Multi-trait association mapping for phosphorous efficiency reveals flexible root architectures in sorghum.

BACKGROUND: On tropical regions, phosphorus (P) fixation onto aluminum and iron oxides in soil clays restricts P diffusion from the soil to the root surface, limiting crop yields. While increased root surface area favors P uptake under low-P availability, the relationship between the three-dimensional arrangement of the root system and P efficiency remains elusive. Here, we simultaneously assessed allelic effects of loci associated with a variety of root and P efficiency traits, in addition to grain yield under low-P availability, using multi-trait genome-wide association. We also set out to establish the relationship between root architectural traits assessed in hydroponics and in a low-P soil. Our goal was to better understand the influence of root morphology and architecture in sorghum performance under low-P availability. RESULT: In general, the same alleles of associated SNPs increased root and P efficiency traits including grain yield in a low-P soil. We found that sorghum P efficiency relies on pleiotropic loci affecting root traits, which enhance grain yield under low-P availability. Root systems with enhanced surface area stemming from lateral root proliferation mostly up to 40 cm soil depth are important for sorghum adaptation to low-P soils, indicating that differences in root morphology leading to enhanced P uptake occur exactly in the soil layer where P is found at the highest concentration. CONCLUSION: Integrated QTLs detected in different mapping populations now provide a comprehensive molecular genetic framework for P efficiency studies in sorghum. This indicated extensive conservation of P efficiency QTL across populations and emphasized the terminal portion of chromosome 3 as an important region for P efficiency in sorghum. Increases in root surface area via enhancement of lateral root development is a relevant trait for sorghum low-P soil adaptation, impacting the overall architecture of the sorghum root system. In turn, particularly concerning the critical trait for water and nutrient uptake, root surface area, root system development in deeper soil layers does not occur at the expense of shallow rooting, which may be a key reason leading to the distinctive sorghum adaptation to tropical soils with multiple abiotic stresses including low P availability and drought.

Lessons from a GWAS study of a wheat pre-breeding program: pyramiding resistance alleles to Fusarium crown rot.

Much has been published on QTL detection for complex traits using bi-parental and multi-parental crosses (linkage analysis) or diversity panels (GWAS studies). While successful for detection, transferability of results to real applications has proven more difficult. Here, we combined a QTL detection approach using a pre-breeding populations which utilized intensive phenotypic selection for the target trait across multiple plant generations, combined with rapid generation turnover (i.e. "speed breeding") to allow cycling of multiple plant generations each year. The reasoning is that QTL mapping information would complement the selection process by identifying the genome regions under selection within the relevant germplasm. Questions to answer were the location of the genomic regions determining response to selection and the origin of the favourable alleles within the pedigree. We used data from a pre-breeding program that aimed at pyramiding different resistance sources to Fusarium crown rot into elite (but susceptible) wheat backgrounds. The population resulted from a complex backcrossing scheme involving multiple resistance donors and multiple elite backgrounds, akin to a MAGIC population (985 genotypes in total, with founders, and two major offspring layers within the pedigree). A significant increase in the resistance level was observed (i.e. a positive response to selection) after the selection process, and 17 regions significantly associated with that response were identified using a GWAS approach. Those regions included known QTL as well as potentially novel regions contributing resistance to Fusarium crown rot. In addition, we were able to trace back the sources of the favourable alleles for each QTL. We demonstrate that QTL detection using breeding populations under selection for the target trait can identify QTL controlling the target trait and that the frequency of the favourable alleles was increased as a response to selection, thereby validating the QTL detected. This is a valuable opportunistic approach that can provide QTL information that is more easily transferred to breeding applications.

Multi-parent multi-environment QTL analysis: an illustration with the EU-NAM Flint population.

KEY MESSAGE: Multi-parent populations multi-environment QTL experiments data should be analysed jointly to estimate the QTL effect variation within the population and between environments. Commonly, QTL detection in multi-parent populations (MPPs) data measured in multiple environments (ME) is done by analyzing genotypic values 'averaged' across environments. This method ignores the environment-specific QTL (QTLxE) effects. Running separate single environment analyses is a possibility to measure QTLxE effects, but those analyses do not model the genetic covariance due to the use of the same genotype in different environments. In this paper, we propose methods to analyse MPP-ME QTL experiments using simultaneously the data from several environments and modelling the genotypic covariance. Using data from the EU-NAM Flint population, we show that these methods estimate the QTLxE effects and that they can improve the quality of the QTL detection. Those methods also have a larger inference power. For example, they can be extended to integrate environmental indices like temperature or precipitation to better understand the mechanisms behind the QTLxE effects. Therefore, our methodology allows the exploitation of the full MPP-ME data potential: to estimate QTL effect variation (a) within the MPP between sub-populations due to different genetic backgrounds and (b) between environments.

Natural Variation in Portuguese Common Bean Germplasm Reveals New Sources of Resistance Against Fusarium oxysporum f. sp. phaseoli and Resistance-Associated Candidate Genes.

Common bean (Phaseolus vulgaris) is one of the most consumed legume crops in the world, and Fusarium wilt, caused by the fungus Fusarium oxysporum f. sp. phaseoli, is one of the major diseases affecting its production. Portugal holds a very promising common bean germplasm with an admixed genetic background that may reveal novel genetic resistance combinations between the original Andean and Mesoamerican gene pools. To identify new sources of Fusarium wilt resistance and detect resistance-associated single-nucleotide polymorphisms (SNPs), we explored, for the first time, a diverse collection of the underused Portuguese common bean germplasm by using genome-wide association analyses. The collection was evaluated for Fusarium wilt resistance under growth chamber conditions, with the highly virulent F. oxysporum f. sp. phaseoli strain FOP-SP1 race 6. Fourteen of the 162 Portuguese accessions evaluated were highly resistant and 71 intermediate. The same collection was genotyped with DNA sequencing arrays, and SNP-resistance associations were tested via a mixed linear model accounting for the genetic relatedness between accessions. The results from the association mapping revealed nine SNPs associated with resistance on chromosomes Pv04, Pv05, Pv07, and Pv08, indicating that Fusarium wilt resistance is under oligogenic control. Putative candidate genes related to phytoalexin biosynthesis, hypersensitive response, and plant primary metabolism were identified. The results reported here highlight the importance of exploring underused germplasm for new sources of resistance and provide new genomic targets for the development of functional markers to support selection in future disease resistance breeding programs.

Genome-wide association study for kernel composition and flour pasting behavior in wholemeal maize flour.

BACKGROUND: Maize is a crop in high demand for food purposes and consumers worldwide are increasingly concerned with food quality. However, breeding for improved quality is a complex task and therefore developing tools to select for better quality products is of great importance. Kernel composition, flour pasting behavior, and flour particle size have been previously identified as crucial for maize-based food quality. In this work we carried out a genome-wide association study to identify genomic regions controlling compositional and pasting properties of maize wholemeal flour. RESULTS: A collection of 132 diverse inbred lines, with a considerable representation of the food used Portuguese unique germplasm, was trialed during two seasons, and harvested samples characterized for main compositional traits, flour pasting parameters and mean particle size. The collection was genotyped with the MaizeSNP50 array. SNP-trait associations were tested using a mixed linear model accounting for genetic relatedness. Fifty-seven genomic regions were identified, associated with the 11 different quality-related traits evaluated. Regions controlling multiple traits were detected and potential candidate genes identified. As an example, for two viscosity parameters that reflect the capacity of the starch to absorb water and swell, the strongest common associated region was located near the dull endosperm 1 gene that encodes a starch synthase and is determinant on the starch endosperm structure in maize. CONCLUSIONS: This study allowed for identifying relevant regions on the maize genome affecting maize kernel composition and flour pasting behavior, candidate genes for the majority of the quality-associated genomic regions, or the most promising target regions to develop molecular tools to increase efficacy and efficiency of quality traits selection (such as "breadability") within maize breeding programs.

Combining pedigree and genomic information to improve prediction quality: an example in sorghum.

KEY MESSAGE: The use of a kinship matrix integrating pedigree- and marker-based relationships optimized the performance of genomic prediction in sorghum, especially for traits of lower heritability. Selection based on genome-wide markers has become an active breeding strategy in crops. Genomic prediction models can make use of pedigree information to account for the residual polygenic effects not captured by markers. Our aim was to evaluate the impact of using pedigree and genomic information on prediction quality of breeding values for different traits in sorghum. We explored BLUP models that use weighted combinations of pedigree and genomic relationship matrices. The optimal weighting factor was empirically determined in order to maximize predictive ability after evaluating a range of candidate weights. The phenotypic data consisted of testcross evaluations of sorghum parental lines across multiple environments. All lines were genotyped, and full pedigree information was available. The performance of the best predictive combined matrix was compared to that of models fitting the component matrices independently. Model performance was assessed using cross-validation technique. Fitting a combined pedigree-genomic matrix with the optimal weight always yielded the largest increases in predictive ability and the largest reductions in prediction bias relative to the simple G-BLUP. However, the weight that optimized prediction varied across traits. The benefits of including pedigree information in the genomic model were more relevant for traits with lower heritability, such as grain yield and stay-green. Our results suggest that the combination of pedigree and genomic relatedness can be used to optimize predictions of complex traits in crops when the additive variation is not fully explained by markers.

How do the type of QTL effect and the form of the residual term influence QTL detection in multi-parent populations? A case study in the maize EU-NAM population.

KEY MESSAGE: In the QTL analysis of multi-parent populations, the inclusion of QTLs with various types of effects can lead to a better description of the phenotypic variation and increased power. For the type of QTL effect in QTL models for multi-parent populations (MPPs), various options exist to define them with respect to their origin. They can be modelled as referring to close parental lines or to further away ancestral founder lines. QTL models for MPPs can also be characterized by the homo- or heterogeneity of variance for polygenic effects. The most suitable model for the origin of the QTL effect and the homo- or heterogeneity of polygenic effects may be a function of the genetic distance distribution between the parents of MPPs. We investigated the statistical properties of various QTL detection models for MPPs taking into account the genetic distances between the parents of the MPP. We evaluated models with different assumptions about the QTL effect and the form of the residual term using cross validation. For the EU-NAM data, we showed that it can be useful to mix in the same model QTLs with different types of effects (parental, ancestral, or bi-allelic). The benefit of using cross-specific residual terms to handle the heterogeneity of variance was less obvious for this particular data set.

Modelling spatial trends in sorghum breeding field trials using a two-dimensional P-spline mixed model.

KEY MESSAGE: A flexible and user-friendly spatial method called SpATS performed comparably to more elaborate and trial-specific spatial models in a series of sorghum breeding trials. Adjustment for spatial trends in plant breeding field trials is essential for efficient evaluation and selection of genotypes. Current mixed model methods of spatial analysis are based on a multi-step modelling process where global and local trends are fitted after trying several candidate spatial models. This paper reports the application of a novel spatial method that accounts for all types of continuous field variation in a single modelling step by fitting a smooth surface. The method uses two-dimensional P-splines with anisotropic smoothing formulated in the mixed model framework, referred to as SpATS model. We applied this methodology to a series of large and partially replicated sorghum breeding trials. The new model was assessed in comparison with the more elaborate standard spatial models that use autoregressive correlation of residuals. The improvements in precision and the predictions of genotypic values produced by the SpATS model were equivalent to those obtained using the best fitting standard spatial models for each trial. One advantage of the approach with SpATS is that all patterns of spatial trend and genetic effects were modelled simultaneously by fitting a single model. Furthermore, we used a flexible model to adequately adjust for field trends. This strategy reduces potential parameter identification problems and simplifies the model selection process. Therefore, the new method should be considered as an efficient and easy-to-use alternative for routine analyses of plant breeding trials.

Ascertainment bias from imputation methods evaluation in wheat.

BACKGROUND: Whole-genome genotyping techniques like Genotyping-by-sequencing (GBS) are being used for genetic studies such as Genome-Wide Association (GWAS) and Genomewide Selection (GS), where different strategies for imputation have been developed. Nevertheless, imputation error may lead to poor performance (i.e. smaller power or higher false positive rate) when complete data is not required as it is for GWAS, and each marker is taken at a time. The aim of this study was to compare the performance of GWAS analysis for Quantitative Trait Loci (QTL) of major and minor effect using different imputation methods when no reference panel is available in a wheat GBS panel. RESULTS: In this study, we compared the power and false positive rate of dissecting quantitative traits for imputed and not-imputed marker score matrices in: (1) a complete molecular marker barley panel array, and (2) a GBS wheat panel with missing data. We found that there is an ascertainment bias in imputation method comparisons. Simulating over a complete matrix and creating missing data at random proved that imputation methods have a poorer performance. Furthermore, we found that when QTL were simulated with imputed data, the imputation methods performed better than the not-imputed ones. On the other hand, when QTL were simulated with not-imputed data, the not-imputed method and one of the imputation methods performed better for dissecting quantitative traits. Moreover, larger differences between imputation methods were detected for QTL of major effect than QTL of minor effect. We also compared the different marker score matrices for GWAS analysis in a real wheat phenotype dataset, and we found minimal differences indicating that imputation did not improve the GWAS performance when a reference panel was not available. CONCLUSIONS: Poorer performance was found in GWAS analysis when an imputed marker score matrix was used, no reference panel is available, in a wheat GBS panel.

Quantitative trait loci and candidate genes underlying genotype by environment interaction in the response of Arabidopsis thaliana to drought.

Drought stress was imposed on two sets of Arabidopsis thaliana genotypes grown in sand under short-day conditions and analysed for several shoot and root growth traits. The response to drought was assessed for quantitative trait locus (QTL) mapping in a genetically diverse set of Arabidopsis accessions using genome-wide association (GWA) mapping, and conventional linkage analysis of a recombinant inbred line (RIL) population. Results showed significant genotype by environment interaction (G×E) for all traits in response to different watering regimes. For the RIL population, the observed G×E was reflected in 17 QTL by environment interactions (Q×E), while 17 additional QTLs were mapped not showing Q×E. GWA mapping identified 58 single nucleotide polymorphism (SNPs) associated with loci displaying Q×E and an additional 16 SNPs associated with loci not showing Q×E. Many candidate genes potentially underlying these loci were suggested. The genes for RPS3C and YLS7 were found to contain conserved amino acid differences when comparing Arabidopsis accessions with strongly contrasting drought response phenotypes, further supporting their candidacy. One of these candidate genes co-located with a QTL mapped in the RIL population.

Genetic diversity and linkage disequilibrium analysis in elite sugar beet breeding lines and wild beet accessions.

Linkage disequilibrium decay in sugar beet is strongly affected by the breeding history, and varies extensively between and along chromosomes, allowing identification of known and unknown signatures of selection. Genetic diversity and linkage disequilibrium (LD) patterns were investigated in 233 elite sugar beet breeding lines and 91 wild beet accessions, using 454 single nucleotide polymorphisms (SNPs) and 418 SNPs, respectively. Principal coordinate analysis suggested the existence of three groups of germplasm, corresponding to the wild beets, the seed parent and the pollen parent breeding pool. LD was investigated in each of these groups, with and without correction for genetic relatedness. Without correction for genetic relatedness, in the pollen as well as the seed parent pool, LD persisted beyond 50 centiMorgan (cM) on four (2, 3, 4 and 5) and three chromosomes (2, 4 and 6), respectively; after correction for genetic relatedness, LD decayed after <6 cM on all chromosomes in both pools. In the wild beet accessions, there was a strong LD decay: on average LD disappeared after 1 cM when LD was calculated with a correction for genetic relatedness. Persistence of LD was not only observed between distant SNPs on the same chromosome, but also between SNPs on different chromosomes. Regions on chromosomes 3 and 4 that harbor disease resistance and monogermy loci showed strong genetic differentiation between the pollen and seed parent pools. Other regions, on chromosomes 8 and 9, for which no a priori information was available with respect to their contribution to the phenotype, still contributed to clustering of lines in the elite breeding material.

Genome-wide association mapping of frost tolerance in barley (Hordeum vulgare L.).

