identification of molecular markers associated with yield...

Identification of molecular markers associated with yield traits in Coconut

Coconut production is facing heavy loss due t o several biotic, ablotic stresses and t he

tight competition for coconut oil by other vegetable oils in the world market (De taffin.

1998). Hence it is necessary to develop techniques, which can improve coconut varieties.

One of the important problems in plantation crops like coconut is the variah~lity in the

yield traits and vide adaptability, warrants the assessment of genet~c diversity al the

seedling stage b eforc t hey are transplanted (Sathyabalan and Mathew, 1983, Bourdeix.

1988: Iyer and Daniodharan. 1994). Identification of high yielding plants at early stage is

vcry important In the case of perennial crop like coconut. But character~zation and

evaluation of coconut genotypes based on morphological and agronomic traits have

found to be time consuming and labor intensive, provides a simplified picture of d~verslty

(Akpan, 1994; Sugimura el al., 1997). Moreover morpholog~cal markers arc

devcloprncnt s t a g specific and highly influenced by environment.

Molecular markers, wh~ch are independent of environmental effects, detect variation at

DNA level. They provide a faster technique to characterize germplasm and can be used

[or accelerating breedmg programmes. Characterization of germplasm and identification

of molecular markers related to yield traits by using RAPD (Randomly Amplified

Polymorphic DNA) and SSR (Simple Sequence Repeat) markers seems to be very

effective, as they are phenotypically neutral compared to the morphological markers.

RAPD markers remain popular because of their simplicity and low development cost.

Among the various DNA marker technologies currently available (Rafalski el al., 1996),

the most informative polymorphic marker system to date is microsatellites or simple

sequence repeats (Tautz and Renz, 1984; Powell el al., 1996). Their high information

content, species specificity, co-domnance and PCR based detection, allows SSRs to be

4 7 an ideal tool for many genetic applications (Bruford and W a y e , 1 993; Queller e / 01. .

1993; Dallas et al.. 1995). Because of the possibility to detect several alleles at a high

frequency, SSRs turned out to be an deal tool for identifying individuals and for

establishing genetic diversity between them.

MATERIALS AND METHODS

plant material used for molecular analysis

Details of plant materials used for the analysis arc described in chapter 2.

Materials for RAPD analysis

Deta~ls of materials used for the RAPD analyses are described in chapter 2.

Materials for SSR analysis

MgCIl, dNTPs, y AT^', Tq Kinase, Tay Polymerase, Fonvard Primer, Reverse Primer,

Ammonium per sulphate, TEMED, Kodak Biomax MR-2 film.

REAGENTS

SSR analysis

10X Tag assay Buffer: lOmM Tris-HCI buffer pH 9.0 containing 50mM KCI, 1.5mM

MgCl1,0.1% Gelatin, 0.05% Triton -XI00 and 0.05% NP40.

5X Running Buffer: 5 4g Tns, 2 7.5g Bonc Acid and 2 Oml o f 0.5M EDTA (pH 8.0)

dissolved in 1000ml of double distilled water.

IOX Loading buffer: 100 mM Tris-HCI buffer pH 8.0 contain~ng lOOmM EDTA (pH

8.0). 0.25% Xylene cyanol. 0.25% Bromophenol blue and 50% Saccharose.

5% Denaturing acryl amide gel solution (4000ml):

Urea (Merc 1.08.488.1000) : 1812 g

I OX TBE : 200 ml

40% (19:l ratio) Acrylamide: his acrylamide wlw (Eurobio 018808): 500 ml

Dissolve the urea completely and make up the volume to 4000 ml w~th water

METHODS

RAPD analysis

Dctails of reagcnts and mcthodology for RAPD analysis is described in chapter 2

SSR analysis

Primer labeling

SSR primers used in the experiment have been listed out in table 3.1. Forward primer of

each primer pair is radiolaheled with ~ A T P " using T4 Klnase.

Stock solutions

Forward Primer : 100mM

Kinase buffer : 1 OX

~ A T P ~ ' : 0.Olmilli Curie1 pI

T4 Kinase : I OLII pl

Procedure

Forward primer required for 35 PCR reactions was labeled in a total reaction volume of

20 p1 with the following components as follows:

Table 3.1. Characteristics of SSR Primers.

?M: Not mapped In the present study

'~ebmn er a/, 2001 " www.neiker.net/link2palm/ Information on rest of the primers (P. Lebrun personnel communication).

orw ward Primer : 0.70 p1

Klnase buffer : 2.00 pl

y ~ . ~ ~ 3 ' : I .05 pl

T4 Kinase : 0.35 p1

Water : 15.9 p1

The reaction mixture was incubated at 37°C for 1 hr. The reaction mixture was incubated

at 65°C for 10 min to stop reactton.

PCR amplification

Amplification was achieved by following the procedure outlined by Lebmn et al., (1999)

Stock Solutions

Taq assay Buffer : 1OX

MgC12 : 50mM

dNTPs : 2mM

Tuq Polynierase : 2Ulpl

Reverse primer : 1 OOpM

20 p1 of labeled Forward Primer

Procedure

Cocktail was prepared for 35 reactions with out template DNA:

Tuq assay Buffer :35.0 p1

dNTPs : 35.0 p1

Labelled Primer( Forward) . 20.0 pl

Reverse Primer : 0 7pl

MgClz : 6.5 p1

Toy Polymerase : 9.0~1

5 0 Water : 72.0~1

5 pI of cocktail was distributed to each PCR tube containing 5 pl of template DNA.

