Simultaneous Dissection of Grain Carotenoid Levels and Kernel Color in Biparental Maize Populations with Yellow-to-Orange Grain

ABSTRACT Maize enriched in provitamin A carotenoids could be key in combatting vitamin A deficiency in human populations relying on maize as a food staple. Consumer studies indicate that orange maize may be regarded as novel and preferred. This study identifies genes of relevance for grain carotenoid concentrations and kernel color, through simultaneous dissection of these traits in 10 families of the U.S. maize nested association mapping panel that have yellow to orange grain. Quantitative trait loci (QTL) were identified via joint-linkage analysis, with phenotypic variation explained for individual kernel color QTL ranging from 2.4 to 17.5%. These QTL were cross-analyzed with significant marker-trait associations in a genome-wide association study that utilized ∼27 million variants. Nine genes were identified: four encoding activities upstream of the core carotenoid pathway, one at the pathway branchpoint, three within the α- or β-pathway branches, and one encoding a carotenoid cleavage dioxygenase. Of these, three exhibited significant pleiotropy between kernel color and one or more carotenoid traits. Kernel color exhibited moderate positive correlations with β-branch and total carotenoids and negligible correlations with α-branch carotenoids. These findings can be leveraged to simultaneously achieve desirable kernel color phenotypes and increase concentrations of provitamin A and other priority carotenoids.

Identification of Quantitative Trait Loci for Related Traits of Stalk Lodging Resistance Using Genome-wide Association Studies in Maize (Zea mays L.)

Background: Stalk lodging is one of the main factors affecting maize (Zea mays L.) yield and limiting mechanized harvesting. Developing maize varieties with high stalk lodging resistance requires exploring the genetic basis of lodging resistance-associated agronomic traits. Stalk strength is an important indicator to evaluate maize lodging and can be evaluated by measuring stalk rind penetrometer resistance (RPR) and stalk buckling strength (SBS). And morphological traits of the stalk for the third internodes length (TIL), fourth internode length (FIL), third internode diameter (TID), and the fourth internode diameter (FID) traits are associated with stalk lodging resistance.Results: In this study, 248 genome-wide association study (GWAS) panel with 83,057 single nucleotide polymorphisms (SNPs) were used to detect the quantitative trait loci (QTLs) for six stalk lodging resistance-related traits. The heritability of all traits ranged from 0.59 to 0.72 in the association mapping panel. A total of 85 significant SNPs were identified for the association mapping panel using best linear unbiased prediction (BLUP) values of all traits. Additionally, five candidate genes were associated with stalk strength traits, which were either directly or indirectly associated with cell wall components. Conclusions: These findings contribute to our understanding of the genetic basis of maize stalk lodging and provide valuable theoretical guidance for lodging resistance in maize breeding in the future.

Genetic Variation in Photosynthetic Responses to Chilling Modulates Proton Motive Force, Cyclic Electron Flow and Photosystem II Photoinhibition

The work demonstrates the use of detailed, high-throughput phenotyping to generate and test mechanistic models to explain the genetic diversity of photosynthetic responses to abiotic stress. We assessed a population of recombinant inbred lines (RILs) of cowpea (Vigna unguiculata. (L.) Walp.) with significant differences in a range of photosynthetic responses to chilling. We found well-defined, colocalized (overlapping) QTL intervals for photosynthetic parameters, suggesting linkages among the redox states of Q, the thylakoid pmf, through effects on cyclic electron flow and photodamage to PSII. We propose that these genetic variations optimize photosynthesis in the tolerant lines under low temperatures, preventing recombination reactions within Photosystem II that can lead to deleterious O production. By contrast, we did not observe linkages to PSI redox state, PSI photodamage or ATP synthase activity, or nyctinastic (diurnally controlled) leaf movements, likely indicating that several proposed models likely do not contribute to the genetic control of photosynthesis at low temperature in our mapping panel. The identified QTL intervals include a range of potential causative genetic components, with direct applications to breeding of photosynthesis for more climate-resilient productivity.

