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Table 3 Accuracy of genomic prediction for BCWD resistance with BayesB using progeny testing families in five GS schemes

From: Genomic selection models double the accuracy of predicted breeding values for bacterial cold water disease resistance compared to a traditional pedigree-based model in rainbow trout aquaculture

GS scheme

Family

Training size

Training–testing relationshipb

\(\pi\) c

SNPsd

DAYSe

STATUSe

Number

Size

\(PA_{GEBV}\) f

\(Bias_{GEBV}\) g

\(PA_{GEBV}\) f

\(Bias_{GEBV}\) g

1

50

20-40a

1473

0.66

0.97

1069

0.71

1.16

0.71

1.01

2

50

20

991

0.50

0.98

713

0.67

1.55

0.67

1.51

3

25

40

979

1.00

0.98

713

0.69

1.26

0.72

1.23

4

25

20

497

1.00

0.987

463

0.53

1.37

0.61

1.66

5

25

20

494

0.00

0.987

463

0.25

3.33

0.22

5.08

  1. A sample of 193 testing fish (from total n = 930 testing fish) were inter-mated to develop 138 progeny testing families (PTF). After disease evaluation of progeny from the 138 PTF (n = 9968), we estimated the mean progeny phenotype (MPP) for each PTF
  2. aIn scheme1, there were two groups of training families: (1) A set of 25 families with 40 offspring each that contributed fish to the testing sample; and (2) A set of 25 families with 20 offspring each that did not contribute fish to the testing sample
  3. bProportion of training fish that were full-sibs (FS) of testing fish: scheme 1 = 0.66 indicates that 66% of training fish were FS of testing fish; scheme 2 = 0.50 indicates that 50% of training fish were FS of testing fish; schemes 3 and 4 = 1.0 indicates that ALL training fish were FS of testing fish; and scheme 5 = 0.0 indicates that NONE of training fish were FS of testing fish (i.e., training and testing fish were sampled from different families)
  4. cBayesB method uses a mixture parameter \(\pi\) that specifies the proportion of loci with zero effect, and the analyses included 35,636 effective SNPs
  5. dNumber of SNPs that are sampled as having non-zero effect \(\left( {1 - \pi } \right)\) and fitted simultaneously in the multiple regression model
  6. eBacterial cold water disease (BCWD) resistance phenotypes: BCWD survival days (DAYS) and survival status (STATUS)
  7. fPredictive ability of GEBV \(\left( {PA_{GEBV} } \right)\) was defined as the correlation of MPP with mid-parent GEBV from each PTF: \(PA_{GEBV} = CORR\left( {MPP, \;Midparent\;GEBV} \right)\)
  8. gBias of GEBV \(\left( {Bias_{GEBV} } \right)\) was defined as the regression coefficient of performance MPP on predicted mid-parent GEBV: \(Bias_{GEBV} = REGRES\left( {MPP, \;Midparent\; GEBV} \right)\). The predicted GEBV for STATUS estimated on the underlying scale of liability were transformed to the observed scale (probability of survival)
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Genetics Selection Evolution

ISSN: 1297-9686