Mixed model approaches for the identification of QTLs within a maize hybrid breeding program.
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ABSTRACT:
SUBMITTER: van Eeuwijk FA
PROVIDER: S-EPMC2793393 | biostudies-other | 2010 Jan
REPOSITORIES: biostudies-other
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Project description:Maize (Zea mays L.) serves as model plant for heterosis research and is the crop where hybrid breeding was pioneered. We analyzed genomic and phenotypic data of 1254 hybrids of a typical maize hybrid breeding program based on the important Dent × Flint heterotic pattern. Our main objectives were to investigate genome properties of the parental lines (e.g., allele frequencies, linkage disequilibrium, and phases) and examine the prospects of genomic prediction of hybrid performance. We found high consistency of linkage phases and large differences in allele frequencies between the Dent and Flint heterotic groups in pericentromeric regions. These results can be explained by the Hill-Robertson effect and support the hypothesis of differential fixation of alleles due to pseudo-overdominance in these regions. In pericentromeric regions we also found indications for consistent marker-QTL linkage between heterotic groups. With prediction methods GBLUP and BayesB, the cross-validation prediction accuracy ranged from 0.75 to 0.92 for grain yield and from 0.59 to 0.95 for grain moisture. The prediction accuracy of untested hybrids was highest, if both parents were parents of other hybrids in the training set, and lowest, if none of them were involved in any training set hybrid. Optimizing the composition of the training set in terms of number of lines and hybrids per line could further increase prediction accuracy. We conclude that genomic prediction facilitates a paradigm shift in hybrid breeding by focusing on the performance of experimental hybrids rather than the performance of parental lines in test crosses.
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Project description:Root system architecture (RSA), also known as root morphology, is critical in plant acquisition of soil resources, plant growth, and yield formation. Many QTLs associated with RSA or root traits in maize have been identified using several bi-parental populations, particularly in response to various environmental factors. In the present study, a meta-analysis of QTLs associated with root traits was performed in maize using 917 QTLs retrieved from 43 mapping studies published from 1998 to 2020. A total of 631 QTLs were projected onto a consensus map involving 19,714 markers, which led to the prediction of 68 meta-QTLs (MQTLs). Among these 68 MQTLs, 36 MQTLs were validated with the marker-trait associations available from previous genome-wide association studies for root traits. The use of comparative genomics approaches revealed several gene models conserved among the maize, sorghum, and rice genomes. Among the conserved genomic regions, the ortho-MQTL analysis uncovered 20 maize MQTLs syntenic to 27 rice MQTLs for root traits. Functional analysis of some high-confidence MQTL regions revealed 442 gene models, which were then subjected to in silico expression analysis, yielding 235 gene models with significant expression in various tissues. Furthermore, 16 known genes viz., DXS2, PHT, RTP1, TUA4, YUC3, YUC6, RTCS1, NSA1, EIN2, NHX1, CPPS4, BIGE1, RCP1, SKUS13, YUC5, and AW330564 associated with various root traits were present within or near the MQTL regions. These results could aid in QTL cloning and pyramiding in developing new maize varieties with specific root architecture for proper plant growth and development under optimum and abiotic stress conditions.
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Project description:Key messageHistorical data from breeding programs can be efficiently used to improve genomic selection accuracy, especially when the training set is optimized to subset individuals most informative of the target testing set. The current strategy for large-scale implementation of genomic selection (GS) at the International Maize and Wheat Improvement Center (CIMMYT) global maize breeding program has been to train models using information from full-sibs in a "test-half-predict-half approach." Although effective, this approach has limitations, as it requires large full-sib populations and limits the ability to shorten variety testing and breeding cycle times. The primary objective of this study was to identify optimal experimental and training set designs to maximize prediction accuracy of GS in CIMMYT's maize breeding programs. Training set (TS) design strategies were evaluated to determine the most efficient use of phenotypic data collected on relatives for genomic prediction (GP) using datasets containing 849 (DS1) and 1389 (DS2) DH-lines evaluated as testcrosses in 2017 and 2018, respectively. Our results show there is merit in the use of multiple bi-parental populations as TS when selected using algorithms to maximize relatedness between the training and prediction sets. In a breeding program where relevant past breeding information is not readily available, the phenotyping expenditure can be spread across connected bi-parental populations by phenotyping only a small number of lines from each population. This significantly improves prediction accuracy compared to within-population prediction, especially when the TS for within full-sib prediction is small. Finally, we demonstrate that prediction accuracy in either sparse testing or "test-half-predict-half" can further be improved by optimizing which lines are planted for phenotyping and which lines are to be only genotyped for advancement based on GP.
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Project description:Heterosis plays a decisive role in maize production worldwide, but its genetic basis remains unclear. In this study, we explored heterosis for ear-weight (EW)-related traits using a North Carolina Experiment III design (Design III) population derived from the elite maize hybrid Zhengdan 958. Quantitative trait loci (QTL) analysis was conducted based on phenotypic data collected from five environments using a high-density linkage map that consisted of 905 single nucleotide polymorphisms (SNP). A total of 38 environmentally stable QTLs were detected, and the numbers for the Z1 and Z2 populations were 18 and 20, respectively. All environmentally stable QTLs for Z2 were characterized by the overdominance effect (OD), which indicated that overdominance was one of the most important contributors to the heterosis of EW-related traits. Consistent with the significant positive correlations between EW-related traits, 9 genomic regions with overlapped QTLs for different traits were found and were located on chromosomes 1 (1), 3 (2), 4 (3), 7 (1), 8 (1), and 9 (1). Compared to previous reports, we found that the genomic regions for heterosis were not always congruent between different hybrids, which suggested that the combination of heterotic loci in different hybrids was genotype-dependent. Collectively, these data provided further evidence that the potential utilization of QTLs for heterosis may be feasible by pyramiding if we treat the QTLs as inherited units.