BACKGROUND: Frost tolerance is a key trait with economic and agronomic importance in barley because it is a major component of winter hardiness, and therefore limits the geographical distribution of the crop and the effective transfer of quality traits between spring and winter crop types. Three main frost tolerance QTL (Fr-H1, Fr-H2 and Fr-H3) have been identified from bi-parental genetic mapping but it can be argued that those mapping populations only capture a portion of the genetic diversity of the species. A genetically broad dataset consisting of 184 genotypes, representative of the barley gene pool cultivated in the Mediterranean basin over an extended time period, was genotyped with 1536 SNP markers. Frost tolerance phenotype scores were collected from two trial sites, Foradada (Spain) and Fiorenzuola (Italy) and combined with the genotypic data in genome wide association analyses (GWAS) using Eigenstrat and kinship approaches to account for population structure. RESULTS: GWAS analyses identified twelve and seven positive SNP associations at Foradada and Fiorenzuola, respectively, using Eigenstrat and six and four, respectively, using kinship. Linkage disequilibrium analyses of the significant SNP associations showed they are genetically independent. In the kinship analysis, two of the significant SNP associations were tightly linked to the Fr-H2 and HvBmy loci on chromosomes 5H and 4HL, respectively. The other significant kinship associations were located in genomic regions that have not previously been associated with cold stress. CONCLUSIONS: Haplotype analysis revealed that most of the significant SNP loci are fixed in the winter or facultative types, while they are freely segregating within the un-adapted spring barley genepool. Although there is a major interest in detecting new variation to improve frost tolerance of available winter and facultative types, from a GWAS perspective, working within the un-adapted spring germplasm pool is an attractive alternative strategy which would minimize statistical issues, simplify the interpretation of the data and identify phenology independent genetic determinants of frost tolerance.

Association mapping of salt tolerance in barley (Hordeum vulgare L.).

A spring barley collection of 192 genotypes from a wide geographical range was used to identify quantitative trait loci (QTLs) for salt tolerance traits by means of an association mapping approach using a thousand SNP marker set. Linkage disequilibrium (LD) decay was found with marker distances spanning 2-8 cM depending on the methods used to account for population structure and genetic relatedness between genotypes. The association panel showed large variation for traits that were highly heritable under salt stress, including biomass production, chlorophyll content, plant height, tiller number, leaf senescence and shoot Na(+), shoot Cl(-) and shoot, root Na(+)/K(+) contents. The significant correlations between these traits and salt tolerance (defined as the biomass produced under salt stress relative to the biomass produced under control conditions) indicate that these traits contribute to (components of) salt tolerance. Association mapping was performed using several methods to account for population structure and minimize false-positive associations. This resulted in the identification of a number of genomic regions that strongly influenced salt tolerance and ion homeostasis, with a major QTL controlling salt tolerance on chromosome 6H, and a strong QTL for ion contents on chromosome 4H.

Genome-wide association studies for agronomical traits in a world wide spring barley collection.

BACKGROUND: Genome-wide association studies (GWAS) based on linkage disequilibrium (LD) provide a promising tool for the detection and fine mapping of quantitative trait loci (QTL) underlying complex agronomic traits. In this study we explored the genetic basis of variation for the traits heading date, plant height, thousand grain weight, starch content and crude protein content in a diverse collection of 224 spring barleys of worldwide origin. The whole panel was genotyped with a customized oligonucleotide pool assay containing 1536 SNPs using Illumina's GoldenGate technology resulting in 957 successful SNPs covering all chromosomes. The morphological trait "row type" (two-rowed spike vs. six-rowed spike) was used to confirm the high level of selectivity and sensitivity of the approach. This study describes the detection of QTL for the above mentioned agronomic traits by GWAS. RESULTS: Population structure in the panel was investigated by various methods and six subgroups that are mainly based on their spike morphology and region of origin. We explored the patterns of linkage disequilibrium (LD) among the whole panel for all seven barley chromosomes. Average LD was observed to decay below a critical level (r2-value 0.2) within a map distance of 5-10 cM. Phenotypic variation within the panel was reasonably large for all the traits. The heritabilities calculated for each trait over multi-environment experiments ranged between 0.90-0.95. Different statistical models were tested to control spurious LD caused by population structure and to calculate the P-value of marker-trait associations. Using a mixed linear model with kinship for controlling spurious LD effects, we found a total of 171 significant marker trait associations, which delineate into 107 QTL regions. Across all traits these can be grouped into 57 novel QTL and 50 QTL that are congruent with previously mapped QTL positions. CONCLUSIONS: Our results demonstrate that the described diverse barley panel can be efficiently used for GWAS of various quantitative traits, provided that population structure is appropriately taken into account. The observed significant marker trait associations provide a refined insight into the genetic architecture of important agronomic traits in barley. However, individual QTL account only for a small portion of phenotypic variation, which may be due to insufficient marker coverage and/or the elimination of rare alleles prior to analysis. The fact that the combined SNP effects fall short of explaining the complete phenotypic variance may support the hypothesis that the expression of a quantitative trait is caused by a large number of very small effects that escape detection. Notwithstanding these limitations, the integration of GWAS with biparental linkage mapping and an ever increasing body of genomic sequence information will facilitate the systematic isolation of agronomically important genes and subsequent analysis of their allelic diversity.

