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1.
G3 (Bethesda) ; 2020 Jun 13.
Artigo em Inglês | MEDLINE | ID: mdl-32527748

RESUMO

""Sparse testing" refers to reduced multi-environment breeding trials in which not all genotypes of interest are grown in each environment. Using genomic-enabled prediction and a model embracing genotype × environment interaction (GE), the non-observed genotype-in-environment combinations can be predicted. Consequently, the overall costs can be reduced and the testing capacities can be increased. The accuracy of predicting the unobserved data depends on different factors including (1) how many genotypes overlap between environments, (2) in how many environments each genotype is grown, and (3) which prediction method is used. In this research, we studied the predictive ability obtained when using a fixed number of plots and different sparse testing designs. The considered designs included the extreme cases of (1) no overlap of genotypes between environments, and (2) complete overlap of the genotypes between environments. In the latter case, the prediction set fully consists of genotypes that have not been tested at all. Moreover, we gradually go from one extreme to the other considering (3) intermediates between the two previous cases with varying numbers of different or non-overlapping (NO)/overlapping (O) genotypes. The empirical study is built upon two different maize hybrid data sets consisting of different genotypes crossed to two different testers (T1 and T2) and each data set was analyzed separately. For each set, phenotypic records on yield from three different environments are available. Three different prediction models were implemented, two main effects models ( M1 and M2 ), and a model ( M3) including the genotype-by-environment interaction term (GE). The results showed that the genome-based model including GE ( M3 ) captured more phenotypic variation than the models that did not include this component. Also, M3 provided higher prediction accuracy than models M1 and M2 for the different allocation scenarios. Reducing the size of the calibration sets decreased the prediction accuracy under all allocation designs with M3 being the less affected model; however, using the genome-enabled models (i.e., M2 and M3 ) the predictive ability is recovered when more genotypes are tested across environments. Our results indicate that a substantial part of the testing resources can be saved when using genome-based models including GE for optimizing sparse testing designs.

2.
Plants (Basel) ; 9(4)2020 Apr 08.
Artigo em Inglês | MEDLINE | ID: mdl-32276322

RESUMO

Prior knowledge on heterosis and quantitative genetic parameters on maize lethal necrosis (MLN) can help the breeders to develop numerous resistant or tolerant hybrids with optimum resources. Our objectives were to (1) estimate the quantitative genetic parameters for MLN disease severity, (2) investigate the efficiency of the prediction of hybrid performance based on parental per se and general combining ability (GCA) effects, and (3) examine the potential of hybrid prediction for MLN resistance or tolerance based on markers. Fifty elite maize inbred lines were selected based on their response to MLN under artificial inoculation. Crosses were made in a half diallel mating design to produce 307 F1 hybrids. All hybrids were evaluated in MLN quarantine facility in Naivasha, Kenya for two seasons under artificial inoculation. All 50 inbreds were genotyped with genotyping-by-sequencing (GBS) SNPs. The phenotypic variation was significant for all traits and the heritability was moderate to high. We observed that hybrids were superior to the mean performance of the parents for disease severity (-14.57%) and area under disease progress curve (AUDPC) (14.9%). Correlations were significant and moderate between line per se and GCA; and mean of parental value with hybrid performance for both disease severity and AUDPC value. Very low and negative correlation was observed between parental lines marker based genetic distance and heterosis. Nevertheless, the correlation of GCA effects was very high with hybrid performance which can suggests as a good predictor of MLN resistance. Genomic prediction of hybrid performance for MLN is high for both traits. We therefore conclude that there is potential for prediction of hybrid performance for MLN. Overall, the estimated quantitative genetic parameters suggest that through targeted approach, it is possible to develop outstanding lines and hybrids for MLN resistance.

