Abstracts
The objective of this work was to assess the genetic diversity and population structure of wheat genotypes, to detect significant and stable genetic associations, as well as to evaluate the efficiency of statistical models to identify chromosome regions responsible for the expression of spike-related traits. Eight important spike characteristics were measured during five growing seasons in Serbia. A set of 30 microsatellite markers positioned near important agronomic loci was used to evaluate genetic diversity, resulting in a total of 349 alleles. The marker-trait associations were analyzed using the general linear and mixed linear models. The results obtained for number of allelic variants per locus (11.5), average polymorphic information content value (0.68), and average gene diversity (0.722) showed that the exceptional level of polymorphism in the genotypes is the main requirement for association studies. The population structure estimated by model-based clustering distributed the genotypes into six subpopulations according to log probability of data. Significant and stable associations were detected on chromosomes 1B, 2A, 2B, 2D, and 6D, which explained from 4.7 to 40.7% of total phenotypic variations. The general linear model identified a significantly larger number of marker-trait associations (192) than the mixed linear model (76). The mixed linear model identified nine markers associated to six traits.
Triticum aestivum; genetic resources; microsatellites; population structure; spike-related traits.
O objetivo deste trabalho foi avaliar a diversidade genética e a estrutura de população de genótipos de trigo, para detectar associações genéticas significativas e estáveis, bem como avaliar a eficácia de modelos estatísticos para identificar as regiões cromossômicas responsáveis pela expressão de características da espiga. Foram determinadas oito importantes características durante cinco safras agrícolas na Sérvia. Uma série de 30 marcadores microssatélites, localizados próximos a locos agronomicamente importantes, foi utilizada para avaliação da diversidade genética, o que resultou num total de 349 alelos. As associações marcador-características foram analisadas com uso dos modelos linear generalizado e linear misto. Os resultados obtidos para número de variantes alélicas por loco (11,5), valor médio de conteúdo de informação polimórfica (0,68) e diversidade genética média (0,722) mostraram que o nível excepcional de polimorfismo nos genótipos é o principal requerimento para estudos de associação. A estrutura da população estimada pelo agrupamento com base no modelo distribuiu os genótipos em seis subpopulações, de acordo com o log da probabilidade dos dados. Associações significativas e estáveis foram detectadas nos cromossomos 1B, 2A, 2B, 2D e 6D, que explicaram de 4,7 a 40,7% do total das variações fenotípicas. O modelo linear generalizado revelou número significativamente maior de associações marcador-características (192) do que o modelo linear misto (76). O modelo linear misto identificou nove marcadores associados a seis características.
Triticum aestivum; recursos genéticos; microssatélites; estrutura da população; características da espiga.
Introduction
Wheat (Triticum aestivum L.) breeders worldwide invest a great deal of
effort into creating cultivars able to challenge rising global issues, such as ongoing
climate changes and a growing world population. A tendency in the breeding process is
introducing novel techniques and approaches that could improve current conventional
breeding programs. Particularly, the advancement in the field of molecular biology by
applying genetic marker technologies and new statistical approaches are powerful tools
for indirect selection of valuable traits through marker-assisted selection (Landjeva et al., 2007LANDJEVA, S.; KORZUN, V.; BÖRNER, A. Molecular markers: actual and
potential contributions to wheat genome characterization and breeding. Euphytica,
v.156, p.271-296, 2007. DOI: 10.1007/s10681-007-9371-0.
https://doi.org/10.1007/s10681-007-9371-...
). The detection of specific
and precisely tagged chromosome regions responsible for the expression of certain
agronomic traits could be an excellent contribution for the selection and generation of
new high-yielding wheat varieties. Likewise, the knowledge of population diversity and
structure is of major importance for an efficient use of elite lines and varieties in a
breeding process (Laido et al., 2013LAIDO, G.; MANGINI, G.; TARANTO, F.; GADALETA, A.; BLANCO, A.;
CATTIVELLI, L.; MARONE, D.; MASTRANGELO, A.M.; PAPA, R.; DE VITA, P. Genetic
diversity and population structure of tetraploid wheats (Triticum turgidum L.)
estimated by SSR, DarT and pedigree data. PloS ONE, v.8, e6720, 2013. DOI:
10.13717/journal.pone.0067280.
https://doi.org/10.13717/journal.pone.00...
).
Spike-related traits are important yield components, which are less environmentally
sensitive and exhibit higher heritability than yield per se (Cuthbert et al., 2008CUTHBERT, J.L.; SOMERS, D.J.; BRULE-BABEL, A.L.; BROWN, P.D.; CROW, G.H.
Molecular mapping of quantitative trait loci for yield and yield components in spring
wheat (Triticum aestivum L.). Theoretical and Applied Genetics, v.117, p.595-608,
2008. DOI: 10.1007/s00122-008-0804-5.
https://doi.org/10.1007/s00122-008-0804-...
