A comprehensive catalogue of regulatory variants in the cattle transcriptome

Liu, Shuli; Gao, Yahui; Canela-Xandri, Oriol; Wang, Sheng; Yu, Ying; Cai, Wentao; Li, Bingjie; Pairo-Castineira, Erola; D’Mellow, Kenton; Rawlik, Konrad; Xia, Charley; Yao, Yuelin; Li, Xiujin; Yan, Ze; Li, Congjun; Rosen, Benjamin D.; VanRaden, P.M.; Zhang, Shengli; Ma, Li; Cole, John B.; Liu, George E.; Tenesa, Albert; Fang, Lingzhao

doi:10.1101/2020.12.01.406280

Cited by 23 publications

(30 citation statements)

References 70 publications

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“…In addition, the brain and the testis exhibited a higher percentage of tissue-enriched proteins, which were also found in human ( Uhlén et al, 2015 ; Wang et al, 2019 ), cattle ( Liu et al, 2020 ), sheep ( Clark et al, 2017 ), and goat ( Muriuki et al, 2019 ), than other organs. The result of the functional analysis showed that brain-enriched genes were significantly associated with synapse and neuron function and testis-enriched genes with spermatogenesis and reproduction in humans ( Wang et al, 2019 ), cattle ( Liu et al, 2020 ), and donkey. Due to the high specificity of function, the expression of these genes may be restricted to spermatogenic cells or neurons.…”

Section: Discussionmentioning

confidence: 92%

“…Whole-genome gene expression arrays detected 17,924 protein-coding genes in mouse ( Su et al, 2004 ). One cattle transcriptome study showed that 16,564 genes were detected by analyzing 100 different tissues and cell types ( Liu et al, 2020 ). In addition, approximately 18,528, 19,921, and 14,426 protein-coding genes were detected in domestic goat ( Muriuki et al, 2019 ), sheep ( Clark et al, 2017 ), and pig ( Freeman et al, 2012 ), respectively.…”

Section: Discussionmentioning

confidence: 99%

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Transcriptome Atlas of 16 Donkey Tissues

Wang

Miao

Zhao³

et al. 2021

Front. Genet.

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Donkeys (Equus asinus) are important livestock with great economic value in meat, skin, and milk production. However, a lack of knowledge of the transcriptome landscape across a wide range of donkey tissues limits genetic selective breeding and conservation. Here we used transcriptomics to describe the transcriptome landscape, classify the tissue-specific gene expression across all primary donkey tissues, and present supplementary analyses on the protein level of additional donkey milk samples. Overall, 16,013 protein-coding genes and 21,983 transcripts were mapped to the reference genome, including 6,778 ubiquitously expressed genes and 2,601 tissue-enriched genes. Functional analysis revealed that the function of the tissue-enriched genes was highly tissue specific. Tissue-elevated genes that could be associated with unique phenotypes in donkey were analyzed. The results showed that, compared with those in human and other livestock, the lysozyme gene in donkey breast was specifically and highly expressed. The calcium-binding lysozyme, encoded by the lysozyme gene, was also detected in high amounts in donkey milk. Given those intact lysozyme genes that predict potentially functional calcium-binding lysozyme found in only a few species (e.g., donkey and horse), the high expression of the lysozyme gene in donkey breast may contribute to the high lysozyme content in donkey milk. Furthermore, 71% of the proteins in donkey milk overlapped with human milk protein, higher than the overlapping rates of bovine, sheep, and swine with humans. The donkey transcriptomic resource contributes to the available genomic resources to interpret the molecular mechanisms underlying phenotype traits.

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Section: Discussionmentioning

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Transcriptome Atlas of 16 Donkey Tissues

Wang

Miao

Zhao³

et al. 2021

Front. Genet.

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“…This study represents a first step to more ambitious projects that could analyze tens of thousands of available RNA-seq datasets to build a GTEx-like atlas reporting cis - and trans - genetic associations with gene expression, as previously performed in human ( The GTEx Consortium, 2020 ) and more recently in cattle ( Liu et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

RNA-Seq Data for Reliable SNP Detection and Genotype Calling: Interest for Coding Variant Characterization and Cis-Regulation Analysis by Allele-Specific Expression in Livestock Species

Jehl

Degalez²,

Bernard³

et al. 2021

Front. Genet.

