Genetic pathway-based hierarchical clustering analysis of older adults with cognitive complaints and amnestic mild cognitive impairment using clinical and neuroimaging phenotypes

Translational Genomic Research: The Association between Genetic Profiles and Cognitive Functioning or Cardiac Function Among Breast Cancer Survivors Completing Chemotherapy

Introduction: Emerging evidence suggests that Chemotherapy (CT) treated breast cancer survivors (BCS) who have "risk variants" in genes may be more susceptible to cognitive impairment (CI) and/or poor cardiac phenotypes. The objective of this preliminary study was to examine whether there is a relationship between genetic variants and objective/subjective cognitive or cardiac phenotypes. Methods and Analysis: BCS were recruited from Moffitt Cancer Center, Morsani College of Medicine, AdventHealth Tampa and Sarasota Memorial Hospital. Genomic DNA were collected at baseline for genotyping analysis. A total of 16 single nucleotide polymorphisms (SNPs) from 14 genes involved in cognitive or cardiac function were evaluated. Three genetic models (additive, dominant, and recessive) were used to test correlation coefficients between genetic variants and objective/subjective measures of cognitive functioning and cardiac outcomes (heart rate, diastolic blood pressure, systolic blood pressure, respiration rate, and oxygen saturation). Results: BCS (207 participants) with a mean age of 56 enrolled in this study. The majority were non-Hispanic white (73.7%), married (63.1%), and received both CT and radiation treatment (77.3%). Three SNPs in genes related to cognitive functioning (rs429358 in APOE, rs1800497 in ANKK1, rs10119 in TOMM40) emerged with the most consistent significant relationship with cognitive outcomes. Among five candidate SNPs related to cardiac functioning, rs8055236 in CDH13 and rs1801133 in MTHER emerged with potential significant relationships with cardiac phenotype. Conclusions: These preliminary results provide initial targets to further examine whether BCS with specific genetic profiles may preferentially benefit from interventions designed to improve cognitive and cardiac functioning following CT.

Clustering suicidal phenotypes and genetic associations with brain-derived neurotrophic factor in patients with substance use disorders

Suicide attempts (SA), especially recurrent SA or serious SA, are common in substance use disorders (SUD). However, the genetic component of SA in SUD samples remains unclear. Brain-derived neurotrophic factor (BDNF) alleles and levels have been repeatedly involved in stress-related psychopathology. This investigation uses a within-cases study of BDNF and associated factors in three suicidal phenotypes ('any', 'recurrent', and 'serious') of outpatients seeking treatment for opiate and/or cocaine use disorder. Phenotypic characterization was ascertained using a semi-structured interview. After thorough quality control, 98 SNPs of BDNF and associated factors (the BDNF pathway) were extracted from whole-genome data, leaving 411 patients of Caucasian ancestry, who had reliable data regarding their SA history. Binary and multinomial regression with the three suicidal phenotypes were further performed to adjust for possible confounders, along with hierarchical clustering and compared to controls (N = 2504). Bayesian analyses were conducted to detect pleiotropy across the suicidal phenotypes. Among 154 (37%) ever suicide attempters, 104 (68%) reported at least one serious SA and 96 (57%) two SA or more. The median number of non-tobacco SUDs was three. The BDNF gene remained associated with lifetime SA in SNP-based (rs7934165, rs10835210) and gene-based tests within the clinical sample. rs10835210 clustered with serious SA. Bayesian analysis identified genetic correlation between 'any' and 'serious' SA regarding rs7934165. Despite limitations, 'serious' SA was shown to share both clinical and genetic risk factors of SA-not otherwise specified, suggesting a shared BDNF-related pathophysiology of SA in this population with multiple SUDs.

