Genotype imputation for genomic selection

Genotype imputation as a cost-effective strategy to increase genotype data for genomic selection in South African beef cattle

Industry Sector: Cattle And Small Stock

Research Focus Area: Livestock production with global competitiveness: Breeding,physiology and management

Research Institute: Agricultural Research Council

Year Of Completion : 2020

Researcher: Mahlako Makgahlela

The Research Team

ProfFWCNeserPhDUniversity of the Free State
DrMDMacNeilPhDDelta Genetics
ProfMMScholtzPhDAgricultural Research Council
MsSMdyogoloMScAgricultural Research Council
DrAAZwanePhDAgricultural Research Council

Executive Summary

The discovery of DNA polymorphisms (single nucleotide polymorphisms (SNP) or simply genomic data) and their cost-effective genotyping platforms have provided breeders and scientists in animal breeding additional tools to select young animals without performance records with much higher accuracies. Breeding programmes incorporating genomic information have achieved substantial increase in genetic improvement for cattle populations around the world. As a start, genotyping strategies are determined to identify individuals that are genotyped to increase the accuracy of predictions, and estimate relationships between candidates more reliably. Meanwhile, accuracy of genome-based schemes is a function of the reference population from which prediction equations for estimating genomic breeding values (GEBV) are developed. Setting up a sizable reference population is costly and remains a challenge for the uptake of genomic selection in South Africa.

Objective Statement

The aim of this research was to assess the accuracy of genotype imputation from low-density (7 931 SNPs or 7K) or medium density (150 000 SNPs or 150K) to high density (777 962 SNPs or 777K) panels using the reference population defined as influential animals explaining substantial genetic variation in the Afrikaner (AFR), Brahman (BRA) and Brangus (BNG).

Project Aims

  1. To evaluate whether the Celtic mutation on the POLL locus is the causative mutation for polledness in Bonsmara and Drakensberger
  2. To perform a genome wide association study of the Polled and Scur genes based on phenotypic data and genotypic data from the GGP Bovine 150K SNP bead chip
  3. To apply sequence data available from the Bovine Genomics Program to finemap the suspected regions for the Polled and Scur genes

Results

In identifying influential animals for the AFR, BRA, BNG and the other beef breeds (i.e., Limousine, Santa Gertrudis, Simbra and Simmentaler), it was found that only 100 ancestors explained approximately 50% of the genetic variation, and about 90% of the genetic variation was explained by 200 ancestors for all breeds. Genetic variation explained by top 1000 important ancestors was 95, 96 and 84% for AFR, BRA and BNG, respectively. Imputation accuracies within breed reference population, measured as the concordance rate, were 96.60, 91.39 and 89.91 using Beagle and 95.3, 92.8 and 96 using FImpute for AFR, BRA and BNG, respectively. Accuracies in multi-breed were lower (±80%) than within-breed reference populations. Furthermore, results demonstrated that accuracy tends to be greater when imputing from low-density 7K to medium-density 150K than bypassing the latter and impute from low-density 7K to high-density 777K. Higher accuracies were observed using Fimpute (93-97 %) versus Beagle (89-95%) and Impute (91-94%).

Conclusion

This study investigated the accuracies of imputation within breed and across breeds by masking actual genotypes in the Afrikaner, Brahman and Brangus breeds, and through genotype imputation from low density 7K or medium density 150K to high-density 777K in the Brahman cattle breed. The reference populations for imputation for all breeds were defined as influential animals with high marginal genetic contributions to young animals who were born in the last decade of the pedigree data, which were found to be few relative to the pedigreed population. Genotyped animals used in this study were few but promising accuracies were observed in within breed reference populations for AFR and BRA. Thus, imputation workflow established in this research could be integrated within the framework for implementation of genomic evaluations of GEBV and genomic selection in the Brahman and Afrikaner cattle breeds.

Popular Article

Genotype imputation: An essential, promising and cheap tool for assembling the reference population for genomic selection in the Afrikaner and Brahman cattle breeds of South Africa

Authors: Dr M.L. Makgahlela, Ms S. Mdyogolo, Prof F.W.C. Neser, Dr M. D. MacNeil, Prof M. M. Scholtz & Prof A. Maiwashe

The world will need to produce 100% more food in the next 40 years than currently produced (UNFAO, 2002). Accordingly, the demand for beef products, being the top valuable livestock product, will continue to increase significantly. Meanwhile, competition for resources will intensify, dictating that livestock systems must increase both productivity and efficiency. More than 60% of the additional food must come through technological innovations. Genomics is among technologies that will play a pivotal role in meeting the increasing demand while safeguarding natural resources and preventing environmental degradation. Genomic selection (GS) is the selection of genetically superior breeding animals based on genomic breeding values (GEBV) calculated from thousands of DNA markers or single nucleotide polymorphisms (SNP. Breeding programmes using GEBV have achieved substantial increase in genetic improvement for cattle populations around the world. The infrastructure for implementing GS within breed is a sufficient reference population of phenotyped (measured economic traits) and genotyped (SNP genotypes) animals. Setting up a sizable reference population requires substantial capital investments, and remains a challenge for the uptake of genomic selection in South Africa. There are several SNP genotyping panels of low-, medium- and high densities in terms of the number of SNP markers. Imputation is a method used to fill missing SNP on the low-density panel using medium- or high-density panel as reference population, without paying for the extra information. It provides an opportunity to achieve a sizable reference population for timely uptake of GS.

The aim of this research was to assess the accuracy of genotype imputation from low-density (7 931 SNPs or 7K) to medium density (150 000 SNPs or 150K) or high density (777 962 SNPs or 777K) panels using the reference population defined as influential animals explaining the genetic diversity in the Afrikaner (AFR), Brahman (BRA) and Brangus (BNG). In identifying influential animals for the AFR, BRA, BNG and the Limousine, Santa Gertrudis, Simbra and Simmentaler, it was found that only 100 ancestors explained approximately 50% of the genetic diversity, and about 90% of the genetic diversity was explained by 200 ancestors for all breeds. Genetic diversity explained by top 1000 important ancestors was 95, 96 and 84% for AFR, BRA and BNG, respectively. Imputation accuracies within breed reference population, measured as correctly imputed SNP, were 96.60, 91.39 and 89.91 using Beagle and 95.3, 92.8 and 96 using FImpute for AFR, BRA and BNG, respectively. Accuracies in multi-breed were lower (±80) than within-breed reference populations. Furthermore, results demonstrated that accuracy tends to be greater when imputing from low-density 7K to medium-density 150K than bypassing the latter and impute from low-density 7K to high-density 777K. Higher accuracies were observed using Fimpute (93-97 %) versus Beagle (89-95) and Impute (91-94). Genotyped animals used in this study were few but promising accuracies were observed in within breed reference population for AFR and BRA. Thus, imputation workflow established in this research could be integrated within the framework for implementation of genomic evaluations of GEBV and genomic selection in the Brahman and Afrikaner cattle breeds.

Please contact the Primary Researcher if you need a copy of the comprehensive report of this project on :mmakgahlela@arc.agric.za

Gene expression: Nguni and Bonsmara cattle

A gene-expression study on the growth performance of Nguni and Bonsmara cattle grown in a feedlot fed a high and low energy diet.

Industry Sector: Cattle And Small Stock

Research Focus Area: Livestock production with global competitiveness: Breeding, physiology and management

Research Institute: Agricultural Research Council

Year Of Completion : 2019

Researcher: Dina Linde

The Research Team

TitleInitialsSurnameHighest QualificationResearch Institution
DrM.M.ScholtzPhDARC-AP
ProfEvan Marle-KosterPhDUP
MrsA. Theunissen MSc Vaalharts Research Station

Executive Summary

Introduction

In South Africa, Nguni cattle are one of the breeds found predominantly in extensive production systems. The Nguni is an indigenous cattle breed and is widely used in crossbreeding systems due to their high fertility and mothering ability. Nguni cattle are also commonly used in communal production systems. The majority of beef in South Africa is produced in feedlots using commercially formulated high energy diets, where preference is given to medium and large framed later maturing cattle that include British types and composites such as the South African Bonsmara. Due to the Nguni’s small frame and low meat yield when compared to British types it is nor preferred as a feedlot animals, however studies have shown that Nguni cattle produce high quality meat. The veldt of South Africa has a varying degree of carrying capacity, however the occasional drought conditions have necessitated the use of alternative production systems such as feedlots for finishing cattle. The use of a lower energy diet in feedlots for indigenous cattle have been suggested and warrants investigation. Nutrigenomics is the study of the effect of nutrition on the genes of the animal by quantifying the gene expression. An improved understanding of the interaction between the nutritional environment and the genetics of the animal can lead to increased efficiency and production. Diets that are different in components or ingredients can result in different phenotypes in the animals. In this study the effect of two feedlot diets with different energy levels have been investigated using a transcriptome approach.

Objective statement

The objective of this study is to determine if there are gene expression differences between Nguni and Bonsmara cattle fed a low or a high energy diet. The gene expression of the cattle can determine the underlying differences in the reaction of the two breeds to the two different diets.

Project Aims

  1. To evaluate whether the Celtic mutation on the POLL locus is the causative mutation for polledness in Bonsmara and Drakensberger
  2. To perform a genome wide association study of the Polled and Scur genes based on phenotypic data and genotypic data from the GGP Bovine 150K SNP bead chip
  3. To apply sequence data available from the Bovine Genomics Program to finemap the suspected regions for the Polled and Scur genes

Results

Performance results showed a higher live weight, carcass weight and marbling score for all bulls fed the high energy diet compared to bulls fed the low energy diet. Only carcass weight and marbling score had significant difference in terms of diet (p < 0.05). Live weight, average daily gain, rib fat, rump fat and eye muscle area were only significant for breed. Diet had a greater effect on the Bonsmara compared to the Nguni according to transcriptomic and phenotypic values. Transcriptomic values showed 3584 differential expressed genes (DEG) between the Bonsmara fed the two different diets, while only a difference of 288 DEG were observed between the Nguni fed the two different diets. Phenotypic values show a difference of 20 kg between the Bonsmara groups and only a 6 kg difference between the Nguni groups. Most DEG were involved with cellular processes and metabolic pathways. A total of 73 differentially expressed genes were observed between the diets across breeds. The genes that were involved in intramuscular fat deposition (CRHR2, NR4A3, MMD) were expressed on a higher level in the bulls on the low energy diet compared to bulls on the high energy diet. Genes that were involved in muscle deposition (PITX2, Leptin, AVP) was expressed higher in the bulls on the high energy diet. Comparing the breeds revealed that 2214 genes were differentially expressed between the Bonsmara and the Nguni. At the end of the feedlot trial a higher expression of marbling genes (SIRT, ND, ADIPOQ) were observed in the Nguni, however this expression was not observed in the marbling scores recorded. Several genes (ASIP, MOGAT, SNAI3) that were involved in fat deposition were upregulated in the Bonsmara. This suggests that the Nguni was still growing at the end of the feedlot trial while the Bonsmara had reached physiological maturity. Little literature could be found on some of the gene showing the highest expression in the groups such as GSTA3, TEX28 and TUBB3. Glutathione-s transferase alpha 3 (GSTA3) is linked to steroidal genesis and could therefore have an influence on myogenesis, however no confirming literature could be found.

Conclusion

The diet had a smaller effect on the Nguni bulls compared to the Bonsmara bulls as observed in the DEG and the carcass weight of the bulls. This might indicate that the Nguni is more adaptable to a variation in feed quality. The Bonsmara bulls had a higher meat yield on the high energy diet, however it seems as if the bulls on the low energy diet had a higher expression of the marbling genes. This is in contrast to the phenotypically higher marbling score. Further research needs to be done as this study had a small sample size (n=40). An extended feedlot period for Nguni cattle should be considered in future studies. This study provides reference data for differentially expressed genes in muscle of South African feedlot cattle.

Popular Article

A low energy feedlot diet may favour our indigenous breeds

Dina Linde1,2, Michiel Scholtz1,3 & Este van Marle-Koster2

Introduction

The phenotypes of animals vary due to differences in their genetics and environment. The expression of the genes of animals can also be influenced by their diet. In South Africa, Sanga cattle (Afrikaner and Nguni) are adapted to various environments and production systems. However, these cattle are mostly found in extensive production systems that make use of natural grazing. They are not preferred in the feedlot, as their smaller frame sizes compared to Bos Taurus types and crossbreds will lead to smaller carcass sizes. The traditional diet fed in the feedlot is a high energy diet, that includes maize and maize by-products. There are however, farmers that believe that a high energy diet does not suite the genetics of Sanga cattle and that these breeds would do better on a lower energy diet.

A study was done to find alternative strategies for Sanga cattle in the feedlot. A diet low in energy (10.9 MJ ME/kg) or a high energy diet (12.5 MJ ME/kg) was fed to Nguni and Bonsmara cattle for a period of a 120 days respectively. During the feeding period the animals were weighed and real time ultrasound scanned for meat quality characteristics such as eye muscle area (EMA). At slaughter, muscle samples were taken to analyse the gene expression data.

In terms of the feedlot data, no significant difference could be found between the diets with regard to live weight, eye muscle area (EMA), rib fat and rump fat in the Nguni. Breed differences were however found. Carcass weight and marbling did however show significant difference as well as an interaction between breed and diet.

Breed had a much bigger effect compared to diet, with 2214 differentially expressed genes (DEG) and 74 DEG, respectively. Of the  genes found differentially expressed between the breeds, several genes known to be involved in marbling (SIRT, ND, COX, ADIPOQ, SERPINF2) was expressed higher in the Nguni compared to the Bonsmara. However, the phenotypic marbling score was higher in the Bonsmara compared to the Nguni.

This lead to the suggestion that the Nguni needed more time (a longer feedlot period) for the expression of the marbling genes to show phenotypically. This is in contrast to the industry that perceives that the Nguni deposits fat too early in comparison to exotic and crossbreds. Sanga cattle tend to first deposit fat intramuscularly, which might be an adaptation mechanism for the harsh conditions in which these cattle lived. It seems, however, that the Nguni only begins depositing muscle tissue after a sufficient layer of fat is deposited. This might be the reason for backgrounding, which is common practise in the Nguni breed.