The amplification was performed in a PTC-100 MJ Research Thermocycler under the

following reaction conditions:

Hot start I Denaturation :94"C for 5 min

Denaturation : 94°C for 30 sec

Annealing

Extension : 72°C for 1 min

F~nal extension : 72 "C for 8 min

Each of the PCR amplified product was mixed with equal volume of 10X DNA loading

buffer and denatured at 94" C for 3 min. 4 PI of the reaction mixture was applied on 5%

denaturing polyacrylamide gels and electrophoresed at 55 W constant power for 2 hr

uslng 1X TBE buffer. 60ml of 5% denaturing acrylamide gel solution was used to cast

one gel. The gels were dried using a gel drier (Bio-Rad, USA) at 80°C for 30 min and

were exposed to Kodak Biomax MR-2 film for 18 hr.

Data analysis

Rlalecular markers

Numerical values of yield triuts (number of nuts per year, number of bunches per year,

weight of kernel, weight of nut, weight of fruit and copra content) werc analyzed against

genotypic data derived by molecular markers (32 SSR and seven RAPD primers)

lndcpendently and in combination.

single-Marker Analysis (SMA):

~ssociation of each of the molecular marker with y~eld traits was computed using SAS

software package v6.12 (SAS, 1989). Analysis of variance using general llnear modcl

(GLM) procedures of SAS was adopted to identify putative markers (SSR and RAPD)

assoc~ated with yield traits. The R' values were determined to find thc extent of

variability explained by these markers. R' values assumed positive or negativc values

(parameter estimate) indicating association of the markcr with increased or decreased

yeld.

Stepwise Multiple regression analysis (SMRA)

Associat~on of SSK and RAPD markers with yield traits was performed lndependcntly as

well as in combination. Stepwise-regression of molecular markers (SSR and RAPD)

against yield traits was performed to identify suitable markers that would account for

progressive quantum Increase in yeld (R'). Further, multiple regression approach was

adopted considering yield traits as independent variable; and RAPD, SSR and combined

RAPD and SSR data as dependent variable. The analysis model was

Where, 'Y' represented yield traits; 'm,' represented hy RAPD and SSR. The 'bJs' are

thc partial regression coefficients that specify the empirical relationship between 'Y' and

'm,'; 'e' is the random error of 'Y' that Includes environmental variation. This would

allow us to ascertain the maximum likelihood solutions of relationships between

Individual quantitative trait and various markers.

Cluster analysis

Phylogenic relationships among the lines were assessed by adopting cluster analysis

uslng STATISTICA software. The dendrogram for y~eld tram were computed based on

Ward's method of clustering, using minimum variance algorithm (Ward, 1963).

Analysis for identification RAPD markers related yield traits

RAPD profiles were analyzed as given in chapter 2. Single marker analysis and Stepw~se

multiple regression analysis for yield traits based on the RAPD profiles showed the

following results.

Number of bunches per year

SMA established association of five RAPD markers with the trait number of bunches pcr

year (Table 3.2). Maximum association was showed by OPG711(HI (29.70%) followed by

OPPlhlluo (20.09?6), OPE67so (12.91%), OPG714m (10.80%) and O P P I ~ W O (0.0957%).

SMRA revealed thc association of 10 markers. which together showed 91.440h

association towards number or bunches per year (Table 3.3.). Out of thc 10 markers

Identilied by SMRA maximum association was showed by OPG71100 (29.70%)).

OPG7, was repeated in both SMA and SMRA with the same variab~lity.

Number of nuts per year

Seven RAPD markers for number of nuts per year were identified by SMA (Table 3.4).

OPG714Do (21.12%) showed maximum association with nut yield in SMA followed by

OPP15hso (19.28%), OPE18400 (15.33%), OPE1814011 (13.93%). OPPISQO (9.98%) and

OPE181250 (5.00%). In SMRA, nine markers togcther showed 88.40% association with

number of nuts per year and the maximum association was showed by OPG714,xl

(21.12%)(Table 3.5). Both SMA and SMRA revealed the strong association of OPG7141wl

with number of nuts per year.

Weight of nut

SMA and SMRA for weight of the nut showed association of seven (Table 3.6) and 10

markers (Table 3.7) respectively. Markers OPP151011,1 (30.88%). 0PE18500 (28.96%),

Table 3.2. Stepwise single marker analysis for number of bunches per year using the

seven RAPD primers.

PE- Parameter estimate NM-Not mappcd

Table 3.3.Stepwise multiple regression analysis for number of bunches per year

using the seven RAPD primers

PE- Parameter estimate NM-Not mapped

Table3.4. Stepwise single marker analysis for number of nuts per year using the

seven RAPD primers


Table 3.5. Stepwise multiple regression analysis for number of nuts per year using

the seven RAPD primers

number


Table 3.6. Stepwise single marker analysis for weight of nut using the seven RAPD

primers


Table 3.7. Stepwise multiple regression analysis for weight of nut using the seven

RAPD primers

Marker ,,, Chromosome number

Partial R2 Total R'


OPG71roa (15.42%). OPG717so (14.28%), 0 P E 6 . i ~ (9.99%), OPG7lono (8.75%) and

OPEl81rn (8.54% were identified by SMA. 10 markers that were ident~fied by SMRA

together accounted for 88.91% association with weight of the nut. Among the 10

markers identified by SMRA, OPP151om (30.88%) showed maximum association with

weight of the nut, which was also the same in the case of SMA.

Weight of the kernel

SMA showed that eight markers are associated with weight of the kernel (Table 3.8) and

a maximum association was showed by the marker OPP151ooo (25.64%) followed by

OPG71~oo (20.70%), OPP1695o (17.02%). OPF24oo (12.56%), OPG71lso (13.90%).

OPE67oo (12.56%), OPE4950 (9.21%) and OPEl8loo0 (7.29%). SMRA revealed the

assoc~ation of 10 markers with weight of the kcrncl (Table 3.9), which together

accounted for 86.3I0/u. Out of the 10 markers OPPISl(ao (25.64%) showed maximum

association with kernel weight. OPPI 5loilewas found in both SMA and SMRA.

Weight of fruit

The SMA and SMRA identified eight and eleven markers respectively for the yield trait

weight of fruit (Table 3.10 and 3.11). Markers OPP1542~ (19.25%). O P F ~ I C , ~ , (18.04%).

OPE67ri1(15.77%), OPG'iloio (15.23%). OPE4son (14.64%), OPE18xoo (13.9%), OPPl51ix1o

(9.89%) and OPE187oo (7.529') were identified by SMA. 0 P P 1 5 4 ~ ~ (19.25%) showed

maximum association with fruit weight in SMRA and all the 10 markers together

accounted for 95.04% variability. OPP15121 showed the maximum association with

welght of fruit in both SMA and SMRA.

Copra content

Eight and nine markers were identified by SMA and SMRA respectively (Table 3.12 &

3.13). Markers OPP16C,So (18 96%), OPP1592il (1 1.79%), OPE4qso (1 5.85%), OPEI8soo

(12.48%). O P E ~ T W (15.77%), OPF24w (11.79%), OPG717so (11.46°/u) and OPP157oo

Table 3.8. Stepwise single marker analysis for weight of kernel using the seven

RAPD primers


OPPlSlwo

OPP 16usil

NM

NM

0.2564

0.1702

0.2564

0.1702

0.0060

0.0291

8'9.09

75.08

Table 3.9. Stepwise multiple regression analysis for weight of kernel using the

seven RAPD primers


Table 3.10. Stepwise single marker analysis for weight of fruit using the seven

RAPD primers


Table 3.11. Stepwise multiple regression analysis for weight of fruit using the seven

RAPD primers


Table 3.12. Stepwise single marker analysis for copra content using the seven

RAPD primers

number


Table 3.13. Stepwise multiple regression analysis for copra content using the seven

RAPD primers

Marker s,,e Chromosome number


5 4 (11.67%) were identified in SMA with high significance value. Nine markers together

account for 82.58% variability in the case of SMRA for copra content. OPP1695,1

(1896%) was found to be maximally associated with copra content In SMRA, which IS

also true in case of SMA.

SSR

Th~rty-two SSR primers produced a total of 185 polymorphic bands (Fig 3.1-3.13) with

slzes ranging from 84 to 282 bp (Table 3.1) with an average of 5.78 bands. Of these, 13

SSRs have been mapped on coconut genome using six different mapping populations

(Herran et a/., 2000; Ritter et a / . , 2000; Lebrun et al., 2001). In t h ~ s study, SSK primer

CnClrE2 resulted In amplification of maximum number of bands (15) ranglng from 115

to 175 bp, whereas, CnCirEl2 produced only two bands of 164 and 174 bp.


SMA revealed nine markers (Table 3.14), CnCirS121~9 (24 88%), CnCirH4'2rf, (22.00%).

CnCirC1ZLa, (21.58%). CnCirS12~ (20.85%), CnCirC1111-i (20.85%). CnC1rA41,,~,

(19.87%). mCnCir861~,4 (19.14%), CnCirH4'2~0 (14.55%) and CnCirSIZn, (10.28%)

associated with yield trait number of bunches per year. SMRA (Tahle 3.15) identified ten

markers, which together accounted for 94.14% variahillty. 0 ut o f w h~ch CnCirSI 216~1

(24.88%) showed maximum association with the trait number of hunches per year.

CnCirSIZlaq (24.88%) was found to be common in both SMA and SMRA with same

variability.


Eight markers CnCiffil l l x H (19.62%). CnCirS7215 (18.67%), CnCirGl lxx, (1 7.69%),

CnCirE12104 (16.57%), CnCirA997 (15.72%). mCnCir861.,4 (15.62%), mCnCir862~0

(1 I.%%), CnCirC7159(1 1.84%) were found to be associated with number of nuts per year

In SMA (Table 3.16). In SMRA CnCirS7*1, (18.67%) showed maximum association

Fig. 3.1. Representative SSR profile of coconut genoopes using primers A. CnCirA3 and B. C'nCirA4

Lanes l..AN32: 2.BR-48: 3..AYl13?: 4.BR80: S.liA210: 6 . U 5 2 0 1 : 7.31YD: 8.GUIIIP: 9.GUZlFI: IO.HUHYI:

11.G12Fl: 12.K.4137: 13.,\V211: IJ .HC2Drl: IS.HUhIPGA3: 16.K.4402: 17.GV1731P: 18.\\.4T; 19.1K32:

20.BR39: 21.KA552: 22.Ii-iZ44: 23.BR59: 24.GL'l4hIP: 25.BRS1: 26.K.4403: 27.BR2.45: 28.KA248:

29.hlYD: 30.GU331P: 31.GCI\IPGR; 32.GU9FI.

Fig. 3.2. Representative SSR profile of coconut genotypes using primers .A. CnCirA8 and B. CnCir.49

Lanes 1.AN32: 2.BR48; 3.ANli32: 4.BR8O: 5.lLA210: 6.K-15201: 7.31YD: 8.GCIJIP: 9.GUZlFl: IU.1iUHl I ;

11.GUZFl; 12.KA1.37: 13.AY211: 1J.HUZDxT; 15.HL-3IPG.-\3; 16.K4402; 17.GU1711P; 18.\VAT; 19.A332:

20.BR.39: 21.liA552: 2Z.liA244; 23.BR59: 24.GUI43IP; 25 .BRSI; 26.Ii.4403: 27.BR245: 28.U4248;

29.1Ik'D: 30.G1'33IP: 31.GUl hlPGR: 32.GU9Fl.

