Gene expression and genetic variation in human atria

BACKGROUND

The human left and right atria have different susceptibilities to develop atrial fibrillation (AF). However, the molecular events related to structural and functional changes that enhance AF susceptibility are still poorly understood.

OBJECTIVE

The purpose of this study was to characterize gene expression and genetic variation in human atria.

METHODS

We studied the gene expression profiles and genetic variations in 53 left atrial and 52 right atrial tissue samples collected from the Myocardial Applied Genomics Network (MAGNet) repository. The tissues were collected from heart failure patients undergoing transplantation and from unused organ donor hearts with normal ventricular function. Gene expression was profiled using the Affymetrix GeneChip Human Genome U133A Array. Genetic variation was profiled using the Affymetrix Genome-Wide Human SNP Array 6.0.

RESULTS

We found that 109 genes were differentially expressed between left and right atrial tissues. A total of 187 and 259 significant cis-associations between transcript levels and genetic variants were identified in left and right atrial tissues, respectively. We also found that a single nucleotide polymorphism at a known AF locus, rs3740293, was associated with the expression of MYOZ1 in both left and right atrial tissues.

CONCLUSION

We found a distinct transcriptional profile between the right and left atrium and extensive cis-associations between atrial transcripts and common genetic variants. Our results implicate MYOZ1 as the causative gene at the chromosome 10q22 locus for AF.

Geographical, environmental and pathophysiological influences on the human blood transcriptome

Gene expression variation provides a read-out of both genetic and environmental influences on gene activity. Geographical, genomic and sociogenomic studies have highlighted how life circumstances of an individual modify the expression of hundreds and in some cases thousands of genes in a co-ordinated manner. This review places such results in the context of a conserved set of 90 transcripts known as Blood Informative Transcripts (BIT) that capture the major conserved components of variation in the peripheral blood transcriptome. Pathophysiological states are also shown to associate with the perturbation of transcript abundance along the major axes. Discussion of false negative rates leads us to argue that simple significance thresholds provide a biased perspective on assessment of differential expression that may cloud the interpretation of studies with small sample sizes.

Association between variants of PRDM1 and NDP52 and Crohn's disease, based on exome sequencing and functional studies

BACKGROUND & AIMS

Genome-wide association studies (GWAS) have identified 140 Crohn's disease (CD) susceptibility loci. For most loci, the variants that cause disease are not known and the genes affected by these variants have not been identified. We aimed to identify variants that cause CD through detailed sequencing, genetic association, expression, and functional studies.

METHODS

We sequenced whole exomes of 42 unrelated subjects with CD and 5 healthy subjects (controls) and then filtered single nucleotide variants by incorporating association results from meta-analyses of CD GWAS and in silico mutation effect prediction algorithms. We then genotyped 9348 subjects with CD, 2868 subjects with ulcerative colitis, and 14,567 control subjects and associated variants analyzed in functional studies using materials from subjects and controls and in vitro model systems.

RESULTS

We identified rare missense mutations in PR domain-containing 1 (PRDM1) and associated these with CD. These mutations increased proliferation of T cells and secretion of cytokines on activation and increased expression of the adhesion molecule L-selectin. A common CD risk allele, identified in GWAS, correlated with reduced expression of PRDM1 in ileal biopsy specimens and peripheral blood mononuclear cells (combined P = 1.6 × 10(-8)). We identified an association between CD and a common missense variant, Val248Ala, in nuclear domain 10 protein 52 (NDP52) (P = 4.83 × 10(-9)). We found that this variant impairs the regulatory functions of NDP52 to inhibit nuclear factor κB activation of genes that regulate inflammation and affect the stability of proteins in Toll-like receptor pathways.

CONCLUSIONS

We have extended the results of GWAS and provide evidence that variants in PRDM1 and NDP52 determine susceptibility to CD. PRDM1 maps adjacent to a CD interval identified in GWAS and encodes a transcription factor expressed by T and B cells. NDP52 is an adaptor protein that functions in selective autophagy of intracellular bacteria and signaling molecules, supporting the role of autophagy in the pathogenesis of CD.

Immune response is a personal matter

Changes in gene expression could be used to predict whether individuals will respond successfully to the influenza vaccine.

Integrative genomic analysis of the human immune response to influenza vaccination

Identification of the host genetic factors that contribute to variation in vaccine responsiveness may uncover important mechanisms affecting vaccine efficacy. We carried out an integrative, longitudinal study combining genetic, transcriptional, and immunologic data in humans given seasonal influenza vaccine. We identified 20 genes exhibiting a transcriptional response to vaccination, significant genotype effects on gene expression, and correlation between the transcriptional and antibody responses. The results show that variation at the level of genes involved in membrane trafficking and antigen processing significantly influences the human response to influenza vaccination. More broadly, we demonstrate that an integrative study design is an efficient alternative to existing methods for the identification of genes involved in complex traits. DOI:http://dx.doi.org/10.7554/eLife.00299.001.

