Expression Quantitative Trait Loci Mapping Research Articles

Learning gene networks underlying clinical phenotypes using SNP perturbation.

Availability of genome sequence, molecular, and clinical phenotype data for large patient cohorts generated by recent technological advances provides an opportunity to dissect the genetic architecture of complex diseases at system level. However, previous analyses of such data have largely focused on the co-localization of SNPs associated with clinical and expression traits, each identified from genome-wide association studies and expression quantitative trait locus mapping. Thus, their description of the molecular mechanisms behind the SNPs influencing clinical phenotypes was limited to the single gene linked to the co-localized SNP. Here we introduce PerturbNet, a statistical framework for learning gene networks that modulate the influence of genetic variants on phenotypes, using genetic variants as naturally occurring perturbation of a biological system. PerturbNet uses a probabilistic graphical model to directly model the cascade of perturbation from genetic variants to the gene network to the phenotype network along with the networks at each layer of the biological system. PerturbNet learns the entire model by solving a single optimization problem with an efficient algorithm that can analyze human genome-wide data within a few hours. PerturbNet inference procedures extract a detailed description of how the gene network modulates the genetic effects on phenotypes. Using simulated and asthma data, we demonstrate that PerturbNet improves statistical power for detecting disease-linked SNPs and identifies gene networks and network modules mediating the SNP effects on traits, providing deeper insights into the underlying molecular mechanisms.

EQTL mapping of the 12S globulin cruciferin gene PGCRURSE5 as a novel candidate associated with starch content in potato tubers

Tuber starch content (TSC) is a very important trait in potato (Solanum tuberosum L.). This study is the first to use expression quantitative trait loci (eQTL) mapping of transcript-derived markers for TSC in potato. Thirty-four differentially expressed genes were selected by comparing the RNA-seq data of contrasting bulked segregants. For the 11 candidate genes, we determined their relative expression levels across the segregating diploid potato population using RT-qPCR. We detected 36 eQTL as candidate genes distributed on all twelve potato chromosomes, and nine of them overlapped with QTL for TSC. Peaks for two eQTL, eAGPaseS-a and ePGRCRURSE5, were close to the corresponding loci of the large subunit of ADP-glucose pyrophosphorylase (AGPaseS-a) and the 12S globulin cruciferin gene (PGCRURSE5), respectively. The eQTL peaks for AGPaseS-a and PGRCRURSE5 explained 41.0 and 28.3% of the phenotypic variation at the transcript level. We showed the association of the DNA markers for AGPaseS-a and PGRCRURSE5 with QTL for TSC, and significant correlation between the expression level of PGRCRURSE5 and TSC. We did not observe a significant correlation between the expression level of AGPaseS-a and TSC. We concluded that the cruciferin gene PGRCRURSE5 is a novel candidate involved in the regulation of starch content in potato tubers.

A new mechanism for a familiar mutation \u2013 bovine DGAT1 K232A modulates gene expression through multi-junction exon splice enhancement

BackgroundThe DGAT1 gene encodes an enzyme responsible for catalysing the terminal reaction in mammary triglyceride synthesis, and underpins a well-known pleiotropic quantitative trait locus (QTL) with a large influence on milk composition phenotypes. Since first described over 15 years ago, a protein-coding variant K232A has been assumed as the causative variant underlying these effects, following in-vitro studies that demonstrated differing levels of triglyceride synthesis between the two protein isoforms.ResultsWe used a large RNAseq dataset to re-examine the underlying mechanisms of this large milk production QTL, and hereby report novel expression-based functions of the chr14 g.1802265AA > GC variant that encodes the DGAT1 K232A substitution. Using expression QTL (eQTL) mapping, we demonstrate a highly-significant mammary eQTL for DGAT1, where the K232A mutation appears as one of the top associated variants for this effect. By conducting in vitro expression and splicing experiments in bovine mammary cell culture, we further show modulation of splicing efficiency by this mutation, likely through disruption of an exon splice enhancer as a consequence of the allele encoding the 232A variant.ConclusionsThe relative contributions of the enzymatic and transcription-based mechanisms now attributed to K232A remain unclear; however, these results suggest that transcriptional impacts contribute to the diversity of lactation effects observed at the DGAT1 locus.

