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Published online 8 January 2009
Published in Agron J 101:106-112 (2009)
DOI: 10.2134/agronj2008.0217
© 2009 American Society of Agronomy
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Classifying Maize Inbred Lines into Heterotic Groups using a Factorial Mating Design

X. M. Fana,*, Y. M. Zhangb, W. H. Yaoa, H. M. Chena, J. Tana, C. X. Xua, X. L. Hana, L. M. Luoa and M. S. Kangc

a Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, Yunnan Province, China
b Research Analyst, John Deere Co., Milan, IL 61264
c Vice Chancellor, Punjab Agricultural Univ., Ludhiana 141 004, India

* Corresponding author (xingmingfan{at}vip.km169.net).


   ABSTRACT
TOP
 NOTES
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES
 
A novel method of using a heterotic group's specific and general combining ability (HSGCA) to assign maize (Zea mays L.) inbred lines into heterotic groups has been proposed recently. The objectives of this study were to (i) assign maize inbred lines to known heterotic groups using this method and (ii) compare efficiency of this method to traditional and molecular methods relative to the percentage of high-yielding hybrids obtained across the total number of the crosses made between testers and lines. An experiment with 23 maize inbred lines crossed to four testers with known heterotic groups was conducted in 2003 and 2004. This study successfully established a clear procedure to classify maize inbred lines into heterotic groups. The HSGCA method increased maize breeding efficiency by 16.7 to 23.6% compared with simple sequence repeat (SSR) and specific combining ability combined line pedigree and hybrid yield information (SCA_PY) methods, respectively. An analysis of variance showed that crosses classified by HSGCA method could explain more variation in maize hybrid yield and produce more predictable yield than the other two methods. The superiority of HSGCA relative to the other two methods is that HSGCA includes both GCA and SCA effect in assigning an unknown maize line to a known maize heterotic group.

Abbreviations: AFLP, amplified fragment length polymorphism • CIMMYT, International Maize and Wheat Improvement Center • GS, genetic similarity • HSGCA, heterotic group's specific and general combining ability of an inbred with a representative tester from a maize heterotic group • HZ4, Huangzao4 • LDRC, Luda Red Cob • RFLP, restriction fragment length polymorphism • SCA_PY, specific combining ability combined line pedigree and hybrid yield information • SSR, simple sequence repeat • TSPT, Tangsipingtou

Received for publication June 28, 2008.
   INTRODUCTION
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES
 
ESTABLISHMENT OF THE BEST COMBINATION of inbreds among the heterotic groups is crucial to the development of successful maize hybrids (Barata and Carena, 2006). The extensive use and investigations of a well-established heterotic pattern, Reid x Lancaster, have been made and has culminated in the development and use of many good maize hybrids in China (Fan et al., 2002; Huang and Li, 2001; Yuan et al., 2002; Wu et al., 2007) and many parts of the world (Moreno-González, 1988; Ordás, 1991; Vasal et al., 1992a, 1992b; Menkir et al., 2004; Melani and Carena, 2005; Barata and Carena, 2006).

Heterotic group classification methods used by researchers have great influence on how a maize line is assigned to a maize heterotic group. Two major heterotic group-classification methods are currently used widely across the world. The traditional method uses specific combining ability with some line-pedigree information and/or field hybrid-yield information (SCA_PY) to assign a maize line to a heterotic group (Kauffman et al., 1982; Li et al., 2001; Fan et al., 2001, 2004; Bhatnagar et al., 2004; Wu et al., 2007). Another method employs various molecular markers to compute genetic similarity (GS) or genetic distance (GD) to assign maize lines to different heterotic groups (Wu et al., 2000; Huang and Li, 2002; Fan et al., 2003a, 2003b; Li et al., 2003; Menkir et al., 2004; Barata and Carena, 2006).

Fan et al. (2001) used a diallel design to study combining abilities among 10 maize lines (five lines from the International Maize and Wheat Improvement Center [CIMMYT] and five major commercial lines from China). According to SCA_PY method, they classified CML171, CML161, CML166 into one heterotic group; Chang 631/o2, Zhongxi 096/o2 into another heterotic group; and Qi 205 into a third heterotic group.

Huang and Li (2001) evaluated 45 maize inbred lines from China, U.S. Corn Belt, and tropical regions by using 44 restriction fragment length polymorphism (RFLP) markers equally distributed across the 10 maize chromosomes. The 45 inbred lines were grouped into six heterotic groups: Mo17 was assigned to group II; Tangsipinttou (TSPT) and Huangzao4 (HZ4) were assigned to group IV and Dan340 was assigned to group VI.

Yuan et al. (2002) used 51 SSR markers to analyze 134 maize inbred lines from both temperate regions (China and U.S. Corn Belt) and CIMMYT. The 134 maize inbred lines formed nine clusters according to genetic similarity (GS) values among the inbred lines. Inbred line Ye478 was assigned to TSPT group; Dan340 to Luda Red Cob (LDRC) group; Mo17 to Zi330 group, Qi205 to Zhong group; and 8129QPM to Lancaster group.

Wu et al. (2007) used NC II design to examine a similar set of inbred lines as employed in the current study. They used the SCA_PY method and combined results from previous studies (Fan et al., 2001; Huang and Li, 2001; Yuan et al., 2002) to classify 27 maize inbred lines into four known maize heterotic groups widely accepted in China (Wang et al., 1998a, 1998b; Zhang et al., 2000; Fan et al., 2003a).

Menkir et al. (2004) used two testers representing the flint and dent heterotic patterns to test 38 tropical maize inbred lines. The two testers successfully classified 23 of the 38 tested inbred lines into two heterotic groups based on the SCA_PY method. However, when both amplified fragment length polymorphism (AFLP) and SSR markers were used to classify the tested lines, the 38 tropical lines were classified into the heterotic groups different from those classified by the SCA_PY method. Thus, they recommended that the molecular marker-based grouping might only serve as a basis for designing and carrying out combining ability studies in the field to establish clearly defined heterotic groups with a greater GS within groups.

Barata and Carena (2006) conducted a similar study as Menkir et al. (2004) to classify 13 elite North Dakota maize inbred lines into current U.S. Corn Belt heterotic groups. In addition, the researchers evaluated the consistency in classification between the SSR and SCA_PY methods using a diallel study. Results showed that heterotic groups of genetically similar germplasms could not be identified accurately and reliably with molecular markers even in diverse germplasm, contrary to what had previously been reported (Barata and Carena, 2006). Therefore, extensive field evaluation was suggested to assign unrelated maize inbred lines to heterotic groups.

Molecular methods to classify inbred lines into heterotic groups are usually fast and eliminate field crossing but have limitations (Menkir et al., 2004; Barata and Carena, 2006). The SCA_PY is still used as a major method for maize heterotic group classification. However, previous experience has shown that due to SCA effects being greatly influenced by the interaction between two inbred lines and by the interaction between hybrids and environments, different studies might assign the same inbred line to different heterotic groups. For example, Fan et al. (2001) classified CML171, CML161, and CML166 into one heterotic group and Qi205 into another heterotic group. In contrast, Wu et al. (2007) classified CML171, CML161, and Qi205 into one heterotic group and CML166 into another heterotic group.

Fan et al. (2008) proposed using a HSGCA to assign an unknown maize line to a known maize heterotic group. The HSGCA is combining ability between a representative tester from a known heterotic group and another maize line. They used the HSGCA method to successfully identify Suwan1 as another heterotic group different from Reid, Lancaster, and other two Chinese maize heterotic groups (Fan et al., 2008). However, following questions remain to be answered: How to use HSGCA method to assign maize inbred lines to heterotic groups? Is the HSGCA method better than SCA_PY and molecular heterotic group classification methods for delineating maize heterotic groups? To answer these questions, the objectives of this study were to (i) establish a procedure to classify maize inbred lines into known heterotic groups with the line x tester design, which uses HSGCA; and (ii) compare efficiency of the HSGCA method with SCA_PY and molecular methods for percentage of high yield hybrids in all intergroup crosses.


   MATERIALS AND METHODS
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES
 
Experimental Materials
The sources and grain texture of 27 maize lines used for this study are listed in Table 1 . The four testers used in this study are Ye478, Mo17, HZ4, and Dan340 which are widely used as representative testers in China to classify maize lines into four heterotic groups: Reid, Lancaster, TSPT, and LDRC, respectively (Wang et al., 1998a, 1998b; Zhang et al., 2000; Fan et al., 2003a; Wu et al., 2007).


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  		Table 1. Sources and grain texture of the 27 maize lines used for the study.

 
Experimental Design for Field Trial
In 2003, at Kunming, China, 23 maize inbred lines were used as female parents and crossed with four testers from four known heterotic groups, which yielded 92 testcrosses. In 2004, the 92 testcrosses were field-tested for grain yield and other important agronomic traits such as ear length, ear diameter, number of kernel-row per ear, number of kernel per row, thousand kernel weights, etc. at three locations in Yunnan. A randomized complete-block design with three replications was used for all locations. Each experimental unit was a single-row plot with a row spacing of 0.7 m and length of 5 m. Distance between two adjacent plants was 0.25 m and the population density was approximately 57,140 plants ha–1. At maturity, a 10-ear sample was harvested from 10 consecutive plants from the middle of each row. After harvest, kernels were air-dried until a constant moisture of 130 g kg–1 was achieved, and then grain yield per plant was determined.

Simple Sequence Repeat and Genetic Similarity Computation
DNA was extracted using a modified CTAB procedure (Murray and Thompson, 1980). PCR reactions and gel electrophoresis were performed according to protocols of CIMMYT's applied molecular genetics laboratory (Hoisington et al., 1998). Primers were excluded from the study if banding patterns were difficult to score accurately on acrylamide gels, and a final set of 77 SSR primers were chosen from 117 SSR primers for further analysis. Each SSR primer was regarded as a locus and each band as an allele. Allele sizes were determined on the basis of their position relative to a molecular marker. The amplified bands were scored as 1 (presence) or 0 (absence), with missing bands being scored as 9.

Polymorphism information content (PIC) for each SSR marker was determined as described by Smith et al. (1997). The PIC is a measure of allele diversity at a locus and

Formula 1[1]
where fi is the frequency of the ith allele. Genetic similarity and GD was estimated from the allele frequency data using a simple matching coefficient, such that

Formula 2[2]
where m is the number of matches and n is the number of mismatches.

The unweighted pair group method using arithmetic averages (UPGMA) method was used to draw dendrogram. The GS matrix and dendrogram were constructed using NTSYS-pc Version 2.1 (Rohlf, 2000).

Heterotic Group's Specific and General Combining Ability Computation

Formula 2

Formula 2

Formula 3[3]
where Xij is the mean yield of the cross between ith tester and jth line, Xj. is the mean yield of the ith tester and X.j is the mean yield of jth line.

Statistical Model and Analyses
The following statistical model was used for the data analysis:

Formula 4[4]

Formula 4
where Yijkl = observed value from each experimental unit; µ = population mean; al = location effect; bkl = block or replication effect within each location; vij = F1 hybrid effect = gi + gj + sij [where gi = general combining ability (GCA) for the ith parental line; gj = GCA effect of jth tester; and sij = specific combining ability (SCA) for the ijth F1 hybrid]; (av)ijl = interaction effect between ith F1 hybrid and lth location; and eijkl = residual effect. Because the three locations selected for this experiment were not a random sample of all possible locations within Yunnan, we treated locations as a fixed factor. Hybrid or cross effect and, consequently, GCA and SCA effects were regarded as fixed effects. Only replication was considered to be a random factor. Thus, significance of location variance was tested against replication-within-location entity. For all other significance tests, experimental error term was used (Table 2 ).


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  		Table 2. Mean squares from ANOVA of 92 test-crosses for grain yield at three environmental conditions of Yunnan province in 2004.

 

   RESULTS AND DISCUSSION
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
CONCLUSIONS
 REFERENCES
 
Analysis of Variance for Grain Yield
Analysis of variance of grain yield from the three locations showed that locations, crosses, and crosses x locations sources of variation were significant (P = 0.01) for all traits studied (Table 2). As the variances of crosses and crosses-by-locations interaction were significant, the crosses variation was further partitioned into GCA and SCA and the interaction variation was further partitioned into GCA x location and SCA x location interactions. The GCA, SCA, GCA x locations were all significant at the 0.01 level and SCA x locations were significant at the 0.05 level for grain yield (Table 2).

Heterotic Group's Specific and General Combining Ability Effects and their Use in Classifying Maize Lines into Known Heterotic Groups
The calculated HSGCA effects for grain yield of the 23 maize inbred lines and the four testers (Dan340, Mo17, HZ4, and Ye478) were shown in Fig. 1a, 1b, 1c, and 1d , respectively. The following procedure was developed for classifying the 23 maize lines into the known maize heterotic groups via the HSGCA method.


Figure 1
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  		Fig. 1. Heterotic group specific and general combining ability between 23 lines and four testers with known heterotic groups. TSPT = Tangsipingtou; LDRC = Luda Red Cob. With (a) tester Dan340; (b) tester Mo17; (c) tester HZ4; (d) tester Ye478.

 
Step 1
Placed all inbred lines with negative HSGCA effects into the same heterotic groups as their tester. All 23 inbred lines, except CML166, were classified into the four known heterotic groups (Table 3 ). At this step, a line might be assigned to more than one heterotic group.


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  		Table 3. Inbred line list in each of four known heterotic groups classified by general combining ability between lines and testers.{dagger}

 
Step 2
If an inbred line was assigned to more than one heterotic group in Step 1, we kept the line in the heterotic group if its HSGCA had the smallest value (or largest negative value) and removed it from other heterotic groups.

Step 3
If a line had a positive HSGCA effect with all representative testers, we were cautious to assign that line to any heterotic group because the line might belong to a heterotic group different from the four testers. In the following heterotic analysis, CML166 was removed for comparison purposes because in SSR analysis, CML166 was also assigned to a heterotic group different from the four known heterotic groups (Fig. 2 ).


Figure 2
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  		Fig. 2. Dendrogram of 27 maize inbred lines based on genetic similarity values using simple sequence repeat (SSR) markers. The dotted line represents genetic similarity (GS) = 0.7140, four testers can be separated by the line, and all the inbred lines were divided into five genetic similarity groups.

 
Heterotic Groups Defined by Specific Combining Ability Combined Line Pedigree and Hybrid Yield Information and Simple Sequence Repeat Classification Methods
Fan et al. (2001) and Wu et al. (2007) used the SCA_PY method to classify a similar set of inbred lines as used in this study into the four known heterotic groups Table 4 .


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  		Table 4. Inbred line list in each of the four known heterotic groups classified by specific combining ability with pedigree and/or hybrid yield information (SCA_PY).{dagger}

 
The GS values between the 23 inbred lines and four representative testers were calculated according to formula (2) and a dendrogram was plotted (Fig. 2). When we drew a line at GS = 0.7140, the four testers used in this study were clearly separated and we were able to classify all 27 lines, except CML166, into four known heterotic groups.

Breeding Efficiency
One of the major purposes of maize hybrid breeding is to develop hybrids with high grain yield. To develop a high-yielding maize hybrid, a breeder usually makes hundreds of crosses among selected inbred lines. Plant breeders design crosses usually according to the heterotic group of the line(s). The breeder would have a better chance of obtaining superior hybrids by making crosses between lines from different maize heterotic groups. Making crosses among lines from interheterotic groups, plant breeders have better chances of obtaining superior high-yielding hybrids from fewer crosses. However, because of unlimited genetic combinations between any two inbred lines, no heterotic group classification method can be perfect. However, it is still highly possible to develop superior hybrid from crosses made within heterotic groups. Thus, a good heterotic group classification method can be defined as one whose classified heterotic groups allow interheterotic group crosses to produce more superior hybrids than the within-group crosses.

Breeding efficiency can be defined as percentage of superior high-yielding hybrids obtained across the total number of interheterotic crosses. To compare the breeding efficiency, we first divided all hybrids into three groups on the basis of their grain yields. Of a total of 88 crosses (excluding all crosses from CML166 because its heterotic group was undefined), 23 highest yielding hybrids were assigned to grain yield group 1, with a mean grain yield >135 g per plant; 22 lowest yielding hybrids into grain yield group 3, with a mean grain yield of <115 g per plant, and the rest of hybrids were assigned to grain yield group 2. Crosses were later divided into intergroup and within-group crosses based on the heterotic groups of the lines used in a cross. Intergroup crosses are the crosses between lines from two different heterotic groups and the within-group crosses are the crosses between lines within the same heterotic group. Finally, the Chi-square test was used to check if same numbers of hybrids were within-group and intergroups from the different heterotic classification methods in grain yield group 1.

The numbers of hybrids in grain yield groups 1, 2, and 3 resulting from HSGCA, SCA_PY, and SSR methods are shown in Table 5 . The Chi-square tests showed that the difference between HSGCA and SSR methods, in yield-group 1, was highly significant (P = 0.0355) and between HSGCA and SCA_PY method was marginally significant (P = 0.0723); strongly suggesting that the different heterotic classification methods were responsible for the differential number of high-yielding hybrids selected from the same number of intergroup crosses.


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  		Table 5. The number of hybrids with mean grain yield greater than 135 g per plant (yield group 1), between 115 and 135 g per plant (yield group 2), and smaller than 115 g per plant (yield group 3) for three different heterotic group classification methods.

