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Development and preliminary evaluation of a 90 K Axiom® SNP array for the allo-octoploid cultivated strawberry Fragaria × ananassa.

Bassil NV, Davis TM, Zhang H, Ficklin S, Mittmann M, Webster T, Mahoney L, Wood D, Alperin ES, Rosyara UR, Koehorst-Vanc Putten H, Monfort A, Sargent DJ, Amaya I, Denoyes B, Bianco L, van Dijk T, Pirani A, Iezzoni A, Main D, Peace C, Yang Y, Whitaker V, Verma S, Bellon L, Brew F, Herrera R, van de Weg E - BMC Genomics (2015)

Bottom Line: Strategies and filtering pipelines were developed to identify and incorporate markers of several types: di-allelic SNPs (66.6%), multi-allelic SNPs (1.8%), indels (10.1%), and ploidy-reducing "haploSNPs" (11.7%).The array's high success rate is likely driven by the presence of naturally occurring variation in ploidy level within the nominally octoploid genome, and by effectiveness of the employed array design and ploidy-reducing strategies.This array enables genetic analyses including generation of high-density linkage maps, identification of quantitative trait loci for economically important traits, and genome-wide association studies, thus providing a basis for marker-assisted breeding in this high value crop.

View Article: PubMed Central - PubMed

Affiliation: USDA-ARS, NCGR, Corvallis, OR, USA. nahla.bassil@ars.usda.gov.

ABSTRACT

Background: A high-throughput genotyping platform is needed to enable marker-assisted breeding in the allo-octoploid cultivated strawberry Fragaria × ananassa. Short-read sequences from one diploid and 19 octoploid accessions were aligned to the diploid Fragaria vesca 'Hawaii 4' reference genome to identify single nucleotide polymorphisms (SNPs) and indels for incorporation into a 90 K Affymetrix® Axiom® array. We report the development and preliminary evaluation of this array.

Results: About 36 million sequence variants were identified in a 19 member, octoploid germplasm panel. Strategies and filtering pipelines were developed to identify and incorporate markers of several types: di-allelic SNPs (66.6%), multi-allelic SNPs (1.8%), indels (10.1%), and ploidy-reducing "haploSNPs" (11.7%). The remaining SNPs included those discovered in the diploid progenitor F. iinumae (3.9%), and speculative "codon-based" SNPs (5.9%). In genotyping 306 octoploid accessions, SNPs were assigned to six classes with Affymetrix's "SNPolisher" R package. The highest quality classes, PolyHigh Resolution (PHR), No Minor Homozygote (NMH), and Off-Target Variant (OTV) comprised 25%, 38%, and 1% of array markers, respectively. These markers were suitable for genetic studies as demonstrated in the full-sib family 'Holiday' × 'Korona' with the generation of a genetic linkage map consisting of 6,594 PHR SNPs evenly distributed across 28 chromosomes with an average density of approximately one marker per 0.5 cM, thus exceeding our goal of one marker per cM.

Conclusions: The Affymetrix IStraw90 Axiom array is the first high-throughput genotyping platform for cultivated strawberry and is commercially available to the worldwide scientific community. The array's high success rate is likely driven by the presence of naturally occurring variation in ploidy level within the nominally octoploid genome, and by effectiveness of the employed array design and ploidy-reducing strategies. This array enables genetic analyses including generation of high-density linkage maps, identification of quantitative trait loci for economically important traits, and genome-wide association studies, thus providing a basis for marker-assisted breeding in this high value crop.

