Introduction
Marker assisted selection in plant breeding has been
found to be very effective in devising more focused and robust crop improvement
programs. As this technique provided more precision and accuracy in selection
thus saves time, resources and efforts required for the development of new
variety/hybrid (Filippi et al. 2015). An important part of this methodology is to
locate the markers in the genome that are tightly linked to the quantitative
trait loci (QTLs) controlling phenotype of the plant (Darvishzadeh 2016). QTL mapping in crop plants
is normally achieved by two frequently utilized techniques i.e., QTL mapping and association mapping (Ilyas et al. 2018). In
conventional QTL mapping, two parents are crossed in an organized way and the
association between phenotypic traits and mapped marker loci allows the
identification of QTLs. Because of few recombination events, the QTL of
interests may not be tightly associated with the marker identified (Myles et al.
2009). Whereas, association mapping which is a relatively new approach
of mapping QTLs, identifies relationship between gene polymorphism and
phenotypic variation in existing germplasm collections without the development
of mapping populations (Fusari et al. 2012). It proves to be
efficient in detecting the markers that are tightly linked to a specific QTL (Abdurakhmonov and Abdukarimov 2008). Association mapping utilizes the population
structure and linkage disequilibrium (LD) information, hence, also known as LD
mapping (Thornsberry et al. 2001). Association mapping provides high resolution
mapping by arresting all the meiosis occurring in the breeding history of the
crop. Moreover, it is cost effective and time saving technique as
compared to QTL mapping or linkage analysis (Ambreen
et al. 2018).
Microsatellites/SSR
markers are frequently being employed for population structure analysis in
association mapping studies because of their proven ability for generation of
more information content as compared to biallelic markers (Fusari et al.
2012; Filippi et al. 2015). In
Pakistan, sunflower (Helianthus annuus
L.) was introduced during 1960’s along with safflower (Carthamus tinctorius) and soybean (Glycine max) to increase the local edible oil production. During 2015–16 sunflower was
cultivated on an area of 866003 hectares in Pakistan and 35000 tons of
vegetable oil was extracted (Ibrar et al. 2018). Worldwide,
sunflower is the fourth biggest vegetable oil producing crop after palm oil (Elaeis guineensis), soybean and canola (Brassica napus) (Rauf et al. 2017). From
hybrid breeding prospective, it is considered as second most important crop
after maize (Zea mays) (Seiler et al.
2017). Domestication of sunflower was started in Pre-Colombian times,
but the breeding efforts for oil types were started in 18th century (Mandel et al.
2011). Heterosis on commercial scale was exploited in sunflower after
the incorporation of CMS (cytoplasmic male sterility) genes by Leclerq (1969) and discovery of male fertility restoration
genes by Kinman (1970) and Lochner
(2011). Assessment of genetic diversity for various agro-morphological traits
is a pre-requisite for manipulating and introgression precisely for achieving
the crop improvement objectives under optimum and less than optimum conditions (Hussain et al.
2018; Noble et al. 2018)
In
this study 109 sunflower genotypes panel were evaluated for genetic diversity,
linkage disequilibrium and population structure so that to detect the SSR loci
associated with nine important agro-morphological traits through association
mapping.
Despite
of considerable progress being made in plant breeding by conventional
approaches; the need to save time and resources, increase accuracy and pace of
improvement had urged plant breeders to use new and improved breeding
strategies by combining new advancement made in the field of genetics and
phenomics. Association mapping has been proved an effective approach being
utilized globally for marker assisted breeding program. Sunflower is among the
most important oilseed crops but only few association mapping studies has been
conducted on this crop so far (Filippi et
al. 2015). Therefore, it is needed to characterize sunflower genotypes to
upsurge the level of understanding regarding sunflower worldwide genetic map.
In this study 109 sunflower genotypes were used to document the level of their
genetic diversity, linkage disequilibrium and population structure through
microsatellite markers. The SSR loci located in proximity of the genes
controlling morphological traits studied could be highlighted at chromosomal
level through association mapping analysis. The information gathered in this
study will be helpful for plant breeders working on the improvement of
morphological traits through directed and precise breeding approaches.
Materials and Methods
Plant material and phenotyping
Present study was performed on
109 sunflower lines (Table 1) maintained by Oilseeds Research Program (ORP) of
National Agricultural Research Center (NARC), Islamabad. For phenotypic
evaluation, these sunflower lines were planted in open field conditions at
NARC, Islamabad during spring 2016 following augmented block design. Each
sunflower line was planted in a 5 m row with row to row distance of 75 cm and
plant to plant distance of 25 cm. NPK fertilizers was applied @ 150:60:60
kg/ha. Complete doze of phosphorus and potassium was applied as basal
along-with half of the nitrogen and remaining half of nitrogen was applied at
first irrigation. Thinning was done after 12–15 days
to ensure proper plant population. Weeding was done manually twice to keep crop
weed free during the growth period. Ten morphological parameters viz. days to flower initiation (days
taken from date of sowing till 5% of the plant of an entry starts flowering),
days to flower completion (days taken from sowing till 95–100%
plants of an entry initiate flowering), days to maturity (days taken from
sowing till 95% of the plants turn their brackets brown), plant height (height
of the plant from soil surface to base of head at maturity), stem curvature
(plant height subtracted from height of the head from the ground surface),
number of leaves per plant, leaf area, hundred seed weight, seed yield and oil
content were recorded for the phenotyping of sunflower material.
DNA extraction and genotyping with SSR markers
Total genomic DNA was
extracted from 10–12 old sunflower seedlings following CTAB (Cetyl trimethylammonium bromide) DNA extraction
protocol (Murray and Thompson 1980). DNA extracted was diluted in 50 in µL of TE buffer for working solution and
stored at -4°C. Purity and concentration of genomic DNA was checked by running
it on 1% agarose gel.
Overall,
40 SSR markers were employed for genotyping (Table 3). These microsatellites
were selected from the 17 linkage groups in sunflower identified by Yu et al. (2002) so that a uniform
representation of sunflower genome could be ensured. For PCR analysis 20 µL of reaction mixture was prepared from
1–1.2 µL DNA solution, 2 µL Taq Buffer, 2.5 µL MgCl2, 2 µL dNTP’s mixture, 0.2 µL Taq Polymerase enzyme, 10.8 µL dd.H2O and 0.8 µL each of forward and reverse primers
(Primers were first diluted with dd.H2O for making their working
solution). A touchdown cycling program was employed to reduce the spontaneous
amplification of the PCR product. Cycling protocol comprises of initial
denaturation at 94ºC for 5 min, followed by 30 cycles of 94ºC for 30 s,
annealing temperature for 30 sec (it ranges from 55ºC to 62ºC for different
primers), extension temperature of 72ºC for 40 sec, with final extension at 72ºC
for 5 min. The PCR products then obtained were run on a 2% agarose gel for
visualization of the amplified segments.
Linkage disequilibrium
Pairwise LD among the SSRs was
calculated using the TASSEL program (v.3.0.174) based
on the allele frequency correlation
(r2) and LD was drawn to represent the pairwise LD measurements
graphically.
Population structure
For population structure
analysis Bayesian clustering approach was followed for the SSR genotyping data
in the Structure program (v.2.3.4) (Falush et al. 2003). For
calculation of ancestry fractions of each cluster an admixture model and
correlated allele frequencies were applied to each accession, 10 independent
runs for each K-value (1–10) were completed with burn-in period of 10,000
followed by 10,000 Markov Chain Monte Carlo repetitions. The delta
K method was implemented in Structure Harvester Program (Earl 2012) to determine the most suitable K-value. An unrooted
neighbor joining phylogenetic tree was drawn using TASSEL program (v.3.0.174).
