Genetic architecture and predictability of seedling root traits in maize (Zea mays L.)

The maize (Zea mays L.) root system is important for proper growth and productivity of the plant. There is substantial genetic and phenotypic variation for root architecture, which gives opportunity for selection. Root traits, however, have not been used as selection criterion mainly due to the difficulty in measuring, as well as their quantitative mode of inheritance. Studying seedling roots offer an opportunity to study multiple individuals and to enable repeated measurements per year as compared to adult root phenotyping. Here we have evaluated phenotypic and genotypic variation within seedling root traits for two panels of inbred lines. Constructed maize association mapping panels were used within both a candidate gene based and genome-wide based association study to connect putative QTL with seedling root traits. Also, to collect seedling root phenotypes a software program ARIA (Automatic Root Image Analysis) was developed in order to allow for larger scale quantitative studies. A Genome-wide association mapping panel was also used as a training population to predict the performance in relation to total root length at the seedling stage of a large set of maize inbred lines. Candidate gene association analyses revealed several polymorphisms within the Rtcl, Rth3, Rum1, and Rul1 genes associated with seedling root traits. Several nucleotide polymorphisms in Rtcl, Rth3, Rum1, and Rul1 were significantly (P\u3c0.05) associated with seedling root traits in maize suggesting that all four tested genes are involved in the maize root development. We developed a new software framework to capture various traits from a single image of seedling roots based on the mathematical notion of converting images of roots into an equivalent graph. This allows automated querying of multiple traits simply as graph operations. This framework is furthermore extendable to 3D tomography image data. Within a Genome-wide association analysis utilizing both a general linear model and mixed linear model, a GWAS study was conducted identifying 268 marker trait associations (p ≤ 5.3x10-7). Analysis of significant SNP markers for multiple traits showed that several were located within gene models with some SNP markers localized within regions with previously identified root quantitative trait loci. Gene model GRMZM2G153722 located on chromosome 4 contained nine significant markers. This predicted gene is expressed in roots and shoots. Finally a Genomic prediction study successfully predicted extreme groups with regard to TRL were significantly different (p=0.0001). The difference of predicted means for TRL between groups was 145.1 cm, and 118.7 cm for observed means, which were significantly different (p=0.001). The accuracy of predicting the rank 1-200 of the validation population based on TRL, longest to shortest was determined using a Spearman correlation to be ρ=0.55. In conclusion this work exemplifies the vast amount of diversity seen within root architecture even at the seedling stage and lays ground work for future studies two build upon moving forward studying roots

Comprehensive phenotypic analysis and quantitative trait locus identification for grain mineral concentration, content, and yield in maize (Zea mays L.)

Biofortification by enhanced mineral density in maize grain through genetic improvement is one of the efficient ways to solve global mineral malnutrition, in which one key step is to detect the corresponding Quantitative Trait Loci (QTL). In this work, a maize recombinant inbred population (RIL) was grown to maturity in four field environments with two locations × two years. Phenotypic data of mineral nutrition concentration, content and yield were determined for grain copper (Cu), iron (Fe), manganese (Mn), zinc (Zn), magnesium (Mg), potassium (K) and phosphorus (P). Analysis of variance (ANOVA) showed significant effects of genotype, location and year for all investigated traits. Location showed the highest effect for all mineral yields, and Zn and Cu content and concentration, while year had the strongest impact for Mn, K, and P content and concentration. Heritabilities (h2) of different traits varied with higher h2 (72-85%) for mineral concentration and content and lower (48-63%) for nutrient yields. Correlation coefficient analysis revealed significant positive correlations for grain concentration between several minerals. P had the closest correlations to other elements, while Cu had the lowest. When environments were analyzed individually, a total of 28, 25, and 12 QTL were identified for nutrient concentration, content and yield, respectively. Among these QTL, 8 QTL were consistent within traits across different environments. These stable QTL may be most promising for controlling mineral accumulation in maize grain. Co-localization of QTL for different traits was found for 12 chromosome regions, suggesting that common processes might contribute seed nutrient accumulatio

Association analysis of genes involved in maize (Zea mays L.) root development with seedling and agronomic traits under contrasting nitrogen levels

