Euphytica (2011) 179:209–225 DOI 10.1007/s10681-010-0235-7
Allelic changes in bread wheat cultivars were associated with long-term wheat trait improvements Yong-Bi Fu • Daryl J. Somers
Received: 6 May 2010 / Accepted: 22 July 2010 / Published online: 8 August 2010 Ó Her Majesty the Queen in Right of Canada 2010
Abstract Genetic impacts under selective breeding of agricultural crops have been frequently investigated with molecular tools, but inadequate attention has been paid to assess genetic changes under longterm genetic improvement of plant traits. Here we analyzed allelic changes with respect to wheat trait improvement in 78 Canadian hard red spring wheat cultivars released from 1845 to 2004 and screened with 370 mapped SSR markers. The improvements in quality, maturity, yield, disease, stem rust, leaf rust, sawfly resistance, and agronomy were considered. A total of 154 (out of 370) loci with significant allelic changes across 21 chromosomes were detected in the 78 wheat cultivars separated into improved versus non-improved groups for eight traits. The number of significant loci for improving a trait ranged from four for quality to 68 for yield and averaged 35. Many more loci with significant allelic reduction for improving a trait were detected than those with significant allelic increase. Selection for early
Y.-B. Fu (&) Plant Gene Resources of Canada, Saskatoon Research Centre, Agriculture and Agri-Food Canada, 107 Science Place, Saskatoon, SK S7N 0X2, Canada e-mail:
[email protected] D. J. Somers Vineland Research and Innovation Centre, 4890 Victoria Avenue North, P.O. Box 4000, Vineland Station, ON L0R 2E0, Canada
maturity introduced more alleles, but improving the other traits purged more alleles. Significantly lower numbers of unique alleles were found in the cultivars with improved traits. The distributions of unique allele counts also varied greatly across the 21 chromosomes with respect to trait improvement. Significant SSR variation between two cultivar groups was observed for improvement in seven traits, but not in stem rust. The proportional SSR variation residing between two groups ranged from 0.014 to 0.118. The proportional SSR variations within the improved cultivar groups consistently were much lower than those within the non-improved groups. These findings clearly demonstrate the association between allelic changes and wheat trait improvements and are useful for understanding the genetic modification of the wheat genome by long-term wheat breeding. Keywords Plant breeding Breeding target trait SSR marker Genetic diversity Triticum aestivum
Introduction Over the last century, plant breeding has made striking improvements of agricultural crops in yield, adaptation, disease resistance, and other traits (Borlaug 2007). These phenotypic shifts are the consequences of the genetic modifications of plant genomes by selective breeding or specifically the
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impacts of directional selections on target genes (Allard 1999). Theoretically, intensive directional selection within a narrow range of plant germplasm can be predicted to eliminate rare alleles, increase favorable allele frequencies, reduce genetic diversity, and increase linkage disequilibrium (Robertson 1960; Zeng and Cockerham 1990; Hedrick 2000; Walsh 2005). However, how long-term selective breeding influences a plant genome remains poorly understood (Fu et al. 2010). Information is largely lacking on genetic changes under long-term genetic improvements of plant traits (Duvick et al. 2004; Fu 2006). Whether or not modern plant breeding reduces crop genetic diversity remains an unresolved issue (Gepts 2006; van de Wouw et al. 2010). Efforts have been made to assess genetic changes under selective breeding with allozyme, RFLP and SSR markers in several American maize breeding programs specific for grain yield, oil and protein (Stuber et al. 1980; Sughroue and Rocheford 1994; Labate et al. 1999; Duvick et al. 2004; Mikkilineni and Rocheford 2005; Hinze et al. 2005). These analyses clearly demonstrated the genetic narrowing of maize breeding materials over generations of selective breeding for individual traits (Allard 1998; Duvick et al. 2004). This narrowing is consistent with the common notion that modern plant breeding is a strong force in reduction of crop genetic diversity (Gepts 2006). However, recent molecular diversity analyses of various crop gene pools established from long-term breeding efforts revealed inconclusive patterns of genetic diversity changes over time (Fu 2006; van de Wouw et al. 2010). Many improved crop gene pools did not show much reduction from early to recent breeding efforts (Donini et al. 2000; Fu 2006; Hyten et al. 2006; Malysheva-Otta et al. 2007; Huang et al. 2007; White et al. 2008), although some molecular evidence existed on the diversity impact of plant breeding (Fu et al. 2003; Roussel et al. 2004; Hao et al. 2006; Fu and Somers 2009). This discrepancy could be explained by many factors including technical limitations of diversity analysis (Fu 2006; Fu and Somers 2009), but the heterogeneity of improved gene pools established from different breeding targets appears to be the major confounding factor. Thus, it would be more informative to determine if genetic changes in an improved gene pool are associated with long-term selective breeding for specific plant traits.
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The Canadian bread wheat (Triticum aestivum L.) gene pool established since 1886 is interesting, as the genetic shift and genome-wide diversity reduction in this gene pool (Fu et al. 2005, 2006; Fu and Somers 2009) appear to reassembly those found in long-term maize breeding programs for specific traits as mentioned above. However, the improved wheat gene pool should be more heterogeneous than those specific maize programs, as it was established with mixed and variable breeding targets over time (McCallum and DePauw 2008). Although the applied breeding methods and the breeding focus on grain yield and quality remained largely unchanged, additional breeding targets existed from adaptation before 1940, resistance to biotic and abiotic stresses such as rust from 1940 to 1990, to end-use quality such as increased grain protein after 1990 (DePauw et al. 1995; McCallum and DePauw 2008). Thus, it would be more insightful to investigate if the genetic changes in the gene pool are associated with genetic improvement of specific wheat traits. A detection of such an association should provide not only support for the early diversity findings revealed in the gene pool and in specific maize breeding programs, but also can help understand the effectiveness of selective breeding for wheat traits in genetic modification of wheat genome. The objective of this study was to extend our previous diversity analysis with trait improvement records to determine if allelic changes are associated with long-term genetic improvement of wheat traits in 78 Canadian hard red spring wheat cultivars released from 1845 to 2004. The improvements in quality, maturity, yield, disease, stem rust, leaf rust, sawfly resistance, and agronomy were considered. Based on trait improvement records, these cultivars were separated into improved versus non-improved groups for a comparison of genetic changes.
