Plant Cell Rep DOI 10.1007/s00299-014-1564-0

ORIGINAL PAPER

Population structure and association mapping of yield contributing agronomic traits in foxtail millet Sarika Gupta • Kajal Kumari • Mehanathan Muthamilarasan Swarup Kumar Parida • Manoj Prasad

•

Received: 29 September 2013 / Revised: 6 December 2013 / Accepted: 31 December 2013 Ó Springer-Verlag Berlin Heidelberg 2014

Abstract Key message Association analyses accounting for population structure and relative kinship identified eight SSR markers (p < 0.01) showing significant association (R2 = 18 %) with nine agronomic traits in foxtail millet. Abstract Association mapping is an efficient tool for identifying genes regulating complex traits. Although association mapping using genomic simple sequence repeat (SSR) markers has been successfully demonstrated in many agronomically important crops, very few reports are available on marker-trait association analysis in foxtail millet. In the present study, 184 foxtail millet accessions from diverse geographical locations were genotyped using 50 SSR markers representing the nine chromosomes of foxtail millet. The genetic diversity within these accessions was examined using a genetic distance-based and a general model-based clustering method. The model-based analysis using 50 SSR markers identified an underlying population structure comprising five sub-populations which corresponded well with distance-based groupings. The phenotyping of plants was carried out in the field for three consecutive years for 20 yield contributing agronomic traits. The linkage disequilibrium analysis considering

Communicated by A. Dhingra.

Electronic supplementary material The online version of this article (doi:10.1007/s00299-014-1564-0) contains supplementary material, which is available to authorized users. S. Gupta
K. Kumari
M. Muthamilarasan
S. K. Parida
M. Prasad (&) National Institute of Plant Genome Research (NIPGR), Aruna Asaf Ali Marg, JNU Campus, New Delhi 110 067, India e-mail: [email protected]

population structure and relative kinship identified eight SSR markers (p \ 0.01) on different chromosomes showing significant association (R2 = 18 %) with nine agronomic traits. Four of these markers were associated with multiple traits. The integration of genetic and physical map information of eight SSR markers with their functional annotation revealed strong association of two markers encoding for phospholipid acyltransferase and ubiquitin carboxyl-terminal hydrolase located on the same chromosome (5) with flag leaf width and grain yield, respectively. Our findings on association mapping is the first report on Indian foxtail millet germplasm and this could be effectively applied in foxtail millet breeding to further uncover marker-trait associations with a large number of markers. Keywords Association mapping
Genetic diversity
Foxtail millet (Setaria italica)
Linkage disequilibrium
Marker-trait association
SSR markers
Population structure

Introduction Foxtail millet [Setaria italica (L.) Beauv.], because of its small diploid genome (1C genome size = 515 Mb), inbreeding nature, abiotic stress-tolerance along with the release of its genome sequence by Beijing Genomics Institute, China and US Department of Energy—Joint Genome Initiative, is on a fast-track to transforming into a model crop (Doust et al. 2009; Zhang et al. 2012; Bennetzen et al. 2012; Lata and Prasad 2013) for studying functional genomics and to probe plant architecture, genome evolution, drought tolerance, and physiology in the bioenergy grasses (Dekker 2003; Diao 2011; Lata et al. 2013). Foxtail millet is one of the important crops in the

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subfamily Panicoideae, distributed widely around warm and temperate regions of the world including Asia, Europe, America, Australia, and Africa, used as grain or forage (Austin 2006). The recent archaeological discoveries revealed that foxtail millet had contributed significantly to human civilizations in Asia and Europe (Li and Wu 1996; Hunt et al. 2008; Barton et al. 2009). The trace for centre of origin was identified in China, where further domestication for food grain took place gradually, 8,700 years ago (Vavilov 1926; Lu et al. 2009). Though exact figures of worldwide production of foxtail millet are not available, taken together with other millet crops production it is nearly 25 million tons (Mt) of grain in 2012 (FAO stat data 2013, http://faostat.fao.org/). Development of genomic resources in foxtail millet and its breeding had made great progress, resulting in significant improvement of yield potential in recent years (Diao 2011; Pandey et al. 2013; Kumari et al. 2013; Muthamilarasan et al. 2013). A diversified germplasm collection plays a key role in both breeding and genomic research for any crop species. More than 30,000 germplasm of foxtail millet have been conserved in different germplasm consortium all over the world viz. Chinese National Gene Bank (CNGB), (26,670 accessions), International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru (1,534 accessions), National Institute of Agro-biological Sciences (NIAS), Japan (1,279 accessions), and the Plant Genetic Resources Conservation Unit (PGRCU), USDA (766 accessions) (Doust et al. 2009). The availability of large germplasm collection facilitates the evaluation of population diversity and genetic structure of germplasm accessions which could further provide vital information for resource management, association mapping, and allele mining for novel variants and crop breeding. Interestingly, genetic structure analysis of landraces has been carried out in many plant species, including foxtail millet (Hirano et al. 2011; Wang et al. 2012; Liu et al. 2011; Jia et al. 2013), rice (Zhang et al. 2009a), maize (Liu et al. 2003), sorghum (Barro-Kondombo et al. 2010), soybean (Guo et al. 2012), coffee (Razafinarivo et al. 2013) and wheat (Liu et al. 2010; Zoric et al. 2012) which have contributed greatly to genomic analysis and breeding of these crops. Considerable efforts have been invested in tracing genes responsible for evolutionarily and agronomically important traits. In previous studies, a number of QTLs for agronomic traits have been identified in foxtail millet by linkage mapping approach, including basal branching (tillering) and axillary branching (Doust et al. 2004), inflorescence branching (Doust et al. 2005), plant height (PH), panicle neck length, stem node number, panicle length (PcL) and seed shattering (Mauro-Herrera et al. 2013). However, with

