Journal: G3: Genes|Genomes|Genetics

Article Title: polyRAD: Genotype Calling with Uncertainty from Sequencing Data in Polyploids and Diploids

doi: 10.1534/g3.118.200913

Figure Lengend Snippet: Genotyping error of EBG, fitPoly, updog, polyRAD, LinkImpute, and rrBLUP in a diversity panel of 565 diploid Miscanthus sinensis . The benefits of incorporating population structure into the genotyping model and using continuous rather than discrete genotypes are illustrated. Genotypes were coded on a scale of 0 to 2. Root mean squared error (RMSE) was calculated between actual genotypes and genotypes ascertained from simulated RAD-seq reads at 395 SNP markers (lower RMSE = higher accuracy). Each point represents one SNP. Median read depth is indicated by color, including genotypes with zero reads. The RMSE for continuous genotypes output by the polyRAD PopStruct LD method is shown on the x-axis, and the RMSE of other methods and types of genotypes (continuous or discrete) is shown on the y-axis. The dashed line indicates the ordinary least-squares regression with slope and intercept estimates, with standard errors. The “norm” model was used with updog. (A) RMSE calculated using only genotypes with more than zero reads. (B) RMSE calculated using only genotypes with zero reads, by genotyping or imputation method and genotype type.

Article Snippet: To test the accuracy of polyRAD, we used datasets from three previously studied populations: 1) RAD-seq data and GoldenGate SNP genotypes from a diversity panel (n = 565) of the outcrossing, diploidized allotetraploid grass Miscanthus sinensis ( Clark et al. 2014 ), 2) RAD-seq data and GoldenGate SNP genotypes from a bi-parental F 1 mapping population (n = 275) of M. sinensis ( Liu et al. 2016a ), and 3) SNP array genotypes from a biparental F 1 mapping population of autotetraploid potato (n = 238) ( da Silva et al. 2017 ).

Techniques:

Journal: G3: Genes|Genomes|Genetics

Article Title: polyRAD: Genotype Calling with Uncertainty from Sequencing Data in Polyploids and Diploids

doi: 10.1534/g3.118.200913

Figure Lengend Snippet: Genotyping error of EBG, fitPoly, updog, polyRAD, and rrBLUP in a simulated tetraploid diversity panel derived from genotypes of 565 diploid Miscanthus sinensis . The benefits of incorporating population structure into the genotyping model and using continuous rather than discrete genotypes are illustrated. Genotypes were coded on a scale of 0 to 4. Root mean squared error (RMSE) was calculated between actual genotypes and genotypes ascertained from simulated RAD-seq reads at 395 SNP markers (lower RMSE = higher accuracy). Each point represents one SNP. Median read depth is indicated by color, including genotypes with zero reads. The RMSE for continuous genotypes output by the polyRAD PopStruct LD method is shown on the x-axis, and the RMSE of other methods and types of genotypes (continuous or discrete) is shown on the y-axis. The dashed line indicates the ordinary least-squares regression with slope and intercept estimates, with standard errors. The “norm” model was used with updog. (A) RMSE calculated using only genotypes with more than zero reads. (B) RMSE calculated using only genotypes with zero reads, by genotyping or imputation method and genotype type. LinkImpute was not included given that it works for diploids only.

Article Snippet: To test the accuracy of polyRAD, we used datasets from three previously studied populations: 1) RAD-seq data and GoldenGate SNP genotypes from a diversity panel (n = 565) of the outcrossing, diploidized allotetraploid grass Miscanthus sinensis ( Clark et al. 2014 ), 2) RAD-seq data and GoldenGate SNP genotypes from a bi-parental F 1 mapping population (n = 275) of M. sinensis ( Liu et al. 2016a ), and 3) SNP array genotypes from a biparental F 1 mapping population of autotetraploid potato (n = 238) ( da Silva et al. 2017 ).

Techniques: Derivative Assay

Journal: G3: Genes|Genomes|Genetics

Article Title: polyRAD: Genotype Calling with Uncertainty from Sequencing Data in Polyploids and Diploids

doi: 10.1534/g3.118.200913

Figure Lengend Snippet: Genotyping error of EBG, fitPoly, updog, polyRAD, LinkImpute, and rrBLUP in an F1 mapping population of 83 diploid Miscanthus sinensis . The benefits of incorporating linkage into the genotyping model and using continuous rather than discrete genotypes are illustrated. Genotypes were coded on a scale of 0 to 2. Root mean squared error (RMSE) was calculated between actual genotypes and genotypes ascertained from simulated RAD-seq reads at 241 SNP markers (lower RMSE = higher accuracy). Each point represents one SNP. Median read depth is indicated by color, including genotypes with zero reads. The RMSE for continuous genotypes output by the polyRAD mapping method with linkage is shown on the x-axis, and the RMSE of other methods and types of genotypes (continuous or discrete) is shown on the y-axis. The dashed line indicates the ordinary least-squares regression with slope and intercept estimates, with standard errors. The “f1” model was used with updog. (A) RMSE calculated using only genotypes with more than zero reads. (B) RMSE calculated using only genotypes with zero reads, by genotyping or imputation method and genotype type.