). The analyses of the genetic control of
spike-related characteristics and of individual effects of different genes and
quantitative trait loci (QTL) could provide specific information and be useful for
indirect determination of yield improvement (Ma et al.,
2007MA, Z.; ZHAO, D.; ZHANG, C.; ZHANG, Z.; XUE, S.; LIN, F.; KONG, Z.; TIAN
D.; LUO Q. Molecular genetic analysis of spike-related traits in wheat using RIL and
immortalized F2 populations. Molecular Genetics and Genomics, v.277, p.31-42, 2007.
DOI: 10.1007/s00438-006-0166-0.
https://doi.org/10.1007/s00438-006-0166-...
). In the last few years, association mapping has been considered one of
the most promising methods for the exploration of the entire genome in the search of
preferred chromosome regions, QTLs, and desired genes (Liu et al., 2010LIU, L.; WANG, L.; YAO, J.; ZHENG, Y.; ZHAO, C. Association mapping of
six agronomic traits on chromosome 4A of wheat (Triticum aestivum L.). Molecular
Plant Breeding, v.1, p.1-10, 2010.). The association mapping approach provides a greater
potential for the identification of targeted QTLs and fine tuning and mapping of genes
at a higher resolution than the previously used linkage mapping. Based on linkage
disequilibrium, association mapping is applied directly to diverse genetic materials,
resulting in a larger number of detected alleles per locus in a more representative
genetic background. It also represents a higher resolution system due to the
recombination events that have been accumulated during selection circles through
evolution and historical breeding processes (Haseneyer
et al., 2010HASENEYER, G.; STRACKE, S.; PIEPHO, H.-P.; SAUER, S.; GEIGER, H.H.;
GRANER, A. DNA polymorphisms and haplotype patterns of transcription factors involved
in barley endosperm development are associated with key agronomic traits. BMC Plant
Biology, v.10, p.1-11, 2010. DOI: 10.1186/1471-2229-10-5.
https://doi.org/10.1186/1471-2229-10-5...
). Cultivars genotyped with high-density markers and their
associations show promise in resolving the genetic basis of complex traits of agronomic
and economic importance (Wang et al., 2012WANG, M.; JIANG, N.; JIA, T.; LEACH, L.; COCKRAM, J.; WAUGH, R.; RAMSAY,
L.; THOMAS, B.; LUO, Z. Genome-wide association mapping of agronomic and morphologic
traits in highly structured populations of barley cultivars. Theoretical and Applied
Genetics, v.124, p.233-246, 2012. DOI: 10.1007/s00122-011-1697-2.
https://doi.org/10.1007/s00122-011-1697-...
). The
analysis of complex traits by association mapping is required for breeders, since it
facilitates even more the application of associated markers in the breeding process. One
of the first association mapping studies in wheat aimed at identifying significant
markers for kernel size and milling quality (Breseghello
& Sorrells, 2006BRESEGHELLO, F.; SORRELLS, M.E. Association analysis as a strategy for
improvement of quantitative traits in plants. Crop Science, v.46, p.1323-1330, 2006.
DOI: 10.2135/cropsci2005.09-0305.
https://doi.org/10.2135/cropsci2005.09-0...
). Subsequently, a large number of works used genome-wide
association studies (GWAS) to detect marker-trait associations (MTAs) for a large number
of traits, including quality traits in soft wheat (Reif
et al., 2011REIF, J.C.; GOWDA, M.; MAURER, H.P.; LONGIN, C.F.H.; KORZUN, V.;
EBMEYER, E.; BOTHE, R.; PIETSCH, C.; WÜRSCHUM, T. Association mapping for quality
traits in soft winter wheat. Theoretical and Applied Genetics, v.122, p.961-970,
2011. DOI: 10.1007/s00122-010-1502-7.
https://doi.org/10.1007/s00122-010-1502-...
), yield and other agronomic traits in wheat (Liu et al., 2010LIU, L.; WANG, L.; YAO, J.; ZHENG, Y.; ZHAO, C. Association mapping of
six agronomic traits on chromosome 4A of wheat (Triticum aestivum L.). Molecular
Plant Breeding, v.1, p.1-10, 2010.), and seed longevity in hexaploid
wheat (Rehman Arif et al., 2012REHMAN ARIF, M.A.; NAGEL, M.; NEUMANN, K.; KOBILJSKI, B.; LOHWASSER, U.
BÖRNER, A.Genetic studies of seed longevity in hexaploid wheat using segregation and
association mapping approaches. Euphytica, v.186, p.1-13, 2012. DOI:
10.1007/s10681-011-0471-5.
https://doi.org/10.1007/s10681-011-0471-...
