Genome-wide association mapping of agronomic traits in sugar beet

Genome-wide analysis for root and leaf architecture traits associated with drought tolerance at the seedling stage in a highly ecologically diverse wheat population

Drought stress occurred at early growth stages in wheat affecting the following growth stages. Therefore, selecting promising drought-tolerant genotypes with highly adapted traits at the seedling stage is an important task for wheat breeders and geneticists. Few research efforts were conducted on the genetic control for drought-adaptive traits at the seedling stage in wheat. In this study, a set of 146 highly diverse spring wheat core collections representing 28 different countries was evaluated under drought stress at the seedling stage. All genotypes were exposed to drought stress for 13 days by water withholding. Leaf traits including seedling length, leaf wilting, days to wilting, leaf area, and leaf rolling were scored. Moreover, root traits such as root length, maximum width, emergence angle, tip angle, and number of roots were scored. Considerable significant genetic variation was found among all genotypes tested in these experiments. The heritability estimates ranged from 0.74 (leaf witling) to 0.99 (root tip angle). A set of nine genotypes were selected and considered drought-tolerant genotypes. Among all leaf traits, shoot length had significant correlations with all root traits under drought stress. The 146 genotypes were genotyped using the Infinium Wheat 15 K single nucleotide polymorphism (SNP) array and diversity arrays technology (DArT) marker platform. The result of genotyping revealed 12,999 SNPs and 2150 DArT markers which were used to run a genome-wide association study (GWAS). The results of GWAS revealed 169 markers associated with leaf and root traits under drought stress. Out of the 169 markers, 82 were considered major quantitative trait loci (QTL). The GWAS revealed 95 candidate genes were identified with 53 genes showing evidence for drought tolerance in wheat, while the remaining candidate genes were considered novel. No shared markers were found between leaf and root traits. The results of the study provided mapping novel markers associated with new root traits at the seedling stage. Also, the selected genotypes from different countries could be employed in future wheat breeding programs not only for improving adaptive drought-tolerant traits but also for expanding genetic diversity.

Genetic diversity, linkage disequilibrium, and population structure of tetraploid wheat landraces originating from Europe and Asia

BACKGROUND

Durum wheat is one of the most important crops, especially in the Mediterranean region. Insight into the genetic diversity of germplasm can improve the breeding program management in various traits. This study was done using single nucleotide polymorphisms (SNP) markers to characterize the genetic distinctiveness and differentiation of tetraploid wheat landraces collected from nine European and Asian countries. A sum of 23,334 polymorphic SNPs was detected in 126 tetraploid wheat landraces in relation to the reference genome.

RESULTS

The number of identified SNPs was 11,613 and 11,721 in A and B genomes, respectively. The highest and lowest diversity was on 6B and 6 A chromosomes, respectively. Structure analysis classified the landraces into two distinct subpopulations (K = 2). Evaluating the principal coordinate analysis (PCoA) and weighted pair-group method using arithmetic averages (WPGMA) clustering results demonstrated that landraces (99.2%) are categorized into one of the two chief subpopulations. Therefore, the grouping pattern did not clearly show the presence of a clear pattern of relationships between genetic diversity and their geographical derivation. Part of this result could be due to the historical exchange between different germplasms. Although the result did not separate landraces based on their region of origin, the landraces collected from Iran were classified into the same group and cluster. Analysis of molecular variance (AMOVA) also confirmed the results of population structure. Finally, Durum wheat landraces in some countries, including Turkey, Russia, Ukraine, and Afghanistan, were highly diverse, while others, including Iran and China, were low-diversity.

CONCLUSION

The recent study concluded that the 126 tetraploid wheat genotypes and their GBS-SNP markers are very appropriate for quantitative trait loci (QTLs) mapping and genome-wide association studies (GWAS). The core collection comprises two distinct subpopulations. Subpopulation II genotypes are the most diverse genotypes, and if they possess desired traits, they may be used in future breeding programs. The degree of diversity in the landraces of countries can provide the ground for the improvement of new cultivars with international cooperation. linkage disequilibrium (LD) hotspot distribution across the genome was investigated, which provides useful information about the genomic regions that contain intriguing genes.

Prediction of additive, epistatic, and dominance effects using models accounting for incomplete inbreeding in parental lines of hybrid rye and sugar beet

Genomic models for prediction of additive and non-additive effects within and across different heterotic groups are lacking for breeding of hybrid crops. In this study, genomic prediction models accounting for incomplete inbreeding in parental lines from two different heterotic groups were developed and evaluated. The models can be used for prediction of general combining ability (GCA) of parental lines from each heterotic group as well as specific combining ability (SCA) of all realized and potential crosses. Here, GCA was estimated as the sum of additive genetic effects and within-group epistasis due to high degree of inbreeding in parental lines. SCA was estimated as the sum of across-group epistasis and dominance effects. Three models were compared. In model 1, it was assumed that each hybrid was produced from two completely inbred parental lines. Model 1 was extended to include three-way hybrids from parental lines with arbitrary levels of inbreeding: In model 2, parents of the three-way hybrids could have any levels of inbreeding, while the grandparents of the maternal parent were assumed completely inbred. In model 3, all parental components could have any levels of inbreeding. Data from commercial breeding programs for hybrid rye and sugar beet was used to evaluate the models. The traits grain yield and root yield were analyzed for rye and sugar beet, respectively. Additive genetic variances were larger than epistatic and dominance variances. The models' predictive abilities for total genetic value, for GCA of each parental line and for SCA were evaluated based on different cross-validation strategies. Predictive abilities were highest for total genetic values and lowest for SCA. Predictive abilities for SCA and for GCA of maternal lines were higher for model 2 and model 3 than for model 1. The implementation of the genomic prediction models in hybrid breeding programs can potentially lead to increased genetic gain in two different ways: I) by facilitating the selection of crossing parents with high GCA within heterotic groups and II) by prediction of SCA of all realized and potential combinations of parental lines to produce hybrids with high total genetic values.