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the progression of molecular biology and bio-statistical tools, association mapping emerged as a new and powerful tool which could be demonstrated to complement and improve previous QTL information identified by linkage analyses in marker-assisted selection (MAS) for different crops. Association mapping appears to be a promising approach for plant breeders as it is based on linkage disequilibrium (LD) and eliminates the main drawback of classical linkage analysis such as, it does not involve the prolonged, lingering and expensive generation of specific genetic populations, rather it allows detection of associations between phenotypic variation and genetic polymorphisms in existing cultivars studied without further development of new mapping populations. Furthermore, this approach can detect larger number of alleles and increase mapping resolution (Yu and Buckler 2006). It also allows exploration of recombination events that had taken place through multiple generations in natural populations resulting in a tight linkage of causal polymorphisms with nearby genomic regions, avoiding the large blocks of linkage that are often obtained from two- or three-generation pedigrees and facilitating the identification of polymorphisms that are associated with quantitative traits. Association mapping originated in human genetics, and it has been widely used in various plant species in identifying phenotype-associated marker and trait-associated phenotypes. Reports on various crops including rice and wheat (Crossa et al. 2007; Reimer et al. 2008; Yan et al. 2009) show the versatility of this approach in identifying markers linked to genes and genomic regions associated with desirable traits. Breseghello and Sorrells (2006) also demonstrated the utility of association mapping in enhancing the information derived from QTL studies through execution of MAS in elite wheat germplasm. Using genomic simple sequence repeat (SSR) markers, association mapping has been successfully demonstrated in rice (Jin et al. 2010), wheat (Liu et al. 2010; Maccaferri et al. 2011; Zoric et al. 2012), barley (Mather et al. 2004), sorghum (Upadhyaya et al. 2012) and chickpea (Kujur et al. 2013). Recently, SNP marker-based genome-wide association mapping for identifying genes controlling agronomic traits using foxtail millet germplasm lines has been reported (Jia et al. 2013). Considering the importance of association mapping for dissecting the complex quantitative and qualitative agronomic traits, we have attempted to; (1) estimate genetic diversity and population structure among a core collection of 184 foxtail germplasm accessions, (2) estimate the extent of LD (linkage disequilibrium) and (3) analyse the association of SSR markers with various yield component agronomic traits in foxtail millet.

Plant Cell Rep Fig. 1 Geographical distribution of 184 foxtail millet accessions

Materials and methods Materials and phenotyping The foxtail millet accessions were collected from different eco-regions representing ten countries (Fig. 1). Based on the geographical distribution of these accessions, 184 accessions from the core collection (Lata et al. 2011) were selected. The purpose of the sampling strategy was to assemble a good representative set of accessions of foxtail millet landraces. The detail of accessions from each country, used in this study is listed in supplementary Table S1. The 184 foxtail millet accessions were phenotyped for 20 yield contributing agronomic traits precisely at experimental field of National Institute of Plant Genome Research (NIPGR, New Delhi) with three replications for 3 years (2009–2011) based on randomized complete block design (RCBD) during warm growing season. The grain yield (t ha-1) was determined after harvesting the plots at full maturity. In addition to grain yield (GY), the following important agronomic/morphological traits were also recorded: thousand grain weight (GW) in gram (g) measured as the average of three samples of 1,000 grains per plot, apical sterility (AS), days of flowering (DF), flag leaf width (FLW) and length (FLL) measured in centimetres (cm), fruit colour (FC) assumed from the seed coat colour, grain shape (GSh), presence of inflorescence brittles (Inf Br), inflorescence compactness (Inf Cp), presence of inflorescence lobe (Inf Lb), inflorescence shape (Inf Sh), leaf colour (LC), lobe compactness (LbCp), panicle length

