Hippocampal transcriptome-guided genetic analysis of correlated episodic memory phenotypes in Alzheimer's disease

Genome-wide association study unravels mechanisms of brain glymphatic activity

Brain glymphatic activity, as indicated by diffusion-tensor imaging analysis along the perivascular space (ALPS) index, is involved in developmental neuropsychiatric and neurodegenerative diseases, but its genetic architecture is poorly understood. Here, we identified 17 unique genome-wide significant loci and 161 candidate genes linked to the ALPS-indexes in a discovery sample of 31,021 individuals from the UK Biobank. Seven loci were replicated in two independent datasets. Genetic signals located at the 2p23.3 locus yielded significantly concordant effects in both young and aging cohorts. Genetic correlation and polygenic overlap analyses indicate a common underlying genetic mechanism between the ALPS-index, ventricular volumes, and cerebrospinal fluid tau levels, with GMNC (3q28) and C16orf95 (16q24.2) as the shared genetic basis. Our findings enhance the understanding of the genetics of the ALPS-index and provide insight for further research into the neurobiological mechanisms of glymphatic clearance activity across the lifespan and its relation to neuropsychiatric phenotypes.

The interaction of obesity with susceptible gene polymorphisms in the relationship with mild cognitive impairment

Mild cognitive impairment (MCI) in the elderly is threatening the mental health of the elderly, and the interaction of some factors is worth exploring. This study aims to explore the interactions of obesity and gene polymorphisms in the relationship with MCI. A total of 2555 community resident dwellings include 444 participants who met MCI criteria recruited from the Ningxia province of China. Fourteen MCI-susceptible single nucleotide polymorphisms were detected using a high-throughput mass spectrometer. The interaction was examined by performing the multifactor dimensionality reduction model and unconditional logistic regression model. Logistic regression showed that obesity (OR = 1.42, 95%CI: 1.04-1.94), rs2075650G allele carrying (OR = 17.95, 95%CI: 1.32-244.95), rs11556505T allele carrying (OR = 0.06, 95%CI: 0.01-0.87) were statistically associated with MCI. Multifactor dimensionality reduction analysis showed a strong antagonistic effect between obesity and rs4402960 (Interaction dendrogram between obesity and rs4402960 is red) and a weak synergy effect on rs7901695 (Interaction dendrogram between obesity and rs7901695 is green). The hierarchical analysis showed obesity is a risk factor for MCI in the non-rs4402960T allele carrier group (OR = 1.55, 95%CI: 1.02-2.35). This study found that obesity is an independent risk factor for MCI, and the interactions with MCI-susceptible gene polymorphisms suggest a possible precision preventive intervention program should be developed to reduce the risk of MCI among individuals with obesity in the community.

Neuron navigators: A novel frontier with physiological and pathological implications

Neuron navigators are microtubule plus-end tracking proteins containing basic and serine rich regions which are encoded by neuron navigator genes (NAVs). Neuron navigator proteins are essential for neurite outgrowth, neuronal migration, and overall neurodevelopment along with some other functions as well. The navigator proteins are substantially expressed in the developing brain and have been reported to be differentially expressed in various tissues at different ages. Over the years, the research has found neuron navigators to be implicated in a spectrum of pathological conditions such as developmental anomalies, neurodegenerative disorders, neuropathic pain, anxiety, cancers, and certain inflammatory conditions. The existing knowledge about neuron navigators remains sparse owing to their differential functions, undiscovered modulators, and unknown molecular mechanisms. Investigating the possible role of neuron navigators in various physiological processes and pathological conditions pose as a novel field that requires extensive research and might provide novel mechanistic insights and understanding of these aspects.