). In bread wheat,
a number of yield-component QTLs was associated with spike-related and adaptive traits
(Neumann et al., 2011NEUMANN, K.; KOBILJSKI, B.; DENČIĆ, S.; VARSHNEY, R.K.; BÖRNER,
A.Genome-wide association mapping: a case study in bread wheat (Triticum aestivum
L.). Molecular Breeding, v.27, p.37-58, 2011. DOI:
10.1007/s11032-010-9411-7.
https://doi.org/10.1007/s11032-010-9411-...
). The Tassel software
(Bradbury et al., 2007BRADBURY, P.J.; ZHANG, Z.; KROON, D.E.; CASSTEVENS, T.M.; RAMDOSS, Y.;
BUCKLER, E.S. TASSEL: software for association mapping of complex traits in diverse
samples. Bioinformatics, v.23, p.2633-2635, 2007. DOI:
10.1093/bioinformatics/btm308.
https://doi.org/10.1093/bioinformatics/b...
) is one of the most
sophisticated software programs with implemented algorithms and methods useful for
association studies. The structure association analysis developed by Pritchard et al. (2000)PRITCHARD, J.K.; STEPHENS, M.; DONNELLY, P. Inference of population
structure using multilocus genotype data. Genetics, v.155, p.945-959,
2000. first uses a set of random
markers to estimate the population structure (Q matrix) and then incorporates this
estimation into a general linear model (GLM) analysis. Yu et al. (2006)YU, J.M.; PRESSOIR, G.; BRIGGS, W.H.; VROH BI, I.; YAMASAKI, M.;
DOEBLEY, J. F.; MCMULLEN, M.D.; GAUT, B.S.; NIELSEN, D.M.; HOLLAND, J.B.; KRESOVICH,
S.; BUCKLER, E.S.A unified mixed-model method for association mapping that accounts
for multiple levels of relatedness. Nature Genetics, v.38, p.203-208, 2006. DOI:
10.1038/ng1702.
https://doi.org/10.1038/ng1702...
developed a new methodology, the mixed linear model (MLM)
method, which incorporates both the population structure and the familial relatedness or
the so-called "kinship" (K matrix), adapted for GWAS, to avoid false associations. This
method is recommended in the absence of available pedigree data for clustering a large
dataset into groups with improved statistical power (Zhang et al., 2010ZHANG, Z.; ERSOZ, E.; LAI, C.-Q.; TODHUNTER, R.J.; TIWARI, H.K.; GORE,
M.A.; BRADBURY, P.J.; YU, J.; ARNETT, D.K.; ORDOVAS, J.M.; BUCKLER, E.S.Mixed linear
model approach adapted for genome-wide association studies. Nature Genetics, v.42,
p.355-360, 2010. DOI: 10.1038/ng.546.
https://doi.org/10.1038/ng.546...
).
The objective of this work was to assess the genetic diversity and population structure of wheat genotypes, to detect significant and stable genetic associations, as well as to evaluate the efficiency of statistical models to identify chromosome regions responsible for the expression of spike-related traits.
Materials and Methods
A set of 283 wheat accessions originating from 24 countries was used for phenotype evaluation (Table 1). These varieties are part of the largest Wheat Core Collection in Serbia, which belongs to the Small Grains Department of the Institute of Field and Vegetable Crops in Novi Sad. The genotypes were sown in a randomized complete block design in a 1.2 m2 plot, containing six rows, with a distance of 20 cm between rows. Field plots were cultivated at Rimski Šančevi (45°20'N, 19°51'E) in Novi Sad, Serbia, by applying standard agrotechnical practices (Malešević et al., 1994MALEŠEVIĆ, M.; STARČEVIĆ, S.; MILOŠEV, D. Terms of cultivation and production technology of grain crops. In: FURMAN, T.(Ed.). The mechanized production of small grains. Novi Sad: Institute of Agriculture Technique, 1994. p.1-17.). The following spike-related traits were measured and recorded for association analysis, during five growing seasons, from 1995 to 1999: spike length, peduncle length, number of spikelets per spike, number of sterile spikelets per spike, spike index, spike weight, grain weight per spike, and grain number per spike.
Wheat (Triticum aestivum) varieties and lines, origin, and distribution of subpopulations (genotype clusters, Q)obtained by the Structure software (Pritchard et al., 2000PRITCHARD, J.K.; STEPHENS, M.; DONNELLY, P. Inference of population structure using multilocus genotype data. Genetics, v.155, p.945-959, 2000.).
Genomic DNA from all varieties (approximately ten plantlets per genotype) was isolated
from fresh young leaves using the CTAB protocol described by Doyle & Doyle (1990)DOYLE, J.J.; DOYLE, J.L. Isolation of plant DNA from fresh tissue.
Focus, v.12, p.13-15, 1990.. Wheat genotype population was profiled
with 30 microsatellite markers out of 41 initial markers, excluding 11 with non-specific
PCR products. The sequences of SSR markers were taken from the GrainGenes database
(GrainGenes, 2014GRAINGENES: a database for Triticeae and Avena. Available at:
<http://wheat.pw.usda.gov/GG2/index.shtml>. Accessed on: 10 Sept.
