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ReQTL: identifying correlations between expressed SNVs and gene expression using RNA-sequencing data.

ABSTRACT: MOTIVATION:By testing for associations between DNA genotypes and gene expression levels, expression quantitative trait locus (eQTL) analyses have been instrumental in understanding how thousands of single nucleotide variants (SNVs) may affect gene expression. As compared to DNA genotypes, RNA genetic variation represents a phenotypic trait that reflects the actual allele content of the studied system. RNA genetic variation at expressed SNV loci can be estimated using the proportion of alleles bearing the variant nucleotide (variant allele fraction, VAFRNA). VAFRNA is a continuous measure which allows for precise allele quantitation in loci where the RNA alleles do not scale with the genotype count. We describe a method to correlate VAFRNA with gene expression and assess its ability to identify genetically regulated expression solely from RNA-sequencing (RNA-seq) datasets. RESULTS:We introduce ReQTL, an eQTL modification which substitutes the DNA allele count for the variant allele fraction at expressed SNV loci in the transcriptome (VAFRNA). We exemplify the method on sets of RNA-seq data from human tissues obtained though the Genotype-Tissue Expression (GTEx) project and demonstrate that ReQTL analyses are computationally feasible and can identify a subset of expressed eQTL loci. AVAILABILITY AND IMPLEMENTATION:A toolkit to perform ReQTL analyses is available at https://github.com/HorvathLab/ReQTL. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.

SUBMITTER: Spurr LF

PROVIDER: S-EPMC7058180 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Project description:The Dongxiang Blue-shelled chicken is one of the most valuable Chinese indigenous poultry breeds. However, compared to the Italian native White Leghorn, although this Chinese breed possesses numerous favorable characteristics, it also exhibits lower growth performance and fertility. Here, we utilized genotyping sequencing data obtained via genome reduction on a sequencing platform to detect 100,114 single nucleotide polymorphisms and perform further biological analysis and functional annotation. We employed cross-population extended haplotype homozygosity, eigenvector decomposition combined with genome-wide association studies (EigenGWAS), and efficient mixed-model association expedited methods to detect areas of the genome that are potential selected regions (PSR) in both chicken breeds, and performed gene ontology (GO) enrichment and quantitative trait loci (QTL) analyses annotating using the Kyoto Encyclopedia of Genes and Genomes. The results of this study revealed a total of 2424 outlier loci (p-value <0.01), of which 2144 occur in the White Leghorn breed and 280 occur in the Dongxiang Blue-shelled chicken. These correspond to 327 and 94 PSRs containing 297 and 54 genes, respectively. The most significantly selected genes in Blue-shelled chicken are TMEM141 and CLIC3, while the SLCO1B3 gene, related to eggshell color, was identified via EigenGWAS. We show that the White Leghorn genes JARID2, RBMS3, GPC3, TRIB2, ROBO1, SAMSN1, OSBP2, and IGFALS are involved in immunity, reproduction, and growth, and thus might represent footprints of the selection process. In contrast, we identified six significantly enriched pathways in the Dongxiang Blue-shelled chicken that are related to amino acid and lipid metabolism as well as signal transduction. Our results also reveal the presence of a GO term associated with cell metabolism that occurs mainly in the White Leghorn breed, while the most significant QTL regions mapped to the Chicken QTL Database (GG_4.0) for the Dongxiang Blue-shelled breed are predominantly related to lesions, bone mineral content, and other related traits compared to tibia length and body weight (i.e., at 14, 28, 42, and 70 d) in the White Leghorn. The results of this study highlight differences in growth, immunity, and egg quality traits between the two breeds, and provide a foundation for the exploration of their genetic mechanisms.

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ReQTL: identifying correlations between expressed SNVs and gene expression using RNA-sequencing data.

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