We investigated the genetic and statistical properties of the nested association mapping (NAM) design currently being implemented in maize (26 diverse founders and 5000 distinct immortal genotypes) to dissect the genetic basis of complex quantitative traits. The NAM design simultaneously exploits the advantages of both linkage analysis and association mapping. We demonstrated the power of NAM for high-power cost-effective genome scans through computer simulations based on empirical marker data and simulated traits with different complexities. With common-parent-specific (CPS) markers genotyped for the founders and the progenies, the inheritance of chromosome segments nested within two adjacent CPS markers was inferred through linkage. Genotyping the founders with additional high-density markers enabled the projection of genetic information, capturing linkage disequilibrium information, from founders to progenies. With 5000 genotypes, 30-79% of the simulated quantitative trait loci (QTL) were precisely identified. By integrating genetic design, natural diversity, and genomics technologies, this new complex trait dissection strategy should greatly facilitate endeavors to link molecular variation with phenotypic variation for various complex traits.

/pdf/genetic-design-and-statistical-power-of-nested-association-6pmd14fv34.pdf

Genetic Design and Statistical Power of Nested Association Mapping in Maize

Heritability (H) on a progeny mean basis is frequently estimated in recurrent selection experiments for the purpose of estimating the expected progress from family selection; however, appropriate measures of precision have been developed for only a few heritability estimators. The objective of this research was to develop a measure of precision for H for certain balanced linear models. Exact confidence intervals for H were derived and are not restricted to a specific experimental design. The confidence intervals were applied to sorghum [Sorghum bicolor (L.) Moench] half-sib family data.

An exact confidence interval for heritability on a progeny mean basis

Plants produce a variety of metabolites that have a critical role in growth and development. Here we present a comprehensive study of maize metabolism, combining genetic, metabolite and expression profiling methodologies to dissect the genetic basis of metabolic diversity in maize kernels. We quantify 983 metabolite features in 702 maize genotypes planted at multiple locations. We identify 1,459 significant locus-trait associations (P≤1.8 × 10(-6)) across three environments through metabolite-based genome-wide association mapping. Most (58.5%) of the identified loci are supported by expression QTLs, and some (14.7%) are validated through linkage mapping. Re-sequencing and candidate gene association analysis identifies potential causal variants for five candidate genes involved in metabolic traits. Two of these genes were further validated by mutant and transgenic analysis. Metabolite features associated with kernel weight could be used as biomarkers to facilitate genetic improvement of maize.

/pdf/metabolome-based-genome-wide-association-study-of-maize-qqp4d4dqbw.pdf

Metabolome-based genome-wide association study of maize kernel leads to novel biochemical insights

Height is one of the most heritable and easily measured traits in maize (Zea mays L.). Given a pedigree or estimates of the genomic identity-by-state among related plants, height is also accurately predictable. But, mapping alleles explaining natural variation in maize height remains a formidable challenge. To address this challenge, we measured the plant height, ear height, flowering time, and node counts of plants grown in .64,500 plots across 13 environments. These plots contained .7300 inbreds representing most publically available maize inbreds in the United States and families of the maize Nested Association Mapping (NAM) panel. Joint- linkage mapping of quantitative trait loci (QTL), fine mapping in near isogenic lines (NILs), genome-wide association studies (GWAS), and genomic best linear unbiased prediction (GBLUP) were performed. The heritability of maize height was estimated to be .90%. Mapping NAM family-nested QTL revealed the largest explained 2.1 6 0.9% of height variation. The effects of two tropical alleles at this QTL were independently validated by fine mapping in NIL families. Several significant associations found by GWAS colocalized with established height loci, including brassinosteroid-deficient dwarf1, dwarf plant1, and semi-dwarf2. GBLUP explained .80% of height variation in the panels and outperformed bootstrap aggregation of family-nested QTL models in evaluations of prediction accuracy. These results revealed maize height was under strong genetic control and had a highly polygenic genetic architecture. They also showed that multiple models of genetic architecture differing in polygenicity and effect sizes can plausibly explain a population's variation in maize height, but they may vary in predictive efficacy.

https://www.genetics.org/content/genetics/196/4/1337.full.pdf

The Genetic Architecture of Maize Height

Physiological and developmental traits that vary over time are difficult to phenotype under relevant growing conditions. In this light, we developed a novel system for phenotyping dynamic traits in the field. System performance was evaluatedon25Pimacotton(GossypiumbarbadenseL.)cultivarsgrownin2011atMaricopa,Arizona.Field-grownplants wereirrigatedunderwellwateredandwater-limitedconditions,withmeasurementstakenatdifferenttimeson3daysinJuly andAugust.Thesystemcarriedfoursetsofsensorstomeasurecanopyheight,reflectanceandtemperaturesimultaneouslyon four adjacent rows, enabling the collection of phenotypic data at a rate of 0.84ha h -1 . Measurements of canopy height, normalised difference vegetation index and temperature all showed large differences among cultivars and expected interactions of cultivars with water regime and time of day. Broad-sense heritabilities (H 2 )were highest for canopy height (H 2 =0.86-0.96), followed by the more environmentally sensitive normalised difference vegetation index (H 2 =0.28-0.90) and temperature (H 2 =0.01-0.90) traits. We also found a strong agreement (r 2 =0.35-0.82) between values obtained by the system, and values from aerial imagery and manual phenotyping approaches. Taken together, these results confirmed the ability of the phenotyping system to measure multiple traits rapidly and accurately.

/pdf/development-and-evaluation-of-a-field-based-high-throughput-16hen9tw0m.pdf

Cuauhtemoc T. Cervantes‐Martínez

Papers

Estimating and Interpreting Heritability for Plant Breeding: An Update