A Genome-wide Association Study Identifies SERPINB10, CRLF3, STX7, LAMP3, IFNG-AS1, and KRT80 As Risk Loci Contributing to Cutaneous Leishmaniasis in Brazil.

BackgroundOur goal was to identify genetic risk factors for cutaneous leishmaniasis (CL) caused by Leishmania braziliensis.MethodsGenotyping 2066 CL cases and 2046 controls using Illumina HumanCoreExomeBeadChips provided data for 4 498 586 imputed single-nucleotide variants (SNVs). A genome-wide association study (GWAS) using linear mixed models took account of genetic diversity/ethnicity/admixture. Post-GWAS positional, expression quantitative trait locus (eQTL) and chromatin interaction mapping was performed in Functional Mapping and Annotation (FUMA). Transcriptional data were compared between lesions and normal skin, and cytokines measured using flow cytometry and Bioplex assay.ResultsPositional mapping identified 32 genomic loci associated with CL, none achieving genome-wide significance (P < 5 × 10−8). Lead SNVs at 23 loci occurred at protein coding or noncoding RNA genes, 15 with eQTLs for functionally relevant cells/tissues and/or showing differential expression in lesions. Of these, the 6 most plausible genetic risk loci were SERPINB10 (Pimputed_1000G = 2.67 × 10−6), CRLF3 (Pimputed_1000G = 5.12 × 10−6), STX7 (Pimputed_1000G = 6.06 × 10−6), KRT80 (Pimputed_1000G = 6.58 × 10−6), LAMP3 (Pimputed_1000G = 6.54 × 10−6), and IFNG-AS1 (Pimputed_1000G = 1.32 × 10−5). LAMP3 (Padjusted = 9.25 × 10−12; +6-fold), STX7 (Padjusted = 7.62 × 10−3; +1.3-fold), and CRLF3 (Padjusted = 9.19 × 10−9; +1.97-fold) were expressed more highly in CL biopsies compared to normal skin; KRT80 (Padjusted = 3.07 × 10−8; −3-fold) was lower. Multiple cis-eQTLs across SERPINB10 mapped to chromatin interaction regions of transcriptional/enhancer activity in neutrophils, monocytes, B cells, and hematopoietic stem cells. Those at IFNG-AS1 mapped to transcriptional/enhancer regions in T, natural killer, and B cells. The percentage of peripheral blood CD3+ T cells making antigen-specific interferon-γ differed significantly by IFNG-AS1 genotype.ConclusionsThis first GWAS for CL identified multiple genetic risk loci including a novel lead to understanding CL pathogenesis through regulation of interferon-γ by IFNG antisense RNA 1.

Neonatal genetics of gene expression reveal potential origins of autoimmune and allergic disease risk

Chronic immune-mediated diseases of adulthood often originate in early childhood. To investigate genetic associations between neonatal immunity and disease, we map expression quantitative trait loci (eQTLs) in resting myeloid cells and CD4+ T cells from cord blood samples, as well as in response to lipopolysaccharide (LPS) or phytohemagglutinin (PHA) stimulation, respectively. Cis-eQTLs are largely specific to cell type or stimulation, and 31% and 52% of genes with cis-eQTLs have response eQTLs (reQTLs) in myeloid cells and T cells, respectively. We identified cis regulatory factors acting as mediators of trans effects. There is extensive colocalisation between condition-specific neonatal cis-eQTLs and variants associated with immune-mediated diseases, in particular CTSH had widespread colocalisation across diseases. Mendelian randomisation shows causal neonatal gene expression effects on disease risk for BTN3A2, HLA-C and others. Our study elucidates the genetics of gene expression in neonatal immune cells, and aetiological origins of autoimmune and allergic diseases.

Gene mapping and functional annotation of GWAS of oral ulcers using FUMA software

Oral ulcers not only influence the physical health of patients, but they also interfere with their quality of life. However, the exact etiology of oral ulcers is not clear. To explore the roles of genetic factors in oral ulcers, a genome-wide association study of the condition in European individuals was re-evaluated by the FUMA v1.3.5e online tool. A total of 380 independent significant single nucleotide polymorphisms (SNPs) and 89 lead SNPs were identified in 34 genomic risk loci. Out of these identified genomic risk loci, 280 possible causal genes were pinpointed by positional mapping and expression quantitative trait locus mapping. Among these genes, 216 novel genes were identified. Furthermore, some genomic loci were mapped to a single gene. Functional annotation of these prioritized genes revealed that the immune response pathway was implicated in the onset of oral ulcers. Overall, our findings revealed novel possible causal genes and demonstrated that the immune response has a crucial role in the occurrence of oral ulcers.

