Kenan Burak Aydin
Ye Bi
Luiz F. Brito
Zafer Ulutaş
Gota Morota- 1School of Animal Sciences, Virginia Tech, Blacksburg, VA, United States
- 2Department of Animal Sciences, Purdue University, West Lafayette, IN, United States
- 3Department of Animal Science, Faculty of Agriculture, Ondokuz Mayis University, Samsun, Türkiye
- 4Center for Advanced Innovation in Agriculture, Virginia Tech, Blacksburg, VA, United States
The livestock industry in Türkiye is vital to the country’s agricultural sector and economy. In particular, sheep products are an important source of income and livelihood for many Turkish smallholder farmers in semi-arid and highland areas. Türkiye is one of the largest sheep producers in the world and its sheep production system is heavily dependent on indigenous breeds. Given the importance of the sheep industry in Türkiye, a systematic literature review on sheep breeding and genetic improvement in the country is needed for the development and optimization of sheep breeding programs using modern approaches, such as genomic selection. Therefore, we conducted a comprehensive literature review on the current characteristics of sheep populations and farms based on the most up-to-date census data and breeding and genetic studies obtained from scientific articles. The number of sheep has increased in recent years, mainly due to the state’s policy of supporting livestock farming and the increase in consumer demand for sheep dairy products with high nutritional and health benefits. Most of the genetic studies on indigenous Turkish sheep have been limited to specific traits and breeds. The use of genomics was found to be incipient, with genomic analysis applied to only two major breeds for heritability or genome-wide association studies. The scope of heritability and genome-wide association studies should be expanded to include traits and breeds that have received little or no attention. It is also worth revisiting genetic diversity studies using genome-wide single nucleotide polymorphism markers. Although there was no report of genomic selection in Turkish sheep to date, genomics could contribute to overcoming the difficulties of implementing traditional pedigree-based breeding programs that require accurate pedigree recording. As indigenous sheep breeds are better adapted to the local environmental conditions, the proper use of breeding strategies will contribute to increased income, food security, and reduced environmental footprint in a sustainable manner.
Introduction
Livestock farming is vital to Turkey’s agricultural sector and economy. Türkiye (Turkey) has extensive areas of meadows, pastures, forests, woodland, and arable land, which give it a great capacity to support a variety of livestock and crops (Akbay and Boz, 2005). Livestock production contributes about 25% of the total value of agricultural production and helps in the economic growth of rural households (Akbay and Boz, 2005). Small ruminant products provide an important source of income and livelihood for numerous farmers and households living in resource-poor areas in highlands and semi-arid territories.
Sheep (Ovis aries) are multipurpose animals that produce meat, milk, and wool (Zygoyiannis, 2006). Sheep are thought to have been domesticated in the Fertile Crescent about 10,000 years ago (Zeder, 2008). Sheep are the second known animal to have been domesticated, following the dog. Sheep production is one of the most important agricultural sectors in Türkiye due to the Turkish dietary preference (Ankarali, 1988). Türkiye is one of the largest sheep producers in Europe and West Asia and has a sheep production system that heavily relies on indigenous breeds. However, in recent years, the number of European breeds imported for dairy and meat production has increased. These sheep breeds are raised in almost all regions of Türkiye, including harsh environmental areas. The Turkish sheep breeds are well adapted to rangelands and can endure droughts and unstable nutrient availability (Ankarali, 1988). For this reason, sheep have been meeting human needs for many years by providing meat, milk, and fleece. They are able to convert grasses on poor rangelands into valuable sources of protein and wool. Sheep meat and milk account for 25% and 5% of domestic production, respectively (Turkish Statistical Institute, 2022). Sheep are commonly reared in extensive systems where ewes give birth once a year and graze on low-input and low-output grasslands unsuitable for cultivation. Due to the high feed costs, grazing plays an important role in Turkish farming. Sheep breeders prefer to raise sheep because they benefit from the forage in the grass and other species, such as cattle, cannot adapt well to harsh environmental conditions. In Türkiye, most sheep are raised for meat production, and the average consumption of 4.2 kg per person makes it an important source of red meat production, especially under harsh climate conditions (Şirin et al., 2017). Lambs are typically slaughtered when they reach 50–55 kg, resulting in a carcass weight of 18–20 kg. Most sheep milk is used in cheese production, with an average cheese consumption of 8.8 kg per person per year. Ewes are typically milked manually for about 3–4 months after weaning (Gürsoy, 2006). Despite the crucial role that sheep production plays in Turkey’s agricultural production and economy, a comprehensive review focused on sheep breeding and genetics in Türkiye has not been documented yet. A comprehensive literature review could guide the development of more effective breeding strategies incorporating modern methods and technologies.
Therefore, the primary objective of this paper was to comprehensively review the literature on sheep breeding and genetics in Türkiye for the past 30 years. This review is based on a thorough literature search using recent government statistics, Web of Science, and Google Scholar databases. The structure of this review article is as follows. First, we examine the current size of the indigenous sheep populations, the geographic locations where they are being raised, and their management practices based on the most recent data available. Next, we review some of the primary sheep breeds in Türkiye, their key characteristics, and their level of population genetic diversity. We also compiled the estimates of genetic parameters and genome-wide association study (GWAS) analyses of economically important traits. Lastly, an overall discussion and suggestions of next steps to be taken for improving sheep breeding and genetics in Türkiye are presented in the last section as concluding remarks.
Sheep population demographics
Figure 1 shows the total number of sheep during the last 30 years in Türkiye (Turkish Statistical Institute, 2022). At present, there are 45 million sheep in the country, where approximately 83% of them are ewes. While the sheep population experienced a steady decline from 1990 to 2002 and remained fairly constant for a few years thereafter, it sharply rose to 45 million later. Several factors have contributed to the decline of the sheep population between 1990 and 2002. These include 1) decrease in pasture and rangeland areas due to conversion to crop production, 2) lower productivity of indigenous sheep breeds, 3) migration of population from rural to urban areas, 4) reluctance of farmers to adopt new technologies, 5) greater government support for poultry and dairy production as opposed to sheep production, and 6) lack of government support policies for sheep production (Gürsoy, 2006). In particular, sheep were replaced by dairy cattle in resource-rich areas and sheep production was forced to move to more remote, resource-limited, and arid areas (Ocak et al., 2010). The combination of these factors has led to the decline of the sheep industry in Türkiye.
FIGURE 1. Bar graph showing the total number of sheep in the last 30 years in Türkiye. The 2022 data were obtained from the Turkish Statistical Institute.
The more recent increase in the sheep population after 2010 is due to the implementation of government support policies. To enhance the profitability of livestock production and improve livelihoods, the government introduced support policies in 2000 and 2005. These policies promoted meat and milk production. For example, livestock support increased from 19 million in 2000 to 1.16 billion dollars in 2015 (Erturk et al., 2015). Likewise, the share of livestock support in total agricultural support was 1.29% in 2000, but increased to 21.8% in 2015. These support policies have strengthened the sheep industry by providing support and incentives to livestock farmers. These policies have contributed to the growth and development of the sector, leading to an increase in the sheep population in Türkiye (Erturk et al., 2015). Another factor contributing to this trend is the recent rise in consumer demand for dairy products that provide significant nutritional and health benefits, such as cheese, yogurt, ice cream, butter, ayran, kefir, and drinking milk. This has increased the popularity of dairy products derived from small ruminants, mainly dairy sheep. The increasing popularity of these dairy products in the markets is due to the changing socio-economic status of consumers. This trend has led to an increase in the number of sheep in Türkiye (Hayaloglu and Karagul-Yuceer, 2011).
