Euphytica (2008) 161:283–291 DOI 10.1007/s10681-007-9481-8
Genetic diversity evolution through participatory maize breeding in Portugal Maria Carlota Vaz Patto Æ Pedro Manuel Moreira Æ Nuno Almeida Æ Zlatko Satovic Æ Silas Pego
Received: 31 October 2006 / Accepted: 6 June 2007 / Published online: 5 July 2007 Ó Springer Science+Business Media B.V. 2007
Abstract Natural, and in particular, artificial (human) selection may pose a danger to the existing crop genetic diversity. Nevertheless, on-farm breeding systems seem to achieve phenotypic improvements even though preserving variability. Using SSR markers, we analysed several selection cycles, over a 20 years period, of a Portuguese on-farm participatory maize OPV-‘Pigarro’ breeding project. No significant differences in allelic richness (Nar), observed heterozygosity (HO), expected heterozygosity (or gene diversity; HE) or inbreeding coefficient (f) were detected among the selection cycles. 58 out of 107 alleles were common to all the selection cycles studied. The analysis of molecular variance showed M. C. Vaz Patto (&) N. Almeida Instituto de Tecnologia Quı´mica e Biolo´gica, Plant Cell Biotechnology Lab, Universidade Nova de Lisboa, Apt. 127, Oeiras 2781-901, Portugal e-mail:
[email protected] P. M. Moreira Departamento de Fitotecnia, Escola Superior Agra´ria de Coimbra, Bencanta, Coimbra 3040-316, Portugal Z. Satovic Faculty of Agriculture, Department of Seed Science and Technology, University of Zagreb, Svetosimunska 25, Zagreb 10000, Croatia S. Pego Estac¸a˜o Agrono´mica Nacional (EAN), Instituto Nacional de Investigac¸a˜o Agra´ria, Av. Repu´blica, Oeiras 2784-505, Portugal
that the variation among selection cycles represented only 7% of the total molecular variation. However, the number of private alleles varied among the selection cycles, being the highest detected at the beginning of the selection project. These findings demonstrate that an allele flow took place during the on-farm selection process of ‘Pigarro’ but the level of genetic diversity was not significantly influenced. Since interesting phenotypic improvements were also achieved, on-farm breeding projects, like this one, should be valued as a way to preserve unique Portuguese maize landraces in risk of disappearing. Keywords Genetic diversity Maize On-farm Participatory breeding SSR Zea mays L.
Introduction Maize was introduced in Portugal during the XVI century and spread rapidly throughout the country. The establishment and further expansion of this new crop during the XVII and XVIII centuries, in a polycrop system (maize + beans + forage), lead to an agricultural revolution, enhancing the rural population’s standard of living. Numerous landraces (open pollinated varieties, OPV) have been developed during the centuries of cultivation, adapted to specific regional growing conditions as well as farmer’s needs. However, after World War II, Portugal was one of the first European countries to introduce
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American hybrids which initially were not well accepted by the Portuguese farmers due to several handicaps as late maturity or kernel type, not fitted for food. Subsequently, due to the big accomplishments of the maize hybrid development in Portugal, with several national breeding stations releasing adapted hybrid varieties, maize landraces were progressively replaced. Nevertheless, since the late 70’s there has been a growing concern that numerous Portuguese maize landraces may have been lost forever. In 1975, Portugal took the first initiative of collection of maize germplasm, and later on, with FAO support, a national collection program was implemented and a national germplasm bank was established in the city of Braga. However, genetic diversity was still being lost on the farmers’ fields. It is said that participatory plant breeding (PPB) may encourage farmers to continue growing landraces by enhancing their current use value (Smale et al. 2003). PPB, with the involvement of farmers, uses mostly material generated from crosses among local landraces, leading to a dynamic form of in situ genetic conservation and genetic enhancement. On the other hand, PPB programs meet the needs of lowinput, small-scale farmers who are often overlooked by conventional crop breeders. The returns from PPB, compared to conventional breeding, are generally higher because it costs less and the benefits to farmers are realised earlier (Virk et al. 2003; Witcomb et al. 2003). Taking all this into account, Silas Pego led, in 1984, a detailed survey on farmer’s maize fields at «Vale do Sousa» Region (Sousa Valley Region) in the Northwest of Portugal. The collected materials were the starting point of a PPB project, with simultaneous on-farm breeding and on-farm conservation objectives (VASO- ‘‘Vale do Sousa’’- project). The project was focused on solving the problem of small farmers with scarce land resources, due to high demographic density; with polycroping production systems, quality being the first priority over quantity. The Sousa valley is a traditional maize cultivation region with polycropping systems for human uses (bread production), which is very fertile, with good water availability and local germplasm adapted to local conditions during centuries of cultivation. This PPB project concerned mainly flint-type OPV landraces with technological ability for the production of the traditional maize bread called ‘‘broa’’. ‘‘Broa’’
