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ABSTRACT: Intense structuring of plant breeding populations challenge the design of the training set (TS) in genomic selection (GS). An important open question is how the TS should be constructed from multiple related or unrelated small bi-parental families to predict progeny from individual crosses. Here, we used a set of five interconnected maize (Zea mays L.) populations of doubled haploid (DH) lines derived from four parents to systematically investigate how the composition of the TS affects the prediction accuracy for lines from individual crosses. A total of 635 DH lines genotyped with 16,741 polymorphic SNPs were evaluated for five traits including Gibberella ear rot severity and three kernel yield component traits. The populations showed a genomic similarity pattern, which reflects the crossing scheme with a clear separation of full sibs, half sibs, and unrelated groups. Prediction accuracies within full sib families of DH lines followed closely theoretical expectations accounting for the influence of sample size and heritability of the trait. Prediction accuracies declined by 42% if full sib DH lines were replaced by half sib DH lines, but statistically significantly better results could be achieved if half sib DH lines were available from both instead of only one parent of the validation population. Once both parents of the validation population were represented in the TS, including more crosses with a constant TS size did not increase accuracies. Unrelated crosses showing opposite linkage phases with the validation population resulted in negative or reduced prediction accuracies, if used alone or in combination with related families, respectively. We suggest identifying and excluding such crosses from the TS. Moreover, the observed variability among populations and traits suggest that these uncertainties must be taken into account in models optimizing the allocation of resources in GS.
Genetics 03/2013; · 4.01 Impact Factor
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ABSTRACT: Chilling sensitivity of maize is a strong limitation for its cultivation in the cooler areas of the northern and southern hemisphere, because reduced growth in early stages impairs on later biomass accumulation. Efficient breeding for chilling tolerance is hampered by both the complex physiological response of maize to chilling temperatures and the difficulty to accurately measure chilling tolerance in the field under fluctuating climatic conditions. For this research we used genome-wide association (GWA) mapping to identify genes underlying chilling tolerance under both controlled and field conditions in a broad germplasm collection of 375 maize inbred lines genotyped with 56,110 SNPs. We identified nineteen highly significant association signals explaining between 5.7 and 52.5 % of the phenotypic variance observed for early growth and chlorophyll fluorescence parameters. The allelic effect of several SNPs identified for early growth was associated with temperature and incident radiation. Candidate genes involved in ethylene signaling, brassinolide, and lignin biosynthesis were found in their vicinity. The frequent involvement of candidate genes into signaling or gene expression regulation underlines the complex response of photosynthetic performance and early growth to climatic conditions, and supports pleiotropism as a major cause of co-locations of quantitative trait loci for these highly polygenic traits.
Plant Cell and Environment 03/2013; · 5.22 Impact Factor
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ABSTRACT: Bayesian methods are a popular choice for genomic prediction of genotypic values. The methodology is well established for traits with approximately Gaussian phenotypic distribution. However, numerous important traits are of dichotomous nature and the phenotypic counts observed follow a Binomial distribution. The standard Gaussian generalized linear models (GLM) are not statistically valid for this type of data. Therefore, we implemented Binomial GLM with logit link function for the BayesB and Bayesian GBLUP genomic prediction methods. We compared these models with their standard Gaussian counterparts using two experimental data sets from plant breeding, one on female fertility in wheat and one on haploid induction in maize, as well as a simulated data set. With the aid of the simulated data referring to a bi-parental population of doubled haploid lines, we further investigated the influence of training set size (N), number of independent Bernoulli trials for trait evaluation (n ( i )) and genetic architecture of the trait on genomic prediction accuracies and abilities in general and on the relative performance of our models. For BayesB, we in addition implemented finite mixture Binomial GLM to account for overdispersion. We found that prediction accuracies increased with increasing N and n ( i ). For the simulated and experimental data sets, we found Binomial GLM to be superior to Gaussian models for small n ( i ), but that for large n ( i ) Gaussian models might be used as ad hoc approximations. We further show with simulated and real data sets that accounting for overdispersion in Binomial data can markedly increase the prediction accuracy.
