Understanding and predicting complex traits: knowledge from cattle.

Area of Science:

• Animal Genetics
• Quantitative Genetics
• Genomic Prediction

Background:

• Complex traits in cattle are influenced by numerous genetic loci, most with small effects.
• Recent selection can lead to moderate-to-large effect loci with low allele frequencies.
• Genomic markers capture substantial, but not all, additive genetic variance due to incomplete linkage disequilibrium.

Purpose of the Study:

• To evaluate the accuracy of genomic prediction for cattle genetic values.
• To determine the sample size required for accurate genomic prediction in cattle.
• To compare genomic prediction requirements between cattle and humans.

Main Methods:

• Utilized statistical models incorporating all genomic markers with random marker effects.
• Assessed the impact of effective population size on prediction accuracy.
• Calculated the number of animals needed for a prediction accuracy of 0.65 in cattle.

Main Results:

• An effective population size of approximately 100 in cattle allows for a prediction accuracy of 0.65 with around 2500 animals.
• Humans, with a larger effective population size (10,000-15,000), require over 145,000 records for similar accuracy.
• Current genomic markers may not capture all genetic variance due to low-frequency causal variants.

Conclusions:

• Genomic prediction is a powerful tool for cattle breeding, requiring manageable sample sizes.
• Effective population size significantly influences the data requirements for accurate genomic prediction.
• Future advancements, like genotyping causal polymorphisms via genome sequencing, may further enhance prediction accuracy.