3.
Virus Res ; 282: 197943, 2020 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-32205142

RESUMO

Maize lethal necrosis (MLN), a complex viral disease, emerged as a serious threat to maize production and the livelihoods of smallholders in eastern Africa since 2011, primarily due to the introduction of maize chlorotic mottle virus (MCMV). The International Maize and Wheat Improvement Center (CIMMYT), in close partnership with national and international partners, implemented a multi-disciplinary and multi-institutional strategy to curb the spread of MLN in sub-Saharan Africa, and mitigate the impact of the disease. The strategy revolved around a) intensive germplasm screening and fast-tracked development and deployment of MLN-tolerant/resistant maize hybrids in Africa-adapted genetic backgrounds; b) optimizing the diagnostic protocols for MLN-causing viruses, especially MCMV, and capacity building of relevant public and private sector institutions on MLN diagnostics and management; c) MLN monitoring and surveillance across sub-Saharan Africa in collaboration with national plant protection organizations (NPPOs); d) partnership with the private seed sector for production and exchange of MLN pathogen-free commercial maize seed; and e) awareness creation among relevant stakeholders about MLN management, including engagement with policy makers. The review concludes by highlighting the need to keep continuous vigil against MLN-causing viruses, and preventing any further spread of the disease to the major maize-growing countries that have not yet reported MLN in sub-Saharan Africa.

4.
Front Plant Sci ; 10: 1502, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31824533

RESUMO

Genomic selection predicts the genomic estimated breeding values (GEBVs) of individuals not previously phenotyped. Several studies have investigated the accuracy of genomic predictions in maize but there is little empirical evidence on the practical performance of lines selected based on phenotype in comparison with those selected solely on GEBVs in advanced testcross yield trials. The main objectives of this study were to (1) empirically compare the performance of tropical maize hybrids selected through phenotypic selection (PS) and genomic selection (GS) under well-watered (WW) and managed drought stress (WS) conditions in Kenya, and (2) compare the cost-benefit analysis of GS and PS. For this study, we used two experimental maize data sets (stage I and stage II yield trials). The stage I data set consisted of 1492 doubled haploid (DH) lines genotyped with rAmpSeq SNPs. A subset of these lines (855) representing various DH populations within the stage I cohort was crossed with an individual single-cross tester chosen to complement each population. These testcross hybrids were evaluated in replicated trials under WW and WS conditions for grain yield and other agronomic traits, while the remaining 637 DH lines were predicted using the 855 lines as a training set. The second data set (stage II) consists of 348 DH lines from the first data set. Among these 348 best DH lines, 172 lines selected were solely based on GEBVs, and 176 lines were selected based on phenotypic performance. Each of the 348 DH lines were crossed with three common testers from complementary heterotic groups, and the resulting 1042 testcross hybrids and six commercial checks were evaluated in four to five WW locations and one WS condition in Kenya. For stage I trials, the cross-validated prediction accuracy for grain yield was 0.67 and 0.65 under WW and WS conditions, respectively. We found similar responses to selection using PS and GS for grain yield other agronomic traits under WW and WS conditions. The top 15% of hybrids advanced through GS and PS gave 21%-23% higher grain yield under WW and 51%-52% more grain yield under WS than the mean of the checks. The GS reduced the cost by 32% over the PS with similar selection gains. We concluded that the use of GS for yield under WW and WS conditions in maize can produce selection candidates with similar performance as those generated from conventional PS, but at a lower cost, and therefore, should be incorporated into maize breeding pipelines to increase breeding program efficiency.

5.
Genes (Basel) ; 11(1)2019 12 23.
Artigo em Inglês | MEDLINE | ID: mdl-31877962

RESUMO

Maize lethal necrosis (MLN), caused by co-infection of maize chlorotic mottle virus and sugarcane mosaic virus, can lead up to 100% yield loss. Identification and validation of genomic regions can facilitate marker assisted breeding for resistance to MLN. Our objectives were to identify marker-trait associations using genome wide association study and assess the potential of genomic prediction for MLN resistance in a large panel of diverse maize lines. A set of 1400 diverse maize tropical inbred lines were evaluated for their response to MLN under artificial inoculation by measuring disease severity or incidence and area under disease progress curve (AUDPC). All lines were genotyped with genotyping by sequencing (GBS) SNPs. The phenotypic variation was significant for all traits and the heritability estimates were moderate to high. GWAS revealed 32 significantly associated SNPs for MLN resistance (at p < 1.0 × 10-6). For disease severity, these significantly associated SNPs individually explained 3-5% of the total phenotypic variance, whereas for AUDPC they explained 3-12% of the total proportion of phenotypic variance. Most of significant SNPs were consistent with the previous studies and assists to validate and fine map the big quantitative trait locus (QTL) regions into few markers' specific regions. A set of putative candidate genes associated with the significant markers were identified and their functions revealed to be directly or indirectly involved in plant defense responses. Genomic prediction revealed reasonable prediction accuracies. The prediction accuracies significantly increased with increasing marker densities and training population size. These results support that MLN is a complex trait controlled by few major and many minor effect genes.