Genotypes with large and well distributed root system might have the potential to adapt to soils with limited nutrient availability. For this purpose, an association study (AS) panel consisting of 74 diverse set of inbred maize lines were screened for seedling root traits and adult plant root traits under two contrasting nitrogen (N) levels (low and high N). Allele re-sequencing of RTCL, RTH3, RUM1, and RUL1 genes related to root development was carried out for AS panel lines. Association analysis was carried out between individual polymorphisms, and both seedling and adult plant traits, while controlling for spurious associations due to population structure and kinship relations. Based on the SNPs identified in RTCL, RTH3, RUM1, and RUL1, lines within the AS panel were grouped into 16, 9, 22, and 7 haplotypes, respectively. Association analysis revealed several polymorphisms within root genes putatively associated with the variability in seedling root and adult plant traits development under contrasting N levels. The highest number of significantly associated SNPs with seedling root traits were found in RTCL (19 SNPs) followed by RUM1 (4 SNPs) and in case of RTH3 and RUL1, two and three SNPs, respectively, were significantly associated with root traits. RTCL and RTH3 were also found to be associated with grain yield. Thus considerable allelic diversity is present within the candidate genes studied and can be utilized to develop functional markers that allow identification of maize lines with improved root architecture and yield under N stress conditions

Miscellany

Art Literature Roy F. Powell Creditshttps://digitalcommons.georgiasouthern.edu/miscell/1001/thumbnail.jp

Genetic architecture and predictability of seedling root traits in maize (Zea mays L.)

The maize (Zea mays L.) root system is important for proper growth and productivity of the plant. There is substantial genetic and phenotypic variation for root architecture, which gives opportunity for selection. Root traits, however, have not been used as selection criterion mainly due to the difficulty in measuring, as well as their quantitative mode of inheritance. Studying seedling roots offer an opportunity to study multiple individuals and to enable repeated measurements per year as compared to adult root phenotyping. Here we have evaluated phenotypic and genotypic variation within seedling root traits for two panels of inbred lines. Constructed maize association mapping panels were used within both a candidate gene based and genome-wide based association study to connect putative QTL with seedling root traits. Also, to collect seedling root phenotypes a software program ARIA (Automatic Root Image Analysis) was developed in order to allow for larger scale quantitative studies. A Genome-wide association mapping panel was also used as a training population to predict the performance in relation to total root length at the seedling stage of a large set of maize inbred lines. Candidate gene association analyses revealed several polymorphisms within the Rtcl, Rth3, Rum1, and Rul1 genes associated with seedling root traits. Several nucleotide polymorphisms in Rtcl, Rth3, Rum1, and Rul1 were significantly (P<0.05) associated with seedling root traits in maize suggesting that all four tested genes are involved in the maize root development. We developed a new software framework to capture various traits from a single image of seedling roots based on the mathematical notion of converting images of roots into an equivalent graph. This allows automated querying of multiple traits simply as graph operations. This framework is furthermore extendable to 3D tomography image data. Within a Genome-wide association analysis utilizing both a general linear model and mixed linear model, a GWAS study was conducted identifying 268 marker trait associations (p ≤ 5.3x10-7). Analysis of significant SNP markers for multiple traits showed that several were located within gene models with some SNP markers localized within regions with previously identified root quantitative trait loci. Gene model GRMZM2G153722 located on chromosome 4 contained nine significant markers. This predicted gene is expressed in roots and shoots. Finally a Genomic prediction study successfully predicted extreme groups with regard to TRL were significantly different (p=0.0001). The difference of predicted means for TRL between groups was 145.1 cm, and 118.7 cm for observed means, which were significantly different (p=0.001). The accuracy of predicting the rank 1-200 of the validation population based on TRL, longest to shortest was determined using a Spearman correlation to be ρ=0.55. In conclusion this work exemplifies the vast amount of diversity seen within root architecture even at the seedling stage and lays ground work for future studies two build upon moving forward studying roots.</p

Analysis of a diseased maple leaf, <i>ARIA’s</i> flexible mainframe will allow multiple uses of the program beyond root phenotyping.

<p>Analysis of a diseased maple leaf, <i>ARIA’s</i> flexible mainframe will allow multiple uses of the program beyond root phenotyping.</p

Comparison of repeatability estimates for both WhinRhizo Pro 9.0 and <i>ARIA.</i>

<p>Comparison of repeatability estimates for both WhinRhizo Pro 9.0 and <i>ARIA.</i></p

Essay on Timbering and Arching of Levels

種別: 卒業論

Manhattan plot displaying all 10 maize chromosomes, showing SNP markers significantly associated with trait Total Root Length measured with <i>ARIA</i>.

<p>Significant SNPs are located on chromosomes 1, 2, and 4.</p