Alternative SNP weighting for single-step genomic best linear unbiased predictor evaluation of stature in US Holsteins in the presence of selected sequence variants
| Autor: | Ignacy Misztal, Andres Legarra, D. A. L. Lourenco, Paul M. VanRaden, Breno O. Fragomeni |
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| Přispěvatelé: | University of Connecticut (UCONN), Dept Anim & Dairy Sci, University of Gerogia, Génétique Physiologie et Systèmes d'Elevage (GenPhySE ), École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, ARS, Agriculture and Food Research Initiative Competitive from the US Department of Agriculture's National Institute of Food and Agriculture 2015-67015-22936 |
| Rok vydání: | 2019 |
| Předmět: |
Male
[SDV.OT]Life Sciences [q-bio]/Other [q-bio.OT] Genotype sequence data Best linear unbiased prediction Residual Polymorphism Single Nucleotide Cross-validation 03 medical and health sciences Statistics Genetics SNP Animals causative variant Selection Genetic genomic prediction 030304 developmental biology Mathematics Oligonucleotide Array Sequence Analysis 0303 health sciences Sequence Models Genetic BayesA 0402 animal and dairy science Reproducibility of Results 04 agricultural and veterinary sciences Genomics Covariance 040201 dairy & animal science Weighting SNP genotyping Phenotype Animal Science and Zoology Cattle Female Food Science Selective Breeding |
| Zdroj: | Journal of Dairy Science Journal of Dairy Science, American Dairy Science Association, 2019, 102 (11), pp.10012-10019. ⟨10.3168/jds.2019-16262⟩ |
| ISSN: | 1525-3198 0022-0302 |
| DOI: | 10.3168/jds.2019-16262⟩ |
| Popis: | International audience; Causal variants inferred from sequence data analysis are expected to increase accuracy of genomic selection. In this work we evaluated the gain in reliability of genomic predictions, for stature in US Holsteins, when adding selected sequence variants to a pre-existent SNP chip. Two prediction methods were tested: de-regressed proofs assuming heterogeneous (genomic BLUP; GB-LUP) residual variances and by single-step GBLUP (ssGBLUP) using actual phenotypes. Phenotypic data included 3,999,631 records for stature on 3,027,304 Holstein cows. Genotypes on 54,087 SNP markers (54k) were available for 26,877 bulls. Additionally, 16,648 selected sequence variants were combined with the 54k markers, for a total of 70,735 (70k) markers. In all methods, SNP in the genomic relationship matrix (G) were unweighted or weighted iteratively, with weights derived either by SNP effects squared or by a nonlinear method that resembles BayesA (nonlinear A). Reliability of genomic predictions were obtained by cross validation. With unweighted G derived from 54k markers, the reliabilities (x 100) were 72.4 for GBLUP and 75.3 for ssGBLUP. With unweighted G derived from 70k markers, the reliabilities were 73.4 and 76.0, respectively. Weighting by nonlinear A changed reliabilities to 73.3, and 75.9, respectively. Addition of selected sequence variants had a small effect on reliabilities. Weighting by quadratic functions reduced reliabilities. Weighting by nonlinear A increased reliabilities for GBLUP but had only a small effect in ssGBLUP. Reliabilities for direct genornic values extracted from ssGBLUP using unweighted G with 54k were higher than reliabilities by any GBLUP. Thus, ssGBLUP seems to capture more information than GBLUP and there is less room for extra reliability. Improvements in GBLUP may be because the weights in G change the covariance structure, which can explain a proportion of the variance that is accounted for when a heterogeneous residual variance is assumed by considering a different number of daughters per bull. |
| Databáze: | OpenAIRE |
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