Expression Quantitative Trait Loci Mapping Research Articles

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.

<i>Cis</i>- and <i>Trans</i>-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.

33 Young Scholar Presentation: Expression quantitative trait loci and allele-specific expression exhibiting joint association with polygenic trait phenotypes in pigs

Abstract Advancements in sequencing technology, improvements in genome annotation, and development of quantitative genetic models have been instrumental to the significant genetic gains achieved in pork production. Several quantitative trait loci (QTL) have been identified for growth, meat quality and carcass composition (GMC) phenotypes, however, the biological mechanisms underlying most QTL remain unknown. Functional genomic analysis can reveal insights on the genetic architecture of complex traits, and transcriptomic profiling of skeletal muscle during the conversion of muscle to meat can identify critical regulators of GMC phenotypes. Gene transcripts obtained with RNA-seq of longissimus muscle from 168 pigs were used to estimate gene expression variation subject to genetic control by mapping expression QTL (eQTL) and allele-specific expression (ASE). A total of 334 eQTL were mapped (FDR≤0.01) and joint association of eQTL with phenotypic QTL (pQTL) segregating in our population revealed 16 genes significantly associated with 21 pQTL for GMC phenotypes. ASE analysis facilitates the identification of cis-acting regulation of transcript abundance. We tested for ASE in 69,502 coding SNP (cSNP) and a total of 18,234 cSNP with significant ASE were identified (FDR≤0.01). A gene-wise conditional analysis fitting all ASE cSNP per gene for each phenotype identified 60 genes associated with GMC phenotypes. A comparison of eQTL with ASE cSNP showed an overlap of 136 genes. Pearson correlations of peak eQTL SNP with ASE cSNP was significant for 51% of these genes. The ASE analysis showed more precision in the identification of cis-acting effects than the genome-wide eQTL analysis; however, both approaches provide valuable information on the regulation of transcript abundance. For instance, we observed 24 genes associated with distant eQTL (trans effects) and exhibiting ASE. This study provides new information on the complex regulation of the pig longissimus muscle transcriptome and associations with measurable differences in economically important phenotypic traits.

A systems genetics analysis in Eucalyptus reveals coordination of metabolic pathways associated with xylan modification in wood‐forming tissues

Acetyl- and methylglucuronic acid decorations of xylan, the dominant hemicellulose in secondary cell walls (SCWs) of woody dicots, affect its interaction with cellulose and lignin to determine SCW structure and extractability. Genes and pathways involved in these modifications may be targets for genetic engineering; however, little is known about the regulation of xylan modifications in woody plants. To address this, we assessed genetic and gene expression variation associated with xylan modification in developing xylem of Eucalyptus grandis×Eucalyptus urophylla interspecific hybrids. Expression quantitative trait locus (eQTL) mapping identified potential regulatory polymorphisms affecting gene expression modules associated with xylan modification. We identified 14 putative xylan modification genes that are members of five expression modules sharing seven trans-eQTL hotspots. The xylan modification genes are prevalent in two expression modules. The first comprises nucleotide sugar interconversion pathways supplying the essential precursors for cellulose and xylan biosynthesis. The second contains genes responsible for phenylalanine biosynthesis and S-adenosylmethionine biosynthesis required for glucuronic acid and monolignol methylation. Co-expression and co-regulation analyses also identified four metabolic sources of acetyl coenxymeA that appear to be transcriptionally coordinated with xylan modification. Our systems genetics analysis may provide new avenues for metabolic engineering to alter wood SCW biology for enhanced biomass processability.

Dynamic genetic regulation of gene expression during cellular differentiation.

Genetic regulation of gene expression is dynamic, as transcription can change during cell differentiation and across cell types. We mapped expression quantitative trait loci (eQTLs) throughout differentiation to elucidate the dynamics of genetic effects on cell type-specific gene expression. We generated time-series RNA sequencing data, capturing 16 time points during the differentiation of induced pluripotent stem cells to cardiomyocytes, in 19 human cell lines. We identified hundreds of dynamic eQTLs that change over time, with enrichment in enhancers of relevant cell types. We also found nonlinear dynamic eQTLs, which affect only intermediate stages of differentiation and cannot be found by using data from mature tissues. These fleeting genetic associations with gene regulation may explain some of the components of complex traits and disease. We highlight one example of a nonlinear eQTL that is associated with body mass index.

