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Article

Genome-Wide SNP Analysis for Milk Performance Traits in Indigenous Sheep: A Case Study in the Egyptian Barki Sheep

by
Ibrahim Abousoliman
1,2,
Henry Reyer
1,
Michael Oster
1,
Eduard Murani
1,
Ismail Mohamed
2 and
Klaus Wimmers
1,3,*
1
Leibniz Institute for Farm Animal Biology, Wilhelm-Stahl-Allee 2, 18196 Dummerstorf, Germany
2
Desert Research Center, Department of Animal and Poultry Breeding, 1 Mathaf El-Matareya St., El-Matareya, 11753 Cairo, Egypt
3
Faculty of Agricultural and Environmental Sciences, University of Rostock, Justus-von-Liebig-Weg 7, 18059 Rostock, Germany
*
Author to whom correspondence should be addressed.
Animals 2021, 11(6), 1671; https://doi.org/10.3390/ani11061671
Submission received: 27 April 2021 / Revised: 28 May 2021 / Accepted: 31 May 2021 / Published: 3 June 2021
(This article belongs to the Special Issue Genetics, Genomics, Transcriptomics, Proteomics, Health and Product Quality of Small Ruminants)
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Abstract

:

Simple Summary

The Barki sheep is one of the three main breeds in Egypt, which is spread mainly throughout the northwestern coastal zone, which has harsh conditions. Considering the harsh, semi-arid habitat of this breed, milk performance traits such as milk yield and milk composition have a very important role in the feeding of newborn lambs and affect their growth during the early stage of life. In this study, rare milk performance data and genomic information of Barki sheep were used to uncover diversified genomic regions that could explain the variability of milk yield and milk quality traits in the studied population of Barki ewes. Genome-wide analysis identified genomic regions harboring interesting candidate genes such as SLC5A8, NUB1, TBC1D1, KLF3 and ABHD5 for milk yield and PPARA and FBLN1 genes for milk quality traits. The findings offer valuable information for obtaining a better understanding of the genetics of milk performance traits and contribute to the genetic improvement of these traits in Barki sheep.

Abstract

Sheep milk yield and milk composition traits play an important role in supplying newborn lambs with essential components such as amino acids, energy, vitamins and immune antibodies and are also of interest in terms of the nutritional value of the milk for human consumption. The aim of this study was to identify genomic regions and candidate genes for milk yield and milk composition traits through genome-wide SNP analyses between high and low performing ewes of the Egyptian Barki sheep breed, which is well adapted to the harsh conditions of North-East Africa. Therefore, out of a herd of 111 ewes of the Egyptian Barki sheep breed (IBD = 0.08), ewes representing extremes in milk yield and milk quality traits (n = 25 for each group of animals) were genotyped using the Illumina OvineSNP50 V2 BeadChip. The fixation index (FST) for each SNP was calculated between the diversified groups. FST values were Z-transformed and used to identify putative SNPs for further analysis (Z(FST) > 10). Genome-wide SNP analysis revealed genomic regions covering promising candidate genes related to milk performance traits such as SLC5A8, NUB1, TBC1D1, KLF3 and ABHD5 for milk yield and PPARA and FBLN1 genes for milk quality trait. The results of this study may contribute to the genetic improvement of milk performance traits in Barki sheep breed and to the general understanding of the genetic contribution to variability in milk yield and quality traits.