(PcL), peduncle length (PdL), plant height (PH) measured in centimetres (cm), plant pigmentation (Pl Pg), tiller number (TN) and tillers maturity (TM) measured in terms of occurrence of fruiting. The variations of morphological traits among different accessions were observed in three independent replicates for three consequent years (2009–2011) in field condition. All the 20 agronomic traits evaluated for 184 accessions in 3 years were analysed using SPSS V14.0 (http://www.ibm.com/software/analy tics/spss) to derive their summary statistics including mean, range, standard deviation, variance and coefficient of variation. The positive and negative correlation among 20 agronomic traits in 184 accessions was measured based on Pearson correlation coefficient at 5 % level of significance in SPSS. The heritability (h2 = r2g/r2p) for each trait was estimated by measuring their genotypic (r2g) and phenotypic (r2p) variance in accessions. SSR genotyping Total genomic DNA of each accession was extracted using a CTAB extraction procedure (Saghai-Maroof et al. 1994) with modifications and subsequently quantified by agarose gel electrophoresis. Genotyping studies were done with 50 genomic SSR markers distributed (physically mapped) on nine chromosomes of foxtail millet (Jia et al. 2009; Pandey et al. 2013) (supplementary Table S2). PCR amplification was performed in a 25 ll total volume containing 1 U of Taq DNA polymerase (Sigma, USA), 50 ng of genomic DNA, 10 lmol of each forward and reverse primer, 0.5 mmol of each dNTPs, and 2.5 ll of 109 PCR reaction

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buffer [500 mM KCl, 200 mM Tris–HCl (pH 8.4), and 3 mM MgCl2] in iCycler thermal controller (Bio-Rad). The PCR profile was: an initial denaturation of 3 min at 94 °C, followed by 35 cycles of 60 s at 94 °C, 60 s at 50–55 °C, and 2 min at 72 °C, and a final extension of 10 min at 72 °C. The amplified products were resolved on 3 % metaphor agarose gel. Amplified PCR products which could not give clear polymorphic pattern were further tested on microchip-based electrophoresis system (MultiNA; Shimadzu Corporation, Kyoto, Japan). Results were confirmed by three replicate assays. Null alleles and nonspecific fragments (errors due to stuttering) amplified by SSR markers were analysed with Microchecker 2.2.3 (van Oosterhout et al. 2004). The clear and reproducible alleles amplified by each SSR marker among 184 accessions were scored based on their fragment size (bp) resolved on a gel. SSR diversity statistics and population structure analysis Polymorphism information content (PIC), frequency of alleles, number of alleles per locus (NA) and expected heterozygosity were calculated to estimate the genetic variation within the 184 accessions from mini-core collection. In addition, genetic differentiation among subpopulations was assessed by Wright’s F-statistics (Weir and Hill 2002). The pairwise significance of F-statistics was obtained after sequential Bonferroni corrections (Rice 1989). Genetic distances were computed among these genotypes and cluster analysis was performed on the Rogers’s distances (Rogers 1972) using the neighbourjoining method (NJ) in PowerMarker software ver2.5 (Liu and Muse 2005). The association panel was analysed for possible population structure with the model-based program STRUCTURE 2.2 (Pritchard et al. 2000) for the 184 foxtail millet accessions using a length of burn-in period 100,000 and the number of iterations 100,000 and a model allowing for admixture and correlated allele frequencies. At least 10 runs of STRUCTURE were performed by setting the number of sub-populations (K) from K = 2 to K = 10. Association analysis Association tests between SSR marker alleles and traits were performed using two approaches: a general linear model (GLM) implemented in TASSEL v.2.0.1 (Bradbury et al. 2007) software that do not consider control for population structure and a mixed linear model (MLM), that controlled for both population structure and kinship (Q ? K model) i.e. using the information on population structure (Q, determined by running the program STRUCTURE at optimized K = 5) to minimize false

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positive associations. Kinship (K value) using SSR genotypic data of 184 accessions was estimated employing SPAGeDi (Hardy and Vekemans 2002). The critical p values for assessing the significance of SSR markers were calculated based on a false discovery rate (FDR) separately for each trait (Benjamini and Hochberg 1995), which was found to be highly stringent. A FDR cutoff of 0.05 was used for determining significance of marker-trait associations (MTAs). The same approach was used to detect significant associations between SSR markers and partial least score (PLS) regression component scores which represent the linear combinations of accession and traits. The p value determines whether a trait is associated with a marker significantly or not, the squared correlation coefficient (r2) was used to estimate association of markers with traits based on LD between each pair of marker loci (Pritchard and Przeworski 2001) using TASSEL (Bradbury et al. 2007). Markers exhibiting a p value less than 0.05 were considered significantly associated with a phenotypic trait.

