Spatial and temporal patterns of associations ... - Wiley Online Library

Hereditas 115: 1-8 (1991)

Spatial and temporal patterns of associations between quantitative characters and resistance to scald in barley QIFA ZHANG’, M. A. SAGHAI MAROOF’ and R. K. WEBSTER3

’ Department of Agronomy, Huazhong Agricultural University, Wuhan, China ’Department of Crop and Soil Environmental Sciences, VPI&SU, Blacksburg, VA 24061-0404, USA

’Department of Plant Pathology, University of California, Davis, C A 95616, USA

ZHANG,Q.. SAGHAI MAROOF,M. A. and WEBSTER, R. K. 1991. Spatial and temporal patterns of associations between quantitative characters and resistance to scald in barley. Sweden. ISSN 0018-0661. Received June 18, 1990. Accepted April 29, 1991

~

Heredifas 1 1 5 1-8. Lund,

Interrelationships of two character sets, quantitative characters and resistance to Rhynchosporium secalis, were studied in a world barley collection and in an experimental barley population, Composite Cross I1 (CCII). The characters within each set were found to be more closely correlated with each other than they were with characters in the other set. Canonical correlation analysis showed that the correlations between sets as a whole were substantial; almost every character contributed, to a larger or lesser extent, to the correlation between these two sets of characters in the world barley. The associations of these characters among accessions from four geographical regions were also studied, and these showed that geographical regions differed strikingly in patterns of both within- and between-set correlations. The within-set correlations for North African accessions appeared to be of the same order of magnitude as the between-set associations; whereas within-set correlations were much larger than between-set associations for the other three regions. No marked correlations between these two sets of characters were detected in various generations of CCII. M . A . Saghai MarooL Department of Crop and Soil Environmental Sciences, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061-0404, USA

Barley scald, caused by Rhynchosporium secalis (Oud.) Davis, is a serious foliar disease in many barley-growing areas of the world. Losses in yield as high as 35% and 40% have been observed in California (SCHALLER 1951) and Australia (ANON 1976), respectively. Disease control through manipulating resistance genes by plant breeding is generally considered as one of the most efficient and economic strategies. Although resistance is widely available and cultivars with different genes for resistance have been developed, certain factors have appeared to pose serious limitations on breeding cultivars for resistance. First, resistant cultivars have often been found to possess undesirable agronomic traits such as poor yield and quality. Second, specific resistance frequently breaks down in the field as a consequence of a changing racial composition in pathogen populations. Much is still to be understood in order for breeding programs to be effective. In particular, comprehensive knowledge would be valuable regarding the. following aspects: (i) interrelationships of disease resistance and agronomically important quantitative characters; (ii) the extent to which these two groups of characters are intra- as well as inter-correlated; and

(iii) the underlying evolutionary forces which have structured the interrelationships of these characters in populations. ZHANG(1985) suggested that the genome structure of barley was composed of interrelated clusters, with loci within each cluster more closely related to each other than loci in different clusters. It has also been reported that resistance to several races of R . secalis is highly correlated both in world barley collections (ZHANGet al. 1987) and in different generations of composite crosses (JACKSON et al. 1982; MUONAet al. 1982; SAGHAI MAROOFet al. 1983). Although attempts have been made to assess the effect of barley scald on various agronomic characters (see SHIPTONet al. 1974 for a review), little is known about the interrelations between groups of quantitative characters and resistance to different races at the population level. The studies reported in this paper were undertaken to determine (i) whether there are associations among scald resistance and particular reproductive characters; (ii) whether such associations are the same in different barley-growing regions or change with the progress of generations in

a barley composite cross, and (iii) what are the possible evolutionary forces responsible for the observed relationships among these characters.

Materials and methods Genetic n1uteriul.r

This study utilized two sets of materials. The first set consisted of 350 accessions of Horrkwni uulgriro L. chosen at random from the USDA barley collection. Details of development and maintenance of the collection were described by MOSEMAN and SMITH (1981). These accessions were placed into I2 groups according to their geographical origin The numbers of accessions sampled from different geographical regions were given by ZHANGct al. (1987). The second set of the genetic material included 200 families from an experimental barley population, Composite Cross I I (CCII), synthesized by H A R L A Nand M A R T I N Iin 1929 by pooling F, seeds from all 378 possible crosses among 28 barley varieties representing a diversity of barleygrowing areas of the world. This population has subsequently been grown at Davis, California under normal agricultural conditions. Fifty seeds drawn at random from each of generations 8 . 13, 23, 45 of CCll were sown. The resulting 200 plants ( 5 0 per generation) were grown to maturity; seed was collected from each plant and kept separate as a single "family". Previous studies of allozynies and morphological markers on the 350 accessions ( Z H A N Get al. 1990) and similar materials from the CClI population ( M U O N A1980) showed that there was very little genetic variation within accessions of the world barley collections and within families of the CCll population

reproducible (JACKSON and WEBSTER 1976; MUONAet al. 1982; JACKSONet al. 1982; GURAs i N c t w 1984). Fifteen seeds from each of the 350 accessions and 10-16 seeds from each of the 200 families were planted in UC soil mix C-2 in metal flats and seedlings allowed to grow in a greenhouse for two weeks before inoculation. Each flat was inoculated with a 50 ml spore suspension ( 2 x 10' spores per ml) of one of the four races and incubated for 72 hours in a growth chamber, where the humidity was kept at 1 0 0 ' % ~The seedlings were returned to the greenhouse and disease rating was done two weeks later. Ten random seedlings from each of the 350 accessions and all the resulting seedlings ( 10- 16, mostly 12) from each of the 200 families were scored following the 0-4 grading described by JACKSONand WEBSTER(1976). The severity of the disease was evaluated using the disease index of ZHANGet al. ( 1987), which recovers information from disease scores 0, I . 2 , 3 and 4 as follows: I

I

where j = the disease score (0, I , 2, 3 and 4) and f, = number of individuals with disease score j. The variance of the disease index, V( DI). is given by

DI can take value from 0 (if every individual in an entry scores 0) to 1 (if every individual scores 4). Qirunt itu t iw c/iurar.t ers

Quantitative characters were studied in replicated field trials. The 350 accessions were grown in the 1982-83 barley-growing season under standard agricultural conditions in Davis, California, USA. Diseu.vc rcvir~tion A randomized complete block design was emThe resistance reaction to four physiological races ployed with each accession replicated three times. of R. secalis was studied for all 350 accessions and Measurements for the characters heading date 200 families derived from CCII. Races 40. 61, 72 (days after March l ) , plant height (cm), tillers per and 74, which belong to different complexity plant, seeds per spike, and weight (6) of 100 kerclasses and which, in combination, are able to nels (hereafter referred to as kernel weight) were overcome all known scald resistance genes (WEB- taken from 5 randomly chosen plants in each row. STER et a[. 1986) were used. The inoculation proceThe 200 families of CCll were planted in the field dures of JACKSON and WEBSTER(1976) were in the 1980-81 growing season following a ranclosely followed. Previous studies showed that the domized complete block design with each family results obtained from these procedures are highly replicated twice. Four quantitative characters,

Heredifas 115 (1991)

RESISTANCE TO SCALD IN BARLEY

heading data (days after March l), plant height (cm), weight (g) of 100 kernels (kernel weight), and yield per plant (g), were measured.

3

s = min ( p , 4). The interrelation for one pair of the linear compounds is unaffected by the relationship of other pairs because the canonical variate pairs are statistically independent of each other.

Statistical analysis Product-moment correlations were calculated for all possible character pairs. The relationship between quantitative characters as one set and disease reaction as another set was studied using canonical correlation analysis (MORRISON1976). For any two sets of variables with p and q elements respectively,

x;=(x,,x2,..