Plant biochemical genetics in the multiomics era

Our understanding of plant biology has been revolutionized by modern genetics and biochemistry. However, biochemical genetics can be traced back to the foundation of Mendelian genetics; indeed, one of Mendel's milestone discoveries of seven characteristics of pea plants later came to be ascribed to a mutation in a starch branching enzyme. Here, we review both current and historical strategies for the elucidation of plant metabolic pathways and the genes that encode their component enzymes and regulators. We use this historical review to discuss a range of classical genetic phenomena including epistasis, canalization, and heterosis as viewed through the lens of contemporary high-throughput data obtained via the array of approaches currently adopted in multiomics studies.

Genetic diversity and genome-wide association study of 13 agronomic traits in 977 Beta vulgaris L. germplasms

BACKGROUND

Sugar beet (Beta vulgaris L.) is an economically essential sugar crop worldwide. Its agronomic traits are highly diverse and phenotypically plastic, influencing taproot yield and quality. The National Beet Medium-term Gene Bank in China maintains more than 1700 beet germplasms with diverse countries of origin. However, it lacks detailed genetic background associated with morphological variability and diversity.

RESULTS

Here, a comprehensive genome-wide association study (GWAS) of 13 agronomic traits was conducted in a panel of 977 sugar beet accessions. Almost all phenotypic traits exhibited wide genetic diversity and high coefficient of variation (CV). A total of 170,750 high-quality single-nucleotide polymorphisms (SNPs) were obtained using the genotyping-by-sequencing (GBS). Neighbour-joining phylogenetic analysis, principal component analysis, population structure and kinship showed no obvious relationships among these genotypes based on subgroups or regional sources. GWAS was carried out using a mixed linear model, and 159 significant associations were detected for these traits. Within the 25 kb linkage disequilibrium decay of the associated markers, NRT1/PTR FAMILY 6.3 (BVRB_5g097760); nudix hydrolase 15 (BVRB_8g182070) and TRANSPORT INHIBITOR RESPONSE 1 (BVRB_8g181550); transcription factor MYB77 (BVRB_2g023500); and ethylene-responsive transcription factor ERF014 (BVRB_1g000090) were predicted to be strongly associated with the taproot traits of root groove depth (RGD); root shape (RS); crown size (CS); and flesh colour (FC), respectively. For the aboveground traits, UDP-glycosyltransferase 79B6 (BVRB_9g223780) and NAC domain-containing protein 7 (BVRB_5g097990); F-box protein At1g10780 (BVRB_6g140760); phosphate transporter PHO1 (BVRB_3g048660); F-box protein CPR1 (BVRB_8g181140); and transcription factor MYB77 (BVRB_2g023500) and alcohol acyltransferase 9 (BVRB_2g023460) might be associated with the hypocotyl colour (HC); plant type (PT); petiole length (PL); cotyledon size (C); and fascicled leaf type (FLT) of sugar beet, respectively. AP-2 complex subunit mu (BVRB_5g106130), trihelix transcription factor ASIL2 (BVRB_2g041790) and late embryogenesis abundant protein 18 (BVRB_5g106150) might be involved in pollen quantity (PQ) variation. The candidate genes extensively participated in hormone response, nitrogen and phosphorus transportation, secondary metabolism, fertilization and embryo maturation.

CONCLUSIONS

The genetic basis of agronomical traits is complicated in heterozygous diploid sugar beet. The putative valuable genes found in this study will help further elucidate the molecular mechanism of each phenotypic trait for beet breeding.

Genome-wide association and epistasis studies reveal the genetic basis of saline-alkali tolerance at the germination stage in rice

Introduction

Saline-alkali stress is one of the main abiotic factors limiting rice production worldwide. With the widespread use of rice direct seeding technology, it has become increasingly important to improve rice saline-alkali tolerance at the germination stage.

Methods

To understand the genetic basis of saline-alkali tolerance and facilitate breeding efforts for developing saline-alkali tolerant rice varieties, the genetic basis of rice saline-alkali tolerance was dissected by phenotyping seven germination-related traits of 736 diverse rice accessions under the saline-alkali stress and control conditions using genome-wide association and epistasis analysis (GWAES).

Results

Totally, 165 main-effect quantitative trait nucleotides (QTNs) and 124 additional epistatic QTNs were identified as significantly associated with saline-alkali tolerance, which explained a significant portion of the total phenotypic variation of the saline-alkali tolerance traits in the 736 rice accessions. Most of these QTNs were located in genomic regions either harboring saline-alkali tolerance QTNs or known genes for saline-alkali tolerance reported previously. Epistasis as an important genetic basis of rice saline-alkali tolerance was validated by genomic best linear unbiased prediction in which inclusion of both main-effect and epistatic QTNs showed a consistently better prediction accuracy than either main-effect or epistatic QTNs alone. Candidate genes for two pairs of important epistatic QTNs were suggested based on combined evidence from the high-resolution mapping plus their reported molecular functions. The first pair included a glycosyltransferase gene LOC_Os02g51900 (UGT85E1) and an E3 ligase gene LOC_Os04g01490 (OsSIRP4), while the second pair comprised an ethylene-responsive transcriptional factor, AP59 (LOC_Os02g43790), and a Bcl-2-associated athanogene gene, OsBAG1 (LOC_Os09g35630) for salt tolerance. Detailed haplotype analyses at both gene promoter and CDS regions of these candidate genes for important QTNs identified favorable haplotype combinations with large effects on saline-alkali tolerance, which can be used to improve rice saline-alkali tolerance by selective introgression.

Discussion

Our findings provided saline-alkali tolerant germplasm resources and valuable genetic information to be used in future functional genomic and breeding efforts of rice saline-alkali tolerance at the germination stage.

Genetic diversity and population structure assessment of Western Canadian barley cooperative trials

Studying the population structure and genetic diversity of historical datasets is a proposed use for association analysis. This is particularly important when the dataset contains traits that are time-consuming or costly to measure. A set of 96 elite barley genotypes, developed from eight breeding programs of the Western Canadian Cooperative Trials were used in the current study. Genetic diversity, allelic variation, and linkage disequilibrium (LD) were investigated using 5063 high-quality SNP markers via the Illumina 9K Barley Infinium iSelect SNP assay. The distribution of SNPs markers across the barley genome ranged from 449 markers on chromosome 1H to 1111 markers on chromosome 5H. The average polymorphism information content (PIC) per locus was 0.275 and ranged from 0.094 to 0.375. Bayesian clustering in STRUCTURE and principal coordinate analysis revealed that the populations are differentiated primarily due to the different breeding program origins and ear-row type into five subpopulations. Analysis of molecular variance based on PhiPT values suggested that high values of genetic diversity were observed within populations and accounted for 90% of the total variance. Subpopulation 5 exhibited the most diversity with the highest values of the diversity indices, which represent the breeding program gene pool of AFC, AAFRD, AU, and BARI. With increasing genetic distance, the LD values, expressed as r², declined to below the critical r^{2 =} 0.18 after 3.91 cM, and the same pattern was observed on each chromosome. Our results identified an important pattern of genetic diversity among the Canadian barley panel that was proposed to be representative of target breeding programs and may have important implications for association mapping in the future. This highlight, that efforts to identify novel variability underlying this diversity may present practical breeding opportunities to develop new barley genotypes.

Genotyping-by-Sequencing Based Molecular Genetic Diversity of Pakistani Bread Wheat (Triticum aestivum L.) Accessions

Spring wheat (Triticum aestivum L.) is one of the most imperative staple food crops, with an annual production of 765 million tons globally to feed ∼40% world population. Genetic diversity in available germplasm is crucial for sustainable wheat improvement to ensure global food security. A diversity panel of 184 Pakistani wheat accessions was genotyped using 123,596 high-quality single nucleotide polymorphism (SNP) markers generated by genotyping-by-sequencing with 42% of the SNPs mapped on B, 36% on A, and 22% on D sub-genomes of wheat. Chromosome 2B contains the most SNPs (9,126), whereas 4D has the least (2,660) markers. The mean polymorphic information content, genetic diversity, and major allele frequency of the population were 0.157, 0.1844, and 0.87, respectively. Analysis of molecular variance revealed a higher genetic diversity (80%) within the sub-population than among the sub-populations (20%). The genome-wide linkage disequilibrium was 0.34 Mbp for the whole wheat genome. Among the three subgenomes, A has the highest LD decay value (0.29 Mbp), followed by B (0.2 Mbp) and D (0.07 Mbp) genomes, respectively. The results of population structure, principal coordinate analysis, phylogenetic tree, and kinship analysis also divided the whole population into three clusters comprising 31, 33, and 120 accessions in group 1, group 2, and group 3, respectively. All groups were dominated by the local wheat accessions. Estimation of genetic diversity will be a baseline for the selection of breeding parents for mutations and the genome-wide association and marker-assisted selection studies.