Identification of Candidate Genes for Skeletal Muscle Injury Prevention in Two Different Types

The study aims to uncover mechanisms on repair process of two different types of skeletal muscle injuries, freezing injury (FI) and contraction-induced injury (CI). GSE5413 was utilized, including 11 eccentric CI, 11 FI, and 3 control samples at 4 time points (6 hours, 1 day, 3 days, and 7 days after injury). Differentially expressed genes (DEGs) separately were selected in FI and CI. Correlation analysis of samples at different time points was performed. Clustering analysis was conducted for DEGs in FI and CI, respectively. Moreover, enrichment analysis and protein/protein interaction network analysis were performed for the specific DEGs. There were 616 and 465 DEGs separately in FI and CI samples. For both FI and CI, samples between 6 hours and 1 day, and between 3 and 7 days, had a close distance. DEGs in FI and CI separately were enriched in leukocyte transendothelial migration (e.g., ICAM1 [intercellular adhesion molecule 1], ITGAM, MMP9) and protein processing in endoplasmic reticulum pathway (e.g., HSPH1, HSP90AA1). In addition, MMP9 (matrix metallopeptidase 9) and ITGAM, and MYC, HSPA1B, and HSPA1A were hub nodes in the networks in FI and CI, respectively. ICAM1, ITGAM, and MMP9 in FI, and MYC and HSP70 family members in CI were biomarkers for injury prevention.

The Translational Potential of Neuroimaging Genomic Analyses To Diagnosis And Treatment In The Mental Disorders

Imaging genomics focuses on characterizing genomic influence on the variation of neurobiological traits, holding promise for illuminating the pathogenesis, reforming the diagnostic system, and precision medicine of mental disorders. This paper aims to provide an overall picture of the current status of neuroimaging-genomic analyses in mental disorders, and how we can increase their translational potential into clinical practice. The review is organized around three perspectives. (a) Towards reliability, generalizability and interpretability, where we summarize the multivariate models and discuss the considerations and trade-offs of using these methods and how reliable findings may be reached, to serve as ground for further delineation. (b) Towards improved diagnosis, where we outline the advantages and challenges of constructing a dimensional transdiagnostic model and how imaging genomic analyses map into this framework to aid in deconstructing heterogeneity and achieving an optimal stratification of patients that better inform treatment planning. (c) Towards improved treatment. Here we highlight recent efforts and progress in elucidating the functional annotations that bridge between genomic risk and neurobiological abnormalities, in detecting genomic predisposition and prodromal neurodevelopmental changes, as well as in identifying imaging genomic biomarkers for predicting treatment response. Providing an overview of the challenges and promises, this review hopefully motivates imaging genomic studies with multivariate, dimensional and transdiagnostic designs for generalizable and interpretable findings that facilitate development of personalized treatment.

Williams Syndrome, Human Self-Domestication, and Language Evolution

Language evolution resulted from changes in our biology, behavior, and culture. One source of these changes might be human self-domestication. Williams syndrome (WS) is a clinical condition with a clearly defined genetic basis which results in a distinctive behavioral and cognitive profile, including enhanced sociability. In this paper we show evidence that the WS phenotype can be satisfactorily construed as a hyper-domesticated human phenotype, plausibly resulting from the effect of the WS hemideletion on selected candidates for domestication and neural crest (NC) function. Specifically, we show that genes involved in animal domestication and NC development and function are significantly dysregulated in the blood of subjects with WS. We also discuss the consequences of this link between domestication and WS for our current understanding of language evolution.

Investigating the Correspondence of Clinical Diagnostic Grouping With Underlying Neurobiological and Phenotypic Clusters Using Unsupervised Machine Learning

Many brain-based disorders are traditionally diagnosed based on clinical interviews and behavioral assessments, which are recognized to be largely imperfect. Therefore, it is necessary to establish neuroimaging-based biomarkers to improve diagnostic precision. Resting-state functional magnetic resonance imaging (rs-fMRI) is a promising technique for the characterization and classification of varying disorders. However, most of these classification methods are supervised, i.e., they require a priori clinical labels to guide classification. In this study, we adopted various unsupervised clustering methods using static and dynamic rs-fMRI connectivity measures to investigate whether the clinical diagnostic grouping of different disorders is grounded in underlying neurobiological and phenotypic clusters. In order to do so, we derived a general analysis pipeline for identifying different brain-based disorders using genetic algorithm-based feature selection, and unsupervised clustering methods on four different datasets; three of them-ADNI, ADHD-200, and ABIDE-which are publicly available, and a fourth one-PTSD and PCS-which was acquired in-house. Using these datasets, the effectiveness of the proposed pipeline was verified on different disorders: Attention Deficit Hyperactivity Disorder (ADHD), Alzheimer's Disease (AD), Autism Spectrum Disorder (ASD), Post-Traumatic Stress Disorder (PTSD), and Post-Concussion Syndrome (PCS). For ADHD and AD, highest similarity was achieved between connectivity and phenotypic clusters, whereas for ASD and PTSD/PCS, highest similarity was achieved between connectivity and clinical diagnostic clusters. For multi-site data (ABIDE and ADHD-200), we report site-specific results. We also reported the effect of elimination of outlier subjects for all four datasets. Overall, our results suggest that neurobiological and phenotypic biomarkers could potentially be used as an aid by the clinician, in additional to currently available clinical diagnostic standards, to improve diagnostic precision. Data and source code used in this work is publicly available at https://github.com/xinyuzhao/identification-of-brain-based-disorders.git.