Association mapping
Association mapping among the
phenotypic data of nine morphological traits and the genotypic data of 40 SSR
markers was performed using the program TASSEL (v 3.0.174). SSR markers with
known linkage groups and their corresponding positions were used. MLM (mixed
linear model) of association mapping that uses both population
structure and kinship matrix was employed so that to minimize the probability
of false association that may arise in GLM (general linear model)
based method. The association between marker and trait was considered
significant at P < 0.05 (Ambreen et al.
2018).
Results
Phenotypic variability and population
structure analysis
High
level of phenotypic variability was observed in the field condition among the
sunflower genotypes for all the studied traits (Table 2). Phenotypic data
collected exhibited a broad variation among the sunflower studied panel making
it an ideal population for documenting the genotypic variability. The data of
ten morphological traits was then later combined with the genotypic data
revealed by the SSR markers genotyping to highlight the underlying genes
controlling these traits. Forty SSR markers were used for the population
structure analysis that amplified a total of 65 bands. The admixture
model-based analysis provided information about the optimal number of
sub-populations. As the value of K increased from 1 to 10 (Fig. 1) LnP(D) also
increases continuously and maximum inflection was noticed as the value of K
changed from 1 to 2 (Fig. 2). This
optimum number of k was further validated by ΔK, a second order
statistics. ΔK value also showed a peak at K=2 (Fig. 3). This shows that there were two
sub-populations in our samples, based on SSR genotyping data. Further analysis
of these 2 sub-populations revealed that these two populations contained
maintainer and CMS lines separately.
Neighbor-joining tree
An unrooted neighbor joining
phylogenetic tree diagram was generated in TASSEL (v.3.0.174) to
compute the level of relatedness among the sunflower accessions. Sunflower
accessions study panel was divided into three clusters namely CI, CII and CIII
(Fig. 4). CI contains A and B lines mainly with some
mixture of open-pollinated and few R-lines as well. Likewise, CIII mainly
consists of R-lines, whereas, C-II contains few R-lines along-with some
open-pollinated lines. This tree diagram validated that female lines are quite
distinct from male (R) lines. However, diversity within the clusters was
limited.
Linkage disequilibrium analysis
Linkage disequilibrium was
assessed among the entire forty SSR markers over 109 sunflower accessions. A
linkage disequilibrium graph was generated on the basis of squared correlation
of allele frequencies. The distribution assembly of squared correlation of
allele frequencies (r2) is shown in Fig. 5. Loci in red, green and blue dots exhibited high level of LD having
their p-value less than 0.0001, 0.001 and 0.01 respectively.
Association mapping analysis
Linked markers along-with
their P-values are shown in Table 4. In this study 11 markers showed a
significant association with the underlying QTLs controlling six studied traits
i.e., head diameter, leaf area, seed yield, days to flower initiation, days to
flower completion and hundred seed weight while no significant marker trait
association was detected for plant height, stem curvature, oil content and
number of leaves per plant. More than one marker was found to have strong
correlation with head diameter and days to flower initiation. A scatter plot
diagram was drawn to illustrate the markers expressing strong association with
the traits studied with threshold value at P
< 0.05 (Fig. 6).
Discussion
Rapid domestication and urge
for more productive cultivars housed with superior quality attributes had
resulted in Table
1: List of Sunflower Accessions
used in present study
|
S.
No. |
Accession
No. |
Source |
S.
No. |
Accession
No. |
Source |
|
1 |
CMS-HAP-12 |
NARC, Islamabad |
56 |
RHP-38 |
NARC, Islamabad |
|
2 |
CMS-HAP-56 |
NARC, Islamabad |
57 |
RHP-77 |
NARC, Islamabad |
|
3 |
CMS-HAP-101 |
NARC, Islamabad |
58 |
RHP-82 |
NARC, Islamabad |
|
4 |
CMS-HAP-54 |
NARC, Islamabad |
59 |
RHP-42 |
NARC, Islamabad |
|
5 |
CMS-HAP-103 |
NARC, Islamabad |
60 |
RHP-73 |
NARC, Islamabad |
|
6 |
CMS-HAP-24 |
NARC, Islamabad |
61 |
RHP-74DN |
NARC, Islamabad |
|
7 |
CMS-HAP-110 |
NARC, Islamabad |
62 |
RHP-7485 |
NARC, Islamabad |
|
8 |
CMS-HAP-112 |
NARC, Islamabad |
63 |
RHP-7490 |
NARC, Islamabad |
|
9 |
CMS-HAP-25 |
NARC, Islamabad |
64 |
RHP-7495 |
NARC, Islamabad |
|
10 |
CMS-HAP-111 |
NARC, Islamabad |
65 |
RHP-7498 |
NARC, Islamabad |
|
11 |
CMS-HAP-10 |
NARC, Islamabad |
66 |
RHP-74100 |
NARC, Islamabad |
|
12 |
CMS-HAP-114 |
NARC, Islamabad |
67 |
RHP-74105 |
NARC, Islamabad |
|
13 |
CMS-HAP-115 |
NARC, Islamabad |
68 |
RHP-74107 |
NARC, Islamabad |
|
14 |
CMS-HAP-03 |
NARC, Islamabad |
69 |
RHP-74108 |
NARC, Islamabad |
|
15 |
CMS-HAP-99 |
NARC, Islamabad |
70 |
RHP-74110 |
NARC, Islamabad |
|
16 |
CMS-HAP-125 |
NARC, Islamabad |
71 |
RHP-74112 |
NARC, Islamabad |
|
17 |
CMS-HAP-118 |
NARC, Islamabad |
72 |
RHP-74115 |
NARC, Islamabad |
|
18 |
CMS-HAP-116 |
NARC, Islamabad |
73 |
RHP-74120 |
NARC, Islamabad |
|
19 |
CMS-HAP-121 |
NARC, Islamabad |
74 |
RHP-74125 |
NARC, Islamabad |
|
20 |
CMS-HAP-117 |
NARC, Islamabad |
75 |
RHP-74128 |
NARC, Islamabad |
|
21 |
CMS-HAP-122 |
NARC, Islamabad |
76 |
RHP-74130 |
NARC, Islamabad |
|
22 |
CMS-HAP-120 |
NARC, Islamabad |
77 |
RHP-71 |
NARC, Islamabad |
|
23 |
CMS-HAP-123 |
NARC, Islamabad |
78 |
SFP-14 |
NARC, Islamabad |
|
24 |
CMS-HAP-102 |
NARC, Islamabad |
79 |
SFP-12 |
NARC, Islamabad |
|
25 |
CMS-HAP-08 |
NARC, Islamabad |
80 |
SFP-10 |
NARC, Islamabad |
|
26 |
CMS-HAP-119 |
NARC, Islamabad |
81 |
SFP-40 |
NARC, Islamabad |
|
27 |
HAP-12 |
NARC, Islamabad |
82 |
SFP-42 |
NARC, Islamabad |
|
28 |
HAP-56 |
NARC, Islamabad |
83 |
SFP-38 |
NARC, Islamabad |
|
29 |
HAP-101 |
NARC, Islamabad |
84 |
SFP-18 |
NARC, Islamabad |
|
30 |
HAP-54 |
NARC, Islamabad |
85 |
SFP-36 |
NARC, Islamabad |
|
31 |
HAP-103 |
NARC, Islamabad |
86 |
SFP-31 |
NARC, Islamabad |
|
32 |
HAP-24 |
NARC, Islamabad |
87 |
SFP-37 |
NARC, Islamabad |
|
33 |
HAP-110 |
NARC, Islamabad |
88 |
SFP-24 |
NARC, Islamabad |
|
34 |
HAP-112 |
NARC, Islamabad |
89 |
SFP-09 |
NARC, Islamabad |
|
35 |
HAP-25 |
NARC, Islamabad |
90 |
SFP-41 |
NARC, Islamabad |
|
36 |
HAP-102 |
NARC, Islamabad |
91 |
SFP-19 |
NARC, Islamabad |
|
37 |
HAP-10 |
NARC, Islamabad |
92 |
SFP-22 |
NARC, Islamabad |
|
38 |
HAP-114 |
NARC, Islamabad |
93 |
SFP-25 |
NARC, Islamabad |
|
39 |
HAP-116 |
NARC, Islamabad |
94 |
SFP-43 |
NARC, Islamabad |
|
40 |
HAP-123 |
NARC, Islamabad |
95 |
SFP-33 |
NARC, Islamabad |
|
41 |
HAP-111 |
NARC, Islamabad |
96 |
SFP-46 |
NARC, Islamabad |
|
42 |
HAP-99 |
NARC, Islamabad |
97 |
SFP-08 |
NARC, Islamabad |
|
43 |
HAP-122 |
NARC, Islamabad |
98 |
SFP-07 |
NARC, Islamabad |
|
44 |
HAP-120 |
NARC, Islamabad |
99 |
SFP-16 |
NARC, Islamabad |
|
45 |
HAP-03 |
NARC, Islamabad |
100 |
SFP-26 |
NARC, Islamabad |
|
46 |
HAP-08 |
NARC, Islamabad |
101 |
SFP-13 |
NARC, Islamabad |
|
47 |
RHP-68 |
NARC, Islamabad |
102 |
SFP-35 |
NARC, Islamabad |
|
48 |
RHP-72 |
NARC, Islamabad |
103 |
SFP-20 |
NARC, Islamabad |
|
49 |
RHP-53 |
NARC, Islamabad |
104 |
SFP-32 |
NARC, Islamabad |
|
50 |
RHP-73-1 |
NARC, Islamabad |
105 |
RHP-83 |
NARC, Islamabad |
|
51 |
RHP-46 |
NARC, Islamabad |
106 |
RHP-84 |
NARC, Islamabad |
|
52 |
RHP-76 |
NARC, Islamabad |
107 |
RHP-88 |
NARC, Islamabad |
|
53 |
RHP-41 |
NARC, Islamabad |
108 |
RHP-86 |
NARC, Islamabad |
|
54 |
RHP-81 |
NARC, Islamabad |
109 |
RHP-89 |
NARC, Islamabad |
|
55 |
RHP-69 |
NARC, Islamabad |
|
|
|
more
yields but at the cost of loss in the genetic diversity. To address the threats
faced by the crops with narrow genetic base like sunflower, scientists are
establishing and maintaining large and genetically divergent germplasm
material. In this present research, 109 diverse sunflower lines that included
A, B R and OPV’s were studied. Ten morphological traits studied showed a high
level of genetic variability in field conditions (Table 2) making this panel of
sunflower genotypes an ideal fit for determining the marker-trait association
by combining the phenotypic and genotypic data. For genotyping 40 SSR markers
that yielded 65 scorable DNA bands. SSR markers have been found to be very
effective as they showed more resolution power than SNPs (Emanuelli et
al. 2013).
Table 2: Mean, range and standard deviation studied
traits among sunflower genotypes
|
Crop traits |
Range |
Mean |
Standard deviation |
Genotypes with highest value |
Genotype with lowest value |
|
DFI |
69 –94 |
79.86 |
5.85 |
RHP-7485 |
CMS-HAP-102 |
|
DFC |
72 – 102 |
91.45 |
8.12 |
RHP-7495 |
CMS-HAP-102 |
|
PH |
121.8 – 246.25 |
185.46 |
27.13 |
SFP-42 |
CMS-HAP-111 |
|
SC |
15.5 – 36.7 |
27.68 |
5.45 |
SFP-12 |
SFP-31 |
|
HD |
9.25 – 20.9 |
14.50 |
2.93 |
HAP-10 |
RHP-69 |
|
L/P |
22 – 4189 |
30.84 |
3.99 |
SFP-33 |
HAP-111 |
|
L/A |
139.08 – 276.48 |
202.31 |
39.13 |
SFP-37 |
CMS-HAP-12 |
|
HSW |
2.76 – 6.94 |
4.92 |
0.98 |
CMS-HAP-111 |
RHP-42 |
|
SY/P |
23.9 – 62.7 |
37.14 |
7.31 |
CMS-HAP-54 |
RHP-41 |
|
OC |
35.6 – 59.93 |
44.18 |
4.99 |
CMS-HAP-12 |
SFP-33 |
DFI: Days to flower initiation;
DFC: Days to flower completion; PH: Plant height; SC: Stem curvature; L/P:
Leaves per plant; L/A: Average leaf area; HSW: 100-seed weight, SY/P: Seed
yield per plant; SY/ha: Seed yield per hectare OC: Oil contents
Table 3: List of SSR markers used to study the association mapping and population structure in sunflower
|
Primer
Name |
Linkage
Group |
Forward
Sequence |
Reverse
Sequence |
|
ORS-605 |
1 |
CGCGTGATGTGACGATTATT |
ACGGAGCAAAGTTTCGAGGT |
|
ORS-543 |
1 |
CCAAGTTTCAGTTACAATCCATGA |
GGTCATTAGGAGTTTGGGATCA |
|
ORS-371 |
1 |
CACACCACCAAACATCAACC |
GGTGCCTTCTCTTCCTTGTG |
|
ORS-453 |
2 |
CCTGTGAGCTACAATACTCCCACA |
GATTCTGATTAGGCGGTGGT |
|
ORS-1053 |
2 |
TTTCATCACATTAGACCATAGACCA |
GGCTTTCCTTCGTGGTTTGTAT |
|
ORS-752 |
3 |
CACTGATGAACAAGTGCGAGA |
ATGATTCCCATACCCACCAA |
|
ORS-924 |
3 |
TAAATCGCCATACCACTCCATC |
TATCAGCAGGAAGAACGCCTAAT |
|
ORS-366 |
4 |
AACCAACTGAGCATTCTTGTGA |
GCGCTAGGTTAAAGAGGACAAA |
|
ORS-1068 |
4 |
AATTTGTCGACGGTGACGATAG |
TTTTGTCATTTCATTACCCAAGG |
|
ORS-337 |
4 |
TTGGTTCATTCATCCTTGGTC |
GGGTTGGTGGTTAATTCGTC |
|
ORS-1024 |
5 |
GGGAAGTGGGCTTGTCTATGTAT |
AACACACCGAAATCACCTATGAA |
|
ORS-533 |
5 |
TGGTGGAGGTCACTATTGGA |
AGGAAAGAAGGAAGCCGAGA |
|
ORS-608 |
6 |
CATGGAAAGCCGAGTTCTCT |
CGTGCGTGATTAACATACCC |
|
ORS-1256 |
6 |
GATGTTGATGTTGGTGAAGTTGC |
CTCCGTCACCTTAAGCACTTGTA |
|
ORS-400 |
7 |
CGAACCCGTCTGTACCGTTT |
ACTTCGTTCACAAGGCACAA |
|
ORS-700 |
7 |
GTACCCACCACGCTTAACCA |
AGTCTTCCACAGCAACGTCA |
|
ORS-830 |
8 |