Beta vulgaris ssp. vulgaris chromosome 8 shows significant association with geosmin concentration in table beet

Geosmin, a degraded sesquiterpene molecule with earthy and musty odor, imbues table beet with its characteristic aroma. Geosmin is heritable and endogenously produced in table beet; its earthy aroma is sought by some consumers but deters others. Geosmin biosynthesis is catalyzed by a bifunctional geosmin synthase enzyme in diverse bacteria and fungi, but a mechanism for geosmin biosynthesis in plants has not been reported. This work employed association analysis and selective genotyping of a segregating F2:3 mapping population to seek QTL associated with geosmin concentration in table beet. GBS reads were aligned to sugar beet reference genome EL10.2, and association analysis revealed two QTL for geosmin concentration on Beta vulgaris ssp. vulgaris chromosome 8. QTL at EL10.2 positions 28,017,624 and 38,488,687 each show effect size 8.7 μg·kg-1 geosmin and explain 8.5% and 6.4% of total variation in geosmin concentration, respectively. Resolution was low due to large recombination bin size and imperfect alignment between the reference genome and mapping population, but population size and selection proportion were sufficient to detect moderate to large effect QTL. This study, the first molecular genetic mapping experiment in table beet, succeeded in finding QTL for geosmin concentration in table beet, and it provides the basis for fine mapping or candidate gene investigation of functional loci for this distinctive sensory trait.

Molecular genetic analysis of spring wheat core collection using genetic diversity, population structure, and linkage disequilibrium

Background

Wheat (Triticum aestivium L.) is an important crop globally which has a complex genome. To identify the parents with useful agronomic characteristics that could be used in the various breeding programs, it is very important to understand the genetic diversity among global wheat genotypes. Also, understanding the genetic diversity is useful in breeding studies such as marker-assisted selection (MAS), genome-wide association studies (GWAS), and genomic selection.

Results

To understand the genetic diversity in wheat, a set of 103 spring wheat genotypes which represented five different continents were used. These genotypes were genotyped using 36,720 genotyping-by-sequencing derived SNPs (GBS-SNPs) which were well distributed across wheat chromosomes. The tested 103-wheat genotypes contained three different subpopulations based on population structure, principle coordinate, and kinship analyses. A significant variation was found within and among the subpopulations based on the AMOVA. Subpopulation 1 was found to be the more diverse subpopulation based on the different allelic patterns (Na, Ne, I, h, and uh). No high linkage disequilibrium was found between the 36,720 SNPs. However, based on the genomic level, D genome was found to have the highest LD compared with the two other genomes A and B. The ratio between the number of significant LD/number of non-significant LD suggested that chromosomes 2D, 5A, and 7B are the highest LD chromosomes in their genomes with a value of 0.08, 0.07, and 0.05, respectively. Based on the LD decay, the D genome was found to be the lowest genome with the highest number of haplotype blocks on chromosome 2D.

Conclusion

The recent study concluded that the 103-spring wheat genotypes and their GBS-SNP markers are very appropriate for GWAS studies and QTL-mapping. The core collection comprises three different subpopulations. Genotypes in subpopulation 1 are the most diverse genotypes and could be used in future breeding programs if they have desired traits. The distribution of LD hotspots across the genome was investigated which provides useful information on the genomic regions that includes interesting genes.

Crop genome-wide association study: a harvest of biological relevance

With the advent of rapid genotyping and next-generation sequencing technologies, genome-wide association study (GWAS) has become a routine strategy for decoding genotype-phenotype associations in many species. More than 1000 such studies over the last decade have revealed substantial genotype-phenotype associations in crops and provided unparalleled opportunities to probe functional genomics. Beyond the many 'hits' obtained, this review summarizes recent efforts to increase our understanding of the genetic architecture of complex traits by focusing on non-main effects including epistasis, pleiotropy, and phenotypic plasticity. We also discuss how these achievements and the remaining gaps in our knowledge will guide future studies. Synthetic association is highlighted as leading to false causality, which is prevalent but largely underestimated. Furthermore, validation evidence is appealing for future GWAS, especially in the context of emerging genome-editing technologies.

Association mapping of drought tolerance-related traits in barley to complement a traditional biparental QTL mapping study

Association mapping of drought-related traits in barley was used to increase the density of existing QTL maps without recreating mapping populations. We used 109 spring barley genotypes exhibiting high or low drought tolerance to elucidate the associations between diversity array technology sequencing (DArTseq) and single nucleotide polymorphism (SNP) markers and various physiological parameters related to plant responses to drought conditions. The study was performed in controlled conditions (growth chambers), drought tolerance was phenotyped in the four-leaf seedlings. We identified 58 associations including 34 new markers (i.e., 16 DArTseq and 18 SNP markers). The results for three markers were consistent with the data obtained in an earlier traditional biparental QTL mapping study. The regions neighboring markers on linkage group 2H contained the highest number of significant marker-trait associations. Five markers related to the photosynthetic activity of photosystem II were detected on chromosome 4H. The lowest number of associations were observed for the sequences neighboring DArT markers on linkage group 6H. A chromosome 3H region related to water use efficiency and net photosynthesis rate in both biparental QTL, and association study, may be particularly valuable, as these parameters correspond to the ability of plants to remain highly productive under water deficit stress. Our findings confirm that association mapping can increase the density of existing QTL maps without recreating mapping populations.

Genome-wide association mapping of black point reaction in common wheat (Triticum aestivum L.)

BACKGROUND

Black point is a serious threat to wheat production and can be managed by host resistance. Marker-assisted selection (MAS) has the potential to accelerate genetic improvement of black point resistance in wheat breeding. We performed a genome-wide association study (GWAS) using the high-density wheat 90 K and 660 K single nucleotide polymorphism (SNP) assays to better understand the genetic basis of black point resistance and identify associated molecular markers.

RESULTS

Black point reactions were evaluated in 166 elite wheat cultivars in five environments. Twenty-five unique loci were identified on chromosomes 2A, 2B, 3A, 3B (2), 3D, 4B (2), 5A (3), 5B (3), 6A, 6B, 6D, 7A (5), 7B and 7D (2), respectively, explaining phenotypic variation ranging from 7.9 to 18.0%. The highest number of loci was detected in the A genome (11), followed by the B (10) and D (4) genomes. Among these, 13 were identified in two or more environments. Seven loci coincided with known genes or quantitative trait locus (QTL), whereas the other 18 were potentially novel loci. Linear regression showed a clear dependence of black point scores on the number of favorable alleles, suggesting that QTL pyramiding will be an effective approach to increase resistance. In silico analysis of sequences of resistance-associated SNPs identified 6 genes possibly involved in oxidase, signal transduction and stress resistance as candidate genes involved in black point reaction.

CONCLUSION

SNP markers significantly associated with black point resistance and accessions with a larger number of resistance alleles can be used to further enhance black point resistance in breeding. This study provides new insights into the genetic architecture of black point reaction.

Insights into the genetic relationships among plants of Beta section Beta using SNP markers

Using a much higher number of SNP markers and larger sample sizes than all the previous studies, we characterized the genetic relationships among wild and cultivated plants of section Beta. We analyzed the genetic variation of Beta section Beta, which includes wild taxa (Beta macrocarpa, B. patula, B. vulgaris subsp. adanensis and B. vulgaris subsp. maritima) and cultivars (fodder beet, sugar beet, garden beet, leaf beet, and swiss chards), using 9724 single nucleotide polymorphism markers. The analyses conducted at the individual level without a priori groups confirmed the strong differentiation of B. macrocarpa and B. vulgaris subsp. adanensis from the other taxa. B. vulgaris subsp. maritima showed a complex genetic structure partly following a geographical pattern, which confounded the differences between this taxon and the cultivated varieties. Cultivated varieties were structured into three main groups: garden beets, fodder and sugar beets, and leaf beets and swiss chards. The genetic structure described here will be helpful to correctly estimate linkage disequilibrium and to test for statistical associations between genetic markers and environmental variables.

Genomewide association study for seeding emergence and tiller number using SNP markers in an elite winter wheat population

Seeding emergence and tiller number are the most important traits for wheat (Triticum aestivum L.) yield, but the inheritance of seeding emergence and tillering is poorly understood. We conducted a genomewide association study focussing on seeding emergence and tiller number at different growth stages with a panel of 205 elite winter wheat accessions. The population was genotyped with a high-density Illumina iSelect 90K SNPs assay. A total of 31 loci were found to be associated with seeding emergence rate (SER) and tiller number in different growth stages. Loci distributed among 12 chromosomes accounted for 5.35 to 11.33% of the observed phenotypic variation. With this information, 10 stable SNPs were identified for eventual development of cleaved amplified polymorphic sequence markers for SER and tiller number in different growth stages. Additionally, a set of elite alleles were identified, such as Ra_c14761_1348-T, which may increase SER by 13.35%, and Excalibur_c11045_236-A and BobWhite_c8436_391-T, which may increase the rate of available tillering by 14.78 and 8.47%, respectively. These results should provide valuable information for marker-assisted selection and parental selection in wheat breeding programmes.

Crop wild relative populations of Beta vulgaris allow direct mapping of agronomically important genes

Rapid identification of agronomically important genes is of pivotal interest for crop breeding. One source of such genes are crop wild relative (CWR) populations. Here we used a CWR population of <200 wild beets (B. vulgaris ssp. maritima), sampled in their natural habitat, to identify the sugar beet (Beta vulgaris ssp. vulgaris) resistance gene Rz2 with a modified version of mapping-by-sequencing (MBS). For that, we generated a draft genome sequence of the wild beet. Our results show the importance of preserving CWR in situ and demonstrate the great potential of CWR for rapid discovery of causal genes relevant for crop improvement. The candidate gene for Rz2 was identified by MBS and subsequently corroborated via RNA interference (RNAi). Rz2 encodes a CC-NB-LRR protein. Access to the DNA sequence of Rz2 opens the path to improvement of resistance towards rhizomania not only by marker-assisted breeding but also by genome editing.

Variation among wild relatives of crop plants can be used to identify genes underlying traits of agronomic importance. Here, the authors show that a modified mapping-by-sequencing approach can rapidly identify the genetic basis for viral resistance in sugar beet using wild beet populations in their natural habitat.

A robust multiple-locus method for quantitative trait locus analysis of non-normally distributed multiple traits

Linear regression-based quantitative trait loci/association mapping methods such as least squares commonly assume normality of residuals. In genetics studies of plants or animals, some quantitative traits may not follow normal distribution because the data include outlying observations or data that are collected from multiple sources, and in such cases the normal regression methods may lose some statistical power to detect quantitative trait loci. In this work, we propose a robust multiple-locus regression approach for analyzing multiple quantitative traits without normality assumption. In our method, the objective function is least absolute deviation (LAD), which corresponds to the assumption of multivariate Laplace distributed residual errors. This distribution has heavier tails than the normal distribution. In addition, we adopt a group LASSO penalty to produce shrinkage estimation of the marker effects and to describe the genetic correlation among phenotypes. Our LAD-LASSO approach is less sensitive to the outliers and is more appropriate for the analysis of data with skewedly distributed phenotypes. Another application of our robust approach is on missing phenotype problem in multiple-trait analysis, where the missing phenotype items can simply be filled with some extreme values, and be treated as outliers. The efficiency of the LAD-LASSO approach is illustrated on both simulated and real data sets.