Genome-wide association study identifies genetic susceptibility loci and pathways of radiation-induced acute oral mucositis

BackgroundRadiation-induced oral mucositis (OM) is one of the most common acute complications for head and neck cancer. Severe OM is associated with radiation treatment breaks, which harms successful tumor management. Radiogenomics studies have indicated that genetic variants are associated with adverse effects of radiotherapy.MethodsA large-scale genome-wide scan was performed in 1467 nasopharyngeal carcinoma patients, including 753 treated with 2D-CRT from Genetic Architecture of the Radiotherapy Toxicity and Prognosis (GARTP) cohort and 714 treated with IMRT (192 from the GARTP and 522 newly recruited). Subgroup analysis by radiotherapy technique was further performed in the top associations. We also performed physical and regulatory mapping of the risk loci and gene set enrichment analysis of the candidate target genes.ResultsWe identified 50 associated genomic loci and 64 genes via positional mapping, expression quantitative trait locus (eQTL) mapping, chromatin interaction mapping and gene-based analysis, and 36 of these loci were replicated in subgroup analysis. Interestingly, one of the top loci located in TNKS, a gene relevant to radiation toxicity, was associated with increased OM risk with OR = 3.72 of the lead SNP rs117157809 (95% CI 2.10–6.57; P = 6.33 × 10−6). Gene set analyses showed that the 64 candidate target genes were enriched in the biological processes of regulating telomere capping and maintenance and telomerase activity (Top P = 7.73 × 10−7).ConclusionsThese results enhance the biological understanding of radiotherapy toxicity. The association signals enriched in telomere function regulation implicate the potential underlying mechanism and warrant further functional investigation and potential individual radiotherapy applications.

Expression Quantitative Trait Loci (eQTL) Mapping in Korean Patients With Crohn's Disease and Identification of Potential Causal Genes Through Integration With Disease Associations.

BackgroundExpression quantitative trait loci (eQTL) datasets have extensively been used to help interpret genome-wide association study signals. Most eQTL analyses have been conducted with populations of European ancestry.ObjectiveTo determine the most functionally relevant genes at the Crohn’s disease (CD) loci identified in genome-wide association studies (GWAS) involving Asian populations and to find novel disease-associated genes, we conducted an eQTL analysis.MethodseQTL analysis was performed using whole-blood RNA-sequencing of 101 Korean patients with CD. FastQTL was used for a pair-wise genome analysis of ∼ 6.5 M SNPs and ∼ 22 K transcripts.ResultsWe identified 135,164 cis-eQTL and 3,816 eGenes with a false discovery rate less than 0.05. A significant proportion of the genes identified in our study overlapped with those identified in previous studies. The significantly enriched pathways of these 3,816 eGenes included neutrophil degranulation and small molecule biosynthetic process. Integrated analysis of CD GWAS with Korean eQTL revealed two putative target genes, TNFSF15 and GPR35, at two previously reported loci, whereas TNFSF15 only with the whole blood data from the Genotype-Tissue Expression (GTEx) project, highlighting the utility of building a population-specific data set, even of modest size. The risk alleles of these genes were found to be associated with lower expression levels of TNFSF15 and GPR35, respectively. Our eQTL browser can be accessed at “http://asan.crohneqtl.com/”.ConclusionThis resource would be useful for studies that need to employ genome-wide association analyses involving Asian populations.

Large‐scale identification of expression quantitative trait loci in Arabidopsis reveals novel candidate regulators of immune responses and other processes

The extensive phenotypic diversity within natural populations of Arabidopsis is associated with differences in gene expression. Transcript levels can be considered as inheritable quantitative traits, and used to map expression quantitative trait loci (eQTL) in genome-wide association studies (GWASs). In order to identify putative genetic determinants for variations in gene expression, we used publicly available genomic and transcript variation data from 665 Arabidopsis accessions and applied the single nucleotide polymorphism-set (Sequence) Kernel Association Test (SKAT) method for the identification of eQTL. Moreover, we used the penalized orthogonal-components regression (POCRE) method to increase the power of statistical tests. Then, gene annotations were used as test units to identify genes that are associated with natural variations in transcript accumulation, which correspond to candidate regulators, some of which may have a broad impact on gene expression. Besides increasing the chances to identify real associations, the analysis using POCRE and SKAT significantly reduced the computational cost required to analyze large datasets. As a proof of concept, we used this approach to identify eQTL that represent novel candidate regulators of immune responses. The versatility of this approach allows its application to any process that is subjected to natural variation among Arabidopsis accessions.