Results Agronomic characteristics The phenotypic data used in this study were based on the mean values recorded for 3 years (2009–2011). The summary statistics including mean, range, standard deviation, variance and coefficient of variation for 20 agronomic traits in 184 accessions were mentioned in Table 1. Coefficient of variation was maximum for InfSh (0.974) followed by InfLb (0.844) and minimum in TM (0.089). High and low heritability of InfSh (85 %) and InfBr (65 %) was evident, respectively (Table 2). Pearson correlation coefficients (at p \ 0.05) of the agronomic traits in the 184 accessions were also evaluated (Table 3). The correlation coefficient among 184 accessions varied from -0.428 between GW and FLW to 0.479 between LC and PiPg with an average of 0.25. A high positive correlation of grain yield with grain weight (0.43), and TNs (0.22) and a negative correlation with PH, flowering time and PcL were observed. These wider phenotypic trait variations among 184 accessions indicated that, the constituted association panel was suitable for association mapping for different yield traits. Genetic diversity of core collection A total of 214 alleles were obtained from the 50 SSR loci scored for the 184 accessions, with an average of 4.3 alleles per locus varying from two to eight. Major allele frequency was in a range of 0.22–0.80 with an average 0.46. The average gene diversity was 0.65 (range from 0.33 to 0.84) and expected heterozygosity for individual loci ranged

Plant Cell Rep Table 1 Summary of genetic diversity of 184 foxtail accessions using 50 mapped SSR microsatellite markers

S. no.

Marker

Allele frequency

Allele no.

Gene diversity

Heterozygosity

PIC

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50

b165 p3 b260 b126 b112 b153 p87 SiGMS2303 b242 b233 b151 SiGMS4692 b163 p61 p85 b225 SiGMS6210 b109 b255 p100 b247 SiGMS7403 SiGMS7563 SiGMS7747 SiGMS7833 SiGMS7964 SiGMS8050 SiGMS8556 p17x b196 p75 b129 SiGMS9034 SiGMS9645 p10 b190 SiGMS10379 b200 p59 b142 b202 SiGMS12222 SiGMS12819 b258 b185 p41 b171 b166 b241 b269 Mean

0.33 0.34 0.22 0.29 0.39 0.35 0.53 0.65 0.32 0.37 0.44 0.45 0.64 0.37 0.63 0.39 0.47 0.22 0.56 0.53 0.39 0.43 0.42 0.39 0.56 0.66 0.49 0.37 0.39 0.49 0.44 0.62 0.31 0.47 0.38 0.29 0.8 0.6 0.4 0.38 0.7 0.59 0.49 0.73 0.35 0.29 0.47 0.47 0.45 0.46 0.46

6 7 7 5 5 4 4 3 4 4 4 4 3 4 3 4 3 8 4 4 4 4 5 5 3 2 3 5 3 4 4 3 6 4 7 4 3 3 5 4 3 4 6 3 4 6 3 5 3 6 4.28

0.78 0.78 0.84 0.78 0.75 0.73 0.64 0.51 0.72 0.71 0.63 0.69 0.52 0.69 0.51 0.68 0.6 0.83 0.59 0.63 0.71 0.68 0.69 0.74 0.55 0.45 0.63 0.74 0.65 0.64 0.69 0.54 0.77 0.57 0.78 0.75 0.33 0.56 0.71 0.71 0.46 0.57 0.67 0.43 0.69 0.79 0.61 0.64 0.64 0.7 0.65

0 0.17 0.26 0 0 0.12 0 0.58 0 0.04 0 0.12 0 0 0 0 0.08 0.44 0.15 0.11 0.19 0.04 0.14 0.09 0 0.03 0.06 0.16 0 0.01 0.06 0 0.13 0.09 0.42 0.99 0.14 0.02 0.17 0 0.06 0.06 0.31 0 0.43 0.01 0 0.01 0 0 0.11

0.75 0.75 0.81 0.75 0.72 0.68 0.59 0.45 0.67 0.65 0.56 0.64 0.46 0.63 0.44 0.62 0.52 0.81 0.52 0.58 0.65 0.63 0.64 0.7 0.47 0.35 0.56 0.7 0.58 0.58 0.64 0.48 0.73 0.47 0.75 0.7 0.31 0.5 0.66 0.66 0.41 0.51 0.63 0.39 0.63 0.76 0.53 0.57 0.57 0.66 0.6

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Plant Cell Rep Table 2 Summary statistics of 20 agronomic traits evaluated in 184 foxtail millet accessions Traits

Range

Mean

Standard deviation

Variance

Coefficient of variation

Heritability (%)

DF

36–65

51.41

5.42

29.43

0.11

78

PH

89.7–194

138.66

19.61

384.64

0.14

75

TN

1.4–8.0

4.31

1.16

1.36

0.27

82

FLL

16.6–56.3

31.92

6.96

48.42

0.22

79

FLW

1.1–6.6

1.87

0.56

0.31

0.30

80

PdL

6.9–31.7

18.80

4.40

19.34

0.23

71

PcL

6.6–32.5

14.64

3.92

15.37

0.27

77

TM

54–101

87.62

7.82

61.22

0.09

80

GY

4.0–42.3

12.91

7.57

57.26

0.59

84

GW

0.8–3.9

2.84

0.64

0.40

0.23

81

PlPg LC

0–1.0 1.0–4.0

0.57 1.31

0.44 0.69

0.20 0.48

0.77 0.53

67 79

InfLb

0.0–3.0

1.48

1.25

1.55

0.84

78

InfBr

0.0–3.0

1.56

0.96

0.93

0.62

65

LbCp

0.0–3.0

1.67

1.11

1.23

0.66

83

InfSh

1.0–2.0

1.54

0.50

0.25

0.97

85

InfCp

0.0–3.0

1.48

0.92

0.85

0.62

79

FC

1.0–5.0

1.65

1.19

1.41

0.72

83

GSh

1.0–3.0

1.60

0.51

0.26

0.32

81

AS

0.0–1.0

0.59

0.49

0.24

0.83

80

from 0.00 to 0.99 with an average 0.11. Polymorphic information content (PIC) values ranged from 0.31 to 0.81 with an average of 0.60 (Table 1). Molecular diversity, population structure and genetic relationships among 184 accessions Two approaches were used in this study to analyse population structure: unrooted NJ tree and Bayesian modelbased clustering. The NJ tree of accessions based on a shared alleles distance matrix indicated a main sub-division of accessions into at least five groups, namely I, II, III, IV and V which included 15, 14, 24, 104 and 27 accessions, respectively (Fig. 2). Distance-based methods often introduce distortions and simplifications in the representation of the relationships among members of larger clusters (Sneath and Sokal 1973) and/or when accessions are related to two or more distinct clusters, as is the case for many modern cultivars obtained by crossing parents from different gene pools (Maccaferri et al. 2005). For these reasons, accessions were also grouped using a model-based Bayesian clustering method (Pritchard et al. 2000; Falush et al. 2003). Admixture model-based simulations were carried out by varying K from 2 to 10 with 10 run for each K using all 184 accessions which showed evident knees at K = 5. The average LnP(D) (log-likelihood) value increased continuously with the increase in K from 1 to 10, however, its most apparent inflection was obtained at one of the best replicates

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of K = 5 (Fig. 3a). The results of population numbers (K) were further confirmed using DK estimation (Evanno et al. 2005). A sharp peak with a maximum value of DK was obtained at K = 5 (Fig. 3b). This confirms the classification of 184 foxtail millet accessions into five distinct population groups with high resolution population structure. The estimated population structure is presented in Fig. 4. Using this approach, 184 accessions were assigned to the corresponding A–E sub-populations, representing 14.6 % (27), 13.5 (25), 49.4 (91), 15.7 (29) and 6.5 % (12) of the germplasm used for analysis. Genetic variation among five identified sub-populations and among 184 accessions within population was tested using AMOVA. The population differentiation based on AMOVA revealed that 6.8, 3.2 and 1.7 % (p \ 0.05) of the total molecular variation in the five sub-populations were attributed to genetic differentiation among populations, within populations (among accessions) and within accessions, respectively. The five sub-populations (A–E) had an Fst equal to 0.24, 0.19, 0.17, 0.18 and 0.25, respectively, supporting the existence of moderate population structures. The overall Fst value estimated within the sub-populations was 0.21 (Table 4). In addition, the genetic distances among these four subgroups by pairwise Fst were measured. It showed variable level of genetic differentiation between inferred populations. The pairwise Fst value ranged from 0.41 to 0.98 with an average 0.54, revealing smallest genetic distances between subpop B and C the largest between subpop C and E (Table 5).