Advanced phenotyping and phenotype data analysis for the study of plant growth and development

Due to an increase in the consumption of food, feed, fuel and to meet global food security needs for the rapidly growing human population, there is a necessity to breed high yielding crops that can adapt to the future climate changes, particularly in developing countries. To solve these global challenges, novel approaches are required to identify quantitative phenotypes and to explain the genetic basis of agriculturally important traits. These advances will facilitate the screening of germplasm with high performance characteristics in resource-limited environments. Recently, plant phenomics has offered and integrated a suite of new technologies, and we are on a path to improve the description of complex plant phenotypes. High-throughput phenotyping platforms have also been developed that capture phenotype data from plants in a non-destructive manner. In this review, we discuss recent developments of high-throughput plant phenotyping infrastructure including imaging techniques and corresponding principles for phenotype data analysis.

From phenotypes to causal sequences: using genome wide association studies to dissect the sequence basis for variation of plant development

Tremendous natural variation of growth and development exists within species. Uncovering the molecular mechanisms that tune growth and development promises to shed light on a broad set of biological issues including genotype to phenotype relations, regulatory mechanisms of biological processes and evolutionary questions. Recent progress in sequencing and data processing capabilities has enabled Genome Wide Association Studies (GWASs) to identify DNA sequence polymorphisms that underlie the variation of biological traits. In the last years, GWASs have proven powerful in revealing the complex genetic bases of many phenotypes in various plant species. Here we highlight successful recent GWASs that uncovered mechanistic and sequence bases of trait variation related to plant growth and development and discuss important considerations for conducting successful GWASs.

Advanced phenotyping and phenotype data analysis for the study of plant growth and development

Due to an increase in the consumption of food, feed, fuel and to meet global food security needs for the rapidly growing human population, there is a necessity to breed high yielding crops that can adapt to the future climate changes, particularly in developing countries. To solve these global challenges, novel approaches are required to identify quantitative phenotypes and to explain the genetic basis of agriculturally important traits. These advances will facilitate the screening of germplasm with high performance characteristics in resource-limited environments. Recently, plant phenomics has offered and integrated a suite of new technologies, and we are on a path to improve the description of complex plant phenotypes. High-throughput phenotyping platforms have also been developed that capture phenotype data from plants in a non-destructive manner. In this review, we discuss recent developments of high-throughput plant phenotyping infrastructure including imaging techniques and corresponding principles for phenotype data analysis.

Evaluation of multi-locus models for genome-wide association studies: a case study in sugar beet

Association mapping has become a widely applied genomic approach to dissect the genetic architecture of complex traits. A major issue for association mapping is the need to control for the confounding effects of population structure, which is commonly done by mixed models incorporating kinship information. In this case study, we employed experimental data from a large sugar beet population to evaluate multi-locus models for association mapping. As in linkage mapping, markers are selected as cofactors to control for population structure and genetic background variation. We compared different biometric models with regard to important quantitative trait locus (QTL) mapping parameters like the false-positive rate, the QTL detection power and the predictive power for the proportion of explained genotypic variance. Employing different approaches we show that the multi-locus model, that is, incorporating cofactors, outperforms the other models, including the mixed model used as a reference model. Thus, multi-locus models are an attractive alternative for association mapping to efficiently detect QTL for knowledge-based breeding.

Reliable in silico identification of sequence polymorphisms and their application for extending the genetic map of sugar beet (Beta vulgaris)

Molecular markers are a highly valuable tool for creating genetic maps. Like in many other crops, sugar beet (Beta vulgaris L.) breeding is increasingly supported by the application of such genetic markers. Single nucleotide polymorphism (SNP) based markers have a high potential for automated analysis and high-throughput genotyping. We developed a bioinformatics workflow that uses Sanger and 2nd-generation sequence data for detection, evaluation and verification of new transcript-associated SNPs from sugar beet. RNAseq data from one parent of an established mapping population were produced by 454-FLX sequencing and compared to Sanger ESTs derived from the other parent. The workflow established for SNP detection considers the quality values of both types of reads, provides polymorphic alignments as well as selection criteria for reliable SNP detection and allows painless generation of new genetic markers within genes. We obtained a total of 14,323 genic SNPs and InDels. According to empirically optimised settings for the quality parameters, we classified these SNPs into four usability categories. Validation of a subset of the in silico detected SNPs by genotyping the mapping population indicated a high success rate of the SNP detection. Finally, a total of 307 new markers were integrated with existing data into a new genetic map of sugar beet which offers improved resolution and the integration of terminal markers.