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production still plays an important economic and social role in Central and Northern Portuguese rural communities. This bread making ability seems to depend on a range of particular traits not found in the available commercial hybrid varieties, and this is probably why traditional maize landraces have not, in these regions, been totally replaced by hybrid varieties. One regional maize OPV was selected based on the farmers needs and introduced in the PPB project, at that time with support of CIMMYT. The selected OPV was named by the farmer as «Pigarro». ‘Pigarro’ is a white flint type, with FAO 300 maturity rating, high level of fasciation, bread making ability and adapted to polycropping (with beans). Since then, a farmer’s selection criterium based on mass selection methodology was applied to ‘Pigarro’, on the farmer’s field, by the farmer himself, but in close collaboration with the breeder (Silas Pego). The breeder, on the other hand, worked side by side with the farmer, using recurrent selection methodologies with careful respect for the local traditional agriculture, accepting low input and intercropping characteristics, and favouring diversity (as the basis for pest and disease tolerance) and quality as priorities. In the case of the farmer’s selection, a two parental control mass selection was applied to ‘Pigarro’. In the field, before pollen shedding, male flowers were detasseled, and the weakest, diseased and pest susceptible plants were removed. After that, and just before harvesting, plants were again selected based on the ear size, root and stalk quality, pest and disease tolerance and prolificacy. Finally, at the storing facilities, after harvesting, selection was focused on the ear length and the kernel row number, avoiding damage to ears. Significant agronomic improvements have been achieved with this approach (Pego and Antunes 1997). The seeds of each selection cycle (in a total of 20 mass selection cycles) have been put in cold storage. Concern has been expressed that genetic diversity might be reduced by natural and artificial (human) selection. The main commercial maize hybrids that have substituted the traditional OPVs worldwide, involve a restricted number of key inbred lines, thus limiting the available genetic diversity. However, it is known that traditional farmers, when selecting their landraces seed, have been successful in preserving variability as a way to guarantee production under
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any circumstances (Pego and Antunes 1997). Only recently an objective assessment of genetic diversity has become possible with the introduction of molecular marker technologies. SSR markers have proven their efficiency as genetic markers to assess genetic diversity evolution in numerous plant species (Khlestkina et al. 2004; Struss and Plieske 1998; Le Clerc et al. 2005; Maccaferi et al. 2003). To study the genetic diversity evolution during traditional maize landraces development, we analysed different selection cycles of the on-farm participatory maize breeding project (VASO project), in progress since 1984, in the Portuguese Sousa Valley region, with simple sequence repeat (SSR) markers.
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biotech) by comparison with internal and external size standards. Size estimates were rounded up or down using the criteria defined by Matsuoka et al. (2002) as described in Vaz Patto et al. (2004). To reduce variance in the estimate of fragment sizes between runs, control samples (B73) were run repetitively on all the gels corresponding to the same SSR locus. The 16 SSR markers used in the study were chosen from MaizeDB based on their repeat unit and bin location. The SSR primers were scattered throughout the maize genome and represented various repeat classes (Table 1). Primer sequences are available from the MaizeDB (www.maizegdb.org). Data analysis
Materials and methods Plant materials PPB using mass selection was carried out within the VASO project each year and OPVs seed was stored from each selection cycle. From each of three different ‘Pigarro’ on-farm mass selection cycles (1984, 1993 and 2004), approximately 30 individuals were randomly selected from seed. In total, 89 individuals were analysed using SSRs markers. In addition, B73, an US inbred line, was used as a control. SSR fingerprints DNA was isolated from 2-week old seedlings, employing a modified CTAB procedure (SaghaiMaroof et al. 1984). SSR marker technique was performed as described by Vaz Patto et al. (2004). Fragment analysis was carried out using an automated laser fluorescence (ALFexpress II) sequencer (Amersham Biosciences). For this, 0.5 ll of each amplification reaction was mixed with 3 ll of formamide loading buffer, 3 ll of TE (pH 8.0) and 0.3 ll of each of two internal sizers labelled fluorescently (Cy5) with sizes flanking the amplified fragments. After denaturation at 948C for 3 min, and cooling down on ice, the 8 ll samples were loaded onto a standard sequencing gel (Repro gel, Amersham Biosciences). Fragment sizes were determined using the computer program ALFwin Fragment analyser v. 1.00 (Amersham Pharmacia