 
Based on the breeding efficiency definition, SSR method identified 17, SCA_PY method 18, and HSGCA 21 high-yielding hybrids from a total of 56 intergroup crosses. The breeding efficiency of the HSGCA method was 23.6% greater than the SSR method and16.7% higher than the SCA_PY method. More importantly, all high-yielding crosses identified by the HSGCA method were among the intergroups crosses, whereas four and three high-yielding crosses defined by SSR and SCA_PY methods, respectively, were obtained from crosses made within-groups. To obtain the same number of superior high-yielding hybrids with SSR and SCA_PY methods, a maize breeder need to evaluate all 88 crosses. Based on these results, the HSGCA method was more efficient at identifying superior hybrids in intergroup crosses relative to SSR and SCA_PY methods.

We further examined the yield performance of the superior hybrids missed by the SSR and SCA_PY methods across three test locations. We found that two of them were among the top three hybrids (first 186 g/plant and third 183.3 g/plant) in Kunming. These results suggested that use of intercrosses between lines only, and classifying maize heterotic groups on the basis of SSR and SCA_PY methods would lead to a higher chance of missing superior hybrids that might be well adapted to a specific location. To determine the superiority of the HSGCA method relative to SSR and SCA_PY methods across three locations, mean yields of all crosses at the three locations were computed. and only crosses with a mean yield >155 g, 160 g, 170 g, and 180 g per plant at each location were retained. The numbers of the hybrids were determined from both intergroup and within-group crosses according to the three heterotic group classification methods (Table 6 ). The HSGCA method was better than SCA_PY and SSR methods in reducing the number of missed superior hybrids among intercrosses.


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  		Table 6. The number of superior hybrids with inter- and within-group crosses for different location mean grain yield per plant by three different heterotic group classification methods.

 
Analysis of Variance of Cross-mean Yield
R-square statistic from ANOVA is widely used to compare if a model is better than other model(s) when one is trying to explain a dependent variable's variation. Because we classified 26 lines into four heterotic groups (CML166 was not used for this ANOVA analysis since it was the only line classified into a different heterotic group), there would be 12 intercrosses, such as Reid x Lancaster, Reid x TSPT, Reid x LDRC, Lancaster x Reid, Lancaster x TSPT and four within-group crosses (i.e., Reid x Reid, Lancaster x Lancaster, TSPT x TSPT, and LDRC x LDRC). We called these different crosses defined by heterotic groups as "cross type". The SAS GLM procedure (SAS Institute, 2002) was employed to compute R-squares when cross-mean yields were modeled with the cross types defined by the three different heterotic group classification methods. The cross types defined by HSGCA method explained 72% of cross-mean yield variation, whereas the cross types defined by SCA_PY and SSR methods only explained 54% and 52%, respectively, of the total variation in yield. These results further indicated that heterotic groups classified by the HSGCA method gave more consistent yield performance. Thus, the yield performance of between inbred lines from different heterotic groups classified by the HSGCA method was more predictable than those by SCA_PY and SSR methods.

Because HSGCA has both GCA effect of a line and the SCA between the line and a representative tester of a heterotic group, it can explain more genetic variation in hybrid yield than the other two methods did. The maize lines between different heterotic groups had been thought to have some fundamental agronomic and genetic differences (Giauffret et al., 2000; Chen et al., 2000; Spengas et al., 2003; Yao et al., 2004; Zhang et al., 2003, 2005; Sun et al., 2007). These fundamental differences should be heritable and thus should be included in maize heterotic group classification.


   CONCLUSIONS
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
REFERENCES
 
This study has successfully established a practical and easy-to-follow procedure to classify maize inbred lines into known heterotic groups. The HSGCA method was more reliable and efficient than traditional maize heterotic group classification methods that use SCA_PY and molecular markers. The HSGCA method was much better than the other two methods in reducing the number of superior hybrids missed in within and across location-specific crosses. The key for HSGCA method is to identify representative tester lines for a known maize heterotic group. Once one or more representative testers have been identified, the HSGCA information from different researchers can be pooled for planning future maize breeding programs.


   ACKNOWLEDGMENTS
 
This research was supported by Yunnan advanced talent introduction project foundation (20080A006) and Yunnan Key Science and Technology Development Project Foundation for 11th 5-year Plan (2006NG06), China. The authors would like to give great thanks to Dr. Han Gengchen (Origin Agritech Limited, Beijing) for his invaluable contributions in conducting SSR analysis. The authors would also like to thank Prof. Huang Bihua (Dehong Institute of Agricultural Science of Yunnan province) and Prof. Han Xuerui (Qujing Institute of Agricultural Science of Yunnan province) for their help in data collection for this study.


   NOTES
TOP
 NOTES
ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES
 
All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher.


   REFERENCES
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES
 





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