Quantitative Traits In Maize Research Articles

Correlation and path coefficient analysis of quantitative traits in maize (Zea mays L.)

Correlation and path coefficient analysis of quantitative traits in maize (Zea mays L.)

Evaluation of Inbred Lines and Their Tester Hybrids and Estimation of Heterozygosity for Some Quantitative Traits in Maize

Nine inbred lines of maize were sowing :(OH, ZP- 301, unn440, Inbreed12, Zp- 607, SH, Ik-58, IK-8 and R-153). The first five was used as a tester (males) and the reminder used as line (females) during at 2014 season (spring) according to method (Line X Tester) to obtain 20 crosses, while the evaluation season was in Shiwan province / Kirkuk governorate of parents and its crosses and the commercial variety (Moring) at spring of 2015 using Random Complete Block Design (RCBD) design with three replicates. The studied traits were: (No. of days to 50% male flowering and female flowering, plant height, ear leaf area, No. of the rows ear-1, No. of the grains row-1, No. of the grains ear-1, 100 grains weight and grain yield plant-1). The most results were summarized as follows: The genotypes (parents, crosses and commercial variety) showed significant differences at 1% level probe for all studied except days to 50% female flowering for parents. Crosses gave highly (Inbred12 X IK-58) for ear leaf area (681.06 cm) and the cross (R-153 X Inbred 12) for both No. of the grains row-1 and grain yield plant-1 (760.23 grain) and (175.457gm) respectively, the cross (Ik-58 x OH) for 100 grains weight (30.387gm). The crosses (IK-58 X OH), (SH X Zp-301), (Zp-301 X Ik-8), (Ik-8 X Un-44052), (IK-8 X Inbred12), (R-153 X Inbred12) and (SH X Zp-607) gave desired significant heterosis over the better parents as well as the commercial variety for most traits included the grain yield plant-1. The parents SH, Zp-607 and Zp-301 to be used in single cross production and synthetic varieties, also the crosses (SH X OH), (SH X Un-44052), and (R-153 X Un-44052) in double cross production and compound crosses because these hybrids put in different main and secondary groups through cluster analysis.

BREEDIT: a multiplex genome editing strategy to improve complex quantitative traits in maize.

Ensuring food security for an ever-growing global population while adapting to climate change is the main challenge for agriculture in the 21st century. Although new technologies are being applied to tackle this problem, we are approaching a plateau in crop improvement using conventional breeding. Recent advances in CRISPR/Cas9-mediated gene engineering have paved the way to accelerate plant breeding to meet this increasing demand. However, many traits are governed by multiple small-effect genes operating in complex interactive networks. Here, we present the gene discovery pipeline BREEDIT, which combines multiplex genome editing of whole gene families with crossing schemes to improve complex traits such as yield and drought tolerance. We induced gene knockouts in 48 growth-related genes into maize (Zea mays) using CRISPR/Cas9 and generated a collection of over 1,000 gene-edited plants. The edited populations displayed (on average) 5%-10% increases in leaf length and up to 20% increases in leaf width compared with the controls. For each gene family, edits in subsets of genes could be associated with enhanced traits, allowing us to reduce the gene space to be considered for trait improvement. BREEDIT could be rapidly applied to generate a diverse collection of mutants to identify promising gene modifications for later use in breeding programs.

Phenotypic and molecular characterization of a set of tropical maize inbred lines from a public breeding program in Brazil

BackgroundThe characterization of genetic diversity and population differentiation for maize inbred lines from breeding programs is of great value in assisting breeders in maintaining and potentially increasing the rate of genetic gain. In our study, we characterized a set of 187 tropical maize inbred lines from the public breeding program of the Universidade Federal de Viçosa (UFV) in Brazil based on 18 agronomic traits and 3,083 single nucleotide polymorphisms (SNP) markers to evaluate whether this set of inbred lines represents a panel of tropical maize inbred lines for association mapping analysis and investigate the population structure and patterns of relationships among the inbred lines from UFV for better exploitation in our maize breeding program.ResultsOur results showed that there was large phenotypic and genotypic variation in the set of tropical maize inbred lines from the UFV maize breeding program. We also found high genetic diversity (GD = 0.34) and low pairwise kinship coefficients among the maize inbred lines (only approximately 4.00 % of the pairwise relative kinship was above 0.50) in the set of inbred lines. The LD decay distance over all ten chromosomes in the entire set of maize lines with r2 = 0.1 was 276,237 kb. Concerning the population structure, our results from the model-based STRUCTURE and principal component analysis methods distinguished the inbred lines into three subpopulations, with high consistency maintained between both results. Additionally, the clustering analysis based on phenotypic and molecular data grouped the inbred lines into 14 and 22 genetic divergence clusters, respectively.ConclusionsOur results indicate that the set of tropical maize inbred lines from UFV maize breeding programs can comprise a panel of tropical maize inbred lines suitable for a genome-wide association study to dissect the variation of complex quantitative traits in maize, mainly in tropical environments. In addition, our results will be very useful for assisting us in the assignment of heterotic groups and the selection of the best parental combinations for new breeding crosses, mapping populations, mapping synthetic populations, guiding crosses that target highly heterotic and yielding hybrids, and predicting untested hybrids in the public breeding program UFV.

Chromatin interaction maps reveal genetic regulation for quantitative traits in maize

Chromatin loops connect regulatory elements to their target genes. They serve as bridges between transcriptional regulation and phenotypic variation in mammals. However, spatial organization of regulatory elements and its impact on gene expression in plants remain unclear. Here, we characterize epigenetic features of active promoter proximal regions and candidate distal regulatory elements to construct high-resolution chromatin interaction maps for maize via long-read chromatin interaction analysis by paired-end tag sequencing (ChIA-PET). The maps indicate that chromatin loops are formed between regulatory elements, and that gene pairs between promoter proximal regions tend to be co-expressed. The maps also demonstrated the topological basis of quantitative trait loci which influence gene expression and phenotype. Many promoter proximal regions are involved in chromatin loops with distal regulatory elements, which regulate important agronomic traits. Collectively, these maps provide a high-resolution view of 3D maize genome architecture, and its role in gene expression and phenotypic variation.

Prospective Targeted Recombination and Genetic Gains for Quantitative Traits in Maize.

Advances in clustered regularly interspaced short palindromic repeats (CRISPR) technology have allowed targeted recombination in specific DNA sequences in yeast (). My objective was to determine if the selection gains from targeted recombination are large enough to warrant the development of targeted recombination technology in plants. Genomewide marker effects for quantitative traits in two maize ( L.) experiments were used to identify targeted recombination points that would maximize the per-chromosome genetic gains in a given cross. With nontargeted recombination in the intermated B73 × Mo17 population, selecting the best out of 180 recombinant inbreds led to a 7.1% gain for testcross yield. Having one targeted recombination on each of the 10 maize chromosomes led to a predicted gain of 15.3% for yield. Targeted recombination therefore led to a predicted relative efficiency () of (0.153 ÷ 0.071) = 212% of targeted recombination compared with nontargeted recombination. For the five other traits in the intermated B73 × Mo17 population and for four traits in 45 other maize crosses, the values ranged from 105 to 600%. The targeted recombination points differed among traits and crosses. Predicted gains increased when the number of targeted recombinations per chromosome increased from one to two. Overall, the results suggested that targeted recombination could double the selection gains for quantitative traits in maize and that the development of targeted recombination technology is worthwhile. Empirical experiments with current marker-assisted breeding procedures are needed to validate the per-chromosome predicted gains.

Germplasm Architecture Revealed through Chromosomal Effects for Quantitative Traits in Maize.

Germplasm architecture refers to how favorable alleles for a given trait are distributed across the genome in a germplasm collection. Our objective was to assess germplasm architecture for quantitative traits among US maize ( L.) inbreds. A total of 271 inbreds were genotyped at 28,626 single nucleotide polymorphism (SNP) loci and phenotyped for anthesis date, plant height, starch and protein concentration, and resistance to northern corn leaf blight (NCLB, caused by ). Chromosomal effects were calculated as the sum of the trait effects of SNP alleles carried on a specific chromosome by an inbred. The chromosomal effects were further decomposed into the mean effects of chromosomes, mean effects of inbreds, and chromosome × inbred effects. On average, none of the 10 maize chromosomes was particularly rich or poor in favorable quantitative trait locus (QTL) alleles. However, extreme values of chromosome × inbred effects often involved chromosomes 5 and 8 for anthesis date, chromosomes 1 and 5 for plant height, and chromosome 9 for protein concentration. Inbreds with one or two chromosomes deficient in favorable alleles were candidates for improvement via chromosome-substitution lines. Specific chromosomes for which each of five genetic backgrounds (B73, Mo17, Oh43, A321, and PH207) were rich or poor for unknown favorable alleles were also identified. Chromosomal effects varied widely even when prior association mapping in the same germplasm collection had failed to identify any QTL. Genomewide marker effects, particularly when partitioned into chromosomal effects, provide a simple way to dissect germplasm architecture for quantitative traits.

Identification of QTL for maize grain yield and kernel-related traits.

Grain yield (GY) is one of the most important and complex quantitative traits in maize (Zea mays L.) breeding practice. Quantitative trait loci (QTLs) for GY and three kernel-related traits were detected in a set of recombinant inbred lines (RILs). One hundred and seven simple sequence repeats (SSRs) and 168 insertion/deletion polymorphism markers (Indels) were used to genotype RILs. Eight QTLs were found to be associated with four yield-related traits: GY, 100-kernel weight (HKW), 10-kernel length (KL), and 10-kernel length width (KW). Each QTL explained between 5.96 (qKL2-1) and 13.05 (qKL1-1) per cent of the phenotypic variance. Notably, one common QTL, located at the marker interval between bnlg1893 and chr2- 236477 (chromosomal bin 2.09) simultaneously controlled GY and HKW; another common QTL, at bin 2.03 was simultaneously responsible for HKW and KW. Of the QTLs identified, only one pair of significant epistatic interaction involved in chromosomal region at bin 2.03 was detected for HKW; no significant QTL × environment interactions were observed. These results provide the common QTLs and for marker-assisted breeding.

An ultra-high-density map as a community resource for discerning the genetic basis of quantitative traits in maize.

BackgroundTo safeguard the food supply for the growing human population, it is important to understand and exploit the genetic basis of quantitative traits. Next-generation sequencing technology performs advantageously and effectively in genetic mapping and genome analysis of diverse genetic resources. Hence, we combined re-sequencing technology and a bin map strategy to construct an ultra-high-density bin map with thousands of bin markers to precisely map a quantitative trait locus.ResultsIn this study, we generated a linkage map containing 1,151,856 high quality SNPs between Mo17 and B73, which were verified in the maize intermated B73 × Mo17 (IBM) Syn10 population. This resource is an excellent complement to existing maize genetic maps available in an online database (iPlant, http://data.maizecode.org/maize/qtl/syn10/). Moreover, in this population combined with the IBM Syn4 RIL population, we detected 135 QTLs for flowering time and plant height traits across the two populations. Eighteen known functional genes and twenty-five candidate genes for flowering time and plant height trait were fine-mapped into a 2.21–4.96 Mb interval. Map expansion and segregation distortion were also analyzed, and evidence for inadvertent selection of early flowering time in the process of mapping population development was observed. Furthermore, an updated integrated map with 1,151,856 high-quality SNPs, 2,916 traditional markers and 6,618 bin markers was constructed. The data were deposited into the iPlant Discovery Environment (DE), which provides a fundamental resource of genetic data for the maize genetic research community.ConclusionsOur findings provide basic essential genetic data for the maize genetic research community. An updated IBM Syn10 population and a reliable, verified high-quality SNP set between Mo17 and B73 will aid in future molecular breeding efforts.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-2242-5) contains supplementary material, which is available to authorized users.

