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In females, X chromosome inactivation (XCI) is an epigenetic, gene dosage compensatory mechanism by inactivation of one copy of X in cells. Random XCI of one of the parental chromosomes results in an approximately equal proportion of cells expressing alleles from either the maternally or paternally inherited active X, and is defined by the XCI ratio. Skewed XCI ratio is suggestive of non-random inactivation, which can play an important role in X-linked genetic conditions. Current methods rely on indirect, semi-quantitative DNA methylation-based assay to estimate XCI ratio. Here we report a direct approach to estimate XCI ratio by integrated, family-trio based whole-exome and mRNA sequencing using phase-by-transmission of alleles coupled with allele-specific expression analysis. We applied this method to in silico data and to a clinical patient with mild cognitive impairment but no clear diagnosis or understanding molecular mechanism underlying the phenotype. Simulation showed that phased and unphased heterozygous allele expression can be used to estimate XCI ratio. Segregation analysis of the patient's exome uncovered a de novo, interstitial, 1.7 Mb deletion on Xp22.31 that originated on the paternally inherited X and previously been associated with heterogeneous, neurological phenotype. Phased, allelic expression data suggested an 83: 20 moderately skewed XCI that favored the expression of the maternally inherited, cytogenetically normal X and suggested that the deleterious affect of the de novo event on the paternal copy may be offset by skewed XCI that favors expression of the wild-type X. This study shows the utility of integrated sequencing approach in XCI ratio estimation.

A 3-year-old female patient presenting with an unknown syndrome of a neonatal progeroid appearance, lipodystrophy, pulmonary hypertension, cutis marmorata, feeding disorder and failure to thrive was investigated by whole-genome sequencing. This revealed a de novo, heterozygous, frame-shift mutation in the Caveolin1 gene (CAV1) (p.Phe160X). Mutations in CAV1, encoding the main component of the caveolae in plasma membranes, cause Berardinelli-Seip congenital lipodystrophy type 3 (BSCL). Although BSCL is recessive, heterozygous carriers either show a reduced phenotype of partial lipodystrophy, pulmonary hypertension, or no phenotype. To investigate the pathogenic mechanisms underlying this syndrome in more depth, we performed next generation RNA sequencing of peripheral blood, which showed several dysregulated pathways in the patient that might be related to the phenotypic progeroid features (apoptosis, DNA repair/replication, mitochondrial). Secondly, we found a significant down-regulation of known Cav1 interaction partners, verifying the dysfunction of CAV1. Other known progeroid genes and lipodystrophy genes were also dysregulated. Next, western blotting of lysates of cultured fibroblasts showed that the patient shows a significantly decreased expression of wild-type CAV1 protein, demonstrating a loss-of-function mutation, though her phenotype is more severe that other heterozygotes with similar mutations. This phenotypic variety could be explained by differences in genetic background. Indications for this are supported by additional rare variants we found in AGPAT2 and LPIN1 lipodystrophy genes. CAV1, AGPAT2 and LPIN1 all play an important role in triacylglycerol (TAG) biosynthesis in adipose tissue, and the defective function in different parts of this pathway, though not all to the same extend, could contribute to a more severe lipoatrophic phenotype in this patient. In conclusion, we report, for the first time, an association of CAV1 dysfunction with a syndrome of severe premature aging and lipodystrophy. This may contribute to a better understanding of the aging process and pathogenic mechanisms that contribute to premature aging.

Background Despite sharing the same genes, identical twins demonstrate substantial variability in behavioral traits and in their risk for disease. Epigenetic factors–DNA and chromatin modifications that affect levels of gene expression without affecting the DNA sequence–are thought to be important in establishing this variability. Epigenetically-mediated differences in the levels of gene expression that are associated with individual variability traditionally are thought to occur only in a gene-specific manner. We challenge this idea by exploring the large-scale organizational patterns of gene expression in an epigenetic model of behavioral variability. Methodology/Findings To study the effects of epigenetic influences on behavioral variability, we examine gene expression in genetically identical mice. Using a novel approach to microarray analysis, we show that variability in the large-scale organization of gene expression levels, rather than differences in the expression levels of specific genes, is associated with individual differences in behavior. Specifically, increased activity in the open field is associated with increased variance of log-transformed measures of gene expression in the hippocampus, a brain region involved in open field activity. Early life experience that increases adult activity in the open field also similarly modifies the variance of gene expression levels. The same association of the variance of gene expression levels with behavioral variability is found with levels of gene expression in the hippocampus of genetically heterogeneous outbred populations of mice, suggesting that variation in the large-scale organization of gene expression levels may also be relevant to phenotypic differences in outbred populations such as humans. We find that the increased variance in gene expression levels is attributable to an increasing separation of several large, log-normally distributed families of gene expression levels. We also show that the presence of these multiple log-normal distributions of gene expression levels is a universal characteristic of gene expression in eurkaryotes. We use data from the MicroArray Quality Control Project (MAQC) to demonstrate that our method is robust and that it reliably detects biological differences in the large-scale organization of gene expression levels. Conclusions Our results contrast with the traditional belief that epigenetic effects on gene expression occur only at the level of specific genes and suggest instead that the large-scale organization of gene expression levels provides important insights into the relationship of gene expression with behavioral variability. Understanding the epigenetic, genetic, and environmental factors that regulate the large-scale organization of gene expression levels, and how changes in this large-scale organization influences brain development and behavior will be a major future challenge in the field of behavioral genomics.