The number of sheep in different regions of Turkey is shown in Figure 2. The 2022 data was obtained from the Turkish Statistical Institute (Turkish Statistical Institute, 2022). Türkiye is commonly divided into seven geographical regions. Out of these regions, there are five main regions where sheep are predominantly found: Central Anatolia, Eastern Anatolia, Southeastern Anatolia, Aegean, and Marmara regions. The Central Anatolia, Eastern Anatolia, and Southeastern Anatolia regions are arid and semi-arid (Ankarali, 1988). The number of sheep varies among the regions, with 10,946,183 in Central Anatolia, 11,803,775 in Eastern Anatolia, 7,608,111 in Southeastern Anatolia, 4,379,581 in Aegean, and 3,886,642 in Marmara. The Mediterranean region has the highest number of goats, possibly due to its mountainous terrain and consumer demand for goat meat.
FIGURE 2. Heat map of Türkiye showing the number of sheep in seven regions in 2022. The data were obtained from the Turkish Statistical Institute.
The number of sheep in each province of Turkey is shown in Figure 3 using the 2022 data from the Turkish Ministry of Agriculture and Forestry. Van is the province with the highest number of sheep, totaling 3,106,786. The Eastern Anatolia region, covering the provinces of Van, Mus, Agri, Erzurum, and Bitlis, is mainly known for small-scale sheep breeding for meat and milk, with vast grazing areas consisting of meadows and pastures. Indigenous breeds known for their modest productivity are primarily used for livestock production in this region. The Central Anatolia region, encompassing the provinces of Ankara, Konya, Eskisehir, Sivas, and Karaman, is renowned for housing the highest number of sheep breeds for meat. The milk-producing Awassi breed of sheep is known to be primarily reared in the Southeastern Anatolia region, which includes Sanliurfa and Diyarbakir. Awassi sheep have become widespread around the world due to the increasing preference for sheep milk in recent years. Dairy and meat sheep breeding is well-known in the Aegean region, which includes the provinces of Izmir, Manisa, Balikesir, and Usak. The most common breed in this region is the Chios (Sakiz) breed. The Marmara region, which includes several provinces such as Kirklareli, Edirne, Bursa, Yalova, and Çanakkale, is primarily known for its substantial sheep population that is utilized for dairy, wool, and meat purposes. The common meat type breed reared in the region is the Kivircik sheep, which is known for its meat quality (Aksoy and Yavuz, 2012).
FIGURE 3. Heat map of Türkiye showing the number of sheep in each province in 2022. The data were obtained from the Ministry of Agriculture and Forestry.
Number of farms and farm size
The number of sheep farms in each province is shown in Figure 4. There are 385,071 farms in total. The provinces with the highest numbers of farms were Van (21,583), Balikesir (17,888), Konya (17,175), Manisa (13,810), and Canakkale (12,965). In Figure 5, the relationship between the number of farms and farm size in Türkiye in 2022 is shown (Turkish Statistical Institute, 2022). The majority of them are smallholder family farms. A smallholder farming system is the basic unit of sheep production in Türkiye, with family members providing most of the farm labor. The majority of farms have between 50 and 149 sheep (28.5%), with the second largest category ranging from 20 to 49 sheep (17.7%). Farms with a capacity of one to four sheep aim to meet the family’s needs for milk, butter, and cheese by rearing animals within or near the household, rather than for sale. Farms with higher capacity are managed for commercial purposes. Recently, the size of Turkish sheep flocks has been increasing, with an average flock size of 85. For comparison, the corresponding population demographics and the number and size of goat farms are shown in Supplementary Figures S1–S3.
FIGURE 4. Heat map of Türkiye representing the number of farms in each province in 2022. The data were obtained from the Ministry of Agriculture and Forestry.
FIGURE 5. Bar graph showing the relationship between the number of farms and farm size in Türkiye in 2022. The data were obtained from the Turkish Statistical Institute.
Sheep breeds in Türkiye
Türkiye is home to 33 different breeds of sheep (Sheep and Goat Breeders’ Associations of Turkey, 2021). The majority of sheep in Türkiye are considered fat-tailed, which means that they have long tails with a fat base. The tails of fat-tailed sheep in Türkiye reach their maximum size before the winter season. This enables them to survive harsh winter conditions and poor feeding periods by utlizing the fat stored in their tails (Yilmaz et al., 2012; 2013). In recent years, European sheep breeds have been increasingly bred for dairy and meat, such as Romanov, Ille de France, German Merino, Lacaune, and Assaf. However, they are primarily raised under indoor conditions. The names of the indigenous Turkish sheep breeds most represented in each province are shown in Figure 6.
FIGURE 6. The names of the indigenous Turkish sheep breeds most represented in each province. The data were obtained from the Ministry of Agriculture and Forestry.
Dual-purpose breeds
Most of the sheep breeds raised for meat production are categorized as dual-purpose breeds. The two most popular breeds are the Akkaraman (White Karaman) and Morkaraman (Red Karaman) sheep, which represent approximately 65% of the total sheep population in Türkiye (Gürsoy, 2006; Uzun et al., 2006). The Akkaraman is the most commonly raised fat-tail dual-purpose breed, which is predominantly found in the Central Anatolia region. It has been reported that 45% of domestic sheep in Türkiye are Akkaraman (Ozmen et al., 2020). They are known for their adaptability to arid environments, cold resilience, disease resistance or tolerance, and ability to thrive on low-quality pastures (Arzik et al., 2022a). The Karakas in Eastern Anatolia, Kangal in Central Anatolia, Savak in Eastern Anatolia, Guney Karaman in the Mediterranean, and Norduz in the Eastern Anatolian regions are recognized as varieties/subtypes of the Akkaraman (Ozmen et al., 2020). Among them, the Guney Karaman, Norduz, and Karakas breeds are considered endangered (Yilmaz et al., 2012). The Morkaraman sheep is also a dual-purpose breed, distinguished by its fat-tailed appearance, and is primarily raised in the Eastern Anatolian region. The Daglic sheep is commonly raised in the Central Anatolia and Aegean regions. The Tuj (Tushin) sheep, with its short tail, comes from the Caucasus region and can be found throughout northeastern Türkiye. The Hemsin sheep is also a fat-tailed sheep limited to a specific area along the eastern Black Sea coast and in northeastern Türkiye (Uzun et al., 2006). Additional uncommon fat-tailed sheep breeds include the Hamdani in the Eastern and Southeastern Anatolia regions, the Hasak in the Central Anatolia region, and the Acipayam and Cine Capari (endangered) in the Aegean region.
The main indigenous thin-tailed sheep breeds in Türkiye are the Kivircik and Karayaka. The Kivircik sheep, which makes up 6%–7% of the sheep population in Türkiye, is renowned for its meat quality and has its origin in Romania and Balkan countries (Öner et al., 2014). They are mainly bred in the Marmara and Aegean regions (Öner et al., 2014). The Karayaka sheep, known for their meat quality, are dual-purpose sheep recognizable by their thin, long tails. They are relatively small in size and are primarily raised in the Black Sea region, where they have developed adaptation traits to thrive in the region’s rainy climate (Uzun et al., 2006). Other thin-tailed breeds with smaller populations include the Bafra, Bandirma, Pirlak, Karya, Gokceada, Tahirova, Hasmer, Polatli, Esme, and Sonmez in the Aegean region.
Dairy production breeds
In general, sheep milk has a high dry matter and fat content. The Awassi and Chios breeds are known for their milk production and high fertility. The Awassi breed is characterized by a fatty tail, while the Chios breed has a thin tail, and both are raised extensively in Türkiye. These sheep are highly adaptable and excel in milk production, making them a crucial asset for many farmers with limited resources (Haile et al., 2019).