Materials and methods Plant materials The 78 Canadian hard red spring wheat cultivars studied here were described in Table 1, including release source, year, genebank accession information. Briefly, the wheat accessions consisted of cultivars introduced since 1845 from four regions (central
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Table 1 Descriptions of 78 wheat cultivars with improvements in eight wheat traits, along with their group coding, release origin, release year, and genebank accession number Cultivara
Improved characteristicsa
Coding for improved characteristicsb
CNd
Q
M
Y
D
S
L
F
A
Sourcec
Year
Red Fife
Wide adaptation, good milling and bread-making quality
1
2
2
2
2
2
2
2
I.GC
1845
11425
Ladoga
Earlier maturing than Red Fife
2
1
2
2
2
2
2
2
I.RU
1887
10921
Stanley
Earlier maturing than Red Fife
2
1
2
2
2
2
2
2
ECORC
1893
11999
Preston
Earlier maturing than Red Fife
2
1
2
2
2
2
2
2
ECORC
1895
11327
Huron
Earlier maturing than Red Fife
2
1
2
2
2
2
2
2
ECORC
1900
1922
Percy White Fife
Earlier maturing than Red Fife Earlier maturing than Red Fife
2 1
1 1
2 2
2 2
2 2
2 2
2 2
2 2
ECORC ECORC
1901 1908
33679 12172
Marquis
Earlier maturing than Red Fife, good milling and breed-making quality
1
1
2
2
2
2
2
2
ECORC
1909
11061
Prelude
Earlier maturing than Marquis
2
1
2
2
2
2
2
2
ECORC
1913
11320
Ruby
Good milling and bread-making quality
1
2
2
2
2
2
2
2
ECORC
1917
11902
Kota
Partial resistance to stem rust
2
1
2
2
1
2
2
2
I.US
1921
1798
Supreme
Earlier maturing than Marquis
2
1
2
2
2
2
2
2
REF
1921
12011
Renfrew
Later maturing than Marquis
2
1
2
2
2
2
2
2
UOA
1924
1796
Broatch’s Whitehead
Taller, late maturing, inferior yield, but with white kernels
2
1
2
2
2
2
2
2
CDC
1925
11140
Garnet
Earlier maturing
2
1
2
2
2
2
2
2
ECORC
1925
10123
Red Bobs #222
Earlier maturing than Marquis
2
1
2
2
2
2
2
2
UOA
1926
11404
Ceres
Better rust resistance than Marquis
2
2
2
1
1
1
2
2
I.US
1928
9774
Reward
Early maturity, good quality
1
1
2
2
2
2
2
2
ECORC
1928
11778
Reliance
Drought resistance
2
2
2
2
2
2
2
2
I.US
1932
11766
Canus
Drought resistance
2
2
2
2
2
2
2
2
UOA
1935
33637
Thatcher
1
1
2
1
1
2
2
2
I.US
1935
12060
Apex
Stem rust resistance, wide adaptation, earlier maturity, quality Stem rust resistance
2
2
2
1
1
2
2
2
CDC
1937
33627
Coronation
Disease resistance
2
2
2
1
2
2
2
2
CRC
1937
9844
Renown
Leaf and stem rust resistance
2
2
2
1
1
1
2
2
CRC
1937
11773
Regent
Leaf and stem rust resistance
2
2
2
1
1
1
2
2
CRC
1939
11444
Redman
Leaf and stem rust resistance
2
2
2
1
1
1
2
2
CRC
1946
11428
Rescue
Resistant to wheat stem sawfly
2
2
2
2
2
2
1
2
SPARC
1946
45654
Saunders
Earlier maturity than Thatcher
2
1
2
2
2
2
2
2
ECORC
1947
11943
Lee
Leaf and stem rust resistance
2
2
2
1
1
1
2
2
I.US
1950
10947
Chinook
Resistant to wheat stem sawfly
2
2
2
2
2
2
1
2
SPARC
1952
1738
Selkirk
Leaf and stem rust resistance
2
2
2
1
1
1
2
2
CRC
1953
11955
Lake
Drought resistance
2
2
2
2
2
2
2
2
SRF
1954
10923
Canthatch
Stem rust resistance
2
2
2
1
1
2
2
2
CRC
1959
9740
Pembina
Leaf and stem rust resistance
2
2
2
1
1
1
2
2
CRC
1959
11280
Cypress
Resistant to wheat stem sawfly
2
2
2
2
2
2
1
2
SPARC
1962
1739
Park Manitou
Early maturity Stem rust resistance
2 2
1 2
2 2
2 1
2 1
2 2
2 2
2 2
LRC CRC
1963 1965
11267 9883
Neepawa
Disease resistance, high yield
2
2
1
1
2
2
2
2
CRC
1969
11189
Canuck
Resistant to wheat stem sawfly
2
2
2
2
2
2
1
2
SPARC
1973
9741
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212
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Table 1 continued Cultivara
Improved characteristicsa
Coding for improved characteristicsb
CNd
Q
M
Y
D
S
L
F
A
Sourcec
Year
Sinton
Leaf and stem rust resistance
2
2
2
1
1
1
2
2
SPARC
1975
11968
Chester
Resistant to wheat stem sawfly
2
2
2
2
2
2
1
2
LBRC
1976
9786
Benito
Early maturity, disease
2
1
2
1
2
2
2
2
CRC
1979
2796
Columbus
Pre-harvest sprouting tolerance
2
2
2
2
2
2
2
1
CRC
1980
37156
Katepwa
Disease resistance
2
2
2
1
2
2
2
2
CRC
1981
38927
Leader
Resistant to wheat stem sawfly
2
2
2
2
2
2
1
2
SPARC
1981
38926
Lancer
Resistant to wheat stem sawfly, pre-harvest sprouting tolerance
2
2
2
2
2
2
1
1
SPARC
1984
17840
Kenyon
Leaf rust
2
2
2
1
2
1
2
2
CDC
1985
43842
Conway
Drought resistance
2
2
2
2
2
2
2
2
CDC
1986
43840
Laura
Lr34 good leaf rust resistance, high yield and good quality
1
2
1
1
2
1
2
2
SPARC
1986
44167
Roblin
Leaf and stem rust resistance
2
2
2
1
1
1
2
2
CRC
1986
43847
CDC Makwa
Disease resistance, high yield
2
2
1
1
2
2
2
2
CDC
1990
52587
Pasqua
Disease resistance, five leaf rust resistance genes drought tolerance
2
2
2
1
2
1
2
2
CRC
1990
106306
AC Minto
Disease resistance, high yield
2
2
1
1
2
2
2
2
CRC
1991
106307
CDC Teal
Disease resistance, high yield
2
2
1
1
2
2
2
2
CDC
1991
52585
CDC Merlin
Disease resistance, high yield
2
2
1
1
2
2
2
2
CDC
1992
52586
AC Domain
Pre-harvest sprouting tolerance, lodging resistant
2
2
2
2
2
2
2
1
CRC
1993
106358
AC Eatonia
Resistance to wheat stem sawfly, pre-harvest sprouting tolerance
2
2
2
2
2