Genome-wide cis-regulatory signatures for modulation of agronomic traits as exemplified by drought yield index (DYI) in chickpea.

Developing functional molecular tags from the cis-regulatory sequence components of genes is vital for their deployment in efficient genetic dissection of complex quantitative traits in crop plants including chickpea. The current study identified 431,194 conserved non-coding SNP (CNSNP) from the cis-regulatory element regions of genes which were annotated on a chickpea genome. These genome-wide CNSNP marker resources are made publicly accessible through a user-friendly web-database ( http://www.cnsnpcicarbase.com ). The CNSNP-based quantitative trait loci (QTL) and expression QTL (eQTL) mapping and genome-wide association study (GWAS) were further integrated with global gene expression landscapes, molecular haplotyping, and DNA-protein interaction study in the association panel and recombinant inbred lines (RIL) mapping population to decode complex genetic architecture of one of the vital seed yield trait under drought stress, drought yield index (DYI), in chickpea. This delineated two constituted natural haplotypes and alleles from a histone H3 protein-coding gene and its transcriptional regulator NAC transcription factor (TF) harboring the major QTLs and trans-acting eQTL governing DYI in chickpea. The effect of CNSNPs in TF-binding cis-element of a histone H3 gene in altering the binding affinity and transcriptional activity of NAC TF based on chromatin immunoprecipitation-quantitative PCR (ChIP-qPCR) assay was evident. The CNSNP-led promising molecular tags scanned will essentially have functional significance to decode transcriptional gene regulatory function and thus can drive translational genomic analysis in chickpea.

On Using Local Ancestry to Characterize the Genetic Architecture of Human Traits: Genetic Regulation of Gene Expression in Multiethnic or Admixed Populations

Understanding the nature of the genetic regulation of gene expression promises to advance our understanding of the genetic basis of disease. However, the methodological impact of the use of local ancestry on high-dimensional omics analyses, including, most prominently, expression quantitative trait loci (eQTL) mapping and trait heritability estimation, in admixed populations remains critically underexplored. Here, we develop a statistical framework that characterizes the relationships among the determinants of the genetic architecture of an important class of molecular traits. We provide a computationally efficient approach to local ancestry analysis in eQTL mapping while increasing control of type I and type II error over traditional approaches. Applying our method to National Institute of General Medical Sciences (NIGMS) and Genotype-Tissue Expression (GTEx) datasets, we show that the use of local ancestry can improve eQTL mapping in admixed and multiethnic populations, respectively. We estimate the trait variance explained by ancestry by using local admixture relatedness between individuals. By using simulations of diverse genetic architectures and degrees of confounding, we show improved accuracy in estimating heritability when accounting for local ancestry similarity. Furthermore, we characterize the sparse versus polygenic components of gene expression in admixed individuals. Our study has important methodological implications for genetic analysis of omics traits across a range of genomic contexts, from a single variant to a prioritized region to the entire genome. Our findings highlight the importance of using local ancestry to better characterize the heritability of complex traits and to more accurately map genetic associations.

Genetical Genomics of Tonic Immobility in the Chicken.

Identifying the molecular mechanisms of animal behaviour is an enduring goal for researchers. Gaining insight into these mechanisms enables us to gain a greater understanding of behaviour and their genetic control. In this paper, we perform Quantitative Trait Loci (QTL) mapping of tonic immobility behaviour in an advanced intercross line between wild and domestic chickens. Genes located within the QTL interval were further investigated using global expression QTL (eQTL) mapping from hypothalamus tissue, as well as causality analysis. This identified five candidate genes, with the genes PRDX4 and ACOT9 emerging as the best supported candidates. In addition, we also investigated the connection between tonic immobility, meat pH and struggling behaviour, as the two candidate genes PRDX4 and ACOT9 have previously been implicated in controlling muscle pH at slaughter. We did not find any phenotypic correlations between tonic immobility, struggling behaviour and muscle pH in a smaller additional cohort, despite these behaviours being repeatable within-test.