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CROP BREEDING, GENETICS & CYTOLOGY

Effect of Recurrent Selection on Combining Ability in Maize Breeding Populations

University of Guelph, Dep. of Plant Agriculture, Crop Science Building, Guelph, ON, N1G 2W1 Canada

^* Corresponding author (lizlee{at}uoguelph.ca)

	ABSTRACT

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION REFERENCES

 
Recurrent selection (RS) is a population improvement method that increases the frequency of favorable alleles while maintaining genetic variation in breeding populations. Twelve University of Guelph RS maize (Zea mays L.) populations selected via reciprocal recurrent selection (RRS), selfed-progeny recurrent selection (S), or a method combining RRS and S (COM), were assessed for changes in the genetic structure of grain yield, grain moisture, and broken stalks, and two associated selection indices. Partitioning of the entry sums of squares from diallel matings of the original (C₀) and advanced (C_A) cycle populations using Gardner and Eberhart's Analysis II and Analysis III indicated genetic improvement occurred for the per se and cross performance of most populations. Accompanying the favorable changes in population performance were less favorable shifts from predominantly additive genetic effects in C₀ to greater nonadditive genetic effects in C_A. This shift did not substantially change the general combining ability estimates (g_i) of most populations. However, for grain yield, the underlying components of g_i effects were altered in their relative importance. General combining ability (GCA) effects in the C₀ were caused primarily by the population per se effects (v_i), while in C_A the GCA effects were caused predominately by parental heterotic effects (h_i).

Abbreviations: API, adjusted performance index • C₀, original cycle • C_A, advanced cycle • COM, combined recurrent selection • GCA, general combining ability • g_i, general combining ability estimate • h, average heterotic effect • h_i, parental heterotic effect • h_ij, total heterosis • OCHU, Ontario crop heat units • RS, recurrent selection • RRS, reciprocal recurrent selection • S, selfed-progeny recurrent selection • SCA, specific combining ability • UPI, unadjusted performance index • RCBD, randomized complete block design • v_i, variety

	INTRODUCTION

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION REFERENCES

 
THE OBJECTIVE of maize population improvement is to increase the frequency of favorable alleles while maintaining genetic variation. These populations then can serve as a potential source of superior inbreds and can inhibit development of a possible genetic ceiling for future hybrid improvement (Duvick, 1992; Kannenberg and Falk, 1995). Recurrent selection is a process of cyclical selection in a breeding population to increase the frequency of favorable alleles, and thus mean performance. In the absence of overdominance, S progeny selection using either S₁ or S₂ lines is considered to be superior to other methods of RS for improvement of the population per se (Lamkey, 1992). Alleles are fixed rapidly and deleterious, homozygous alleles are exposed and eliminated early in selection (Weyhrich et al., 1998). Population improvement through S is the result of direct selection favoring additive genetic effects because there is no masking effect of a tester. Reciprocal recurrent selection theoretically improves both additive and nonadditive gene action between two maize populations of complementary heterotic patterns (Comstock et al., 1949). Because the population testcross is used for selection, the population per se performance will be improved only indirectly. Testcross methods of population improvement, including RRS, have improved GCA with genetically narrow-based as well as broad-based testers (Walejko and Russell, 1977; Horner et al., 1989). Combining the S and RRS methods (COM) simultaneously should permit the benefits of nonadditive and additive genetic effects in testcross and per se evaluation, respectively, to be realized (Goulas and Lonnquist, 1976; Dhillon, 1991). Progress from COM selection is expected to be the summation of expected progress for each individual method (Hallauer and Miranda Filho, 1988).

The variance of the crosses from a diallel mating scheme can be partitioned to GCA and specific combining ability (SCA). Griffing (1956a) demonstrated that two times the GCA variance contained all the additive (A) variance and a portion of the A x A epistatic variance. Conversely, the SCA variance component contained all the dominance (D) variance and the remaining portion of the epistatic variance, including the remainder of the A x A as well as the A x D and D x D effects. Thus, GCA variance is usually associated with additive genetic effects, while SCA variance is associated with nonadditive effects. Another approach to partitioning the entry variance from a diallel analysis is to use Analysis II and Analysis III of Gardner and Eberhart (1966). Analysis III differs from Method 4 (Griffing, 1956b) in that parents are included in the test, providing an additional parents vs. crosses contrast, but are not included in the estimation of effects. The parents vs. crosses contrast is an indication of heterosis. Traits exhibiting significant heterosis can be partitioned into variety (v_i) and total heterosis (h_ij) sources with Analysis II and estimates of effects are functions of those in Analysis III (Gardner and Eberhart, 1966).

In a population diallel study, genetic changes between cycles of selection have been attributed to favorable but nonsignificant improvement in GCA for grain yield and lodging using S₁ RS (Garay et al., 1996). Increased grain yield in crosses was noted in related populations selected by S₁ and half-sib RS, but not in unrelated populations using the same methods (Genter and Eberhart, 1974). Furthermore, population effects, including v_i, explained most of the between-cycle diallel variance for grain moisture and stalk lodging, while grain yield also was affected by average (h) and parental (h_i) heterotic effects (Genter and Eberhart, 1974). Total heterosis often accounts for >50% of entry sum of squares for grain yield in population diallels of parents derived from mass selection (Dudley et al., 1977), open-pollinated varieties (Miranda Filho and Vencovsky, 1984), and mixes of adapted and exotic germplasm (Crossa et al., 1987). In this paper, two diallels of University of Guelph maize breeding populations involving, respectively, their originating gene pool (C₀) and their most advanced selection cycle (C_A), were analyzed to determine genetic progress and to examine the changes in the genetic structure of the populations following RS.

	MATERIALS AND METHODS

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION REFERENCES

 
Genetic Materials
The 11 C₀ and 12 C_A (C₂ to C₆) early [2550–2900 Ontario Crop Heat Units (OCHU) (Brown and Bootsma, 1993)] University of Guelph maize breeding populations in this study included CG-SynA (S), CG-SynB (S), CG-Wigor (RRS) and CG-SynA (RRS), CG-HOPE Elite A (RRS) and CG-HOPE Elite B (RRS), CG-Stiff Stalk (COM) and CG-Lancaster (COM), CG-CBI (RRS) and CG-CBII (RRS), and CG-Cross Canada Gene Pool A (RRS) and CG-CCGP B (RRS). The two CCGP populations were selected from a common C₀, resulting in 12 C_A populations and 11 C₀ populations. All other populations were unrelated except that CG-SynA (RRS) and CG-SynA (S) originated from different cycles of selection in the CG-SynA gene pool. Further information regarding the populations can be found elsewhere (Lee, 2002). Unadjusted (UPI) and adjusted (API) performance indices were used as the selection criteria for advancement of families until the populations had attained a level of maturity comparable with adapted germplasm for 2600 OCHU. The UPI was the ratio of grain yield to the percentage of grain moisture at harvest, while the API was the same ratio, but based on standing plants only; that is, whole-plot grain yield times the percentage of plants neither lodged nor with broken stalks. Progenies were ranked by each index and selection was based on the sum of ranks to identify those progenies for intercrossing to form the next cycle. When satisfactory grain moisture at harvest was attained, selection was based only on the sum of ranks for grain yield and grain yield of standing plants. In general, 20 selections were intermated to form a new cycle.

In 1999, separate half-diallel matings, not including reciprocals, were made using the C₀ populations [n(n - 1)/2 = 55 crosses] and C_A [n(n - 1)/2 = 66 crosses]. For each cross, six ears were pollinated with bulked pollen from six plants from the other population. Reciprocal crosses were then bulked, resulting in a total of 12 ears per cross. Individual plants were used as either males or females in the crosses, but not both, for a total of 24 S₀ plants, representing 24 gametes from each population. Seed was increased for each of the 11 C₀ and 12 C_A parental populations via chain sibbing and at least 70 individual ears were harvested from each population. Equal amounts of seed from each ear of the populations per se were bulked for use in the experimental trials.

Experimental Procedure and Data Collection
The experiment included 144 entries, 55 crosses from the C₀ diallel mating, 66 crosses from the C_A diallel mating, 11 C₀ populations per se, and 12 advanced populations per se. Yield trials were grown in 2000 and 2001 at three southwestern Ontario locations [Alma (2500 OCHU), Elora (2600 OCHU), and Woodstock (2850 OCHU)] using a 12-by-12 partially balanced lattice design with three replications at each location. The soil type at all locations is Guelph loam (fine-loamy, mixed, active, mesic Haplic Glossudalfs). Fertilizer was applied based on soil tests at the rate of 155, 58, and 74 kg ha^-1 (2000) and 122, 75, and 57 kg ha^-1 (N, P₂O₅, and K₂O, respectively) supplemented with 33 690 L ha^-1 liquid swine manure (2001) in Alma; 140, 50, and 50 kg ha^-1 (2000) and 135, 32, and 76 kg ha^-1 (2001) in Elora; and 150, 66, and 108 kg ha^-1 (2000) and 167, 59, and 75 kg ha^-1 (2001) in Woodstock. Weeds were controlled using conventional herbicides. Experimental units were two-row plots, 5.78 m long, with a spacing of 0.76 m between rows. Trials were overplanted and thinned at the six-leaf stage to uniform stands of 68 000 plants ha^-1 (60 plants plot^-1). All trials were machine planted [New Idea four-row planter (New Idea Co., Cold Water, OH, USA)] in May with ALMACO (Allan Machine Co., Nevada, IA, USA) planting cones and machine harvested (ALMACO SPC40-2 two row combine) in October or November. Three traits and two indices were measured, respectively: machine-harvested grain yield (kg ha^-1) adjusted to 155 g kg^-1 grain moisture, percentage grain moisture at harvest, and percentage broken stalks (plants broken at or below the ear or inclined >45° from the vertical), and UPI and API were calculated. All five parameters were analyzed and will be referred to as traits.

Statistical Analysis
Data were combined across locations and analyzed as a partially-balanced lattice using PROC MIXED and a randomized complete block design (RCBD) using PROC GLM (SAS, 1996). The efficiency of the lattice design was tested by determining the ratio of the lattice MS_error x 100/RCBD MS_error for each analyzed trait. Analysis of variance indicated only slight gains in efficiency using a lattice over a RCBD, and thus the RCBD analysis and means were used. The average efficiencies of the analyses for grain yield, grain moisture, broken stalks, UPI, and API were 103, 109, 109, 103, and 102%, respectively. Combined data were analyzed according to the linear model

where Y_rge is the measured trait of genotype g in replicate r at environment e; µ is the grand mean; _g and ß_e are the genotype and environment main effects; _r(ß_e) is the replicate effect nested within an environment; _gß_e is the interaction between main effects; and _rge is the random experimental error. Raccoon (Procyon lotor) damaged plots in Woodstock (2000) were adjusted after harvest to full stands per plot. Full stands (60 plants per plot) were divided by the number of plants standing after raccoon damage and multiplied by the recorded grain yield at harvest. Broken stalks in the damaged plots were calculated as those that lodged between raccoon damage and harvest. Genotypes were considered fixed, while environments and blocks were considered random model effects.

Genotype cross variance was partitioned and g_i and SCA (s_ij) estimates were calculated on a plot basis using Analysis III of Gardner and Eberhart (1966) according to Kang (1994) and Zhang and Kang (1997). The model for estimating the combining ability effects for each cross was

where µ_c was the mean of the population crosses, g_i (or g_j) was the GCA effect, and s_ij was the SCA effect for the cross between the ith and jth parents such that s_ij = s_ji (no reciprocal effect). The restrictions applied to the combining ability estimates were that g_i = 0, and that s_ij = 0 for each jth parent. For traits exhibiting significant heterosis, Analysis II of Gardner and Eberhart (1966) was used to partition the variance among the crosses according to Zhang and Kang (1997) and to obtain estimates in an electronic spreadsheet as suggested by Singh (1978) according to the following model:

where µ_v and v_i (or v_j) were the population per se mean and effect of the ith (or jth) parent, respectively. The difference between the per se and cross means (u_c– u_v) was h, and h_i (or h_j) was equal to the g_i estimate minus half the v_i effect (h_i = g_i– v_i/2); where = 0 when i = j and there was no heterosis, and where heterosis was present, = 1 when i j.

