**5. –Omic techniques to analyse the response to heat stress**

Quantitative genetic analyses presented earlier have been used for the assessment of the animals' response to HS through its effect on milk production and fitness traits [21]. As a result, we can conclude that the genetic component of response to climatic constraints is not negligible and, therefore, to include traits related to thermotolerance of the animals in breeding programmes may help to face the challenge of climate change on goat production.

A number of studies conducted to detect genomic regions and genes associated with heat tolerance via GWAS, mainly in cattle [58–60], have pointed at genes coding for fibroblast growth factors (FGFs), dehydrogenase‐reductase member 3 (DHRS3), involved in the embryonic development in humans, and junctophilin 1 (JPH1), whose expression has been found to be upregulated in the hypothalamus of chickens subjected to HS, as candidate to be associated with HS. Carabaño et al. [61] using a GWAS approach have also validated some of the gene families already found in the literature in relation to HS, such as heat shock proteins (HSP, DNAJ), heat shock protein factors (HSF), mechanisms of immunoresponse (IL and CD) or NADH dehydrogenase (NDUF) families, FMO and FDF coding for growth factor relevant in the remodelling of the mammary gland after lactation. These findings corroborate the com-

Characterisation of Goats' Response to Heat Stress: Tools to Improve Heat Tolerance

http://dx.doi.org/10.5772/intechopen.70080

339

plexity of HS effect, involving apoptotic, immunological and metabolic responses.

in humans.

Less attention has been paid on this topic in goats, as evidenced by the lower number of studies found in the literature. The extensive nature of its production system and the lower production level compared to dairy cattle have led to the belief that HS effects would be lower in this species. Genes found to be associated with response to HS in goats include HSP genes, genes associated with production traits, regulating respiration rate or playing roles in heat generation among others. Thus, Zidi et al. [23], conducting a GWAS analysis in a Spanish local goat, identified an HSP gene, the kappa casein gene CSN3 and some genes encoding enzymes such as malic enzyme (ME1) or acetyl‐coenzyme A carboxylase alpha (ACACA). Though candidate genes were coherent with what is expected, the approach followed for this analysis somehow favoured the presence of false‐positive signals because the slope used as a pseudo‐phenotype in the GWAS analysis was estimated based on EBVs obtained from a reaction norm model without de‐regressing them [63]. In another study on Egyptian desert sheep and goats, Elbeltagy et al. [64] found the GRID2 neurotransmitter receptor as genes associated with HS, affecting neuronal apoptosis and PDLIM5 (ontogenesis), or the SLC27, NR2F6 and DRD2 that have been found to be associated with heat generation and detection of temperature stimulus or homeostatic processes. Finally, Carabaño et al. [61] found a relevant signal for fat and protein yields response to heat common to the three dairy species (cattle, sheep and goats) pointing out to a region in Chromosome 6 where a gene encoding a member of the potassium channel‐interacting proteins (KCNIP4) that regulate processes of defence against hypoxia and associated to hyperactivity disorders

Future works on ‐Omics could contribute to develop powerful tools to select animals not reactive to thermal stress; however, finding phenotypes of thermal response is still a handicap. Genome analysis of HS response should take advantage of the new technologies recently

The studies on the genetic variability of the response to HS in dairy cattle [50] opened the opportunity to reduce the unfavourable effects of that stress on milk yields through selection, which represents a relatively cheap way (once milk recording and weather data are

implemented for measuring biomarkers and proxies of thermotolerance in animals.

**6. Improving resilience to heat stress**

The development of ‐omic technologies has provided some powerful techniques to characterise the response of the animal to HS, either by evaluating (co)‐expression patterns of genes in specific tissues [34, 56] or as a tool to understand the complexity of the genetic background of animal's reaction to thermal stress.

