Mining for Single Nucleotide Polymorphisms and Insertions/Deletions in Maize Expressed Sequence Tag Data

Abstract

We have developed a computer based method to identify candidate single nucleotide polymorphisms (SNPs) and small insertions/deletions from expressed sequence tag data. Using a redundancy-based approach, valid SNPs are distinguished from erroneous sequence by their representation multiple times in an alignment of sequence reads. A second measure of validity was also calculated based on the cosegregation of the SNP pattern between multiple SNP loci in an alignment. The utility of this method was demonstrated by applying it to 102,551 maize (Zea mays) expressed sequence tag sequences. A total of 14,832 candidate polymorphisms were identified with an SNP redundancy score of two or greater. Segregation of these SNPs with haplotype indicates that candidate SNPs with high redundancy and cosegregation confidence scores are likely to represent true SNPs. This was confirmed by validation of 264 candidate SNPs from 27 loci, with a range of redundancy and cosegregation scores, in four inbred maize lines. The SNP transition/transversion ratio and insertion/deletion size frequencies correspond to those observed by direct sequencing methods of SNP discovery and suggest that the majority of predicted SNPs and insertion/deletions identified using this approach represent true genetic variation in maize.The development of high-throughput methods for the detection of single nucleotide polymorphisms (SNPs) and small indels (insertion/deletions) has led to a revolution in their use as molecular markers. SNPs are increasingly becoming the marker of choice in genetic analysis and are used routinely as markers in agricultural breeding programs (Gupta et al., 2001). They also have many uses in human genetics, such as for the detection of alleles associated with genetic diseases and the identification of individuals (Nikiforov et al., 1994). SNPs are invaluable as a tool for genome mapping, offering the potential for generating very high-density genetic maps, which can be used to develop haplotyping systems for genes or regions of interest (Rafalski, 2002). The low mutation rate of SNPs also makes them excellent markers for studying complex genetic traits and as a tool for the understanding of genome evolution (Syvanen, 2001).Unlike random amplified polymorphic DNAs and RFLPs, SNPs are direct markers because sequence information provides the exact nature of the allelic variants. They are far more prevalent than microsatellites and, therefore, may provide a high density of markers near a locus of interest. Recent evidence has shown that when comparing human DNA from two individuals, SNPs are found on average every 1 to 2 kb (Clifford et al., 2000;Deutsch et al., 2001). Limited work has been carried out to examine the occurrence of SNPs in plants, although these preliminary studies have indicated that SNPs appear to be even more abundant in plant systems than in the human genome. Germano and Klein (1999) identified five SNPs in 1 kb of nDNA of Picea rubens and Picea mariana, and also discovered SNPs in the chloroplasts of these species. Rec...

Breeding rootstocks for Prunus species: Advances in genetic and genomics of peach and cherry as a model

“…A number of methods to identify SNPs have been described (Ganal et al, 2009): by searching expressed sequence tag (EST) databases (Batley et al, 2003), amplicon re-sequencing (Choi et al, 2007), complete sequence of a genome (Velasco et al, 2007), and more recently, high throughput sequencing technology (Barbazuk et al, 2007). Advances in next-generation techniques have reduced the cost of DNA sequencing to the point where it is now feasible to perform genotypingby-sequencing (GBS) to analyze small-and large-sized genomes with high diversity and allow the identification of thousands of markers for a species (Elshire et al, 2011;Poland et al, 2012;Ward et al, 2013;Guajardo et al, 2015).…”

Genetic Improvement of Maize by Marker-Assisted Breeding

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Bioinformatics tools for development of fast and cost effective simple sequence repeat (SSR), and single nucleotide polymorphisms (SNP) markers from expressed sequence tags (ESTs)

“…For computational SNP discovery, two important points should be considered. First, the program should be able to distinguish allelic variation from sequence variation between paralogous sequences (Marth et al, 1999;Le Dantec et al, 2004;Batley et al, 2003). Secondly, the program should be able to recognize sequencing errors which are usually caused by poor quality sequences, especially for EST data (Picoult-Newberg et al, 1999;Garg et al, 1999;Batley et al, 2003;Matukumalli et al, 2006).…”