Identification of SSR markers associated with height using pool-based genome-wide association mapping in sorghum

Sorghum has been proposed as a potential energy crop. However, it has been traditionally bred for grain yield and forage quality, not traits related to bioenergy production. To develop tools for genetic improvement of bioenergy-related traits such as height, genetic markers associated with these traits have first to be identified. Association mapping has been extensively used in humans and in some crop plants for this purpose. However, genome-wide association mapping using the whole association panel is costly and time-consuming. A variation of this method called pool-based genome-wide association mapping has been extensively used in humans. In this variation, pools of individuals with contrasting phenotypes, instead of the whole panel, are screened with genetic markers and polymorphic markers are confirmed by screening the individuals in the pools. Here, we identified several new simple sequence repeats (SSR) markers associated with height using this pool-based genome-wide association mapping in sorghum. After screening the tall and short pools of sorghum accessions from the sorghum Mini Core collection developed at the International Crops Research Institute for the Semi-Arid Tropics with 703 SSR markers, we have identified four markers that are closely associated with sorghum height on chromosomes 2, 6, and 9. Comparison with published maps indicates that all four markers are clustered with markers previously mapped to height or height-related traits and with candidate genes involved in regulating plant height such as FtsZ, Ugt, and GA 2-oxidase. The mapping method can be applied to other crop plants for which a high-throughput genome-wide association mapping platform is not yet available.     

An Arabidopsis example of association mapping in structured samples

A potentially serious disadvantage of association mapping is the fact that marker-trait associations may arise from confounding population structure as well as from linkage to causative polymorphisms. Using genome-wide marker data, we have previously demonstrated that the problem can be severe in a global sample of 95 Arabidopsis thaliana accessions, and that established methods for controlling for population structure are generally insufficient. Here, we use the same sample together with a number of flowering-related phenotypes and data-perturbation simulations to evaluate a wider range of methods for controlling for population structure. We find that, in terms of reducing the false-positive rate while maintaining statistical power, a recently introduced mixed-model approach that takes genome-wide differences in relatedness into account via estimated pairwise kinship coefficients generally performs best. By combining the association results with results from linkage mapping in F2 crosses, we identify one previously known true positive and several promising new associations, but also demonstrate the existence of both false positives and false negatives. Our results illustrate the potential of genome-wide association scans as a tool for dissecting the genetics of natural variation, while at the same time highlighting the pitfalls. The importance of study design is clear; our study is severely under-powered both in terms of sample size and marker density. Our results also provide a striking demonstration of confounding by population structure. While statistical methods can be used to ameliorate this problem, they cannot always be effective and are certainly not a substitute for independent evidence, such as that obtained via crosses or transgenic experiments. Ultimately, association mapping is a powerful tool for identifying a list of candidates that is short enough to permit further genetic study.     

Novel Genes Affecting the Interaction between the Cabbage Whitefly and Arabidopsis Uncovered by Genome-Wide Association Mapping

Plants have evolved a variety of ways to defend themselves against biotic attackers. This has resulted in the presence of substantial variation in defense mechanisms among plants, even within a species. Genome-wide association (GWA) mapping is a useful tool to study the genetic architecture of traits, but has so far only had limited exploitation in studies of plant defense. Here, we study the genetic architecture of defense against the phloem-feeding insect cabbage whitefly (Aleyrodes proletella) in Arabidopsis thaliana. We determined whitefly performance, i.e. the survival and reproduction of whitefly females, on 360 worldwide selected natural accessions and subsequently performed GWA mapping using 214,051 SNPs. Substantial variation for whitefly adult survival and oviposition rate (number of eggs laid per female per day) was observed between the accessions. We identified 39 candidate SNPs for either whitefly adult survival or oviposition rate, all with relatively small effects, underpinning the complex architecture of defense traits. Among the corresponding candidate genes, i.e. genes in linkage disequilibrium (LD) with candidate SNPs, none have previously been identified as a gene playing a role in the interaction between plants and phloem-feeding insects. Whitefly performance on knock-out mutants of a number of candidate genes was significantly affected, validating the potential of GWA mapping for novel gene discovery in plant-insect interactions. Our results show that GWA analysis is a very useful tool to gain insight into the genetic architecture of plant defense against herbivorous insects, i.e. we identified and validated several genes affecting whitefly performance that have not previously been related to plant defense against herbivorous insects.     

Quantitative trait loci controlling leaf venation in Arabidopsis

Leaf veins provide the mechanical support and are responsible for the transport of nutrients and water to the plant. High vein density is a prerequisite for plants to have C4 photosynthesis. We investigated the genetic variation and genetic architecture of leaf venation traits within the species Arabidopsis thaliana using natural variation. Leaf venation traits, including leaf vein density (LVD) were analysed in 66 worldwide accessions and 399 lines of the multi‐parent advanced generation intercross population. It was shown that there is no correlation between LVD and photosynthesis parameters within A. thaliana. Association mapping was performed for LVD and identified 16 and 17 putative quantitative trait loci (QTLs) in the multi‐parent advanced generation intercross and worldwide sets, respectively. There was no overlap between the identified QTLs suggesting that many genes can affect the traits. In addition, linkage mapping was performed using two biparental recombinant inbred line populations. Combining linkage and association mapping revealed seven candidate genes. For one of the candidate genes, RCI2c, we demonstrated its function in leaf venation patterning.     

Evidence for a large-scale population structure of Arabidopsis thaliana from genome-wide single nucleotide polymorphism markers

Population-based methods for the genetic mapping of adaptive traits and the analysis of natural selection require that the population structure and demographic history of a species are taken into account. We characterized geographic patterns of genetic variation in the model plant Arabidopsis thaliana by genotyping 115 genome-wide single nucleotide polymorphism (SNP) markers in 351 accessions from the whole species range using a matrix-assisted laser desorption/ionization time-of-flight assay, and by sequencing of nine unlinked short genomic regions in a subset of 64 accessions. The observed frequency distribution of SNPs is not consistent with a constant-size neutral model of sequence polymorphism due to an excess of rare polymorphisms. There is evidence for a significant population structure as indicated by differences in genetic diversity between geographic regions. Accessions from Central Asia have a low level of polymorphism and an increased level of genome-wide linkage disequilibrium (LD) relative to accessions from the Iberian Peninsula and Central Europe. Cluster analysis with the structure program grouped Eurasian accessions into K=6 clusters. Accessions from the Iberian Peninsula and from Central Asia constitute distinct populations, whereas Central and Eastern European accessions represent admixed populations in which genomes were reshuffled by historical recombination events. These patterns likely result from a rapid postglacial recolonization of Eurasia from glacial refugial populations. Our analyses suggest that mapping populations for association or LD mapping should be chosen from regional rather than a species-wide sample or identified genetically as sets of individuals with similar average genetic distances. Electronic Supplementary Material Supplementary material is available for this article at and is accessible for authorized users.     

Genome-wide association study for oat (Avena sativa L.) beta-glucan concentration using germplasm of worldwide origin

Detection of quantitative trait loci (QTL) controlling complex traits followed by selection has become a common approach for selection in crop plants. The QTL are most often identified by linkage mapping using experimental F₂, backcross, advanced inbred, or doubled haploid families. An alternative approach for QTL detection are genome-wide association studies (GWAS) that use pre-existing lines such as those found in breeding programs. We explored the implementation of GWAS in oat (Avena sativa L.) to identify QTL affecting β-glucan concentration, a soluble dietary fiber with several human health benefits when consumed as a whole grain. A total of 431 lines of worldwide origin were tested over 2?years and genotyped using Diversity Array Technology (DArT) markers. A mixed model approach was used where both population structure fixed effects and pair-wise kinship random effects were included. Various mixed models that differed with respect to population structure and kinship were tested for their ability to control for false positives. As expected, given the level of population structure previously described in oat, population structure did not play a large role in controlling for false positives. Three independent markers were significantly associated with β-glucan concentration. Significant marker sequences were compared with rice and one of the three showed sequence homology to genes localized on rice chromosome seven adjacent to the CslF gene family, known to have β-glucan synthase function. Results indicate that GWAS in oat can be a successful option for QTL detection, more so with future development of higher-density markers.     