1. Introduction

Sheep have been raised for milk production for thousands of years, before most other mammalian species [1]. In many countries around the world, especially in the Mediterranean region, sheep milk and its products are widely consumed by humans and are considered an important food resource. Sheep contribute about 5% of the total annual milk production in Egypt, whereas cows and buffaloes are the major suppliers of milk. This is a reflection of the management system of sheep milk production, which is characterized by subsistence and smallholder farming systems [2]. Sheep milk is highly similar to human milk in fatty acids composition, which makes it a suitable raw material for infant formula production [3]. Moreover, milk is the most important feed resource for newborn lambs during the early stage of their lifetime, from birth to weaning age (90 days), providing energy and proteins for growth and antibodies against infections and diseases [4]. Milk components such as fat, protein and lactose are important indicators of milk quality, which affects the growth and healthy feeding of the newborn lambs. Sheep milk production and composition are influenced by genetic and environmental factors. Estimates of heritability for milk yield, fat content and protein content in some sheep breeds such as Churra ewes were 0.32, 0.29 and 0.41, respectively [5,6]. The Barki sheep is one of the three most important breeds in Egypt, as it has adapted well to the harsh environmental conditions of Egypt’s northwestern coastal zone, where it is raised for meat, as its main product, and milk, as its by-product. The total population of Barki sheep is about 470,000 heads, which are owned by small holders [7]. The current Barki sheep breeding is characterized by a phenotypic selection approach considering mainly the number of offspring and the growth performance of lambs. In addition, the general health status is included, which enables ewes and lambs to cope with the harsh environmental conditions. Neither a structured breeding program nor a genetic selection program is applied. The amount of milk produced by Barki sheep in particular is low compared to the other native Egyptian breeds or worldwide breeds, possibly due to the absence of any attempts to perform phenotypic or genomic selection of milk performance traits in this breed. This low production affects lambs’ growth and viability and increases the percentage of the lambs lost due to inanition. It is also noticeable that the production of milk and its composition varies greatly between individuals in the Barki sheep breed, which is attributed to both genetic and environmental factors [8]. Therefore, it is feasible to study the differences between high and low productive individuals. The development of high-density SNP arrays and bioinformatics tools enables researchers to detect genomic regions that contribute to phenotypic variation in different livestock species, using different approaches based on linkage disequilibrium, allele frequency or haplotype characteristics [9]. To gain further knowledge about the genetic architecture, the fixation index (FST) approach of Weir and Cockerham is a suitable method, also for small data sets, to uncover genomic differences between experimental populations or groups and detect genomic regions with divergent allelic frequencies indicating putative candidate genes [10,11]. In this context, several studies were performed using genome-wide SNP data and revealed some candidate genes for milk traits in dairy cattle [12,13,14], sheep [15,16] and goats [17,18]. Previously, FST approach was conducted to detect some candidate genes for productive and reproductive traits such as fertility in Egyptian native Rahmani sheep breed [19]. The aim of the current study is to explore genomic differences of Barki ewes divergent in milk performance traits, thereby identifying genomic regions and candidate genes related to milk yield and milk composition.

2. Materials and Methods

2.1. Animals and Phenotypes

The experiment was conducted in accordance with all ethical and animal welfare standards of the Desert Research Center, taking into account all regulations in compliance with the European Union Directive for the Protection of Experimental Animals (2010/63/EU). A population of 111 Egyptian Barki ewes aged between 4 and 5 years was kept in the farms of Desert Research Centre, Ministry of Agriculture, Egypt under an intensive system and housed in semi-open yards for one breeding season. All ewes in the study were sired by 10 rams. Throughout the experimental period, ewes were fed daily on a feed concentrate (0.75 kg per head) and clover hay (0.5 kg per head). Fresh water was available to sheep ad libitum. Ewes were in the same parity and lactation period. Milk yield was recorded from parturition for a period of 3 months by hand milking in the morning and evening. Daily milk yield was measured by summation of the morning and evening milking. Total milk yield was calculated by summation of the daily milk yields for 90 days. Milk was sampled and stored at −20 °C. Milk from mixed samples of morning and afternoon milk were analyzed for percentages of fat, protein, lactose, and total solids using milko-scan (130 A/SN Foss Electric, Hillerod, Denmark). For genetic analysis of milk traits, both milk yield and milk composition served as selection criteria. For milk yield, in total 50 ewes were selected from the two tails of the phenotypic distribution and divided into two subgroups (high milk yield—HMY represent top 25 animals and low milk yield—LMY represent bottom 25 animals), each representing the extreme phenotypes for the milk yield trait. For milk composition, the measured values for fat, protein, lactose and total solids were used for a principal component analysis to calculate animal-individual eigenvalues. Therefore, the phenotypic correlation matrix was used to compute principal components using R statistical software [20]. The first and second principal components explained about 59.7% and 19.3% of the phenotypic variance of the traits. The first principal component was considered for grouping of animals according to milk quality (Supplementary Figure S1). Ewes having extreme negative loadings on PC1 were considered as high milk quality (HMQ) animals (n = 25), whereas individuals with extreme positive loading on PC1 were assigned to the low milk quality (LMQ) group (n = 25). Student’s t-test was used to compute the differences between the group means using SPSS V20 (IBM, New York, NY, USA). Phenotypic Spearman correlation coefficients among milk performance traits and PC1 were calculated for all animals (n = 111).

2.2. Genotyping and Quality Control

DNA was extracted from blood samples, collected from the jugular vein of all ewes, using the G-spin Total DNA Extraction kit (iNtRON Biotechnology, Seoul, Korea) according to the manufacturer’s instructions. Out of the entire population of 111 animals, 71 ewes were genotyped using the Illumina OvineSNP50 V2 BeadChip (Illumina, San Diego, CA, USA). The genetic relatedness of all pairs of ewes was assessed by calculating relative identity–by-descent (IBD) probabilities, which revealed an average relatedness of 0.08. The raw signal intensities of the 53,516 SNPs on the chip were imaged using the iScan Reader (Illumina). The signals were converted into genotype calls using the Genome Studio software (version 2.0). The SNPs with genotype call rates <90%, minor allele frequencies (MAF) <0.03 [21] and significant deviation from Hardy–Weinberg equilibrium at p < 10−6 were removed from analysis using JMP Genomics software (version 9). Base pair positions and names of SNP markers were updated to the latest version of the ovine genome of Texel breed (Oar_v3.1 accessed on 6 July 2020). SNPs not located on autosomes and lacking rs identifiers were excluded. After quality control, 49,184 SNPs were used for analyses.

