eQTL mapping identifies insertion- and deletion-specific eQTLs in multiple tissues

Genome-wide expression QTL mapping reveals the highly dynamic regulatory landscape of a major wheat pathogen

BACKGROUND

In agricultural ecosystems, outbreaks of diseases are frequent and pose a significant threat to food security. A successful pathogen undergoes a complex and well-timed sequence of regulatory changes to avoid detection by the host immune system; hence, well-tuned gene regulation is essential for survival. However, the extent to which the regulatory polymorphisms in a pathogen population provide an adaptive advantage is poorly understood.

RESULTS

We used Zymoseptoria tritici, one of the most important pathogens of wheat, to generate a genome-wide map of regulatory polymorphism governing gene expression. We investigated genome-wide transcription levels of 146 strains grown under nutrient starvation and performed expression quantitative trait loci (eQTL) mapping. We identified cis-eQTLs for 65.3% of all genes and the majority of all eQTL loci are within 2kb upstream and downstream of the transcription start site (TSS). We also show that polymorphism in different gene elements contributes disproportionally to gene expression variation. Investigating regulatory polymorphism in gene categories, we found an enrichment of regulatory variants for genes predicted to be important for fungal pathogenesis but with comparatively low effect size, suggesting a separate layer of gene regulation involving epigenetics. We also show that previously reported trait-associated SNPs in pathogen populations are frequently cis-regulatory variants of neighboring genes with implications for the trait architecture.

CONCLUSIONS

Overall, our study provides extensive evidence that single populations segregate large-scale regulatory variation and are likely to fuel rapid adaptation to resistant hosts and environmental change.

RASA3 is a candidate gene in sickle cell disease-associated pulmonary hypertension and pulmonary arterial hypertension

Pulmonary hypertension (PH) is associated with significant morbidity and mortality. RASA3 is a GTPase activating protein integral to angiogenesis and endothelial barrier function. In this study, we explore the association of RASA3 genetic variation with PH risk in patients with sickle cell disease (SCD)-associated PH and pulmonary arterial hypertension (PAH). Cis-expression quantitative trait loci (eQTL) were queried for RASA3 using whole genome genotype arrays and gene expression profiles derived from peripheral blood mononuclear cells (PBMC) of three SCD cohorts. Genome-wide single nucleotide polymorphisms (SNPs) near or in the RASA3 gene that may associate with lung RASA3 expression were identified, reduced to 9 tagging SNPs for RASA3 and associated with markers of PH. Associations between the top RASA3 SNP and PAH severity were corroborated using data from the PAH Biobank and analyzed based on European or African ancestry (EA, AA). We found that PBMC RASA3 expression was lower in patients with SCD-associated PH as defined by echocardiography and right heart catheterization and was associated with higher mortality. One eQTL for RASA3 (rs9525228) was identified, with the risk allele correlating with PH risk, higher tricuspid regurgitant jet velocity and higher pulmonary vascular resistance in patients with SCD-associated PH. rs9525228 associated with markers of precapillary PH and decreased survival in individuals of EA but not AA. In conclusion, RASA3 is a novel candidate gene in SCD-associated PH and PAH, with RASA3 expression appearing to be protective. Further studies are ongoing to delineate the role of RASA3 in PH.

High-coverage whole-genome sequencing of the expanded 1000 Genomes Project cohort including 602 trios

The 1000 Genomes Project (1kGP) is the largest fully open resource of whole-genome sequencing (WGS) data consented for public distribution without access or use restrictions. The final, phase 3 release of the 1kGP included 2,504 unrelated samples from 26 populations and was based primarily on low-coverage WGS. Here, we present a high-coverage 3,202-sample WGS 1kGP resource, which now includes 602 complete trios, sequenced to a depth of 30X using Illumina. We performed single-nucleotide variant (SNV) and short insertion and deletion (INDEL) discovery and generated a comprehensive set of structural variants (SVs) by integrating multiple analytic methods through a machine learning model. We show gains in sensitivity and precision of variant calls compared to phase 3, especially among rare SNVs as well as INDELs and SVs spanning frequency spectrum. We also generated an improved reference imputation panel, making variants discovered here accessible for association studies.