2014.
http://wheat.pw.usda.gov/GG2/index.shtml...
) (Table 2). The additional variety Chinese Spring was used as a
positive control. Microsatellites were positioned along almost all three genomes and
located near previously detected important QTLs. PCR amplifications were carried out
according to the protocols given by Röder et al. (1998). The reaction in 10 µL volume
contained 30 ng of DNA template, 1x buffer solution, 2 mmol L-1 dNTPs, 1.5
mmol L-1 MgCl2, 10 pmol of fluorescently labeled forward and
unlabeled reverse primers, and 1 unit of Taq polymerase. PCR started
with an initial denaturation at 94°C for 5 min, followed by 40 cycles of 94°C for 30 s,
52-62°C for 45 s, and 72°C for 45 s. The final extension was 10 min at 72°C. The PCR
amplicons were separated by size using capillary electrophoresis on an ABI Prism 3130
genetic analyzer (Applied Biosystems, Foster City, CA, USA). The reaction volume of 10
µL consisted of 2 µL of mixed differently-labeled PCR products, 0.2 µL of GeneScan 500
LIZ size standard (Applied Biosystems, Foster City, CA, USA), and 7.8 µL of Hi-Di
formamide. The dye-labeled products were identified by fluorescence detection, and
microsatellite analysis was performed using the GeneMapper software, version 4.0
(Applied Biosystems, Foster City, CA, USA).
Microsatellite markers, sequences of forward and reverse primers, annealing temperature (Tm), repeated motif, and expected amplicons in the Chinese Spring variety of wheat (Triticum aestivum), used as a positive control.
The parameters of genetic diversity were calculated with the PowerMarker software,
version 3.25 (Liu & Muse, 2005LIU, K.J.; MUSE, S.V. PowerMarker: an integrated analysis environment
for genetic marker analysis. Bioinformatics, v.21, p.2128-2129, 2005. DOI:
10.1093/bioinformatics/bti282.
https://doi.org/10.1093/bioinformatics/b...
). The
population structure based on genetic data was estimated by the Bayesian algorithm
implemented in the Structure software, version 2.3.4 (Pritchard et al., 2000PRITCHARD, J.K.; STEPHENS, M.; DONNELLY, P. Inference of population
structure using multilocus genotype data. Genetics, v.155, p.945-959,
2000.). The hypothetical number of clusters was set ranging
from 1 to 20, whereas the length of the burn-in and the Markov chain Monte Carlo (MCMC)
were determined at 100.000. The real number of subpopulations was obtained by comparing
log probabilities of data Pr [X|K], and corrections were done according to Evanno et al. (2005)EVANNO, G.; REGNAUT, S.; GOUDET, J. Detecting the number of clusters of
individuals using the software STRUCTURE: a simulation study. Molecular Ecology,
v.14, p.2611-2620, 2005. DOI: 10.1111/j.1365-294X.2005.02553.x.
https://doi.org/10.1111/j.1365-294X.2005...
. The selection of the most
appropriate number of subgroups was a critical step for further association analysis.
Determination of internal genetic structure was done by additional analysis through
principal coordinate analysis (PCoA).
The marker-trait associations were analyzed in the Tassel software, version 2.1. (Bradbury et al., 2007BRADBURY, P.J.; ZHANG, Z.; KROON, D.E.; CASSTEVENS, T.M.; RAMDOSS, Y.;
BUCKLER, E.S. TASSEL: software for association mapping of complex traits in diverse
samples. Bioinformatics, v.23, p.2633-2635, 2007. DOI:
10.1093/bioinformatics/btm308.
https://doi.org/10.1093/bioinformatics/b...
) using two models: GLM and MLM
(Yu et al., 2006YU, J.M.; PRESSOIR, G.; BRIGGS, W.H.; VROH BI, I.; YAMASAKI, M.;
DOEBLEY, J. F.; MCMULLEN, M.D.; GAUT, B.S.; NIELSEN, D.M.; HOLLAND, J.B.; KRESOVICH,
S.; BUCKLER, E.S.A unified mixed-model method for association mapping that accounts
for multiple levels of relatedness. Nature Genetics, v.38, p.203-208, 2006. DOI:
10.1038/ng1702.
https://doi.org/10.1038/ng1702...
