Analysis of allele-specific expression of seven candidate genes involved in lipid metabolism in pig skeletal muscle and fat tissues reveals allelic imbalance of ACACA, LEP, SCD, and TNF

Abstract Background Genetic analysis of gene expression level is a promising approach for characterizing candidate genes that are involved in complex economic traits such as meat quality. In the present study, we conducted expression quantitative trait loci (eQTL) and allele-specific expression (ASE) analyses based on RNA-sequencing (RNAseq) data from the longissimus muscle of 189 Duroc × Luchuan crossed pigs in order to identify some candidate genes for meat quality traits. Results Using a genome-wide association study based on a mixed linear model, we identified 7192 cis-eQTL corresponding to 2098 cis-genes (p ≤ 1.33e-3, FDR ≤ 0.05) and 6400 trans-eQTL corresponding to 863 trans-genes (p ≤ 1.13e-6, FDR ≤ 0.05). ASE analysis using RNAseq SNPs identified 9815 significant ASE-SNPs in 2253 unique genes. Integrative analysis between the cis-eQTL and ASE target genes identified 540 common genes, including 33 genes with expression levels that were correlated with at least one meat quality trait. Among these 540 common genes, 63 have been reported previously as candidate genes for meat quality traits, such as PHKG1 (q-value = 1.67e-6 for the leading SNP in the cis-eQTL analysis), NUDT7 (q-value = 5.67e-13), FADS2 (q-value = 8.44e-5), and DGAT2 (q-value = 1.24e-3). Conclusions The present study confirmed several previously published candidate genes and identified some novel candidate genes for meat quality traits via eQTL and ASE analyses, which will be useful to prioritize candidate genes in further studies.

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Bulk segregant RNA-seq reveals expression and positional candidate genes and allele-specific expression for disease resistance against enteric septicemia of catfish

BMC Genomics ◽

10.1186/1471-2164-14-929 ◽

2013 ◽

Vol 14 (1) ◽

pp. 929 ◽

Cited By ~ 53

Author(s):

Ruijia Wang ◽

Luyang Sun ◽

Lisui Bao ◽

Jiaren Zhang ◽

Yanliang Jiang ◽

...

Keyword(s):

Disease Resistance ◽

Candidate Genes ◽

Rna Seq ◽

Specific Expression ◽

Allele Specific Expression ◽

Positional Candidate ◽

Allele Specific ◽

Enteric Septicemia

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Transcriptome analysis revealed chimeric RNAs, single nucleotide polymorphisms and allele-specific expression in porcine prenatal skeletal muscle

Scientific Reports ◽

10.1038/srep29039 ◽

2016 ◽

Vol 6 (1) ◽

Cited By ~ 7

Author(s):

Yalan Yang ◽

Zhonglin Tang ◽

Xinhao Fan ◽

Kui Xu ◽

Yulian Mu ◽

...

Keyword(s):

Skeletal Muscle ◽

Single Nucleotide Polymorphisms ◽

Transcriptome Analysis ◽

Nucleotide Polymorphisms ◽

Specific Expression ◽

Single Nucleotide ◽

Allele Specific Expression ◽

Chimeric Rnas ◽

Allele Specific

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Cis-regulatory changes in locomotor genes are associated with the evolution of burrowing behavior

10.1101/2021.09.27.462036 ◽

2021 ◽

Author(s):

Caroline K. Hu ◽

Ryan A. York ◽

Hillery C. Metz ◽

Nicole L. Bedford ◽

Hunter B. Fraser ◽

...

Keyword(s):