Population-Scale Polymorphic Short Tandem Repeat Provides an Alternative Strategy for Allele Mining in Cotton

Short tandem repeats (STRs), which vary in size due to featuring variable numbers of repeat units, are present throughout most eukaryotic genomes. To date, few population-scale studies identifying STRs have been reported for crops. Here, we constructed a high-density polymorphic STR map by investigating polymorphic STRs from 911 Gossypium hirsutum accessions. In total, we identified 556,426 polymorphic STRs with an average length of 21.1 bp, of which 69.08% were biallelic. Moreover, 7,718 (1.39%) were identified in the exons of 6,021 genes, which were significantly enriched in transcription, ribosome biogenesis, and signal transduction. Only 5.88% of those exonic STRs altered open reading frames, of which 97.16% were trinucleotide. An alternative strategy STR-GWAS analysis revealed that 824 STRs were significantly associated with agronomic traits, including 491 novel alleles that undetectable by previous SNP-GWAS methods. For instance, a novel polymorphic STR consisting of GAACCA repeats was identified in GH_D06G1697, with its (GAACCA)₅ allele increasing fiber length by 1.96-4.83% relative to the (GAACCA)₄ allele. The database CottonSTRDB was further developed to facilitate use of STR datasets in breeding programs. Our study provides functional roles for STRs in influencing complex traits, an alternative strategy STR-GWAS for allele mining, and a database serving the cotton community as a valuable resource.

Transcriptome-wide Profiling of Cerebral Cavernous Malformations Patients Reveal Important Long noncoding RNA molecular signatures

Cerebral cavernous malformations (CCMs) are low-flow vascular malformations in the brain associated with recurrent hemorrhage and seizures. The current treatment of CCMs relies solely on surgical intervention. Henceforth, alternative non-invasive therapies are urgently needed to help prevent subsequent hemorrhagic episodes. Long non-coding RNAs (lncRNAs) belong to the class of non-coding RNAs and are known to regulate gene transcription and involved in chromatin remodeling via various mechanism. Despite accumulating evidence demonstrating the role of lncRNAs in cerebrovascular disorders, their identification in CCMs pathology remains unknown. The objective of the current study was to identify lncRNAs associated with CCMs pathogenesis using patient cohorts having 10 CCM patients and 4 controls from brain. Executing next generation sequencing, we performed whole transcriptome sequencing (RNA-seq) analysis and identified 1,967 lncRNAs and 4,928 protein coding genes (PCGs) to be differentially expressed in CCMs patients. Among these, we selected top 6 differentially expressed lncRNAs each having significant correlative expression with more than 100 differentially expressed PCGs. The differential expression status of the top lncRNAs, SMIM25 and LBX2-AS1 in CCMs was further confirmed by qRT-PCR analysis. Additionally, gene set enrichment analysis of correlated PCGs revealed critical pathways related to vascular signaling and important biological processes relevant to CCMs pathophysiology. Here, by transcriptome-wide approach we demonstrate that lncRNAs are prevalent in CCMs disease and are likely to play critical roles in regulating important signaling pathways involved in the disease progression. We believe, that detailed future investigations on this set of identified lncRNAs can provide useful insights into the biology and, ultimately, contribute in preventing this debilitating disease.

Genome-wide expression profiling-based copy number variations and colorectal cancer risk in Chinese

Genetic factors play important roles in colorectal carcinogenesis. This study was aimed to evaluate the effects of gene expression-related copy number variations (CNVs) on the risk of colorectal cancer in Chinese. Expression Quantitative Trait Locus (eQTL) mapping was conducted to explore the most regulatable gene expressions by CNVs among the whole genome based on publicly available data. Then a case-control study was performed to evaluate the associations between copy numbers of the most regulatable genes and colorectal cancer. The influence of the target CNVs on the expression of corresponding gene and protein was verified in colorectal tissue, and the biological effects of these CNVs on cell-cycle arrest and apoptosis of colon cancer cell lines were further detected. The eQTL revealed the most significant association between CNV of HM3_CNP_342 and gene expressions of human leukocyte antigen (HLA)-DQA1 and HLA-DQB1 among the whole genome. The later case-control study found that amplified HLA-DQB1 was inversely associated with colorectal cancer risk (odds ratio = 0.73; 95% confidence interval: 0.58-0.93), especially among those with a family history of cancer. The positive association between amplified HLA-DQB1 and upregulation of gene and protein was validated in colorectal tissue. In addition, overexpression of HLA-DQB1 in dendritic cells promoted cell-cycle arrest and apoptosis of cocultured SW480 and HCT116 cell lines, and vice versa. Our study suggests that the amplified copy number of HLA-DQB1 is associated with lower risk of colorectal cancer and able to induce the apoptosis of colon cancer cells, which implies the potential of HLA class II in cancer predisposition and immunotherapy.

