Journal: Scientific Reports

Article Title: Utilization of a Wheat660K SNP array-derived high-density genetic map for high-resolution mapping of a major QTL for kernel number

doi: 10.1038/s41598-017-04028-6

Figure Lengend Snippet: Synteny of the mapped SNPs from the Wheat660K SNP array (in blue ) with that from the Wheat90K array (in red ) based on their common CSS-assembled contigs.

Article Snippet: Genomic DNA was extracted and hybridized on the Wheat660K SNP genotyping array by Compass Biotechnology Company (Beijing, China).

Techniques:

Journal: Scientific Reports

Article Title: Utilization of a Wheat660K SNP array-derived high-density genetic map for high-resolution mapping of a major QTL for kernel number

doi: 10.1038/s41598-017-04028-6

Figure Lengend Snippet: Synteny of the mapped SNPs from the Wheat660K SNP array (in blue ) with that from the Wheat820K array (in green ) based on their common CSS-assembled contigs. Notes: The genetic maps of chromosomes 3A, 4B, 5D, 6D and 7B derived from the Wheat820K SNP array are shown in inverted form, with long arms at the top and short arms at the bottom . In this figure, these inversions were corrected.

Article Snippet: Genomic DNA was extracted and hybridized on the Wheat660K SNP genotyping array by Compass Biotechnology Company (Beijing, China).

Techniques: Derivative Assay

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: Fig. 8 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.

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: Fig. 8 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.

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: Fig. 8 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.

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: Fig. 8 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.

Techniques: Derivative Assay, Sequencing