2
1
1
SPARC
1993
106365
AC Michael
Tolerance to head melanosis
2
2
2
1
2
2
2
2
LRC
1993
52557
Invader
Disease resistance, high yield
2
2
1
1
2
2
2
2
APAU
1993
106366
AC Barrie
1
2
1
1
2
2
2
2
SPARC
1994
106318
AC Cora
High yield and good quality, disease resistance Disease resistance (Lr21)
2
2
2
1
2
1
2
2
CRC
1994
106353
Pacific
BW90, Lr34, yield
2
2
1
2
2
1
2
2
CRC
1994
106352
AC Majestic
High yield, pre-harvest sprouting resistance
2
2
1
2
2
2
2
1
CRC
1995
106357
AC Cadillac
Lr34, good yield and protein, large heavy kernels, bunt resistant Bt10
1
2
1
1
2
1
2
2
SPARC
1996
106337
AC Elsa
High yield, high protein, resistance to leaf and stem rusts
1
2
1
1
1
1
2
2
SPARC
1996
106314
AC Vista
Quality increase, pre-harvest sprouting resistance
1
2
2
2
2
2
2
1
SPARC
1996
106343
AC Intrepid
Good yield, early maturity, leaf and stem rust resistant, bunt resistant
2
1
1
1
1
1
2
2
SPARC
1997
106338
AC Splendor
Early maturity, good yield, high protein
1
1
1
2
2
2
2
2
CRC
1997
106351
McKenzie
High yielding, Lr21 for leaf rust resistance
2
2
1
1
2
1
2
2
SWP
1997
106330
AC Abbey
Resistance to wheat stem sawfly
2
2
2
2
2
2
1
2
SPARC
1998
106336
Alikat
Agronomy, aluminum tolerance
2
2
2
2
2
2
2
1
UOA
1998
106416
Prodigy
High yield, high protein, resistant to leaf and stem rust.
1
2
1
1
1
1
2
2
SWP
1998
106329
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Table 1 continued Cultivara
Improved characteristicsa
Coding for improved characteristicsb
CNd
Q
M
Y
D
S
L
F
A
Sourcec
Year
Superb
High yielding, lodging resistant
2
2
1
2
2
2
2
1
CRC
2001
Journey
Good yield, lodging resistance
2
2
1
2
2
2
2
1
SWP
2002
106382 106379
Lovitt
Lr21, preharvest sprouting resistance
2
2
2
1
2
1
2
1
SPARC
2002
106395
Lillian
Resistance to wheat stem sawfly, leaf rust resistance
2
2
2
2
2
1
1
2
SPARC
2003
106394
Harvest
Early maturity, high yield
2
1
1
2
2
2
2
2
CRC
2004
106393
Snowbird
Good yield, lodging resistance, leaf rust resistance
2
2
1
2
2
1
2
1
CRC
2004
106388
a
Dominant check cultivars over time are highlighted in italics. Trait improvement data were copied from several sources as described in the text
b
Group coding for improvement in eight traits. The number of 1 or 2 stands for a cultivar joining to the group with or without improvement of each trait, respectively. Q quality, Y high yield, M early maturity, D resistance to all the possible reported diseases, S stem rust resistance, L leaf rust resistance, F sawfly resistance, A agronomy including lodging, preharvest sprouting tolerance c
The code for the origin or breeding program from which a cultivar was developed. APAU AgriPro and Agricore United joint breeding program; CDC Crop Development Centre, Univ. of Saskatchewan; CRC Cereal Research Centre, Winnipeg; ECORC Eastern Cereal and Oilseed Research Centre, Ottawa; I Introductions from Galicia region of central Europe (I.GC), Russia (I.RU), and USA (I.US); LRC Lacombe Research Centre; LBRC Lethbridge Research Centre; REF Rosthern Experimental Farm; SPARC Semiarid Prairie Agricultural Research Centre, Swift Current; SRF Scott Research Farm; SWP Saskatchewan Wheat Pool; and UOA = Univ. of Alberta d
CN Canadian National accession number in the wheat collection held at Plant Gene Resources of Canada
Europe, Russia, and U.S.A.) and developed from seven major groups of the Canadian wheat breeding program from 1893 to 2004. The major breeding groups were Crop Development Centre, Saskatoon; Cereal Research Centre, Winnipeg; Eastern Cereal and Oilseed Research Centre, Ottawa; Semiarid Prairie Agricultural Research Centre, Swift Current; Lacombe Research Centre, Lethbridge Research Centre, Scott Research Farm; University of Alberta, Edmonton; and AgriPro and Agricore United joint breeding program, Saskatchewan Wheat Pool, Rosthern Experimental Farm. These breeding groups, however, applied similar breeding approaches and essentially shared the same breeding targets over time to improve productivity, disease resistance, resistance to abiotic stress, and end-use quality over the longterm breeding efforts (DePauw et al. 1995). The selection of these representative cultivars also depended on the availability on molecular data and cultivar description. SSR data and cultivar description The SSR data used for this analysis were originally collected from the previous effort to genotype four
panels of (or about 350 accessions of) elite wheat germplasm with polymorphic 370 SSR markers. The genotyping effort, including cultivar selection, tissue preparation, DNA extraction, primer screening and genotyping, and SSR data collection, has been described in Somers et al. (2007) and Fu and Somers (2009). The screened 370 markers were mapped across 21 wheat chromosomes and spanned the wheat genome with 10 cM per every marker. The genotyped germplasm included durum wheat, spring and winter wheat, but this analysis considered only hard red spring wheat, as the hard red spring wheat breeding represented the major effort of the Canadian bread wheat breeding over the past century. Effort was made to collect the information on genetic improvement of various traits in these 78 wheat cultivars. The collection sources included Slinkard and Knott (1995), Morrison (1960), Neatby (1942), GRIN-CA website (http://pgrc3.agr.gc.ca/ index_e.html), wheat pedigree website (http://genbank. vurv.cz/wheat/pedigree/pedigree.asp), and wheat variety selector website (http://www1.agric.gov.ab.ca/general/ cropvart.nsf/Varieties). Most cultivars released in Canada have a cultivar description published or possess a cultivar record. Consequently, a trait
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improvement in a cultivar compared to its check (or reference) cultivar(s) in a breeding period can be tracked. Note that most of the assayed cultivars were the check cultivars for the cultivars released after them and dominant check cultivars are given in Table 1. Based on the collected cultivar records, genetic improvements in eight major traits were identified and used to separate 78 cultivars with or without improvement in those traits into improved or nonimproved groups, respectively. As illustrated in Table 1, cultivars with the improvement record of a specific trait were classified into the improved group with code of 1, and those cultivars without the improvement record of the specific trait were separated into the non-improved group with code of 2. Coding cultivars for improvement in one trait was independent of coding for improvement in the other traits and consequently, a cultivar with records of multi-traits improvements would join the improved groups multiple times for those traits. For example, the cultivar Thatcher with records of ‘‘stem rust resistance, wide adaptation, earlier maturity, quality’’ joined the improved groups for quality, maturity, disease, and stem rust, but the non-improved groups for yield, leaf rust, sawfly, and agronomy. This classification considered only the overall incremental change of a trait over time for a cultivar, not the actual performance of the cultivar in the trait, and it is feasible for the assessment of the breeding impacts accumulated over time on the trait.
Euphytica (2011) 179:209–225
improved and non-improved groups for each trait was determined following the random permutation procedure described in Fu (2010). The random permutation procedure allows for a significance test of the difference in allelic counts for groups of variable cultivar numbers. This analysis was done separately for each chromosome and for all the chromosomes. The number of loci with significant allelic decrease or increase was also calculated in the improved group when compared with the non-improved group for each trait. Third, an analysis of molecular variance was performed using the Arlequin program (Excoffier et al. 2005) and using GenAlEx v6 software (Peakall and Smouse 2005) to estimate the proportional SSR variations resided between and within cultivar groups for improvement in each trait. The proportional SSR variations were tested for significance with 9,999 random permutations. This analysis was done with respect to chromosome and trait improvement. Fourth, a principal coordinates (PCO) analysis was made based on their similarity matrix generated with simple matching method using the NTSYS-pc program (Rohlf 1997) to assess the genetic associations of individual cultivars with different trait improvements. Plots were made of the first three resulting principal coordinate scores to illustrate the cultivar associations for each trait improvement.
Results Data analysis The analysis of SSR data for 78 wheat cultivars with respect to specific trait improvement was made in several steps. First, the number of loci and alleles were counted with respect to chromosome, followed by allelic counts with respect to trait improvement. The allelic counts for two cultivar groups for each trait also considered the number of the same alleles (measured with fragment size) present in both groups and the number of unique alleles for either group. This was repeated for each chromosome and for all chromosomes. This counting was done with a SAS program specifically written in SAS IML (SAS Institute Inc. 2008). Second, the number of loci showing a significant allelic change in the 78 cultivars separated into
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The selected SSR data for the 78 wheat cultivars consisted of 370 polymorphic loci with 2301 alleles detected from 336 genomic SSR primer pairs. Two primer pairs (wmc48 and gwm497) detected three loci each and 30 primer pairs revealed two loci each. These markers were widely distributed over all 21 chromosomes, with an average of 6.52 cM between adjacent markers (Table 2). The number of loci per chromosome ranged from seven in chromosome 4D to 24 in chromosomes 1B, 5A and 7A (Table 3). The number of alleles detected per locus ranged from 2 to 18 and averaged 6.2. The observed allelic frequencies ranged from 0.013 to 0.987 with an average of 0.143. There were 1302, 1538 and 1734 alleles with occurrence frequencies of 0.05, 0.10, and 0.15 or smaller, respectively. This means 56.6% of the