Wool production breeds
Most of the wool-type sheep breeds in Türkiye were developed through crossbreeding. In the 1930s, the German Mutton Merino sheep were imported to Türkiye with the aim of improving the overall performance and wool quality of the local sheep breeds (Yalçın, 1986; Ankarali, 1988). The Karacabey State Farm in the South Marmara region developed the Turkish Merino (Karacabey Merino) sheep by crossing the German Mutton Merino with the Kivircik as part of a breeding program. The aim of this initiative was to improve the characteristics of the local sheep by incorporating desirable traits from the German Mutton Merino breed. The Turkish Merino is approximately 95% German Mutton Merino and 5% Kivircik (Yalçın, 1986). Although the Turkish Merino breed is known for its dual-purpose nature, it excels in wool production and mothering ability. The current population of the Turkish Merinos is approximately 4 million. It has been reported that the fleece quality of the Turkish Merino sheep is comparable to Australian Merino wool standards (Atav et al., 2023). Similarly, crossbreeding between the German Mutton Merino and the Akkaraman began in the 1950s at the Konya State Farm (Konya, Türkiye), resulted in the development of the Central Anatolian Merino (Yalçın, 1986; Ankarali, 1988). The Central Anatolian Merino is approximately 80% German Mutton Merino and 20% Akkaraman (Yalçın, 1986). The Malya state farm (Kirsehir, Türkiye) also performed crossbreeding between 35% German Mutton Merino and 65% Akkaraman, resulting in the Malya breed in the Central Anatolia region (Yilmaz et al., 2012).
Genetic diversity
Assessing the genetic diversity of populations is essential for developing genetic conservation programs and sustainable breeding strategies. This is particularly important for Türkiye because the number of some indigenous sheep breeds is decreasing due to non-systematic crossbreeding (Ozmen et al., 2020). Most of the studies on the genetic diversity of Turkish sheep have been carried out using either mitochondrial DNA or microsatellites.
Table 1 summarizes genetic diversity studies of Turkish sheep populations using mitochondrial DNA. Sample sizes ranged from 69 to 628. Sheep can be classified into five major mitochondrial haplogroups (A–E) (Hiendleder et al., 1998; Tapio et al., 2006). It has been reported that three mitochondrial haplogroups (A, B, and C) are prevalent in indigenous Turkish sheep breeds (Pedrosa et al., 2005; Meadows et al., 2007; Demirci et al., 2013; Oner et al., 2013; Kirikci et al., 2018).
TABLE 1. Summary of Turkish sheep genetic diversity studies using mitochondrial DNA.
Table 2 summarizes genetic diversity studies of Turkish sheep populations based on microsatellite markers, which are the most commonly used molecular information to perform genetic diversity studies in Turkish sheep. Sample sizes ranged from 64 to 594, and the number of microsatellites used ranged from 9 to 30. The first analysis of genetic diversity using microsatellites was conducted in 2006 Gutiérrez-Gil et al. (2006); Uzun et al. (2006). High within-breed variability and low inbreeding rates were observed among the Akkaraman, Morkaraman, Tuj, Hemsin, and Karayaka breeds Gutiérrez-Gil et al. (2006). A between-breed analysis indicated a close relationship between Akkaraman, Morkaraman, and Tuj, while Hemsin and Karayaka breeds were distinct from the others (Uzun et al., 2006). The Akkaraman and Morkaraman breeds exhibited the strongest genetic relatedness. The study reported the potential admixture between Awassi, Karakas, Karayaka, Morkaraman, Norduz, and Tuj breeds, with Cine Capari and Karya being separated (Yilmaz et al., 2014). Additionally, Pirlak and Turkish Merino were found to be distinct from the Kivircik sheep (Öner et al., 2014). The Gokceada and Chios, which are raised on the coast of the Aegean region, were grouped together, while the Kivircik and Turkish Merino were found to have a distinct genetic profile with evidence of admixture (Yilmaz et al., 2015). This finding aligns with the historical development of Turkish sheep breeding, as the Turkish Merino resulted from a cross between the German Mutton Merino and Kivircik breeds. Similarly, in another study, the Turkish Merino breed exhibited a distinct cluster in comparison to the Akkaraman, Guney Karaman, and Kivircik breeds (Ameur et al., 2020). Analysis of the genetic diversity of Akkaraman subtypes revealed the presence of three distinct clusters: Kangal-Akkaraman, Karakas-Akkaraman, Norduz, and Morkaraman were grouped together, while Savak-Akkaraman and Awassi were clearly separated from all the other sheep populations (Ozmen et al., 2020).
TABLE 2. Summary of genetic diversity studies using microsatellites in Turkish sheep.
Subpopulation analysis of individual breeds was also performed. For example, although the Guney Karaman, Norduz, Kangal, and Karakas are considered to be varieties of the Akkaraman, clustering results showed that the Guney Karaman and Norduz are genetically different from the Kangal and Karakas. This may be due to different breeding practices and environmental conditions over the course of many years (Karsli et al., 2020). The inbreeding coefficients of the Kangal (0.26) and Guney Karaman (0.24) were higher than those of Karakas (0.16) and Norduz (0.17), which may require some management to reduce inbreeding (Karsli et al., 2020). A study of Karayaka sheep subpopulations scattered in four provinces showed that they are genetically different from each other, suggesting that the Karayaka has discrete subpopulations (Kirikci et al., 2020). In addition, the analysis of three different flocks of the Cine Capari showed high genetic variability based on unique allele numbers in different loci (Cemal et al., 2013).
Pedigree- and genomic-based variance component analysis
Quantifying the magnitude of variance components is critical to assess the proportion of phenotypic variance controlled by genetics (i.e., heritability estimates) for each economically important trait to design effective breeding objectives.
Growth and linear type traits
Growth traits have been extensively investigated in genetic studies due to the increasing demand for sheep meat production in Türkiye. Figure 7 shows a bubble plot of pedigree and genomic heritability estimates of Turkish sheep for growth traits collected from the literature (Table 3). The heritability analyses for growth traits were conducted for six breeds, namely, the Akkaraman, Awassi, Central Anatolian Merino, Karayaka, Sonmez, and Turkish Meriono. Overall, the Akkaraman and Turkish Merino were the two most studied breeds. Most papers reported pedigree-based heritability estimates, with genome-based estimates being rare (Figure 7). The Akkaraman breed is the only one with genomic heritability estimates. A common model used in the literature included additive genetic, maternal genetic, and maternal environmental effects. Growth traits were the most studied traits for variance component analysis. For example, there were 9, 6, 5, and 3 papers reporting heritability estimates for birth weight, average daily gain (pre-weaning), weaning weight, and average daily gain (post-weaning), respectively (Table 3). For the major growth traits, heritability estimates ranged from 0.03 to 0.54 for birth weight (Ekiz et al., 2004; Ozcan et al., 2005; Koyuncu and Duru, 2009; Ozder et al., 2009; Taskin et al., 2012; Ulutas et al., 2013; Haile et al., 2019; Behrem, 2021; Kizilaslan et al., 2022), from 0.09 to 0.61 for average daily gain (pre-weaning) (Ozcan et al., 2005; Ozder et al., 2009; Taskin et al., 2012; Haile et al., 2019; Behrem, 2021; Kizilaslan et al., 2022), from 0.06 to 0.38 for weaning weight (Ekiz et al., 2004; Ozcan et al., 2005; Haile et al., 2019; Behrem, 2021; Kizilaslan et al., 2022), and from 0.49 to 0.61 for average daily gain (post-weaning) (Ozder et al., 2009; Taskin et al., 2012; Kizilaslan et al., 2022). Overall, estimates of genetic correlations between growth traits were moderate to high (Ozcan et al., 2005; Ozder et al., 2009; Haile et al., 2019; Kizilaslan et al., 2022). For example, genetic correlations between birth weight, weaning weight, yearling weight, and average daily gain (pre-weaning) were positive (Ozcan et al., 2005; Haile et al., 2019; Kizilaslan et al., 2022). In addition, birth weight showed moderate to high positive correlations with 2-month weight, 6-month weight, 12-month weight, and average daily gain (post-weaning) (Ozder et al., 2009). However, another study reported a negative genetic correlation between birth weight and weaning weight, and birth weight and average daily gain (pre-weaning) (Behrem, 2021).
FIGURE 7. Bubble plot of pedigree- and genomic-based heritability estimates of Turkish sheep for growth and linear type traits collected from the literature. The size of the bubble is proportional to the size of the sample. Shapes and colors indicate different types of genetic relationship matrices and breeds.
TABLE 3. Summary of genetic parameter estimation studies for growth traits in Turkish sheep populations.
Figure 8 shows a bubble plot of maternal heritability estimates for growth traits found in the literature. The influence of maternal effects has been most studied for birth weight, with low to moderate maternal heritability estimates (Figure 8). Negative genetic correlations between additive and maternal effects have been reported for growth traits (Ekiz et al., 2004; Ozcan et al., 2005; Koyuncu and Duru, 2009; Ozder et al., 2009; Ulutas et al., 2013), which is consistent with the literature from non-Turkish sheep breeds (Maria et al., 1993; Tosh and Kemp, 1994). It has been argued that a negative genetic correlation between additive and maternal genetic effects may inhibit an increase in species size (Cundiff, 1972). Also, maternal permanent environmental variance was an important source of variation in birth weight (Ekiz et al., 2004; Ozcan et al., 2005; Koyuncu and Duru, 2009; Haile et al., 2019). To date, genomic data has not been utilized to estimate the maternal contribution to variance components.
FIGURE 8. Bubble plot of pedigree-based maternal heritability estimates of Turkish sheep for growth traits collected from the literature. The size of the bubble is proportional to the sample size. Colors indicate different types of relationship breeds.
There was only one paper that reported genomic heritability estimates for linear type traits (Figure 7; Table 4), which was performed only in the Akkaraman (Kizilaslan et al., 2022). Linear type traits showed low to moderate genomic heritability estimates ranging from 0.07 to 0.52 (Kizilaslan et al., 2022). Positive and high genetic correlations were reported between growth and linear type traits, indicating that linear type traits serve as a useful indicator for growth traits (Kizilaslan et al., 2022). No pedigree-based heritability analysis was found in the literature.
TABLE 4. Summary of genetic parameter estimation studies for linear type traits in Turkish sheep populations.
Milk traits
There was only one paper reporting pedigree-based heritability estimates for milk traits from the Turksih Awassi (Haile et al., 2019) (Table 5). Overall, pedigree-based heritability estimates for milk traits in the Awassi ranged from low to moderate (Figure 9). In particular, milk yield (0.29) and lactation length (0.16) had the highest and lowest heritability estimates, respectively.
TABLE 5. Summary of genetic parameter estimation studies for milk, wool, and reproduction traits in Turkish sheep populations.
FIGURE 9. Bubble plot of heritability estimates of Turkish sheep for milk, wool, reproduction, gastrointestinal parasite resistance, and blood traits collected from the literature. The size of the bubble is proportional to the sample size. Shapes and colors indicate different types of genetic relationship matrices and breeds.
Wool traits
Heritability estimates for wool traits were reported in the Turkish Merino and Akkaraman (Ozcan et al., 2005; Arzik et al., 2023) (Table 5). The pedigree-based heritability estimate for greasy fleece weight was 0.08 (Figure 9). Greasy fleece weight showed a high favorable genetic correlation with birth weight, weaning weight, and average daily gain, indicating that selection for growth traits is expected to result in increased greasy fleece weight (Ozcan et al., 2005). Overall, genomic heritability estimates for wool were higher, ranging from 0.22 to 0.63 (Arzik et al., 2023).
Reproduction traits
Heritability estimates for reproductive traits in the Turkish Merino and Awassi were all low, ranging from 0.03 to 0.08, suggesting a strong influence of environmental factors (Ekiz et al., 2005; Haile et al., 2019) (Figure 9; Table 5). Lambing interval showed a large negative genetic correlation with litter weight at birth and litter weight at weaning (Haile et al., 2019). There was also a high genetic correlation between litter weight at birth and litter weight at weaning. Although lambing interval showed moderate correlations with milk traits, litter weight at birth and litter weight at weaning exhibited negative correlations with milk traits (Haile et al., 2019).
Gastrointestinal parasite resistance and blood traits
Heritability estimates for some less studied traits were also available in the literature (Figure 9; Table 6). Genomic heritability estimates for gastrointestinal parasite resistance, such as nematode egg count, tapeworm egg count, and coccidian fecal oocyst count, were recently reported in the Akkaraman (Arzik et al., 2022b). These estimates were low to moderate, ranging from 0 to 0.34. Similarly, genomic heritability estimates for blood traits collected to evaluate general health status in the Akkaraman ranged from low to moderate, with white blood cell showing the largest heritability of 0.55 (Arzik et al., 2022a).
TABLE 6. Summary of genetic parameter estimation studies for gastrointestinal parasite resistance and blood traits in Turkish sheep populations.
Genome-wide association studies
Figure 10 shows candidate genes reported in the GWAS literature for a variety of traits in Turkish sheep. There were four genome-wide association analysis papers performed on two breeds, the Akkaraman and Esme (Table 7). A total of 89 unique genes were reported for 43 traits, including growth, linear type, carcass composition, wool, gastrointestinal parasite resistance, and blood traits. Some genes were reported for more than one trait. More than half of the genes reported were significant at a chromosome-wide level rather than a genome-wide level, probably due to the small sample size used in the association analyses. Chromosome 2 had the largest number of candidate genes, followed by chromosomes 12, 3, and 1, while no genes were reported for chromosomes 10, 11, and 16.
FIGURE 10. Bubble plot of important genes for growth, liner type, carcass composition, wool, gastrointestinal parasite resistance and blood traits reported in the literature. Bubble size is proportional to sample size. Colors represent CWAS: Chromosome-wide association anlaysis. GWAS: Genome-wide association analysis.
TABLE 7. Summary of association analysis studies in Turkish sheep populations.
Overall, multiple genes were found for each trait in the growth and carcass composition categories. In contrast, most of the linear type, wool, gastrointestinal parasite resistance, and blood traits had one or two genes identified. Growth traits were the only group of traits studied in both breeds. Candidate genes were found for both Akkaraman and Esme for birth weight and average daily gain (pre-weaning), but there was no overlap. A number of genes were suggested for possible influence on several growth, linear type, and carcass composition traits. For example, PTGDR was associated with weaning weight and average daily gain (6 months), SCD5 with chest width and gigot roundness rear view, TLE4 with rump width and gigot roundness side view, ZNF641, DUSP5, and TEAD1 for average daily gain (pre-weaning) and weaning weight, SMOX for average daily gain (pre-weaning) and ultrasonic measurements of Longissimus dorsi muscle depth, and MYADM for hematocrit, mean corpuscular volume, and mean corpuscular volume concentration. Some of these genes have also been reported in non-Turkish sheep breeds based on selection signature analysis or GWAS (Moradi et al., 2012; Xu et al., 2021).
Discussion
In this study, we reviewed the populations, breeds, and genetic analysis of sheep populations in Türkiye. Sheep are an important small ruminant that provide income and play an important role in the livelihood of Turkish farmers. Their breeds represent a wide genetic and phenotypic diversity adapted to different environments in semi-arid and highland areas. The importance of sheep breeding is anticipated to increase due to the consistent rise in the number of sheep over the past decade.
Past genetic studies
Genetic diversity studies are among the earliest genetic analyses conducted in Turkish sheep. While mitochondrial DNA and microsatellites have been used for genetic diversity analyses, the use of single nucleotide polymorphisms (SNPs) has not been explored. Further studies utilizing high-density SNPs distributed throughout the chromosomes are needed to obtain a more detailed evaluation of the level of genetic differentiation and to verify the findings obtained from the analyses of mitochondrial DNA and microsatellites.
Heritability estimates were reported for a variety of traits, including growth and linear type, milk, wool, reproduction, gastrointestinal parasite resistance, and blood traits. Growth and linear type traits are central to the Turkish sheep program because of their importance for production, efficiency, and profitability. Their heritability estimates have been reported for six breeds. However, studies on the rest of the traits do not represent a wide range of sheep breeds that exist in Türkiye. For example, only one, two, two, one, and one breeds have been studied for milk, wool, reproduction, gastrointestinal parasite resistance, and blood traits, respectively. Notably, the Akkaraman is the only breed for which genomic heritability estimates have been reported. Further phenotyping and genetic data collection are necessary to conduct heritability analysis for understudied breeds.
