Identification of RILs for agronomic and grain quality traits in rice through Intraspecific crosses

Silva, Raissa Martins da; Thurow, Liamara Bahr; Nardino, Maicon; Oliveira, Victoria Freitas de; Lopes, Jennifer Luz; Maltzahn, Latoia Eduarda; Venske, Eduardo; Pegoraro, Camila; Maia, Luciano Carlos da; Oliveira, Antonio Costa

doi:10.1590/1984-70332024v24n1a05

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ARTICLE • Crop Breed. Appl. Biotechnol. 24 (1) • 2024 • https://doi.org/10.1590/1984-70332024v24n1a05 copy

Identification of RILs for agronomic and grain quality traits in rice through Intraspecific crosses

Authorship SCIMAGO INSTITUTIONS RANKINGS

Abstract

This study aimed to select recombinant lines and explore phenotypic and genotypic correlations using BLUP. It was conducted in Capão do Leão/RS during two years, in an incomplete block design with intercalary controls, with four replications. 131 and 128 RILs were tested in the F₆ in the F₇ generations, respectively. Plant height; days to flowering; panicle length; number of panicles per plant; number of fertile and sterile spikelets per panicle; one hundred grains weight; yield per plant; broken, chalky, white-belly and red-streaked grains; vitreous whiteness; gelatinization temperature; and apparent amylose content were obtained. According to the study, line F105 is an elite line for improving grain quality, exhibiting high amylose content (27.041%). Canonical (r=0.817), phenotypic (0.541) and genotypic (0.808) correlations inferred that groups of grain quality and agronomic traits were not independent and there was a tendency for the amylose content to be associated with grain yield.

Keywords:
Oryza sativa L.; biometric models; BLUP analysis; cooking quality; industrial quality

Trait/ F₆	Sample size	Minimum	Mean	Maximum	CV (%)	Variance	SD	LCI	UCI
PL	1121	12.82	21.57	77.56	19.91	18.44	4.29	21.30	21.82
NFS	1121	1.20	93.81	363.40	38.03	1273.09	35.68	91.58	95.94
NSS	1121	1.00	25.72	175.00	85.12	479.20	21.89	24.35	27.02
PH	1121	55.00	96.50	152.00	17.27	277.69	16.66	95.46	97.50
NPP	1121	2.00	13.91	66.00	47.26	43.20	6.57	13.50	14.30
HGW	1121	1.34	2.78	4.74	13.25	0.14	0.37	2.76	2.81
YP	1121	1.05	29.15	144.53	57.56	281.57	16.78	28.10	30.15
DF	1121	71.00	88.48	117.00	10.31	83.19	9.12	87.91	89.03
Trait/ F₇	Sample size	Minimum	Mean	Maximum	CV (%)	Variance	SD	LCI	UCI
PL	1387	12.25	19.92	29.88	15.68	9.76	3.12	19.75	20.09
NFS	1387	0.00	55.11	210.40	44.20	593.30	24.36	53.74	56.42
NSS	1387	2.00	29.05	164.40	74.37	466.62	21.60	27.83	30.21
PH	1417	43.00	91.20	140.00	16.78	234.04	15.30	90.35	92.01
NPP	1386	1.00	12.67	147.00	47.81	36.68	6.06	12.33	12.99
HGW	1384	0.05	2.70	5.22	18.16	0.24	0.49	2.67	2.72
YP	1384	0.05	17.15	74.37	58.31	100.03	10.00	16.59	17.69
Trait/ F₆	Sample size	Minimum	Mean	Maximum	CV (%)	Variance	SD	LCI	UCI
VW	415	108.31	120.27	128.61	3.06	13.52	3.68	119.89	120.63
BG	415	0.00	3.72	26.76	114.23	18.10	4.25	4.14	3.22
CG	415	0.00	0.02	0.47	247.40	0.00	0.06	0.02	0.03
WG	415	0.00	0.25	4.77	203.36	0.26	0.51	0.20	0.30
RG	415	0.00	0.08	2.25	254.54	0.04	0.21	0.06	0.10
ACC	126	10.26	17.99	30.24	26.61	22.93	4.79	17.09	18.85
GT	126	1.00	5.60	7.00	33.24	3.46	1.86	5.25	5.93

