Preface Animal Breeding is the selective mating of individuals to obtain desirable traits in future generations. Animal breeding has played a crucial role in shaping the relationship between humans and animals for thousands of years. From the period of domestication, the practice of selective breeding has been both an art and a science. However, with the dawn of the information age, the landscape of animal breeding is evolving rapidly. Today, data-driven approaches are paving the way for ground-breaking advancements in genetic improvement and conservation efforts. Statistics is an integral part of the science of animal breeding and the process of genetic evaluation and conservation involves analysis of data, diverse in nature. The voluminous and unique nature of animal breeding data warrants need for maintenance, storage, retrieval, processing, classification, analysis, validation and interpretation for effective application in selection of animals. In this context the book on “Data Acquisition and Analysis in Animal Breeding” was conceived and within these pages, we can delve into the essential aspects of collecting and analysing data, unlocking the potential for use in practical animal breeding.

Domestic livestock plays a major role in meeting the food requirement of human beings. Especially with dwindling resources, improvement in production efficiency is primary to meet the ever-increasing demand for animal protein. The role of an animal breeder is to bring about a sustained improvement in the quality and quantity of such products. Over the years, there has been an enormous improvement in the production ability of livestock and statistical analysis has played a predominant role in the application of principles of animal breeding to improve the productivity of animals. The combination of genetics and statistics developed into a separate branch of science called biometry.

Maintaining the required type of information in the form of records is primary for any type of animal breeding analysis and this chapter explains the importance of records and ways to maintain them in the form of registers or computer databases. The formats for different types of registers to be maintained are also given as a template in appendix. Any breeding plan is formulated to improve the economic traits depending on the utility of species reared and the chapter lists important economic traits of livestock and poultry with definitions and formulae, where ever required. Basic record maintenance is a must for any animal breeding program. Every farm or breeding program is expected to maintain records in the form of registers or computerized databases. The goal of animal breeding is to select mates with the objective to improve economically important traits. In this chapter, the most common economic traits in different species of livestock are described. Even though the list is not exhaustive the basic economic traits that have been worked for improvement are explained. Breeders also explore new composite traits which are a combination of these base traits.

Animal breeding data is unique in terms of volume, statistical analysis, type of information required etc. The large volume of data extended spatially and temporally warrants additional tools in the form of databases to handle data and enable accuracy, retrieval, efficient utilization and adaptability to software. This chapter explains the nature of animal breeding data and the importance of databases with examples and explanations. Animals are raised for some utility including meat, milk, hide, wool etc., and efforts are on to continuously improve the quality and quantity of these products. Some of the important economic traits in this regard were already discussed in earlier chapters. Record maintenance is primary for decision-making with respect to the selection of animals. The details of record maintenance including the format of registers are already explained in chapter 2. Apart from the actual information required for economic traits, pedigree information forms an important part of animal breeding data.

Basic knowledge of statistics is important to understand animal breeding analysis. In this chapter, the hypothesis testing, principles of experimental design, types of data and probability distribution are described with examples using appropriate software.

Correlations and regression are important components of animal breeding analysis. The genetic correlation is an important genetic parameter that is useful in the planning of breeding programs involving multiple traits. In this chapter, the basic concepts of correlation and regression including method of estimation, assumptions and test of significance are presented. An example using SPSS software is also given at the end for better understanding.

It is known that the phenotype of an individual is a combination of genotype and environment. Animal breeding analysis is mainly done to partition the genetic part from the environmental part so as to assess the actual genetic worth of the animal. In this process correction for the known environmental part is integral to any animal breeding analysis. In this chapter, a list of common factors affecting important economic traits are described. The analytical part for these effects will be explained in later chapters. Correction or standardization of data is very important in any animal breeding analysis. Unless the candidates under selection are placed on a common platform, various factors affecting the trait will lead to bias in assessing the performance of the individual with respect to a particular trait. For eg. a lamb born during a favorable season, a lactating cow in its third parity, first lactation yield of a cow with longer age at first calving.

The key genetic parameters available with the animal breeder to attain improvement are heritability, repeatability and genetic correlation. This chapter will mainly deal with the theoretical considerations of these parameters. The goal of an animal breeder is to bring about permanent improvement in different economic traits, either individually (univariate) or simultaneously in multiple traits (multitrait). The phenotypic value available from the animal is a combination of genotypic value and environmental deviation. By permanent improvement in a trait, we mean the improvement in genotypic value, which could be carried over to future generations. The environmental deviations are spatially and temporally specific, and thus will not be the same over generations. The genotypic value, itself is made up of additive effect, dominance deviation and interaction deviation. The additive effect is a value of genes (the sum of the average effect of genes) and hence is more important for the breeder as only the genes are passed on to the next generation.

Estimation of variance is primary for any breeding program as all the genetic parameters are dependent on these estimates. The properties of an estimate, their assumptions, type of data, availability of data, and computational requirement make it important to understand the method of estimation of variance components so as to make a wise choice that would suit the breeder’s requirement. This chapter explains the methods of estimation of variance with merits and demerits of each and the reader could use these as a guide to choose the right method of estimation. The genetic parameters are the basis for any animal breeding program and the important parameters such as heritability, repeatability and genetic correlation are ratios of (co)variances and their reliability depends on the accurate estimation of variance components. Estimates of variance components are used extensively in animal breeding.

Linear models have been extensively used in animal breeding. This chapter explains the basics of linear models including description; fixed, random and mixed models; model diagnostics and covariates. The concept of linear models in animal breeding is explained with representation and actual examples of use in different types of livestock. Introduction to Linear Models A model can be defined as a physical, mathematical or otherwise logical representation of a system, entity, phenomenon, or process. Linear models are a way of describing response variables in terms of a linear combination of the predictor variable. They provide a framework for a large set of models whose common goal is to explain or predict a quantitative dependent variable by a set of independent variables that can be categorical or quantitative.

Traits such as growth, lactation test day yield, semen evaluation are longitudinal traits measured repeatedly on the animal and random regression models (RRM) have been found to be suitable for modeling these traits, The methodology used, data requirement, assumptions, validity, software available and application of RRM are explained in this chapter. Growth, test-day milk yield, and semen traits are examples of longitudinal traits measured repeatedly on the animal and random regression models (RRM) have been found suitable for modeling such traits. RRM is based on a covariance function which could be defined as “a continuous function to give the variance and covariance of traits measured at different points on a trajectory” (van der Werf, 2001). Kirkpatrick et al. (1990) showed that variance components for longitudinal data can be modelled through covariance functions. Random regression models (RRM) and the resulting covariance function have been recognized as ideally suited for the analysis of longitudinal data (Hill, 1998; Meyer, 1998).

Milk yield is one of the most important economic traits and modelling the lactation curve is important with respect to the unique shape with ascending, persistent and descending phases. The non-linear relationship of daily milk yield with days in milk has led to extensive research in modelling the lactation curve. The nature of the lactation curve, different models, identifying the best model of fit, factors affecting the curve parameters and orthogonal polynomials to model the curve are discussed in this chapter.

The aim of the breeder is to improve on various economic traits through selection. However, due to the lesser number of parents used, over a period of time, there creeps in the effect of inbreeding and loss of genetic variability which in turn causes depression and loss of fitness. This is more prominent in finite endangered populations and evaluation for genetic variability also forms an integral part of the animal breeding analysis. Genetic variability is an integral part of domestic animal populations as fitness is directly related to the variability.

Meta-analyses has been used extensively to combine the results of existing studies and obtain powerful estimates of a parameter. In animal breeding, attempts have been made to obtain meta-analysis estimates for genetic parameters. This tool is more pertinent to animal breeding on account of heterogeneity in terms of location, sample size and other factors in the estimation of genetic parameters. This chapter explains the background, methodology, precautions and implications of meta-analysis with respect to genetic parameters. A meta-analysis is a powerful tool of statistics that could combine several estimates from different studies for a parameter and the resulting weighted estimate could be used as a representative value for the particular parameter. However, not all the studies are similar with respect to location, sample size, and methodology used and thus various precautions are to be taken right from the selection of studies to incorporating of between-study heterogeneity in the models. In animal breeding, met-analysis is very much useful for obtaining meaningful estimates of genetic parameters and this chapter explains the methodology of meta-analysis in animal breeding.

This chapter describes various multivariate techniques which are useful in animal breeding. The genetic correlation with multiple response variables is already explained in the chapters on Correlation and Genetic Parameters. The other techniques including MANOVA, Principal Component Analysis, Factor Analysis, Cluster Analysis and Discriminant Analysis are explained. Multivariate analysis is referred to a set of data with multiple response variables in general and such data is encountered often in animal breeding analysis. The simplest form of multivariate analysis is the genetic correlation where two or more traits are analysed together as response variables with factors affecting them on the right-hand side (RHS) and details of these are already explained in earlier chapters.

The large volume, type and nature of animal breeding data and the complex nature of analytical requirement warrants additional tools for handling, storage, retrieval and solving complex equations. Accuracy, speed, computational requirement, applicability, etc., has resulted in the development of numerous software exclusive for animal breeding data analysis. The database management system is already explained in the chapter on ‘Nature of animal breeding data and database management’. This chapter gives a note on the commonly used animal breeding software.

  Appendix References