Genome Association Mapping Research Articles

Leveraging genomics and temporal high-throughput phenotyping to enhance association mapping and yield prediction in sesame.

Sesame (Sesamum indicum) is an important oilseed crop with rising demand owing to its nutritional and health benefits. There is an urgent need to develop and integrate new genomic-based breeding strategies to meet these future demands. While genomic resources have advanced genetic research in sesame, the implementation of high-throughput phenotyping and genetic analysis of longitudinal traits remains limited. Here, we combined high-throughput phenotyping and random regression models to investigate the dynamics of plant height, leaf area index, and five spectral vegetation indices throughout the sesame growing seasons in a diversity panel. Modeling the temporal phenotypic and additive genetic trajectories revealed distinct patterns corresponding to the sesame growth cycle. We also conducted longitudinal genomic prediction and association mapping of plant height using various models and cross-validation schemes. Moderate prediction accuracy was obtained when predicting new genotypes at each time point, and moderate to high values were obtained when forecasting future phenotypes. Association mapping revealed three genomic regions in linkage groups 6, 8, and 11, conferring trait variation over time and growth rate. Furthermore, we leveraged correlations between the temporal trait and seed-yield and applied multi-trait genomic prediction. We obtained an improvement over single-trait analysis, especially when phenotypes from earlier time points were used, highlighting the potential of using a high-throughput phenotyping platform as a selection tool. Our results shed light on the genetic control of longitudinal traits in sesame and underscore the potential of high-throughput phenotyping to detect a wide range of traits and genotypes that can inform sesame breeding efforts to enhance yield.

Genomic prediction and association mapping of maize grain yield in multi-environment trials based on reaction norm models.

Genotype-by-environment interaction (GEI) is among the greatest challenges for maize breeding programs. Strong GEI limits both the prediction of genotype performance across variable environmental conditions and the identification of genomic regions associated with grain yield. Incorporating GEI into yield prediction models has been shown to improve prediction accuracy of yield; nevertheless, more work is needed to further understand this complex interaction across populations and environments. The main objectives of this study were to: 1) assess GEI in maize grain yield based on reaction norm models and predict hybrid performance across a gradient of environmental (EG) conditions and 2) perform a genome-wide association study (GWAS) and post-GWAS analyses for maize grain yield using data from 2014 to 2017 of the Genomes to Fields initiative hybrid trial. After quality control, 2,126 hybrids with genotypic and phenotypic data were assessed across 86 environments representing combinations of locations and years, although not all hybrids were evaluated in all environments. Heritability was greater in higher-yielding environments due to an increase in genetic variability in these environments in comparison to the low-yielding environments. GWAS was carried out for yield and five single nucleotide polymorphisms (SNPs) with the highest magnitude of effect were selected in each environment for follow-up analyses. Many candidate genes in proximity of selected SNPs have been previously reported with roles in stress response. Genomic prediction was performed to assess prediction accuracy of previously tested or untested hybrids in environments from a new growing season. Prediction accuracy was 0.34 for cross validation across years (CV0-Predicted EG) and 0.21 for cross validation across years with only untested hybrids (CV00-Predicted EG) when compared to Best Linear Unbiased Prediction (BLUPs) that did not utilize genotypic or environmental relationships. Prediction accuracy improved to 0.80 (CV0-Predicted EG) and 0.60 (CV00-Predicted EG) when compared to the whole-dataset model that used the genomic relationships and the environmental gradient of all environments in the study. These results identify regions of the genome for future selection to improve yield and a methodology to increase the number of hybrids evaluated across locations of a multi-environment trial through genomic prediction.

Phenotypic and molecular analyses in diploid and tetraploid genotypes of Solanum tuberosum L. reveal promising genotypes and candidate genes associated with phenolic compounds, ascorbic acid contents, and antioxidant activity.

Potato tubers contain biochemical compounds with antioxidant properties that benefit human health. However, the genomic basis of the production of antioxidant compounds in potatoes has largely remained unexplored. Therefore, we report the first genome-wide association study (GWAS) based on 4488 single nucleotide polymorphism (SNP) markers and the phenotypic evaluation of Total Phenols Content (TPC), Ascorbic Acid Content (AAC), and Antioxidant Activity (AA) traits in 404 diverse potato genotypes (84 diploids and 320 tetraploids) conserved at the Colombian germplasm bank that administers AGROSAVIA. The concentration of antioxidant compounds correlated to the skin tuber color and ploidy level. Especially, purple-blackish tetraploid tubers had the highest TPC (2062.41 ± 547.37 mg GAE), while diploid pink-red tubers presented the highest AA (DDPH: 14967.1 ± 4687.79 μmol TE; FRAP: 2208.63 ± 797.35 mg AAE) and AAC (4.52 mg ± 0.68 AA). The index selection allowed us to choose 20 promising genotypes with the highest values for the antioxidant compounds. Genome Association mapping identified 58 SNP-Trait Associations (STAs) with single-locus models and 28 Quantitative Trait Nucleotide (QTNs) with multi-locus models associated with the evaluated traits. Among models, eight STAs/QTNs related to TPC, AAC, and AA were detected in common, flanking seven candidate genes, from which four were pleiotropic. The combination in one population of diploid and tetraploid genotypes enabled the identification of more genetic associations. However, the GWAS analysis implemented independently in populations detected some regions in common between diploids and tetraploids not detected in the mixed population. Candidate genes have molecular functions involved in phenolic compounds, ascorbic acid biosynthesis, and antioxidant responses concerning plant abiotic stress. All candidate genes identified in this study can be used for further expression analysis validation and future implementation in marker-assisted selection pre-breeding platforms targeting fortified materials. Our study further revealed the importance of potato germplasm conserved in national genebanks, such as AGROSAVIA's, as a valuable genetic resource to improve existing potato varieties.

Genomic Association Mapping of Apparent Amylose and Protein Concentration in Milled Rice

Apparent amylose and protein concentrations (AAC and PC) affect the milling, cooking, and eating quality of rice. This study was conducted to assess the phenotypic and genotypic variation and to identify genomic regions and putative candidate genes associated with AAC and PC in milled rice grain. Two hundred and seventeen and 207 diverse rice accessions were grown at the Texas A&M AgriLife Research Center in 2018 and 2019, respectively. Milled rice samples were analyzed for AAC and PC using the iodine colorimetry and Dumas method, respectively. Genome-wide association studies (GWAS) for AAC and PC were conducted using 872,556 single nucleotide polymorphism (SNP) markers following the mixed linear model. Significant variation among the accessions was found for both variables each year. Associations between 32 SNPs with PC and seven SNPs with AAC were detected. Gene models linked to these SNPs have a wide range of biological functions, including protein and carbohydrate metabolism, DNA methylation, and response to abiotic and biotic stresses. Seven of the identified SNPs colocalized with previously reported quantitative trait loci (QTL) for protein concentration. Fine-mapping of significant genomic regions and gene validation are necessary for this information to aid in marker-assisted breeding for improved grain quality.

Genetic and Molecular Regulation of Seed Storage Proteins (SSPs) to Improve Protein Nutritional Value of Oilseed Rape (Brassica napus L.) Seeds.

The world-wide demand for additional protein sources for human nutrition and animal feed keeps rising due to rapidly growing world population. Oilseed rape is a second important oil producing crop and the by-product of the oil production is a protein rich meal. The protein in rapeseed meal finds its application in animal feed and various industrial purposes, but its improvement is of great interest, especially for non-ruminants and poultry feed. To be able to manipulate the quality and quantity of seed protein in oilseed rape, understanding genetic architecture of seed storage protein (SSPs) synthesis and accumulation in this crop species is of great interest. For this, application of modern molecular breeding tools such as whole genome sequencing, genotyping, association mapping, and genome editing methods implemented in oilseed rape seed protein improvement would be of great interest. This review examines current knowledge and opportunities to manipulate of SSPs in oilseed rape to improve its quality, quantity and digestibility.

GWAS by GBLUP: Single and Multimarker EMMAX and Bayes Factors, with an Example in Detection of a Major Gene for Horse Gait.

Bayesian models for genomic prediction and association mapping are being increasingly used in genetics analysis of quantitative traits. Given a point estimate of variance components, the popular methods SNP-BLUP and GBLUP result in joint estimates of the effect of all markers on the analyzed trait; single and multiple marker frequentist tests (EMMAX) can be constructed from these estimates. Indeed, BLUP methods can be seen simultaneously as Bayesian or frequentist methods. So far there is no formal method to produce Bayesian statistics from GBLUP. Here we show that the Bayes Factor, a commonly admitted statistical procedure, can be computed as the ratio of two normal densities: the first, of the estimate of the marker effect over its posterior standard deviation; the second of the null hypothesis (a value of 0 over the prior standard deviation). We extend the BF to pool evidence from several markers and of several traits. A real data set that we analyze, with ours and existing methods, analyzes 630 horses genotyped for 41711 polymorphic SNPs for the trait “outcome of the qualification test” (which addresses gait, or ambling, of horses) for which a known major gene exists. In the horse data, single marker EMMAX shows a significant effect at the right place at Bonferroni level. The BF points to the same location although with low numerical values. The strength of evidence combining information from several consecutive markers increases using the BF and decreases using EMMAX, which comes from a fundamental difference in the Bayesian and frequentist schools of hypothesis testing. We conclude that our BF method complements frequentist EMMAX analyses because it provides a better pooling of evidence across markers, although its use for primary detection is unclear due to the lack of defined rejection thresholds.

Bayesian Approximate Kernel Regression With Variable Selection

ABSTRACTNonlinear kernel regression models are often used in statistics and machine learning because they are more accurate than linear models. Variable selection for kernel regression models is a challenge partly because, unlike the linear regression setting, there is no clear concept of an effect size for regression coefficients. In this article, we propose a novel framework that provides an effect size analog for each explanatory variable in Bayesian kernel regression models when the kernel is shift-invariant—for example, the Gaussian kernel. We use function analytic properties of shift-invariant reproducing kernel Hilbert spaces (RKHS) to define a linear vector space that: (i) captures nonlinear structure, and (ii) can be projected onto the original explanatory variables. This projection onto the original explanatory variables serves as an analog of effect sizes. The specific function analytic property we use is that shift-invariant kernel functions can be approximated via random Fourier bases. Based on the random Fourier expansion, we propose a computationally efficient class of Bayesian approximate kernel regression (BAKR) models for both nonlinear regression and binary classification for which one can compute an analog of effect sizes. We illustrate the utility of BAKR by examining two important problems in statistical genetics: genomic selection (i.e., phenotypic prediction) and association mapping (i.e., inference of significant variants or loci). State-of-the-art methods for genomic selection and association mapping are based on kernel regression and linear models, respectively. BAKR is the first method that is competitive in both settings. Supplementary materials for this article are available online.

Multiplex confounding factor correction for genomic association mapping with squared sparse linear mixed model.

Genome-wide Association Study has presented a promising way to understand the association between human genomes and complex traits. Many simple polymorphic loci have been shown to explain a significant fraction of phenotypic variability. However, challenges remain in the non-triviality of explaining complex traits associated with multifactorial genetic loci, especially considering the confounding factors caused by population structure, family structure, and cryptic relatedness. In this paper, we propose a Squared-LMM (LMM2) model, aiming to jointly correct population and genetic confounding factors. We offer two strategies of utilizing LMM2 for association mapping: 1) It serves as an extension of univariate LMM, which could effectively correct population structure, but consider each SNP in isolation. 2) It is integrated with the multivariate regression model to discover association relationship between complex traits and multifactorial genetic loci. We refer to this second model as sparse Squared-LMM (sLMM2). Further, we extend LMM2/sLMM2 by raising the power of our squared model to the LMMn/sLMMn model. We demonstrate the practical use of our model with synthetic phenotypic variants generated from genetic loci of Arabidopsis Thaliana. The experiment shows that our method achieves a more accurate and significant prediction on the association relationship between traits and loci. We also evaluate our models on collected phenotypes and genotypes with the number of candidate genes that the models could discover. The results suggest the potential and promising usage of our method in genome-wide association studies.

Integrating Bayesian genomic cline analyses and association mapping of morphological and ecological traits to dissect reproductive isolation and introgression in a Louisiana Iris hybrid zone.

Hybrid zones provide unique opportunities to examine reproductive isolation and introgression in nature. We utilized 45,384 single nucleotide polymorphism (SNP) loci to perform association mapping of 14 floral, vegetative and ecological traits that differ between Iris hexagona and Iris fulva, and to investigate, using a Bayesian genomic cline (BGC) framework, patterns of genomic introgression in a large and phenotypically diverse hybrid zone in southern Louisiana. Many loci of small effect size were consistently found to be associated with phenotypic variation across all traits, and several individual loci were revealed to influence phenotypic variation across multiple traits. Patterns of genomic introgression were quite heterogeneous throughout the Louisiana Iris genome, with I.hexagona alleles tending to be favoured over those of I. fulva. Loci that were found to have exceptional patterns of introgression were also found to be significantly associated with phenotypic variation in a small number of morphological traits. However, this was the exception rather than the rule, as most loci that were associated with morphological trait variation were not significantly associated with excess ancestry. These findings provide insights into the complexity of the genomic architecture of phenotypic differences and are a first step towards identifying loci that are associated with both trait variation and reproductive isolation in nature.

Genomic Prediction and Association Mapping of Curd-Related Traits in Gene Bank Accessions of Cauliflower.

Genetic resources are an important source of genetic variation for plant breeding. Genome-wide association studies (GWAS) and genomic prediction greatly facilitate the analysis and utilization of useful genetic diversity for improving complex phenotypic traits in crop plants. We explored the potential of GWAS and genomic prediction for improving curd-related traits in cauliflower (Brassica oleracea var. botrytis) by combining 174 randomly selected cauliflower gene bank accessions from two different gene banks. The collection was genotyped with genotyping-by-sequencing (GBS) and phenotyped for six curd-related traits at two locations and three growing seasons. A GWAS analysis based on 120,693 single-nucleotide polymorphisms identified a total of 24 significant associations for curd-related traits. The potential for genomic prediction was assessed with a genomic best linear unbiased prediction model and BayesB. Prediction abilities ranged from 0.10 to 0.66 for different traits and did not differ between prediction methods. Imputation of missing genotypes only slightly improved prediction ability. Our results demonstrate that GWAS and genomic prediction in combination with GBS and phenotyping of highly heritable traits can be used to identify useful quantitative trait loci and genotypes among genetically diverse gene bank material for subsequent utilization as genetic resources in cauliflower breeding.

Genetic diversity, population structure, and linkage disequilibrium of elite and local apple accessions from Belgium using the IRSC array

The identification of molecular markers associated with economic and quality traits will help improve breeding for new apple (Malus × domestica Borkh.) cultivars. Tools such as the 8K apple SNP array developed by the RosBREED consortium allow for high-throughput genotyping of SNP polymorphisms within collections. However, genetic characterization and the identification of population stratification and kinship within germplasm collections is a fundamental prerequisite for identifying robust marker–trait associations. In this study, a collection of apple germplasm originally developed for plant architectural studies and consisting of both non-commercial/local and elite accessions was genotyped using the 8K apple SNP array to identify cryptic relationships between accessions, to analyze population structure and to calculate the linkage disequilibrium (LD). A total of nine pairs of synonyms and several triploids accessions were identified within the 130 accessions genotyped. In addition, most of the known parent-child relations were confirmed, and several putative, previously unknown parent-child relations were identified among the local accessions. No clear subgroups could be identified although some separation between local and elite accessions was evident. The study of LD showed a rapid decay in our collection, indicating that a larger number of SNPs is necessary to perform whole genome association mapping. Finally, an association mapping effort for architectural traits was carried out on a small number of accessions to estimate the feasibility of this approach.

Novel Innate Immune Genes Regulating the Macrophage Response to Gram Positive Bacteria.

