Matrix eQTL Research Articles

A Genome-Wide Association Study of Respiratory Syncytial Virus Infection Severity in Infants.

Respiratory syncytial virus (RSV) is a significant cause of infant morbidity and mortality worldwide. Most children experience at least one 1 RSV infection by the age of two 2 years, but not all develop severe disease. However, the understanding of genetic risk factors for severe RSV is incomplete. Consequently, we conducted a genome-wide association study of RSV severity. Disease severity was assessed by the ReSVinet scale, in a cohort of 251 infants aged 1 week to 1 year. Genotyping data were collected from multiple European study sites as part of the RESCEU Consortium. Linear regression models were used to assess the impact of genotype on RSV severity and gene expression as measured by microarray. While no SNPs reached the genome-wide statistical significance threshold (P < 5 × 10-8), we identified 816 candidate SNPs with a P-value of <1 × 10-4. Functional annotation of candidate SNPs highlighted genes relevant to neutrophil trafficking and cytoskeletal functions, including LSP1 and RAB27A. Moreover, SNPs within the RAB27A locus significantly altered gene expression (false discovery rate, FDR P < .05). These findings may provide insights into genetic mechanisms driving severe RSV infection, offering biologically relevant information for future investigations.

PSI-10 Prioritizing Fertility-Related Genes in Cattle Through Systems Genomics Data Integration

Abstract Female fertility significantly affects the efficiency of cow-calf production. Despite the collective efforts to understand the complex regulation of reproductive traits, causative genes and/or mutations have yet to be reported. Using omics technologies, such as gene expression, may unveil potential regulatory mechanisms and provide opportunities that link genotype to phenotype. Among them, expression quantitative trait loci (eQTL) are a powerful tool to identify biological mechanisms and potential markers for improving fertility. This study aimed to identify eQTLs underlying fertility-related traits and their co-localization with previously annotated QTLs. RNA-Seq from uterine epithelial cells (GSE171577) from crossbreed cows (n = 18 non-pregnant – NP and n = 25 pregnant – P) were used for gene expression analysis and SNP calling. Raw data quality control was performed, and read mapping was carried out using STAR. Genes were normalized to counts per million (CPM) and then the CPM values were log2 transformed using edgeR. Next, uniquely mapped reads were used for variant detection using the GATK software. Significant eQTLs were compared with the Animal Genome cattle database (QTLdb) to identify overlapping QTL regions. After quality control, 43 samples, 203,404 SNPs, and 15,029 genes were used for eQTL analysis. Using an additive linear model from the Matrix eQTL R-package, we identified 3,989 cis- and 7,683 trans-eQTLs for 1,120 and 2,503 genes, respectively (FDR &amp;lt; 0.05). A total of 50 cis-eQTLs were previously reported as differentially expressed genes, including CD37, CXCL3, PILRA, and PPP6R1 genes. The QTLs from QTLdb overlapping with our eQTLs were associated with traits such as age at puberty, conception rate, fertility index, and luteal activity (FDR &amp;lt; 0.05). Additionally, QTLs for production traits, such as milk production and body weight gain, were over-represented by the eQTLs we have identified. Significant biological processes underlying cis-regulated genes retrieved from ShinyGO were related to antigen processing and presentation. Likewise, trans-eQTLs genes were involved with transcription cis-regulatory region binding and transcription regulator activity. Our study provides novel regulatory and potential causative genetic variants, biological pathways, and genes underlying fertility in cows. Annotation and co-localization of detected eQTLs retrieved genomic regions previously reported as QTLs for production and reproduction-related traits. Further investigation, however, is required to untangle the mechanisms modulating gene expression and its effects on fertility-related traits.

