Feed efficiency indexes in hair sheep: meat quality and associated genes. Review

Arce-Recinos, Carlos; Chay-Canul, Alfonso Juventino; Alarcón-Zúñiga, Baldomero; Ramos-Juárez, Jesús Alberto; Vargas-Villamil, Luis Manuel; Aranda-Ibáñez, Emilio Manuel; Sánchez-Villegas, Nathaly del Carmen; Lopes Dias da Costa, Ricardo; Arce-Recinos, Carlos; Chay-Canul, Alfonso Juventino; Alarcón-Zúñiga, Baldomero; Ramos-Juárez, Jesús Alberto; Vargas-Villamil, Luis Manuel; Aranda-Ibáñez, Emilio Manuel; Sánchez-Villegas, Nathaly del Carmen; Lopes Dias da Costa, Ricardo

doi:10.22319/rmcp.v12i2.5642

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Revista mexicana de ciencias pecuarias

versão On-line ISSN 2448-6698versão impressa ISSN 2007-1124

Rev. mex. de cienc. pecuarias vol.12 no.2 Mérida Abr./Jun. 2021 Epub 15-Nov-2021

https://doi.org/10.22319/rmcp.v12i2.5642

Reviews

Feed efficiency indexes in hair sheep: meat quality and associated genes. Review

Carlos Arce-Recinos^a

Alfonso Juventino Chay-Canul^b^*

Baldomero Alarcón-Zúñiga^c

Jesús Alberto Ramos-Juárez^a

Luis Manuel Vargas-Villamil^a

Emilio Manuel Aranda-Ibáñez^a

Nathaly del Carmen Sánchez-Villegas^a

Ricardo Lopes Dias da Costa^d

^{^a} Colegio de Postgraduados. Campus Tabasco. Periférico Carlos A. Molina, Km 3.5. Carretera Cárdenas-Huimanguillo. 86500 H. Cárdenas, Tabasco, México.

^{^b} Universidad Juárez Autónoma de Tabasco. División Académica de Ciencias Agropecuarias, Tabasco, México.

^{^c} Universidad Autónoma Chapingo. Departamento de Zootecnia, Estado de México, México.

^{^d} Instituto de Zootecnia. São Paulo, Brasil.

Abstract

Hair sheep are essential for meat production in tropical regions, where feed efficiency has been little evaluated. Feed consumption represents more than 70 % of the costs. Therefore, animals with high feed efficiency could increase the profitability of the production system. There exist tools that help select individuals with increased feed efficiency without compromising the quality of the product. This review aims to identify these genetic-molecular and statistical tools, such as residual feed intake (RFI) and residual intake and gain (RIG). Previous studies report differences ranging from 9 to 30 % in the dry matter intake (DMI) of efficient and inefficient animals, maintaining a similar daily weight gain (DWG) using the RFI index. Moreover, the DMI is similar using the RIG index. Although, the DWG of efficient animals is higher by up to 50 g d^-1, reducing feed conversion by one kg. This difference is attributed to a group of genes associated with feed efficiency (Adra2a, Gfra1, Gh, Glis1, Il1rapl1, Lep, Lepr, Mc4r, Oxsm, Pde8b, Rarb, Ryr2, Sox5, Sox6, and Trdn). These genes could be used to select hair sheep with high feed efficiency, considering the genes associated with meat quality (Capns1, Cast, Dgat1, Fabp4, Igf-i, Lep, Mstn, and Scd).

Key words Feed efficiency; Meat quality; Genes; Hair sheep

Resumen

Los ovinos de pelo desempeñan un papel importante en la producción de carne en zonas tropicales, donde los estudios de eficiencia alimenticia han sido poco evaluados. El consumo de alimento representa más del 70 % de los costos; por lo tanto, la selección de animales con alta eficiencia alimenticia puede mejorar la rentabilidad del sistema de producción. Se han desarrollado herramientas que permiten seleccionar individuos con mayor eficiencia alimenticia sin comprometer la calidad del producto. Por lo que esta revisión tiene la finalidad de identificar estas herramientas genético-moleculares y estadísticas, como son, el consumo de alimento residual (CAR) y ganancia e ingesta residual (GIR). En la literatura consultada, se reportan diferencias en el consumo de materia seca (CMS) en un rango del 9 al 30 % entre animales eficientes e ineficientes, manteniendo una ganancia diaria de peso (GDP) similar empleando el índice CAR. Por otro lado, utilizando el índice GIR los CMS son similares, aunque la GDP en animales eficientes es mayor hasta en 50 g día^-1, reduciendo la conversión alimenticia en un kilo. Esta diferencia se asume a un conjunto de genes asociados a la eficiencia alimenticia (Adra2a, Gfra1, Gh, Glis1, Il1rapl1, Lep, Lepr, Mc4r, Oxsm, Pde8b, Rarb, Ryr2, Sox5 y Sox6, Trdn), que pudieran ser utilizados para la selección de ovinos de razas de pelo con alta eficiencia alimenticia, teniendo en cuenta los genes relacionados con la calidad de carne (Capns1, Cast, Dgat1, Fabp4, Igf-i, Lep, Mstn y Scd).

Palabras clave Eficiencia alimenticia; Calidad de carne; Genes; Ovinos de pelo

Introduction

The global sheep population in 2017 consisted of 1,202 million heads. Approximately 74 % of this population is distributed between Asia and Africa (42.25 and 31.7 %, respectively). The remaining 26 % is located in the rest of the continents. Although America has the smallest sheep population (6.76 %), its average carcass weight is higher (18.6 kg) than that of the other continents, only surpassed by Oceania (21.6 kg)¹.

In Mexico, sheep production is one of the livestock activities with more presence regarding territorial distribution. According to the preliminary figures of 2018, the sheep population reached 8'683,835 heads²; approximately 11 % of this population in the American continent, distributed in around 53,000 production units. About 53 % is located in the center of the country, 24 % in the south-southeast, and 23 % in the north³. Pelibuey is one of the most numerous breeds; it is used as breeding stock due to its maternal ability, high prolificacy, rusticity, resistance to parasites, and great adaptation to the various climatic conditions in the country⁴.

Moreover, feed intake is one of the most important factors in intensive meat production systems, representing more than 70 % of total production costs⁵. Therefore, the selection of animals with high feed efficiency, those that require a lower feed intake to maintain their performance or increase their production with a similar intake, could increase the unit’s profitability⁶. Reducing feed costs would help keep profitable prices within a fluctuating agricultural supply market and competitiveness in the global market.

Traditionally, the meat production livestock industry has used feed conversion (FC) to assess feed efficiency⁷. Nonetheless, this measure is questionable because the DMI is highly correlated with body size and production level⁸; this tends to select animals with high DWG. However, animals with high DMI are also selected, which increases production costs⁷.

Taking a different approach, other authors have defined feed efficiency as the animal capacity to reach a specific weight with a lower DMI⁹. Ruminants' efficiency is low compared to other species. However, they can transform nonfood resources for humans (forages and non-protein nitrogen) into high-quality food (animal protein)⁷.

Consequently, several tools have been sought to help explain, predict, and select individuals with greater efficiency in feed utilization and energy intake. Residual feed intake (RFI) being among the most used ones⁹^,¹⁰. RFI is defined as the difference between the real and the expected feed intake for a specific weight and production level during an established period⁸^,¹¹. This tool identifies the animals with the greatest efficiency of feed utilization, improving the herd's genetics and reducing the production costs of each increased kilogram of live weight⁸. Koch et al⁹ proposed the Residual Gain (RG) index; this tool estimates the expected gain for a specific production level and identifies the animals with the highest weight gain rates.

A new indicator of feed efficiency was recently proposed, the Residual Intake and Gain (RIG) index. This indicator retains the selection characteristic of RFI and RG, which are independent of body weight. RIG selects the animals with the greatest DWG and the lowest DMI since it correlates negatively with the DMI and positively with the DWG¹².

The meat industry is not only interested in the efficiency of feed utilization but also the quality of the final product. Meat quality includes various traits, such as physicochemical attributes (tenderness, color, fat content, intramuscular, and water holding capacity), palatability factors (flavor, juiciness, and smell), and food safety characteristics¹³. These quality traits influence the decision-making of the consumer and the meat processing industry¹⁴. On this matter, several studies have used RFI to determine the effect of feed efficiency on meat quality. They have reported that selecting efficient Angus bovines (low RFI) does not hurt meat quality¹⁵^,¹⁶. However, recent studies in Nelore cattle have reported conflicting results. Some authors observed that efficiency does not affect meat quality and the calpain system¹⁷^,¹⁸, yet other studies report the opposite. Therefore, animals with low RFI tend to have tougher meat¹⁹. This undesirable characteristic is regulated by protein turnover and specific enzymes (especially those in the calpain system), which carry out the muscle postmortem proteolysis²⁰.

Furthermore, previous reports mention that proteolysis is related to the maintenance energy requirement (MER); a high protein degradation rate is associated with a higher MER²¹. Additionally, the most efficient animals have a lower maintenance metabolizable energy requirement¹⁷^,²¹. Thus, in Nelore cattle, the most efficient animals have a low protein degradation rate²², which is associated with a higher shear force at different maturation times (0 d= 4.50 vs 4.00, 7 d= 4.22 vs 3.61, 21 d= 3.27 v. 2.69 kg/cm²), low myofibril fragmentation index (37.0 vs 42 %), and high content of soluble collagen (17.7 vs 14.9 %), resulting in lower meat quality¹⁹.

Moreover, the development of molecular genetics, sequencing, and selective gene amplification techniques has increased the detection of genes that have a marked effect on traits of interest, i.e., feed efficiency and meat quality. This allows the detection of the genomic sequences associated with these genes and the establishment of selection programs based on molecular markers²³.

A genetic marker is a specific DNA sequence with a known location in a chromosome; this sequence either has a specific function or is associated with the phenotypic expression level of a trait²⁴. The use of genetic markers helps with problems faced during traditional selection by selecting genetically superior individuals²⁵. Furthermore, the markers can predict improvement values for the individuals selected at birth more precisely than the classic pedigree index, reducing the generation interval²³. Therefore, this study aimed to review the feed efficiency indexes and their relationship with meat quality and the genes associated with these traits in hair sheep.

Feed efficiency indexes

The RFI and RG indicators were proposed by Koch et al⁹ after observing that feeding affects the maintenance of live weight and daily weight gain. They suggested that feed intake can be adjusted to body weight and weight gain, dividing it into two components: 1) The feed intake expected for a specific performance or production level and 2) A residual portion. The residual portion of feed intake could be used to identify animals that deviate below their expected level of feed intake (negative RFI); this allows comparing animals with different production levels during the measurement period.

The RFI has been used as a selection criterion in beef cattle breeding programs. Heifers with low RFI are more efficient regarding feed utilization than those with a high RFI²⁶; additionally, their progeny tend to behave more efficiently²⁷. The estimated heritability of this characteristic is moderate (0.27-0.58) and independent from growth and production level⁹^,²⁸^,²⁹^,³⁰, and it has no adverse effect on other economically important characteristics, such as meat quality¹⁵.

Furthermore, RFI reduces livestock's environmental impact because animals with low RFI tend to produce lower amounts of methane (CH₄) per unit of consumed dry matter due to their lower DMI and best energy use efficiency³¹^,³²^,³³. Therefore, RFI is one of the carbon dioxide (CO₂) and CH₄ mitigation strategies of livestock.

The main advantages of using RFI as a selection criterion are improved feed efficiency⁸^,³⁴ and increased productivity in the breeding sector, reducing the area used per animal unit³⁵.

The novel RIG index improves feed efficiency and identifies animals with greater growth rates and lower fat proportion without affecting meat and carcass quality, reducing confinement and slaughter times of animals because they reach their commercial weight at early ages³⁶.

The studies that involve the RFI and RIG indexes have been mainly carried out in cattle, pigs, and poultry. These indexes have also been evaluated in temperate climate sheep. Although some studies have included Brazilian hair sheep crosses, the Santa Inés and Pantaneira breeds stand out⁵^,³⁶^,³⁷^,³⁸; there is also a study in Dorper sheep³⁹ (Table 1 and 2). In Mexico, this tool is just starting to be implemented, so there are few studies; there is only one previous study in the Rambouillet breed⁴⁰. Therefore, the behavior of hair sheep is still unknown (Table 1).

Table 1 Production parameters in sheep classified by residual feed intake (RFI)

	Residual feed intake
Breeds	Low	Medium	High	Low	Medium	High	Low	Medium	High	DDMI	Author
Breeds	Daily weight gain			Dry matter intake			Feed conversion			%	Author
½D½SI	0.280	-	0.270	1.24^b	-	1.41^a	4.43^b	-	5.15^a	12.06	5
RHS	0.260	-	0.240	2.23^b	-	3.22^a	-	-	-	30.74	6
–T¼P	0.321^a	0.277^b	0.306^ab	1.34^b	1.35^b	1.52^b	4.18^a	4.90^b	5.00^b	11.84	37
½D½SI	0.284	0.301	0.286	1.25^**	1.37^**	1.44^**	-	-	-	13.19	38
Dorper	0.266		0.253	2.63^b		3.00^a	5.94^b		6.91^a	12.33	39
Rambouillet	0.180	0.170	0.180	1.39^c	1.48^b	1.67^a	-	-	-	16.77	40
Targhee	0.350	0.330	0.360	1.92^b	2.02^b	2.32^a	6.58^b	7.71^a	7.83^a	17.24	45
Ghezel	0.210	-	0.200	1.01^b	-	1.12^a	4.95^b	-	5.53^a	9.82	46
Ile de France	0.329	-	0.335	1.42^b	-	1.63^a	4.35	-	4.93	12.88	47
Targhee	0.297	0.302	0.286	2.15^b	2.31^b	2.52^a	-	-	-	14.68	48
Targhee	0.294	-	0.293	2.21^b	-	2.43^a	-	-	-	9.05	49
RHS	-	-	-	2.10^b	-	2.89^a	-	-	-	27.34	50
Kurdi	0.260	-	0.260	1.82^b	-	2.11^a	-	-	-	13.74	51
Hu	0.280	-	0.250	1.50^b	-	1.72^a	-	-	-	12.80	52
Ghezel	0.280	-	0.290	1.52^b	-	1.72^a	5.47	-	5.93	11.63	53
Hu	0.250	0.260	0.260	1.09^c	1.25^b	1.33^a	4.51^c	4.84^b	5.39^a	18.04	54
Hu	0.260	-	0.270	1.05^b	-	1.48^a	3.92^b	-	5.62^a	29.05	55

DDMI= Difference in dry matter intake (%), ½D½SI= ½Dorper ½Santa Inés, RHS= Rambouillet, Hampshire, and Suffolk, –T¼P= –Texel ¼Pantaneira.

**^{, abc}= Significant differences.

Table 2 Production parameters in sheep classified by residual intake and gain (RIG)

	Residual intake and gain
Breed	Low	Medium	High	Low	Medium	High	Low	Medium	High	Low	Medium	High	Author
Breed	Dry matter intake			Daily weight gain			Feed conversion			Feed efficiency
½D½SI	1.39^a	-	1.31^b	0.26^b	-	0.30^a	5.32^a	-	4.28^b	0.19^b		0.23^a	5
–T¼P	1.28	1.27	1.22	0.26^b	0.29^a	0.31^a	4.99^a	4.28^b	3.91^c	0.20^c	0.24^b	0.26^a	36
½D½SI	1.41	1.37	1.31	0.26**	0.29**	0.30**	5.36*	4.61*	4.27*	0.18*	0.21*	0.23*	38

½D½SI= ½Dorper ½Santa Inés, –T¼P= –Texel ¼Pantaneira, *= Calculated data, **^{, abc}= Significant differences.

Estimating the RFI and RIG indexes

The RFI determines the expected DMI and is estimated through a multiple linear regression equation as a function of mean metabolic weight (MMW) and DWG.

The model used by Koch et al⁹:

Yi= β0+β1GDPi+β2PMMi+εi

Where:

Yi= Dry matter intake of the i-th animal.

β0= Regression intercept.

β1GDPi= partial regression coefficient of dry matter intake in the i-th DWG of the animal.

β2PMMi= partial regression coefficient of dry matter intake in the i-th mean metabolic weight of the animal.

εi= residual error in the dry matter intake of the i-th animal.

Moreover, RG helps estimate the expected DWG through a multiple linear regression as a function of DMI and MMW.

Yi=β0+β1CMSi+β2PMMi+Ԑi

Where:

Y_i= Weight gain of the i-th animal.

β0= Regression intercept.

β1CMSi= partial regression coefficient of the DWG of the i-th DMI of the animal.

β2PMMi= partial regression coefficient of the DWG of the i-th MMW of the animal.

εi= residual error in the DWG of the i-th animal.

RIG is calculated with the two previously described models using the following equation: GIRCAR* -1+GR. The index requires prior standardization (Mean = 0 and Standard Deviation = 1) of the RFI and RG.

After determining the RFI, animals are classified into high (>0.5 SD above the mean, higher feed consumption than expected for maintenance and production; thus, lower efficiency), medium (±0.5 SD from the mean), and low RFI (<0.5 SD below the mean, lower feed consumption; thus, higher efficiency)⁴¹. The same categorization procedure is used to determine the RIG groups. However, high RIG indicates greater efficiency, and low RIG means lower efficiency.

Intervening physiologic factors

There are numerous and interrelated physiologic mechanisms associated with higher feed utilization efficiency. However, they have not been completely elucidated. Richardson and Herd⁴² synthesized the results of a series of experiments in Angus cattle selected divergently for RFI. They estimated the proportion of the variation in RFI that explains the following processes: protein turnover, tissue metabolism and stress (37 %), digestibility (10 %), increase of heat and fermentation (9 %), physical activity (9 %), body composition (5 %), and feeding patterns (2 %). The mechanisms responsible for more than 25 % of the variation in RFI are not yet known. The physiologic processes associated with the variation in feed utilization efficiency have been grouped into five categories: 1) Feed intake capacity, 2) Feed digestion, 3) Metabolism (anabolism and catabolism), 4) Heat production related to digestion and physical activity, and 5) Thermoregulation³⁰.

Knowing the biological processes involved and the degree to which they contribute to the feed efficiency of hair sheep is crucial since information is scarce in these breeds. Therefore, it is necessary to carry out studies that help understand how these mechanisms favor this behavior, which would allow the selection of more efficient and productive animals.

Feed intake and productive performance

Voluntary feed intake is regulated by a complex interaction between neuroendocrine control mechanisms and the physicochemical properties of feed; this interaction changes according to the physiologic state of the animal⁴³. Moreover, feed intake directly correlates with the energy used for digestion; at higher the intake, the higher the energy expenditure. This results from an increase in digestive organ size and the energy used in the tissues of these organs³⁰. This energy expenditure is known as heat increase during fermentation; in ruminants, it represents approximately 9 % of the metabolizable energy intake⁴⁴.

Most studies (Table 1) indicate that sheep with low RFI show the same DWG as animals with high RFI⁵^,⁶^,³⁸^-⁴⁰^,⁴⁵^-⁵⁵; animals with low RFI have a better energy use efficiency³³. However, Rocha et al³⁷ reported significant differences in DWG, making energy use efficiency more noticeable. In all the studies, DMI was lower on the most efficient animals, with a difference ranging from 9 to 30 % compared with those less efficient. Therefore, it is expected that the more efficient animals show a better FC (Table 1).

Previous studies in sheep, using the RIG index⁵^,³⁶^,³⁸, have shown that sheep classified with high RIG have a lower DMI, higher DWG, lower FC, and higher feed efficiency (FE) (Table 2). Although the difference in DMI is not as high as that observed with RFI, the DWG differs by up to 50 g d^-1 _. Moreover, FC differs in more than one kg, so the FE is greater in animals with high RIG. Considering that feed is the most significant production cost in animal production systems, lower feed consumption and greater weight gains represent an important reduction in operation costs and increase the profitability of the production units and the efficient use of the supplied energy.

Methane production

The rumen microbial ecosystem is extremely diverse. It includes Eukarya, Archaea, and Bacteria phylotypes that interact between them, the feed, and the host, with densities of 10¹⁰ bacteria mL^-1, 10⁶ protozoa mL^-1, and 10³ fungi ml^-1 of ruminal fluid⁵⁶.

The diversity and concentration of the organisms that live in the rumen are influence by several factors, such as diet, breed, age, health status, environment, and geographic location⁵⁷. However, diet is considered the primary determinant of ruminal microbial diversity and the fermentation parameter in cattle and sheep⁵⁸. Thus, animals fed forage have a more diverse microbial ecosystem, with more frequent methanogenic groups, compared to those provided concentrate-based diets⁵⁹. This implies greater use of free H₂ (produced by a more acetic fermentation) to reduce CO₂ into CH₄⁶⁰_.

The results reported by Henderson et al⁶¹ indicate that regardless of the type of diet and geographic location, there is a central microbiome in the rumen comprised of seven groups. These groups represent 67.1% of the bacterial sequences in the global analyzed samples. This major group includes Prevotella, Butyrivibrio, Ruminicoccus, Lachnospiraceae, Ruminococcaceae, Bacteroidales, and Clostridiales. However, some genera are more abundant with specific diets. For example, Bacteroidales, Clostridiales, Fibrobacter, and Ruminococcaceae are more abundant in animals fed with forage; Prevotella and Succinivibrionaceae are more abundant with concentrate-based diets. Moreover, in the same study, they reported that the archaea population is constituted mainly by Methanobrevibacter gottschalkii and Methanobrevibacter ruminantium, representing 74 % of all the archaea inside the rumen. Other found species were Methanosphaera sp. and two groups belonging to Methanomassiliicoccaceae. These five species constituted 89 % of the total archaea; Methanobacterias being one of the main species that utilize free H₂ to reduce CO₂ to CH₄⁶². These species are associated with fibrous diets, where fermentation is more acetic and H₂ release is higher

Recent studies indicate that feed efficiency is related to CH₄ production³¹^,⁶²^,⁶³. It has been reported that the methanogenic communities in animals with high RFI are more diverse compared to efficient animals, with a high prevalence of M. stadtmaniae and Methanobrevibacter sp. Thus, animals with high RFI emit more CO₂ and CH₄ due to their higher fiber intake, which increases ruminal CH₄ production. Animals with low RFI tend to modify their bacteria consortia. Therefore, they can use the fibrous components of the ration more efficiently, reducing the passage rate and increasing digestibility. Thus, completely fermenting rations at a ruminal level⁶².

Using statistical prediction models, female sheep have shown significant differences in CH₄ emissions. Emissions are lower in animals with low and medium RFI, compared to those with high RFI (0.025^a, 0.028^a, and 0.032^b CH₄ kg^-1 d^-1, respectively). However, no differences were observed in male sheep due to the lack of significant difference in the DMI of efficient and inefficient animals⁴⁰.

Furthermore, greater efficiency could be related to bacteria that modify the fermentation pattern towards a more propionic fermentation, which favors meat production³³. Propionate is the main substrate contributing to the gluconeogenesis process; glucose is required as an energy source in protein synthesis⁴³. Previous studies have reported greater propionate concentrations in highly efficient sheep (low RFI) fed concentrate-based diets, compared to those less efficient (41.2 vs 30.2 % Molar)⁶. Therefore, animal selection based on feed efficiency indexes could reduce the greenhouse gases (GHG) produced by sheep.

Candidate genes associated with feed

Various studies have reported many single nucleotide polymorphisms (SNP) associated with feed efficiency in bovine species⁶⁴^-⁶⁷. Few studies have focused on sheep. Knowing the genes implicated in the biological processes related to desirable, productive characteristics (feed efficiency and meat quality) of farm animals²⁰^,⁵⁵^,⁶⁸^-⁸⁶ helps understand the relationship between these parameters and then use these genes as molecular markers for the selection of animals with desired traits (Table 3).