Genic and nongenic contributions to natural variation of quantitative traits in maize

The complex genomes of many economically important crops present tremendous challenges to understand the genetic control of many quantitative traits with great importance in crop production, adaptation, and evolution. Advances in genomic technology need to be integrated with strategic genetic design and novel perspectives to break new ground. Complementary to individual-gene–targeted research, which remains challenging, a global assessment of the genomic distribution of trait-associated SNPs (TASs) discovered from genome scans of quantitative traits can provide insights into the genetic architecture and contribute to the design of future studies. Here we report the first systematic tabulation of the relative contribution of different genomic regions to quantitative trait variation in maize. We found that TASs were enriched in the nongenic regions, particularly within a 5-kb window upstream of genes, which highlights the importance of polymorphisms regulating gene expression in shaping the natural variation. Consistent with these findings, TASs collectively explained 44%–59% of the total phenotypic variation across maize quantitative traits, and on average, 79% of the explained variation could be attributed to TASs located in genes or within 5 kb upstream of genes, which together comprise only 13% of the genome. Our findings suggest that efficient, cost-effective genome-wide association studies (GWAS) in species with complex genomes can focus on genic and promoter regions.

Combining Ability and Heterosis for Yield and Quantitative Traits in Maize, Zea mays (L.)

Combining ability analysis was conducted in late maturing maize (Zea mays L.) inbred lines for yield attributes. Both additive and non additive gene effects were present in the material under study. Variance due to gca was preponderant for days to 50% pollen shedding, days to 50% silk emergence, plant height, ear height, number of kernel rows, number of kernels per row and grain yield indicating importance of additive genes in controlling the traits. Three lines KML-70, KML-223 and KML-286 and two testers, KML-36 and KML-226 were good general combiners for grain yield. The crosses KML-57 x KML-226, KML-70 x KML-3, KML-70 x KML-29 and KML-286 x KML-29 are desirable with significant positive sca, heterosis and high mean values for grain yield. KML-223 x KML-3 recorded significant negative heterosis for days to 75 per cent dry husk, combined with significant negative sca and low mean performance. Hence, this hybrid could be suitable for medium maturity situations.

The relationship between parental genetic or phenotypic divergence and progeny variation in the maize nested association mapping population

Appropriate selection of parents for the development of mapping populations is pivotal to maximizing the power of quantitative trait loci detection. Trait genotypic variation within a family is indicative of the family's informativeness for genetic studies. Accurate prediction of the most useful parental combinations within a species would help guide quantitative genetics studies. We tested the reliability of genotypic and phenotypic distance estimators between pairs of maize inbred lines to predict genotypic variation for quantitative traits within families derived from biparental crosses. We developed 25 families composed of ~200 random recombinant inbred lines each from crosses between a common reference parent inbred, B73, and 25 diverse maize inbreds. Parents and families were evaluated for 19 quantitative traits across up to 11 environments. Genetic distances (GDs) among parents were estimated with 44 simple sequence repeat and 2303 single-nucleotide polymorphism markers. GDs among parents had no predictive value for progeny variation, which is most likely due to the choice of neutral markers. In contrast, we observed for about half of the traits measured a positive correlation between phenotypic parental distances and within-family genetic variance estimates. Consequently, the choice of promising segregating populations can be based on selecting phenotypically diverse parents. These results are congruent with models of genetic architecture that posit numerous genes affecting quantitative traits, each segregating for allelic series, with dispersal of allelic effects across diverse genetic material. This architecture, common to many quantitative traits in maize, limits the predictive value of parental genotypic or phenotypic values on progeny variance.

Characterization of a global germplasm collection and its potential utilization for analysis of complex quantitative traits in maize

Association mapping is a powerful approach for exploring the molecular basis of phenotypic variations in plants. A maize (Zea mays L.) association mapping panel including 527 inbred lines with tropical, subtropical and temperate backgrounds, representing the global maize diversity, was genotyped using 1,536 single nucleotide polymorphisms (SNPs). In total, 926 SNPs with minor allele frequencies of ≥0.1 were used to estimate the pattern of genetic diversity and relatedness among individuals. The analysis revealed broad phenotypic diversity and complex genetic relatedness in the maize panel. Two different Bayesian approaches identified three specific subpopulations, which were then reconfirmed by principal component analysis (PCA) and tree-based analyses. Marker–trait associations were performed to assess the suitability of different models for false-positive correction by population structure (Q matrix/PCA) and familial kinship (K matrix) alone or in combination in this panel. The K, Q + K and PCA + K models could reduce the false positives, and the Q + K model performed slightly better for flowering time, ear height and ear diameter. Our findings suggest that this maize panel is suitable for association mapping in order to understand the relationship between genotypic and phenotypic variations for agriculturally complex quantitative traits using optimal statistical methods.

Prospects for Genomewide Selection for Quantitative Traits in Maize

The availability of cheap and abundant molecular markers in maize (Zea mays L.) has allowed breeders to ask how molecular markers may best be used to achieve breeding progress, without conditioning the question on how breeding has traditionally been done. Genomewide selection refers to marker-based selection without first identifying a subset of markers with significant effects. Our objectives were to assess the response due to genomewide selection compared with marker-assisted recurrent selection (MARS) and to determine the extent to which phenotyping can be minimized and genotyping maximized in genomewide selection. We simulated genomewide selection by evaluating doubled haploids for testcross performance in Cycle 0, followed by two cycles of selection based on markers. Individuals were genotyped for NM markers, and breeding values associated with each of the NM markers were predicted and were all used in genomewide selection. We found that across different numbers of quantitative trait loci (20, 40, and 100) and levels of heritability, the response to genomewide selection was 18 to 43% larger than the response to MARS. Responses to selection were maintained when the number of doubled haploids phenotyped and genotyped in Cycle 0 was reduced and the number of plants genotyped in Cycles 1 and 2 was increased. Such schemes that minimize phenotyping and maximize genotyping would be feasible only if the cost per marker data point is reduced to about 2 cents. The convenient but incorrect assumption of equal marker variances led to only a minimal loss in the response to genomewide selection. We conclude that genomewide selection, as a brute-force and black-box procedure that exploits cheap and abundant molecular markers, is superior to MARS in maize.

Marker analysis of quantitative traits in maize by ISSR-PCR

The segregating maize population (GK26 x Mo17)F2 has been used for identification of ISSR markers able to reveal a significant difference between alleles by a quantitative index. Confidence ranges have been determined for variation in 17 quantitative traits. Variations in the traits under study correlate with the inheritance of 16 marker loci have been found. The nature of these correlations and the possibility of chromosomal mapping of genetic markers are discussed.

Genetic Mapping in Maize with Hybrid Progeny Across Testers and Generations: Grain Yield and Grain Moisture

Most complex quantitative traits in maize (Zea mays L.), especially grain yield, display low correlations between the trait values observed in inbred and hybrid progeny. Comparisons of quantitative trait loci (QTL) controlling inbred per se and hybrid performance are needed to understand the underlying genetic factors, and to determine the utility of QTL detected in the two progeny types. DNA markers were used to identify QTL for grain yield and grain moisture in hybrid progeny of F2:3 and F6:8 lines from a Mo17xH99 population. For both generations, testcross progeny were developed by crossing the lines to three inbred testers (B91, A632, B73). Each testcross population was evaluated in field trials with two replications in eight environments. The testcross progeny from the two generations were evaluated at the same locations but in different years. QTL were identified within each testcross population and for mean testcross (MTC) performance. Individual tester QTL effects were not consistent in rank or detection across generations; however, parental contributions were consistent. MTC effects were more consistent across generations with most of the QTL with large effects being detected across generations. QTL detected with only one tester were not necessarily detected for the other two testers, especially for grain yield, but parental contributions were consistent when QTL were detected in a region for more than one tester. The QTL for grain yield identified in this population for inbred and hybrid progeny show only partial correspondence, indicating that marker‐assisted selection programs would need to identify and incorporate QTL for both progeny types.