Organ reserve, excess metabolic capacity, and aging

"Organ reserve" refers to the ability of an organ to successfully return to its original physiological state following repeated episodes of stress. Clinical evidence shows that organ reserve correlates with the ability of older adults to cope with an added workload or stress, suggesting a role in the process of aging. Although organ reserve is well documented clinically, it is not clearly defined at the molecular level. Interestingly, several metabolic pathways exhibit excess metabolic capacities (e.g., bioenergetics pathway, antioxidants system, plasticity). These pathways comprise molecular components that have an excess of quantity and/or activity than that required for basic physiological demand in vivo (e.g., mitochondrial complex IV or glycolytic enzymes). We propose that the excess in mtDNA copy number and tandem DNA repeats of telomeres are additional examples of intrinsically embedded structural components that could comprise excess capacity. These excess capacities may grant intermediary metabolism the ability to instantly cope with, or manage, added workload or stress. Therefore, excess metabolic capacities could be viewed as an innate mechanism of adaptability that substantiates organ reserve and contributes to the cellular defense systems. If metabolic excess capacities or organ reserves are impaired or exhausted, the ability of the cell to cope with stress is reduced. Under these circumstances cell senescence, transformation, or death occurs. In this review, we discuss excess metabolic and structural capacities as integrated metabolic pathways in relation to organ reserve and cellular aging.

Multiple genetic imaging study of the association between cholesterol metabolism and brain functional alterations in individuals with risk factors for Alzheimer's disease

Alzheimer's disease (AD) is a clinically and genetically heterogeneous neurodegenerative disease. Genes involved in cholesterol metabolism may play a role in the pathological changes of AD. However, the imaging genetics-based endophenotypes derived from polymorphisms in multiple functionally related genes are unclear in individuals with risk factors for AD. Forty-three amnestic mild cognitive impairment (aMCI) subjects and 30 healthy controls underwent resting-state functional magnetic resonance imaging (fMRI) measurements of brain topological organization. Thirty-three previously suggested tagging single nucleotide polymorphisms (SNPs) from 12 candidate genes in the cholesterol metabolism pathway were further investigated. A cholesterol metabolism pathway gene-based imaging genetics approach was then utilized to investigate disease-related differences between the groups based on genotype-by-aMCI interactions. The cholesterol metabolism pathway genes exerted widespread effects on the cortico-subcortical-cerebellar spontaneous brain activity. Meanwhile, left lateralization of global brain connectivity was associated with cholesterol metabolism pathway genes. The APOE rs429358 variation significantly influenced the brain network characteristics, affecting the activation of nodes as well as the connectivity of edges in aMCI subjects.The cholesterol metabolism pathway gene-based imaging genetics approach may provide new opportunities to understand the mechanisms underlying AD and suggested that APOE rs429358 is a core genetic variation that is associated with disease-related differences in brain function.