Assuntos
Resistência à Doença/genética , Sementes/genética , Zea mays/genética , Mapeamento Cromossômico/métodos , Cromossomos de Plantas/genética , Estudo de Associação Genômica Ampla , Genômica/métodos , Genótipo , Fenótipo , Doenças das Plantas/genética , Doenças das Plantas/virologia , Polimorfismo de Nucleotídeo Único/genética , Potyvirus/patogenicidade , Locos de Características Quantitativas/genética , Sementes/virologia , Tombusviridae/patogenicidade , Zea mays/virologia
6.
Theor Appl Genet ; 132(8): 2381-2399, 2019 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-31098757

RESUMO

KEY MESSAGE: Analysis of the genetic architecture of MCMV and MLN resistance in maize doubled-haploid populations revealed QTLs with major effects on chromosomes 3 and 6 that were consistent across genetic backgrounds and environments. Two major-effect QTLs, qMCMV3-108/qMLN3-108 and qMCMV6-17/qMLN6-17, were identified as conferring resistance to both MCMV and MLN. Maize lethal necrosis (MLN) is a serious threat to the food security of maize-growing smallholders in sub-Saharan Africa. The ability of the maize chlorotic mottle virus (MCMV) to interact with other members of the Potyviridae causes severe yield losses in the form of MLN. The objective of the present study was to gain insights and validate the genetic architecture of resistance to MCMV and MLN in maize. We applied linkage mapping to three doubled-haploid populations and a genome-wide association study (GWAS) on 380 diverse maize lines. For all the populations, phenotypic variation for MCMV and MLN was significant, and heritability was moderate to high. Linkage mapping revealed 13 quantitative trait loci (QTLs) for MCMV resistance and 12 QTLs conferring MLN resistance. One major-effect QTL, qMCMV3-108/qMLN3-108, was consistent across populations for both MCMV and MLN resistance. Joint linkage association mapping (JLAM) revealed 18 and 21 main-effect QTLs for MCMV and MLN resistance, respectively. Another major-effect QTL, qMCMV6-17/qMLN6-17, was detected for both MCMV and MLN resistance. The GWAS revealed a total of 54 SNPs (MCMV-13 and MLN-41) significantly associated (P ≤ 5.60 × 10-05) with MCMV and MLN resistance. Most of the GWAS-identified SNPs were within or adjacent to the QTLs detected through linkage mapping. The prediction accuracy for within populations as well as the combined populations is promising; however, the accuracy was low across populations. Overall, MCMV resistance is controlled by a few major and many minor-effect loci and seems more complex than the genetic architecture for MLN resistance.


Assuntos
Ligação Genética , Genoma de Planta , Estudo de Associação Genômica Ampla , Doenças das Plantas/virologia , Sementes/genética , Tombusviridae/genética , Zea mays/genética , Zea mays/virologia , Alelos , Área Sob a Curva , Fenótipo , Doenças das Plantas/genética , Polimorfismo de Nucleotídeo Único/genética , Locos de Características Quantitativas/genética , Clima Tropical
7.
Genes (Basel) ; 11(1)2019 12 26.
Artigo em Inglês | MEDLINE | ID: mdl-31888105

RESUMO

Maize lethal necrosis (MLN) occurs when maize chlorotic mottle virus (MCMV) and sugarcane mosaic virus (SCMV) co-infect maize plant. Yield loss of up to 100% can be experienced under severe infections. Identification and validation of genomic regions and their flanking markers can facilitate marker assisted breeding for resistance to MLN. To understand the status of previously identified quantitative trait loci (QTL)in diverse genetic background, F3 progenies derived from seven bi-parental populations were genotyped using 500 selected kompetitive allele specific PCR (KASP) SNPs. The F3 progenies were evaluated under artificial MLN inoculation for three seasons. Phenotypic analyses revealed significant variability (P ≤ 0.01) among genotypes for responses to MLN infections, with high heritability estimates (0.62 to 0.82) for MLN disease severity and AUDPC values. Linkage mapping and joint linkage association mapping revealed at least seven major QTL (qMLN3_130 and qMLN3_142, qMLN5_190 and qMLN5_202, qMLN6_85 and qMLN6_157 qMLN8_10 and qMLN9_142) spread across the 7-biparetal populations, for resistance to MLN infections and were consistent with those reported previously. The seven QTL appeared to be stable across genetic backgrounds and across environments. Therefore, these QTL could be useful for marker assisted breeding for resistance to MLN.


