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Table 4 Genomic prediction (GP) accuracy for five agronomic traits in an association panel including 286 Iran bread wheat accessions grown under terminal drought stress (TDS) and well-watered (WW) conditions using high-throughput image analysis results as fixed effects in the univariate (UV) GP models

From: A simple, cost-effective high-throughput image analysis pipeline improves genomic prediction accuracy for days to maturity in wheat

Model

Method

TDS

WW

DTH

DTM

DHTM

PH

GY

DTH

DTM

DHTM

PH

GY

UV1

RR-BLUP

0.10

0.10

0.10

0.06

0.06

0.09

0.08

0.04

0.06

0.05

GBLUP

0.12

0.10

0.10

0.06

0.07

0.09

0.08

0.04

0.06

0.06

BA

0.11

0.11

0.10

− 0.05

0.11

0.09

0.09

0.02

− 0.04

0.12

BB

0.11

0.11

0.10

− 0.02

0.10

0.10

0.09

0.03

− 0.02

0.12

BC \(\pi \)

0.12

0.11

0.10

− 0.01

0.10

0.09

0.09

0.04

− 0.01

0.11

BL

0.11

0.12

0.10

− 0.06

0.08

0.11

0.08

0.03

− 0.04

0.12

BRR

0.11

0.10

0.10

− 0.03

0.10

0.10

0.09

0.03

− 0.01

0.11

UV2

RR-BLUP

0.15

0.38

0.16

0.11

0.15

0.23

0.37

0.01

0.23

− 0.03

GBLUP

0.17

0.38

0.15

0.10

0.12

0.21

0.36

− 0.01

0.23

− 0.03

BA

0.18

0.37

0.15

0.01

0.15

0.19

0.32

0.03

0.13

0.11

BB

0.18

0.38

0.15

0.02

0.15

0.21

0.34

0.03

0.16

0.10

BC \(\pi \)

0.18

0.38

0.16

0.03

0.16

0.22

0.34

0.03

0.18

0.09

BL

0.19

0.37

0.15

− 0.02

0.14

0.19

0.34

0.02

0.10

0.07

BRR

0.19

0.39

0.16

0.04

0.16

0.22

0.34

0.04

0.19

0.09

UV3

RR-BLUP

0.15

0.42

0.19

0.15

0.12

− 0.05

0.36

0.23

− 0.06

0.19

GBLUP

0.14

0.42

0.19

0.13

0.09

0.01

0.35

0.22

− 0.02

0.17

BA

0.15

0.39

0.20

0.02

0.12

0.09

0.31

0.17

− 0.04

0.18

BB

0.15

0.41

0.20

0.05

0.12

0.08

0.31

0.18

− 0.02

0.18

BC \(\pi \)

0.15

0.41

0.21

0.06

0.13

0.08

0.32

0.19

− 0.01

0.18

BL

0.16

0.40

0.20

0.01

0.11

0.06

0.30

0.16

− 0.05

0.18

BRR

0.16

0.41

0.21

0.07

0.13

0.08

0.32

0.20

− 0.01

0.19

UV4

RR-BLUP

0.14

0.44

0.17

0.14

0.13

0.22

0.42

0.23

0.25

0.18

GBLUP

0.16

0.44

0.18

0.12

0.12

0.21

0.41

0.21

0.24

0.16

BA

0.17

0.44

0.19

0.02

0.15

0.20

0.38

0.16

0.15

0.18

BB

0.17

0.44

0.19

0.05

0.14

0.20

0.39

0.17

0.18

0.18

BC \(\pi \)

0.17

0.44

0.20

0.06

0.14

0.21

0.40

0.19

0.20

0.17

BL

0.17

0.43

0.20

− 0.01

0.16

0.20

0.38

0.13

0.11

0.19

BRR

0.18

0.45

0.20

0.07

0.15

0.21

0.39

0.19

0.20

0.18

  1. The average of accuracies was reported across folds and repeats. No covariate was used in the UV1 model. TOR as a covariate was used in the UV2 model. STR as a covariate was used in the UV3 model. Both TOR and STR as covariates were utilized in the UV4 model
  2. GBLUP genomic best linear unbiased prediction, RR-BLUP ridge regression-best linear unbiased prediction, BA Bayesian A, BB Bayesian B, BC \(\pi \) Bayesian C \(\pi \), BL Bayesian LASSO, BRR Bayesian ridge regression, DTH days to heading, DTM days to maturity, DHTM duration of heading-to-maturity, PH plant height (cm), GY grain yield (kg/m2), TOR tolerance ratio, STR stress ratio