This document summarizes initial results from modeling genome-wide predictions in peach. Eleven peach populations from four sites were genotyped and phenotyped for traits like fruit weight, sugar content, and acidity. Genotypes were imputed and a repeatability model was used to estimate heritability and predict trait values based on genomic relationships. Preliminary results showed variable predictive ability across populations and traits. Next steps include standardizing the data analysis and interpreting the results to draft a paper on genomic predictions in peach breeding.

1. Eucarpia 2015
Filippo Biscarini
Nelson Nazzicari, Marco Bink, Pere Arus, Maria Jose Aranzana, Ignazio Verde, Sabrina Micali, Thierry
Pascal, Benedicte Quilot-Turion, Patrick Lambert, Igor Pacheco Cruz, Daniele Bassi, and Laura Rossini
Modelling genome-
wide predictions in
peach: first results
and perspectives
FruitBreedomics International Conference

2. Motivation
A little background!
● Genome-enabled predictions: popular in human (e.g. disease
risk) and livestock (e.g. dairy cattle) genetics
● Relevant also in plant genetics: crops and trees
● Interest in peach breeding, too!

3. Plant material
populations and traits
● 11 populations from 4
sites
● 3 traits: fruit weight,
sugar content (Brix)
and titrable acidity
● max n. records per
population x trait
combination
● yellow/red: done/to-be-
done

4. Phenotypes
description
● wide phenotypic
variability
● range: 51.7 – 191.8
(fruit weight); 12.04 –
16.21 (sugar content);
7.78 – 16.50 (acidity)
● larger for fruit weight
and acidity, lower for
sugar content
● red: max values;
yellow: min values
σ
μ

5. Genotypes
description
● all populations genotyped with:
peach 9K SNP-chip
● different data editing (so far): call-
rate, monomorphic SNP
● n. SNP after editing
● low residual missing rate: 0.23 –
5.56%
σ
μ

6. Imputation
results
● “Beagle”
● Little imputation
experiment
● Complete datasets, MAF
0.25-0.45
● 10 replicates per
threshold
● Imputation accuracy:
decreases with amounts
of missing genotypes
● Full data: 0.62 – 0.97
● Worst-case scenario:
MB1.73 x EarlyGold:
5.5% missing genotypes
x 0.95 imputation
accuracy = 0.3% errors

7. Genomic predictions
Let's get to the meat!
● Use SNP genotypes to predict unobserved (e.g. future) performances
● Phenotypic data over multiple years (1 to 5) → repeated observations model
● A.k.a. “repeatability model”
● 5-fold cross-validation: used to estimate the accuracy of genomic predictions
● 10-50 repetitions, so far (depending on population-trait)

8. Genomic predictions
The model
● Xb: systematic effects (mean, year)
● Za: additive genetic effect
● Wpe: permanent envrionment effects
● e: residuals
y=Xb+ Za+Wpe+e

9. Genomic predictions
The model
Var( y)=ZGZ ' σa
2
+WIW ' σpe
2
+I σe
2
● G: matrix of estimated genomic relationships (IBS)
● À la Van Raden (2008)
● G-BLUP: BLUP model in which the genomic information was introduced
through the variance-covariance matrix
● ASREML (REML), BGLR (MCMC)

10. Genomic predictions
The model
● Heritability: h
2
=
σa
2
σa
2
+σpe
2
+σe
2
● Repeatability: rep=
σa
2
+σ pe
2
σa
2
+σpe
2
+σe
2
● Predictive ability: r=cor( y , ̂y) ̂yik=μ+̂yeark +̂ai+̂pei

11. Heritability
Results

12. Repeatability
Results

13. Predictive ability

14. Next steps
● Genomic predictions in peach trees are feasible
● Preliminary results: very variable!
● Sample size, specific crosses, n. of replicates, trait definition ...
● Methods and (bio)informatics pipelines ready: 100%!
● Align data: retrieve all genotypes and phenotypes, apply same editing
policies etc …: almost there!
● Same n. of repetitions all over
● Interpret results
● Start drafting paper