Journal: Experimental Hematology

Article Title: Determination of complex subclonal structures of hematological malignancies by multiplexed genotyping of blood progenitor colonies

doi: 10.1016/j.exphem.2017.09.011

Figure Lengend Snippet: Multiplexed SNP genotyping is an efficient method for genotyping BFU-E colonies . Number of colonies for which a genotype could be called out of 96 colonies for each mutation is shown. Five genes were genotyped by capillary sequencing and multiplexed SNP genotyping. Shaded regions highlight the number of colonies for which the same genotype was called by both technologies.

Article Snippet: The diluted amplified product was loaded onto the 192.24 Dynamic Array IFC for SNP Genotyping (BMK-M-192.24GT, Fluidigm) alongside a sample premixture including ROX reference dye and real-time master mixture.

Techniques: Mutagenesis, Sequencing

Journal: BMC Biology

Article Title: Imputation strategies for genomic prediction using nanopore sequencing

doi: 10.1186/s12915-023-01782-0

Figure Lengend Snippet: Correlations between body weight (BW) genomic estimated breeding values (GEBV) derived from 35 k SNP array genotypes and BW GEBVs derived from Oxford Nanopore Technologies (ONT) data. ONT GEBVs were imputed using four different imputation strategies and across five sequencing coverages. Labels at the top of the figure indicate the imputation method used starting from left to right with GLIMPSE , minor allele count (MAC) genotyping with Beagle5.2 , quality score (Q-score) genotyping with Beagle5.2 and QUILT . SNP reference panel size is indicated by the minor allele frequency (MAF) filter on the right-hand side in descending order of size from top to bottom. The largest panel had 48,203,338 SNP and was referred to as the No MAF filter panel, while the smallest panel was referred to as the bovine high density (HD) SNP which had only the 641 k SNP used to calculate the GEBVs. Error bars indicate 95% confidence of the Pearson correlation

Article Snippet: B) Genomic prediction bias for body weight (BW), defined as> \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathrm\beta}_2-1$$\end{document} β 2 - 1 >, where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathrm\beta}_2$$\end{document} β 2 is the regression coefficient of the bovine HD SNP array genomic estimated breeding value (GEBV) ~ Oxford Nanopore Technologies GEBV derived using QUILT and GLIMPSE.

Techniques: Derivative Assay, Sequencing

Journal: BMC Biology

Article Title: Imputation strategies for genomic prediction using nanopore sequencing

doi: 10.1186/s12915-023-01782-0

Figure Lengend Snippet: Genomic prediction bias, defined as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\beta }_{1}-1$$\end{document} β 1 - 1 , where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\beta }_{1}$$\end{document} β 1 is the regression coefficient of the 35 k SNP array genomic estimated breeding values ~ Oxford Nanopore Technologies derived genomic estimated breeding values, for the four different imputation approaches across the sequencing coverages for four traits: body weight (BW), body condition score (BCS), corpus luteum score (CL score) and hip height (HH). Labels at the top of each figure indicate the imputation method used starting with GLIMPSE , minor allele count (MAC) genotyping with Beagle5.2 , quality score (Q-score) genotyping with Beagle5.2 and QUILT . Prediction bias was also calculated across five different SNP reference panel sizes which were created using minor allele frequency (MAF) filters from whole genome sequence SNP. The smallest SNP reference panel, the bovine high definition (HD) SNP, had only the 641 k SNP used to calculate the GEBVs

Article Snippet: B) Genomic prediction bias for body weight (BW), defined as> \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathrm\beta}_2-1$$\end{document} β 2 - 1 >, where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathrm\beta}_2$$\end{document} β 2 is the regression coefficient of the bovine HD SNP array genomic estimated breeding value (GEBV) ~ Oxford Nanopore Technologies GEBV derived using QUILT and GLIMPSE.

Techniques: Derivative Assay, Sequencing

Journal: BMC Biology

Article Title: Imputation strategies for genomic prediction using nanopore sequencing

doi: 10.1186/s12915-023-01782-0

Figure Lengend Snippet: Imputation accuracy for genotypes derived from low-coverage Oxford Nanopore Technologies (ONT) sequence data imputed using QUILT and GLIMPSE and compared to bovine HD SNP array genotypes. ONT genotypes were imputed across five different sequencing coverages and using five different imputation reference panels. The imputation accuracy of genotypes imputed from the low-density SNP array to the HD array density is illustrated by the green dashed line

Article Snippet: B) Genomic prediction bias for body weight (BW), defined as> \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathrm\beta}_2-1$$\end{document} β 2 - 1 >, where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathrm\beta}_2$$\end{document} β 2 is the regression coefficient of the bovine HD SNP array genomic estimated breeding value (GEBV) ~ Oxford Nanopore Technologies GEBV derived using QUILT and GLIMPSE.

