DNA methylation differences in Alzheimer's disease (AD) have been reported. Here, we conducted a meta-analysis of more than 1000 prefrontal cortex brain samples to prioritize the most consistent ...methylation differences in multiple cohorts. Using a uniform analysis pipeline, we identified 3751 CpGs and 119 differentially methylated regions (DMRs) significantly associated with Braak stage. Our analysis identified differentially methylated genes such as MAMSTR, AGAP2, and AZU1. The most significant DMR identified is located on the MAMSTR gene, which encodes a cofactor that stimulates MEF2C. Notably, MEF2C cooperates with another transcription factor, PU.1, a central hub in the AD gene network. Our enrichment analysis highlighted the potential roles of the immune system and polycomb repressive complex 2 in pathological AD. These results may help facilitate future mechanistic and biomarker discovery studies in AD.
In this study we have identified key genes that are critical in development of astrocytic tumors. Meta-analysis of microarray studies which compared normal tissue to astrocytoma revealed a set of 646 ...differentially expressed genes in the majority of astrocytoma. Reverse engineering of these 646 genes using Bayesian network analysis produced a gene network for each grade of astrocytoma (Grade I-IV), and 'key genes' within each grade were identified. Genes found to be most influential to development of the highest grade of astrocytoma, Glioblastoma multiforme were: COL4A1, EGFR, BTF3, MPP2, RAB31, CDK4, CD99, ANXA2, TOP2A, and SERBP1. All of these genes were up-regulated, except MPP2 (down regulated). These 10 genes were able to predict tumor status with 96-100% confidence when using logistic regression, cross validation, and the support vector machine analysis. Markov genes interact with NFkβ, ERK, MAPK, VEGF, growth hormone and collagen to produce a network whose top biological functions are cancer, neurological disease, and cellular movement. Three of the 10 genes - EGFR, COL4A1, and CDK4, in particular, seemed to be potential 'hubs of activity'. Modified expression of these 10 Markov Blanket genes increases lifetime risk of developing glioblastoma compared to the normal population. The glioblastoma risk estimates were dramatically increased with joint effects of 4 or more than 4 Markov Blanket genes. Joint interaction effects of 4, 5, 6, 7, 8, 9 or 10 Markov Blanket genes produced 9, 13, 20.9, 26.7, 52.8, 53.2, 78.1 or 85.9%, respectively, increase in lifetime risk of developing glioblastoma compared to normal population. In summary, it appears that modified expression of several 'key genes' may be required for the development of glioblastoma. Further studies are needed to validate these 'key genes' as useful tools for early detection and novel therapeutic options for these tumors.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The ApoE ε4 allele is the most significant genetic risk factor for late-onset Alzheimer disease. The risk conferred by ε4, however, differs across populations, with populations of African ancestry ...showing lower ε4 risk compared to those of European or Asian ancestry. The cause of this heterogeneity in risk effect is currently unknown; it may be due to environmental or cultural factors correlated with ancestry, or it may be due to genetic variation local to the ApoE region that differs among populations. Exploring these hypotheses may lead to novel, population-specific therapeutics and risk predictions. To test these hypotheses, we analyzed ApoE genotypes and genome-wide array data in individuals from African American and Puerto Rican populations. A total of 1,766 African American and 220 Puerto Rican individuals with late-onset Alzheimer disease, and 3,730 African American and 169 Puerto Rican cognitively healthy individuals (> 65 years) participated in the study. We first assessed average ancestry across the genome ("global" ancestry) and then tested it for interaction with ApoE genotypes. Next, we assessed the ancestral background of ApoE alleles ("local" ancestry) and tested if ancestry local to ApoE influenced Alzheimer disease risk while controlling for global ancestry. Measures of global ancestry showed no interaction with ApoE risk (Puerto Rican: p-value = 0.49; African American: p-value = 0.65). Conversely, ancestry local to the ApoE region showed an interaction with the ApoE ε4 allele in both populations (Puerto Rican: p-value = 0.019; African American: p-value = 0.005). ApoE ε4 alleles on an African background conferred a lower risk than those with a European ancestral background, regardless of population (Puerto Rican: OR = 1.26 on African background, OR = 4.49 on European; African American: OR = 2.34 on African background, OR = 3.05 on European background). Factors contributing to the lower risk effect in the ApoE gene ε4 allele are likely due to ancestry-specific genetic factors near ApoE rather than non-genetic ethnic, cultural, and environmental factors.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Background
A major goal of the ADSP is to fully reveal the genetic architecture of AD/ADRD across diverse ancestral populations with the hope that new discoveries and the therapeutic or prevention ...strategies they enable will benefit all ancestral groups.
