The expression of a gene can vary across individuals in the general population, as well as between monozygotic twins. This variable expression is assumed to be due to the influence of both genetic and nongenetic factors. Yet little evidence supporting this assumption has been obtained from empirical data. In this study, we used expression data from a large twin cohort to investigate the influences of genetic and nongenetic factors on variable gene expression. We focused on a set of expression variability QTL (evQTL)-i.e., genetic loci associated with the variance, as opposed to the mean, of gene expression. We identified evQTL for 99, 56, and 79 genes in lymphoblastoid cell lines, skin, and fat, respectively. The differences in gene expression, measured by the relative mean difference (RMD), tended to be larger between pairs of dizygotic (DZ) twins than between pairs of monozygotic (MZ) twins, showing that genetic background influenced the expression variability. Furthermore, a more profound RMD was observed between pairs of MZ twins whose genotypes were associated with greater expression variability than the RMD found between pairs of MZ twins whose genotypes were associated with smaller expression variability. This suggests that nongenetic (e.g., environmental) factors contribute to the variable expression. Lastly, we demonstrated that the formation of evQTL is likely due to partial linkages between eQTL SNPs that are additively associated with the mean of gene expression; in most cases, no epistatic effect is involved. Our findings have implications for understanding divergent sources of gene expression variability.V ARIATION and variability are central concepts in biology (Hallgrímsson and Hall 2005). Although often used interchangeably in the scientific literature, the two are not synonymous. Variation refers to the differences among individuals, whereas variability refers to the potential of a population to vary (Wagner 1995;Wagner and Altenberg 1996). In many cases, greater phenotypic variability (e.g., transcriptional noise) is disadvantageous (Kemkemer et al. 2002;Bahar et al. 2006;Wang and Zhang 2011) unless it gives rise to greater organismal plasticity-first at the level of an individual organism and eventually at the population level. Genetic factors resulting in more variable phenotypes become favored when they enable a population to more effectively respond to environmental changes (Hill and Zhang 2004;Kaern et al. 2005;Acar et al. 2008;Zhang et al. 2009). Thus, understanding to what extent and in what ways genotypes influence phenotypic variability is of fundamental importance.Much effort has been focused on identifying genetic loci such as expression quantitative trait loci, or eQTL (Stranger et al. 2005(Stranger et al. , 2007Choy et al. 2008;Montgomery et al. 2010;Pickrell et al. 2010;Montgomery and Dermitzakis 2011), that affect the average value of a phenotype, while ignoring those that affect the variance of a phenotype. However, there is increasing evidence across species for genet...
Appaloosa horses are predisposed to equine recurrent uveitis (ERU), an immune-mediated disease characterized by recurring inflammation of the uveal tract in the eye, which is the leading cause of blindness in horses. Nine genetic markers from the ECA1 region responsible for the spotted coat color of Appaloosa horses, and 13 microsatellites spanning the equine major histocompatibility complex (ELA) on ECA20, were evaluated for association with ERU in a group of 53 Appaloosa ERU cases and 43 healthy Appaloosa controls. Three markers were significantly associated (corrected P-value <0.05): a SNP within intron 11 of the TRPM1 gene on ECA1, an ELA class I microsatellite located near the boundary of the ELA class III and class II regions and an ELA class II microsatellite located in intron 1 of the DRA gene. Association between these three genetic markers and the ERU phenotype was confirmed in a second population of 24 insidious ERU Appaloosa cases and 16 Appaloosa controls. The relative odds of being an ERU case for each allele of these three markers were estimated by fitting a logistic mixed model with each of the associated markers independently and with all three markers simultaneously. The risk model using these markers classified ~80% of ERU cases and 75% of controls in the second population as moderate or high risk, and low risk respectively. Future studies to refine the associations at ECA1 and ELA loci and identify functional variants could uncover alleles conferring susceptibility to ERU in Appaloosa horses.