Assuntos
Mapeamento Cromossômico/métodos , Resistência à Doença , Locos de Características Quantitativas , Zea mays/crescimento & desenvolvimento , Fenótipo , Melhoramento Vegetal , Potyvirus/patogenicidade , Análise de Componente Principal , Tombusviridae/patogenicidade , Zea mays/genética , Zea mays/virologia
8.
Mol Breed ; 38(5): 66, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-29773962

RESUMO

In sub-Saharan Africa, maize is the key determinant of food security for smallholder farmers. The sudden outbreak of maize lethal necrosis (MLN) disease is seriously threatening the maize production in the region. Understanding the genetic basis of MLN resistance is crucial. In this study, we used four biparental populations applied linkage mapping and joint linkage mapping approaches to identify and validate the MLN resistance-associated genomic regions. All populations were genotyped with low to high density markers and phenotyped in multiple environments against MLN under artificial inoculation. Phenotypic variation for MLN resistance was significant and heritability was moderate to high in all four populations for both early and late stages of disease infection. Linkage mapping revealed three major quantitative trait loci (QTL) on chromosomes 3, 6, and 9 that were consistently detected in at least two of the four populations. Phenotypic variance explained by a single QTL in each population ranged from 3.9% in population 1 to 43.8% in population 2. Joint linkage association mapping across three populations with three biometric models together revealed 16 and 10 main effect QTL for MLN-early and MLN-late, respectively. The QTL identified on chromosomes 3, 5, 6, and 9 were consistent with the QTL identified by linkage mapping. Ridge regression best linear unbiased prediction with five-fold cross-validation revealed high accuracy for prediction across populations for both MLN-early and MLN-late. Overall, the study discovered and validated the presence of major effect QTL on chromosomes 3, 6, and 9 which can be potential candidates for marker-assisted breeding to improve the MLN resistance.

9.
Front Plant Sci ; 8: 1916, 2017.
Artigo em Inglês | MEDLINE | ID: mdl-29167677

RESUMO

Genomic selection is being used increasingly in plant breeding to accelerate genetic gain per unit time. One of the most important applications of genomic selection in maize breeding is to predict and select the best un-phenotyped lines in bi-parental populations based on genomic estimated breeding values. In the present study, 22 bi-parental tropical maize populations genotyped with low density SNPs were used to evaluate the genomic prediction accuracy (rMG ) of the six trait-environment combinations under various levels of training population size (TPS) and marker density (MD), and assess the effect of trait heritability (h2 ), TPS and MD on rMG estimation. Our results showed that: (1) moderate rMG values were obtained for different trait-environment combinations, when 50% of the total genotypes was used as training population and ~200 SNPs were used for prediction; (2) rMG increased with an increase in h2 , TPS and MD, both correlation and variance analyses showed that h2 is the most important factor and MD is the least important factor on rMG estimation for most of the trait-environment combinations; (3) predictions between pairwise half-sib populations showed that the rMG values for all the six trait-environment combinations were centered around zero, 49% predictions had rMG values above zero; (4) the trend observed in rMG differed with the trend observed in rMG /h, and h is the square root of heritability of the predicted trait, it indicated that both rMG and rMG /h values should be presented in GS study to show the accuracy of genomic selection and the relative accuracy of genomic selection compared with phenotypic selection, respectively. This study provides useful information to maize breeders to design genomic selection workflow in their breeding programs.

10.
Trends Plant Sci ; 22(11): 961-975, 2017 11.
Artigo em Inglês | MEDLINE | ID: mdl-28965742

RESUMO

Genomic selection (GS) facilitates the rapid selection of superior genotypes and accelerates the breeding cycle. In this review, we discuss the history, principles, and basis of GS and genomic-enabled prediction (GP) as well as the genetics and statistical complexities of GP models, including genomic genotype×environment (G×E) interactions. We also examine the accuracy of GP models and methods for two cereal crops and two legume crops based on random cross-validation. GS applied to maize breeding has shown tangible genetic gains. Based on GP results, we speculate how GS in germplasm enhancement (i.e., prebreeding) programs could accelerate the flow of genes from gene bank accessions to elite lines. Recent advances in hyperspectral image technology could be combined with GS and pedigree-assisted breeding.