2.3. Genome Wide FST Calculation

SNPRelate R package was used to calculate the FST of Weir and Cockerham for each SNP between the subgroups (LMY-HMY and LMQ-HMQ) [22]. The resultant distribution of FST values were Z-transformed and the extreme tail of the distribution was used to identify putative SNPs for further analysis, using a threshold Z(FST) > 10. In addition, all SNPs that passed the cutoff threshold at Z(FST) > 5 were listed in Supplementary Tables S1 and S2 [23]. Manhattan plots of the genome-wide Z(FST) values were performed using qqman package in R software. Genomic regions with the highest Z(FST) values were considered as region of interest. Genes within 1 megabase (Mb) regions up- and downstream of SNPs with highest Z(FST) values were scrutinized based on positional and functional evidences according to the Ensembl database. Genes harboring a highlighted SNP were considered positional candidate genes. Genes within the 2-Mb window were considered functional candidate genes, taking into account their functional relationship to phenotypes using available gene annotations from the GeneCards (http://www.genecards.org (accessed on: 3 February 2021) and Uniprot (http://www.uniprot.org (accessed on: 3 February 2021) databases.

3. Results

3.1. Phenotypic Data of Milk Performance Traits

Descriptive statistics of milk yield and milk composition, comprising fat, protein, lactose and total solids percentages and principal component 1 (PC1) for milk quality (MQ), are shown for the Barki subgroups in Table 1. A high MQ is indicated by negative loadings on PC1, whereas a low MQ is represented by positive values.
Significant correlation coefficients were determined between the milk composition traits (Table 2). The highest correlation coefficient was obtained for TS and P (0.83), followed by the coefficients of F and TS (0.47), and TS and L (0.43). There was no considerable correlation between milk yield and milk composition traits. The correlation coefficients obtained for PC1 showed that all milk composition traits are correlated to varying degrees by PC1, with protein and total solids having the highest correlation coefficients.

3.2. Detection of Genomic Regions and Candidate Genes

The animals were divided into two subgroups representing extreme phenotypes for milk yield (HMY and LMY) and milk composition (HMQ and LMQ). Z(FST) values were calculated to investigate the genomic differences between the groups using a genome-wide SNP panel. For milk yield, a number of genomic regions and SNPs were indicated to differentiate between the groups (Z(FST) > 10, Figure 1). These regions and SNPs were distributed on the chromosomes 1, 3, 4, 6, 12, 18 and 19 (Table 3). Within these genomic regions, OR6C75, ANO4, MCTP2 and SNRK were identified as positional candidate genes. Moreover, SLC5A8, NUB1, TBC1D1, KLF3 and ABHD5 were proposed as functional candidate genes, which are known to affect lactation, mammary gland development and secretion and fatty acids’ synthesis and lipids’ metabolism.
Figure 2 shows the Manhattan plot representation of SNP-specific Z(FST) values for milk quality. A genomic region and corresponding SNPs located on chromosome 3 are highlighted to be linked to this trait in the Barki sheep population (Table 3). Positional and functional candidate genes derived by the selected SNPs are indicated in Table 3. Within the genomic region on chromosome 3, ATXN10 gene was identified as a positional candidate gene. Moreover, FBLN1 and PPARA genes were designated as functional candidate genes in the identified genomic region.

