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High throughput sequencing in mice: a platform comparison identifies a preponderance of cryptic SNPs.

Walter NA, Bottomly D, Laderas T, Mooney MA, Darakjian P, Searles RP, Harrington CA, McWeeney SK, Hitzemann R, Buck KJ - BMC Genomics (2009)

Bottom Line: Polymorphisms result in a high incidence of false positive and false negative results in hybridization based analyses and hinder the identification of the true variation underlying genetically determined differences in physiology and behavior.Using the same templates on both platforms, we compared realignments and single nucleotide polymorphism (SNP) detection with an 80 fold average read depth across platforms and samples.Furthermore, we confirmed 40 missense SNPs and discovered 36 new missense SNPs.

View Article: PubMed Central - HTML - PubMed

Affiliation: Research and Development Service, Portland VA Medical Center, Portland, OR, USA. waltern@ohsu.edu

ABSTRACT

Background: Allelic variation is the cornerstone of genetically determined differences in gene expression, gene product structure, physiology, and behavior. However, allelic variation, particularly cryptic (unknown or not annotated) variation, is problematic for follow up analyses. Polymorphisms result in a high incidence of false positive and false negative results in hybridization based analyses and hinder the identification of the true variation underlying genetically determined differences in physiology and behavior. Given the proliferation of mouse genetic models (e.g., knockout models, selectively bred lines, heterogeneous stocks derived from standard inbred strains and wild mice) and the wealth of gene expression microarray and phenotypic studies using genetic models, the impact of naturally-occurring polymorphisms on these data is critical. With the advent of next-generation, high-throughput sequencing, we are now in a position to determine to what extent polymorphisms are currently cryptic in such models and their impact on downstream analyses.

Results: We sequenced the two most commonly used inbred mouse strains, DBA/2J and C57BL/6J, across a region of chromosome 1 (171.6 - 174.6 megabases) using two next generation high-throughput sequencing platforms: Applied Biosystems (SOLiD) and Illumina (Genome Analyzer). Using the same templates on both platforms, we compared realignments and single nucleotide polymorphism (SNP) detection with an 80 fold average read depth across platforms and samples. While public datasets currently annotate 4,527 SNPs between the two strains in this interval, thorough high-throughput sequencing identified a total of 11,824 SNPs in the interval, including 7,663 new SNPs. Furthermore, we confirmed 40 missense SNPs and discovered 36 new missense SNPs.

Conclusion: Comparisons utilizing even two of the best characterized mouse genetic models, DBA/2J and C57BL/6J, indicate that more than half of naturally-occurring SNPs remain cryptic. The magnitude of this problem is compounded when using more divergent or poorly annotated genetic models. This warrants full genomic sequencing of the mouse strains used as genetic models.

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Density of PARC SNPs and protein coding genes. B6 vs. D2 PARC SNPs are binned in 5000 bp intervals. The blue lines indicate how many SNPs are currently annotated in the public MPD database, and the red lines show how many PARC SNPs were discovered by custom HTS with realignment of this 3 Mb interval of chromosome 1. SNP dense and SNP sparse regions are apparent. A total of only 16 SNPs were detected between 171.6 – 172.9 Mb, whereas 11,808 SNPs were detected between 172.9 – 174.6 Mb. Below, the black blocks identify the locations of the 79 protein coding genes annotated by Ensembl in this interval.
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Figure 4: Density of PARC SNPs and protein coding genes. B6 vs. D2 PARC SNPs are binned in 5000 bp intervals. The blue lines indicate how many SNPs are currently annotated in the public MPD database, and the red lines show how many PARC SNPs were discovered by custom HTS with realignment of this 3 Mb interval of chromosome 1. SNP dense and SNP sparse regions are apparent. A total of only 16 SNPs were detected between 171.6 – 172.9 Mb, whereas 11,808 SNPs were detected between 172.9 – 174.6 Mb. Below, the black blocks identify the locations of the 79 protein coding genes annotated by Ensembl in this interval.

Mentions: This chromosome 1 interval spans a clear haplotype break, resulting in a SNP sparse region (171.6–172.9 Mb) and a more distal SNP dense region (172.9 – 174.6 Mb) (Figure 4). The SNP sparse region contains only 16 PARC SNPs based on our custom sequencing, and the SNP dense region harbors 11,808 D2 vs. B6 PARC SNPs. This data further defines the haplotype break between D2 and B6 and, importantly, provides additional genetic markers for fine mapping within the SNP-sparse region which previously was not possible [6]. Additionally, full SNP annotation will inform future SNP array chips allowing for more precise genotyping.