Table 3 Genes associated with sheep feed efficiency and meat quality

Symbol	Gene	Chrom	Biological process	Param	Author
Glis1	Glis Family Zinc Finger 1	1	Significantly promotes human and mouse fibroblast reprogramming into induced pluripotent stem cells during embryonic development. It is highly expressed in the fertilized ovum, moderately expressed in metaphase II oocytes, and weakly expressed in two-cell embryos. Additionally, this gene is associated with the regulation (including the transcription factor Foxa2, several genes of the Wnt and Esrrb families) of genes involved in the mesenchymal-epithelial transition, a crucial process in somatic cell reprogramming.	RFI	68-70, 87
Sox5 and Sox6	SRY-related box -5 and -6 transcription factor	15	Its expression is related to an efficient process of chondrogenesis, although the Sox9 gene is required to activate and maintain chondrocyte-specific genes. The Sox5 and Sox6 genes significantly increase the transcriptional activity of Sox9, ensuring its binding to DNA.
Il1rapl1	Interleukin 1 Receptor Accessory Protein Like 1	X	Related to intellectual disability and autism spectrum disorders promoted by the absence of the Il1rapl1 protein. Mutations in Il1rapl1 result in the absence of the protein or the production of a dysfunctional protein in humans.
Lepr	Leptin receptor	1	It produces a protein of the same name that, when combined with Leptin, triggers a series of chemical signals (JAK/STAT signaling pathway) that activate the receptor and transphosphorylate the JAK molecules associated with it. This pathway participates in energy homeostasis.	RFI	71, 88
Trdn	Triadin	8	It regulates the release of Ca²⁺ through the Ryr2 and Casq2 calcium release channels in the sarcoplasmic reticulum; this is a crucial step for the contraction of the skeletal and cardiac muscles. In humans, the lack of triadin results in cardiac arrhythmia with sudden cardiac death.	DWG	72-74, 89
Oxsm	Mitochondrial 3- oxoacyl-ACP synthase	26	An enzyme related to the synthetic α-lipoic acid pathway. Its activity is essential for the elongation of the fatty acid chains in the production of α-lipoic acid. α-lipoic acid deficiency represents a risk factor for diabetes.	DWG
Rarb	Retinoic acid receptor beta	26	Overall, retinoic acid receptors are essential for retinoic acid signaling during embryonic development and organogenesis. Mice lacking two isotypes of Rara, Rarb, Rarg show some characteristics of vitamin A deficiency syndromes in fetal and postnatal stages, as well as some congenital malformations.	DWG
Lep	Leptin	4	Hormone synthesized in the adipose tissue with an important role in the regulation of appetite and energy metabolism. Additionally, leptin has been linked to fat deposition in mammals.	DWG	75, 90
				FC	75, 88
				pH	75, 96
				Tenderness	75, 96
Gh	Growth hormone	11	Activates anabolic processes that regulate the increase in body size and skeletal growth. It controls and coordinates the flow of metabolic processes, such as stored fat mobilization and fatty acid and glucose catabolism in tissues.	DWG	76, 91
Mc4r	Melanocortin-4 receptor	23	This receptor is predominantly expressed in the hypothalamic appetite regulator nucleus; it regulates food intake and energy homeostasis.	DWG	77, 92
Adra2a	Alpha-2A adrenergic receptor	22	Catecholamine regulator; associated with energy metabolism. This receptor also participates in the adrenaline pathway and can regulate energy metabolism through the secretion of adrenaline, which affects FC.	FC	51, 55, 72, 78
Ryr2	Ryanodine receptor 2	25	Main channel of Ca²⁺ release from the sarcoplasmic reticulum in ventricular myocytes. This receptor is related to heart disease. This receptor also participates in the adrenaline pathway and can regulate energy metabolism through the secretion of adrenaline, which affects FC.	FC	51, 55, 72, 78
Pde8b	Phosphodiesterase 8B	7	This gene encodes a cyclic adenosine monophosphate-specific phosphodiesterase that regulates thyroid-stimulating hormone levels. The thyroid synthesizes thyroxine, which binds to the receptors to control biological processes, such as gene expression, growth, development, and metabolism.	FE	79-81, 93
Gfra1	GDNF family receptor alpha 1	22	Associated with the tyrosine kinase receptor, which regulates cell proliferation, growth factors, and neuronal development and differentiation.	FE	79-81, 93
Fabp4	Adipocyte fatty acid-binding protein	9	Known as intracellular lipid chaperons, they bind and transport long chain fatty acids in mammals. In cattle, these proteins are associated with growth, fat deposition, and carcass traits.	pH	82, 97
				Tenderness
				WHC
Capns1	Calpain small subunit 1	14	Mainly associated with the postmortem degradation of myofibrillar proteins and the production of free amino acids, resulting in meat tenderization.	WHC	20, 14
Capns1	Calpain small subunit 1	14		Color	20, 14
Cast	Calpastatin	5	This enzyme inhibits calpain activity and is related to the regulation of muscle protein degradation. The inhibition of muscle protein degradation by the calpastatin system increases production efficiency but affects meat tenderness.	Tenderness	20, 101,102
				WHC	20, 102
				Color	20, 102
Dgat1	Diacylglycerol O- acyltransferase 1	9	This enzyme modulates the synthesis of triglycerides and regulates their circulation. Additionally, it is directly related to glucose metabolism, obesity, insulin resistance, and hepatic steatosis.	Tenderness	83, 103
Dgat1	Diacylglycerol O- acyltransferase 1	9		WHC	83, 103
Igf-1	Insulin-like growth factor 1	3	This protein participates in the control of skeletal growth and cell differentiation by activating the cell cycle.	WHC	84, 106
Mstn	Myostatin	2	Myostatin is a potent negative regulator of muscle mass in mammals. The natural mutations in Mstn inactivate or suppress the protein, which increases musculature. The skeletal muscles affected by these mutations increase their myofibrils (hyperplasia) and, to a lesser extent, the cross-sectional area of the myofibers (hypertrophy). These mutations have a greater impact on homozygous individuals compared to heterozygous individuals.	WHC	85, 107
Scd	Stearoyl-CoA Desaturase	22	It regulates lipid synthesis and oxidation.	Color	86, 110

Chrom= Chromosome, Param= Parameter, RFI= residual feed intake, DWG= daily weight gain, FC= feed conversion, FE= feed efficiency, WHC= water holding capacity.

Cockrum et al⁸⁷ identified markers through genome-wide association studies (GWAS) with a nominal threshold of P<3.02^-4 in sheep genes associated with RFI. The candidate genes were: Glis Family Zinc Finger 1 (Glis1), SRY-related box -5 and -6 transcription factor (Sox5, Sox6), and Interleukin 1 Receptor Accessory Protein Like 1 (Il1rapl1). Another gene associated with this index is the Leptin receptor (Lepr). The association of a SNP in exon 2 of Lepr has also been reported in lactating ewes (P<0.05); the homozygous CC genotype had the highest RFI (2.579^a), compared to the TC (1.218^b) and TT (1.005^b) genotypes⁸⁸.