4. Discussion

The averages of milk yield and milk composition traits (fat, protein, lactose and total solids percentages) in this study were similar to the previously recorded values of Barki ewes with 44.7 kg, 4.17%, 3.34%, 5.01% and 15.88%, respectively [24]. The results of the correlation analysis between the recorded milk traits confirmed the positive correlation among milk composition traits [25]. In contrast to other studies in sheep and cattle, there was no considerable negative correlation between milk yield and milk composition traits, possibly due to the overall low milk production of Barki sheep and the limited breeding efforts on these traits. Furthermore, a positive correlation was revealed between PC1 and milk composition traits as shown in Table 2. For comparison, the average milk yield of the Rahmani breed (70.75 kg), which is another important indigenous Egyptian sheep breed, was reported to be significantly higher [26]. The correlation results among milk yield and milk components were in agreement with those of ewes from the ancient Iberian Churra breed, which also have low average milk performance [6]. The ewes in this study were considered not substantially related according to genetic relatedness (IBD = 0.08) and were suitable for the application of the FST approach [27]. For milk yield, a total of seven genomic regions were identified to differentiate comparing HMY and LMY animals as shown in Table 3. Scrutiny of the genes in the identified genomic regions revealed functional candidates on chromosomes 3, 4, 6, 18 and 19. Several QTL for milk yield on these chromosomes in different genomic regions in the Sheep Genome were reported previously in various sheep breeds [8,28,29,30,31,32,33]. The same genomic region on chromosome 18 was detected to be associated with milk yield in East Friesian and Dorset sheep breeds [32]. In the genomic region on chromosome 3 at 169.8 Mb, Solute Carrier Family 5 (Sodium/Monocarboxylate Cotransporter) Member 8 (SLC5A8) was previously reported to be associated with milk yield in Italian Holstein dairy cows [34]. The genomic region on chromosome 4 harbors the Negative Regulator of Ubiquitin-Like Proteins 1 (NUB1) gene as one of the proposed genes affecting milk yield and contributing to the proteasomal degradation pathway. NUB1 was previously proposed as a strong candidate gene explaining the variation in milk yield in Gir × Holstein (Girolando) crossbreed animals [35]. The QTL on chromosome 6 at 57 Mb includes TBC1 Domain Family Member 1 (TBC1D1) and Kruppel-Like Factor 3 (KLF3). Selection signatures study in dairy and beef cattle revealed TBC1D1 as candidate for milk production [36]. In Holstein cows, a scan for polymorphisms in TBCID1 yielded two SNPs associated with milk protein yield [37] and another SNP associated with fat and protein percentages [38]. The importance of KLF3 was suggested in Chinese Holstein cows based on its physiological and biochemical functions in many processes such as cell proliferation, differentiation, homeostasis and apoptosis [39,40]. Moreover, a SNP in KLF3 was significantly associated with milk yield and protein yield also in Chinese Holstein [41]. The Abhydrolase Domain Containing 5 (ABHD5) gene, which resides on chromosome 19 at 15.5 Mb, represents a prospective functional candidate, based on its important role in lipid metabolism, the energy balance signaling pathway and triglyceride metabolism in dairy cows and Qinchuan cattle [42,43].
For milk quality, a genomic region on chromosome 3 was shown to be differentiated between HMQ and LMQ ewes, confirming previously reported QTL for milk fat percentage [30,33], protein percentage [44,45] and lactose percentage [46]. Within this genomic region on chromosome 3, PPARA and FBLN1 genes were proposed as candidates. The Peroxisome Proliferator Activated Receptor Alpha (PPARA) gene located at 220.6 Mb is a member of the PPARs family, which has a critical role in the regulation of milk fat synthesis in lactating ruminants [47]. PPARA is one of the genes involved in lipid metabolism in mammary gland in dairy cows [48]. In Charolais × German Holstein cross-breed dairy cows, PPARA was associated with milk yield and protein synthesis [49]. In line with the results of the Barki study, Fibulin 1 (FBLN1) located on chromosome 3 at 220 Mb was reported to be associated with milk protein yield and protein percentage in dairy cattle [25]. In addition, FBLN1 was reported to play a critical role in the development and cell differentiation of the mammary gland [50]. However, due to the limited sample size available for Barki sheep in the current study, the results deserve further investigation involving a larger number of animals and other indigenous sheep breeds.

5. Conclusions

The results of the genome-wide analysis uncovered some genomic regions contributing to variability in milk performance traits such as milk yield and milk quality in Bakri sheep. These regions harbor some interesting functional candidate genes such as SLC5A8, NUB1, TBC1D1, KLF3 and ABHD5 for milk yield, and PPARA and FBLN1 for milk quality traits. These genes deserve further investigation to analyze the association between genetic variations of these genes and their respective milk phenotypes. Given the current absence of structured genetic improvement programs in Barki sheep, the current analysis provides insights into genomic regions that are critical for milk quantity and quality in ruminants. Our findings offer valuable information for the future improvement of milk performance traits and the associated assurance of offspring supply in the Barki sheep breed.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/ani11061671/s1. Table S1: Genomic positions derived from SNPs differentiating between ewes divergent in milk yield (Z(FST) > 5), Table S2: Genomic positions derived from SNPs differentiating between ewes divergent in milk quality (Z(FST) > 5), Figure S1: Eigenvalues from the principle component analysis for milk quality traits. Animals have been assigned to low (red) and high (blue) milk quality.

Author Contributions

Conceptualization, I.M. and K.W.; Methodology, I.A., H.R. and E.M.; formal analysis, I.A., H.R. and M.O.; resources, I.M. and K.W.; data curation, I.A. and H.R.; writing—original draft preparation, I.A.; writing—review and editing, all authors; supervision, H.R. and K.W.; funding acquisition, I.M. and K.W. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by a grant to I.A. from the Sector for Missions and Cultural Affairs, Ministry of Higher Education, Egypt, and co-financed by the Leibniz Institute for Farm Animal Biology (FBN), Germany.

Institutional Review Board Statement

All animal handling procedures and samples collection are done with the approval of the Department of Animal Health (DRC) Committee, do not require an animal experimentation permit according to the regulations of the Desert Research Center (DCR) Ethics Board, and complained in their implementation with the European Union Directive on the Protection of the Experimental Animals (2010/63/EU).