Method
While the project’s ‘Discovery Phase’ (2012‐2017) sequenced predominantly non‐Hispanic White individuals of European ancestry (NHW‐EA) whole‐exome sequencing: N = 10,061; whole‐genome sequencing (WGS): 197 NHW‐EA and 351 Hispanic/Latino (HL) familial individuals, the ‘Discovery‐Extension Phase’ of the project added WGS on 1,183 NHW‐EA, 1,141 HL, and 1,070 non‐Hispanic Black individuals with African ancestry (NHB‐AA). The ADSP’s current phase, the Follow‐Up Study (FUS) (2018‐2023) has targeted existing ancestrally diverse and unique cohorts with clinical AD/ADRD data. Over 40,000 individuals have been ascertained and ∼32,000 sequenced to date (ancestry distribution: 9,192 NHB‐AA; 9,952 HL; 13,531 NHW‐EA, 4,600 East Asian, 2,760 Indian, 89 Amerindian).
Result
Despite relatively small sample sizes, important instances of unique AD/ADRD genetic variation have been identified in HL and NHB‐AA studies, including population‐specific rare/low‐frequency variants, and evidence of the importance of ancestral background in conferring risk for genetic factors such as APOE ε4. Power studies show ∼16,100 cases and ∼16,100 controls are needed per ancestry for discovery of risk/protective variants with minor allele frequency (MAF) of 0.5% and odds ratios (OR) of 2.0 at genome‐wide significance (P = 5×10−8). Using region‐based testing ∼10,000 cases and ∼10,000 controls are needed for finding variants with MAF = 0.005% and ORs = 1.4. To this end, the next phase of the ADSP, the “ADSP‐FUS 2.0: The Diverse Population Initiative” NIH_PAR‐21‐212 aims to ensure there are enough study participants to achieve statistical power for rare variant analysis in the largest US populations, with a particular focus on HL, NHB‐AA, and Asian populations. Initiatives such as the Asian cohort for Alzheimer’s disease (ACAD), focusing on increasing recruitment of Asians in AD/ADRD studies, are also in development.
Conclusion
These datasets, some of which will include data for assessing social and environmental influences of AD/ADRD, will be an invaluable resource for the AD research community and will enhance ongoing efforts for the identification of shared and novel genetic risk factors for AD/ADRD across populations.
The X chromosome is often omitted in disease association studies despite containing thousands of genes that may provide insight into well-known sex differences in the risk of Alzheimer's disease ...(AD).
To model the expression of X chromosome genes and evaluate their impact on AD risk in a sex-stratified manner.
Using elastic net, we evaluated multiple modeling strategies in a set of 175 whole blood samples and 126 brain cortex samples, with whole genome sequencing and RNA-seq data. SNPs (MAF > 0.05) within the cis-regulatory window were used to train tissue-specific models of each gene. We apply the best models in both tissues to sex-stratified summary statistics from a meta-analysis of Alzheimer's Disease Genetics Consortium (ADGC) studies to identify AD-related genes on the X chromosome.
Across different model parameters, sample sex, and tissue types, we modeled the expression of 217 genes (95 genes in blood and 135 genes in brain cortex). The average model R2 was 0.12 (range from 0.03 to 0.34). We also compared sex-stratified and sex-combined models on the X chromosome. We further investigated genes that escaped X chromosome inactivation (XCI) to determine if their genetic regulation patterns were distinct. We found ten genes associated with AD at p < 0.05, with only ARMCX6 in female brain cortex (p = 0.008) nearing the significance threshold after adjusting for multiple testing (α = 0.002).
We optimized the expression prediction of X chromosome genes, applied these models to sex-stratified AD GWAS summary statistics, and identified one putative AD risk gene, ARMCX6.
Rich data from large biobanks, coupled with increasingly accessible association statistics from genome-wide association studies (GWAS), provide great opportunities to dissect the complex ...relationships among human traits and diseases. We introduce BADGERS, a powerful method to perform polygenic score-based biobank-wide association scans. Compared to traditional approaches, BADGERS uses GWAS summary statistics as input and does not require multiple traits to be measured in the same cohort. We applied BADGERS to two independent datasets for late-onset Alzheimer's disease (AD; n=61,212). Among 1738 traits in the UK biobank, we identified 48 significant associations for AD. Family history, high cholesterol, and numerous traits related to intelligence and education showed strong and independent associations with AD. Furthermore, we identified 41 significant associations for a variety of AD endophenotypes. While family history and high cholesterol were strongly associated with AD subgroups and pathologies, only intelligence and education-related traits predicted pre-clinical cognitive phenotypes. These results provide novel insights into the distinct biological processes underlying various risk factors for AD.