Recent studies have reported the association of DWG and specific SNPs; these associations can be considered in selecting animals with better productive performance. For example, in sheep, three genes have been associated with DWG. The triadin gene (Trdn) is in chromosome 8, and the 3-oxoacyl-ACP synthase (Oxsm) and Retinoic acid receptor beta (Rarb) genes are located in chromosome 26⁸⁹. Furthermore, the Leptin gene (Lep) has been associated with DWG, with significant differences (P<0.05) in the DWG (six-month weaning) of heterozygous BC, AB, and AC genotypes than in the homozygous AA and CC (116, 103, 99, 94, and 94 g d^-1, respectively)⁹⁰. In the Salsk breed, significant differences (P<0.001) were observed in the growth hormone (Gh) genotypes; AB was superior to AA (128.64 vs 81.51 g d^-1)⁹¹. The Melanocortin-4 receptor gene (Mc4r) has also been associated with DWG. A SNP located in the 3' untranslated region of the gene (NM_001126370.2) causes a G>A nucleotide variation in the 1016 position. The heterozygous GA genotype was superior to the homozygous GG at 120 (210.23 vs 192.01 g/d) and 180 d (166.35 v. 155.66 g/d) of fattening. Furthermore, SNP 292 G> A was detected with a variation in amino acid 98 Gly> Arg, which affected the eye area of the Longissmus muscle⁹².

The association of FC with some genes has been reported. In exon 3 of the Lep gene in lactating ewes, significant differences (P<0.001) were found in the genotypes of a SNP with amino acid variation (c.314 G>A, Arg>Gln). The GC genotype showed lower FC (2.019 kg) compared to the AG genotype (3.886 kg) in milk production⁸⁸. Additionally, the g.1429 C>A and g.1117 A>C synonym mutations in the Alpha-2A adrenergic receptor (Adra2a) and Ryanodine receptor 2 (Ryr2) genes had a positive effect with this efficiency indicator. In Adra2a, three genotypes were identified (CC, CA, and AA); the homozygous CC genotype had the lowest FC (4.67^b, 5.18^a, and 5.14^a kg, respectively). As for Ryr2, similar genotypes were identified. However, the homozygous had the lowest FC, but it was statistically similar to the CC genotype (5.14^b, 5.08^b, and 5.46^a kg, respectively)⁵⁵. Recently, in Santa Inés sheep, the GDNF family receptor alpha 1 (Gfra1) and Phosphodiesterase (Pde8b) genes have been associated with FE⁹³.

The genes implicated in feed efficiency can help identify superior individuals using molecular techniques. These techniques have been scarcely used in hair sheep. Their use will help identify and select, at an early age, those individuals with higher feed efficiency, reducing the generation interval.

Meat quality and associated candidate genes

Previous studies in sheep⁵^,³⁷^,³⁹^,⁴⁶^-⁴⁹^,⁵¹ suggest that carcass characteristics (Longissimus area, subcutaneous fat thickness , and Longissimus muscle depth) are not negatively affected when using the RFI index. However, regarding carcass yield, there tends to be a significant difference (P<0.1) between efficient and inefficient animals³⁷^,⁵⁴. Moreover, genes have been associated with the physicochemical parameters that determine meat quality, such as pH, tenderness, water holding capacity, and color.

In small ruminants, a normal pH ranges from 5.5 to 5.8⁹⁴ and is related to desirable characteristics in meat quality, such as color, shear force, and water holding capacity⁹⁵. Some studies have demonstrated the relationship between the pH and the polymorphism of some genes. A previous study reported the association of the Lep gene (intron 2, g.103 A>G) in the Suffolk breed and identified the AA and AG genotypes. The homozygous genotype had a lower pH value (5.53) when compared to the heterozygous (P<0.05)⁹⁶. Moreover, genotypes of the Fatty acid-binding protein gene (Fabp4) were identified in Chinese sheep with an effect on pH (P<0.1). The AG heterozygous genotype had a lower pH (6.3); AA and GG had a pH of 6.5⁹⁷. Although the final pH was higher than that reported as desirable in the literature⁹⁴.

Tenderness

As rigor mortis begins, sarcomeres shorten, and myofibrils undergo transverse contraction, increasing shear force. Within myofibrils, protein density increases in specific areas when the space between myofilaments decreases. Therefore, it is likely that this space reduction reduces the protease activity in the myofibril proteins, affecting meat tenderness⁹⁸. The decrease in temperature and pH in the carcass, along with the increase in cytoplasmic calcium, activates proteolytic enzymes, such as caspases and calpains⁹⁵, improving meat tenderness. Calpains are responsible for up to 90% of the proteolytic tenderizing of meat⁹⁹. Other proteolytic systems in the muscle, such as the lysosomal proteases and the multicatalytic proteasome complex, participate in cytoskeleton proteolysis and meat tenderizing, although to a lesser extent¹⁰⁰.

In sheep, some genes have been associated with shear force. Calpastatin (Cast) being the main gene associated with the texture. In Iranian breeds, significant differences have been reported between Cast genotypes (B, C, D, I)¹⁰¹. Genotype I required a shear force of 8.39 kg; genotype C required 12.69 kg. Sheep with genotype I are more desirable for this parameter. Additionally, a previous study reported a nucleotide variation (197A>T) in exon 6 of Cast, changing amino acid 66 from glutamine (Gln) to leucine (Leu). The heterozygous AT genotype had a lower shear force than the homozygous AA (6.68 vs 8.71 kg). For this same gene, two genotypes were detected on the Awassi breed. These genotypes showed significantly different (P<0.05) shear forces. The MN genotype had a higher force than the MM genotype (4.36 and 3.98 kg, respectively)¹⁰². In Chinese breeds, previous studies have reported the association between Diacylglycerol O-acyltransferase 1 (Dgat1) genotypes and tenderness. The TT genotype required a lower force than TC and CC (2.30, 2.69, and 2.73 kg)¹⁰³. Also, in Chinese breeds, the association of Fabp4 genotypes and tenderness has been previously reported. The AA genotype was more tender than the AG and GG genotypes (2.24, 2.78, and 2.88 kg, respectively, P<0.05)⁹⁷. Lep is another gene associated with this parameter. For example, previous studies have reported the polymorphism of this gene in the Suffolk breed (intron 2, g.103 A>G). The shear force of the AA genotype is lower than that of the AG genotype (3.6 and 4.7 kg, respectively)⁹⁶.

