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Array-based genotyping in S.cerevisiae using semi-supervised clustering.

Bourgon R, Mancera E, Brozzi A, Steinmetz LM, Huber W - Bioinformatics (2009)

Bottom Line: The resulting data permit the identification of loci at which genetic variation is associated with quantitative traits, or fine mapping of meiotic recombination, which is a key determinant of genetic diversity among individuals.We also demonstrate that oligonucleotide probe response depends significantly on genomic background, even when the probe's specific target sequence is unchanged.As a result, supervised classifiers trained on reference strains may not generalize well to diverged strains; ssG's semi-supervised approach, on the other hand, adapts automatically.

View Article: PubMed Central - PubMed

Affiliation: EMBL, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK. bourgon@ebi.ac.uk

ABSTRACT

Motivation: Microarrays provide an accurate and cost-effective method for genotyping large numbers of individuals at high resolution. The resulting data permit the identification of loci at which genetic variation is associated with quantitative traits, or fine mapping of meiotic recombination, which is a key determinant of genetic diversity among individuals. Several issues inherent to short oligonucleotide arrays -- cross-hybridization, or variability in probe response to target -- have the potential to produce genotyping errors. There is a need for improved statistical methods for array-based genotyping.

Results: We developed ssGenotyping (ssG), a multivariate, semi-supervised approach for using microarrays to genotype haploid individuals at thousands of polymorphic sites. Using a meiotic recombination dataset, we show that ssG is more accurate than existing supervised classification methods, and that it produces denser marker coverage. The ssG algorithm is able to fit probe-specific affinity differences and to detect and filter spurious signal, permitting high-confidence genotyping at nucleotide resolution. We also demonstrate that oligonucleotide probe response depends significantly on genomic background, even when the probe's specific target sequence is unchanged. As a result, supervised classifiers trained on reference strains may not generalize well to diverged strains; ssG's semi-supervised approach, on the other hand, adapts automatically.

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Typical genotype call behavior (30 segregants and 600 markers on chromosome IV). (A) Unfiltered ssG genotype calls include numerous putative single-marker genotype switches, as well as multi-marker regions with a large excess of one genotype. (B) Array-, polymorphism- and call-level filtering reduced marker density (by 30.5%), but also substantially reduced the error rate, as shown in Figure 5. Most short events vanish, even though none of ssG's filters explicitly removes short events. (C) SNPscanner's heuristic filters discard substantially more calls (52.5%). Results are largely in agreement with those of ssG, but more putative short events remain. For two, identified in red, we examine probe behavior more closely in Figure 6.
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Figure 4: Typical genotype call behavior (30 segregants and 600 markers on chromosome IV). (A) Unfiltered ssG genotype calls include numerous putative single-marker genotype switches, as well as multi-marker regions with a large excess of one genotype. (B) Array-, polymorphism- and call-level filtering reduced marker density (by 30.5%), but also substantially reduced the error rate, as shown in Figure 5. Most short events vanish, even though none of ssG's filters explicitly removes short events. (C) SNPscanner's heuristic filters discard substantially more calls (52.5%). Results are largely in agreement with those of ssG, but more putative short events remain. For two, identified in red, we examine probe behavior more closely in Figure 6.

Mentions: One objective of Mancera et al. (2008) was the characterization of short non-crossover gene conversion events. The number of putative small events seen in unfiltered ssG (Fig. 4A) or SNPscanner (data not shown) calls, however is far too large given our understanding of the biology. The ssG filters discussed in Section 2.3 removed most small events (Fig. 4B). Importantly, these filters are based only on properties of the inferred distributions and , not on event size; therefore, they are not biased against small events.Fig. 4.


Array-based genotyping in S.cerevisiae using semi-supervised clustering.

Bourgon R, Mancera E, Brozzi A, Steinmetz LM, Huber W - Bioinformatics (2009)

Typical genotype call behavior (30 segregants and 600 markers on chromosome IV). (A) Unfiltered ssG genotype calls include numerous putative single-marker genotype switches, as well as multi-marker regions with a large excess of one genotype. (B) Array-, polymorphism- and call-level filtering reduced marker density (by 30.5%), but also substantially reduced the error rate, as shown in Figure 5. Most short events vanish, even though none of ssG's filters explicitly removes short events. (C) SNPscanner's heuristic filters discard substantially more calls (52.5%). Results are largely in agreement with those of ssG, but more putative short events remain. For two, identified in red, we examine probe behavior more closely in Figure 6.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2666814&req=5

Figure 4: Typical genotype call behavior (30 segregants and 600 markers on chromosome IV). (A) Unfiltered ssG genotype calls include numerous putative single-marker genotype switches, as well as multi-marker regions with a large excess of one genotype. (B) Array-, polymorphism- and call-level filtering reduced marker density (by 30.5%), but also substantially reduced the error rate, as shown in Figure 5. Most short events vanish, even though none of ssG's filters explicitly removes short events. (C) SNPscanner's heuristic filters discard substantially more calls (52.5%). Results are largely in agreement with those of ssG, but more putative short events remain. For two, identified in red, we examine probe behavior more closely in Figure 6.
Mentions: One objective of Mancera et al. (2008) was the characterization of short non-crossover gene conversion events. The number of putative small events seen in unfiltered ssG (Fig. 4A) or SNPscanner (data not shown) calls, however is far too large given our understanding of the biology. The ssG filters discussed in Section 2.3 removed most small events (Fig. 4B). Importantly, these filters are based only on properties of the inferred distributions and , not on event size; therefore, they are not biased against small events.Fig. 4.

Bottom Line: The resulting data permit the identification of loci at which genetic variation is associated with quantitative traits, or fine mapping of meiotic recombination, which is a key determinant of genetic diversity among individuals.We also demonstrate that oligonucleotide probe response depends significantly on genomic background, even when the probe's specific target sequence is unchanged.As a result, supervised classifiers trained on reference strains may not generalize well to diverged strains; ssG's semi-supervised approach, on the other hand, adapts automatically.

View Article: PubMed Central - PubMed

Affiliation: EMBL, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK. bourgon@ebi.ac.uk

ABSTRACT

Motivation: Microarrays provide an accurate and cost-effective method for genotyping large numbers of individuals at high resolution. The resulting data permit the identification of loci at which genetic variation is associated with quantitative traits, or fine mapping of meiotic recombination, which is a key determinant of genetic diversity among individuals. Several issues inherent to short oligonucleotide arrays -- cross-hybridization, or variability in probe response to target -- have the potential to produce genotyping errors. There is a need for improved statistical methods for array-based genotyping.

Results: We developed ssGenotyping (ssG), a multivariate, semi-supervised approach for using microarrays to genotype haploid individuals at thousands of polymorphic sites. Using a meiotic recombination dataset, we show that ssG is more accurate than existing supervised classification methods, and that it produces denser marker coverage. The ssG algorithm is able to fit probe-specific affinity differences and to detect and filter spurious signal, permitting high-confidence genotyping at nucleotide resolution. We also demonstrate that oligonucleotide probe response depends significantly on genomic background, even when the probe's specific target sequence is unchanged. As a result, supervised classifiers trained on reference strains may not generalize well to diverged strains; ssG's semi-supervised approach, on the other hand, adapts automatically.

Show MeSH