Two methods of SNPs pre-selection based on single marker regression for the estimation of genomic breeding values (G-EBVs) were compared using simulated data provided by the XII QTL-MAS workshop: i) Bonferroni correction of the significance threshold and ii) Permutation test to obtain the reference distribution of the null hypothesis and identify significant markers at P<0.01 and P<0.001 significance thresholds. From the set of markers significant at P<0.001, random subsets of 50% and 25% markers were extracted, to evaluate the effect of further reducing the number of significant SNPs on G-EBV predictions. The Bonferroni correction method allowed the identification of 595 significant SNPs that gave the best G-EBV accuracies in prediction generations (82.80%). The permutation methods gave slightly lower G-EBV accuracies even if a larger number of SNPs resulted significant (2,053 and 1,352 for 0.01 and 0.001 significance thresholds, respectively). Interestingly, halving or dividing by four the number of SNPs significant at P<0.001 resulted in an only slightly decrease of G-EBV accuracies. The genetic structure of the simulated population with few QTL carrying large effects, might have favoured the Bonferroni method.

Use of different marker pre-selection methods based on single SNP regression in the estimation of Genomic-EBVs / Dimauro, Corrado; Nicolazzi, Ezequiel Luis; Negrini, Riccardo. - In: ITALIAN JOURNAL OF ANIMAL SCIENCE. - ISSN 1828-051X. - 8:Suppl. 2(2009), pp. 117-119.

Use of different marker pre-selection methods based on single SNP regression in the estimation of Genomic-EBVs

Dimauro, Corrado;
2009

Abstract

Two methods of SNPs pre-selection based on single marker regression for the estimation of genomic breeding values (G-EBVs) were compared using simulated data provided by the XII QTL-MAS workshop: i) Bonferroni correction of the significance threshold and ii) Permutation test to obtain the reference distribution of the null hypothesis and identify significant markers at P<0.01 and P<0.001 significance thresholds. From the set of markers significant at P<0.001, random subsets of 50% and 25% markers were extracted, to evaluate the effect of further reducing the number of significant SNPs on G-EBV predictions. The Bonferroni correction method allowed the identification of 595 significant SNPs that gave the best G-EBV accuracies in prediction generations (82.80%). The permutation methods gave slightly lower G-EBV accuracies even if a larger number of SNPs resulted significant (2,053 and 1,352 for 0.01 and 0.001 significance thresholds, respectively). Interestingly, halving or dividing by four the number of SNPs significant at P<0.001 resulted in an only slightly decrease of G-EBV accuracies. The genetic structure of the simulated population with few QTL carrying large effects, might have favoured the Bonferroni method.
Use of different marker pre-selection methods based on single SNP regression in the estimation of Genomic-EBVs / Dimauro, Corrado; Nicolazzi, Ezequiel Luis; Negrini, Riccardo. - In: ITALIAN JOURNAL OF ANIMAL SCIENCE. - ISSN 1828-051X. - 8:Suppl. 2(2009), pp. 117-119.
File in questo prodotto:
File Dimensione Formato  
Nicolazzi_E_Articolo_2009_Use.pdf

accesso aperto

Tipologia: Versione editoriale (versione finale pubblicata)
Licenza: Non specificato
Dimensione 155.58 kB
Formato Adobe PDF
155.58 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11388/263181
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact