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

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

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 genomic best linear unbiased prediction (ssGBLUP) mod...

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Bibliographic Details
Main Authors: 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
Format: Article
Language:deu
Published: BMC 2020-08-01
Series:Genetics Selection Evolution
Online Access:http://link.springer.com/article/10.1186/s12711-020-00568-0

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http://link.springer.com/article/10.1186/s12711-020-00568-0

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