Several studies to dilucidate the genetic mechanisms underlying the response to heat stress have been performed using metabolic cages analysing patterns of gene expression occurring in blood and milk with either microarrays or RNA‐seq. In blood, Hamzaoui et al. [57] using Affymetrix GeneChip Bovine in Murciano‐Granadina goats in late lactation identified 39 and 74 genes whose expression was up‐ and down‐regulated, respectively, by HS (*P* < 0.05). These genes were mainly related to biological processes and, to a lower extent, to molecular functions and cellular components. Moreover, ingenuity pathway analysis detected important pathways related to cell proliferation and death, free radical scavenging, inflammatory response, lipid metabolism and glycolysis/gluconeogenesis. Transcription regulators affected by HS were SATB1 (global chromatin organiser) and PPARD (which might be related to insulin resistance). The HS elicited changes in gene expression related to transcriptional regulation and metabolic processes. On the other hand, gene expression in milk cells has also been studied using RNA‐seq by Salama et al. [35]. These authors have showed how decreases in protein and fat in milk composition are accompanied by downregulation in the gene expression of casein, fat and lactose synthesis and upregulation in the expression of genes related to milk cathepsins. This is an evidence of how findings in quantitative studies are the phenotypic expression on the underlying genetic mechanisms.

In parallel to transcriptomic analyses, a number of genome‐wide association studies (GWAS) have been run mostly in cattle with the objective of identifying genomic regions associated with the signs of response to HS, based on production traits [58, 59], or physiological signs such as respiration rate or rectal temperatures [60]. Ideally, joining transcriptomic and genomic information together will help to find causal mutations (eQTLs) useful to address an effective selection favouring thermal tolerance in livestock.

A comprehensive review of the main challenges of performing GWAS analysis for HS response in dairy ruminants can be found in Carabaño et al. [61]. These authors highlighted the difficulty of dealing with milk recording data to find a phenotype measuring heat‐stress response independent from milk production level in dairy production (cattle, sheep and goats). Separating production level from tolerance to thermal stress is very complex in dairy cattle because both components show a high correlation but not as much in sheep and goats [61]. Principal components analysis can be used to find variables related with heat tolerance independently from production level [59, 62].

A number of studies conducted to detect genomic regions and genes associated with heat tolerance via GWAS, mainly in cattle [58–60], have pointed at genes coding for fibroblast growth factors (FGFs), dehydrogenase‐reductase member 3 (DHRS3), involved in the embryonic development in humans, and junctophilin 1 (JPH1), whose expression has been found to be upregulated in the hypothalamus of chickens subjected to HS, as candidate to be associated with HS. Carabaño et al. [61] using a GWAS approach have also validated some of the gene families already found in the literature in relation to HS, such as heat shock proteins (HSP, DNAJ), heat shock protein factors (HSF), mechanisms of immunoresponse (IL and CD) or NADH dehydrogenase (NDUF) families, FMO and FDF coding for growth factor relevant in the remodelling of the mammary gland after lactation. These findings corroborate the complexity of HS effect, involving apoptotic, immunological and metabolic responses.

Less attention has been paid on this topic in goats, as evidenced by the lower number of studies found in the literature. The extensive nature of its production system and the lower production level compared to dairy cattle have led to the belief that HS effects would be lower in this species. Genes found to be associated with response to HS in goats include HSP genes, genes associated with production traits, regulating respiration rate or playing roles in heat generation among others. Thus, Zidi et al. [23], conducting a GWAS analysis in a Spanish local goat, identified an HSP gene, the kappa casein gene CSN3 and some genes encoding enzymes such as malic enzyme (ME1) or acetyl‐coenzyme A carboxylase alpha (ACACA). Though candidate genes were coherent with what is expected, the approach followed for this analysis somehow favoured the presence of false‐positive signals because the slope used as a pseudo‐phenotype in the GWAS analysis was estimated based on EBVs obtained from a reaction norm model without de‐regressing them [63]. In another study on Egyptian desert sheep and goats, Elbeltagy et al. [64] found the GRID2 neurotransmitter receptor as genes associated with HS, affecting neuronal apoptosis and PDLIM5 (ontogenesis), or the SLC27, NR2F6 and DRD2 that have been found to be associated with heat generation and detection of temperature stimulus or homeostatic processes. Finally, Carabaño et al. [61] found a relevant signal for fat and protein yields response to heat common to the three dairy species (cattle, sheep and goats) pointing out to a region in Chromosome 6 where a gene encoding a member of the potassium channel‐interacting proteins (KCNIP4) that regulate processes of defence against hypoxia and associated to hyperactivity disorders in humans.

Future works on ‐Omics could contribute to develop powerful tools to select animals not reactive to thermal stress; however, finding phenotypes of thermal response is still a handicap. Genome analysis of HS response should take advantage of the new technologies recently implemented for measuring biomarkers and proxies of thermotolerance in animals.

