Comparison of mixed-model approaches for association mapping in rapeseed, potato, sugar beet, maize, and Arabidopsis

Dissecting the Genetic Architecture of Morphological Traits in Sunflower (Helianthus annuus L.)

The sunflower (Helianthus annuus L.) is one of the most essential oil crops in the world. Several component traits, including flowering time, plant height, stem diameter, seed weight, and kernel weight, determine sunflower seed and oil yield. Although the genetic mechanisms governing the variation of these yield-related traits have been studied using various approaches, genome-wide association studies (GWAS) have not been widely applied to sunflowers. In this study, a set of 342 sunflower accessions was evaluated in 2019 and 2020 using an incomplete randomized block design, and GWAS was conducted utilizing two complementary approaches: the mixed linear model (MLM) and the fixed and random model circulating probability unification (farmCPU) model by fitting 226,779 high-quality SNPs. As a result, GWAS identified a number of trait-associated SNPs. Those SNPs were located close to several genes that may serve as a basis for further molecular characterization and provide promising targets for sunflower yield improvement.

GWAS for the identification of introgressed candidate genes of Sinapis alba with increased branching numbers in backcross lines of the allohexaploid Brassica

Plant architecture is a crucial determinant of crop yield. The number of primary (PB) and secondary branches (SB) is particularly significant in shaping the architecture of Indian mustard. In this study, we analyzed a panel of 86 backcross introgression lines (BCILs) derived from the first stable allohexaploid Brassicas with 170 Sinapis alba genome-specific SSR markers to identify associated markers with higher PB and SB through association mapping. The structure analysis revealed three subpopulations, i.e., P1, P2, and P3, in the association panel containing a total of 11, 33, and 42 BCILs, respectively. We identified five novel SSR markers linked to higher PB and SB. Subsequently, we explored the 20 kb up- and downstream regions of these SSR markers to predict candidate genes for improved branching and annotated them through BLASTN. As a result, we predicted 47 complete genes within the 40 kb regions of all trait-linked markers, among which 35 were identified as candidate genes for higher PB and SB numbers in BCILs. These candidate genes were orthologous to ANT, RAMOSUS, RAX, MAX, MP, SEU, REV, etc., branching genes. The remaining 12 genes were annotated for additional roles using BLASTP with protein databases. This study identified five novel S. alba genome-specific SSR markers associated with increased PB and SB, as well as 35 candidate genes contributing to plant architecture through improved branching numbers. To the best of our knowledge, this is the first report of introgressive genes for higher branching numbers in B. juncea from S. alba.

Genome-wide association study in two-row spring barley landraces identifies QTL associated with plantlets root system architecture traits in well-watered and osmotic stress conditions

Water availability is undoubtedly one of the most important environmental factors affecting crop production. Drought causes a gradual deprivation of water in the soil from top to deep layers and can occur at diverse stages of plant development. Roots are the first organs that perceive water deficit in soil and their adaptive development contributes to drought adaptation. Domestication has contributed to a bottleneck in genetic diversity. Wild species or landraces represent a pool of genetic diversity that has not been exploited yet in breeding program. In this study, we used a collection of 230 two-row spring barley landraces to detect phenotypic variation in root system plasticity in response to drought and to identify new quantitative trait loci (QTL) involved in root system architecture under diverse growth conditions. For this purpose, young seedlings grown for 21 days in pouches under control and osmotic-stress conditions were phenotyped and genotyped using the barley 50k iSelect SNP array, and genome-wide association studies (GWAS) were conducted using three different GWAS methods (MLM GAPIT, FarmCPU, and BLINK) to detect genotype/phenotype associations. In total, 276 significant marker-trait associations (MTAs; p-value (FDR)< 0.05) were identified for root (14 and 12 traits under osmotic-stress and control conditions, respectively) and for three shoot traits under both conditions. In total, 52 QTL (multi-trait or identified by at least two different GWAS approaches) were investigated to identify genes representing promising candidates with a role in root development and adaptation to drought stress.

Multi-model genome-wide association studies of leaf anatomical traits and vein architecture in rice

Introduction

The anatomy of rice leaves is closely related to photosynthesis and grain yield. Therefore, exploring insight into the quantitative trait loci (QTLs) and alleles related to rice flag leaf anatomical and vein traits is vital for rice improvement.

Methods

Here, we aimed to explore the genetic architecture of eight flag leaf traits using one single-locus model; mixed-linear model (MLM), and two multi-locus models; fixed and random model circulating probability unification (FarmCPU) and Bayesian information and linkage disequilibrium iteratively nested keyway (BLINK). We performed multi-model GWAS using 329 rice accessions of RDP1 with 700K single-nucleotide polymorphisms (SNPs) markers.

Results

The phenotypic correlation results indicated that rice flag leaf thickness was strongly correlated with leaf mesophyll cells layer (ML) and thickness of both major and minor veins. All three models were able to identify several significant loci associated with the traits. MLM identified three non-synonymous SNPs near NARROW LEAF 1 (NAL1) in association with ML and the distance between minor veins (IVD) traits.

Discussion

Several numbers of significant SNPs associated with known gene function in leaf development and yield traits were detected by multi-model GWAS performed in this study. Our findings indicate that flag leaf traits could be improved via molecular breeding and can be one of the targets in high-yield rice development.

Interpretation of Manhattan Plots and Other Outputs of Genome-Wide Association Studies

With increasing marker density, estimation of recombination rate between a marker and a causal mutation using linkage analysis becomes less important. Instead, linkage disequilibrium (LD) becomes the major indicator for gene mapping through genome-wide association studies (GWAS). In addition to the linkage between the marker and the causal mutation, many other factors may contribute to the LD, including population structure and cryptic relationships among individuals. As statistical methods and software evolve to improve statistical power and computing speed in GWAS, the corresponding outputs must also evolve to facilitate the interpretation of input data, the analytical process, and final association results. In this chapter, our descriptions focus on (1) considerations in creating a Manhattan plot displaying the strength of LD and locations of markers across a genome; (2) criteria for genome-wide significance threshold and the different appearance of Manhattan plots in single-locus and multiple-locus models; (3) exploration of population structure and kinship among individuals; (4) quantile-quantile (QQ) plot; (5) LD decay across the genome and LD between the associated markers and their neighbors; (6) exploration of individual and marker information on Manhattan and QQ plots via interactive visualization using HTML. The ultimate objective of this chapter is to help users to connect input data to GWAS outputs to balance power and false positives, and connect GWAS outputs to the selection of candidate genes using LD extent.

Genetic diversity and population structure analysis for morphological traits in upland cotton (Gossypium hirsutum L.)

A total of 96 different genotypes of upland cotton (Gossypium hirsutum) were selected from the breeding material and germplasm available at CCS HAU, India, to find the novel marker-trait associations for morphological traits used for registration of variety in upland cotton. Twenty-three morphological traits of the selected genotypes were recorded in field trials conducted in two replication of randomized block design during Kharif 2018 and 2019. A total of 11 traits showed sufficient variations in the screened germplasm and the same were further used for association mapping. A total of 168 SSRs were used for genotyping, of which 97 SSRs showed polymorphism amplifying 293 different alleles with an average of 3.02 alleles per SSR. Clustering, principal component analysis, and population structure analysis advocated that the current germplasm panel has enough diversity to be considered for association mapping. A total of 20 significant marker-trait associations were identified by the mixed linear model (MLM) and compressed mixed linear model (CMLM), of which 15 were common to both models, hence considered as promising associations. To the best of our knowledge, it is a first attempt to identify the linked markers in relation to morphological traits for the cotton crop. Results of the present study will be highly useful in speeding up variety registration programmes of upland cotton complementing to Distinctiveness, Uniformity, and Stability (DUS) testing.

