Genomic signatures of recent selection were identified in 2918 Italian Holstein bulls born between 1987 and 2007 using a birth date regression on EBVs, and the analysis of changes in allele frequencies. Under strong directional selection, allele frequencies rapidly change and permit the identification of genomic regions that carry genes controlling production, functional or type traits. Genotype data from SELMOL, PROZOO and INNOVAGEN projects were used along with EBVs (Estimated Breeding Value) for 32 production and morphological traits of the genotyped animals, provided by the Italian Holstein association (ANAFI). Bulls were genotyped with BovineSNP50 and BovineHD SNPchips. Imputation using SNPchiMp v.1 and BEAGLE (v.3) was used to obtain HD genotypes for all individuals. A total of 2918 animals and 613,956 SNPs were included in the working dataset, after quality control. Birth date regressed Protein Yield EBVs, show a strong positive trend in the birth date interval analyzed. To detect genomic regions involved, we first identified animals with outlier PLUS- and MINUS-variant EBVs, over the total range of birth years (164 bulls, group 1) and in each birth year (159 bulls, group 2). Then, allele frequencies were obtained for each SNP, in PLUS and MINUS variants pools. Finally, we calculated the absolute allele frequency difference between PLUS and MINUS pools within each group and identified genomic regions with high values by overlapping sliding windows of 50 SNPs. Comparing the information from the plus and minus pool identified 0.53% shared windows in genomic regions under recent selection. A ~1.2 Mb region on BTA13 (from position 23.2 to 24.4Mb) had the highest absolute mean difference across datasets. This birth date based analysis is a novel and potentially powerful approach to identify regions under recent selection associated with production, type and functional traits.
Birth date regression to identify genomic signatures of recent selection in Italian Holstein / A. Talenti, M. Milanesi, E.L. Nicolazzi, L. Nicoloso, S. Frattini, B. Coizet, G. Pagnacco, J.L. Williams, P.A. Marsan, P. Crepaldi. - In: ITALIAN JOURNAL OF ANIMAL SCIENCE. - ISSN 1594-4077. - 14:suppl. 1(2015 Jun), pp. 28-28. ((Intervento presentato al 21. convegno ASPA Congress tenutosi a Milano nel 2015.
Birth date regression to identify genomic signatures of recent selection in Italian Holstein
A. TalentiPrimo
;L. Nicoloso;S. Frattini;B. Coizet;G. Pagnacco;P. CrepaldiUltimo
2015
Abstract
Genomic signatures of recent selection were identified in 2918 Italian Holstein bulls born between 1987 and 2007 using a birth date regression on EBVs, and the analysis of changes in allele frequencies. Under strong directional selection, allele frequencies rapidly change and permit the identification of genomic regions that carry genes controlling production, functional or type traits. Genotype data from SELMOL, PROZOO and INNOVAGEN projects were used along with EBVs (Estimated Breeding Value) for 32 production and morphological traits of the genotyped animals, provided by the Italian Holstein association (ANAFI). Bulls were genotyped with BovineSNP50 and BovineHD SNPchips. Imputation using SNPchiMp v.1 and BEAGLE (v.3) was used to obtain HD genotypes for all individuals. A total of 2918 animals and 613,956 SNPs were included in the working dataset, after quality control. Birth date regressed Protein Yield EBVs, show a strong positive trend in the birth date interval analyzed. To detect genomic regions involved, we first identified animals with outlier PLUS- and MINUS-variant EBVs, over the total range of birth years (164 bulls, group 1) and in each birth year (159 bulls, group 2). Then, allele frequencies were obtained for each SNP, in PLUS and MINUS variants pools. Finally, we calculated the absolute allele frequency difference between PLUS and MINUS pools within each group and identified genomic regions with high values by overlapping sliding windows of 50 SNPs. Comparing the information from the plus and minus pool identified 0.53% shared windows in genomic regions under recent selection. A ~1.2 Mb region on BTA13 (from position 23.2 to 24.4Mb) had the highest absolute mean difference across datasets. This birth date based analysis is a novel and potentially powerful approach to identify regions under recent selection associated with production, type and functional traits.File | Dimensione | Formato | |
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