Analysis of Whole Genome Resequencing Datasets from a Worldwide Sample of Sheep Breeds to Identify Potential Causal Mutations Influencing Milk Composition Traits

Marina, H.; Gutiérrez-Gil, Beatriz; Esteban-Blanco, Cristina; Pelayo, R.; Arranz, Juan José

doi:10.3390/ani10091542

Cited by 8 publications

(12 citation statements)

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“…From the raw sequencing data, genotypes for SNP markers obtained by performing a variant calling analysis, following Marina et␣al . (2020), were considered for further comparison with the genotypes generated by SNP‐chip genotyping. The complete variant calling analysis performed has been previously described by our research group (see Marina et␣al .…”

Section: Methodsmentioning

confidence: 99%

“…The complete variant calling analysis performed has been previously described by our research group (see Marina et␣al . 2020, for details) and included the following steps: (i) the quality evaluation of the raw paired‐end reads was performed with the fastqc program (Andrews et␣al . 2015); (ii) the poor quality reads were filtered with Trimmomatic (Bolger et␣al .…”

Section: Methodsmentioning

confidence: 99%

“…In addition to SNP array genotyping, the 31 DNA sheep samples considered in this work were subjected to WGR using paired-end Illumina sequencing technology (Illumina HiSeq 2000 and HiSeq 2500 sequencers). From the raw sequencing data, genotypes for SNP markers obtained by performing a variant calling analysis, following Marina et al (2020), were considered for further comparison with the genotypes generated by SNP-chip genotyping. The complete variant calling analysis performed has been previously described by our research group (see Marina et al 2020, for details) and included the following steps: (i) the quality evaluation of the raw paired-end reads was performed with the FASTQC program (Andrews et al 2015); (ii) the poor quality reads were filtered with Trimmomatic (Bolger et al 2014), using filter parameters to paired-end samples (-phred33, LEADING:5, TRAILING:5 SLIDINGWINDOW:4:20, MIN-LEN:36 ILLUMINACLIP: Trimmomatic-0.33/adapters/ TruSeq 3-PE.fa:2:30:10); (iii) sample alignments against the Oar_v.3.1 and Oar_rambouillet_v3.1 ovine reference genome assemblies (https://www.ensembl.org/index.html) were performed with the program Burrows-Wheeler Aligner using the algorithm of maximal exact matches (mem); (iv) data manipulation and statistics analysis were performed using SAMTOOLS , Picard (Wysoker et al 2019) were used to add the identifier code for each of the known variants through the Ensembl database as the reference; and (viii) the SNPEFF program (Cingolani et al 2012) was used to extract genotypes for variants also genotyped through the two considered SNPchip platforms.…”

Section: Variant Calling Analysis Of Wgr Datasetsmentioning

confidence: 99%

“…2015; Marina et␣al . 2020). Based on these factors, one can easily identify the need to jointly analyse SNP‐chip datasets generated with different platforms for different purposes (e.g., meta‐analyses in different populations, merging of low‐ and high‐density chips genotyped in the same population).…”

Section: Introductionmentioning

confidence: 99%

“…In contrast, Affymetrix Axiom technology is a two-colour, ligation-based assay using 30-mer oligonucleotide probes that allow simultaneous genotyping of 384 samples with 50K SNPs or 96 individuals with 650K SNPs (Hoffmann et al 2011). The development of specific custom chips that are adapted to new versions of the genome or particular situations, such as the imputation of microsatellites used in determining paternity in specific animal populations, has become increasingly frequent (Nicolazzi et al 2015;Marina et al 2020). Based on these factors, one can easily identify the need to jointly analyse SNP-chip datasets generated with different platforms for different purposes (e.g., metaanalyses in different populations, merging of low-and high-density chips genotyped in the same population).…”

Section: Introductionmentioning

confidence: 99%

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Study on the concordance between different SNP‐genotyping platforms in sheep

et al. 2021

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Different SNP genotyping technologies are commonly used in multiple studies to perform QTL detection, genotype imputation, and genomic predictions. Therefore, genotyping errors cannot be ignored, as they can reduce the accuracy of different procedures applied in genomic selection, such as genomic imputation, genomic predictions, and false-positive results in genome-wide association studies. Currently, whole-genome resequencing (WGR) also offers the potential for variant calling analysis and high-throughput genotyping. WGR might overshadow array-based genotyping technologies due to the larger amount and precision of the genomic information provided; however, its comparatively higher price per individual still limits its use in larger populations. Thus, the objective of this work was to evaluate the accuracy of the two most popular SNP-chip technologies, namely, Affymetrix and Illumina, for high-throughput genotyping in sheep considering high-coverage WGR datasets as references. Analyses were performed using two reference sheep genome assemblies, the popular Oar_v3.1 reference genome and the latest available version Oar_rambouillet_v1.0. Our results demonstrate that the genotypes from both platforms are suggested to have high concordance rates with the genotypes determined from reference WGR datasets (96.59% and 99.51% for Affymetrix and Illumina technologies, respectively). The concordance results provided in the current study can pinpoint low reproducible markers across multiple platforms used for sheep genotyping data. Comparing results using two reference genome assemblies also informs how genome assembly quality can influence genotype concordance rates among different genotyping platforms. Moreover, we describe an efficient pipeline to test the reliability of markers included in sheep SNP-chip panels against WGR datasets available on public databases. This pipeline may be helpful for discarding low-reliability markers before exploiting genomic information for gene mapping analyses or genomic prediction.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Variant Calling Analysis Of Wgr Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Study on the concordance between different SNP‐genotyping platforms in sheep

