Establishing a core collection from the integration of morpho-agronomical, phytopathological and molecular data

Martins, Francielle Alline; Silva, Derly José Henriques da; Carneiro, Pedro Crescêncio Souza

doi:10.5935/1806-6690.20150072

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Crop Science • Rev. Ciênc. Agron. 46 (4) • Oct-Dec 2015 • https://doi.org/10.5935/1806-6690.20150072 copy

Establishing a core collection from the integration of morpho-agronomical, phytopathological and molecular data

Estabelecimento de coleção nuclear a partir da integração de dados morfoagronômicos, fitopatológicos e moleculares

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

The aim of this study was to establish and compare, as to their representativeness, core collections obtained from quantitative data, multicategoric, molecular and collections that covering all this information simultaneously. Ten subcollections were established from 67 tomato accessions of the Germplasm Bank of the Universidade Federal de Viçosa (BGH-UFV), characterized according to 19 quantitative traits, 30 multicategoric characters, 52 ISSR loci and to the reaction to three pathogens. These subcollections were defined by the combination of the nature of data collected and the sampling rate. The COD-20 subcollection stood out in 20% intensity of sampling by has higher rates of coincidence amplitude followed by more appropriate values of variance. At 30% intensity, subcollection MOL-30 was as efficient as the subcollection COD-30 when considering only the rates of coincidence of the amplitude and the variances. However, the graphical analysis of the variability showed a slight superiority of subcollection COD-30 in maintaining the variability, especially regarding multicategoric characters. So whenever data from different sources are available, should be prioritized the establishment of core collections from the integration of these data, since these were more representative when the amplitude coefficient, variance, and retention index of variability, are regarded simultaneously.

Key words:
Retention rate variability; Amplitude coefficient; Germplasm; Variance

BGH-UFV	CG	BGH-UFV	CG	BGH-UFV	CG	BGH-UFV	CG
166	SC	990	SAL	1532	SAL	2211	SAL
181	SAL	991	SC	1538	SC	2213	SAL
279	SC	992	SC	1706	SC	2214	SAL
322	SAL	993	SC	1708	SC	2216	AP
349	SAL	994	SC	1985	SAL	2219	SAL
468	SAL	997	SAL	1987	SAL	2223	AP
489	SAL	1019	SAL	1988	SAL	2229	SAL
773	CHE	1020	SC	1989	SAL	2234	AP
850	SAL	1211	SAL	1990	ITA	3472	SAL
970	SC	1214	SAL	1991	SAL	4006	SC
975	SC	1254	CHE	1992	SAL	4035	SC
978	SAL	1282	SAL	1993	SAL	4053	SC
980	CHE	1485	SAL	2119	SAL	4054	ITA
981	SAL	1490	SC	2202	SAL	4055	SC
985	SAL	1497	SC	2203	SAL	4206	SC
987	SAL	1498	SC	2205	SAL	4309	SC
989	SAL	1499	SAL	2208	SAL

	Stratum2	20% density	30% density
	SAL	850, 1989, 2211, 989 and 1211	850, 1989, 2211, 989, 1211, 1991, 489 and 1282
	SC	994, 4053, 1020 and 1490	994, 4053, 1020, 1490, 4006 and 970
QUANT¹	ITA	4054	1990 and 4054
	AP	2216 and 2234	2216 and 2234
	CHE	980 and 1254	980 and 1254
	SAL	489, 1987, 2203, 989 and 2213	489, 1987, 2203, 989, 2213, 1989, 2202 and 181
	SC	975, 4053, 1708 and 991	975, 4053, 1708, 991, 4035 and 1497
MULT	ITA	1990	1990 and 4054
	AP	2216 and 2234	2216 and 2234
	CHE	980 and 1254	980 and 1254
	SAL	181, 2211, 1282, 1987 and 1499	181, 2211, 1282, 1987, 1499, 997, 1985 and 1993
	SC	1490, 1498, 1497 and 4055	1490, 1498, 1497, 4055, 4035 and 994
MOL	ITA	4054	1990 and 4054
	AP	2216 and 2234	2216 and 2234
	CHE	773 and 980	773 and 980
	SAL	489, 1993, 2202, 181 and 1987	489, 1993, 2202, 181, 1987, 850, 2208 and 1282
	SC	975, 4035, 1490 and 4309	975, 4035, 1490, 4309, 991 and 1497
COD	ITA	1990	1990 and 4054
	AP	2216 and 2234	2216 and 2234
	CHE	980 and 1254	980 and 1254
	SAL	850, 2202, 1282, 181 and 1993	850, 2202, 1282, 181, 1993, 2208, 489 and 2211
	SC	991, 4035, 1490 and 975	991, 4035, 1490, 975, 4053 and 166
SUM	ITA	1990	1990 and 4054
	AP	2216 and 2234	2216 and 2234
	CHE	980 and 1254	980 and 1254