). The Q matrix for further
association analysis was determined based on the average value of three iterations of
log probability of data obtained by the Structure software (Pritchard et al., 2000PRITCHARD, J.K.; STEPHENS, M.; DONNELLY, P. Inference of population
structure using multilocus genotype data. Genetics, v.155, p.945-959,
2000.). In order to define the level of genetic
covariance between pairs of individuals, a kinship (K) analysis was carried out by
molecular data, converting the distance matrix to a similarity matrix using the Tassel
software (Bradbury et al., 2007BRADBURY, P.J.; ZHANG, Z.; KROON, D.E.; CASSTEVENS, T.M.; RAMDOSS, Y.;
BUCKLER, E.S. TASSEL: software for association mapping of complex traits in diverse
samples. Bioinformatics, v.23, p.2633-2635, 2007. DOI:
10.1093/bioinformatics/btm308.
https://doi.org/10.1093/bioinformatics/b...
). The magnitude
of QTL effects was explained by the R2 parameter. The descriptive statistics
of all phenotypic data was performed in the Statistica software, version 10 (Statsoft,
Tulsa, OK, USA).
Results and Discussion
A total of 349 alleles was detected in 30 SSR loci, and the mean number of alleles per
loci was 11.5 (Table 3). This result was higher
than the diversity (7.2) found among USA wheat accessions (Chao et al., 2007CHAO, S.M.; ZHANG, W.J.; DUBCOVSKY, J.; SORRELS, M. Evaluation of
genetic diversity and genome-wide linkage disequilibrium among U.S. wheat (Triticum
aestivum L.) germplasm representing different market classes. Crop Science, v.47,
p.1018-1030, 2007. DOI: 10.2135/cropsci2006.06.0434.
https://doi.org/10.2135/cropsci2006.06.0...
). Chen et al.
(2003)CHEN, X.M.; HE, Z.H.; SHI, J.R.; XIA, L.Q.; WARD, R.; ZHOU, Y.; JIANG,
G.L. Genetic diversity of high quality winter wheat varieties (lines) based on SSR
markers. Acta Agronomica Sinica, v.29, p.13-19, 2003. reported extremely low values of mean alleles per locus and other
polymorphism parameters as a result of the genotype's specific region of origin, which
led to a narrowing of genetic diversity. The sufficient genetic variation observed in
the material evaluated in the present study was confirmed in other studies with the
materials from the same core collection (Kobiljski et
al., 2002KOBILJSKI, B.; QUARRIE, S.; DENČIĆ, S.; KIRBY, J.; IVEGES, M. Genetic
diversity of the Novi Sad Wheat Core Collection revealed by microsatellites. Cellular
and Molecular Biology Letters, v.7, p.685-694, 2002. DOI:
10.1038/nature12028.
https://doi.org/10.1038/nature12028...
). However, since the previous analysis was performed on only 96
genotypes, the mean average number of alleles per locus (7.96) was lower than in the
present study. The average number of polymorphic information content (PIC) value was
0.688, representing a highly significant level of genetic polymorphism. Considering the
cosmopolitan origin of the studied varieties (Table
1), the breeding material indicates a broad genetic diversity that proved to
be an excellent base for further research.
The population structure distributed genotypes into six subpopulations using log
probability of data obtained by the Structure software (Figure 1), whereas the corrections of the number of clusters (ΔK) according
to Evanno et al. (2005)EVANNO, G.; REGNAUT, S.; GOUDET, J. Detecting the number of clusters of
individuals using the software STRUCTURE: a simulation study. Molecular Ecology,
v.14, p.2611-2620, 2005. DOI: 10.1111/j.1365-294X.2005.02553.x.
https://doi.org/10.1111/j.1365-294X.2005...
indicated the
distribution of genotypes into three existing subpopulations (Figure 2). Evanno's corrections generally predicted the existence of
two or three subpopulations regardless of the number and diversity of the investigated
materials (Vigouroux et al., 2008VIGOUROUX, Y.; GLAUBITZ, J.C.; MATSUOKA, Y.; GOODMAN, M.M.; SANCHEZ,
J.G.; DOEBLEY, J. Population structure and genetic diversity of new world maize races
assessed by DNA microsatellites. American Journal of Botany, v.95, p.1240-1253, 2008.
DOI: 10.3732/ajb.0800097.
https://doi.org/10.3732/ajb.0800097...
), which was
confirmed in the present study. The classification of 283 genotypes was more effective
in discriminating the genotypes toward log probability of data. The largest group (Q5)
consisted of 114 genotypes, mainly originating from Serbia, whereas the smallest group
(Q3) included 18 cultivars, mostly from the USA. The other subpopulations consisted of
37 genotypes (Q1), with diverse geographic origin; 45 genotypes (Q2), mostly from
England and France; 30 genotypes (Q4), from Croatia; and 40 genotypes (Q6), from the
USA.
Population structure of 283 wheat (Triticum aestivum) genotypes estimated using the model-based Bayesian algorithm implemented in the Structure software (Pritchard et al., 2000PRITCHARD, J.K.; STEPHENS, M.; DONNELLY, P. Inference of population structure using multilocus genotype data. Genetics, v.155, p.945-959, 2000.) performed with 30 microsatellite loci. Q1 to Q6, genotype clusters on the Q matrix.