Applications of the 1000 Genomes Project resources

The 1000 Genomes Project created a valuable, worldwide reference for human genetic variation. Common uses of the 1000 Genomes dataset include genotype imputation supporting Genome-wide Association Studies, mapping expression Quantitative Trait Loci, filtering non-pathogenic variants from exome, whole genome and cancer genome sequencing projects, and genetic analysis of population structure and molecular evolution. In this article, we will highlight some of the multiple ways that the 1000 Genomes data can be and has been utilized for genetic studies.

A high-quality human reference panel reveals the complexity and distribution of genomic structural variants

Structural variation (SV) represents a major source of differences between individual human genomes and has been linked to disease phenotypes. However, the majority of studies provide neither a global view of the full spectrum of these variants nor integrate them into reference panels of genetic variation. Here, we analyse whole genome sequencing data of 769 individuals from 250 Dutch families, and provide a haplotype-resolved map of 1.9 million genome variants across 9 different variant classes, including novel forms of complex indels, and retrotransposition-mediated insertions of mobile elements and processed RNAs. A large proportion are previously under reported variants sized between 21 and 100 bp. We detect 4 megabases of novel sequence, encoding 11 new transcripts. Finally, we show 191 known, trait-associated SNPs to be in strong linkage disequilibrium with SVs and demonstrate that our panel facilitates accurate imputation of SVs in unrelated individuals.

Characterization of candidate genes in inflammatory bowel disease-associated risk loci

GWAS have linked SNPs to risk of inflammatory bowel disease (IBD), but a systematic characterization of disease-associated genes has been lacking. Prior studies utilized microarrays that did not capture many genes encoded within risk loci or defined expression quantitative trait loci (eQTLs) using peripheral blood, which is not the target tissue in IBD. To address these gaps, we sought to characterize the expression of IBD-associated risk genes in disease-relevant tissues and in the setting of active IBD. Terminal ileal (TI) and colonic mucosal tissues were obtained from patients with Crohn's disease or ulcerative colitis and from healthy controls. We developed a NanoString code set to profile 678 genes within IBD risk loci. A subset of patients and controls were genotyped for IBD-associated risk SNPs. Analyses included differential expression and variance analysis, weighted gene coexpression network analysis, and eQTL analysis. We identified 116 genes that discriminate between healthy TI and colon samples and uncovered patterns in variance of gene expression that highlight heterogeneity of disease. We identified 107 coexpressed gene pairs for which transcriptional regulation is either conserved or reversed in an inflammation-independent or -dependent manner. We demonstrate that on average approximately 60% of disease-associated genes are differentially expressed in inflamed tissue. Last, we identified eQTLs with either genotype-only effects on expression or an interaction effect between genotype and inflammation. Our data reinforce tissue specificity of expression in disease-associated candidate genes, highlight genes and gene pairs that are regulated in disease-relevant tissue and inflammation, and provide a foundation to advance the understanding of IBD pathogenesis.

PExFInS: An Integrative Post-GWAS Explorer for Functional Indels and SNPs

Expression quantitative trait loci (eQTLs) mapping and linkage disequilibrium (LD) analysis have been widely employed to interpret findings of genome-wide association studies (GWAS). With the availability of deep sequencing data of 423 lymphoblastoid cell lines (LCLs) from six global populations and the microarray expression data, we performed eQTL analysis, identified more than 228 K SNP cis-eQTLs and 21 K indel cis-eQTLs and generated a LCL cis-eQTL database. We demonstrate that the percentages of population-shared and population-specific cis-eQTLs are comparable; while indel cis-eQTLs in the population-specific subsection make more contribution to gene expression variations than those in the population-shared subsection. We found cis-eQTLs, especially the population-shared cis-eQTLs are significantly enriched toward transcription start site. Moreover, the National Human Genome Research Institute cataloged GWAS SNPs are enriched for LCL cis-eQTLs. Specifically, 32.8% GWAS SNPs are LCL cis-eQTLs, among which 12.5% can be tagged by indel cis-eQTLs, suggesting the fundamental contribution of indel cis-eQTLs to GWAS association signals. To search for functional indels and SNPs tagging GWAS SNPs, a pipeline Post-GWAS Explorer for Functional Indels and SNPs (PExFInS) has been developed, integrating LD analysis, functional annotation from public databases, cis-eQTL mapping with our LCL cis-eQTL database and other published cis-eQTL datasets.