A combination of genome-wide association study and transcriptome analysis in leaf epidermis identifies candidate genes involved in cuticular wax biosynthesis in Brassica napus

BACKGROUND

Brassica napus L. is one of the most important oil crops in the world. However, climate-change-induced environmental stresses negatively impact on its yield and quality. Cuticular waxes are known to protect plants from various abiotic/biotic stresses. Dissecting the genetic and biochemical basis underlying cuticular waxes is important to breed cultivars with improved stress tolerance.

RESULTS

Here a genome-wide association study (GWAS) of 192 B. napus cultivars and inbred lines was used to identify single-nucleotide polymorphisms (SNPs) associated with leaf waxes. A total of 202 SNPs was found to be significantly associated with 31 wax traits including total wax coverage and the amounts of wax classes and wax compounds. Next, epidermal peels from leaves of both high-wax load (HW) and low-wax load (LW) lines were isolated and used to analyze transcript profiles of all GWAS-identified genes. Consequently, 147 SNPs were revealed to have differential expressions between HW and LW lines, among which 344 SNP corresponding genes exhibited up-regulated while 448 exhibited down-regulated expressions in LW when compared to those in HW. According to the gene annotation information, some differentially expressed genes were classified into plant acyl lipid metabolism, including fatty acid-related pathways, wax and cutin biosynthesis pathway and wax secretion. Some genes involved in cell wall formation and stress responses have also been identified.

CONCLUSIONS

Combination of GWAS with transcriptomic analysis revealed a number of directly or indirectly wax-related genes and their associated SNPs. These results could provide clues for further validation of SNPs for marker-assisted breeding and provide new insights into the genetic control of wax biosynthesis and improving stress tolerance of B. napus.

Phenotypic and molecular dissection of grain quality using the USDA rice mini-core collection

Grain quality is a major breeding objective and paramount to food production. This study was aimed to phenotypically and molecularly dissect the rice grain quality, especially amylose content (AC), grain protein content (GPC) and alkali spreading value (ASV), using the USDA rice mini-core collection representing the world-wide rice germplasm lines. Grain chemical analysis combined with genome-wide association study (GWAS) was used for the study. A wide genetic variation was observed for these grain quality traits in the mini-core collection. Germplasm lines unique in AC, GPC and ASV and desirable for grain quality improvement were identified. The genetic diversity of the collection was re-analyzed using new SNPs, thus providing a more precise genotypic information about the collection. Furthermore, ten loci significantly associated with these grain quality traits were identified through GWAS using 22947 high-quality SNPs. These results, therefore, provide knowledge, resources and molecular tools for efficient rice grain quality improvement.

Comparing Different Statistical Models and Multiple Testing Corrections for Association Mapping in Soybean and Maize

Association mapping (AM) is a powerful tool for fine mapping complex trait variation down to nucleotide sequences by exploiting historical recombination events. A major problem in AM is controlling false positives that can arise from population structure and family relatedness. False positives are often controlled by incorporating covariates for structure and kinship in mixed linear models (MLM). These MLM-based methods are single locus models and can introduce false negatives due to over fitting of the model. In this study, eight different statistical models, ranging from single-locus to multilocus, were compared for AM for three traits differing in heritability in two crop species: soybean (Glycine max L.) and maize (Zea mays L.). Soybean and maize were chosen, in part, due to their highly differentiated rate of linkage disequilibrium (LD) decay, which can influence false positive and false negative rates. The fixed and random model circulating probability unification (FarmCPU) performed better than other models based on an analysis of Q-Q plots and on the identification of the known number of quantitative trait loci (QTLs) in a simulated data set. These results indicate that the FarmCPU controls both false positives and false negatives. Six qualitative traits in soybean with known published genomic positions were also used to compare these models, and results indicated that the FarmCPU consistently identified a single highly significant SNP closest to these known published genes. Multiple comparison adjustments (Bonferroni, false discovery rate, and positive false discovery rate) were compared for these models using a simulated trait having 60% heritability and 20 QTLs. Multiple comparison adjustments were overly conservative for MLM, CMLM, ECMLM, and MLMM and did not find any significant markers; in contrast, ANOVA, GLM, and SUPER models found an excessive number of markers, far more than 20 QTLs. The FarmCPU model, using less conservative methods (false discovery rate, and positive false discovery rate) identified 10 QTLs, which was closer to the simulated number of QTLs than the number found by other models.

GWAS analysis in spring barley (Hordeum vulgare L.) for morphological traits exposed to drought

Association analysis based on linkage disequilibrium has become a common and powerful approach for detection of QTLs underlying complex agronomic traits including drought tolerance. To determine marker/trait association, 148 modern European spring barley cultivars were evaluated under drought stress. Associations of morphological traits with AFLP/SSR markers were investigated based on the mixed linear model using the TASSEL_3.0. Population structure was estimated using various methods including Bayesian clustering model by STRUCTURE software, PCoA analysis, NJ dendrogram and Hierarchical Clustering. Linkage disequilibrium patterns were explored among the whole genome and each chromosome separately. All the analysis for population structure divided the population into two sub-groups. Linkage disequilibrium analysis showed that by increasing genetic distance, LD decreases. Totally, 167 significant marker trait associations were found which delineated into 65 QTLs in both treatments. Two stable QTLs on 5H at 86.880 cM were detected for Internode Length and on 3H at 126.421 cM for flag leaf length in drought stress treatment. Fourteen QTLs were co-localized with previously reported QTLs and others were novel. The results indicate that these putative genomic regions contain genes that have pleiotropic effects on morphological traits in drought condition.

Relevance of genetic relationship in GWAS and genomic prediction

The objective of this study was to analyze the relevance of relationship information on the identification of low heritability quantitative trait loci (QTLs) from a genome-wide association study (GWAS) and on the genomic prediction of complex traits in human, animal and cross-pollinating populations. The simulation-based data sets included 50 samples of 1000 individuals of seven populations derived from a common population with linkage disequilibrium. The populations had non-inbred and inbred progeny structure (50 to 200) with varying number of members (5 to 20). The individuals were genotyped for 10,000 single nucleotide polymorphisms (SNPs) and phenotyped for a quantitative trait controlled by 10 QTLs and 90 minor genes showing dominance. The SNP density was 0.1 cM and the narrow sense heritability was 25%. The QTL heritabilities ranged from 1.1 to 2.9%. We applied mixed model approaches for both GWAS and genomic prediction using pedigree-based and genomic relationship matrices. For GWAS, the observed false discovery rate was kept below the significance level of 5%, the power of detection for the low heritability QTLs ranged from 14 to 50%, and the average bias between significant SNPs and a QTL ranged from less than 0.01 to 0.23 cM. The QTL detection power was consistently higher using genomic relationship matrix. Regardless of population and training set size, genomic prediction provided higher prediction accuracy of complex trait when compared to pedigree-based prediction. The accuracy of genomic prediction when there is relatedness between individuals in the training set and the reference population is much higher than the value for unrelated individuals.

A Novel QTL for Powdery Mildew Resistance in Nordic Spring Barley (Hordeum vulgare L. ssp. vulgare) Revealed by Genome-Wide Association Study

The powdery mildew fungus, Blumeria graminis f. sp. hordei is a worldwide threat to barley (Hordeum vulgare L. ssp. vulgare) production. One way to control the disease is by the development and deployment of resistant cultivars. A genome-wide association study was performed in a Nordic spring barley panel consisting of 169 genotypes, to identify marker-trait associations significant for powdery mildew. Powdery mildew was scored during three years (2012-2014) in four different locations within the Nordic region. There were strong correlations between data from all locations and years. In total four QTLs were identified, one located on chromosome 4H in the same region as the previously identified mlo locus and three on chromosome 6H. Out of these three QTLs identified on chromosome 6H, two are in the same region as previously reported QTLs for powdery mildew resistance, whereas one QTL appears to be novel. The top NCBI BLASTn hit of the SNP markers within the novel QTL predicted the responsible gene to be the 26S proteasome regulatory subunit, RPN1, which is required for innate immunity and powdery mildew-induced cell death in Arabidopsis. The results from this study have revealed SNP marker candidates that can be exploited for use in marker-assisted selection and stacking of genes for powdery mildew resistance in barley.