Precision phenotyping of biomass accumulation in triticale reveals temporal genetic patterns of regulation

To extend agricultural productivity by knowledge-based breeding and tailor varieties adapted to specific environmental conditions, it is imperative to improve our ability to assess the dynamic changes of the phenome of crops under field conditions. To this end, we have developed a precision phenotyping platform that combines various sensors for a non-invasive, high-throughput and high-dimensional phenotyping of small grain cereals. This platform yielded high prediction accuracies and heritabilities for biomass of triticale. Genetic variation for biomass accumulation was dissected with 647 doubled haploid lines derived from four families. Employing a genome-wide association mapping approach, two major quantitative trait loci (QTL) for biomass were identified and the genetic architecture of biomass accumulation was found to be characterized by dynamic temporal patterns. Our findings highlight the potential of precision phenotyping to assess the dynamic genetics of complex traits, especially those not amenable to traditional phenotyping.

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.

Cross-validation in association mapping and its relevance for the estimation of QTL parameters of complex traits

Association mapping has become a widely applied genomic approach to identify quantitative trait loci (QTL) and dissect the genetic architecture of complex traits. However, approaches to assess the quality of the obtained QTL results are lacking. We therefore evaluated the potential of cross-validation in association mapping based on a large sugar beet data set. Our results show that the proportion of the population that should be used as estimation and validation sets, respectively, depends on the size of the mapping population. Generally, a fivefold cross-validation, that is, 20% of the lines as independent validation set, appears appropriate for commonly used population sizes. The predictive power for the proportion of genotypic variance explained by QTL was overestimated by on average 38% indicating a strong bias in the estimated QTL effects. The cross-validated predictive power ranged between 4 and 50%, which are more realistic estimates of this parameter for complex traits. In addition, QTL frequency distributions can be used to assess the precision of QTL position estimates and the robustness of the detected QTL. In summary, cross-validation can be a valuable tool to assess the quality of QTL parameters in association mapping.

Genomic selection in sugar beet breeding populations

Background

Genomic selection exploits dense genome-wide marker data to predict breeding values. In this study we used a large sugar beet population of 924 lines representing different germplasm types present in breeding populations: unselected segregating families and diverse lines from more advanced stages of selection. All lines have been intensively phenotyped in multi-location field trials for six agronomically important traits and genotyped with 677 SNP markers.

Results

We used ridge regression best linear unbiased prediction in combination with fivefold cross-validation and obtained high prediction accuracies for all except one trait. In addition, we investigated whether a calibration developed based on a training population composed of diverse lines is suited to predict the phenotypic performance within families. Our results show that the prediction accuracy is lower than that obtained within the diverse set of lines, but comparable to that obtained by cross-validation within the respective families.

Conclusions

The results presented in this study suggest that a training population derived from intensively phenotyped and genotyped diverse lines from a breeding program does hold potential to build up robust calibration models for genomic selection. Taken together, our results indicate that genomic selection is a valuable tool and can thus complement the genomics toolbox in sugar beet breeding.

Population structure, genetic diversity and linkage disequilibrium in elite winter wheat assessed with SNP and SSR markers

Modern genomics approaches rely on the availability of high-throughput and high-density genotyping platforms. A major breakthrough in wheat genotyping was the development of an SNP array. In this study, we used a diverse panel of 172 elite European winter wheat lines to evaluate the utility of the SNP array for genomic analyses in wheat germplasm derived from breeding programs. We investigated population structure and genetic relatedness and found that the results obtained with SNP and SSR markers differ. This suggests that additional research is required to determine the optimum approach for the investigation of population structure and kinship. Our analysis of linkage disequilibrium (LD) showed that LD decays within approximately 5-10 cM. Moreover, we found that LD is variable along chromosomes. Our results suggest that the number of SNPs needs to be increased further to obtain a higher coverage of the chromosomes. Taken together, SNPs can be a valuable tool for genomics approaches and for a knowledge-based improvement of wheat.

Effect of inter- and intragenic epistasis on the heritability of oil content in rapeseed (Brassica napus L.)

The loci detected by association mapping which are involved in the expression of important agronomic traits in crops often explain only a small proportion of the total genotypic variance. Here, 17 SNPs derived from 9 candidate genes from the triacylglycerol biosynthetic pathway were studied in an association analysis in a population of 685 diverse elite rapeseed inbred lines. The 685 lines were evaluated for oil content, as well as for glucosinolates, yield, and thousand-kernel weight in field trials at 4 locations. We detected main effects for most of the studied genes illustrating that genetic diversity for oil content can be exploited by the selection of favorable alleles. In addition to main effects, both intergenic and intragenic epistasis was detected that contributes to a considerable amount to the genotypic variance observed for oil content. The proportion of explained genotypic variance was doubled when in addition to main effects epistasis was considered. Therefore, a knowledge-based improvement of oil content in rapeseed should also take such favorable epistatic interactions into account. Our results suggest, that the observed high contribution of epistasis may to some extent explain the missing heritability in genome-wide association studies.