An exploratory study of host polymorphisms in genes that clinically characterize breast cancer tumors and pretreatment cognitive performance in breast cancer survivors

PURPOSE

Inspired by the hypothesis that heterogeneity in the biology of breast cancers at the cellular level may account for cognitive dysfunction symptom variability in survivors, the current study explored relationships between host single-nucleotide polymorphisms (SNPs) in 25 breast cancer-related candidate genes (AURKA, BAG1, BCL2, BIRC5, CCNB1, CD68, CENPA, CMC2, CTSL2, DIAPH3, ERBB2, ESR1, GRB7, GSTM1, MELK, MKI67, MMP11, MYBL2, NDC80, ORC6, PGR, RACGAP1, RFC4, RRM2, and SCUBE2), identified from clinically relevant prognostic multigene-expression profiles for breast cancer, and pretreatment cognitive performance.

PATIENTS AND METHODS

The sample (n=220) was comprised of 138 postmenopausal women newly diagnosed with early stage breast cancer and 82 postmenopausal age- and education-matched healthy controls without breast cancer. Cognitive performance was assessed after primary surgery but prior to initiation of adjuvant chemotherapy and/or hormonal therapy using a comprehensive battery of neuropsychological tests encompassing eight cognitive function composite domains: attention, concentration, executive function, mental flexibility, psychomotor speed, verbal memory, visual memory, and visual working memory. In total, 131 SNPs were included in the analysis. Standard and robust multiple linear regression modeling was used to examine relationships between each domain and the presence or absence of one or more minor alleles for each SNP. Genetic risk/protection scores (GRSs) were calculated for each domain to evaluate the collective effect of possession of multiple risk/protective alleles.

RESULTS

With the exception of CMC2, MMP11, and RACGAP1, significant (P<0.05) SNP main effect and/or SNP by future prescribed treatment group interactions were observed for every gene between at least one domain and one or more SNPs. All GRSs were found to be significantly (P<0.001) associated with each respective domain score.

CONCLUSION

Associations between host SNPs and computed GRSs and variability in pretreatment cognitive function performance support the study hypothesis, and warrant further investigations to identify biomarkers for breast cancer-related cognitive dysfunction.

Genetic analysis of quantitative phenotypes in AD and MCI: imaging, cognition and biomarkers

The Genetics Core of the Alzheimer's Disease Neuroimaging Initiative (ADNI), formally established in 2009, aims to provide resources and facilitate research related to genetic predictors of multidimensional Alzheimer's disease (AD)-related phenotypes. Here, we provide a systematic review of genetic studies published between 2009 and 2012 where either ADNI APOE genotype or genome-wide association study (GWAS) data were used. We review and synthesize ADNI genetic associations with disease status or quantitative disease endophenotypes including structural and functional neuroimaging, fluid biomarker assays, and cognitive performance. We also discuss the diverse analytical strategies used in these studies, including univariate and multivariate analysis, meta-analysis, pathway analysis, and interaction and network analysis. Finally, we perform pathway and network enrichment analyses of these ADNI genetic associations to highlight key mechanisms that may drive disease onset and trajectory. Major ADNI findings included all the top 10 AD genes and several of these (e.g., APOE, BIN1, CLU, CR1, and PICALM) were corroborated by ADNI imaging, fluid and cognitive phenotypes. ADNI imaging genetics studies discovered novel findings (e.g., FRMD6) that were later replicated on different data sets. Several other genes (e.g., APOC1, FTO, GRIN2B, MAGI2, and TOMM40) were associated with multiple ADNI phenotypes, warranting further investigation on other data sets. The broad availability and wide scope of ADNI genetic and phenotypic data has advanced our understanding of the genetic basis of AD and has nominated novel targets for future studies employing next-generation sequencing and convergent multi-omics approaches, and for clinical drug and biomarker development.

A review of multivariate analyses in imaging genetics

Recent advances in neuroimaging technology and molecular genetics provide the unique opportunity to investigate genetic influence on the variation of brain attributes. Since the year 2000, when the initial publication on brain imaging and genetics was released, imaging genetics has been a rapidly growing research approach with increasing publications every year. Several reviews have been offered to the research community focusing on various study designs. In addition to study design, analytic tools and their proper implementation are also critical to the success of a study. In this review, we survey recent publications using data from neuroimaging and genetics, focusing on methods capturing multivariate effects accommodating the large number of variables from both imaging data and genetic data. We group the analyses of genetic or genomic data into either a priori driven or data driven approach, including gene-set enrichment analysis, multifactor dimensionality reduction, principal component analysis, independent component analysis (ICA), and clustering. For the analyses of imaging data, ICA and extensions of ICA are the most widely used multivariate methods. Given detailed reviews of multivariate analyses of imaging data available elsewhere, we provide a brief summary here that includes a recently proposed method known as independent vector analysis. Finally, we review methods focused on bridging the imaging and genetic data by establishing multivariate and multiple genotype-phenotype-associations, including sparse partial least squares, sparse canonical correlation analysis, sparse reduced rank regression and parallel ICA. These methods are designed to extract latent variables from both genetic and imaging data, which become new genotypes and phenotypes, and the links between the new genotype-phenotype pairs are maximized using different cost functions. The relationship between these methods along with their assumptions, advantages, and limitations are discussed.

Pathways to neurodegeneration: mechanistic insights from GWAS in Alzheimer's disease, Parkinson's disease, and related disorders

The discovery of causative genetic mutations in affected family members has historically dominated our understanding of neurodegenerative diseases such as Alzheimer's disease (AD), Parkinson's disease (PD), frontotemporal dementia (FTD), and amyotrophic lateral sclerosis (ALS). Nevertheless, most cases of neurodegenerative disease are not explained by Mendelian inheritance of known genetic variants, but instead are thought to have a complex etiology with numerous genetic and environmental factors contributing to susceptibility. Although unbiased genome-wide association studies (GWAS) have identified novel associations to neurodegenerative diseases, most of these hits explain only modest fractions of disease heritability. In addition, despite the substantial overlap of clinical and pathologic features among major neurodegenerative diseases, surprisingly few GWAS-implicated variants appear to exhibit cross-disease association. These realities suggest limitations of the focus on individual genetic variants and create challenges for the development of diagnostic and therapeutic strategies, which traditionally target an isolated molecule or mechanistic step. Recently, GWAS of complex diseases and traits have focused less on individual susceptibility variants and instead have emphasized the biological pathways and networks revealed by genetic associations. This new paradigm draws on the hypothesis that fundamental disease processes may be influenced on a personalized basis by a combination of variants - some common and others rare, some protective and others deleterious - in key genes and pathways. Here, we review and synthesize the major pathways implicated in neurodegeneration, focusing on GWAS from the most prevalent neurodegenerative disorders, AD and PD. Using literature mining, we also discover a novel regulatory network that is enriched with AD- and PD-associated genes and centered on the SP1 and AP-1 (Jun/Fos) transcription factors. Overall, this pathway- and network-driven model highlights several potential shared mechanisms in AD and PD that will inform future studies of these and other neurodegenerative disorders. These insights also suggest that biomarker and treatment strategies may require simultaneous targeting of multiple components, including some specific to disease stage, in order to assess and modulate neurodegeneration. Pathways and networks will provide ideal vehicles for integrating relevant findings from GWAS and other modalities to enhance clinical translation.

Genome-wide pathway analysis of memory impairment in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort implicates gene candidates, canonical pathways, and networks

Memory deficits are prominent features of mild cognitive impairment (MCI) and Alzheimer's disease (AD). The genetic architecture underlying these memory deficits likely involves the combined effects of multiple genetic variants operative within numerous biological pathways. In order to identify functional pathways associated with memory impairment, we performed a pathway enrichment analysis on genome-wide association data from 742 Alzheimer's Disease Neuroimaging Initiative (ADNI) participants. A composite measure of memory was generated as the phenotype for this analysis by applying modern psychometric theory to item-level data from the ADNI neuropsychological test battery. Using the GSA-SNP software tool, we identified 27 canonical, expertly-curated pathways with enrichment (FDR-corrected p-value < 0.05) against this composite memory score. Processes classically understood to be involved in memory consolidation, such as neurotransmitter receptor-mediated calcium signaling and long-term potentiation, were highly represented among the enriched pathways. In addition, pathways related to cell adhesion, neuronal differentiation and guided outgrowth, and glucose- and inflammation-related signaling were also enriched. Among genes that were highly-represented in these enriched pathways, we found indications of coordinated relationships, including one large gene set that is subject to regulation by the SP1 transcription factor, and another set that displays co-localized expression in normal brain tissue along with known AD risk genes. These results 1) demonstrate that psychometrically-derived composite memory scores are an effective phenotype for genetic investigations of memory impairment and 2) highlight the promise of pathway analysis in elucidating key mechanistic targets for future studies and for therapeutic interventions.

Biphasic functional regulation in hippocampus of rat with chronic cerebral hypoperfusion induced by permanent occlusion of bilateral common carotid artery

Background

Chronic cerebral hypoperfusion induced by permanent occlusion of the bilateral common carotid artery (BCCAO) in rats has been commonly used for the study of Alzheimer’s disease and vascular dementia. Despite the apparent cognitive dysfunction in rats with BCCAO, the molecular markers or pathways involved in the pathological alternation have not been clearly identified.

Methods

Temporal changes (sham, 21, 35, 45, 55 and 70 days) in gene expression in the hippocampus of rats after BCCAO were measured using time-course microarray analysis. Gene Ontology (GO) and pathway analyses were performed to identify the functional involvement of temporally regulated genes in BCCAO.

Results

Two major gene expression patterns were observed in the hippocampus of rats after BCCAO. One pattern, which was composed of 341 early up-regulated genes after the surgical procedure, was dominantly involved in immune-related biological functions (false discovery rate [FDR]<0.01). Another pattern composed of 182 temporally delayed down-regulated genes was involved in sensory perception such as olfactory and cognition functions (FDR<0.01). In addition to the two gene expression patterns, the temporal change of GO and the pathway activities using all differentially expressed genes also confirmed that an immune response was the main early change, whereas sensory functions were delayed responses. Moreover, we identified FADD and SOCS3 as possible core genes in the sensory function loss process using text-based mining and interaction network analysis.

Conclusions

The biphasic regulatory mechanism first reported here could provide molecular evidence of BCCAO-induced impaired memory in rats as well as mechanism of the development of vascular dementia.

Pathway analysis of genomic data: concepts, methods, and prospects for future development

Genome-wide data sets are increasingly being used to identify biological pathways and networks underlying complex diseases. In particular, analyzing genomic data through sets defined by functional pathways offers the potential of greater power for discovery and natural connections to biological mechanisms. With the burgeoning availability of next-generation sequencing, this is an opportune moment to revisit strategies for pathway-based analysis of genomic data. Here, we synthesize relevant concepts and extant methodologies to guide investigators in study design and execution. We also highlight ongoing challenges and proposed solutions. As relevant analytical strategies mature, pathways and networks will be ideally placed to integrate data from diverse -omics sources to harness the extensive, rich information related to disease and treatment mechanisms.

Rheumatoid arthritis-associated polymorphisms are not protective against Alzheimer's disease

Background

Rheumatoid arthritis (RA) and Alzheimer's disease (AD) are inversely associated. To test the hypothesis that genetic elements associated with increased RA risk are associated with decreased AD risk, we evaluated RA genetic risk factors recently identified in genome-wide association studies (GWAS) for their association with AD in a two-stage, case-control analysis.

Results

In our Stage 1 analysis of ~800 AD and ~1,200 non-AD individuals, three of seventeen RA-associated SNPs were nominally associated with AD (p < 0.05) with one SNP, rs2837960, retaining significance after correction for multiple testing (p = 0.03). The rs2837960_G (minor) allele, which is associated with increased RA risk, was associated with increased AD risk. Analysis of these three SNPs in a Stage 2 population, consisting of ~1,100 AD and ~2,600 non-AD individuals, did not confirm their association with AD. Analysis of Stage 1 and 2 combined suggested that rs2837960 shows a trend for association with AD. When the Stage 2 population was age-matched for the Stage 1 population, rs2837960 exhibited a non-significant trend with AD. Combined analysis of Stage 1 and the age-matched Stage 2 subset showed a significant association of rs2837960 with AD (p = 0.002, OR 1.24) that retained significance following correction for age, sex and APOE (p = 0.02, OR = 1.20). Rs2837960 is near BACE2, which encodes an aspartic protease capable of processing the AD-associated amyloid precursor protein. Testing for an association between rs2837960 and the expression of BACE2 isoforms in human brain, we observed a trend between rs2837960 and the total expression of BACE2 and the expression of a BACE2 transcript lacking exon 7 (p = 0.07 and 0.10, respectively).