Between the Nguni fed the high energy diet and the Nguni fed the low energy diet, 288 genes were differentially expressed. The different levels of energy in the diets seem to result in different components being deposited. Various genes (PITX2, PAX, Leptin, AVP, OXT) involved in muscle deposition were upregulated in the bulls that received the high energy diet, compared to the bulls fed the low energy diet. This is also seen in the phenotypic results with the carcass weight of the bulls fed the high energy diet being higher compared to the bulls fed the low energy diet.

The difference in carcass weight is very small between the Nguni fed the high energy diet and the Nguni fed the lower energy diet (6 kg). However, genes that influence intramuscular fat deposition (SPARC, CRNR2, CHRND, NR4A3, MMD) were elevated in the bulls that received the low energy diet compared to the bulls that received the high energy diet. This was also not shown in the phenotypic traits. Extending the period of feeding may result in the bulls that received the low energy diet to express the phenotype.

It seems that the low energy diet suite the Sanga cattle better, when compared to the traditional high energy diet fed in the feedlots. This should be further investigated as this study had a relatively small sample size (n=40). Furthermore, as these animals are ruminants, it would be also interesting to study the rumen microbiome in another study of this kind.

Dina Linde1,2, Michiel Scholtz1,3 & Este van Marle-Koster2

1ARC – Animal Production, Private bag X2, Irene, 0062, South Africa; 2Department of Animal and Wildlife science, University of Pretoria, Pretoria, 0002, South Africa; 3Department of Animal, Wildlife and Grassland Sciences, University of the Free State, Bloemfontein, 9300; South Africa;

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Please contact the Primary Researcher if you need a copy of the comprehensive report of this project on :mmakgahlela@arc.agric.za

Gene expression: Nguni and Bonsmara

A gene-expression study on the growth performance of Nguni and Bonsmara cattle grown in a feedlot fed a high and low energy diet

Industry Sector: Cattle And Small Stock

Research Focus Area: Livestock production with global competitiveness: Breeding,physiology and management

Research Institute: Agricultural Research Council

Year Of Completion : 2019

Researcher: Dina Linde

The Research Team

TitleInitialsSurnameHighest QualificationResearch Institution
DrM.M.ScholtzPhDARC-AP
ProfEvan Marle-KosterPhDUP
MrsA. Theunissen MSc Vaalharts Research Station

Executive Summary

Introduction

In South Africa, Nguni cattle are one of the breeds found predominantly in extensive production systems. The Nguni is an indigenous cattle breed and is widely used in crossbreeding systems due to their high fertility and mothering ability. Nguni cattle are also commonly used in communal production systems. The majority of beef in South Africa is produced in feedlots using commercially formulated high energy diets, where preference is given to medium and large framed later maturing cattle that include British types and composites such as the South African Bonsmara. Due to the Nguni’s small frame and low meat yield when compared to British types it is nor preferred as a feedlot animals, however studies have shown that Nguni cattle produce high quality meat.

The veldt of South Africa has a varying degree of carrying capacity, however the occasional drought conditions have necessitated the use of alternative production systems such as feedlots for finishing cattle. The use of a lower energy diet in feedlots for indigenous cattle have been suggested and warrants investigation. Nutrigenomics is the study of the effect of nutrition on the genes of the animal by quantifying the gene expression. An improved understanding of the interaction between the nutritional environment and the genetics of the animal can lead to increased efficiency and production. Diets that are different in components or ingredients can result in different phenotypes in the animals. In this study the effect of two feedlot diets with different energy levels have been investigated using a transcriptome approach.

Objective statement

The objective of this study is to determine if there are gene expression differences between Nguni and Bonsmara cattle fed a low or a high energy diet. The gene expression of the cattle can determine the underlying differences in the reaction of the two breeds to the two different diets.

Project Aims

  1. To evaluate whether the Celtic mutation on the POLL locus is the causative mutation for polledness in Bonsmara and Drakensberger
  2. To perform a genome wide association study of the Polled and Scur genes based on phenotypic data and genotypic data from the GGP Bovine 150K SNP bead chip
  3. To apply sequence data available from the Bovine Genomics Program to finemap the suspected regions for the Polled and Scur genes

Results

Conclusion

Performance results showed a higher live weight, carcass weight and marbling score for all bulls fed the high energy diet compared to bulls fed the low energy diet. Only carcass weight and marbling score had significant difference in terms of diet (p < 0.05). Live weight, average daily gain, rib fat, rump fat and eye muscle area were only significant for breed. Diet had a greater effect on the Bonsmara compared to the Nguni according to transcriptomic and phenotypic values. Transcriptomic values showed 3584 differential expressed genes (DEG) between the Bonsmara fed the two different diets, while only a difference of 288 DEG were observed between the Nguni fed the two different diets. Phenotypic values show a difference of 20 kg between the Bonsmara groups and only a 6 kg difference between the Nguni groups. Most DEG were involved with cellular processes and metabolic pathways. A total of 73 differentially expressed genes were observed between the diets across breeds. The genes that were involved in intramuscular fat deposition (CRHR2, NR4A3, MMD) were expressed on a higher level in the bulls on the low energy diet compared to bulls on the high energy diet. Genes that were involved in muscle deposition (PITX2, Leptin, AVP) was expressed higher in the bulls on the high energy diet. Comparing the breeds revealed that 2214 genes were differentially expressed between the Bonsmara and the Nguni.

At the end of the feedlot trial a higher expression of marbling genes (SIRT, ND, ADIPOQ) were observed in the Nguni, however this expression was not observed in the marbling scores recorded. Several genes (ASIP, MOGAT, SNAI3) that were involved in fat deposition were upregulated in the Bonsmara. This suggests that the Nguni was still growing at the end of the feedlot trial while the Bonsmara had reached physiological maturity. Little literature could be found on some of the gene showing the highest expression in the groups such as GSTA3, TEX28 and TUBB3. Glutathione-s transferase alpha 3 (GSTA3) is linked to steroidal genesis and could therefore have an influence on myogenesis, however no confirming literature could be found.

Conclusion

The diet had a smaller effect on the Nguni bulls compared to the Bonsmara bulls as observed in the DEG and the carcass weight of the bulls. This might indicate that the Nguni is more adaptable to a variation in feed quality. The Bonsmara bulls had a higher meat yield on the high energy diet, however it seems as if the bulls on the low energy diet had a higher expression of the marbling genes. This is in contrast to the phenotypically higher marbling score. Further research needs to be done as this study had a small sample size (n=40). An extended feedlot period for Nguni cattle should be considered in future studies. This study provides reference data for differentially expressed genes in muscle of South African feedlot cattle.

Popular Article – A low energy feedlot diet may favour our indigenous breeds

Dina Linde1,2, Michiel Scholtz1,3 & Este van Marle-Koster2

1ARC – Animal Production, Private Bag X2, Irene, 0062, South Africa; 2Department Of Animal And Wildlife Science, University Of Pretoria, Pretoria, 0002, South Africa; 3Department Of Animal, Wildlife And Grassland Sciences, University Of The Free State, Bloemfontein, 9300; South Africa;

The phenotypes of animals vary due to differences in their genetics and environment. The expression of the genes of animals can also be influenced by their diet. In South Africa, Sanga cattle (Afrikaner and Nguni) are adapted to various environments and production systems. However, these cattle are mostly found in extensive production systems that make use of natural grazing. They are not preferred in the feedlot, as their smaller frame sizes compared to Bos Taurus types and crossbreds will lead to smaller carcass sizes. The traditional diet fed in the feedlot is a high energy diet, that includes maize and maize by-products. There are however, farmers that believe that a high energy diet does not suite the genetics of Sanga cattle and that these breeds would do better on a lower energy diet.

A study was done to find alternative strategies for Sanga cattle in the feedlot. A diet low in energy (10.9 MJ ME/kg) or a high energy diet (12.5 MJ ME/kg) was fed to Nguni and Bonsmara cattle for a period of a 120 days respectively. During the feeding period the animals were weighed and real time ultrasound scanned for meat quality characteristics such as eye muscle area (EMA). At slaughter, muscle samples were taken to analyse the gene expression data.

In terms of the feedlot data, no significant difference could be found between the diets with regard to live weight, eye muscle area (EMA), rib fat and rump fat in the Nguni. Breed differences were however found. Carcass weight and marbling did however show significant difference as well as an interaction between breed and diet.

Breed had a much bigger effect compared to diet, with 2214 differentially expressed genes (DEG) and 74 DEG, respectively. Of the  genes found differentially expressed between the breeds, several genes known to be involved in marbling (SIRT, ND, COX, ADIPOQ, SERPINF2) was expressed higher in the Nguni compared to the Bonsmara. However, the phenotypic marbling score was higher in the Bonsmara compared to the Nguni.

This lead to the suggestion that the Nguni needed more time (a longer feedlot period) for the expression of the marbling genes to show phenotypically. This is in contrast to the industry that perceives that the Nguni deposits fat too early in comparison to exotic and crossbreds. Sanga cattle tend to first deposit fat intramuscularly, which might be an adaptation mechanism for the harsh conditions in which these cattle lived. It seems, however, that the Nguni only begins depositing muscle tissue after a sufficient layer of fat is deposited. This might be the reason for backgrounding, which is common practise in the Nguni breed.

Between the Nguni fed the high energy diet and the Nguni fed the low energy diet, 288 genes were differentially expressed. The different levels of energy in the diets seem to result in different components being deposited. Various genes (PITX2, PAX, Leptin, AVP, OXT) involved in muscle deposition were upregulated in the bulls that received the high energy diet, compared to the bulls fed the low energy diet. This is also seen in the phenotypic results with the carcass weight of the bulls fed the high energy diet being higher compared to the bulls fed the low energy diet.

The difference in carcass weight is very small between the Nguni fed the high energy diet and the Nguni fed the lower energy diet (6 kg). However, genes that influence intramuscular fat deposition (SPARC, CRNR2, CHRND, NR4A3, MMD) were elevated in the bulls that received the low energy diet compared to the bulls that received the high energy diet. This was also not shown in the phenotypic traits. Extending the period of feeding may result in the bulls that received the low energy diet to express the phenotype.

It seems that the low energy diet suite the Sanga cattle better, when compared to the traditional high energy diet fed in the feedlots. This should be further investigated as this study had a relatively small sample size (n=40). Furthermore, as these animals are ruminants, it would be also interesting to study the rumen microbiome in another study of this kind.

Footer and Tags and Categorisation

Please contact the Primary Researcher if you need a copy of the comprehensive report of this project on :mmakgahlela@arc.agric.za

Genotype imputation for indigenous beef cattle

Genotype imputation as a genomic strategy for the South African Drakensberger beef breed

Industry Sector: Cattle And Small Stock

Research Focus Area: Livestock Production With Global Competitiveness: Breeding,Physiology And Management

Research Institute: Department Of Agriculture Forest And Fisheries (DAFF)

Year Of Completion : 2019

Researcher: Carina Visser

The Research Team

TitleInitialsSurnameHighest QualificationResearch Institution
DrM.M.ScholtzPhDARC-AP
ProfEvan Marle-KosterPhDUP
MrsA. Theunissen MSc Vaalharts Research Station

Executive Summary

The SA Drakensberger is a medium-framed breed with a sleek, black coat. Considering its history as one of the oldest indigenous breeds, its prominent role in the present beef industry and its potential for improving the beef cattle gene pool in the future; there is value in characterizing the SA Drakensberger on the genomic level. There has recently been interest in incorporating genomic information into selection strategies for this breed. Apart from the fact that the implementation of genomic technologies relies on diligent phenotyping efforts, accurate and complete pedigree recording; genomic selection also requires adequate SNP genotyping profiles (Meuwissen et al., 2001). The SA Drakensberger meets the requirements for genomic selection with 100% participation in SA Stud Book’s Logix Beef performance recording scheme as well as an extensive recorded pedigree profile (SA Stud Book, 2017). Theoretically, current EBVs can therefore be enhanced with the use of genomics if financial resources allow the generation of adequate high-density genotypic profiles. Imputation is a statistical methodology that relies on the genomic segments shared within a breed, or a group of genetically similar breeds, to predict genotypic information for SNPs that were not physically genotyped (Marchini et al., 2007). The main advantage of this methodology is the reduction in genotyping costs by allowing genotyping to be undertaken using lower density SNP panels. The utility of such low-density panels for applications such as genomic selection will depend on the accuracy with which un-genotyped SNPs can be imputed to higher density from such lower density panels. Even though imputation is integrated into routine genomic evaluations internationally, the utility of this methodology has not been evaluated for indigenous cattle resources. Considering that these breeds often have admixed genomes, applying imputation requires optimization for such breeds and this includes the SA Drakensberger.

Objective Statement

The objective of this research project was to comprehensively study the validity of genotype imputation, from lower-density single nucleotide polymorphism (SNP) panels to higher density, for the economically-important SA Drakensberger beef cattle breed towards cost-effectively implementing genomically-enhanced breed improvement strategies such as genomic selection for this indigenous breed in the future.

Project Aims

  1. To evaluate whether the Celtic mutation on the POLL locus is the causative mutation for polledness in Bonsmara and Drakensberger
  2. To perform a genome wide association study of the Polled and Scur genes based on phenotypic data and genotypic data from the GGP Bovine 150K SNP bead chip
  3. To apply sequence data available from the Bovine Genomics Program to finemap the suspected regions for the Polled and Scur genes

Results

Results generated from the first part of this study indicated that differences in genomic characteristics such as minor allele frequency (MAF), linkage disequilibrium (LD) and runs of homozygosity (ROH) exists between chromosomes. Mean genome-wide MAF was, for example, estimated to be 0.26 with chromosome-specific MAF ranging from 0.24 (Bos Taurus Autosome; BTA14) to 0.28 (BTA21). This was supported by the proportion of low-MAF (< 5%) SNPs estimated, which indicated 16.0% of SNPs to be classified as low-MAF SNPs on BTA14. The inter-SNP LD was generally weak, ranging from mean r²=0.11 (BTA28) to r²=0.17 (BTA14) for SNPs separated by≤1Mb and r²=0.20 extended only up to<30 kb. LD was weaker between SNP pairs including low-MAF SNPs. Consensus ROH segments were identified and the most prevalent of these occurred on BTA14 and was identified in ∼23% of the sampled population. The ROH length characteristics furthermore pointed towards more ancient inbreeding, reflecting known historic bottleneck events.