Assuntos
Genoma de Planta , Modelos Genéticos , Melhoramento Vegetal/métodos , Seleção Genética , Produtos Agrícolas/genética , Interação Gene-Ambiente , Sequenciamento de Nucleotídeos em Larga Escala , Aprendizado de Máquina , Polimorfismo de Nucleotídeo Único , Locos de Características Quantitativas , Zea mays/genética
11.
Plant Breed ; 136(2): 197-205, 2017 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-28781399

RESUMO

Drought and poor soil fertility are among the major abiotic stresses affecting maize productivity in sub-Saharan Africa. Maize breeding efforts at the International Maize and Wheat Improvement Center (CIMMYT) have focused on incorporating drought stress tolerance and nitrogen-use efficiency (NUE) into tropical maize germplasm. The objectives of this study were to estimate the general combining ability (GCA) and specific combining ability (SCA) of selected maize inbred lines under drought stress (DS), low-nitrogen (LN) and optimum moisture and nitrogen (optimum) conditions, and to assess the yield potential and stability of experimental hybrids under these management conditions. Forty-nine experimental three-way cross hybrids, generated from a 7 × 7 line by tester crosses, and six commercial checks were evaluated across 11 optimum, DS and LN sites in Kenya in 2014 using an alpha lattice design with two replicates per entry at each site. DS reduced both grain yield (GY) and plant height (PH), while anthesis-silking interval (ASI) increased under both DS and LN. Hybrids 'L4/T2' and 'L4/T1' were found to be superior and stable, while inbreds 'L4' and 'L6' were good combiners for GY and other secondary traits across sites. Additive variance played a greater role for most traits under the three management conditions, suggesting that further progress in the improvement of these traits should be possible. GY under optimum conditions was positively correlated with GY under both DS and LN conditions, but GY under DS and LN was not correlated. Our results suggest the feasibility for simultaneous improvement in grain yield performance of genotypes under optimum, DS and LN conditions.

12.
Crop Prot ; 89: 202-208, 2016 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-27812235

RESUMO

A study was conducted to assess the performance of maize hybrids with Bt event MON810 (Bt-hybrids) against the maize stem borer Busseola fusca (Fuller) in a biosafety greenhouse (BGH) and against the spotted stem borer Chilo partellus (Swinhoe) under confined field trials (CFT) in Kenya for three seasons during 2013-2014. The study comprised 14 non-commercialized hybrids (seven pairs of near-isogenic Bt and non-Bt hybrids) and four non-Bt commercial hybrids. Each plant was artificially infested twice with 10 first instar larvae. In CFT, plants were infested with C. partellus 14 and 24 days after planting; in BGH, plants were infested with B. fusca 21 and 31 days after planting. In CFT, the seven Bt hybrids significantly differed from their non-Bt counterparts for leaf damage, number of exit holes, percent tunnel length, and grain yield. When averaged over three seasons, Bt-hybrids gave the highest grain yield (9.7 t ha-1), followed by non-Bt hybrids (6.9 t ha-1) and commercial checks (6 t ha-1). Bt-hybrids had the least number of exit holes and percent tunnel length in all the seasons as compared to the non-Bt hybrids and commercial checks. In BGH trials, Bt-hybrids consistently suffered less leaf damage than their non-Bt near isolines. The study demonstrated that MON810 was effective in controlling B. fusca and C. partellus. Bt-maize, therefore, has great potential to reduce the risk of maize grain losses in Africa due to stem borers, and will enable the smallholder farmers to produce high-quality grain with increased yield, reduced insecticide inputs, and improved food security.