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Project description:BACKGROUND: In recent years, several attempts have been made in plant genetics to detect QTL by using association mapping methods. The objectives of this study were to (i) evaluate various methods for association mapping in five plant species and (ii) for three traits in each of the plant species compare the Topt, the restricted maximum likelihood (REML) estimate of the conditional probability that two genotypes carry at the same locus alleles that are identical in state but not identical by descent. In order to compare the association mapping methods based on scenarios with realistic estimates of population structure and familial relatedness, we analyzed phenotypic and genotypic data of rapeseed, potato, sugar beet, maize, and Arabidopsis. For the same reason, QTL effects were simulated on top of the observed phenotypic values when examining the adjusted power for QTL detection. RESULTS: The correlation between the Topt values identified using REML deviance profiles and profiles of the mean of squared difference between observed and expected P values was 0.83. CONCLUSION: The mixed-model association mapping approaches using a kinship matrix, which was based on Topt, were more appropriate for association mapping than the recently proposed QK method with respect to the adherence to the nominal alpha level and the adjusted power for QTL detection. Furthermore, we showed that Topt differs considerably among the five plant species but only marginally among different traits.
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Project description:Assessment of genetic purity of parental inbred lines and their resultant F1 hybrids is an essential quality control check in maize hybrid breeding, variety release and seed production. In this study, genetic purity, parent-offspring relationship and diversity among the inbred lines were assessed using 92 single-nucleotide polymorphism (SNP) markers. A total of 188 maize genotypes, comprising of 26 inbred lines, four doubled haploid (DH) lines and 158 single-cross maize hybrids were investigated in this study using Kompetitive Allele Specific Polymerase Chain Reaction (KASP) genotyping assays. The bi-allelic data was analyzed for genetic purity and diversity parameters using GenAlex software. The SNP markers were highly polymorphic and 90% had polymorphic information content (PIC) values of > 0.3. Pairwise genetic distances among the lines ranged from 0.05 to 0.56, indicating a high level of dissimilarity among the inbred lines. A maximum genetic distance of (0.56) was observed between inbred lines CKDHL0089 and CML443 while the lowest (0.05) was between I-42 and I-40. The majority (67%) of the inbred lines studied were genetically pure with residual heterozygosity of <5%, while only 33% had heterozygosity levels of >5%. Inbred lines, which were not pure, require purification through further inbreeding. Cluster analysis partitioned the lines into three distinct genetic clusters with the potential to contribute new beneficial alleles to the maize breeding program. Out of the 68 hybrids (43%) that passed the parent-offspring test, seven hybrids namely; SCHP29, SCHP95, SCHP94, SCHP134, SCHP44, SCHP114 and SCHP126, were selected as potential candidates for further evaluation and release due to their outstanding yield performance.
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Project description:Previous studies have reported promising differences in the quality of kernels from farmers' maize populations collected in a Portuguese region known to produce maize-based bread. However, several limitations have been identified in the previous characterizations of those populations, such as a limited set of quality traits accessed and a missing accurate agronomic performance evaluation. The objectives of this study were to perform a more detailed quality characterization of Portuguese farmers' maize populations; to estimate their agronomic performance in a broader range of environments; and to integrate quality, agronomic, and molecular data in the setting up of decision-making tools for the establishment of a quality-oriented participatory maize breeding program. Sixteen farmers' maize populations, together with 10 other maize populations chosen for comparison purposes, were multiplied in a common-garden experiment for quality evaluation. Flour obtained from each population was used to study kernel composition (protein, fat, fiber), flour's pasting behavior, and bioactive compound levels (carotenoids, tocopherols, phenolic compounds). These maize populations were evaluated for grain yield and ear weight in nine locations across Portugal; the populations' adaptability and stability were evaluated using additive main effects and multiplication interaction (AMMI) model analysis. The phenotypic characterization of each population was complemented with a molecular characterization, in which 30 individuals per population were genotyped with 20 microsatellites. Almost all farmers' populations were clustered into the same quality-group characterized by high levels of protein and fiber, low levels of carotenoids, volatile aldehydes, ?- and ?-tocopherols, and breakdown viscosity. Within this quality-group, variability on particular quality traits (color and some bioactive compounds) could still be found. Regarding the agronomic performance, farmers' maize populations had low, but considerably stable, grain yields across the tested environments. As for their genetic diversity, each farmers' population was genetically heterogeneous; nonetheless, all farmers' populations were distinct from each other's. In conclusion, and taking into consideration different quality improvement objectives, the integration of the data generated within this study allowed the outline and exploration of alternative directions for future breeding activities. As a consequence, more informed choices will optimize the use of the resources available and improve the efficiency of participatory breeding activities.
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