Genome-Wide SNP Markers Based on SLAF-Seq Uncover Breeding Traces in Rapeseed (Brassica napus L.)

Single Nucleotide Polymorphisms (SNPs) are the most abundant and richest form of genomic polymorphism, and hence make highly favorable markers for genetic map construction and genome-wide association studies. In this study, a total of 300 rapeseed accessions (278 representative of Chinese germplasm, plus 22 outgroup accessions of different origins and ecotypes) were collected and sequenced using Specific-Locus Amplified Fragment Sequencing (SLAF-seq) technology, obtaining 660.25M reads with an average sequencing depth of 6.27 × and a mean Q30 of 85.96%. Based on the 238,711 polymorphic SLAF tags a total of 1,197,282 SNPs were discovered, and a subset of 201,817 SNPs with minor allele frequency >0.05 and integrity >0.8 were selected. Of these, 30,877 were designated SNP "hotspots," and 41 SNP-rich genomic regions could be delineated, with 100 genes associated with plant resistance, vernalization response, and signal transduction detected in these regions. Subsequent analysis of genetic diversity, linkage disequilibrium (LD), and population structure in the 300 accessions was carried out based on the 201,817 SNPs. Nine subpopulations were observed based on the population structure analysis. Hierarchical clustering and principal component analysis divided the 300 varieties roughly in accordance with their ecotype origins. However, spring-type varieties were intermingled with semi-winter type varieties, indicating frequent hybridization between spring and semi-winter ecotypes in China. In addition, LD decay across the whole genome averaged 299 kb when r² = 0.1, but the LD decay in the A genome (43 kb) was much shorter than in the C genome (1,455 kb), supporting the targeted introgression of the A genome from progenitor species B. rapa into Chinese rapeseed. This study also lays the foundation for genetic analysis of important agronomic traits using this rapeseed population.

Genome-Wide SNP Markers Based on SLAF-Seq Uncover Breeding Traces in Rapeseed (Brassica napus L.)

Single Nucleotide Polymorphisms (SNPs) are the most abundant and richest form of genomic polymorphism, and hence make highly favorable markers for genetic map construction and genome-wide association studies. In this study, a total of 300 rapeseed accessions (278 representative of Chinese germplasm, plus 22 outgroup accessions of different origins and ecotypes) were collected and sequenced using Specific-Locus Amplified Fragment Sequencing (SLAF-seq) technology, obtaining 660.25M reads with an average sequencing depth of 6.27 × and a mean Q30 of 85.96%. Based on the 238,711 polymorphic SLAF tags a total of 1,197,282 SNPs were discovered, and a subset of 201,817 SNPs with minor allele frequency >0.05 and integrity >0.8 were selected. Of these, 30,877 were designated SNP "hotspots," and 41 SNP-rich genomic regions could be delineated, with 100 genes associated with plant resistance, vernalization response, and signal transduction detected in these regions. Subsequent analysis of genetic diversity, linkage disequilibrium (LD), and population structure in the 300 accessions was carried out based on the 201,817 SNPs. Nine subpopulations were observed based on the population structure analysis. Hierarchical clustering and principal component analysis divided the 300 varieties roughly in accordance with their ecotype origins. However, spring-type varieties were intermingled with semi-winter type varieties, indicating frequent hybridization between spring and semi-winter ecotypes in China. In addition, LD decay across the whole genome averaged 299 kb when r² = 0.1, but the LD decay in the A genome (43 kb) was much shorter than in the C genome (1,455 kb), supporting the targeted introgression of the A genome from progenitor species B. rapa into Chinese rapeseed. This study also lays the foundation for genetic analysis of important agronomic traits using this rapeseed population.

Association Mapping for Aluminum Tolerance in a Core Collection of Rice Landraces

Trivalent aluminum (Al³⁺) has drastic effect on the rice production in acidic soils. Elite genes for aluminum (Al) tolerance might exist in rice landraces. Therefore, the purpose of this research is to mine the elite genes within rice landraces. Association mapping for Al tolerance traits [i.e., relative root elongation (RRE)] was performed by using a core collection of 150 accessions of rice landraces (i.e., Ting's rice core collection). Our results showed that the Ting's rice core collection possessed a wide-range of phenotypic variation for Al tolerance, and the index of Al tolerance (RRE) was ranged from 0.22 to 0.89. Moreover, the groups with different origins and compositions of indica and japonica rice showed different degrees of tolerance to varying levels of Al. These rice landraces were further screened with 274 simple sequence repeat markers, and association mapping was performed using a mixed linear model approach. The mapping results showed that a total of 23 significant (P < 0.05) trait-marker associations were detected for Al tolerance. Of these, three associations (13%) were identical to the quantitative trait loci reported previously, and other 20 associations were reported for the first time in this study. The proportion of phenotypic variance (R²) explained by 23 significant associations ranged from 5.03 to 20.03% for Al tolerance. We detected several elite alleles for Al tolerance based on multiple comparisons of allelic effects, which could be used to develop Al tolerant rice cultivars through marker-assisted breeding.

Linkage disequilibrium, SNP frequency change due to selection, and association mapping in popcorn chromosome regions containing QTLs for quality traits

The objectives of this study were to assess linkage disequilibrium (LD) and selection-induced changes in single nucleotide polymorphism (SNP) frequency, and to perform association mapping in popcorn chromosome regions containing quantitative trait loci (QTLs) for quality traits. Seven tropical and two temperate popcorn populations were genotyped for 96 SNPs chosen in chromosome regions containing QTLs for quality traits. The populations were phenotyped for expansion volume, 100-kernel weight, kernel sphericity, and kernel density. The LD statistics were the difference between the observed and expected haplotype frequencies (D), the proportion of D relative to the expected maximum value in the population, and the square of the correlation between the values of alleles at two loci. Association mapping was based on least squares and Bayesian approaches. In the tropical populations, D-values greater than 0.10 were observed for SNPs separated by 100-150 Mb, while most of the D-values in the temperate populations were less than 0.05. Selection for expansion volume indirectly led to increase in LD values, population differentiation, and significant changes in SNP frequency. Some associations were observed for expansion volume and the other quality traits. The candidate genes are involved with starch, storage protein, lipid, and cell wall polysaccharides synthesis.

Comparison of statistical models for nested association mapping in rapeseed (Brassica napus L.) through computer simulations

BACKGROUND

Rapeseed (Brassica napus L.) is an important oilseed crop throughout the world, serving as source for edible oil and renewable energy. Development of nested association mapping (NAM) population and methods is of importance for quantitative trait locus (QTL) mapping in rapeseed. The objectives of the research were to compare the power of QTL detection 1- β(∗) (β(∗) is the empirical type II error rate) (i) of two mating designs, double haploid (DH-NAM) and backcross (BC-NAM), (ii) of different statistical models, and (iii) for different genetic situations.

RESULTS

The computer simulations were based on the empirical data of a single nucleotide polymorphism (SNP) set of 790 SNPs from 30 sequenced conserved genes of 51 accessions of world-wide diverse B. napus germplasm. The results showed that a joint composite interval mapping (JCIM) model had significantly higher power of QTL detection than a single marker model. The DH-NAM mating design showed a slightly higher power of QTL detection than the BC-NAM mating design. The JCIM model considering QTL effects nested within subpopulations showed higher power of QTL detection than the JCIM model considering QTL effects across subpopulations, when examing a scenario in which there were interaction effects by a few QTLs interacting with a few background markers as well as a scenario in which there were interaction effects by many QTLs (≥ 25) each with more than 10 background markers and the proportion of total variance explained by the interactions was higher than 75 %.

CONCLUSIONS

The results of our study support the optimal design as well as analysis of NAM populations, especially in rapeseed.

Characterizing Variation of Branch Angle and Genome-Wide Association Mapping in Rapeseed (Brassica napus L.)

Changes in the rapeseed branch angle alter plant architecture, allowing more efficient light capture as planting density increases. In this study, a natural population of rapeseed was grown in three environments and evaluated for branch angle trait to characterize their phenotypic patterns and genotype with a 60K Brassica Infinium SNP array. Significant phenotypic variation was observed from 20 to 70°. As a result, 25 significant quantitative trait loci (QTL) associated with branch angle were identified on chromosomes A2, A3, A7, C3, C5, and C7 by the MLM model in TASSEL 4.0. Orthologs of the functional candidate genes involved in branch angle were identified. Among the key QTL, the peak SNPs were close to the key orthologous genes BnaA.Lazy1 and BnaC.Lazy1 on A3 and C3 homologous genome blocks. With the exception of Lazy (LA) orthologous genes, SQUMOSA PROMOTER BINDING PROTEIN LIKE 14 (SPL14) and an auxin-responsive GRETCHEN HAGEN 3 (GH3) genes from Arabidopsis thaliana were identified close to two clusters of SNPs on the A7 and C7 chromosomes. These findings on multiple novel loci and candidate genes of branch angle will be useful for further understanding and genetic improvement of plant architecture in rapeseed.

Genome-wide DArT and SNP scan for QTL associated with resistance to stripe rust (Puccinia striiformis f. sp. tritici) in elite ICARDA wheat (Triticum aestivum L.) germplasm

Identified DArT and SNP markers including a first reported QTL on 3AS, validated large effect APR on 3BS. The different genes can be used to incorporate stripe resistance in cultivated varieties. Stripe rust [yellow rust, caused by Puccinia striiformis f. sp. tritici (Pst)] is a serious disease in wheat (Triticum aestivum). This study employed genome-wide association mapping (GWAM) to identify markers linked to stripe rust resistance genes using Diversity Arrays Technology (DArT(®)) and single-nucleotide polymorphism (SNP) Infinium 9K assays in 200 ICARDA wheat genotypes, phenotyped for seedling and adult plant resistance in two sites over two growing seasons in Syria. Only 25.8 % of the genotypes showed resistance at seedling stage while about 33 and 44 % showed moderate resistance and resistance response, respectively. Mixed-linear model adjusted for false discovery rate at p < 0.05 identified 12 DArT and 29 SNP markers on chromosome arms 3AS, 3AL, 1AL, 2AL, 2BS, 2BL, 3BS, 3BL, 5BL, 6AL, and 7DS significantly linked to Pst resistance genes. Of these, the locus on 3AS has not been previously reported to confer resistance to stripe rust in wheat. The QTL on 3AS, 3AL, 1AL, 2AL, and 2BS were effective at seedling and adult plant growth stages while those on 3BS, 3BL, 5BL, 6AL and 7DS were effective at adult plant stage. The 3BS QTL was validated in Cham-6 × Cham-8 recombinant inbred line population; composite interval analysis identified a stripe resistance QTL flanked by the DArT marker, wPt-798970, contributed by Cham-6 parent which accounted for 31.2 % of the phenotypic variation. The DArT marker "wPt-798970" lies 1.6 cM away from the 3BS QTL detected within GWAM. Epistatic interactions were also investigated; only the QTL on 1AL, 3AS and 6AL exhibited interactions with other loci. These results suggest that GWAM can be an effective approach for identifying and improving resistance to stripe rust in wheat.

Association mapping of seedling resistance to spot form net blotch in a worldwide collection of barley

Spot form net blotch (SFNB), caused by the necrotrophic fungal pathogen Pyrenophora teres f. maculata, is an important foliar disease of barley in major production regions around the world. Deployment of adequate host resistance is challenging because the virulence of P. teres f. maculata is highly variable and characterized minor-effect resistances are typically ineffective against the diverse pathogen populations. A world barley core collection consisting of 2,062 barley accessions of diverse origin and genotype were phenotyped at the seedling stage with four P. teres f. maculata isolates collected from the United States (FGO), New Zealand (NZKF2), Australia (SG1), and Denmark (DEN 2.6). Of the 2,062 barley accessions phenotyped, 1,480 were genotyped with the Illumina barley iSelect chip and passed the quality controls with 5,954 polymorphic markers used for further association mapping analysis. Genome-wide association mapping was utilized to identify and map resistance loci from the seedling disease response data and the single nucleotide polymorphism (SNP) marker data. The best among six different regression models was identified for each isolate and association analysis was performed separately for each. A total of 138 significant (-log10P value>3.0) marker-trait associations (MTA) were detected. Using a 5 cM cutoff, a total of 10, 8, 13, and 10 quantitative trait loci (QTL) associated with SFNB resistance were identified for the FGO, SG1, NZKF2, and DEN 2.6 isolates, respectively. Loci containing from 1 to 34 MTA were identified on all seven barley chromosomes with one locus at 66 to 69 cM on chromosome 2H common to all four isolates. Six distinct loci were identified by the association mapping (AM) analysis that corresponded to previously characterized SFNB resistance QTL identified by biparental population analysis (QRpt4, QRpt6, Rpt4, Rpt6, Rpt7, and a QTL on 4H that was not given a provisional gene or QTL nomenclature). The 21 putative novel loci identified may represent a broad spectrum of resistance and or susceptibility loci. This is the first comprehensive AM study to characterize SFNB resistance loci underlying broad populations of the barley host and P. teres f. maculata pathogen.

Starch phosphorylation in potato tubers is influenced by allelic variation in the genes encoding glucan water dikinase, starch branching enzymes I and II, and starch synthase III

Starch phosphorylation is an important aspect of plant metabolism due to its role in starch degradation. Moreover, the degree of phosphorylation of starch determines its physicochemical properties and is therefore relevant for industrial uses of starch. Currently, starch is chemically phosphorylated to increase viscosity and paste stability. Potato cultivars with elevated starch phosphorylation would make this process unnecessary, thereby bestowing economic and environmental benefits. Starch phosphorylation is a complex trait which has been previously shown by antisense gene repression to be influenced by a number of genes including those involved in starch synthesis and degradation. We have used an association mapping approach to discover genetic markers associated with the degree of starch phosphorylation. A diverse collection of 193 potato lines was grown in replicated field trials, and the levels of starch phosphorylation at the C6 and C3 positions of the glucosyl residues were determined by mass spectrometry of hydrolyzed starch from tubers. In addition, the potato lines were genotyped by amplicon sequencing and microsatellite analysis, focusing on candidate genes known to be involved in starch synthesis. As potato is an autotetraploid, genotyping included determination of allele dosage. Significant associations (p < 0.001) were found with SNPs in the glucan water dikinase (GWD), starch branching enzyme I (SBEI) and the starch synthase III (SSIII) genes, and with a SSR allele in the SBEII gene. SNPs in the GWD gene were associated with C6 phosphorylation, whereas polymorphisms in the SBEI and SBEII genes were associated with both C6 and C3 phosphorylation and the SNP in the SSIII gene was associated with C3 phosphorylation. These allelic variants have potential as genetic markers for starch phosphorylation in potato.

