Heritability and tissue specificity of expression quantitative trait loci

A role for heritable transcriptomic variation in maize adaptation to temperate environments

Background

Transcription bridges genetic information and phenotypes. Here, we evaluated how changes in transcriptional regulation enable maize (Zea mays), a crop originally domesticated in the tropics, to adapt to temperate environments.

Result

We generated 572 unique RNA-seq datasets from the roots of 340 maize genotypes. Genes involved in core processes such as cell division, chromosome organization and cytoskeleton organization showed lower heritability of gene expression, while genes involved in anti-oxidation activity exhibited higher expression heritability. An expression genome-wide association study (eGWAS) identified 19,602 expression quantitative trait loci (eQTLs) associated with the expression of 11,444 genes. A GWAS for alternative splicing identified 49,897 splicing QTLs (sQTLs) for 7614 genes. Genes harboring both cis-eQTLs and cis-sQTLs in linkage disequilibrium were disproportionately likely to encode transcription factors or were annotated as responding to one or more stresses. Independent component analysis of gene expression data identified loci regulating co-expression modules involved in oxidation reduction, response to water deprivation, plastid biogenesis, protein biogenesis, and plant-pathogen interaction. Several genes involved in cell proliferation, flower development, DNA replication, and gene silencing showed lower gene expression variation explained by genetic factors between temperate and tropical maize lines. A GWAS of 27 previously published phenotypes identified several candidate genes overlapping with genomic intervals showing signatures of selection during adaptation to temperate environments.

Conclusion

Our results illustrate how maize transcriptional regulatory networks enable changes in transcriptional regulation to adapt to temperate regions.

Supplementary information

The online version contains supplementary material available at 10.1186/s13059-023-02891-3.

Robust and rigorous identification of tissue-specific genes by statistically extending tau score

OBJECTIVES

In this study, we aimed to identify tissue-specific genes for various human tissues/organs more robustly and rigorously by extending the tau score algorithm.

INTRODUCTION

Tissue-specific genes are a class of genes whose functions and expressions are preferred in one or several tissues restrictedly. Identification of tissue-specific genes is essential for discovering multi-cellular biological processes such as tissue-specific molecular regulations, tissue development, physiology, and the pathogenesis of tissue-associated diseases.

MATERIALS AND METHODS

Gene expression data derived from five large RNA sequencing (RNA-seq) projects, spanning 96 different human tissues, were retrieved from ArrayExpress and ExpressionAtlas. The first step is categorizing genes using significant filters and tau score as a specificity index. After calculating tau for each gene in all datasets separately, statistical distance from the maximum expression level was estimated using a new meaningful procedure. Specific expression of a gene in one or several tissues was calculated after the integration of tau and statistical distance estimation, which is called as extended tau approach. Obtained tissue-specific genes for 96 different human tissues were functionally annotated, and some comparisons were carried out to show the effectiveness of the extended tau method.

RESULTS AND DISCUSSION

Categorization of genes based on expression level and identification of tissue-specific genes for a large number of tissues/organs were executed. Genes were successfully assigned to multiple tissues by generating the extended tau approach as opposed to the original tau score, which can assign tissue specificity to single tissue only.

Epigenetic control of chromosome-associated lncRNA genes essential for replication and stability

ASARs are long noncoding RNA genes that control replication timing of entire human chromosomes in cis. The three known ASAR genes are located on human chromosomes 6 and 15, and are essential for chromosome integrity. To identify ASARs on all human chromosomes we utilize a set of distinctive ASAR characteristics that allow for the identification of hundreds of autosomal loci with epigenetically controlled, allele-restricted behavior in expression and replication timing of coding and noncoding genes, and is distinct from genomic imprinting. Disruption of noncoding RNA genes at five of five tested loci result in chromosome-wide delayed replication and chromosomal instability, validating their ASAR activity. In addition to the three known essential cis-acting chromosomal loci, origins, centromeres, and telomeres, we propose that all mammalian chromosomes also contain "Inactivation/Stability Centers" that display allele-restricted epigenetic regulation of protein coding and noncoding ASAR genes that are essential for replication and stability of each chromosome.

The regulatory landscape of multiple brain regions in outbred heterogeneous stock rats

Heterogeneous Stock (HS) rats are a genetically diverse outbred rat population that is widely used for studying genetics of behavioral and physiological traits. Mapping Quantitative Trait Loci (QTL) associated with transcriptional changes would help to identify mechanisms underlying these traits. We generated genotype and transcriptome data for five brain regions from 88 HS rats. We identified 21 392 cis-QTLs associated with expression and splicing changes across all five brain regions and validated their effects using allele specific expression data. We identified 80 cases where eQTLs were colocalized with genome-wide association study (GWAS) results from nine physiological traits. Comparing our dataset to human data from the Genotype-Tissue Expression (GTEx) project, we found that the HS rat data yields twice as many significant eQTLs as a similarly sized human dataset. We also identified a modest but highly significant correlation between genetic regulatory variation among orthologous genes. Surprisingly, we found less genetic variation in gene regulation in HS rats relative to humans, though we still found eQTLs for the orthologs of many human genes for which eQTLs had not been found. These data are available from the RatGTEx data portal (RatGTEx.org) and will enable new discoveries of the genetic influences of complex traits.

Telescoping bimodal latent Dirichlet allocation to identify expression QTLs across tissues

Expression quantitative trait loci (eQTLs), or single-nucleotide polymorphisms that affect average gene expression levels, provide important insights into context-specific gene regulation. Classic eQTL analyses use one-to-one association tests, which test gene-variant pairs individually and ignore correlations induced by gene regulatory networks and linkage disequilibrium. Probabilistic topic models, such as latent Dirichlet allocation, estimate latent topics for a collection of count observations. Prior multimodal frameworks that bridge genotype and expression data assume matched sample numbers between modalities. However, many data sets have a nested structure where one individual has several associated gene expression samples and a single germline genotype vector. Here, we build a telescoping bimodal latent Dirichlet allocation (TBLDA) framework to learn shared topics across gene expression and genotype data that allows multiple RNA sequencing samples to correspond to a single individual's genotype. By using raw count data, our model avoids possible adulteration via normalization procedures. Ancestral structure is captured in a genotype-specific latent space, effectively removing it from shared components. Using GTEx v8 expression data across 10 tissues and genotype data, we show that the estimated topics capture meaningful and robust biological signal in both modalities and identify associations within and across tissue types. We identify 4,645 cis-eQTLs and 995 trans-eQTLs by conducting eQTL mapping between the most informative features in each topic. Our TBLDA model is able to identify associations using raw sequencing count data when the samples in two separate data modalities are matched one-to-many, as is often the case in biological data. Our code is freely available at https://github.com/gewirtz/TBLDA.

Identifying causal genes for depression via integration of the proteome and transcriptome from brain and blood

Genome-wide association studies (GWASs) have identified numerous risk genes for depression. Nevertheless, genes crucial for understanding the molecular mechanisms of depression and effective antidepressant drug targets are largely unknown. Addressing this, we aimed to highlight potentially causal genes by systematically integrating the brain and blood protein and expression quantitative trait loci (QTL) data with a depression GWAS dataset via a statistical framework including Mendelian randomization (MR), Bayesian colocalization, and Steiger filtering analysis. In summary, we identified three candidate genes (TMEM106B, RAB27B, and GMPPB) based on brain data and two genes (TMEM106B and NEGR1) based on blood data with consistent robust evidence at both the protein and transcriptional levels. Furthermore, the protein-protein interaction (PPI) network provided new insights into the interaction between brain and blood in depression. Collectively, four genes (TMEM106B, RAB27B, GMPPB, and NEGR1) affect depression by influencing protein and gene expression level, which could guide future researches on candidate genes investigations in animal studies as well as prioritize antidepressant drug targets.

