Genomic prediction of seedling root length in maize (Zea mays L.).

Genotypes with extreme phenotypes are valuable for studying 'difficult' quantitative traits. Genomic prediction (GP) might allow the identification of such extremes by phenotyping a training population of limited size and predicting genotypes with extreme phenotypes in large sequences of germplasm collections. We tested this approach employing seedling root traits in maize and the extensively genotyped Ames Panel. A training population made up of 384 inbred lines from the Ames Panel was phenotyped by extracting root traits from images using the software program aria. A ridge regression best linear unbiased prediction strategy was used to train a GP model. Genomic estimated breeding values for the trait 'total root length' (TRL) were predicted for 2431 inbred lines, which had previously been genotyped by sequencing. Selections were made for 100 extreme TRL lines and those with the predicted longest or shortest TRL were validated for TRL and other root traits. The two predicted extreme groups with regard to TRL were significantly different (P = 0.0001). The difference in 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 between 1 and 200 of the validation population based on TRL (longest to shortest) was determined using a Spearman correlation to be ρ = 0.55. Taken together, our results support the idea that GP may be a useful approach for identifying the most informative genotypes in sequenced germplasm collections to facilitate experiments for quantitative inherited traits.

Genome-wide association analysis of seedling root development in maize (Zea mays L.).

BACKGROUND: Plants rely on the root system for anchorage to the ground and the acquisition and absorption of nutrients critical to sustaining productivity. A genome wide association analysis enables one to analyze allelic diversity of complex traits and identify superior alleles. 384 inbred lines from the Ames panel were genotyped with 681,257 single nucleotide polymorphism markers using Genotyping-by-Sequencing technology and 22 seedling root architecture traits were phenotyped. RESULTS: Utilizing both a general linear model and mixed linear model, a GWAS study was conducted identifying 268 marker trait associations (p ≤ 5.3×10(-7)). Analysis of significant SNP markers for multiple traits showed that several were located within gene models with some SNP markers localized within regions of 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. CONCLUSION: This study identifies putatively associated SNP markers associated with root traits at the seedling stage. Some SNPs were located within or near (<1 kb) gene models. These gene models identify possible candidate genes involved in root development at the seedling stage. These and respective linked or functional markers could be targets for breeders for marker assisted selection of seedling root traits.

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

A better understanding of the genetic control of root development might allow one to develop lines with root systems with 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.

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

KEY MESSAGE: Understanding the correlations of seven minerals for concentration, content and yield in maize grain, and exploring their genetic basis will help breeders to develop high grain quality maize. Biofortification by enhanced mineral accumulation in grain through genetic improvement is an efficient way to solve global nutrient malnutrition, in which one key step is to detect the underlying quantitative trait loci (QTL). Herein, a maize recombinant inbred population (RIL) was field grown to maturity across four environments (two locations × two years). Phenotypic data for grain mineral concentration, content and yield were determined for copper (Cu), iron (Fe), manganese (Mn), zinc (Zn), magnesium (Mg), potassium (K) and phosphorus (P). Significant effects of genotype, location and year were observed for all investigated traits. The strongest location effects were found for Zn accumulation traits probably due to distinct soil Zn availabilities across locations. Heritability (H (2)) of different traits varied with higher H (2) (72-85 %) for mineral concentration and content, and lower (48-63 %) for mineral yield. Significant positive correlations for grain concentration were revealed between several minerals. QTL analysis revealed 28, 25, and 12 QTL for mineral concentration, content and yield, respectively; and identified 8 stable QTL across at least two environments. All these QTL were assigned into 12 distinct QTL clusters. A cluster at chromosome Bin 6.07/6.08 contained 6 QTL for kernel weight, mineral concentration (Mg) and content (Zn, K, Mg, P). Another cluster at Bin 4.05/4.06 contained a stable QTL for Mn concentration, which were previously identified in other maize and rice RIL populations. These results highlighted the phenotypic and genetic performance of grain mineral accumulation, and revealed two promising chromosomal regions for genetic improvement of grain biofortification in maize.

Analysis of maize (Zea mays L.) seedling roots with the high-throughput image analysis tool ARIA (Automatic Root Image Analysis).

The maize root system is crucial for plant establishment as well as water and nutrient uptake. 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 them, as well as their quantitative mode of inheritance. Seedling root traits offer an opportunity to study multiple individuals and to enable repeated measurements per year as compared to adult root phenotyping. We developed a new software framework to capture various traits from a single image of seedling roots. This framework is 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. In order to evaluate this tool, a subset of the 384 inbred lines from the Ames panel, for which extensive genotype by sequencing data are available, was investigated. A genome wide association study was applied to this panel for two traits, Total Root Length and Total Surface Area, captured from seedling root images from WinRhizo Pro 9.0 and the current framework (called ARIA) for comparison using 135,311 single nucleotide polymorphism markers. The trait Total Root Length was found to have significant SNPs in similar regions of the genome when analyzed by both programs. This high-throughput trait capture software system allows for large phenotyping experiments and can help to establish relationships between developmental stages between seedling and adult traits in the future.

Association analysis of single nucleotide polymorphisms in candidate genes with root traits in maize (Zea mays L.) seedlings.

Several genes involved in maize root development have been isolated. Identification of SNPs associated with root traits would enable the selection of maize lines with better root architecture that might help to improve N uptake, and consequently plant growth particularly under N deficient conditions. In the present study, an association study (AS) panel consisting of 74 maize inbred lines was screened for seedling root traits in 6, 10, and 14-day-old seedlings. Allele re-sequencing of candidate root genes Rtcl, Rth3, Rum1, and Rul1 was also carried out in the same AS panel lines. All four candidate genes displayed different levels of nucleotide diversity, haplotype diversity and linkage disequilibrium. Gene based association analyses were carried out between individual polymorphisms in candidate genes, and root traits measured in 6, 10, and 14-day-old maize seedlings. 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. Thus considerable allelic variation present in these root genes can be exploited for improving maize root characteristics.