-0.145

-0.036

PlPg

LC

0.027

0.142

0.008

FC

GSh

AS

-0.010

-0.096

GW

InfCp

0.057

GY

0.143

0.229

TM

InfSh

-0.149

PcL

0.081

-0.100

PdL

LbCp

-0.095

FLW

-0.084

0.016

FLL

-0.053

0.059

InfLb

0.146

TN

InfBr

1

PH

DF

DF

Traits

0.094

-0.291

-0.019

0.107

-0.208

0.017

0.006

0.013

0.122

0.202

-0.038

-0.102

0.320

0.328

0.140

0.093

0.286

0.062

1

PH

0.053

-0.085

-0.087

-0.111

-0.038

0.017

0.046

-0.152

0.012

0.040

0.216

0.369

0.108

0.111

-0.172

0.076

0.099

1

TN

0.065

-0.052

-0.032

-0.031

0.010

-0.038

-0.194

-0.037

0.188

0.189

-0.377

0.326

-0.040

0.397

-0.217

0.430

1

FLL

0.122

0.120

-0.117

-0.101

0.162

-0.139

-0.277

0.122

0.062

0.027

-0.428

0.358

-0.194

0.275

-0.272

1

FLW

-0.044

-0.223

0.074

0.173

-0.276

0.216

0.257

-0.077

-0.072

-0.034

0.419

-0.244

0.185

0.047

1

PdL

0.062

-0.188

0.067

-0.063

-0.056

-0.045

-0.072

0.078

0.068

0.105

-0.315

0.420

-0.090

1

PcL

-0.003

-0.283

-0.109

0.243

-0.193

0.154

0.120

-0.210

-0.143

-0.017

0.318

-0.131

1

TM

-0.012

-0.059

-0.091

-0.168

0.135

-0.026

-0.127

-0.078

0.124

0.102

0.428

1

GY

-0.090

-0.096

-0.005

0.180

-0.189

0.166

0.218

-0.116

-0.082

-0.186

1

GW

-0.101

-0.206

-0.031

0.004

-0.330

0.036

0.200

-0.054

0.479

1

PlPg

-0.028

-0.021

0.019

-0.133

-0.153

0.035

0.149

-0.021

1

LC

Table 3 Correlation among 20 agronomic traits evaluated in 184 foxtail millet accessions at significance p value \0.05

0.141

0.196

0.006

-0.231

0.287

-0.344

-0.247

1

InfLb

-0.185

-0.190

0.057

0.050

-0.380

0.078

1

InfBr

-0.109

-0.175

-0.134

0.200

-0.160

1

LbCp

0.074

0.454

-0.079

-0.247

1

InfSh

-0.033

-0.147

0.119

1

InfCp

0.018

0.077

1

FC

0.135

1

GSh

1

AS

Plant Cell Rep

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Plant Cell Rep Fig. 2 Cluster tree derived by Neighbour-joining (NJ) method based on 50 SSR markers between 184 accessions of foxtail millet

V

I

II

III

IV

Linkage disequilibrium (LD) and marker-trait associations The background LD (unlinked) in the genome created by population structure was used to determine specific critical value of LD. Extent of genome-wide LD was evaluated through pairwise comparisons among the 50 marker loci (Fig. 5). The LD pattern including a total 44 estimates expressed by r2 averaged 0.26 ranging from 0.15 to 0.32. The 95th percentile of the distribution of unlinked r2 estimates was used as a population-specific threshold for this parameter as an evidence of linkage (Breseghello and Sorrells 2006), and the critical value is 0.28. Only 44 (from GLM) estimates of LD parameter r2 were significant (p \ 0.05) among the pairwise comparisons of all 50 markers among 184 accessions, indicative of support for further analysis of MTAs. In the present study, association mapping was used to search for linked/responsive marker for the agronomic traits. Using both GLM and MLM approaches, we identified eight SSR markers associated with nine different agronomic traits at the p \ 0.05 probability level, and contributing 6–25 % of the phenotypic variation. In combination, all eight markers explained an average of 18.4 %

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of the trait variation. A significant correlation was shown by one SSR marker for DF, Inf Br, Inf Cp, Gr Sh, PcL, two for GY and three for 1,000-GW, FLW and PdL. It was observed that, P59, b129 and b185 were associated with FLW; b129, b260 and p75 with grain weight (1,000); b129, b225 and p75 with PdL; and p75 and b129 with grain yield (Table 6). Of eight markers, three were associated with more than one trait including SSR b129 which showed significant R2 value for multiple traits like FLW (0.08), PdL (0.15), GY (0.10), Inf Br (0.08), PcL (0.06) and 1,000 GW (0.19). SSR marker p75 was found to be associated with 1,000 GW (0.13), Pd L (0.12) and GY (0.20) and b225 marker showed association with PdL (0.11) and Inf Cp (0.08) and, therefore, these could be regarded as multi-trait MTAs (Table 6).