Correction of number of clusters (ΔK) according to Evanno et al. (2005)EVANNO, G.; REGNAUT, S.; GOUDET, J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Molecular Ecology, v.14, p.2611-2620, 2005. DOI: 10.1111/j.1365-294X.2005.02553.x.
https://doi.org/10.1111/j.1365-294X.2005... for the different Bayesian clustering analyses implemented by the Structure software (Pritchard et al., 2000PRITCHARD, J.K.; STEPHENS, M.; DONNELLY, P. Inference of population structure using multilocus genotype data. Genetics, v.155, p.945-959, 2000.).
The distribution could be explained partly by geographical origin and partly by pedigree
data. Likewise, strict distribution according to origin is difficult because of the use
of breeding and elite lines through and by different breeding centers. Even the
distribution of genotypes originating from the same regions points to a similar
selective pressure in wheat breeding during domestication and the subsequent breeding
process (Laido et al., 2013LAIDO, G.; MANGINI, G.; TARANTO, F.; GADALETA, A.; BLANCO, A.;
CATTIVELLI, L.; MARONE, D.; MASTRANGELO, A.M.; PAPA, R.; DE VITA, P. Genetic
diversity and population structure of tetraploid wheats (Triticum turgidum L.)
estimated by SSR, DarT and pedigree data. PloS ONE, v.8, e6720, 2013. DOI:
10.13717/journal.pone.0067280.
https://doi.org/10.13717/journal.pone.00...
). Moreover, internal
genetic structure using PCoA separated the largest subpopulation (Q5) and group (Q2)
mostly consistent with grouping by the Structure software (Figure 3). In addition, the groups from Croatia (Q4) and from the
USA (Q3) took a particular position in the coordinate system, whereas the remaining two
clusters (Q1 and Q6) showed dispersed distribution in the coordinate system. However,
certain overlapping within some subpopulations could be a result of the frequent use of
certain varieties as parents, as well as of the inclusion of a great number of genotypes
into the analysis. Population structure determined by model-based clustering in the
Structure software was the most appropriate tool for determining genetic structure and a
key component for further association studies (Yu et
al., 2006YU, J.M.; PRESSOIR, G.; BRIGGS, W.H.; VROH BI, I.; YAMASAKI, M.;
DOEBLEY, J. F.; MCMULLEN, M.D.; GAUT, B.S.; NIELSEN, D.M.; HOLLAND, J.B.; KRESOVICH,
S.; BUCKLER, E.S.A unified mixed-model method for association mapping that accounts
for multiple levels of relatedness. Nature Genetics, v.38, p.203-208, 2006. DOI:
10.1038/ng1702.
https://doi.org/10.1038/ng1702...
).
Principal coordinate analysis of the 283 wheat (Triticum aestivum) varieties. Each mark represents a sample obtained by the Structure software (Pritchard et al., 2000PRITCHARD, J.K.; STEPHENS, M.; DONNELLY, P. Inference of population structure using multilocus genotype data. Genetics, v.155, p.945-959, 2000.). Q1 to Q6, genotype clusters on the Q matrix.
The total number of detected marker-trait associations in the five evaluation years was
of 192 using the GLM method, but decreased to 76 for all analyzed traits and years using
the MLM approach (Table 4). The advantage of the
MLM approach is the detection of more real loci associated with agronomic traits,
without false positive associations (Zhang et al.,
2013ZHANG, K.; WANG, J.; ZHANG, L.; RONG, C.; ZHAO, F.; PENG, T.; LI, H.;
CHENG, D.; LIU, X.; QIN, H.; ZHANG, A.; TONG, Y.; WANG, D. Association analysis of
genomic loci important for grain weight control in elite common wheat varieties
cultivated with variable water and fertilizer supply. PLoS ONE, v.8, e57853, 2013.
DOI: 10.1371/journal.pone.0057853.
https://doi.org/10.1371/journal.pone.005...
). Neumann et al. (2011)NEUMANN, K.; KOBILJSKI, B.; DENČIĆ, S.; VARSHNEY, R.K.; BÖRNER,
A.Genome-wide association mapping: a case study in bread wheat (Triticum aestivum
L.). Molecular Breeding, v.27, p.37-58, 2011. DOI:
10.1007/s11032-010-9411-7.
https://doi.org/10.1007/s11032-010-9411-...
suggested
the usefulness of both models because a great number of associations could be neglected
using only the MLM, resulting in many MTAs that might not be recognized as potential
loci. This statement is in accordance with Yu et al.
(2009)YU, J.; ZHANG, Y.; ZHU, C.; TABANAO, D.A.; PRESSOIR, G.; TUINSTRA, M.R.;
KRESOVICH, S.; TODHUNTER, R.J.; BUCKLER, E.S.Simulation appraisal of the adequacy of
number of background markers for relationship estimation in association mapping.
