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ARS Home » Plains Area » Miles City, Montana » Livestock and Range Research Laboratory » Research » Publications at this Location » Publication #402330
Research Project: Identifying and Mitigating Factors that Limit Beef Production Efficiency

Location: Livestock and Range Research Laboratory

Title: Alternative SNP weighting for multi-step and single-step genomic BLUP in the presence of causative variants

Author
item SANTANA, BRUNA - University Of Connecticut
item RISER, MOLLY - University Of Connecticut
item Hay, El Hamidi
item FRAGOMENI, BRENO - University Of Connecticut

Submitted to: Journal of Animal Breeding and Genetics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/2/2023
Publication Date: 8/7/2023
Citation: Santana, B., Riser, M., Hay, E.A., Fragomeni, B. 2023. Alternative SNP weighting for multi-step and single-step genomic BLUP in the presence of causative variants. Journal of Animal Breeding and Genetics. 140(6):679-694. https://doi.org/10.1111/jbg.12817.
DOI: https://doi.org/10.1111/jbg.12817

Interpretive Summary: With the advent of genomic information, genomic selection has been successfully implemented in many sectors of livestock production. However, genomic selection still suffers from several limitations such as the accuracy of prediction of genetic merit. The objective of this study was to evaluate the weighting of genetic markers in improving the accuracy of genomic selection using several methods. The results showed that the accuracy is dependent on the genetic architecture of the trait, the marker panel, and the statistical method of prediction. The non-uniform weighting approaches were more suitable for less polygenic traits.

Technical Abstract: Accuracy of genetic selection in dairy can be increased by the adoption of new technologies, such as the inclusion of sequence data. In simulation studies, assigning different weights to causative SNP markers led to better predictions depending on the genomic prediction method used. However, it is still not clear how the weights should be calculated. Our objective is to evaluate the accuracy of a multi-step method (GBLUP) and ssGBLUP with simulated data using regular SNP, causal variants (QTN), and the combination of both. Additionally, we compared accuracies of all previous scenarios using alternatives of SNP weighting. Data were simulated, assuming a single trait with heritability of 0.3. The effective population size (Ne) was approximately 200. The pedigree contained 440,000 animals, and approximately 16,800 individuals were genotyped. A total of 49,974 SNP markers were evenly placed throughout the genome; and 100, 1000, and 2000 causative QTN were simulated. Both GBLUP and ssGBLUP were used in this study. Uniform, quadratic, and nonlinear SNP weights were evaluated. The QTN addition into panels led to significant accuracy gains. NonlinearA was demonstrated superior to quadratic weighting and uniform approaches, however, results from NonlinearA were dependent on the equation parameters. The non-uniform weighting approaches were more suitable for less polygenic scenarios. Finally, SNP weighting might help elucidate trait architecture features based on changes in the accuracy of genomic prediction.