Host variation in Toll-like receptors and other innate immune signaling molecules alters infection susceptibility. However, only a portion of the variability observed in the innate immune response is accounted for by known genes in these pathways. Thus, the identification of additional genes that regulate the response to Gram positive bacteria is warranted. Bone marrow-derived macrophages (BMMs) from 43 inbred mouse strains were stimulated with lipotechoic acid (LTA), a major component of the Gram positive bacterial cell wall. Concentrations of the proinflammatory cytokines IL-6, IL-12, and TNF-α were measured. In silico whole genome association (WGA) mapping was performed using cytokine responses followed by network analysis to prioritize candidate genes. To determine which candidate genes could be responsible for regulating the LTA response, candidate genes were inhibited using RNA interference (RNAi) and were overexpressed in RAW264.7 macrophages. BMMs from Bdkrb1-deficient mice were used to assess the effect of Bdkrb1 gene deletion on the response to LTA, heat-killed Streptococcus pneumoniae, and heat-killed Staphylococcus aureus WGA mapping identified 117 loci: IL-6 analysis yielded 20 loci (average locus size = 0.133 Mb; 18 genes), IL-12 analysis produced 5 loci (0.201 Mb average; 7 genes), and TNF-α analysis yielded 92 loci (0.464 Mb average; 186 genes of which 46 were prioritized by network analysis). The follow-up small interfering RNA screen of 71 target genes identified four genes (Bdkrb1, Blnk, Fbxo17, and Nkx6-1) whose inhibition resulted in significantly reduced cytokine production following LTA stimulation. Overexpression of these four genes resulted in significantly increased cytokine production in response to LTA. Bdkrb1-deficient macrophages were less responsive to LTA and heat-killed S. aureus, validating the genetic and RNAi approach to identify novel regulators of the response to LTA. We have identified four innate immune response genes that may contribute to Gram positive bacterial susceptibility.

Sequence-based Association Analysis Reveals an MGST1 eQTL with Pleiotropic Effects on Bovine Milk Composition.

The mammary gland is a prolific lipogenic organ, synthesising copious amounts of triglycerides for secretion into milk. The fat content of milk varies widely both between and within species, and recent independent genome-wide association studies have highlighted a milk fat percentage quantitative trait locus (QTL) of large effect on bovine chromosome 5. Although both EPS8 and MGST1 have been proposed to underlie these signals, the causative status of these genes has not been functionally confirmed. To investigate this QTL in detail, we report genome sequence-based imputation and association mapping in a population of 64,244 taurine cattle. This analysis reveals a cluster of 17 non-coding variants spanning MGST1 that are highly associated with milk fat percentage, and a range of other milk composition traits. Further, we exploit a high-depth mammary RNA sequence dataset to conduct expression QTL (eQTL) mapping in 375 lactating cows, revealing a strong MGST1 eQTL underpinning these effects. These data demonstrate the utility of DNA and RNA sequence-based association mapping, and implicate MGST1, a gene with no obvious mechanistic relationship to milk composition regulation, as causally involved in these processes.

Genome-Wide Association Mapping and Genomic Prediction Elucidate the Genetic Architecture of Morphological Traits in Arabidopsis.

Quantitative traits in plants are controlled by a large number of genes and their interaction with the environment. To disentangle the genetic architecture of such traits, natural variation within species can be explored by studying genotype-phenotype relationships. Genome-wide association studies that link phenotypes to thousands of single nucleotide polymorphism markers are nowadays common practice for such analyses. In many cases, however, the identified individual loci cannot fully explain the heritability estimates, suggesting missing heritability. We analyzed 349 Arabidopsis accessions and found extensive variation and high heritabilities for different morphological traits. The number of significant genome-wide associations was, however, very low. The application of genomic prediction models that take into account the effects of all individual loci may greatly enhance the elucidation of the genetic architecture of quantitative traits in plants. Here, genomic prediction models revealed different genetic architectures for the morphological traits. Integrating genomic prediction and association mapping enabled the assignment of many plausible candidate genes explaining the observed variation. These genes were analyzed for functional and sequence diversity, and good indications that natural allelic variation in many of these genes contributes to phenotypic variation were obtained. For ACS11, an ethylene biosynthesis gene, haplotype differences explaining variation in the ratio of petiole and leaf length could be identified.

Natural variation in flavonol accumulation in Arabidopsis is determined by the flavonol glucosyltransferase BGLU6.

