Genomic Era Introduction

Difference between genetics and genomics

Genetics: “The study of heredity (i.e. Inheritance).” Focuses on how characteristics are inherited using one gene or a few genes. Uses pedigree + phenotypes + one/few mutations and requires simpler designs and lab techniques. Genomics: “The study of all an organism’s genes (i.e. the genome).” Requires advanced data analytics, statistical models, sequencing platforms, and high-performance computing.

Why genomics is important

Genomics improves difficult or expensive-to-measure traits (feed efficiency), low heritability traits (fertility/fitness), sex-limited traits (milk yield, butterfat), and traits expressed late in life (carcass traits, marbling, tenderness). Genomics can double genetic improvement rate and decrease generation interval.

Examples of difficult traits genomics can improve

Feed efficiency requires measuring individual feed intake using systems such as Calan-gate, GrowSafe, and SmartFeed systems.

Three major branches of genomics

1. Structural genomics = studying the physical nature of genomes including sequencing and mapping genomes. Includes SNPs, Indels, CNVs, QTL mapping, and GWAS. 2. Functional genomics = studying expression and function of the genome using RNA-Seq, CHIP-Seq, and eQTL. 3. Comparative genomics = comparing genomes of different organisms to identify conserved sequences and evolutionary changes.

Structural genomics definition

“Studying the physical nature of genomes and includes the sequencing and mapping of genomes.”

Functional genomics definition

“studying the expression and function of the entire genome.”

Comparative genomics definition

“Comparing the genomes of different organisms.” Also: “Comparative genomics can be used to define important structural sequences that are identical in many genomes and to detect evolutionary changes across genomes.”

Genome sequencing technologies

Sanger DNA sequencing (1977–1990s), DNA microarrays (mid-1990s), second-generation DNA sequencing (~2007), and third-generation/single-molecule sequencing (~2010). Sequencing technologies provide very high-resolution snapshots of nucleic acids.

Illumina sequencing platforms examples

GAII = 1.6 billion nt/day (2008). HiSeq 2000 = 75 billion nt/day (2011). NovaSeq 5000/6000 = 1–3 trillion nt/day (2017). NovaSeq X series = 1–16 trillion nt/day (2022–2023).

Why high-performance computing (HPC) is important in genomics

Genomics generates massive sequencing datasets that require advanced computing power and storage for analysis.

Genetic variants (markers)

Types of genetic variation include SNPs, INDELs, CNVs, and STRs (microsatellites). These markers are generated from genome sequencing and are used in structural and quantitative genomics applications.

Genotype definition

“Genotype: It is a combination of alleles at a locus or loci for an individual.”

SNP definition

A SNP (single nucleotide polymorphism) is “a substitution of a single nucleotide that occurs at a specific position in the genome where minor allele frequency is more than 1%.”

SNP genotypes

Homozygote (A/A), Homozygote (G/G), and Heterozygote (A/G).

Important SNP concept

Using sequencing or genotyping assays, it is possible to determine which alleles animals possess at a SNP locus.

INDEL definition

INDEL = insertion/deletion mutation. Occurs when DNA is lost or gained on a small scale (

Effects of INDELs

Indels can change protein sequences and shift the codon reading frame.

CNV definition

A CNV (Copy Number Variant) is a DNA segment for which copy-number differences have been observed. CNVs may range from one kilobase to several megabases and may contain multiple genes.

Important CNV concept

Animals can be genotyped for CNVs similarly to SNPs and INDELs.

Development of commercial SNP/INDEL chips

Commercial genotyping chips rapidly increased in density from ~3K and 50K SNP chips to high-density and sequence-level panels containing hundreds of thousands to millions of variants.

Applications of structural/quantitative genomics

QTL mapping, genomic selection, genomic diversity, biomarker development, heterosis/hybrid vigor, customized marker panels, haplotypes/genetic recessives, traceability, parentage verification, and understanding biology.

QTL mapping definition

“The process of finding genomic regions associated with variation in phenotypes.”

Three approaches to QTL mapping

1. Linkage analysis (designed crosses; uses 1–few markers). 2. Candidate gene approach (uses 1–100s of variants). 3. GWAS (uses thousands to millions of variants).

Key principle of QTL detection

If the marker and QTL are linked, comparing marker genotypes (MM vs mm) is equivalent to comparing QTL genotypes (QQ vs qq).

Linkage equilibrium

Allele combinations occur independently. Example in slides: AB, ab, aB, and Ab gametes each occur at ~25%.

Linkage disequilibrium (LD) definition

LD occurs when alleles at different loci are inherited together more often than expected by chance due to physical linkage.

Example of linkage disequilibrium

In LD, parental combinations such as AB and ab may occur much more frequently (49%) than recombinant combinations Ab and aB (1%).

Importance of linkage disequilibrium

LD allows markers to indirectly track nearby disease/QTL variants because linked markers are inherited together.

Association analysis model

y = μ + SNP + other factors + g + e where y = phenotype, μ = mean, SNP = genotype effect, g = polygenic effect, and e = residual error.

Polygenic effect in association analysis

The accumulated effect of all SNPs captured by the genomic relationship matrix (GRM).

Additive effect estimation for a marker

Additive Effect of Allele i = ½ × (Average Homozygote1 − Average Homozygote2). Example: Additive effect of allele T = ½ × (Average TT − Average CC).

Marker Assisted Selection (MAS) definition

“Using the marker genotype to select individuals with the best linked QTL allele.” Marker genotype assists in selecting animals with the best QTL genotype.

How well does MAS work?

Results vary widely. Simulations show responses ranging from less response to 40–50% improvement, but 2–5% improvement is more realistic in practice.

Genomic selection definition

A highly effective approach for quantitative traits using high-density SNP panels, genome sequence genotypes, GWAS, and genomic prediction to estimate genomic breeding values.

Features of genomic selection

Uses high-density SNP panels (100K, 777K, millions of markers), genome-wide association analyses, and genomic breeding values/genomic-enhanced breeding values.

Principle of genomic selection

A reference population with known genotypes and phenotypes is used to estimate prediction equations. Selection candidates are genotyped and genomic breeding values are calculated to identify superior breeders.

Reference population in genomic selection

A population with known phenotypes and genotypes used to estimate marker effects and prediction equations.

Genomic breeding value calculation concept

Genomic breeding values are calculated by summing marker effects across SNPs using prediction equations.

Simplified genomic breeding value example

Estimated SNP effects: SNP1 = +4, SNP2 = +2, SNP3 = +1, SNP4 = −3. Total genomic breeding value is obtained by summing SNP genotype contributions across loci.

Difference between MAS and genomic selection

MAS focuses on a few linked markers/QTLs, while genomic selection uses genome-wide high-density markers and captures effects across the entire genome.

Why genomic selection is more effective for quantitative traits

Quantitative traits are controlled by many genes of small effect, so using genome-wide markers captures more additive genetic variance than focusing on a few QTLs.

True/False high-yield concepts

Genetics usually studies one/few genes TRUE. Genomics studies the entire genome TRUE. Genomics can improve low-heritability traits TRUE. SNPs are single nucleotide substitutions TRUE. INDELs can shift the reading frame TRUE. CNVs involve copy-number differences TRUE. GWAS can use millions of markers TRUE. Linkage disequilibrium means alleles are inherited together more often than expected TRUE. MAS usually uses a few markers TRUE. Genomic selection uses genome-wide markers TRUE.

Most important conceptual distinction between MAS and genomic selection

MAS tracks a few linked QTL markers, while genomic selection predicts breeding value using dense genome-wide marker information across the whole genome.

Why linkage disequilibrium is essential for genomics applications

LD allows markers to act as proxies for nearby causal mutations/QTL because linked alleles tend to be inherited together.

Most important advantage of genomic selection

Improves prediction accuracy and allows earlier selection, reducing generation interval and increasing genetic gain.