Genome wide association studies using a new nonparametric model reveal the genetic architecture of 17 agronomic traits in an enlarged maize association panel

Association mapping is a powerful approach for dissecting the genetic architecture of complex quantitative traits using high-density SNP markers in maize. Here, we expanded our association panel size from 368 to 513 inbred lines with 0.5 million high quality SNPs using a two-step data-imputation method which combines identity by descent (IBD) based projection and k-nearest neighbor (KNN) algorithm. Genome-wide association studies (GWAS) were carried out for 17 agronomic traits with a panel of 513 inbred lines applying both mixed linear model (MLM) and a new method, the Anderson-Darling (A-D) test. Ten loci for five traits were identified using the MLM method at the Bonferroni-corrected threshold −log₁₀ (P) >5.74 (α = 1). Many loci ranging from one to 34 loci (107 loci for plant height) were identified for 17 traits using the A-D test at the Bonferroni-corrected threshold −log₁₀ (P) >7.05 (α = 0.05) using 556809 SNPs. Many known loci and new candidate loci were only observed by the A-D test, a few of which were also detected in independent linkage analysis. This study indicates that combining IBD based projection and KNN algorithm is an efficient imputation method for inferring large missing genotype segments. In addition, we showed that the A-D test is a useful complement for GWAS analysis of complex quantitative traits. Especially for traits with abnormal phenotype distribution, controlled by moderate effect loci or rare variations, the A-D test balances false positives and statistical power. The candidate SNPs and associated genes also provide a rich resource for maize genetics and breeding.

Genotype imputation has been used widely in the analysis of genome-wide association studies (GWAS) to boost power and fine-map associations. We developed a two-step data imputation method to meet the challenge of large proportion missing genotypes. GWAS have uncovered an extensive genetic architecture of complex quantitative traits using high-density SNP markers in maize in the past few years. Here, GWAS were carried out for 17 agronomic traits with a panel of 513 inbred lines applying both mixed linear model and a new method, the Anderson-Darling (A-D) test. We intend to show that the A-D test is a complement to current GWAS methods, especially for complex quantitative traits controlled by moderate effect loci or rare variations and with abnormal phenotype distribution. In addition, the traits associated QTL identified here provide a rich resource for maize genetics and breeding.

Recovering power in association mapping panels with variable levels of linkage disequilibrium

Association mapping has permitted the discovery of major QTL in many species. It can be applied to existing populations and, as a consequence, it is generally necessary to take into account structure and relatedness among individuals in the statistical model to control false positives. We analytically studied power in association studies by computing noncentrality parameter of the tests and its relationship with parameters characterizing diversity (genetic differentiation between groups and allele frequencies) and kinship between individuals. Investigation of three different maize diversity panels genotyped with the 50k SNPs array highlighted contrasted average power among panels and revealed gaps of power of classical mixed models in regions with high linkage disequilibrium (LD). These gaps could be related to the fact that markers are used for both testing association and estimating relatedness. We thus considered two alternative approaches to estimating the kinship matrix to recover power in regions of high LD. In the first one, we estimated the kinship with all the markers that are not located on the same chromosome than the tested SNP. In the second one, correlation between markers was taken into account to weight the contribution of each marker to the kinship. Simulations revealed that these two approaches were efficient to control false positives and were more powerful than classical models.

Association mapping of yield-related traits and SSR markers in wild soybean (Glycine soja Sieb. and Zucc.)

Wild soybean, the progenitor of cultivated soybean, is an important gene pool for ongoing soybean breeding efforts. To identify yield-enhancing quantitative trait locus (QTL) or gene from wild soybean, 113 wild soybeans accessions were phenotyped for five yield-related traits and genotyped with 85 simple sequence repeat (SSR) markers to conduct association mapping. A total of 892 alleles were detected for the 85 SSR markers, with an average 10.49 alleles; the corresponding PIC values ranged from 0.07 to 0.92, with an average 0.73. The genetic diversity of each SSR marker ranged from 0.07 to 0.93, with an average 0.75. A total of 18 SSR markers were identified for the five traits. Two SSR markers, sct_010 and satt316, which are associated with the yield per plant were stably expressed over two years at two experimental locations. Our results suggested that association mapping can be an effective approach for identifying QTL from wild soybean.

Association mapping in crop plants: opportunities and challenges

The research area of association mapping (AM) is currently receiving major attention for genetic studies of quantitative traits in all major crops. However, the level of success and utility of AM achieved for crop improvement is not comparable to that in the area of human health care for diagnosis of complex human diseases. These AM studies in plants, as in humans, became possible due to the availability of DNA-based molecular markers and a variety of sophisticated statistical tools that are evolving on a regular basis. In this chapter, we first briefly review the significance of a variety of populations that are used in AM studies, then briefly describe the molecular markers and high-throughput genotyping strategies, and finally describe the approaches used for AM studies. The major part of the chapter is, however, devoted to analysis of reasons why the results of AM have been underutilized in plant breeding. We also examine the opportunities available and challenges faced while using AM for crop improvement programs. This includes a detailed discussion of the issues that have plagued AM studies, and the solutions that have become available to deal with these issues, so that in future, the results of AM studies may prove increasingly fruitful for crop improvement programs.

Constructing linkage map based on a four-way cross population

Currently, developing genetic linkage map mostly use the derived-populations from crossing of two homogenous parents, which only covers limited genetic diversity and is inappropriate for some species, such as tobacco with lower diversity in genome. It is very general that there are no sufficient polymorphic markers to construct linkage map and ineffective to conduct marker-assisted selection (MAS) and quantitative trait locus (QTL) mapping based on lower density linkage map. This study proposed a method for developing genetic linkage map based on a four-way cross population. Computer simulation was conducted to investigate the feasibility and effectiveness of the method and a supporting program was designed. The main procedures and features of the proposed method were summarized as follows: 1) estimating genetic distance of any paired markers based on maximum likelihood method; 2) splitting all markers into different groups (linkage group) by cluster analysis based on genetic distance of markers; 3) for each linkage group, two end markers were first determined, then the marker order could be determined by inserting other markers in appropriate position by distance analysis of any three neighboring markers. Monte Carlo simulation showed that the proposed method is feasible, effective, and applicable in other derived populations from crossing of two homogenous parents.

Allelic variation in a cellulose synthase gene (PtoCesA4) associated with growth and wood properties in Populus tomentosa

Lignocellulosic biomass from trees provides a renewable feedstock for biofuels, lumber, pulp, paper, and other uses. Dissecting the mechanism underlying natural variation of the complex traits controlling growth and lignocellulose biosynthesis in trees can enable marker-assisted breeding to improve wood quality and yield. Here, we combined linkage disequilibrium (LD)-based association analysis with traditional linkage analysis to detect the genetic effect of a Populus tomentosa cellulose synthase gene, PtoCesA4. PtoCesA4 is strongly expressed in developing xylem and leaves. Nucleotide diversity and LD in PtoCesA4, sampled from the P. tomentosa natural distribution, revealed that PtoCesA4 harbors high single nucleotide polymorphism (SNP) diversity (πT = 0.0080 and θw = 0.0098) and low LD (r(2) ≥ 0.1, within 1400 bp), demonstrating that the potential of a candidate-gene-based LD approach in understanding the molecular basis underlying quantitative variation in this species. By combining single SNP, multi-SNP, and haplotype-based associations in an association population of 460 individuals with single SNP linkage analysis in a family-based linkage populations (1200 individuals), we identified three strong associations (false discovery rate Q < 0.05) in both populations. These include two nonsynonymous markers (SNP49 associated with α-cellulose content and SNP59 associated with fiber width) and a noncoding marker (SNP18 associated with α-cellulose content). Variation in RNA transcript abundance among genotypic classes of SNP49 was confirmed in these two populations. Therefore, combining different methods allowed us to examine functional PtoCesA4 allelic variation underlying natural variation in complex quantitative traits related to growth and lignocellulosic biosynthesis.

Unraveling the genetic basis of seed tocopherol content and composition in rapeseed (Brassica napus L.)