Article Snippet: To test the accuracy of polyRAD, we used datasets from three previously studied populations: 1) RAD-seq data and GoldenGate SNP genotypes from a diversity panel (n = 565) of the outcrossing, diploidized allotetraploid grass Miscanthus sinensis ( Clark et al. 2014 ), 2) RAD-seq data and GoldenGate SNP genotypes from a bi-parental F 1 mapping population (n = 275) of M. sinensis ( Liu et al. 2016a ), and 3) SNP array genotypes from a biparental F 1 mapping population of autotetraploid potato (n = 238) ( da Silva et al. 2017 ).

Techniques:

Journal: G3: Genes|Genomes|Genetics

Article Title: polyRAD: Genotype Calling with Uncertainty from Sequencing Data in Polyploids and Diploids

doi: 10.1534/g3.118.200913

Figure Lengend Snippet: Genotyping error of EBG, updog, polyRAD, and rrBLUP in an F1 mapping population of tetraploid potato with 238 progeny. The benefits of incorporating linkage into the genotyping model and using continuous rather than discrete genotypes are illustrated. Genotypes were coded on a scale of 0 to 4. Root mean squared error (RMSE) was calculated between actual genotypes and genotypes ascertained from simulated RAD-seq reads at 2538 SNP markers (lower RMSE = higher accuracy). Each point represents one SNP. Median read depth is indicated by color, including genotypes with zero reads. The RMSE for continuous genotypes output by the polyRAD mapping method with linkage is shown on the x-axis, and the RMSE of other methods and types of genotypes (continuous or discrete) is shown on the y-axis. The dashed line indicates the ordinary least-squares regression with slope and intercept estimates, with standard errors. The “f1” model was used with updog. fitPoly results are omitted since it failed for all markers, and LinkImpute was not run since LinkImpute is for diploids only. (A) RMSE calculated using only genotypes with more than zero reads. (B) RMSE calculated using only genotypes with zero reads, by genotyping or imputation method and genotype type.

Article Snippet: To test the accuracy of polyRAD, we used datasets from three previously studied populations: 1) RAD-seq data and GoldenGate SNP genotypes from a diversity panel (n = 565) of the outcrossing, diploidized allotetraploid grass Miscanthus sinensis ( Clark et al. 2014 ), 2) RAD-seq data and GoldenGate SNP genotypes from a bi-parental F 1 mapping population (n = 275) of M. sinensis ( Liu et al. 2016a ), and 3) SNP array genotypes from a biparental F 1 mapping population of autotetraploid potato (n = 238) ( da Silva et al. 2017 ).

Techniques:

Journal: Molecular Ecology

Article Title: Targeted re‐sequencing confirms the importance of chemosensory genes in aphid host race differentiation

doi: 10.1111/mec.13818

Figure Lengend Snippet: Squared loadings for SNP s in each principal component of the GoldenGate SNP genotyping data set plotted against squared loadings for the most strongly correlated principal component in the capture sequencing data set (left to right, top to bottom: capture PC 1 vs. SNP genotyping PC 2, capture genotyping PC 2 vs. SNP genotyping PC 1, capture PC 3 vs. SNP genotyping PC 1, capture PC 4 vs. SNP genotyping PC 4, capture genotyping PC 5 vs. SNP genotyping PC 5, and maximum squared loading capture genotyping vs. maximum squared loading SNP genotyping). Black = control, pink = P450, green = chemosensory.

Article Snippet: By repeating outlier analyses on eight races, we confirm that differences in chemosensory genes are important to the divergence of the broader spectrum of pea aphid races, and incorporating more localities in our GoldenGate SNP genotyping data set allowed us to confirm a direct link between plant choice and chemosensory differences, distinct from other environmental variables that might be correlated with differences between single populations. amova showed large contributions of race and small contributions of locality to genetic variation, a pattern that was more pronounced in chemosensory than in control genes, supporting the relationship between chemosensory gene divergence and race in the face of gene flow.

Techniques: Sequencing

Journal: Molecular Ecology

Article Title: Targeted re‐sequencing confirms the importance of chemosensory genes in aphid host race differentiation

doi: 10.1111/mec.13818

Figure Lengend Snippet: Outliers in each data set (Smadja et al ., Capture Sequencing and GoldenGate SNP genotyping), for genes present in all data sets, two data sets and just one data set each. Smadja et al . ( 2012 ) and Capture sequencing outliers with P < 0.05 Poisson probability of the observed or a greater number of SNP outliers given the number of SNPs in the gene and the overall proportion of outliers. Outliers from GoldenGate SNP genotyping are genes containing a SNP with a significant loading ( q < 0.05) in PCAdapt

Article Snippet: By repeating outlier analyses on eight races, we confirm that differences in chemosensory genes are important to the divergence of the broader spectrum of pea aphid races, and incorporating more localities in our GoldenGate SNP genotyping data set allowed us to confirm a direct link between plant choice and chemosensory differences, distinct from other environmental variables that might be correlated with differences between single populations. amova showed large contributions of race and small contributions of locality to genetic variation, a pattern that was more pronounced in chemosensory than in control genes, supporting the relationship between chemosensory gene divergence and race in the face of gene flow.

Techniques: Sequencing