SFP Genotyping From Affymetrix Arrays Is Robust But Largely Detects Cis-acting Expression Regulators

Abstract

The recent development of Affymetrix chips designed from assembled EST sequences has spawned considerable interest in identifying single-feature polymorphisms (SFPs) from transcriptome data. SFPs are valuable genetic markers that potentially offer a physical link to the structural genes themselves. However, most current SFP prediction methodologies were developed for sequenced species although SFPs are particularly valuable for species with complex and unsequenced genomes. To establish the sensitivity and specificity of prediction, we explored four methods for identifying SFPs from experiments involving two tissues in two commercial barleys and their doubled-haploid progeny. The methods were compared in terms of numbers of SFPs predicted and their ability to identify known sequence polymorphisms in the features, to confirm existing SNP genotypes and to match existing maps and individual haplotypes. We identified .4000 separate SFPs that accurately predicted the SNP genotype of .98% of the doubled-haploid (DH) lines. They were highly enriched for features containing sequence polymorphisms but all methods uniformly identified a majority of SFPs ($64%) in features for which there was no sequence polymorphism while 5% mapped to different locations, indicating that ''SFPs'' mainly represent polymorphism in cis-acting regulators. All methods are efficient and robust at predicting markers for gene mapping.

Array-based high-throughput DNA markers for crop improvement

“…The false positives are believed to result due to alternative splicing or polyadenylation, gene duplications, chance alignments with RNA from another region, gene expression markers (resulting from polymorphism(s) at trans-acting regulators and secondary structures in target DNA) or SNPs that occur immediately adjacent to the position of a 25mer probe (cf. Luo et al, 2007;Zhu and Salmeron, 2007;Potokina et al, 2008). The number of these false positives can be reduced by (i) sampling more than one tissues per genotype (Rostoks et al, 2005;Luo et al, 2007); (ii) excluding sequence duplications during array design, (iii) studying segregation pattern in the progeny of the cross made from genotypes under question (Luo et al, 2007), (iv) adjusting stringency parameters (Luo et al, 2007) and through (v) the use of replication arrays.…”

“…Luo et al, 2007;Zhu and Salmeron, 2007;Potokina et al, 2008). The number of these false positives can be reduced by (i) sampling more than one tissues per genotype (Rostoks et al, 2005;Luo et al, 2007); (ii) excluding sequence duplications during array design, (iii) studying segregation pattern in the progeny of the cross made from genotypes under question (Luo et al, 2007), (iv) adjusting stringency parameters (Luo et al, 2007) and through (v) the use of replication arrays. Similarly, false negatives can be attributed to position (in nucleotides) of known SNP(s) on the corresponding array feature, and can be reduced by increasing feature density per gene (Rostoks et al, 2005).…”

“…Other statistical methods that are specifically dedicated to SFP/SNP detection include 'robustified projection pursuit' (Cui et al, 2005), GeSNP (Greenhall et al, 2007) and two other methods developed by Luo et al (2007). In a recent study, three methods were compared by testing their relative efficiencies to detect SFPs associated with known barley SNPs (already mapped using same population).…”

“…In a recent study, three methods were compared by testing their relative efficiencies to detect SFPs associated with known barley SNPs (already mapped using same population). It was concluded that different methods have their own advantages/disadvantages, and each is useful only under specific circumstances (Luo et al, 2007). For instance, the method developed by Winzeler et al (1998) is appropriate for SFP prediction from genomic DNA microarray data only, because it assumes that DNA molecules uniformly hybridize onto a microarray chip across all genes.…”

“…It may, therefore, be important to take into account the assumptions associated with each method, while selecting for an analytical method to predict SFPs. Two methods recently developed by Luo et al (2007) address most of these issues and can be used for SFP analysis in seed plants. A web-based program 'GeSNP', which was initially designed to detect SNPs from microarray data, was recently tested for its suitability to predict SFPs in mice, humans and chimpanzees (Greenhall et al, 2007), but its use for plants has yet to be tried.…”