Revealing the architecture of gene regulation: the promise of eQTL studies

Joint analyses of multi-tissue Hi-C and eQTL data demonstrate close spatial proximity between eQTLs and their target genes

Background

Gene regulation is important for cells and tissues to function. It has been studied from two aspects at the genomic level, the identification of expression quantitative trait loci (eQTLs) and identification of long-range chromatin interactions. It is important to understand their relationship, such as whether eQTLs regulate their target genes through physical chromatin interaction. Although chromatin interactions have been widely believed to be one of the main mechanisms underlying eQTLs, most evidence came from studies of cell lines and yet no direct evidence exists for tissues.

Results

We performed various joint analyses of eQTL and high-throughput chromatin conformation capture (Hi-C) data from 11 human primary tissue types and 2 human cell lines. We found that chromatin interaction frequency is positively associated with the number of genes that have eQTLs and that eQTLs and their target genes tend to fall into the same topologically associating domain (TAD). These results are consistent across all tissues and cell lines we evaluated. Moreover, in 6 out of 11 tissues (aorta, dorsolateral prefrontal cortex, hippocampus, pancreas, small bowel, and spleen), tissue-specific eQTLs are significantly enriched in tissue-specific frequently interacting regions (FIREs).

Conclusions

Our data have demonstrated the close spatial proximity between eQTLs and their target genes among multiple human primary tissues.

Electronic supplementary material

The online version of this article (10.1186/s12863-019-0744-x) contains supplementary material, which is available to authorized users.

Postmortem brain tissue as an underutilized resource to study the molecular pathology of neuropsychiatric disorders across different ethnic populations

In recent years, large scale meta-analysis of genome-wide association studies (GWAS) have reliably identified genetic polymorphisms associated with neuropsychiatric disorders such as schizophrenia (SCZ), bipolar disorder (BPD) and major depressive disorder (MDD). However, the majority of disease-associated single nucleotide polymorphisms (SNPs) appear within functionally ambiguous non-coding genomic regions. Recently, increased emphasis has been placed on identifying the functional relevance of disease-associated variants via correlating risk polymorphisms with gene expression levels in etiologically relevant tissues. For neuropsychiatric disorders, the etiologically relevant tissue is brain, which requires robust postmortem sample sizes from varying genetic backgrounds. While small sample sizes are of decreasing concern, postmortem brain databases are composed almost exclusively of Caucasian samples, which significantly limits study design and result interpretation. In this review, we highlight the importance of gene expression and expression quantitative loci (eQTL) studies in clinically relevant postmortem tissue while addressing the current limitations of existing postmortem brain databases. Finally, we introduce future collaborations to develop postmortem brain databases for neuropsychiatric disorders from Chinese and Asian subpopulations.

Gene expression imputation across multiple brain regions provides insights into schizophrenia risk

Transcriptomic imputation approaches combine eQTL reference panels with large-scale genotype data in order to test associations between disease and gene expression. These genic associations could elucidate signals in complex genome-wide association study (GWAS) loci and may disentangle the role of different tissues in disease development. We used the largest eQTL reference panel for the dorso-lateral prefrontal cortex (DLPFC) to create a set of gene expression predictors and demonstrate their utility. We applied DLPFC and 12 GTEx-brain predictors to 40,299 schizophrenia cases and 65,264 matched controls for a large transcriptomic imputation study of schizophrenia. We identified 413 genic associations across 13 brain regions. Stepwise conditioning identified 67 non-MHC genes, of which 14 did not fall within previous GWAS loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple porphyric disorder pathways. We investigated developmental expression patterns among the 67 non-MHC genes and identified specific groups of pre- and postnatal expression.

Bayesian Inference for Mixed Model-Based Genome-Wide Analysis of Expression Quantitative Trait Loci by Gibbs Sampling

The importance of expression quantitative trait locus (eQTL) has been emphasized in understanding the genetic basis of cellular activities and complex phenotypes. Mixed models can be employed to effectively identify eQTLs by explaining polygenic effects. In these mixed models, the polygenic effects are considered as random variables, and their variability is explained by the polygenic variance component. The polygenic and residual variance components are first estimated, and then eQTL effects are estimated depending on the variance component estimates within the frequentist mixed model framework. The Bayesian approach to the mixed model-based genome-wide eQTL analysis can also be applied to estimate the parameters that exhibit various benefits. Bayesian inferences on unknown parameters are based on their marginal posterior distributions, and the marginalization of the joint posterior distribution is a challenging task. This problem can be solved by employing a numerical algorithm of integrals called Gibbs sampling as a Markov chain Monte Carlo. This article reviews the mixed model-based Bayesian eQTL analysis by Gibbs sampling. Theoretical and practical issues of Bayesian inference are discussed using a concise description of Bayesian modeling and the corresponding Gibbs sampling. The strengths of Bayesian inference are also discussed. Posterior probability distribution in the Bayesian inference reflects uncertainty in unknown parameters. This factor is useful in the context of eQTL analysis where a sample size is too small to apply the frequentist approach. Bayesian inference based on the posterior that reflects prior knowledge, will be increasingly preferred with the accumulation of eQTL data. Extensive use of the mixed model-based Bayesian eQTL analysis will accelerate understanding of eQTLs exhibiting various regulatory functions.

Transcriptome-referenced association study of clove shape traits in garlic

Genome-wide association studies are a powerful approach for identifying genes related to complex traits in organisms, but are limited by the requirement for a reference genome sequence of the species under study. To circumvent this problem, we propose a transcriptome-referenced association study (TRAS) that utilizes a transcriptome generated by single-molecule long-read sequencing as a reference sequence to score population variation at both transcript sequence and expression levels. Candidate transcripts are identified when both scores are associated with a trait and their potential interactions are ascertained by expression quantitative trait loci analysis. Applying this method to characterize garlic clove shape traits in 102 landraces, we identified 22 candidate transcripts, most of which showed extensive interactions. Eight transcripts were long non-coding RNAs (lncRNAs), and the others were proteins involved mainly in carbohydrate metabolism, protein degradation, etc. TRAS, as an efficient tool for association study independent of a reference genome, extends the applicability of association studies to a broad range of species.

Integrated Functional Genomic Analysis Enables Annotation of Kidney Genome-Wide Association Study Loci

BACKGROUND

Linking genetic risk loci identified by genome-wide association studies (GWAS) to their causal genes remains a major challenge. Disease-associated genetic variants are concentrated in regions containing regulatory DNA elements, such as promoters and enhancers. Although researchers have previously published DNA maps of these regulatory regions for kidney tubule cells and glomerular endothelial cells, maps for podocytes and mesangial cells have not been available.

METHODS

We generated regulatory DNA maps (DNase-seq) and paired gene expression profiles (RNA-seq) from primary outgrowth cultures of human glomeruli that were composed mainly of podocytes and mesangial cells. We generated similar datasets from renal cortex cultures, to compare with those of the glomerular cultures. Because regulatory DNA elements can act on target genes across large genomic distances, we also generated a chromatin conformation map from freshly isolated human glomeruli.

RESULTS

We identified thousands of unique regulatory DNA elements, many located close to transcription factor genes, which the glomerular and cortex samples expressed at different levels. We found that genetic variants associated with kidney diseases (GWAS) and kidney expression quantitative trait loci were enriched in regulatory DNA regions. By combining GWAS, epigenomic, and chromatin conformation data, we functionally annotated 46 kidney disease genes.

CONCLUSIONS

We demonstrate a powerful approach to functionally connect kidney disease-/trait-associated loci to their target genes by leveraging unique regulatory DNA maps and integrated epigenomic and genetic analysis. This process can be applied to other kidney cell types and will enhance our understanding of genome regulation and its effects on gene expression in kidney disease.

Gene Regulatory Networks: A Primer in Biological Processes and Statistical Modelling

Modelling gene regulatory networks requires not only a thorough understanding of the biological system depicted, but also the ability to accurately represent this system from a mathematical perspective. Throughout this chapter, we aim to familiarize the reader with the biological processes and molecular factors at play in the process of gene expression regulation. We first describe the different interactions controlling each step of the expression process, from transcription to mRNA and protein decay. In the second section, we provide statistical tools to accurately represent this biological complexity in the form of mathematical models. Among other considerations, we discuss the topological properties of biological networks, the application of deterministic and stochastic frameworks, and the quantitative modelling of regulation. We particularly focus on the use of such models for the simulation of expression data that can serve as a benchmark for the testing of network inference algorithms.

Genetic ancestry, admixture and health determinants in Latin America

BACKGROUND

Modern Latin American populations were formed via genetic admixture among ancestral source populations from Africa, the Americas and Europe. We are interested in studying how combinations of genetic ancestry in admixed Latin American populations may impact genomic determinants of health and disease. For this study, we characterized the impact of ancestry and admixture on genetic variants that underlie health- and disease-related phenotypes in population genomic samples from Colombia, Mexico, Peru, and Puerto Rico.

RESULTS

We analyzed a total of 347 admixed Latin American genomes along with 1102 putative ancestral source genomes from Africans, Europeans, and Native Americans. We characterized the genetic ancestry, relatedness, and admixture patterns for each of the admixed Latin American genomes, finding a spectrum of ancestry proportions within and between populations. We then identified single nucleotide polymorphisms (SNPs) with anomalous ancestry-enrichment patterns, i.e. SNPs that exist in any given Latin American population at a higher frequency than expected based on the population's genetic ancestry profile. For this set of ancestry-enriched SNPs, we inspected their phenotypic impact on disease, metabolism, and the immune system. All four of the Latin American populations show ancestry-enrichment for a number of shared pathways, yielding evidence of similar selection pressures on these populations during their evolution. For example, all four populations show ancestry-enriched SNPs in multiple genes from immune system pathways, such as the cytokine receptor interaction, T cell receptor signaling, and antigen presentation pathways. We also found SNPs with excess African or European ancestry that are associated with ancestry-specific gene expression patterns and play crucial roles in the immune system and infectious disease responses. Genes from both the innate and adaptive immune system were found to be regulated by ancestry-enriched SNPs with population-specific regulatory effects.

CONCLUSIONS

Ancestry-enriched SNPs in Latin American populations have a substantial effect on health- and disease-related phenotypes. The concordant impact observed for same phenotypes across populations points to a process of adaptive introgression, whereby ancestry-enriched SNPs with specific functional utility appear to have been retained in modern populations by virtue of their effects on health and fitness.

Plasma angiopoietin-2 as a potential causal marker in sepsis-associated ARDS development: evidence from Mendelian randomization and mediation analysis

PURPOSE

A causal biomarker for acute respiratory distress syndrome (ARDS) could fuel precision therapy options. Plasma angiopoietin-2 (ANG2), a vascular permeability marker, is a strong candidate on the basis of experimental and observational evidence. We used genetic causal inference methods-Mendelian randomization and mediation-to infer potential effects of plasma ANG2.

METHODS

We genotyped 703 septic subjects, measured ICU admission plasma ANG2, and performed a quantitative trait loci (QTL) analysis to determine variants in the ANGPT2 gene associated with plasma ANG2 (p < 0.005). We then used linear regression and post-estimation analysis to genetically predict plasma ANG2 and tested genetically predicted ANG2 for ARDS association using logistic regression. We estimated the proportion of the genetic effect explained by plasma ANG2 using mediation analysis.

RESULTS

Plasma ANG2 was strongly associated with ARDS (OR 1.59 (95% CI 1.35, 1.88) per log). Five ANGPT2 variants were associated with ANG2 in European ancestry subjects (n = 404). Rs2442608C, the most extreme cis QTL (coefficient 0.22, 95% CI 0.09-0.36, p = 0.001), was associated with higher ARDS risk: adjusted OR 1.38 (95% CI 1.01, 1.87), p = 0.042. No significant QTL were identified in African ancestry subjects. Genetically predicted plasma ANG2 was associated with ARDS risk: adjusted OR 2.25 (95% CI 1.06-4.78), p = 0.035. Plasma ANG2 mediated 34% of the rs2442608C-related ARDS risk.

CONCLUSIONS

In septic European ancestry subjects, the strongest ANG2-determining ANGPT2 genetic variant is associated with higher ARDS risk. Plasma ANG2 may be a causal factor in ARDS development. Strategies to reduce plasma ANG2 warrant testing to prevent or treat sepsis-associated ARDS.

Colon-specific eQTL analysis to inform on functional SNPs

Background

Genome-wide association studies on colorectal cancer have identified more than 60 susceptibility loci, but for most of them there is no clear knowledge of functionality or the underlying gene responsible for the risk modification. Expression quantitative trail loci (eQTL) may provide functional information for such single nucleotide polymorphisms (SNPs).

Methods

We have performed detailed eQTL analysis specific for colon tissue on a series of 97 colon tumours, their paired adjacent normal mucosa and 47 colon mucosa samples donated by healthy individuals. R package MatrixEQTL was used to search for genome-wide cis-eQTL and trans-eQTL fitting linear models adjusted for age, gender and tissue type to rank transformed expression data.

Results

The cis-eQTL analyses has revealed 29,073 SNP-gene associations with permutation-adjusted P-values < 0.01. These correspond to 363 unique genes. The trans-eQTL analysis identified 10,665 significant SNP-gene associations, most of them in the same chromosome, further than 1 Mb of the gene. We provide a web tool to search for specific SNPs or genes. The tool calculates Pearson or Spearman correlation, and allows to select tissue type for analysis. Data and plots can be exported.

Conclusions

This resource should be useful to prioritise SNPs for further functional studies and to identify relevant genes behind identified loci.

Genetic and nongenetic profiling of milk pregnancy-associated glycoproteins in Holstein cattle

Pregnancy-associated glycoproteins (PAG) are secreted by the trophoblast and are detectable in maternal circulation around the time of attachment of the fetal placenta, as well as in blood and milk throughout gestation. The understanding of the genetic mechanisms controlling PAG levels can confer advantages for livestock breeding programs given the precocity and the ease of obtaining this phenotype from routine pregnancy diagnosis. The aim of this study was to characterize PAG levels by estimating genetic parameters and correlations with other dairy traits, and to identify genomic regions and candidate genes associated with PAG levels in milk. The PAG data consisted of pregnancy diagnoses using commercial assays from 2012 to 2017, and genotype data consisted of 54,123 SNP markers for 2,352 individuals (embryos and dams). The model included contemporary group (herd, year, and season) and embryo age as fixed effects, and random embryonic (direct) and maternal (indirect) genetic effects. Using genomic data, the estimated heritability for direct and maternal genetic effects (± standard deviations) were 0.23 ± 0.05 and 0.11 ± 0.05, respectively. The genetic correlation between these effects was almost zero (0.001 ± 0.06). A preliminary analysis revealed low correlations between milk PAG levels and other dairy traits. The genome-wide association analysis was performed using 2 approaches: single-marker and single-step using all markers. Four genomic regions with direct genetic effects were detected on Bos taurus autosome (BTA) 6, BTA7, BTA19, and BTA29 of the embryonic genome. The BTA29 locus was within the bovine PAG gene cluster, suggesting a cis-regulatory quantitative trait locus on the PAG expression. However, other associations, without an obvious link to PAG expression, could be related to the transportation of PAG and their concentration in milk. Only 1 region from the maternal genome, on BTA4, had a significant indirect effect, where WNT2 is a candidate gene related to placenta vascularization and gestation health. Collectively, our results suggest a moderate genetic control of milk PAG levels from both maternal and fetal genomes, but larger studies are needed to fully evaluate the usefulness of milk PAG in the genetic evaluation of fetal growth and cow fertility.

Prioritizing candidate genes post-GWAS using multiple sources of data for mastitis resistance in dairy cattle

Background

Improving resistance to mastitis, one of the costliest diseases in dairy production, has become an important objective in dairy cattle breeding. However, mastitis resistance is influenced by many genes involved in multiple processes, including the response to infection, inflammation, and post-infection healing. Low genetic heritability, environmental variations, and farm management differences further complicate the identification of links between genetic variants and mastitis resistance. Consequently, studies of the genetics of variation in mastitis resistance in dairy cattle lack agreement about the responsible genes.

Results

We associated 15,552,968 imputed whole-genome sequencing markers for 5147 Nordic Holstein cattle with mastitis resistance in a genome-wide association study (GWAS). Next, we augmented P-values for markers in genes in the associated regions using Gene Ontology terms, Kyoto Encyclopedia of Genes and Genomes pathway analysis, and mammalian phenotype database. To confirm results of gene-based analyses, we used gene expression data from E. coli-challenged cow udders. We identified 22 independent quantitative trait loci (QTL) that collectively explained 14% of the variance in breeding values for resistance to clinical mastitis (CM). Using association test statistics with multiple pieces of independent information on gene function and differential expression during bacterial infection, we suggested putative causal genes with biological relevance for 12 QTL affecting resistance to CM in dairy cattle.

Conclusion

Combining information on the nearest positional genes, gene-based analyses, and differential gene expression data from RNA-seq, we identified putative causal genes (candidate genes with biological evidence) in QTL for mastitis resistance in Nordic Holstein cattle. The same strategy can be applied for other traits.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5050-x) contains supplementary material, which is available to authorized users.

DeFine: deep convolutional neural networks accurately quantify intensities of transcription factor-DNA binding and facilitate evaluation of functional non-coding variants

The complex system of gene expression is regulated by the cell type-specific binding of transcription factors (TFs) to regulatory elements. Identifying variants that disrupt TF binding and lead to human diseases remains a great challenge. To address this, we implement sequence-based deep learning models that accurately predict the TF binding intensities to given DNA sequences. In addition to accurately classifying TF-DNA binding or unbinding, our models are capable of accurately predicting real-valued TF binding intensities by leveraging large-scale TF ChIP-seq data. The changes in the TF binding intensities between the altered sequence and the reference sequence reflect the degree of functional impact for the variant. This enables us to develop the tool DeFine (Deep learning based Functional impact of non-coding variants evaluator, http://define.cbi.pku.edu.cn) with improved performance for assessing the functional impact of non-coding variants including SNPs and indels. DeFine accurately identifies the causal functional non-coding variants from disease-associated variants in GWAS. DeFine is an effective and easy-to-use tool that facilities systematic prioritization of functional non-coding variants.

Landscape of Conditional eQTL in Dorsolateral Prefrontal Cortex and Co-localization with Schizophrenia GWAS

Causal genes and variants within genome-wide association study (GWAS) loci can be identified by integrating GWAS statistics with expression quantitative trait loci (eQTL) and determining which variants underlie both GWAS and eQTL signals. Most analyses, however, consider only the marginal eQTL signal, rather than dissect this signal into multiple conditionally independent signals for each gene. Here we show that analyzing conditional eQTL signatures, which could be important under specific cellular or temporal contexts, leads to improved fine mapping of GWAS associations. Using genotypes and gene expression levels from post-mortem human brain samples (n = 467) reported by the CommonMind Consortium (CMC), we find that conditional eQTL are widespread; 63% of genes with primary eQTL also have conditional eQTL. In addition, genomic features associated with conditional eQTL are consistent with context-specific (e.g., tissue-, cell type-, or developmental time point-specific) regulation of gene expression. Integrating the 2014 Psychiatric Genomics Consortium schizophrenia (SCZ) GWAS and CMC primary and conditional eQTL data reveals 40 loci with strong evidence for co-localization (posterior probability > 0.8), including six loci with co-localization of conditional eQTL. Our co-localization analyses support previously reported genes, identify novel genes associated with schizophrenia risk, and provide specific hypotheses for their functional follow-up.

Estimation of cis-eQTL effect sizes using a log of linear model

The study of expression Quantitative Trait Loci (eQTL) is an important problem in genomics and biomedicine. While detection (testing) of eQTL associations has been widely studied, less work has been devoted to the estimation of eQTL effect size. To reduce false positives, detection methods frequently rely on linear modeling of rank-based normalized or log-transformed gene expression data. Unfortunately, these approaches do not correspond to the simplest model of eQTL action, and thus yield estimates of eQTL association that can be uninterpretable and inaccurate. In this article, we propose a new, log-of-linear model for eQTL action, termed ACME, that captures allelic contributions to cis-acting eQTLs in an additive fashion, yielding effect size estimates that correspond to a biologically coherent model of cis-eQTLs. We describe a non-linear least-squares algorithm to fit the model by maximum likelihood, and obtain corresponding p-values. We perform careful investigation of the model using a combination of simulated data and data from the Genotype Tissue Expression (GTEx) project. Our results reveal little evidence for dominance effects, a parsimonious result that accords with a simple biological model for allele-specific expression and supports use of the ACME model. We show that Type-I error is well-controlled under our approach in a realistic setting, so that rank-based normalizations are unnecessary. Furthermore, we show that such normalizations can be detrimental to power and estimation accuracy under the proposed model. We then show, through effect size analyses of whole-genome cis-eQTLs in the GTEx data, that using standard normalizations instead of ACME noticeably affects the ranking and sign of estimates.

Recent Genetics and Epigenetics Approaches to PTSD

PURPOSE OF REVIEW

Following a life-threatening traumatic exposure, about 10% of those exposed are at considerable risk for developing posttraumatic stress disorder (PTSD), a severe and disabling syndrome characterized by uncontrollable intrusive memories, nightmares, avoidance behaviors, and hyperarousal in addition to impaired cognition and negative emotion symptoms. This review will explore recent genetic and epigenetic approaches to PTSD that explain some of the differential risk following trauma exposure.

RECENT FINDINGS

A substantial portion of the variance explaining differential risk responses to trauma exposure may be explained by differential inherited and acquired genetic and epigenetic risk. This biological risk is complemented by alterations in the functional regulation of genes via environmentally induced epigenetic changes, including prior childhood and adult trauma exposure. This review will cover recent findings from large-scale genome-wide association studies as well as newer epigenome-wide studies. We will also discuss future "phenome-wide" studies utilizing electronic medical records as well as targeted genetic studies focusing on mechanistic ways in which specific genetic or epigenetic alterations regulate the biological risk for PTSD.

Expression analysis of candidate genes for fatty acid composition in adipose tissue and identification of regulatory regions

The aim of this work was to study the genetic basis of the backfat expression of lipid-related genes associated with meat quality traits in pigs. We performed a genome-wide association study with the backfat gene expression measured in 44 genes by qPCR and the PorcineSNP60 BeadChip genotypes in 115 Iberian x Landrace backcross animals. A total of 193 expression-associated SNPs located in 19 chromosomal regions were associated with expression levels of ACSM5, ELOVL6, FABP4, FADS2, and SLC27A4 genes. Three expression quantitative trail loci (eQTLs) corresponding to ACSM5, FABP4, and FADS2 were classified as cis-acting eQTLs, whereas the remaining 16 eQTLs have trans-regulatory effects. Remarkably, a SNP in the ACSM5 promoter region and a SNP in the 3′UTR region of FABP4 were the most associated polymorphisms with the ACSM5 and FABP4 expression levels, respectively. Moreover, relevant lipid-related genes mapped in the trans-eQTLs regions associated with the ACSM5, FABP4, FADS2, and SLC27A4 genes. Interestingly, a trans-eQTL hotspot on SSC13 regulating the gene expression of ELOVL6, ELOLV5, and SCD, three important genes implicated in the elongation and desaturation of fatty acids, was identified. These findings provide new data to further understand the functional regulatory mechanisms implicated in the variation of fatty acid composition in pigs.

An integrative functional genomics framework for effective identification of novel regulatory variants in genome-phenome studies

BACKGROUND

Genome-phenome studies have identified thousands of variants that are statistically associated with disease or traits; however, their functional roles are largely unclear. A comprehensive investigation of regulatory mechanisms and the gene regulatory networks between phenome-wide association study (PheWAS) and genome-wide association study (GWAS) is needed to identify novel regulatory variants contributing to risk for human diseases.

METHODS

In this study, we developed an integrative functional genomics framework that maps 215,107 significant single nucleotide polymorphism (SNP) traits generated from the PheWAS Catalog and 28,870 genome-wide significant SNP traits collected from the GWAS Catalog into a global human genome regulatory map via incorporating various functional annotation data, including transcription factor (TF)-based motifs, promoters, enhancers, and expression quantitative trait loci (eQTLs) generated from four major functional genomics databases: FANTOM5, ENCODE, NIH Roadmap, and Genotype-Tissue Expression (GTEx). In addition, we performed a tissue-specific regulatory circuit analysis through the integration of the identified regulatory variants and tissue-specific gene expression profiles in 7051 samples across 32 tissues from GTEx.

RESULTS

We found that the disease-associated loci in both the PheWAS and GWAS Catalogs were significantly enriched with functional SNPs. The integration of functional annotations significantly improved the power of detecting novel associations in PheWAS, through which we found a number of functional associations with strong regulatory evidence in the PheWAS Catalog. Finally, we constructed tissue-specific regulatory circuits for several complex traits: mental diseases, autoimmune diseases, and cancer, via exploring tissue-specific TF-promoter/enhancer-target gene interaction networks. We uncovered several promising tissue-specific regulatory TFs or genes for Alzheimer's disease (e.g. ZIC1 and STX1B) and asthma (e.g. CSF3 and IL1RL1).

CONCLUSIONS

This study offers powerful tools for exploring the functional consequences of variants generated from genome-phenome association studies in terms of their mechanisms on affecting multiple complex diseases and traits.

The phosphatidylinositide 3-kinase (PI3K) signaling pathway is a determinant of zileuton response in adults with asthma

Variable responsiveness to zileuton, a leukotriene antagonist used to treat asthma, may be due in part to genetic variation. While individual SNPs were previously associated with zileuton-related lung function changes, specific quantitative trait loci (QTLs) and biological pathways that may contribute have not been identified. In this study, we investigated the hypothesis that genetic variation within biological pathways is associated with zileuton response. We performed an integrative QTL mapping and pathway enrichment study to investigate data from a GWAS of zileuton response, in addition to mRNA expression profiles and leukotriene production data from lymphoblastoid cell lines (LCLs) (derived from asthmatics) that were treated with zileuton or ethanol (control). We identified 1060 QTLs jointly associated with zileuton-related differential LTB₄ production in LCLs and lung function change in patients taking zileuton, of which eight QTLs were also significantly associated with persistent LTB₄ production in LCLs following zileuton treatment (i.e., ‘poor’ responders). Four nominally significant trans-eQTLs were predicted to regulate three candidate genes (SELL, MTF2, and GAL), the expression of which was significantly reduced in LCLs following zileuton treatment. Gene and pathway enrichment analyses of QTL associations identified multiple genes and pathways, predominantly related to phosphatidyl inositol signaling via PI3K. We validated the PI3K pathway activation status in a subset of LCLs demonstrating variable zileuton-related LTB₄ production, and show that in contrast to LCLs that responded to zileuton, the PI3K pathway was activated in poor responder LCLs. Collectively, these findings demonstrate a role for the PIK3 pathway and its targets as important determinants of differential responsiveness to zileuton.

Genetic variation and gene expression across multiple tissues and developmental stages in a nonhuman primate

By analyzing multitissue gene expression and genome-wide genetic variation data in samples from a vervet monkey pedigree, we generated a transcriptome resource and produced the first catalog of expression quantitative trait loci (eQTLs) in a nonhuman primate model. This catalog contains more genome-wide significant eQTLs per sample than comparable human resources and identifies sex- and age-related expression patterns. Findings include a master regulatory locus that likely has a role in immune function and a locus regulating hippocampal long noncoding RNAs (lncRNAs), whose expression correlates with hippocampal volume. This resource will facilitate genetic investigation of quantitative traits, including brain and behavioral phenotypes relevant to neuropsychiatric disorders.

cis-Acting Complex-Trait-Associated lincRNA Expression Correlates with Modulation of Chromosomal Architecture

Intergenic long noncoding RNAs (lincRNAs) are the largest class of transcripts in the human genome. Although many have recently been linked to complex human traits, the underlying mechanisms for most of these transcripts remain undetermined. We investigated the regulatory roles of a high-confidence and reproducible set of 69 trait-relevant lincRNAs (TR-lincRNAs) in human lymphoblastoid cells whose biological relevance is supported by their evolutionary conservation during recent human history and genetic interactions with other trait-associated loci. Their enrichment in enhancer-like chromatin signatures, interactions with nearby trait-relevant protein-coding loci, and preferential location at topologically associated domain (TAD) boundaries provide evidence that TR-lincRNAs likely regulate proximal trait-relevant gene expression in cis by modulating local chromosomal architecture. This is consistent with the positive and significant correlation found between TR-lincRNA abundance and intra-TAD DNA-DNA contacts. Our results provide insights into the molecular mode of action by which TR-lincRNAs contribute to complex human traits.

Cis-SNPs Set Testing and PrediXcan Analysis for Gene Expression Data using Linear Mixed Models

Understanding the functional mechanism of SNPs identified in GWAS on complex diseases is currently a challenging task. The studies of expression quantitative trait loci (eQTL) have shown that regulatory variants play a crucial role in the function of associated SNPs. Detecting significant genes (called eGenes) in eQTL studies and analyzing the effect sizes of cis-SNPs can offer important implications on the genetic architecture of associated SNPs and interpretations of the molecular basis of diseases. We applied linear mixed models (LMM) to the gene expression level and constructed likelihood ratio tests (LRT) to test for eGene in the Geuvadis data. We identified about 11% genes as eGenes in the Geuvadis data and found some eGenes were enriched in approximately independent linkage disequilibrium (LD) blocks (e.g. MHC). We further performed PrediXcan analysis for seven diseases in the WTCCC data with weights estimated using LMM and identified 64, 5, 21 and 1 significant genes (p < 0.05 after Bonferroni correction) associated with T1D, CD, RA and T2D. We found most of the significant genes of T1D and RA were also located within the MHC region. Our results provide strong evidence that gene expression plays an intermediate role for the associated variants in GWAS.

Environmental and genetic determinants of transcriptional plasticity in Chinook salmon

Variation in gene transcription is widely believed to be the mechanistic basis of phenotypically plastic traits; however, comparatively little is known about the inheritance patterns of transcriptional variation that would allow us to predict its response to selection. In addition, acclimation to different environmental conditions influences acute transcriptional responses to stress and it is unclear if these effects are heritable. To address these gaps in knowledge, we assayed levels of messenger RNA for 14 candidate genes at rest and in response to a 24-h confinement stress for 72 half-sib families of Chinook salmon reared in two different environments (hatchery and semi-natural stream channel). We observed extensive plasticity for mRNA levels of metabolic and stress response genes and demonstrated that mRNA level plasticity due to rearing environment affects mRNA level plasticity in response to stress. These effects have important implications for natural populations experiencing multiple stressors. We identified genotype-by-environment interactions for mRNA levels that were dominated by maternal effects; however, mRNA level response to challenge also exhibited a non-additive genetic basis. Our results indicate that while plasticity for mRNA levels can evolve, predicting the outcome of selection will be difficult. The inconsistency in genetic architecture among treatment groups suggests there is considerable cryptic genetic variation for gene expression.

Interchromosomal Transfer of Immune Regulation During Infection of Barley with the Powdery Mildew Pathogen

Powdery mildew pathogens colonize over 9500 plant species, causing critical yield loss. The Ascomycete fungus, Blumeria graminis f. sp. hordei (Bgh), causes powdery mildew disease in barley (Hordeum vulgare L.). Successful infection begins with penetration of host epidermal cells, culminating in haustorial feeding structures, facilitating delivery of fungal effectors to the plant and exchange of nutrients from host to pathogen. We used expression Quantitative Trait Locus (eQTL) analysis to dissect the temporal control of immunity-associated gene expression in a doubled haploid barley population challenged with Bgh. Two highly significant regions possessing trans eQTL were identified near the telomeric ends of chromosomes (Chr) 2HL and 1HS. Within these regions reside diverse resistance loci derived from barley landrace H. laevigatum (MlLa) and H. vulgare cv. Algerian (Mla1), which associate with the altered expression of 961 and 3296 genes during fungal penetration of the host and haustorial development, respectively. Regulatory control of transcript levels for 299 of the 961 genes is reprioritized from MlLa on 2HL to Mla1 on 1HS as infection progresses, with 292 of the 299 alternating the allele responsible for higher expression, including Adaptin Protein-2 subunit μ AP2M and Vesicle Associated Membrane Protein VAMP72 subfamily members VAMP721/722. AP2M mediates effector-triggered immunity (ETI) via endocytosis of plasma membrane receptor components. VAMP721/722 and SNAP33 form a Soluble N-ethylmaleimide-sensitive factor Attachment Protein REceptor (SNARE) complex with SYP121 (PEN1), which is engaged in pathogen associated molecular pattern (PAMP)-triggered immunity via exocytosis. We postulate that genes regulated by alternate chromosomal positions are repurposed as part of a conserved immune complex to respond to different pathogen attack scenarios.

Systematic Analysis of Cell-to-Cell Expression Variation of T Lymphocytes in a Human Cohort Identifies Aging and Genetic Associations

Cell-to-cell expression variation (CEV) is a prevalent feature of even well-defined cell populations, but its functions, particularly at the organismal level, are not well understood. Using single-cell data obtained via high-dimensional flow cytometry of T cells as a model, we introduce an analysis framework for quantifying CEV in primary cell populations and studying its functional associations in human cohorts. Analyses of 840 CEV phenotypes spanning multiple baseline measurements of 14 proteins in 28 T cell subpopulations suggest that the quantitative extent of CEV can exhibit substantial subject-to-subject differences and yet remain stable within healthy individuals over months. We linked CEV to age and disease-associated genetic polymorphisms, thus implicating CEV as a biomarker of aging and disease susceptibility and suggesting that it might play an important role in health and disease. Our dataset, interactive figures, and software for computing CEV with flow cytometry data provide a resource for exploring CEV functions.

Identification of expression quantitative trait loci (eQTLs) in human peripheral blood mononuclear cells (PBMCs) and shared with liver and brain

PBMCs are essential for immunity and involved in various diseases. To identify genetic variations contributing to PBMCs transcriptome-wide gene expression, we performed a genome-wide eQTL analysis by using genome-wide SNPs data and transcriptome-wide mRNA expression data. To assess whether there are common regulation patterns shared among different tissues/organs, public datasets were utilized to identify common eQTLs shared with PBMCs in lymphoblastoid, monocytes, liver, and brain. Allelic expression imbalance (AEI) assay was employed to validate representative eQTLs identified. We identified 443 cis- and 2386 trans-eSNPs (FDR <0.05), which regulated 128 and 635 target genes, respectively. A transcriptome-wide expression regulation network was constructed, highlighting the importance of 28 pleiotropic eSNPs and 18 dually (cis- and trans-) regulated genes. Three genes, that is, TIPRL, HSPB8, and EGLN3, were commonly regulated by hundreds of eSNPs and constituted a very complex interaction network. Strikingly, the missense SNP rs371513 trans- regulated 25 target genes, which were functionally related to poly(A) RNA binding. Among 8904 eQTLs (P < 0.001) identified herein in PBMCs, a minority (163) was overlapped with lymphoblastoid, monocytes, liver, and/or brain. Besides, two cis-eSNPs in PBMC were confirmed by AEI. The present results demonstrated a comprehensive expression regulation network for human PBMCs and may provide novel insights into the pathogenesis of immunological diseases related to PBMCs.

Correlation analyses revealed global microRNA-mRNA expression associations in human peripheral blood mononuclear cells

MicroRNAs (miRNAs) can regulate gene expression through binding to complementary sites in the 3'-untranslated regions of target mRNAs, which will lead to existence of correlation in expression between miRNA and mRNA. However, the miRNA-mRNA correlation patterns are complex and remain largely unclear yet. To establish the global correlation patterns in human peripheral blood mononuclear cells (PBMCs), multiple miRNA-mRNA correlation analyses and expression quantitative trait locus (eQTL) analysis were conducted in this study. We predicted and achieved 861 miRNA-mRNA pairs (65 miRNAs, 412 mRNAs) using multiple bioinformatics programs, and found global negative miRNA-mRNA correlations in PBMC from all 46 study subjects. Among the 861 pairs of correlations, 19.5% were significant (P < 0.05) and ~70% were negative. The correlation network was complex and highlighted key miRNAs/genes in PBMC. Some miRNAs, such as hsa-miR-29a, hsa-miR-148a, regulate a cluster of target genes. Some genes, e.g., TNRC6A, are regulated by multiple miRNAs. The identified genes tend to be enriched in molecular functions of DNA and RNA binding, and biological processes such as protein transport, regulation of translation and chromatin modification. The results provided a global view of the miRNA-mRNA expression correlation profile in human PBMCs, which would facilitate in-depth investigation of biological functions of key miRNAs/mRNAs and better understanding of the pathogenesis underlying PBMC-related diseases.

Naturally Segregating Variation at Ugt86Dd Contributes to Nicotine Resistance in Drosophila melanogaster

Identifying the sequence polymorphisms underlying complex trait variation is a key goal of genetics research, since knowing the precise causative molecular events allows insight into the pathways governing trait variation. Genetic analysis of complex traits in model systems regularly starts by constructing QTL maps, but generally fails to identify causative sequence polymorphisms. Previously we mapped a series of QTL contributing to resistance to nicotine in a Drosophila melanogaster multiparental mapping resource and here use a battery of functional tests to resolve QTL to the molecular level. One large-effect QTL resided over a cluster of UDP-glucuronosyltransferases, and quantitative complementation tests using deficiencies eliminating subsets of these detoxification genes revealed allelic variation impacting resistance. RNAseq showed that Ugt86Dd had significantly higher expression in genotypes that are more resistant to nicotine, and anterior midgut-specific RNA interference (RNAi) of this gene reduced resistance. We discovered a segregating 22-bp frameshift deletion in Ugt86Dd, and accounting for the InDel during mapping largely eliminates the QTL, implying the event explains the bulk of the effect of the mapped locus. CRISPR/Cas9 editing of a relatively resistant genotype to generate lesions in Ugt86Dd that recapitulate the naturally occurring putative loss-of-function allele, leads to a large reduction in resistance. Despite this major effect of the deletion, the allele appears to be very rare in wild-caught populations and likely explains only a small fraction of the natural variation for the trait. Nonetheless, this putatively causative coding InDel can be a launchpad for future mechanistic exploration of xenobiotic detoxification.

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.

A network-driven approach for genome-wide association mapping

MOTIVATION

It remains a challenge to detect associations between genotypes and phenotypes because of insufficient sample sizes and complex underlying mechanisms involved in associations. Fortunately, it is becoming more feasible to obtain gene expression data in addition to genotypes and phenotypes, giving us new opportunities to detect true genotype-phenotype associations while unveiling their association mechanisms.

RESULTS

In this article, we propose a novel method, NETAM, that accurately detects associations between SNPs and phenotypes, as well as gene traits involved in such associations. We take a network-driven approach: NETAM first constructs an association network, where nodes represent SNPs, gene traits or phenotypes, and edges represent the strength of association between two nodes. NETAM assigns a score to each path from an SNP to a phenotype, and then identifies significant paths based on the scores. In our simulation study, we show that NETAM finds significantly more phenotype-associated SNPs than traditional genotype-phenotype association analysis under false positive control, taking advantage of gene expression data. Furthermore, we applied NETAM on late-onset Alzheimer's disease data and identified 477 significant path associations, among which we analyzed paths related to beta-amyloid, estrogen, and nicotine pathways. We also provide hypothetical biological pathways to explain our findings.

AVAILABILITY AND IMPLEMENTATION

Software is available at http://www.sailing.cs.cmu.edu/

CONTACT

: epxing@cs.cmu.edu.

Using genomic annotations increases statistical power to detect eGenes

Motivation: Expression quantitative trait loci (eQTLs) are genetic variants that affect gene expression. In eQTL studies, one important task is to find eGenes or genes whose expressions are associated with at least one eQTL. The standard statistical method to determine whether a gene is an eGene requires association testing at all nearby variants and the permutation test to correct for multiple testing. The standard method however does not consider genomic annotation of the variants. In practice, variants near gene transcription start sites (TSSs) or certain histone modifications are likely to regulate gene expression. In this article, we introduce a novel eGene detection method that considers this empirical evidence and thereby increases the statistical power.

Results: We applied our method to the liver Genotype-Tissue Expression (GTEx) data using distance from TSSs, DNase hypersensitivity sites, and six histone modifications as the genomic annotations for the variants. Each of these annotations helped us detected more candidate eGenes. Distance from TSS appears to be the most important annotation; specifically, using this annotation, our method discovered 50% more candidate eGenes than the standard permutation method.

Contact:buhm.han@amc.seoul.kr or eeskin@cs.ucla.edu

SPIRE, a modular pipeline for eQTL analysis of RNA-Seq data, reveals a regulatory hotspot controlling miRNA expression in C. elegans

The interpretation of genome-wide association study is difficult, as it is hard to understand how polymorphisms can affect gene regulation, in particular for trans-regulatory elements located far from their controlling gene. Using RNA or protein expression data as phenotypes, it is possible to correlate their variations with specific genotypes. This technique is usually referred to as expression Quantitative Trait Loci (eQTLs) analysis and only few packages exist for the integration of genotype patterns and expression profiles. In particular, tools are needed for the analysis of next-generation sequencing (NGS) data on a genome-wide scale, which is essential to identify eQTLs able to control a large number of genes (hotspots). Here we present SPIRE (Software for Polymorphism Identification Regulating Expression), a generic, modular and functionally highly flexible pipeline for eQTL processing. SPIRE integrates different univariate and multivariate approaches for eQTL analysis, paying particular attention to the scalability of the procedure in order to support cis- as well as trans-mapping, thus allowing the identification of hotspots in NGS data. In particular, we demonstrated how SPIRE can handle big association study datasets, reproducing published results and improving the identification of trans-eQTLs. Furthermore, we employed the pipeline to analyse novel data concerning the genotypes of two different C. elegans strains (N2 and Hawaii) and related miRNA expression data, obtained using RNA-Seq. A miRNA regulatory hotspot was identified in chromosome 1, overlapping the transcription factor grh-1, known to be involved in the early phases of embryonic development of C. elegans. In a follow-up qPCR experiment we were able to verify most of the predicted eQTLs, as well as to show, for a novel miRNA, a significant difference in the sequences of the two analysed strains of C. elegans. SPIRE is publicly available as open source software at , together with some example data, a readme file, supplementary material and a short tutorial.

Genetic Dissection of Nutrition-Induced Plasticity in Insulin/Insulin-Like Growth Factor Signaling and Median Life Span in a Drosophila Multiparent Population

The nutritional environments that organisms experience are inherently variable, requiring tight coordination of how resources are allocated to different functions relative to the total amount of resources available. A growing body of evidence supports the hypothesis that key endocrine pathways play a fundamental role in this coordination. In particular, the insulin/insulin-like growth factor signaling (IIS) and target of rapamycin (TOR) pathways have been implicated in nutrition-dependent changes in metabolism and nutrient allocation. However, little is known about the genetic basis of standing variation in IIS/TOR or how diet-dependent changes in expression in this pathway influence phenotypes related to resource allocation. To characterize natural genetic variation in the IIS/TOR pathway, we used >250 recombinant inbred lines (RILs) derived from a multiparental mapping population, the Drosophila Synthetic Population Resource, to map transcript-level QTL of genes encoding 52 core IIS/TOR components in three different nutritional environments [dietary restriction (DR), control (C), and high sugar (HS)]. Nearly all genes, 87%, were significantly differentially expressed between diets, though not always in ways predicted by loss-of-function mutants. We identified cis (i.e., local) expression QTL (eQTL) for six genes, all of which are significant in multiple nutrient environments. Further, we identified trans (i.e., distant) eQTL for two genes, specific to a single nutrient environment. Our results are consistent with many small changes in the IIS/TOR pathways. A discriminant function analysis for the C and DR treatments identified a pattern of gene expression associated with the diet treatment. Mapping the composite discriminant function scores revealed a significant global eQTL within the DR diet. A correlation between the discriminant function scores and the median life span (r = 0.46) provides evidence that gene expression changes in response to diet are associated with longevity in these RILs.

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.

Mitonuclear Interactions Mediate Transcriptional Responses to Hypoxia in Drosophila

Among the major challenges in quantitative genetics and personalized medicine is to understand how gene × gene interactions (G × G: epistasis) and gene × environment interactions (G × E) underlie phenotypic variation. Here, we use the intimate relationship between mitochondria and oxygen availability to dissect the roles of nuclear DNA (nDNA) variation, mitochondrial DNA (mtDNA) variation, hypoxia, and their interactions on gene expression in Drosophila melanogaster. Mitochondria provide an important evolutionary and medical context for understanding G × G and G × E given their central role in integrating cellular signals. We hypothesized that hypoxia would alter mitonuclear communication and gene expression patterns. We show that first order nDNA, mtDNA, and hypoxia effects vary between the sexes, along with mitonuclear epistasis and G × G × E effects. Females were generally more sensitive to genetic and environmental perturbation. While dozens to hundreds of genes are altered by hypoxia in individual genotypes, we found very little overlap among mitonuclear genotypes for genes that were significantly differentially expressed as a consequence of hypoxia; excluding the gene hairy. Oxidative phosphorylation genes were among the most influenced by hypoxia and mtDNA, and exposure to hypoxia increased the signature of mtDNA effects, suggesting retrograde signaling between mtDNA and nDNA. We identified nDNA-encoded genes in the electron transport chain (succinate dehydrogenase) that exhibit female-specific mtDNA effects. Our findings have important implications for personalized medicine, the sex-specific nature of mitonuclear communication, and gene × gene coevolution under variable or changing environments.

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.

Prediction of gene expression with cis-SNPs using mixed models and regularization methods

Background

It has been shown that gene expression in human tissues is heritable, thus predicting gene expression using only SNPs becomes possible. The prediction of gene expression can offer important implications on the genetic architecture of individual functional associated SNPs and further interpretations of the molecular basis underlying human diseases.

Methods

We compared three types of methods for predicting gene expression using only cis-SNPs, including the polygenic model, i.e. linear mixed model (LMM), two sparse models, i.e. Lasso and elastic net (ENET), and the hybrid of LMM and sparse model, i.e. Bayesian sparse linear mixed model (BSLMM). The three kinds of prediction methods have very different assumptions of underlying genetic architectures. These methods were evaluated using simulations under various scenarios, and were applied to the Geuvadis gene expression data.

Results

The simulations showed that these four prediction methods (i.e. Lasso, ENET, LMM and BSLMM) behaved best when their respective modeling assumptions were satisfied, but BSLMM had a robust performance across a range of scenarios. According to R² of these models in the Geuvadis data, the four methods performed quite similarly. We did not observe any clustering or enrichment of predictive genes (defined as genes with R² ≥ 0.05) across the chromosomes, and also did not see there was any clear relationship between the proportion of the predictive genes and the proportion of genes in each chromosome. However, an interesting finding in the Geuvadis data was that highly predictive genes (e.g. R² ≥ 0.30) may have sparse genetic architectures since Lasso, ENET and BSLMM outperformed LMM for these genes; and this observation was validated in another gene expression data. We further showed that the predictive genes were enriched in approximately independent LD blocks.

Conclusions

Gene expression can be predicted with only cis-SNPs using well-developed prediction models and these predictive genes were enriched in some approximately independent LD blocks. The prediction of gene expression can shed some light on the functional interpretation for identified SNPs in GWASs.

Genetic regulation of gene expression in the epileptic human hippocampus

Epilepsy is a serious and common neurological disorder. Expression quantitative loci (eQTL) analysis is a vital aid for the identification and interpretation of disease-risk loci. Many eQTLs operate in a tissue- and condition-specific manner. We have performed the first genome-wide cis-eQTL analysis of human hippocampal tissue to include not only normal (n = 22) but also epileptic (n = 22) samples. We demonstrate that disease-associated variants from an epilepsy GWAS meta-analysis and a febrile seizures (FS) GWAS are significantly more enriched with epilepsy-eQTLs than with normal hippocampal eQTLs from two larger independent published studies. In contrast, GWAS meta-analyses of two other brain diseases associated with hippocampal pathology (Alzheimer's disease and schizophrenia) are more enriched with normal hippocampal eQTLs than with epilepsy-eQTLs. These observations suggest that an eQTL analysis that includes disease-affected brain tissue is advantageous for detecting additional risk SNPs for the afflicting and closely related disorders, but not for distinct diseases affecting the same brain regions. We also show that epilepsy eQTLs are enriched within epilepsy-causing genes: an epilepsy cis-gene is significantly more likely to be a causal gene for a Mendelian epilepsy syndrome than to be a causal gene for another Mendelian disorder. Epilepsy cis-genes, compared to normal hippocampal cis-genes, are more enriched within epilepsy-causing genes. Hence, we utilize the epilepsy eQTL data for the functional interpretation of epilepsy disease-risk variants and, thereby, highlight novel potential causal genes for sporadic epilepsy. In conclusion, an epilepsy-eQTL analysis is superior to normal hippocampal tissue eQTL analyses for identifying the variants and genes underlying epilepsy.

Methods, Tools and Current Perspectives in Proteogenomics

With combined technological advancements in high-throughput next-generation sequencing and deep mass spectrometry-based proteomics, proteogenomics, i.e. the integrative analysis of proteomic and genomic data, has emerged as a new research field. Early efforts in the field were focused on improving protein identification using sample-specific genomic and transcriptomic sequencing data. More recently, integrative analysis of quantitative measurements from genomic and proteomic studies have identified novel insights into gene expression regulation, cell signaling, and disease. Many methods and tools have been developed or adapted to enable an array of integrative proteogenomic approaches and in this article, we systematically classify published methods and tools into four major categories, (1) Sequence-centric proteogenomics; (2) Analysis of proteogenomic relationships; (3) Integrative modeling of proteogenomic data; and (4) Data sharing and visualization. We provide a comprehensive review of methods and available tools in each category and highlight their typical applications.

Gene Regulatory Elements, Major Drivers of Human Disease

Gene expression changes, the driving forces for cellular diversity in multicellular organisms, are regulated by a diverse set of gene regulatory elements that direct transcription in specific cells. Mutations in these elements, ranging from chromosomal aberrations to single-nucleotide polymorphisms, are a major cause of human disease. However, we currently have a very limited understanding of how regulatory element genotypes lead to specific phenotypes. In this review, we discuss the various methods of regulatory element identification, the different types of mutations they harbor, and their impact on human disease. We highlight how these variations can affect transcription of multiple genes in gene regulatory networks. In addition, we describe how novel technologies, such as massively parallel reporter assays and CRISPR/Cas9 genome editing, are beginning to provide a better understanding of the functional roles that these elements have and how their alteration can lead to specific phenotypes.

Large-Scale trans-eQTLs Affect Hundreds of Transcripts and Mediate Patterns of Transcriptional Co-regulation

Efforts to decipher the causal relationships between differences in gene regulation and corresponding differences in phenotype have been stymied by several basic technical challenges. Although detecting local, cis-eQTLs is now routine, trans-eQTLs, which are distant from the genes of origin, are far more difficult to find because millions of SNPs must currently be compared to thousands of transcripts. Here, we demonstrate an alternative approach: we looked for SNPs associated with the expression of many genes simultaneously and found that hundreds of trans-eQTLs each affect hundreds of transcripts in lymphoblastoid cell lines across three African populations. These trans-eQTLs target the same genes across the three populations and show the same direction of effect. We discovered that target transcripts of a high-confidence set of trans-eQTLs encode proteins that interact more frequently than expected by chance, are bound by the same transcription factors, and are enriched for pathway annotations indicative of roles in basic cell homeostasis. We thus demonstrate that our approach can uncover trans-acting transcriptional control circuits that affect co-regulated groups of genes: a key to understanding how cellular pathways and processes are orchestrated.

Gene co-expression network connectivity is an important determinant of selective constraint

While several studies have investigated general properties of the genetic architecture of natural variation in gene expression, few of these have considered natural, outbreeding populations. In parallel, systems biology has established that a general feature of biological networks is that they are scale-free, rendering them buffered against random mutations. To date, few studies have attempted to examine the relationship between the selective processes acting to maintain natural variation of gene expression and the associated co-expression network structure. Here we utilised RNA-Sequencing to assay gene expression in winter buds undergoing bud flush in a natural population of Populus tremula, an outbreeding forest tree species. We performed expression Quantitative Trait Locus (eQTL) mapping and identified 164,290 significant eQTLs associating 6,241 unique genes (eGenes) with 147,419 unique SNPs (eSNPs). We found approximately four times as many local as distant eQTLs, with local eQTLs having significantly higher effect sizes. eQTLs were primarily located in regulatory regions of genes (UTRs or flanking regions), regardless of whether they were local or distant. We used the gene expression data to infer a co-expression network and investigated the relationship between network topology, the genetic architecture of gene expression and signatures of selection. Within the co-expression network, eGenes were underrepresented in network module cores (hubs) and overrepresented in the periphery of the network, with a negative correlation between eQTL effect size and network connectivity. We additionally found that module core genes have experienced stronger selective constraint on coding and non-coding sequence, with connectivity associated with signatures of selection. Our integrated genetics and genomics results suggest that purifying selection is the primary mechanism underlying the genetic architecture of natural variation in gene expression assayed in flushing leaf buds of P. tremula and that connectivity within the co-expression network is linked to the strength of purifying selection.

Multiple functional SNPs in differentially expressed genes modify risk and survival of non-small cell lung cancer in Chinese female non-smokers

DNA genotype can affect gene expression, and gene expression can influence the onset and progression of diseases. Here we conducted a comprehensive study, we integrated analysis of gene expression profile and single nucleotide polymorphism (SNP) microarray data in order to scan out the critical genetic changes that participate in the onset and development of non-small cell lung cancer (NSCLC). Gene expression profile datasets were downloaded from the GEO database. Firstly, differentially expressed genes (DEGs) between NSCLC samples and adjacent normal samples were identified. Next, by STRING database, protein-protein interaction (PPI) network was constructed. At the same time, hub genes in PPI network were identified. Then, some functional SNPs in hub genes that may affect gene expression have been annotated. Finally, we carried a study to explore the relationship between functional SNPs and NSCLC risk and overall survival in Chinese female non-smokers. A total of 488 DEGs were identified in our study. There are 29 proteins with a higher degree of connectivity in the PPI network, including FOS, IL6 and MMP9. By using database annotation, we got 8 candidate functional SNPs that may affect the expression level of hub proteins. In the case-control study, we found that rs4754-T allele, rs959173-C allele and rs2239144-G allele were the protective allele of NSCLC risk. In dominant model, rs4754-CT+TT genotype were associated with a shorter survival time. In general, our study provides a novel research direction in the field of multi-omic data integration, and helps us find some critical genetic changes in disease.