.,XJ

and

' . ,xp+q),

~;=(xp+l,xp+2,.

canonical correlation analysis constructs linear compounds cvI = a', XI, cf, = b', X,,

cu,=aLX,,

cf,=bLX,,

such that the sample correlation between cu, and cfl is the greatest and that between cv2 and cfr is the greatest among all the linear compounds uncorrelated with cvI and cf,, and so on for all

Results Scald resistance and quantitative characters in world barley and in CCII

The disease indices and measurements of quantitative characters for the world barley and the four generations of CCII are listed in Table 1, also listed are those of barleys from four geographical regions: Northwest Europe, Middle South Asia, East Asia, and North Africa. Only those regions which were represented by more than 30 accessions were included in more detailed analyses. Worldwide, the DI value for race 40 was the highest (0.743), followed by race 72 (0.651), race 61 (0.585) and race 74 (0.453). This trend holds in all the regions except N. W. Europe, in which the DI values of races 61 and 72 were about equal. It is also clear that, for given races, disease indices varied greatly from one region to another. For example, the DI values of race 40 for the four regions are ordered as: E. Asia (0.831) > M. S. Asia (0.81 1) > N. W. Europe (0.690) > N. Africa (0.654) and the difference between M. S. Asia and

Table 1. Means and standard errors (in parenthesis) for disease indices of four races of R. secalis, and quantitative characters of barley from four geographical regions of the world and four generations of Composite Cross I1 Race

Race

Race

Race

40

61

72

74

Heading Date

Plant Height

World

0.743 (0.006)

0.585 (0.007)

0.651 (0.006)

0.453 (0.007)

36.14 (9.36)

104.59 (20.44)

N. W. Europe

0.690 (0.014)

0.552 (0.01s)

0.535 (0.013)

0.467 (0.0 IS)

40.64 (9.30)

M. S. Asia

0.811 (0.0 14)

0.579 (0.018)

0.776 (0.01 3)

0.429 (0.017)

E. Asia

0.831 (0.013)

0.674 (0.016)

0.785 (0.01 4)

N. Africa

0.654 (0.022)

0.436 (0.021)

F8

0.942 (0.007)

F,J

Tillers per plant

Seeds Per spike

Weight of 100 kernels

Yield

11.52 (6.61)

68.62 (25.64)

4.38 (3.23)

104.43 (32.66)

12.73 (6.43)

53.10 (26.12)

4.45 (2.39)

29.99 (7.53)

94.53 (14.82)

11.24 (6.94)

67.85 (22.32)

4.14 (2.98)

0.638 (0.016)

30.88 (7.67)

103.89 (18.42)

9.34 (5.84)

82.02 (18.05)

3.37 (2.43)

0.631 (0.01 8)

0.394 (0.0 19)

36.36 (8.67)

99.23 (15.65)

10.85 (6.14)

62.61 (23.46)

4.82 (3.58)

0.948 (0.006)

0.955 (0.006)

0.984 (0.004)

32.97 (7.80)

120.39 (9.69)

3.72 (0.49)

13.14 (15.06)

0.941 (0.006)

0.986 (0.004)

0.988 (0.003)

0.979 (0.005)

31.23 (9.00)

121.92 ( 1 I .96)

3.61 (0.53)

13.29 (15.53)

F2J

0.884 (0.012)

0.921 (0.008)

0.956 (0.006)

0.912 (0.009)

30.74 (8.39)

119.92 (10.71)

3.80 (0.41)

13.56 (16.65)

F45

0.199 (0.013)

0.608 (0.014)

0.976 (0.004)

0.422 (0.013)

34.72 (5.01)

123.39 (9.38)

3.79 (0.32)

10.82 (12.37)