The GDA software (Lewis and Zaykin 2001) was used for calculating allele frequencies and estimating the average number of alleles (Na), number of private alleles (Npa), the observed and expected heterozygosities (HO, HE) and fixation index (f) in each selection cycle. FSTAT v. 2.9.3.2 programme package (Goudet 1995, 2002) was used for estimating the allelic richness Nar as the measure of the number of alleles per locus independent of sample size. The estimates of Nar, HO, HE and f in each selection cycle were compared using the Kruskal-Wallis test in SAS software (SAS Institute 1999). GENEPOP v. 3.4 (Raymond and Rousset 1995) was used to test genotypic frequencies for conformance to Hardy-Weinberg (HW) expectations, to test the loci for linkage disequilibrium and to estimate the significance of genic differentiation between selection cycle pairs. All significance tests were based on a Markov chain method (Guo and Thompson 1992; Raymond and Rousset 1995) using 10,000 de-memorization steps, 100 batches and 5,000 iterations per batch. Sequential Bonferroni adjustments (Holm 1979; Rice 1989) were applied to correct for the effect of multiple tests using SAS Release 8.02 (SAS Institute 1999). The distribution of gene diversity was conducted according to the model proposed by Nei (1973), in which the total genetic diversity mean (HT) is partitioned in two components: the gene diversity mean within selection cycles (HS), and between cycles (DST). The proportion of total gene diversity between cycles (GST), or genetic differentiation, was
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Table 1 Repeat motifs, size ranges and number of alleles for 16 SSR loci used in 89 maize plants from three selection cycles (SC1984, SC1993, SC2004) Locus
Repeat motif
Bin location
Size range
No. of alleles SC1984
SC1993
SC2004
Total
umc1013
GA
1.08
129–167
5
5
4
5
umc1823
TG
2.02
85–165
12
10
8
14
umc1635 umc1907
GAAGG AT
2.05 3.05
119–144 109–173
3 13
3 9
4 9
4 17
umc1528
TGCG
3.07
148–164
3
3
3
3
bmc2323
AG
5.04
144–196
11
9
9
13
umc1524
GGACTG
5.06
128–158
4
4
3
4
umc1143
AAAAT
6.00
73–83
3
3
3
3
umc1229
AG
6.01
218–260
12
10
9
14
umc1066
GCCAGA
7.01
138–150
2
2
3
3
umc1483
ACG
8.01
154–160
3
3
3
3
umc1858
TA
8.04
117–159
6
4
6
7
umc1279
CCT
9.00
91–100
4
2
3
4
umc1120
GGCAT
9.04
92–112
3
3
4
4
umc2067
CATG
10.03
143–155
3
3
3
3
umc2021
TGG
10.07
112–136
5
6
6
6
92
79
80
107
Total Average
calculated as GST = DST/HT. The partition of total genetic diversity was performed separately for SC1984 vs. SC1993, SC1993 vs. SC2004, and SC1984 vs. SC2004 using FSTAT. The proportion-of-shared-alleles distance (Bowcock et al. 1994) between pairs of individuals was calculated using MICROSAT (Minch et al. 1997) and the distance matrix was subjected to the analysis of molecular variance (AMOVA; Excoffier et al. 1992) using ARLEQUIN version 2.000 (Schneider et al. 2000). The significance of /-statistics was obtained non-parametrically after 106 permutations.
Results A total of 107 alleles were detected within 89 individuals across 16 SSR markers (Table 1). The number of alleles/locus ranging from 3 (umc1528, umc1143, umc1066, umc1483 and umc2067) to 17 (umc1907), with a mean value of 6.69 alleles/locus. Out of 107 alleles, 58 were common to all the selection cycles, 28 were detected in two out of three
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5.75
4.94
5.00
6.69
selection cycles, while 21 were private alleles. Average frequencies of private alleles (4.45%) and alleles found in two out of three selection cycles (6.45%) were considerably lower than the average frequencies of common alleles (24.97%). As expected, the highest number of private alleles (12) was detected in SC1984. The average number of alleles per selection cycle was the highest in SC1984 (5.750) and the lowest in SC1993 (4.938), but the gene diversity (HE) had even slightly increased from 0.599 in SC1984 to 0.612 in SC2004. However, no significant differences were observed among the three selection cycles in any of the analysed parameters including Nar, observed heterozygosity (HO), expected heterozygosity (or gene diversity; HE) and inbreeding coefficient (f) (Table 2). After accounting for multiple comparisons, only three loci (umc1823, umc1907, umc1229) were significantly out of Hardy-Weinberg equilibrium (P < 0.05) in all the selection cycles, showing excess of homozyogotes. Additionally, locus umc1524 showed significant heterozygotes excess in SC2004 (Table 3).
