Brain xQTL map: Integrating the genetic architecture of the human brain transcriptome and epigenome

Ng, Bernard; White, Charles C.; Klein, Hans Ulrich; Sieberts, Solveig K.; McCabe, Cristin; Patrick, Ellis; Xu, Jishu; Yu, Lei; Gaiteri, Chris; Bennett, David A.; Mostafavi, Sara; Jager, Philip L. De

doi:10.1101/142927

Cited by 8 publications

(13 citation statements)

References 57 publications

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“…More interestingly, however, we found that genetic variants that affect RNA splicing were most enriched (14× fold-enrichment) in variants associated to PD at p-value < 10 −5 . This observation is consistent across two different DLFPC datasets 18,21 , and motivated us to explore the hypothesis that PD-associated variants may affect RNA expression or splicing of nearby genes in myeloid cells and in cells from the CNS.…”

Section: Heritability Enrichment Of Expressed Genes Identifies Pd-relmentioning

confidence: 52%

“…To further support the results from our splicing TWAS analysis, we performed additional analyses to replicate our TWAS in independent transcriptome 21 and GWAS datasets (23andMe cohort 27 ). We first assessed whether the DLPFC TWAS results could be replicated in an independent transcriptomic reference panel by performing TWAS using DLPFC RNA-seq data from ROS/MAP 22 .…”

Section: Dlpfc Sqtls (Supplementarymentioning

confidence: 85%

“…We next asked whether PD-associated variants were enriched among molecular (including expression, splicing, histone, and DNA methylation) quantitative trait loci (QTLs) in dorsolateral prefrontal cortex (DLFPC) 21,22 and immune cell-types 5,23 ( Fig. 1c).…”

Section: Heritability Enrichment Of Expressed Genes Identifies Pd-relmentioning

confidence: 99%

“…Individuals were genotyped on Illumina Human 610 Quad custom arrays.The gene expression data is available via https://ega-archive.org/studies/EGAS00001000411. (4) ROS/MAP RNA-seq data generation was previously described in22,21 . ROS/MAP is a prospective cohort of aging individuals, where individuals are healthy at enrollment and 38% have clinical Alzheimer's disease at the time of death.…”

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confidence: 99%

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Prioritizing Parkinson’s Disease genes using population-scale transcriptomic data

Wong

Humphrey

et al. 2017

Preprint

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Genome-wide association studies (GWAS) have identified over 41 susceptibility loci associated with late-onset Parkinson's Disease (PD) but identifying putative causal genes and the underlying mechanisms remains challenging. To address this, we leveraged large-scale transcriptomic datasets to prioritize genes that are likely to affect PD. We found 29 gene associations in peripheral monocytes, and 44 gene associations whose expression or differential splicing in prefrontal cortex is associated with PD. This includes many novel genes but also known associations such as MAPT, for which we found that variation in exon 3 splicing explains the common genetic association. Genes identified in our analyses are more likely to interact physically with known PD genes and belong to the same or related pathways including lysosomal and innate immune function. Overall, our study provides a strong foundation for further mechanistic studies that will elucidate the molecular drivers of PD.

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Section: Heritability Enrichment Of Expressed Genes Identifies Pd-relmentioning

confidence: 52%

Section: Dlpfc Sqtls (Supplementarymentioning

confidence: 85%

Section: Heritability Enrichment Of Expressed Genes Identifies Pd-relmentioning

confidence: 99%

mentioning

confidence: 99%

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Prioritizing Parkinson’s Disease genes using population-scale transcriptomic data

Wong

Humphrey

et al. 2017

Preprint

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“…A summary description of all the data sets can be found in Supplementary Table 1, Supplementary Table 3, and Supplementary Table 6. All the samples were of European descent and the summary data available to us were derived from individual-level data that passed stringent quantify control (QC) 9,11,20,22,[33][34][35]46 . The SNPs in all eQTL/mQTL data sets were from imputation of the genotyped data to the 1000 Genomes Project (1KGP) reference panels 47 , and only the common SNPs (MAF > 0.01) were included in analyses.…”

