Genetics of Gene Expression in the Aging Human Brain Reveal TDP-43 Proteinopathy Pathophysiology

BACKGROUND: Parkinson's disease (PD) causes severe motor and cognitive disabilities that result from the progressive loss of dopamine neurons in the substantia nigra. The rs12456492 variant in the RIT2 gene has been repeatedly associated with increased risk for Parkinson's disease. From a transcriptomic perspective, a meta-analysis found that RIT2 gene expression is correlated with pH in the human brain. OBJECTIVE: To assess pH associations at the RIT2-SYT4 locus. METHODS: Linear models to examine two datasets that assayed rs12456492, gene expression, and pH in the postmortem human brain. RESULTS: Using the BrainEAC dataset, we replicate the positive correlation between RIT2 gene expression and pH in the human brain. Furthermore, we found that the relationship between expression and pH is influenced by rs12456492. When tested across ten brain regions, this interaction is specifically found in the substantia nigra. A similar association was found for the co-localized SYT4 gene. In addition, SYT4 associations are stronger in a combined model with both genes, and the SYT4 interaction appears to be specific to males. In the GTEx dataset, the pH associations involving rs12456492 and expression of either SYT4 and RIT2 was not seen. This null finding may be due to the short postmortem intervals (PMI) of the GTEx tissue samples. In the BrainEAC data, we tested the effect of PMI and only observed the interactions in the longer PMI samples. CONCLUSIONS: These previously unknown associations suggest novel mechanistic roles for rs12456492, RIT2, and SYT4 in the regulation of pH in the substantia nigra.

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Faculty Opinions recommendation of Genetics of gene expression in primary immune cells identifies cell type-specific master regulators and roles of HLA alleles.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.716997873.792302882 ◽

2012 ◽

Author(s):

Michael McDermott ◽

Michael Morgan

Keyword(s):

Gene Expression ◽

Immune Cells ◽

Cell Type ◽

Hla Alleles ◽

Master Regulators ◽

Genetics Of Gene Expression ◽

Cell Type Specific

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The Functional False Discovery Rate with Applications to Genomics

10.1101/241133 ◽

2017 ◽

Cited By ~ 2

Author(s):

Xiongzhi Chen ◽

David G. Robinson ◽

John D. Storey

Keyword(s):

Gene Expression ◽

False Discovery Rate ◽

Genetic Marker ◽

Read Depth ◽

False Discovery Rates ◽

Additional Information ◽

False Discovery ◽

Gene Expression Trait ◽

Genetics Of Gene Expression ◽

False Discoveries

AbstractThe false discovery rate measures the proportion of false discoveries among a set of hypothesis tests called significant. This quantity is typically estimated based on p-values or test statistics. In some scenarios, there is additional information available that may be used to more accurately estimate the false discovery rate. We develop a new framework for formulating and estimating false discovery rates and q-values when an additional piece of information, which we call an “informative variable”, is available. For a given test, the informative variable provides information about the prior probability a null hypothesis is true or the power of that particular test. The false discovery rate is then treated as a function of this informative variable. We consider two applications in genomics. Our first is a genetics of gene expression (eQTL) experiment in yeast where every genetic marker and gene expression trait pair are tested for associations. The informative variable in this case is the distance between each genetic marker and gene. Our second application is to detect differentially expressed genes in an RNA-seq study carried out in mice. The informative variable in this study is the per-gene read depth. The framework we develop is quite general, and it should be useful in a broad range of scientific applications.

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Characterization of the nuclear and cytosolic transcriptomes in human brain tissue reveals new insights into the subcellular distribution of RNA transcripts

10.1101/2020.04.08.031419 ◽

2020 ◽

Author(s):

Ammar Zaghlool ◽

Adnan Niazi ◽

Åsa K. Björklund ◽

Jakub Orzechowski Westholm ◽

Adam Ameur ◽

...

Keyword(s):

Gene Expression ◽

Subcellular Localization ◽

Human Brain ◽

Expression Analysis ◽

Differential Expression Analysis ◽

Adult Brain ◽

Sequencing Data ◽

Human Brain Tissue ◽

Protein Coding ◽

The Impact

AbstractTranscriptome analysis has mainly relied on analyzing RNA sequencing data from whole cells, overlooking the impact of subcellular RNA localization and its influence on our understanding of gene function, and interpretation of gene expression signatures in cells. Here, we performed a comprehensive analysis of cytosolic and nuclear transcriptomes in human fetal and adult brain samples. We show significant differences in RNA expression for protein-coding and lncRNA genes between cytosol and nucleus. Transcripts displaying differential subcellular localization belong to particular functional categories and display tissue-specific localization patterns. We also show that transcripts encoding the nuclear-encoded mitochondrial proteins are significantly enriched in the cytosol compared to the rest of protein-coding genes. Further investigation of the use of the cytosolic or the nuclear transcriptome for differential gene expression analysis indicates important differences in results depending on the cellular compartment. These differences were manifested at the level of transcript types and the number of differentially expressed genes. Our data provide a resource of RNA subcellular localization in the human brain and highlight differences in using the cytosolic or the nuclear transcriptomes for differential expression analysis.

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