Flavonols are colourless secondary metabolites, primarily regarded as UV-protection pigments that are deposited in plants in their glycosylated forms. The glycosylation of flavonols is mainly catalysed by UDP-sugar-dependent glycosyltransferases (UGTs). Although the structures of flavonol glycosides accumulating in Arabidopsis thaliana are known, many genes involved in the flavonol glycosylation pathway are yet to be discovered. The flavonol glycoside profiles of seedlings from 81 naturally occurring A. thaliana accessions were screened using high performance thin layer chromatography. A qualitative variation in flavonol 3-O-gentiobioside 7-O-rhamnoside (F3GG7R) content was identified. Ler × Col-0 recombinant inbred line mapping and whole genome association mapping led to the identification of a glycoside hydrolase family 1-type gene, At1g60270/BGLU6, that encodes a homolog of acyl-glucose-dependent glucosyltransferases involved in the glycosylation of anthocyanins, possibly localized in the cytoplasm, and that is co-expressed with genes linked to phenylpropanoid biosynthesis. A causal single nucleotide polymorphism introducing a premature stop codon in non-producer accessions was found to be absent in the producers. Several other naturally occurring loss-of-function alleles were also identified. Two independent bglu6 T-DNA insertion mutants from the producer accessions showed loss of F3GG7R. Furthermore, bglu6 mutant lines complemented with the genomic Ler BGLU6 gene confirmed that BGLU6 is essential for production of F3GGR7. We have thus identified an accession-specific gene that causes a qualitative difference in flavonol glycoside accumulation in A. thaliana strains. This gene encodes a flavonol 3-O-glucoside: 6″-O-glucosyltransferase that does not belong to the large canonical family of flavonol glycosyltransferases that use UDP-conjugates as the activated sugar donor substrate.

Populus endo-β-1,4-glucanases gene family: genomic organization, phylogenetic analysis, expression profiles and association mapping.

Extensive characterization of the poplar GH9 gene family provides new insights into GH9 function and evolution in woody species, and may drive novel progress for molecular breeding in trees. In higher plants, endo-β-1,4-glucanases (cellulases) belonging to the glycosyl hydrolase family 9 (GH9) have roles in cell wall synthesis, remodeling and degradation. To increase the understanding of the GH9 family in perennial woody species, we conducted an extensive characterization of the GH9 family in the model tree species, Populus. We characterized 25 putative GH9 members in Populus with three subclasses (A, B, and C), using structures and bioinformatic analysis. Phylogenetic analyses of 114 GH9s from plant (dicot, monocot, and conifer) and bacterial species (outgroup) demonstrated that plant GH9s are monophyletic with respect to bacteria GH9s. Three subclasses, A, B, and C, of plant GH9 are formed before the divergence of angiosperms and gymnosperms. Chromosomal localization and duplications of GH9s in the Populus genome showed that eight paralogous pairs remained in conserved positions on segmental duplicated blocks, suggesting duplication of chromosomal segments has contributed to the family expansion. By examining tissue-specific expression profiles for all 25 members, we found that GH9 members exhibited distinct but partially overlapping expression patterns, while certain members have higher transcript abundance in mature or developing xylem. Based on our understanding of intraspecific variation and linkage disequilibrium of two KORRIGANs (PtoKOR1 and PtoKOR2) in natural population of Populus tomentosa, two non-synonymous SNPs in PtoKOR1 associated with fiber width and holocellulose content were obtained. Characterizations of the poplar GH9 family provide new insights into GH9 function and evolution in woody species, and may drive novel progress for molecular breeding in trees.

Genomic selection and association mapping in rice (Oryza sativa): effect of trait genetic architecture, training population composition, marker number and statistical model on accuracy of rice genomic selection in elite, tropical rice breeding lines.

Genomic Selection (GS) is a new breeding method in which genome-wide markers are used to predict the breeding value of individuals in a breeding population. GS has been shown to improve breeding efficiency in dairy cattle and several crop plant species, and here we evaluate for the first time its efficacy for breeding inbred lines of rice. We performed a genome-wide association study (GWAS) in conjunction with five-fold GS cross-validation on a population of 363 elite breeding lines from the International Rice Research Institute's (IRRI) irrigated rice breeding program and herein report the GS results. The population was genotyped with 73,147 markers using genotyping-by-sequencing. The training population, statistical method used to build the GS model, number of markers, and trait were varied to determine their effect on prediction accuracy. For all three traits, genomic prediction models outperformed prediction based on pedigree records alone. Prediction accuracies ranged from 0.31 and 0.34 for grain yield and plant height to 0.63 for flowering time. Analyses using subsets of the full marker set suggest that using one marker every 0.2 cM is sufficient for genomic selection in this collection of rice breeding materials. RR-BLUP was the best performing statistical method for grain yield where no large effect QTL were detected by GWAS, while for flowering time, where a single very large effect QTL was detected, the non-GS multiple linear regression method outperformed GS models. For plant height, in which four mid-sized QTL were identified by GWAS, random forest produced the most consistently accurate GS models. Our results suggest that GS, informed by GWAS interpretations of genetic architecture and population structure, could become an effective tool for increasing the efficiency of rice breeding as the costs of genotyping continue to decline.

