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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Animal Genomics and Improvement Laboratory » Research » Publications at this Location » Publication #376067
Research Project: Improving Dairy Animals by Increasing Accuracy of Genomic Prediction, Evaluating New Traits, and Redefining Selection Goals

Location: Animal Genomics and Improvement Laboratory

Title: Marker selection and genomic prediction of economically important traits multiple traits using imputed high-density genotypes for 5 breeds of dairy cattle

Author
item Al-Khudhair, Ahmed
item Vanraden, Paul
item LI, BINGJIE - Sruc-Scotland'S Rural College
item Null, Daniel

Submitted to: Journal of Dairy Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/22/2020
Publication Date: N/A
Citation: N/A

Interpretive Summary: Genomic prediction has become very effective at increasing genetic progress in large populations such as the Holstein breed. Accuracy of prediction in smaller breeds can also be improved by selecting additional markers with larger effects from high-density chips. This study selected such markers from genotypes for 5 dairy breeds, included those selected markers in routine predictions, and provided the marker list to genotyping laboratories for developing future chips. More accurate predictions should improve progress for many traits in each breed.

Technical Abstract: Genotypes for 351,461 Holsteins, 347,570 Jerseys, 42,346 Brown Swiss, 9,364 Ayrshires (including Scandinavian Red), and 4,599 Guernseys were imputed to high density (HD). The separate HD reference populations included Illumina BovineHD genotypes for 4,012 Holsteins, 407 Jerseys, 181 Brown Swiss, 527 Ayrshires, and 147 Guernseys. The 643,059 variants included the HD SNP and all 79,254 (80K) genetic markers and QTL used in routine national genomic evaluations for dairy cattle. The variants were not pruned for high linkage disequilibrium as in previous HD studies of only Holsteins. Chromosome locations used the ARS-UCD1 map which has improved performances in marker locations, sequence alignment, and genotype imputation compared to the previous UMD 3.1 reference assembly. Imputation using Findhap (version 3) with 24 processors took <2 d for each breed. Before imputation, approximately 91 to 97% of genotypes in each breed were unknown for each breed; after imputation, 1.1% of Holstein, 3.2% of Jersey, 6.7% of Brown Swiss, 4.8% of Ayrshire, and 4.2% of Guernsey alleles remained unknown. Prediction and SNP selection results for Jerseys are presented as an example. Allele effects for 26 traits were estimated using phenotypic reference populations that included up to 6,157 Jersey males and 110,130 Jersey females. Convergence took 4 to 8 d using 1 processor per trait and up to 800 iterations. Correlations of HD with 80K genomic predictions for young animals averaged 0.986; correlations were highest for yield traits (about 0.991). Correlations for foot angle and rear legs (side view) were lowest (0.981and 0.982, respectively). Some HD effects were more than twice as large as the largest 80K SNP effect, and HD markers had larger effects than nearby 80K markers for many breed-trait combinations. Direct gene tests also had the largest effects for several breeds and traits. Previous studies selected and included markers with large effects for Holstein traits; adding the newly selected HD markers should improve non-Holstein and crossbred genomic predictions.