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In addition to their common usages to study gene expression, RNA-seq data accumulated over the last 10 years are a yet-unexploited resource of SNPs in numerous individuals from different populations. SNP detection by RNA-seq is particularly interesting for livestock species since whole genome sequencing is expensive and exome sequencing tools are unavailable. These SNPs detected in expressed regions can be used to characterize variants affecting protein functions, and to study cis-regulated genes by analyzing allele-specific expression (ASE) in the tissue of interest. However, gene expression can be highly variable, and filters for SNP detection using the popular GATK toolkit are not yet standardized, making SNP detection and genotype calling by RNA-seq a challenging endeavor. We compared SNP calling results using GATK suggested filters, on two chicken populations for which both RNA-seq and DNA-seq data were available for the same samples of the same tissue. We showed, in expressed regions, a RNA-seq precision of 91% (SNPs detected by RNA-seq and shared by DNA-seq) and we characterized the remaining 9% of SNPs. We then studied the genotype (GT) obtained by RNA-seq and the impact of two factors (GT call-rate and read number per GT) on the concordance of GT with DNA-seq; we proposed thresholds for them leading to a 95% concordance. Applying these thresholds to 767 multi-tissue RNA-seq of 382 birds of 11 chicken populations, we found 9.5 M SNPs in total, of which ∼550,000 SNPs per tissue and population with a reliable GT (call rate ≥ 50%) and among them, ∼340,000 with a MAF ≥ 10%. We showed that such RNA-seq data from one tissue can be used to (i) detect SNPs with a strong predicted impact on proteins, despite their scarcity in each population (16,307 SIFT deleterious missenses and 590 stop-gained), (ii) study, on a large scale, cis-regulations of gene expression, with ∼81% of protein-coding and 68% of long non-coding genes (TPM ≥ 1) that can be analyzed for ASE, and with ∼29% of them that were cis-regulated, and (iii) analyze population genetic using such SNPs located in expressed regions. This work shows that RNA-seq data can be used with good confidence to detect SNPs and associated GT within various populations and used them for different analyses as GTEx studies.

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“…[ 5 – 7 ]), and expression quantitative trait locus studies (e.g. [ 8 – 11 ]) will help prioritize non-coding variants, that are likely to explain a substantial part of the genetic variation in quantitative traits (quantified by [ 12 ] in cattle), but with effects that are challenging to predict from DNA sequence. We expect that this will make challenges in the identification of causative variants more pressing in the near future.…”

Section: Introductionmentioning

confidence: 99%

Evidence for and localization of proposed causative variants in cattle and pig genomes

Johnsson

Jungnickel

2021

Genet Sel Evol

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Background This paper reviews the localization of published potential causative variants in contemporary pig and cattle reference genomes, and the evidence for their causality. In spite of the difficulties inherent to the identification of causative variants from genetic mapping and genome-wide association studies, researchers in animal genetics have proposed putative causative variants for several traits relevant to livestock breeding. Results For this review, we read the literature that supports potential causative variants in 13 genes (ABCG2, DGAT1, GHR, IGF2, MC4R, MSTN, NR6A1, PHGK1, PRKAG3, PLRL, RYR1, SYNGR2 and VRTN) in cattle and pigs, and localized them in contemporary reference genomes. We review the evidence for their causality, by aiming to separate the evidence for the locus, the proposed causative gene and the proposed causative variant, and report the bioinformatic searches and tactics needed to localize the sequence variants in the cattle or pig genome. Conclusions Taken together, there is usually good evidence for the association at the locus level, some evidence for a specific causative gene at eight of the loci, and some experimental evidence for a specific causative variant at six of the loci. We recommend that researchers who report new potential causative variants use referenced coordinate systems, show local sequence context, and submit variants to repositories.

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A comprehensive catalogue of regulatory variants in the cattle transcriptome

Cited by 23 publications

References 70 publications

Transcriptome Atlas of 16 Donkey Tissues

Transcriptome Atlas of 16 Donkey Tissues

RNA-Seq Data for Reliable SNP Detection and Genotype Calling: Interest for Coding Variant Characterization and Cis-Regulation Analysis by Allele-Specific Expression in Livestock Species

Evidence for and localization of proposed causative variants in cattle and pig genomes

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