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Table 2 Admixture components allow predicting several quantitative functional traits in M. truncatula. (a) Linear model between admixture components and final plant height before harvest. (b) Linear model between admixture components and number of leaves at about 2 weeks. (c) Linear model between admixture components and number of nodules below 5 cm of root growth. (d) Linear model between admixture components and number of nodules in top 5 cm of roots. (e) Linear model between admixture components and total number of nodules. Raw data from Stanton-Geddes et al. [73]

From: WhoGEM: an admixture-based prediction machine accurately predicts quantitative functional traits in plants

 

Estimate

Std. error

t value

Pr (. > |t|)

(a)

 Intercept

14.1608

0.3506

40.39

0.0000

 South Tunisian Coastal

5.7582

1.1345

5.08

0.0000

 Greek

3.6659

0.6954

5.27

0.0000

 North Tunisian Coastal

5.0274

1.1228

4.48

0.0000

 Spanish Coastal

3.7344

0.9770

3.82

0.0002

 r2 = 0.21. P = 1.5 × 10−11

(b)

 Intercept

2.8234

0.0444

63.54

0.0000

 French

− 0.4447

0.1482

− 3.00

0.0030

 Atlas

0.1995

0.1007

1.98

0.0488

 r2 = 0.05. P = 7.3 × 10−4

(c)

 Intercept

14.7805

0.5198

28.44

0.0000

 Spanish Coastal

5.0685

1.7406

2.91

0.0040

 South Tunisian Coastal

− 4.9235

2.0711

− 2.38

0.0183

 r2 = 0.06. P = 4.2 × 10−4

(d)

 Intercept

5.1755

0.2108

24.55

0.0000

 South Tunisian Coastal

− 2.4748

0.7870

− 3.14

0.0019

 Spanish Coastal

2.6320

0.6618

3.98

0.0001

 Algiers

3.1375

0.8330

3.77

0.0002

 r2 = 0.15. P = 1.1 × 10−8

(e)

 Intercept

19.7448

0.6824

28.93

0.0000

 Spanish Coastal

7.9483

2.1422

3.71

0.0003

 South Tunisian Coastal

− 7.1137

2.5473

− 2.79

0.0057

 Algiers

5.3812

2.6964

2.00

0.0472

 r2 = 0.10. P = 6 × 10−6