Mapping and manipulating quantitative traits in maize

Maize has been used effectively as a model organism in the development and evaluation of molecular markers for the identification, mapping and manipulation of major genes affecting the expression of quantitative traits in plants. Although quantitative geneticists have recognized the possibility of major loci, the general dogma bad emerged that quantitative traits were controlled by many loci, each with a small effect. This interpretation sent a signal to the molecular biologist not to bother with quantitative traits because it would be essentially impossible to isolate a gene responsible for the trait. Recent results from numerous mapping studies have shown that quantitative traits are controlled by, at least some, factors with major effects, and have given credibility to the conclusion that major loci exist and that one might be able to study them. Positive results from marker-facilitated selection and introgression studies have further strengthened this conclusion.

Inheritance of Grain Yield and Other Quantitative Traits in Maize

SUMMARYCombining ability analysis was carried out on diallel crosses of 20 yellow maize varieties in four environments. General combining ability variance (σ2g) was of greater importance than specific combining ability (σ2s) in the inheritance of all traits except grain yield and ear length, where the reverse was true. Interaction components (σ2ge, σ2se) were greater than the respective main components (σ2g, σ2s) for grain yield. The study brought out the prominent role of genotype-environmental interactions. Heritability in the broad sense was very high for all traits except grain yield and grain moisture, and narrow sense heritability was also high for all traits except grain yield and ear length.

Epistasis in Maize (Zea mays L.): IV. Crosses Among Lines Selected for Superior Intervariety Single Cross Performances1

Five quantitative traits in maize (Zea mays L.) were investigated to evaluate the statistical significance of epistasis as well as its importance in hybrid predictions in 18 sets of single, three‐way, and double crosses. Parental lines for the sets were selectively grouped based on superior specific combining ability evaluated in interpopulation single‐cross combinations. Although variability analyses failed to detect significant epistatic effects in yield and lodging, mean comparisons showed small but significant epistatic contributions in yield, ear number, plant height, and ear height.Comparisons of the relative importance of errors caused by epistatic effects with those errors caused by genotype by environmental interaction effects in predicting hybrid performances were made. For yield, ear number, and ear height, predictions based on data from single environments showed greater discrepancies due to genotype by environmental interaction effects than to epistatic effects. Also, for yield and ear number, the use of single crosses to predict three‐way and double crosses in other environments was more reliable than the use of observed performances of three‐way and double crosses to predict their respective performances in other environments.

Estimating Genetic Variance in Maize by Use of Single and Three‐way Crosses among Unselected Inbred Lines1

Unweighted least squares and maximum likelihood procedures were used and compared for the estimation of genetic variance for eight quantitative traits in maize (Zea mays L.). The genetic material was developed from unselected inbred lines isolated from a strain of Krug Yellow Dent maize. All possible single and 3‐way crosses were produced from 60 inbred lines, which traced back to 51 S0 plants. The mean squares from the diallel and triallel analyses were used in estimating the genetic components of variance.Fitting the error and a six‐parameter genetic model showed that: 1) it was not possible to obtain realistic estimates of the epistatic components, although significant effects were detected in the analyses of variance; 2) the estimates of additive geuetic variance were significant for all traits for both estimation procedures; 3) the nonadditive components accounted for only a small proportion of the total genetic variance; 4) three iterations of the maximum likelihood procedure were sufficient to stabilize the estimates; and 5) the maximum likelihood procedure generally reduced the errors of the estimates.For the two‐parameter genetic model the largest proportion of the total genetic variance was additive for all traits. The estimates of deviations due to dominance were larger than twice their standard errors for all traits in the two‐parameter model for the combined single and three‐way cross data. The frequency of significant interactions with environments was higher for the additive than for the dominance effects.