Hierarchical analysis of RNA-seq reads improves the accuracy of allele-specific expression

Analysis of differential gene expression and alternative splicing is significantly influenced by choice of reference genome

RNA-seq analysis has enabled the evaluation of transcriptional changes in many species including nonmodel organisms. However, in most species only a single reference genome is available and RNA-seq reads from highly divergent varieties are typically aligned to this reference. Here, we quantify the impacts of the choice of mapping genome in rice where three high-quality reference genomes are available. We aligned RNA-seq data from a popular productive rice variety to three different reference genomes and found that the identification of differentially expressed genes differed depending on which reference genome was used for mapping. Furthermore, the ability to detect differentially used transcript isoforms was profoundly affected by the choice of reference genome: Only 30% of the differentially used splicing features were detected when reads were mapped to the more commonly used, but more distantly related reference genome. This demonstrated that gene expression and splicing analysis varies considerably depending on the mapping reference genome, and that analysis of individuals that are distantly related to an available reference genome may be improved by acquisition of new genomic reference material. We observed that these differences in transcriptome analysis are, in part, due to the presence of single nucleotide polymorphisms between the sequenced individual and each respective reference genome, as well as annotation differences between the reference genomes that exist even between syntenic orthologs. We conclude that even between two closely related genomes of similar quality, using the reference genome that is most closely related to the species being sampled significantly improves transcriptome analysis.

Identification of Pre-symptomatic Gene Signatures That Predict Resilience to Cognitive Decline in the Genetically Diverse AD-BXD Model

Across the population, individuals exhibit a wide variation of susceptibility or resilience to developing Alzheimer’s disease (AD). Identifying specific factors that promote resilience would provide insight into disease mechanisms and nominate potential targets for therapeutic intervention. Here, we use transcriptome profiling to identify gene networks present in the pre-symptomatic AD mouse brain relating to neuroinflammation, brain vasculature, extracellular matrix organization, and synaptic signaling that predict cognitive performance at an advanced age. We highlight putative drivers of these observed relationships, including Itgb2, Fcgr2b, Slc6a14, and Gper1, which represent prime targets through which to promote resilience prior to overt symptom onset. In addition, we identify a genomic region on chromosome 2 containing variants that directly modulate resilience network expression. Overall, work here highlights new potential drivers of resilience to AD and contributes significantly to our understanding of early, potentially causal, disease mechanisms.

Harnessing Genetic Complexity to Enhance Translatability of Alzheimer's Disease Mouse Models: A Path toward Precision Medicine

An individual's genetic makeup plays a large role in determining susceptibility to Alzheimer's disease (AD) but has largely been ignored in preclinical studies. To test the hypothesis that incorporating genetic diversity into mouse models of AD would improve translational potential, we combined a well-established mouse model of AD with a genetically diverse reference panel to generate mice that harbor identical high-risk human mutations but differ across the remainder of their genome. We first show that genetic variation profoundly modifies the impact of human AD mutations on both cognitive and pathological phenotypes. We then validate this complex AD model by demonstrating high degrees of genetic, transcriptomic, and phenotypic overlap with human AD. Overall, work here both introduces a novel AD mouse population as an innovative and reproducible resource for the study of mechanisms underlying AD and provides evidence that preclinical models incorporating genetic diversity may better translate to human disease.

Genetic Drivers of Pancreatic Islet Function

The majority of gene loci that have been associated with type 2 diabetes play a role in pancreatic islet function. To evaluate the role of islet gene expression in the etiology of diabetes, we sensitized a genetically diverse mouse population with a Western diet high in fat (45% kcal) and sucrose (34%) and carried out genome-wide association mapping of diabetes-related phenotypes. We quantified mRNA abundance in the islets and identified 18,820 expression QTL. We applied mediation analysis to identify candidate causal driver genes at loci that affect the abundance of numerous transcripts. These include two genes previously associated with monogenic diabetes (PDX1 and HNF4A), as well as three genes with nominal association with diabetes-related traits in humans (FAM83E, IL6ST, and SAT2). We grouped transcripts into gene modules and mapped regulatory loci for modules enriched with transcripts specific for α-cells, and another specific for δ-cells. However, no single module enriched for β-cell-specific transcripts, suggesting heterogeneity of gene expression patterns within the β-cell population. A module enriched in transcripts associated with branched-chain amino acid metabolism was the most strongly correlated with physiological traits that reflect insulin resistance. Although the mice in this study were not overtly diabetic, the analysis of pancreatic islet gene expression under dietary-induced stress enabled us to identify correlated variation in groups of genes that are functionally linked to diabetes-associated physiological traits. Our analysis suggests an expected degree of concordance between diabetes-associated loci in the mouse and those found in human populations, and demonstrates how the mouse can provide evidence to support nominal associations found in human genome-wide association mapping.