Theoretical and Applied Genetics 02/2013; · 3.30 Impact Factor
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ABSTRACT: Northern corn leaf blight (NCLB), a severe fungal disease causing yield losses worldwide, is most effectively controlled by resistant varieties. Genomic prediction could greatly aid resistance breeding efforts. However, the development of accurate prediction models requires large training sets of genotyped and phenotyped individuals. Maize hybrid breeding is based on distinct heterotic groups that maximize heterosis (the dent and flint groups in Central Europe). The resulting allocation of resources to parallel breeding programs challenges the establishment of sufficiently sized training sets within groups. Therefore, using training sets combining both heterotic groups might be a possibility of increasing training set sizes and thereby prediction accuracies. The objectives of our study were to assess the prospect of genomic prediction of NCLB resistance in maize and the benefit of a training set that combines two heterotic groups. Our data comprised 100 dent and 97 flint lines, phenotyped for NCLB resistance per se and genotyped with high-density single-nucleotide polymorphism marker data. A genomic BLUP model was used to predict genotypic values. Prediction accuracies reached a maximum of 0.706 (dent) and 0.690 (flint), and there was a strong positive response to increases in training set size. The use of combined training sets led to significantly greater prediction accuracies for both heterotic groups. Our results encourage the application of genomic prediction in NCLB-resistance breeding programs and the use of combined training sets.
G3 (Bethesda, Md.). 02/2013; 3(2):197-203.
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ABSTRACT: Production of maternal haploids via a male inducer can greatly accelerate maize breeding and is an interesting biological phenomenon in double fertilization. However, the mechanism behind haploid induction remains elusive. Segregation distortion, which is increasingly recognized as a potentially powerful evolutionary force, has recently been observed during maternal haploid induction in maize. The results present here showed that both male gametophytic and zygotic selection contributed to severe segregation distortion of a locus, named segregation distortion 1 (sed1), during maternal haploid induction in maize. Interestingly, analysis of reciprocal crosses showed that sed1 is expressed in the male gametophyte. A novel mapping strategy based on segregation distortion has been used to fine-map this locus. Strong selection for the presence of the sed1 haplotype from inducers in kernels with haploid formation and defects could be detected in the segregating population. Dual-pollination experiments showed that viable pollen grains from inducers had poor pollen competitive ability against pollen from normal genotypes. Although defective kernels and haploids have different phenotypes, they are most probably caused by the sed1 locus, and possible mechanisms for production of maternal haploids and the associated segregation distortion are discussed. This research also provides new insights into the process of double fertilization.
Journal of Experimental Botany 01/2013; · 5.36 Impact Factor
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ABSTRACT: Landraces are valuable genetic resources for broadening the genetic base of elite germplasm in maize. Extensive exploitation of landraces has been hampered by their genetic heterogeneity and heavy genetic load. These limitations may be overcome by the doubled haploid (DH) technique. A set of 132 DH lines derived from three European landraces and 106 elite flint (EF) lines were genotyped for 56,110 single nucleotide polymorphism (SNP) markers and evaluated in field trials at five locations in Germany in 2010 for several agronomic traits. In addition, the landraces were compared with synthetic populations produced by intermating DH lines derived from the respective landrace. Our objectives were to (1) evaluate the phenotypic and molecular diversity captured within DH lines derived from European landraces, (2) assess the breeding potential (usefulness) of DH lines derived from landraces to broaden the genetic base of the EF germplasm, and (3) compare the performance of each landrace with the synthetic population produced from the respective DH lines. Large genotypic variances among DH lines derived from landraces allowed the identification of DH lines with grain yields comparable to those of EF lines. Selected DH lines may thus be introgressed into elite germplasm without impairing its yield level. Large genetic distances of the DH lines to the EF lines demonstrated the potential of DH lines derived from landraces to broaden the genetic base of the EF germplasm. The comparison of landraces with their respective synthetic population showed no yield improvement and no reduction of phenotypic diversity. Owing to the low population structure and rapid decrease of linkage disequilibrium within populations of DH lines derived from landraces, these would be an ideal tool for association mapping. Altogether, the DH technology opens new opportunities for characterizing and utilizing the genetic diversity present in gene bank accessions of maize.
PLoS ONE 01/2013; 8(2):e57234. · 4.09 Impact Factor
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Vanessa S Windhausen,
Gary N Atlin,
John M Hickey,
Jose Crossa,
Jean-Luc Jannink,
Mark E Sorrells,
Babu Raman,
Jill E Cairns,
Amsal Tarekegne,
Kassa Semagn,
Yoseph Beyene,
Pichet Grudloyma,
Frank Technow,
Christian Riedelsheimer, Albrecht E Melchinger
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ABSTRACT: Genomic prediction is expected to considerably increase genetic gains by increasing selection intensity and accelerating the breeding cycle. In this study, marker effects estimated in 255 diverse maize (Zea mays L.) hybrids were used to predict grain yield, anthesis date, and anthesis-silking interval within the diversity panel and testcross progenies of 30 F(2)-derived lines from each of five populations. Although up to 25% of the genetic variance could be explained by cross validation within the diversity panel, the prediction of testcross performance of F(2)-derived lines using marker effects estimated in the diversity panel was on average zero. Hybrids in the diversity panel could be grouped into eight breeding populations differing in mean performance. When performance was predicted separately for each breeding population on the basis of marker effects estimated in the other populations, predictive ability was low (i.e., 0.12 for grain yield). These results suggest that prediction resulted mostly from differences in mean performance of the breeding populations and less from the relationship between the training and validation sets or linkage disequilibrium with causal variants underlying the predicted traits. Potential uses for genomic prediction in maize hybrid breeding are discussed emphasizing the need of (1) a clear definition of the breeding scenario in which genomic prediction should be applied (i.e., prediction among or within populations), (2) a detailed analysis of the population structure before performing cross validation, and (3) larger training sets with strong genetic relationship to the validation set.
G3 (Bethesda, Md.). 11/2012; 2(11):1427-36.
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ABSTRACT: Recent advances in high-throughput sequencing technologies have triggered a shift toward single-nucleotide polymorphism (SNP) markers. A systematic bias can be introduced if SNPs are ascertained in a small panel of genotypes and then used for characterizing a larger population (ascertainment bias). With the objective of evaluating a potential ascertainment bias of the Illumina MaizeSNP50 array with respect to elite European maize dent and flint inbred lines, we compared the genetic diversity among these materials based on 731 amplified fragment length polymorphisms (AFLPs), 186 simple sequence repeats (SSRs), 41,434 SNPs of the MaizeSNP50 array (SNP-A), and two subsets of it, i.e., 30,068 Panzea (SNP-P) and 11,366 Syngenta markers (SNP-S). We evaluated the bias effects on major allele frequency, allele number, gene diversity, modified Roger's distance (MRD), and on molecular variance (AMOVA). We revealed ascertainment bias in SNP-A, compared to AFLPs and SSRs. It affected especially European flint lines analyzed with markers (SNP-S) specifically developed to maximize differences among North American dent germplasm. The bias affected all genetic parameters, but did not substantially alter the relative distances between inbred lines within groups. For these reasons, we conclude that the SNP markers of the MaizeSNP50 array can be employed for breeding purposes in the investigated material. However, attention should be paid in case of comparisons between genotypes belonging to different heterotic groups. In this case, it is advisable to prefer a marker subset with potentially low ascertainment bias, like in our case the SNP-P marker set.
Theoretical and Applied Genetics 09/2012; · 3.30 Impact Factor
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ABSTRACT: BACKGROUND: There is increasing empirical evidence that whole-genome prediction (WGP) is a powerful tool for predicting line and hybrid performance in maize. However, there is a lack of knowledge about the sensitivity of WGP models towards the genetic architecture of the trait. Whereas previous studies exclusively focused on highly polygenic traits, important agronomic traits such as disease resistances, nutrifunctional or climate adaptational traits have a genetic architecture which is either much less complex or unknown. For such cases, information about model robustness and guidelines for model selection are lacking. Here, we compared five WGP models with different assumptions about the distribution of the underlying genetic effects. As contrasting model traits, we chose three highly polygenic agronomic traits and three metabolites each with a major QTL explaining 22 to 30 % of the genetic variance in a panel of 289 diverse maize inbred lines genotyped with 56,110 SNPs. RESULTS: We found the five WGP models to be remarkable robust towards trait architecture with the largest differences in prediction accuracies ranging between 0.05 and 0.14 for the same trait, most likely as the result of the high level of linkage disequilibrium prevailing in elite maize germplasm. Whereas RR-BLUP performed best for the agronomic traits, it was inferior to LASSO or elastic net for the three metabolites. We found the approach of genome partitioning of genetic variance, first applied in human genetics, as useful in guiding the breeder which model to choose, if prior knowledge of the trait architecture is lacking. CONCLUSIONS: Our results suggest that in diverse germplasm of elite maize inbred lines with a high level of LD, WGP models differ only slightly in their accuracies, irrespective of the number and effects of QTL found in previous linkage or association mapping studies. However, small gains in prediction accuracies can be achieved if the WGP model is selected according to the genetic architecture of the trait. If the trait architecture is unknown e.g. for novel traits which only recently received attention in breeding, we suggest to inspect the distribution of the genetic variance explained by each chromosome for guiding model selection in WGP.
BMC Genomics 09/2012; 13(1):452. · 4.07 Impact Factor
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ABSTRACT: Identifying high performing hybrids is an essential part of every maize breeding program. Genomic prediction of maize hybrid performance allows to identify promising hybrids, when they themselves or other hybrids produced from their parents were not tested in field trials. Using simulations, we investigated the effects of marker density (10, 1, 0.3 marker per mega base pair, Mbp(-1)), convergent or divergent parental populations, number of parents tested in other combinations (2, 1, 0), genetic model (including population-specific and/or dominance marker effects or not), and estimation method (GBLUP or BayesB) on the prediction accuracy. We based our simulations on marker genotypes of Central European flint and dent inbred lines from an ongoing maize breeding program. To simulate convergent or divergent parent populations, we generated phenotypes by assigning QTL to markers with similar or very different allele frequencies in both pools, respectively. Prediction accuracies increased with marker density and number of parents tested and were higher under divergent compared with convergent parental populations. Modeling marker effects as population-specific slightly improved prediction accuracy under lower marker densities (1 and 0.3 Mbp(-1)). This indicated that modeling marker effects as population-specific will be most beneficial under low linkage disequilibrium. Incorporating dominance effects improved prediction accuracies considerably for convergent parent populations, where dominance results in major contributions of SCA effects to the genetic variance among inter-population hybrids. While the general trends regarding the effects of the aforementioned influence factors on prediction accuracy were similar for GBLUP and BayesB, the latter method produced significantly higher accuracies for models incorporating dominance.
Theoretical and Applied Genetics 06/2012; 125(6):1181-94. · 3.30 Impact Factor
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ABSTRACT: The diversity of metabolites found in plants is by far greater than in most other organisms. Metabolic profiling techniques, which measure many of these compounds simultaneously, enabled investigating the regulation of metabolic networks and proved to be useful for predicting important agronomic traits. However, little is known about the genetic basis of metabolites in crops such as maize. Here, a set of 289 diverse maize inbred lines was genotyped with 56,110 SNPs and assayed for 118 biochemical compounds in the leaves of young plants, as well as for agronomic traits of mature plants in field trials. Metabolite concentrations had on average a repeatability of 0.73 and showed a correlation pattern that largely reflected their functional grouping. Genome-wide association mapping with correction for population structure and cryptic relatedness identified for 26 distinct metabolites strong associations with SNPs, explaining up to 32.0% of the observed genetic variance. On nine chromosomes, we detected 15 distinct SNP-metabolite associations, each of which explained more then 15% of the genetic variance. For lignin precursors, including p-coumaric acid and caffeic acid, we found strong associations (P values to ) with a region on chromosome 9 harboring cinnamoyl-CoA reductase, a key enzyme in monolignol synthesis and a target for improving the quality of lignocellulosic biomass by genetic engineering approaches. Moreover, lignin precursors correlated significantly with lignin content, plant height, and dry matter yield, suggesting that metabolites represent promising connecting links for narrowing the genotype-phenotype gap of complex agronomic traits.
Proceedings of the National Academy of Sciences 05/2012; 109(23):8872-7. · 9.68 Impact Factor
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ABSTRACT: BACKGROUND: Setosphaeria turcica is a fungal pathogen that causes northern corn leaf blight (NCLB) which is a serious foliar disease in maize. In order to unravel the genetic architecture of the resistance against this disease, a vast association mapping panel comprising 1487 European maize inbred lines was used to (i) identify chromosomal regions affecting flowering time (FT) and northern corn leaf blight (NCLB) resistance, (ii) examine the epistatic interactions of the identified chromosomal regions with the genetic background on an individual molecular marker basis, and (iii) dissect the correlation between NCLB resistance and FT. RESULTS: The single marker analyses performed for 8 244 single nucleotide polymorphism (SNP) markers revealed seven, four, and four SNP markers significantly (alpha D 0.05, amplicon wise Bonferroni correction) associated with FT, NCLB, and NCLB resistance corrected for FT, respectively. These markers explained individually between 0.36 and 14.29% of the genetic variance of the corresponding trait. DISCUSSION: The very well interpretable pattern of SNP associations observed for FT suggested that data from applied plant breeding programs can be used to dissect polygenic traits. This in turn indicates that the associations identified for NCLB resistance might be successfully used in marker-assisted selection programs. Furthermore, the associated genes are also of interest for further research concerning the mechanism of resistance to NCLB and plant diseases in general, because some of the associated genes have not been mentioned in this context so far.
BMC Plant Biology 04/2012; 12(1):56. · 3.45 Impact Factor
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ABSTRACT: For in vivo production of doubled haploid (DH) lines in maize, the rate of haploid induction is of crucial importance. Maternal
haploid induction depends primarily on the inducer used as a pollinator. However, the source germplasm used as a maternal
parent and the environmental conditions for induction may also influence haploid induction and these aspects have not been
examined in tropical maize so far. The objectives of our study were to (i) monitor the variation for haploid induction rate
(HIR) among diverse source germplasm in tropical maize, (ii) determine the relative importance of general (GCA) and specific
(SCA) combining abilities for HIR, and (iii) investigate the influence of summer and winter seasons and genotype×season
interactions on this trait. Ten inbreds were mated in a half diallel design. The resulting 45 F1 single crosses were pollinated with the haploid inducer hybrid RWS×UH400 during the summer 2008 and winter 2009 seasons
in a lowland tropical environment in Mexico. HIR of the single crosses averaged over seasons ranged from 2.90 to 9.66% with
an overall mean of 6.74%. Mean HIR was significantly (P<0.01) higher during the winter (7.37%) than summer season (6.11%). Significant (P<0.01) variation was observed due to GCA effects of parental inbreds of single crosses but not for SCA, GCA×season and
SCA×season interactions. Our study underpins that a higher HIR in tropical maize can be obtained by selecting appropriate
source germplasm and undertaking pollination under favorable environmental conditions.
KeywordsHaploid induction rate–Tropical source germplasm–Diallel crosses–General combining ability–Season–Maize
Euphytica 04/2012; 180(2):219-226. · 1.55 Impact Factor
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ABSTRACT: Computer simulations are a useful tool to solve problems in population genetics for which no analytical solutions are available.
We developed Plabsoft, a powerful and flexible software for population genetic simulation and data analysis. Various mating
systems can be simulated, comprising planned crosses, random mating, partial selfing, selfing, single-seed descent, double
haploids, topcrosses, and factorials. Selection can be simulated according to selection indices based on phenotypic values
and/or molecular marker scores. Data analysis routines are provided to analyze simulated and experimental datasets for allele
and genotype frequencies, genotypic and phenotypic values and variances, molecular genetic diversity, linkage disequilibrium,
and parameters to optimize marker-assisted backcrossing programs. Plabsoft has already been employed in numerous studies,
we chose some of them to illustrate the functionality of the software.
Euphytica 04/2012; 161(1):133-139. · 1.55 Impact Factor
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Sankalp U Bhosale,
Benjamin Stich,
H Frederick W Rattunde,
Eva Weltzien,
Bettina I G Haussmann,
C Thomas Hash,
Punna Ramu,
Hugo E Cuevas,
Andrew H Paterson, Albrecht E Melchinger,
Heiko K Parzies
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ABSTRACT: Photoperiod-sensitive flowering is a key adaptive trait for sorghum (Sorghum bicolor) in West and Central Africa. In this study we performed an association analysis to investigate the effect of polymorphisms within the genes putatively related to variation in flowering time on photoperiod-sensitive flowering in sorghum. For this purpose a genetically characterized panel of 219 sorghum accessions from West and Central Africa was evaluated for their photoperiod response index (PRI) based on two sowing dates under field conditions.
Sorghum accessions used in our study were genotyped for single nucleotide polymorphisms (SNPs) in six genes putatively involved in the photoperiodic control of flowering time. Applying a mixed model approach and previously-determined population structure parameters to these candidate genes, we found significant associations between several SNPs with PRI for the genes CRYPTOCHROME 1 (CRY1-b1) and GIGANTEA (GI).
The negative values of Tajima's D, found for the genes of our study, suggested that purifying selection has acted on genes involved in photoperiodic control of flowering time in sorghum. The SNP markers of our study that showed significant associations with PRI can be used to create functional markers to serve as important tools for marker-assisted selection of photoperiod-sensitive cultivars in sorghum.
BMC Plant Biology 03/2012; 12:32. · 3.45 Impact Factor
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ABSTRACT: In plant breeding, a large number of progenies that will be discarded later in the breeding process must be phenotyped and marker genotyped for conducting QTL analysis. In many cases, phenotypic preselection of lines could be useful. However, in QTL analyses even moderate preselection can have a significant effect on the power of QTL detection and estimation of effects of the target traits. In this study, we provide exact formulas for quantifying the change of allele frequencies within marker classes, expectations of marker contrasts and the variance of the marker contrasts under truncation selection, for the general case of two QTL affecting the target trait and a correlated trait. We focused on homozygous lines derived at random from biparental crosses. The effects of linkage between the marker and the QTL under selection as well as the effect of selection on a correlated trait can be quantified with the given formulas. Theoretical results clearly show that depending on the magnitude of QTL effects, high selection intensities can lead to a dramatic reduction in power of QTL detection and that approximations based on the infinitesimal model deviate substantially from exact solutions. The presented formulas are valuable for choosing appropriate selection intensity when performing QTL mapping experiments on the data on phenotypically preselected traits and enable the calculation and bias correction of the effects of QTL under selection. Application of our theory to experimental data revealed that selection-induced bias of QTL effects can be successfully corrected.
Theoretical and Applied Genetics 02/2012; 124(3):543-53. · 3.30 Impact Factor
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ABSTRACT: Maize is both an exciting model organism in plant genetics and also the most important crop worldwide for food, animal feed and bioenergy production. Recent genome-wide association and metabolic profiling studies aimed to resolve quantitative traits to their causal genetic loci and key metabolic regulators. Here we present a complementary approach that exploits large-scale genomic and metabolic information to predict complex, highly polygenic traits in hybrid testcrosses. We crossed 285 diverse Dent inbred lines from worldwide sources with two testers and predicted their combining abilities for seven biomass- and bioenergy-related traits using 56,110 SNPs and 130 metabolites. Whole-genome and metabolic prediction models were built by fitting effects for all SNPs or metabolites. Prediction accuracies ranged from 0.72 to 0.81 for SNPs and from 0.60 to 0.80 for metabolites, allowing a reliable screening of large collections of diverse inbred lines for their potential to create superior hybrids.