Genetic Architecture of Gene Expression in European and African Americans: An eQTL Mapping Study in GENOA

Most existing expression quantitative trait locus (eQTL) mapping studies have been focused on individuals of European ancestry and are underrepresented in other populations including populations with African ancestry. Lack of large-scale well-powered eQTL mapping studies in populations with African ancestry can both impede the dissemination of eQTL mapping results that would otherwise benefit individuals with African ancestry and hinder the comparable analysis for understanding how gene regulation is shaped through evolution. We fill this critical knowledge gap by performing a large-scale in-depth eQTL mapping study on 1,032 African Americans (AA) and 801 European Americans (EA) in the GENOA cohort. We identified a total of 354,931 eSNPs in AA and 371,309 eSNPs in EA, with 112,316eSNPs overlapped between the two. We found that eQTL harboring genes (eGenes) are enriched in metabolic pathways and tend to have higher SNP heritability compared to non-eGenes. We found that eGenes that are common in the two populations tend to be less conserved than eGenes that are unique to one population, which are less conserved than non-eGenes. Through conditional analysis, we found that eGenes in AA tend to harbor more independent eQTLs than eGenes in EA, suggesting potentially diverse genetic architecture underlying expression variation in the two populations. Finally, the large sample sizes in GENOA allow us to construct accurate expression prediction models in both AA and EA, facilitating powerful transcriptome-wide association studies. Overall, our results represent an important step toward revealing the genetic architecture underlying expression variation in African Americans.

Meta Gene Regulatory Networks in Maize Highlight Functionally Relevant Regulatory Interactions.

The regulation of gene expression is central to many biological processes. Gene regulatory networks (GRNs) link transcription factors (TFs) to their target genes and represent maps of potential transcriptional regulation. Here, we analyzed a large number of publically available maize (Zea mays) transcriptome data sets including >6000 RNA sequencing samples to generate 45 coexpression-based GRNs that represent potential regulatory relationships between TFs and other genes in different populations of samples (cross-tissue, cross-genotype, and tissue-and-genotype samples). While these networks are all enriched for biologically relevant interactions, different networks capture distinct TF-target associations and biological processes. By examining the power of our coexpression-based GRNs to accurately predict covarying TF-target relationships in natural variation data sets, we found that presence/absence changes rather than quantitative changes in TF gene expression are more likely associated with changes in target gene expression. Integrating information from our TF-target predictions and previous expression quantitative trait loci (eQTL) mapping results provided support for 68 TFs underlying 74 previously identified trans-eQTL hotspots spanning a variety of metabolic pathways. This study highlights the utility of developing multiple GRNs within a species to detect putative regulators of important plant pathways and provides potential targets for breeding or biotechnological applications.

Gene expression networks in the Drosophila Genetic Reference Panel.

A major challenge in modern biology is to understand how naturally occurring variation in DNA sequences affects complex organismal traits through networks of intermediate molecular phenotypes. This question is best addressed in a genetic mapping population in which all molecular polymorphisms are known and for which molecular endophenotypes and complex traits are assessed on the same genotypes. Here, we performed deep RNA sequencing of 200 Drosophila Genetic Reference Panel inbred lines with complete genome sequences and for which phenotypes of many quantitative traits have been evaluated. We mapped expression quantitative trait loci for annotated genes, novel transcribed regions, transposable elements, and microbial species. We identified host variants that affect expression of transposable elements, independent of their copy number, as well as microbiome composition. We constructed sex-specific expression quantitative trait locus regulatory networks. These networks are enriched for novel transcribed regions and target genes in heterochromatin and euchromatic regions of reduced recombination, as well as genes regulating transposable element expression. This study provides new insights regarding the role of natural genetic variation in regulating gene expression and generates testable hypotheses for future functional analyses.