CAAGTGCATTAGGTGGTTCTAACA |
GCCCTCTGACTGTTGTATGACTG |
|
ORS-599 |
8 |
TTCCCTATCACACGCCTCTC |
GAAAGGAAGTAGCGGTGGTG |
|
ORS-882 |
9 |
AAACCGGCATGTAAGATATTCG |
ATCGGGAGCAGAAGAAGAGTATG |
|
ORS-617 |
9 |
GGTACTTGGTATTCATGGGTCAT |
GACACCGCCAACTTAACACTT |
|
ORS-795 |
9 |
CGCTAGTTACACCGCAGATG |
TGTCCACAGGTTGAAGATCG |
|
ORS-613 |
10 |
GTAAACCCTAGGTCAATTTGCAG |
ATCTCCGGAAAACATTCTCG |
|
ORS-1088 |
10 |
ACTATCGAACCTCCCTCCAAAC |
GGATTTCTTTCATCTTTGTGGTG |
|
ORS-433 |
10 |
CCGAGGTTTGATCGCTATTT |
AGCGTTTGTGATTTGATTACGA |
|
ORS-769 |
11 |
GTTTATTTATGTAGAAATGTTCTGGAA |
ATGTGGTGGTAAGGGTTGTTG |
|
ORS-697 |
11 |
TTGGGCTGTGGTTCCTTAAC |
AAGAGATGGGAGTGTTGATGC |
|
ORS-1085 |
12 |
GACCTCAAGGCATGCTAACACTC |
ACTAAGTGTGTGGACGGGGAAA |
|
ORS-1040 |
12 |
CTGCTGATCGTTTCTTGGATAGA |
TGCTAATCCTTCTAATCAACTTCCAC |
|
ORS-879 |
13 |
GAACCTCCCTTTGTCTGCATATC |
CTCCGGTTGCTGTTGATGTCT |
|
ORS-781 |
13 |
GTCAACCCATGACCCAAACC |
GATGTGGAGGAGAGAGGGTGT |
|
ORS-511 |
13 |
TGGCTCAGATTAAGTTCACACAG |
CGGGTTGCGAGTAACAGGTA |
|
ORS-307 |
14 |
CAGTTCCCTGAAACCAATTCA |
GCAGTAGAAGATGACGGGATG |
|
ORS-1086 |
14 |
TTGTTTGTCGCACACTCAAGATT |
ATTATCGGCACATCTTTGGATTT |
|
ORS-857 |
15 |
ACATCCGAACGAAGGACAATC |
CAAGAAAGTATGTCACCCAATAGCA |
|
ORS-562 |
15 |
CACACACACAAACCCTAGCTCT |
CAATCATATCGAGCACACATCA |
|
ORS-768 |
16 |
CCACTCATCATCAAGCCTAACA |
AGGTGGTGCTGGTTGTAGGT |
|
ORS-1064 |
16 |
TGAATGATCTATGAGTGGTGATGG |
ACTCGCAGTGGTAAGTCGTTAGG |
|
ORS-495 |
16 |
CCAGGATTAGGTAGCTTAGTTCG |
GCGATCTGAGGTTGACTCGT |
|
ORS-811 |
17 |
CCTTCTCCTCAATCTTTGGCTA |
AGGAATGAAATGGGTGTGTGT |
|
ORS-845 |
17 |
GGTGCCCTATCTTCATTCTCTG |
CTAAAGGGTATCACACATTTGACATT |
SSR
based genomic data is the most common method of studying the genetic diversity
and population structure analysis. Genomic SSR’s are useful marker types
because of their abundance in genome, higher polymorphic content and
reproducibility (Filippi et al. 2015). It has also been reported in recent studies
that SSR markers produce same results as obtained through SNPs from GBS (Souza et al.
2018).
In
depth knowledge of population structure in important to avoid any spurious or
false associations (Flint‐Garcia et al. 2005).
Based on the diagrams of ΔK and LnP(D), the sunflower accessions were
divided into two sub-populations as highest peak was observed at K = 2. Mandel et al.