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Acknowledgments

The authors thank Angela Garve for her excellent technical help.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Zervas, G.; Tsiplakou, E. The effect of feeding systems on the characteristics of products from small ruminants. Small Ruminant Res. 2011, 101, 140–149. [Google Scholar] [CrossRef]
  2. Sallam, A. A missense mutation in the coding region of the toll-like receptor 4 gene affects milk traits in Barki sheep. Anim. Biosci. 2021, 34, 489–498. [Google Scholar] [CrossRef] [PubMed]
  3. Martin, C.R.; Ling, P.; Blackburn, G.L. Review of Infant Feeding: Key Features of Breast Milk and Infant Formula. Nutrients 2016, 8, 279. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Kalyankar, S.D. Sheep: Milk. In Encyclopedia of Food and Health; Caballero, B., Finglas, P.M., Toldrá, F., Eds.; Academic Press: Oxford, UK, 2016; pp. 758–763. [Google Scholar]
  5. Milan, P.; Mekic, C.; Zujovic, M. Genetic principles relating to improvement of milk yield in sheep and goats. Biotech. Anim. Husb. 2005, 21, 73–78. [Google Scholar]
  6. Othmane, M.H.; Carriedo, J.A.; Primitivo, F.S.; De La Fuente, L.F. Genetic parameters for lactation traits of milking ewes: Protein content and composition, fat, somatic cells and individual laboratory cheese yield. Genet. Sel. Evol. 2002, 34, 581–596. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  7. Sallam, A.M.; Ibrahim, A.H.; Alsheikh, S.M. Estimation of genetic parameters and variance components of pre-weaning growth traits in Barki lambs. Small Rumin Res. 2019, 173, 94–100. [Google Scholar] [CrossRef]
  8. Abousoliman, I.; Reyer, H.; Oster, M.; Muráni, E.; Mourad, M.; Rashed, M.A.; Mohamed, I.; Wimmers, K. Analysis of candidate genes for growth and milk performance traits in the Egyptian Barki sheep. Animals 2020, 10, 197. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  9. Saravanan, K.A.; Panigrahi, M.; Kumar, H.; Bhushan, B.; Dutt, T.; Mishra, B.P. Selection signatures in livestock genome: A review of concepts, approaches and applications. Livest. Sci. 2020, 241, 104257. [Google Scholar] [CrossRef]
  10. Weir, B.S.; Cockerham, C.C. Estimating f-statistics for the analysis of population structure. Evolution 1984, 38, 1358–1370. [Google Scholar] [CrossRef]
  11. Porto-Neto, L.R.; Lee, S.H.; Gondro, C. Detection of signatures of selection using Fst. Methods Mol. Biol. 2013, 1019, 423–436. [Google Scholar] [CrossRef]
  12. Bouwman, A.C.; Bovenhuis, H.; Visker, M.H.P.W.; Arendonk, J.A.M. Genome-wide association of milk fatty acids in Dutch dairy cattle. BMC Genet. 2011, 12, 43. [Google Scholar] [CrossRef] [Green Version]
  13. Li, C.; Sun, D.; Jiang, L.; Liu, J.; Zhang, Q.; Zhang, Y.; Zhang, S. Advances on genome-wide association study for economically important traits in dairy cattle. Hereditas 2012, 34, 545–550. [Google Scholar] [CrossRef]
  14. Maxa, J.; Neuditschko, M.; Russ, I.; Förster, M.; Medugorac, I. Genome-wide association mapping of milk production traits in Braunvieh cattle. J. Dairy Sci. 2012, 95, 5357–5364. [Google Scholar] [CrossRef] [Green Version]
  15. Saridaki, A.; Antonakos, G.; Theodorides, H.; Zoidis, A.L. Combined haplotype blocks regression and multi-locus mixed model analysis reveals novel candidate genes associated with milk traits in dairy sheep. Livest. Sci. 2019, 220, 8–16. [Google Scholar] [CrossRef]
  16. García-Gámez, E.; Gutiérrez-Gil, B.; Sahana, G.; Arranz, J. 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. [Google Scholar] [CrossRef]
  17. Scholtens, M.; Jiang, A.; Smith, A.; Littlejohn, M.; Blair, H. Genome-wide association studies of lactation yields of milk, fat, protein and somatic cell score in New Zealand dairy goats. J. Anim. Sci. Biotechnol. 2020, 11, 55. [Google Scholar] [CrossRef]
  18. Mucha, S.; Morde, R.; Coffey, M.; Conington, J. Genome-wide association study of conformation and milk yield in mixed-breed dairy goats. J. Dairy Sci. 2018, 101, 2213–2225. [Google Scholar] [CrossRef]