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Table 2 Descriptions of each locus detected with a significant allelic increase or decrease in the improved group of wheat cultivars when compared with the non-improved group of wheat cultivars for eight wheat traits Cha
Dist (cM)a
Locus
NAa
Improvement in traitb Q
M
Y
D
1A
6.1
gdm33
9
1A
24.6
cfd15
3
1A
48.4
wmc24
8
-5*
-4*
1A
56.3
gwm164
6
-3*
-2*
1A
56.8
barc148
6
-4***
1A
57.0
cfd59
4
-2*
1A
60.7
gwm135
6
-6***
1A
61.7
wmc278
4
-2**
1A
64.5
wmc183
6
1A
68.5
wmc312
11
1A 1A
70.8 98.0
cfa2129 wmc59
12 6
-6*
1A
113.8
barc158
5
3**
1A
124.1
cfa2219
9
1B
24.9
barc8
11
-5*
1B
25.9
gwm413
8
-4*
1B
31.8
wmc419
9
1B
33.4
gwm273
8
1B
34.3
gwm11
11
1B
34.3
barc137
9
1B
34.8
wmc626
16
-6*
-5* -6*
1* -3*
1B
47.3
wmc134
5
-4***
1B
64.1
gwm124
2
1**
1B 1B
90.3 91.5
wmc830 wmc44
8 13
1D
13.7
barc149
3
1D
23.2
wmc336
1
1D
50.6
cfd72
9
1D
53.9
barc148
14
1D
59.2
wmc216
4
1D
75.4
gdm126
3
1D
84.1
cfd63
7
2A
28.3
wmc177
7
2A
52.5
gwm95
4
2A
73.7
gwm312
8
2A
139.9
wmc658
12
2B 2B
7.7 40.4
wmc382 gwm429
1 6
-5* -7*
8
5
-4**
-8*
5
8
-4*
-4*
-6* 3**
barc181
cfd65
-6*
-4*
cfd48
wmc429
-4*
-9*
38.3
52.3
A
-4** 1*
39.8
52.7
F
1**
1B
1D
L
-4*
1B
1D
S
-5**
-5*
-4** -6* 1** -1* 2* -4* -4* -11*
-8*
2* -2* 1* -3* -2* -6* 1* -1* -3**
123
216
Euphytica (2011) 179:209–225
Table 2 continued Cha
Dist (cM)a
Locus
NAa
Improvement in traitb Q
M
Y
D
2B
59.8
barc18
11
-7*
-6**
2B
61.1
barc167
8
-4*
-3*
2B
63.4
wmc477
7
-4**
-3*
2B
71.9
gwm388
4
-1*
2B
82.4
cfd73
8
-6**
2B
93.4
wmc332
9
2B
99.6
wmc149
10
2D
0.0
barc124
8
cfd56
6.6
wmc112
12 8
-5*
2D
40.7
gwm484
12
-9*
2D
48.2
gwm102
6
-4*
2D
59.5
cfd2
4
-1*
2D
65.8
cfd116
4
1*
2D 2D
68.9 73.1
wmc601 gwm157
16 3
90.9
gwm539
11
gwm301
8
3A
0.0
wmc11
5
3A
5.7
wmc532
10
3A
36.7
barc45
5
3A
44.9
gwm5
7
3A
64.4
cfa2262
4
3A
73.8
cfa2193
9
-7** -8*
-5* -6*
-7*
-10*
-2* -4* 2* -3** 1*
-2* -5* -4*
-4*
-4* -3* -5*
3A
83.3
wmc559
7
3A
105.0
wmc594
16
3B
7.2
barc147
5
3B
72.4
wmc418
5
3B
97.2
barc206
1
-1**
-4*
-4*
-12** -4*
2* -3*
3B
105.4
wmc687
7
3B
111.2
barc77
8
3B
122.5
gwm299
7
3B
142.9
wmc632
8
-7*
3D
19.7
gwm383
7
-6*
-6* -4*
3D
23.5
wmc492
3
-2*
3D
30.1
gwm456
7
-6*
3D
32.8
wmc656
9
4A
7.6
wmc491
13
-8* -4*
4A
7.8
wmc48
6
4A
8.4
cfd71
3
4A
12.1
gwm610
6
4A
24.9
wmc650
11
4A
26.6
barc170
9
123
A
-8*
28.0
107.0
F
-4*
2D
2D
L
1*
2D
2D
S
-8*
1*** -4* 1* -8*
-9* -7*
-7*
Euphytica (2011) 179:209–225
217
Table 2 continued Cha
Dist (cM)a
Locus
NAa
Improvement in traitb Q
M
Y
4A
37.7
wmc468
8
4A
39.5
wmc707
16
3*
4A
49.1
gwm494
5
4A
68.1
wmc232
6
-4***
4A
82.9
wmc313
6
-5***
4B
17.3
wmc413
3
D
S
L
F
A
-6* -11*
-7*
-8* 1*
-4* -4*
2*
-5**
1*
4B
24.5
gwm251
8
4B
37.7
barc20
5
-6***
4D
28.3
wmc48
6
-4*
4D
34.5
wmc457
3
-1*
4D
66.7
cfd84
5
5A
23.1
gwm443
6
-4*
5A
27.8
wmc713
12
4**
5A
44.9
cfa2190
9
-7*
5A 5A
57.0 59.0
wmc805 gwm304
5 6
-3*
5A
76.6
gwm617
13
-9*
5A
80.3
wmc415
3
-2**
5A
100.5
wmc445
3
-2*
5A
107.3
cfa2141
10
5A
110.9
barc232
14
5A
113.8
cfa2185
2
5A
137.5
gwm126
4
1*
5B
0.0
cfd5
5
-4*
5B
60.0
wmc376
3
5B
67.6
gwm335
16
5B
72.6
gwm371
11
5B
80.3
wmc415
6
3**
5B
84.0
wmc537
9
4**
5B 5B
88.5 117.2
gwm554 gwm408
5 5
5B
164.4
gwm497
5
5D
0.0
wmc233
3
5D
3.7
cfd18
9
-7* -3**
3*
-3* -10* -6**
-7*
-4* -1* -3** -9* -9* 1**
-2* -8* -9** -5* -4* -2* -4* 2** -5*
5D
9.0
gwm190
5
5D
13.5
cfa2104
4
5D
14.3
cfd189
5
5D
48.8
gwm182
3
-1**
5D
69.5
cfd29
13
-9***
5D
97.6
cfd10
3
5D
110.0
wmc765
18
6A
95.3
gwm617
7
6A
148.2
wmc254
5
-2* 2*
2***
-3* -9* 1** -9** -4* -4*
1*
123
218
Euphytica (2011) 179:209–225
Table 2 continued Cha
Dist (cM)a
Locus
NAa
Improvement in traitb Q
Y
6
D
S
-5***
-5*
6B
9.2
6B
17.0
cfd13
14
6D
0.0
cfd49
11
7A
7.6
gwm635
5
7A
14.1
wmc646
6
7A
39.6
wmc283
8
7A
47.1
barc127
7
7A
47.7
cfa2028
5
7A
91.7
cfa2257
2
-1*
7A
102.0
wmc790
11
-9*
7A
131.2
wmc809
10
7B
0.0
wmc606
10
7B
41.2
gwm537
11
-8**
7B
47.4
gwm400
10
-8**
7B 7B
56.6 65.0
wmc758 gwm333
15 6
-12* -5***
7B
68.2
wmc396
4
-3***
7B
143.4
cfa2040
5
-4*
7B
149.9
gwm146
4
7D
21.0
wmc506
12
7D
51.7
cfd31
8
7D
71.7
wmc463
7
7D
78.4
gwm44
9
7D
98.8
barc172
6
-3*
7D
135.9
gwm428
4
-2*
7D
142.8
wmc634
15
-8**
a
wmc487
M
L
F
A
-12* 2*
-8**
-5*
-3* -5** -5** -5*** 2*
-8**
-7***
-7*
3** -5* -5*
-3** 1* -5* 2*
-4* -7**
-5*
Ch Chromosome, Dist distance, NA number of alleles detected for the locus in the 78 wheat cultivars
b
Q quality, Y high yield, M early maturity, D resistance to all the possible reported diseases, S stem rust resistance, L leaf rust resistance, F sawfly resistance, A agronomy including lodging, preharvest sprouting tolerance. Positive or negative number represents increased or decreased number of alleles at the locus, respectively, followed by the star(s) for the significance level of random permutation test: * P \ 0.05, ** P \ 0.01, *** P \ 0.001