Imputation of 3 million SNPs in the Arabidopsis regional mapping population

Natural variation has become a prime resource to identify genetic variants that contribute to phenotypic variation. The regional mapping (RegMap) population is one of the most important populations for studying natural variation in Arabidopsis thaliana, and has been used in a large number of association studies and in studies on climatic adaptation. However, only 413 RegMap accessions have been completely sequenced, as part of the 1001 Genomes (1001G) Project, while the remaining 894 accessions have only been genotyped with the Affymetrix 250k chip. As a consequence, most association studies involving the RegMap are either restricted to the sequenced accessions, reducing power, or rely on a limited set of SNPs. Here we impute millions of SNPs to the 894 accessions that are exclusive to the RegMap, using the 1135 accessions of the 1001G Project as the reference panel. We assess imputation accuracy using a novel cross‐validation scheme, which we show provides a more reliable measure of accuracy than existing methods. After filtering out low accuracy SNPs, we obtain high‐quality genotypic information for 2029 accessions and 3 million markers. To illustrate the benefits of these imputed data, we reconducted genome‐wide association studies on five stress‐related traits and could identify novel candidate genes.     

Genetic diversity and population structure of a diverse set of rice germplasm for association mapping

Germplasm diversity is the mainstay for crop improvement and genetic dissection of complex traits. Understanding genetic diversity, population structure, and the level and distribution of linkage disequilibrium (LD) in target populations is of great importance and a prerequisite for association mapping. In this study, 100 genome-wide simple sequence repeat (SSR) markers were used to assess genetic diversity, population structure, and LD of 416 rice accessions including landraces, cultivars and breeding lines collected mostly in China. A model-based population structure analysis divided the rice materials into seven subpopulations. 63% of the SSR pairs in these accessions were in LD, which was mostly due to an overall population structure, since the number of locus pairs in LD was reduced sharply within each subpopulation, with the SSR pairs in LD ranging from 5.9 to 22.9%. Among those SSR pairs showing significant LD, the intrachromosomal LD had an average of 25–50 cM in different subpopulations. Analysis of the phenotypic diversity of 25 traits showed that the population structure accounted for an average of 22.4% of phenotypic variation. An example association mapping for starch quality traits using both the candidate gene mapping and genome-wide mapping strategies based on the estimated population structure was conducted. Candidate gene mapping confirmed that the Wx and starch synthase IIa (SSIIa) genes could be identified as strongly associated with apparent amylose content (AAC) and pasting temperature (PT), respectively. More importantly, we revealed that the Wx gene was also strongly associated with PT. In addition to the major genes, we found five and seven SSRs were associated with AAC and PT, respectively, some of which have not been detected in previous linkage mapping studies. The results suggested that the population may be useful for the genome-wide marker–trait association mapping. This new association population has the potential to identify quantitative trait loci (QTL) with small effects, which will aid in dissecting complex traits and in exploiting the rich diversity present in rice germplasm.     

Genome-Wide Association Study of Grain Appearance and Milling Quality in a Worldwide Collection of Indica Rice Germplasm

Grain appearance quality and milling quality are the main determinants of market value of rice. Breeding for improved grain quality is a major objective of rice breeding worldwide. Identification of genes/QTL controlling quality traits is the prerequisite for increasing breeding efficiency through marker-assisted selection. Here, we reported a genome-wide association study in indica rice to identify QTL associated with 10 appearance and milling quality related traits, including grain length, grain width, grain length to width ratio, grain thickness, thousand grain weight, degree of endosperm chalkiness, percentage of grains with chalkiness, brown rice rate, milled rice rate and head milled rice rate. A diversity panel consisting of 272 indica accessions collected worldwide was evaluated in four locations including Hangzhou, Jingzhou, Sanya and Shenzhen representing indica rice production environments in China and genotyped using genotyping-by-sequencing and Diversity Arrays Technology based on next-generation sequencing technique called DArTseq™. A wide range of variation was observed for all traits in all environments. A total of 16 different association analysis models were compared to determine the best model for each trait-environment combination. Association mapping based on 18,824 high quality markers yielded 38 QTL for the 10 traits. Five of the detected QTL corresponded to known genes or fine mapped QTL. Among the 33 novel QTL identified, qDEC1.1 (qGLWR1.1), qBRR2.2 (qGL2.1), qTGW2.1 (qGL2.2), qGW11.1 (qMRR11.1) and qGL7.1 affected multiple traits with relatively large effects and/or were detected in multiple environments. The research provided an insight of the genetic architecture of rice grain quality and important information for mining genes/QTL with large effects within indica accessions for rice breeding.     