Fig. 3.4. Representati~e SSR profile of coconut genot!pes using primers .A. CnCirC3' and B. CnCirC7

Lanes 1.AN32: 2.BR48: 3.AKli32: 4.BR80: S.K.4210: b.KAS201; 7.311.D: U.GUI3IP; 9.GC21Fl: IO.HLH1'1;

II.GU2FI: 12.1i.4137: 13 .. 4Y2il: 11.HU2D\T; 15.HU31PG.43; 1 6 . U 4 0 2 : 17.GU1711P; 18.1V.AT; 19.AN32:

20.BR79: 21.kL-1552: 22.K.4241: 23.BR59: 24.GVI43IP: 25.BRSI: 26.UA403: 27.BRZ45: 28.KA248: 29. RIYD:

30.GV3RlP; 31 .GL-1 RIPGR; 32.GC9F1.

Fig. 3.9. Representatke SSR profile of coconut genohpes using prirnerc A. CnCirG4 and B. CnCirH1'

Lanes 1.4Pi32: 2.BR18: 3..AX1/32: 4.BR80: 5.KA210; 6.K45201: 7.31YD: 8.GCI\IP: 9.GU21Fl; IO.IILfI\ 1;

II.GU2f;l: 12.li-1137: 13..4N2!1: 11.HL2DxT: 15.HL1SIPG-\3: 16.K4402: 17.GU17>1P; 18.\Vi\T: 19.iN32;

20.BR39; 21.L4552: 22.K4214: 23.BR59: 21.GU1431P: 25.BRSI; 26.K4403: 27.BR215: 28.IG\248: 29. 31%-D:

30.GU33IP: 31.GVI31PGR: 32.GU9F1.

Table 3.14. Stepwise single marker analysis for number of bunches per year using

the 32 SSR primers

Marker,,,, Chromosome Partial R2 Total R2 1 number 1 1


Table 3.15. Stepwise multiple regression analysis for number of bunches per year

using the 32 SSR primers


Table 3.16. Stepwise single marker analysis for number of nuts per year using the 32

SSR primers


Marker ,,,,

CnCirA90~

C11Cirs7~~5

mCnCir861~

Chromosome number

NM

3

12

Partial R

0.1 572

0.1867

0.1562

Total R'

0.1 572

0.1867

0.1562

Prob>F

0.0367

0.0217

0 0374

PE

-70.90

188.11

-81.35

u ~ t h number o f n uts pe r y m and 1 0 markns together accounted for 94.98% vari~ncc

(Table 3.1 7).

Weight of out

SMA and SMRA for *eight of the nut showed association of eight (Table 3.18) and 10

SSR markers (Table 3.19) respectively. M arkcrs I dentitied h y S MA were CnCirB6202

(24.63%). CnCirRll l lo (21.7756), CnClrH7113 (20.25%), c n C ~ r E ? , ; ~ (17.01%).

CnCirA91(,! (17.91%). CnCi rE21~ (16.36%), CnCirB52,,, (16.24':'O) and TnCirSIl,,?

(15.55%). In both SMA and SMRA CnCirB62~2 repeated w ~ t h maximum variability

Markers identified by SMRA accounted for 94.26% vartabil~ty towards weight oTthe nut.


For weight of the kernel, nine markers were ident~fied hy SMA and SMRA (Tahle 3.20

and 3.21). Markers identified by SMA are CnCirB5272 (28.23%). CnCirE217~ (26.601%),

CnClrEl l IV!I (25.47%). CnClrS121;7 (23.66%), CnCirA4?!1~~ (19.86'X), C I I C ~ ~ A ~ ~ I , ,

(10.57%). CnCirA91,,) ( 15.01%). CnC1rD82~~ (14.57%) and C ~ C I ~ B ~ ~ ~ ~ : ( 13.30"). Both

SMA and SMRA showed that CnCirB5212 is associated with weight of kernel w~th

maximum variability. Nine markers identified by SMRA accounted for 94.84'X)

assoclatlon w ~ t h wcigh~ of thc kerliel.

Weight of the fruit

The SMA and SMRA tdentttied eight and nine markers respectively for the yield tralt

wetght of the h i t (Table 3.22 and 3.23). Markers identified by SMA arc CnCirH7111

(26.29%), mCnCir47114 (14.39%), CnCirC3'201, (14.03%), CnCirRlllrb (13.43%).

C n c ~ r S l ~ ~ ~ (13.03%), mCnCir47nz (12.09%), CnCirA3::,, (10.92%) and CnClrA312~,

(10.92%). Nlne markers ident~fied by SMRA showed 92.55% association with weight of

Tahle3.17. Stepwise multiple regression analysis for number of nuts per year using

the 32 SSR primers

Marker,,,, Chromosome Partial R~ Total R Proh>F PE I I I I I I


Table 3.18. Stepwise single marker analysis for weight of nut using the 32 SSR

primers

PE- Parameter cdmate NM-Not mapped

Marker Chromosome

number Partial R' Total R' Prob>F PE

Table 3.19. Stepwise multiple regression analysis for weight of nut using the 32 SSR

primers

Partial R Total R' Proh,F -i Marker ,,,,


Chromosome number

Table 3.20. Stepwise single marker analysis for weight of kernel using the 32 SSR

primers

Marker ,,, Chromosome Partial R! Total R> Prob>F number c


Table 3.21. Stepwise multiple regression analysis for weight of kernel using the 32

SSR primers


Table 3.22. Stepwise single marker analysis for weight of fruit using the 32 SSR

primers


Table 3.23. Stepwise multiple regression analysis for weight of fruit using the 32

SSR primers


Marker,,,, Chromosome numher

Partla1 R' Total R' Prob>F PE

56 the fruit. In both SMA and SMRA. CnCirH71~i (26.29%) showed maximum association

with weight of the fruit.

Copra content

SMA established association of 11 SSR markers CnCirEZlj1 (49.48%). CnCirB5?jl

(26.52%), (23.34%). CnCirEll l~o (20.99%). CnCirARlxll (19.88'').

('nCirA322il (19.57%), mCnCir47124 (19.57%), mCnCir8blh4 (17.82'%)), 1nCnCir47,~~

(16 60%). CnCirS121,2 (16.12%) and CnCirA42oo (15.06%) with the yield trait copra

content (Table 3.24). SMRA revealed the association of nine markers, which together

accounted for 97.22% copra yield (Table 3.25). C ~ C I ~ E ~ ~ ~ ~ (49.48%) was found to he

associated with copra content with maximum variability in both SMA and SMRA.

Comhined Stepwise Multiple Regression Analysis of SSR and RAPD


Numbcr ofbunchcs pcr ycar -9.37 + [(-1.25) O P G ~ I I ~ O ] + [(-2.87) CnCirA4l~,~,] + [(3.50)

CoCirS12177] + [(-3.75) CnCirABn"] t [(-3.75) O P P 1 6 ~ ~ ~ , , ]

t l(2.62) OPE18i,s0] + [(2.62) mCnC1r47lirI + [(1.75)

C n C 1 r S 7 ~ ~ ~ ~ 1 1 [(1.75) C n C 1 r E 2 ~ ~ ~ 1 1 0.00027

In the combined analysis ninc markers showed strong association with number of

bunches per year and they together accounted for 95.29% variab~l~ty ('Iablc 3.2b).

Equat~on above shows that five markers CnCirS12177, OPE181>5ll, mCnCir47114.

CnCirS72iIi and CnCirE211s contributed to enhanced number of bunches and O P G ~ ~ I O U ,

OPG71 100. CnCirA8~7~ and OPP16~so were the markers responsible for reduced number of

bunches. The PEs of each marker indicates the strength of association and direction of

impact on the yield trait number of bunches per year.

Table 3.24. Stepwise single marker analysis for copra content using the 32 SSR

primers


Marker,,,,

CnClrA9lp

C ~ C I ~ S ~ ~ , , ~

Chromosome number

14

NM

Partial R~

0.2334

0.1612

Tolal R'

0.2334

0.1612

P r o b F

0.0092

0.0342

PE

166.73

-80.57

Table 3.26. Stepwise multiple regression analysis for number of bunches per year

using combined data of RAPD and SSR analysis



Number of nuts per year= 158.99 + [(I 13.31) OPG7141nl + [(-136.83) CnCirE21~I] +

[(-104.68) OPP1542rl + [(-I 10.89) CnCirE1 ZI~,,I] + [(-40.81)

OPEIS~IIO] + [(84.61) CnCirE7z141 + [(48.07) CnCirE?l<t] t

[(-97.63) C n C i r E l l l ~ ] + [(50.62) CnCirH71l;I + [(13.70)

CnCirH7131] + 0.00053.

Combined analysis for number of nuts per year revealed ten markers, whlch togetlier

accountcd for 96.82% association. OPG714i~i, CnCirE721~. CnCirE2151, CnCirH7),1 and

CnClrH7111 showed association with increased number of nuts and markers Cn('irE21,1,

OPPI 5421, CnClrEI 211,2, OPE1 8c0,~ and CnCirEl 110" were found to bc assoc~alcd with

reduced numher of nuts (Table 3.27)

Weight o f nut

Wc~ght ofn ut = 292.00 + [(330.00) OPP151illio] t [(-378.00) CnCirD82411 t [(494.00)

1 i iCnCir l19~~] + [(494.00) OPE400] + [(-I 1 .I 8) CnCirB5271,] +

[(-I61 35) OPPI~~IOIJ] + [(-161.35) C ~ C I ~ G ~ ~ , , ~ ] + [(135.15)

CnCirC71~1 + [(92.54) 0PPl5"50] + [(136.10) OPE1 81251,]+ 0.00099

Tcn markers showed association with weight of nut (Tablc 3.28) and lhcy logclhcr

accounlcd Tor 96.56'1/,, var~ahil~ty. OPP151ellll, r n C n C ~ r l l O ~ ~ ~ , OPE4c,r0, CnCirC71s<1,

OPPISxso and OPE1812~0 showed positivc association with weight o i thc nut. Whcrcas

C ~ C I ~ D S ~ , , ~ CnCirB527h. OPP16sooandCnC1rC4~~,~ are negative markers for thts trait


Weight of the kernel = 54.66 - [(l53.50) CnCirBSli~] + [(196.50) CnCirA9111il + [(31.00)

OPP151noo] + [(71.83) OPE184o0l + [(-108.33) CnCirC3'1~1 +

Table 3.27. Stepwise multiple regression analysis for number of nuts per gear using

combined data of RAPD and SSR analysis

Marker ,,,, Chromosome Part~al R2 Total R' Prob>F 1 number 1 1 1

PE-Parameter estimate NM-Not mapped

Table 3.28. Stepwise multiple regression analysis for weight of nut using the


PE- Parameter estimate NM-No1 mapped

Marker,,,,

OPPl 6yoo

CnCirG41(,~

CnCirC71sg

OPP 15sso

Prob>F Chromosome number

PE

NM

13

NM

NM

Partial R'

0.0389

0.0372

0.0296

0.0261

Total R'

0.8585

0.8957

0.9254

0.95 15

0.0255

0.0147

0.0129

0.0060

-161.35

-161.35

135.15

92.54

Combined analysis identified ten markers, which together showed 90.87%) associat~on

n ~ t h weight of the kernel (Table 3.29). CnCirB5:tl. C ~ C I ~ A ~ ~ ~ , , O P P I ~ I , ~ ~ , , OPE18400.