179 Identification of Regulatory Mutations Affecting Complex Traits

Abstract Understanding genetic bases controlling complex traits of economic importance is fundamental to improve breeding programs. Recent efforts from FAANG consortium resulted in significant progress in the identification of regulatory regions in farm animals. However, most studies in cattle have focused on Bos taurus, and limited information is available for Bos indicus. Considering that Bos indicus is the predominant subspecies in tropical regions, the objective of this work was to identify regulatory variants associated with meat quality by combining information from whole genome sequence (WGS), gene expression (RNA-seq), and open chromatin regions (ATAC-seq). For genome wide association study (GWAS), an imputed panel of SNPs was obtained from WGS of 26 sires, a Bovine HD 770K panel of 374 progenies, and SNPs called from RNA-seq data of Longissimus thoracis muscle (LT) of 192 progenies. The SNPs were identified using GATK4, and the dataset included almost 4.5 million variants that were pruned to 553,581 tag-SNPs (PLINK, r2 = 0.8). ATAC-seq data was obtained from duplicated samples of LT collected from two animals. Phenotype data included back fat thickness (BFT), intramuscular fat (IMF), ribeye area (REA) and shear force (SF). Association of genotypes (553,581 SNPs) and gene expression (12,991 genes), performed in Matrix eQTL resulted in 36,916 cis-eQTL and 14,408 trans-eQTL (FDR &amp;lt; 0.05). For GWAS analysis of the eQTLs with phenotypic traits, we used a linear model in PLINK program (considering population stratification, contemporary group and hot carcass weight). GWAS resulted in 24 eQTLs associated with IMF, three with BFT, 12 with REA, and eight with SF (FDR &amp;lt; 0.10). ATAC-seq analysis (using nfcore/atacseq pipeline implemented in Nextflow) resulted in 33,734 consensus peaks with a mean size of 2.4 kb. The overlap analyses among GWAS, eQTL, and ATAC-seq positions resulted in two, four and one putative regulatory regions that control gene expression and are associated with BFT, IMF and SF, respectively. Functional studies are still necessary to establish if identified mutations are the causal mutation. In conclusion, integration of GWAS, RNAseq, and ATAC-Seq data can result in identification of regulatory mutations associated with phenotypes of economic importance.

Identifying genetic loci that are associated with changes in gene expression in PTSD in a South African cohort.

The molecular mechanisms underlying posttraumatic stress disorder (PTSD) are yet to be fully elucidated, especially in underrepresented population groups. Expression quantitative trait loci (eQTLs) are DNA sequence variants that influence gene expression, in a local (cis-) or distal (trans-) manner, and subsequently impact cellular, tissue, and system physiology. This study aims to identify genetic loci associated with gene expression changes in a South African PTSD cohort. Genome-wide genotype and RNA-sequencing data were obtained from 32 trauma-exposed controls and 35 PTSD cases of mixed-ancestry, as part of the SHARED ROOTS project. The first approach utilised 108 937 single-nucleotide polymorphisms (SNPs) (MAF > 10%) and 11 312 genes with Matrix eQTL to map potential eQTLs, while controlling for covariates as appropriate. The second analysis was focused on 5638 SNPs related to a previously calculated PTSD polygenic risk score for this cohort. SNP-gene pairs were considered eQTLs if they surpassed Bonferroni correction and had a false discovery rate <0.05. We did not identify eQTLs that significantly influenced gene expression in a PTSD-dependent manner. However, several known cis-eQTLs, independent of PTSD diagnosis, were observed. rs8521 (C > T) was associated with TAGLN and SIDT2 expression, and rs11085906 (C > T) was associated with ZNF333 expression. This exploratory study provides insight into the molecular mechanisms associated with PTSD in a non-European, admixed sample population. This study was limited by the cross-sectional design and insufficient statistical power. Overall, this study should encourage further multi-omics approaches towards investigating PTSD in diverse populations.

Multivariate adaptive shrinkage improves cross-population transcriptome prediction and association studies in underrepresented populations

Transcriptome prediction models built with data from European-descent individuals are less accurate when applied to different populations because of differences in linkage disequilibrium patterns and allele frequencies. We hypothesized that methods that leverage shared regulatory effects across different conditions, in this case, across different populations, may improve cross-population transcriptome prediction. To test this hypothesis, we made transcriptome prediction models for use in transcriptome-wide association studies (TWASs) using different methods (elastic net, joint-tissue imputation [JTI], matrix expression quantitative trait loci [Matrix eQTL], multivariate adaptive shrinkage in R [MASHR], and transcriptome-integrated genetic association resource [TIGAR]) and tested their out-of-sample transcriptome prediction accuracy in population-matched and cross-population scenarios. Additionally, to evaluate model applicability in TWASs, we integrated publicly available multiethnic genome-wide association study (GWAS) summary statistics from the Population Architecture using Genomics and Epidemiology (PAGE) study and Pan-ancestry genetic analysis of the UK Biobank (PanUKBB) with our developed transcriptome prediction models. In regard to transcriptome prediction accuracy, MASHR models performed better or the same as other methods in both population-matched and cross-population transcriptome predictions. Furthermore, in multiethnic TWASs, MASHR models yielded more discoveries that replicate in both PAGE and PanUKBB across all methods analyzed, including loci previously mapped in GWASs and loci previously not found in GWASs. Overall, our study demonstrates the importance of using methods that benefit from different populations' effect size estimates in order to improve TWASs for multiethnic or underrepresented populations.