Gene expression as an intermediate molecular phenotype has been a focus of research interest. In particular, studies of expression quantitative trait loci (eQTL) have offered promise for understanding gene regulation through the discovery of genetic variants that explain variation in gene expression levels. Existing eQTL methods are designed for assessing the effects of common variants, but not rare variants. Here, we address the problem by establishing a novel analytical framework for evaluating the effects of rare or private variants on gene expression. Our method starts from the identification of outlier individuals that show markedly different gene expression from the majority of a population, and then reveals the contributions of private SNPs to the aberrant gene expression in these outliers. Using population-scale mRNA sequencing data, we identify outlier individuals using a multivariate approach. We find that outlier individuals are more readily detected with respect to gene sets that include genes involved in cellular regulation and signal transduction, and less likely to be detected with respect to the gene sets with genes involved in metabolic pathways and other fundamental molecular functions. Analysis of polymorphic data suggests that private SNPs of outlier individuals are enriched in the enhancer and promoter regions of corresponding aberrantly-expressed genes, suggesting a specific regulatory role of private SNPs, while the commonly-occurring regulatory genetic variants (i.e., eQTL SNPs) show little evidence of involvement. Additional data suggest that non-genetic factors may also underlie aberrant gene expression. Taken together, our findings advance a novel viewpoint relevant to situations wherein common eQTLs fail to predict gene expression when heritable, rare inter-individual variation exists. The analytical framework we describe, taking into consideration the reality of differential phenotypic robustness, may be valuable for investigating complex traits and conditions.
Antecedent viral infection may contribute to increased susceptibility to several neurological diseases, such as multiple sclerosis and Parkinson’s disease. Variation in clinical presentations of these diseases is often associated with gender, genetic background, or a combination of these and other factors. The complicated etiologies of these virally influenced diseases are difficult to study in conventional laboratory mouse models, which display a very limited number of phenotypes. We have used the genetically and phenotypically diverse Collaborative Cross mouse panel to examine complex neurological phenotypes after viral infection. Female and male mice from 18 CC strains were evaluated using a multifaceted phenotyping pipeline to define their unique disease profiles following infection with Theiler’s Murine Encephalomyelitis Virus, a neurotropic virus. We identified 4 distinct disease progression profiles based on limb-specific paresis and paralysis, tremors and seizures, and other clinical signs, along with separate gait profiles. We found that mice of the same strain had more similar profiles compared to those of different strains, and also identified strains and phenotypic parameters in which sex played a significant role in profile differences. These results demonstrate the value of using CC mice for studying complex disease subtypes influenced by sex and genetic background. Our findings will be useful for developing novel mouse models of virally induced neurological diseases with heterogenous presentation, an important step for designing personalized, precise treatments.
A comprehensive second-generation whole genome radiation hybrid (RH II), cytogenetic and comparative map of the horse genome (2n = 64) has been developed using the 5000rad horse × hamster radiation hybrid panel and fluorescence in situ hybridization (FISH). The map contains 4,103 markers (3,816 RH; 1,144 FISH) assigned to all 31 pairs of autosomes and the X chromosome. The RH maps of individual chromosomes are anchored and oriented using 857 cytogenetic markers. The overall resolution of the map is one marker per 775 kilobase pairs (kb), which represents a more than five-fold improvement over the first-generation map. The RH II incorporates 920 markers shared jointly with the two recently reported meiotic maps. Consequently the two maps were aligned with the RH II maps of individual autosomes and the X chromosome. Additionally, a comparative map of the horse genome was generated by connecting 1,904 loci on the horse map with genome sequences available for eight diverse vertebrates to highlight regions of evolutionarily conserved syntenies, linkages, and chromosomal breakpoints. The integrated map thus obtained presents the most comprehensive information on the physical and comparative organization of the equine genome and will assist future assemblies of whole genome BAC fingerprint maps and the genome sequence. It will also serve as a tool to identify genes governing health, disease and performance traits in horses and assist us in understanding the evolution of the equine genome in relation to other species.
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