Genome-wide association mapping of salinity tolerance in rice (Oryza sativa)

Salinity tolerance in rice is highly desirable to sustain production in areas rendered saline due to various reasons. It is a complex quantitative trait having different components, which can be dissected effectively by genome-wide association study (GWAS). Here, we implemented GWAS to identify loci controlling salinity tolerance in rice. A custom-designed array based on 6,000 single nucleotide polymorphisms (SNPs) in as many stress-responsive genes, distributed at an average physical interval of <100 kb on 12 rice chromosomes, was used to genotype 220 rice accessions using Infinium high-throughput assay. Genetic association was analysed with 12 different traits recorded on these accessions under field conditions at reproductive stage. We identified 20 SNPs (loci) significantly associated with Na⁺/K⁺ ratio, and 44 SNPs with other traits observed under stress condition. The loci identified for various salinity indices through GWAS explained 5–18% of the phenotypic variance. The region harbouring Saltol, a major quantitative trait loci (QTLs) on chromosome 1 in rice, which is known to control salinity tolerance at seedling stage, was detected as a major association with Na⁺/K⁺ ratio measured at reproductive stage in our study. In addition to Saltol, we also found GWAS peaks representing new QTLs on chromosomes 4, 6 and 7. The current association mapping panel contained mostly indica accessions that can serve as source of novel salt tolerance genes and alleles. The gene-based SNP array used in this study was found cost-effective and efficient in unveiling genomic regions/candidate genes regulating salinity stress tolerance in rice.     

Metabolic source isotopic pair labeling and genome-wide association are complementary tools for the identification of metabolite–gene associations in plants

The optimal extraction of information from untargeted metabolomics analyses is a continuing challenge. Here, we describe an approach that combines stable isotope labeling, liquid chromatography– mass spectrometry (LC–MS), and a computational pipeline to automatically identify metabolites produced from a selected metabolic precursor. We identified the subset of the soluble metabolome generated from phenylalanine (Phe) in Arabidopsis thaliana, which we refer to as the Phe-derived metabolome (FDM) In addition to identifying Phe-derived metabolites present in a single wild-type reference accession, the FDM was established in nine enzymatic and regulatory mutants in the phenylpropanoid pathway. To identify genes associated with variation in Phe-derived metabolites in Arabidopsis, MS features collected by untargeted metabolite profiling of an Arabidopsis diversity panel were retrospectively annotated to the FDM and natural genetic variants responsible for differences in accumulation of FDM features were identified by genome-wide association. Large differences in Phe-derived metabolite accumulation and presence/absence variation of abundant metabolites were observed in the nine mutants as well as between accessions from the diversity panel. Many Phe-derived metabolites that accumulated in mutants also accumulated in non-Col-0 accessions and was associated to genes with known or suspected functions in the phenylpropanoid pathway as well as genes with no known functions. Overall, we show that cataloguing a biochemical pathway’s products through isotopic labeling across genetic variants can substantially contribute to the identification of metabolites and genes associated with their biosynthesis.

An isotopic labeling and LC–MS pipeline to identify metabolites produced from Phe and its integration with genome-wide association identifies genes associated with the phenylpropanoid pathway.     

Uncovering Genes and Ploidy Involved in the High Diversity in Root Hair Density,Length and Response to Local Scarce Phosphate in Arabidopsis thaliana

Plant root hairs increase the root surface to enhance the uptake of sparingly soluble and immobile nutrients, such as the essential nutrient phosphorus, from the soil. Here, root hair traits and the response to scarce local phosphorus concentration were studied in 166 accessions of Arabidopsis thaliana using split plates. Root hair density and length were correlated, but highly variable among accessions. Surprisingly, the well-known increase in root hair density under low phosphorus was mostly restricted to genotypes that had less and shorter root hairs under P sufficient conditions. By contrast, several accessions with dense and long root hairs even had lower hair density or shorter hairs in local scarce phosphorus. Furthermore, accessions with whole-genome duplications developed more dense but phosphorus-insensitive root hairs. The impact of genome duplication on root hair density was confirmed by comparing tetraploid accessions with their diploid ancestors. Genome-wide association mapping identified candidate genes potentially involved in root hair responses tp scarce local phosphate. Knock-out mutants in identified candidate genes (CYR1, At1g32360 and RLP48) were isolated and differences in root hair traits in the mutants were confirmed. The large diversity in root hair traits among accessions and the diverse response when local phosphorus is scarce is a rich resource for further functional analyses.     

