Biology subjects

James J Cai

Publications and source records attributed to James J Cai.

3 recordsLinked to original sources

bioRxiv2016

Aging shapes the population-mean and -dispersion of gene expression in human brains

Human aging is associated with cognitive decline and an increased risk of neurodegenerative disease. Our objective for this study was to evaluate potential relationships between age and variation in gene expression across different regions of the brain. We analyzed the Genotype-Tissue Expression (GTEx) data from 54 and 101 tissue samples across 13 brain regions in post-mortem donors of European descent aged between 20 and 70 years at death. After accounting for the effects of covariates and hidden confounding factors, we identified 1,446 protein-coding genes whose expression in one or more brain regions is correlated with chronological age at a false discovery rate of 5%. These genes are involved in various biological processes including apoptosis, mRNA splicing, amino acid biosynthesis, and neurotransmitter transport. The distribution of these genes among brain regions is uneven, suggesting variable regional responses to aging. We also found that the aging response of many genes, e.g., TP37 and C1QA, depends on individuals genotypic backgrounds. Finally, using dispersion-specific analysis, we identified genes such as IL7R, MS4A4E, and TERF1/TERF2 whose expressions are differentially dispersed by aging, i.e., variances differ between age groups. Our results demonstrate that age-related gene expression is brain region-specific, genotype-dependent, and associated with both mean and dispersion changes. Our findings provide a foundation for more sophisticated gene expression modeling in the studies of age-related neurodegenerative diseases.

Genetics↗

bioRxiv2015

Exploiting aberrant mRNA expression in autism for gene discovery and diagnosis

Autism spectrum disorder (ASD) is characterized by substantial phenotypic and genetic heterogeneity, which greatly complicates the identification of genetic factors that contribute to the disease. Study designs have mainly focused on group differences between cases and controls. The problem is that, by their nature, group difference-based methods (e.g., differential expression analysis) blur or collapse the heterogeneity within groups. By ignoring genes with variable within-group expression, an important axis of genetic heterogeneity contributing to expression variability among affected individuals has been overlooked. To this end, we develop a new gene expression analysis method--aberrant gene expression analysis, based on the multivariate distance commonly used for outlier detection. Our method detects the discrepancies in gene expression dispersion between groups and identifies genes with significantly different expression variability. Using this new method, we re-visited RNA sequencing data generated from post-mortem brain tissues of 47 ASD and 57 control samples. We identified 54 functional gene sets whose expression dispersion in ASD samples is more pronounced than that in controls, as well as 76 co-expression modules present in controls but absent in ASD samples due to ASD-specific aberrant gene expression. We also exploited aberrantly expressed genes as biomarkers for ASD diagnosis. With a whole blood expression data set, we identified three aberrantly expressed gene sets whose expression levels serve as discriminating variables achieving >70% classification accuracy. In summary, our method represents a novel discovery and diagnostic strategy for ASD. Our findings may help open an expression variability-centered research avenue for other genetically heterogeneous disorders.

Genetics↗

bioRxiv2015

Epistasis and destabilizing mutations shape gene expression variability in humans via distinct modes of action

Increasing evidence shows that, like phenotypic mean, phenotypic variance is also genetically determined, but the underlying mechanisms of genetic control over the variance remain obscure. Here, we conducted variance-association mapping analyses to identify expression variability QTLs (evQTLs), i.e., genomic loci associated with gene expression variance, in humans. We discovered that common genetic variations may contribute to increasing gene expression variability via two distinct modes of action--epistasis and destabilization. Specifically, the epistasis model explains a quarter of the identified evQTLs, of which the formation is attributed to the presence of \"third-party\" eQTLs that influence the level of gene expression in a fraction, rather than the entire set, of sampled individuals. The destabilization model explains the other three-quarters of evQTLs, which tend to be associated with mutations that disrupt the stability of the transcription process of genes. To show the destabilizing effect, we measured discordant gene expression between monozygotic twins, and time-course stability of gene expression in single samples using repetitive qRT-PCR assays. The destabilizing evQTL SNPs were found to be associated with more pronounced expression discordance between twin pairs and less stable gene expression in single samples. Together, our results suggest that common SNPs may work interactively or independently to shape the variability of gene expression in humans. These findings contribute to the understanding of the mechanisms of genetic control over phenotypic variance and may have implications for the development of variability-centered analytic methods for quantitative trait mapping.

Genetics↗