Genetic Control of Human Brain Transcript Expression in Alzheimer Disease

Article (PDF Available)inThe American Journal of Human Genetics 84(4):445-58 · May 2009with85 Reads
DOI: 10.1016/j.ajhg.2009.03.011 · Source: PubMed
We recently surveyed the relationship between the human brain transcriptome and genome in a series of neuropathologically normal postmortem samples. We have now analyzed additional samples with a confirmed pathologic diagnosis of late-onset Alzheimer disease (LOAD; final n = 188 controls, 176 cases). Nine percent of the cortical transcripts that we analyzed had expression profiles correlated with their genotypes in the combined cohort, and approximately 5% of transcripts had SNP-transcript relationships that could distinguish LOAD samples. Two of these transcripts have been previously implicated in LOAD candidate-gene SNP-expression screens. This study shows how the relationship between common inherited genetic variants and brain transcript expression can be used in the study of human brain disorders. We suggest that studying the transcriptome as a quantitative endo-phenotype has greater power for discovering risk SNPs influencing expression than the use of discrete diagnostic categories such as presence or absence of disease.

Full-text (PDF)

Available from: Amanda J Myers
    • "Transcripts that were detected in less than 90% of the case or 90% of the control series were excluded from our study. Finally 8650 high quality transcripts were obtained (Webster et al., 2009). Of the 188 controls, 40 were APOE ε4 carriers and 148 were non-carriers. "
    [Show abstract] [Hide abstract] ABSTRACT: APOE ε4 is the strongest genetic risk factor for late-onset Alzheimer’s disease (AD) and accounts for 50-65% of late-onset AD. Late-onset AD patients carrying or not carrying APOE ε4 manifest many clinico-pathological distinctions. Thus, we applied a weighted gene co-expression network analysis to identify specific co-expression modules in AD based on APOE ε4 stratification. Two specific modules were identified in AD APOE ε4 carriers and one module was identified in non-carriers. The hub genes of one module of AD APOE ε4 carriers were ISOC1, ENO3, GDF10, GNB3, XPO4, ACLY and MATN2. The other module of AD APOE ε4 carriers consisted of 10 hub genes including ANO3, ARPP21, HPCA, RASD2, PCP4 and ADORA2A. The module of AD APOE ε4 non-carriers consisted of 16 hub genes including DUSP5, TNFRSF18, ZNF331, DNAJB5 and RIN1. The module of AD APOE ε4 carriers including ISOC1 and ENO3 and the module of non-carriers contained the most highly connected hub gene clusters. mRNA expression of the genes in the cluster of the ISOC1 and ENO3 module of carriers was shown to be correlated in a time-dependent manner under APOE ε4 treatment but not under APOE ε3 treatment. In contrast, mRNA expression of the genes in the cluster of non-carriers’ module was correlated under APOE ε3 treatment but not under APOE ε4 treatment. The modules of carriers demonstrated genetic bases and were mainly enriched in hereditary disorders and neurological diseases, energy metabolism-associated signaling and G protein-coupled receptor-associated pathways. The module including ISOC1 and ENO3 harbored two conserved promoter motifs in its hub gene cluster that could be regulated by common transcription factors and miRNAs. The module of non-carriers was mainly enriched in neurological, immunological and cardiovascular diseases and was correlated with Parkinson’s disease. These data demonstrate that AD in APOE ε4 carriers involves more genetic factors and particular biological processes, whereas AD in APOE ε4 non-carriers shares more common pathways with other types of diseases. The study reveals differential genetic bases and pathogenic and pathological processes between carriers and non-carriers, providing new insight into the mechanisms of the differences between APOE ε4 carriers and non-carriers in AD.
    Full-text · Article · Jul 2016
    • "The TGEN2 data set comprised clinically-and neuropathologically characterized brain donors, 668 with LOAD and 365 CNEs without dementia or significant LOAD pathology. The samples were obtained from 21 different National Institute on Aging-support LOAD Center brain banks and from the Miami Brain Bank as previously described (Caselli et al., 2007; Liang et al., 2011; Reiman et al., 2007; Webster et al., 2009). Additional samples from other brain banks in the United States, United Kingdom, and the Netherlands were obtained in the same manner. "
    [Show abstract] [Hide abstract] ABSTRACT: Late-onset Alzheimer disease (LOAD) has a complex genetic etiology, involving locus heterogeneity, polygenic inheritance and gene-gene interactions; however, the investigation of interactions in recent GWAS has been limited. We used a biological knowledge-driven approach to evaluate gene-gene interactions for consistency across thirteen datasets from the Alzheimer Disease Genetics Consortium. Fifteen SNP-SNP pairs within three gene-gene combinations were identified: SIRT1 x ABCB1, PSAP x PEBP4, and GRIN2B x ADRA1A. Additionally, we extend a previously identified interaction from an endophenotype analysis between RYR3 x CACNA1C. Finally, post hoc gene expression analyses of the implicated SNPs further implicate SIRT1 and ABCB1, and implicate CDH23 which was most recently identified as an AD risk locus in an epigenetic analysis of AD. The observed interactions in this manuscript highlight ways in which genotypic variation related to disease may depend on the genetic context in which it occurs. Further, our results highlight the utility of evaluating genetic interactions to explain additional variance in AD risk and identify novel molecular mechanisms of AD pathogenesis.
    Full-text · Article · Nov 2015
    • "These transcriptional findings were corroborated by two independent, publicly available, microarray datasets. The first dataset (GSE15522) examined cortex transcription of 363 individuals and the second dataset (GSE44770) examined transcription in the prefrontal cortex of 227 individuals [31, 33]. Independent analysis of each dataset yielded similar findings with both datasets having a significant decrease of both WIF1 (Wnt-Inhibitory Factor 1) and DIO2 (Type II Iodothyronine Deiodinase) in LOAD, whereas RAR- RES3 (Retinoic Acid Receptor Responder 3) was significantly increased in LOAD (Fig. 1). "
    [Show abstract] [Hide abstract] ABSTRACT: Previous transcriptome studies observed disrupted cellular processes in late-onset Alzheimer's disease (LOAD), yet it is unclear whether these changes are specific to LOAD, or are common to general neurodegeneration. In this study, we address this question by examining transcription in LOAD and comparing it to cognitively normal controls and a cohort of "disease controls." Differential transcription was examined using RNA-seq, which allows for the examination of protein coding genes, non-coding RNAs, and splicing. Significant transcription differences specific to LOAD were observed in five genes: C10orf105, DIO2, a lincRNA, RARRES3, and WIF1. These findings were replicated in two independent publicly available microarray data sets. Network analyses, performed on 2,504 genes with moderate transcription differences in LOAD, reveal that these genes aggregate into seven networks. Two networks involved in myelination and innate immune response specifically correlated to LOAD. FRMD4B and ST18, hub genes within the myelination network, were previously implicated in LOAD. Of the five significant genes, WIF1 and RARRES3 are directly implicated in the myelination process; the other three genes are located within the network. LOAD specific changes in DNA methylation were located throughout the genome and substantial changes in methylation were identified within the myelination network. Splicing differences specific to LOAD were observed across the genome and were decreased in all seven networks. DNA methylation had reduced influence on transcription within LOAD in the myelination network when compared to both controls. These results hint at the molecular underpinnings of LOAD and indicate several key processes, genes, and networks specific to the disease.
    Article · Nov 2014
Show more

Recommended publications

Discover more