  19. El-Halawany, N.; Zhou, X.; Al-Tohamy, A.; El-Sayd, Y.; Shawky, A.; Michal, J.; Jiang, Z. Genome-wide screening of candidate genes for improving fertility in Egyptian native Rahmani sheep. Anim. Genet. 2016, 47, 513. [Google Scholar] [CrossRef]
  20. Laenoi, W.; Rangkasenee, N.; Uddin, M.; Cinar, M.; Wimmers, K.; Schellander, K. Association and expression study of MMP3, TGFβ1 and COL10A1 as candidate genes for leg weakness-related traits in pigs. Mol. Biol. Rep. 2012, 39, 3893–3901. [Google Scholar] [CrossRef]
  21. Zhang, L.; Liu, J.; Zhao, F.; Ren, H.; Xu, L.; Lu, J. Genome-Wide Association Studies for Growth and Meat Production Traits in Sheep. PLoS ONE 2013, 8, e66569. [Google Scholar] [CrossRef] [Green Version]
  22. Zheng, X.; Levine, D.; Shen, J.; Gogarten, S.M.; Laurie, C.; Weir, B.S. A High-performance Computing Toolset for Relatedness and Principal Component Analysis of SNP Data. Bioinformatics 2012, 28, 3326–3328. [Google Scholar] [CrossRef] [Green Version]
  23. Yaxin, Y.; Zhangyuan, P.; Ran, D. Whole-Genome Sequencing of Bamei Mutton Sheep for Screening the Genes and SNPs Associated with Litter Size under Selection. Res. Square 2021, 1, 1–17. [Google Scholar] [CrossRef]
  24. Farrag, B.; El-Hawy, A.; El-Bassiony, M. Improving Reproductive and Productive Efficiency of Barki Sheep by using GnRHand Selenium. World’s Vet. J. 2017, 7, 128–136. [Google Scholar]
  25. Raven, L.-A.; Cocks, B.; Pryce, J.; Hayes, B. Genes of the RNASE5 pathway contain SNP associated with milk production traits in dairy cattle. Genet. Sel. Evol. 2013, 45, 25. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  26. Abd-Allah, M.; Abass, S.; Allam, F. Factors affecting the milk yield and composition of Rahmani and Chios sheep. Glob. J. Dairy Farm. Milk Prod. 2013, 1, 53–59. [Google Scholar]
  27. Miller, M.B.; Basu, S.; McGue, M. The Minnesota Center for Twin and Family Research genome-wide association study. Twin Res. Hum. Genet. 2012, 15, 767–774. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  28. Calvo, J.H.; Marcos, S.; Beattie, A.E.; Gonzalez, C.; Jurado, J.J.; Serrano, M. Ovine alpha-amylase genes: Isolation, linkage mapping and association analysis with milk traits. Anim. Genet. 2004, 35, 329–332. [Google Scholar] [CrossRef] [PubMed]
  29. Calvo, J.H.; Martínez-Royo, A.; Beattie, A.E.; Dodds, K.G.; Marcos-Carcavilla, A.; Serrano, M. Fine mapping of genes on sheep chromosome 1 and their association with milk traits. Anim. Genet. 2006, 37, 205–210. [Google Scholar] [CrossRef]
  30. Raadsma, H.W.; Jonas, E.; McGill, D.; Thomosom, P. Mapping quantitative trait loci (QTL) in sheep. II. Meta-assembly and identification of novel QTL for milk production traits in sheep. Genet. Sel. Evol. 2009, 41, 45. [Google Scholar] [CrossRef] [Green Version]
  31. Arnyasi, M.; Komlósi, I.; Lien, S.; Czeglédi, L.; Nagy, S.; Jávor, A. Searching for DNA markers for milk production and composition on chromosome 6 in sheep. J. Anim. Breed. Genet. 2009, 126, 142–147. [Google Scholar] [CrossRef]
  32. Mateescu, R.G.; Thonney, M.L. Genetic mapping of quantitative trait loci for milk production in sheep. Anim. Genet. 2010, 41, 460–466. [Google Scholar] [CrossRef]
  33. Garcia-Gámez, E.; Gutiérrez-Gil, B.; Suarez-Vega, A.; de la Fuente, L.F.; Arranz, J.J. Identification of quantitative trait loci underlying milk traits in Spanish dairy sheep using linkage plus combined linkage disequilibrium and linkage analysis approaches. J. Dairy Sci. 2013, 96, 6059–6069. [Google Scholar] [CrossRef] [Green Version]
  34. Cellesi, M.; Dimauro, C.; Sorbolini, S.; Macciotta, N. Maximum difference analysis: A new empirical method for genome-wide association studies. Ital. J. Anim. Sci. 2016, 15, 396–406. [Google Scholar] [CrossRef] [Green Version]
  35. da Cruz, A.S.; Silva, D.; Minasi, L.; da Cruz, A. Single-Nucleotide Polymorphism variations associated with specific genes putatively identified enhanced genetic predisposition for 305-day milk yield in the Girolando crossbreed. Front. Genet. 2021, 11, 573344. [Google Scholar] [CrossRef]
