Weighted single-step genomic best linear unbiased prediction integrating variants selected from sequencing data by association and bioinformatics analyses

Autor: Aoxing Liu, Mogens Sandø Lund, Didier Boichard, Emre Karaman, Bernt Guldbrandtsen, Sebastien Fritz, Gert Pedersen Aamand, Ulrik Sander Nielsen, Goutam Sahana, Yachun Wang, Guosheng Su

Publikováno v: Genetics Selection Evolution, Vol 52, Iss 1, Pp 1-17 (2020)

Abstract Background Sequencing data enable the detection of causal loci or single nucleotide polymorphisms (SNPs) highly linked to causal loci to improve genomic prediction. However, until now, studies on integrating such SNPs using a single-step gen

Externí odkaz: https://doaj.org/article/3f75709fef3c4f9eaf8ea1d04ea3480a

A 660-Kb deletion with antagonistic effects on fertility and milk production segregates at high frequency in Nordic Red cattle: additional evidence for the common occurrence of balancing selection in livestock.

Autor: Naveen Kumar Kadri, Goutam Sahana, Carole Charlier, Terhi Iso-Touru, Bernt Guldbrandtsen, Latifa Karim, Ulrik Sander Nielsen, Frank Panitz, Gert Pedersen Aamand, Nina Schulman, Michel Georges, Johanna Vilkki, Mogens Sandø Lund, Tom Druet

Publikováno v: PLoS Genetics, Vol 10, Iss 1, p e1004049 (2014)

In dairy cattle, the widespread use of artificial insemination has resulted in increased selection intensity, which has led to spectacular increase in productivity. However, cow fertility has concomitantly severely declined. It is generally assumed t

Externí odkaz: https://doaj.org/article/d11c114a650a46cbaaa57ec6bcf08d10

Practical implementation of genetic groups in single-step genomic evaluations with Woodbury matrix identity–based genomic relationship inverse

Autor: Minna Koivula, Ismo Strandén, Esa Mäntysaari, Gert Pedersen Aamand

Publikováno v: Journal of Dairy Science. 104:10049-10058

The growing amount of genomic information in dairy cattle has increased computational and modeling challenges in the single-step evaluations. The computational challenges are due to the dense inverses of genomic (G) and pedigree (A22) relationship ma

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::09f899c73ae87511a9947aa58093570c
https://doi.org/10.3168/jds.2020-19821

Imputation for sequencing variants preselected to a customized low-density chip

Autor: Didier Boichard, Emre Karaman, Xiaowei Mao, Yachun Wang, Gert Pedersen Aamand, Aoxing Liu, Sébastien Fritz, Mogens Sandø Lund, Guosheng Su

Publikováno v: Scientific Reports, Vol 10, Iss 1, Pp 1-9 (2020)
Liu, A, Lund, M S, Boichard, D, Mao, X, Karaman, E, Fritz, S, Aamand, G P, Wang, Y & Su, G 2020, ' Imputation for sequencing variants preselected to a customized low-density chip ', Scientific Reports, vol. 10, 9524 . https://doi.org/10.1038/s41598-020-66523-7
Scientific Reports
Scientific Reports, Nature Publishing Group, 2020, 10 (1), pp.9524. ⟨10.1038/s41598-020-66523-7⟩

the sequencing variants preselected from association analyses and bioinformatics analyses could improve genomic prediction. In this study, the imputation of sequencing SNPs preselected from major dairy breeds in Denmark-Finland-Sweden (DFS) and Franc

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::92c01ffa50ecb962b463d8a350e00c48
http://link.springer.com/article/10.1038/s41598-020-66523-7

Weighted single-step genomic best linear unbiased prediction integrating variants selected from sequencing data by association and bioinformatics analyses

Autor: Bernt Guldbrandtsen, Didier Boichard, Goutam Sahana, Emre Karaman, Aoxing Liu, Yachun Wang, Guosheng Su, Mogens Sandø Lund, Sébastien Fritz, U. S. Nielsen, Gert Pedersen Aamand

Publikováno v: Liu, A, Lund, M S, Boichard, D, Karaman, E, Guldbrandtsen, B, Fritz, S, Aamand, G P, Nielsen, U S, Sahana, G, Wang, Y & Su, G 2020, ' Weighted single-step genomic best linear unbiased prediction integrating variants selected from sequencing data by association and bioinformatics analyses ', Genetics Selection Evolution, vol. 52, no. 1, 48 . https://doi.org/10.1186/s12711-020-00568-0
Genetics, Selection, Evolution : GSE
Genetics Selection Evolution, Vol 52, Iss 1, Pp 1-17 (2020)
Genetics Selection Evolution
Genetics Selection Evolution, BioMed Central, 2020, 52 (1), pp.48. ⟨10.1186/s12711-020-00568-0⟩

International audience; Background: Sequencing data enable the detection of causal loci or single nucleotide polymorphisms (SNPs) highly linked to causal loci to improve genomic prediction. However, until now, studies on integrating such SNPs using a

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b67700b69dd3cfd3eca876082f97ddca
https://pure.au.dk/portal/da/publications/weighted-singlestep-genomic-best-linear-unbiased-prediction-integrating-variants-selected-from-sequencing-data-by-association-and-bioinformatics-analyses(af14ec23-4867-4a01-90ec-906cb9f88485).html