137-OR: Metabolite Ratios and Development of Type 1 Diabetes

Ratios between metabolites more directly reflect flux through chemical reactions than individual metabolites. Variation in metabolites and their ratios are determined, in part, by genetic factors. Thus, genetic loci associated with variation in metabolite ratios may help disentangle genetic from non-genetic processes captured by the metabolome. We integrated metabolite ratios with genetics to predict changes preceding type 1 diabetes (T1D) in The Environmental Determinants of Diabetes in the Young (TEDDY) study. Quantitative trait loci (QTLs) were identified for 132 metabolite ratios previously associated with progression from islet autoimmunity (IA) to T1D. Ratios were calculated from untargeted metabolomics data available at the time of seroconversion to IA for 338 cases and 921 matched controls in TEDDY. Genotyping was performed using a genome-wide array with custom content with imputation to the TOPMed reference panel. Ratio-SNP associations were estimated using Matrix eQTL software, adjusting for case-control status and genetic relatedness. Genome-wide significant association (P &amp;lt; 4.9×10-9) was observed at 202 independent loci, involving 49 metabolite ratios. Twenty-six loci (12.9%) involved ratios between polyunsaturated fatty-acids (PUFAs) and fatty acid esters of hydroxy fatty acids (FAHFAs), a novel class of bioactive lipids with antidiabetic and anti-inflammatory effects. Variation in the metabolite ratio of eicosadienoic acid to FAHFA (18:1/O-15:1) was significantly determined by a site on chromosome 3 (ITPR1); ITPR1 encodes a receptor integral to calcium signaling and glucose-induced insulin secretion. Other genetic loci contributing to variation of PUFA:FAHFA ratios reside in genes controlling inflammatory and innate immune response (e.g., TSPO, TTLL12, NCAM1) and apoptosis (PPM1L). These novel metabolite ratio QTLs, some with known association with T1D risk, expand potential biomarkers of T1D risk, with PUFA:FAHFA ratios representing candidate FAHFA biosynthesis genes. Disclosure R.K. Johnson: None. A. Romero: None. E. Willems: Employee; Amgen Inc. S.S. Rich: None. M. Rewers: Research Support; Provention Bio, Juvenile Diabetes Research Foundation (JDRF), Hemsley Charitable Trust, Dexcom, Inc., Janssen Research &amp; Development, LLC. O. Fiehn: None. J.M. Norris: None. Funding National Institute of Diabetes and Digestive and Kidney Diseases (R03DK127472); National Institute of Allergy and Infectious Diseases; National Institute of Child Health and Human Development; National Institute of Environmental Health Sciences; JDRF; Centers for Disease Control and Prevention

Refined expression quantitative trait locus analysis on adenocarcinoma at the gastroesophageal junction reveals susceptibility and prognostic markers.

Objectives: This study aimed to explore cell type level expression quantitative trait loci (eQTL) in adenocarcinoma at the gastroesophageal junction (ACGEJ) and identify susceptibility and prognosis markers. Methods: Whole-genome sequencing (WGS) was performed on 120 paired samples from Chinese ACGEJ patients. Germline mutations were detected by GATK tools. RNA sequencing (RNA-seq) data on ACGEJ samples were taken from our previous studies. Public single-cell RNA sequencing (scRNA-seq) data were used to produce the proportion of epithelial cells. Matrix eQTL and a linear mixed model were used to identify condition-specific cis-eQTLs. The R package coloc was used to perform co-localization analysis with the public data of genome-wide association studies (GWASs). Log-rank and Cox regression tests were used to identify survival-associated eQTL and genes. Functions of candidate risk loci were explored by experimental validation. Results: Refined eQTL analyses of paired ACGEJ samples were performed and 2,036 potential ACGEJ-specific eQTLs with East Asian specificity were identified in total. ACGEJ-gain eQTLs were enriched at promoter regions more than ACGEJ-loss eQTLs. rs658524 was identified as the top eQTL close to the transcription start site of its paired gene (CTSW). rs2240191-RASAL1, rs4236599-FOXP2, rs4947311-PSORS1C1, rs13134812-LOC391674, and rs17508585-CDK13-DT were identified as ACGEJ-specific susceptibility eQTLs. rs309483-LINC01355 was associated with the overall survival of ACGEJ patients. We explored functions of candidate eQTLs such as rs658524, rs309483, rs2240191, and rs4947311 by experimental validation. Conclusion: This study provides new risk loci for ACGEJ susceptibility and effective disease prognosis biomarkers.