Exploiting gene expression variation to capture gene-environment interactions for disease

Gene-environment interactions have long been recognized as a fundamental concept in evolutionary, quantitative, and medical genetics. In the genomics era, study of how environment and genome interact to shape gene expression variation is relevant to understanding the genetic architecture of complex phenotypes. While genetic analysis of gene expression variation focused on main effects, little is known about the extent of interaction effects implicating regulatory variants and their consequences on transcriptional variation. Here we survey the current state of the concept of transcriptional gene-environment interactions and discuss its utility for mapping disease phenotypes in light of the insights gained from genome-wide association studies of gene expression.

Using eQTL weights to improve power for genome-wide association studies: a genetic study of childhood asthma

Increasing evidence suggests that single nucleotide polymorphisms (SNPs) associated with complex traits are more likely to be expression quantitative trait loci (eQTLs). Incorporating eQTL information hence has potential to increase power of genome-wide association studies (GWAS). In this paper, we propose using eQTL weights as prior information in SNP based association tests to improve test power while maintaining control of the family-wise error rate (FWER) or the false discovery rate (FDR). We apply the proposed methods to the analysis of a GWAS for childhood asthma consisting of 1296 unrelated individuals with German ancestry. The results confirm that eQTLs are enriched for previously reported asthma SNPs. We also find that some SNPs are insignificant using procedures without eQTL weighting, but become significant using eQTL-weighted Bonferroni or Benjamini-Hochberg procedures, while controlling the same FWER or FDR level. Some of these SNPs have been reported by independent studies in recent literature. The results suggest that the eQTL-weighted procedures provide a promising approach for improving power of GWAS. We also report the results of our methods applied to the large-scale European GABRIEL consortium data.

Mapping Novel Pathways in Cardiovascular Disease Using eQTL Data: The Past, Present, and Future of Gene Expression Analysis

Genome-wide association studies (GWAS) have identified genetic variants associated with numerous cardiovascular and metabolic diseases. Newly identified polymorphisms associated with myocardial infarction, dyslipidemia, hypertension, diabetes, and insulin resistance suggest novel mechanistic pathways that underlie these and other complex diseases. Working out the connections between the polymorphisms identified in GWAS and their biological mechanisms has been especially challenging given the number of non-coding variants identified thus far. In this review, we discuss the utility of expression quantitative trait locus (eQTL) databases in the study of non-coding variants with respect to cardiovascular and metabolic phenotypes. Recent successes in using eQTL data to link variants with functional candidate genes will be reviewed, and the shortcomings of this approach will be outlined. Finally, we discuss the emerging next generation of eQTL studies that take advantage of the ability to generate induced pluripotent stem cell lines from population cohorts.

eQTL Epistasis - Challenges and Computational Approaches

The determination of expression quantitative trait loci (eQTL) epistasis – a form of functional interaction between genetic loci that affect gene expression – is an important step toward the thorough understanding of gene regulation. Since gene expression has emerged as an “intermediate” molecular phenotype eQTL epistasis might help to explain the relationship between genotype and higher level organismal phenotypes such as diseases. A characteristic feature of eQTL analysis is the big number of tests required to identify associations between gene expression and genetic loci variability. This problem is aggravated, when epistatic effects between eQTLs are analyzed. In this review, we discuss recent algorithmic approaches for the detection of eQTL epistasis and highlight lessons that can be learned from current methods.

Allelic expression imbalance screening of genes in chromosome 1q21-24 region to identify functional variants for Type 2 diabetes susceptibility

Type 2 diabetes (T2D)-associated SNPs are more likely to be expression quantitative trait loci (eQTLs). The allelic expression imbalance (AEI) analysis is the measure of relative expression between two allelic transcripts and is the most sensitive measurement to detect cis-regulatory effects. We performed AEI screening to detect cis-regulators for genes expressed in transformed lymphocytes of 190 Caucasian (CA) and African American (AA) subjects to identify functional variants for T2D susceptibility in the chromosome 1q21-24 region of linkage. Among transcribed SNPs studied in 115 genes, significant AEI (P < 0.001) occurred in 28 and 30 genes in CA and AA subjects, respectively. Analysis of the effect of selected AEI-SNPs (≥10% mean AEI) on total gene expression further established the cis-eQTLs in thioesterase superfamily member-4 (THEM4) (rs13320, P = 0.027), and IGSF8 (rs1131891, P = 0.02). Examination of published genome-wide association data identified significant associations (P < 0.01) of three AEI-SNPs with T2D in the DIAGRAM-v3 dataset. Six AEI single nucleotide polymorphisms, including rs13320 (P = 1.35E-04) in THEM4, were associated with glucose homeostasis traits in the MAGIC dataset. Evaluation of AEI-SNPs for association with glucose homeostasis traits in 611 nondiabetic subjects showed lower AIRG (P = 0.005) in those with TT/TC genotype for rs13320. THEM4 expression in adipose was higher (P = 0.005) in subjects carrying the T allele; in vitro analysis with luciferase construct confirmed the higher expression of the T allele. Resequencing of THEM4 exons in 192 CA subjects revealed four coding nonsynonymous variants, but did not explain transmission of T2D in 718 subjects from 67 Caucasian pedigrees. Our study indicates the role of a cis-regulatory SNP in THEM4 that may influence T2D predisposition by modulating glucose homeostasis.