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Related in: MedlinePlus

Relationship of diploid clustering to mean genic read depth inF.×ananassa‘Winter Dawn’, displayed forPHRdi-allelic SNPs (A) and forPHRSNP-SNPs (B). Black dot positions, which are identical in panels A and B, represent the mean read depth category (X axis), and the relative frequency of this read depth category (Y axis). The size of each dot is directly proportional to the fraction of di-allelic SNP (A) or SNP-SNP (B) markers that displayed diploid-like clustering. For example, the green arrow in (A) points to a large black dot with mean read depth category of approximately 28× (X axis) and a category frequency of approximately 0.01 (Y axis). The red arrow in (A) points to a small black dot corresponding to a mean read depth category of about 100× and a category frequency of about 0.015. The larger dot sizes (green arrows) occur in (A) in regions of comparatively low read depth, and in (B) in regions of comparatively high read depth. Conversely, smaller dot sizes occur in (A) in regions of comparatively high read depth, and in (B) in regions of comparatively low read depth (red arrows).
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Fig9: Relationship of diploid clustering to mean genic read depth inF.×ananassa‘Winter Dawn’, displayed forPHRdi-allelic SNPs (A) and forPHRSNP-SNPs (B). Black dot positions, which are identical in panels A and B, represent the mean read depth category (X axis), and the relative frequency of this read depth category (Y axis). The size of each dot is directly proportional to the fraction of di-allelic SNP (A) or SNP-SNP (B) markers that displayed diploid-like clustering. For example, the green arrow in (A) points to a large black dot with mean read depth category of approximately 28× (X axis) and a category frequency of approximately 0.01 (Y axis). The red arrow in (A) points to a small black dot corresponding to a mean read depth category of about 100× and a category frequency of about 0.015. The larger dot sizes (green arrows) occur in (A) in regions of comparatively low read depth, and in (B) in regions of comparatively high read depth. Conversely, smaller dot sizes occur in (A) in regions of comparatively high read depth, and in (B) in regions of comparatively low read depth (red arrows).

Mentions: As exemplified by the plot for F. ×ananassa ‘Winter Dawn’ (Figure 9), the read depth distributions of SNPs were distinctly bi-modal and sometimes tri-modal, indicating an underlying discontinuous distribution as would be expected if the genome was partitioned into regions that were effectively diploid, tetraploid, hexaploid, or octoploid, the latter being the predominant component (Figure 9: highest peak in distribution). When frequencies of PHR markers that exhibited diploid clustering in each read depth category were computed and depicted graphically for di-allelic SNPs (Figure 9A) and for SNP-SNPs (Figure 9B), diploid clustering for the di-allelic SNPs was most prevalent at marker sites that displayed reduced read depth, as would be expected if elevated diploid clustering was related to localized biological (i.e., actual) ploidy reduction. In contrast, diploid clustering for the SNP-SNPs was more prevalent in the zones of higher read depth, and particularly at the presumed octoploid level, indicating that the diploid clustering resulted from the effectiveness of the technical ploidy reduction strategy and not from underlying biological ploidy reduction.Figure 9


Development and preliminary evaluation of a 90 K Axiom® SNP array for the allo-octoploid cultivated strawberry Fragaria × ananassa.

Bassil NV, Davis TM, Zhang H, Ficklin S, Mittmann M, Webster T, Mahoney L, Wood D, Alperin ES, Rosyara UR, Koehorst-Vanc Putten H, Monfort A, Sargent DJ, Amaya I, Denoyes B, Bianco L, van Dijk T, Pirani A, Iezzoni A, Main D, Peace C, Yang Y, Whitaker V, Verma S, Bellon L, Brew F, Herrera R, van de Weg E - BMC Genomics (2015)

Relationship of diploid clustering to mean genic read depth inF.×ananassa‘Winter Dawn’, displayed forPHRdi-allelic SNPs (A) and forPHRSNP-SNPs (B). Black dot positions, which are identical in panels A and B, represent the mean read depth category (X axis), and the relative frequency of this read depth category (Y axis). The size of each dot is directly proportional to the fraction of di-allelic SNP (A) or SNP-SNP (B) markers that displayed diploid-like clustering. For example, the green arrow in (A) points to a large black dot with mean read depth category of approximately 28× (X axis) and a category frequency of approximately 0.01 (Y axis). The red arrow in (A) points to a small black dot corresponding to a mean read depth category of about 100× and a category frequency of about 0.015. The larger dot sizes (green arrows) occur in (A) in regions of comparatively low read depth, and in (B) in regions of comparatively high read depth. Conversely, smaller dot sizes occur in (A) in regions of comparatively high read depth, and in (B) in regions of comparatively low read depth (red arrows).
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4374422&req=5