Integrative Analysis Between Genome-Wide Association Study and Expression Quantitative Trait Loci Reveals Bovine Muscle Gene Expression Regulatory Polymorphisms Associated With Intramuscular Fat and Backfat Thickness.

Understanding the architecture of gene expression is fundamental to unravel the molecular mechanisms regulating complex traits in bovine, such as intramuscular fat content (IMF) and backfat thickness (BFT). These traits are economically important for the beef industry since they affect carcass and meat quality. Our main goal was to identify gene expression regulatory polymorphisms within genomic regions (QTL) associated with IMF and BFT in Nellore cattle. For that, we used RNA-Seq data from 193 Nellore steers to perform SNP calling analysis. Then, we combined the RNA-Seq SNP and a high-density SNP panel to obtain a new dataset for further genome-wide association analysis (GWAS), totaling 534,928 SNPs. GWAS was performed using the Bayes B model. Twenty-one relevant QTL were associated with our target traits. The expression quantitative trait loci (eQTL) analysis was performed using Matrix eQTL with the complete SNP dataset and 12,991 genes, revealing a total of 71,033 cis and 36,497 trans-eQTL (FDR < 0.05). Intersecting with QTL for IMF, we found 231 eQTL regulating the expression levels of 117 genes. Within those eQTL, three predicted deleterious SNPs were identified. We also identified 109 eQTL associated with BFT and affecting the expression of 54 genes. This study revealed genomic regions and regulatory SNPs associated with fat deposition in Nellore cattle. We highlight the transcription factors FOXP4, FOXO3, ZSCAN2, and EBF4, involved in lipid metabolism-related pathways. These results helped us to improve our knowledge about the genetic architecture behind important traits in cattle.

Identification of eQTLs and differential gene expression associated with fetal programming in beef cattle.

This study assessed differential gene expression and identified expression quantitative trait loci (eQTLs) from samples of Longissimus lumborum muscle from bulls at 15months of age submitted to different prenatal nutrition. Upon confirmation of pregnancy, 126 dams were separated into three diet treatments varying the period of inclusion of energy protein supplementation (NP, PP, and FP). At calving, 63 males were genotyped with GGP LD BeadChip. The skeletal muscle of 15 bulls was sequenced (RNA-seq) at 15months of age. The EdgeR package was used for differential gene expression and principal component analysis (PCA), and the Matrix eQTL package was used for the eQTLs analysis (R statistical). The functional enrichment analysis was performed using the MetaCore® software. No genes differentially expressed were found between treatments (FDR > 0.05); nevertheless, we found 179 cis-tag-eQTLs and 20,762 trans-tag-eQTLs (FDR < 0.05) after linkage disequilibrium analysis. The functional enrichment analysis identified terms from gene ontology related to genes associated to trans-eQTLs (FDR < 0.05) as well as metabolic pathways (> gScore). Most biological pathways and genes found had been previously associated to fetal programming. The different prenatal supplementation strategies did not impact on muscle transcriptome of bulls. Additionally, there is a link between genotype and gene expression levels related to developmental traits in Nellore cattle.

Abstract 6242: Association of prostate cancer risk variants with gene expression in prostate stroma

Abstract Background: Prostate cancer genome-wide association studies have identified &amp;gt;260 germline risk variants which provide strong risk prediction for prostate cancer. Functional interpretation and biological mechanisms of these risk variants are not always clear. Many of the prostate cancer risk variants are expression quantitative trait loci (eQTLs), but studies have mostly been performed in tumor or epithelial-enriched normal prostate tissue. Given emerging evidence supporting that stroma contributes to the carcinogenic process, we examined the associations of prostate cancer risk variants with the expression of genes in prostate stroma. Methods: We generated mRNA expression data for 13,577 genes with the Illumina TruSeq RNA Exome library preparation kit from stromal samples in specimens from participants of the Health Professionals Follow-up Study and Physicians’ Health Study cohorts. Specifically, we profiled 172 stromal samples adjacent to tumor and 86 paired stroma distant from tumor. We included 264 prostate cancer risk variants genotyped data and imputed using a common reference panel (1,000 Genomes Phase I release). We performed cis- (≤1 Mb from the SNP (single nucleotide polymorphism) to the gene) and trans- eQTL analyses using a linear regression model in the R package Matrix eQTL. Results: Thirty-four SNPs were significantly (P &amp;lt; 0.01) associated with cis-expression of 41 nearby genes in prostate stroma adjacent to tumor, and 41 SNPs with 52 nearby genes for stroma distant from tumor. Genome-wide, 86 SNPs were significantly (FDR &amp;lt; 0.10) associated with the expression of 126 genes in stroma adjacent to tumor, and 64 SNPs with 291 genes for stroma distant from tumor. Conclusions: This is the first study to comprehensively examine associations between prostate cancer risk variants and gene expression in prostate stroma. We observed some previous reported associations but also novel associations which may suggest genes that contribute to prostate cancer risk within the stromal tissue. Identifications of eQTLs within prostate stroma may suggest potential mechanisms in specific cell types for their effect on disease. Citation Format: Chaoran Ma, Yinglu Zhou, Anna Plym, Giuseppe Nicolò Fanelli, Massimo Loda, Lorelei Mucci, Svitlana Tyekucheva, Kathryn Penney. Association of prostate cancer risk variants with gene expression in prostate stroma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6242.

Integrative analysis of liver-specific non-coding regulatory SNPs associated with the risk of coronary artery disease

Genetic factors underlying coronary artery disease (CAD) have been widely studied using genome-wide association studies (GWASs). However, the functional understanding of the CAD loci has been limited by the fact that a majority of GWAS variants are located within non-coding regions with no functional role. High cholesterol and dysregulation of the liver metabolism such as non-alcoholic fatty liver disease confer an increased risk of CAD. Here, we studied the function of non-coding single-nucleotide polymorphisms in CAD GWAS loci located within liver-specific enhancer elements by identifying their potential target genes using liver cis-eQTL analysis and promoter Capture Hi-C in HepG2 cells. Altogether, 734 target genes were identified of which 121 exhibited correlations to liver-related traits. To identify potentially causal regulatory SNPs, the allele-specific enhancer activity was analyzed by (1) sequence-based computational predictions, (2) quantification of allele-specific transcription factor binding, and (3) STARR-seq massively parallel reporter assay. Altogether, our analysis identified 1,277 unique SNPs that display allele-specific regulatory activity. Among these, susceptibility enhancers near important cholesterol homeostasis genes (APOB, APOC1, APOE, and LIPA) were identified, suggesting that altered gene regulatory activity could represent another way by which genetic variation regulates serum lipoprotein levels. Using CRISPR-based perturbation, we demonstrate how the deletion/activation of a single enhancer leads to changes in the expression of many target genes located in a shared chromatin interaction domain. Our integrative genomics approach represents a comprehensive effort in identifying putative causal regulatory regions and target genes that could predispose to clinical manifestation of CAD by affecting liver function.

Identification of loci associated with susceptibility to bovine paratuberculosis and with the dysregulation of the MECOM, eEF1A2, and U1 spliceosomal RNA expression

Although genome-wide association studies have identified single nucleotide polymorphisms (SNPs) associated with the susceptibility to Mycobacterium avium subsp. paratuberculosis (MAP) infection, only a few functional mutations for bovine paratuberculosis (PTB) have been characterized. Expression quantitative trait loci (eQTLs) are genetic variants typically located in gene regulatory regions that alter gene expression in an allele-specific manner. eQTLs can be considered as functional links between genomic variants, gene expression, and ultimately phenotype. In the current study, peripheral blood (PB) and ileocecal valve (ICV) gene expression was quantified by RNA-Seq from fourteen Holstein cattle with no lesions and with PTB-associated histopathological lesions in gut tissues. Genotypes were generated from the Illumina LD EuroG10K BeadChip. The associations between gene expression levels (normalized read counts) and genetic variants were analyzed by a linear regression analysis using R Matrix eQTL 2.2. This approach allowed the identification of 192 and 48 cis-eQTLs associated with the expression of 145 and 43 genes in the PB and ICV samples, respectively. To investigate potential relationships between these cis-eQTLs and MAP infection, a case–control study was performed using the genotypes for all the identified cis-eQTLs and phenotypical data (histopathology, ELISA for MAP-antibodies detection, tissue PCR, and bacteriological culture) of 986 culled cows. Our results suggested that the heterozygous genotype in the cis-eQTL-rs43744169 (T/C) was associated with the up-regulation of the MDS1 and EVI1 complex (MECOM) expression, with positive ELISA, PCR, and bacteriological culture results, and with increased risk of progression to clinical PTB. As supporting evidence, the presence of the minor allele was associated with higher MECOM levels in plasma samples from infected cows and with increased MAP survival in an ex-vivo macrophage killing assay. Moreover, the presence of the two minor alleles in the cis-eQTL-rs110345285 (C/C) was associated with the dysregulation of the eukaryotic elongation factor 1-α2 (eEF1A2) expression and with increased ELISA (OD) values. Finally, the presence of the minor allele in the cis-eQTL rs109859270 (C/T) was associated with the up-regulation of the U1 spliceosomal RNA expression and with an increased risk of progression to clinical PTB. The introduction of these novel functional variants into marker-assisted breeding programs is expected to have a relevant effect on PTB control.

An application of matrix eQTL to billions hypothesis testing to identify expression quantitative trait loci in genome wide association studies of inflammatory bowel disease

An application of matrix eQTL to billions hypothesis testing to identify expression quantitative trait loci in genome wide association studies of inflammatory bowel disease

Identification of eQTLs and sQTLs associated with meat quality in beef

BackgroundTranscription has a substantial genetic control and genetic dissection of gene expression could help us understand the genetic architecture of complex phenotypes such as meat quality in cattle. The objectives of the present research were: 1) to perform eQTL and sQTL mapping analyses for meat quality traits in longissimus dorsi muscle; 2) to uncover genes whose expression is influenced by local or distant genetic variation; 3) to identify expression and splicing hot spots; and 4) to uncover genomic regions affecting the expression of multiple genes.ResultsEighty steers were selected for phenotyping, genotyping and RNA-seq evaluation. A panel of traits related to meat quality was recorded in longissimus dorsi muscle. Information on 112,042 SNPs and expression data on 8588 autosomal genes and 87,770 exons from 8467 genes were included in an expression and splicing quantitative trait loci (QTL) mapping (eQTL and sQTL, respectively). A gene, exon and isoform differential expression analysis previously carried out in this population identified 1352 genes, referred to as DEG, as explaining part of the variability associated with meat quality traits. The eQTL and sQTL mapping was performed using a linear regression model in the R package Matrix eQTL. Genotype and year of birth were included as fixed effects, and population structure was accounted for by including as a covariate the first PC from a PCA analysis on genotypic data. The identified QTLs were classified as cis or trans using 1 Mb as the maximum distance between the associated SNP and the gene being analyzed. A total of 8377 eQTLs were identified, including 75.6% trans, 10.4% cis, 12.5% DEG trans and 1.5% DEG cis; while 11,929 sQTLs were uncovered: 66.1% trans, 16.9% DEG trans, 14% cis and 3% DEG cis. Twenty-seven expression master regulators and 13 splicing master regulators were identified and were classified as membrane-associated or cytoskeletal proteins, transcription factors or DNA methylases. These genes could control the expression of other genes through cell signaling or by a direct transcriptional activation/repression mechanism.ConclusionIn the present analysis, we show that eQTL and sQTL mapping makes possible positional identification of gene and isoform expression regulators.