13.
Euphytica ; 208: 285-297, 2016.
Artigo em Inglês | MEDLINE | ID: mdl-27397932

RESUMO

A marker-assisted recurrent selection (MARS) program was undertaken in sub-Saharan Africa to improve grain yield under drought-stress in 10 biparental tropical maize populations. The objectives of the present study were to evaluate the performance of C1S2-derived hybrids obtained after three MARS cycles (one cycle of recombination (C1), followed by two generations of selfing (S2), and to study yield stability under both drought-stress (DS) and well-watered (WW) conditions. For each of the 10 populations, we evaluated hybrids developed by crossing 47-74 C1S2 lines advanced through MARS, the best five S5 lines developed through pedigree selection, and the founder parents with a single-cross tester from a complementary heterotic group. The hybrids and five commercial checks were evaluated in Kenya under 1-3 DS and 3-5 WW conditions with two replications. Combined across DS locations, the top 10 C1S2-derived hybrids from each of the 10 biparental populations produced 0.5-46.3 and 11.1-55.1 % higher mean grain yields than hybrids developed using pedigree selection and the commercial checks, respectively. Across WW locations, the best 10 hybrids derived from C1S2 of each population produced 3.4-13.3 and 7.9-36.5 % higher grain yields than hybrids derived using conventional pedigree breeding and the commercial checks, respectively. Mean days to anthesis of the best 10 C1S2 hybrids were comparable to those of hybrids developed using the pedigree method, the founder parents and the commercial checks, with a maximum difference of 3.5 days among the different groups. However, plant height was significantly (P < 0.01) different in most pairwise comparisons. Our results showed the superiority of MARS over pedigree selection for improving diverse tropical maize populations as sources of improved lines for stress-prone environments and thus MARS can be effectively integrated into mainstream maize breeding programs.

14.
G3 (Bethesda) ; 5(10): 2155-64, 2015 Aug 18.
Artigo em Inglês | MEDLINE | ID: mdl-26290571

RESUMO

A genomic selection index (GSI) is a linear combination of genomic estimated breeding values that uses genomic markers to predict the net genetic merit and select parents from a nonphenotyped testing population. Some authors have proposed a GSI; however, they have not used simulated or real data to validate the GSI theory and have not explained how to estimate the GSI selection response and the GSI expected genetic gain per selection cycle for the unobserved traits after the first selection cycle to obtain information about the genetic gains in each subsequent selection cycle. In this paper, we develop the theory of a GSI and apply it to two simulated and four real data sets with four traits. Also, we numerically compare its efficiency with that of the phenotypic selection index (PSI) by using the ratio of the GSI response over the PSI response, and the PSI and GSI expected genetic gain per selection cycle for observed and unobserved traits, respectively. In addition, we used the Technow inequality to compare GSI vs. PSI efficiency. Results from the simulated data were confirmed by the real data, indicating that GSI was more efficient than PSI per unit of time.


Assuntos
Simulação por Computador , Modelos Genéticos , Seleção Genética , Algoritmos , Conjuntos de Dados como Assunto
15.
Theor Appl Genet ; 128(10): 1957-68, 2015 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-26152570

RESUMO

KEY MESSAGE: Genome-wide association analysis in tropical and subtropical maize germplasm revealed that MLND resistance is influenced by multiple genomic regions with small to medium effects. The maize lethal necrosis disease (MLND) caused by synergistic interaction of Maize chlorotic mottle virus and Sugarcane mosaic virus, and has emerged as a serious threat to maize production in eastern Africa since 2011. Our objective was to gain insights into the genetic architecture underlying the resistance to MLND by genome-wide association study (GWAS) and genomic selection. We used two association mapping (AM) panels comprising a total of 615 diverse tropical/subtropical maize inbred lines. All the lines were evaluated against MLND under artificial inoculation. Both the panels were genotyped using genotyping-by-sequencing. Phenotypic variation for MLND resistance was significant and heritability was moderately high in both the panels. Few promising lines with high resistance to MLND were identified to be used as potential donors. GWAS revealed 24 SNPs that were significantly associated (P < 3 × 10(-5)) with MLND resistance. These SNPs are located within or adjacent to 20 putative candidate genes that are associated with plant disease resistance. Ridge regression best linear unbiased prediction with five-fold cross-validation revealed higher prediction accuracy for IMAS-AM panel (0.56) over DTMA-AM (0.36) panel. The prediction accuracy for both within and across panels is promising; inclusion of MLND resistance associated SNPs into the prediction model further improved the accuracy. Overall, the study revealed that resistance to MLND is controlled by multiple loci with small to medium effects and the SNPs identified by GWAS can be used as potential candidates in MLND resistance breeding program.