Gene Expression ◽

Candidate Genes ◽

Sister Species ◽

Behavioral Context ◽

Specific Expression ◽

Allele Specific Expression ◽

Regulatory Changes ◽

Allele Specific ◽

Burrowing Behavior ◽

Regulated Gene Expression

SummaryHow evolution modifies complex, innate behaviors is largely unknown. Divergence in many morphological traits has been linked, at least in part, to cis-regulatory changes in gene expression, a pattern also observed in some behaviors of recently diverged populations. Given this, we compared the gene expression in the brains of two interfertile sister species of Peromyscus mice, including allele-specific expression (ASE) of their F1 hybrids, that show large and heritable differences in burrowing behavior. Because cis-regulation may contribute to constitutive as well as activity-dependent gene expression, we also captured a molecular signature of burrowing circuit divergence by quantifying gene expression in mice shortly after burrowing. We found that several thousand genes were differentially expressed between the two sister species regardless of behavioral context, with several thousand more showing behavior-dependent differences. Allele-specific expression in F1 hybrids showed a similar pattern, suggesting that much of the differential expression is driven by cis-regulatory divergence. Genes related to locomotor coordination showed the strongest signals of lineage-specific selection on burrowing-induced cis-regulatory changes. By comparing these candidate genes to independent quantitative trait locus (QTL) mapping data, we found that the closest QTL markers to these candidate genes are associated with variation in burrow shape, demonstrating an enrichment for candidate locomotor genes near segregating causal loci. Together, our results provide insight into how cis-regulated gene expression can depend on behavioral context as well as how this dynamic regulatory divergence between species can be integrated with forward genetics to enrich our understanding of the genetic basis of behavioral evolution.

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Allele-specific expression is widespread in Bos indicus muscle and affects meat quality candidate genes

Scientific Reports ◽

10.1038/s41598-020-67089-0 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Marcela Maria de Souza ◽

Adhemar Zerlotini ◽

Marina Ibelli Pereira Rocha ◽

Jennifer Jessica Bruscadin ◽

Wellison Jarles da Silva Diniz ◽

...

Keyword(s):

Candidate Genes ◽

Meat Quality ◽

Bos Indicus ◽

Specific Expression ◽

Allele Specific Expression ◽

Allele Specific

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Unexpected variability of allelic imbalance estimates from RNA sequencing

10.1101/2020.02.18.948323 ◽

2020 ◽

Cited By ~ 1

Author(s):

Asia Mendelevich ◽

Svetlana Vinogradova ◽

Saumya Gupta ◽

Andrey A. Mironov ◽

Shamil Sunyaev ◽

...

Keyword(s):

Gene Expression ◽

Rna Sequencing ◽

Allelic Imbalance ◽

Experimental Methods ◽

Specific Gene ◽

Differential Analysis ◽

Specific Expression ◽

Allele Specific Expression ◽

Allele Specific ◽

Noise Models

RNA sequencing and other experimental methods that produce large amounts of data are increasingly dominant in molecular biology. However, the noise properties of these techniques have not been fully understood. We assessed the reproducibility of allele-specific expression measurements by conducting replicate sequencing experiments from the same RNA sample. Surprisingly, variation in the estimates of allelic imbalance (AI) between technical replicates was up to 7-fold higher than expected from commonly applied noise models. We show that AI overdispersion varies substantially between replicates and between experimental series, appears to arise during the construction of sequencing libraries, and can be measured by comparing technical replicates. We demonstrate that compensation for AI overdispersion greatly reduces technical variation and enables reliable differential analysis of allele-specific expression across samples and across experiments. Conversely, not taking AI overdispersion into account can lead to a substantial number of false positives in analysis of allele-specific gene expression

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Elimination of reference mapping bias reveals robust immune related allele-specific expression in crossbred sheep

10.1101/619122 ◽

2019 ◽

Cited By ~ 1

Author(s):

Mazdak Salavati ◽

Stephen J. Bush ◽

Sergio Palma-Vera ◽

Mary E. B. McCulloch ◽

David A. Hume ◽

...

Keyword(s):

