Biology subjects

Taru Tukiainen

Publications and source records attributed to Taru Tukiainen.

4 recordsLinked to original sources

bioRxiv2016

Landscape of X chromosome inactivation across human tissues

X chromosome inactivation (XCI) silences the transcription from one of the two X chromosomes in mammalian female cells to balance expression dosage between XX females and XY males. XCI is, however, characteristically incomplete in humans: up to one third of X-chromosomal genes are expressed from both the active and inactive X chromosomes (Xa and Xi, respectively) in female cells, with the degree of \"escape\" from inactivation varying between genes and individuals1,2 (Fig. 1). However, the extent to which XCI is shared between cells and tissues remains poorly characterized3,4, as does the degree to which incomplete XCI manifests as detectable sex differences in gene expression5 and phenotypic traits6. Here we report a systematic survey of XCI using a combination of over 5,500 transcriptomes from 449 individuals spanning 29 tissues, and 940 single-cell transcriptomes, integrated with genomic sequence data (Fig. 1). By combining information across these data types we show that XCI at the 683 X-chromosomal genes assessed is generally uniform across human tissues, but identify examples of heterogeneity between tissues, individuals and cells. We show that incomplete XCI affects at least 23% of X-chromosomal genes, identify seven new escape genes supported by multiple lines of evidence, and demonstrate that escape from XCI results in sex biases in gene expression, thus establishing incomplete XCI as a likely mechanism introducing phenotypic diversity6,7. Overall, this updated catalogue of XCI across human tissues informs our understanding of the extent and impact of the incompleteness in the maintenance of XCI.\n\nO_FIG O_LINKSMALLFIG WIDTH=114 HEIGHT=200 SRC=\"FIGDIR/small/073957_fig1.gif\" ALT=\"Figure 1\">\nView larger version (33K):\norg.highwire.dtl.DTLVardef@1c902f7org.highwire.dtl.DTLVardef@1e1b14dorg.highwire.dtl.DTLVardef@3f56feorg.highwire.dtl.DTLVardef@687c65_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1.C_FLOATNO Schematic overview of the study. Previous expression-based surveys of XCI1,2 have established the incomplete and variable nature of XCI, showing that XCI is incomplete for up to 30% of X-chromosomal genes, but these studies have been limited in the tissue types and samples assessed. To investigate the landscape of XCI across human tissues, we combined three approaches: 1) sex-biases in expression using population-level GTEx data across 29 tissue types, 2) allelic expression in 16 tissue samples from a female GTEx donor with fully skewed XCI, and 3) validation using single cell RNA-seq by combining allelic expression and genotype phasing. WGS, whole genome sequencing; WES, whole exome sequencing; scRNA-seq, single-cell RNA-seq.\n\nC_FIG

Genomics↗

bioRxiv2016

Improving genetic diagnosis in Mendelian disease with transcriptome sequencing

Exome and whole-genome sequencing are becoming increasingly routine approaches in Mendelian disease diagnosis. Despite their success, the current diagnostic rate for genomic analyses across a variety of rare diseases is approximately 25-50%. Here, we explore the utility of transcriptome sequencing (RNA-seq) as a complementary diagnostic tool in a cohort of 50 patients with genetically undiagnosed rare muscle disorders. We describe an integrated approach to analyze patient muscle RNA-seq, leveraging an analysis framework focused on the detection of transcript-level changes that are unique to the patient compared to over 180 control skeletal muscle samples. We demonstrate the power of RNA-seq to validate candidate splice-disrupting mutations and to identify splice-altering variants in both exonic and deep intronic regions, yielding an overall diagnosis rate of 35%. We also report the discovery of a highly recurrent de novo intronic mutation in COL6A1 that results in a dominantly acting splice-gain event, disrupting the critical glycine repeat motif of the triple helical domain. We identify this pathogenic variant in a total of 27 genetically unsolved patients in an external collagen VI-like dystrophy cohort, thus explaining approximately 25% of patients clinically suggestive of collagen VI dystrophy in whom prior genetic analysis is negative. Overall, this study represents a large systematic application of transcriptome sequencing to rare disease diagnosis and highlights its utility for the detection and interpretation of variants missed by current standard diagnostic approaches.\n\nOne Sentence SummaryTranscriptome sequencing improves the diagnostic rate for Mendelian disease in patients for whom genetic analysis has not returned a diagnosis.

Genomics↗

bioRxiv2016

Mergeomics: integration of diverse genomics resources to identify pathogenic perturbations to biological systems

Mergeomics is a computational pipeline (http://mergeomics.research.idre.ucla.edu/Download/Package/) that integrates multidimensional omics-disease associations, functional genomics, canonical pathways and gene-gene interaction networks to generate mechanistic hypotheses. It first identifies biological pathways and tissue-specific gene subnetworks that are perturbed by disease-associated molecular entities. The disease-associated subnetworks are then projected onto tissue-specific gene-gene interaction networks to identify local hubs as potential key drivers of pathological perturbations. The pipeline is modular and can be applied across species and platform boundaries, and uniquely conducts pathway/network level meta-analysis of multiple genomic studies of various data types. Application of Mergeomics to cholesterol datasets revealed novel regulators of cholesterol metabolism.

Systems Biology↗

bioRxiv2015

Analysis of protein-coding genetic variation in 60,706 humans

Large-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. Here we describe the aggregation and analysis of high-quality exome (protein-coding region) sequence data for 60,706 individuals of diverse ethnicities generated as part of the Exome Aggregation Consortium (ExAC). The resulting catalogue of human genetic diversity contains an average of one variant every eight bases of the exome, and provides direct evidence for the presence of widespread mutational recurrence. We show that this catalogue can be used to calculate objective metrics of pathogenicity for sequence variants, and to identify genes subject to strong selection against various classes of mutation; we identify 3,230 genes with near-complete depletion of truncating variants, 72% of which have no currently established human disease phenotype. Finally, we demonstrate that these data can be used for the efficient filtering of candidate disease-causing variants, and for the discovery of human \"knockout\" variants in protein-coding genes.

Genomics↗