For the second and main object preliminary results were generated to understand the necessary dynamics, in terms of size and composition, of an appropriate sub-population to use as a reference for estimation of haplotypes to be imputed from. Initial results indicated that a larger reference population would improve imputation accuracy. For example, it was observed that a 4% increase in imputation accuracy could achieved when the ratio of reference:test population was 90:10 versus 75:25; imputation accuracy improved from 0.981 (range: 0.895-0.997) to 0.985 (range: 0.905-0.996) when the former versus the latter scenario was used. It was further observed that using a reference population consisting of animals with closer genetic relatedness to the test population would also improve imputation accuracy. A strong correlation of 0.817 (P<0.001) was observed between the mean genetic relatedness of animals in the test population, with animals in the reference population, and their resulting imputation accuracy.

This was supported by estimates showing mean imputation accuracy of 0.994 as opposed to 0.982 for animals that had both as opposed to no parents in the reference population. The influence of using different low-density SNP panels, consisting of varying density and SNP content, on more specifically animal-wise and SNP-wise imputation accuracy was then determined. Animal-wise imputation accuracy improved when the SNP density of the lower-density panel improved; correlation-based imputation accuracy ranged (minimum to maximum) from 0.625-0.990, 0.728-0.994, 0.830-0.996, 0.885-0.998 and 0.918-0.999 when 2 500, 5 000, 10 000, 20 000 and 50 000 SNPs when SNPs were randomly chosen. The variation between animals, as well as the degree of improvement in accuracy, became smaller with increasing SNP density. Improvements of 0.043 units were seen when SNPs were doubled from 2 500 to 5 000 SNPs, as opposed to an improvement 0f only 0.007 units when SNPs were (more than) doubled from 20 000 to 50 000 SNPs. Selection of SNPs based on both MAF and LD attributes proved to be the best selection strategy to maximize imputation accuracy and random selection produced the worst imputation accuracy. Mean imputation accuracy exceeding 97% (less than 3% errors) could be achieved by using only 5 000 SNPs when this method of selection was used; using other methods of selection this accuracy was only achieved when double the amount of SNPs (10 000) was used. In terms of SNP-wise imputation accuracy, accuracy estimates were lower for SNPs located on the chromosomal extremes and if the MAF of these SNPs was low. For chromosome 19, which was the chromosome with the worst mean imputation accuracy for most scenarios, SNPs located in the first (n=32), middle (n=42) and last (n=64) 1Mb of this chromosome, for example, had mean SNP-wise correlation-based accuracy measures of 0.640, 0.810 and 0.577. The difference in SNP-wise imputation accuracy moreover was 0.071 between SNPs in the highest (0.4<MAF≤0.5) and lowest MAF bins (0.01<MAF≤0.1); imputation accuracy was better for SNPs with higher MAF.

Results generated to achieve the final aim of this study are still preliminary and in the process of being analyzed. Preliminary results, however, shows strong correlations between conventionally-estimated EBVs and GEBVs, with the inclusion of genomic information being advantageous to breeding value estimation. The difference in GEBV accuracies estimated from true- versus imputed genotypes was small thus far, depending on the per animal imputation accuracy; the discrepancy is expected to be larger for animals with lower mean imputation accuracy.

Conclusion

The variation observed in genomic characteristics such as MAF and LD conformed to expectations and supported previous research suggesting that the SA Drakensberger is a composite breed with an admixed genome and heterogenous genomic architecture. This variation across the genome allowed variation in imputation accuracy between different chromosomes and genomic regions within chromosomes to be pre-empted. Genotype imputation is a valid genomic strategy for the SA Drakensberger breed and this study concluded that a genotyping panel consisting approximately 10 000 SNPs would suffice in achieving less than 3% imputation errors. Results presented further suggests that if such a panel were to be designed, that the SNPs considered for inclusion would have to be selected based on selection criteria, such as MAF and LD, specific to the SA Drakensberger breed. Considering that no Sanga-specific genotyping panel currently exists, it would be recommended that these SNPs be chosen from re-sequencing efforts, i.e. from a pool of SNPs that are identified as specific to the breed, and not necessarily from a pool of SNPs that are available on taurine- and/or indicine-derived genotyping platforms. The reason for this is that low MAF, because of ascertainment bias, was the most influential factor affecting  achievable imputation accuracy and therefore poses a concern. This study showed that it will be a valid strategy to integrate genotype imputation routinely into future genomic evaluation pipelines for the SA Drakensberger breed as imputation errors are expected to have a negligible effect on resulting GEBV accuracies. Finally, the inferences made from this study may be transferable to other Sanga breeds and may provide guidelines for consideration in future genomic endeavours for these breeds.

Popular Article

Genotype imputation as a genomic strategy for the South African Drakensberger beef breed by SF Lashmar, C Visser and FC Muchadeyi

The Drakensberger is a medium-framed breed of cattle with a sleek, black coat. It is believed to be one of South Africa’s oldest Sanga breeds and was developed from an ancestral population of cattle that was first sighted in 1659 in the Bredasdorp area of the Western Cape province. These cattle ancestors, also described as black in colour, belonged to native tribes and were crossbred with Dutch cattle of the Groningen breed, which were imported by European settlers in the 1700s. By this introduction of European Bos Taurus genetics, the development of the SA Drakensberger was initiated. The modern SA Drakensberger, as it is presently known, was however only recognized in 1947 when the SA Drakensberger Breeders’ Society was established. The breed therefore underwent a process of development that spanned centuries, whereby it withstood many harsh challenges in its history and this has led to the hardy breed it is today. Nicknamed the “profit breed”, the Drakensberger is both adapted and highly productive within SA’s beef producing environment and has a long history of diligent performance recording. In fact, it was the first breed to receive estimated breeding values (EBVs) using best linear unbiased prediction (BLUP) methodology, as performance testing was made compulsory to all breeders since 1980. Participation by Drakensberger breeders in SA Stud Book’s Logix Beef performance recording scheme is still at 100% today (SA Stud Book, 2017) and extensive pedigree records are available. Considering all of this, there has recently been interest in further enriching breed improvement strategies for the SA Drakensberger with genetic information in the form of genomic selection.

To implement genomic selection can significantly improve the efficiency of selection processes, and hence accelerate genetic progress, for the SA Drakensberger breed. This selection strategy, however, requires large numbers of animals to be “tested”, referred to as “genotyped”, for a high density of single nucleotide polymorphism markers (SNPs) in order to make reliable scientific deductions and to produce accurate genomic estimated breeding values (GEBVs) for farmers or breeders. From experience, international researchers have suggested 1 000 animals to be included in a training- or reference population to deduce the prediction equations that will be used in calculating GEBVs for selection candidates. Generating the amount of data to fulfill the number of genotyped animals necessary in the training population alone can become unfeasibly expensive, especially in developing countries, considering that the cost of genotyping an animal for about 150 000 SNPs is currently approximated at ZAR200 per animal. The cost of genotyping can, however, be alleviated by genotyping animals with SNP chips containing lower numbers of SNPs and “imputing” to higher density.

In statistical terms, imputation refers to the process of replacing missing data with substituted values. In the context of genomics, genotype imputation refers to a method of predicting SNP genotypes for SNPs that are either missing or were not physically genotyped. The genotypes are predicted based on patterns observed from a more complete data set of SNPs that are available for a group of animals that are representative of a specific breed. Consider for example that we have a young animal tested for 10 000 markers (which would be referred to as a “low-density SNP panel”) and the parents of this animal are tested for 100 000 markers (which would be referred to as a “high-density SNP panel”). Given the genetic relationship between the parents and the offspring, and the fact that these animals share large parts of the DNA, we can “impute” or infer the “missing” 90 000 markers for the young animal by making certain statistical assumptions using the principles of genetics. On a larger scale: if a “reference” population (consisting of older, high-impact animals with many offspring in the national herd) is genotyped for a high density of genetic markers (let’s say 150 000 SNPs) and a “test” population (younger, commercial animals in the national herd) is genotyped for a smaller subset of these SNPs (let’s say 50 000 SNPs), the 150 000-SNP genotype profile can be imputed for the “test” animals. The prerequisite is, however, that the animals in the reference- and test populations need to be related in some way, in other words they need to share underlying genetic patterns. These shared patterns can be used to fill in the gaps in SNP information. The imputed SNPs i.e. the 100 000 “missing” SNPs not included on the lower density panel, can however only be used in downstream application such as genomic selection if they were accurately imputed or assigned otherwise inaccurate scientific deductions will be made.

Imputation is now almost routinely included in genomic evaluation processes abroad because this methodology has been optimized, through trial-and-error and studying the factors influencing “imputability” of SNPs, for the most popular international breed. To be able to make use of this methodology within the South African beef industry, and more specifically for local breeds, requires a process of validation and this has not yet been performed for breeds such as the SA Drakensberger. The aim of the study was therefore to comprehensively evaluate genotype imputation for the SA Drakensberger breed so that it can be routinely applied in a GS pipeline.

The first step in the process of validation was to investigate the genomic characteristics of the breed. The genomic characteristics of SNPs have previously been shown to have an influence on the accuracy with which genotypes can be imputed. The genome of each animal is subdivided into different structures, called chromosomes, and on each of these chromosomes differences may furthermore exist between different DNA segments depending on the origin of these segments i.e. from which animal in the pedigree that part of DNA was inherited. As a result of the history of the SA Drakensberger, the genomes of animals belonging to this breed are expected to be composite i.e. containing genomic segments from both Bos taurus and Bos indicus. Certain parameters can provide more information on the SNPs within each of these segments and these include the minor allele frequency (MAF) and linkage disequilibrium (LD). The MAF gives an indication of the value of a specific SNP to the breed in question; if the MAF is high, it is an indication that both alleles of the SNP are present amongst the animals in the breed i.e. the SNP is informative. The LD provides an indication of the relationship between adjacent SNPs; if SNPs are in high LD, a “block” of SNPs can be inherited together and animals share larger parts of the genome with one another. This improves the ability of SNPs in these regions to be imputed. The software, Plink, was used to quantify these parameters on a per chromosome basis. Results showed that there was variation in these genomic characteristics between different chromosomes and this led us to expect differences in imputation accuracy between chromosomes.

The logical next step was to calculate the actual achievable imputation accuracy. The accuracy of imputation was calculated for imputation from several custom-derived low-density panels. To achieve this objective, different sets of SNPs were extracted from the SNP data available (150K SNP data) to mimic possible lower-density SNP panels. Panels containing 2 500, 5 000, 10 000, 20 000 and 50 000 SNPs were tested. The choice of SNPs to be included on each of these panels were based on certain SNP selection strategies i.e. different criteria were used to select the SNPs. The different strategies of selection included 1) selecting SNPs randomly, 2) selecting SNPs so that they were approximately evenly spaced, 3) selecting only SNPs with the highest MAF and 4) selecting SNPs based on a score combining its MAF and relationship to neighbouring SNPs (LD). Imputation was done using a software called FImpute and our findings suggested that a low-density SNP panel consisting of approximately 10 000 SNPs that were selected based on their MAF and LD information will be optimal. Using such a panel resulted in less than 3% imputation errors.

The final step was to determine the influence of mistakenly imputed SNPs on the accuracy of GEBVs and hence on genomic selection. The “single-step” approach to GS was tested using software called Mix99. Breeding values were calculated using 1) only pedigree information (traditional), 2) using true genotypic data (GEBV) and 3) using imputed genotypic data (imputed GEBV). These different breeding values were compared to determine whether imputation accuracy had an effect. Our preliminary findings suggest that the inclusion of genomic data is advantageous and that there is a minimal effect on GEBV accuracy estimates if imputation accuracy was good.

To conclude, results from this study indicated that imputation is a valid genomic strategy towards cost-effectively implementing GS for an indigenous breed such as the SA Drakensberger despite the uniqueness and complexity of its genome. The outcomes of this study may moreover be transferable to other Sanga breeds and may provide a set of guidelines for genomic studies requiring imputation in the future. Even though this study has shown that a more affordable lower-density panel can be developed from choosing SNPs with high MAF in indigenous breeds from currently available genotyping platforms, it would be invaluable for future genomic endeavours to develop a Sanga-specific panel using breed-specific SNPs identified from re-sequencing efforts.

Please contact the Primary Researcher if you need a copy of the comprehensive report of this project on :mmakgahlela@arc.agric.za

Genetic markers for Haemonchus contortus in sheep


Genome wide association study to identify genetic markers associated with resistance to Haemonchus contortus in sheep

Industry Sector: Cattle And Small Stock

Research Focus Area: Livestock production with global competitiveness: Breeding,physiology and management

Research Institute: Department of Agriculture Forerst and Fisheries (DAFF)

Year Of Completion : 2019

Researcher: Margeretha Snyman

The Research Team

TitleInitialsSurnameHighest QualificationResearch Institution
DrCVisserPhDUP
DrPSomaPhDARC
DrFCMuchadeyiPhDARC-BTP
DrADFischerBVScQueenstown PVL
MrNJDlamimiMScARC

Aims Of The Project

  1. Collect blood samples and data on resistance to H. contortus on a sheep flock on a farm with major Haemonchus anthelmintic resistance problems
  2. Analyse the data, estimate genetic parameters and develop a protocol for recording of resistance to H. contortus under SA conditions
  3. Conduct various genomic studies to identify genetic markers linked to resistance

Executive Summary

The objective of this study was to compare four commonly used growth promotants in a commercial sheep feedlot. The steroidal growth promotants chosen for this trial were Ralgro (zeranol), Revalor G (Rev G; TBA/oestrogen- 17β), Revalor H (Rev H; TBA/oestrogen- 17β) and Zilmax® (zilpaterol hydrochloride). The growth promotants were compared with one another and within three sex groups, namely ewe, ram and wether (castrates), to determine which molecule or combination of molecules, if any, had the most benefit and profitability when measured against a control group.  Sheep were stratified based on initial weights and then randomly allocated to treatment groups in a completely randomised control study. All sheep originated from the same farm, and they were of  similar age, breed,  transport method,  processing method, feed (the only difference being  the groups receiving Zilmax® during the last 18 days of feeding, making provision for 3 days withdrawal), weather conditions, housing and time on feed. A time constant termination date was used in this study, in order to measure the performance of lambs in treatment groups over time.

The issue of resistance of internal parasite species to worm remedies is widespread throughout South Africa and the world and affects all small stock farmers. Especially Haemonchus contortus causes major losses among sheep in the summer rainfall regions in South Africa. For some areas, farming with animals resistant to nematodes seems to be the only solution in the long run. Genetic variation in resistance to nematode infestation in sheep, based on faecal egg count (FEC) as a criterion, has been reported for various breeds. Successful breeding programs for resistance have been reported for Australian and New Zealand sheep. There are, however, no large scale active breeding programs for resistance in South African sheep.

Because of the difficulty of routinely collecting phenotypic data associated with resistance to internal parasites, suitable data sets for the estimation of genetic parameters for resistance against H. contortus are scarce in South Africa. The history of and recent selection practices followed in the Wauldby Dohne Merino flock makes it an ideal resource for research into resistance to H. contortus in South African sheep.

In 2011, a project aimed at selection for resistance against H. contortus was started on the Wauldby Dohne Merino flock. Apart from full pedigree information, data on faecal egg counts (FEC), Famacha© score (FAM) and body condition score (BCS) were collected annually on all lambs born since 2011. FEC, FAM and BCS of all lambs were recorded from the middle of January onwards. FAM was recorded weekly and FEC and BCS every 14 days until the end of June when Haemonchus challenge had decreased. Lambs were only drenched when they had a FAM of 2.5 or more, in conjunction with a BCS of less than 1.5. Any lamb that was drenched was recorded as “Dosed” and those lambs that did not require any drenching as “Not dosed”. Replacement rams and ewes were selected from the animals that did not need dosing on the basis of a selection index incorporating FEC, FAM and BCS.

Data were analysed to compile protocols for selection against resistance to H. contortus in SA sheep.

Objective

The objective of this study is to identify of genetic markers for resistance to Haemonchus contortus in SA sheep, which could be included in the selection plan. See aims for the objectives of the three phases of the projects.

Results

The Dohne Merino lambs at Wauldby were subjected to severe H. contortus challenge. This is evident from the very high maximum FEC values recorded, even at the last two recordings during June. FEC ranged from 0 to 54100 epg among recordings over the trial period. Despite the high FEC challenge, mean FAM was still low, which is indicative of the high resilient status of the Wauldby flock. Dosing status had a significant effect on FAM, BCS and FEC. The Not dosed lambs had lower FEC, higher BCS and lower FAM compared to the lambs that were dosed. FAM increased and BCS decreased as the number of treatments received increased.

During the first year of the trial, 33% of the ram lambs and 45% of the ewe lambs were not dosed throughout the annual recording period. These percentages increased annually until 77% of the ram lambs and 82% of the ewe lambs did not need any drenching in 2016. One of the most significant results of the trial to date was the increase in percentage offspring of the sires that did not need drenching. The best performing sire used during 2011 had 53% lambs that did not need drenching, while 74% lambs of the poorest sire needed anthelmintic treatment. In 2016, the best performing sire had 97% lambs that did not need drenching, while 37% lambs of the poorest sire needed anthelmintic treatment.

FAM had a high genetic correlation and moderate phenotypic correlation with FEC. The highest heritability and repeatability of the resistance traits were recorded for BCS, but BCS had a moderate genetic and a low phenotypic correlation with FEC. In this study, BCS of the lamb, in combination with FAM, was considered in the decision whether to treat the lamb or not. However, due to the low phenotypic correlation between BCS and faecal egg count, BCS of an animal by itself is not an accurate indication of the existing level of H. contortus infection. The low phenotypic correlation estimated between BCS and FEC in this study is also indicative that other factors apart from worm load influence BCS in growing lambs.

Body weight, fleece weight and coefficient of variation of fibre diameter had favourable genetic correlations with FEC, FAM and BCS, while fibre diameter and staple length were unfavourably correlated with FEC. Inclusion of FEC in the selection protocol should therefore not adversely affect body weight and wool production.

As far as the application of FAM as criterion for the selection of resilient or resistant sires and dams is concerned, it should be used in combination with other resistance indicators such as FEC. During the high challenge summer rainfall period FAM will be recorded weekly or bi-weekly, therefore more FAM recordings will be available for inclusion in a final selection protocol. Due to its favourable genetic correlations with FEC and the production traits, and the fact that BCS of the Not dosed lambs in this study was higher than BCS of the Dosed lambs, BCS could be included in the selection protocol to be used for selection against resistance to H. contortus. BCS and FEC can be recorded at the beginning (January), at the peak (middle to end of March) and towards the end of the H. contortus season (June). Lambs that did not require any anthelmintic treatment up until selection age could be selected on the basis of a selection protocol incorporating these FEC and BCS recordings, together with all the recorded FAM.

The following selection indices, including FEC with or without incorporating FAM and BCS, were compiled / suggested:

  • SI1 = (-1 x FEC169 -1 x FAM +1 x BCS169) +10
  • SI2 = (-1 x FEC169 -1 x FAM) +10
  • SI3 = (-1 x FEC169) +10

As far as the genomic study is concerned, there were definite genetic differences among the animals in the flock and three genetic clusters were observed. Animals in the most resistant cluster had significantly lower FEC, lower FAM and higher BCS than the animals in the other two clusters. The sires of the animals in the resistant cluster also had more favourable EBVs for FEC and FAM.

The results of the genomic study further indicated the possibility of selection signatures on the same chromosomes in the Wauldby Merino animals than those on which QTL for faecal egg count and H. contortus FEC are reported in the sheep genomic databases. These will be further investigated in a comprehensive GWAS study.

Conclusion

The results indicate that progress was made when selecting for resistance to H. contortus in the Wauldby Dohne Merino flock.
There is genetic variation in host resistance against H. contortus in the Wauldby Dohne Merino flock. Sires in one genetic cluster are highly resistant and can be used in a breeding program to develop sheep that are resistant to H. contortus infections.
Moderate heritabilities and genetic correlations were estimated for and among FAM, BCS and FEC in this flock. Except for the unfavourable genetic correlation with fibre diameter, no detrimental genetic correlations between the resistance and production traits were estimated.
These results were used to develop protocols for selection for resistance to H. contortus under South African conditions. The developed protocols need to be validated on various farms before they can be implemented on a wider scale.

Popular Article

PROTOCOL FOR SELECTION FOR RESISTANCE TO HAEMONCHUS CONTORTUS IN SOUTH AFRICAN DOHNE MERINO SHEEP

Authors: M.A. Snyman, Grootfontein Agricultural Development Institute, Private Bag X529, Middelburg (EC), 5900 GrethaSn@Daff.Gov.Za. A.D. Fisher, Queenstown Provincial Veterinary Laboratory, Private Bag X7093, Queenstown, 5320 Alan.Fisher@Awe.Co.Za

INTRODUCTION

The issue of resistance of internal parasite species to worm remedies is widespread throughout South Africa and the world and affects all small stock farmers. Haemonchus contortus is the most important parasite and causes the most losses among sheep in the summer rainfall regions of South Africa. For some areas, farming with animals resistant to nematode infestation seems to be the only solution in the long run.

Because of the difficulty of routinely collecting phenotypic data associated with resistance to internal parasites, suitable data sets for the estimation of genetic parameters for resistance against H. contortus are scarce in South Africa. The history of and recent selection practices followed in the Wauldby Dohne Merino flock makes it an ideal resource for research into resistance to H. contortus in South African sheep. The farm Wauldby is located in the Stutterheim district in the Eastern Cape Province in a high summer rainfall area (800 mm annually). Wauldby has a well-documented history of heavy H. contortus challenge and H. contortus resistance and in the past the farm was used for several trials relating to resistance of H. contortus to anthelmintics.

In 2011, a project aimed at selection for resistance against H. contortus was started on the Wauldby Dohne Merino flock. Data on faecal egg counts (FEC), Famacha© score (FAM) and body condition score (BCS) were collected annually on all lambs born since 2011. FEC, FAM and BCS of all lambs were recorded from the middle of January onwards. FAM was recorded weekly and FEC and BCS every 14 days until the end of June when Haemonchus challenge had decreased. Lambs were only drenched when they had a FAM of 2.5 or more, in conjunction with a BCS of less than 1.5. Any lamb that was drenched was recorded as “Dosed” and those lambs that did not require any drenching as “Not dosed”. Data on all lambs were recorded throughout until the end of June, irrespective whether they needed drenching or not.

Selection in the flock was aimed at increasing resistance to H. contortus, while maintaining reproductive performance, body weight, wool weight and fibre diameter and improving wool quality traits. Selection for the production traits was done on the basis of selection indices and BLUP of breeding values for the mentioned traits measured at 14 months of age. Selection for resistance to H. contortus was based on a selection index incorporating FEC, FAM and BCS.

A selection line, in which the most resistant ewes were mated to the most resistant rams, has been established in 2012 as part of the project. These animals were run together with the rest of the flock animals, except during mating. Only ram and ewe lambs that had never been drenched were considered for selection into the selection line. Three rams and about 20 young ewes were selected annually for the selection line since 2012. Currently the selection line consists of 120 ewes, which are mated in three groups of 40 ewes each to the three most resistant rams in single sire mating camps. All progeny born in both the selection line and the rest of the flock were evaluated together. Rams and ewes performing the best in terms of resistance could be selected for the selection line, whether their parents came from the selection line or the other flock animals.

RESULTS TO DATE

The data collected over the years were used to estimated heritabilities and genetic correlations among the traits. Moderate heritabilities for and favourable genetic correlations among FEC, FAM and BCS were estimated. It will be impractical and expensive to record FEC every second week under commercial farming conditions. A combination of FEC recordings at the beginning, at the peak (middle to end of March) and towards the end of the season, proved to be the best alternative for selection purposes.

FAM had a high genetic correlation with FEC. In this study on-going first stage selection was done by identifying animals unsuitable for inclusion in the selection line on the basis of FAM and BCS. Identifying animals that required anthelminthic treatment according to FAM will ensure that only truly susceptible animals are identified and destined to be culled. Resilient as well as resistant animals will not be targeted and will remain untreated and available for final stage selection. As far as the application of FAM as criterion for the selection of resilient or resistant sires and dams is concerned, it should be used in combination with other resistance traits such as FEC. During the high challenge summer rainfall period, FAM will be recorded weekly or bi-weekly, therefore more FAM recordings will be available for inclusion in a final selection protocol.

The highest heritability and repeatability of the resistance traits were recorded for BCS, but BCS had a moderate genetic correlation with FEC. In this study, BCS of the lamb, in combination with FAM, were considered in the decision whether to treat the lamb or not. However, due to the low phenotypic correlation between BCS and FEC, BCS of an animal by itself is not an accurate indication of the existing level of H. contortus infection. By the time BCS is affected by H. contortus per se, the animal would have shown other clinical signs of Haemonchosis. Due to the fact that BCS of the Not dosed lambs in this study was higher than BCS of the Dosed lambs, BCS was included in one of the selection indices. BCS and FEC can be recorded at the beginning (January), at the peak (middle to end of March) and towards the end of the H. contortus season (June), as mentioned above.

SELECTION INDICES (SI)

The following selection indices, including FEC with or without incorporating FAM and BCS, were compiled / suggested:

SI1 = (-1 x FEC169 -1 x FAM +1 x BCS169) +10

SI2 = (-1 x FEC169 -1 x FAM) +10

SI3 = (-1 x FEC169) +10

For all the animals on which data were collected to date in the Wauldby flock, these three SI options were calculated. These three selection indices were evaluated on the data of the Wauldby animals born in 2015, 2016 and 2017. When the data of all the available animals were evaluated, basically the same animals will be selected with SI1 and SI2. Where selection is based only on FEC (SI3), somewhat different animals were selected in some years than when FAM and BCS were included in the selection index.

When the data of only a selected proportion of 5% rams and 25% ewes were evaluated, again basically the same animals will be selected with SI1 and SI2. However, rather different animals will be selected when only FEC was used as selection criteria. What this implies is that selection should preferably be done on SI1 or SI2. FAM should be included together with FEC, but the inclusion of BCS is optional.

PROTOCOL FOR SELECTION FOR RESISTANCE AGAINST H. CONTORTUS

The following protocols can be followed for selection for resistance against H. Contortus in stud and commercial flocks respectively.

Stud animals

Follow the normal internal parasite control program before weaning, i.e. routine pooled FEC.If the lambs needed to be drench before weaning, FEC, FAM and BCS of all the lambs could be recorded before drenching. All lambs could then be drenched after data collection.After weaning, recording of individual ram and ewe lambs should take place.FAM should be recorded every 14 days until the end of June when Haemonchus challenge has decreased.Individual FEC and BCS should be recorded at the beginning (January) and twice during the summer season (March and May).Lambs should only be drenched when they have a FAM of 2.5 or more.Any lamb that was drenched should be noted and culled.Replacement rams and ewes should be selected from the animals that did not need dosing on the basis of one of the above selection indices incorporating FEC and FAM, with or without BCS.Adult ewes should only be drenched on FAM (Targeted selective treatment). Note and cull ewes that need repeated drenching.Evaluate existing sires on the performance of their offspring.If rams are bought, buy only rams resistant to internal parasites.

Commercial animals

Follow the normal internal parasite control program before weaning, i.e. routine pooled FEC.After weaning only ewe lambs should be recorded.FAM should be recorded every 14 days until the Haemonchus challenge has decreased.FEC should be monitored through monthly pooled FEC samples.Lambs should only be drenched when they have a FAM of 2.5 or more.Any ewe lamb that was drenched should be noted and lambs that needed 2 or more drenchings should be culled.Adult ewes should only be drenched on FAM (Targeted selective treatment). Note and cull ewes that need repeated drenching.Individual FEC of all adult rams should be recorded during the peak Haemonchus season. Before faecal sampling, the rams should not receive any anthelmintic treatment for at least 3 to 4 weeks. Cull rams with too high FEC.Buy only rams resistant to internal parasites.

CONCLUSIONS

Progress was made when selecting for resistance to H. contortus in the Wauldby Dohne Merino flock. These results were used to develop protocols for selection for resistance to H. contortus under South African conditions. The developed protocols need to be validated on various farms before they can be implemented on a wider scale.

 ACKNOWLEDGEMENTS

Mr Robbie Blaine and the personnel at Wauldby for their valuable contribution in the execution of the project and RMRD-SA for funding of the project.

Conclusions

Please contact the Primary Researcher if you need a copy of the comprehensive report of this project –

Greta Snyman on GrethaSn@daff.gov.za

Innovative management for beef productivity

Innovative management to increase beef productivity in South Africa : Phase II

Industry Sector: Cattle and Small Stock

Research focus area: Livestock production with global competitiveness

Research Institute: ARC – Animal Production Institute

Researcher: Ms SM Grobler MSc (agric) Animal Science

Research Team:

TitleInitialsSurnameHighest Qualification
DrM.M..ScholtzDSc (Agric) Animal Science
DrA.MaiwashePhD (Agric) Animal Science
MrP.J.J.BreytenbachMSc (Agric) Animal Science
DrJ.P.C.GreylingPhD (Agric) Animal Science
DrF.W.C.Neser
PhD (Agric) Animal Science

Final report approved: 2016

Aims of the project

  • To establish if synchronization can lead to an increase in the total mass of calves weaned from a limited calving season, most likely by decreasing the days to calving, but also by increasing number of calves born
  • To establish if breeding replacement heifers at 14 months have an economic advantage over breeding heifers at 26 months in term of reproductive performance
  • To establish the impact of the two different grazing strategies on veld condition, grass species composition and basal vegetation cover over time

Executive Summary

This report presents the final results of the combined first and second phase of the project: Innovative management for improved productivity: Beef.  The motivation for this project was that the South African beef market has changed with a need for livestock research and development to think in terms of a livestock systems approach.  This entails the combination of sound natural resource utilization, forage management and reproduction management to ensure a sustainable production enterprise over time through the allocation of limited resources.

When the project was planned it was clear that a period of 3 years was not enough to evaluate extremely valuable long term effects on herd life and veld condition; however, the RMRD-SA only fund projects for a maximum of 3 years and therefore a second application was approved to cover the remaining two-year project period.

South Africa is still a net importer of beef. Therefore, by increasing off take in the beef sector, South Africa can move towards self-sufficiency. With fertility being regarded as one of the main components influencing total beef herd efficiency, it is essential that the quoted calving percentage of 62% in the commercial beef sector of South Africa must be improved.  If the long calving seasons can be shortened and the calving percentage increased, more and heavier calves with a more uniform age can be weaned.  Cows that calve early also have a better chance of conceiving in the next breeding season and are generally seen as the more fertile animals

Development, production and quality of replacement heifers is a crucial component in the extensive beef production system.  In general, beef heifers are managed to calve for the first time at three years of age, but in some cases mating of heifers at one year of age have been advocated. 

All extensive beef production systems in South Africa are dependent on natural veld and it is well documented that veld condition have a huge influence on a number of beef production parameters. Studies conducted on natural veld have concentrated mainly on aspects that affect herd efficiency, including calving percentage, pre-weaning growth and supplementation of cows and calves.  However, none of the studies focused on the reproduction performance of beef cattle mated naturally after synchronization, heifer age at breeding and effect of grazing system on veld condition.

The aim of the study was to evaluate: the effect of estrous synchronization followed by natural mating on the calving percentage and calving distribution of multiparous beef cows and heifers; effect of breeding heifers at either 14 months or 26 months of age and the evaluation of a high utilized grazing system and controlled selective grazing on veld condition and animal performance.  The effects of climate on cow-calf production characteristics over time was also evaluated.

The study was conducted from 2009 to 2015 at the Roodeplaat experimental farm (REF) of the ARC-Animal Production Institute (25°34’11.27’’S; 28°22’05.36’’E) on 900 ha of natural rangeland described as Sourish Mixed Bushveld.  The experimental herd (n=92) was divided in four sub-herds consisting of 23 cows each at the beginning of the project in 2009.  It was ensured that the four sub-herds were as uniform as possible at the beginning of the project e.g. age, weight, previous number of calves. Within each sub-herd, 50% of the cows and heifers were synchronized prior to the commencement of the breeding season. Two sub-herds were subjected to high utilized grazing and two sub-herds were subjected to controlled selective grazing. The two grazing systems were related to the use of 30% or 60% of the available grass dry matter.  Half the heifers were mated at 14 months and the other half at 26 months.

Results from this study indicated that calving percentage and body condition score did not differ significantly (P=0.54) between cows that was either synchronized or not synchronized followed by natural mating.  However, estrous synchronization prior to natural mating did influence the average days to conception with synchronized cows calving earlier, except for 2012 in the calving season.  Over the six-year project period 15% more cows from the synchronized group conceived within 293 days after the onset of the breeding season. Calves from the synchronized cows weaned on average 5kg heavier than the cows that were not synchronized although this difference was not significant.

Conception rates of heifers mated at 26 months were significantly (P<0.05) higher than heifers mated at 14 months of age.  It would seem that it may be more viable to breed Bonsmara heifers in an extensive production system in the Sourish Mixed Bushveld region at 26 months of age for the first time.  Synchronization of 14 month old heifers did not improve conception rate over 14 month old heifers bred naturally.  However, the calving percentage of synchronized heifers bred at 26 months was 6% higher than the non-synchronized heifers.

Almost no veld condition change was recorded except for veld condition scores for both controlled selective grazing and high utilization grazing.  In addition, the results indicate a tendency that high utilization grazing improved veld condition score and grass species composition over that of controlled selective grazing, but the duration of the study is too short to make a definite conclusion on the effect of grazing strategy on veld condition.

It was also shown that grazing strategy did not have a significant influence on cow weight and calf growth over the six-year period, indicating that both grazing strategies are sustainable in the Sourish Mixed Bushveld if carrying capacity is adhered to. 

With the significant differences between years (P ≤ 0.05) for calving percentage, cow weight at calving, cow weight at weaning, calf birth weight, calf weaning weight and body condition score over the six-year observation period, the effect of seasonal temperature, relative humidity and rainfall is elucidated.  Forward stepwise regression procedures were performed to determine what climatic data were involved in cow and calf weight at birth and weaning as well as calving percentage.  In spite of the high standard errors (which were probably due to the small sample size), maximum relative humidity one month prior to the start of the breeding season, made a major contribution to explain calving percentage and minimum temperature within the last month of the 3 month breeding season, had a low negative correlation with calving percentage.   It can be speculated that high humidity in the study region (Sourish Mixed Bushveld) is an indication of warm and wet conditions, negatively impacting cow and bull comfort, leading to lower conception rates.  The negative correlation between minimum temperature within the last month of the breeding season and calving percentage may indicate that the cows were unable to cool down at night during the warmer summer months of the year, leading to lower conception rates and resorptions. The researcher acknowledge that the available herd size may be a limitation and that a bigger herd or sub-herds’ size combined with bigger land size could benefit the project outcome, possibly resulting in more significant differences and/or enhanced interpretation of results

Conferences

  1. Grobler, S.M., Scholtz, M.M. & J.P.C. Greyling, 2013.  Reproduction performance of beef cattle mated naturally after synchronization in the Central Bushveld Bioregion.  South African Society of Animal Science 47th Congress – University of the Free State, Bloemfontein, Free State Province 23-26 June 2013. Poster
  2. Grobler, S.M., Breytenbach, P.J.J. & M.M. Scholtz, 2013.  Effects of 2 grazing systems on veld in the Marikana Thornveld.  Grassland Society of Southern Africa.  48th annual Congress – Modimolle, Limpopo Province 15-19 July 2013.  Presentation
  3. Grobler, S.M., Scholtz, M.M., Neser, F.W.C., Greyling, J.P.C, Morey, L. & F. Calitz, 2016.  Reproductibve performance of extensively managed beef heifers mated at 14 months or 24 months in the Marikana Thornveld.  51th annual Congress – Stellenbosch, Western Cape Province 3 – 5 July 2016.  Poster

scientific articles

  1. Grobler, S.M., Scholtz, M.M., Greyling, J.P.C. & F.W.C. Neser, 2014.  Reproduction performance of beef cattle mated naturally following synchronization in the Central Bushveld bioregion of South Africa. S. Afr. J. Anim. Sci. 44: S70-S74
  2. Grobler, S.M., Scholtz, M.M., & Schwalbach, L.M.J. & J.P.C. Greyling, 2013.  Effect of synchronization on calving date following natural mating in beef cattle.  Appl. Anim. Husb. Rural Develop. 6:15-17

Popular Article

  • Grobler, S.M., Scholtz, M.M. & Breytenbach, P.J.J., 2014. Innovation = improved productivity. Red Meat/Rooi vleis. Agri Connect Pty (Ltd), Pretoria. Vol 5(1): 74-77

PhD Thesis submitted

  • Grobler, S.M., 2016. Alternative management systems to increase beef production under extensive conditions. PhD thesis. University of the Free State, Bloemfontein.

Popular Article

Will follow later

Supplementation of ruminants on winter pastures

Supplementation of ruminants on winter pastures

Industry Sector: Cattle and Small Stock

Research focus area: Livestock production with global competitiveness

Research Institute: University of Pretoria

Researcher: Prof Willem.A. van Niekerk PhD (Agric) Animal Science

Research Team:

TitleInitialsSurnameHighest Qualification
ProfLourens. J.ErasmusPhD (Agric) Animal Science
DrA.Hassan
PhD (Agric) Animal Science
MrR.J.Coetzer
MSc (Agric) Animal Science
MrHMynhardtMSc (Agric) Animal Science

Final report approved: 2016

Aims of the project

  • To develop a cost-effective supplementation strategy for ruminants under low quality winter forage conditions
  • To maintain body weight during the wineter season by assessing different sources and levels of nutrients that enhances poor quality roughage utilisation
  • To investigate intake, fiber degradation and microbial protein production when various types and levels of nutrients are supplemented to ruminants kept at maintenance under extensive conditions

Executive Summary

A series of studies was conducted to evaluate differential energy and nitrogen (N) sources as supplemental feed to sheep grazing low quality winter grazing in the High veldt. Knowledge on supplementation under local conditions are limiting as the majority of supplementation studies are funded and performed in the more temperate areas. Results indicated that higher N and energy inclusion levels are necessary to optimize ruminant production under local conditions compared to temperate areas. In addition, the ratio of fermentable energy to available protein is an important parameter in optimizing supplementation programs. It is concluded that the supplementary recommendations from the current feeding tables does not describe the requirements and nutrient quality of the tropical grasses satisfactorily and as such, cannot be used to predict supplementation responses by the tropical forage fed ruminant.  del can be used for further sensitivity analyses and “what if” scenarios as well as a database to answer specific questions.

Popular

SUPPLEMENTATION OF SHEEP GRAZING LOW QUALITY GRASSES WITH UREA AND STARCH

BY:  *H. MYNHARDT, W. A. VAN NIEKERK AND L. J. ERASMUS, UNIVERSITY OF PRETORIA

Every year sheep might lose up to 30% of their summer body weight gain during the dry winter periods in the high veldt.  While these weight losses have an economic impact on its own, it also is associated with an increased susceptibility for diseases and parasitic infestations and decreased reproductive performances. It generally is considered that protein or non-protein nitrogen (NPN) supplementation is necessary to limit these weight losses during these periods. However, due to the type of grass found in the High veldt area of Southern Africa, data is limiting on the effects of supplementation of ruminants grazing these types of grasses (See box: Differences between C4 and C3 grasses). As such, supplementations recommendations derived from current feeding tables seldom satisfy the needs of the grazing ruminant in Southern Africa. Therefore, a series of studies was conducted at the University of Pretoria to determine and quantify the requirements of the ruminant grazing low quality Eragrostis curvula hay commonly found in the Southern Africa High veldt.

* References and correspondence can be obtained from the author: hermanmynhardt@yahoo.com


Box 1: Differences between C4 and C3 Grasses

The acronyms C3 and C4 refer to the first product of the photosynthetic processes in the respective grasses with the first product of photosynthesis in the C3 grass being phosphoglycerate (a 3 carbon structure) while for the C4 plant, the corresponding molecule is a 4 carbon molecule (oxaloacetate). C3 grasses are temperate grasses and are adapted to the temperate regions of the world where rainfall is more constant with maximum temperatures seldom topping 22 OC. In contrast, C4 grasses are more adapted to the subtropical and tropical climates with temperatures frequently topping 25oC during the growth period. These areas also are associated with seasonal droughts and the occasional frost. Due to these extremes in temperatures and seasonal droughts, C4 grasses contain more bundle sheath cells and less available nutrients compared to C3 grasses during all maturity stages. Ruminant production therefore in general is significantly lower in ruminants grazing C4 grasses compared to temperate C3 grasses, especially during the dormant stage of the grass where lignification of the C4 grasses reduces the availability of the nutrients even further. As such, supplementation requirements and responses differ between ruminants grazing these grasses. However, the majority of supplementation studies in the past have been conducted on C3 grasses as it is found more in the European countries where research funding is more available. As such, as more studies conducted on low quality C3 grasses are incorporated in the current feeding tables, supplementation requirements derived from these tables to the low quality tropical forage fed ruminant are not always accurate. As such, the need was established to conduct research through the financial support of the **RMRD-SA on the nutritional requirements of the low quality tropical forage fed ruminant in order to improve ruminant production in Southern Africa.


*RMRD -SA – Red Meat and Research Development, South Africa



Results and Discussion

Forage intake and digestibility was not influenced by either the level of urea or starch supplementation to the wethers. However, CP-balance, measured as CP intake – CP excretion in the faeces and urine, increased from 12.5 g CP/day in the LU wethers up to 70 g CP/day in the EHU wethers. Based on these observations, only the EHU treatment supplied sufficient protein to potentially satisfy the needs of the 50 kg wethers as they require 65 – 70 g CP for maintenance. These recommendations are significantly higher than the recommendations set in the current feeding standards, however, it is in alignment with the observations and recommendations set out by **Leng (1995) studying ruminants grazing tropical grasses in Australia.

Forage intake and digestibility was not influenced by either the level of urea or starch supplementation to the wethers. However, CP-balance, measured as CP intake – CP excretion in the faeces and urine, increased from 12.5 g CP/day in the LU wethers up to 70 g CP/day in the EHU wethers. Based on these observations, only the EHU treatment supplied sufficient protein to potentially satisfy the needs of the 50 kg wethers as they require 65 – 70 g CP for maintenance. These recommendations are significantly higher than the recommendations set in the current feeding standards, however, it is in alignment with the observations and recommendations set out by **Leng (1995) studying ruminants grazing tropical grasses in Australia.

HIGHER LEVELS OF PROTEIN AND ENERGY SUPPLEMENTATION IS NECCESARY TO OPTIMISE THE GRAZING RUMINANT IN THE S.A. HIGH VELDT DURING THE DRY WINTER MONTHS

An important parameter in ruminant nutrition is microbial protein synthesis (MPS) as it gives an indication of the efficiency of the rumen microbes. During the dry winter months, MPS generally decreases due to the lack of available nutrients in the roughages (Leng, 1990, 1995) which decreases the productivity of the animal which is experienced as weight loss by the farmer.  In this study, MPS increased almost 50% from 78 g MPS to 106 g MPS as the level of starch supplemented was increased from 200 (LS) to 280 (HS) g starch/day. This observation is in agreement with suggestions made by Leng, (1990; 1995) that energy is an important nutrient driving MPS in the tropical forage fed ruminant, provided that the protein requirements of the ruminant have been met. Interestingly, energy supplementation for the temperate forage fed ruminant is not always advocated as these grasses contain higher concentrations of water soluble carbohydrates compared to the tropical grass.

Based on the above results, higher levels of both protein and energy supplementation is necessary to optimise ruminant production during the dry winter months in the High Veldt. The question now was asked whether there was an “ideal” quantity of protein and energy to be supplemented to ruminants grazing low quality “tropical” forages.

Graph 1 is a schematic representation of MPS per unit CP intake (MNS: N intake) while Graph 2 represents the mean rumen ammonia nitrogen (RAN) concentration as influenced by the ratio of starch supplemented to available protein intake.

Graph 1

Urea supplementation across all three starch treatments affected the MPS: CP ratio similarly with the ratio decreasing from almost 3 to below 1 where the wethers were supplemented with the higher urea treatments (HU and EHU). It is important to note that alleviated MPS: CP levels (above 1) could be indicative of CP deficiency as more microbial protein was synthesized in the rumen compared to dietary CP intake. The additional CP required to produce the microbial protein under these circumstances is derived from body protein catabolism which in itself, is an inefficient process, resulting in an excessive body weight loss. As such, in this trial, it is suggested that the protein intake of the wethers supplemented with at least 26.4 g urea/day (HU) was sufficient to meet the requirements of the wethers.

Graph 2

An inverse relationship was observed between RAN and the ratio of starch: digestible protein intake (Graph 2) with RAN decreasing and plateau between 5 and 10 mg RAN/ dL rumen fluid as the ratio increased. An inflexion point was observed where RAN increased exponentially to levels as high as 25 and even 30 mg RAN/dL rumen fluid as the ratio decreased below 2: 1. This graph highlights the importance of supplementation of both rumen available energy sources (starch in this instance) as the supplementation of only RDP sources to the ruminant could lead to an increased risk of ammonia toxicity under these circumstances.

Conclusion

The results from this study suggest that the supplementation requirements of 50 kg wethers grazing low quality tropical forages (2.7% CP) differs to the current feeding standards as:

  • Higher levels of protein (urea supplementation up to 26.4 g urea per day per wether or 3% urea of the total DM intake) is necessary to optimise CP balance in the tropical forage ruminant.
  • Starch supplementation (up to 280 g/wether/day or almost 20% of the total DM intake) in addition to urea supplementation is necessary as tropical grasses not only are deficient in protein, but also in easy available energy.
  • For wethers grazing low quality tropical grasses, the ideal ratio of starch supplemented to digestible protein intake lies between 2 and 3: 1.
  • Additional research is necessary to study the effects of other energy sources and protein sources on rumen environment and the production parameters of the tropical forage fed ruminant as these sources might have different availabilities compared to urea and pure starch within the rumen.

The authors wish to thank the Red Meat Industry and Research Development (RMRD) for their financial support of this study.

Please contact the Primary Researcher if you need a copy of the comprehensive report of this project –
Willem van Niekerk on willem.vanniekerk@up.ac.za

Genomic markers in beef tenderness

The effectiveness of genomic markers in predicting the meat tenderness in pure beef genotypes under South African production and slaughter conditions

Industry Sector: Cattle and Small Stock

Research focus area: Livestock production with global competitiveness: Breeding, physiology and management

Research Institute: Agricultural Research Council – Animal Production Institute

Researcher: Dr L Frylinck PhD

Title Initials Surname Highest Qualificaion
Prof PE Strydom PhD Animal Science
Ms A Basson MSc

Year of completion : 2018

Aims of the project

  • To determine the expression of genomic markers in five South African purebred genotypes – Bos indicus (Brahman), Sanga type (Nguni), British Bos taurus (Angus), European Bos taurus (Charolais) and the composite (Bonsmara) for genes associated with beef tenderness in meat.
  • To determine the relationship between the actual physiological tenderness characteristics under South African production and slaughter conditions of the meat from these five main South African genotypes and the known DNA-marker information.
  • To assess the phenotypic variation in meat tenderness within South African selected pure beef genotypes under the same environmental conditions and to build a tenderness prediction model.

Executive Summary

Purebred South African bulls of 5 breeds (n=166) were finished on a grain diet at the Animal Production Institute of the Agricultural Research Council (API-ARC), Irene. Breeds included Angus (n=27; representative of British Bos taurus), Brahman (n=35; Zebu type Bos indicus), Bonsmara (n=35; South African composite breed with large Sanga contribution), Charolais (n=34; European Bos taurus) and Nguni (n=35; Sanga type Bos taurus africanus). Animals were sampled over 3 slaughter periods in 2011 (50 animals), 2012 (50 animals) and 2014/2015 (66 animals). Bulls were sourced from breeders that are registered with the appropriate breeders’ associations and were progeny of registered pure breed bulls and cows. Bulls were ≃9 months old when entering the feedlot and reared under feedlot conditions for ≃120 days to ≃12 months old. Bullas were slaughtered to yield A2/3 carcasses (zero permanent incisors, lean to medium fatness). Bulls were penned overnight with access to water before slaughter following captive bolt immobilization at the abattoir of the API-ARC. All treatments and procedures were approved by the Ethics Committee of the Agricultural Research Council (ARC AEC-I 2010 001).

To determine whether the effects of genotype were additive to electrical stimulation, the right half of the carcass was electrical stimulated for 15 seconds at 500V peak, using 5 ms pulses at 15 pulses per second and directly chilled at 4 °C. The left half of the carcass was not electrically stimulated (served as a control), while chilling was delayed for 6 hours (at 10 °C) to allow for the full development of metabolic processes within muscle fibers before chilling at 4 °C.

Animal measurements included weights, recorded during the feedlot growth period to determine body weight gain (total gain and average daily gain) and liver body weight (BW) measured on the day before slaughter as a final weight. Carcass measurements included hot carcass weight (HCW; used to calculate dressing percentage), cold carcass weight (used to determine carcass mass loss), EMA (in the thoracic region at T9/10), pH and temperature (measured at the lumbar end of the LTL). Beef quality estimates measured from samples collected directly from the carcass or from LTL excised from the lumbar region (L6) up to the thoracic region (T9/10) included myofibrillar fragment length (MFL), Warner-Bratzler shear force (WBSF), calpain enzyme system activities, sarcomere length (SL), colour measurements, energy metabolites, collagen (content and solubility) and water-holding capacity (WHC). Colour was determined using the CIE L*A*b* colour convention with measurements of L*, a*, b*, C* and hab over the ageing period. Energy metabolites included the concentrations of glycogen, glucose 6-phosphate, glucose, lactate, creatine phosphate and ATP determined at 1 h, 3 h, 6 h and 20 h post‑mortem.

The genes that are most likely to affect beef quality, specifically tenderness, as those of the calpain enzyme system. Calpain-1, calpain-2, calpain-3 and calpastatin are all found in the sarcoplasm and are known to determine post‑mortem proteolysis. The genes for these proteins can therefore be identified as causative to proteolysis at least, but potentially also for beef tenderness. We therefore used the 114 SNPs located in these causative genes (capn1capn2capn3 and cast respectively) to determine their genotypic distribution, as well as the association of these genotypes with beef quality traits in order to determine the importance of these genes in determining the quality (tenderness) phenotype. These data were used to identify possible markers for genomic selection (GS), once they were validated for tenderness in South African beef breeds.

  • The capn1 gene (on BTA29) was validated for beef tenderness, with a large number of strong associations (relatively high correlations) with estimates of beef tenderness, found in both the ES and the NS treatment groups. It correlated especially with MFL as a measure of physical tenderness (r2= 0.07 to 0.15), with fewer SNPs explaining the phenotypic variation in WBSF (r2 = 0.09 to 0.10). Almost no associations occurred with calpain-1 enzyme activity itself, but the effects of the SNPs in capn1 was rather a change in the responsiveness of the enzyme to calpastatin inhibition, as shown by several relatively strong correlations (r2 = 0.07 – 0.12) to the relative calpastatin inhibition per calpain(-s).
  • The capn2 gene (on BTA16) was validated for beef tenderness, explaining the phenotypic variation in, especially, the activities of calpain-1 and calpain-2 (r2 = 0.07 – 0.11). Although effects on enzyme activities were evident, these changes only resulted in a few significant associations of the genotypes with physical tenderness MFL (r2 = 0.07 – 0.09).
  • The capn3 gene (on BTA10) exhibited very few associations with beef quality. The protein coded by this gene is responsible for background proteolysis and does not cause variation in tenderness. The lack of an effect of these SNPs on tenderness is therefore unsurprising.
  • The cast gene (on BTA7) is quite large (136,434 bp) and contained a large number of SNPs (63), of which only 4 exhibited extensive effects on tenderness. Many of the correlations with MFL ranged between 0.07 – 0.11, although a few SNPs exhibited strong phenotypic correlations with MFL (r2 = 0.12 – 0.16), while associations with WBSF were less common and less pronounced (r2 = 0.07 – 0.11). These differences in physical tenderness were only in part explained by differences in the total and /or relative inhibition of calpastatin of protease enzyme activities (r2 = 0.07 – 0.12).

Using SNPs of the Illumina Bovine HD SNP BeadChip the capn1capn2 and cast genes were verified for tenderness in SA purebred beef cattle. The amount of phenotypic variation in tenderness estimates explained by some of these SNPs were large, making them useful targets for genomic selection in these breeds. Both Nguni and Bonsmara exhibited high allelic frequencies for alleles that were favorable for tenderness, giving them the genetic potential to produce tender beef.

Popular Article

Inheemse rasse soos die Nguni en Bonsmara het die genetiese potensiaal om sagte vleis te produseer

Basson, A

Inleiding

Hierdie proef is onderneem om vleisbeesgenetika in Suid-Afrikaanse (SA) rasse te ondersoek. As deel van die proef is daar getoets of die rasse wat algemeen vir kruisteling in SA gebruik word, verskil in die verspreiding van voordelige gene vir sagtheid (en ander vleiseienskappe), met spesifieke fokus op die inheemse Bonsmara en Nguni. Die karkasse is gehalveer om die een helfte elektries te stimuleer en dadelik te verkoel, terwyl die ander helfte as kontrole gedien het. Hier is verkoeling vir 6 ure uitgestel om die normale perimortem prosesse soos energieverskaffing in metabolisme, genoeg tyd te gee om te ontwikkel, voordat hierdie nie-gestimuleerde karkas-helftes verkoel is.

Daar is verskeie vrae waarvoor ons antwoorde soek met hierdie navorsing. Ons weet dat die Nguni oor die genetiese en biochemiese potensiaal beskik om sagte vleis te produseer (Frylinck et al., 2009), maar hoe vergelyk dit met Bonsmara, Angus, Charolais en Brahman? Kan die Nguni onder die regte slagtoestande, sagte vleis produseer? Kan ons deur middel van genomiese seleksie (GS) die kwaliteit van beesvleis verbeter in die industrie, waar elektriese stimulering dalk die invloed van voordelige gene sou uitkanselleer, of is verbeterde genetika se positiewe invloed op kwaliteit steeds waargeneem na stimulering?

Die Proef

Vyf vleisbeesrasse is in die proef ingesluit; Angus en Charolais as Bos taurus rasse, Brahman as Zebu-tipe Bos indicus, Bonsmara as ‘n inheemse kruisbeesras met ‘n groot Sanga-tipe bydra en Nguni as inheemse Sanga-tipe Bos taurus africanus. Die stoetbulle is afgerond in die voerkraal tot naastenby 12 maande oud voor slagting, of ‘n karkasklassifisering van A2/3. ‘n Groot aantal monsters is versamel van die Longissimus lumborum et thoracis spier (lende) om die toestande rondom slagting te bepaal, asook lendeskywe wat vakuum-verseël is en verouder is vir 3, 9, 14 en 20 dae, om die invloed van veroudering op vleiskwaliteit te bepaal (met of sonder elektriese stimulering).

Vleis se Kwaliteitseienskappe

Vir kwantitatiewe eienskappe is daar ‘n baie groot aantal gene wat ‘n eienskap bepaal en elkeen van hierdie gene dra slegs ‘n klein proporsie by tot die uiteindelike resultaat, byvoorbeeld sagte vleis. Elkeen van hierdie gene kan honderde (selfs duidende) variasies toon op ‘n molekulêre vlak. Enkel-nukleotied polimorfismes (single nucleotide polymorphisms = SNPs) wat die verskil in een enkele DNA molekule is, kan soms ‘n relatiewe groot invloed op die fenotipe hê. Hierdie SNPs (uitgespreek “snips”) is wat ons geïdentifiseer en getoets het binne-in gene wat sagtheid behoort te beïnvloed.

Genetika en Fisiologie

Spier in die lewendige dier het ‘n baie rigiede proteïenstruktuur wat hoogs ge-orden is, terwyl die omskakelings na vleis in die karkas ‘n ontwrigting van hierdie orde behels – hoe meer die speirstrukture ontwrig word, hoe sagter is die vleis. Die kalpaïen ensiem-sisteem (spesifieke proteases) dra grootliks by tot die ontwikkeling van die finale sagtheid van vleis. Alhoewel kalpaïen‑1 en kalpastatien (die inhibeerder van kalpaïen) die grootste bydra lewer tot die degradering van die proteïene in vleis om dit sagter te maak, kan kalpaïen‑2 en kalpaïen‑3 dalk ook hiertoe bydra. Ons het dus diere met die Bovine-HD SNP BeadChip van Illumina genotipeer vir die gene van die ensieme kalpaïen‑1 (capn1 in chromosoom 29), kalpaïen‑2 (capn2 in chromosoom 16), en kalpaïen‑3 (capn3 in chromosoom 10), asook die ensiem-inhibeerder, kalpastatien (cast in chromosoom 7). Ons bepaal dus eerstens watter gene fisiologies belangrik is en analiseer dan al die geen-variante (of SNPs) om die korrelasie tussen hierdie variante en vleiskwaliteit van die diere te bepaal. ‘n Groot voordeel van hierdie navorsing, wat dit onderskei van ander werk, is dat ons ‘n baie gedetaileerde prentjie van die fisiologie van die vleis het, deur meting van verskeie eienskappe (met of sonder behandeling), gekoppel aan redelik indiepte inligting omtrent die genotipes van hierdie funksionele gene.

Resultate

Brahman bulle (rooi in die grafiek) het deurgaans die hoogste vlakke van kalpastatien per kalpaïene getoon, wat bygedra het tot meer intakte spierveselstrukture (langer miofibril fragment lengtes – MFL) asook verhoogde taaiheid (hoë Warner-Bratzler snyweerstande of WBSW gemeet in kg). In teenstelling het die Nguni (turquois in die grafiek) heelwat laer inhibering van ensiemwerking deur kalpastatien getoon, wat in sommige gevalle die laagste van al die rasse was, met ander woorde die Nguni was die ras met die mees voordelige biochemie. In die Bonsmara was die patroon vir biochemiese en strukturele veranderinge baie soortgelyk aan dié van Nguni’s en die sagtheid van die lendeskywe (verlaging in snyweerstande) het vinnig verbeter tussen dag 3 en 9 van veroudering. Teen 14 dae se veroudering het die snyweerstande gestabiliseer en Bonsmara bulle het nie dieselfde sagtheid as die Nguni bereik nie, inteendeel, hulle snyweerstande was soortgelyk aan Brahman en Charolais.

Kalpaïen-1 is die belangrikste protease wat sagtheid bepaal en die kalpaïen‑1 geen (capn1) behoort dus by te dra tot vleiskwaliteit. Die grootste invloed van capn1 was om die proteïenstruktuur te ontwrig, deur middel van laer relatiewe kalpastatien inhibisie per kalpaïen aktiwiteit. Ons het sterk korrelasies vir verskeie SNPs in hierdie geen geïdentifiseer waar veral MFL (maar ook party van die snyweerstande), sowat 15-20% laer was in die “voordelige” genotipe (voordelig vir sagtheid).

Die kalpaïen-2 ensiem is verantwoordelik vir die ontwikkeling van agtergrond-sagtheid en die geen (capn2) was ge-assosieer met sowat 12 – 15% hoër protease ensiemaktiwiteit, wat in sommige SNPs met soveel as 38% hoër ensiem aktiwiteit geassosieer was. Dit was egter tot ‘n kleiner mate met die bevordering van sagtheid en die ontwrigting van vesels geassosieer.

Kalpastatien aksie kan ‘n groot invloed op sagtheid hê. In die lewendige dier funksioneer dit om die kalpaïen protease ensiemaktiwiteit, wat sellulêre proteïene groot skade sou kon aanrig, in beheer te hou. In die prosesse wat spier omskakel na vleis toe, verhoed dit ook die afbraak van spierproteïene, maar in dié geval sal dit dan die ontwikkeling van sagtheid benadeel. In die kalpastatien geen (cast) was daar ‘n relatief klein aantal SNPs wat ‘n redelike groot invloed op die ontwrigting van spiervesel proteïene gehad het. Die MFL was nagenoeg 10 – 15% laer, terwyl sommige van die SNPs se “voordelige” genotipes tot  meer as ‘n 20% verbetering in die MFL gelei het (i.e. korter lengtes). Dit was gedeeltelik verduidelik deur ‘n verlaging in die totale eenhede kalpastatien werking, met soveel as 20% laer inhibisie vanaf kalpastatien, gekoppel aan ‘n redelike verbetering in die sagtheid van die vleis, veral in die vroeë tot intermediêre stadiums van veroudering.

Bespreking

Uit die 4 gene wat hier getoets is, is die kalpaïen‑1 en kalpastatien gene veral geskik vir genomiese seleksie in Suid-Afrikaanse vleisbeesrasse, terwyl ‘n paar van die SNPs in die kalpaïen-2 geen ook potensiaal toon. Rasverskille in sagtheidseienskappe (fisiese en biochemies) word gereflekteer in verskille in die verspreiding van genotipes tussen die verskillende rasse (sien tabel hier onder)..

Totale Aantal Voordelige Allele*
cast capn1 capn2
Angus (n=27) 189 146 220
Bonsmara (n=35) 270 209 174
Brahman (n=35) 237 39 141
Charolais (n=34) 217 147 215
Nguni (n=35) 256 233 241

* Die groen blok dui die ras met die grootste aantal voordelige allele vir sagtheid aan

Nguni’s hét die genetiese potensiaal om sagte vleis te produseer, maar die noemenswaardige ligter karkasse is geneig om te vinnig te verkoel wat beteken die vleis raak te koud vir metaboliese ensieme om energie optimaal te benut, terwyl die struktuur binne miofibrille ook sub-optimaal word vir die proteases se ensiemwerking. In hierdie proef het Nguni’s die “beste genetika” gehad en die allele wat voordelige is vir sagtheid in die gene wat hier getoets is, was volop in Nguni’s.

Please contact the Primary Researcher if you need a copy of the comprehensive report of this project – Lorinda Frylinck on lorinda@arc.agric.za

Discovery of single nucleotide polymorphisms

Genome-wide genetic marker discovery in South African indigenous cattle breeds using next generation sequencing

Industry Sector: Cattle and Small Stock

Research Focus Area: Livestock production with global competitiveness: Animal growth, nutrition and management

Research Institute: Agriculture Research Institute – Animal Production Institute

Researcher: Dr. Avhashoni Zwane

Title Initials Surname Highest Qualification
Prof. Azwihangwisi Maiwashe PhD
Prof Este Van Marle-Koster PhD
Prof Jerry Taylor PhD
Prof Mahlako Makgahlela PhD
Dr Ananyo Choudhury PhD
Dr Farai Muchadeyi PhD

Year of completion : 2018

Aims Of The Project

  • To conduct a genome wide search for new SNPs in local cattle breeds
  • To validate newly identified SNPs using Run 5 data from the 1000 Bull Genomes Project and perform functional annotation and enrichment analysis
  • To identify selective sweeps and a panel of SNP markers to discriminate between the three indigenous breeds

Executive Summary

South African (SA) livestock has played an important role in food security country’s sustainability. Due to the important role of indigenous cattle breeds in SA, it is crucial for these breeds to be included in the generation of genotypic and sequence data. Genomic data provide opportunity for various genetic investigations including identification of breed-informative markers, selective sweeps and genome-wide association studies (GWAS). In this study sequence data were generated and used in combination with genotypic data to conduct a SNP discovery in the three indigenous SA breeds (Afrikaner, Drakensberger, and Nguni) and study potential selective sweeps and identify panel of breed-specific markers. Commercial bovine SNP assays, (BovineSNP50 and GGP-80K) were used for identifying the breed-informative markers, while an approach of breed pooled samples were used for sequencing. Sequencing of the three breeds generated approximately 1.8 billion (184 Giga-bp) of high quality paired-end reads which 99 % reads mapped to the bovine reference genome (UMD 3.1), with an average coverage of 21.1-fold. A total of 17.6 million variants were identified across the three breeds with the highest number of variants identified in NGI (12,514,597) than in AFR (11,165,172) and the DRA (7,049,802). In total 89 % of variants were SNPs and 11 % were Indels. On average, 85 % of the total SNPs identified were also shared among the breeds from 1000 Bull Genomes Project data and the remaining 15 % of SNPs were unique to SA indigenous breeds. Novel SNPs were further annotated to identify genes enriched in novel SNPs. In total, 461, 478 and 542 genomic regions identified from the top (5%) windows were enriched for novel variants (p < 0.001). A total of 174 putative breed-specific SNPs were identified across the breeds and showed the overall 100% breed allocation using PCA and GeneClass 2. This study provides the first analysis of sequence data to discover SNPs in indigenous SA cattle breeds and the results provide insight into the genetic composition of the breeds and offer the potential for further applications in their genetic improvement.

POPULAR ARTICLE

IDENTIFICATION OF SELECTIVE SWEEPS IN AFRIKANER, DRAKENSBERGER AND NGUNI CATTLE USING GENOME-WIDE SEQUENCE DATA

A.A. Zwane1,2, A. Choudhury, M.L. Makgahlela1, E. van Marle-Köster2, A. Maiwashe1,5 and J.F. Taylor4
1Department of Animal Breeding and Genetics, ARC-API, P/Bag X2, Irene, 0062, 2Department of Animal and Wildlife Sciences, University of Pretoria, P/Bag X20, Hatfield, Pretoria, 0028, 3Sydney Brenner Institute of Molecular Bioscience, University of the Witwatersrand, P/Bag 3, Wits, Gauteng, 2050, 4Division of Animal Sciences, University of Missouri, 920 East Campus Drive, Columbia, MO 65211-5300, USA, 5Department of Animal, Wildlife and Grassland Sciences, University of the Free State, Bloemfontein 9300, South Africa
#Corresponding author: zwanea@arc.agric.za

Background: Whole-genome sequencing now provides a suitable platform to examine the entire genome for the identification of selective sweeps. Indigenous South African (SA) breeds including Afrikaner (AFR), Drakensberger (DRA), and Nguni (NGI) are important genetic resources for SA cattle production. These breeds were subjected to strong selection leading to changes in their morphology, physiology and behaviour.

Aim: The aim of this study was to identify selective sweeps that shaped phenotypic diversity among indigenous SA breeds.
Methodologies: Whole genome sequencing of pools of DNA from AFR, DRA, and NGI was performed using an Illumina HiSeq 2000 and 17.6 million variants were discovered across the breeds. To identify the selective sweep regions, SNPs were used to calculate Z-transformations of the pooled heterozygosity (ZHp) in each of the three breeds using a 150 kb sliding window to compute the ZHp Z-scores in each breed. The results were used to plot the distribution of SNP counts within the windows. The regions of selective sweeps were represented by the lower ZHp Z-scores with the minimum threshold of -4. Animal QTL database was used to determine the gene ontology of the genes identified in selective sweep regions.

Results: In total 688 candidate selective sweeps, with the ZHp Z-score ≤ −4 were identified across the three breeds with 223 putative selective sweeps (ZHp Z-score ≤ -5). About 93 regions had extremely low ZHp Z-scores (ZHp scores ≤ −6). These are the regions subjected to selection segninatures. Using animal QTLdb, several genes were identified, e.g., ESM1, CNOT6, ASIC5, KIT and MITF, associated with phenotypic variation in livestock species (Zielak-Steciwko et al., 2014; Fallahsharoudi et al., 2016).

Discussion: The ability to detect selective sweep regions provided useful genomic information for these breeds, whereas functional analysis of these regions revealed the presence of genes of biological and economic importance.
Conclusions and recommendations: This study provides a broad insight into the events that happened during recent selection events and artificial selection processes that have shaped the livestock genome. More work is needed to characterise genomic regions and genes identified in this study.

Please contact the Primary Researcher if you need a copy of the comprehensive report of this project –  vhashoni Zwane  on Zwanea@arc.agric.za

Crossbreeding Afrikaner, Bonsmara and Nguni cows

Crossbreeding effects with specialized sire lines in Afrikaner, Bonsmara and Nguni beef cattle herds

Industry Sector: Cattle and Small Stock

Research Focus Area: Livestock production with global competitiveness: Breeding, physiology and management

Research Institute: Agriculture Research Institute – Animal Production Institute

Researcher: Dr. M Scholtz

Title Initials Surname Highest Qualification
Mrs. A. Theunissen MSc
Prof F W C Neser Ph.D.
Mr. L De Lange Nat. Dipl.
Mr. T Jonker M.Sc.
Mr. F J Jordaan M.Sc. (Agric)
Dr M D MacNeil Ph.D.
Mr. O Ntwaeagae B.Tech
Mr. W Pieterson Nat. Dipl.
Ms. M C Mokolobate M.Sc. (Agric)
Ms. G M Pyoos B.Sc. (Agric. Sci.)
Ms. M Mokgadi M.Tech

Year of completion : 2018

Aims Of The Project

  • 1. To estimate the genetic and phenotypic trends in the dam lines
  • 2. To evaluate crossbreeding systems and quantify the phenotypic progress made in economically important traits in crossbred cattle for beef production
  • 3. To characterize the additive and non-additive genetic effects for production and health traits in progeny of terminal sires and dam line breeding cows
  • 4. To validate an existing simulation model for the development of breeding objectives for specialized sire lines on Landrace breed cows for use in small scale and commercial farming that better meet commercial feedlot requirements
  • 5. To make recommendations with regard to future selection and management of beef herds in warm arid areas
  • 6. To evaluate alternative production systems in anticipation of global warming

Executive Summary

Climate has been changing and these changes are predicted to be highly dynamic. Increasing frequencies of heat stress, drought and flooding events are likely, and these will have adverse effects livestock production. It is therefore important that production systems utilizing local landrace and adapted breeds that are better adapted to warmer climates, be investigated.

In South Africa extensive cattle farming dominate primary cattle production systems, while more than 80% of all beef cattle slaughtered in the formal sector in South Africa originate from commercial feedlots. A total of 67% of feedlot animals are crossbreds, indicating that crossbreeding is playing a significant role in the commercial industry in South Africa. Well-structured crossbreeding systems allows producers to capture benefits from complementarity and heterosis.

The study is being conducted at Vaalharts Research Station. The aim is to use the Afrikaner, Bonsmara and Nguni as dam lines in crosses with specialized sire lines from British (represented by Angus) and European (represented by Simmentaler) breeds. In addition these dam lines were also mated with Afrikaner, Bonsmara and Nguni bulls in all combinations. This is producing 15 different genotypes.

It is anticipated that the information from five breeding seasons will be needed for the a more comprehensive study. Currently the information from three seasons are available and have been summarized. A protocol for Phase 2 of the study has been submitted.