Risperidone response in patients with schizophrenia drives DNA methylation changes in immune and neuronal systems

Background: The choice of efficient antipsychotic therapy for schizophrenia relies on a time-consuming trial-and-error approach, whereas the social and economic burdens of the disease call for faster alternatives. Material & methods: In a search for predictive biomarkers of antipsychotic response, blood methylomes of 28 patients were analyzed before and 4 weeks into risperidone therapy. Results: Several CpGs exhibiting response-specific temporal dynamics were identified in otherwise temporally stable methylomes and noticeable global response-related differences were observed between good and bad responders. These were associated with genes involved in immunity, neurotransmission and neuronal development. Polymorphisms in many of these genes were previously linked with schizophrenia etiology and antipsychotic response. Conclusion: Antipsychotic response seems to be shaped by both stable and medication-induced methylation differences.

Neuroimaging Genetics and Network Analysis in Alzheimer's Disease

The issue of the genetics in brain imaging phenotypes serves as a crucial link between two distinct scientific fields: neuroimaging genetics (NG). The articles included here provide solid proof that this NG link has considerable synergy. There is a suitable collection of articles that offer a wide range of viewpoints on how genetic variations affect brain structure and function. They serve as illustrations of several study approaches used in contemporary genetics and neuroscience. Genome-wide association studies and candidate-gene association are two examples of genetic techniques. Cortical gray matter structural/volumetric measures from magnetic resonance imaging (MRI) are sources of information on brain phenotypes. Together, they show how various scientific disciplines have benefited from significant technological advances, such as the single-nucleotide polymorphism array in genetics and the development of increasingly higher-resolution MRI imaging. Moreover, we discuss NG's contribution to expanding our knowledge about the heterogeneity within Alzheimer's disease as well as the benefits of different network analyses.

Identification of SNPs and Candidate Genes for Milk Production Ability in Yorkshire Pigs

Sow milk production ability is an important limiting factor impacting suboptimal growth and the survival of piglets. Through pig genetic improvement, litter sizes have been increased. Larger litters need more suckling mammary glands, which results in increased milk from the lactating sow. Hence, there is much significance to exploring sow lactation performance. For milk production ability, it is not practical to directly measure the milk yield, we used litter weight gain (LWG) throughout sow lactation as an indicator. In this study, we estimated the heritability of LWG, namely, 0.18 ± 0.07. We then performed a GWAS, and detected seven significant SNPs, namely, Sus scrofa Chromosome (SSC) 2: ASGA0010040 (p = 7.73E-11); SSC2:MARC0029355 (p = 1.30E-08), SSC6: WU_10.2_6_65751151 (p = 1.32E-10), SSC7: MARC0058875 (p = 4.99E-09), SSC10: WU_10.2_10_49571394 (p = 6.79E-08), SSC11: M1GA0014659 (p = 1.19E-07), and SSC15: MARC0042106 (p = 1.16E-07). We performed the distribution of phenotypes corresponding to the genotypes of seven significant SNPs and showed that ASGA0010040, MARC0029355, MARC0058875, WU_10.2_10_49571394, M1GA0014659, and MARC0042106 had extreme phenotypic values that corresponded to the homozygous genotypes, while the intermediate values corresponded to the heterozygous genotypes. We screened for flanking regions ± 200 kb nearby the seven significant SNPs, and identified 38 genes in total. Among them, 28 of the candidates were involved in lactose metabolism, colostrum immunity, milk protein, and milk fat by functional enrichment analysis. Through the combined analysis between 28 candidate genes and transcriptome data of the sow mammary gland, we found nine commons (ANO3, MUC15, DISP3, FBXO6, CLCN6, HLA-DRA, SLA-DRB1, SLA-DQB1, and SLA-DQA1). Furthermore, by comparing the chromosome positions of the candidate genes with the quantitative trait locus (QTLs) as previously reported, a total of 17 genes were found to be within 0.86–94.02 Mb of the reported QTLs for sow milk production ability, in which, NAV2 was found to be located with 0.86 Mb of the QTL region ssc2: 40936355. In conclusion, we identified seven significant SNPs located on SSC2, 6, 7, 10, 11, and 15, and propose 28 candidate genes for the ability to produce milk in Yorkshire pigs, 10 of which were key candidates.

Detecting Risk Gene and Pathogenic Brain Region in EMCI Using a Novel GERF Algorithm Based on Brain Imaging and Genetic Data

Fusion analysis of disease-related multi-modal data is becoming increasingly important to illuminate the pathogenesis of complex brain diseases. However, owing to the small amount and high dimension of multi-modal data, current machine learning methods do not fully achieve the high veracity and reliability of fusion feature selection. In this paper, we propose a genetic-evolutionary random forest (GERF) algorithm to discover the risk genes and disease-related brain regions of early mild cognitive impairment (EMCI) based on the genetic data and resting-state functional magnetic resonance imaging (rs-fMRI) data. Classical correlation analysis method is used to explore the association between brain regions and genes, and fusion features are constructed. The genetic-evolutionary idea is introduced to enhance the classification performance, and to extract the optimal features effectively. The proposed GERF algorithm is evaluated by the public Alzheimer's Disease Neuroimaging Initiative (ADNI) database, and the results show that the algorithm achieves satisfactory classification accuracy in small sample learning. Moreover, we compare the GERF algorithm with other methods to prove its superiority. Furthermore, we propose the overall framework of detecting pathogenic factors, which can be accurately and efficiently applied to the multi-modal data analysis of EMCI and be able to extend to other diseases. This work provides a novel insight for early diagnosis and clinicopathologic analysis of EMCI, which facilitates clinical medicine to control further deterioration of diseases and is good for the accurate electric shock using transcranial magnetic stimulation.

Omics sciences for systems biology in Alzheimer's disease: State-of-the-art of the evidence

Alzheimer's disease (AD) is characterized by non-linear, genetic-driven pathophysiological dynamics with high heterogeneity in biological alterations and disease spatial-temporal progression. Human in-vivo and post-mortem studies point out a failure of multi-level biological networks underlying AD pathophysiology, including proteostasis (amyloid-β and tau), synaptic homeostasis, inflammatory and immune responses, lipid and energy metabolism, oxidative stress. Therefore, a holistic, systems-level approach is needed to fully capture AD multi-faceted pathophysiology. Omics sciences - genomics, epigenomics, transcriptomics, proteomics, metabolomics, lipidomics - embedded in the systems biology (SB) theoretical and computational framework can generate explainable readouts describing the entire biological continuum of a disease. Such path in Neurology is encouraged by the promising results of omics sciences and SB approaches in Oncology, where stage-driven pathway-based therapies have been developed in line with the precision medicine paradigm. Multi-omics data integrated in SB network approaches will help detect and chart AD upstream pathomechanistic alterations and downstream molecular effects occurring in preclinical stages. Finally, integrating omics and neuroimaging data - i.e., neuroimaging-omics - will identify multi-dimensional biological signatures essential to track the clinical-biological trajectories, at the subpopulation or even individual level.

Risk Variants in Three Alzheimer's Disease Genes Show Association with EEG Endophenotypes

Background:

Dementia due to Alzheimer’s disease (AD) is a complex neurodegenerative disorder, which much of heritability remains unexplained. At the clinical level, one of the most common physiological alterations is the slowing of oscillatory brain activity, measurable by electroencephalography (EEG). Relative power (RP) at the conventional frequency bands (i.e., delta, theta, alpha, beta-1, and beta-2) can be considered as AD endophenotypes.

Objective:

The aim of this work is to analyze the association between sixteen genes previously related with AD: APOE, PICALM, CLU, BCHE, CETP, CR1, SLC6A3, GRIN2 β, SORL1, TOMM40, GSK3 β, UNC5C, OPRD1, NAV2, HOMER2, and IL1RAP, and the slowing of the brain activity, assessed by means of RP at the aforementioned frequency bands.

Methods:

An Iberian cohort of 45 elderly controls, 45 individuals with mild cognitive impairment, and 109 AD patients in the three stages of the disease was considered. Genomic information and brain activity of each subject were analyzed.

Results:

The slowing of brain activity was observed in carriers of risk alleles in IL1RAP (rs10212109, rs9823517, rs4687150), UNC5C (rs17024131), and NAV2 (rs1425227, rs862785) genes, regardless of the disease status and situation towards the strongest risk factors: age, sex, and APOE ɛ4 presence.

Conclusion:

Endophenotypes reduce the complexity of the general phenotype and genetic variants with a major effect on those specific traits may be then identified. The found associations in this work are novel and may contribute to the comprehension of AD pathogenesis, each with a different biological role, and influencing multiple factors involved in brain physiology.

A Comprehensive Analysis of Unique and Recurrent Copy Number Variations in Alzheimer's Disease and its Related Disorders

BACKGROUND

Copy number variations (CNVs) play an important role in the genetic etiology of various neurological disorders, including Alzheimer's disease (AD). Type 2 diabetes mellitus (T2DM) and major depressive disorder (MDD) were shown to have share mechanisms and signaling pathways with AD.

OBJECTIVE

We aimed to assess CNVs regions that may harbor genes contributing to AD, T2DM, and MDD in 67 Saudi familial and sporadic AD patients, with no alterations in the known genes of AD and genotyped previously for APOE.

METHODS

DNA was analyzed using the CytoScan-HD array. Two layers of filtering criteria were applied. All the identified CNVs were checked in the Database of Genomic Variants (DGV).

RESULTS

A total of 1086 CNVs (565 gains and 521 losses) were identified in our study. We found 73 CNVs harboring genes that may be associated with AD, T2DM or MDD. Nineteen CNVs were novel. Most importantly, 42 CNVs were unique in our studied cohort existing only in one patient. Two large gains on chromosomes 1 and 13 harbored genes implicated in the studied disorders. We identified CNVs in genes that encode proteins involved in the metabolism of amyloid-β peptide (AGRN, APBA2, CR1, CR2, IGF2R, KIAA0125, MBP, RER1, RTN4R, VDR and WISPI) or Tau proteins (CACNAIC, CELF2, DUSP22, HTRA1 and SLC2A14).

CONCLUSION

The present work provided information on the presence of CNVs related to AD, T2DM, and MDD in Saudi Alzheimer's patients.

Expression pattern of Nav2 in the murine CNS with development

Neuron navigator 2 (NAV2, RAINB1, POMFIL2, HELAD1, unc53H2) is essential for nervous system development. In the present study the spatial distribution of Nav2 transcript in mouse CNS during embryonic, postnatal and adult life is examined. Because multiple NAV2 proteins are predicted based on alternate promoter usage and RNA splicing, in situ hybridization was performed using probes designed to the 5' and 3' ends of the Nav2 transcript, and PCR products using primer sets spanning the length of the mRNA were also examined by real time PCR (qPCR). These studies support full-length Nav2 transcript as the predominant form in the wild-type mouse CNS. The developing cortex, hippocampus, thalamus, olfactory bulb, and granule cells (GC) within the cerebellum show the highest expression, with a similar staining pattern using either the 5'Nav2 or 3'Nav2 probe. Nav2 is expressed in GC precursors migrating over the cerebellar primordium as well as in the postmitotic premigratory cells of the external granule cell layer (EGL). It is expressed in the cornu ammonis (CA) and dentate gyrus (DG) throughout hippocampal development. In situ hybridization was combined with immunohistochemistry for Ki67, CTIP2 and Nissl staining to follow Nav2 transcript location during cortical development, where it is observed in neuroepithelial cells exiting the germinal compartments, as well as later in the cortical plate (CP) and developing cortical layers. The highest levels of Nav2 in all brain regions studied are observed in late gestation and early postnatal life which coincides with times when neurons are migrating and differentiating. A hypomorphic mouse that lacks the full-length transcript but expresses shorter transcript shows little staining in the CNS with either probe set except at the base of the cerebellum, where a shorter Nav2 transcript is detected. Using dual fluorescent probe in situ hybridization studies, these cells are identified as oligodendrocytes and are detected using both Olig1 and the 3'Nav2 probe. The identification of full-length Nav2 as the primary transcript in numerous brain regions suggests NAV2 could play a role in CNS development beyond that of its well-established role in the cerebellum.