detected alleles were present only in four or fewer cultivars. A total of 154 loci with significant allelic changes (P \ 0.05) were detected in the 78 wheat cultivars separated into improved and non-improved groups for eight traits (Table 2). These significant loci with a total of 1136 alleles were widely distributed across 21 chromosomes and a majority of them were involved with multiple trait improvements. For example, the locus gwm273 with eight alleles on the chromosome 1B displayed significant allelic changes associated with the genetic improvements of three traits
123
(maturity, yield and disease; Table 2). Also, some significant loci associated with the improvements of yield, disease, and sawfly resistance were located closely on the same chromosomes. For example, eight significant loci on the chromosome 1A were associated with the yield improvement. Similarly, improving sawfly resistance generated four significant loci on the chromosome 3D. The number of significant loci differed greatly for the improvements of eight traits, ranging from four for quality to 68 for yield and averaging 35 (Table 3). Overall, many more loci with significant allelic
Euphytica (2011) 179:209–225
219
Table 3 Chromosome-specific number of loci displaying a significant allelic decrease or increase in the improved group of wheat cultivars when compared with the non-improved group of wheat cultivars in specific wheat traitsa Chromosome
No. of loci
Improvement in trait Quality
Maturity
Yield
Disease
1A
23
(1)
9
5(1)
1B
24
(2)
6(1)
8
1D
23
(3)
2
2
2A
17
(1)
2
1
2B
19
5
4
2D
22
2(2)
3
5
3A
14
(1)
4
7
3B
23
1
1(1)
1
1
(2)
8
(1) 2
1
1
3D
14 17
4B 4D
14 7
5A
24
1(1)
6
3
5B
22
(2)
2
2
5D
21
(2)
2
6
6A
9
Agronomy
2(1)
2
1
1
(1)
3 1
1
1
1 2 4
(1)
1
(1)
1
8 10
(1)
1
1
7A
24
1(1)
5
1
7B
15
(1)
7
1
7D
20
(2)
2
5
a
(1)
(2)
6D
370
Sawfly
1
3
6B
Total
Leaf rust
2
4A
Sub-total
Stem rust
1
1(1)
3
1
2(1)
1
1
3
1
(2)
1
(1)
1
1 1
1 1
3(1)
7(24)
67(1)
56(1)
5(5)
7
17(5)
12(1)
4
31
68
57
10
7
22
13
The number of loci with a significant allelic increase is given in parentheses. The significance level used was at P \ 0.05
reduction were detected than those with significant allelic increase (Table 3). Only the improved group for maturity displayed many more loci (24) with significant allelic increase than those (7) with significant decrease (Table 3). The distributions of significant loci also varied over the 21 chromosomes for these eight traits. For example, the improvement of three traits (maturity, yield and disease) resulted in 31, 68 and 57 significant loci, respectively, that were widely distributed over the 21 chromosomes. The improvement of the other traits generated significant allelic changes only on some chromosomes. Comparison of allelic characteristics between alleles of significant and non-significant loci associated with the improvements of eight traits revealed no marked differences for the allelic distributions associated with allelic frequencies (Fig. 1a). However, the significant loci had slightly more alleles with
frequencies less than 0.07 and fewer alleles with frequencies greater than 0.30 than the non-significant loci. Assessment of allelic characteristics for alleles from the loci with significant allelic decrease or increase revealed similar allelic distributions. For example, a similar pattern of allelic distributions with respect to allelic frequency was observed when a comparison was made between 523 SSR alleles from 67 significant loci with decreased alleles by yield improvement and the 193 SSR alleles from 24 significant loci with increased alleles by selection for early maturity (Fig. 1b). At the frequency of 0.03 or lower, however, more alleles were added by the selection for early maturity than those removed by the improvement of yield (Fig. 1b). Such allelic changes were consistent with the broad genetic base of the wheat cultivars with earlier maturity and with the narrow base of the wheat cultivars with improved
123
220
a
50
PC0 Axis 2 (8.9%)
40
30
20
.06 .04 .02 0.00 -.02
-.06
0.03 0.06 0.09 0.12 0.15 0.18 0.21 0.24 0.27 0.30 >0.30
Frequency of occurrence in 78 wheat cultivars
The percentage of the total alleles
Filled circles for cultivars with earlier maturity
-.04
10
0
b
.10 .08
From significant loci From non-significant loci
60 From loci with decreased alleles From loci with increased alleles
50 40
b PCO Axis 2 (8.9%)
The percentage of the total alleles
a
Euphytica (2011) 179:209–225
Filled circles for cultivars with improvement in yield
.08 .06 .04 .02 0.00 -.02 -.04
30
-.06
20
-.08 -.10
-.08
-.06
-.04
-.02
0.00
.02
.04
.06
.08
PCO Axis 1 (19.5%) 10 0 0.03 0.06 0.09 0.12 0.15 0.18 0.21 0.24 0.27 0.30 >0.30
Frequency of occurrence in 78 wheat cultivars
Fig. 1 The percentage of the total SSR alleles in relation to allelic frequency. a The percentage of the 1136 SSR alleles from 154 loci with significant allelic changes and the percentage of the 1165 SSR alleles from 216 loci with nonsignificant allelic changes. b The percentage of the 523 SSR alleles from 67 significant loci with decreased alleles by improved yield and the percentage of 192 SSR alleles from 24 significant loci with increased alleles by selection for early maturity