BACKGROUND

Tocopherols are important antioxidants in vegetable oils; when present as vitamin E, tocopherols are an essential nutrient for humans and livestock. Rapeseed (Brassica napus L, AACC, 2 n = 38) is one of the most important oil crops and a major source of tocopherols. Although the tocopherol biosynthetic pathway has been well elucidated in the model photosynthetic organisms Arabidopsis thaliana and Synechocystis sp. PCC6803, knowledge about the genetic basis of tocopherol biosynthesis in seeds of rapeseed is scant. This project was carried out to dissect the genetic basis of seed tocopherol content and composition in rapeseed through quantitative trait loci (QTL) detection, genome-wide association analysis, and homologous gene mapping.

METHODOLOGY/PRINCIPAL FINDINGS

We used a segregating Tapidor × Ningyou7 doubled haploid (TNDH) population, its reconstructed F(2) (RC-F(2)) population, and a panel of 142 rapeseed accessions (association panel). Genetic effects mainly contributed to phenotypic variations in tocopherol content and composition; environmental effects were also identified. Thirty-three unique QTL were detected for tocopherol content and composition in TNDH and RC-F(2) populations. Of these, seven QTL co-localized with candidate sequences associated with tocopherol biosynthesis through in silico and linkage mapping. Several near-isogenic lines carrying introgressions from the parent with higher tocopherol content showed highly increased tocopherol content compared with the recurrent parent. Genome-wide association analysis was performed with 142 B. napus accessions. Sixty-one loci were significantly associated with tocopherol content and composition, 11 of which were localized within the confidence intervals of tocopherol QTL.

CONCLUSIONS/SIGNIFICANCE

This joint QTL, candidate gene, and association mapping study sheds light on the genetic basis of seed tocopherol biosynthesis in rapeseed. The sequences presented here may be used for marker-assisted selection of oilseed rape lines with superior tocopherol content and composition.

Population structure and linkage disequilibrium in Lupinus albus L. germplasm and its implication for association mapping

White lupin (Lupinus albus L.) has been around since 300 B.C. and is recognized for its ability to grow on poor soils and application as green manure in addition to seed harvest. The seed has very high levels of protein (33-47 %) and oil (6-13 %). It also has many secondary metabolites that are potentially of nutraceutical value to animals and humans. Despite such a great potential, lupins role in modern agriculture began only in the twentieth century. Although a large collection of Lupinus germplasm accessions is available worldwide, rarely have they been genetically characterized. Additionally, scarce genomic resources in terms of recombinant populations and genome information have been generated for L. albus. With the advancement in association mapping methods, the natural populations have the potential to replace the recombinant populations in gene mapping and marker-trait associations. Therefore, we studied the genetic similarity, population structure and marker-trait association in a USDA germplasm collection for their current and future application in this crop improvement. A total of 122 PI (Plant Inventory) lines were screened with 18 AFLP primer pairs that generated 2,277 fragments. A subset of 892 polymorphic markers with MAF >0.05 (minor allele frequency) were used for association mapping. The cluster analysis failed to group accessions on the basis of their passport information, and a weak structure and low linkage disequilibrium (LD) were observed indicating the usefulness of the collection for association mapping. Moreover, we were also able to identify two markers (a p value of 1.53 × 10(-4) and 2.3 × 10(-4)) that explained 22.69 and 20.5 % of seed weight variation determined using R (LR) (2) . The implications of lack of geographic clustering, population structure, low LD and the ability of AFLP to map seed weight trait using association mapping and the usefulness of the PI collections in breeding programs are discussed.

Gene-based single nucleotide polymorphism markers for genetic and association mapping in common bean

Background

In common bean, expressed sequence tags (ESTs) are an underestimated source of gene-based markers such as insertion-deletions (Indels) or single-nucleotide polymorphisms (SNPs). However, due to the nature of these conserved sequences, detection of markers is difficult and portrays low levels of polymorphism. Therefore, development of intron-spanning EST-SNP markers can be a valuable resource for genetic experiments such as genetic mapping and association studies.

Results

In this study, a total of 313 new gene-based markers were developed at target genes. Intronic variation was deeply explored in order to capture more polymorphism. Introns were putatively identified after comparing the common bean ESTs with the soybean genome, and the primers were designed over intron-flanking regions. The intronic regions were evaluated for parental polymorphisms using the single strand conformational polymorphism (SSCP) technique and Sequenom MassARRAY system. A total of 53 new marker loci were placed on an integrated molecular map in the DOR364 × G19833 recombinant inbred line (RIL) population. The new linkage map was used to build a consensus map, merging the linkage maps of the BAT93 × JALO EEP558 and DOR364 × BAT477 populations. A total of 1,060 markers were mapped, with a total map length of 2,041 cM across 11 linkage groups. As a second application of the generated resource, a diversity panel with 93 genotypes was evaluated with 173 SNP markers using the MassARRAY-platform and KASPar technology. These results were coupled with previous SSR evaluations and drought tolerance assays carried out on the same individuals. This agglomerative dataset was examined, in order to discover marker-trait associations, using general linear model (GLM) and mixed linear model (MLM). Some significant associations with yield components were identified, and were consistent with previous findings.

Conclusions

In short, this study illustrates the power of intron-based markers for linkage and association mapping in common bean. The utility of these markers is discussed in relation with the usefulness of microsatellites, the molecular markers by excellence in this crop.

Genome-wide association mapping of leaf metabolic profiles for dissecting complex traits in maize

The diversity of metabolites found in plants is by far greater than in most other organisms. Metabolic profiling techniques, which measure many of these compounds simultaneously, enabled investigating the regulation of metabolic networks and proved to be useful for predicting important agronomic traits. However, little is known about the genetic basis of metabolites in crops such as maize. Here, a set of 289 diverse maize inbred lines was genotyped with 56,110 SNPs and assayed for 118 biochemical compounds in the leaves of young plants, as well as for agronomic traits of mature plants in field trials. Metabolite concentrations had on average a repeatability of 0.73 and showed a correlation pattern that largely reflected their functional grouping. Genome-wide association mapping with correction for population structure and cryptic relatedness identified for 26 distinct metabolites strong associations with SNPs, explaining up to 32.0% of the observed genetic variance. On nine chromosomes, we detected 15 distinct SNP-metabolite associations, each of which explained more then 15% of the genetic variance. For lignin precursors, including p-coumaric acid and caffeic acid, we found strong associations (P values to ) with a region on chromosome 9 harboring cinnamoyl-CoA reductase, a key enzyme in monolignol synthesis and a target for improving the quality of lignocellulosic biomass by genetic engineering approaches. Moreover, lignin precursors correlated significantly with lignin content, plant height, and dry matter yield, suggesting that metabolites represent promising connecting links for narrowing the genotype-phenotype gap of complex agronomic traits.

Genome-wide association mapping of flowering time and northern corn leaf blight (Setosphaeria turcica) resistance in a vast commercial maize germplasm set

BACKGROUND

Setosphaeria turcica is a fungal pathogen that causes northern corn leaf blight (NCLB) which is a serious foliar disease in maize. In order to unravel the genetic architecture of the resistance against this disease, a vast association mapping panel comprising 1487 European maize inbred lines was used to (i) identify chromosomal regions affecting flowering time (FT) and northern corn leaf blight (NCLB) resistance, (ii) examine the epistatic interactions of the identified chromosomal regions with the genetic background on an individual molecular marker basis, and (iii) dissect the correlation between NCLB resistance and FT.

RESULTS

The single marker analyses performed for 8 244 single nucleotide polymorphism (SNP) markers revealed seven, four, and four SNP markers significantly (α=0.05, amplicon wise Bonferroni correction) associated with FT, NCLB, and NCLB resistance corrected for FT, respectively. These markers explained individually between 0.36 and 14.29% of the genetic variance of the corresponding trait.

CONCLUSIONS

The very well interpretable pattern of SNP associations observed for FT suggested that data from applied plant breeding programs can be used to dissect polygenic traits. This in turn indicates that the associations identified for NCLB resistance might be successfully used in marker-assisted selection programs. Furthermore, the associated genes are also of interest for further research concerning the mechanism of resistance to NCLB and plant diseases in general, because some of the associated genes have not been mentioned in this context so far.