Plant Genome, v.2, p.63-77, 2009. DOI:
10.3835/plantgenome2008.09.0009.
https://doi.org/10.3835/plantgenome2008....
, who proposed that new loci detected by GLM are also useful and should
be additionally validated to avoid false-positive associations. Furthermore, the
differences detected by these two models could be trait-dependent (Neumann et al., 2011NEUMANN, K.; KOBILJSKI, B.; DENČIĆ, S.; VARSHNEY, R.K.; BÖRNER,
A.Genome-wide association mapping: a case study in bread wheat (Triticum aestivum
L.). Molecular Breeding, v.27, p.37-58, 2011. DOI:
10.1007/s11032-010-9411-7.
https://doi.org/10.1007/s11032-010-9411-...
).
It is important to highlight that only the stable associations detected in more than three evaluation years, at 1% probability, using the GLM and MLM approaches, were reported (Table 5). Four closely located markers (wmc18, wmc167, wmc144, and gwm157) on chromosome 2D were significant for the detection of QTLs for number of spikelets per spike, number of sterile spikelets per spike, and grain number per spike. This observation agrees with the results of high partial correlations obtained for these traits (Table 6). Besides being a carrier of three key genes for height reduction (Rht8), photoperiod (Ppd1), and yellow rust (Yr16), which are essential for adaptation, chromosome 2D contained most markers associated with the agronomically important traits.
Partial correlations of significant phenotypic traits with mean values for each genotype and coefficient of variation (CV) for each trait.
The proximity region of the Ppd-1 gene, near gwm484,
was responsible for the expression of many yield components and spike morphology,
showing its high value for wheat improvement (Dodig et
al., 2012DODIG, D.; ZORIĆ, M.; KOBILJSKI, B.; SAVIĆ, J.; KANDIĆ, V.; QUARRIE, S.;
BARNES, J. Genetic and association mapping study of wheat agronomic traits under
contrasting water regimes. International Journal of Molecular Sciences, v.13,
p.6167-6188, 2012. DOI: 10.3390/ijms13056167.
https://doi.org/10.3390/ijms13056167...
). On the integrated genetic map of this chromosome created with
scaffolds and markers in Aegilops tauschii, Jia et al. (2013)JIA, J.; ZHAO, S.; KONG, X.; LI, Y.; ZHAO, G.; HE, W.; APPELS, R.;
PFEIFER, M.; TAO, Y.; ZHANG, X.; JING, R.; ZHANG, C.; MA, Y.; GAO, L.; GAO, C.;
SPANNAGL, M.; MAYER, K.F.X.; LI, D.; PAN, S.; ZHENG, F.; HU, Q.; XIA, X.; LI, J.;
LIANG, Q.; CHEN, J.; WICKER, T.; GOU, C.; KUANG, H.; HE, G.; LUO, Y.; KELLER, B.;
XIA, Q.; LU, P.; WANG, J.; ZOU, H.; ZHANG, R.; XU, J.; GAO, J.; MIDDLETON, C.; QUAN,
Z.; LIU, G.; WANG, J.; INTERNATIONAL WHEAT GENOME SEQUENCING CONSORTIUM; YANG, H.;
LIU, X.; HE, Z.; MAO, L.; WANG, J. Aegilops tauschii draft genome sequence reveals a
gene repertoire for wheat adaptation. Nature, v.496, p.91-95, 2013. DOI:
10.1038/nature12028.
https://doi.org/10.1038/nature12028...
identified 33 QTLs or genes. One of them was the
QTL for test weight near marker wmc167, which was significant for
spike-related traits in the present study. Marker wmc144 showed the
highest effect on phenotypic variation of spikelets per spike with mean value of 40.7%.
QTLs for grain number per spike and spike length were found in association with marker
gwm294, derived by Yao et al.
(2009)YAO, J.; WANG, L.; LIU, L.; ZHAO, C. ZHENG, Y.Association mapping of
agronomic traits on chromosome 2A of wheat. Genetica, v.137, p.67-75, 2009. DOI:
10.1007/s10709-009-9351-5.
https://doi.org/10.1007/s10709-009-9351-...
, located on the long arms of chromosome 2A. In the present study, this
marker showed similar effects on the phenotypic variation of these traits (13 and 5%,
respectively) (Table 5). Also, two markers
(gwm294 and cfa2086) on chromosome 2A were
associated with peduncle length apart from the previously detected QTL for this trait on
chromosome 6A (Neumann et al., 2011NEUMANN, K.; KOBILJSKI, B.; DENČIĆ, S.; VARSHNEY, R.K.; BÖRNER,
A.Genome-wide association mapping: a case study in bread wheat (Triticum aestivum
L.). Molecular Breeding, v.27, p.37-58, 2011. DOI:
10.1007/s11032-010-9411-7.
https://doi.org/10.1007/s11032-010-9411-...
