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A haplotype map of allohexaploid wheat reveals distinct patterns of selection on homoeologous genomes.

Jordan KW, Wang S, Lun Y, Gardiner LJ, MacLachlan R, Hucl P, Wiebe K, Wong D, Forrest KL, IWGS ConsortiumSharpe AG, Sidebottom CH, Hall N, Toomajian C, Close T, Dubcovsky J, Akhunova A, Talbert L, Bansal UK, Bariana HS, Hayden MJ, Pozniak C, Jeddeloh JA, Hall A, Akhunov E - Genome Biol. (2015)

Bottom Line: These selected regions are enriched for loci associated with agronomic traits detected in genome-wide association studies.Evidence suggests that directional selection in allopolyploids rarely acted on multiple parallel advantageous mutations across homoeologous regions, likely indicating that a fitness benefit could be obtained by a mutation at any one of the homoeologs.Additional advantageous variants in other homoelogs probably either contributed little benefit, or were unavailable in populations subjected to directional selection.

View Article: PubMed Central - PubMed

Affiliation: Department Plant Pathology, Kansas State University, Manhattan, KS, 66506, USA. kwjordan@k-state.edu.

ABSTRACT

Background: Bread wheat is an allopolyploid species with a large, highly repetitive genome. To investigate the impact of selection on variants distributed among homoeologous wheat genomes and to build a foundation for understanding genotype-phenotype relationships, we performed population-scale re-sequencing of a diverse panel of wheat lines.

Results: A sample of 62 diverse lines was re-sequenced using the whole exome capture and genotyping-by-sequencing approaches. We describe the allele frequency, functional significance, and chromosomal distribution of 1.57 million single nucleotide polymorphisms and 161,719 small indels. Our results suggest that duplicated homoeologous genes are under purifying selection. We find contrasting patterns of variation and inter-variant associations among wheat genomes; this, in addition to demographic factors, could be explained by differences in the effect of directional selection on duplicated homoeologs. Only a small fraction of the homoeologous regions harboring selected variants overlapped among the wheat genomes in any given wheat line. These selected regions are enriched for loci associated with agronomic traits detected in genome-wide association studies.

Conclusions: Evidence suggests that directional selection in allopolyploids rarely acted on multiple parallel advantageous mutations across homoeologous regions, likely indicating that a fitness benefit could be obtained by a mutation at any one of the homoeologs. Additional advantageous variants in other homoelogs probably either contributed little benefit, or were unavailable in populations subjected to directional selection. We hypothesize that allopolyploidy may have increased the likelihood of beneficial allele recovery by broadening the set of possible selection targets.

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Genotype imputation. (a) Relationship between the accuracy of genotype imputation and the percentage of missing data, which is estimated after removing genotypes over a range of genotype calling probability thresholds. Imputation in Opata (solid lines) and Rialto (dashed lines) cultivars was performed using the reference panel of 60 lines (Opata and Rialto cultivars were excluded) genotyped using the 90 K iSelect assay. (b) Genotype imputation at disease resistance loci. Two GWAS regions overlapped with the positions of the previously mapped Lr37/Yr17/Sr38 (middle panel) and Lr68 (right panel) disease resistance loci; the markers associated with these loci showed highest similarity to CSS contigs 2AS-5264433 and 7BL-6748067, respectively. SNP sites directly genotyped using the 90 K SNP array are shown as red dots; imputed SNPs are shown as black dots.
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Fig3: Genotype imputation. (a) Relationship between the accuracy of genotype imputation and the percentage of missing data, which is estimated after removing genotypes over a range of genotype calling probability thresholds. Imputation in Opata (solid lines) and Rialto (dashed lines) cultivars was performed using the reference panel of 60 lines (Opata and Rialto cultivars were excluded) genotyped using the 90 K iSelect assay. (b) Genotype imputation at disease resistance loci. Two GWAS regions overlapped with the positions of the previously mapped Lr37/Yr17/Sr38 (middle panel) and Lr68 (right panel) disease resistance loci; the markers associated with these loci showed highest similarity to CSS contigs 2AS-5264433 and 7BL-6748067, respectively. SNP sites directly genotyped using the 90 K SNP array are shown as red dots; imputed SNPs are shown as black dots.

Mentions: First, we sequentially selected each cultivar from our panel of 62 lines and, after ‘hiding’ all SNP sites besides those overlapping with the public 90 K SNP array [14], we used the WEC SNP data in the remaining 61 lines to predict the ‘hidden’ variants. Depending on the selected wheat line, using a genotype calling probability cutoff of 0.6, the accuracy of genotype predictions assessed by comparing with the observed data was in the range of 93% to 97% (Figure 3a; Table S12 in Additional file 4). This genotype probability cutoff value resulted in the removal of 5% to 15% of the data (Figure 3a), and allowed imputation of up to 549,918 SNPs. The accuracy of SNP imputation varied among the wheat genomes reflecting the inter-genomic differences in the extent of LD (Figure 2a). For example, the highest imputation accuracy was achieved for the D genome, which also showed the highest levels of inter-variant LD.Figure 3


A haplotype map of allohexaploid wheat reveals distinct patterns of selection on homoeologous genomes.