Haplotype associated RNA expression (HARE) improves prediction of complex traits in maize

Genomic prediction typically relies on associations between single-site polymorphisms and traits of interest. This representation of genomic variability has been successful for predicting many complex traits. However, it usually cannot capture the combination of alleles in haplotypes and it has generated little insight about the biological function of polymorphisms. Here we present a novel and cost-effective method for imputing cis haplotype associated RNA expression (HARE), studied their transferability across tissues, and evaluated genomic prediction models within and across populations. HARE focuses on tightly linked cis acting causal variants in the immediate vicinity of the gene, while excluding trans effects from diffusion and metabolism. Therefore, HARE estimates were more transferrable across different tissues and populations compared to measured transcript expression. We also showed that HARE estimates captured one-third of the variation in gene expression. HARE estimates were used in genomic prediction models evaluated within and across two diverse maize panels–a diverse association panel (Goodman Association panel) and a large half-sib panel (Nested Association Mapping panel)–for predicting 26 complex traits. HARE resulted in up to 15% higher prediction accuracy than control approaches that preserved haplotype structure, suggesting that HARE carried functional information in addition to information about haplotype structure. The largest increase was observed when the model was trained in the Nested Association Mapping panel and tested in the Goodman Association panel. Additionally, HARE yielded higher within-population prediction accuracy as compared to measured expression values. The accuracy achieved by measured expression was variable across tissues, whereas accuracy by HARE was more stable across tissues. Therefore, imputing RNA expression of genes by haplotype is stable, cost-effective, and transferable across populations.

Genome-wide association mapping within a single Arabidopsis thaliana population reveals a richer genetic architecture for defensive metabolite diversity

A paradoxical finding from genome-wide association studies (GWAS) in plants is that variation in metabolite profiles typically maps to a small number of loci, despite the complexity of underlying biosynthetic pathways. This discrepancy may partially arise from limitations presented by geographically diverse mapping panels. Properties of metabolic pathways that impede GWAS by diluting the additive effect of a causal variant, such as allelic and genic heterogeneity and epistasis, would be expected to increase in severity with the geographic range of the mapping panel. We hypothesized that a population from a single locality would reveal an expanded set of associated loci. We tested this in a French Arabidopsis thaliana population (< 1 km transect) by profiling and conducting GWAS for glucosinolates, a suite of defensive metabolites that have been studied in depth through functional and genetic mapping approaches. For two distinct classes of glucosinolates, we discovered more associations at biosynthetic loci than previous GWAS with continental-scale mapping panels. Candidate genes underlying novel associations were supported by concordance between their observed effects in the TOU-A population and previous functional genetic and biochemical characterization. Local populations complement geographically diverse mapping panels to reveal a more complete genetic architecture for metabolic traits.

Genetic diversity in rice (Oryza sativa L.) landraces of Sikkim-Himalaya and early insight into their use in genome-wide association analysis

The Sikkim-Himalaya represents one of the unique reservoirs of rice genetic resources in India owing to the presence of a large number of landraces adapted to extreme climatic and edaphic conditions. This valuable gene pool is now under threat due to the introduction of high-yielding varieties, wanting suitable genetic intervention for enhancing their productivity, and thus economic viability. Development of lodging resistant, high-yielding varieties tolerant to soil acidity through association mapping aided marker-assisted breeding programmes can help achieve this in a fast and efficient manner. But this requires information on genetic diversity, population structure, etc., of the germplasm collection, which is strikingly lacking. We, therefore, characterized a set of 53 rice landraces from Sikkim to address the above issues. The results revealed moderate diversity, poor divergence and high gene flow in our germplasm collection attesting its utility as an association mapping panel. Further, a total of 115 putative marker-trait associations (P < 0.05, R2 ≥ 10%) were obtained using the general linear model and mixed linear models of which 25 were identical in both. Some of the associated markers were positioned in those regions where Qualitative Trait Loci have been previously identified in rice, providing credence to our results. The resources generated from this study will benefit the rice breeders from this region and elsewhere for targeting the yield and related traits, in addition to conservation efforts by the interested researchers.

Generation of a High-Density Genetic Map of Pepper (Capsicum annuum L.) by SLAF-seq and QTL Analysis of Phytophthora capsici Resistance

