Missing genotypes are a common feature of high density SNP datasets obtained using SNP chip technology and this is likely to decrease the accuracy of genomic selection. This problem can be circumvented by imputing the missing genotypes with estimated genotypes. When implementing imputation, the criteria used for SNP data quality control and whether to perform imputation before or after data quality control need to consider. In this paper, we compared six strategies of imputation and quality control using different imputation methods, different quality control criteria and by changing the order of imputation and quality control, against a real dataset of milk production traits in Chinese Holstein cattle. The results demonstrated that, no matter what imputation method and quality control criteria were used, strategies with imputation before quality control performed better than strategies with imputation after quality control in terms of accuracy of genomic selection. The different imputation methods and quality control criteria did not significantly influence the accuracy of genomic selection. We concluded that performing imputation before quality control could increase the accuracy of genomic selection, especially when the rate of missing genotypes is high and the reference population is small.
Introduction Genome-wide association(GWA) studies have begun to apply to genetic researches and attempt to und...
Jiapeng Zhou~1,Lanjuan Zhao~2,Jian Li~3,Peng Xiao~2,Qin Zhang~1,Hongwen Deng~3 1.State Key Laboratory for Agrobiotechnology,Key Laboratory of Animal Genetics and Breeding of the Ministry of Agriculture, College of Animal Science and Technology,China Agricultural University,Beijing,China
Genomic selection (GS) is a marker-assisted selection method, in which high density markers covering the whole genome are used simultaneously for individual genetic evaluation via genomic estimated breeding values (GEBVs). GS can increase the accuracy of selection, shorten the generation interval by selecting individuals at the early stage of life, and accelerate genetic progress. With the availability of high density whole genome SNP (single nucleotide polymorphism) chips for livestock, GS is reshaping the conventional animal breeding systems. In many countries, GS is becoming the major genetic evaluation method for bull selection in dairy cattle and GS may soon completely replace the traditional genetic evaluation system. In recent years, GS has become an important research topic in animal, plant and aquiculture breeding and many exciting results have been reported. In this paper, the methods for obtaining GEBVs, factors affecting the accuracy of GEBVs, and the current status of implementation of GS in livestock are reviewed. Some unresolved issues related to GS in livestock are also discussed.
General pedigrees are very common in farm animals,and the recent availability of large panels of SNPs in domestic species has given new momentum to the search for the mutations underlying variation in quantitative traits.In this paper,we proposed a new transmission disequilibrium test approach,called the pedigree transmission disequilibrium test,which deals with general pedigrees and quantitative traits in farm animals.Compared with the existing pedigree disequilibrium test (PDT) and general linear model-based method QTDT,our approach performed better with higher power and lower type I error,especially in scenarios where the quantitative trait locus (QTL) effect was small.We also investigated the application of our approach in selective genotyping design.Our simulation studies indicated that it was plausible to implement a selective genotyping strategy in the proposed pedigree transmission disequilibrium test.We found that our approach performed equally well or better when only some proportion of the individuals in the two tails were genotyped compared with its performance when all the individuals in the pedigree were genotyped.