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Table 2 Genetic variability estimates for three selection cycles Selection cycle
n
Na
Nar
Npa
HO
HE
f
SC1984
30
5.750
3.861
12
0.498
0.599
0.170
SC1993
29
4.938
3.611
3
0.588
0.606
0.031
SC2004 Mean
30
5.000 5.229
3.640 3.896
6
0.529 0.538
0.612 0.606
0.140 0.114
0.238
0.996
0.310
P*
0.938
* P-value of Kruskal-Wallis test among selection cycles. n: number of individuals, Na: average number of alleles, Nar: allelic richness, Npa: number of private alleles, HO: observed heterozygosity, HE: gene diversity or expected heterozygosity, f: inbreeding coefficient Table 3 Inbreeding coefficients per locus and selection cycle and significant deviations from Hardy-Weinberg equilibrium
Table 4 P-values for test of null hypothesis that assumes identical allelic distribution across cycles Locus
SC1984/ SC1993
SC1993/ SC2004
SC1984/ SC2004
bmc2323
1.000*
0.036
0.004
umc1013
1.000
0.000
0.095
umc1066
0.091
1.000
1.000
umc1120
1.000
0.687
0.022
umc1143
1.000
0.672
0.907
umc1229
0.000
0.278
0.000
umc1279
1.000
1.000
1.000
umc1483
1.000
1.000
1.000
umc1524
1.000
0.890
1.000
umc1528
0.493
1.000
1.000
umc1635 umc1823
1.000 0.000
1.000 0.000
1.000 0.000
umc1858
0.011
0.000
0.000
umc1907
1.000
0.116
0.004
umc2021
1.000
1.000
1.000
umc2067
1.000
1.000
1.000
Significant deviations from Hardy-Weinberg equilibrium after sequential Bonferroni corrections: ‘‘**’’ corresponds to significance at the 1% nominal level, and ‘‘*’’ significance at the 5% nominal level; no marking depicts non-significant values
No. of significant tests
3
4
6
Among a total of 360 tests for linkage disequilibrium between pairs of loci, 30 were significant at P < 0.05 (data not shown). Nevertheless, no test was found significant after applying sequential Bonferroni correction for multiple testing. The differentiation tests for the allele frequency distribution between selection cycles were significant for three loci between SC1984 and SC1993, for four between SC1993 and SC2004 and for 6 out of 16 loci between SC1984 and SC2004 (Table 4). Hence, despite of 20 years of continuous selection, not only was the number of alleles shared between cycles
considerable, but also the cycles SC1984 and SC2004 did not differ greatly in allelic frequencies. The comparison of the gene diversity among the three selection cycles showed that the total gene diversity (HT) of two different cycles essentially originated from the gene diversity within a cycle (HS). The gene diversity among cycles accounted for less than 3% of the HT (Table 5). Similarly, AMOVA results showed that 93.61% of variation was attributable to within-selection cycles diversity indicating that a great proportion of the genetic diversity is maintained within each selection
Locus
SC1984
SC1993
SC2004
umc1013
0.461
0.281
0.387
umc1635
0.239
0.319
0.165
umc1823
0.638**
0.177*
0.475**
umc1528
0.162
0.193
0.058
umc1907
0.497**
0.344**
0.537**
umc1524
0.664
0.365
0.843**
bmc2323
0.110
0.025
0.005
umc1143
0.053
0.425
0.182
umc1229
0.311*
0.385**
0.350*
umc1066
0.033
0.055
0.123
umc1858
0.081
0.082
0.041
umc1483
0.223
0.106
0.290
umc1279
0.111
0.147
0.101
umc1120
0.232
0.048
0.388
umc2067 umc2021
0.287 0.219
0.025 0.127
0.230 0.150
* P-value as obtained after sequential Bonferroni corrections
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Table 5 Distribution of gene diversity between selection cycles Comparison
HT
HS
DST
GST
SC1984 vs. SC1993
0.612
0.603
0.009
0.014
SC1993 vs. SC2004
0.629
0.610
0.019
0.030
SC1984 vs. SC2004
0.620
0.607
0.014
0.022
All cycles
0.625
0.607
0.018
0.029
Table 6 Results of AMOVA analysis for the partitioning of SSR variation among and within selection cycles Comparison
% Total variance Between
/-statistics
P(/)
Within
SC1984 vs. SC1993
4.13
95.87
0.041