Genotype partitions were tested using their respective source x environment mean squares while the pooled error term was used to test sources partitioned from the entry x environment source. Least significant differences from zero and between estimates were calculated according to Griffing (1956b) and Zhang and Kang (1997) for Analysis III and for Analysis II according to Singh (1978) and Zhang and Kang (1997). The calculation of LSDs for v_i, h_i, and g_i estimates from zero and between estimates were, respectively,

where p was the number of parents in the diallel cross, n was the error degrees freedom, e was the number of environments, r was the number of blocks, and MS_error was the respective source x environment mean square. Rank correlations were calculated between C₀ and C_A g_i, v_i, and h_i estimates and between grain yield and other trait g_i estimates. The t tests for independent samples and unequal variances were performed for all traits between the C₀ and C_A g_i, v_i, and h_i estimates for each respective population (Steel and Torrie, 1980). All hypotheses were tested with a Type I error () rate of 0.05.

	RESULTS AND DISCUSSION

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION REFERENCES

 
Analysis of Variance
Environments, genotypes, and the genotype x environment interaction were significant sources of variation for all traits (Table 1) . Genotype sums of squares were initially partitioned into C₀, C_A, and C₀ vs. C_A. The C₀ vs. C_A contrast was significant for all traits, suggesting that genetic improvement in the per se performance was made. Genetic improvement was observed in most populations for grain yield and the indices both on a per se basis as well as in crosses. Only CCGP A (RRS) and CCGP B (RRS), which were at C₂ of selection, failed to show a significant favorable improvement in some crosses (data not shown). Only 24 gametes from each population were sampled for any cross. This approach samples predominant alleles and linkage blocks; rare alleles and linkage blocks are likely not represented in the population crosses. Crosses involving a particular population behaved consistently; for example, crosses involving Lancaster were generally low-performing, crosses involving Stiff Stalk were generally high-performing (data not shown), indicating that small sample size did not bias the results.

The crosses source of variation was further partitioned into GCA and SCA, which were significant for all traits in both C₀ and C_A. The F test of the ratio of the GCA to SCA mean squares was used to test the significance of the amount of additive vs. nonadditive variation, assuming that SCA is randomly and normally distributed with constant variance (Lin and Binns, 1991). The F test of the ratio of GCA to SCA mean squares was significant for all traits in the C₀ and C_A (data not shown), and was consistently larger in C₀ than in C_A for all traits (2.2, 2.2, 2.2, 3.0, and 3.6 times greater for grain yield, grain moisture, broken stalks, UPI, and API, respectively). The reduction in magnitude of the GCA to SCA mean squares ratio indicates that nonadditive effects increased in C_A at the expense of additive effects.

Analysis II
For traits with significant heterotic effects in the C₀ and C_A (i.e., grain yield, UPI, and API), the genotypic variance was partitioned using Analysis II (Gardner and Eberhart, 1966) into v_i and h_ij (Table 2) . Total heterosis was further partitioned into average, parent, and SCA effects. All sources of variation partitioned from genotypes were significant except for average heterosis for API in the C₀ (not shown). Total heterosis increased from C₀ to C_A for grain yield, UPI, and API (Table 2). There was a significant reduction in the percentage of v_i variance in C_A relative to C₀ for all traits. The increase in percentage of h_ij in C_A was due to an increase in the relative importance of the average heterosis component and a decrease in the relative importance of the SCA component. The GCA to SCA mean square ratios from Analysis III indicated that nonadditive effects were becoming larger in C_A. This was further supported by an increase in the relative importance of the h_ij partitioned from C₀ to C_A.

The estimates from Analysis II and Analysis III are related to one another because g_i = h_i + (v_i/2) (Gardner and Eberhart, 1966). Within the C₀, rank correlations were significant between the grain yield g_i and v_i estimates (r = 0.8; significant at = 0.01) but not between the g_i and h_i estimates (r = 0.2; not significant). However, in C_A the grain yield g_i estimates were significantly correlated with the h_i estimates (r = 0.7; significant at = 0.01), but not with the v_i estimates (r = 0.5; not significant). Consequently, even though there were not major changes in the grain yield g_i estimates from C₀ to C_A for most of the populations, the underlying genetic cause changed. In C₀, grain yield g_i estimates were associated with the population per se effects (v_i), while in C_A the grain yield g_i estimates were associated with the h_i.

Results indicated that h_ij was higher in C_A of this study than in other population diallel studies of adapted germplasm (Dudley et al., 1977; Miranda Filho and Vencovsky, 1984), and approached that observed in studies involving exotic germplasm (Crossa et al., 1987). However, in the studies involving adapted germplasm, the components of h_ij accounted for a similar percentage of the h_ij sums of squares; average heterosis being more important and parental heterosis the least important (Dudley et al., 1977; Miranda Filho and Vencovsky, 1984). Our findings are consistent with v_i and average heterosis effects explaining most of the variation (Genter and Eberhart, 1974), although SCA was significant for all traits, making performance prediction based on v_i or GCA effects problematic.

Two populations, SS (COM) and Elite B (RRS), had favorable, significant g_i estimates for grain yield in C_A (Table 3). The apparent yield advantage of SS (COM), indicated by the g_i estimate, was due to its ability as a parent to utilize a longer growing season, as shown by the significant positive g_i estimate for grain moisture in C_A. However, when the crosses were corrected for maturity differences using the UPI, SS (COM) was not superior to other populations, as shown by the nonsignificant g_i estimate for UPI. Conversely, the significant grain yield g_i estimate for Elite B (RRS) was not due to utilizing a longer growing season shown by the significant negative g_i estimate for grain moisture in C_A. When corrected for maturity differences using the UPI, Elite B (RRS) still possessed a significant positive g_i estimate in C_A.

In this paper, we examined how RS affected the genetic structure of 12 populations. Recurrent selection resulted in genetic improvement in both the per se and cross performance of most for the populations. Accompanying the favorable changes in population performance were less favorable shifts from predominantly additive genetic effects in the C₀ to greater nonadditive genetic effects in the C_A. Most of the populations (10 of 12) used in this study were improved using RRS, which is designed to make use of both additive and nonadditive effects (Comstock et al., 1949). In practice, an increase in nonadditive, relative to additive genetic effects, has been observed in other RRS populations (Popi and Kannenberg 1993), which is consistent with the results of our study. This shift did not substantially change the g_i estimates of most populations. However, in the case of grain yield, the underlying components of g_i effects were altered in their relative importance. The GCA effects in C₀ were caused primarily by the population per se effects (v_i), while in C_A they were caused predominately by the h_i.

	ACKNOWLEDGMENTS

 
We are most grateful for the technical assistance of B. Good, M.J. Ash, and R. Chakravarty.

	NOTES

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION REFERENCES

 
Part of a thesis submitted by T.K. Doerksen in partial fulfillment of the requirements for the M.Sc. degree. Financial support from the Ontario Ministry of Agriculture and Food, and the National Sciences and Engineering Research Council of Canada.

Received for publication June 11, 2002.

	REFERENCES

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION REFERENCES

 

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This Article Abstract Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in ISI Web of Science Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via ISI Web of Science (4) Citing Articles via Google Scholar Google Scholar Articles by Doerksen, T. K. Articles by Lee, E. A. Search for Related Content PubMed Articles by Doerksen, T. K. Articles by Lee, E. A. Agricola Articles by Doerksen, T. K. Articles by Lee, E. A. Related Collections Crop Genetics Maize Maize Biometrics