7 research outputs found
Genomic prediction using haplotypes in Brown Swiss
No full textIn order to improve accuracy of genomic selection different approaches have been suggested. One possibility is to use haplotypes instead of SNPs. It is thought that by the usage of haplotypes the number of effects to estimate should be decreased and the accuracy should be increased because the haplotype should catch the causal variants better than from LD with SNPs. Different definitions of the length of haplotypes are possible. The haplotypes can either be determined by the number of SNPs in a haplotype, by the length in base pairs or by linkage disequilibrium (LD) measures. For this study we used four different definitions of haplotype lengths either based on physical length in bp or on LD measures. We used haplotypes with a length of 250kb or 1Mb, we defined the LD based groups in PLINK and either included or excluded SNPs that were not included in any LD block. We estimated genomic breeding values with each of these haplotype definitions and compared prediction accuracy to that achieved with 50K SNPs for four traits in Brown Swiss. The traits were protein yield, non-return rate 56 in heifers, somatic cell score and stature. Estimation of genomic breeding values was carried out applying a BayesC model. We found trait-specific differences in the ranking of the scenarios. However, differences in accuracies between scenarios within trait were relatively low and using haplotypes only marginally increased the accuracy of genomic breeding values. The number of variables to be fitted increased relative to the SNP model especially for scenarios where the haplotypes were defined by physical length
National single-step genomic method that integrates multi-national genomic information
No full textThe aim of this paper was to develop a national single-step genomic BLUP that integrates multi-national genomic estimated breeding values (EBV) and associated reliabilities without double counting dependent data contributions from the different evaluations. Simultaneous use of all data, including phenotypes, pedigree, and genotypes, is a condition to obtain unbiased EBV. However, this condition is not always fully met, mainly due to unavailability of foreign raw data for imported animals. In dairy cattle genetic evaluations, this issue is traditionally tackled through the multiple across-country evaluation (MACE) of sires, performed by Interbull Centre (Uppsala, Sweden). Multiple across-country evaluation regresses all the available national information onto a joint pedigree to obtain country-specific rankings of all sires without sharing the raw data. In the context of genomic selection, the issue is handled by exchanging sire genotypes and by using MACE information (i.e., MACE EBV and reliabilities), as a valuable source of "phenotypic" data. Although all the available data are considered, these "multi-national" genomic evaluations use multi-step methods assuming independence of various sources of information, which is not met in all situations. We developed a method that handles this by single-step genomic evaluation that jointly (1) uses national phenotypic, genomic, and pedigree data; (2) uses multi-national genomic information; and (3) avoids double counting dependent data contributions from an animal's own records and relatives' records. The method was demonstrated by integrating multi-national genomic EBV and reliabilities of Brown Swiss sires, included in the InterGenomics consortium at Interbull Centre, into the national evaluation in Slovenia. The results showed that the method could (1) increase reliability of a national (genomic) evaluation; (2) provide consistent ranking of all animals: bulls, cows, and young animals; and (3) increase the size of a genomic training population. These features provide more efficient and transparent selection throughout a breeding program.</p
Genome-wide association study in Brown Swiss for udder traits based on sequence data
No full textIdentification of QTL, especially of causative variants within QTL is still challenging. Higher SNP densities aid the identification and fine-mapping of QTL. Based on imputed sequence data we performed GWAS for udder traits in Brown Swiss cattle. The GWAS was performed using a mixed-model approach with deregressed breeding values as phenotypes. The traits investigated included: udder depth (UD), fore udder attachment (FUA), rear udder width (RUW), rear udder height (RUH), fore udder length (FUL), and central ligament (CL). We found significant associations on BTA 3 (UD, FUA), BTA 5 (UD), BTA 17 (FUL, RUW, CL) and BTA 20 (FUA). A single gene was located in the significantly associated regions on BTA 5 (ABCC9) and BTA 20 (HCN1). The region on BTA 17 spans almost 3 Mb and includes 74 genes (maximal region for all the traits combined) and the region on BTA 3 includes 91 genes across 3 Mb. We also looked for associated missense variants in these intervals. Neither for ABCC9 nor for HCN1 we could identify such a variant. On BTA 17 we identified 2 missense variants that were significantly associated with CL. On BTA 3, 11 missense variants were significantly associated with UD and/or FUA. The advantages of using imputed sequence data compared to SNP chip genotypes are mainly through the inclusion of potential causative variants
Genomic prediction in cattle based on sequence data
No full textOne of the factors influencing the accuracy of genomic prediction is the density of SNP data used for prediction. We used sequence genotypes from the 1000 Bull Genomes Project (Run 5) as reference data to impute the whole-genome sequences of around 22,000 Brown Swiss and 15,000 Holstein, Simmental and Swiss Fleckvieh cattle to whole-genome sequences. We used FImpute to obtain HD genotypes and imputed with Minimac from HD to sequence data (16,184,800 variants). We report here the results for non return-rate 56 in heifers which are available already for Brown Swiss. For effect estimation deregressed breeding values of 2,018 Brown Swiss bulls with reliabilities above 0.65 were used. We used the BayesC approach implemented in gbcpp. Accuracy of genomic prediction was calculated as the correlation between the deregressed breeding values and the predicted direct genomic breeding value in a set of 240 of young bulls with accurate breeding values. LD pruned sequence data (5,812,425 SNPs; r=0.412) and sequence data (12,973,772 SNPs; r=0.407) yielded a higher accuracy than 50k data (38,009 SNPs; r=0.400) and missense data (34,184 SNPs; r=0,372).
