Alternative methods improve the accuracy of genomic prediction using information from a causal point mutation in a dairy sheep model

Abstract

Background Genomic evaluation is usually based on a set of markers assumed to be linked with causal mutations. Selection and precise management of major genes and the remaining polygenic component might be improved by including causal polymorphisms in the evaluation models. In this study, various methods involving a known mutation were used to estimate prediction accuracy. The SOCS2 gene, which influences body growth, milk production and somatic cell scores, a proxy for mastitis, was studied as an example in dairy sheep. Methods The data comprised 1,503,148 phenotypes and 9844 54K SNPs genotypes. The SOCS2 SNP was genotyped for 4297 animals and imputed in the above 9844 animals. Breeding values and their accuracies were estimated for each of nine traits by using single-step approaches. Pedigree-based BLUP, single-step genomic BLUP (ssGBLUP) involving the 54K ovine SNPs chip, and four weighted ssGBLUP (WssGBLUP) methods were compared. In WssGBLUP methods, weights are assigned to SNPs depending on their effect on the trait. The ssGBLUP and WssGBLUP methods were again tested after including the SOCS2 causal mutation as a SNP. Finally, the Gene Content approach was tested, which uses a multiple-trait model that considers the SOCS2 genotype as a trait. Results EBV accuracies were increased by 14.03% between the pedigree-based BLUP and ssGBLUP methods and by 3.99% between ssGBLUP and WssGBLUP. Adding the SOCS2 SNP to ssGBLUP methods led to an average gain of 0.26%. Construction of the kinship matrix and estimation of breeding values was generally improved by placing emphasis on SNPs in regions with a strong effect on traits. In the absence of chip data, the Gene Content method, compared to pedigree-based BLUP, efficiently accounted for partial genotyping information on SOCS2 as accuracy was increased by 6.25%. This method also allowed dissociation of the genetic component due to the major gene from the remaining polygenic component. Conclusions Causal mutations with a moderate to strong effect can be captured with conventional SNP chips by applying appropriate genomic evaluation methods. The Gene Content method provides an efficient way to account for causal mutations in populations lacking genome-wide genotyping.

Genomic Prediction Using Alternative Strategies of Weighted Single-Step Genomic BLUP for Yearling Weight and Carcass Traits in Hanwoo Beef Cattle

“…Our findings showed that the window weighting performed better than single ones for CW, EMA, and YW ( Figure 4). Also, the highest accuracy was obtained using 100 SNPs for EMA and 75 SNPs for CW and YW, which might be due to QTL size (Oget et al, 2019). In a study using simulation, Hassani et al [46] showed the worst prediction accuracy occurred using the single SNP method.…”

“…The results of most studies showed that maximum accuracy is achieved at the second iteration. For instance, Wang et al [ 22 ], Teissier et al [ 23 , 35 ], and Oget et al [ 24 ] exhibited that the most accurate genomic evaluation was obtained at the second iteration. Nonetheless, the highest accuracy of genomic prediction was observed at the third [ 12 ] and the fourth [ 11 , 12 ] iteration for BFT and CW in Hanwoo beef cattle.…”

“…The superiority of ssGBLUP over pedigree-based BLUP or GBLUP approaches for the prediction of breeding values has been reported in previous studies on Hanwoo cattle [ 18 , 26 , 38 ] and other beef cattle breeds [ 30 , 33 , 39 ]. Other studies on French dairy goats [ 23 , 40 ] and sheep [ 24 ] identified that ssGBLUP was more accurate than either the pedigree-based BLUP or GBLUP for milk production traits, udder type traits, and somatic cell scores.…”

“…Likewise, Teissier et al [ 23 ] achieved an up to 3% increase in the accuracy of genomic prediction on protein content using WssGBLUP over ssGBLUP, as the α S1 -casein gene is well known to segregate in the Alpine and Saanen breeds. In a recent study, Oget et al [ 24 ] also showed that WssGBLUP performed more accurately than ssGBLUP, ranging from 2.06% to 8.75% for production, somatic cell score, and type traits which were affected by the major gene SOCS2 in Lacaune dairy sheep. On the other hand, accuracies from ssGBLUP and WssGBLUP analyses were near the same for BT and MS ( Figure 2 ).…”

“…Zhang et al [ 6 ] developed several iterative weighting strategies with simulation data and indicated that the use of a common weight as a window could improve the accuracy of prediction compared with single SNP weighting. Also, Teissier et al [ 23 ] and Oget et al [ 24 ] depicted that WssGBLUP and its alternatives performed more accurately than ssGBLUP in French dairy goats and sheep.…”