Incorporating spatial-anatomical similarity into the VGWAS framework for AD biomarker detection

MOTIVATION

The detection of potential biomarkers of Alzheimer's disease (AD) is crucial for its early prediction, diagnosis and treatment. Voxel-wise genome-wide association study (VGWAS) is a commonly used method in imaging genomics and usually applied to detect AD biomarkers in imaging and genetic data. However, existing VGWAS methods entail large computational cost and disregard spatial correlations within imaging data. A novel method is proposed to solve these issues.

RESULTS

We introduce a novel method to incorporate spatial correlations into a VGWAS framework for the detection of potential AD biomarkers. To consider the characteristics of AD, we first present a modification of a simple linear iterative clustering method for spatial grouping in an anatomically meaningful manner. Second, we propose a spatial-anatomical similarity matrix to incorporate correlations among voxels. Finally, we detect the potential AD biomarkers from imaging and genetic data by using a fast VGWAS method and test our method on 708 subjects obtained from an Alzheimer's Disease Neuroimaging Initiative dataset. Results show that our method can successfully detect some new risk genes and clusters of AD. The detected imaging and genetic biomarkers are used as predictors to classify AD/normal control subjects, and a high accuracy of AD/normal control classification is achieved. To the best of our knowledge, the association between imaging and genetic data has yet to be systematically investigated while building statistical models for classifying AD subjects to create a link between imaging genetics and AD. Therefore, our method may provide a new way to gain insights into the underlying pathological mechanism of AD.

AVAILABILITY AND IMPLEMENTATION

https://github.com/Meiyan88/SASM-VGWAS.

Network approaches to systems biology analysis of complex disease: integrative methods for multi-omics data

In the past decade, significant progress has been made in complex disease research across multiple omics layers from genome, transcriptome and proteome to metabolome. There is an increasing awareness of the importance of biological interconnections, and much success has been achieved using systems biology approaches. However, because of the typical focus on one single omics layer at a time, existing systems biology findings explain only a modest portion of complex disease. Recent advances in multi-omics data collection and sharing present us new opportunities for studying complex diseases in a more comprehensive fashion, and yet simultaneously create new challenges considering the unprecedented data dimensionality and diversity. Here, our goal is to review extant and emerging network approaches that can be applied across multiple biological layers to facilitate a more comprehensive and integrative multilayered omics analysis of complex diseases.

FGWAS: Functional genome wide association analysis

Functional phenotypes (e.g., subcortical surface representation), which commonly arise in imaging genetic studies, have been used to detect putative genes for complexly inherited neuropsychiatric and neurodegenerative disorders. However, existing statistical methods largely ignore the functional features (e.g., functional smoothness and correlation). The aim of this paper is to develop a functional genome-wide association analysis (FGWAS) framework to efficiently carry out whole-genome analyses of functional phenotypes. FGWAS consists of three components: a multivariate varying coefficient model, a global sure independence screening procedure, and a test procedure. Compared with the standard multivariate regression model, the multivariate varying coefficient model explicitly models the functional features of functional phenotypes through the integration of smooth coefficient functions and functional principal component analysis. Statistically, compared with existing methods for genome-wide association studies (GWAS), FGWAS can substantially boost the detection power for discovering important genetic variants influencing brain structure and function. Simulation studies show that FGWAS outperforms existing GWAS methods for searching sparse signals in an extremely large search space, while controlling for the family-wise error rate. We have successfully applied FGWAS to large-scale analysis of data from the Alzheimer's Disease Neuroimaging Initiative for 708 subjects, 30,000 vertices on the left and right hippocampal surfaces, and 501,584 SNPs.