Methylation quantitative trait loci analysis in Korean exposome study

Environmental exposure and genotype variation influence DNA methylation. Studies on the effects of genotype variation were performed mainly on European ancestries. We analyzed the genetic effects on cord blood methylation of Koreans. As part of the Korean Exposome study project, DNA was extracted from 192 cord blood samples for analysis. Cord blood samples were genotyped via Asian Precision Medicine Research Array analysis and methylation was measured using the Methylation EPIC Beadchip kits. The associations between genotypes and CpG methylation were analyzed with matrix eQTL. Conditional analysis revealed 34,425 methylation quantitative trait loci (mQTLs), and trans-mQTLs constituted 7.2% of all the associated CpG sites. About 80% of the total trans-associations were trans-chromosomal and the related SNPs were concentrated on chromosome 19. According to the results of DAVID, cis-mQTL-related SNPs resulting in amino acid substitutions were related to signal peptides or glycosylation. We identified genotype variations associated with DNA methylation in the cord blood obtained from Koreans.

Expression Quantitative Trait Loci (eQTL) Analysis in Cancer.

Expression quantitative trait loci (eQTL) analysis links variations in gene expression levels to genotypes. Analyzing both cis- and trans-eQTLs from tumor samples can provide an intermediate phenotype between genetic variation and complex traits to better understand how risk alleles contribute to tumorigenesis and development. Here we describe a detailed workflow for identifying eQTLs in cancer using existing packages and software. The key package is Matrix eQTL, which requires input data of genotypes, genes expression, and covariates. This pipeline can be easily applied in a related research field.

Characterization of eQTLs associated with androstenone by RNA sequencing in porcine testis.

Characterization of genetic variants affecting genome-wide gene expression levels (expression quantitative trait loci or eQTLs) in pig testes may improve our understanding of genetic architecture of boar taint (an animal welfare trait) and helps in genome-assisted or genomic selection programs. The aims of this study were to identify eQTLs associated with androstenone, to find candidate eQTLs for low androstenone, and to validate the top eQTL by reverse transcriptase quantitative PCR (RT-qPCR). Gene expression profiles were obtained by RNA sequencing in testis from Danish cross-bred pigs and genotype data by 80K single nucleotide polymorphism panel. A total of 262 eQTLs [false discovery rate (FDR) < 0.05] were identified by using two software packages: Matrix eQTL and Krux eQTL. Of these, 149 cis-acting eQTLs were significantly associated with androstenone concentrations and gene expression (FDR < 0.05). The eQTLs were associated with several genes of boar taint relevance including CYP1A2, CYB5D1, and SPHK2. One eQTL gene, AMPH, was differentially expressed (FDR < 0.05) and affected by chicory. Five candidate eQTLs associated with low androstenone concentrations were discovered, including the top eQTL associated with CYP1A2. RT-qPCR confirmed target gene expression to be significantly (P < 0.05) different based on eQTL genotypes. Furthermore, eQTLs were enriched as QTLs for 15 boar taint related traits from the PigQTLdb. This is the first study to report eQTLs in testes of commercial crossbred pigs used in pork production and to reveal genetic architecture of boar taint. Potential applications include development of a DNA test and in advanced genomic selection models for boar taint.

Abstract 1218: An eQTL and mQTL study of 147 genetic variants associated with prostate cancer susceptibility

