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Table 3 Accuracy of the best prediction models with haplotype additive values compared to the best SNP models

From: Haplotype genomic prediction of phenotypic values based on chromosome distance and gene boundaries using low-coverage sequencing in Duroc pigs

 

Trait

AGW

ADG

BJS

FCR

LMA

LMD

BF

TN

SNP accuracy for predicting phenotypic values \({\hat{\text{R}}}_{{0}_{\text{jp}}}=\text{corr}({\hat{\text{g}}}_{{0}_{\text{j}}},{\text{y}}_{0})\)

 A-only, Model-6

0.251

0.258

0.258

0.197

0.402

0.363

0.387

0.401

 A + D, Model-5

0.252

0.259

0.244

0.202

0.401

0.364

0.387

0.405

 A + D over A (%)

0.381

0.328

− 5.588

2.624

− 0.123

0.496

0.174

0.856

Best SNP prediction model (the SNP model in italic font, A-only or A + D)

 \({ \hat{\text{R}}}_{{0}_{\text{jp}}}=\text{corr}({\hat{\text{g}}}_{{0}_{\text{j}}},{\text{y}}_{0})\)

0.252 ± 0.032

0.259 ± 0.028

0.258 ± 0.033

0.202 ± 0.052

0.402 ± 0.042

0.364 ± 0.055

0.387 ± 0.061

0.405 ± 0.055

 \({\text{ R}}_{{0}_{\text{jp}}}={\text{R}}_{{0}_{\text{j}}}\sqrt{{\text{h}}_{\text{j}}^{2}}\)

0.285 ± 0.010

0.290 ± 0.009

0.156 ± 0.013

0.241 ± 0.012

0.432 ± 0.008

0.418 ± 0.010

0.407 ± 0.015

0.405 ± 0.010

 \({\text{ R}}_{{0}_{\text{j}}}=\text{corr}({\hat{\text{g}}}_{{0}_{\text{j}}},{\text{g}}_{{0}_{\text{j}}})\)

0.613 ± 0.010

0.624 ± 0.01

0.559 ± 0.016

0.579 ± 0.015

0.757 ± 0.004

0.722 ± 0.009

0.718 ± 0.010

0.707 ± 0.007

Haplotype prediction accuracy

 Best model

H

H

H

D + H

H

A + D + H

A + D + H

A + D + H

 Best blocking

500 kb

500 kb

100 kb

1 Mb

Genes

Genes

1 Mb

Genes

 \({ \hat{\text{R}}}_{{0}_{\text{jp}}}=\text{corr}({\hat{\text{g}}}_{{0}_{\text{j}}},{\text{y}}_{0})\)

0.270 ± 0.029

0.276 ± 0.027

0.277 ± 0.026

0.212 ± 0.066

0.413 ± 0.043

0.371 ± 0.055

0.392 ± 0.062

0.406 ± 0.053

 Accuracy increase (%)

7.14

6.56

7.36

4.95

2.74

1.92

1.29

0.25

 \({\text{ R}}_{{0}_{\text{jp}}}={\text{R}}_{{0}_{\text{j}}}\sqrt{{\text{h}}_{\text{j}}^{2}}\)

0.292 ± 0.006

0.298 ± 0.005

0.178 ± 0.011

0.248 ± 0.014

0.431 ± 0.006

0.417 ± 0.010

0.413 ± 0.015

0.401 ± 0.010

 \({\text{ R}}_{{0}_{\text{j}}}=\text{corr}({\hat{\text{g}}}_{{0}_{\text{j}}},{\text{g}}_{{0}_{\text{j}}})\)

0.647 ± 0.005

0.650 ± 0.004

0.572 ± 0.012

0.549 ± 0.012

0.743 ± 0.004

0.710 ± 0.008

0.693 ± 0.009

0.695 ± 0.007

  1. \({\hat{\text{R}}}_{{0}_{\text{jp}}}\), observed accuracy of predicting phenotypic values; \({\text{R}}_{{0}_{\text{jp}}}\), theoretical accuracy of predicting phenotypic values; \({\text{R}}_{{0}_{\text{j}}}\), theoretical accuracy of predicting genotypic values; accuracy increase is the percentage increase in observed accuracy of predicting phenotypic values under the best haplotype model relative to the observed accuracy of the best SNP model (in italic font); A, SNP additive values; D, SNP dominance values; H, haplotype additive values; AGW, age at 100 kg live weight; ADG, daily gain; BJS, body judging score; FCR, feed conversion ratio; LMA, loin muscle area; LMD, loin muscle depth; BF, back fat thickness; TN, teat number