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Table 3 Accuracy of 17 traits in the loblolly pine population r(Deregressed Phenotypes, GEBVs)

From: Improving accuracy of genomic prediction by genetic architecture based priors in a Bayesian model

Trait category

Traits

GBLUP

BayesB

BayesBÏ€

BayesCÏ€

Growth

HT

0.376 ± 0.003

0.351 ± 0.003

0.363 ± 0.002

0.374 ± 0.002

HTLC

0.451 ± 0.002

0.449 ± 0.002

0.448 ± 0.002

0.449 ± 0.001

BHLC

0.487 ± 0.006

0.468 ± 0.007

0.479 ± 0.007

0.487 ± 0.002

DBH

0.458 ± 0.002

0.436 ± 0.003

0.446 ± 0.003

0.458 ± 0.002

Development

CWAL

0.381 ± 0.003

0.386 ± 0.003

0.388 ± 0.003

0.382 ± 0.002

CWAC

0.468 ± 0.002

0.468 ± 0.002

0.474 ± 0.002

0.469 ± 0.002

BD

0.262 ± 0.004

0.263 ± 0.004

0.265 ± 0.004

0.264 ± 0.003

BA

0.512 ± 0.003

0.497 ± 0.002

0.500 ± 0.003

0.512 ± 0.002

Rootnum_bin

0.277 ± 0.003

0.272 ± 0.004

0.279 ± 0.003

0.275 ± 0.002

Rootnum

0.262 ± 0.003

0.245 ± 0.003

0.253 ± 0.003

0.261 ± 0.002

Disease resistance

Rust_bin

0.306 ± 0.004

0.368 ± 0.004

0.353 ± 0.004

0.32 ± 0.003

Rust_gall_vol

0.259 ± 0.005

0.325 ± 0.006

0.292 ± 0.006

0.267 ± 0.004

Wood quality

Stiffness

0.424 ± 0.003

0.401 ± 0.003

0.410 ± 0.003

0.422 ± 0.002

Lignin

0.179 ± 0.005

0.173 ± 0.005

0.176 ± 0.005

0.178 ± 0.003

LateWood

0.254 ± 0.003

0.254 ± 0.003

0.257 ± 0.003

0.253 ± 0.002

Density

0.239 ± 0.003

0.226 ± 0.003

0.234 ± 0.003

0.239 ± 0.002

C5C6

0.264 ± 0.004

0.247 ± 0.004

0.257 ± 0.004

0.262 ± 0.003

Mean accuracy

–

0.345 ± 0.003

0.343 ± 0.004

0.346 ± 0.004

0.345 ± 0.002

  1. The highest accuracies (Mean ± SE) among methods in relevant traits and subpopulations are in bold faces