4

Q

ZHANCi ET AL

N . W. Europe is statistically highly significant. Similar large geographical differences were also obvious for the other three races. In the CCll population, it is interesting to note that the disease level decreased progressively as the generations advanced for three of the four races (races 40, 61 and 74). By F,,, the population was resistant to race 74 (DI = 0.422), highly resistant to race 40 (DI = 0.199). and much less susceptible to race 61 (DI = 0.608) than were the earlier generations. Little change was observed in the levels of resistance to race 72 from early to late generations. For the quantitative characters, however, there were no significant differences for any character either among various geographical regions or in different generations of CCII.

within-set pairwise correlations were much higher than between-set correlations. The covariant structure of these two sets was further assessed using canonical correlation analysis. A Bartlett's test at probability 0.05 suggested that two canonical variable pairs were needed in order to express the dependency between these two sets of characters. The correlation coefficient of the first canonical variate pair, which represented the largest possible correlation between these two sets of characters, was 0.360 with probability 0.000 (Table 3); the second canonical correlation coT d k 3 Loading ol' lirst Iwu canoniciil variable pairs and niultiple correlations of each c h n r ~ r r in r one set with ;ill the characters in the other s e ~for resistance 10 four riiues of R. sr(rdi.s a n d live quantitative characters in world barley

The pattern of association between scald resistance and quantitative characters in the worldwide sample

Multiple correlation

Ch.rirliCter . I

We will describe the analyses of correlation for the worldwide data in detail for illustrative purposes. All the six ( I00'!h) possible pairwise correlations within the set of resistance to four races of R. .sec~u/irwere significant at the 0.05 probability level (Table 2 ) ; the average absolute correlation coefficient /?I within this set was 0.320. Eight of the ten (80'Xl) pairwise correlations within the set of five quantitative characters were significant, the ]PI of this set was 0.227. However, only eight (40%) among the 20 possible pairs of correlations between resistances to the four races and five quantitative characters were significant; and the value of If1 was 0.096. Thus it appears that, on average, the

CVDI"

CVD2

R-'

P

0.791 0.463 0.753 0.556

-0.021 -0.701 -0.319 -0.736

0.0X4 0.047

0.000 0 01x

0.082

0.000 0.001

Race 40 Race 61

Uace 72 R x e 74

0 073

CFQI,'

CFQ2

-0.751

0.052 -0.300

-0.053 -0.503 -0.064 -0.403 --O.64X

('anon icitl correlation coefficient

0.360

0.132

P

0 no0

0.010

Heading date Plant height Tillers/planr Seedsispike

Kernel weight

-0.I92 -0.549

0.083 0023

0.000

0.049

0.004 (1.379

013 0.036 (I

Within quaiititative characters

Proportion ol' Populelloil

rllI15

If/

Worldwide N Africa E . Africa M . S . Asia N . W. Europe FH F,,

0.1 10 0.337 0.17X 0.307 0.256 n 273 0.273

F?,

0.273

F,i

0.273

sigiiilicent (correlations)

lr/

0.320 0.21 I 0.305 0.216

1.00 0.17 0.67

0.127

0.335

0 67 0.67 0.67

0.444 0.493 0.474 0.558

0.33

0.50 0.X3

0.072

T V L ) and Ct:Q are ceiionic;tl ~iiriiitesfor disease and qua111it;it i b e characlers, respeclively

7iihic 2. A suinm;iry oi' all the pairwise correlatioiib wiiliin the sct of rrsistmcr 10 lour races o/' R. . w d f . s . within the set quen~itativecharacters iind between t h a e two 'iets of uhiiriicturs

Within scald rrcisi;incc

0.128

0.759 0 305 0.3 I 3 0.217 0 . I01 0.1 X4

0.157 0.1 x9

rlllli IS the crittciil value of being signilkant at the 0.05 probability level

Between resistaiice nnd quanlitative &. '11 -,deters

Pruporrlon 01' \igniliunt (correialions) 0.xo 0.30 0 70 0.60 0 50 0.00 0.17 017

0.17

1i.l

0.096 0.23x 0.200 0.187 0. I 3 0 0 101

0 I27 0.117 0.171

Proportiiw of signtlicont (correlations) 0.40 0.15 0.35 0.15 0 10

0.06 0.06 0.06 (1.25

or

RESISTANCE TO SCALD IN BARLEY

Herediros 115 (1991)