Techniques: Derivative Assay, Sequencing

Journal: BMC Biology

Article Title: Imputation strategies for genomic prediction using nanopore sequencing

doi: 10.1186/s12915-023-01782-0

Figure Lengend Snippet: A Correlations between genomic estimated breeding values (GEBVs) derived from Oxford Nanopore Technologies (ONT) sequence data and GEBVs derived from bovine HD SNP array genotypes for body weight (BW). ONT-derived GEBVs were imputed using QUILT and GLIMPSE and calculated across five coverages and five SNP panels. The different SNP reference panels were created using minor allele frequency (MAF) filters to reduce the size of the panels down from whole genome sequence SNP. The largest panel had 48,203,338 SNP and was referred to as the No MAF filter panel, while the smallest panel was referred to as the bovine high definition (HD) SNP panel and featured only the 641 k SNP used to calculate the GEBVs. SNP array genotypes were from the Illumina bovine HD SNP array. The correlation for each trait between GEBVs calculated from the 35 k GGP SNP array imputed to 700 k and GEBVs calculated from the Illumina bovine HD SNP array are indicated by the dashed line. The colour of each bar indicates how well the ONT derived GEBV accuracies compare to the 35 K SNP array accuracies. Error bars indicate 95% confidence interval of the Pearson correlation. B Genomic prediction bias for body weight (BW), defined as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\beta }_{2}-1$$\end{document} β 2 - 1 , where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\beta }_{2}$$\end{document} β 2 is the regression coefficient of the bovine HD SNP array genomic estimated breeding value (GEBV) ~ Oxford Nanopore Technologies GEBV derived using QUILT and GLIMPSE. The prediction bias of the HD SNP array GEBVs ~ 35 k SNP array GEBVs are displayed for each trait by the dotted lines, where the colour of the line corresponds to the colour of the trait in the figure legend

Article Snippet: B) Genomic prediction bias for body weight (BW), defined as> \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathrm\beta}_2-1$$\end{document} β 2 - 1 >, where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathrm\beta}_2$$\end{document} β 2 is the regression coefficient of the bovine HD SNP array genomic estimated breeding value (GEBV) ~ Oxford Nanopore Technologies GEBV derived using QUILT and GLIMPSE.

Techniques: Derivative Assay, Sequencing

Journal: BMC Biology

Article Title: Imputation strategies for genomic prediction using nanopore sequencing

doi: 10.1186/s12915-023-01782-0

Figure Lengend Snippet: Change in body weight genomic estimated breeding values (GEBV) quartile rankings between the HD SNP array GEBVs and GEBVs derived from five different Oxford Nanopore Technologies (ONT) sequencing coverages. ONT GEBVs were derived using either GLIMPSE or QUILT for genotype imputation. Three different imputation reference panels were used: The first reference panel included all 48 million SNP; the second reference panel used a minor allele frequency (MAF) filter of > 0.2 and had 9.5 million SNP. The third reference panel included only the 700,000 SNP in the bovine HD SNP array

Article Snippet: B) Genomic prediction bias for body weight (BW), defined as> \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathrm\beta}_2-1$$\end{document} β 2 - 1 >, where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathrm\beta}_2$$\end{document} β 2 is the regression coefficient of the bovine HD SNP array genomic estimated breeding value (GEBV) ~ Oxford Nanopore Technologies GEBV derived using QUILT and GLIMPSE.

Techniques: Derivative Assay, Sequencing

Journal: BMC Biology

Article Title: Imputation strategies for genomic prediction using nanopore sequencing

doi: 10.1186/s12915-023-01782-0

Figure Lengend Snippet: Genotype concordance for genotypes derived from low-coverage Oxford Nanopore Technologies (ONT) sequence data imputed using GLIMPSE and QUILT. Twelve animals were sequenced twice at two separate time points and genotypes were calculated separately for each of the sequencing runs. Five different sequencing coverages were evaluated for the two different imputation methods as well as five different imputation reference panel sizes. The largest imputation reference panel used 48 million SNP, a minor allele frequency (MAF) cutoff of 0.1, 0.2 and 0.3, was used to subset the 48 million SNP reference panel down. The final imputation reference panel used only the 700 k SNP in the bovine HD SNP array

Article Snippet: B) Genomic prediction bias for body weight (BW), defined as> \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathrm\beta}_2-1$$\end{document} β 2 - 1 >, where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathrm\beta}_2$$\end{document} β 2 is the regression coefficient of the bovine HD SNP array genomic estimated breeding value (GEBV) ~ Oxford Nanopore Technologies GEBV derived using QUILT and GLIMPSE.

Techniques: Derivative Assay, Sequencing

Journal: Frontiers in Plant Science

Article Title: Novel Design of Imputation-Enabled SNP Arrays for Breeding and Research Applications Supporting Multi-Species Hybridization

doi: 10.3389/fpls.2021.756877

Figure Lengend Snippet: Principal component analysis (PCA) plots showing genetic diversity of wheat and barley accessions used for SNP discovery. (A) About 6,087 wheat accessions were genotyped with the iSelect wheat 90K SNP array (black), exome-sequenced accessions used for linkage disequilibrium (LD) analysis (red), and synthetic derivative accessions capturing D-genome diversity (blue); and (B) 19,778 barley accessions genotyped with GBS (black), with exome-sequenced accessions used for LD analysis (red).

Article Snippet: SNPs on the Infinium 90K SNP array were derived from the transcriptome sequence of 19 bread wheat accessions and 18 tetraploid accessions, while those on the Axiom 820K arrays were based on exome capture sequence from 43 bread wheat and wild species accessions representing the primary, secondary, and tertiary gene pools.

Techniques: