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  • Research article
  • Open Access

Variant discovery in the sheep milk transcriptome using RNA sequencing

  • 1,
  • 1,
  • 2,
  • 3 and
  • 1Email author
BMC Genomics201718:170

Received: 25 August 2016

Accepted: 10 February 2017

Published: 15 February 2017



The identification of genetic variation underlying desired phenotypes is one of the main challenges of current livestock genetic research. High-throughput transcriptome sequencing (RNA-Seq) offers new opportunities for the detection of transcriptome variants (SNPs and short indels) in different tissues and species. In this study, we used RNA-Seq on Milk Sheep Somatic Cells (MSCs) with the goal of characterizing the genetic variation within the coding regions of the milk transcriptome in Churra and Assaf sheep, two common dairy sheep breeds farmed in Spain.


A total of 216,637 variants were detected in the MSCs transcriptome of the eight ewes analyzed. Among them, a total of 57,795 variants were detected in the regions harboring Quantitative Trait Loci (QTL) for milk yield, protein percentage and fat percentage, of which 21.44% were novel variants. Among the total variants detected, 561 (2.52%) and 1,649 (7.42%) were predicted to produce high or moderate impact changes in the corresponding transcriptional unit, respectively. In the functional enrichment analysis of the genes positioned within selected QTL regions harboring novel relevant functional variants (high and moderate impact), the KEGG pathway with the highest enrichment was “protein processing in endoplasmic reticulum”. Additionally, a total of 504 and 1,063 variants were identified in the genes encoding principal milk proteins and molecules involved in the lipid metabolism, respectively. Of these variants, 20 mutations were found to have putative relevant effects on the encoded proteins.


We present herein the first transcriptomic approach aimed at identifying genetic variants of the genes expressed in the lactating mammary gland of sheep. Through the transcriptome analysis of variability within regions harboring QTL for milk yield, protein percentage and fat percentage, we have found several pathways and genes that harbor mutations that could affect dairy production traits. Moreover, remarkable variants were also found in candidate genes coding for major milk proteins and proteins related to milk fat metabolism. Several of the SNPs found in this study could be included as suitable markers in genotyping platforms or custom SNP arrays to perform association analyses in commercial populations and apply genomic selection protocols in the dairy production industry.


  • Dairy Sheep
  • Milk Somatic Cells
  • RNA-Seq
  • Transcriptome Variants


The identification of genetic variation underlying desired phenotypes is one of the main challenges in current dairy genetic research. The higher content of sheep milk in total solids when compared to cow and goat milk favors its greater aptitude for cheese production [1]. Therefore, genetic variation within genes that influence the total solid content of milk is of crucial interest in dairy sheep breeding because this variability could be linked to milk composition, milk quality and cheese production.

Over the years, several studies on polymorphisms in ovine major milk proteins (caseins and whey proteins) have appeared due to the potential association of these polymorphisms with milk yield, milk composition and milk technological aspects [14]. Additionally, as the majority of dairy sheep traits are complex, research on dairy Quantitative Trait Loci (QTL) mapping has also been widely performed. To date, 1,336 sheep QTL influencing 212 different traits have been reported in a total of 119 publications (; accessed at 24 November 2016) [5]. In relation to milk traits, 242 QTL have been reported [5]. However, the traditional methodology used for QTL mapping with genome-wide sparse microsatellite markers or with low/middle density Single Nucleotide Polymorphism (SNP) genotyping platforms makes it difficult to identify the true causal mutations underlying these complex traits.

Over the last few years, the constant improvement of high-throughput sequencing platforms and the availability of genome sequencing data have facilitated the detection of a substantial number of genetic variants in livestock [6, 7]. The identification of this genomic variation is crucial to the rapid identification of mutations that compromise animal health and productivity but also to build a database of polymorphisms that could be used as molecular markers for more accurate genomic predictions and genome-wide association studies [6].

High-throughput transcriptome sequencing technology (RNA-Seq) has been developed to identify and quantify gene expression in different tissues [8, 9]. Moreover, RNA-Seq also offers new opportunities for the efficient detection of transcriptome variants (SNPs and short indels) in different tissues and species [10, 11]. In this way, when compared to whole genome sequencing, RNA-Seq offers a cheaper alternative to identifying variation and, possibly, discovering the causal mutations underlying the analyzed phenotypes [12, 13].

In this study, we used RNA-Seq on Milk Sheep Somatic Cells (MSCs) with the goal of characterizing the genetic variation in the coding regions of the milk transcriptome in two dairy sheep breeds, Churra and Assaf, that are commonly farmed in Spain. In addition to the general characterization of variations in the sheep milk transcriptome, we focused our analysis on the detection of variability within the coding regions harboring QTL for milk yield, fat percentage and protein percentage and in the genes codifying for major milk proteins and enzymes related to milk fat metabolism. Thus, this analysis has allowed for the discovery of functionally relevant variants within genes related to dairy production traits that could be exploited by dairy sheep breeding programs after further research confirms the possible associations with phenotypes of interest.

Results and discussion

Sequencing and mapping

Milk samples from eight ewes (four Churra and four Assaf) were collected at different lactation time points (days 10, 50, 120 and 150 after lambing). Based on the quality score of the RNA (RIN > 7), we sequenced the MSCs transcriptome from eight animals on days 10, 50 and 150 of lactation and from six animals on day 120 of lactation. A total of 1,116 million paired-end reads was obtained from the transcriptome sequencing of the 30 milk samples analyzed. An alignment of the reads to the Ovis aries Oar_v3.1 genome yielded a mean of 88.10% of the reads per RNA-Seq sample that aligned to unique locations in the ovine genome. After merging the replicates from the same animal at the different sampling time-points and marking the duplicates on the resulting merged bam files, we found that an average of 119.33 million non-duplicated paired-end reads per animal mapped to the Oarv3.1 genome assembly. General RNA-Seq metrics obtained with the RSeQC software [14] that consider the annotation bed file of the reference sheep genome are summarized in Table 1. In our dataset of the sheep MSCs transcriptome, an average of 120.47 million tags per animal were defined. The term “tag” accounted for the number of times one read is spliced. The RSeQC program assigned an average of 110.08 million tags per merged sample to the annotated sheep genome regions. Therefore, approximately 10.39 million tags were not assigned to annotated regions, suggesting that approximately 10 million tags per sample mapped to intergenic regions. The comparative analysis performed in a previous study of the assembled transcripts of this RNA-Seq dataset with the ovine genome assembly Oar_v3.1 revealed that up to the 62% of the transcripts detected in the MSCs genome were intergenic [15]. These results reflect the incompleteness of the current annotation of the sheep transcriptome and presume the presence of non-annotated transcripts that could codify for novel proteins or constitute functional noncoding RNAs, like long noncoding RNAs (lncRNAs), microRNAs (miRNAs), short interfering RNAs (siRNAs), Piwi-interacting RNAs (piRNAs) or small nucleolar RNAs (snoRNAs). In the human genome the transcriptome functional non-coding elements have been estimated to constitute up to 98% of transcripts [16]. The identification of these functional elements in animals is one of the goals of the Functional Annotation of Animal Genomes (FAANG) project [17].
Table 1

Summary of sequencing results according to the annotation performed in this study of the MSC transcriptome based on the sheep genome reference Oar_v3.1

Total Reads (paired end)



Total Tags



Total Assigned















































a CDS Coding DNA sequence; b 5′UTR leader untranslated sequence; c 3′UTR trailer untranslated sequence; d TSS Transcription Start Site; e TES Transcription End Site

By focusing on assigned tags, as could be expected, the vast majority of tags mapped to coding genome regions. Specifically, we found an average of 65.85 million tags per animal, or 2008.93 tags/kb that mapped to CDSs (Table 1).

Variant detection and functional annotation

A total of 216,637 variants were detected in the MSCs transcriptome of the eight ewes analyzed after the variants were filtered (Table 2; Additional file 1). Of these variants, approximately the 78% were previously annotated in dbSNP (version 143). Among the total variants identified, 197,948 were SNPs and 18,689 were indels. The transition to transversion (Ts/Tv) ratio was 2.4, which was slightly higher than the 2.0-2.2 genome-wide Ts/Tv ratio reported in relation to human whole-genome sequence data [18]. However, this ratio is generally higher in exomes due to the increased presence of methylated cytosine in CpG dinucleotides in exonic regions [19].
Table 2

Summary statistics of the identified variants


Counts SnpEff

Counts VEP

Variants processed















Effects by impact

















Effects by type

















































































Considering SNPs and Indels, the variant density across the genome (Fig. 1) showed a more or less uniform distribution, with three regions showing a high density of variants that should be noted (more than 800 variants/Mb). Two of these regions with high densities of variants were located on chromosome 20 (OAR20) at OAR20:26–27 Mb and OAR20:27–28 Mb, with 858 and 1321 variants/Mb, respectively. The Major Histocompatibility Complex (MHC) of sheep is located in a region of chromosome 20 [20] that corresponds to the 2 Mb region with high variability detected in this study. This region on OAR20 was also identified to harbor a putative QTL for milk yield-related traits [21]. The other region with a high number of variants (972 variants/Mb) is located on OAR6 (OAR6:85–86 Mb) and is related to the genomic location of ovine genes coding for the milk caseins (OAR6: 85,087,000-85,318,000). The large number of variants positioned in this region could be due to the high transcription levels of caseins in the lactating mammary gland. The high transcription rate of the casein cluster region, with an average of 3.48 million of tags per kb of exon, refers to the transcription of both exons and the surrounding intronic regions. Hence, it is remarkable that a very high number of tags per kb of intron was found in the casein cluster region (7011.22 tags per kb of intron) when compared with the average across the whole sheep genome (16.86 tags per kb of intron). Previous RNA-Seq analysis suggest that the pattern of the intronic sequence read coverage in RNA-Seq could be explained by an inefficient poly(A)+ purification [22], the presence of intronic reads flanked by poly(A)+ stretches [23] or by transcripts undertaking splicing after polyadenylation [23].
Figure 1
Fig. 1

Genome-wide variant densities. Manhattan plot showing the variant density (number of SNPs per Mb) on the Y-axis and the positions of the genome across the 26 ovine autosomes and the X chromosome on the X-axis

The annotation analyses performed with SnpEff [24] and Variant Effect Predictor (VEP) [25] are summarized in Table 2. The number of variants processed with SnpEff was higher (216,637) when compared to the variants processed with the VEP software (212,742) because SnpEff performs the annotation of the variants present in the whole domestic sheep genome (Oar_v3.1), chromosomes and scaffolds, whereas VEP only annotates variants within ovine chromosomes. Variants were assigned to four types of biological impact based on the significance of the effect of the variant: high (e.g., frame shift, stop gain/loss, start loss, etc.); moderate (e.g., nonsynonymous coding changes, codon insertion/deletion, etc.); low (e.g., synonymous changes etc.); or modifier (used for terms with hard-to-predict effects and markers) (Table 2). The number of functional effects assigned was larger than the number of loci because the categories were not mutually exclusive. Among the total number of effects detected, the vast majority of the variants were predicted to have modifier impacts by both software programs (312,170 with SnpEff and 232,768 with VEP) (Table 2). This is because most of the variants detected were located in downstream gene regions (Table 2). Among the distribution of the variants by type of effect, the results of the two annotation tools were generally consistent (Table 2). Only two non-coding categories show marked discrepancies as follows: the variants annotated as intergenic regions and the variants annotated as non-coding transcript variants (Table 2). A higher number of variants were found by SnpEff than by VEP in intergenic regions (96,639 and 16,991, respectively), which could be due to the different performances of the annotation algorithms. The VEP software found a greater number of non-coding transcript variants than SnpEff (9,492 and 10 variants, respectively) because VEP annotates regulatory region variants without providing additional datasets to the software [25].

Among the results described in Table 2, it is remarkable the large proportion of variants identified within non-coding regions (e.g. downstream, intergenic, intronic variants) which could indicate the presence of variants in unannotated exons and/or noncoding but functionally transcribed genomic regions. As we have pointed above, the 62% of the transcripts detected within the ovine MSCs transcriptome were intergenic and moreover, the 11% were classified as potentially novel isoforms [15]. Therefore, the detection of variants out of known protein coding regions can be expected. Furthermore, these results agree with the results found in previous studies in cattle and human [26, 27]. However, further research needs to be done in the identification of transcriptome functional elements in livestock genomes to elucidate the potential role of the variants detected within no-coding regions.

Variants in QTL regions

A total of 57,795 variants were detected within the selected regions harboring QTL for milk yield, protein percentage and fat percentage. Among them, 78.56% were mutations already described in SNPdb (version 143). Most QTL in dairy sheep have been mapped with low-density maps, resulting in the detection of the significant effect within large confidence intervals. Hence, the high amount of variants detected in this work within ovine QTL for dairy traits could be related to the low mapping resolution of many of the previously identified QTL effects.

Due to the large number of total variants found, we focused our further exploratory study on the novel variants detected. Among the 12,389 novel variants identified within QTL regions, 9,118 were SNPs, 2,161 were insertions and 1,110 were deletions. Approximately 82.15% of the identified novel variants were considered sequence modifiers; the remaining (~17,85%) were inferred to produce high impact (2.52%), moderate impact (7.42%) or low impact (7.91%) changes in the corresponding transcriptional unit (Fig. 2).
Figure 2
Fig. 2

Functional characterization established by SnpEff and VEP software for the novel variants identified in this study within the QTL previously reported for milk yield, milk protein percentage and milk fat percentage. a Distribution of the novel variants by impact; b Distribution of moderate impact novel variants within QTL regions by functional effect; c Distribution of high impact novel variants within QTL regions by functional effect

Considering that the variants found within QTL regions may have been a consequence of selective pressures related to dairy production traits, we performed a functional enrichment analysis of the genes containing the variants with high and moderate functional impacts. For this analysis, we considered the variants that were classified as high and moderate impact variants (Fig. 2) by the two annotation software programs used, SnpEff [24] and VEP [25]. However, based on the large number of moderate missense variants identified by both programs (Fig. 2), we performed additional filtering to consider only the missense mutations predicted to be deleterious by SIFT [28], an external tool implemented in the VEP software that predicts the effects of an amino acid substitution on protein function. Hence, after discarding those variants predicted to be tolerated, a final total of 371 unique genes containing relevant functional variants (Additional file 2) were used to perform a functional enrichment analysis using the WEB-based Gene SeT AnaLysis Toolkit (WebGestalt) [29]. These genes were categorized by 14 enriched KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway terms (p adj < 0.05) (Additional file 3). The highest enriched KEGG pathway was “protein processing in endoplasmic reticulum” with a p adj of 2.60e-05. Metabolic processes in endoplasmic reticulum (ER) are associated with the synthesis and folding of membrane and secretory proteins as well as lipid synthesis. Under certain stress conditions (such as high levels of carbon-based molecules, free fatty acids, cytokines, and hypoxia), the accumulation of unfolded/misfolded proteins activates the ER stress signaling response [30, 31]. The mammary gland faces high metabolic stress during lactation due to the elevated rates of protein and fat synthesis. In our study, the majority of the genes with relevant functional variants enriched in the KEGG pathway “protein processing in ER” were related to the ER stress response (CAPN2, HSP90B1, PLAA, DERL2, DNAJB2, VCP, UBQLN1, SSR1). Mutations in these genes could be related to a different response of the overloaded ER in mutated animals during lactation, suggesting that these mutations could be a consequence of selective pressure for milk production traits. The high and moderate impact variants found in these genes and the animal genotypes for these variants are summarized in the additional information (Additional file 4).

Among the remaining enriched KEGG pathways (p adj < 0.005) found in this analysis (Additional file 3), “Jak-STAT signaling pathway”, “RNA transport” and “Fatty acid elongation” should be highlighted due to the putative influence of the genes within these pathways in milk yield or milk protein and fat content (see relevant variants and associated genes in Additional file 4). The Jak-STAT signaling pathway is directly implicated in milk protein expression by the mammary gland during lactation [32, 33]. Among the variants found in the genes within this pathway, the variant found in the signal transducer and activator of transcription 4 (STAT4) gene is noteworthy because variants in the orthologous bovine gene have been significantly associated with milk yield and protein percentage [34, 35].

In the “RNA transport” pathway, it is worthwhile to highlight variants within the EIF4G3, EIF3I, and EIF3D genes. These three genes code for the eukaryotic translation initiation factors 4 Gamma 3, 3 Subunit I and 3 Subunit D, respectively. The binding of eIF4G to eIF3 is regulated by insulin via the association of mTOR with eIF3, which causes the initiation of translation in the mTOR signaling pathway [36, 37]. This pathway is implicated in the positive control of protein synthesis, and studies in ruminants have highlighted the crucial role of the mTOR signaling pathway in the regulation of milk protein synthesis [38].

The following two genes were enriched in the “Fatty acid elongation in mitochondria” KEGG pathway: PPT2 and ACAA2. PPT2 is located within the ovine MHC region and encodes a member of the palmitoyl-protein thioesterase family, which has significant thioesterase activity against lipids with chain lengths of 10 or fewer carbons and 18 or more carbons [39]. The ACAA2 gene codes for the acetyl-CoA acyltransferase 2, a protein involved in lipid metabolism that catabolizes the last step in fatty acid β-oxidation. In Chios sheep, a single nucleotide polymorphism in ACAA2 was identified and associated with the milk yield phenotype [40].

Variants in sheep-cheese candidate genes

Variants in genes related to milk protein content

Variability related to milk protein content was evaluated in the genes codifying for major milk proteins, i.e., within the genes encoding caseins (casein α-S1 (CSN1S1), casein α-S2 (CSN1S2), casein β (CSN2), and casein κ (CSN3)) and whey proteins (α-lactalbumin (LALBA) and β-lactoglobulin (PAEP)). After variant filtration a total of 504 variants were identified within these genes. Among these variants, 80 (15.9%) variants were novel, and 424 (84.1%) variants were previously annotated in SNPdb (version 143). Most of the detected variants in the major milk protein genes (452) were single nucleotide polymorphisms (SNPs). There were also 29 deletions and 23 insertions.

A high number of the variants found in the genes codifying for major milk proteins were positioned in introns (482). The large number of tags mapped to introns within the casein cluster, which was pointed above, together with the higher variability generally expected in non-coding regions may explain the high level of genetic variation identified in this region.

Among the variants detected in the coding regions by both software programs (SnpEff and VEP), we found one splice donor variant, which was classified as a high impact effect mutation, and ten missense variants. These mutations found within protein genes are summarized in Table 3. The splice donor variant found in the CSN1S2 gene is a novel variant that was detected in the two studied breeds (allele frequency of 0.625). This variant affects a putative splice donor site at the third intron of the CSN1S2 gene (GCA_000298735.1:6:85186875:G:A). Thus, this SNP could cause intron retention resulting in a novel isoform of CSN1S2, which should be confirmed by further research.
Table 3

Functionally relevant variants in genes codifying for major milk proteins

Variant a


Allele Freq



















































Splice donor




















a For described variants rs identifier is indicated and novel variants are described with the unique ID “INSDC Genome accession:CHROM:POS:REF:ALT”.

Missense variants in the ovine casein genes, which lead to amino acid changes in the protein products, comprise a group of SNPs that are of particular interest because some of these variants have been demonstrated to influence the composition and/or technological properties of milk (reviewed by Moioli et al. [41]). Among the missense variants detected in this study (Table 3), one was in CSN1S1, two were in CSN2 and three were in CSN1S2; no missense variants were found in CSN3. This result agrees with the fact that CSN3 is considered to be monomorphic in sheep [1]. Missense variants detected in the CSN1S2 gene are relevant due to their relationships with known protein alleles. The deleterious variant rs430397133 was detected in the CSN1S2 gene in one heterozygous Churra ewe (allele frequency of 0.125). The same animal was heterozygous for the other two missense variants found in CSN1S2, named rs424657035 and rs399378277, which were predicted to be tolerated. The mature protein of the known CSN1S2*B’ variant harbors these three missense mutations [42]. The deleterious variant rs430397133, which causes the Asp90Tyr substitution, is responsible for the higher isoelectric point of the B protein variant that allows for its differentiation from CSN1S2*A [43]. An advantageous effect of CSN1S2*B in comparison to CSN1S2*A in terms of milk, fat and protein yield, and protein content has been reported [3]. In this study, we also found the variants responsible for αs2-CN protein alleles G (rs424657035) and G’ (rs424657035 and rs399378277). However, at the protein level, the G and G’ alleles are hidden by the CSN1S2*A phenotype in isoelectric focusing [3].

In the CSN1S1 gene, we found a previously described missense variant (rs420959261). This SNP is responsible for the p.Thr209Ile substitution, which differentiates the protein variant CSN1S1*C’, the supposed ancestral variant, from CSN1S1*C” [44].

Two known SNPs, rs430298704 and rs416941267, were detected within the CSN2 gene. The rs430298704 SNP is a missense variant causing the substitution p.Met199Val which is classified as tolerated. This mutation causes the A and G protein alleles of β-casein. Corral et al. [45] found that in Merino sheep the GG genotype for this variant was associated with an increase in milk production, whereas the AA genotype was associated with an increase in protein and fat percentage. The rs416941267 is a missense variant causing the amino acid exchange p.Leu212Ile associated to the CSN2*X protein allele described by Chessa et al. [46].

One already described missense SNP, rs403176291, was detected within the LALBA gene in both breeds. This mutation causes the amino acid change p.Val27Ala classified as deleterious by SIFT [28] and that has been suggested to be a Quantitative Trait Nucleotide (QTN) influencing milk protein percentage [47].

Regarding the PAEP (LGB) gene, which encodes the milk β-lactoglobulin protein, our analysis identified the missense variant (rs430610497) that differentiates protein alleles A and B of β-lactoglobulin [48, 49]. This mutation causes the substitution p.Tyr36His and was found in both breeds. A higher aptitude for cheese processing has been shown in AA ewes due to a shorter clotting time, better rate of curd firming and a higher cheese yield [2]. The C allele of β-lactoglobulin [50] was not found in this study. This rare C variant has been only found in few breeds, including Merinoland, Latxa, Carranzana, Spanish Merino, Serra da Estrela, White Merino, and Black Merino [2]. However, at position c.500 of the PAEP gene, we detected trialelic missense variants, rs600923112 and rs600923112, which cause two amino acid substitutions in the protein (p.Gln167Leu and p.Gln167Arg, respectively). The p.Gln167Leu amino acid change was found in the two studied breeds, whereas the p.Gln167Arg substitution was found only in Assaf sheep. These seem to be important mutations, as both amino acid changes are predicted to be deleterious by SIFT [28]. To our knowledge, these mutations are not related to described protein alleles in the β-lactoglobulin so further research should be conducted to elucidate their possible functional consequences.

Variants in genes related to milk fat content

To find variability in candidate genes related to milk fat content, we filtered the mutations positioned within a total of 17 genes (Table 4) that have been previously related to milk fat metabolism [51].
Table 4

Milk fat candidate genes considered in this study

Gene symbol



Butyrophilin Subfamily 1 Member A1


Acetyl-CoA Carboxylase Alpha


Fatty Acid Binding Protein 3


Carboxyl Ester Lipase


Acyl-CoA Synthetase Long-Chain Family Member 1


Lipoprotein Lipase


Acyl-CoA Synthetase Short-Chain Family Member 2


Xanthine Dehydrogenase


Glycerol-3-Phosphate Acyltransferase, Mitochondrial


Diazepam Binding Inhibitor, Acyl-CoA Binding Protein


Very Low Density Lipoprotein Receptor


Diacylglycerol O-Acyltransferase 1


Perilipin 2


Stearoyl-CoA Desaturase


Lipin 1


Solute Carrier Family 27 Member 6


Fatty Acid Synthase

We detected a total of 1,063 variants in the transcriptomic regions containing the studied genes related to lipid metabolism. The majority of the variants within these genes (953; 89.65%) were previously annotated in SNPdb (version 143). Among the variants detected, 990 were SNPs, 24 were insertions, and 49 were deletions. As these variants occurred in the genomic regions encoding caseins and whey proteins, the highest proportion of mutations were located within intronic regions (920; 86.39%).

According to the functional effects by impact found in the fat-related genes, we identified four (0.38%) variants with high impact, 27 (2.54%) with moderate impact, 100 (9.39%) with low impact and 934 (87.7%) with a modifier impact. Among the moderate variants, we found a disruptive inframe deletion and 26 missense mutations, of which four were classified as deleterious by SIFT [28]. The functionally relevant variants within genes related to mammary gland fat metabolism are indicated in Table 5.
Table 5

Functionally relevant variants detected in the milk fat candidate genes considered in this study



Allele Freq










High-Splice donor






Missense-Deleterious (0)






High-Splice aceptor






Missense-Deleterious (0.02)






Missense-Deleterious (0)






High-Splice aceptor






Moderate-Inframe deletion






Missense-deleterious-low_confidence (0.04)






High-Splice donor


a For described variants rs identifier is indicated and novel variants are described with the unique ID “INSDC Genome accession:CHROM:POS:REF:ALT

The highest number of functionally relevant variants were found in the XDH gene. Two splice acceptor mutations and an inframe deletion were found in both breeds (Table 5). It should be noted that the inframe deletion (GCA_000298735.1:3:92239411:CCGCCCCTCTTCCCGGGCGCCCCCATCTTCTTTTCCA:C) was found in homozygosis in the eight ewes analyzed, which could mean that the XDH sequence is not well-characterized at this genomic location. Moreover, two deleterious missense SNPs were found only in Assaf ewes (allele frequency of 0.125). XDH encodes the xanthine dehydrogenase, a protein implicated in milk fat globule secretion [52]. Hence, mutations in this gene could alter the mechanisms underlying lipid droplet secretion.

PLIN2 encodes the perilipin 2/adipophilin protein. Adipophilin is reported to have a role in the packaging of triglycerides for secretion as milk lipids in the mammary gland [53]. Moreover, the absence of adipophilin has been associated with the formation of smaller intracellular fat globules [54]. The splice donor variant found within PLIN2 (GCA_000298735.1:2:87107748:C:A) gene is a novel variant that was detected in both breeds (allele frequency of 0.5). This variant affects a splice donor site at the first intron of the PLIN2 gene. Thus, this SNP could cause intron retention and a novel isoform.

A novel missense variant within the LPIN1 gene (GCA_000298735.1:3:20585665:C:T), causing the amino acid substitution p.Arg781Trp at the protein level, and classified as deleterious by SIFT [28], was found in heterozygosis in one Assaf sheep. LPIN1 encodes the lipin-1 protein, an enzyme implicated in triacylglycerol synthesis [32]. Additionally, a role for lipin-1 in the transcriptional regulation of other genes involved in milk lipid synthesis has been suggested in relation to the mTOR, PPARα and PPARγ regulatory pathways [5557].

In the FASN gene, we detected a known missense mutation (rs604791005) that causes the amino acid change p.Gly2312Ala. This polymorphism was found in heterozygosis in one Churra ewe. FASN encodes a fatty acid synthase responsible for de novo fatty-acid biosynthesis in the mammary gland [58]. In cattle, several polymorphisms in this gene have been associated with milk fat content and fatty acid composition [5964]. In Churra sheep, two QTL affecting capric acid and polyunsaturated fatty acid contents were mapped to the genomic region harboring the FASN gene [65], although the variability identified in this gene did not appear to be directly related to these QTL [65]. Therefore, the missense polymorphism described in this study should be further analyzed to assess its possible association with the QTL previously described in Churra sheep.

The splice donor variant found in the ACSL1 gene is a novel variant that was detected in both breeds (allele frequency of 0.5). This variant (GCA_000298735.1:26:13949071:C:T) affects the first base of the 5′ splice donor region of the second intron of ACSL1, which encodes an acyl-CoA synthetase long-chain family member 1. This protein is implicated in the activation of long chain fatty acids [32].


We present herein the first transcriptomic approach performed to identify the genetic variants of the lactating mammary gland in sheep. Through the transcriptome analysis of variability within regions harboring QTL for milk yield, protein percentage and fat percentage, we found several pathways and genes that could harbor mutations with relevant effects on dairy production traits. Moreover, remarkable variants were also found in candidate genes coding for major milk proteins and enzymes related to milk fat metabolism. Further research is required to estimate the allele frequencies and determine the phenotypic effects of the functionally relevant variants found through this RNA-Seq approach in commercial sheep populations. Additionally, several of the SNPs found in this study could be included as suitable markers in genotyping platforms or custom SNP-arrays to perform association analyses in commercial populations and apply genomic selection protocols in the dairy production industry.


Animals and sampling

For this study, a MSCs transcriptome dataset from Assaf and Spanish Churra dairy sheep breeds was used. The dataset is available in the Gene Expression Omnibus (GEO) database under the accession number GSE74825. The source of the animals and the sampling process protocol are described in detail in the related data descriptor manuscript [66]. The milk samples of eight healthy sheep (four Churra and four Assaf ewes) belonging to the commercial farm of the University of León were collected on days 10 (D10), 50 (D50), 120 (D120) and 150 (D150) after lambing. At each sampling time-point, we collected 50 ml of milk from each ewe one hour after the routine milking at 8 a.m. and ten minutes after the administration of five IUs of Oxytocin Facilpart (Syva, León, Spain). The time-point for milk collection was chosen to maximize the concentration of MSCs. Previous studies have indicated that the diurnal time point with the highest concentration of MSCs occurs one hour after milking [67]. Moreover, oxytocin was administered with the aim of stimulating its mechanical effect on myoepithelial contraction and thus the flattening of the alveolar lumen, which causes the release of residual post-milking milk containing a higher concentration of exfoliated MECs [68].

Ethics statement

All protocols involving animals were approved by the Animal Welfare Committee of the University of Leon, Spain, following the proceedings described in Spanish and EU legislations (Law 32/2007, R.D. 1201/2005, and Council Directive 2010/63/EU).

Library preparation and sequencing

Somatic cell separation and RNA extraction were performed as described by Suárez-Vega et al. (2016) [66]. The integrity of the RNA was assessed using an Agilent 2100 Bioanalyzer device (Agilent Technologies, Santa Clara, CA, USA). The RNA integrity value (RIN) of the samples ranged between 7.1 and 9. Paired-end libraries with fragments of 300 bp were prepared using the True-Seq RNA-Seq sample preparation Kit v2 (Illumina, San Diego, CA, USA). The fragments were sequenced on an Illumina Hi-Seq 2000 sequencer (Fasteris SA, Plan-les-Ouates, Switzerland).

Alignment, variant identification and annotation

The read qualities of the RNA-Seq libraries were evaluated using FastQC [69]. Using the STAR aligner [70] the reads were mapped against the ovine genome assembly v.3.1. (Oar_v3.1 [71]). After the alignment, Samtools [72] was used to convert sam files to bam files and then to sort and merge the bam files from the same animal at different time-points. Metrics from the bam files were obtained with RSeQC software [14] based on the annotation bed file of the Oar_v3.1 sheep assembly obtained from the UCSC Genome Browser [73]. Then, Picard [74] was used to add read groups and mark duplicated reads on the merged bam files. SNP and Indel calling was performed using the Genome Analysis Toolkit (GATK, version 3.4.46) software package following GATK best practices [75]. To obtain high-quality variants, strict filter conditions were applied using vcffilter [76] and SnpSift [77] (Variation Quality (QUAL) >30, Mapping Quality (MQ) >40, Quality By Depth. (QD) >5, Fisher Strand (FS) <60 and a minimum Depth of coverage (DP) >5 in all the samples). The bcftools “annotate –c ID” option [72] and the ovine reference vcf file downloaded from the Ensembl database (SNPdb-version 143) were used to annotate the known variants detected in our study.

Two software programs, SnpEff [24] and Variant Effect Predictor [25], were used to predict the functional consequences of the detected variants. SnpEff allows users to define specific intervals and customize the annotation of the variants. Considering that the final aim of this study is the characterization of the transcriptome variants that may be of special interest for the dairy industry, we used SnpEff to select (i) the variants included within previously reported sheep QTL studies for milk protein percentage, milk fat percentage and milk yield [5] and (ii) the variants included within candidate genes related to milk protein and fat content. The selection of the variants included in these two types of target regions (QTL and candidate genes) was performed according to the following criteria.

Filtering variants in QTL regions affecting milk production traits

The coordinates of the genomic regions containing the QTL related to milk protein percentage, milk fat percentage and milk yield, based on the annotation of the SheepQTLdb [5], were downloaded from the Ensembl database [71]. This information, provided as a bed file (Additional file 5), was used by the SnpEff software (−fi option) to retain only the variants matching the target QTL intervals from the total number of variants identified through the GATK protocol. Due to the high number of variants detected in the selected QTL regions (57,795), those variants already described in the Ensembl database were filtered out using vcftools [78]. Among the novel variants, we selected those which were predicted by the two annotation analyses (SnpEff and VEP) to have relevant functional consequences. Thus, we retained those variants that were classified in terms of their functional consequences as “high” and “moderate” by the two different software programs. Due to the large number of variants classified as “moderate”, within the moderate missense variants, we selected those predicted to be “deleterious” by the VEP option “--sift b” [25]. This option allows the use of the SIFT tool [28] for any of the variants annotated as missense. SIFT is an algorithm that predicts whether an amino acid substitution will have a deleterious effect on the protein function [28]. Finally, we extracted the names of the genes containing these functionally relevant mutations and used them to perform a functional enrichment analysis with the Web-based Gene Set Analysis Toolkit (WebGestalt) [29].

Filtering variants on protein and fat candidate genes

The candidate genes selected for a detailed analysis of their genetic variability in the studied dataset included those codifying for major milk constituent proteins (CSN1S1, CSN1S2, CSN2, CSN3, PAEP, LALBA) and 17 genes related to mammary gland lipid metabolism (Table  4). These genes were selected based on a previous study by our research group that evaluated the gene expression of candidate milk genes in the milk sheep transcriptome that affect cheese-related traits [51]. To obtain the variants within the target genes selected for the study, we used the –fi option from SnpEff followed by a bed file with the coordinates of the selected genes (Additional files 6 and 7) and the –onlyTr option followed by a file with an ID list with the Ensembl transcripts name of the selected genes. From all the variants detected within the candidate cheese-yield genes, we focused further our analyses on those mutations that could have relevant consequences. Hence, the variants classified by the two software programs as having “high” and “moderate” functional impacts were selected.



Acetyl-CoA Acyltransferase 2


Acetyl-CoA Carboxylase Alpha


Acyl-CoA Synthetase Long-Chain Family Member 1


Acyl-CoA Synthetase Short-Chain Family Member 2


Alternative Allele


Butyrophilin Subfamily 1 Member A1


Coding Sequence


Carboxyl Ester Lipase




Casein Alpha S1


Casein Alpha S2


Casein Beta


Casein Kappa


Day 10 after lambing


Day 120 after lambing


Day 150 after lambing


Day 50 after lambing


Diazepam Binding Inhibitor, Acyl-CoA Binding Protein


NCBI database of genetic variation


Diacylglycerol O-Acyltransferase 1


Depth of Coverage


Eukaryotic Translation Initiation Factor 3 Subunit D


Eukaryotic Translation Initiation Factor 3 Subunit I


Eukaryotic Translation Initiation Factor 4 Gamma 3


Endoplasmic Reticulum


European Union


Functional Annotation of Animal Genomes


Fatty Acid Binding Protein 3


Fatty Acid Synthase


Fisher Strand


Genome Analysis Toolkit


Gene Expression Omnibus


Glycerol-3-Phosphate Acyltransferase, Mitochondrial





INSDC Genome accession: 

International Nucleotide Sequence Database Collaboration Genome accesion


International Units




Kyoto Encyclopedia of Genes and Genomes


Lactalbumin Alpha


long noncoding RNAs


Lipin 1


Lipoprotein Lipase




Milk Epithelial Cells


Major Histocompatibility Complex




Mapping Quality


Milk Somatic Cells


Mechanistic Target Of Rapamycin


Progestagen Associated Endometrial Protein (Beta-lactoglobulin)


Piwi-interacting RNAs


Perilipin 2


Position in the chromosome


Peroxisome Proliferator Activated Receptor Alpha


Peroxisome Proliferator Activated Receptor Gamma


Palmitoyl-Protein Thioesterase 2


Quality By Depth


Quantitative Trait Loci


Variation Quality


Reference Allele


RNA Integrity Number


Ribonucleic acid


RNA sequencing


Stearoyl-CoA Desaturase


short interfering RNAs


Solute Carrier Family 27 Member 6


small nucleolar RNAs


Single Nucleotide Polymorphism


Genetic variant annotation and effect prediction toolbox


Signal Transducer And Activator Of Transcription 4


Transition to Transversion


Variant Effect Predictor


Very Low Density Lipoprotein Receptor


WEB-based Gene SeT AnaLysis Toolkit


Xanthine Dehydrogenase



The support and availability to the computing facilities of the Foundation of Supercomputing Center of Castile and León (FCSCL) ( is greatly acknowledged.


This work is included in the framework of the project AGL2015-66035-R funded by the Spanish Ministry of Economy and Competitiveness (MINECO) and co-funded by European Regional Development Fund. B.G.G. is funded through the Spanish ‘Ramón y Cajal’ Program (RYC-2012-10230) from the MINECO.

Availability of data and materials

The datasets generated during and/or analyzed during the current study are available in the Gene Expression Omnibus (GEO) repository, under the accession number GSE74825.

Authors’ contributions

Conceived and designed the experiments: JJA. Performed the experiments: ASV, BGG and JJA. Analyzed the data: ASV, GTK and CK .Wrote the paper: ASV, BGG and JJA. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

All protocols involving animals were approved by the Animal Welfare Committee of the University of Leon, Spain, following proceedings described in Spanish and EU legislations (Law 32/2007, R.D. 1201/2005, and Council Directive 2010/63/EU). All animals used in this study were handled in strict accordance with good clinical practices and all efforts were made to minimize suffering.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

Departamento de Producción Animal, Facultad de Veterinaria, Universidad de León, León, Spain
INRA, Plateforme bioinformatique Toulouse Midi-Pyrénées, UR875 Biométrie et Intelligence Artificielle, Castanet-Tolosan Cedex, France
GenPhySE, Université de Toulouse, INRA, INPT, ENVT, Castanet, France


  1. Selvaggi M, Laudadio V, Dario C, Tufarelli V. Investigating the genetic polymorphism of sheep milk proteins: a useful tool for dairy production. J Sci Food Agric. 2014;94:3090–9.View ArticlePubMedGoogle Scholar
  2. Selvaggi M. β-Lactoglobulin gene polymorphisms in sheep and effects on milk production traits: A Review. Adv Anim Vet Sci. 2015;3:478–84.View ArticleGoogle Scholar
  3. Giambra IJ, Brandt H, Erhardt G. Milk protein variants are highly associated with milk performance traits in East Friesian Dairy and Lacaune sheep. Small Rumin Res. 2014;121:382–94.View ArticleGoogle Scholar
  4. Amigo L, Recio I, Ramos M. Genetic polymorphism of ovine milk proteins: its influence on technological properties of milk- a review. Int Dairy J. 2000;10:135–49.View ArticleGoogle Scholar
  5. Hu Z-L, Park CA, Reecy JM. Developmental progress and current status of the Animal QTLdb. Nucleic Acids Res Oxford University Press. 2016;44:D827–33.View ArticleGoogle Scholar
  6. Daetwyler HD, Capitan A, Pausch H, Stothard P, van Binsbergen R, Brøndum RF, et al. Whole-genome sequencing of 234 bulls facilitates mapping of monogenic and complex traits in cattle. Nat Genet. 2014;46:858–65.View ArticlePubMedGoogle Scholar
  7. Georges M. Towards sequence-based genomic selection of cattle. Nat Genet. 2014;46:807–9.View ArticlePubMedGoogle Scholar
  8. Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Methods. 2008;5:621–8.Google Scholar
  9. Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2009;10:57–63.View ArticlePubMedPubMed CentralGoogle Scholar
  10. Cánovas A, Rincon G, Islas-Trejo A, Wickramasinghe S, Medrano JF. SNP discovery in the bovine milk transcriptome using RNA-Seq technology. Mamm Genome. 2010;21:592–8.View ArticlePubMedPubMed CentralGoogle Scholar
  11. Cox LA, Glenn JP, Spradling KD, Nijland MJ, Garcia R, Nathanielsz PW, et al. A genome resource to address mechanisms of developmental programming: determination of the fetal sheep heart transcriptome. J Physiol. 2012;590:2873–84.View ArticlePubMedPubMed CentralGoogle Scholar
  12. Hudson NJ, Dalrymple BP, Reverter A, Hudson N, Reverter A, Dalrymple B, et al. Beyond differential expression: the quest for causal mutations and effector molecules. BMC Genomics. 2012;13:356.View ArticlePubMedPubMed CentralGoogle Scholar
  13. Suárez-Vega A, Gutiérrez-Gil B, Benavides J, Perez V, Tosser-Klopp G, Klopp C, et al. Combining GWAS and RNA-Seq approaches for detection of the causal mutation for hereditary junctional epidermolysis bullosa in sheep. PLoS One. 2015;10:e0126416.View ArticlePubMedPubMed CentralGoogle Scholar
  14. Wang L, Wang S, Li W. RSeQC: quality control of RNA-seq experiments. Bioinformatics. 2012;28:2184–5.View ArticlePubMedGoogle Scholar
  15. Suárez-Vega A, Gutiérrez-Gil B, Klopp C, Robert-Granie C, Tosser-Klopp G, Arranz JJ. Characterization and comparative analysis of the milk transcriptome in two dairy sheep breeds using RNA sequencing. Sci Rep. 2015;5:18399.View ArticlePubMedPubMed CentralGoogle Scholar
  16. Peschansky VJ, Wahlestedt C. Non-coding RNAs as direct and indirect modulators of epigenetic regulation. Epigenetics. 2014;9:3–12.View ArticlePubMedGoogle Scholar
  17. Andersson L, Archibald AL, Bottema CD, Brauning R, Burgess SC, Burt DW, et al. Coordinated international action to accelerate genome-to-phenome with FAANG, the Functional Annotation of Animal Genomes project. Genome Biol BioMed Central. 2015;16:57.View ArticleGoogle Scholar
  18. DePristo MA, Banks E, Poplin R, Garimella KV, Maguire JR, Hartl C, et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 2011;43:491–8.Google Scholar
  19. Hodges E, Smith AD, Kendall J, Xuan Z, Ravi K, Rooks M, et al. High definition profiling of mammalian DNA methylation by array capture and single molecule bisulfite sequencing. Genome Res Cold Spring Harbor Lab. 2009;19:1593–605.Google Scholar
  20. Dukkipati VSR, Blair HT, Garrick DJ, Murray A. “Ovar-Mhc” - ovine major histocompatibility complex: structure and gene polymorphisms. Genet Mol Res. 2006;5:581–608.PubMedGoogle Scholar
  21. Mateescu RG, Thonney ML. Genetic mapping of quantitative trait loci for milk production in sheep. Anim Genet. 2010;41:460–6.View ArticlePubMedGoogle Scholar
  22. Wetterbom A, Ameur A, Feuk L, Gyllensten U, Cavelier L, Chen F, et al. Identification of novel exons and transcribed regions by chimpanzee transcriptome sequencing. Genome Biol. 2010;11:R78.View ArticlePubMedPubMed CentralGoogle Scholar
  23. Ameur A, Zaghlool A, Halvardson J, Wetterbom A, Gyllensten U, Cavelier L, et al. Total RNA sequencing reveals nascent transcription and widespread co-transcriptional splicing in the human brain. Nat Struct Mol Biol. 2011;18:1435–40.View ArticlePubMedGoogle Scholar
  24. Cingolani P, Platts A, Wang LL, Coon M, Nguyen T, Wang L, et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin). 2012;6:80–92.View ArticleGoogle Scholar
  25. McLaren W, Gil L, Hunt SE, Riat HS, Ritchie GRS, Thormann A, et al. The Ensembl Variant Effect Predictor. Genome Biol. 2016;17:122.View ArticlePubMedPubMed CentralGoogle Scholar
  26. Djari A, Esquerré D, Weiss B, Martins F, Meersseman C, Boussaha M, et al. Gene-based single nucleotide polymorphism discovery in bovine muscle using next-generation transcriptomic sequencing. BMC Genomics. 2013;14:307.View ArticlePubMedPubMed CentralGoogle Scholar
  27. Piskol R, Ramaswami G, Li JB. Reliable identification of genomic variants from RNA-Seq data. Am J Hum Genet. 2013;93:641–51.View ArticlePubMedPubMed CentralGoogle Scholar
  28. Kumar P, Henikoff S, Ng PC. Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat Protoc. 2009;4:1073–81.View ArticlePubMedGoogle Scholar
  29. Wang J, Duncan D, Shi Z, Zhang B. WEB-based GEne SeT AnaLysis Toolkit (WebGestalt): update 2013. Nucleic Acids Res. 2013;41:W77–83.View ArticlePubMedPubMed CentralGoogle Scholar
  30. Fonseca SG, Gromada J, Urano F. Endoplasmic reticulum stress and pancreatic β-cell death. Trends Endocrinol Metab. 2011;22:266–74.PubMedPubMed CentralGoogle Scholar
  31. Gopinath RK, Leu J-Y. Hsp90 maintains proteostasis of the galactose utilization pathway to prevent cell lethality. Mol Cell Biol. 2016;36:1412–24.View ArticlePubMedPubMed CentralGoogle Scholar
  32. Bionaz M, Loor JJ. Gene networks driving bovine milk fat synthesis during the lactation cycle. BMC Genomics. 2008;9:366.View ArticlePubMedPubMed CentralGoogle Scholar
  33. Rupp R, Senin P, Sarry J, Allain C, Tasca C, Ligat L, et al. A point mutation in Suppressor Of Cytokine Signalling 2 (Socs2) increases the susceptibility to inflammation of the mammary gland while associated with higher body weight and size and higher milk production in a sheep model. PLoS One. 2015. doi:10.1371/journal.pgen.1005629.Google Scholar
  34. Zhang F, Huang J, Li Q, Ju Z, Li J, Shi F, et al. Novel single nucleotide polymorphisms (SNPs) of the bovine STAT4 gene and their associations with production traits in Chinese Holstein cattle. African J Biotechnol. 2010;9:4003–8.View ArticleGoogle Scholar
  35. Song XM, Zhang L, Jiang JF, Shi FX, Jiang YQ. An SduI polymorphism at intron 20 of the Chinese Holstein cow STAT4 gene and its effect on milk performance traits. Genet Mol Res. 2013;12:1593–602.View ArticlePubMedGoogle Scholar
  36. LeFebvre AK, Korneeva NL, Trutschl M, Cvek U, Duzan RD, Bradley CA, et al. Translation initiation factor eIF4G-1 binds to eIF3 through the eIF3e subunit. J Biol Chem. 2006;281:22917–32.View ArticlePubMedPubMed CentralGoogle Scholar
  37. Laplante M, Sabatini DM. mTOR signaling at a glance. J Cell Sci. 2009;122:3589–94.View ArticlePubMedPubMed CentralGoogle Scholar
  38. Bionaz M, Loor JJ. Gene networks driving bovine mammary protein synthesis during the lactation cycle. Bioinforma Biol Insights. 2011;5:83–985.Google Scholar
  39. Calero G, Gupta P, Nonato MC, Tandel S, Biehl ER, Hofmann SL, et al. The crystal ctructure of Palmitoyl Protein Thioesterase-2 (PPT2) reveals the basis for divergent substrate specificities of the two lysosomal thioesterases, PPT1 and PPT2. J Biol Chem. 2003;278:37957–64.View ArticlePubMedGoogle Scholar
  40. Orford M, Hadjipavlou G, Tzamaloukas O, Chatziplis D, Koumas A, Mavrogenis A, et al. A single nucleotide polymorphism in the acetyl-coenzyme A acyltransferase 2 (ACAA2) gene is associated with milk yield in Chios sheep. J Dairy Sci. 2012;95:3419–27.View ArticlePubMedGoogle Scholar
  41. Moioli B, D’Andrea M, Pilla F. Candidate genes affecting sheep and goat milk quality. Small Rumin Res. 2007;68:179–92.View ArticleGoogle Scholar
  42. Tetens JL, Drögemüller C, Thaller G, Tetens J. DNA-based identification of novel ovine milk protein gene variants. Small Rumin Res. 2014;121:225–31.View ArticleGoogle Scholar
  43. Picariello G, Rignanese D, Chessa S, Ceriotti G, Trani A, Caroli A, et al. Characterization and genetic study of the ovine alphaS2-casein (CSN1S2) allele B. Protein J. 2009;28:333–40.View ArticlePubMedGoogle Scholar
  44. Ceriotti G, Chessa S, Bolla P, Budelli E, Bianchi L, Duranti E, et al. Single Nucleotide polymorphisms in the ovine casein genes detected by polymerase chain reaction-single strand conformation polymorphism. J Dairy Sci. 2004;87:2606–13.View ArticlePubMedGoogle Scholar
  45. Corral JM, Padilla JA, Izquierdo M. Associations between milk protein genetic polymorphisms and milk production traits in Merino sheep breed. Livest Sci. 2010;129:73–9.View ArticleGoogle Scholar
  46. Chessa S, Rignanese D, Berbenni M, Ceriotti G, Martini M, Pagnacco G, et al. New genetic polymorphisms within ovine β- and αS2-caseins. Small Rumin Res. 2010;88:84–8.Google Scholar
  47. Garcia-Gamez E, Gutierrez-Gil B, Sahana G, Sanchez JP, Bayon Y, Arranz JJ. GWA analysis for milk production traits in dairy sheep and genetic support for a QTN influencing milk protein percentage in the LALBA gene. PLoS One. 2012;7:e47782.View ArticlePubMedPubMed CentralGoogle Scholar
  48. Ali S, McClenaghan M, Simons JP, Clark AJ. Characterisation of the alleles encoding ovine ß-lactoglobulins A and B. Gene. 1990;91:201–7.View ArticlePubMedGoogle Scholar
  49. Bell K, McKenzie HA. The whey proteins of ovine milk: β-lactoglobulins A and B. Biochim Biophys Acta. 1967;147:123–34.View ArticlePubMedGoogle Scholar
  50. Erhardt G. Evidence for a third allele at the β-lactoglobulin (β-Lg) locus of sheep milk and its occurrence in different breeds. Anim Genet. 2009;20:197–204.View ArticleGoogle Scholar
  51. Suárez-Vega A, Gutiérrez-Gil B, Arranz JJ. Transcriptome expression analysis of candidate milk genes affecting cheese-related traits in 2 sheep breeds. J Dairy Sci. 2016;99:6381–90.View ArticlePubMedGoogle Scholar
  52. McManaman JL, Russell TD, Schaack J, Orlicky DJ, Robenek H. Molecular determinants of milk lipid secretion. J Mammary Gland Biol Neoplasia. 2007;12:259–68.View ArticlePubMedGoogle Scholar
  53. Russell TD, Palmer CA, Orlicky DJ, Bales ES, Chang BH-J, Chan L, et al. Mammary glands of adipophilin-null mice produce an amino-terminally truncated form of adipophilin that mediates milk lipid droplet formation and secretion. J Lipid Res. 2008;49:206–16.View ArticlePubMedGoogle Scholar
  54. Russell TD, Schaack J, Orlicky DJ, Palmer C, Chang BH-J, Chan L, et al. Adipophilin regulates maturation of cytoplasmic lipid droplets and alveolae in differentiating mammary glands. J Cell Sci. 2011;124:3247–53.View ArticlePubMedPubMed CentralGoogle Scholar
  55. Huffman TA, Mothe-Satney I, Lawrence JC. Insulin-stimulated phosphorylation of lipin mediated by the mammalian target of rapamycin. Proc Natl Acad Sci. 2002;99:1047–52.View ArticlePubMedPubMed CentralGoogle Scholar
  56. Finck BN, Gropler MC, Chen Z, Leone TC, Croce MA, Harris TE, et al. Lipin 1 is an inducible amplifier of the hepatic PGC-1α/PPARα regulatory pathway. Cell Metab. 2006;4:199–210.View ArticlePubMedGoogle Scholar
  57. Reue K, Zhang P. The lipin protein family: Dual roles in lipid biosynthesis and gene expression. FEBS Lett. 2008;582:90–6.View ArticlePubMedGoogle Scholar
  58. Smith S. The animal fatty acid synthase: one gene, one polypeptide, seven enzymes. FASEB J. 1994;8:1248–59.PubMedGoogle Scholar
  59. Roy R, Ordovas L, Zaragoza P, Romero A, Moreno C, Altarriba J, et al. Association of polymorphisms in the bovine FASN gene with milk-fat content. Anim Genet. 2006;37:215–8.View ArticlePubMedGoogle Scholar
  60. Morris CA, Cullen NG, Glass BC, Hyndman DL, Manley TR, Hickey SM, et al. Fatty acid synthase effects on bovine adipose fat and milk fat. Mamm Genome. 2007;18:64–74.View ArticlePubMedGoogle Scholar
  61. Zhang S, Knight TJ, Reecy JM, Beitz DC. DNA polymorphisms in bovine fatty acid synthase are associated with beef fatty acid composition. Anim Genet. 2008;39:62–70.View ArticlePubMedGoogle Scholar
  62. Abe T, Saburi J, Hasebe H, Nakagawa T, Misumi S, Nade T, et al. Novel mutations of the FASN gene and their effect on fatty acid composition in Japanese Black Beef. Biochem Genet. 2009;47:397–411.View ArticlePubMedGoogle Scholar
  63. Schennink A, Bovenhuis H, Léon-Kloosterziel KM, Van Arendonk JAM, Visker MHPW. Effect of polymorphisms in the FASN, OLR1, PPARGC1A, PRL and STAT5A genes on bovine milk-fat composition. Anim Genet. 2009;40:909–16.View ArticlePubMedGoogle Scholar
  64. Matsumoto H, Inada S, Kobayashi E, Abe T, Hasebe H, Sasazaki S, et al. Identification of SNPs in the FASN gene and their effect on fatty acid milk composition in Holstein cattle. Livest Sci. 2012;144:281–4.View ArticleGoogle Scholar
  65. García-Fernández M, Gutiérrez-Gil B, García-Gámez E, Sánchez JP, Arranz JJ. The identification of QTL that affect the fatty acid composition of milk on sheep chromosome 11. Anim Genet. 2010;41:324–8.View ArticlePubMedGoogle Scholar
  66. Suárez-Vega A, Gutiérrez-Gil B, Klopp C, Tosser-Klopp G, Arranz J-J, Marioni JC, et al. Comprehensive RNA-Seq profiling to evaluate lactating sheep mammary gland transcriptome. Sci Data. 2016;3:160051.View ArticlePubMedPubMed CentralGoogle Scholar
  67. Gonzalo C, Carriedo JA, Gomez JD, Gomez LD, San Primitivo F. Diurnal variation in the somatic cell count of ewe milk. J Dairy Sci. 1994;77:1856–9.View ArticlePubMedGoogle Scholar
  68. Peris C, Molina P, Fernandez N, Rodriguez M, Torres A. Variation in somatic cell count, California mastitis test, and electrical conductivity among various fractions of ewe’s milk. J Dairy Sci. 1991;74:1553–60.View ArticlePubMedGoogle Scholar
  69. Andrews S. FastQC A Quality Control tool for High Throughput Sequence Data. Babraham Bioinformatics. 2012. Accessed 24 Aug 2016.
  70. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29:15–21.View ArticlePubMedGoogle Scholar
  71. International Sheep Genome Consortium. Ovis aries Oar_v3.1, INSDC Assembly. Ensembl database. 2012. Accessed 24 Aug 2016.
  72. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009;25:2078–9.View ArticlePubMedPubMed CentralGoogle Scholar
  73. Karolchik D, Hinrichs AS, Furey TS, Roskin KM, Sugnet CW, Haussler D, et al. The UCSC Table Browser data retrieval tool. Nucleic Acids Res. 2004;32:493D–6D.View ArticleGoogle Scholar
  74. Wysoker A, Tibbetts K, McCowan M, Homer N, Fennell T. Picard Tools. (2010). Accessed 24 Aug 2016.
  75. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, et al. The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010;20:1297–303.View ArticlePubMedPubMed CentralGoogle Scholar
  76. Garrison E. Vcflib: A C++ library for parsing and manipulating VCF files. Available from: (2012). Accessed 24 Aug 2016.
  77. Cingolani P, Patel VM, Coon M, Nguyen T, Land SJ, Ruden DM, et al. Using Drosophila melanogaster as a model for genotoxic chemical mutational studies with a new program. SnpSift Front Genet. 2012;3:35.PubMedGoogle Scholar
  78. Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA, et al. The variant call format and VCFtools. Bioinformatics. 2011;27:2156–8.View ArticlePubMedPubMed CentralGoogle Scholar


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