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.

&lt;i&gt;Cis&lt;/i&gt;- and &lt;i&gt;Trans&lt;/i&gt;-Acting Expression Quantitative Trait Loci of Long Non-Coding RNA Impacts the Tumor Immune Microenvironment

Background: Long noncoding RNAs (lncRNAs) are implicated in various human cancers. However, the genetic regulation and clinical significance of most lncRNAs in cancers remain unknown. Method: In this study, we performed expression quantitative trait loci (eQTLs) mapping of lncRNA (elncRNA) in 11 cancer types using The Cancer Genome Atlas (TCGA) data and characterized the role of elncRNAs in the setting of genomic location, cancer association and drug sensitivity prediction. Furthermore, we performed instrumental variable (IV) analysis to dissect the downstream biological perturbation by eQTL-elncRNA pairs. Finding: 10.86% cis-eQTLs and 1.67% trans-eQTLs were related to known cancer risk-associated loci. The elncRNAs were significantly enriched in lncRNA predictors of anticancer drug sensitivity. We found that the target genes affected by eQTL-elncRNA associations are enriched in the immune system processes. We also found that eQTL-elncRNA associations can impact the fraction of immune cell types. In ovarian cancer, the rs34631313-AC092580.4 pair was shown to associate with immune-related genes (FASLG/GZMM/PYHIN1TRAT1), and with increased percentages of CD8+ T cells and M1 Macrophage. The rs9546285(13q12.3)-LINC00426 pair was shown to associate with the expression of IFNG/TNIP3/DTHD1/ZBED2 and with a higher fraction of CD8+ T cells and a lower fraction of M2 macrophages in kidney renal clear cell carcinoma. Interpretation: We revealed the genetic regulation of lncRNAs in cancers and explored the role of eQTL-elncRNA in cancer immunology. Our findings provide valuable genetic and lncRNA biomarkers for drug sensitivity and cancer immune therapy. Funding Statement:This study was supported in part by the National Natural Science Foundation of China (Grant No. 31371289 to Qiyuan Li), the Fundamental Research Funds for the Chinese Central Universities (20720190101 to Qiyuan Li), the Young Scientists Fund of the National Natural Science Foundation of China (Grant No. 81802823 to Ying Zhou), the Natural Science Foundation of Fujian Province of China (Grant No. 2018J01054 to Ying Zhou), and the Major Project of Shanghai Science and Technology Commission of China, (Grant No. 18441901700 to Wenzhi Li). Declaration of Interests: The authors declare that they have no competing interests. Ethics Approval Statement: The authors stated: Not applicable.

FastLORS: Joint modelling for expression quantitative trait loci mapping in R

FastLORS is a software package that implements a new algorithm to solve sparse multivariate regression for expression quantitative trait loci (eQTLs) mapping. FastLORS solves the same optimization problem as LORS, an existing popular algorithm. The optimization problem is solved through inexact block coordinate descent with updates by proximal gradient steps, which reduces the computational cost compared with LORS. We apply LORS and FastLORS to a real dataset for eQTL mapping and demonstrate that FastLORS delivers comparable results with LORS in much less computing time.

High-Order Association Mapping for Expression Quantitative Trait Loci.

Mapping expression quantitative trait loci (eQTLs) is an important avenue to identify putative genetic variants in regulatory regions. Famed eQTL mapping methods exploit the mean effects of locus-wise genetic variants on expression quantitative traits. Despite their successes, such methods are suboptimal because they neglect high-order heterogeneity inherent in genetic variants and covariates. High-order effects of observed loci are common due to their connections to various latent factors, i.e., latent interactions among genes and environmental factors. In this chapter, we introduce a new scheme to harmoniously integrate mean and high-order effects of genetic variants on expression quantitative trait. We rigorously evaluate its validity and utility of signal augmentation.

EQTL Mapping Using Transcription Factor Affinity.

In the last decades, thousands of common genetic variants have been associated with human diseases by genome-wide association studies (GWAS). However, the functional interpretation of GWAS hits is usually nontrivial, especially because most of them lay outside the coding genome. These noncoding variants presumably exert their effect by altering gene expression levels; therefore, expression quantitative trait loci (eQTL) mapping analyses represent an important step in understanding their functional relevance and identifying the target genes. Here we describe an alternative strategy for the detection of eQTL that takes into account the combined effect of genetic variants within regulatory regions and leverages the idea that changes in gene expression often are the consequence of the alteration of transcription factor (TF) binding.