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.

A candidate gene-based association study of tocopherol content and composition in rapeseed (Brassica napus)

Rapeseed (Brassica napus L.) is the most important oil crop of temperate climates. Rapeseed oil contains tocopherols, also known as vitamin E, which is an indispensable nutrient for humans and animals due to its antioxidant and radical scavenging abilities. Moreover, tocopherols are also important for the oxidative stability of vegetable oils. Therefore, seed oil with increased tocopherol content or altered tocopherol composition is a target for breeding. We investigated the role of nucleotide variations within candidate genes from the tocopherol biosynthesis pathway. Field trials were carried out with 229 accessions from a worldwide B. napus collection which was divided into two panels of 96 and 133 accessions. Seed tocopherol content and composition were measured by HPLC. High heritabilities were found for both traits, ranging from 0.62 to 0.94. We identified polymorphisms by sequencing selected regions of the tocopherol genes from the 96 accession panel. Subsequently, we determined the population structure (Q) and relative kinship (K) as detected by genotyping with genome-wide distributed SSR markers. Association studies were performed using two models, the structure-based GLM + Q and the PK-mixed model. Between 26 and 12 polymorphisms within two genes (BnaX.VTE3.a, BnaA.PDS1.c) were significantly associated with tocopherol traits. The SNPs explained up to 16.93% of the genetic variance for tocopherol composition and up to 10.48% for total tocopherol content. Based on the sequence information we designed CAPS markers for genotyping the 133 accessions from the second panel. Significant associations with various tocopherol traits confirmed the results from the first experiment. We demonstrate that the polymorphisms within the tocopherol genes clearly impact tocopherol content and composition in B. napus seeds. We suggest that these nucleotide variations may be used as selectable markers for breeding rapeseed with enhanced tocopherol quality.

Detection of epistatic interactions in association mapping populations: an example from tetraploid potato

Epistatic interactions among loci are expected to contribute substantially to variation of quantitative traits. The objectives of our research were to (i) compare a classical mixed-model approach with a combined mixed-model and analysis of variance approach for detecting epistatic interactions; (ii) examine using computer simulations the statistical power to detect additive-additive, additive-dominance and dominance-dominance epistatic interactions and (iii) detect epistatic interactions between candidate genes for resistance to leaf blight in a set of tetraploid potato clones. Our study was based on the genotypic and phenotypic data of 184 tetraploid potato cultivars as well as computer simulations. The number of significant (α* =1 × 10(-6)) epistatic interactions ranged for the three examined traits from 3 to 32. Our findings suggested that the combined mixed-model and analysis of variance approach leads in comparison with the classical mixed-model approach not to an increased rate of false-positives. The results of the computer simulations suggested that, if molecular markers are available that are in high LD (D'>0.9) with the trait-coding loci, the statistical power to detect epistatic interactions, which explain 5-10% of the phenotypic variance, was of a size that seems promising for their detection.

Association mapping of quantitative resistance to Phaeosphaeria nodorum in spring wheat landraces from the USDA National Small Grains Collection

Stagonospora nodorum blotch (SNB), caused by Phaeosphaeria nodorum, is a destructive disease of wheat (Triticum aestivum) found throughout the United States. Host resistance is the only economically feasible option for managing the disease; however, few SNB-resistant wheat cultivars are known to exist. In this study, we report findings from an association mapping (AM) of resistance to P. nodorum in 567 spring wheat landraces of diverse geographic origin. The accessions were evaluated for seedling resistance to P. nodorum in a greenhouse. Phenotypic data and 625 polymorphic diversity array technology (DArT) markers have been used for linkage disequilibrium (LD) and association analyses. The results showed that seven DArT markers on five chromosomes (2D, 3B, 5B, 6A, and 7A) were significantly associated with resistance to P. nodorum. Genetic regions on 2D, 3B, and 5B correspond to previously mapped quantitative trait loci (QTL) conferring resistance to P. nodorum whereas the remaining QTL appeared to be novel. These results demonstrate that the use of AM is an effective method for identifying new genomic regions associated with resistance to P. nodorum in spring wheat landraces. Additionally, the novel resistance found in this study could be useful in wheat breeding aimed at controlling SNB.

Genetic variation, population structure, and linkage disequilibrium in European elite germplasm of perennial ryegrass

Perennial ryegrass (Lolium perenne L.) is a highly valued temperate climate grass species grown as forage crop and for amenity uses. Due to its outbreeding nature and recent domestication, a high degree of genetic diversity is expected among cultivars. The aim of this study was to assess the extent of linkage disequilibrium (LD) within European elite germplasm and to evaluate the appropriate methodology for genetic association mapping in perennial ryegrass. A high level of genetic diversity was observed in a set of 380 perennial ryegrass elite genotypes when genotyped with 40 SSRs and 2 STS markers. A Bayesian structure analysis identified two subpopulations, which were confirmed by principal coordinate analysis (PCoA). One subpopulation consisted mainly of genotypes originating from the UK, while germplasm mostly from Continental Europe was grouped into the second subpopulation. LD (r(2)) decay was rapid and occurred within 0.4cM across European varieties, when population structure was taken into consideration. However, an extended LD of up to 6.6cM was detected within the variety Aberdart. High genetic diversity and rapid LD decay provide means for high resolution association mapping in elite materials of perennial ryegrass. However, different strategies need to be applied depending on the material used. Genome-wide association study (GWAS) with several hundred markers can be applied within synthetic varieties to identify large (up to 10cM) genomic regions affecting trait variation. A combination of available and novel DNA markers is needed to achieve resolution required for GWAS in elite breeding materials. An even higher marker density of several million SNPs might be needed for GWAS in diverse ecotype collections, potentially resulting in quantitative trait polymorphism (QTP) identification.

Mixed model association mapping for fusarium head blight resistance in tunisian-derived durum wheat populations

Sources of resistance to Fusarium head blight (FHB) in wheat are mostly restricted to Chinese hexaploid genotypes. The effort to incorporate the resistance from hexaploid wheat or wild relatives to cultivated durum wheat (Triticum turgidum L. var. durum Desf.) have not been successful in providing resistance to the level of the donor parents. In this study, we used 171 BC₁F₆ and 169 BC₁F₇ lines derived from crossing of four Tunisian tetraploid sources of resistance (Tun7, Tun18, Tun34, Tun36) with durum cultivars ‘Ben,’ ‘Maier,’ ‘Lebsock,’ and ‘Mountrail’ for association studies. The Tun18 and Tun7 FHB resistances were found to be comparable to the best hexaploid wheat sources. A new significant QTL for FHB resistance was identified on the long arm of chromosome 5B (Qfhs.ndsu-5BL) with both association and classical QTL mapping analysis. Linkage disequilibrium (LD) blocks extending up to 40 cM were evident in these populations. The linear mixed model considering the structure (Q or P) and the kinship matrix (K_T) estimated by restricted maximum likelihood (REML) was identified as the best for association studies in a mixture of wheat populations from a breeding program. The results of association mapping analysis also demonstrated a region on the short arm of chromosome 3B as potentially linked to FHB resistance. This region is in proximity of major FHB resistance gene fhb1 reported in hexaploid wheat. A possibility of having susceptibility or suppressor of resistance gene(s) on durum wheat chromosome 2A was further confirmed in this material, explaining the problem in developing resistant genotypes without counter selection against this region.

