Abstract
High-density single nucleotide polymorphism (SNP) genotyping arrays are a powerful tool for studying genomic patterns of diversity, inferring ancestral relationships between individuals in populations and studying marker–trait associations in mapping experiments. We developed a genotyping array including about 90 000 gene-associated SNPs and used it to characterize genetic variation in allohexaploid and allotetraploid wheat populations. The array includes a significant fraction of common genome-wide distributed SNPs that are represented in populations of diverse geographical origin. We used density-based spatial clustering algorithms to enable high-throughput genotype calling in complex data sets obtained for polyploid wheat. We show that these model-free clustering algorithms provide accurate genotype calling in the presence of multiple clusters including clusters with low signal intensity resulting from significant sequence divergence at the target SNP site or gene deletions. Assays that detect low-intensity clusters can provide insight into the distribution of presence–absence variation (PAV) in wheat populations. A total of 46 977 SNPs from the wheat 90K array were genetically mapped using a combination of eight mapping populations. The developed array and cluster identification algorithms provide an opportunity to infer detailed haplotype structure in polyploid wheat and will serve as an invaluable resource for diversity studies and investigating the genetic basis of trait variation in wheat.
Highlights
High-density single nucleotide polymorphism (SNP) data are widely used to detect marker–trait associations in quantitative trait locus (QTL) mapping experiments and genome-wide association studies (GWAS) (Cook et al, 2012; Jia et al, 2013; Tian et al, 2011; Zhao et al, 2011)
Sets of informative SNPs selected based on their distribution across the genome, minor allele frequency (MAF) and intervariant linkage disequilibrium (LD), have been used to design high-density genotyping assays based on various technological principles (Cavanagh et al, 2013; Ganal et al, 2011; Kim et al, 2007; Song et al, 2013)
We demonstrate the utility of the developed array and genotype calling algorithms to reliably detect SNPs across worldwide wheat populations including hexaploid and tetraploid cultivars and landraces
Summary
High-density single nucleotide polymorphism (SNP) data are widely used to detect marker–trait associations in quantitative trait locus (QTL) mapping experiments and genome-wide association studies (GWAS) (Cook et al, 2012; Jia et al, 2013; Tian et al, 2011; Zhao et al, 2011). The GWAS of 413 diverse rice accessions using a 44K SNP genotyping chip identified dozens of alleles controlling 34 morphological, developmental and agronomic traits (Zhao et al, 2011). The 50K maize SNP chip has been used to study the genetic control of maize kernel composition in a nested association mapping panel (Cook et al, 2012) and identify signatures of wild relative allele introgressions in the maize genome (Hufford et al, 2012). The recently developed 9K SNP wheat chip was used to detect genomic regions targeted by breeding and improvement selection in wheat (Cavanagh et al, 2013)
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