). This trait
has attracted great interest in recent studies due to its importance in avoiding ear
diseases. Grain number per spike is one of the most important yield components of wheat
(Ma et al., 2007MA, Z.; ZHAO, D.; ZHANG, C.; ZHANG, Z.; XUE, S.; LIN, F.; KONG, Z.; TIAN
D.; LUO Q. Molecular genetic analysis of spike-related traits in wheat using RIL and
immortalized F2 populations. Molecular Genetics and Genomics, v.277, p.31-42, 2007.
DOI: 10.1007/s00438-006-0166-0.
https://doi.org/10.1007/s00438-006-0166-...
), which was associated with
the largest number of markers evaluated, i.e., five (Table 5). The specific marker for grain number was barc101
(2BL), which has not been previously associated with this trait, indicating the presence
of a new QTL. The presence of QTLs near marker gwm11 for a large number
of agronomic and adaptive traits has been proven by Wang
et al. (2009)WANG, R.X.; HAI, L.; ZHANG, X. Y.; YOU, G.X.; YAN, C.S.; XIAO, S.H. QTL
mapping for grain filling rate and yield-related traits in RILs of the Chinese winter
wheat population Heshangmai x Yu8679. Theoretical and Applied Genetics, v.118,
p.313-325, 2009. DOI: 10.1007/s00122-008-0901-5.
https://doi.org/10.1007/s00122-008-0901-...
, whereas, in the present study, the only association of this
marker was found with sterile spikelets per spike. Only a limited number of QTL studies
for sterile spikelet number per spike have been documented (Ma et al., 2007MA, Z.; ZHAO, D.; ZHANG, C.; ZHANG, Z.; XUE, S.; LIN, F.; KONG, Z.; TIAN
D.; LUO Q. Molecular genetic analysis of spike-related traits in wheat using RIL and
immortalized F2 populations. Molecular Genetics and Genomics, v.277, p.31-42, 2007.
DOI: 10.1007/s00438-006-0166-0.
https://doi.org/10.1007/s00438-006-0166-...
). The coefficient of variation for sterile spikelets
per spike obtained by descriptive statistics was extremely high (Table 6), probably due to the selection of a relatively small number
of varieties with branched architecture of wheat spikes. Grain weight per spike and
spike weight were the only traits with absence of stable associations in more evaluation
years. Using the collection of genotypes with a high level of polymorphism for
association analysis and finding stable QTLs over a course of multiple years could be
useful for the breeding process (Maccaferri et al.,
2008MACCAFERRI, M.; SANGUINETI, M.C.; CORNETI, S.; ORTEGA, J.L.A.; SALEM,
M.B.; BORT, J.; DEAMBROGIO, E.; GARCIA DEL MORAL, L.F.; DEMONTIS, A.; EL-AHMED, A.;
MAALOUF, F.; MACHLAB, H.; MARTOS, V.; MORAGUES, M.; MOTAWAJ, J.; NACHIT, M.;
NSERALLAH, N.; OUABBOU, H.; ROYO, C.; SLAMA, A.; TUBEROSA, R. Quantitative trait loci
for grain yield and adaptation of durum wheat (Triticum durum Desf.) across a wide
range of water availability. Genetics, v.178, p.489-511, 2008. DOI:
10.1534/genetics.107.077297.
https://doi.org/10.1534/genetics.107.077...
). A potential new flowering-time gene on chromosome 6D
(psp3200) was detected in similar material from the same core
collection under contrasting water regimes (Dodig et
al., 2012DODIG, D.; ZORIĆ, M.; KOBILJSKI, B.; SAVIĆ, J.; KANDIĆ, V.; QUARRIE, S.;
BARNES, J. Genetic and association mapping study of wheat agronomic traits under
contrasting water regimes. International Journal of Molecular Sciences, v.13,
p.6167-6188, 2012. DOI: 10.3390/ijms13056167.
https://doi.org/10.3390/ijms13056167...
). However, this region has not shown importance for spike
characteristics considering field conditions. The unique association between marker
wmc333 on chromosome 6A and spike length detected in the present
study could indicate the presence of new potential QTL with minor effect.
Conclusions
-
The evaluated collection of wheat (Triticum aestivum) genotypes shows genetic diversity, and population structure is an important tool for association analysis.
-
A significant number of associations is stable for six spike-related traits.
-
The statistical models evaluated increase the accuracy and power of the association analysis.
-
The new chromosome regions identified as responsible for spike-related traits are useful for wheat breeding programs.
Acknowledgments
To the Ministry of Education, Science and Technological development of Serbia (Project number TR31066), for support.
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Publication Dates
-
Publication in this collection
Feb 2015
History
-
Received
07 Aug 2014 -
Accepted
26 Jan 2015