OPFZ400 and OPP15,,, are associated with increase in wcight of the kernel. Markers

associated with reduced weight of the kernel were CnClrC3'1s8, CnClrEI I 1x1~. CnCirC3'17~,

and mCnCir86192

Weight of the fruit

We~ght of the fruit ; 700.26 + [(393.04) CnCirH71u] + [(830.56) O P E ~ , V ~ ] +

[(-394.95) CnCirRl 1 1 1 ~ 1 t [(407.21) OPP15clro] + [(-674.04)

CnCirS81021 + [(-402.56) OPP157,(] + [(277.65) CnCirl171~~] t

[(493 30) OPE47si,] + [(357.04) CnCirS121i,,1 + [(301.82)

CnClrRI 1,41;] + 0.00062.

Combined analysis for wcight of the fm~l ident~fied ten markcrs, which togethcr showcd

96.64%) assoc~ation w~th this tralt. CnCirH7111, OPE~~~O,OPPIS~I~,~,CIICI~H~I~I, OPE47rrl.

CnC1rS12~,,~andCnCirRl 114Rare responsible for the increase in we~ght of the fruit and

markers CnCirRl 1 CnCirSBIo2, OPP15775 are associated with reduced weight of the

rru~t (Table 3.30).

Copra content

Copra content = 17.00 + [(150.00) CnClrE21711 + [(I 15.00) CnClrH4'21xJ + [(83.00)

CnC1rG42011 + [(98.00) CnCirD82r4] + [(93.00) CnCirC41(,8] +

[(36.00) O P P ~ ~ ~ I K I I I ] + [(28.00) CnCirD82531 + [(36.00) O P G ~ I I I ~ O ] +

0.00093.

Table 3.29. Stepwise multiple regression analysis for weight of kernel using the


Marker ,,,, Chromosome Partial R' 1 1 n u m k r 1

OPE 18400 NM 0.0739 I I

Total R I Proh,F

PE- Parameter estimatc NM-Not mapped

Table 3.30. Stepwise multiple regression analysis for weight of fruit using the


Marker ,,,, Chromosome Partla1 R number

Total R'


5 :1 Combined analysis for copra content revealed the assoctatlon of eight markers, wh~ch

together accounted for 96.47:' variability. Markers C ~ C I ~ E ? ~ , , , C ~ C I ~ H ~ ' : ~ ~ ,

CnCtrG4201, C ~ C I ~ D B ~ ~ ~ , . C ~ C ~ ~ G ~ I ~ ~ . O P P I ~ I ~ . C ~ C I ~ D S ~ ~ ~ , O P G ~ I > , , , I arc tdentificd to hc

associated with increased copra yeld (Table3.3 1).

DISCUSSION

Marker asststed selectton (MAS) has been proved to bc a good method to ~nip~.ovc

hreedlng programs. MAS has been appl~ed In d~fferent crops to Improve agrononiically

tmportant traits (Chandrababu el ul., 2003; Lecomte el ol., 2004). Before atternptlng

molecular marker in coconut, wc tried to identify morphological markers related to

agronomically important traits (Chapter 2). Morphological markcrs usually do not

rcqulre special~zed equipment. factltties or human resources other than that used 111 a

trad~t~onal breedlng program. Morphological markers like plant he~ght, trunk girlh, total

nunibcr of leaves, lcngth of thc leaflct, length of leaflet hcarlng portlon, lcngth of 10

tnternodes and age at first flowering showed good correlation wlth yield traits like

number of bunches pcr year, number of nuts per year, weight of the nut, we~ght of'the

frutt, welght of the kcmcl and copra contcnt (Table 2.3). Howcvcr, thcse morphological

tralts can be only asscssed aftcr 11 starts bcaring and are influenced by cnvironnient.

Therefore it warrants the need for developing DNA based markers, whlch arc

phenotypically and environmentally ~ndcpendent, to Identify plants with high yield tralts.

In agricultural research statlons they maintaln all the detalls ahout Individual trees 'l'hc

ylcld data from farmer's fields were collected with maximum accuracy All thc farms wc

selected for sample collect~on are from different agro climatic rcglons of South Incl~a

(Table 2 I) . From each farm we selected low, medium and h ~ g h yylldlng palms.

Earlier studies suggcst that tall coconuts are cross-polltnated, wh~le dwarfs arc

predominantly self-poll~nated. Therefore developing mapping populat~ons in coconuts

for llnkage analysis was a Herculean task. However, mapping populat~ons, wcrc

generated for constructing molecular linkage maps Molecular llnkage maps uslng MY0

Table 3.31. Stepwise multiple regression analysis for copra content using the



Marker,,,, Chromosome number

Partial R' Total R Prob>F PE

6'0 \ l.AGT, EAT x PRD, EAT x RIT and CRD x RIT IS now b e ~ n g used to assess genetlc

d~versity among coconut genotypes and also to establish thcir identity (Duran c.1 01..

1997, Perera et ul., 1998: Lebmn el 01.. 1999).