Sub-collection¹ 1 QUANT-20: sub-collection formed from quantitative data; MULT-20: sub-collection formed from multi-category data; MOL-20: sub-collection formed from molecular data; COD-20: sub-collection formed from the integration of quantitative, multi-category and molecular data by the encoding of quantitative into multi-category data; SUM-20: sub-collection formed from the integration of quantitative, multi-category and molecular data through algebraic matrix addition; IC: initial collection.	Quantitative² 2 Corresponds to the group of characteristics used to estimate AC and variance.	Multi-category² 2 Corresponds to the group of characteristics used to estimate AC and variance.	Molecular² 2 Corresponds to the group of characteristics used to estimate AC and variance.
QUANT-20	0.90 (204325.8^** * ** significant at 5% and 1% probability, )³ 3 Parenthesis: variance values;	0.80 (1.6508^ns ns not significant at 5% probability by F-max test )	0.79 (0.1106^* * ** significant at 5% and 1% probability, )
MULT-20	0.77 (119051.5^* * ** significant at 5% and 1% probability, )	0.82 (2.0951^** * ** significant at 5% and 1% probability, )	0.81 (0.1135^** * ** significant at 5% and 1% probability, )
MOL-20	0.85 (120873.5^* * ** significant at 5% and 1% probability, )	0.78 (1.5764^ns ns not significant at 5% probability by F-max test )	0.92 (0.1431^** * ** significant at 5% and 1% probability, )
COD-20	0.86 (138148.4^ns ns not significant at 5% probability by F-max test )	0.85 (1.9171^ns ns not significant at 5% probability by F-max test )	0.90 (0.1359^** * ** significant at 5% and 1% probability, )
SUM-20	0.91 (144725.7^ns ns not significant at 5% probability by F-max test )	0.83 (1.9431^* * ** significant at 5% and 1% probability, )	0.88 (0.1307^** * ** significant at 5% and 1% probability, )
IC	(144671)	(1.6938)	(0.0987)

Sub-collection¹ 1 QUANT-30: sub-collection formed from quantitative data; MULT-30: sub-collection formed from multi-category data; MOL-30: sub-collection formed from molecular data; COD-30: sub-collection formed from the integration of quantitative, multi-category and molecular data by the encoding of quantitative into multi-category data; SUM-30: sub-collection formed from the integration of quantitative, multi-category and molecular data through algebraic matrix addition; IC: initial collection.	Quantitative² 2 Corresponds to the group of characteristics used to estimate AC and variance.	Multi-category² 2 Corresponds to the group of characteristics used to estimate AC and variance.	Molecular² 2 Corresponds to the group of characteristics used to estimate AC and variance.
QUANT-30	0.94 (213747.5^** * ** significant at 5% and 1% probability, )³ 3 Parenthesis: variance values;	0.85 (1.7624^ns ns not significant at 5% probability by F-max test )	0.83 (0.1020^ns ns not significant at 5% probability by F-max test )
MULT-30	0.89 (163845.9^ns ns not significant at 5% probability by F-max test )	0.90 (1.9856^* * ** significant at 5% and 1% probability, )	0.94 (0.1131^** * ** significant at 5% and 1% probability, )
MOL-30	0.92 (158051.7^ns ns not significant at 5% probability by F-max test )	0.85 (1.6108^ns ns not significant at 5% probability by F-max test )	0.98 (0.1334^** * ** significant at 5% and 1% probability, )
COD-30	0.92 (150664.5^ns ns not significant at 5% probability by F-max test )	0.86 (1.8708^ns ns not significant at 5% probability by F-max test )	0.94 (0.1279^** * ** significant at 5% and 1% probability, )
SUM-30	0.91 (172489.8*)	0.85 (1.8032^ns ns not significant at 5% probability by F-max test )	0.90 (0.1219^** * ** significant at 5% and 1% probability, )
IC	(144671)	(1.6938)	(0.0987)

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