(2013) studied 433 sunflower lines and
%2051-58_files/image002.jpg)
Fig. 1: Population structure of 109 sunflower accessions based
on SSR genotyping data at K=2 (Structure 2.3.4)
%2051-58_files/image004.png)
Fig. 2: Diagram of LnP(D) of
possible clusters (K) from 1 to 10
Table 4: List of traits along
with significantly associated markers (P <
0.05)
|
Trait |
Marker |
P- value |
|
Head diameter |
ORS-1088 |
0.0054 |
|
Head diameter |
ORS-371 |
0.0054 |
|
Leaf area |
ORS-1085 |
0.0079 |
|
Seed yield per plant |
ORS-769 |
0.019 |
|
Head diameter |
ORS-608 |
0.025 |
|
Head diameter |
ORS-608 |
0.025 |
|
Head diameter |
ORS-608 |
0.025 |
|
Days to flower initiation |
ORS-433 |
0.027 |
|
Days to flower completion |
ORS-433 |
0.027 |
|
Hundred seed weight |
ORS-811 |
0.028 |
|
Days to flower initiation |
ORS-605 |
0.033 |
%2051-58_files/image006.png)
Fig. 3: ΔK based on the
rate of change of LnP(D) between successive K values
fund two sub-populations based on the optimum K
value. In sunflower population studies CMS and R lines tends to group
separately from each other and this trend has been observed previously by (Lochner 2011; Ibrar
et al. 2018)
Linkage
disequilibrium is an important analysis while performing marker assisted
selection and association mapping analysis. It is considered as a non-random
association of alleles at different loci present on the same chromosome. It
assumes the co-segregation of a specific trait and DNA marker and by using this
information locates the QTLs or major genes (Darvishzadeh
2016). Resolution of
association mapping depends on the structure of LD across the genome. LD is dependent
on various factors like outcrossing, inbreeding, population size, physical
separation and recombination frequency between loci, mutation rate, selection,
historical sub-division and admixture of populations and genomic rearrangements
(Ilyas et
al. 2018). Presence of linkage disequilibrium is a pre-requisite for
any association mapping studies as LD determines the significance of
association among QTL’s and the phenotype (Maulana et al. 2018). Mandel et al.
(2011) also reported two distinct populations of sunflower one primarily
composed of R lines and the other of B or female lines. This distinction among
R and B lines is may be due to their separate breeding history and origin (Lochner 2011). The rare mixing of B lines in R line group may arise because of
continuous breeding efforts as sunflower breeders had introgressed superior
traits from one genotype to the other for the development of superior inbred
lines for hybrid development (Fick and Miller 1997).
This study provided us to detect QTLs controlling some
important morphological traits like head diameter, days to flower initiation,
leaf area, 100 seed weight, days to flower completion and seed yield per plant.
These traits expressed a strong correlation with seed yield in sunflower
(Arshad et al. 2010; Jalil et al. 2014); thus these markers can
be used for designing sunflower breeding programs for increased seed yield
alongwith short duration. However for determination of marker-trait association
with other important characters like oil content and plant height further
evaluation should be carried out. Neighbor joining tree shows
two sub-populations in the sunflower studied panel along-with a minor cluster
containing OPVs. However, out of two major clusters one contains mostly A and B
lines while other predominantly contains R lines. Similar clustering pattern
has previously been reported by Lochner (2011)
and Ibrar et
al. (2018).
Conclusion
As very few association mapping
studies have been conducted on this crop; therefore, in this effort forty SSR
markers was utilized to identify marker-trait association. MLM based approach
of association mapping coupled with Q+K model identified eleven SSR markers
that have been found to be in proximity (p<0.05) of the genes
controlling six phenotypic traits i.e., head diameter, days to flower
initiation and completion, leaf area, seed yield and 100-seed weight. The
marker-trait association identified could be used in designing sunflower
improvement/breeding programs with more precision and efficacy thus saving the
time and resources needed for new cultivar/hybrid development.
Acknowledgements
The author is highly thankful
to Higher Education Commission of Pakistan (HEC) for providing funds to conduct
this research under the Indigenous PhD Fellowship Program, Phase-II Batch-I.
Plant genetic resources institute, NARC, Islamabad for providing the lab
facilities and Oilseeds Research Program NARC for providing the sunflower
accessions and field facilities.
%2051-58_files/image008.png)
Fig. 6: A scatter plot depicting the
marker-traits association
%2051-58_files/image010.jpg)
Fig. 4: An unrooted neighbor
joining phylogenetic tree of 109 sunflower accessions based on SSR data. C I
(Cluster I) consists of mainly CMS and maintainer lines, C II (Cluster II)
consists of OPV’s and C III (Cluster III) contains restorer lines
%2051-58_files/image012.jpg)
Fig. 5: LD distribution pattern based on
squared correlation of allele frequencies (r2). Each cell represents
the comparison of two pairs of marker sites with color codes for the presence
of significant linkage disequilibrium
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