SSR and RAPD analysis yielded 276 polymorphic bands, were used to correlate their

assoc~ation with yield traits by SMA and SMRA. We also undcrsland the gcnctics of

marker-trait association by treating the marker data of SSR and RAPD ~ndependrntly and

In comblnat~on.

RAPD


Marker OPG71loo was identified by SMA, SMRA and combined analysis ( lahle

3.2.3.3.3.26). 11 is a strong negative marker (-ve PE) and can be used to idcnt~fy plants

w~th I ow n unibcr o fbunches H owevcr O P G ~ I ~ , ~ showed association with nunibcr of

hunches per year in both SM.4 and SMRA. OPG71~oo can hc used to sclcct plants with

h~gh numbcr of hunches (+ve PE).


0PG71300 was ~dentificd by SMA. SMRA and comb~ned analys~s (Table 3.4. 3.5, 3.27) as

positive marker to select plants with high number of nuts ( 4 ve PF). Four nlarkcrs

O P G ~ I J ~ ~ , OPP15921,. O P E ~ S I , and OPIil812511 showed assoc~ation w ~ t h numher of nuts per

year in both SMA and SMRA. Out of these OPG71400 and OPE181zv, were assoc~ated

with increase in number of nuts (+ve parameter estimate) where as OPPl 5u211 and OPE4rw1

are markers to Identify trees with lower number of nuts (-ve PE).

Weight of nut

OPP15,001, is found to be associated with tncreased weight of the nut by SMA, SMRA and

combined analysis (Table 3.6, 3.7, 3.28). F ~ v e markers O P P I ~ I ~ K , ~ , . 0PEl8s01,. OPEIRI~IH,,

6 1 O P G ~ I A W and OPG717ro were ~dentified for the trait weight of the nut in hoth SMA and

SMRA. All the four except OPEl8j*,(-ve PE) can he used to select plants w ~ t h h~gh nut

weight.

Weight o f the kernel

SMA. SMRA and comhincd analysis identified OPPISI1~ili as nlarker for ~ncreased hcrncl

we~ght (Tahle 3.8,3.9.3 29). Four markers OPPI 5,001i.0PEl Blaxi, OPE~,IIII , and OPG7~11i11

were repeated in hoth SMA and SMRA. OPE495U is a -ve marker, can he used to idcnt~fy

plants w ~ t h reduced kernel weight. OPPl51~w~i, OPE1811HiI, and OPG7ia,,I arc +\,c markcrs

for plants hearing nuts with high kernel welghl.

Weight of the fruit

OPE~X, , is found to he associated with increased weight o l thc fnul by SMA. SMRA and

comhlned analys~s (Table 3.10. 3.1 1. 3.30). Four markcrs, ldcnlified hy both SMA and

SMRA arc OPP1Sr21, OPE6751i, OPPISlo,~, and OPE18x,jl,. Markers OPEh7r,, and

OPPI 511jo~~ arc +be markcrs for h igh fruit weight, whereas OPP1542~ and 0 PEI Rxl,~, arc

assoc~atcd with reduccd we~ght of the nut.

Copra content

Among the markers OPPlOoli,, OPPl5910 and O P E l S r ~ , were ident~ficd hy hoth SMA and

SMRA. OPPlGoi,, and OPP15<j21i are +ve markers for h ~ g h copra y~cld (Tahle 3 12. 3.13.

3.31) OPE1 Srllil 1s a marker w ~ t h v c PE for copra contenl.

SSR

Number of hunches per year

None of the markers were repeated for the trait number of hunches per year, by SMA,

SMRA and comhlned analysis (Table 3.14, 3.15, 3.26). CnCirS121asandCnClrH4'230arc

6 2 the two markers identified by both SMA and SMRA. Both CnC1rS12~~,n and CnCirH4':1~~

are -ve markers, which IS useful in Identifying plants with low numher of bunches.

CnClrH4' has been mapped on chromosome 3.


Three markers CnCirS'il15, CnCirC' l l?~ and mCnCir8hlpr, showed assaclation with number

ofnuts per year in both SMA and SMRA (Table 3.16. 3.17). CnClrS7215 a n d C n C i r C 7 1 ~ ~

arc markers, which are associated with increased number of nuts and mCnCir801.,4 1s w ~ t h

ve PE assoc~ated will1 tralt for low number of nuts, mCnCir86 has hccn mapped on

chromosome 9.

Weight of nut

Among the two markers CnCirEZ,,, and CnClrRI 1 which were identlficd by both

SMA and SMKA, CnC1rE2171 is associated wlth increased weight of the nut and

CnCirRI 1 IS associated with reduced nut weight (Table 3.18, 3.19).


Markers CnCirBS2:z and CnCirAVloi were repeated in SMA, SMRA and con~h~ncd

analysis (Table 3.20, 3.21, 3.29). Both these markers were associated w ~ t h increased

a e ~ g h t of the kernel. Markers CnCirHS:,:, CnCirA9101, CnCirBh~~,.. CnC1rD8155 and

Cr1CirE2~~1 were identified in both SMA and SMRA All the five markers can bc uscd to

select plants w ~ t h high kernel weight. CnCirA9 and CnClrD8 have been mappcd on

chromosome 8 and 14 respectively.

Weight of the fruit

Moreover CnCirH71il and CnClrRlI were identified in SMA, SMRA and comhlncd

analys~s for trait fruit we~ght (Table 3.22, 3.23, 3.30). CnCirl-171,1 1s associated w ~ t h

6 3 h~gher h i t weight, can be used in select~on of plants bearing high weight fruits and

CnCirRI I lib is a negatlve marker for we~ght of the fruit

Copra content

CnCirEZlil is identified as marker associated with increased copra content in SMA.