Integration of Multi-omics Data for Expression Quantitative Trait Loci (eQTL) Analysis and eQTL Epistasis.

Expression quantitative trait loci (eQTL) mapping studies identify genetic loci that regulate gene expression. eQTL mapping studies can capture gene regulatory interactions and provide insight into the genetic mechanism of biological systems. Recently, the integration of multi-omics data, such as single-nucleotide polymorphisms (SNPs), copy number variations (CNVs), DNA methylation, and gene expression, plays an important role in elucidating complex biological systems, since biological systems involve a sequence of complex interactions between various biological processes. This chapter introduces multi-omics data that have been used in many eQTL studies and integrative methodologies that incorporate multi-omics data for eQTL studies. Furthermore, we describe a statistical approach that can detect nonlinear causal relationships between eQTLs, called eQTL epistasis, and its importance.

Transcriptome-wide association study reveals candidate causal genes for lung cancer.

We have recently completed the largest GWAS on lung cancer including 29,266 cases and 56,450 controls of European descent. The goal of our study has been to integrate the complete GWAS results with a large-scale expression quantitative trait loci (eQTL) mapping study in human lung tissues (n = 1,038) to identify candidate causal genes for lung cancer. We performed transcriptome-wide association study (TWAS) for lung cancer overall, by histology (adenocarcinoma, squamous cell carcinoma and small cell lung cancer) and smoking subgroups (never- and ever-smokers). We performed replication analysis using lung data from the Genotype-Tissue Expression (GTEx) project. DNA damage assays were performed in human lung fibroblasts for selected TWAS genes. As expected, the main TWAS signal for all histological subtypes and ever-smokers was on chromosome 15q25. The gene most strongly associated with lung cancer at this locus using the TWAS approach was IREB2 (pTWAS = 1.09E-99), where lower predicted expression increased lung cancer risk. A new lung adenocarcinoma susceptibility locus was revealed on 9p13.3 and associated with higher predicted expression of AQP3 (pTWAS = 3.72E-6). Among the 45 previously described lung cancer GWAS loci, we mapped candidate target gene for 17 of them. The association AQP3-adenocarcinoma on 9p13.3 was replicated using GTEx (pTWAS = 6.55E-5). Consistent with the effect of risk alleles on gene expression levels, IREB2 knockdown and AQP3 overproduction promote endogenous DNA damage. These findings indicate genes whose expression in lung tissue directly influences lung cancer risk.

Candidate Domestication-Related Genes Revealed by Expression Quantitative Trait Loci Mapping of Narrow-Leafed Lupin (Lupinus angustifolius L.).

The last century has witnessed rapid domestication of the narrow-leafed lupin (Lupinus angustifolius L.) as a grain legume crop, exploiting discovered alleles conferring low-alkaloid content (iucundus), vernalization independence (Ku and Julius), and reduced pod shattering (lentus and tardus). In this study, a L. angustifolius mapping population was subjected to massive analysis of cDNA ends (MACE). The MACE yielded 4185 single nucleotide polymorphism (SNP) markers for linkage map improvement and 30,595 transcriptomic profiles for expression quantitative trait loci (eQTL) mapping. The eQTL highlighted a high number of cis- and trans-regulated alkaloid biosynthesis genes with gene expression orchestrated by a regulatory agent localized at iucundus locus, supporting the concept that ETHYLENE RESPONSIVE TRANSCRIPTION FACTOR RAP2-7 may control low-alkaloid phenotype. The analysis of Ku shed light on the vernalization response via FLOWERING LOCUS T and FD regulon in L. angustifolius, providing transcriptomic evidence for the contribution of several genes acting in C-repeat binding factor (CBF) cold responsiveness and in UDP-glycosyltransferases pathways. Research on lentus selected a DUF1218 domain protein as a candidate gene controlling the orientation of the sclerified endocarp and a homolog of DETOXIFICATION14 for purplish hue of young pods. An ABCG transporter was identified as a hypothetical contributor to sclerenchyma fortification underlying tardus phenotype.