Discussion Over the years, SSRs have become the markers of choice due to their abundance, co-dominance and locus-specific nature. Consequently, these markers have been extensively used for gene tagging, genetic mapping and genetic diversity analysis in a number of crop plants including

Plant Cell Rep Table 4 Mean value of Fst within population Mean value of Fst within population PopA

0.2447

PopB

0.1903

PopC

0.1721

PopD

0.1866

PopE

0.2491

Table 5 Genetic distances between different groups from structure analysis

Fig. 3 Optimization of the number of populations (K value) varying from K = 1–10 to determine best possible population number for 184 foxtail millet accessions using a STRUCTURE documented by Pritchard et al. (2000) and b DK based on Evanno et al. (2005)

foxtail millet (Jia et al. 2009; Liu et al. 2011; Wang et al. 2012; Gupta et al. 2012; Pandey et al. 2013). In the present study, we used a set of 50 genomic SSR markers covering all nine foxtail millet chromosomes to evaluate diversity, population structure and trait association mapping in foxtail millet. A total of 214 alleles were obtained from the 50 SSR loci scored for the 184 accessions, with an average of 4.3 alleles per locus varying from two to eight which is comparable to Zoysiagrass (4.8, Tsuruta et al. 2005). Conversely, the average allele per locus observed in the present study is higher than the earlier studies in foxtail millet (2.4, Lin et al. 2011; 2.2, Gupta et al. 2012; 2.1, Pandey et al. 2013), sorghum (2.3, Hokanson et al. 1998). Further, the average of 4.3 alleles per locus is lower than earlier reports

PopA

PopB

PopC

PopD

PopE

PopA

0

0.67

0.69

0.47

0.77

PopB

0.71

0

0.42

0.52

0.92

PopC

0.72

0.41

0

0.48

0.88

PopD

0.52

0.46

0.49

0

0.84

PopE

0.81

0.91

0.98

0.91

0

in foxtail millet (6.16, Jia et al. 2009; 14.04, Liu et al. 2011) and wheat (7.4, Prasad et al. 2000). The average gene diversity was 0.65 (ranging from 0.33 to 0.84) and expected heterozygosity for individual loci ranged from 0.00 to 0.99 with an average 0.11. PIC values ranged from 0.31 to 0.81 with an average of 0.60 which conforms to the previous studies reported in foxtail millet (0.69, Jia et al. 2009; 0.72, Liu et al. 2011) and Zoysiagrass (0.69, Tsuruta et al. 2005). The average PIC of 0.60 is higher than earlier reports in foxtail millet (0.45, Gupta et al. 2012) and wheat (0.31, Bohn et al. 1999). On contrary, it is lower than earlier studies in maize (0.72, Pejic et al. 1998) and wheat (0.79, Prasad et al. 2000). Diversity using structure and cluster analysis data was examined for their ecological distribution, but tight association could not be established between structure, traits and ecological groups. On the whole, two separate analyses done in this study basically agreed with each other with minor inconsistency. Cluster I consists of 15 accessions, out of which the major proportion belong to subpop A, II mainly belongs to subpop D with 14 accessions, III consists of 24 accessions of which 14 accessions belong to subpop B, the major proportion of cluster IV (81 accessions) mainly belongs to subpop C and cluster V belongs to

Fig. 4 Admixture bar plot showing the five subpopulations and membership assignment

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Plant Cell Rep Fig. 5 Linkage disequilibrium patterns among 184 accessions genotyped with 50 SSR markers. The squared correlation coefficients (r2) for each pair of markers are presented in the upper triangle and their corresponding p values in the lower triangle

Table 6 Marker-trait association with p value and marker-trait regression coefficient derived from 50 SSR markers and 184 accessions as a core collection in foxtail millet Traits