Relationship between gene expression networks and muscle contractile physiology differences in Anolis lizards

Muscles facilitate most animal behavior, from eating to fleeing. However, to generate the variation in behavior necessary for survival, different muscles must perform differently; for instance, sprinting requires multiple rapid muscle contractions, whereas biting may require fewer contractions but greater force. Here, we use a transcriptomic approach to identify genes associated with variation in muscle contractile physiology among different muscles from the same individual. We measured differential gene expression between a leg and jaw muscle of Anolis lizards known to differ in muscle contractile physiology and performance. For each individual, one muscle was used to measure muscle contractile physiology, including contractile velocity (V_max and V₄₀), specific tension, power ratio, and twitch time, whereas the contralateral muscle was used to extract RNA for transcriptomic sequencing. Using the transcriptomic data, we found clear clustering of muscle type. Expression of genes clustered in gene ontology (GO) terms related to muscle contraction and extracellular matrix was, on average, negatively correlated with V_max and slower twitch times but positively correlated to power ratio and V₄₀. Conversely, genes related to the GO terms related to aerobic respiration were downregulated in muscles with higher power ratio and V₄₀, and over-expressed as twitch time decreased. Determining the molecular mechanisms that underlie variation in muscle contractile physiology can begin to explain how organisms are able to optimize behavior under variable conditions. Future studies pursuing the effects of differential gene expression across muscle types in different environments might inform researchers about how differences develop across species, populations, and individuals varying in ecological history.

Systems genetics in the rat HXB/BXH family identifies Tti2 as a pleiotropic quantitative trait gene for adult hippocampal neurogenesis and serum glucose

Neurogenesis in the adult hippocampus contributes to learning and memory in the healthy brain but is dysregulated in metabolic and neurodegenerative diseases. The molecular relationships between neural stem cell activity, adult neurogenesis, and global metabolism are largely unknown. Here we applied unbiased systems genetics methods to quantify genetic covariation among adult neurogenesis and metabolic phenotypes in peripheral tissues of a genetically diverse family of rat strains, derived from a cross between the spontaneously hypertensive (SHR/OlaIpcv) strain and Brown Norway (BN-Lx/Cub). The HXB/BXH family is a very well established model to dissect genetic variants that modulate metabolic and cardiovascular diseases and we have accumulated deep phenome and transcriptome data in a FAIR-compliant resource for systematic and integrative analyses. Here we measured rates of precursor cell proliferation, survival of new neurons, and gene expression in the hippocampus of the entire HXB/BXH family, including both parents. These data were combined with published metabolic phenotypes to detect a neurometabolic quantitative trait locus (QTL) for serum glucose and neuronal survival on Chromosome 16: 62.1-66.3 Mb. We subsequently fine-mapped the key phenotype to a locus that includes the Telo2-interacting protein 2 gene (Tti2)-a chaperone that modulates the activity and stability of PIKK kinases. To verify the hypothesis that differences in neurogenesis and glucose levels are caused by a polymorphism in Tti2, we generated a targeted frameshift mutation on the SHR/OlaIpcv background. Heterozygous SHR-Tti2+/- mutants had lower rates of hippocampal neurogenesis and hallmarks of dysglycemia compared to wild-type littermates. Our findings highlight Tti2 as a causal genetic link between glucose metabolism and structural brain plasticity. In humans, more than 800 genomic variants are linked to TTI2 expression, seven of which have associations to protein and blood stem cell factor concentrations, blood pressure and frontotemporal dementia.

Cap analysis of gene expression reveals alternative promoter usage in a rat model of hypertension

The role of alternative promoter usage in tissue-specific gene expression has been well established; however, its role in complex diseases is poorly understood. We performed cap analysis of gene expression (CAGE) sequencing from the left ventricle of a rat model of hypertension, the spontaneously hypertensive rat (SHR), and a normotensive strain, Brown Norway to understand the role of alternative promoter usage in complex disease. We identified 26,560 CAGE-defined transcription start sites in the rat left ventricle, including 1,970 novel cardiac transcription start sites. We identified 28 genes with alternative promoter usage between SHR and Brown Norway, which could lead to protein isoforms differing at the amino terminus between two strains and 475 promoter switching events altering the length of the 5' UTR. We found that the shift in Insr promoter usage was significantly associated with insulin levels and blood pressure within a panel of HXB/BXH recombinant inbred rat strains, suggesting that hyperinsulinemia due to insulin resistance might lead to hypertension in SHR. Our study provides a preliminary evidence of alternative promoter usage in complex diseases.

Inheritance of gene expression throughout fruit development in chili pepper

Gene expression is the primary molecular phenotype and can be estimated in specific organs or tissues at particular times. Here we analyzed genome-wide inheritance of gene expression in fruits of chili pepper (Capsicum annuum L.) in reciprocal crosses between a domesticated and a wild accession, estimating this parameter during fruit development. We defined a general hierarchical schema to classify gene expression inheritance which can be employed for any quantitative trait. We found that inheritance of gene expression is affected by both, the time of fruit development as well as the direction of the cross, and propose that such variations could be common in many developmental processes. We conclude that classification of inheritance patterns is important to have a better understanding of the mechanisms underlying gene expression regulation, and demonstrate that sets of genes with specific inheritance pattern at particular times of fruit development are enriched in different biological processes, molecular functions and cell components. All curated data and functions for analysis and visualization are publicly available as an R package.

A trans locus causes a ribosomopathy in hypertrophic hearts that affects mRNA translation in a protein length-dependent fashion

BACKGROUND

Little is known about the impact of trans-acting genetic variation on the rates with which proteins are synthesized by ribosomes. Here, we investigate the influence of such distant genetic loci on the efficiency of mRNA translation and define their contribution to the development of complex disease phenotypes within a panel of rat recombinant inbred lines.

RESULTS

We identify several tissue-specific master regulatory hotspots that each control the translation rates of multiple proteins. One of these loci is restricted to hypertrophic hearts, where it drives a translatome-wide and protein length-dependent change in translational efficiency, altering the stoichiometric translation rates of sarcomere proteins. Mechanistic dissection of this locus across multiple congenic lines points to a translation machinery defect, characterized by marked differences in polysome profiles and misregulation of the small nucleolar RNA SNORA48. Strikingly, from yeast to humans, we observe reproducible protein length-dependent shifts in translational efficiency as a conserved hallmark of translation machinery mutants, including those that cause ribosomopathies. Depending on the factor mutated, a pre-existing negative correlation between protein length and translation rates could either be enhanced or reduced, which we propose to result from mRNA-specific imbalances in canonical translation initiation and reinitiation rates.

CONCLUSIONS

We show that distant genetic control of mRNA translation is abundant in mammalian tissues, exemplified by a single genomic locus that triggers a translation-driven molecular mechanism. Our work illustrates the complexity through which genetic variation can drive phenotypic variability between individuals and thereby contribute to complex disease.

Variable expression quantitative trait loci analysis of breast cancer risk variants

Genome wide association studies (GWAS) have identified more than 180 variants associated with breast cancer risk, however the underlying functional mechanisms and biological pathways which confer disease susceptibility remain largely unknown. As gene expression traits are under genetic regulation we hypothesise that differences in gene expression variability may identify causal breast cancer susceptibility genes. We performed variable expression quantitative trait loci (veQTL) analysis using tissue-specific expression data from the Genotype-Tissue Expression (GTEx) Common Fund Project. veQTL analysis identified 70 associations (p < 5 × 10^–8) consisting of 60 genes and 27 breast cancer risk variants, including 55 veQTL that were observed in breast tissue only. Pathway analysis of genes associated with breast-specific veQTL revealed an enrichment of four genes (CYP11B1, CYP17A1 HSD3B2 and STAR) involved in the C21-steroidal biosynthesis pathway that converts cholesterol to breast-related hormones (e.g. oestrogen). Each of these four genes were significantly more variable in individuals homozygous for rs11075995 (A/A) breast cancer risk allele located in the FTO gene, which encodes an RNA demethylase. The A/A allele was also found associated with reduced expression of FTO, suggesting an epi-transcriptomic mechanism may underlie the dysregulation of genes involved in hormonal biosynthesis leading to an increased risk of breast cancer. These findings provide evidence that genetic variants govern high levels of expression variance in breast tissue, thus building a more comprehensive insight into the underlying biology of breast cancer risk loci.

