Fundamentals of Animal Breeding: Principles and Practice

A Practical Introduction for Undergraduate Students

Author

Austin Putz

Published

November 18, 2025

Welcome

Animal Breeding and Genetics: A Practical Introduction

Welcome to Animal Breeding and Genetics—your comprehensive guide to understanding how we improve livestock populations through systematic genetic selection. This textbook is designed as the first course in animal breeding for undergraduate students in animal science programs.

🎯 Quick Start

New to animal breeding? Start with Chapter 1 to understand the industry, then work through Chapters 2-6 for core concepts. Want to dive into genomics? Jump to Chapters 12-13 after covering the foundations.

Why Animal Breeding Matters

Animal breeding has transformed livestock productivity over the past century:

  • Dairy cattle produce 4-5× more milk than their ancestors from the 1950s
  • Broiler chickens reach market weight in half the time with dramatically improved feed efficiency
  • Swine produce larger litters with leaner, faster-growing offspring
  • Beef cattle exhibit improved carcass quality, feed efficiency, and maternal ability

These improvements directly impact:

  • 🌍 Food security: Efficient animal protein production for a growing population
  • ♻️ Sustainability: Reduced environmental footprint per unit of product
  • 💰 Economic viability: Improved profitability for livestock producers
  • 🐄 Animal welfare: Selection for health, longevity, and reduced stress

Understanding animal breeding is essential for careers in production agriculture, genetics companies, breed associations, and agricultural research.

Who This Book Is For

Primary Audience

  • Undergraduate students (juniors/seniors) taking their first animal breeding course
  • Graduate students seeking a practical foundation before advanced quantitative genetics
  • Industry professionals updating their knowledge of modern breeding methods
  • Livestock producers wanting to understand genetic improvement

Prerequisites

To get the most from this book, you should have completed:

✅ Introductory genetics (Mendelian genetics, basic molecular biology) ✅ Basic statistics (mean, variance, correlation, simple linear regression) ✅ Introduction to R (or willingness to learn alongside the course)

New to R?

Don’t worry! Appendix B provides comprehensive R resources. You can learn R as you work through the book.

What You’ll Learn: Chapter-by-Chapter Overview

Part 1: Foundations of Animal Breeding

Chapter 1: Introduction to Animal Breeding

Learn how the animal breeding industry works:

  • What is animal breeding and why does it matter?
  • How genetics companies generate revenue (semen sales, females, embryos, genomic testing)
  • Industry structure by species (swine, poultry, dairy, beef, horses)
  • Major players: Genus/PIC, Cobb-Vantress, Select Sires, breed associations
  • Pyramid breeding structures (nucleus-multiplier-commercial)

Key takeaway: Understand the business context that drives breeding decisions.

Chapter 2: Selection Basics

Master the fundamentals of genetic selection:

  • Traits of economic importance (production, reproduction, health, efficiency, quality)
  • Phenotyping: how we measure traits accurately
  • Data recording and quality control
  • Defining breeding objectives
  • Introduction to True Breeding Value (TBV)

Key takeaway: High-quality data is the foundation of genetic improvement.

Chapter 3: Basic Genetic Model

Learn to partition phenotype into genetic and environmental components:

  • The basic model: y = μ + A + E
  • Additive vs. dominance vs. epistatic effects
  • Why additive effects drive selection response
  • Variance partitioning (σ²_P = σ²_A + σ²_D + σ²_E)
  • Parent-offspring relationships

Key takeaway: Only additive genetic effects are reliably transmitted to offspring.

Chapter 4: Quantitative vs. Simply Inherited Traits

Understand different types of genetic architecture:

  • Simply inherited traits (single genes, major effects): coat color, polled/horned, genetic defects
  • Quantitative traits (many genes, small effects): growth, reproduction, production
  • Why most economic traits are quantitative
  • Threshold traits and mixed models
  • Modern genomics bridges the gap

Key takeaway: Breeding strategies differ for simply inherited vs. quantitative traits.

Part 2: Genetic Parameters

Chapter 5: Heritability and Repeatability

Learn the most important parameter in animal breeding:

  • Heritability (h²): proportion of variance due to additive genetics
  • Interpreting heritability (high: >0.40; moderate: 0.20-0.40; low: <0.20)
  • Common misconceptions about heritability
  • Repeatability (r): upper limit of heritability
  • Permanent environmental effects (PE)

Examples: Growth traits (h² = 0.35), reproductive traits (h² = 0.10), carcass traits (h² = 0.45)

Key takeaway: Higher heritability means faster response to selection.

Chapter 6: Breeder’s Equation and Selection Response

Predict genetic progress with the breeder’s equation:

  • The equation: R = (i × r × σ_A) / L
  • Selection intensity (i): how hard we select
  • Accuracy (r): how well we predict breeding values
  • Genetic standard deviation (σ_A): amount of genetic variation
  • Generation interval (L): age of parents when offspring are born
  • Trade-offs among the four factors (especially accuracy vs. generation interval)
  • Comparing selection strategies (mass selection, progeny testing, genomic selection)

Key takeaway: Genomic selection resolves the accuracy vs. generation interval trade-off.

Part 3: Selection Methods

Chapter 7: Estimating Breeding Values

Learn how to predict genetic merit:

  • Simple methods: own performance, pedigree index, progeny average
  • Contemporary groups: accounting for environmental differences
  • BLUP (Best Linear Unbiased Prediction) - the modern standard
  • Understanding EBVs (Estimated Breeding Values)
  • Accuracy and reliability of predictions
  • Examples: Dairy Net Merit, Beef EPDs, Swine EBVs

Key takeaway: BLUP uses all information optimally to estimate breeding values.

Chapter 8: Genetic Correlations and Correlated Response

Understand how traits are genetically linked:

  • Genetic correlation (r_A): correlation between breeding values for two traits
  • Causes: pleiotropy (same genes) or linkage (genes on same chromosome)
  • Interpreting correlations: positive, negative (antagonistic), zero
  • Correlated response to selection
  • Important examples:
    • Dairy: milk yield vs. fertility (r_A ≈ -0.35) ❌
    • Swine: growth rate vs. backfat (r_A ≈ +0.30) ❌
    • Beef: weaning weight vs. yearling weight (r_A ≈ +0.70) ✅

Key takeaway: Negative genetic correlations require multi-trait selection.

Chapter 9: Multiple Trait Selection and Selection Index

Learn to balance improvement across multiple traits:

  • The need for multi-trait selection
  • Breeding goal (H) vs. selection index (I)
  • Economic weights: how much is each trait worth?
  • Selection index theory: b = P⁻¹ G v
  • Calculating and interpreting index weights
  • Real-world examples: Dairy Net Merit, Beef Terminal Index, Swine Sow Index

Key takeaway: Selection indices optimally combine traits based on economic value and genetic parameters.

Part 4: Mating Systems and Crossbreeding

Chapter 10: Mating Strategies and Optimum Contribution Selection

Learn to make smart mating decisions:

  • Selection vs. mating decisions
  • Types of mating systems (random, assortative, compensatory)
  • Managing inbreeding and genetic diversity
  • Inbreeding coefficient (F) and inbreeding depression
  • Optimum Contribution Selection (OCS): maximize gain while constraining inbreeding
  • Software tools: MateSel, AlphaMate

Key takeaway: Mating decisions affect both genetic gain and long-term genetic diversity.

Chapter 11: Crossbreeding and Hybrid Vigor

Exploit heterosis for improved performance:

  • What is crossbreeding and why crossbreed?
  • Heterosis (hybrid vigor): superior performance of crossbreds
  • Genetic basis: dominance, overdominance, epistasis
  • Traits with high heterosis: reproductive traits (5-20%)
  • Crossbreeding systems: terminal cross, rotational, composite breeds
  • Industry structure:
    • Swine: (Large White × Landrace) F₁ × Duroc terminal sire
    • Beef: Crossbred cows × terminal sire
    • Poultry: Proprietary four-way crosses

Key takeaway: Crossbreeding is nearly universal in commercial swine and poultry production.

Part 5: Genomic Tools

Chapter 12: Introduction to Genomics

Learn about DNA markers and genomic technologies:

  • What is genomics in animal breeding?
  • SNPs (Single Nucleotide Polymorphisms): the most common genetic markers
  • SNP chips (arrays): genotyping thousands to millions of SNPs
  • Bioinformatics basics: quality control, imputation
  • Whole genome sequencing (WGS)
  • Applications: genomic selection, parentage verification, GWAS, inbreeding management

Key takeaway: Genomic technologies have revolutionized animal breeding since ~2008.

Chapter 13: Genomic Selection

Understand modern genomic prediction methods:

  • What is genomic selection? Predicting breeding values from SNP data
  • Why it works: linkage disequilibrium (LD) between SNPs and causal mutations
  • Genomic BLUP (GBLUP): using genomic relationship matrix (G)
  • SNP-BLUP: estimating effects of individual SNPs
  • Bayesian methods: BayesA, BayesB, BayesC (allow SNPs to have different effects)
  • Single-step genomic BLUP (ssGBLUP): the industry standard
  • Impact: doubled rate of genetic gain in dairy cattle

Key takeaway: Genomic selection enables high-accuracy predictions for young animals without waiting for progeny.

Part 6: Practical Applications

Chapter 14: Practical Animal Breeding and Data Skills

Build practical skills for industry careers:

  • SQL basics for querying breeding databases
  • APIs for accessing genomic data and services
  • Data management and quality control
  • Animal identification systems (RFID, DNA-based parentage)
  • Phenotypic culling vs. genetic selection
  • Career paths: geneticist, data manager, reproductive technician, bioinformatician
  • Software used in industry: BLUPF90, ASReml, MateSel

Key takeaway: Successful breeders need both theoretical knowledge and practical data skills.

Appendices

Appendix A: Datasets - Documentation of simulated datasets used throughout the book Appendix B: R Resources - Guide to R packages for animal breeding (tidyverse, lme4, AGHmatrix, BGLR, rrBLUP, AlphaSimR) Appendix C: Glossary - Alphabetical reference of genetic terms

What Makes This Book Different

🐄 Livestock-Focused

Every example comes from real livestock breeding: swine, poultry, dairy, beef, horses, sheep

📊 R Programming Integrated

Learn to perform calculations and analyze data using R (code included but hidden by default)

🏭 Industry Context

Understand how genetics companies work and make money

📈 Modern Genomics

Covers genomic selection methods used in today’s breeding programs

🎓 Undergraduate-Friendly

Clear explanations with intuition before equations; practical emphasis over mathematical proofs

💡 Interactive Learning (Coming Soon)

Links to Shiny apps for hands-on exploration of breeding concepts

How to Use This Book

For a Full Course (14-16 weeks)

Work through Chapters 1-13 sequentially, approximately one chapter per week. Use Chapter 14 as a capstone or integrate throughout.

For Self-Study

  • Core sequence: Chapters 1-6 (foundations and genetic parameters)
  • Then choose your path:
    • Selection focus → Chapters 7-9
    • Crossbreeding focus → Chapters 10-11
    • Genomics focus → Chapters 12-13
    • Career preparation → Chapter 14

Tips for Success

  1. Read conceptually first - understand the “why” before the equations
  2. Work through examples - do calculations by hand before checking solutions
  3. Run the R code - experiment with changing parameters
  4. Use the practice problems - test your understanding at the end of each chapter
  5. Refer to appendices - datasets, R resources, and glossary as needed

Software Requirements

R and RStudio

This book uses R (version ≥ 4.0) and RStudio. Both are free and open-source.

Installation: 1. Download R from CRAN 2. Download RStudio from Posit

Key R Packages

Install packages used throughout the book:

install.packages(c(
  # Data manipulation and visualization
  "tidyverse",

  # Pedigree and relationships
  "pedigree", "nadiv", "AGHmatrix",

  # Mixed models and genetic evaluation
  "lme4", "lmerTest",

  # Genomic prediction
  "BGLR", "rrBLUP", "brms",

  # Simulation
  "AlphaSimR",

  # Database and utilities
  "DBI", "RSQLite", "knitr", "kableExtra"
))

See Appendix B for detailed package descriptions and documentation.

About the Author

Austin Putz has extensive experience in animal breeding and quantitative genetics, with expertise in swine, beef cattle, and dairy cattle breeding programs. [Additional bio details to be added]

Acknowledgments

This book builds on decades of research and teaching in quantitative genetics and animal breeding. Special thanks to:

  • The pioneers of quantitative genetics whose work forms this field’s foundation
  • Industry partners who shared their expertise and real-world perspectives
  • Students whose questions shaped the explanations in this book
  • Colleagues who reviewed drafts and provided invaluable feedback

Getting Started

Ready to begin? Start with Chapter 1: Introduction to Animal Breeding to understand the industry context, then progress through the foundational concepts in Chapters 2-6.

Welcome to the fascinating world of animal breeding and genetics!

Note: This is an open educational resource. All code and datasets are freely available for teaching and learning.