Genomic Selection and SNP Effects

Definition of GWAS: Genome-Wide Association Studies (GWAS) involve scanning genomes from many individuals to find genetic variants associated with specific traits.
SNPs (Single Nucleotide Polymorphisms): These are molecular markers that represent variations in a single nucleotide that occur at a specific position in the genome.
- Identified SNPs are used in GWAS to signify regions of interest.

Purpose of Fine Mapping: The process of zooming into a specific region after identifying significant SNPs during GWAS.
- Goal: To identify other variants present in the region that may not be in the initial marker set.
- Significance: Discovering variants specific to the population being studied, which may enhance specificity in predictions.
Additional Variants: While some SNPs are used as roadmaps in the broader GWAS, fine mapping may reveal more specific variants or SNPs located within the haplotype that were not previously considered.

Critical Importance: The reference population plays an essential role in calculating effective and accurate estimated breeding values (EBVs).
- Provides the connection between phenotype (observable characteristics) and genotype (genetic constitution).
- It helps to contextualize GWAS findings by comparing them to specific breeds or populations of interest.
Phenotype to Genotype Connection: Reference populations are used to determine how the regions identified in GWAS apply to specific breeds, such as dogs and wolves where SNP markers can be shared due to close evolutionary relationships.

Estimated Breeding Values (EBVs):
- Importance: They serve to predict the likely genetic contribution of an individual to the next generation based on genomic information.
- The pivotal idea discussed is estimating genomic breeding values (GEBVs).
Genomic Estimated Breeding Values (GEBVs): Defined as the sum of the product of each SNP genotype and its corresponding SNP effect.
- Formula:
  $GEBV = ext{SNP}_1 imes ext{Effect}_1 + ext{SNP}_2 imes ext{Effect}_2 + ext{SNP}_3 imes ext{Effect}_3 + …$

Understanding SNP Effects: This relates to how much each allele impacts the phenotype, i.e., heritable traits.
- Favorable Allele: Refers to the allele that influences the phenotype positively, e.g., promoting higher egg production in chickens.
- Notation for Alleles:
- Zero (0): No copies of the favorable allele.
- One (1): One copy of the favorable allele.
- Two (2): Two copies of the favorable allele.

Goal: Increase the number of eggs laid by chickens before they begin laying.
Process: Using a reference population to determine the impact of SNPs before phenotype data is available.
- Identifying SNPs: The reference population provides data on SNP effects as well as phenotypic outcomes (e.g., egg count).
SNP Effects and Calculation for Chickens: Detailed example showcasing the SNP effects for four SNPs and their respective favorable alleles:
- SNP 1: Effect = 1.5
- SNP 2: Effect = 2.5
- SNP 3: Effect = 1.0
- SNP 4: Effect = -0.5 (example of a negative effect for context)
Bird A vs. Bird B Genotypes for Four SNPs:
- Bird A: 2 copies of SNP 1, 0 of SNP 2, 1 of SNP 3, 0 of SNP 4.
- Bird B: 1 copy of SNP 1, 2 of SNP 2, 1 of SNP 3, 0 of SNP 4.
Calculating GEBVs for Birds:
- For Bird A:
  $GEBV_A = (2 imes 1.5) + (0 imes 2.5) + (1 imes 1.0) + (0 imes -0.5)$
- For Bird B:
  $GEBV_B = (1 imes 1.5) + (2 imes 2.5) + (1 imes 1.0) + (0 imes -0.5)$
Conclusion of Example: Demonstrates how using SNP effects from the reference population can effectively calculate predicted phenotypes even before the actual phenotype data is available.

The reference population is pivotal in establishing SNP effects and deriving accurate breeding values.
Ensuring genetic relationship to the population of interest is crucial for predictive accuracy in breeding programs.