Fig9: Relationship of diploid clustering to mean genic read depth inF.×ananassa‘Winter Dawn’, displayed forPHRdi-allelic SNPs (A) and forPHRSNP-SNPs (B). Black dot positions, which are identical in panels A and B, represent the mean read depth category (X axis), and the relative frequency of this read depth category (Y axis). The size of each dot is directly proportional to the fraction of di-allelic SNP (A) or SNP-SNP (B) markers that displayed diploid-like clustering. For example, the green arrow in (A) points to a large black dot with mean read depth category of approximately 28× (X axis) and a category frequency of approximately 0.01 (Y axis). The red arrow in (A) points to a small black dot corresponding to a mean read depth category of about 100× and a category frequency of about 0.015. The larger dot sizes (green arrows) occur in (A) in regions of comparatively low read depth, and in (B) in regions of comparatively high read depth. Conversely, smaller dot sizes occur in (A) in regions of comparatively high read depth, and in (B) in regions of comparatively low read depth (red arrows).
Mentions: As exemplified by the plot for F. ×ananassa ‘Winter Dawn’ (Figure 9), the read depth distributions of SNPs were distinctly bi-modal and sometimes tri-modal, indicating an underlying discontinuous distribution as would be expected if the genome was partitioned into regions that were effectively diploid, tetraploid, hexaploid, or octoploid, the latter being the predominant component (Figure 9: highest peak in distribution). When frequencies of PHR markers that exhibited diploid clustering in each read depth category were computed and depicted graphically for di-allelic SNPs (Figure 9A) and for SNP-SNPs (Figure 9B), diploid clustering for the di-allelic SNPs was most prevalent at marker sites that displayed reduced read depth, as would be expected if elevated diploid clustering was related to localized biological (i.e., actual) ploidy reduction. In contrast, diploid clustering for the SNP-SNPs was more prevalent in the zones of higher read depth, and particularly at the presumed octoploid level, indicating that the diploid clustering resulted from the effectiveness of the technical ploidy reduction strategy and not from underlying biological ploidy reduction.Figure 9

Bottom Line: Strategies and filtering pipelines were developed to identify and incorporate markers of several types: di-allelic SNPs (66.6%), multi-allelic SNPs (1.8%), indels (10.1%), and ploidy-reducing "haploSNPs" (11.7%).The array's high success rate is likely driven by the presence of naturally occurring variation in ploidy level within the nominally octoploid genome, and by effectiveness of the employed array design and ploidy-reducing strategies.This array enables genetic analyses including generation of high-density linkage maps, identification of quantitative trait loci for economically important traits, and genome-wide association studies, thus providing a basis for marker-assisted breeding in this high value crop.

View Article: PubMed Central - PubMed

Affiliation: USDA-ARS, NCGR, Corvallis, OR, USA. nahla.bassil@ars.usda.gov.

ABSTRACT

Background: A high-throughput genotyping platform is needed to enable marker-assisted breeding in the allo-octoploid cultivated strawberry Fragaria × ananassa. Short-read sequences from one diploid and 19 octoploid accessions were aligned to the diploid Fragaria vesca 'Hawaii 4' reference genome to identify single nucleotide polymorphisms (SNPs) and indels for incorporation into a 90 K Affymetrix® Axiom® array. We report the development and preliminary evaluation of this array.

Results: About 36 million sequence variants were identified in a 19 member, octoploid germplasm panel. Strategies and filtering pipelines were developed to identify and incorporate markers of several types: di-allelic SNPs (66.6%), multi-allelic SNPs (1.8%), indels (10.1%), and ploidy-reducing "haploSNPs" (11.7%). The remaining SNPs included those discovered in the diploid progenitor F. iinumae (3.9%), and speculative "codon-based" SNPs (5.9%). In genotyping 306 octoploid accessions, SNPs were assigned to six classes with Affymetrix's "SNPolisher" R package. The highest quality classes, PolyHigh Resolution (PHR), No Minor Homozygote (NMH), and Off-Target Variant (OTV) comprised 25%, 38%, and 1% of array markers, respectively. These markers were suitable for genetic studies as demonstrated in the full-sib family 'Holiday' × 'Korona' with the generation of a genetic linkage map consisting of 6,594 PHR SNPs evenly distributed across 28 chromosomes with an average density of approximately one marker per 0.5 cM, thus exceeding our goal of one marker per cM.

Conclusions: The Affymetrix IStraw90 Axiom array is the first high-throughput genotyping platform for cultivated strawberry and is commercially available to the worldwide scientific community. The array's high success rate is likely driven by the presence of naturally occurring variation in ploidy level within the nominally octoploid genome, and by effectiveness of the employed array design and ploidy-reducing strategies. This array enables genetic analyses including generation of high-density linkage maps, identification of quantitative trait loci for economically important traits, and genome-wide association studies, thus providing a basis for marker-assisted breeding in this high value crop.

Show MeSH
Related in: MedlinePlus