efficient was 0.232 (P = 0.010). Thus, these two canonical variate pairs independently provided evidence that there were weak but significant correlations between quantitative characters and scald resistance. The algebraic sign and magnitude of the loading of the ith canonical variate on the j t h character indicate the direction and magnitude of the correlation between the j t h character and the ith canonical variate. The canonical variable loadings of the first two variate pairs are listed in Table 3. The first canonical variable of resistance to scald (designated as CVDl) placed heavier weights on race 40 (0.791) and race 72 (0.753). The loading of the first canonical variable of quantitative characters (designated as CFQ1) was the largest on heading date (-0.751) and also substantial on tillers per plant ( -0.549). Thus this canonical variate pair showed an association between resistance to races 40 and 72 in the disease set, and heading date and tillers per plant among quantitative characters; accessions with later heading and fewer tillers were more susceptible to races 40 and 72. The second canonical variable for resistance to the four races of scald (designated as CVD2) was dominated by race 74; whereas the concomitant variable (designated as CFQ2) was largely contributed by kernel weight, plant height and seeds per spike. Thus, the association represented by the second canonical variate pair is that barley accessions susceptible to race 74 are short and set fewer and smaller seeds. and vice versa.

5

The correlation between each character in one set and all the characters in the other set was further evaluated by a multiple correlation analysis. The squared multiple correlation coefficients ( R 2 )of resistance to each of the races 40, 72 and 74 with all the quantitative characters were about the same order of magnitude as was the R’ of heading date with resistance to the four races. These multiple correlation coefficients were much larger than those of the remaining characters (Table 3). Thus, resistances to races 40, 72 and 74, and heading date are major contributors to the observed correlation between these two sets of characters. The pattern of association between scald resistance and quantitative characters in four geographical regions

The proportion of significant correlations and the It/ values for these four regions are listed in Table 2, from which it can be seen that, on average, the within-set correlations were much larger than between-set correlations in regions E. Asia, M. S. Asia and N. W. Europe. The between-set correlations were about equal to the within set correlations in barleys from N. Africa. Canonical correlation analyses showed that the overall correlations between scald resistance and quantitative characters were highly significant from N. African accessions, nearly significant for barleys from E. Asia and M. S. Asia, and not significant for N. W. European accessions (Table 4). Parallel

Table 4. Loading of first canonical variable pair and multiple correlation of each character in one set with all the characters in the other set for resistance to four races of R. secalis and five quantitative characters in barley accessions from three regions

Character Race Race Race Race

40 61

72 74

Heading date Plant height Tillers/plant Seeds/spike Kernel weight Canonical correlation coefficient P

N. Africa

E. Asia

M. S. Asia

Multiple correlation

Multiple correlation

Multiple correlation

CVDI

RZ

P

CVDl

R2

P

0.437 0.345 -0.300 0.864 CFQl -0.379 -0.446 0.278 0.573 -0.792

0.334 0.259 0.295 0.483

0.059 0.155 0.100 0.0005

-0,200 0.447 -0.494 0.671

0.203 0.107 0.185 0.272

0.093 0.413 0.126 0.024

0.143 0.266 0.128 0.372 0.457

0.367 0.070 0.431 0.011 0.002

CFQl -0.055 -0.686 -0.582 0.628 -0.774

0.200 0.164 0.166 0.139 0.243

0,044 0.096 0.093 0.159 0.016

0.788 0.003

0.588 0.052

CVDl

0.805 0.373 0.627 0.151 CFQl -0.730 -0.740 -0.111 -0.456 -0.055

0.619 0.069

R2

P

0.296 0.246 0.112

0.043 0.369 0.096 0.513

0.212 0.266 0.116 0.188 0.147

0.080 0.028 0.367 0.122 0.235

0.144

6

Hereditas I I5 (1991)