## **6. Improving resilience to heat stress**

**5. –Omic techniques to analyse the response to heat stress**

animal's reaction to thermal stress.

338 Goat Science

typic expression on the underlying genetic mechanisms.

effective selection favouring thermal tolerance in livestock.

independently from production level [59, 62].

Quantitative genetic analyses presented earlier have been used for the assessment of the animals' response to HS through its effect on milk production and fitness traits [21]. As a result, we can conclude that the genetic component of response to climatic constraints is not negligible and, therefore, to include traits related to thermotolerance of the animals in breeding

The development of ‐omic technologies has provided some powerful techniques to characterise the response of the animal to HS, either by evaluating (co)‐expression patterns of genes in specific tissues [34, 56] or as a tool to understand the complexity of the genetic background of

Several studies to dilucidate the genetic mechanisms underlying the response to heat stress have been performed using metabolic cages analysing patterns of gene expression occurring in blood and milk with either microarrays or RNA‐seq. In blood, Hamzaoui et al. [57] using Affymetrix GeneChip Bovine in Murciano‐Granadina goats in late lactation identified 39 and 74 genes whose expression was up‐ and down‐regulated, respectively, by HS (*P* < 0.05). These genes were mainly related to biological processes and, to a lower extent, to molecular functions and cellular components. Moreover, ingenuity pathway analysis detected important pathways related to cell proliferation and death, free radical scavenging, inflammatory response, lipid metabolism and glycolysis/gluconeogenesis. Transcription regulators affected by HS were SATB1 (global chromatin organiser) and PPARD (which might be related to insulin resistance). The HS elicited changes in gene expression related to transcriptional regulation and metabolic processes. On the other hand, gene expression in milk cells has also been studied using RNA‐seq by Salama et al. [35]. These authors have showed how decreases in protein and fat in milk composition are accompanied by downregulation in the gene expression of casein, fat and lactose synthesis and upregulation in the expression of genes related to milk cathepsins. This is an evidence of how findings in quantitative studies are the pheno-

In parallel to transcriptomic analyses, a number of genome‐wide association studies (GWAS) have been run mostly in cattle with the objective of identifying genomic regions associated with the signs of response to HS, based on production traits [58, 59], or physiological signs such as respiration rate or rectal temperatures [60]. Ideally, joining transcriptomic and genomic information together will help to find causal mutations (eQTLs) useful to address an

A comprehensive review of the main challenges of performing GWAS analysis for HS response in dairy ruminants can be found in Carabaño et al. [61]. These authors highlighted the difficulty of dealing with milk recording data to find a phenotype measuring heat‐stress response independent from milk production level in dairy production (cattle, sheep and goats). Separating production level from tolerance to thermal stress is very complex in dairy cattle because both components show a high correlation but not as much in sheep and goats [61]. Principal components analysis can be used to find variables related with heat tolerance

programmes may help to face the challenge of climate change on goat production.

The studies on the genetic variability of the response to HS in dairy cattle [50] opened the opportunity to reduce the unfavourable effects of that stress on milk yields through selection, which represents a relatively cheap way (once milk recording and weather data are available) that can complement the ways of reducing temperature in farms through costly heat abatement systems. Formerly described results show that applying random regression methods to the data from milk recording, together with the climatic information from the meteorological stations close to the farms, goats can be genetically evaluated for their response to HS and robust or tolerant animals can be selected. Selection criteria can focus on increasing the tolerance threshold or the slope of decay of the considered trait, but the estimation of the genetic value for the threshold of tolerance of each animal in goats is not easy due to the scarce and noisy information available (only six‐ to eight‐test day per lactation) [61]. However, in all studies, in both dairy cattle and goats, it has been found that most of the observed variability of the response is associated to the production level (the intercept coefficient of the response) and only a small fraction of the variation is associated to the slope coefficient. Furthermore, there is an antagonistic relation between the intercept and the slope. Therefore, selecting for a lower decay of yield (lower negative or positive slope) would lead to a negative effect on the level of production. Carabaño et al. [61, 62] have proposed using canonical variables resulting from the eigendecomposition of the additive genetic random regression coefficient (co)variance matrices derived from the norm of reaction models formerly described. The canonical variable explaining the largest proportion of the variation of genetic values of animals across the range of values of THI for milk yield is linked to the production level and only a small proportion of about 10% can be used to select animals tolerant to HS without compromising the level of production. On the contrary, in the case of milk components there is a canonical variable explaining a larger part (up to 25%) of the genetic variation for heat tolerance independent of the production level. This could be a good selection criterion to get heat‐tolerant animals for the trait fat plus protein yield, which is the most important trait in goats' selection programmes. According to Carabaño et al. [61], genomic information may play an important role in identifying genomic variants present in animals showing high production levels and a low rate of decay due to HS. Other possible tools to be used for selecting heat‐tolerant animals could be the use of single nucleotide polymorphism (SNP) markers, found through GWAS analyses, associated with physiological indicators of the response to HS like rectal temperature and respiration and sweating rate. Biomarkers determined in milk by means of mid‐infrared technology, routinely used for milk composition analyses in milk‐recording programmes, and other recent discoveries of genetic mechanisms involved in heat tolerance through transcriptomics and proteomics may also contribute to find selection tools to improve the response to HS without impairing production levels [61].

