway and found sex-specific genetic correlations between memory and cardiovascular, immune, and education traits. DISCUSSION: This study showed memory is highly and comparably heritable across sexes, as well as highlighted novel sex-specific genes, pathways, and genetic correlations that related to late-life memory. HIGHLIGHTS: Demonstrated the heritable component of late-life memory is similar across sexes. Identified two genetic loci with a sex-interaction with baseline memory. Identified an X-chromosome locus associated with memory decline in females. Highlighted sex-specific candidate genes and pathways associated with memory. Revealed sex-specific shared genetic architecture between memory and complex traits.

Longitudinal change in memory performance as a strong endophenotype for Alzheimer's disease.

INTRODUCTION: Although large-scale genome-wide association studies (GWAS) have been conducted on AD, few have been conducted on continuous measures of memory performance and memory decline. METHODS: We conducted a cross-ancestry GWAS on memory performance (in 27,633 participants) and memory decline (in 22,365 participants; 129,201 observations) by leveraging harmonized cognitive data from four aging cohorts. RESULTS: We found high heritability for two ancestry backgrounds. Further, we found a novel ancestry locus for memory decline on chromosome 4 (rs6848524) and three loci in the non-Hispanic Black ancestry group for memory performance on chromosomes 2 (rs111471504), 7 (rs4142249), and 15 (rs74381744). In our gene-level analysis, we found novel genes for memory decline on chromosomes 1 (SLC25A44), 11 (BSX), and 15 (DPP8). Memory performance and memory decline shared genetic architecture with AD-related traits, neuropsychiatric traits, and autoimmune traits. DISCUSSION: We discovered several novel loci, genes, and genetic correlations associated with late-life memory performance and decline. HIGHLIGHTS: Late-life memory has high heritability that is similar across ancestries. We discovered four novel variants associated with late-life memory. We identified four novel genes associated with late-life memory. Late-life memory shares genetic architecture with psychiatric/autoimmune traits.