Assuntos
Resistência à Doença/genética , Vírus do Mosaico/patogenicidade , Doenças das Plantas/genética , Zea mays/genética , Estudos de Associação Genética , Genótipo , Fenótipo , Melhoramento Vegetal , Doenças das Plantas/virologia , Polimorfismo de Nucleotídeo Único , Zea mays/virologia
16.
Theor Appl Genet ; 128(9): 1839-54, 2015 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-26081946

RESUMO

Msv1 , the major QTL for MSV resistance was delimited to an interval of 0.87 cM on chromosome 1 at 87 Mb and production markers with high prediction accuracy were developed. Maize streak virus (MSV) disease is a devastating disease in the Sub-Saharan Africa (SSA), which causes significant yield loss in maize. Resistance to MSV has previously been mapped to a major QTL (Msv1) on chromosome 1 that is germplasm and environment independent and to several minor loci elsewhere in the genome. In this study, Msv1 was fine-mapped through QTL isogenic recombinant strategy using a large F 2 population of CML206 × CML312 to an interval of 0.87 cM on chromosome 1. Genome-wide association study was conducted in the DTMA (Drought Tolerant Maize for Africa)-Association mapping panel with 278 tropical/sub-tropical breeding lines from CIMMYT using the high-density genotyping-by-sequencing (GBS) markers. This study identified 19 SNPs in the region between 82 and 93 Mb on chromosome 1(B73 RefGen_V2) at a P < 1.00E-04, which coincided with the fine-mapped region of Msv1. Haplotype trend regression identified a haplotype block significantly associated with response to MSV. Three SNPs in this haplotype block at 87 Mb on chromosome 1 had an accuracy of 0.94 in predicting the disease reaction in a collection of breeding lines with known responses to MSV infection. In two biparental populations, selection for resistant Msv1 haplotype demonstrated a reduction of 1.03-1.39 units on a rating scale of 1-5, compared to the susceptible haplotype. High-throughput KASP assays have been developed for these three SNPs to enable routine marker screening in the breeding pipeline for MSV resistance.


Assuntos
Mapeamento Cromossômico , Resistência à Doença/genética , Vírus do Listrado do Milho , Doenças das Plantas/genética , Locos de Características Quantitativas , Zea mays/genética , Cromossomos de Plantas , Marcadores Genéticos , Haplótipos , Fenótipo , Melhoramento Vegetal , Polimorfismo de Nucleotídeo Único , Zea mays/virologia
17.
G3 (Bethesda) ; 3(11): 1903-26, 2013 Nov 06.
Artigo em Inglês | MEDLINE | ID: mdl-24022750

RESUMO

Genotyping-by-sequencing (GBS) technologies have proven capacity for delivering large numbers of marker genotypes with potentially less ascertainment bias than standard single nucleotide polymorphism (SNP) arrays. Therefore, GBS has become an attractive alternative technology for genomic selection. However, the use of GBS data poses important challenges, and the accuracy of genomic prediction using GBS is currently undergoing investigation in several crops, including maize, wheat, and cassava. The main objective of this study was to evaluate various methods for incorporating GBS information and compare them with pedigree models for predicting genetic values of lines from two maize populations evaluated for different traits measured in different environments (experiments 1 and 2). Given that GBS data come with a large percentage of uncalled genotypes, we evaluated methods using nonimputed, imputed, and GBS-inferred haplotypes of different lengths (short or long). GBS and pedigree data were incorporated into statistical models using either the genomic best linear unbiased predictors (GBLUP) or the reproducing kernel Hilbert spaces (RKHS) regressions, and prediction accuracy was quantified using cross-validation methods. The following results were found: relative to pedigree or marker-only models, there were consistent gains in prediction accuracy by combining pedigree and GBS data; there was increased predictive ability when using imputed or nonimputed GBS data over inferred haplotype in experiment 1, or nonimputed GBS and information-based imputed short and long haplotypes, as compared to the other methods in experiment 2; the level of prediction accuracy achieved using GBS data in experiment 2 is comparable to those reported by previous authors who analyzed this data set using SNP arrays; and GBLUP and RKHS models with pedigree with nonimputed and imputed GBS data provided the best prediction correlations for the three traits in experiment 1, whereas for experiment 2 RKHS provided slightly better prediction than GBLUP for drought-stressed environments, and both models provided similar predictions in well-watered environments.