Q.ZHANG ET AL.

results were also obtained from multiple correlation analyses (Table 4). The pattern of association between disease resistance and quantitative characters in CCII generations The same simple and canonical correlation analyses were applied to the data of the four generations of CCII. The results are summarized in Tables 2 and 5 . In general, correlations within the set of scald resistance were highly significant in this population, whereas correlations within the set of quantitative characters and between the two sets were very small. It is also evident from Table 2 that there was small but steady increase from early to late generations in the pairwise correlations both within each set and between the two sets. This is also true for the overall associations between the two sets as revealed by the canonical correlation analyses (Table 5).

- 6 - 0

mmr-m

c - w m N O C M

0 0 0 0

0 0 0 0

- 9 f q

??NO

m r n v ~ p 0 0 0 0

0 0 0 0

m d o N

Nr-NC M m N m

- W - - O

0 0 0 0

& O O O O

y 1 - y 0'911'4

"3

h

l

u

l

l

4

N

s:

Discussion We have analyzed the pattern of resistance to four races fo R. secalis and several quantitative characters in a sample of the world barley collection and four generations of Composite Cross 11. The data showed that the observed disease indices differed from one race to another in the world barley, and in all the four geographical regions. Since these accessions were sampled at random from the world barley collection, these results indicated that the races differed in aggressiveness with race 40 being the most aggressive race and race 74 the least aggressive. The analyses have demonstrated that, on average, the correlation within each character set is much larger than that between different character sets. This is true for the worldwide data as well as for accessions from three of the four regions studied. ZHANG(1985) suggested that the genome structure of barley wdS composed of interrelated clusters, with loci within each cluster more closely related to each other than loci in different clusters. The present study has provided supporting evidence for this view of genome structure. The mechanism which leads to the formation of these clusters is not clear. There has been evidence (GURASINGHE 1984) that genes for resistance to races 40, 61, and 74 are closely linked, whereas resistance to race 72 is due to two complementary genes which are inherited independently of each

I

I

-

N

W

0chm-r

4

m m m -

~

moo

1199

\99-??

0 0 0 0

0 0 0 0

N

s:

2 14

5

u

4

N

K

L.

c

E

6

~ m m M b M N

?'9'1N 0 0 0 0

m o l n m b - P I N O N

Q'b.?\P?'? ~

0

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- --PQ r I -N -

m rW- r0- m b W

0 0 0 0

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OYP."!

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Hrredrkr! 115 (1991)

other and of thc other three loci as well. In our present study, the correlations among races 40, 61, and 74 are, on averagc, higher than they are with race 72 in almost every case. Thus, physical linkage of these genes may largely account for the formation of the cluster of resistance. As for the quantitative characters, since all the characters studied are related to reproduction and are of economical importance, artificial and natural selection as well as pleiotropy may all have played roles in the development of such clusters. It should be noted that the associations between these'two sets of Characters are also substantial as indicated by the canonical correlations and the proportions of significant correlations. It is clearly demonstrated, especially in the worldwide data, that these two sets of characters are associated on a multiple-character basis. Almost every character has contributed significantly to the total correlations between the two sets. This may have important bearing on breeding for disease resistance. It suggests that it is difficult to improve simultaneously many quantitative characters and the level of resistance to many races of the pathogen. The analyses also demonstrated that the observed disease indices varied from one geographical region to another, as did the features of associations exhibited by these two sets of characters among accessions from different geographical regions. Table 2 revealed two distinct patterns regarding the relative magnitudes of within- and between-set correlations. The N. African accessions exhibit the first pattern in which within-set correlations are of the same order of magnitude as between-set correlations. The other three regions, in addition to the worldwide data, exhibit the other pattern in which within-set correlations are, on average, consistently larger than between-set correlations. Our canonical correlation studies have established that the major links between resistance and quantitative characters also differ from one region to another. In N . African accessions, the association has mainly resulted from the correlation between race 74, and kernel weight and seeds per spike; in E. Asian barleys, the association is chiefly due to race 40 with kernel weight and heading date; while in M . S. Asian barleys the link between race 40, and plant height and heading date accounts for most of the association between these two sets of characters. On the contrary, scald resistance and quantitative characters are not significantly associated among N. W. European accessions.