A bountiful harvest: genomic insights into crop domestication phenotypes

Human selection during crop domestication has resulted in remarkable transformations of plant phenotypes, providing a window into the genetic basis of morphological evolution. Recent progress in our understanding of the genetic architecture of novel plant traits has emerged from combining advanced molecular technologies with improved experimental designs, including nested association mapping, genome-wide association studies, population genetic screens for signatures of selection, and candidate gene approaches. These studies reveal a diversity of underlying causative mutations affecting phenotypes important in plant domestication and crop improvement, including coding sequence substitutions, presence/absence and copy number variation, transposon activation leading to novel gene structures and expression patterns, diversification following gene duplication, and polyploidy leading to altered combinatorial capabilities. The genomic regions unknowingly targeted by human selection include both structural and regulatory genes, often with results that propagate through the transcriptome as well as to other levels in the biosynthetic and morphogenetic networks.

Comparison of biometrical approaches for QTL detection in multiple segregating families

Detection of QTL in multiple segregating families possesses many advantages over the classical QTL mapping in biparental populations. It has thus become increasingly popular, and different biometrical approaches are available to analyze such data sets. We empirically compared an approach based on linkage mapping methodology with an association mapping approach. To this end, we used a large population of 788 elite maize lines derived from six biparental families genotyped with 857 SNP markers. In addition, we constructed genetic maps with reduced marker densities to assess the dependency of the performance of both mapping approaches on the marker density. We used cross-validation and resample model averaging and found that while association mapping performed better under high marker densities, this was reversed under low marker densities. In addition to main effect QTL, we also detected epistatic interactions. Our results suggest that both approaches will profit from a further increase in marker density and that a cross-validation should be applied irrespective of the biometrical approach.

Mapping QTL for agronomic traits in breeding populations

Detection of quantitative trait loci (QTL) in breeding populations offers the advantage that these QTL are of direct relevance for the improvement of crops via knowledge-based breeding. As phenotypic data are routinely generated in breeding programs and the costs for genotyping are constantly decreasing, it is tempting to exploit this information to unravel the genetic architecture underlying important agronomic traits in crops. This review characterizes the germplasm from breeding populations available for QTL detection, provides a classification of the different QTL mapping approaches that are available, and highlights important considerations concerning study design and biometrical models suitable for QTL analysis.

Genome-wide evaluation of genetic diversity and linkage disequilibrium in winter and spring triticale (x Triticosecale Wittmack)

Background

Recent advances in genotyping with high-density markers nowadays enable genome-wide genomic analyses in crops. A detailed characterisation of the population structure and linkage disequilibrium (LD) is essential for the application of genomic approaches and consequently for knowledge-based breeding. In this study we used the triticale-specific DArT array to analyze population structure, genetic diversity, and LD in a worldwide set of 161 winter and spring triticale lines.

Results

The principal coordinate analysis revealed that the first principal coordinate divides the triticale population into two clusters according to their growth habit. The density distributions of the first ten principal coordinates revealed that several show a distribution indicative of population structure. In addition, we observed relatedness within growth habits which was higher among the spring types than among the winter types. The genome-wide analysis of polymorphic information content (PIC) showed that the PIC is variable among and along chromosomes and that especially the R genome of spring types possesses a reduced genetic diversity. We also found that several chromosomes showed regions of high genetic distance between the two growth habits, indicative of divergent selection. Regarding linkage disequilibrium, the A and B genomes showed a similar LD of 0.24 for closely linked markers and a decay within approximately 12 cM. LD in the R genome was lower with 0.19 and decayed within a shorter map distance of approximately 5 cM. The extent of LD was generally higher for the spring types compared to the winter types. In addition, we observed strong variability of LD along the chromosomes.

Conclusions

Our results confirm winter and spring growth habit are the major contributors to population structure in triticale, and a family structure exists in both growth types. The specific patterns of genetic diversity observed within these types, such as the low diversity on some rye chromosomes of spring habits, provide a basis for targeted broadening of the available breeding germplasm. In addition, the genome-wide analysis of the extent and the pattern of LD will assist scientists and breeders alike in the implementation and the interpretation of association mapping in triticale.

Mapping QTL for agronomic traits in breeding populations

Detection of quantitative trait loci (QTL) in breeding populations offers the advantage that these QTL are of direct relevance for the improvement of crops via knowledge-based breeding. As phenotypic data are routinely generated in breeding programs and the costs for genotyping are constantly decreasing, it is tempting to exploit this information to unravel the genetic architecture underlying important agronomic traits in crops. This review characterizes the germplasm from breeding populations available for QTL detection, provides a classification of the different QTL mapping approaches that are available, and highlights important considerations concerning study design and biometrical models suitable for QTL analysis.

Dissecting the genetic architecture of agronomic traits in multiple segregating populations in rapeseed (Brassica napus L.)

Detection of QTL in multiple segregating populations is of high interest as it includes more alleles than mapping in a single biparental population. In addition, such populations are routinely generated in applied plant breeding programs and can thus be used to identify QTL which are of direct relevance for a marker-assisted improvement of elite germplasm. Multiple-line cross QTL mapping and joint linkage association mapping were used for QTL detection. We empirically compared these two different biometrical approaches with regard to QTL detection for important agronomic traits in nine segregating populations of elite rapeseed lines. The plants were intensively phenotyped in multi-location field trials and genotyped with 253 SNP markers. Both approaches detected several additive QTL for diverse traits, including flowering time, plant height, protein content, oil content, glucosinolate content, and grain yield. In addition, we identified one epistatic QTL for flowering time. Consequently, both approaches appear suited for QTL detection in multiple segregating populations.