Ancient homology underlies adaptive mimetic diversity across butterflies.

Convergent evolution provides a rare, natural experiment with which to test the predictability of adaptation at the molecular level. Little is known about the molecular basis of convergence over macro-evolutionary timescales. Here we use a combination of positional cloning, population genomic resequencing, association mapping and developmental data to demonstrate that positionally orthologous nucleotide variants in the upstream region of the same gene, WntA, are responsible for parallel mimetic variation in two butterfly lineages that diverged >65 million years ago. Furthermore, characterization of spatial patterns of WntA expression during development suggests that alternative regulatory mechanisms underlie wing pattern variation in each system. Taken together, our results reveal a strikingly predictable molecular basis for phenotypic convergence over deep evolutionary time.

High density SNP and SSR-based genetic maps of two independent oil palm hybrids

BackgroundOil palm is an important perennial oil crop with an extremely long selection cycle of 10 to 12 years. As such, any tool that speeds up its genetic improvement process, such as marker-assisted breeding is invaluable. Previously, genetic linkage maps based on AFLP, RFLP and SSR markers were developed and QTLs for fatty acid composition and yield components identified. High density genetic maps of crosses of different genetic backgrounds are indispensable tools for investigating oil palm genetics. They are also useful for comparative mapping analyses to identify markers closely linked to traits of interest.ResultsA 4.5 K customized oil palm SNP array was developed using the Illumina Infinium platform. The SNPs and 252 SSRs were genotyped on two mapping populations, an intraspecific cross with 87 palms and an interspecific cross with 108 palms. Parental maps with 16 linkage groups (LGs), were constructed for the three fruit forms of E. guineensis (dura, pisifera and tenera). Map resolution was further increased by integrating the dura and pisifera maps into an intraspecific integrated map with 1,331 markers spanning 1,867 cM. We also report the first map of a Colombian E. oleifera, comprising 10 LGs with 65 markers spanning 471 cM. Although not very dense due to the high level of homozygosity in E. oleifera, the LGs were successfully integrated with the LGs of the tenera map. Direct comparison between the parental maps identified 603 transferable markers polymorphic in at least two of the parents. Further analysis revealed a high degree of marker transferability covering 1,075 cM, between the intra- and interspecific integrated maps. The interspecific cross displayed higher segregation distortion than the intraspecific cross. However, inclusion of distorted markers in the genetic maps did not disrupt the marker order and no map expansion was observed.ConclusionsThe high density SNP and SSR-based genetic maps reported in this paper have greatly improved marker density and genome coverage in comparison with the first reference map based on AFLP and SSR markers. Therefore, it is foreseen that they will be more useful for fine mapping of QTLs and whole genome association mapping studies in oil palm.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-309) contains supplementary material, which is available to authorized users.

Whole genome scanning and association mapping identified a significant association between growth and a SNP in the IFABP-a gene of the Asian seabass.

BackgroundAquaculture is the quickest growing sector in agriculture. However, QTL for important traits have been only identified in a few aquaculture species. We conducted QTL mapping for growth traits in an Asian seabass F2 family with 359 individuals using 123 microsatellites and 22 SNPs, and performed association mapping in four populations with 881 individuals.ResultsTwelve and nine significant QTL, as well as 14 and 10 suggestive QTL were detected for growth traits at six and nine months post hatch, respectively. These QTL explained 0.9-12.0% of the phenotypic variance. For body weight, two QTL intervals at two stages were overlapped while the others were mapped onto different positions. The IFABP-a gene located in a significant QTL interval for growth on LG5 was cloned and characterized. A SNP in exon 3 of the gene was significantly associated with growth traits in different populations.ConclusionsThe results of QTL mapping for growth traits suggest that growth at different stages was controlled by some common QTL and some different QTL. Positional candidate genes and association mapping suggest that the IFABP-a is a strong candidate gene for growth. Our data supply a basis for fine mapping QTL, marker-assisted selection and further detailed analysis of the functions of the IFABP-a gene in fish growth.