Gene and QTL detection in a three-way barley cross under selection by a mixed model with kinship information using SNPs.

Quantitative trait locus (QTL) detection is commonly performed by analysis of designed segregating populations derived from two inbred parental lines, where absence of selection, mutation and genetic drift is assumed. Even for designed populations, selection cannot always be avoided, with as consequence varying correlation between genotypes instead of uniform correlation. Akin to linkage disequilibrium mapping, ignoring this type of genetic relatedness will increase the rate of false-positives. In this paper, we advocate using mixed models including genetic relatedness, or 'kinship' information for QTL detection in populations where selection forces operated. We demonstrate our case with a three-way barley cross, designed to segregate for dwarfing, vernalization and spike morphology genes, in which selection occurred. The population of 161 inbred lines was screened with 1,536 single nucleotide polymorphisms (SNPs), and used for gene and QTL detection. The coefficient of coancestry matrix was estimated based on the SNPs and imposed to structure the distribution of random genotypic effects. The model incorporating kinship, coancestry, information was consistently superior to the one without kinship (according to the Akaike information criterion). We show, for three traits, that ignoring the coancestry information results in an unrealistically high number of marker-trait associations, without providing clear conclusions about QTL locations. We used a number of widely recognized dwarfing and vernalization genes known to segregate in the studied population as landmarks or references to assess the agreement of the mapping results with a priori candidate gene expectations. Additional QTLs to the major genes were detected for all traits as well.

The influence of QTL allelic diversity on QTL detection in multi-parent populations: a simulation study in sugar beet.

BACKGROUND: Multi-parent populations (MPPs) are important resources for studying plant genetic architecture and detecting quantitative trait loci (QTLs). In MPPs, the QTL effects can show various levels of allelic diversity, which can be an important factor influencing the detection of QTLs. In MPPs, the allelic effects can be more or less specific. They can depend on an ancestor, a parent or the combination of parents in a cross. In this paper, we evaluated the effect of QTL allelic diversity on the QTL detection power in MPPs. RESULTS: We simulated: a) cross-specific QTLs; b) parental and ancestral QTLs; and c) bi-allelic QTLs. Inspired by a real application in sugar beet, we tested different MPP designs (diallel, chessboard, factorial, and NAM) derived from five or nine parents to explore the ability to sample genetic diversity and detect QTLs. Using a fixed total population size, the QTL detection power was larger in MPPs with fewer but larger crosses derived from a reduced number of parents. The use of a larger set of parents was useful to detect rare alleles with a large phenotypic effect. The benefit of using a larger set of parents was however conditioned on an increase of the total population size. We also determined empirical confidence intervals for QTL location to compare the resolution of different designs. For QTLs representing 6% of the phenotypic variation, using 1600 F2 offspring individuals, we found average 95% confidence intervals over different designs of 49 and 25 cM for cross-specific and bi-allelic QTLs, respectively. CONCLUSIONS: MPPs derived from less parents with few but large crosses generally increased the QTL detection power. Using a larger set of parents to cover a wider genetic diversity can be useful to detect QTLs with a reduced minor allele frequency when the QTL effect is large and when the total population size is increased.

Training Set Construction for Genomic Prediction in Auto-Tetraploids: An Example in Potato.