Extent and genome-wide distribution of linkage disequilibrium in commercial maize germplasm

Association mapping is based on linkage disequilibrium (LD) resulting from historical recombinations and helps understanding the genetic basis of complex traits. Many factors affect LD and, therefore, it must be determined empirically in the germplasm under investigation to examine the prospects of successful genome-wide association mapping. The objectives of our study were to (1) examine the extent of LD with simple sequence repeat (SSR) and single nucleotide polymorphism (SNP) markers in 1,537 commercial maize inbred lines belonging to four heterotic pools, (2) compare the LD patterns determined by these two marker types, (3) evaluate the number of SNP markers needed to perform genome-wide association analyses, and (4) investigate temporal trends of LD. Mean values of the squared correlation coefficient ([Formula: see text]) were almost identical for unlinked, linked, and adjacent SSR marker pairs. In contrast, [Formula: see text] values were lowest for the unlinked SNP loci and highest for the SNPs within amplicons. LD decay varied across the different heterotic pools and the individual chromosomes. The SSR markers employed in the present study are not adequate for association analysis, because of insufficient marker density for the germplasm evaluated. Based on the decay of LD in the various heterotic pools, we would need between 4,000 and 65,000 SNP markers to detect with a reasonable power associations with rather large quantitative trait loci (QTL). A much higher marker density is required to identify QTL with smaller effects. However, not only the total number of markers but also their distribution among and along the chromosomes are primordial for undertaking powerful association analyses.

Effect of population structure corrections on the results of association mapping tests in complex maize diversity panels

Association mapping of sequence polymorphisms underlying the phenotypic variability of quantitative agronomical traits is now a widely used method in plant genetics. However, due to the common presence of a complex genetic structure within the plant diversity panels, spurious associations are expected to be highly frequent. Several methods have thus been suggested to control for panel structure. They mainly rely on ad hoc criteria for selecting the number of ancestral groups; which is often not evident for the complex panels that are commonly used in maize. It was thus necessary to evaluate the effect of the selected structure models on the association mapping results. A real maize data set (342 maize inbred lines and 12,000 SNPs) was used for this study. The panel structure was estimated using both Bayesian and dimensional reduction methods, considering an increasing number of ancestral groups. Effect on association tests depends in particular on the number of ancestral groups and on the trait analyzed. The results also show that using a high number of ancestral groups leads to an over-corrected model in which all causal loci vanish. Finally the results of all models tested were combined in a meta-analysis approach. In this way, robust associations were highlighted for each analyzed trait.

Association genetics in Solanum tuberosum provides new insights into potato tuber bruising and enzymatic tissue discoloration

BACKGROUND

Most agronomic plant traits result from complex molecular networks involving multiple genes and from environmental factors. One such trait is the enzymatic discoloration of fruit and tuber tissues initiated by mechanical impact (bruising). Tuber susceptibility to bruising is a complex trait of the cultivated potato (Solanum tuberosum) that is crucial for crop quality. As phenotypic evaluation of bruising is cumbersome, the application of diagnostic molecular markers would empower the selection of low bruising potato varieties. The genetic factors and molecular networks underlying enzymatic tissue discoloration are sparsely known. Hitherto there is no association study dealing with tuber bruising and diagnostic markers for enzymatic discoloration are rare.

RESULTS

The natural genetic diversity for bruising susceptibility was evaluated in elite middle European potato germplasm in order to elucidate its molecular basis. Association genetics using a candidate gene approach identified allelic variants in genes that function in tuber bruising and enzymatic browning. Two hundred and five tetraploid potato varieties and breeding clones related by descent were evaluated for two years in six environments for tuber bruising susceptibility, specific gravity, yield, shape and plant maturity. Correlations were found between different traits. In total 362 polymorphic DNA fragments, derived from 33 candidate genes and 29 SSR loci, were scored in the population and tested for association with the traits using a mixed model approach, which takes into account population structure and kinship. Twenty one highly significant (p < 0.001) and robust marker-trait associations were identified.

CONCLUSIONS

The observed trait correlations and associated marker fragments provide new insight in the molecular basis of bruising susceptibility and its natural variation. The markers diagnostic for increased or decreased bruising susceptibility will facilitate the combination of superior alleles in breeding programs. In addition, this study presents novel candidates that might control enzymatic tissue discoloration and tuber bruising. Their validation and characterization will increase the knowledge about the underlying biological processes.

Controlling Population Structure in Human Genetic Association Studies with Samples of Unrelated Individuals

In genetic studies, associations between genotypes and phenotypes may be confounded by unrecognized population structure and/or admixture. Studies have shown that even in European populations, which are thought to be relatively homogeneous, population stratification exists and can affect the validity of association studies. A number of methods have been proposed to address this issue in recent years. Among them, the mixed-model based approach and the principal component-based approach have several advantages over other methods. However, these approaches have not been thoroughly evaluated on large human datasets. The objectives of this study are to (1) evaluate and compare the performance of the mixed-model approach and the principal component-based approach for genetic association mapping using human data consisting of unrelated individuals, and (2) understand the relationship between these two approaches. To achieve these goals, we simulate datasets based on the HapMap data under various scenarios. Our results indicate that the mixed-model approach performs well in controlling for population structure/admixture. It has similar performance as that based on principal component analysis. However, the approach combining mixed-model and principal component analysis does not perform as well as either method itself.

A general method for controlling the genome-wide type I error rate in linkage and association mapping experiments in plants

Control of the genome-wide type I error rate (GWER) is an important issue in association mapping and linkage mapping experiments. For the latter, different approaches, such as permutation procedures or Bonferroni correction, were proposed. The permutation test, however, cannot account for population structure present in most association mapping populations. This can lead to false positive associations. The Bonferroni correction is applicable, but usually on the conservative side, because correlation of tests cannot be exploited. Therefore, a new approach is proposed, which controls the genome-wide error rate, while accounting for population structure. This approach is based on a simulation procedure that is equally applicable in a linkage and an association-mapping context. Using the parameter settings of three real data sets, it is shown that the procedure provides control of the GWER and the generalized genome-wide type I error rate (GWER(k)).

Software engineering the mixed model for genome-wide association studies on large samples

Mixed models improve the ability to detect phenotype-genotype associations in the presence of population stratification and multiple levels of relatedness in genome-wide association studies (GWAS), but for large data sets the resource consumption becomes impractical. At the same time, the sample size and number of markers used for GWAS is increasing dramatically, resulting in greater statistical power to detect those associations. The use of mixed models with increasingly large data sets depends on the availability of software for analyzing those models. While multiple software packages implement the mixed model method, no single package provides the best combination of fast computation, ability to handle large samples, flexible modeling and ease of use. Key elements of association analysis with mixed models are reviewed, including modeling phenotype-genotype associations using mixed models, population stratification, kinship and its estimation, variance component estimation, use of best linear unbiased predictors or residuals in place of raw phenotype, improving efficiency and software-user interaction. The available software packages are evaluated, and suggestions made for future software development.

Single nucleotide polymorphisms in the allene oxide synthase 2 gene are associated with field resistance to late blight in populations of tetraploid potato cultivars

The oomycete Phytophthora infestans causes late blight, the most relevant disease of potato (Solanum tuberosum) worldwide. Field resistance to late blight is a complex trait. When potatoes are cultivated under long day conditions in temperate climates, this resistance is correlated with late plant maturity, an undesirable characteristic. Identification of natural gene variation underlying late blight resistance not compromised by late maturity will facilitate the selection of resistant cultivars and give new insight in the mechanisms controlling quantitative pathogen resistance. We tested 24 candidate loci for association with field resistance to late blight and plant maturity in a population of 184 tetraploid potato individuals. The individuals were genotyped for 230 single nucleotide polymorphisms (SNPs) and 166 microsatellite alleles. For association analysis we used a mixed model, taking into account population structure, kinship, allele substitution and interaction effects of the marker alleles at a locus with four allele doses. Nine SNPs were associated with maturity corrected resistance (P < 0.001), which collectively explained 50% of the genetic variance of this trait. A major association was found at the StAOS2 locus encoding allene oxide synthase 2, a key enzyme in the biosynthesis of jasmonates, plant hormones that function in defense signaling. This finding supports StAOS2 as being one of the factors controlling natural variation of pathogen resistance.