Similar to heritability studies, GWAS results were available in limited breeds and reported only in the Akkaraman and Esme. The primary limiting factor of GWAS studies was their small sample size. All studies had less than 1,000 genotyped animals, and therefore require further evaluation. The utility of whole-genome regression methods such as BayesB, BayesCπ, or BayesR also needs to be assessed since the only model used thus far has been single-marker regression linear mixed models. While some GWAS papers have been published, there is currently no literature available on genomic prediction analysis in Turkish sheep.
Current challenges
A community-based public animal breeding program implemented in Türkiye since 2006 has been collecting pedigree and yield data, and contributing to the genetic improvement of local breeds to some extent. The major challenge in developing breeding programs for indigenous sheep breeds in Türkiye is the lack of comprehensive pedigree data due to limited access to artificial insemination services and the widespread practice of natural mating in smallholder farming systems. This absence of pedigree data resulting from uncontrolled mating has greatly impeded the genetic improvement of Turkish sheep using conventional structured breeding programs that rely on pedigree-based best linear unbiased prediction (BLUP) (Henderson, 1975). In addition, Türkiye shares other similar bottlenecks often observed in smallholder farming systems around the world (Kosgey et al., 2006; Kosgey and Okeyo, 2007). These include lack of genotyping infrastructure and well designed contemporary groups, insufficient farmer organizations, and management of animals with unimproved genotypes. Nonetheless, the implementation of genetic evaluation using genomic information can alleviate the problem of pedigree insufficiency, and facilitate the establishment of effective breeding programs (Mrode et al., 2018). The importance of accurate pedigree recording diminishes with the availability of genomics, as genetic relatedness at the genomic level offers more accurate estimations than pedigree data. Although animal breeding and genetics aims to understand the relationship between genome and phenome, phenotyping instruments for routine use and data management are still limited. In addition, there is a need for researchers with appropriate expertise in sheep breeding, quantitative genetics, and data science (Ducrocq et al., 2018).
Genomic selection
Genomic selection makes selection decisions on animals using genomic prediction (Meuwissen et al., 2001), and it has been widely used to perform genomic evaluations of many livestock species (Meuwissen et al., 2016; Rupp et al., 2016). Genomic prediction produces genomic estimated breeding values of animals using molecular information, such as high-density SNPs, that is available across the entire genome. The principle idea is that genome-wide SNPs tag quantitative trait loci in the genome via linkage disequilibrium, acting as markers. This allows SNPs to capture genetic variation without having to identify all causal variants in advance. Selection of animals based on genomic estimated breeding values is expected to increase genetic gain, mainly due to the reduced generation interval and more accurate estimation of genetic parameters compared to pedigree-derived estimated breeding values. When applied in the context of the BLUP framework, genomic prediction is referred to as genomic best linear unbiased prediction. In the past, genotyping large numbers of animals was prohibitively expensive. However, recent advances in biotechnology have made the cost of genotyping animals more affordable than ever before, opening up opportunities for genomic selection when coupled with advances in statistical methods (Meuwissen et al., 2001). The gains in accuracy achieved by SNPs compared to pedigree ranged from 0.05 to 0.27 (Rupp et al., 2016). Genomic selection can be particularly useful for meat production traits, such as carcass yield, carcass composition, and meat quality traits, that are measured later in life and often require animal sacrifice. In a French sheep meat breeding program, genomic selection was reported to add value over pedigree-based selection in terms of genetic gains (Shumbusho et al., 2013) and economic returns (Shumbusho et al., 2016). It may not be a suitable choice given the difficulty of accurate pedigree recording in a systematic manner in Türkiye, but a BLUP methodology to integrate pedigree and genomic information is also available (Misztal et al., 2009). Potential quantitative trait loci detected in GWAS can be used to weight SNPs to increase prediction accuracy (Santana et al., 2023). Türkiye has started investing in molecular technologies for sheep breeding over the last 5 years. However, the application of these technologies has not yet been fully realized. Although the application of genomic selection in Turkish sheep is still in its infancy, there is no doubt that it will achieve significant success in the coming years as routinely recorded genotyping and phenotyping systems are established. In addition to production traits, future genomic selection studies should include phenotypes that are difficult or expensive to measure, such as reproductive performance, meat quality, feed efficiency, longevity, and disease susceptibility traits.
Next steps forward
One of the first steps in implementing genomic selection is to identify the breeding objective traits and carefully define the selection criteria for the particular environments being considered. Then, sustainable and routinely recorded reliable phenotyping systems must be established, followed by a cost-benefit analysis to determine which genotyping platform to use. Currently, low, medium, and high density genotyping chips for sheep are available on the market (Rupp et al., 2016). If only selected animals can be genotyped with higher density chips, imputation can be used to impute low density genotypes to medium or high density genotypes. In addition, genotyping-by-sequencing and low-pass sequencing have been used in recent years to obtain genomic information in sheep instead of SNP chips (Dodds et al., 2021).
The critical step is to create a reference population consisting of individuals with phenotypic records and genotypes. A reference population is used to estimate marker-phenotype association, which allows prediction of genomic estimated breeding values. The development of large reference populations is necessary because accuracy and expected genetic gain are proportional to the size of the reference population. The size of purebred sheep reference populations in other countries is reported to be in the range of 2,000 to 6,000 (Rupp et al., 2016). If a reference population size for each purebred is not sufficient, multi-breed or crossbred genomic evaluations can serve as an alternative solution (Daetwyler et al., 2012). This is particularly important for Turkish sheep because crossbreeding is not uncommon to combine adaptation and production ability under various environments. In the case of Turkish sheep breeds that have been exported to other regions of the world where there is structured breeding programs, there might be opportunities for across-country genomic predictions as evaluated in New Zealand and Norwegian sheep breeds with similar development history (Oliveira et al., 2020; 2022). Sufficient DNA extraction laboratories, computing facilities, and data storage infrastructure are needed to analyze thousands of animals using whole-genome regression. In addition, the use of information and sensing technologies for phenotyping has the potential to accelerate the data collection process (Morota et al., 2018). For example, digital tools such as mobile phones and tablets have been successfully used in low- and middle-income countries to collect phenotypic data and return management information to farmers to help them make informed decisions, given the availability of reliable internet connectivity (Mrode et al., 2020). A reference population needs to be updated occasionally and genetic progress needs to be closely monitored. Collectively, a variety of phenotypic data coupled with genomic resources could be used to design comprehensive breeding objectives for sheep by developing appropriate selection indexes tailored to the needs in Türkiye. The use of genomics is also proving useful in parentage verification, determining breed composition, and better managing inbreeding because genomic data provide more accurate estimates of genetic relationships between individuals than pedigree records. Increased rates of inbreeding discovered through certain studies call for caution. For instance, the high level of inbreeding observed among Karakas and Norduz sheep requires attention.
Finally, establishing effective multi-organizational or multi-national partnerships involving all sectors of the sheep breeding chain is a key to success (Burrow et al., 2021). For example, collaboration with established resource populations in other countries with similar management systems or environmental conditions may increase the accuracy of genomic estimated breeding values or the co-development of phenotyping platforms. These resource populations and infrastructure are expected to provide opportunities for genetic improvement of flocks and bring the economic, social, and environmental benefits of genomic selection to smallholder farmers. One bottom-up approach we can learn from when introducing or transferring a new breeding program to smallholder farms is community-based breeding programs (Mueller et al., 2015). Farmers are the key players in community-based breeding programs, as opposed to centralized or government-controlled breeding programs. The feasibility of community-based breeding programs resulting in measurable genetic gains in growth traits has been reported for sheep in Ethiopia (Mirkena et al., 2012; Gizaw et al., 2014; Haile et al., 2020). An important factor to consider is the participation of farmers in the process of planning and implementation, with shared breeding objectives (Wurzinger et al., 2011). An example of farmer participation is involvement in decisions about research agendas and resource allocation. Many attempts to establish new genetic improvement programs for low-input smallholder livestock systems have failed when financial support ended or government priorities changed, if farmers were not involved in the programs or were limited to providing information to researchers. For this reason, genomic selection breeding programs should be self-sustaining in the long term.