Assuntos
Genoma de Planta , Zea mays/genética , Cruzamento , Cromossomos/química , Cromossomos/metabolismo , Genótipo , Haplótipos , Modelos Genéticos , Fenótipo , Polimorfismo de Nucleotídeo Único , Análise de Sequência de DNA
18.
BMC Genomics ; 14: 313, 2013 May 10.
Artigo em Inglês | MEDLINE | ID: mdl-23663209

RESUMO

BACKGROUND: Identification of QTL with large phenotypic effects conserved across genetic backgrounds and environments is one of the prerequisites for crop improvement using marker assisted selection (MAS). The objectives of this study were to identify meta-QTL (mQTL) for grain yield (GY) and anthesis silking interval (ASI) across 18 bi-parental maize populations evaluated in the same conditions across 2-4 managed water stressed and 3-4 well watered environments. RESULTS: The meta-analyses identified 68 mQTL (9 QTL specific to ASI, 15 specific to GY, and 44 for both GY and ASI). Mean phenotypic variance explained by each mQTL varied from 1.2 to 13.1% and the overall average was 6.5%. Few QTL were detected under both environmental treatments and/or multiple (>4 populations) genetic backgrounds. The number and 95% genetic and physical confidence intervals of the mQTL were highly reduced compared to the QTL identified in the original studies. Each physical interval of the mQTL consisted of 5 to 926 candidate genes. CONCLUSIONS: Meta-analyses reduced the number of QTL by 68% and narrowed the confidence intervals up to 12-fold. At least the 4 mQTL (mQTL2.2, mQTL6.1, mQTL7.5 and mQTL9.2) associated with GY under both water-stressed and well-watered environments and detected up to 6 populations may be considered for fine mapping and validation to confirm effects in different genetic backgrounds and pyramid them into new drought resistant breeding lines. This is the first extensive report on meta-analysis of data from over 3100 individuals genotyped using the same SNP platform and evaluated in the same conditions across a wide range of managed water-stressed and well-watered environments.


Assuntos
Meio Ambiente , Flores/crescimento & desenvolvimento , Locos de Características Quantitativas , Estresse Fisiológico/efeitos dos fármacos , Água/farmacologia , Zea mays/crescimento & desenvolvimento , Zea mays/genética , Mapeamento Cromossômico , Relação Dose-Resposta a Droga , Flores/efeitos dos fármacos , Flores/genética , Genótipo , Fenótipo , Estresse Fisiológico/genética , Zea mays/efeitos dos fármacos , Zea mays/fisiologia
19.
G3 (Bethesda) ; 2(11): 1427-36, 2012 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-23173094

RESUMO

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.


Assuntos
Cruzamento , Quimera/genética , Genoma de Planta , Zea mays/genética , Análise de Variância , Meio Ambiente , Variação Genética , Modelos Estatísticos , Probabilidade , Característica Quantitativa Herdável
20.
Theor Appl Genet ; 125(7): 1487-501, 2012 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-22801872

RESUMO

Quality control (QC) genotyping is an important component in breeding, but to our knowledge there are not well established protocols for its implementation in practical breeding programs. The objectives of our study were to (a) ascertain genetic identity among 2-4 seed sources of the same inbred line, (b) evaluate the extent of genetic homogeneity within inbred lines, and (c) identify a subset of highly informative single-nucleotide polymorphism (SNP) markers for routine and low-cost QC genotyping and suggest guidelines for data interpretation. We used a total of 28 maize inbred lines to study genetic identity among different seed sources by genotyping them with 532 and 1,065 SNPs using the KASPar and GoldenGate platforms, respectively. An additional set of 544 inbred lines was used for studying genetic homogeneity. The proportion of alleles that differed between seed sources of the same inbred line varied from 0.1 to 42.3 %. Seed sources exhibiting high levels of genetic distance are mis-labeled, while those with lower levels of difference are contaminated or still segregating. Genetic homogeneity varied from 68.7 to 100 % with 71.3 % of the inbred lines considered to be homogenous. Based on the data sets obtained for a wide range of sample sizes and diverse genetic backgrounds, we recommended a subset of 50-100 SNPs for routine and low-cost QC genotyping, verified them in a different set of double haploid and inbred lines, and outlined a protocol that could be used to minimize errors in genetic analyses and breeding.


Assuntos
Técnicas de Genotipagem/métodos , Técnicas de Genotipagem/normas , Endogamia , Clima Tropical , Zea mays/genética , Alelos , Heterogeneidade Genética , Loci Gênicos/genética , Genótipo , Haploidia , Filogenia , Polimorfismo de Nucleotídeo Único/genética , Controle de Qualidade , Seleção Genética
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