Conclusions

RA-associated SNPs are generally not associated with AD. Moreover, rs2837960_G is associated with increased risk of both RA and, in individuals less than 80 years of age, with AD. Overall, these results contest the hypothesis that genetic variants associated with RA confer protection against AD. Further investigation of rs2837960 is necessary to elucidate the mechanism by which rs2837960 contributes to both AD and RA risk, likely via modulation of BACE2 expression.

Genome-wide association study of CSF biomarkers Abeta1-42, t-tau, and p-tau181p in the ADNI cohort

OBJECTIVES

CSF levels of Aβ1-42, t-tau, and p-tau181p are potential early diagnostic markers for probable Alzheimer disease (AD). The influence of genetic variation on these markers has been investigated for candidate genes but not on a genome-wide basis. We report a genome-wide association study (GWAS) of CSF biomarkers (Aβ1-42, t-tau, p-tau181p, p-tau181p/Aβ1-42, and t-tau/Aβ1-42).

METHODS

A total of 374 non-Hispanic Caucasian participants in the Alzheimer's Disease Neuroimaging Initiative cohort with quality-controlled CSF and genotype data were included in this analysis. The main effect of single nucleotide polymorphisms (SNPs) under an additive genetic model was assessed on each of 5 CSF biomarkers. The p values of all SNPs for each CSF biomarker were adjusted for multiple comparisons by the Bonferroni method. We focused on SNPs with corrected p<0.01 (uncorrected p<3.10×10(-8)) and secondarily examined SNPs with uncorrected p values less than 10(-5) to identify potential candidates.

RESULTS

Four SNPs in the regions of the APOE, LOC100129500, TOMM40, and EPC2 genes reached genome-wide significance for associations with one or more CSF biomarkers. SNPs in CCDC134, ABCG2, SREBF2, and NFATC4, although not reaching genome-wide significance, were identified as potential candidates.

CONCLUSIONS

In addition to known candidate genes, APOE, TOMM40, and one hypothetical gene LOC100129500 partially overlapping APOE; one novel gene, EPC2, and several other interesting genes were associated with CSF biomarkers that are related to AD. These findings, especially the new EPC2 results, require replication in independent cohorts.

Whole genome association study of brain-wide imaging phenotypes for identifying quantitative trait loci in MCI and AD: A study of the ADNI cohort

A genome-wide, whole brain approach to investigate genetic effects on neuroimaging phenotypes for identifying quantitative trait loci is described. The Alzheimer's Disease Neuroimaging Initiative 1.5 T MRI and genetic dataset was investigated using voxel-based morphometry (VBM) and FreeSurfer parcellation followed by genome-wide association studies (GWAS). One hundred forty-two measures of grey matter (GM) density, volume, and cortical thickness were extracted from baseline scans. GWAS, using PLINK, were performed on each phenotype using quality-controlled genotype and scan data including 530,992 of 620,903 single nucleotide polymorphisms (SNPs) and 733 of 818 participants (175 AD, 354 amnestic mild cognitive impairment, MCI, and 204 healthy controls, HC). Hierarchical clustering and heat maps were used to analyze the GWAS results and associations are reported at two significance thresholds (p<10(-7) and p<10(-6)). As expected, SNPs in the APOE and TOMM40 genes were confirmed as markers strongly associated with multiple brain regions. Other top SNPs were proximal to the EPHA4, TP63 and NXPH1 genes. Detailed image analyses of rs6463843 (flanking NXPH1) revealed reduced global and regional GM density across diagnostic groups in TT relative to GG homozygotes. Interaction analysis indicated that AD patients homozygous for the T allele showed differential vulnerability to right hippocampal GM density loss. NXPH1 codes for a protein implicated in promotion of adhesion between dendrites and axons, a key factor in synaptic integrity, the loss of which is a hallmark of AD. A genome-wide, whole brain search strategy has the potential to reveal novel candidate genes and loci warranting further investigation and replication.