Training set construction is an important prerequisite to Genomic Prediction (GP), and while this has been studied in diploids, polyploids have not received the same attention. Polyploidy is a common feature in many crop plants, like for example banana and blueberry, but also potato which is the third most important crop in the world in terms of food consumption, after rice and wheat. The aim of this study was to investigate the impact of different training set construction methods using a publicly available diversity panel of tetraploid potatoes. Four methods of training set construction were compared: simple random sampling, stratified random sampling, genetic distance sampling and sampling based on the coefficient of determination (CDmean). For stratified random sampling, population structure analyses were carried out in order to define sub-populations, but since sub-populations accounted for only 16.6% of genetic variation, there were negligible differences between stratified and simple random sampling. For genetic distance sampling, four genetic distance measures were compared and though they performed similarly, Euclidean distance was the most consistent. In the majority of cases the CDmean method was the best sampling method, and compared to simple random sampling gave improvements of 4-14% in cross-validation scenarios, and 2-8% in scenarios with an independent test set, while genetic distance sampling gave improvements of 5.5-10.5% and 0.4-4.5%. No interaction was found between sampling method and the statistical model for the traits analyzed.

Common bean SNP alleles and candidate genes affecting photosynthesis under contrasting water regimes.

Water deficit is a major worldwide constraint to common bean (Phaseolus vulgaris L.) production, being photosynthesis one of the most affected physiological processes. To gain insights into the genetic basis of the photosynthetic response of common bean under water-limited conditions, a collection of 158 Portuguese accessions was grown under both well-watered and water-deficit regimes. Leaf gas-exchange parameters were measured and photosynthetic pigments quantified. The same collection was genotyped using SNP arrays, and SNP-trait associations tested considering a linear mixed model accounting for the genetic relatedness among accessions. A total of 133 SNP-trait associations were identified for net CO2 assimilation rate, transpiration rate, stomatal conductance, and chlorophylls a and b, carotenes, and xanthophyll contents. Ninety of these associations were detected under water-deficit and 43 under well-watered conditions, with only two associations common to both treatments. Identified candidate genes revealed that stomatal regulation, protein translocation across membranes, redox mechanisms, hormone, and osmotic stress signaling were the most relevant processes involved in common bean response to water-limited conditions. These candidates are now preferential targets for common bean water-deficit-tolerance breeding. Additionally, new sources of water-deficit tolerance of Andean, Mesoamerican, and admixed origin were detected as accessions valuable for breeding, and not yet explored.

Volatilome-Genome-Wide Association Study on Wholemeal Maize Flour.

Odor and aroma, resulting from the perception of volatiles by the olfactory receptors, are important in consumer food acceptance. To develop more efficient molecular breeding tools to improve the odor/aroma on maize (Zea mays L.), a staple food crop, increasing the knowledge on the genetic basis of maize volatilome is needed. In this work, we conducted a genome-wide association study on a unique germplasm collection to identify genomic regions controlling maize wholemeal flour's volatilome. We identified 64 regions on the maize genome and candidate genes controlling the levels of 15 volatiles, mainly aldehydes. As an example, the Zm00001d033623 gene was within a region associated with 2-octenal (E) and 2-nonenal (E), two byproducts of linoleic acid oxidation. This gene codes for linoleate 9S-lipoxygenase, an enzyme responsible for oxidizing linoleic acid. This knowledge can now support the development of molecular tools to increase the selection efficacy/efficiency of these volatiles within maize breeding programs.

Alleles to Enhance Antioxidant Content in Maize-A Genome-Wide Association Approach.

The interest in antioxidant compound breeding in maize (Zea mays L.), a major food crop, has increased in recent years. However, breeding of antioxidant compounds in maize can be hampered, given the complex genetic nature of these compounds. In this work, we followed a genome-wide association approach, using a unique germplasm collection (containing Portuguese germplasm), to study the genetic basis of several antioxidants in maize. Sixty-seven genomic regions associated with seven antioxidant compounds and two color-related traits were identified. Several significant associations were located within or near genes involved in the carotenoid (Zm00001d036345) and tocopherol biosynthetic pathways (Zm00001d017746). Some indications of a negative selection against α-tocopherol levels were detected in the Portuguese maize germplasm. The strongest single nucleotide polymorphism (SNP)-trait associations and the SNP alleles with larger effect sizes were pinpointed and set as priority for future validation studies; these associations detected now constitute a benchmark for developing molecular selection tools for antioxidant compound selection in maize.