SMRA and combined analysis (Table 3.24. 3.25. 3.31). Marlier CnCi rA8:~~ showed

association with the trait copra content in both SMA and SMRA. CnCirE2nl and

CnCirA8rxc1 are t v e markers for plants with higll copra yield.

Combined analysis

Combined analysis revealed a string of RAPD and SSR markers that arc associatcd with

the yield traits. In combined analysis, number of bunchcs per year, (Tablc 1.26) is

associated with six SSR markers out of which CnCirA410a and mCoCir47114 are mapped

on chromosome 12 and 1 3 respect~vely. A nalys~s for n umber o i n uts p cr y ear (Tablc

727) showed assoclatlon of seven SSR markers, out of which CnClrE7jlj and

CnClrEl l are mappcd on chromosome I I and 13 respectively. Three SSR markers

CnCirD82j1, mCnCirl19224 and CnCirC41hH identified out of fivc are mappcd on

chromosome 1.8 and 13 rcspcct~vely werc associated with wc~ght of the nut (Table 3 28).

The threc SSR markcrs CnCirAYlol, CnCirEl ll8i, and mCnCirR(i~~,~ associatcd w~th

weight of the kernel (Tablc 3.29) are mapped on chromosome 14, 11 and 9 respectively.

None of the SSR triarkers assoc~ated with we~ght of the fruit (Table 3.30) by combined

analysis has been mapped on the chromosome. For copra content (Tablc 3.31) eight SSR

markers identified by combined analysis, of which fivc markcrs - CnCirH4'21~

(Chromosome 3). CnClrC42ol (Chromosome 13), CnCirD82~ (Chromosome R),

CnCirG410x (Chromosome 13) and CnCirD825, (Chromosome 8) were mappcd on the

coconut genome.

6 1 Pleiotropic effect

A phenomenon in which one gene controls more than one trait IS known as ple~otropy In

our study we have tdenttfied pleiotropic effects where one marker is assoctated a t th more

than one yeld tralt. Pleiotropism has heen reported in yeld related and root rclaled tratts

111 rice (Venuprasad ef a/.. 2002).

16 markers showed pleiotroplsm in SMRA of M P D . OPG7140u and OPEIR12ro wcre

associated wtth four tralts numher of bunches and nuts per year. wetght of the nut and

kernel (Table 3.3. 3 5 . 3.7, 3.9). O P P I S I M ~ ~ ~ was assoc~ated wtth numher of hunchcs per

year, wcight of nut, kernel and fruit (Table 3.3. 3.7, 3 9. 3.1 1). OPPISr2< showcd

association with five yield tram- number of bunches and nuts per year, wc~ght of thc nut.

kernel and fruit (Tahle 3.3. 3.5, 3.7, 3.9, 3.1 I). Both OPP15~,11, and OPPISx51i showcd

assoelallon with number of nuts pcr year and copra content (Tablc 3.5. 3 13). Thrcc

markers OPE18xisi. OPEIXt,~o and OPP154:5 wcre found to he associated with numher of

nuts per year and we~ght of frutt (Table 3.5. 3.1 1). OPE1811x,o showed assoclatlon with

numher of bunches per year. weigh1 of nut and kernel (Table 3.3, 3 7. 3.9). OPE411111, is

found to be associated w ~ t h two characters, a e ~ g h t of nut and copra contenl (Tahlc 3.7,

3.13). O P G 7 1 3 1 ~ ~ showed association with weight of thc nut, kcrnel, fru~l and copra

content (Table 3.7, 3.0, 3.1 1.3.1 3). OPPI S5~ ,~ , showed pletotrop~snr for wctght of the

kcrnel and copra content (Tahle 3 9, 3 13) and OPPI6,2,,,, for we~ght of thc kerncl and

frurt (Tahle 3 9, 3.1 1)

10 markers showed plc~otroptsm in SMRA of SSR. CnCirS121noshowed association w~th

number of bunches per year and we~ght of kernel (Tablc 3.1 5. 3.21). ( ' nC1rC7~~~ showcd

assoclatron w ~ t h three tratts, number of hunches and nuts pcr year, and we~ght of kcrnel

(Table 3.15, 3.17. 3 .2 1 ) C 11CirS7~15 showed assoc~ation w ~ t h nurnhcr of hunches and

nuts per year (Table 3.1 5, 3 17). CnCtrE2131 was associated w ~ t h number of nuts per year

and we~ght of the nut (Table 3.17, 3.19). CnCirB6202 showed assoctation with weight of

the nut and kernel (3.19, 3.21). CnCirE21,~ was associated with we~ght of the nut and

kernel. and copra content (Table 3.19, 3.21, 3.25). C nCirR1 l and CnCtrH71,~~ werc

6 5 related to weight of the nut and fruit (Table 3.19, 3.23). CnCirA8?*(1 showed association

with weight of the fruit and copra content (Table 3.?3,3.25).

Combined analysis revealed five markers showing pleiotropism. Both ('nC1rH7~,, and

CnCirH71~3 are associated with number of nuts per year and weight of fruit (Tahlc 3.27.

3.30). CnCirG41hx is associated with two yield traits, weight of the nut and copra content

(Table 3.28. 3.31). OPP15lmo is found to be associated with weight of nut and kerncl.

and copra content (Table 3.28, 3.29, 3.31). OPP157: 1s showing association with weight

of kernel and fruit (Table 3.29,3.30).

By this study a set of markers that can be used for selecting high yielding coconut palms

were identified. These markers will he highly useful In the hrecdtng progranimc of

perennial crop like coconut.

identification of molecular markers associated with yield...

Documents