High throughput sequencing in mice: a platform comparison identifies a preponderance of cryptic SNPs.

Walter NA, Bottomly D, Laderas T, Mooney MA, Darakjian P, Searles RP, Harrington CA, McWeeney SK, Hitzemann R, Buck KJ - BMC Genomics (2009)

Density of PARC SNPs and protein coding genes. B6 vs. D2 PARC SNPs are binned in 5000 bp intervals. The blue lines indicate how many SNPs are currently annotated in the public MPD database, and the red lines show how many PARC SNPs were discovered by custom HTS with realignment of this 3 Mb interval of chromosome 1. SNP dense and SNP sparse regions are apparent. A total of only 16 SNPs were detected between 171.6 – 172.9 Mb, whereas 11,808 SNPs were detected between 172.9 – 174.6 Mb. Below, the black blocks identify the locations of the 79 protein coding genes annotated by Ensembl in this interval.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2743714&req=5

Figure 4: Density of PARC SNPs and protein coding genes. B6 vs. D2 PARC SNPs are binned in 5000 bp intervals. The blue lines indicate how many SNPs are currently annotated in the public MPD database, and the red lines show how many PARC SNPs were discovered by custom HTS with realignment of this 3 Mb interval of chromosome 1. SNP dense and SNP sparse regions are apparent. A total of only 16 SNPs were detected between 171.6 – 172.9 Mb, whereas 11,808 SNPs were detected between 172.9 – 174.6 Mb. Below, the black blocks identify the locations of the 79 protein coding genes annotated by Ensembl in this interval.
Mentions: This chromosome 1 interval spans a clear haplotype break, resulting in a SNP sparse region (171.6–172.9 Mb) and a more distal SNP dense region (172.9 – 174.6 Mb) (Figure 4). The SNP sparse region contains only 16 PARC SNPs based on our custom sequencing, and the SNP dense region harbors 11,808 D2 vs. B6 PARC SNPs. This data further defines the haplotype break between D2 and B6 and, importantly, provides additional genetic markers for fine mapping within the SNP-sparse region which previously was not possible [6]. Additionally, full SNP annotation will inform future SNP array chips allowing for more precise genotyping.

Bottom Line: Polymorphisms result in a high incidence of false positive and false negative results in hybridization based analyses and hinder the identification of the true variation underlying genetically determined differences in physiology and behavior.Using the same templates on both platforms, we compared realignments and single nucleotide polymorphism (SNP) detection with an 80 fold average read depth across platforms and samples.Furthermore, we confirmed 40 missense SNPs and discovered 36 new missense SNPs.

View Article: PubMed Central - HTML - PubMed

Affiliation: Research and Development Service, Portland VA Medical Center, Portland, OR, USA. waltern@ohsu.edu

ABSTRACT

Background: Allelic variation is the cornerstone of genetically determined differences in gene expression, gene product structure, physiology, and behavior. However, allelic variation, particularly cryptic (unknown or not annotated) variation, is problematic for follow up analyses. Polymorphisms result in a high incidence of false positive and false negative results in hybridization based analyses and hinder the identification of the true variation underlying genetically determined differences in physiology and behavior. Given the proliferation of mouse genetic models (e.g., knockout models, selectively bred lines, heterogeneous stocks derived from standard inbred strains and wild mice) and the wealth of gene expression microarray and phenotypic studies using genetic models, the impact of naturally-occurring polymorphisms on these data is critical. With the advent of next-generation, high-throughput sequencing, we are now in a position to determine to what extent polymorphisms are currently cryptic in such models and their impact on downstream analyses.

Results: We sequenced the two most commonly used inbred mouse strains, DBA/2J and C57BL/6J, across a region of chromosome 1 (171.6 - 174.6 megabases) using two next generation high-throughput sequencing platforms: Applied Biosystems (SOLiD) and Illumina (Genome Analyzer). Using the same templates on both platforms, we compared realignments and single nucleotide polymorphism (SNP) detection with an 80 fold average read depth across platforms and samples. While public datasets currently annotate 4,527 SNPs between the two strains in this interval, thorough high-throughput sequencing identified a total of 11,824 SNPs in the interval, including 7,663 new SNPs. Furthermore, we confirmed 40 missense SNPs and discovered 36 new missense SNPs.

Conclusion: Comparisons utilizing even two of the best characterized mouse genetic models, DBA/2J and C57BL/6J, indicate that more than half of naturally-occurring SNPs remain cryptic. The magnitude of this problem is compounded when using more divergent or poorly annotated genetic models. This warrants full genomic sequencing of the mouse strains used as genetic models.

Show MeSH