Prospects for advancing defense to cereal rusts through genetical genomics

Rusts are one of the most severe threats to cereal crops because new pathogen races emerge regularly, resulting in infestations that lead to large yield losses. In 1999, a new race of stem rust, Puccinia graminis f. sp. tritici (Pgt TTKSK or Ug99), was discovered in Uganda. Most of the wheat and barley cultivars grown currently worldwide are susceptible to this new race. Pgt TTKSK has already spread northward into Iran and will likely spread eastward throughout the Indian subcontinent in the near future. This scenario is not unique to stem rust; new races of leaf rust (Puccinia triticina) and stripe rust (Puccinia striiformis) have also emerged recently. One strategy for countering the persistent adaptability of these pathogens is to stack complete- and partial-resistance genes, which requires significant breeding efforts in order to reduce deleterious effects of linkage drag. These varied resistance combinations are typically more difficult for the pathogen to defeat, since they would be predicted to apply lower selection pressure. Genetical genomics or expression Quantitative Trait Locus (eQTL) analysis enables the identification of regulatory loci that control the expression of many to hundreds of genes. Integrated deployment of these technologies coupled with efficient phenotyping offers significant potential to elucidate the regulatory nodes in genetic networks that orchestrate host defense responses. The focus of this review will be to present advances in genetical genomic experimental designs and analysis, particularly as they apply to the prospects for discovering partial disease resistance alleles in cereals.

Impact of the Interaction between 3'-UTR SNPs and microRNA on the Expression of Human Xenobiotic Metabolism Enzyme and Transporter Genes

Genetic variation in the expression of human xenobiotic metabolism enzymes and transporters (XMETs) leads to inter-individual variability in metabolism of therapeutic agents as well as differed susceptibility to various diseases. Recent expression quantitative traits loci (eQTL) mapping in a few human cells/tissues have identified a number of single nucleotide polymorphisms (SNPs) significantly associated with mRNA expression of many XMET genes. These eQTLs are therefore important candidate markers for pharmacogenetic studies. However, questions remain about whether these SNPs are causative and in what mechanism these SNPs may function. Given the important role of microRNAs (miRs) in gene transcription regulation, we hypothesize that those eQTLs or their proxies in strong linkage disequilibrium (LD) altering miR targeting are likely causative SNPs affecting gene expression. The aim of this study is to identify eQTLs potentially regulating major XMETs via interference with miR targeting. To this end, we performed a genome-wide screening for eQTLs for 409 genes encoding major drug metabolism enzymes, transporters and transcription factors, in publically available eQTL datasets generated from the HapMap lymphoblastoid cell lines and human liver and brain tissue. As a result, 308 eQTLs significantly (p < 10⁻⁵) associated with mRNA expression of 101 genes were identified. We further identified 7,869 SNPs in strong LD (r² ≥ 0.8) with these eQTLs using the 1,000 Genome SNP data. Among these 8,177 SNPs, 27 are located in the 3′-UTR of 14 genes. Using two algorithms predicting miR-SNP interaction, we found that almost all these SNPs (26 out of 27) were predicted to create, abolish, or change the target site for miRs in both algorithms. Many of these miRs were also expressed in the same tissue that the eQTL were identified. Our study provides a strong rationale for continued investigation for the functions of these eQTLs in pharmacogenetic settings.