African descent populations have a lower Alzheimer disease risk from ApoE ε4 compared to other populations. Ancestry analysis showed that the difference in risk between African and European ...populations lies in the ancestral genomic background surrounding the ApoE locus (local ancestry). Identifying the mechanism(s) of this protection could lead to greater insight into the etiology of Alzheimer disease and more personalized therapeutic intervention. Our objective is to follow up the local ancestry finding and identify the genetic variants that drive this risk difference and result in a lower risk for developing Alzheimer disease in African ancestry populations. We performed association analyses using a logistic regression model with the ApoE ε4 allele as an interaction term and adjusted for genome-wide ancestry, age, and sex. Discovery analysis included imputed SNP data of 1,850 Alzheimer disease and 4,331 cognitively intact African American individuals. We performed replication analyses on 63 whole genome sequenced Alzheimer disease and 648 cognitively intact Ibadan individuals. Additionally, we reproduced results using whole-genome sequencing of 273 Alzheimer disease and 275 cognitively intact admixed Puerto Rican individuals. A further comparison was done with SNP imputation from an additional 8,463 Alzheimer disease and 11,365 cognitively intact non-Hispanic White individuals. We identified a significant interaction between the ApoE ε4 allele and the SNP rs10423769_A allele, (β = -0.54,SE = 0.12,p-value = 7.50x10-6) in the discovery data set, and replicated this finding in Ibadan (β = -1.32,SE = 0.52,p-value = 1.15x10-2) and Puerto Rican (β = -1.27,SE = 0.64,p-value = 4.91x10-2) individuals. The non-Hispanic Whites analyses showed an interaction trending in the "protective" direction but failing to pass a 0.05 significance threshold (β = -1.51,SE = 0.84,p-value = 7.26x10-2). The presence of the rs10423769_A allele reduces the odds ratio for Alzheimer disease risk from 7.2 for ApoE ε4/ε4 carriers lacking the A allele to 2.1 for ApoE ε4/ε4 carriers with at least one A allele. This locus is located approximately 2 mB upstream of the ApoE locus, in a large cluster of pregnancy specific beta-1 glycoproteins on chromosome 19 and lies within a long noncoding RNA, ENSG00000282943. This study identified a new African-ancestry specific locus that reduces the risk effect of ApoE ε4 for developing Alzheimer disease. The mechanism of the interaction with ApoEε4 is not known but suggests a novel mechanism for reducing the risk for ε4 carriers opening the possibility for potential ancestry-specific therapeutic intervention.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
There is a paucity of genetic studies of Alzheimer Disease (AD) in individuals of African Ancestry, despite evidence suggesting increased risk of AD in the African American (AA) population. We ...performed whole-genome sequencing (WGS) and multipoint linkage analyses in 51 multi-generational AA AD families ascertained through the Research in African American Alzheimer Disease Initiative (REAAADI) and the National Institute on Aging Late Onset Alzheimer's disease (NIA-LOAD) Family Based Study. Variants were prioritized on minor allele frequency (<0.01), functional potential of coding and noncoding variants, co-segregation with AD and presence in multi-ancestry ADSP release 3 WGS data. We identified a significant linkage signal on chromosome 5q35 (HLOD=3.3) driven by nine families. Haplotype segregation analysis in the family with highest LOD score identified a 3'UTR variant in INSYN2B with the most functional evidence. Four other linked AA families harbor within-family shared variants located in INSYN2B's promoter or enhancer regions. This AA family-based finding shows the importance of diversifying population-level genetic data to better understand the genetic determinants of AD on a global scale.
Background
Epigenome‐wide association studies (EWAS) often detect a large number of differentially methylated CpGs, many are located far from genes, complicating the interpretation of their ...functionalities. Therefore, there is a critical need to better understand the functional impact of these CpGs. Recent studies demonstrated methylated CpGs can affect gene transcription by either increase or decrease transcription factor binding strengths. To prioritize significant CpGs from EWAS, an integrative analysis that assesses the impact of CpG methylation on TF regulatory activities is proposed.
Method
We developed a new method and software, MethReg, that analyzes matched DNA‐methylation and gene‐expression data, along with external transcription factor (TF) binding information, to evaluate, prioritize, and annotate CpG sites with high regulatory potential. By simultaneous modeling three key elements that contribute to gene transcription (CpG methylation, target gene expression and TF activity), MethReg identifies TF‐target gene associations that are present only in a subset of samples with high (or low) methylation levels at the CpG that influences TF activities, which can be missed in analyses that use all samples.
Result
We performed a MethReg analysis for the ROSMAP Alzheimer's Disease (AD) dataset with DNA methylation and RNA‐seq data for 529 independent subjects. MethReg identified 60 methylation sensitive transcription factors, many of which are well‐known regulators for AD such as TCF12, SPI1, NR3C1, CEBPB, GABPA, and others.
Conclusion
Although many of these significant TFs have been previously implicated in AD pathology, their specific roles in transcription regulation and the identification of their targets in AD remain to be investigated. Currently available tools only identify the TFs but do not consider CpGs or provide detailed information on the relevant target genes. In contrast, MethReg fills this critical gap by nominating plausible TF‐target associations that are mediated by DNA methylation. Therefore, MethReg analysis, which leverages additional gene expression data and provides more comprehensive information on transcription regulation for the TFs, complements existing approaches.
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