Section: Summary Data Of Cis-eqtl Cis-mqtl and Gwasmentioning

confidence: 99%

Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood

Zeng

Zhang

et al. 2018

Preprint

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Understanding the difference in genetic regulation of gene expression between brain and blood is important for discovering genes associated with brain-related traits and disorders. Here, we estimate the correlation of genetic effects at the top associated cis-expression (cis-eQTLs or cis-mQTLs) between brain and blood for genes expressed (or CpG sites methylated) in both tissues, while accounting for errors in their estimated effects (rb). Using publicly available data (n = 72 to 1,366), we find that the genetic effects of cis-eQTLs (PeQTL < 5´10 -8 ) or mQTLs (PmQTL < 1´10 -10 ) are highly correlated between independent brain and blood samples ( " = 0.70 with SE = 0.015 for cis-eQTL and " = 0.78 with SE = 0.006 for cis-mQTLs). Using meta-analyzed brain eQTL/mQTL data (n = 526 to 1,194), we identify 61 genes and 167 DNA methylation (DNAm) sites associated with 4 brain-related traits and disorders. Most of these associations are a subset of the discoveries (97 genes and 295 DNAm sites) using data from blood with larger sample sizes (n = 1,980 to 14,115). We further find that cis-eQTLs with tissue-specific effects are approximately uniformly distributed across all the functional annotation categories, and that mean difference in gene expression level between brain and blood is almost independent of the difference in the corresponding cis-eQTL effect. Our results demonstrate the gain of power in gene discovery for brain-related phenotypes using blood cis-eQTL or cis-mQTL data with large sample sizes.Charitable Foundation. This study makes use of data from dbGaP (accessions: phs000428.v1.p1 and phs000424.v6.p1), UK Biobank Resource (application number: 12514), UK10K project and CommonMind Consortium. A full list of acknowledgements to these data sets can be found in Supplementary Note. The members of the eQTLGen Consortium are (in alphabetical order):

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Integrative analyses of splicing in the aging brain: role in susceptibility to Alzheimer’s Disease

Wong

et al. 2017

Preprint

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We use deep sequencing to identify sources of variation in mRNA splicing in the dorsolateral prefrontal cortex (DLFPC) of 450 subjects from two prospective cohort studies of aging. Hundreds of aberrant pre-mRNA splicing events are reproducibly associated with Alzheimer's Disease (AD). We also generate a catalog of splicing quantitative trait loci (sQTL) effects in the human cortex: splicing of 3,198 genes is influenced by genetic variation. sQTLs are enriched among those variants influencing DNA methylation and histone acetylation. In assessing known AD loci, we report that altered splicing is the mechanism for the effects of the PICALM, CLU, and PTK2B susceptibility alleles. Further, we leverage our sQTL catalog to identify genes whose aberrant splicing is associated with AD and mediated by genetics. This transcriptomewide association study identified 21 genes with significant associations, many of which are found in AD GWAS loci, but 8 are in novel AD loci, including FUS, which is a known amyotrophic lateral sclerosis (ALS) gene. This highlights an intriguing shared genetic architecture that is further elaborated by the convergence of old and new AD genes in autophagy-lysosomal-related pathways already implicated in AD and other neurodegenerative diseases. Overall, this study of the aging brain's transcriptome provides evidence that dysregulation of mRNA splicing is a feature of AD and is, in some genetically-driven cases, causal.peer-reviewed)

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Brain xQTL map: Integrating the genetic architecture of the human brain transcriptome and epigenome

Cited by 8 publications

References 57 publications

Prioritizing Parkinson’s Disease genes using population-scale transcriptomic data

Prioritizing Parkinson’s Disease genes using population-scale transcriptomic data

Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood

Integrative analyses of splicing in the aging brain: role in susceptibility to Alzheimer’s Disease

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