Genetic variability for the response to heat stress was observed. Heritability of milk traits and

Characterisation of Goats' Response to Heat Stress: Tools to Improve Heat Tolerance

http://dx.doi.org/10.5772/intechopen.70080

The GWAS and transcriptomic analyses showed some candidate genes possibly associated to the response to heat stress, which evidence the complexity of such a response involving

A part of the results of experiments referred to in this chapter have been obtained in the framework of the projects AGL2007‐66161‐C02 and INIA‐FEDER: RTA2011‐00108, financed by Spanish (Ministry of Economy) and European (ERDF) funds, with the collaboration of the Spanish Meteorological State Agency (Agencia Estatal de Meteorología—AEMET), which provided the meteorological records, and the breeders associations of Murciano‐Granadina (CAPRIGRAN), Malagueña (CABRAMA), Florida (ACRIFLOR) and Payoya goats which

, Manuel Ramón<sup>3</sup>

2 National Institute for Agricultural and Food Research and Technology (INIA), Madrid,

3 Regional Center for Reproduction and Animal Breeding (CERSYRA‐IRIAF), Valdepeñas

[1] Rauw WM, Kanis E, Noordhuizen‐Stassen EN, Grommers FJ. Undesirable side effects of selection for high production efficiency in farm animals: A review. Livestock Production

[2] Ravagnolo O, Misztal I. Genetic component of heat stress in dairy cattle, parameter estimation. Journal of Dairy Science. 2000;**83**:2126‐2130. DOI: 10.3168/jds.S0022‐0302(00)75095‐8

[3] Bohmanova J, Misztal I, Colet JB. Temperature‐humidity indices as indicators of milk production losses due to heat stress. Journal of Dairy Science. 2007;**90**:1947‐1956. DOI:

Science. 1998;**56**:15‐33. DOI: 10.1016/s0301‐6226(98)00147‐x

, Antonio Molina<sup>1</sup>

, Clara Diaz<sup>2</sup>

and

341

genetic values of animals diminish when heath load increases.

apoptotic, immunological and metabolic process.

**Acknowledgements**

**Author details**

Juan M. Serradilla<sup>1</sup>

(Ciudad Real), Spain

10.3168/jds.2006‐513

**References**

Spain

Alberto Menéndez‐Buxadera<sup>1</sup>

allowed for the use of their data bases.

\*, María J. Carabaño<sup>2</sup>

\*Address all correspondence to: pa1semaj@uco.es

1 University of Córdoba, Córdoba, Spain

#### **7. Conclusions**

Traditionally, it has been considered that goats are better adapted to semiarid and hot climates than cattle and sheep; however, the results of the studies carried out in native Spanish breed formerly describe showed that goats also suffer some physiological effects derived from the exposure to high temperatures with a negative impact on yields.

A negative effect on milk traits of both high and low temperatures was observed in three Spanish native breeds of goats. The effect is higher in high‐yielding animals.

Genetic variability for the response to heat stress was observed. Heritability of milk traits and genetic values of animals diminish when heath load increases.

The GWAS and transcriptomic analyses showed some candidate genes possibly associated to the response to heat stress, which evidence the complexity of such a response involving apoptotic, immunological and metabolic process.