Trait ID

Markers

Chromosome

Adjusted p value

R2

Days of flowering

DF

SiGMS9645

6

0.0009

0.15

Flag leaf width

FLW

b129

5

0.0009

0.08

Flag leaf width

FLW

p59

7

0.0009

0.25

Flag leaf width

FLW

b185

8

0.0009

0.13

Peduncle length

PdL

b225

3

0.0009

0.11

Peduncle length

PdL

b129

5

0.0009

0.15

Peduncle length

PdL

p75

5

0.008

0.12

Grain yield

GY

p75

5

0.0009

0.21

Grain yield

GY

b129

5

0.0009

0.10

1,000 grain wt

GW

b260

1

0.0009

0.25

1,000 grain wt

GW

p75

5

0.0009

0.13

1,000 grain wt

GW

b129

5

0.0009

0.20

Inflorescence bristles Grain shape

InfBr GSh

b129 p61

5 3

0.0009 0.01

0.09 0.06

Panicle length

PcL

b129

5

0.05

0.06

Inflorescence compactness

InfCp

b225

3

0.05

0.09

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Plant Cell Rep

subpop E with 15 accessions out of 26 accessions. The small discrepancy between groupings from the two methods observed was not unexpected, because cluster analysis assigned a fixed branch position to each accession and structure analysis resulted in a sub-population membership percentage, and the highest percentage was used to assign individuals to groups for easy interpretation. There are mixed proportions of accessions with admixture with parental genotypes in sub-populations defined by structure which might have grouped in different defined clusters. In subpop A, the percentage of mixed individuals was found to be 18 %, likewise 13 % for B, 13 % for B and C, 20 % for D and 12 % for E. Association mapping is used to determine the degree to which gene and trait or phenotype and genotype vary together in sampled population on the basis of linkage disequilibrium (Zondervan and Cardon 2004). It is assumed that, if a marker is associated with a phenotypic trait, it should associate with others that highly correlate with this trait. In the present study, we identified eight SSR markers associated (with overall 18 % association potential) with different yield contributing agronomic traits. These trait-associated SSR markers once validated can be used for identification of genes/QTLs regulating the agronomic traits and eventually for marker-assisted genetic enhancement of foxtail millet. In the present study, multitrait association has been shown by different markers with significant r2 value like SSR b129 that is correlated with traits like FLW (0.08), PdL (0.15), GY (0.10), Inf Br (0.08), PcL (0.06) and GW (0.19), p75 with GY (0.20), GW (0.13) and PdL (0.15). The SSR b129 along with SSR p75 on chromosome 5 correlate with traits including grain weight and yield suggesting significant association of traits responsible for yield which could be further selected for genotypic and phenotypic studies. Similarly, SSRs b225 and p61 showed association with Inf Cp and grain shape, respectively, and these could also be considered as candidate markers to study grain quality and yield. SSR markers b129 (which codes for Ubiquitin carboxyl-terminal hydrolase) and p75 (codes for phospholipid acyltransferase) are located on the same chromosome (5) of foxtail millet indicating the presence of a linkage block or QTL region for the yield-associated genes (Fig. 6). Acyltransferase and Ubiquitin carboxyl-terminal hydrolase have been reported in several crop plants for their function in seed development and yield. For instance, Zhang et al. (2009b) reported acyltransferases in Arabidopsis as an essential element for normal pollen and seed development and the expression of Ubiquitin carboxyl-terminal hydrolase in the panicle of rice (Wong et al. 2007) and during flowering stages (Moon et al. 2009) have been reported. However, there are many challenges with using significantly associated markers for marker-assisted breeding. In

Chr5 CM 0.0

Chr5 Start

Mb 0.0

7.9

139.1

p75

178.8

b129 End

Linkage map (Jia et al. 2009)

Start

b129 (Ubiquitin carboxyterminal hydrolase)

30.5

p75 (Phospholipid acyltransferase)

47.2

End

Physical map (Present study)

Fig. 6 Chromosome location of markers (p75 and b129) linked to important agronomic traits in foxtail millet. Physical positions of markers indicated in Mb on physical map with reference linkage map position in centiMorgan (cM)

the present study, we attempted to discover trait-associated loci in foxtail millet and succeeded in identifying eight markers which are associated with different agronomic traits, though further confirmation is still required to correlate with the gene alleles for better understanding and utilization. The SNP marker-based genome-wide association mapping mostly using the 916 Chinese germplasm lines for identifying genes associated with agronomic traits has been reported recently (Jia et al. 2013). In contrast our findings on trait associations using SSR markers is the first report on Indian foxtail millet germplasm to our knowledge and would enhance QTL information and thus improvement of foxtail millet. Further, the release of foxtail millet genome sequences has generated large-scale genomic resources such as SSRs, EST-derived SSRs and intron length polymorphic markers for high-throughput genotyping applications. The availability of these resources in Foxtail millet Marker Database (Suresh et al. 2013) provides an option to researchers and breeders in choosing markers for downstream experimentation towards crop improvement in millets and related grasses. Acknowledgments Thanks are due to the Director, National Institute of Plant Genome Research (NIPGR), New Delhi, India for providing facilities. This work area was supported by the core grant of NIPGR. Dr. Sarika Gupta and Mr. Mehanathan Muthamilarasan acknowledge the award of DST-Young Scientist and Junior Research Fellowship from the Dept. of Science and Technology (DST) and University Grants Commission, New Delhi, respectively. We are also thankful to the National Bureau of Plant Genetic Resources, New Delhi/Hyderabad/Akola, India for providing the seed materials.

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Plant Cell Rep

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