The phenotypic trends in production traits of the three breeds over 25 years revealed an increase in cow productivity in all the breeds varying from 10% in the Bonsmara to 18.3% in the Afrikaner, where cow productivity was defined as kg calf weaned per Large Stock Unit mated. This also resulted in a decrease in the carbon footprint of up to 12%. The bottom line is that cow productivity can be improved if the weaning weight of the calf relative to the weight of the cow can be increased; and the inter-calving period reduced. Well-structured crossbreeding should have a much bigger effect on this and therefore the environmental impact, will be included in the final analyses of this study.

The simulation study indicated that breed, weaner and carcass price have an influence in the gross income from weaner and ox production systems. The simulation model in question can be used to quantify the benefits from the different crosses on completion of the study on condition that it is based on sound assumptions regarding weaner and carcass prices.

The information on 550 weaner calves and 125 feedlot bulls are currently available. The heaviest weaning weights are from Simmentaler sires on Afrikaner (220 kg) and Bonsmara (213 kg) dams, as well as Angus sires on Bonsmara (252 kg) dams. The lightest weaner calves were produced from purebred Ngunis (171 kg) and Angus sires on Nguni dams (173 kg). The severe draught and extreme heat of the 2015/2016 summer season had a big effect on the Angus and Simmentaler sired calves. The Sanga sired calves and Angus/Simmentaler sired calves had the same weaning weight (171 kg) in this season. In contrast, the 2016/2017 summer season was cooler and wetter, resulting in the weaning weight of the Angus/Simmentaler sired calves being 27 kg heavier than the Sanga sired calves (210 kg versus 183 kg). This demonstrates the importance of including the effect of climate on the pre- and post-weaning performance in Phase 2 of the experiment.

At the completion of the study all the information will be updated and this baseline information used to evaluate how effective the current crossbreeding systems in South Africa are and to quantify the direct and maternal heterotic effects, the possible/promising advantages of structured crossbreeding, as well as the effect of climate.

The very dry and hot 2015/2016 season also had an effect on the post weaning feed intake and growth. For example, the ADG of the Angus and Simmentaler types decreased by 17%, whereas that of the Sanga and Sanga derived types (Afrikaner, Bonsmara, Nguni) decreased by 9%, as a result of the heat waves experienced.

It is foreseen that indigenous and adapted beef breeds may become more important in South Africa as a consequence of climate change that will result in more challenging environments. The use of specialized sire and dam lines offer an opportunity to increase output by taking advantage of heterosis and complementarity. The effects of weather patterns on beef production in South Africa should also be estimated and thereafter, mitigation strategies developed in the era of climate change to ensure optimal production efficiency.

With the information collect from the GrowSafe system, it will be possible to study feed and water intake patterns as well as behavior of individual animals and different genotypes. This may give valuable information on the effect of climate on animal performance and behavior.

This study produced one M.Sc. thesis, 8 peer reviewed scientific articles, chapters in books and conference proceedings, as well as 8 popular articles.

Popular Article

The principles behind climate smart beef cow efficiency through utilization of structured crossbreeding

Theunissen1, M.C. Mokolobate2 & M.M. Scholtz2,3

1Northern Cape Department of Agricultural, Land Reform and Ruswral Development, Private Bag X9, Jan Kempdorp 8550, South Africa

2ARC-Animal Production Institute, Private Bag X2, Irene, 0062, South Africa

3University of the Free State, Bloemfontein, 9300, South Africa; South Africa

atheunissen@ncpg.gov.za (Corresponding author)

 Background and deliberations

With the ever swelling costs of production, beef cattle producers in South Africa have a sure challenge for sustainability. This is aggravated by the vagaries of climate change. The country’s most recent vulnerability was displayed during the 2015 drought, which was the warmest year ever recorded and was accompanied by extreme heat. The beef industry is one of the agricultural sectors that need to focus on both adaptation and mitigation strategies in response to  greenhouse gas (GHG) emissions and global warming.

The utilization of more hardy breed resources in a changing production environment is one of the alternative strategies to be considered. The most fundamental factor in this strategy will be the emphasis on a high reproductive rate of the selected breeds in the particular environment to increase the overall efficiency of the beef cattle enterprise.

Another alternate is the use of sustainable crossbreeding systems that pool indigenous and exotic breeds, but with retention of the genetic resources, which have shown to be an effective means to reduces GHG, as it has been shown to increase reproduction and production levels in overseas and in local studies. In this regard, a newly developed more sophisticated Large Stock Unit (LSU) calculator by Neser (2012) and Mokolobate (2015) and the measurement of cow efficiency (to calculate kg calf weaned/kg LSU of the dam); initiated an evaluation tool for “cross-bred” selection and breeding to improve cow efficiency; as long as the nutritional needs of animals are fully met.

This expression of cow efficiency is an improved replacement for the biological definition of kg calf weaned/kg mature cow weight that not only has two variables of which anyone or both in the ratio can change to have the same answer, but does not express beef production in terms of an assigned nutrient intake. The advantage of the new biological expression of cow efficiency is that the method increases output and reduces input, which will then support and facilitate the implementation of climate smart production, adaptation and mitigation measures.

Initially Meissner et al. (1983) defined a LSU on the basis of the nutrient requirement of a unit.  However, with differences in frame sizes there are differences in the voluntary feed intake between such animals although they have the same body weight. The LSU equivalents for beef cattle of different frame sizes also vary according to physiological phases, eg. heifers (over 12 months of age) and lactating cows. Table 1 shows examples of the refined estimations of LSU equivalents according to frame sizes of cows that was derived with the calculator.

Table 1: LSU equivalents for beef cattle of different frame sizes and physiological phases

Weight

Kg

Small Frame Medium Frame Large Frame
Heifer (>12 months) Cow &

Calf

Heifer (>12 months) Cow &

Calf

Heifer (>12 months) Cow &

Calf

150 0.37 X X X X X
175 0.42 X X X X X
200 0.47 X 0.50 X X X
225 0.52 0.83 0.56 X X X
250 0.57 0.89 0.61 X 0.67 X
275 0.61 0.95 0.66 X 0.72 X
300 0.66 1.00 0.70 1.05 0.77 X
325 0.70 1.06 0.75 1.11 0.82 X
350 0.73 1.11 0.80 1.17 0.88 X
375 0.77 1.16 0.84 1.23 0.93 1.48
400 0.80 1.22 0.89 1.29 0.98 1.55
425 0.83 1.27 0.93 1.34 1.03 1.61
450 0.85 1.32 0.97 1.40 1.08 1.66
475 X 1.37 1.01 1.45 1.13 1.72
500 X 1.42 1.05 1.50 1.18 1.78
525 X 1.47 1.08 1.55 1.23 1.83
550 X 1.52 1.12 1.60 1.28 1.88
575 X 1.57 X 1.65 1.33 1.93
600 X 1.61 X 1.69 1.38 1.98
625 X X X 1.74 1.43 2.02
650 X X X 1.78 X 2.07
675 X X X X X 2.11
700 X X X X X 2.15

Crossbreeding has proved to increase cow efficiency when it is measured and calculated with the LSU caculator. Table 2 demonstrates the results of a study that was done at Vaalharts Research Station that used mature cows of different breeds. The cow efficiency, estimated by kg calf weaned / cow LSU (KgC/LSU), for the Afrikaner (A), Brahman (B), Charolais (C), Hereford (H) and Simmentaler (S) breed types were calculated according to their different frame sizes and expressed as percentage deviation from the Afrikaner breed in brackets.

Table 2 The estimated cow efficiency (KgC/LSU) for the 29 different breed types and percentage deviation from the Afrikaner breed in brackets

  Sire Breed
 

Dam breed

Afrikaner  (A) Brahman  (B) Charoloais  (C) Hereford  (H) Simmentaler  (S)
A 142.6

(0.0)

144.2

(1.1%)

145.7

(2.1%)

151.2

(6.0%)

143.7

(0.7%)

B 142.0

(-0.4%)

C 124.9

(-12.4%)

H 149.3

(4.6%)

S 139.3

(-2.3%)

BA 148.9

(4.4%)

147.1

(3.1%)

155.6

(9.1%)

162.0

(13.6%)

160.1

(12.3%)

CA 152.3

(6.7%)

155.5

(9.0%)

154.5

(8.3%)

157.1

(10.1%)

158.4

(11.0%)

HA 155.7

(9.2%)

170.1

(19.2%)

175.1

(22.7%)

161.2

(13.0%)

176.8

(23.9%)

SA 155.9

(9.3%)

156.6

(9.8%)

161.1

(12.9%)

163.8

(14.8%)

162.1

(13.6%)

Table 2 shows that with the exception of the Hereford, purebred dams were less efficient than purebred Afrikaner dams under the particular environmental conditions. The purebred Charolais (C) dam was the least efficient dam out of all the genotypes. Crossbreeding the Afrikaner (A) dam line with Brahman (B), Charolais (C), Hereford (H) and Simmentaler (S) as sire lines indicated small effects (between +0.7 to +6.0%) on KgC/LSU above that of the purebred Afrikaner (A). However, the efficiency in the F1 cow increased relative to that of the purebred exotic cows. For example, the cow efficiency of the CA cow, compared to pure C cow increased with +14.5% (from -12.4% to +2.1%).

In the case of FI cows the HA was unsurpassed and increased cow efficiency on average by +17.6%, while the BA, CA and SA dam lines increased cow efficiency by +8.5, +9.0 and +12.1% respectively. Continental and Zebu sire lines mated to the most productive HA crossbred dam line in a three-breed system (S x HA, C x HA and B x HA) increased KgC/LSU on average by +22.7, +23.9 and +19.2% respectively, against that of the A x HA backcross with +9.2%.

The improvement demonstrated in the study concurs with that of Schoeman (2010), which indicated that crossbreeding improves calf/cow efficiency when measured as energy requirements or input costs per kg of equivalent steer weight. Although the effect of heterosis on individual traits is normally relatively small, the cumulative effect on composite traits, such as weight of calf weaned per cow exposed are immense which explains the superiority in kgC/LSU as a composite trait. Conversely, researchers cautioned on the attempt to extrapolate research results to all environments other than those similar to where the studies were conducted because of the presence of genotype x environment interactions.

While KgC/LSU as trait on its own can be used to rank productive cows in a contemporary group, it cannot be used to plan breeding strategies. Fertility, or the number of calves weaned in a cow group should certainly also be considered as a complementary factor that influences cow efficiency. In this study the net effect on weaning rate (WR) was that crossbred dams outperformed their purebred contemporaries by 8%.

Cow efficiency can then be estimated as follows: Y = WR x KgC/LSU

where Y = cow efficiency.

Since weaning rate has a low heritability and largely depends on the climatic and managerial (environmental) factors of a particular farm, this trait can contribute to large deviations in the estimated cow efficiencies that were obtained in Table 2. When weaning rate is included in the metioned Vaalharts study, it showed that when compared to the A, only purebred H and S cows have increased cow efficiency potential (+11.4 and 5.3% respectively). Two-breed progeny of the A dam line increased cow efficiency on average by +16.5%. All these increases are ascribed to the increased WR of the breeds compared to that of the A, B and C pure-breeds.

While A sire line backcrosses increased cow efficiency on average by +20.3%, three-breed progeny from B, C, H and S sire lines had average increases of +21.6, +24.4, +30.2 and 34.8% respectively. The S x HA showed the notable increase of 49.7%. Similarly, the BA, CA, HA and SA dam lines respectively had average increases of +24.1, +18.9. +36.6 and +25.2% on cow efficiency. All crossbred dam genotypes increased cow efficiency, the only exceptions being a trivial increase of +0.6% of the B x CA genotype. In this study the Pearson correlation between kgC/LSU (cow efficiency without WR included) and WR x kgC/LSU (cow efficiency with WR included) is 0.88%.

In the current Vaalharts crossbreeding project, the Bonsmara and Nguni are added to the Afrikaner as dam lines. These dam lines are mated to Angus and Simmentaler as specialized sire lines. In addition, the dam lines are also inter-mated in all possible combinations. The result is 15 different genotypes. The data will be analysed similar to that of the previous crossbreeding project.

Conclusions

A sophisticated Large Stock Unit (LSU) calculator can be used for the measurement of cow efficiency (to calculate kg calf weaned/kg LSU of the dam) of different frame sizes and without additional inputs. Cross-breeding has shown to increase cow efficiency; as long as cow frame sizes do not increase up to a point where the nutritional needs of animals are not fully being met. Increases in cow efficiency (weaning rate x kg calf/large stock unit) in two-breed and three-breed cattle was mainly derived from differences in frame size, fitness and relationships between calf weight and cow Large Stock Units.

The fact that there are large differences in cow efficiency in reproductive cows point to genetic differences and holds the potential for cow ranking and improvement through selection in contemporary groups. Optimum crossbreeding strategies may increase cow efficiency up to a notable 49.7%. This will support climate smart beef production, since it will reduce resource use and reduce the carbon footprint per unit of product produced.

Acknowledgement

This work is based on research supported in part by Red Meat Research and Development South Africa and the National Research Foundation of South Africa (NRF), under grants UID 75122, 75123 and 90097. The Grant-holder acknowledges that opinions, findings and conclusions or recommendations expressed in any publication generated by the NRF-supported research are that of the authors and that the NRF accepts no liability whatsoever in this regard.

References

Meissner, H.H., Hofmeyr, H.S., Van Rensburg, W.J.J. & Pienaar, J.P., 1983. Classification of livestock for realistic prediction of substitution values in terms of a biologically defined Large Stock Unit. Tech. Comm. No. 175. Department of Agriculture, Pretoria.

Mokolobate, M.C., 2015.Novelty traits to improve cow-calf efficiency in climate smart beef production systems. MSc. Dissertation. University of the Free State, Bloemfontein, South Africa.

Neser, F.W.C., 2012. http://www.rpo.co.za/documents/pptrpo/proffrikkieneser.pdf

Schoeman, S.J., 2010. Crossbreeding in beef cattle. In: Beef Breeding in South Africa. 2nd Edition. Agricultural Research Council, Pretoria. ISBN-13 978-1-86849-391-3 pp 21-32.

Please contact the Primary Researcher if you need a copy of the comprehensive report of this project – Michiel Scholtz on gscholtz@arc.agric.za