RESISTANCE TO SCALD IN BARLEY

7

Geographical differentiation of genetic variation has often been interpreted as resulting from either founding effects or differential selection. Damage of the barley crop by R. serulis has been reported in many barley-growing areas of the world (SHIPTON et a[. 1974). Local differentiations of racial composition in pathogen populations are common in many plant diseases ( P A R K E R1985), and plant populations are capable of responding quickly to disease pressure as demonstrated by the rapidly decreased disease susceptibility of the CCII populations in the present study. Furthermore, all the quantitative characters in the present study are important fitness components, which have large effects on the survival of the crop plants. Thus, the observed geographical patterns of associations between these two sets of characters could certainly have resulted as a consequence of artificial and/or natural selection for local adaptation. However, we are unable to determine whether the observed associations have resulted from correlated or independent adaptations of these two sets of characters without detailed information on the severity of the disease and the racial compositions of the pathogen populations in these areas. The rapid differential changes in the level of resistance to these four races in the CCII population have certainly been responses to selection pressures experienced by this population. A puzzling feature of our results is the lack of correlation between these two sets of characters in the CCII population. Very similar results have been reported by MUONA et al. 1984 in the same population. However, caution is required in the interpretation of the results. Two aspects may be relevant: (i) it has been repeatedly reported that high infection by barley scald leads to heavy loss of yield due to reduction in seed weight (SHIPTON et al. 1974), as well as to reduction in seeds per spike and tillers per plant (JAMES et al. 1968). Other effects reported are reduction in plant height and delay in plant development (OZOE1956); and (ii) the best performing genotypes in one disease infection level may not be best performing genotypes in another disease level. In both respects, genes governing resistance also exert effects on quantitative characters (pleiotropic effects of genes), to the extent that the disease can affect. The pleiotropic effects of genes for resistance could be complex. Taking yield as an example, when the amount of disease is low, individuals carrying resistance genes may appear to be lower yielding than those carrying susceptibility genes because of the limitation of the yielding

8

Q . ZHANG ET AL.

Hcwrh/o.s I15 ( I Y Y I )

potential, resulting in a negative correlation between resistance and yielding characters; the reverse may be true when the amount of disease is high, in which case the resistant genotypes may out-yield the susceptible ones, leading to a positive correlation between disease resistance and yield components. Resistance and yield, however, may not be correlated when the amount of disease is at an intermediate level. Thus, the phenotypic effect of a given gene for resistance of quantitative traits would be largely determined by the level of disease infection, and it would be ideal to test such correlations in the presence of various disease levels in order to characterize the interrelationship of these two sets of traits. Thus, based on the present results, the conclusions are not readily available regarding the evolutionary dependency between scald resistance and quantitative characters in this experimental population.