yield. The principal coordinates analysis revealed the wide spread of the 22 cultivars with earlier maturity (Fig. 2a) and the narrow clustering of the 20 cultivars with improved yield (Fig. 2b), when compared with the other corresponding cultivars. Counting unique alleles in two cultivar groups for improvement in eight traits revealed significant lower numbers of unique alleles in the improved groups (Table 4). For example, 13 wheat cultivars with improved quality had 106 unique alleles, while the other 65 cultivars without improved quality displayed 1142 unique alleles. However, 22 wheat cultivars with earlier maturity displayed a significantly higher
123
Fig. 2 Genetic associations of the 78 wheat cultivars as revealed by a principal coordinates analysis of 2301 SSR alleles at 370 loci across 21 chromosomes. a a wide spread of the 22 cultivars with earlier maturity over the plot. b a narrow clustering of the 20 cultivars with improved yield. Note that a and b are the same plots with different labels for improved cultivars
number (399) of unique alleles than expected. The distributions of unique allele counts also varied greatly among different chromosomes with respect to trait improvement (Table 4). For example, the numbers of unique alleles for two cultivar groups for disease greatly differed at the chromosomes 1B and 5A with similar numbers of loci and detected alleles. Note that these variations in unique allele count were partly associated with the unequal numbers of cultivars for these cultivar groups. To take into account the variable group sizes and the unequal sampling of wheat chromosomes, the analysis of molecular variance between two cultivar groups was performed with respect to wheat trait. Significant SSR variation between two cultivar groups was observed for the improvements of seven traits, except for the trait of stem rust (Table 5). The proportional SSR variation residing between two groups ranged from 1.4% (for improved leaf rust
Euphytica (2011) 179:209–225
221
Table 4 Comparative counts of unique SSR alleles in 78 wheat cultivars separated into two groups with and without improvement in eight wheat traitsa Chr
NL
NA
Improvement in trait Quality
Maturity
Yield
Disease
Stem rust
Leaf rust
Sawfly
Agronomy
1A
23
144
5/67
19/48
6/82
12/53
8/55
8/56
15/85
6/84
1B
24
158
6/80
32/43
5/86
10/65
10/65
9/68
5/98
6/88
1D
23
107
2/55
23/21
3/54
9/35
4/40
5/38
3/64
3/58
2A
17
116
9/51
23/31
5/71
13/46
14/44
10/47
5/76
4/75
2B
19
109
5/38
15/27
3/51
8/35
6/38
7/37
3/59
5/58
2D
22
153
5/74
24/44
7/79
11/59
8/68
5/67
10/85
1/88
3A
14
96
5/49
16/29
2/52
7/36
7/39
4/43
2/63
5/56
3B
23
116
4/51
14/33
7/51
14/30
9/37
12/35
5/71
4/59
3D
14
64
3/26
8/18
3/23
7/16
9/16
7/16
1/44
1/28
4A
17
131
7/74
27/50
5/87
18/53
18/55
15/58
10/69
2/87
4B 4D
14 7
95 33
5/42 1/12
14/37 2/14
8/41 2/10
12/36 5/7
13/35 2/12
11/40 5/7
5/63 3/16
9/46 0/12
5A
24
159
15/88
35/60
1/101
17/76
18/79
13/80
13/104
4/97
5B
22
127
4/63
28/32
6/66
9/51
9/55
9/55
6/78
4/64
5D
21
113
4/59
19/37
10/53
5/45
5/48
7/45
9/54
7/49
6A
9
56
2/29
5/18
3/29
6/17
5/22
8/16
5/33
3/30
6B
8
68
0/42
16/21
6/39
8/32
6/30
4/32
2/44
2/45
6D
10
56
2/28
9/13
4/30
4/21
2/18
4/22
3/32
0/33
7A
24
150
4/89
18/62
6/87
22/53
19/55
17/54
16/89
6/84
7B
15
109
6/59
23/33
4/76
15/42
14/41
13/43
6/69
2/78
7D
20
141
12/66
29/40
11/78
18/61
10/66
11/66
6/87
3/91
370
2301
106/1142
399/711
107/1246
230/869
196/918
184/925
133/1383
77/1310
13/65
22/56
20/58
34/44
17/61
22/56
10/68
Total
Number of cultivars
11/67
a
The numbers in each column for improved trait represent the allelic counts for improved and non-improved cultivar groups for a trait, respectively. Chr chromosome, NL number of loci, and NA number of alleles
resistance) to 11.8% (for improved sawfly resistance). More interestingly, the proportional SSR variations within the improved groups consistently were much lower than those within the non-improved group (Table 5). For example, the group with improved yield had 20.2% of the total SSR variation while the non-improved group for yield displayed 72.6%. Similarly, the group with earlier maturity displayed only 28.2% of the total SSR variation while the group with later maturity had 67.2%. The proportional SSR variation residing between two cultivar groups also varied among 21 chromosomes for any traits examined (Table 5). Interestingly, the significant SSR variation was observed for each chromosome by selection for early maturity, high yield and disease resistance, while only for some chromosomes by improving quality, stem rust, leaf
rust, sawfly resistance, and agronomy. Also, the improvement of sawfly resistance generated the largest SSR differences between two cultivar groups for a large number of chromosomes (Table 5).