Abstract Genome-wide association studies by the PRACTICAL consortium have identified and validated 147 single nucleotide polymorphisms (SNPs) associated with prostate cancer (PCa) risk (Schumacher et al., Nat Genet 2017). However, most of these genetic variants are in noncoding regions, which poses a challenge for understanding the molecular mechanisms underlying these risk loci. Herein we study both expression quantitative trait loci (eQTL) and methylation quantitative trait loci (mQTL) among these PCa risk loci to identify molecular alterations associated with these genetic variants. Data were available for a Fred Hutchinson (FH)-based cohort of patients diagnosed with localized stage PCa, including SNP genotypes (Infinium iCOGS and OncoArray-500K BeadChips) from the PRACTICAL consortium and genome-wide DNA methylation (Infinium Human Methylation450 BeadChip) and gene expression (HumanHT-12 v4 BeadChip) profiling of primary tumor tissues obtained at radical prostatectomy. Missing genotypes were imputed using the October 2014 (Phase 3) release of the 1KGP data as the reference panel. These data were used to identify eQTL (N=355) and mQTL (N=377) in cis-acting associations where the transcripts or CpG sites are located within 2Mb (±1Mb) of the genetic variants. The Matrix eQTL software was used to conduct eQTL and mQTL analyses. In total, 4,300 SNP-transcript pairs and 104,600 SNP-CpG pairs were tested for eQTL and mQTL, respectively, among which we identified 64 eQTL-transcript pairs (37 PCa risk loci and transcripts in 54 genes) and 1,405 mQTL-CpG pairs (116 PCa risk loci and CpGs in 321 genes), after correction for multiple testing (FDR&amp;lt;0.05). The remaining 29 (20%) PCa risk loci were neither eQTLs nor mQTLs. Interestingly, 35 (95%) eQTLs were also mQTLs, while the majority of mQTLs were not eQTLs (Fisher's exact test of independence, p=0.005), suggesting that DNA methylation may have alternative biologic regulatory functions other than directly affecting gene expression. Several of these genes associated with eQTLs such as INCENP, NUDT11, RGS17, ASCL2, C10orf32, MSMB, BHLHA15, and IRX4 were previously found in PCa. Furthermore, we discovered a number of novel genes such as HLA-DRB3 and HLA-DRB4 associated with eQTLs in our data. Further validation using the TCGA-PRAD data is under way. In summary, we identified eQTLs and mQTLs among newly established genetic risk loci for PCa. These results provide new insight into both the genetic and epigenetic mechanisms through which these SNPs may alter susceptibility to PCa. Citation Format: Xiaoyu Wang, Anqi Cheng, the PRACTICAL Consortium, Janet L. Stanford, James Y. Dai. An eQTL and mQTL study of 147 genetic variants associated with prostate cancer susceptibility [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1218.

A mega-analysis of expression quantitative trait loci (eQTL) provides insight into the regulatory architecture of gene expression variation in liver

Genome-wide association studies (GWAS) have identified numerous genetic variants in the human genome associated with diseases and traits. Nevertheless, for most loci the causative variant is still unknown. Expression quantitative trait loci (eQTL) in disease relevant tissues is an excellent approach to correlate genetic association with gene expression. While liver is the primary site of gene transcription for two pathways relevant to age-related macular degeneration (AMD), namely the complement system and cholesterol metabolism, we explored the contribution of AMD associated variants to modulate liver gene expression. We extracted publicly available data and computed the largest eQTL data set for liver tissue to date. Genotypes and expression data from all studies underwent rigorous quality control. Subsequently, Matrix eQTL was used to identify significant local eQTL. In total, liver samples from 588 individuals revealed 202,489 significant eQTL variants affecting 1,959 genes (Q-Value < 0.001). In addition, a further 101 independent eQTL signals were identified in 93 of the 1,959 eQTL genes. Importantly, our results independently reinforce the notion that high density lipoprotein metabolism plays a role in AMD pathogenesis. Taken together, our study generated a first comprehensive map reflecting the genetic regulatory landscape of gene expression in liver.

Gene, Environment and Methylation (GEM): a tool suite to efficiently navigate large scale epigenome wide association studies and integrate genotype and interaction between genotype and environment.

BackgroundThe interplay among genetic, environment and epigenetic variation is not fully understood. Advances in high-throughput genotyping methods, high-density DNA methylation detection and well-characterized sample collections, enable epigenetic association studies at the genomic and population levels (EWAS). The field has extended to interrogate the interaction of environmental and genetic (GxE) influences on epigenetic variation. Also, the detection of methylation quantitative trait loci (methQTLs) and their association with health status has enhanced our knowledge of epigenetic mechanisms in disease trajectory. However analysis of this type of data brings computational challenges and there are few practical solutions to enable large scale studies in standard computational environments.ResultsGEM is a highly efficient R tool suite for performing epigenome wide association studies (EWAS). GEM provides three major functions named GEM_Emodel, GEM_Gmodel and GEM_GxEmodel to study the interplay of Gene, Environment and Methylation (GEM). Within GEM, the pre-existing “Matrix eQTL” package is utilized and extended to study methylation quantitative trait loci (methQTL) and the interaction of genotype and environment (GxE) to determine DNA methylation variation, using matrix based iterative correlation and memory-efficient data analysis. Benchmarking presented here on a publicly available dataset, demonstrated that GEM can facilitate reliable genome-wide methQTL and GxE analysis on a standard laptop computer within minutes.ConclusionsThe GEM package facilitates efficient EWAS study in large cohorts. It is written in R code and can be freely downloaded from Bioconductor at https://www.bioconductor.org/packages/GEM/.