s m t l i s and losses in grain yield of spring barley. AJIIIA . /J/J/. B i d 6 2 : 273 288 MORRISON,D. F 1976. Mullivariate Statistical Methods. McGrciii,-HilI Book C U J I I ~ ( I I I J MOSEMAN, J. G. and S M I T HD. . H.. J R . 1981. Purpose. development, utilization. maintenance and status of USDA barley collection. In: Bw/vv Generics I V , Proc. 4th I n / . Biir/c,j. GLYWI.Swip. Eitlinhurgh. p. 67 70 MUONA.0. 19x0. Evolution of correlated systems in barley: morphological and en7yme polymorphisms, quantitative charPh.D. T/iesis. Unii,. C d i l . . acters and disease resistance. Dell J S MUONA,0.. AL.LARD.R. W. and WFHSTFR,R . K 1982. Evolution of resistance to Rh~ncliosporiirni .secci/is ( O u d . ) Davis in Tlteor. App/. Gene/. 6 1 : 209 714 barley Composite Cross 11. M U O N A0.. , ALLARD.R . W. and WFRSTFR,R . K . 1984. Evolution of disease resistance and quantitative characters in barley Composite Cross 11: independent or correlated'? Nerec1irlr.v for: 143 148 Ozot, S . 1956. Studies on the R h ~ n t ~ h o ~ ~ f c J r iscald u J f I of barley and its control. Shinion Prefc.crio.o/ Apric. /ns/. Bid/. I : I I72 PARKtR, M. A. 1985. Local population ditferentiation for compatibility in a n annual legume and its host-specific Fungal pathogen. t?'U/U/iUJJ 39: 71 3 723 S A G H AMAROOF, I M. A,. WFRSTER,R. K . and ALLARD.R . W. 1983. Evolution of resistance to scald. powdery mildew, and net blotch in barley Composite Cross I1 populations. . Theor. A e k ~ i ( i i I / e ~ / g e ~ ~. ~The e n r .authors wish to thank two anonymous Appl. Gener. 66: 279 283 reviewers for the very helpful conimonts on ;in earlier version of S C H A L L t R . C. W. 1951. The effect of mildew and scald infection this manuscript. on yield and quality of barley. A g r ~ n J. . 43: 183 188 SHIPTON,W. A., BOYD. W. J. and AI.I, S. M. 1974. Scald of barley. R o r P / m / f ~ l h d 5.1: . 839 861 WFHSTER.R . K.. S A G H AMAROOF. I M . A. and AI-LARD.R. W. References 1986. Evolutionary response or barley Composite Cross I I ANON.1976. Pests and diseases W. Ai~.s/.D q ~ / Agrfc.. AJlJlii. to R / ? ~ ~ n ~ ~ h o s p o ,.si~ccrIi.s i r r , ~ i analyzed by pathogen complexity Rip., p. 27 and by gene-by-race relationships. /?J~/Op(J//iU/iJgJ 76: 661 G L I K A S I N G HP.F . D. A. 1984. The inheritance of scald (R/I.vJI668 cIiosporiunl sec1rli.T) resistance i i i experimental populations of ZHANG,Q. 1985. Interrelationship of allozymes. morphological Unil-. C d f : ~ ( J M barley (HurcIewi d x u r c ) . P h . 0 . TI~i~.sis. markers. disease resistance and quantitative characters in the HARLAN, H. V. iind M A R T I N M. I , L. 1929. A composite hybrid genetic system of barley Hordwni iwIgcv JACKSON.L. F. and Wt.HSTk.R. R. K. 1976. Race differentiation. Z H A N G Q.. , WEBSTER,R . K. and A L L A R DR. , W. 1987. Geo.scwr/i.s) in Calidistribution, and frequency of RhyJli~/io.sporiiiJff graphical distribution and associations of resistance to four P I ~ y / c ~ p r i / h 66: o / ( ~719 ~ ~ ~725 fornia. races of Rh?.nc/i*i.sporiirrff Sc,cci//A. ~ / J ~ ' / i ~ p ~ J / h c77: l / c l 352 g~ JACKSON.L. F.. WEHSTtR. R. K., AI.I.ARD.R. W. and K A H l . F R . 357 A. L . 1982. Genetic analysis of changes in scald resistance in ZHANG. Q.. SAGHAI MARODF. M. A. and AILARD.R. W. 1990. P l ~ y i i ~ p o r I i o l c72: ~ y ~1069 ~ 1072 barley composite Cross V. Worldwide pattern of multilocus structure in barley determined JAMES.w . C.. JFNKINS, J. E E and JtMMtTT. J. L. 1968. The by discrete log-linear multivariate analyses. .~ Theor. Apppl. relationship between leaf blotch caused by R/il~Jl~hovporiuJfi Goner. XO: 121 128 ~

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