Discussion This SSR analysis represents the first attempt to assess allelic changes in an improved wheat gene pool with respect to long-term plant trait improvement. Several interesting findings were obtained. First, improving eight traits since 1845 introduced significant allelic changes at 154 (out of 370) loci across the 21 wheat chromosomes. Second, the number of significant loci for a trait improvement varied greatly, ranging from four for improving
123
222
Euphytica (2011) 179:209–225
Table 5 The significant percentages of the total SSR variation resided between two groups of wheat cultivars with and without improvement in specific wheat traits Chromosome
Improvement in trait* Quality
Maturity
Yield
Disease
1A
7.7b
9.4c
6.5c
1B
2.2a
8.5c
4.5c
1D
3.9b
6.5c
2.4a
2A
4.1b
3.2b
4.0b
2B
2.6a
7.2c
5.8b
2D
6.5b
Leaf rust
2.2a
Sawfly
Agronomy
12.6b
8.0b
6.3b
8.5b
9.9b
8.0c
3.2a
5.4b
1.9a
3A
3.8b
5.5b
3.7b
2.1a
3B
8.1c
12.1c
9.1c
2.8a
3D
3.4c
3.9a
2.0a
8a
4A
7.8c
12.8c
9.2c
11.3b
4B 4D
5.9c 7.5c
2.3a 4.0a
5.9c 2.9a
5A
3.5c
4.9b
5.8c
17.8c
5B
3.0a
6.6c
5.3c
15.3c
5D
5.4b
Stem rust
3.4a
6A
13.5c
2.3a
19.9c
6.1a 14.3c
22.7c 7.2a 3.2a
4.5b
11.2c
5.9c
16.5c
5.7b
2.6a
3.2a
5.2b
8.2b
1.3b
6.9b
6.6b
6B
3.9a
5.7c
7.0c
3.8c
6D
7.4b
4.9b
4.5a
2.9a
7A
4.0b
6.2c
4.1c
7B
5.5b
9.1c
5.9b
7D
3.8b
7.1c
3.6c
3.3a
11.2c
1.6a
21.1c
4.1a
6.7b
6.5a 5.2b
All chromosomes Between-group
2.1a
4.6c
7.2c
5.0c
0.8
1.4a
11.8c
5.0b
GWI**
15.4
28.2
20.2
35.6
20.9
26.7
9.6
11.7
GOI**
82.5
67.2
72.6
59.4
78.3
71.9
78.6
83.3
* The letter a, b, c following the percentage represents the significance levels at P \ 0.05, P \ 0.01, P \ 0.001, respectively ** GWI = the percentage of the total SSR variation residing within the improved cultivar group for a trait and GOI = the percentage of the total SSR variation residing within the non-improved cultivar group for a trait
quality to 68 for improving yield and averaging 35. Third, many more loci with significant allelic reduction for improving a trait were detected than those with significant allelic increase. Selection for early maturity introduced more alleles, but improving the other traits purged more alleles. Fourth, significant SSR variation was observed between two cultivar groups for improvement in seven traits, but not in stem rust. The proportional SSR variations within the improved cultivar groups consistently were much lower than those within those non-improved cultivar groups. These findings clearly demonstrate the association between allelic changes and wheat trait improvements and are useful for understanding the
123
effectiveness of modern wheat breeding in genetic modification of the wheat genome. The detected association between allelic changes and wheat trait improvement presents more convincing evidence that the century-long bread wheat breeding reduced allelic diversity, and consequently provides support for the early diversity reports obtained from different analyses of the same gene pool (Fu et al. 2005, 2006; Fu and Somers 2009). The most interesting finding is that allelic reduction or increase was dependent on the improvement of specific traits. Selection for early maturity introduced more alleles, but improving the other traits eliminated more alleles. This finding would pose a difficulty to
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reason the nature of allelic changes for each trait to be improved in a breeding program. The most significant finding is that improving different traits introduced significant allelic changes at different loci across the whole genome. Thus, a different breeding target would generate a different influence on a plant genome. The findings presented here suggest that the alleles eliminated by improving wheat traits may not be completely associated with undesirable or non-target traits. First, a large proportion of alleles undetected in the cultivars with improved traits were infrequent. Second, allelic reduction was significantly associated with the improvement in seven traits, while allelic increase was associated only with the improvement in one trait. Our findings also imply that not all of the alleles eliminated by improving wheat traits were of non-adaptive value, as many loci with significant allelic reduction were also associated with the improvement in multiple traits. Thus, it could reason that some eliminated alleles were either selective or linked to selected genes, although the proportions of either type of reduced alleles remain to be determined. The allelic changes detected here were associated with the overall incremental change of a trait over time and may or may not be those genetic loci controlling the analyzed traits per se. Long-term improvement of a trait is expected to influence genes associated with the trait and nearby chromosomal regions via linkage (Hedrick 2000). However, how many of the significant loci reported here (Table 2) were directly associated with the improved traits, or reflected as the background changes generated by a long-term genetic improvement, remains to be determined. For example, four significant loci on chromosomes 2B, 3B, 4A, and 5A for quality improvement (Table 2) might be partly associated with those quantitative trait loci (QTLs) for several components of bread-making quality (e.g., see Li et al. 2009), but were not related to glutenin loci in the homoeologous group 1. Our data are of limits to detect QTLs controlling these traits and not adequate to characterize genetic changes at these loci under selective breeding (Fu et al. 2010). Our analysis also has several limitations. First, the traits examined here were assumed to reflect real breeding targets for improvement over time, but this assumption may not be completely true. A long-term
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genetic improvement of a target trait may indirectly improve a non-target trait but this analysis would have considered the non-target trait as a target trait. Second, some traits examined here are largely cumulative and compositional, not specific like those in specific maize programs with well-defined targets such as grain yield, oil or protein. For example, the leaf rust was defined as a breeding target for improvement in this analysis, but the early effort may have improved on leaf rust race 1 and the recent effort on leaf rust race 48. Thus, various sets of genes in different chromosomal regions may have been under artificial selection for leaf rust. Third, the group coding of wheat cultivars may not be completely accurate, depending on the cultivar records. Incorrect and incomplete records may bias the diversity analysis. Fourth, the genetic improvement of a trait was classified, not quantified, and consequently genetic comparison may not be precise. Fifth, the studied list of wheat cultivars may not be complete for the Canadian bread wheat breeding. Analyzing extra new cultivars may affect the association tests, particularly at the locus level. It is possible that the resolution of association testing may vary for different bread target traits. In spite of these limitations, the findings presented here are significant for understanding the genetic impacts of modern plant breeding on a plant genome. First, a breeding effort can change allelic composition of the breeding gene pool. As demonstrated, the Canadian bread wheat breeding largely eliminated infrequent alleles, although new alleles were also introduced. Second, the extent of allelic changes depends on the breeding target traits. Improving genetically complex traits such as yield or disease would influence many parts of the genome, as many genes are targeted for selection. Third, a breeding effort may increase or decrease related alleles, also depending on the breeding target traits. This dependence adds more uncertainty to predict allelic changes in an improved gene pool and could complicate the explanation of the inconsistent diversity patterns observed in many other improved gene pools. However, the analysis performed here should stimulate more research in determining allelic changes under selective breeding for trait improvement, as this research can also help to identify related loci in a specific breeding program like those in maize. Assessments of allelic changes in other
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improved gene pools with respect to trait improvement will help to draw a general picture on how plant breeding influences a plant genome. Acknowledgments The authors would like to thank Ms. Debbie Miranda for her assistance in the management of wheat SSR data and three anonymous reviewers on the early version of the manuscript.
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