Genomic selection in farm animals: accuracy of prediction and applications with imputed whole-genome sequencing data in chicken

Genomic prediction has been successfully applied in many livestock breeding schemes, based on different densities of single nucleotide polymorphism (SNP) array data. With the availability of whole-genome sequencing (WGS) data, which may contain the causal mutations, there are a growing number of studies to conducting genomic prediction with WGS data. The main objective of this thesis was to investigate the possibility of imputing SNP array data up to the whole genome sequence level (Chapter 2) and then perform genomic prediction based on the imputed WGS data and SNP array data with different genomic relationship matrices to account for genetic architecture (Chapter 3). To further understand the accuracy of genomic prediction, a simulation study was performed to determine the degree of overestimation of the accuracy of genomic prediction, in order to propose a new method (Chapter 4). The technical progress in the last decade has made it possible to sequence millions of DNA reads in a relatively short time frame. Several variant callers based on different algorithms have emerged and have made it possible to extract SNPs out of the whole-genome sequence. Often, only a few individuals of a population are sequenced completely and imputation is used to obtain genotypes for all sequence-based SNP loci for other individuals that have been genotyped for a subset of SNPs using a genotyping array. Thus, in Chapter 2 we first compared the sets of variants detected with different variant callers, namely GATK, freebayes and SAMtools, and checked the quality of genotypes of the called variants in a set of 50 fully sequenced white and brown layers. There were 1,741,573 SNPs detected by all three callers on the studied chromosomes 3, 6, and 28, which was 71.6% (81.6%, 88.0%) of SNPs detected by GATK (SAMtools, freebayes) in total. Genotype concordance (GC), defined as the proportion of individuals whose array-derived genotypes are the same as the sequence-derived genotypes over all non-missing SNPs on the array, was 0.98 with GATK, 0.98 with SAMtools, and 0.97 with freebayes averaged over all SNPs on the studied chromosomes, respectively. Furthermore, for GATK (SAMtools, freebayes) 90 (88, 75) percent of variants had high values (>0.9) for other quality measures (non-reference sensitivity, non-reference genotype concordance and precision). Performance of all variant callers studied was very good in general, particularly for GATK and SAMtools. Second, we assessed the imputation accuracy (measured as the correlation between imputed and true genotype per SNP and per individual and genotype conflict between father-progeny pairs) when imputing from high density SNP array data to whole-genome sequence using data from approximately 1000 individuals from six generations. Three different imputation programs (Minimac, FImpute and IMPUTE2) were checked in different validation scenarios. Across all imputation programs, correlation between true and imputed genotypes was >0.95 on average with randomly masked 1000 SNPs from the SNP array and >0.85 for a leave-one-out cross-validation within sequenced individuals. FImpute performed slightly worse than Minimac and IMPUTE2 in terms of genotype correlation, especially for SNPs with low minor allele frequency, however, it did have the lowest numbers in Mendelian conflicts in available father-progeny pairs. Correlations of real and imputed genotypes remained constantly high even if individuals to be imputed were several generations away from the sequenced individuals. In conclusion, among three variant callers tested GATK proved the relatively better performance; Minimac proved the relatively better performance comparing to the other two imputation programs tested. Based on the conclusions in Chapter 2, we applied a genomic prediction with imputed WGS in Chapter 3. A commercial brown layer line comprising of 892 chickens from 6 generations was used in the study. These chickens were genotyped with a high density array data. Using the WGS data of 25 individuals, those array data were imputed up to the sequence level. The imputation was done with Minimac3, which needs pre-phased data generated with Beagle4. Accuracy of genomic prediction was measured as the correlation between de-regressed proofs and direct genomic breeding values of eggshell strength, feed intake and laying rate. In this study, besides the accuracy of genomic prediction based on array data and WGS data, accuracy based on different genomic relationship matrices to account for genetic architecture was investigated. The alternative weighting factors used were uniform, -(〖log〗_10 P) from a t-test of genome wide association study, and the square of estimated SNP effects from random regression BLUP. Best linear unbiased prediction given genetic architecture (BLUP|GA) was investigated as well. Prediction with uniform weights (the original GBLUP) was implemented with all SNPs or with only genic SNPs, both based on array and imputed whole sequence data. Averaging over the studied traits, predictive ability with only genic SNPs in WGS data was 0.366 ± 0.075, which was the highest predictive ability observed in the current study. Genomic prediction with genic SNPs in high density array data provided the second highest accuracy (0.361 ± 0.072). The prediction with -(〖log〗_10 P) or squares of SNP effects as weighting factors for building a genomic relationship matrix or BLUP|GA did not lead to higher accuracy, compared to that with uniform weights, regardless of the SNP set used. The results from this study showed that little or no benefit was gained when using all imputed WGS data to perform genomic prediction compared to using HD array data, regardless of the different SNP weightings tested. However, higher predictive ability was observed when using only genic SNPs extracted from the WGS data for genomic prediction. Decisions of genomic selection schemes are made based on the genomic breeding values (GBV) of selection candidates. Thus, the accuracy of GBV is a relevant parameter, as it reflects the stability of the prediction and the possibility that the GBV might change when more information becomes available. It is also one of the key factors in expected response to selection, which is also known as breeders’ equation. Accuracy of genomic prediction, however, is difficult to assess, considering true breeding values (TBV) of the candidates are not available in reality. In previous studies, several methods are proposed to assess the accuracy of GBV by using population and trait parameters (e.g. the effective population size, the reliability of quasi-phenotypes used, the number of independent chromosome segments) or parameters inferred from the mixed model equations. In practice, most approaches were found to overestimate the accuracy of genomic prediction. Thus, in Chapter 4 we tested several approaches used in previous studies based on simulated data under a variety of parameters mimicking different livestock breeding programs (i.e. a cattle-like and a pig-like as well as a basic scenario) and measured the magnitude of overestimation. Then we proposed a novel and computationally feasible method. Based on the comparison in Chapter 4, the new method provided a better prediction for the accuracy of GBV. The method still had one unknown parameter, for which we suggested an approach to approximate its value from a suitable data set reflecting two separate time points. In conclusion, the new approach provided a better assessment of the accuracy of GBVs in many cases.