A Genome-Wide Association Analysis Reveals Epistatic Cancellation of Additive Genetic Variance for Root Length in Arabidopsis thaliana

Efforts to identify loci underlying complex traits generally assume that most genetic variance is additive. Here, we examined the genetics of Arabidopsis thaliana root length and found that the genomic narrow-sense heritability for this trait in the examined population was statistically zero. The low amount of additive genetic variance that could be captured by the genome-wide genotypes likely explains why no associations to root length could be found using standard additive-model-based genome-wide association (GWA) approaches. However, as the broad-sense heritability for root length was significantly larger, and primarily due to epistasis, we also performed an epistatic GWA analysis to map loci contributing to the epistatic genetic variance. Four interacting pairs of loci were revealed, involving seven chromosomal loci that passed a standard multiple-testing corrected significance threshold. The genotype-phenotype maps for these pairs revealed epistasis that cancelled out the additive genetic variance, explaining why these loci were not detected in the additive GWA analysis. Small population sizes, such as in our experiment, increase the risk of identifying false epistatic interactions due to testing for associations with very large numbers of multi-marker genotypes in few phenotyped individuals. Therefore, we estimated the false-positive risk using a new statistical approach that suggested half of the associated pairs to be true positive associations. Our experimental evaluation of candidate genes within the seven associated loci suggests that this estimate is conservative; we identified functional candidate genes that affected root development in four loci that were part of three of the pairs. The statistical epistatic analyses were thus indispensable for confirming known, and identifying new, candidate genes for root length in this population of wild-collected A. thaliana accessions. We also illustrate how epistatic cancellation of the additive genetic variance explains the insignificant narrow-sense and significant broad-sense heritability by using a combination of careful statistical epistatic analyses and functional genetic experiments.     

Independent FLC Mutations as Causes of Flowering-Time Variation in Arabidopsis thaliana and Capsella rubella

Capsella rubella is an inbreeding annual forb closely related to Arabidopsis thaliana, a model species widely used for studying natural variation in adaptive traits such as flowering time. Although mutations in dozens of genes can affect flowering of A. thaliana in the laboratory, only a handful of such genes vary in natural populations. Chief among these are FRIGIDA (FRI) and FLOWERING LOCUS C (FLC). Common and rare FRI mutations along with rare FLC mutations explain a large fraction of flowering-time variation in A. thaliana. Here we document flowering time under different conditions in 20 C. rubella accessions from across the species’ range. Similar to A. thaliana, vernalization, long photoperiods and elevated ambient temperature generally promote flowering. In this collection of C. rubella accessions, we did not find any obvious loss-of-function FRI alleles. Using mapping-by-sequencing with two strains that have contrasting flowering behaviors, we identified a splice-site mutation in FLC as the likely cause of early flowering in accession 1408. However, other similarly early C. rubella accessions did not share this mutation. We conclude that the genetic basis of flowering-time variation in C. rubella is complex, despite this very young species having undergone an extreme genetic bottleneck when it split from C. grandiflora a few tens of thousands of years ago.     

1,135 ionomes reveal the global pattern of leaf and seed mineral nutrient and trace element diversity in Arabidopsis thaliana

Soil is a heterogeneous reservoir of essential elements needed for plant growth and development. Plants have evolved mechanisms to balance their nutritional needs based on availability of nutrients. This has led to genetically based variation in the elemental composition, the ‘ionome’, of plants, both within and between species. We explore this natural variation using a panel of wild-collected, geographically widespread Arabidopsis thaliana accessions from the 1001 Genomes Project including over 1,135 accessions, and the 19 parental accessions of the Multi-parent Advanced Generation Inter-Cross (MAGIC) panel, all with full-genome sequences available. We present an experimental design pipeline for high-throughput ionomic screenings and analyses with improved normalisation procedures to account for errors and variability in conditions often encountered in large-scale, high-throughput data collection. We report quantification of the complete leaf and seed ionome of the entire collection using this pipeline and a digital tool, Ion Explorer , to interact with the dataset. We describe the pattern of natural ionomic variation across the A. thaliana species and identify several accessions with extreme ionomic profiles. It forms a valuable resource for exploratory genetic mapping studies to identify genes underlying natural variation in leaf and seed ionome and genetic adaptation of plants to soil conditions.