Genetic Improvement of Dairy Animals through Molecular Approaches

In past few decades, remarkable growth in milk production in India took place primarily due to increasing number of cattle and buffalos, level of nutrition, management and health care. But, the average production per animal has remained relatively much lower as compared to the annual average production of Holstein cows all over the world. Bovine genetic improvement in our country is extremely slow as be deficient in efficient genetic evaluation of animals in the breeding program based on accurate records on larger sample size. The progeny testing for cattle and buffalo bulls to evaluate genetic potential is going on but the infrastructure is inadequate and the number of bulls and daughters per bull in organized herds is too small leads to low intensity of selection. In developed countries, young bulls are tested based on 70-100 daughters’ records whereas, in India it is based upon only 5-10 progenies per sire leads to low accuracy or low genetic progress in the estimation of breeding value. Accurate assessment of genetic potential of sires for the production of the next generation of cows with high yielding potential is the biggest challenge in Indian cattle breeding. The sequencing of the genomes of the major livestock species places animal breeding on the threshold of a new genomic era. The application of genetic information in animal breeding will be more beneficial for developing countries like India where the large-scale data-system is not well established. The genomic selection programs have made it possible to evaluate the genetic potential of animals at a very early stage of life. 

Genomic selection is a method of selection which utilizes of variations in DNA sequences among individuals as an aid to discriminate the animals along with pedigree and individual performance data, to predict the genetic potential of individuals will improve the accuracy. Improved molecular technology in recent years allowed screening of thousands of locations along the whole genome of individual in low expenses. Genomic selection can augment rates of genetic progress for all the important traits in dairy cattle including traits with low heritability which were difficult to improve using alone the traditional progeny testing. Genomic selection will be especially useful for the traits where accuracy of conventional selection is low, such as:  

1. Traits with low heritability 

2. Traits that are measured late in life, such that traits recording are not available at the time of selection 

3. Slaughter quality traits (available after slaughter of the animals) 

4. Traits for disease resistance (expensive and risky challenge) 

Application of MAS 

Marker assisted selection (MAS) is an indirect selection process where a trait of interest is selected from which marker is tightly linked. MAS is more effective for traits with low heritability, sex or time limited expression or after slaughter recording etc. The efficiency of application of MAS depends upon a number of factors. 

• Identification of all possible phenotypes from all different alleles 

• Demonstrates measurable differences in expression between trait 

• No effect on trait of interest and varies depending on the allele at the marker loci 

• No interaction among the markers allowing the use of many at the same time in a segregating population 

• Abundant in number 

• Polymorphic in nature

Identification of Quantitative Trait loci (QTL)  

Quantitative trait loci (QTL) may be defined as a cluster of genes that are closely linked and associated with some trait. Hence their inheritance is complex. Typically, QTLs underlie continuous traits (those traits that vary continuously, e.g. height) as opposed to discrete traits. At least one marker should be essentially in linkage disequilibrium (LO) with each quantitative trait loci (QTL) for estimation of breeding value. Genomic prediction approach has revolutionized animal breeding in the last few years and now almost all the developed countries in the world have used estimated breeding value to select young bull based on genomic data. Sires can be selected early in their life (2 year old as compared to 5-6 years as in progeny testing) with the use of genomic selection. The relative advantage of genomic selection will be more compared to progeny testing in which the generation interval is longer.  

Positional Cloning of QTL  

Positional cloning is a technique that is used in genetic screening to identify specific areas of interest in the genome, and then determine what they do but this method has low resolution. It involves whole genome (WG) microsatellite QTL scan followed by fine-mapping and then the identification and testing of candidate mutations within each QTL. Strategies which can identify simultaneously QTL is needed, as there could be multiple QTL underlying the economically important traits. By sampling a large number of half-sib families we can capture all of the genetic diversity that is present and it allows the differentiation of pleiotropic and closely linked QTL. However, for closely linked QTL, it should be possible to identify families that segregate for only one of the QTL. Because so many QTL appear to affect the large number of recorded traits, any randomly selected marker has a strong likelihood of being associated with at least 1 trait.  

Whole-genome selection approach  

Whole genome selection (WGS) is an approach utilizing DNA markers distributed throughout the entire genome. Genes affecting most of the economically important traits are distributed throughout the genome and there are relatively few that have large effects with many more genes with progressively smaller effects. Conventional marker-assisted selection (MAS) aims only on regions which more probably influence the trait of interest thus leaves much of the genetic variation unaccounted. In contrast, whole genome selection puts the greatest emphasis on those regions with the largest effects, while still accounting fittingly for the remaining genetic variation in the genome. It uses genotypes of thousands of single nucleotide polymorphism (SNP) markers to predict breeding values (EBVs). Whole genome selection has been applied successfully to the selection of young bulls as well as for extensively recorded traits (e.g., milk production). 

Applications of Markers  

The ability to predict the total genetic merit of livestock using molecular or biochemical markers would allow the opportunity to completely redesign animal breeding programs. For example, the need to progeny test dairy bulls for the milk production of their daughters or beef bulls for the meat tenderness of their steer progeny will evanesce and the ability to manage groups of feedlot steers according to their most profitable market opportunity will materialize. However, this can only be accomplished by technologies that are able to explain economically significant amounts of the (primarily additive) genetic variation that underlies each trait. There appear to be several reasons for this, and the 2 most significant appear to be that insufficient markers have been identified to explain more than a few percent of the variation in any livestock trait and few systematic approaches have been made toward the integration of molecular data into the genetic evaluation systems of any livestock species. Next generation sequencing technologies appear to be poised to revolutionize livestock genomics. Mapping of genes for economic traits in farm animals is of importance both for improving efficiency of breeding, as well as for gaining an improved understanding of the biological mechanisms that cause genetic differences in traits.  

Future strategies for genomic selection  

• Exploration and management of genetic resources in terms of SNPs, ESTs, DNA sequences and genomic libraries.

• Establishment of large reference populations for validation of genetic markers in Indian agro-climatic conditions

• Develop programmeon associate herd scheme for increasing population base for progeny testing using conventional selection and developing gene markers of varied populations.  

• Development of breeding plans incorporating genomic selection for cattle and buffaloes in India. 

• To study genotype x environment and genotype x genotype interaction that will help in selecting the best genotype under Indian climatic condition. 

• The tools can be directly used for selection of breeding animals. 

• Genomic selection can bypass the bottleneck of non-availability of field records in India, required for accurate evaluation of genetic potential. 

• Develop tools to introduce genomic prediction and identification of Quantitative Trait Loci (QTL) to cattle and buffalo breeding in India. 

• Identification of QTL regions affecting production, fertility and disease resistance trait will lead to the identification of genes with major effects on economically important traits and understanding the biology behind these complex traits. 

Conclusion 

Genomic prediction of the genetic merit of selection candidates relies on dense marker maps to predict breeding values. In developing countries like India, where large scale data collection is not extensively practiced; genomic information has a huge potential to benefit in animal breeding. Indigenous cattle and buffalos from India are known for their disease resistance and adaptability to hot and humid climatic conditions, but have low productivity. Thus their true potential can be utilized while improving productivity with an aid of genomic selection. However this will only be realized in collaboration with well-established genomic breeding programs like the European countries, which is based on decades of accurate recordings of animal performance.  

Authors

Himanshu Mehta, Bharti Deshmukh and Neeraj Kashyap 

Guru Angad Dev Veterinary and Animal Sciences University, Ludhiana, Punjab