	PL	NFS	NSS	PH	NPP	HGW	YP	DF	VW	BG	CG	WG	RG	ACC	GT
PL	-	0.292	0.429*	0.472*	-0.102	0.176	0.176	0.136	-0.147	-0.149	-0.062	-0.087	-0.035	0.201	-0.119
NFS	0.279	-	0.572*	0.141	-0.128	-0.445*	0.285	-0.177	-0.385*	-0.041	0.128	0.113	0.047	0.472*	0.334*
NSS	0.498⁺	0.685⁺	-	0.377*	-0.175	-0.286	0.092	0.027	-0.427*	-0.038	0.003	0.003	-0.06	0.345*	0.178
PH	0.521⁺	0.130	0.413⁺	-	-0.086	0.180	0.156	0.194	-0.098	0.053	-0.220	-0.339*	-0.127	0.028	0.058
NPP	-0.186	-0.293	-0.253	-0.161	-	-0.001	0.715*	-0.132	-0.015	0.083	0.247	0.084	0.194	0.264	-0.103
HGW	0.214	-0.491⁺	-0.300	0.194	0.015	-	0.377*	0.164	-0.010	0.080	0.064	0.133	0.008	-0.041	-0.196
YP	0.173	0.221	0.110	0.147	0.635⁺	0.228	-	-0.047	-0.352*	0.179	0.406*	0.259	0.411*	0.541*	0.075
DF	0.171	-0.192	0.023	0.206	-0.167	0.354⁺	-0.054	-	-0.199	-0.355*	0.008	0.343*	-0.132	-0.090	0.160
VW	-0.172	-0.437⁺	-0.467⁺	-0.108	-0.037	-0.013	-0.447⁺	-0.213	-	-0.152	-0.221	-0.397*	-0.303*	-0.329*	-0.418*
BG	-0.169	-0.040	-0.039	0.060	0.126	0.087	0.243	-0.375⁺	-0.145	-	0.234	-0.146	0.163	0.086	-0.010
CG	-0.073	0.147	0.007	-0.242	0.357⁺	0.075	0.531⁺	0.010	-0.191	0.238	-	0.538*	0.269	0.306*	0.009
WG	-0.101	0.133	0.004	-0.367⁺	0.117	0.142	0.323	0.370⁺	-0.398⁺	-0.166	0.547⁺	-	0.379*	0.124	0.009
RG	-0.034	0.063	-0.079	-0.135	0.275	0.008	0.553⁺	-0.133	-0.321	0.177	0.297	0.386⁺	-	0.135	-0.004
ACC	0.248	0.643⁺	0.431⁺	0.018	0.419⁺	-0.057	0.808⁺	-0.100	-0.408⁺	0.119	0.393⁺	0.159	0.166	-	0.205
GT	-0.134	0.449⁺	0.237	0.056	-0.142	-0.225	0.106	0.192	-0.498⁺	-0.015	0.003	0.010	0.010	0.207	-

Traits	Canonical Pairs
Traits	1°	2°	3°	4°	5°
Group 1
VW¹	-0.717	-0.190	0.286	-0.283	-0.136
BG	-0.032	-0.623	0.107	0.325	0.690
CG	0.558	-0.222	0.353	-0.192	0.085
WG	0.696	0.298	0.533	-0.289	0.135
RG	0.319	-0.573	0.453	0.010	-0.322
AAC	0.716	-0.312	-0.360	-0.017	-0.003
GT	0.346	0.250	-0.008	0.875	-0.187
Group 2
PL	0.129	-0.066	-0.658	-0.418	-0.338
NFS	0.566	0.014	-0.444	0.404	-0.192
NSS	0.405	0.042	-0.776	0.170	0.462
PH	-0.153	-0.121	-0.663	0.330	-0.069
NPP	0.237	-0.368	0.052	-0.342	-0.136
HGW	0.020	-0.007	0.107	-0.412	0.564
YP	0.676	-0.529	-0.382	-0.092	-0.105
DF	0.248	0.708	0.072	-0.800	-0.121
r	0.817	0.714	0.561	0.431	0.326
Chi-Square	1070.361	617.152	323.698	168.253	83.506
df	56	42	30	20	12
p	<0.01	<0.01	<0.01	<0.01	<0.01

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