Assessing natural variations in gene expression in humans by comparing with monozygotic twins using microarrays

Quantitative variation in gene expression in humans is the outcome of various factors, including differences in genetic background, gender, age, and environment. However, the extent of the influence of these factors on gene expression is not clear. We attempted to address this issue by carrying out gene expression profiling in blood leukocytes with 13 individuals (including 5 pairs of monozygotic twins) on 10,000 genes using HG-U95Av2 oligonucleotide microarrays. The proportion of differentially expressed genes between monozygotic twins was low (up to 1.76%). Most of the variations belonged to the least variable category. These genes, exhibiting "random variations," did not show clear preference to any functional class, although "signaling and communication" and "immune and related functions" generally topped the list. The extent of variation in gene expression increased in comparisons between unrelated individuals (up to 14.13%). Most of the genes (89%) exhibiting random variations in twins also varied in expression in unrelated individuals. As with twins, signaling and communication topped the list, and substantial variations were observed in all three categories: least variable, moderately variable, and most variable. An important outcome of this study was that the housekeeping genes were nearly insensitive to random variations but appeared to be more susceptible to genetic differences. However, the highly expressed housekeeping genes exhibited low variation and appeared to be insensitive to all known factors. Gene expression profiling in monozygotic twins can provide useful data for the assessment of natural variation in gene expression in humans

Expoldb: expression linked polymorphism database with inbuilt tools for analysis of expression and simple repeats

BACKGROUND: Quantitative variation in gene expression has been proposed to underlie phenotypic variation among human individuals. A facilitating step towards understanding the basis for gene expression variability is associating genome wide transcription patterns with potential cis modifiers of gene expression. DESCRIPTION: EXPOLDB, a novel Database, is a new effort addressing this need by providing information on gene expression levels variability across individuals, as well as the presence and features of potentially polymorphic (TG/CA)(n )repeats. EXPOLDB thus enables associating transcription levels with the presence and length of (TG/CA)(n )repeats. One of the unique features of this database is the display of expression data for 5 pairs of monozygotic twins, which allows identification of genes whose variability in expression, are influenced by non-genetic factors including environment. In addition to queries by gene name, EXPOLDB allows for queries by a pathway name. Users can also upload their list of HGNC (HUGO (The Human Genome Organisation) Gene Nomenclature Committee) symbols for interrogating expression patterns. The online application 'SimRep' can be used to find simple repeats in a given nucleotide sequence. To help illustrate primary applications, case examples of Housekeeping genes and the RUNX gene family, as well as one example of glycolytic pathway genes are provided. CONCLUSION: The uniqueness of EXPOLDB is in facilitating the association of genome wide transcription variations with the presence and type of polymorphic repeats while offering the feature for identifying genes whose expression variability are influenced by non genetic factors including environment. In addition, the database allows comprehensive querying including functional information on biochemical pathways of the human genes. EXPOLDB can be accessed a

VAV1 and BAFF, via NFκB pathway, are genetic risk factors for myasthenia gravis

Objective To identify novel genetic loci that predispose to early‐onset myasthenia gravis (EOMG) applying a two‐stage association study, exploration, and replication strategy. Methods Thirty‐four loci and one confirmation loci, human leukocyte antigen (HLA)‐DRA, were selected as candidate genes by team members of groups involved in different research aspects of MG. In the exploration step, these candidate genes were genotyped in 384 EOMG and 384 matched controls and significant difference in allele frequency were found in eight genes. In the replication step, eight candidate genes and one confirmation loci were genotyped in 1177 EOMG patients and 814 controls, from nine European centres. Results Allele frequency differences were found in four novel loci: CD86, AKAP12, VAV1, B‐cell activating factor (BAFF), and tumor necrosis factor‐alpha (TNF‐α), and these differences were consistent in all nine cohorts. Haplotype trend test supported the differences in allele frequencies between cases and controls. In addition, allele frequency difference in female versus male patients at HLA‐DRA and TNF‐α loci were observed. Interpretation The genetic associations to EOMG outside the HLA complex are novel and of interest as VAV1 is a key signal transducer essential for T‐ and B‐cell activation, and BAFF is a cytokine that plays important roles in the proliferation and differentiation of B‐cells. Moreover, we noted striking epistasis between the predisposing VAV1 and BAFF haplotypes; they conferred a greater risk in combination than alone. These, and CD86, share the same signaling pathway, namely nuclear factor‐kappaB (NFκB), thus implicating dysregulation of proinflammatory signaling in predisposition to EOMG

Using Synthetic Mouse Spike-In Transcripts to Evaluate RNA-Seq Analysis Tools

<div><p>One of the key applications of next-generation sequencing (NGS) technologies is RNA-Seq for transcriptome genome-wide analysis. Although multiple studies have evaluated and benchmarked RNA-Seq tools dedicated to gene level analysis, few studies have assessed their effectiveness on the transcript-isoform level. Alternative splicing is a naturally occurring phenomenon in <a href="http://en.wikipedia.org/wiki/Eukaryote" target="_blank">eukaryotes</a>, significantly increasing the <a href="http://en.wikipedia.org/wiki/Biodiversity" target="_blank">biodiversity</a> of proteins that can be encoded by the genome. The aim of this study was to assess and compare the ability of the bioinformatics approaches and tools to assemble, quantify and detect differentially expressed transcripts using RNA-Seq data, in a controlled experiment. To this end, <i>in vitro</i> synthesized mouse spike-in control transcripts were added to the total RNA of differentiating mouse embryonic bodies, and their expression patterns were measured. This novel approach was used to assess the accuracy of the tools, as established by comparing the observed results versus the results expected of the mouse controlled spiked-in transcripts. We found that detection of differential expression at the gene level is adequate, yet on the transcript-isoform level, all tools tested lacked <a href="http://en.wikipedia.org/wiki/Accuracy_and_precision" target="_blank">accuracy and precision</a>.</p></div

Linear regression model using normalized RSEM FPKM values for all spike-ins.

<p>Linear regression model using normalized RSEM FPKM values for all spike-ins.</p

Linear regression model using normalized DESeq2-HTSeq FPKM values for single loci spike-ins.

<p>Linear regression model using normalized DESeq2-HTSeq FPKM values for single loci spike-ins.</p