The results will be further evaluated by investigating more traits and breeds
Genome-wide association study in Brown Swiss for fertility traits based on sequence data
No full textFertility in dairy cattle has declined continuously during the last years. In Switzerland low fertility is one of the most important culling reason. Therefore this trait complex is of great economic importance. But fertility traits are functional traits with low heritabilities. We performed GWAS in Brown Swiss and Holstein cattle for the fertility traits: non-return rate after 56 days in heifers (NRH) and cows (NRC); days between first and last insemination in heifers (FLIH) and cows (FLIC); and days to first service (DFS). We used deregressed breeding values as input phenotypes and imputed whole-genome sequence dosages as genotypes. In Holstein (2,336 to 3,257 individuals) we could not identify any significant region for any of the investigated traits. For Brown Swiss cattle (1,392 to 3,278 individuals) however we could identify a QTL on BTA 17 for NRH and FLIH. This locus also showed a suggestive association with NRC and FLIC. The region significantly associated to NRH and FLIH comprises 27 genes. Among the associated variants two non-synonymous variants in the genes GAS2L and ASCC2 could be identified. These two variants could be potentially causative ariants
Genome-wide association studies of fertility and calving traits in Brown Swiss cattle using imputed whole-genome sequences
No full textBACKGROUND:
The detection of quantitative trait loci has accelerated with recent developments in genomics. The introduction of genomic selection in combination with sequencing efforts has made a large amount of genotypic data available. Functional traits such as fertility and calving traits have been included in routine genomic estimation of breeding values making large quantities of phenotypic data available for these traits. This data was used to investigate the genetics underlying fertility and calving traits and to identify potentially causative genomic regions and variants. We performed genome-wide association studies for 13 functional traits related to female fertility as well as for direct and maternal calving ease based on imputed whole-genome sequences. Deregressed breeding values from ~1000-5000 bulls per trait were used to test for associations with approximately 10 million imputed sequence SNPs.
RESULTS:
We identified a QTL on BTA17 associated with non-return rate at 56 days and with interval from first to last insemination. We found two significantly associated non-synonymous SNPs within this QTL region. Two more QTL for fertility traits were identified on BTA25 and 29. A single QTL was identified for maternal calving traits on BTA13 whereas three QTL on BTA19, 21 and 25 were identified for direct calving traits. The QTL on BTA19 co-localizes with the reported BH2 haplotype. The QTL on BTA25 is concordant for fertility and calving traits and co-localizes with a QTL previously reported to influence stature and related traits in Brown Swiss dairy cattle.
CONCLUSION:
The detection of QTL and their causative variants remains challenging. Combining comprehensive phenotypic data with imputed whole genome sequences seems promising. We present a QTL on BTA17 for female fertility in dairy cattle with two significantly associated non-synonymous SNPs, along with five additional QTL for fertility traits and calving traits. For all of these we fine mapped the regions and suggest candidate genes and candidate variants
Short communication : Genomic prediction using imputed whole-genome sequence variants in Brown Swiss Cattle
No full textThe accuracy of genomic prediction determines response to selection. It has been hypothesized that accuracy of genomic breeding values can be increased by a higher density of variants. We used imputed whole-genome sequence data and various single nucleotide polymorphism (SNP) selection criteria to estimate genomic breeding values in Brown Swiss cattle. The extreme scenarios were 50K SNP chip data and whole-genome sequence data with intermediate scenarios using linkage disequilibrium-pruned whole-genome sequence variants, only variants predicted to be missense, or the top 50K variants from genome-wide association studies. We estimated genomic breeding values for 3 traits (somatic cell score, nonreturn rate in heifers, and stature) and found differences in accuracy levels between traits. However, among different SNP sets, accuracy was very similar. In our analyses, sequence data led to a marginal increase in accuracy for 1 trait and was lower than 50K for the other traits. We concluded that the inclusion of imputed whole-genome sequence data does not lead to increased accuracy of genomic prediction with the methods
