Improved Accuracy of Genomic Prediction for Traits with Rare QTL by Fitting Haplotypes

Authors: , , ,


Genomic prediction estimates breeding values by exploiting linkage disequilibrium (LD) between quantitative trait loci (QTL) and single nucleotide polymorphisms (SNPs). High LD cannot occur when QTL and SNPs have different minor allele frequencies (MAF). Marker panels tend to use SNPs with high MAF and will have limited ability to predict rare QTL alleles. In practice, increasing SNP density has not improved prediction accuracy. A possible reason is that many traits are characterized by rare QTL. In that case, linear models fitting haplotypes could have advantage because haplotypes can be in complete LD with QTL alleles. SNP genotypes were simulated to resemble 600K chip for the bovine genome. Genomic breeding values were predicted using either SNP genotypes or non-overlapping haplotypes. When QTL had low MAF, the haplotype model had significantly higher accuracy than the SNP model. Results show that fitting haplotypes can improve the accuracy of genomic prediction for traits controlled by rare QTL.

Keywords: Animal Science, ASL R3023

How to Cite: Sun, X. , Fernando, R. L. , Garrick, D. J. & Dekkers, J. C. (2015) “Improved Accuracy of Genomic Prediction for Traits with Rare QTL by Fitting Haplotypes”, Iowa State University Animal Industry Report. 12(1). doi: https://doi.org/10.31274/ans_air-180814-1339