et al. 2021

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Host genetic control on rumen microbiota and its impact on dairy traits in sheep

et al. 2022

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Background Milk yield and fine composition in sheep depend on the volatile and long-chain fatty acids, microbial proteins, vitamins produced through feedstuff digestion by the rumen microbiota. In cattle, the host genome has been shown to have a low to moderate genetic control on rumen microbiota abundance but a high control on dairy traits with heritabilities higher than 0.30. There is little information on the genetic correlations and quantitative trait loci (QTL) that simultaneously affect rumen microbiota abundance and dairy traits in ruminants, especially in sheep. Thus, our aim was to quantify the effect of the host genetics on rumen bacterial abundance and the genetic correlations between rumen bacterial abundance and several dairy traits, and to identify QTL that are associated with both rumen bacterial abundance and milk traits. Results Our results in Lacaune sheep show that the heritability of rumen bacterial abundance ranges from 0 to 0.29 and that the heritability of 306 operational taxonomic units (OTU) is significantly different from 0. Of these 306 OTU, 96 that belong mainly to the Prevotellaceae, Lachnospiraceae and Ruminococcaceae bacterial families show strong genetic correlations with milk fatty acids and proteins (absolute values ranging from 0.33 to 0.99). Genome-wide association studies revealed a QTL for alpha-lactalbumin concentration in milk on Ovis aries chromosome (OAR) 11, and six QTL for rumen bacterial abundances i.e., for two OTU belonging to the genera Prevotella (OAR3 and 5), Rikeneleaceae_RC9_gut_group (OAR5), Ruminococcus (OAR5), an unknown genus of order Clostridia UCG-014 (OAR10), and CAG-352 (OAR11). None of these detected regions are simultaneously associated with rumen bacterial abundance and dairy traits, but the bacterial families Prevotellaceae, Lachnospiraceae and F082 show colocalized signals on OAR3, 5, 15 and 26. Conclusions In Lacaune dairy sheep, rumen microbiota abundance is partially controlled by the host genetics and is poorly genetically linked with milk protein and fatty acid compositions, and three main bacterial families, Prevotellaceae, Lachnospiraceae and F082, show specific associations with OAR3, 5, 15 and 26.

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A cell transcriptomic profile provides insights into adipocytes of porcine mammary gland across development

Fan,

Jin,

et al. 2023

J Animal Sci Biotechnol

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Background Studying the composition and developmental mechanisms in mammary gland is crucial for healthy growth of newborns. The mammary gland is inherently heterogeneous, and its physiological function dependents on the gene expression of multiple cell types. Most studies focused on epithelial cells, disregarding the role of neighboring adipocytes. Results Here, we constructed the largest transcriptomic dataset of porcine mammary gland cells thus far. The dataset captured 126,829 high-quality nuclei from physiological mammary glands across five developmental stages (d 90 of gestation, G90; d 0 after lactation, L0; d 20 after lactation, L20; 2 d post natural involution, PI2; 7 d post natural involution, PI7). Seven cell types were identified, including epithelial cells, adipocytes, endothelial cells, fibroblasts cells, immune cells, myoepithelial cells and precursor cells. Our data indicate that mammary glands at different developmental stages have distinct phenotypic and transcriptional signatures. During late gestation (G90), the differentiation and proliferation of adipocytes were inhibited. Meanwhile, partly epithelial cells were completely differentiated. Pseudo-time analysis showed that epithelial cells undergo three stages to achieve lactation, including cellular differentiation, hormone sensing, and metabolic activation. During lactation (L0 and L20), adipocytes area accounts for less than 0.5% of mammary glands. To maintain their own survival, the adipocyte exhibited a poorly differentiated state and a proliferative capacity. Epithelial cells initiate lactation upon hormonal stimulation. After fulfilling lactation mission, their undergo physiological death under high intensity lactation. Interestingly, the physiological dead cells seem to be actively cleared by immune cells via CCL21-ACKR4 pathway. This biological process may be an important mechanism for maintaining homeostasis of the mammary gland. During natural involution (PI2 and PI7), epithelial cell populations dedifferentiate into mesenchymal stem cells to maintain the lactation potential of mammary glands for the next lactation cycle. Conclusion The molecular mechanisms of dedifferentiation, proliferation and redifferentiation of adipocytes and epithelial cells were revealed from late pregnancy to natural involution. This cell transcriptomic profile constitutes an essential reference for future studies in the development and remodeling of the mammary gland at different stages.

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Analysis of Whole Genome Resequencing Datasets from a Worldwide Sample of Sheep Breeds to Identify Potential Causal Mutations Influencing Milk Composition Traits

Cited by 8 publications

References 56 publications

Study on the concordance between different SNP‐genotyping platforms in sheep

Study on the concordance between different SNP‐genotyping platforms in sheep

Host genetic control on rumen microbiota and its impact on dairy traits in sheep

A cell transcriptomic profile provides insights into adipocytes of porcine mammary gland across development

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