  36. Zhao, F.; McParland, S.; Kearney, F.; Du, L.; Berry, D. Detection of selection signatures in dairy and beef cattle using high-density genomic information. Genet. Sel. Evol. 2015, 47, 49. [Google Scholar] [CrossRef] [Green Version]
  37. Liu, R.; Sun, D.; Wang, Y.; Yu, Y.; Zhang, Y. Fine mapping QTLs affecting milk production traits on BTA6 in Chinese Holstein with SNP markers. J. Integr. Agric. 2013, 12, 110–117. [Google Scholar] [CrossRef]
  38. Jiang, J.; Liu, L.; Gao, Y.; Shi, L.; Sun, D. Determination of genetic associations between indels in 11 candidate genes and milk composition traits in Chinese Holstein population. BMC Genet. 2019, 20, 48. [Google Scholar] [CrossRef] [Green Version]
  39. Xu, Q.; Mei, G.; Sun, D.; Liu, J. Detection of genetic association and functional polymorphisms of UGDH affecting milk production trait in Chinese Holstein cattle. BMC Genom. 2012, 13, 590. [Google Scholar] [CrossRef] [Green Version]
  40. Eaton, S.A.; Funnell, A.; Sue, N.; Pearson, R.; Crossley, M. A Network of Krüppel-like Factors (Klfs): Klf8 is repressed by Klf3 and activated by Klf1 in vivo. J. Biol. Chem. 2008, 283, 26937–26947. [Google Scholar] [CrossRef] [Green Version]
  41. Zongjun, Y.; Mei, G.; Liu, Y.; Ding, X.; Zhang, Q. Polymorphism Identification and Association with Milk Production Traits of KLF3 Gene in a Chinese Holstein Population. J. Anim. Vet. Adv. 2010, 9, 2784–2787. [Google Scholar] [CrossRef] [Green Version]
  42. Contreras, G.A.; Strieder-Barboza, C.; Raphael, W. Adipose tissue lipolysis and remodeling during the transition period of dairy cows. J. Anim. Sci. Biotechnol. 2017, 8, 41. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  43. Wang, X.; Khan, R.; Raza, S.; Zan, L. Molecular characterization of ABHD5 gene promoter in intramuscular preadipocytes of Qinchuan cattle: Roles of Evi1 and C/EBPα. Gene 2019, 690, 38–47. [Google Scholar] [CrossRef]
  44. Gutiérrez-Gil, B.; Zarei, M.; Alvarez, L.; Bayón, Y.; Arranz, J. Quantitative trait loci underlying milk production traits in sheep. Anim. Genet. 2009, 40, 423–434. [Google Scholar] [CrossRef] [PubMed]
  45. García-Gámez, E.; Gutiérrez-Gil, B.; Sánchez, J.P.; Arranz, J.J. Replication and refinement of a quantitative trait locus influencing milk protein percentage on ovine chromosome 3. Anim. Genet. 2012, 43, 636–641. [Google Scholar] [CrossRef] [PubMed]
  46. Jawasreh, K.; Amareen, A.A.; Aad, P. Effect and Interaction of β-Lactoglobulin, Kappa Casein, and Prolactin Genes on Milk Production and Composition of Awassi Sheep. Animals 2019, 9, 382. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  47. Bionaz, M.; Chen, S.; Khan, M.; Loor, J. Functional Role of PPARs in Ruminants: Potential Targets for Fine-Tuning Metabolism during Growth and Lactation. PPAR Res. 2013, 2013, 684159. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  48. Bernard, L.; Toral, P.G.; Chilliard, Y. Comparison of mammary lipid metabolism in dairy cows and goats fed diets supplemented with starch, plant oil, or fish oil. J. Dairy Sci. 2017, 100, 9338–9351. [Google Scholar] [CrossRef] [Green Version]
  49. Friedrich, J.; Brand, B.; Ponsuksili, S.; Kuehn, C.; Schwerin, M. Detection of genetic variants affecting cattle behaviour and their impact on milk production: A genome-wide association study. Anim. Genet. 2016, 47, 12–18. [Google Scholar] [CrossRef]
  50. Menzies, K.K.; Lefevre, C.; Macmillan, K.; Nicholas, K. Insulin regulates milk protein synthesis at multiple levels in the bovine mammary gland. Funct. Integr. Genom. 2009, 9, 197–217. [Google Scholar] [CrossRef]
Animals 11 01671 g001 550
Figure 1. Manhattan plot of Z(FST) values at each SNP for milk yield. The red dots represent the SNPs that passed the cut-off threshold at Z(FST) = 10 and are labelled with candidate genes within a 2 Mb window.
Figure 1. Manhattan plot of Z(FST) values at each SNP for milk yield. The red dots represent the SNPs that passed the cut-off threshold at Z(FST) = 10 and are labelled with candidate genes within a 2 Mb window.