Pepper (Capsicum annuum L.) is an economically significant global crop and condiment. Its yield can be severely reduced by the oomycete plant pathogen, Phytophthora capsici (P. capsici). Here, a high-density genetic map was created with a mapping panel of F2 populations obtained from 150 individuals of parental lines PI201234 and 1287 and specific-locus amplified fragment sequencing (SLAF) that was then utilized to identify loci that are related to resistance to P. capsici. The sequencing depth of the genetic map was 108.74-fold for the male parent, 126.25-fold for the female parent, and 22.73-fold for the offspring. A high-resolution genetic map consisting of 5565 markers and 12 linkage groups was generated for pepper, covering 1535.69 cM and an average marker distance of 0.28 cM. One major quantitative trait locus (QTL) for the P. capsici resistance (CQPc5.1) was identified on Chr05 that explained the observed 11.758% phenotypic variance. A total of 23 candidate genes located within the QTL CQPc5.1 interval were identified, which included the candidate gene Capana05g000595 that encodes the RPP8-like protein as well as two candidate genes Capana05g000596 and Capana05g000597 that encodes a RPP13-like protein. Quantitative reverse-transcription PCR (qRT-PCR) revealed higher expression levels of Capana05g000595, Capana05g000596, and Capana05g000597 in P. capsici resistance accessions, suggesting their association with P. capsici resistance in pepper.

Haplotype Associated RNA Expression (HARE) Improves Prediction of Complex Traits in Maize

Genomic prediction typically relies on associations between single-site polymorphisms and traits of interest. This representation of genomic variability has been successful for prediction within populations. However, it usually cannot capture the complex effects due to combination of alleles in haplotypes. Therefore, accuracy across populations has usually been low. Here we present a novel and cost-effective method for imputing cis haplotype associated RNA expression (HARE, RNA expression of genes by haplotype), studied their transferability across tissues, and evaluated genomic prediction models within and across populations. HARE focuses on tightly linked cis acting causal variants in the immediate vicinity of the gene, while excluding trans effects from diffusion and metabolism, so it would be more transferrable across different tissues and populations. We showed that HARE estimates captured one-third of the variation in gene expression and were more transferable across diverse tissues than the measured transcript expression. HARE estimates were used in genomic prediction models evaluated within and across two diverse maize panels – a diverse association panel (Goodman Association panel) and a large half-sib panel (Nested Association Mapping panel) – for predicting 26 complex traits. HARE resulted in up to 15% higher prediction accuracy than control approaches that preserved haplotype structure, suggesting that HARE carried functional information in addition to information about haplotype structure. The largest increase was observed when the model was trained in the Nested Association Mapping panel and tested in the Goodman Association panel. Additionally, HARE yielded higher within-population prediction accuracy as compared to measured expression values. The accuracy achieved by measured expression was variable across tissues whereas accuracy using HARE was more stable across tissues. Therefore, imputing RNA expression of genes by haplotype is stable, cost-effective, and transferable across populations.Author summaryThe increasing availability of genomic data has been widely used in the prediction of many traits. However, genome wide prediction has been mostly carried out within populations and without explicit modeling of RNA or protein expression. In this study, we explored the prediction of field traits within and across populations using estimated RNA expression attributable to only the DNA sequence around a gene. We showed that the estimated RNA expression was more transferable than overall measured RNA expression. We improved prediction of field traits up to 15% using estimated gene expression as compared to observed expression or gene sequence alone. Overall, these findings indicate that structural and functional information in the gene sequence are highly transferable.

Comparison of Genomic Selection Models for Exploring Predictive Ability of Complex Traits in Breeding Programs

Traits with a complex unknown genetic architecture are common in breeding programs. However, they pose a challenge for selection due to a combination of complex environmental and pleiotropic effects that impede the ability to create mapping populations to characterize the trait’s genetic basis. One such trait, seedling emergence of wheat (Triticum aestivumL.) from deep planting, presents a unique opportunity to explore the best method to use and implement GS models to predict a complex trait. 17 GS models were compared using two training populations, consisting of 473 genotypes from a diverse association mapping panel (DP) phenotyped from 2015-2019 and the other training population consisting of 643 breeding lines phenotyped in 2015 and 2020 in Lind, WA with 40,368 markers. There were only a few significant differences between GS models, with support vector machines reaching the highest accuracy of 0.56 in a single breeding line trial using cross-validations. However, the consistent moderate accuracy of cBLUP and other parametric models indicates no need to implement computationally demanding non-parametric models for complex traits. There was an increase in accuracy using cross-validations from 0.40 to 0.41 and independent validations from 0.10 to 0.17 using diversity panels lines to breeding lines. The environmental effects of complex traits can be overcome by combining years of the same populations. Overall, our study showed that breeders can accurately predict and implement GS for a complex trait by using parametric models within their own breeding programs with increased accuracy as they combine training populations over the years.