Conclusions
The role of sheep breeding and genetics in Türkiye in supporting the incomes and livelihoods of smallholder farmers is expected to grow as the number of sheep has increased in recent times. Historically, sheep in Türkiye were raised mainly for meat production, but there has recently been a surge in demand for dairy products with high nutritional and health benefits. Although there are some genetic studies in indigenous Turkish sheep in the literature, genetic diversity studies need to be revisited using genome-wide SNPs, and the scope of genetic parameter estimation and GWAS studies should be expanded to cover traits and breeds that have previously been overlooked. Genomic selection has not yet been applied, but it has the potential to overcome the difficulties of implementing traditional pedigree-based breeding programs where pedigree recording is required. The use of genomics also contributes to parentage verification, determination of breed composition, and better management of inbreeding. The establishment of sustainable systems for genotyping and phenotyping is vital. Given their superior adaptation to local environments, utilizing indigenous sheep breeds would improve food security and reduce environmental impact in Türkiye.
Author contributions
KA: Conceptualization, Data curation, Formal Analysis, Investigation, Visualization, Writing–original draft. YB: Visualization, Writing–review and editing. LB: Writing–review and editing. ZU;: Writing–review and editing. GM: Conceptualization, Project administration, Supervision, Writing–original draft.
Funding
The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.
Acknowledgments
KA acknowledges a scholarship from the Ministry of National Education Turkey and Ministry of Agriculture and Forestry. The authors thank the Ministry of Agriculture and Forestry in Türkiye for providing the most recent census data.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.
Publisher’s note
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Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fgene.2024.1308113/full#supplementary-material
References
Akbay, C., and Boz, I. (2005). Turkey’s livestock sector: production, consumption and policies. Livest. Res. Rural Dev. 17 (9), 1–11.
Aksoy, A., and Yavuz, F. (2012).Ciftcilerin kucukbas hayvan yetistiriciligini birakma neden-lerinin analizi: dogu anadolu bolgesi ornegi. Anadolu Tarım Bilim. Derg. 27 (2), 76–79.
Ameur, A. A., Yilmaz, O., Nezih, A., Cemal, I., and Gaouar, S. B. S. (2020). Assessment of genetic diversity of Turkish and Algerian native sheep breeds. Acta Agric. Slov. 115 (1), 5–14. doi:10.14720/aas.2020.115.1.1229
Ankarali, B. (1988). “Genetic improvement of sheep in Turkey,” in Increasing small ruminant productivity in semi-arid areas: proceedings of a workshop held at the international center for agricultural research in the dry areas, aleppo, Syria, 30 november to 3 december 1987 (Springer), 177–180.
Arzik, Y., Kizilaslan, M., Behrem, S., White, S. N., Piel, L. M., and Cinar, M. U. (2023). Genome-wide scan of wool production traits in akkaraman sheep. Genes 14 (3), 713. doi:10.3390/genes14030713
Arzik, Y., Kizilaslan, M., White, S. N., Piel, L. M., and Cinar, M. U. (2022a). Estimates of genomic heritability and genome-wide association studies for blood parameters in Akkaraman sheep. Sci. Rep. 12 (1), 18477. doi:10.1038/s41598-022-22966-8
Arzik, Y., Kizilaslan, M., White, S. N., Piel, L. M., and Çınar, M. U. (2022b). Genomic analysis of gastrointestinal parasite resistance in akkaraman sheep. Genes 13 (12), 2177. doi:10.3390/genes13122177
Atav, R., Buğdaycı, B., Şen, A., Ergünay, U., Gürkan Ünal, P., Özkan Ünal, E., et al. (2023). Creating a high-quality wool-oriented Turkish merino herd and investigation of mechanical and dyeability properties of fabrics produced from Turkish merino in comparison with Australian merino. Color. Technol. 136 (6), 689–702. doi:10.1111/cote.12680
Behrem, S. (2021). Estimation of genetic parameters for pre-weaning growth traits in central anatolian merino sheep. Small Ruminant Res. 197, 106319. doi:10.1016/j.smallrumres.2021.106319
Burrow, H. M., Mrode, R., Mwai, A. O., Coffey, M. P., and Hayes, B. J. (2021). Challenges and opportunities in applying genomic selection to ruminants owned by smallholder farmers. Agriculture 11 (11), 1172. doi:10.3390/agriculture11111172
Cemal, I., Yilmaz, O., Karaca, O., Binbas, P., and Ata, N. (2013). Analysis of genetic diversity in indigenous Cine Capari sheep under conservation by microsatellite markers. Kafkas Universitesi Veteriner Fakultesi Derg. 19 (3). doi:10.9775/kvfd.2012.7857
Cundiff, L. V. (1972). The role of maternal effects in animal breeding: VIII. Comparative aspects of maternal effects. J. Animal Sci. 35 (6), 1335–1337. doi:10.2527/jas1972.3561335x
Daetwyler, H., Kemper, K., Van Der Werf, J., and Hayes, B. J. (2012). Components of the accuracy of genomic prediction in a multi-breed sheep population. J. Animal Sci. 90 (10), 3375–3384. doi:10.2527/jas.2011-4557
Demirci, S., Koban Baştanlar, E., Dağtaş, N. D., Pişkin, E., Engin, A., Özer, F., et al. (2013). Mitochondrial DNA diversity of modern, ancient and wild sheep (Ovis gmelinii anatolica) from Turkey: new insights on the evolutionary history of sheep. PloS One 8 (12), e81952. doi:10.1371/journal.pone.0081952
Dodds, K., Jakobsen, J., Wheeler, M., Blichfeldt, T., Brauning, R., Baird, H., et al. (2021). A genomic comparison of australian, New Zealand and Norwegian dairy goat populations. Proc. Assoc. Advmt. Anim. Breed. Genet. 24, 90–93.
Ducrocq, V., Laloe, D., Swaminathan, M., Rognon, X., Tixier-Boichard, M., and Zerjal, T. (2018). Genomics for ruminants in developing countries: from principles to practice. Front. Genet. 9, 251. doi:10.3389/fgene.2018.00251
Ekiz, B., Özcan, M., Yilmaz, A., and Ceyhan, A. (2004). Estimates of genetic parameters for direct and maternal effects with six different models on birth and weaning weights of Turkish Merino lambs. Turkish J. Veterinary and Animal Sci. 28 (2), 383–389.
Ekiz, B., Özcan, M., Yilmaz, A., and Ceyhan, A. (2005). Estimates of phenotypic and genetic parameters for Ewe productivity traits of Turkish Merino (Karacabey Merino) sheep. Turkish J. Veterinary and Animal Sci. 29 (2), 557–564.
Erturk, Y. E., Yalcin, S., and Yilmaz, O. (2015). Livestock support policies in Turkey since 2000. Vi. Sci. Agric. Symposium” Agrosym 2015, 2019–2027.