Water holding capacity (WHC)

WHC is defined as the ability of meat to retain its total or partial water content¹⁰⁴; it is closely related to the pH and isoelectric point of muscle proteins (pH 5.1-5, net charge 0). Thus, under these conditions, WHC is minimized⁹⁸. This parameter is evaluated by drip loss and cooking loss tests. The first test measures the water lost because of gravity¹⁰⁵, i.e., the extracellular water. In contrast, the second test measures the water loss derived from cooking the meat¹⁰⁴.

In Awassi sheep, the Cast gene is related to cooking loss, with differences (P<0.05) between the MM and MN genotypes. The homozygous genotype had the highest percentage of water loss (48.45 and 45.69 %, respectively)¹⁰².

Moreover, genes associated with the drip loss parameter have been previously identified. For example, three genotypes of the Dgat1gene were identified; the water loss in the TT genotype was lower than that of TC and CC, which showed similar losses (67.1, 92.6, and 92.4 g kg^-1)¹⁰³. Furthermore, the Fabp4 gene is also associated with this parameter. Of the AA, AG, and GG genotypes, AA had the lowest loss percentage (8.86, 9.48, and 9.39 %, respectively), although there were no significant differences (P<0.1)⁹⁷. Polymorphisms of the Calpain small subunit 1 (Capns1) gene have also been associated with WHC. Five genotypes with different water loss percentages (P<0.01) have been identified. The genotype B1B1 had a 4.11 % water loss, while A1A1, A1B1, A1C1, and B1C1 ranged from 2.23 to 3.30 %¹⁴. Additionally, two genotypes of the Insulin-like growth factor 1 (Igf-1) with significant effects on drip water loss have been reported. The homozygous AA genotype lost 2.47 %, while the heterozygous AB lost 3.33 %¹⁰⁶. Furthermore, the polymorphism of the Myostatin (Mstn) gene has also been associated with this parameter. Two genotypes with significant differences (P<0.05) in their water loss percentages have been identified. The AA genotype had a water loss of 2.5 %, while AE lost 3.5 % of water¹⁰⁷.

Color

Meat color is largely the main attractive factor for the consumer, who perceives this parameter as a sign of freshness and quality; thus, red color in sheep meat is preferable. The color of meat changes as the myoglobin pigments in the meat surface interacts with oxygen, changing from deoxymyoglobin (purple) to oxymyoglobin (red) to metmyoglobin (brown)¹⁰⁸. The CIE-L* (black-white), a* (red-green), and b* (blue-yellow) values have been used to determine meat color. A light reflectance ratio of 630/580 nm is used to detect the chemical changes that result from the oxygenation or oxidation of myoglobin¹⁰⁹.

In Merino sheep, significant differences (P<0.05) in the L* reflectance coordinates between Capns1 genotypes (A1A1, B1B1, A1B1, A1C1, and B1C1) have been reported. Genotypes B1B1 and A1C1 showed the lowest and highest luminosity (38.05 and 41.13, respectively)¹⁴. Like calpains, the antagonist of Cast is associated with color. Significant differences (P<0.05) in L* have been observed between the MM and MN genotypes in Awassi sheep; the luminosity of the homozygous genotype was higher than that of the heterozygous (37.60 and 32.47, respectively)¹⁰². In Iranian sheep, two genotypes (A and B) were identified for the Stearoyl-CoA Desaturase (Scd) gene. These genotypes showed significant differences in L* and a*; the B genotype had a higher L* (40.96 and 43.16, respectively) than A, while A had a higher a* value than B (16.0 and 15.08, respectively)¹¹⁰.

In hair sheep, no previous study has evaluated the genes associated with carcass characteristics and meat quality. Therefore, using molecular techniques that evaluate these genes is critical to accelerating the genetic improvement of hair breeds.

Conclusions

RFI and RIG are indexes that allow identifying and selecting animals with high feed utilization efficiency. In sheep, a negative effect on the carcass characteristics has not been detected. The heritability of feed utilization efficiency is moderate and is associated with multiple genes. These genes can be used as molecular markers for genetic improvement. Therefore, the study of these indexes and the use of molecular techniques in the selection and breeding of hair sheep could help predict animal behavior. Furthermore, some of the genes related to carcass characteristics and meat quality can be included in the breeding programs of these breeds. This would promote the development of sheep farming since more efficient animals have lower feed requirements without affecting growth rate (RFI), or greater weight gains with similar feed intake (RIG), reducing production costs and increasing the profitability of the production units. In addition to producing the quality food demanded by the global market and contributing to reducing the ecological footprint of livestock.

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Received: March 16, 2020; Accepted: September 11, 2020

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