Animals 11 01671 g001
Animals 11 01671 g002 550
Figure 2. Manhattan plot of Z(FST) values at each SNP comparing LMQ and HMQ Barki ewes. The red dots represent the SNPs that passed the cut-off threshold at Z(FST) = 10 and are labelled with candidate genes within a 2 Mb window.
Figure 2. Manhattan plot of Z(FST) values at each SNP comparing LMQ and HMQ Barki ewes. The red dots represent the SNPs that passed the cut-off threshold at Z(FST) = 10 and are labelled with candidate genes within a 2 Mb window.
Animals 11 01671 g002
Table
Table 1. Descriptive statistics of milk performance traits.
Table 1. Descriptive statistics of milk performance traits.
TraitGroup *NMeanSEMinMaxp Value **
Milk yield MY (kg)HMY2541.972.0231.5072.00p < 0.001
LMY2517.200.719.9023.40
Milk quality (MQ; PC1)HMQ25−1.110.08−0.63−2.21p < 0.001
LMQ252.050.294.910.14
Milk fat (%)HMQ256.280.264.789.60p < 0.001
LMQ252.610.131.453.60
Milk protein (%)HMQ256.850.255.209.20p < 0.001
LMQ254.090.102.854.60
Milk lactose (%)HMQ257.990.147.309.90p < 0.001
LMQ255.140.221.016.10
Total solids (%)HMQ2525.490.8720.0033.10p < 0.001
LMQ2514.830.1812.6816.24
* HMY = High milk yield, LMY = Low milk yield, HMQ = High milk quality, LMQ = Low milk quality. ** p value computed using t-test, SE = Standard error, PC1 = Principal component 1.
Table
Table 2. Phenotypic Spearman correlation among milk performance traits.
Table 2. Phenotypic Spearman correlation among milk performance traits.
TraitMYFatProteinLactoseTotal SolidsPC1
MY1
Fat−0.121
Protein−0.050.38 **1
Lactose0.050.26 **0.29 **1
Total Solids0.060.47 **0.83 **0.43 **1
PC1−0.060.69 **0.90 **0.61 **0.94 **1
** Highly significant correlation (p < 0.01) using t-test for the significance. PC1 = Principal component 1.
Table
Table 3. Genomic positions and putative candidate genes derived from SNPs differentiating between ewes divergent in milk yield and milk quality (Z(FST) > 10).
Table 3. Genomic positions and putative candidate genes derived from SNPs differentiating between ewes divergent in milk yield and milk quality (Z(FST) > 10).
TraitRs NameChrPositionMAFFSTZ(FST)Candidate Genes *
Milk yieldrs41209272111587533750.4770.26310.68ENSOARG00000017360
rs42821747931641774060.4290.27811.32OR6C76, OR6C1, OR6C75
rs43073602531698232140.3740.27010.99GAS2L3, SLC5A8, ANO4
rs42035194841093385290.3320.31913.07ENSOARG00000001351
rs39905026641133621350.3500.25410.30NUB1, RHEB
rs4183942166574519340.2010.25510.34TBC1D1, KLF3
rs42734372612154243500.2280.28711.70ENSOARG00000025431, ENSOARG00000025432
rs41262691012799665740.4210.24710.04CRB1, DENND1B
rs43029763418118415410.4330.25910.53MCTP2
rs42365448819152022340.3520.28111.48GASK1A, ANO10, SNRK-ABHD5
Milk qualityrs40870081832201032170.3700.30010.14ATXN10, FBLN1, PPARA
rs41424412032200484410.4850.29910.09FBLN1, ATXN10, PPARA
* Gene names in bold = functional candidate genes; underlined = positional candidate genes; only italic = closest up- and downstream located genes within 1 Mb window; Chr = Chromosome, MAF = Minor allele frequency.
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Abousoliman, I.; Reyer, H.; Oster, M.; Murani, E.; Mohamed, I.; Wimmers, K. Genome-Wide SNP Analysis for Milk Performance Traits in Indigenous Sheep: A Case Study in the Egyptian Barki Sheep. Animals 2021, 11, 1671. https://doi.org/10.3390/ani11061671

AMA Style

Abousoliman I, Reyer H, Oster M, Murani E, Mohamed I, Wimmers K. Genome-Wide SNP Analysis for Milk Performance Traits in Indigenous Sheep: A Case Study in the Egyptian Barki Sheep. Animals. 2021; 11(6):1671. https://doi.org/10.3390/ani11061671

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Abousoliman, Ibrahim, Henry Reyer, Michael Oster, Eduard Murani, Ismail Mohamed, and Klaus Wimmers. 2021. "Genome-Wide SNP Analysis for Milk Performance Traits in Indigenous Sheep: A Case Study in the Egyptian Barki Sheep" Animals 11, no. 6: 1671. https://doi.org/10.3390/ani11061671

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