Nature Genetics 02/2012; 44(2):217-20. · 35.53 Impact Factor
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ABSTRACT: The in vivo haploid induction approach offers several advantages compared to the in vitro induction approach and recurrent self-pollination. It is currently the method of choice for inbred line development in many commercial maize breeding programs. Here, we describe the in vivo approach for generation of maternal doubled haploids (DHs). It involves four steps: (1) induction of haploidy by pollinating source germplasm with pollen of a haploid inducer; (2) identification of putative haploid seeds (seeds with a haploid embryo) using a seed coloration marker system; (3) doubling of chromosomes of putative haploids by treating seedlings with a mitotic inhibitor; and (4) verification of putative doubled haploids with a stalk color marker and self-pollination of true doubled haploid plants to multiply their seed.
Methods in molecular biology (Clifton, N.J.) 01/2012; 877:161-72.
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ABSTRACT: Breeding maize for use as a biogas substrate (biogas maize) has recently gained considerable importance. To optimize hybrid breeding programs, information about line per se performance (LP) of inbreds and its relation to their general combining ability (GCA) is required. The objectives of our research were to (1) estimate variance components and heritability of LP for agronomic and quality traits relevant to biogas production, (2) study correlations among traits as well as between LP and GCA, and (3) discuss implications for breeding of biogas maize. We evaluated 285 diverse dent maize inbred lines in six environments. Data were recorded on agronomic and quality traits, including dry matter yield (DMY), methane fermentation yield (MFY), and their product, methane yield (MY), as the main target trait. In agreement with observations made for GCA in a companion study, variation in MY was mainly determined by DMY. MFY, which showed moderate correlation with lignin but only weak correlation with starch, revealed only low genotypic variation. Thus, our results favor selection of genotypes with high DMY and less focus on ear proportion for biogas maize. Genotypic correlations between LP and GCA [r (g) (LP, GCA)] were highest (≥0.94) for maturity traits (days to silking, dry matter concentration) and moderate (≥0.65) for DMY and MY. Multistage selection is recommended. Selection for GCA of maturity traits, plant height, and to some extent also quality traits and DMY on the level of LP looks promising.
Theoretical and Applied Genetics 12/2011; 124(6):981-8. · 3.30 Impact Factor
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ABSTRACT: Biofuels have gained importance recently and the use of maize biomass as substrate in biogas plants for production of methane has increased tremendously in Germany. The objectives of our research were to (1) estimate variance components and heritability for different traits relevant to biogas production in testcrosses (TCs) of maize, (2) study correlations among traits, and (3) discuss strategies to breed maize as a substrate for biogas fermenters. We evaluated 570 TCs of 285 diverse dent maize lines crossed with two flint single-cross testers in six environments. Data were recorded on agronomic and quality traits, including dry matter yield (DMY), methane fermentation yield (MFY), and methane yield (MY), the product of DMY and MFY, as the main target trait. Estimates of variance components showed general combining ability (GCA) to be the major source of variation. Estimates of heritability exceeded 0.67 for all traits and were even much greater in most instances. Methane yield was perfectly correlated with DMY but not with MFY, indicating that variation in MY is primarily determined by DMY. Further, DMY had a larger heritability and coefficient of genetic variation than MFY. Hence, for improving MY, selection should primarily focus on DMY rather than MFY. Further, maize breeding for biogas production may diverge from that for forage production because in the former case, quality traits seem to be of much lower importance.
Theoretical and Applied Genetics 12/2011; 124(6):971-80. · 3.30 Impact Factor