Gene Expression ◽

Complex Traits ◽

Allelic Imbalance ◽

Cell Types ◽

Rna Seq ◽

Specific Expression ◽

Allele Specific Expression ◽

Gene Expression Atlas ◽

Expression Atlas ◽

Allele Specific

AbstractPervasive allelic variation at both gene and single nucleotide level (SNV) between individuals is commonly associated with complex traits in humans and animals. Allele-specific expression (ASE) analysis, using RNA-Seq, can provide a detailed annotation of allelic imbalance and infer the existence of cis-acting transcriptional regulation. However, variant detection in RNA-Seq data is compromised by biased mapping of reads to the reference DNA sequence. In this manuscript we describe an unbiased standardised computational pipeline for allele-specific expression analysis using RNA-Seq data, which we have adapted and developed using tools available under open licence. The analysis pipeline we present is designed to minimise reference bias while providing accurate profiling of allele-specific expression across tissues and cell types. Using this methodology, we were able to profile pervasive allelic imbalance across tissues and cell types, at both the gene and SNV level, in Texel x Scottish Blackface sheep, using the sheep gene expression atlas dataset. ASE profiles were pervasive in each sheep and across all tissue types investigated. However, ASE profiles shared across tissues were limited and instead they tended to be highly tissue-specific. These tissue-specific ASE profiles may underlie the expression of economically important traits and could be utilized as weighted SNVs, for example, to improve the accuracy of genomic selection in breeding programmes for sheep. An additional benefit of the pipeline is that it does not require parental genotypes and can therefore be applied to other RNA-Seq datasets for livestock, including those available on the Functional Annotation of Animal Genomes (FAANG) data portal. This study is the first global characterisation of moderate to extreme ASE in tissues and cell types from sheep. We have applied a robust methodology for ASE profiling, to provide both a novel analysis of the multi-dimensional sheep gene expression atlas dataset, and a foundation for identifying the regulatory and expressed elements of the genome that are driving complex traits in livestock.

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Investigation of allele specific expression in various tissues of broiler chickens using the detection tool VADT

Scientific Reports ◽

10.1038/s41598-021-83459-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

M. Joseph Tomlinson ◽

Shawn W. Polson ◽

Jing Qiu ◽

Juniper A. Lake ◽

William Lee ◽

...

Keyword(s):

Broiler Chickens ◽

Nucleotide Polymorphisms ◽

Rna Seq ◽

Specific Expression ◽

Single Nucleotide ◽

Allele Specific Expression ◽

Detection Tool ◽

Commercial Broiler ◽

Significant Phenomenon ◽

Allele Specific

AbstractDifferential abundance of allelic transcripts in a diploid organism, commonly referred to as allele specific expression (ASE), is a biologically significant phenomenon and can be examined using single nucleotide polymorphisms (SNPs) from RNA-seq. Quantifying ASE aids in our ability to identify and understand cis-regulatory mechanisms that influence gene expression, and thereby assist in identifying causal mutations. This study examines ASE in breast muscle, abdominal fat, and liver of commercial broiler chickens using variants called from a large sub-set of the samples (n = 68). ASE analysis was performed using a custom software called VCF ASE Detection Tool (VADT), which detects ASE of biallelic SNPs using a binomial test. On average ~ 174,000 SNPs in each tissue passed our filtering criteria and were considered informative, of which ~ 24,000 (~ 14%) showed ASE. Of all ASE SNPs, only 3.7% exhibited ASE in all three tissues, with ~ 83% showing ASE specific to a single tissue. When ASE genes (genes containing ASE SNPs) were compared between tissues, the overlap among all three tissues increased to 20.1%. Our results indicate that ASE genes show tissue-specific enrichment patterns, but all three tissues showed enrichment for pathways involved in translation.

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Replicate sequencing libraries are important for quantification of allelic imbalance

Nature Communications ◽

10.1038/s41467-021-23544-8 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Asia Mendelevich ◽

Svetlana Vinogradova ◽

Saumya Gupta ◽

Andrey A. Mironov ◽

Shamil R. Sunyaev ◽

...

Keyword(s):

Allelic Imbalance ◽

False Positive Rate ◽

Error Rates ◽

Differential Analysis ◽

Rna Seq ◽

Specific Expression ◽

Technical Noise ◽

Specific Analysis ◽

Positive Rate ◽

Allele Specific

AbstractA sensitive approach to quantitative analysis of transcriptional regulation in diploid organisms is analysis of allelic imbalance (AI) in RNA sequencing (RNA-seq) data. A near-universal practice in such studies is to prepare and sequence only one library per RNA sample. We present theoretical and experimental evidence that data from a single RNA-seq library is insufficient for reliable quantification of the contribution of technical noise to the observed AI signal; consequently, reliance on one-replicate experimental design can lead to unaccounted-for variation in error rates in allele-specific analysis. We develop a computational approach, Qllelic, that accurately accounts for technical noise by making use of replicate RNA-seq libraries. Testing on new and existing datasets shows that application of Qllelic greatly decreases false positive rate in allele-specific analysis while conserving appropriate signal, and thus greatly improves reproducibility of AI estimates. We explore sources of technical overdispersion in observed AI signal and conclude by discussing design of RNA-seq studies addressing two biologically important questions: quantification of transcriptome-wide AI in one sample, and differential analysis of allele-specific expression between samples.

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