Sex-specific modulation of gene expression networks in murine hypothalamus

The hypothalamus contains nuclei and cell populations that are critical in reproduction and that differ significantly between the sexes in structure and function. To examine the molecular and genetic basis for these differences, we quantified gene expression in the hypothalamus of 39 pairs of adult male and female mice belonging to the BXD strains. This experimental design enabled us to define hypothalamic gene coexpression networks and provided robust estimates of absolute expression differences. As expected, sex has the strongest effect on the expression of genes on the X and Y chromosomes (e.g., Uty, Xist, Kdm6a). Transcripts associated with the endocrine system and neuropeptide signaling also differ significantly. Sex-differentiated transcripts often have well delimited expression within specific hypothalamic nuclei that have roles in reproduction. For instance, the estrogen receptor (Esr1) and neurokinin B (Tac2) genes have intense expression in the medial preoptic and arcuate nuclei and comparatively high expression in females. Despite the strong effect of sex on single transcripts, the global pattern of covariance among transcripts is well preserved, and consequently, males and females have well matched coexpression modules. However, there are sex-specific hub genes in functionally equivalent modules. For example, only in males is the Y-linked gene, Uty, a highly connected transcript in a network that regulates chromatin modification and gene transcription. In females, the X chromosome paralog, Kdm6a, takes the place of Uty in the same network. We also find significant effect of sex on genetic regulation and the same network in males and females can be associated with markedly different regulatory loci. With the exception of a few sex-specific modules, our analysis reveals a system in which sets of functionally related transcripts are organized into stable sex-independent networks that are controlled at a higher level by sex-specific modulators.

Expression quantitative trait Loci for extreme host response to influenza a in pre-collaborative cross mice

Outbreaks of influenza occur on a yearly basis, causing a wide range of symptoms across the human population. Although evidence exists that the host response to influenza infection is influenced by genetic differences in the host, this has not been studied in a system with genetic diversity mirroring that of the human population. Here we used mice from 44 influenza-infected pre-Collaborative Cross lines determined to have extreme phenotypes with regard to the host response to influenza A virus infection. Global transcriptome profiling identified 2671 transcripts that were significantly differentially expressed between mice that showed a severe ("high") and mild ("low") response to infection. Expression quantitative trait loci mapping was performed on those transcripts that were differentially expressed because of differences in host response phenotype to identify putative regulatory regions potentially controlling their expression. Twenty-one significant expression quantitative trait loci were identified, which allowed direct examination of genes associated with regulation of host response to infection. To perform initial validation of our findings, quantitative polymerase chain reaction was performed in the infected founder strains, and we were able to confirm or partially confirm more than 70% of those tested. In addition, we explored putative causal and reactive (downstream) relationships between the significantly regulated genes and others in the high or low response groups using structural equation modeling. By using systems approaches and a genetically diverse population, we were able to develop a novel framework for identifying the underlying biological subnetworks under host genetic control during influenza virus infection.

Exploring the shallow end; estimating information content in transcriptomics studies

Transcriptomics is a major platform to study organismal biology. The advent of new parallel sequencing technologies has opened up a new avenue of transcriptomics with ever deeper and deeper sequencing to identify and quantify each and every transcript in a sample. However, this may not be the best usage of the parallel sequencing technology for all transcriptomics experiments. I utilized the Shannon Entropy approach to estimate the information contained within a transcriptomics experiment and tested the ability of shallow RNAseq to capture the majority of this information. This analysis showed that it was possible to capture nearly all of the network or genomic information present in a variety of transcriptomics experiments using a subset of the most abundant 5000 transcripts or less within any given sample. Thus, it appears that it should be possible and affordable to conduct large scale factorial analysis with a high degree of replication using parallel sequencing technologies.

Identifying genetic loci and spleen gene coexpression networks underlying immunophenotypes in BXD recombinant inbred mice

The immune system plays a pivotal role in the susceptibility to and progression of a variety of diseases. Due to a strong genetic basis, heritable differences in immune function may contribute to differential disease susceptibility between individuals. Genetic reference populations, such as the BXD (C57BL/6J × DBA/2J) panel of recombinant inbred (RI) mouse strains, provide unique models through which to integrate baseline phenotypes in healthy individuals with heritable risk for disease because of the ability to combine data collected from these populations across both multiple studies and time. We performed basic immunophenotyping (e.g., percentage of circulating B and T lymphocytes and CD4(+) and CD8(+) T cell subpopulations) in peripheral blood of healthy mice from 41 BXD RI strains to define the immunophenotypic variation in this strain panel and to characterize the genetic architecture that underlies these traits. Significant QTL models that explained the majority (50-77%) of phenotypic variance were derived for each trait and for the T:B cell and CD4(+):CD8(+) ratios. Combining QTL mapping with spleen gene expression data uncovered two quantitative trait transcripts, Ptprk and Acp1, as candidates for heritable differences in the relative abundance of helper and cytotoxic T cells. These data will be valuable in extracting genetic correlates of the immune system in the BXD panel. In addition, they will be a useful resource for prospective, phenotype-driven model selection to test hypotheses about differential disease or environmental susceptibility between individuals with baseline differences in the composition of the immune system.