Jordan KW, Wang S, Lun Y, Gardiner LJ, MacLachlan R, Hucl P, Wiebe K, Wong D, Forrest KL, IWGS ConsortiumSharpe AG, Sidebottom CH, Hall N, Toomajian C, Close T, Dubcovsky J, Akhunova A, Talbert L, Bansal UK, Bariana HS, Hayden MJ, Pozniak C, Jeddeloh JA, Hall A, Akhunov E - Genome Biol. (2015)

Genotype imputation. (a) Relationship between the accuracy of genotype imputation and the percentage of missing data, which is estimated after removing genotypes over a range of genotype calling probability thresholds. Imputation in Opata (solid lines) and Rialto (dashed lines) cultivars was performed using the reference panel of 60 lines (Opata and Rialto cultivars were excluded) genotyped using the 90 K iSelect assay. (b) Genotype imputation at disease resistance loci. Two GWAS regions overlapped with the positions of the previously mapped Lr37/Yr17/Sr38 (middle panel) and Lr68 (right panel) disease resistance loci; the markers associated with these loci showed highest similarity to CSS contigs 2AS-5264433 and 7BL-6748067, respectively. SNP sites directly genotyped using the 90 K SNP array are shown as red dots; imputed SNPs are shown as black dots.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4389885&req=5

Fig3: Genotype imputation. (a) Relationship between the accuracy of genotype imputation and the percentage of missing data, which is estimated after removing genotypes over a range of genotype calling probability thresholds. Imputation in Opata (solid lines) and Rialto (dashed lines) cultivars was performed using the reference panel of 60 lines (Opata and Rialto cultivars were excluded) genotyped using the 90 K iSelect assay. (b) Genotype imputation at disease resistance loci. Two GWAS regions overlapped with the positions of the previously mapped Lr37/Yr17/Sr38 (middle panel) and Lr68 (right panel) disease resistance loci; the markers associated with these loci showed highest similarity to CSS contigs 2AS-5264433 and 7BL-6748067, respectively. SNP sites directly genotyped using the 90 K SNP array are shown as red dots; imputed SNPs are shown as black dots.
Mentions: First, we sequentially selected each cultivar from our panel of 62 lines and, after ‘hiding’ all SNP sites besides those overlapping with the public 90 K SNP array [14], we used the WEC SNP data in the remaining 61 lines to predict the ‘hidden’ variants. Depending on the selected wheat line, using a genotype calling probability cutoff of 0.6, the accuracy of genotype predictions assessed by comparing with the observed data was in the range of 93% to 97% (Figure 3a; Table S12 in Additional file 4). This genotype probability cutoff value resulted in the removal of 5% to 15% of the data (Figure 3a), and allowed imputation of up to 549,918 SNPs. The accuracy of SNP imputation varied among the wheat genomes reflecting the inter-genomic differences in the extent of LD (Figure 2a). For example, the highest imputation accuracy was achieved for the D genome, which also showed the highest levels of inter-variant LD.Figure 3

Bottom Line: These selected regions are enriched for loci associated with agronomic traits detected in genome-wide association studies.Evidence suggests that directional selection in allopolyploids rarely acted on multiple parallel advantageous mutations across homoeologous regions, likely indicating that a fitness benefit could be obtained by a mutation at any one of the homoeologs.Additional advantageous variants in other homoelogs probably either contributed little benefit, or were unavailable in populations subjected to directional selection.

View Article: PubMed Central - PubMed

Affiliation: Department Plant Pathology, Kansas State University, Manhattan, KS, 66506, USA. kwjordan@k-state.edu.

ABSTRACT

Background: Bread wheat is an allopolyploid species with a large, highly repetitive genome. To investigate the impact of selection on variants distributed among homoeologous wheat genomes and to build a foundation for understanding genotype-phenotype relationships, we performed population-scale re-sequencing of a diverse panel of wheat lines.

Results: A sample of 62 diverse lines was re-sequenced using the whole exome capture and genotyping-by-sequencing approaches. We describe the allele frequency, functional significance, and chromosomal distribution of 1.57 million single nucleotide polymorphisms and 161,719 small indels. Our results suggest that duplicated homoeologous genes are under purifying selection. We find contrasting patterns of variation and inter-variant associations among wheat genomes; this, in addition to demographic factors, could be explained by differences in the effect of directional selection on duplicated homoeologs. Only a small fraction of the homoeologous regions harboring selected variants overlapped among the wheat genomes in any given wheat line. These selected regions are enriched for loci associated with agronomic traits detected in genome-wide association studies.

Conclusions: Evidence suggests that directional selection in allopolyploids rarely acted on multiple parallel advantageous mutations across homoeologous regions, likely indicating that a fitness benefit could be obtained by a mutation at any one of the homoeologs. Additional advantageous variants in other homoelogs probably either contributed little benefit, or were unavailable in populations subjected to directional selection. We hypothesize that allopolyploidy may have increased the likelihood of beneficial allele recovery by broadening the set of possible selection targets.

Show MeSH