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Basic concepts on GS
ΔG = i r σA / L breeder → i, r, L trade-offs → r <> L maximize r/L [i ], integrate (more) precise information more rapidly → GWE
G
P+G
G
P+G
P+G
G
G
GS addresses 3 of 4 components of genetic gain: • generation interval L : early evaluation • selection intensity i : evaluation/costs • accuracy r : information integration
Basic concepts on GS
𝑦 = µ + �𝑥𝑖𝑖𝛽𝑖 + 𝜖
𝑢 𝐵𝐵𝐵𝐵 = 𝑋𝛽
𝑢~𝑁(0,𝐺𝐺2𝑢)
𝐺 = 𝑋𝑋′/2�𝑝𝑝
What is prediction accuracy? • most common metric to assess prediction accuracy is
the correlation between estimated and true breeding values (or proxy)
• cross-validation
P G
model
G P’ P
model
P’
What affects prediction accuracy? • relationships between training & prediction sets • size of training & prediction sets • heritabilities (& correlations when multiple
traits)
• marker density (when low) • statistical model (clear with simulations, no so
clear with real data) • level of LD
sam
ple
1
sam
ple
2
Task scientific content: prediction accuracy
fonction drawPedigree [pedantics]
GS evaluation with real data: prediction accuracy versus training/prediction set sizes
P+G
minimize ϴ
G
max ϴ
training set
prediction set