Family-based association analysis of NAV2 gene with the risk and age at onset of Alzheimer's disease

The neuron navigator 2 (NAV2) gene is highly expressed in brain and involved in the nervous system development and may play a role in Alzheimer's disease (AD). We aimed to investigate the associations of 317 single-nucleotide polymorphisms (SNPs) in the NAV2 gene with the risk and age at onset (AAO) of AD using a family-based sample (1266 AD cases and 1279 healthy relatives). Association with the risk of AD was assessed using family-based association test -generalized estimating equations (FBAT- GEE) statistics while the association with AAO as a quantitative trait was evaluated using the FBAT-Wilcoxon statistic. Single marker analysis showed that 20 SNPs were significantly associated with the risk of AD (top SNP rs7112354 with p=8.46×10-4) and 11 SNPs were associated with AAO (top SNP rs1354269 with p=2.87×10-3). Interestingly, two SNPs rs17614100 and rs12364788 were associated with both the risk (p=1.7×10-2 and 2.71×10-2; respectively) and AAO (p=1.85×10-3 and 6.06×10-3; respectively). Haplotype analyses further supported the results of single marker analyses. In addition, functional analysis showed that NAV2 mRNA had significant expression across ten human brain regions examined and significantly correlated with APOE expression in four of ten regions. The present study is the first study providing evidence of several genetic variants within the NAV2 gene influencing the risk and AAO of AD.

Single-nucleotide polymorphisms are associated with cognitive decline at Alzheimer's disease conversion within mild cognitive impairment patients

INTRODUCTION

The growing public threat of Alzheimer's disease (AD) has raised the urgency to quantify the degree of cognitive decline during the conversion process of mild cognitive impairment (MCI) to AD and its underlying genetic pathway. The aim of this article was to test genetic common variants associated with accelerated cognitive decline after the conversion of MCI to AD.

METHODS

In 583 subjects with MCI enrolled in the Alzheimer's Disease Neuroimaging Initiative (ADNI; ADNI-1, ADNI-Go, and ADNI-2), 245 MCI participants converted to AD at follow-up. We tested the interaction effects between individual single-nucleotide polymorphisms and AD diagnosis trajectory on the longitudinal Alzheimer's Disease Assessment Scale-Cognition scores.

RESULTS

Our findings reveal six genes, including BDH1, ST6GAL1, RAB20, PDS5B, ADARB2, and SPSB1, which are directly or indirectly related to MCI conversion to AD.

DISCUSSION

This genome-wide association study sheds light on a genetic mechanism of longitudinal cognitive changes during the transition period from MCI to AD.

Meta-Analysis of Transcriptome Data Related to Hippocampus Biopsies and iPSC-Derived Neuronal Cells from Alzheimer's Disease Patients Reveals an Association with FOXA1 and FOXA2 Gene Regulatory Networks

Although the incidence of Alzheimer's disease (AD) is continuously increasing in the aging population worldwide, effective therapies are not available. The interplay between causative genetic and environmental factors is partially understood. Meta-analyses have been performed on aspects such as polymorphisms, cytokines, and cognitive training. Here, we propose a meta-analysis approach based on hierarchical clustering analysis of a reliable training set of hippocampus biopsies, which is condensed to a gene expression signature. This gene expression signature was applied to various test sets of brain biopsies and iPSC-derived neuronal cell models to demonstrate its ability to distinguish AD samples from control. Thus, our identified AD-gene signature may form the basis for determination of biomarkers that are urgently needed to overcome current diagnostic shortfalls. Intriguingly, the well-described AD-related genes APP and APOE are not within the signature because their gene expression profiles show a lower correlation to the disease phenotype than genes from the signature. This is in line with the differing characteristics of the disease as early-/late-onset or with/without genetic predisposition. To investigate the gene signature's systemic role(s), signaling pathways, gene ontologies, and transcription factors were analyzed which revealed over-representation of response to stress, regulation of cellular metabolic processes, and reactive oxygen species. Additionally, our results clearly point to an important role of FOXA1 and FOXA2 gene regulatory networks in the etiology of AD. This finding is in corroboration with the recently reported major role of the dopaminergic system in the development of AD and its regulation by FOXA1 and FOXA2.