Reciprocal monoallelic expression of ASAR lncRNA genes controls replication timing of human chromosome 6

DNA replication occurs on mammalian chromosomes in a cell-type distinctive temporal order known as the replication timing program. We previously found that disruption of the noncanonical lncRNA genes ASAR6 and ASAR15 results in delayed replication timing and delayed mitotic chromosome condensation of human chromosomes 6 and 15, respectively. ASAR6 and ASAR15 display random monoallelic expression and display asynchronous replication between alleles that is coordinated with other random monoallelic genes on their respective chromosomes. Disruption of the expressed allele, but not the silent allele, of ASAR6 leads to delayed replication, activation of the previously silent alleles of linked monoallelic genes, and structural instability of human chromosome 6. In this report, we describe a second lncRNA gene (ASAR6-141) on human chromosome 6 that when disrupted results in delayed replication timing in cisASAR6-141 is subject to random monoallelic expression and asynchronous replication and is expressed from the opposite chromosome 6 homolog as ASAR6 ASAR6-141 RNA, like ASAR6 and ASAR15 RNAs, contains a high L1 content and remains associated with the chromosome territory where it is transcribed. Three classes of cis-acting elements control proper chromosome function in mammals: origins of replication, centromeres, and telomeres, which are responsible for replication, segregation, and stability of all chromosomes. Our work supports a fourth type of essential chromosomal element, the "Inactivation/Stability Center," which expresses ASAR lncRNAs responsible for proper replication timing, monoallelic expression, and structural stability of each chromosome.

Quantitative genetic analysis deciphers the impact of cis and trans regulation on cell-to-cell variability in protein expression levels

Identifying the factors that shape protein expression variability in complex multi-cellular organisms has primarily focused on promoter architecture and regulation of single-cell expression in cis. However, this targeted approach has to date been unable to identify major regulators of cell-to-cell gene expression variability in humans. To address this, we have combined single-cell protein expression measurements in the human immune system using flow cytometry with a quantitative genetics analysis. For the majority of proteins whose variability in expression has a heritable component, we find that genetic variants act in trans, with notably fewer variants acting in cis. Furthermore, we highlight using Mendelian Randomization that these variability-Quantitative Trait Loci might be driven by the cis regulation of upstream genes. This indicates that natural selection may balance the impact of gene regulation in cis with downstream impacts on expression variability in trans.

Conserved properties of genetic architecture of renal and fat transcriptomes in rat models of insulin resistance

To define renal molecular mechanisms that are affected by permanent hyperglycaemia and might promote phenotypes relevant to diabetic nephropathy, we carried out linkage analysis of genome-wide gene transcription in the kidneys of F2 offspring from the Goto-Kakizaki (GK) rat model of type 2 diabetes and normoglycaemic Brown Norway (BN) rats. We mapped 2526 statistically significant expression quantitative trait loci (eQTLs) in the cross. More than 40% of eQTLs mapped in the close vicinity of the linked transcripts, underlying possible cis-regulatory mechanisms of gene expression. We identified eQTL hotspots on chromosomes 5 and 9 regulating the expression of 80-165 genes, sex or cross direction effects, and enriched metabolic and immunological processes by segregating GK alleles. Comparative analysis with adipose tissue eQTLs in the same cross showed that 496 eQTLs, in addition to the top enriched biological pathways, are conserved in the two tissues. Extensive similarities in eQTLs mapped in the GK rat and in the spontaneously hypertensive rat (SHR) suggest a common aetiology of disease phenotypes common to the two strains, including insulin resistance, which is a prominent pathophysiological feature in both GK rats and SHRs. Our data shed light on shared and tissue-specific molecular mechanisms that might underlie aetiological aspects of insulin resistance in the context of spontaneously occurring hyperglycaemia and hypertension.

Summary: Kidney and fat expression QTL mapping in rat models of spontaneously occurring insulin resistance associated with either diabetes or hypertension reveals conserved gene expression regulation, suggesting shared aetiology of disease phenotypes.

Impact of Genetic Variation on Stress-Related Ethanol Consumption

BACKGROUND

The effect of stress on alcohol consumption in humans is highly variable, and the underlying processes are not yet understood. Attempts to model a positive relationship between stress and increased ethanol (EtOH) consumption in animals have been only modestly successful. Our hypothesis is that individual differences in stress effects on EtOH consumption are mediated by genetics.

METHODS

We measured alcohol consumption, using the drinking-in-the-dark (DID) paradigm in females from 2 inbred mouse strains, C57BL/6J (B6) and DBA/2J (D2), and 35 of their inbred progeny (the BXD family). A control group was maintained in normal housing and a stress group was exposed to chronic mild stress (CMS), consisting of unpredictable stressors over 7 weeks. These included predator, social, and environmental perturbations. Alcohol intake was measured over 16 weeks in both groups during baseline (preceding 5-week period), CMS (intervening 7-week period), and post-CMS (final 4-week period).

RESULTS

We detected a strong effect of CMS on alcohol intake. A few strains demonstrated CMS-related increased alcohol consumption; however, most showed decreased intake. We identified 1 nearly significant quantitative trait locus on chromosome 5 that contains the neuronal nitric oxide synthase gene (Nos1). The expression of Nos1 is frequently changed following alcohol exposure, and variants in this gene segregating among the BXD population may modulate alcohol intake in response to stress.

CONCLUSIONS

The results we present here represent the first study to combine chronic stress and alcohol consumption in a genetic reference population of mice. Differences in susceptibility to the effects of stressful environments vis-à-vis alcohol use disorders would suggest that the differences have at least some basis in genetic constitution. We have also nominated a likely candidate gene underlying the large individual differences in effects of stress on alcohol consumption.

Trans Effects on Gene Expression Can Drive Omnigenic Inheritance

Early genome-wide association studies (GWASs) led to the surprising discovery that, for typical complex traits, most of the heritability is due to huge numbers of common variants with tiny effect sizes. Previously, we argued that new models are needed to understand these patterns. Here, we provide a formal model in which genetic contributions to complex traits are partitioned into direct effects from core genes and indirect effects from peripheral genes acting in trans. We propose that most heritability is driven by weak trans-eQTL SNPs, whose effects are mediated through peripheral genes to impact the expression of core genes. In particular, if the core genes for a trait tend to be co-regulated, then the effects of peripheral variation can be amplified such that nearly all of the genetic variance is driven by weak trans effects. Thus, our model proposes a framework for understanding key features of the architecture of complex traits.

Trait Loci Mapping and CSF Proteome

Recent advance in high-throughput proteome analysis has enabled genome-wide and proteome-wide analyses of associations between single nucleotide polymorphisms and protein expression levels. Protein quantitative trait locus (pQTL) studies using cerebrospinal fluid (CSF) and DNA samples may provide valuable insights into the genetic basis and molecular mechanisms regulating protein expression in the central nervous system. In this chapter, we describe a step-by-step procedures of CSF collection and pQTL analysis, by using PLINK and R software.

Systems Genetics Approaches in Rat Identify Novel Genes and Gene Networks Associated With Cardiac Conduction

Background Electrocardiographic ( ECG ) parameters are regarded as intermediate phenotypes of cardiac arrhythmias. Insight into the genetic underpinnings of these parameters is expected to contribute to the understanding of cardiac arrhythmia mechanisms. Here we used HXB / BXH recombinant inbred rat strains to uncover genetic loci and candidate genes modulating ECG parameters. Methods and Results RR interval, PR interval, QRS duration, and QT c interval were measured from ECG s obtained in 6 male rats from each of the 29 available HXB / BXH recombinant inbred strains. Genes at loci displaying significant quantitative trait loci (QTL) effects were prioritized by assessing the presence of protein-altering variants, and by assessment of cis expression QTL ( eQTL ) effects and correlation of transcript abundance to the respective trait in the heart. Cardiac RNA -seq data were additionally used to generate gene co-expression networks. QTL analysis of ECG parameters identified 2 QTL for PR interval, respectively, on chromosomes 10 and 17. At the chromosome 10 QTL , cis- eQTL effects were identified for Acbd4, Cd300lg, Fam171a2, and Arhgap27; the transcript abundance in the heart of these 4 genes was correlated with PR interval. At the chromosome 17 QTL , a cis- eQTL was uncovered for Nhlrc1 candidate gene; the transcript abundance of this gene was also correlated with PR interval. Co-expression analysis furthermore identified 50 gene networks, 6 of which were correlated with PR interval or QRS duration, both parameters of cardiac conduction. Conclusions These newly identified genetic loci and gene networks associated with the ECG parameters of cardiac conduction provide a starting point for future studies with the potential of identifying novel mechanisms underlying cardiac electrical function.

Identification of expression quantitative trait loci of MTOR associated with the progression of glioma

Mechanistic target of rapamycin (MTOR) encodes a key modulator of cell growth, proliferation, and apoptosis. Previous studies have demonstrated that the dysregulation of MTOR is involved in the development and progression of several types of cancer, including glioma. In the present study, a comprehensive analysis was conducted to examine whether the expression quantitative trait loci (eQTLs) of MTOR are associated with the progression of glioma. Candidate eQTLs of MTOR were obtained from the Genotype-Tissue Expression eQTL Browser. The Kaplan-Meier method and multivariate Cox model were used to analyze the progression-free survival time of glioma patients. Based on the analysis of 138 glioma patients, one eQTL of MTOR, rs4845964, was demonstrated to be significantly associated with the progression of glioma in a dominant manner. The adjusted hazard ratios (HRs) for patients with the AG or AA genotype at rs4845964 were 2.82 [95% confidence interval (CI), 1.27-6.27; P=0.0111] and 2.79 (95% CI, 1.10-7.07; P=0.0312), respectively, compared with those with the GG genotype. When the rs4845964 AG and AA genotypes were combined for analysis, the HR was 2.70 (95% CI, 1.25-5.82; P=0.0114) vs. the GG genotype. Stratified analyses revealed similar associations between the rs4845964 genotypes and the progression of glioma in all subgroups (following stratification by age, sex and tumor grade). These results demonstrate for the first time that the MTOR eQTL rs4845964 is associated with the progression of glioma.

Transcriptome response of Atlantic salmon (Salmo salar) to competition with ecologically similar non-native species

Non-native species may be introduced either intentionally or unintentionally, and their impact can range from benign to highly disruptive. Non-native salmonids were introduced into Lake Ontario, Canada, to provide recreational fishing opportunities; however, the establishment of those species has been proposed as a significant barrier to the reintroduction of native Atlantic salmon (Salmo salar) due to intense interspecific competition. In this study, we compared population differences of Atlantic salmon in transcriptome response to interspecific competition. We reared Atlantic salmon from two populations (LaHave River and Sebago Lake) with fish of each of three non-native salmonids (Chinook salmon Oncorhynchus tshawytscha, rainbow trout O. mykiss, and brown trout S. trutta) in artificial streams. We used RNA-seq to assess transcriptome differences between the Atlantic salmon populations and the responses of these populations to the interspecific competition treatments after 10 months of competition in the stream tanks. We found that population differences in gene expression were generally greater than the effects of interspecific competition. Interestingly, we found that the two Atlantic salmon populations exhibited similar responses to interspecific competition based on functional gene ontologies, but the specific genes within those ontologies were different. Our transcriptome analyses suggest that the most stressful competitor (as measured by the highest number of differentially expressed genes) differs between the two study populations. Our transcriptome characterization highlights the importance of source population selection for conservation applications, as organisms with different evolutionary histories can possess different transcriptional responses to the same biotic stressors. The results also indicate that generalized predictions of the response of native species to interactions with introduced species may not be appropriate without incorporating potential population-specific response to introduced species.

Principles and methods of in-silico prioritization of non-coding regulatory variants

Over a decade of genome-wide association, studies have made great strides toward the detection of genes and genetic mechanisms underlying complex traits. However, the majority of associated loci reside in non-coding regions that are functionally uncharacterized in general. Now, the availability of large-scale tissue and cell type-specific transcriptome and epigenome data enables us to elucidate how non-coding genetic variants can affect gene expressions and are associated with phenotypic changes. Here, we provide an overview of this emerging field in human genomics, summarizing available data resources and state-of-the-art analytic methods to facilitate in-silico prioritization of non-coding regulatory mutations. We also highlight the limitations of current approaches and discuss the direction of much-needed future research.

Deciphering the genetic regulation of peripheral blood transcriptome in pigs through expression genome-wide association study and allele-specific expression analysis

BACKGROUND

Efforts to improve sustainability in livestock production systems have focused on two objectives: investigating the genetic control of immune function as it pertains to robustness and disease resistance, and finding predictive markers for use in breeding programs. In this context, the peripheral blood transcriptome represents an important source of biological information about an individual's health and immunological status, and has been proposed for use as an intermediate phenotype to measure immune capacity. The objective of this work was to study the genetic architecture of variation in gene expression in the blood of healthy young pigs using two approaches: an expression genome-wide association study (eGWAS) and allele-specific expression (ASE) analysis.

RESULTS

The blood transcriptomes of 60-day-old Large White pigs were analyzed by expression microarrays for eGWAS (242 animals) and by RNA-Seq for ASE analysis (38 animals). Using eGWAS, the expression levels of 1901 genes were found to be associated with expression quantitative trait loci (eQTLs). We recovered 2839 local and 1752 distant associations (Single Nucleotide Polymorphism or SNP located less or more than 1 Mb from expression probe, respectively). ASE analyses confirmed the extensive cis-regulation of gene transcription in blood, and revealed allelic imbalance in 2286 SNPs, which affected 763 genes. eQTLs and ASE-genes were widely distributed on all chromosomes. By analyzing mutually overlapping eGWAS results, we were able to describe putative regulatory networks, which were further refined using ASE data. At the functional level, genes with genetically controlled expression that were detected by eGWAS and/or ASE analyses were significantly enriched in biological processes related to RNA processing and immune function. Indeed, numerous distant and local regulatory relationships were detected within the major histocompatibility complex region on chromosome 7, revealing ASE for most class I and II genes.

CONCLUSIONS

This study represents, to the best of our knowledge, the first genome-wide map of the genetic control of gene expression in porcine peripheral blood. These results represent an interesting resource for the identification of genetic markers and blood biomarkers associated with variations in immunity traits in pigs, as well as any other complex traits for which blood is an appropriate surrogate tissue.

ATP2C2 and DYX1C1 are putative modulators of dyslexia-related MMR

BACKGROUND

Dyslexia is a specific learning disorder affecting reading and spelling abilities. Its prevalence is ~5% in German-speaking individuals. Although the etiology of dyslexia largely remains to be determined, comprehensive evidence supports deficient phonological processing as a major contributing factor. An important prerequisite for phonological processing is auditory discrimination and, thus, essential for acquiring reading and spelling skills. The event-related potential Mismatch Response (MMR) is an indicator for auditory discrimination capabilities with dyslexics showing an altered late component of MMR in response to auditory input.

METHODS

In this study, we comprehensively analyzed associations of dyslexia-specific late MMRs with genetic variants previously reported to be associated with dyslexia-related phenotypes in multiple studies comprising 25 independent single-nucleotide polymorphisms (SNPs) within 10 genes.

RESULTS

First, we demonstrated validity of these SNPs for dyslexia in our sample by showing that additional inclusion of a polygenic risk score improved prediction of impaired writing compared with a model that used MMR alone. Secondly, a multifactorial regression analysis was conducted to uncover the subset of the 25 SNPs that is associated with the dyslexia-specific late component of MMR. In total, four independent SNPs within DYX1C1 and ATP2C2 were found to be associated with MMR stronger than expected from multiple testing. To explore potential pathomechanisms, we annotated these variants with functional data including tissue-specific expression analysis and eQTLs.

CONCLUSION

Our findings corroborate the late component of MMR as a potential endophenotype for dyslexia and support tripartite relationships between dyslexia-related SNPs, the late component of MMR and dyslexia.

Interplay of cis and trans mechanisms driving transcription factor binding and gene expression evolution

Noncoding regulatory variants play a central role in the genetics of human diseases and in evolution. Here we measure allele-specific transcription factor binding occupancy of three liver-specific transcription factors between crosses of two inbred mouse strains to elucidate the regulatory mechanisms underlying transcription factor binding variations in mammals. Our results highlight the pre-eminence of cis-acting variants on transcription factor occupancy divergence. Transcription factor binding differences linked to cis-acting variants generally exhibit additive inheritance, while those linked to trans-acting variants are most often dominantly inherited. Cis-acting variants lead to local coordination of transcription factor occupancies that decay with distance; distal coordination is also observed and may be modulated by long-range chromatin contacts. Our results reveal the regulatory mechanisms that interplay to drive transcription factor occupancy, chromatin state, and gene expression in complex mammalian cell states.

Reconstructing the Molecular Function of Genetic Variation in Regulatory Networks

Over the past decade, genetic studies have recognized hundreds of polymorphic DNA loci called response QTLs (reQTLs) as potential contributors to interindividual variation in transcriptional responses to stimulations. Such reQTLs commonly affect the transduction of signals along the regulatory network that controls gene transcription. Identifying the pathways through which reQTLs perturb the underlying network has been a major challenge. Here, we present GEVIN ("Genome-wide Embedding of Variation In Networks"), a methodology that simultaneously identifies a reQTL and the particular pathway in which the reQTL affects downstream signal transduction along the network. Using synthetic data, we show that this algorithm outperforms existing pathway identification and reQTL identification methods. We applied GEVIN to the analysis of murine and human dendritic cells in response to pathogenic components. These analyses revealed significant reQTLs together with their perturbed Toll-like receptor signaling pathways. GEVIN thus offers a powerful framework that renders a comprehensive picture of disease-related DNA loci and their molecular functions within regulatory networks.

Genome-wide quantitative trait loci mapping of the human cerebrospinal fluid proteome

Cerebrospinal fluid (CSF) is virtually the only one accessible source of proteins derived from the central nervous system (CNS) of living humans and possibly reflects the pathophysiology of a variety of neuropsychiatric diseases. However, little is known regarding the genetic basis of variation in protein levels of human CSF. We examined CSF levels of 1,126 proteins in 133 subjects and performed a genome-wide association analysis of 514,227 single nucleotide polymorphisms (SNPs) to detect protein quantitative trait loci (pQTLs). To be conservative, Spearman's correlation was used to identify an association between genotypes of SNPs and protein levels. A total of 421 cis and 25 trans SNP-protein pairs were significantly correlated at a false discovery rate (FDR) of less than 0.01 (nominal P < 7.66 × 10-9). Cis-only analysis revealed additional 580 SNP-protein pairs with FDR < 0.01 (nominal P < 2.13 × 10-5). pQTL SNPs were more likely, compared to non-pQTL SNPs, to be a disease/trait-associated variants identified by previous genome-wide association studies. The present findings suggest that genetic variations play an important role in the regulation of protein expression in the CNS. The obtained database may serve as a valuable resource to understand the genetic bases for CNS protein expression pattern in humans.

Integrating Next-Generation Genomic Sequencing and Mass Spectrometry To Estimate Allele-Specific Protein Abundance in Human Brain

Gene expression contributes to phenotypic traits and human disease. To date, comparatively less is known about regulators of protein abundance, which is also under genetic control and likely influences clinical phenotypes. However, identifying and quantifying allele-specific protein abundance by bottom-up proteomics is challenging since single nucleotide variants (SNVs) that alter protein sequence are not considered in standard human protein databases. To address this, we developed the GenPro software and used it to create personalized protein databases (PPDs) to identify single amino acid variants (SAAVs) at the protein level from whole exome sequencing. In silico assessment of PPDs generated by GenPro revealed only a 1% increase in tryptic search space compared to a direct translation of all human transcripts and an equivalent search space compared to the UniProtKB reference database. To identify a large unbiased number of SAAV peptides, we performed high-resolution mass spectrometry-based proteomics for two human post-mortem brain samples and searched the collected MS/MS spectra against their respective PPD. We found an average of ∼117 000 unique peptides mapping to ∼9300 protein groups for each sample, and of these, 977 were unique variant peptides. We found that over 400 reference and SAAV peptide pairs were, on average, equally abundant in human brain by label-free ion intensity measurements and confirmed the absolute levels of three reference and SAAV peptide pairs using heavy labeled peptides standards coupled with parallel reaction monitoring (PRM). Our results highlight the utility of integrating genomic and proteomic sequencing data to identify sample-specific SAAV peptides and support the hypothesis that most alleles are equally expressed in human brain.

Quantitative trait loci from identification to exploitation for crop improvement

Advancement in the field of genetics and genomics after the discovery of Mendel's laws of inheritance has led to map the genes controlling qualitative and quantitative traits in crop plant species. Mapping of genomic regions controlling the variation of quantitatively inherited traits has become routine after the advent of different types of molecular markers. Recently, the next generation sequencing methods have accelerated the research on QTL analysis. These efforts have led to the identification of more closely linked molecular markers with gene/QTLs and also identified markers even within gene/QTL controlling the trait of interest. Efforts have also been made towards cloning gene/QTLs or identification of potential candidate genes responsible for a trait. Further new concepts like crop QTLome and QTL prioritization have accelerated precise application of QTLs for genetic improvement of complex traits. In the past years, efforts have also been made in exploitation of a number of QTL for improving grain yield or other agronomic traits in various crops through markers assisted selection leading to cultivation of these improved varieties at farmers' field. In present article, we reviewed QTLs from their identification to exploitation in plant breeding programs and also reviewed that how improved cultivars developed through introgression of QTLs have improved the yield productivity in many crops.

Relating quantitative variation within a behavior to variation in transcription

Many studies have shown that variation in transcription is associated with changes in behavioral state, or with variation within a state, but little has been done to address if the same genes are involved in both. Here, we investigate the transcriptional basis of variation in parental provisioning using two species of burying beetle, Nicrophorus orbicollis and Nicrophorus vespilloides. We used RNA-seq to compare transcription in parents that provided high amounts of provisioning behavior versus low amounts in males and females of each species. We found no overarching transcriptional patterns distinguishing high from low caring parents, and no informative transcripts that displayed particularly large expression differences in either sex. However, we did find subtler gene expression differences between high and low provisioning parents that are consistent across both sexes and species. Furthermore, we show that transcripts previously implicated in transitioning into parental care in N. vespilloides had high variance in the levels of transcription and were unusually likely to display differential expression between high and low provisioning parents. Thus, quantitative behavioral variation appears to reflect many transcriptional differences of small effect. Furthermore, the same transcripts required for the transition between behavioral states are also related to variation within a behavioral state.

An independent component analysis confounding factor correction framework for identifying broad impact expression quantitative trait loci

Genome-wide expression Quantitative Trait Loci (eQTL) studies in humans have provided numerous insights into the genetics of both gene expression and complex diseases. While the majority of eQTL identified in genome-wide analyses impact a single gene, eQTL that impact many genes are particularly valuable for network modeling and disease analysis. To enable the identification of such broad impact eQTL, we introduce CONFETI: Confounding Factor Estimation Through Independent component analysis. CONFETI is designed to address two conflicting issues when searching for broad impact eQTL: the need to account for non-genetic confounding factors that can lower the power of the analysis or produce broad impact eQTL false positives, and the tendency of methods that account for confounding factors to model broad impact eQTL as non-genetic variation. The key advance of the CONFETI framework is the use of Independent Component Analysis (ICA) to identify variation likely caused by broad impact eQTL when constructing the sample covariance matrix used for the random effect in a mixed model. We show that CONFETI has better performance than other mixed model confounding factor methods when considering broad impact eQTL recovery from synthetic data. We also used the CONFETI framework and these same confounding factor methods to identify eQTL that replicate between matched twin pair datasets in the Multiple Tissue Human Expression Resource (MuTHER), the Depression Genes Networks study (DGN), the Netherlands Study of Depression and Anxiety (NESDA), and multiple tissue types in the Genotype-Tissue Expression (GTEx) consortium. These analyses identified both cis-eQTL and trans-eQTL impacting individual genes, and CONFETI had better or comparable performance to other mixed model confounding factor analysis methods when identifying such eQTL. In these analyses, we were able to identify and replicate a few broad impact eQTL although the overall number was small even when applying CONFETI. In light of these results, we discuss the broad impact eQTL that have been previously reported from the analysis of human data and suggest that considerable caution should be exercised when making biological inferences based on these reported eQTL.

Genetic, physiological and comparative genomic studies of hypertension and insulin resistance in the spontaneously hypertensive rat

We previously mapped hypertension-related insulin resistance quantitative trait loci (QTLs) to rat chromosomes 4, 12 and 16 using adipocytes from F2 crosses between spontaneously hypertensive (SHR) and Wistar Kyoto (WKY) rats, and subsequently identified Cd36 as the gene underlying the chromosome 4 locus. The identity of the chromosome 12 and 16 genes remains unknown. To identify whole-body phenotypes associated with the chromosome 12 and 16 linkage regions, we generated and characterised new congenic strains, with WKY donor segments introgressed onto an SHR genetic background, for the chromosome 12 and 16 linkage regions. We found a >50% increase in insulin sensitivity in both the chromosome 12 and 16 strains. Blood pressure and left ventricular mass were reduced in the two congenic strains consistent with the congenic segments harbouring SHR genes for insulin resistance, hypertension and cardiac hypertrophy. Integrated genomic analysis, using physiological and whole-genome sequence data across 42 rat strains, identified variants within the congenic regions in Upk3bl, RGD1565131 and AABR06087018.1 that were associated with blood pressure, cardiac mass and insulin sensitivity. Quantitative trait transcript analysis across 29 recombinant inbred strains showed correlation between expression of Hspb1, Zkscan5 and Pdgfrl with adipocyte volume, systolic blood pressure and cardiac mass, respectively. Comparative genome analysis showed a marked enrichment of orthologues for human GWAS-associated genes for insulin resistance within the syntenic regions of both the chromosome 12 and 16 congenic intervals. Our study defines whole-body phenotypes associated with the SHR chromosome 12 and 16 insulin-resistance QTLs, identifies candidate genes for these SHR QTLs and finds human orthologues of rat genes in these regions that associate with related human traits. Further study of these genes in the congenic strains will lead to robust identification of the underlying genes and cellular mechanisms.

Summary: Comparative genome analyses identify candidate genes for hypertension and insulin resistance on rat chromosomes 12 and 16, and marked enrichment of insulin resistance genes in the syntenic regions of the human genome.

Systems Genetics as a Tool to Identify Master Genetic Regulators in Complex Disease

Systems genetics stems from systems biology and similarly employs integrative modeling approaches to describe the perturbations and phenotypic effects observed in a complex system. However, in the case of systems genetics the main source of perturbation is naturally occurring genetic variation, which can be analyzed at the systems-level to explain the observed variation in phenotypic traits. In contrast with conventional single-variant association approaches, the success of systems genetics has been in the identification of gene networks and molecular pathways that underlie complex disease. In addition, systems genetics has proven useful in the discovery of master trans-acting genetic regulators of functional networks and pathways, which in many cases revealed unexpected gene targets for disease. Here we detail the central components of a fully integrated systems genetics approach to complex disease, starting from assessment of genetic and gene expression variation, linking DNA sequence variation to mRNA (expression QTL mapping), gene regulatory network analysis and mapping the genetic control of regulatory networks. By summarizing a few illustrative (and successful) examples, we highlight how different data-modeling strategies can be effectively integrated in a systems genetics study.

Transcriptome Profiling in Rat Inbred Strains and Experimental Cross Reveals Discrepant Genetic Architecture of Genome-Wide Gene Expression

To test the impact of genetic heterogeneity on cis- and trans-mediated mechanisms of gene expression regulation, we profiled the transcriptome of adipose tissue in 20 inbred congenic strains derived from diabetic Goto-Kakizaki (GK) rats and Brown-Norway (BN) controls, which contain well-defined blocks (1-183 Mb) of genetic polymorphisms, and in 123 genetically heterogeneous rats of an (GK × BN)F2 offspring. Within each congenic we identified 73-1351 differentially expressed genes (DEGs), only 7.7% of which mapped within the congenic blocks, and which may be regulated in cis The remainder localized outside the blocks, and therefore must be regulated in trans Most trans-regulated genes exhibited approximately twofold expression changes, consistent with monoallelic expression. Altered biological pathways were replicated between congenic strains sharing blocks of genetic polymorphisms, but polymorphisms at different loci also had redundant effects on transcription of common distant genes and pathways. We mapped 2735 expression quantitative trait loci (eQTL) in the F2 cross, including 26% predominantly cis-regulated genes, which validated DEGs in congenic strains. A hotspot of >300 eQTL in a 10 cM region of chromosome 1 was enriched in DEGs in a congenic strain. However, many DEGs among GK, BN and congenic strains did not replicate as eQTL in F2 hybrids, demonstrating distinct mechanisms of gene expression when alleles segregate in an outbred population or are fixed homozygous across the entire genome or in short genomic regions. Our analysis provides conceptual advances in our understanding of the complex architecture of genome expression and pathway regulation, and suggests a prominent impact of epistasis and monoallelic expression on gene transcription.

Association of genetic variations and gene expression in a family-based study

BACKGROUND

Expression quantitative trait locus (eQTL) maps are considered a valuable resource in studying complex diseases. The availability of gene expression data from the Genetic Analysis Workshop 19 (GAW19) provides a great opportunity to investigate the association of gene expression with genetic variants in blood.

METHODS

A total of 267 samples with gene expression and whole genome sequencing data were employed in this study. We used linear mixed models with genetic random effects along with a permutation procedure to create an eQTL map. The eQTL map was further tested in terms of functional implication, including the enrichment in disease-related variants and in regulatory regions.

RESULTS

We identified 22,869 significant eQTLs from the GAW19 data set. These eQTLs were highly enriched with genetic loci associated with blood pressure and DNase hypersensitive regions. In addition, the majority of genes associated with eQTLs showed moderate to high heritability (h2 > 0.4).

CONCLUSIONS

We successfully created an eQTL map from the GAW19 data set. Our study indicated that the eQTLs were enriched within regulatory regions, and tended to have relatively high heritability.

From integrative genomics to systems genetics in the rat to link genotypes to phenotypes

Complementary to traditional gene mapping approaches used to identify the hereditary components of complex diseases, integrative genomics and systems genetics have emerged as powerful strategies to decipher the key genetic drivers of molecular pathways that underlie disease. Broadly speaking, integrative genomics aims to link cellular-level traits (such as mRNA expression) to the genome to identify their genetic determinants. With the characterization of several cellular-level traits within the same system, the integrative genomics approach evolved into a more comprehensive study design, called systems genetics, which aims to unravel the complex biological networks and pathways involved in disease, and in turn map their genetic control points. The first fully integrated systems genetics study was carried out in rats, and the results, which revealed conserved trans-acting genetic regulation of a pro-inflammatory network relevant to type 1 diabetes, were translated to humans. Many studies using different organisms subsequently stemmed from this example. The aim of this Review is to describe the most recent advances in the fields of integrative genomics and systems genetics applied in the rat, with a focus on studies of complex diseases ranging from inflammatory to cardiometabolic disorders. We aim to provide the genetics community with a comprehensive insight into how the systems genetics approach came to life, starting from the first integrative genomics strategies [such as expression quantitative trait loci (eQTLs) mapping] and concluding with the most sophisticated gene network-based analyses in multiple systems and disease states. Although not limited to studies that have been directly translated to humans, we will focus particularly on the successful investigations in the rat that have led to primary discoveries of genes and pathways relevant to human disease.

Role of genomics and transcriptomics in selection of reintroduction source populations

The use and importance of reintroduction as a conservation tool to return a species to its historical range from which it has been extirpated will increase as climate change and human development accelerate habitat loss and population extinctions. Although the number of reintroduction attempts has increased rapidly over the past 2 decades, the success rate is generally low. As a result of population differences in fitness-related traits and divergent responses to environmental stresses, population performance upon reintroduction is highly variable, and it is generally agreed that selecting an appropriate source population is a critical component of a successful reintroduction. Conservation genomics is an emerging field that addresses long-standing challenges in conservation, and the potential for using novel molecular genetic approaches to inform and improve conservation efforts is high. Because the successful establishment and persistence of reintroduced populations is highly dependent on the functional genetic variation and environmental stress tolerance of the source population, we propose the application of conservation genomics and transcriptomics to guide reintroduction practices. Specifically, we propose using genome-wide functional loci to estimate genetic variation of source populations. This estimate can then be used to predict the potential for adaptation. We also propose using transcriptional profiling to measure the expression response of fitness-related genes to environmental stresses as a proxy for acclimation (tolerance) capacity. Appropriate application of conservation genomics and transcriptomics has the potential to dramatically enhance reintroduction success in a time of rapidly declining biodiversity and accelerating environmental change.

Collapsed methylation quantitative trait loci analysis for low frequency and rare variants

BACKGROUND

Single variant approaches have been successful in identifying DNA methylation quantitative trait loci (mQTL), although as with complex traits they lack the statistical power to identify the effects from rare genetic variants. We have undertaken extensive analyses to identify regions of low frequency and rare variants that are associated with DNA methylation levels.

METHODS

We used repeated measurements of DNA methylation from five different life stages in human blood, taken from the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort. Variants were collapsed across CpG islands and their flanking regions to identify variants collectively associated with methylation, where no single variant was individually responsible for the observed signal. All analyses were undertaken using the sequence kernel association test.

RESULTS

For loci where no individual variant mQTL was observed based on a single variant analysis, we identified 95 unique regions where the combined effect of low frequency variants (MAF ≤ 5%) provided strong evidence of association with methylation. For loci where there was previous evidence of an individual variant mQTL, a further 3 regions provided evidence of association between multiple low frequency variants and methylation levels. Effects were observed consistently across 5 different time points in the lifecourse and evidence of replication in the TwinsUK and Exeter cohorts was also identified.

CONCLUSION

We have demonstrated the potential of this novel approach to mQTL analysis by analysing the combined effect of multiple low frequency or rare variants. Future studies should benefit from applying this approach as a complementary follow up to single variant analyses.

Population-specific genome-wide mapping of expression quantitative trait loci in the colon of Han Chinese

OBJECTIVE

To establish the colonic expression quantitative trait locus map in Han Chinese population and provide a functional reference for interpreting genetic associations of diseases such as inflammatory bowel disease (IBD).

METHODS

Colonic mucosal biopsies and peripheral blood samples were obtained from 48 Chinese Han individuals (24 ulcerative colitis patients and 24 healthy controls). Transcription profiling was performed using human whole genome expression array. Genotyping was done using a population-specific genotype array. Imputation was performed using IMPUTE2. Association between genotypes and gene expression was analyzed using a Matrix Expression Quantitative Trait Loci (eQTL) R package to identify eQTL. We used ChIPpeakAnno R package for annotation of the eQTL. Linkage disequilibrium between the eQTL and IBD risk loci was also investigated.

RESULTS

We identified 6 377 single nucleotide polymorphism-transcript interactions (cis-eQTL) in the colon of the Chinese participants. Most of the eQTL located near the transcription starting sites and overlapped with histone modification marks on the genome. A significant proportion of the eQTL were found to be within transcription factor-binding sites. Two IBD risk loci were found to be colon cis-eQTL in Chinese individuals, and 51 cis-eQTL were identified in another 18 IBD risk loci.

CONCLUSIONS

This study defined a population-specific catalogue of colon eQTL in the Chinese population. Potential functional variants of IBD association signals were identified. We provided a useful reference dataset for fine mapping IBD risk loci and identifying causal variants in the Chinese Han population.

Genetic Regulation of Transcriptional Variation in Natural Arabidopsis thaliana Accessions

An increased knowledge of the genetic regulation of expression in Arabidopsis thaliana is likely to provide important insights about the basis of the plant’s extensive phenotypic variation. Here, we reanalyzed two publicly available datasets with genome-wide data on genetic and transcript variation in large collections of natural A. thaliana accessions. Transcripts from more than half of all genes were detected in the leaves of all accessions, and from nearly all annotated genes in at least one accession. Thousands of genes had high transcript levels in some accessions, but no transcripts at all in others, and this pattern was correlated with the genome-wide genotype. In total, 2669 eQTL were mapped in the largest population, and 717 of them were replicated in the other population. A total of 646 cis-eQTL-regulated genes that lacked detectable transcripts in some accessions was found, and for 159 of these we identified one, or several, common structural variants in the populations that were shown to be likely contributors to the lack of detectable RNA transcripts for these genes. This study thus provides new insights into the overall genetic regulation of global gene expression diversity in the leaf of natural A. thaliana accessions. Further, it also shows that strong cis-acting polymorphisms, many of which are likely to be structural variations, make important contributions to the transcriptional variation in the worldwide A. thaliana population.

Von Willebrand Factor Gene Variants Associate with Herpes simplex Encephalitis

Herpes simplex encephalitis (HSE) is a rare complication of Herpes simplex virus type-1 infection. It results in severe parenchymal damage in the brain. Although viral latency in neurons is very common in the population, it remains unclear why certain individuals develop HSE. Here we explore potential host genetic variants predisposing to HSE. In order to investigate this we used a rat HSE model comparing the HSE susceptible SHR (Spontaneously Hypertensive Rats) with the asymptomatic infection of BN (Brown Norway). Notably, both strains have HSV-1 spread to the CNS at four days after infection. A genome wide linkage analysis of 29 infected HXB/BXH RILs (recombinant inbred lines-generated from the prior two strains), displayed variable susceptibility to HSE enabling the definition of a significant QTL (quantitative trait locus) named Hse6 towards the end of chromosome 4 (160.89-174Mb) containing the Vwf (von Willebrand factor) gene. This was the only gene in the QTL with both cis-regulation in the brain and included several non-synonymous SNPs (single nucleotide polymorphism). Intriguingly, in human chromosome 12 several SNPs within the intronic region between exon 43 and 44 of the VWF gene were associated with human HSE pathogenesis. In particular, rs917859 is nominally associated with an odds ratio of 1.5 (95% CI 1.11-2.02; p-value = 0.008) after genotyping in 115 HSE cases and 428 controls. Although there are possibly several genetic and environmental factors involved in development of HSE, our study identifies variants of the VWF gene as candidates for susceptibility in experimental and human HSE.

Autoimmune diseases - connecting risk alleles with molecular traits of the immune system

Genome-wide strategies have driven the discovery of more than 300 susceptibility loci for autoimmune diseases. However, for almost all loci, understanding of the mechanisms leading to autoimmunity remains limited, and most variants that are likely to be causal are in non-coding regions of the genome. A critical next step will be to identify the in vivo and ex vivo immunophenotypes that are affected by risk variants. To do this, key cell types and cell states that are implicated in autoimmune diseases will need to be defined. Functional genomic annotations from these cell types and states can then be used to resolve candidate genes and causal variants. Together with longitudinal studies, this approach may yield pivotal insights into how autoimmunity is triggered.

Modelling local gene networks increases power to detect trans-acting genetic effects on gene expression

Expression quantitative trait loci (eQTL) mapping is a widely used tool to study the genetics of gene expression. Confounding factors and the burden of multiple testing limit the ability to map distal trans eQTLs, which is important to understand downstream genetic effects on genes and pathways. We propose a two-stage linear mixed model that first learns local directed gene-regulatory networks to then condition on the expression levels of selected genes. We show that this covariate selection approach controls for confounding factors and regulatory context, thereby increasing eQTL detection power and improving the consistency between studies. GNet-LMM is available at: https://github.com/PMBio/GNetLMM.

eQTL analysis links inflammatory bowel disease associated 1q21 locus to ECM1 gene

Genome-wide association studies (GWAS) have been highly successful in inflammatory bowel disease (IBD) with 163 confirmed associations so far. We used expression quantitative trait loci (eQTL) mapping to analyze IBD associated regions for which causative gene from the region is still unknown. First, we performed an extensive literature search and in silico analysis of published GWAS in IBD and eQTL studies and extracted 402 IBD associated SNPs assigned to 208 candidate loci, and 9562 eQTL correlations. When crossing GWA and eQTL data we found that for 50 % of loci there is no eQTL gene, while for 31.2 % we can determine one gene, for 11.1 % two genes and for the remaining 7.7 % three or more genes. Based on that we selected loci with one, two, and three or more eQTL genes and analyzed them in peripheral blood lymphocytes and intestine tissue samples of 606 Slovene patients with IBD and in 449 controls. Association analysis of selected SNPs showed statistical significance for three (rs2631372 and rs1050152 on 5q locus and rs13294 on 1q locus) out of six selected SNPs with at least one phenotype. Furthermore, with eQTL analysis of selected chromosomal regions, we confirmed a link between SNP and gene for four (SLC22A5 on 5q, ECM1 on 1q, ORMDL3 on 17q, and PUS10 on 2p locus) out of five selected regions. For 1q21 loci, we confirmed gene ECM1 as the most plausible gene from this region to be involved in pathogenesis of IBD and thereby contributed new eQTL correlation from this genomic region.

Learning from Co-expression Networks: Possibilities and Challenges

Plants are fascinating and complex organisms. A comprehensive understanding of the organization, function and evolution of plant genes is essential to disentangle important biological processes and to advance crop engineering and breeding strategies. The ultimate aim in deciphering complex biological processes is the discovery of causal genes and regulatory mechanisms controlling these processes. The recent surge of omics data has opened the door to a system-wide understanding of the flow of biological information underlying complex traits. However, dealing with the corresponding large data sets represents a challenging endeavor that calls for the development of powerful bioinformatics methods. A popular approach is the construction and analysis of gene networks. Such networks are often used for genome-wide representation of the complex functional organization of biological systems. Network based on similarity in gene expression are called (gene) co-expression networks. One of the major application of gene co-expression networks is the functional annotation of unknown genes. Constructing co-expression networks is generally straightforward. In contrast, the resulting network of connected genes can become very complex, which limits its biological interpretation. Several strategies can be employed to enhance the interpretation of the networks. A strategy in coherence with the biological question addressed needs to be established to infer reliable networks. Additional benefits can be gained from network-based strategies using prior knowledge and data integration to further enhance the elucidation of gene regulatory relationships. As a result, biological networks provide many more applications beyond the simple visualization of co-expressed genes. In this study we review the different approaches for co-expression network inference in plants. We analyse integrative genomics strategies used in recent studies that successfully identified candidate genes taking advantage of gene co-expression networks. Additionally, we discuss promising bioinformatics approaches that predict networks for specific purposes.

MT-HESS: an efficient Bayesian approach for simultaneous association detection in OMICS datasets, with application to eQTL mapping in multiple tissues

MOTIVATION

Analysing the joint association between a large set of responses and predictors is a fundamental statistical task in integrative genomics, exemplified by numerous expression Quantitative Trait Loci (eQTL) studies. Of particular interest are the so-called ': hotspots ': , important genetic variants that regulate the expression of many genes. Recently, attention has focussed on whether eQTLs are common to several tissues, cell-types or, more generally, conditions or whether they are specific to a particular condition.

RESULTS

We have implemented MT-HESS, a Bayesian hierarchical model that analyses the association between a large set of predictors, e.g. SNPs, and many responses, e.g. gene expression, in multiple tissues, cells or conditions. Our Bayesian sparse regression algorithm goes beyond ': one-at-a-time ': association tests between SNPs and responses and uses a fully multivariate model search across all linear combinations of SNPs, coupled with a model of the correlation between condition/tissue-specific responses. In addition, we use a hierarchical structure to leverage shared information across different genes, thus improving the detection of hotspots. We show the increase of power resulting from our new approach in an extensive simulation study. Our analysis of two case studies highlights new hotspots that would remain undetected by standard approaches and shows how greater prediction power can be achieved when several tissues are jointly considered.

AVAILABILITY AND IMPLEMENTATION

C[Formula: see text] source code and documentation including compilation instructions are available under GNU licence at http://www.mrc-bsu.cam.ac.uk/software/.

Ulcerative Colitis Database: An Integrated Database and Toolkit for Gene Function and Medication Involved in Ulcerative Colitis

BACKGROUND

Over the last decade, a massive amount of well-annotated genomic data has been accumulated on the pathogenesis and therapies for ulcerative colitis (UC). However, a comprehensive repository is not available yet.

METHODS

Ulcerative Colitis Database (UCDB) was constructed using text mining followed by manually curating on the literature to collect the reliable information of UC-related genes, drugs, and susceptibility loci. UC DNA microarray data were collected. R packages were used to implement gene expression analysis toolkit.

RESULTS

UCDB includes 4 separate but closely related components: "UC GENE," "UC DRUG," "UC LOCUS," and "UC ANALYSIS." The UC GENE contains comprehensive information for 1151 UC-related genes manually curated from 2919 publications. The UC DRUG includes information for 248 drugs manually curated from 2344 publications. "UC LOCUS" includes 110 UC susceptibility SNP loci, which were collected from 12 Genome-Wide Association Studies. A comprehensive expression quantitative trait loci browser was also implemented. The UC ANALYSIS is an expression analysis toolkit for 37 UC expression array data sets, which contains 1098 samples. The toolkit can be used to do gene expression correlation, clustering, differentially expressed, and Gene Set Enrichment Analysis (GSEA).

CONCLUSIONS

UCDB provides a comprehensive collection of well-curated UC-related genes and drugs, and straightforward interfaces for gene expression analyses. UCDB is a useful leading resource for both basic and clinical research and will benefit UC community worldwide. UCDB is freely accessible at http://seiwertlab.uchicago.edu/UCDB.

Variable impact of chronic stress on spatial learning and memory in BXD mice

The effects of chronic stress on learning are highly variable across individuals. This variability stems from gene-environment interactions. However, the mechanisms by which stress affects genetic predictors of learning are unclear. Thus, we aim to determine whether the genetic pathways that predict spatial memory performance are altered by previous exposure to chronic stress. Sixty-two BXD recombinant inbred strains of mice, as well as parent strains C57BL/6J and DBA/2J, were randomly assigned as behavioral control or to a chronic variable stress paradigm and then underwent behavioral testing to assess spatial memory and learning performance using the Morris water maze. Quantitative trait loci (QTL) mapping was completed for average escape latency times for both control and stress animals. Loci on chromosomes 5 and 10 were found in both control and stress environmental populations; eight additional loci were found to be unique to either the control or stress environment. In sum, results indicate that certain genetic loci predict spatial memory performance regardless of prior stress exposure, while exposure to stress also reveals unique genetic predictors of training during the memory task. Thus, we find that genetic predictors contributing to spatial learning and memory are susceptible to the presence of chronic stress.