Gizaw, S., Getachew, T., Goshme, S., Valle-Zárate, A., van Arendonk, J. A., Kemp, S., et al. (2014). Efficiency of selection for body weight in a cooperative village breeding program of menz sheep under smallholder farming system. Animal 8 (8), 1249–1254. doi:10.1017/S1751731113002024
Gürsoy, O. (2006). Economics and profitability of sheep and goat production in Turkey under new support regimes and market conditions. Small Ruminant Res. 62 (3), 181–191. doi:10.1016/j.smallrumres.2005.08.013
Gutiérrez-Gil, B., Uzun, M., Arranz, J.-J., San Primitivo, F., Yildiz, S., Cenesiz, M., et al. (2006). Genetic diversity in Turkish sheep. Acta Agric. Scand. Sect. A 56 (1), 1–7. doi:10.1080/09064700600641681
Haile, A., Getachew, T., Mirkena, T., Duguma, G., Gizaw, S., Wurzinger, M., et al. (2020). Community-based sheep breeding programs generated substantial genetic gains and socioeconomic benefits. Animal 14 (7), 1362–1370. doi:10.1017/S1751731120000269
Haile, A., Hilali, M., Hassen, H., Lobo, R., and Rischkowsky, B. (2019). Estimates of genetic parameters and genetic trends for growth, reproduction, milk production and milk composition traits of Awassi sheep. Animal 13 (2), 240–247. doi:10.1017/S1751731118001374
Hayaloglu, A., and Karagul-Yuceer, Y. (2011). Utilization and characterization of small ruminants’ milk and milk products in Turkey: current status and new perspectives. Small Ruminant Res. 101 (1-3), 73–83. doi:10.1016/j.smallrumres.2011.09.027
Henderson, C. R. (1975). Best linear unbiased estimation and prediction under a selection model. Biometrics 31, 423–447. doi:10.2307/2529430
Hiendleder, S., Mainz, K., Plante, Y., and Lewalski, H. (1998). Analysis of mitochondrial DNA indicates that domestic sheep are derived from two different ancestral maternal sources: no evidence for contributions from urial and argali sheep. J. Hered. 89 (2), 113–120. doi:10.1093/jhered/89.2.113
Karsli, B. A., Demir, E., Fidan, H. G., and Karsli, T. (2020). Assessment of genetic diversity and differentiation among four indigenous Turkish sheep breeds using microsatellites. Arch. Anim. Breed. 63 (1), 165–172. doi:10.5194/aab-63-165-2020
Kirikci, K., Cam, M. A., and Mercan, L. (2020). Genetic diversity and relationship among indigenous Turkish Karayaka sheep subpopulations. Arch. Anim. Breed. 63 (2), 269–275. doi:10.5194/aab-63-269-2020
Kirikci, K., Noce, A., Cam, M. A., Mercan, L., and Amills, M. (2018). The analysis of mitochondrial data indicates the existence of population substructure in Karayaka sheep. Small Ruminant Res. 162, 25–29. doi:10.1016/j.smallrumres.2018.02.007
Kizilaslan, M., Arzik, Y., White, S. N., Piel, L. M., and Cinar, M. U. (2022). Genetic parameters and genomic regions underlying growth and linear type traits in Akkaraman sheep. Genes 13 (8), 1414. doi:10.3390/genes13081414
Kosgey, I., Baker, R., Udo, H., and Van Arendonk, J. A. (2006). Successes and failures of small ruminant breeding programmes in the tropics: a review. Small Ruminant Res. 61 (1), 13–28. doi:10.1016/j.smallrumres.2005.01.003
Kosgey, I., and Okeyo, A. (2007). Genetic improvement of small ruminants in low-input, smallholder production systems: technical and infrastructural issues. Small Ruminant Res. 70 (1), 76–88. doi:10.1016/j.smallrumres.2007.01.007
Koyuncu, M., and Duru, S. (2009). Estimates of (co) variance components for direct and maternal effects on birth weight of Karacabey Merino lambs. Turkish J. Veterinary and Animal Sci. 33 (3), 235–240. doi:10.3906/vet-0801-8
Maria, G., Boldman, K., and Van Vleck, L. D. (1993). Estimates of variances due to direct and maternal effects for growth traits of romanov sheep. J. Animal Sci. 71 (4), 845–849. doi:10.2527/1993.714845x
Meadows, J. R., Cemal, I., Karaca, O., Gootwine, E., and Kijas, J. W. (2007). Five ovine mitochondrial lineages identified from sheep breeds of the near East. Genetics 175 (3), 1371–1379. doi:10.1534/genetics.106.068353
Meuwissen, T., Hayes, B., and Goddard, M. (2016). Genomic selection: a paradigm shift in animal breeding. Anim. Front. 6 (1), 6–14. doi:10.2527/af.2016-0002
Meuwissen, T. H., Hayes, B. J., and Goddard, M. (2001). Prediction of total genetic value using genome-wide dense marker maps. Genetics 157 (4), 1819–1829. doi:10.1093/genetics/157.4.1819
Mirkena, T., Duguma, G., Willam, A., Wurzinger, M., Haile, A., Rischkowsky, B., et al. (2012). Community-based alternative breeding plans for indigenous sheep breeds in four agro-ecological zones of Ethiopia. J. Animal Breed. Genet. 129 (3), 244–253. doi:10.1111/j.1439-0388.2011.00970.x
Misztal, I., Legarra, A., and Aguilar, I. (2009). Computing procedures for genetic evaluation including phenotypic, full pedigree, and genomic information. J. Dairy Sci. 92 (9), 4648–4655. doi:10.3168/jds.2009-2064
Moradi, M. H., Nejati-Javaremi, A., Moradi-Shahrbabak, M., Dodds, K. G., and McEwan, J. C. (2012). Genomic scan of selective sweeps in thin and fat tail sheep breeds for identifying of candidate regions associated with fat deposition. BMC Genet. 13 (1), 10–15. doi:10.1186/1471-2156-13-10
Morota, G., Ventura, R. V., Silva, F. F., Koyama, M., and Fernando, S. C. (2018). Big data analytics and precision animal agriculture symposium: machine learning and data mining advance predictive big data analysis in precision animal agriculture. J. Animal Sci. 96 (4), 1540–1550. doi:10.1093/jas/sky014
Mrode, R., Ekine Dzivenu, C., Marshall, K., Chagunda, M. G. G., Muasa, B. S., Ojango, J., et al. (2020). Phenomics and its potential impact on livestock development in low-income countries: innovative applications of emerging related digital technology. Anim. Front. 10 (2), 6–11. doi:10.1093/af/vfaa002
Mrode, R., Tarekegn, G., Mwacharo, J. M., and Djikeng, A. (2018). Invited review: genomic selection for small ruminants in developed countries: how applicable for the rest of the world? Animal 12 (7), 1333–1340. doi:10.1017/S1751731117003688
Mueller, J. P., Rischkowsky, B., Haile, A., Philipsson, J., Mwai, O., Besbes, B., et al. (2015). Community-based livestock breeding programmes: essentials and examples. J. Animal Breed. Genet. 132 (2), 155–168. doi:10.1111/jbg.12136
Ocak, S., Davran, M. K., and Güney, O. (2010). Small ruminant production in Turkey: highlighting in goat production. Trop. Animal Health Prod. 42, 155–159. doi:10.1007/s11250-009-9402-z
Oliveira, H. R., McEwan, J. C., Jakobsen, J., Blichfeldt, T., Meuwissen, T., Pickering, N., et al. (2020). Genetic connectedness between Norwegian white sheep and New Zealand composite sheep populations with similar development history. Front. Genet. 11, 371. doi:10.3389/fgene.2020.00371
Oliveira, H. R. d., McEwan, J. C., Jakobsen, J. H., Blichfeldt, T., Meuwissen, T. H., Pickering, N. K., et al. (2022). Across-country genomic predictions in Norwegian and New Zealand composite sheep populations with similar development history. J. Animal Breed. Genet. 139 (1), 1–12. doi:10.1111/jbg.12642
Oner, Y., Calvo, J. H., and Elmaci, C. (2013). Investigation of the genetic diversity among native Turkish sheep breeds using mtDNA polymorphisms. Trop. Animal Health Prod. 45, 947–951. doi:10.1007/s11250-012-0313-z
Öner, Y., Üstüner, H., Orman, A., Yilmaz, O., and Yılmaz, A. (2014). Genetic diversity of Kıvırcık sheep breed reared in different regions and its relationship with other sheep breeds in Turkey. Italian J. Animal Sci. 13 (3), 3382. doi:10.4081/ijas.2014.3382
Ozcan, M., Ekiz, B., Yilmaz, A., and Ceyhan, A. (2005). Genetic parameter estimates for lamb growth traits and greasy fleece weight at first shearing in Turkish Merino sheep. Small Ruminant Res. 56 (1-3), 215–222. doi:10.1016/j.smallrumres.2004.06.009
Ozder, M., Sezenler, T., Refik, A., Ceyhan, A., et al. (2009). Genetic and non-genetic parameter estimates for growth traits in Turkish Merino lambs. J. Animal Veterinary Adv. 8 (9), 1729–1734.
Ozmen, O., Kul, S., and Gok, T. (2020). Determination of genetic diversity of the Akkaraman sheep breed from Turkey. Small Ruminant Res. 182, 37–45. doi:10.1016/j.smallrumres.2019.10.009
Pedrosa, S., Uzun, M., Arranz, J.-J., Gutiérrez-Gil, B., San Primitivo, F., and Bayón, Y. (2005). Evidence of three maternal lineages in Near Eastern sheep supporting multiple domestication events. Proc. R. Soc. B Biol. Sci. 272 (1577), 2211–2217. doi:10.1098/rspb.2005.3204
Rupp, R., Mucha, S., Larroque, H., McEwan, J., and Conington, J. (2016). Genomic application in sheep and goat breeding. Anim. Front. 6 (1), 39–44. doi:10.2527/af.2016-0006
Santana, B. F., Riser, M., Hay, E. H. A., and de Oliveira Fragomeni, B. (2023). Alternative snp weighting for multi-step and single-step genomic blup in the presence of causative variants. J. Animal Breed. Genet. Early view 140, 679–694. doi:10.1111/jbg.12817
Sheep and Goat Breeders’ Associations of Turkey (2021). Turkiyekoyunkeci. Available at http://turkiyekoyunkeci.org/tr/Anasayfa/(Accessed June 10, 2023).
Shumbusho, F., Raoul, J., Astruc, J.-M., Palhiere, I., and Elsen, J. M. (2013). Potential benefits of genomic selection on genetic gain of small ruminant breeding programs. J. Animal Sci. 91 (8), 3644–3657. doi:10.2527/jas.2012-6205
Shumbusho, F., Raoul, J., Astruc, J.-M., Palhiere, I., Lemarié, S., Fugeray-Scarbel, A., et al. (2016). Economic evaluation of genomic selection in small ruminants: a sheep meat breeding program. Animal 10 (6), 1033–1041. doi:10.1017/S1751731115002049
Şirin, E., Aksoy, Y., Uğurlu, M., Çiçek, Ü., Önenç, A., Ulutaş, Z., et al. (2017). The relationship between muscle fiber characteristics and some meat quality parameters in Turkish native sheep breeds. Small Ruminant Res. 150, 46–51. doi:10.1016/j.smallrumres.2017.03.012
Tapio, M., Marzanov, N., Ozerov, M., Cinkulov, M., Gonzarenko, G., Kiselyova, T., et al. (2006). Sheep mitochondrial DNA variation in European, caucasian, and central asian areas. Mol. Biol. Evol. 23 (9), 1776–1783. doi:10.1093/molbev/msl043
Taskin, T., Kaymakci, M., Sönmez, R., Yılmaz, M., and Bardakcioglu, H. E. (2012). Genetic and non-genetic parameter estimates for growth traits of Sönmez lambs. Age, 258(13):180–67a.
Tosh, J., and Kemp, R. (1994). Estimation of variance components for lamb weights in three sheep populations. J. Animal Sci. 72 (5), 1184–1190. doi:10.2527/1994.7251184x
Turkish Statistical Institute (2022). Tuik. Available at https://www.tuik.gov.tr/(Accessed June 10, 2023).
Ulutas, Z., Sirin, E., Aksoy, Y., Sahin, A., and Kuran, M. (2013). Estimates of (co) variance components for direct and maternal effects on birth weight of karayaka lambs. Trop. Animal Health Prod. 45, 953–956. doi:10.1007/s11250-012-0314-y
Uzun, M., Gutiérrez-Gil, B., Arranz, J.-J., Primitivo, F. S., Saatci, M., Kaya, M., et al. (2006). Genetic relationships among Turkish sheep. Genet. Sel. Evol. 38, 513–524. doi:10.1186/1297-9686-38-5-513
Wurzinger, M., Sölkner, J., and Iñiguez, L. (2011). Important aspects and limitations in considering community-based breeding programs for low-input smallholder livestock systems. Small Ruminant Res. 98 (1-3), 170–175. doi:10.1016/j.smallrumres.2011.03.035
Xu, S.-S., Gao, L., Shen, M., and Lyu, F. (2021). Whole-genome selective scans detect genes associated with important phenotypic traits in sheep (Ovis aries). Front. Genet. 12, 738879. doi:10.3389/fgene.2021.738879
Yalçın, B. (1986). Sheep and goats in Turkey. Food and agriculture organization (FAO). Rome: Animal Production and Health Paper. (60).
Yilmaz, O., Cemal, I., and Karaca, O. (2014). Genetic diversity in nine native Turkish sheep breeds based on microsatellite analysis. Anim. Genet. 45 (4), 604–608. doi:10.1111/age.12173
Yilmaz, O., Cengiz, F., Ertugrul, M., and Wilson, R. T. (2013). The domestic livestock resources of Turkey: sheep breeds and cross-breeds and their conservation status. Anim. Genet. Resour. 52, 147–163. doi:10.1017/s2078633613000015
Yilmaz, O., Kizilaslan, M., Arzik, Y., Behrem, S., Ata, N., Karaca, O., et al. (2022). Genome-wide association studies of preweaning growth and in vivo carcass composition traits in Esme sheep. J. Animal Breed. Genet. 139 (1), 26–39. doi:10.1111/jbg.12640
Yilmaz, O., Sezenler, T., Sevim, S., Cemal, I., Karadag, O., Yaman, Y., et al. (2015). Genetic relationships among four Turkish sheep breeds using microsatellites. Turkish J. Veterinary and Animal Sci. 39 (5), 576–582. doi:10.3906/vet-1411-46
Yilmaz, O., Wilson, R. T., Askin, K., and Ertugrul, M. (2012). The farm animal genetic resources of Turkey: sheep-I-common and rare breeds. Bitlis Eren Univ. J. Sci. Technol. 2 (1), 1–5. doi:10.17678/beuscitech.47143
Zeder, M. A. (2008). Domestication and early agriculture in the mediterranean basin: origins, diffusion, and impact. Proc. Natl. Acad. Sci. 105 (33), 11597–11604. doi:10.1073/pnas.0801317105
Keywords: association analysis, breeding, genomic selection, small ruminant, sheep
Citation: Aydin KB, Bi Y, Brito LF, Ulutaş Z and Morota G (2024) Review of sheep breeding and genetic research in Türkiye. Front. Genet. 15:1308113. doi: 10.3389/fgene.2024.1308113
Received: 05 October 2023; Accepted: 11 January 2024;
Published: 25 January 2024.
Edited by:
Arianna Manunza, National Research Council (CNR), ItalyReviewed by:
Thiruvenkadan Aranganoor Kannan, Tamil Nadu Veterinary and Animal Sciences University, IndiaOnur Yilmaz, Adnan Menderes University, Türkiye
Copyright © 2024 Aydin, Bi, Brito, Ulutaş and Morota. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Gota Morota, morota@vt.edu
†ORCID: Kenan Burak Aydin, orcid.org/0000-0003-1511-023X; Ye Bi, orcid.org/0000-0001-7871-5856; Luiz F. Brito, orcid.org/0000-0002-5819-0922; Zafer Ulutaş, orcid.org/0000-0002-7661-2172; Gota Morota, orcid.org/0000-0002-3567-6911