Dataset Information

Bulk segregant analysis using single nucleotide polymorphism microarrays.

ABSTRACT: Bulk segregant analysis (BSA) using microarrays, and extreme array mapping (XAM) have recently been used to rapidly identify genomic regions associated with phenotypes in multiple species. These experiments, however, require the identification of single feature polymorphisms (SFP) between the cross parents for each new combination of genotypes, which raises the cost of experiments. The availability of the genomic polymorphism data in Arabidopsis thaliana, coupled with the efficient designs of Single Nucleotide Polymorphism (SNP) genotyping arrays removes the requirement for SFP detection and lowers the per array cost, thereby lowering the overall cost per experiment. To demonstrate that these approaches would be functional on SNP arrays and determine confidence intervals, we analyzed hybridizations of natural accessions to the Arabidopsis ATSNPTILE array and simulated BSA or XAM given a variety of gene models, populations, and bulk selection parameters. Our results show a striking degree of correlation between the genotyping output of both methods, which suggests that the benefit of SFP genotyping in context of BSA can be had with the cheaper, more efficient SNP arrays. As a final proof of concept, we hybridized the DNA from bulks of an F2 mapping population of a Sulfur and Selenium ionomics mutant to both the Arabidopsis ATTILE1R and ATSNPTILE arrays, which produced almost identical results. We have produced R scripts that prompt the user for the required parameters and perform the BSA analysis using the ATSNPTILE1 array and have provided them as supplemental data files.

SUBMITTER: Becker A

PROVIDER: S-EPMC3029305 | biostudies-literature | 2011 Jan

REPOSITORIES: biostudies-literature

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Bulk segregant analysis using single nucleotide polymorphism microarrays.

Becker Anthony A Chao Dai-Yin DY Zhang Xu X Salt David E DE Baxter Ivan I

PloS one 20110127 1

Bulk segregant analysis (BSA) using microarrays, and extreme array mapping (XAM) have recently been used to rapidly identify genomic regions associated with phenotypes in multiple species. These experiments, however, require the identification of single feature polymorphisms (SFP) between the cross parents for each new combination of genotypes, which raises the cost of experiments. The availability of the genomic polymorphism data in Arabidopsis thaliana, coupled with the efficient designs of Si ...[more]

PMID: 21297997

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Project description:BackgroundGenome wide association studies (GWAS) are a powerful tool for identifying quantitative trait loci (QTL) and causal single nucleotide polymorphisms (SNPs)/genes associated with various important traits in crop species. Typically, GWAS in crops are performed using a panel of inbred lines, where multiple replicates of the same inbred are measured and the average phenotype is taken as the response variable. Here we describe and evaluate single plant GWAS (sp-GWAS) for performing a GWAS on individual plants, which does not require an association panel of inbreds. Instead sp-GWAS relies on the phenotypes and genotypes from individual plants sampled from a randomly mating population. Importantly, we demonstrate how sp-GWAS can be efficiently combined with a bulk segregant analysis (BSA) experiment to rapidly corroborate evidence for significant SNPs.ResultsIn this study we used the Shoepeg maize landrace, collected as an open pollinating variety from a farm in Southern Missouri in the 1960's, to evaluate whether sp-GWAS coupled with BSA can efficiently and powerfully used to detect significant association of SNPs for plant height (PH). Plant were grown in 8 locations across two years and in total 768 individuals were genotyped and phenotyped for sp-GWAS. A total of 306?k polymorphic markers in 768 individuals evaluated via association analysis detected 25 significant SNPs (P???0.00001) for PH. The results from our single-plant GWAS were further validated by bulk segregant analysis (BSA) for PH. BSA sequencing was performed on the same population by selecting tall and short plants as separate bulks. This approach identified 37 genomic regions for plant height. Of the 25 significant SNPs from GWAS, the three most significant SNPs co-localize with regions identified by BSA.ConclusionOverall, this study demonstrates that sp-GWAS coupled with BSA can be a useful tool for detecting significant SNPs and identifying candidate genes. This result is particularly useful for species/populations where association panels are not readily available.

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Bulk segregant analysis using single nucleotide polymorphism microarrays.

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Bulk segregant analysis using single nucleotide polymorphism microarrays.

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