Short communication: Genomic prediction using imputed whole-genome sequence variants in Brown Swiss Cattle

Frischknecht, Mirjam; Meuwissen, T.H.E.; Bapst, Beat; Seefried, Franz R.; Flury, Christine; Garrick, Dorian J.; Signer-Hasler, Heidi; Stricker, Christian; Bieber, Anna; Fries, R.; Ruß, Ingolf; Sölkner, Johann; Bagnato, A.; Gredler-Grandl, Birgit

doi:10.3168/jds.2017-12890

Cited by 40 publications

(37 citation statements)

References 18 publications

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“…The impact of imputation accuracy on genomic selection accuracy was studied by several authors. The genotype densification allowed to increase the genomic evaluation accuracy depending on the architecture of evaluated traits [65,66]. Moreover, genomic selection accuracy increased with better imputation accuracies [26,28].…”

Section: Discussionmentioning

confidence: 99%

Sequence Imputation from Low Density Single Nucleotide Polymorphism Panel in a Black Poplar Breeding population

Pégard¹,

Rogier²,

Berard

et al. 2018

Preprint

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Background: Genomic selection accuracy increases with the use of high SNP (single nucleotide polymorphism) coverage. However, such gains in coverage come at high costs, preventing their operational implementation by breeders. Low density panels imputed to higher densities offer a cheaper alternative. Our study is one of the first to explore the imputation in a tree species: black poplar. About 1000 pure-breed Populus nigra trees corresponding to a subsample of the French breeding population were selected and genotyped with a 12K custom Infinium Bead-Chip. Forty-three of those individuals corresponding mostly to nodal trees in the pedigree were fully sequenced (reference), while the remaining majority (target) was imputed from 8K to 1.4 million SNPs using FImpute. Each SNP and individual was evaluated for imputation errors by leave-one-out cross validation in the training sample of 43 sequenced trees. Some summary statistics such as Hardy Weinberg Equilibrium exact test p-value, quality of sequencing, depth of sequencing per site and per individual, minor allele frequency, marker density ratio or SNP information redundancy were calculated. Principal component and Boruta analyses were used on all these parameters to rank the factors affecting the quality of imputation. Additionally, we characterize the impact of the relatedness between reference population and target population.Results: During the imputation process, we used 7,540 SNPs from the chip to impute 1,438,827 SNPs from sequences along the 19 Chromosomes. At the individual level, imputation accuracy was very high with a proportion of SNPs correctly imputed between 0.84 and 0.99. The variation in accuracies was mostly due to differences in relatedness between individuals. At a SNP level, the imputation quality strongly depended on genotyped SNP density and to a lesser extent on the original minor allele frequency. The imputation did not appear to result in an increase of linkage disequilibrium. The genotype densification not only brought a better distribution of markers all along the genome, but also we did not detect any substantial bias in annotation categories.Conclusions: This study shows that it is possible to impute low-density marker panels to whole genome sequence with good accuracy under certain conditions that could be common to many breeding populations.

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Section: Discussionmentioning

confidence: 99%

Sequence Imputation from Low Density Single Nucleotide Polymorphism Panel in a Black Poplar Breeding population

Pégard¹,

Rogier²,

Berard

et al. 2018

Preprint

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“…In the present study, we identified 9 interesting genes controlling fertility on BTA17. Frischknecht et al (2018) found a QTL region for nonreturn rate at d 56, and the interval from first to last insemination on BTA8 in Brown Swiss cattle. Liu et al (2017) reported strong associations with conception rate at first insemination and the interval to first insemination in Chinese and Nordic Holstein cows on BTA23 and for heifers on BTA17.…”

Section: Discussionmentioning

confidence: 90%

Genome-wide association study of normal and atypical progesterone profiles in Holstein-Friesian dairy cows

Nyman

Duchemin

Koning

et al. 2019

Journal of Dairy Science

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Female fertility has a major role in dairy production and affects the profitability of dairy cattle. The genetic progress obtained by traditional selection can be slow because of the low heritability of classical fertility traits. Endocrine fertility traits based on progesterone concentration in milk have higher heritability and more directly reflect the cow's own reproductive physiology. The aim of our study was to identify genomic regions for 7 endocrine fertility traits in dairy cows by performing a genome-wide association study with 54,000 SNP. The next step was to fine-map targeted genomic regions with significant SNP using imputed sequences to identify potential candidate genes associated with the normal and atypical progesterone profiles. The association between a SNP and a phenotype was assessed by a single SNP analysis, using a linear mixed model that included a random polygenic effect. Phenotypes and genotypes were available for 1,126 primiparous and multiparous Holstein-Friesian cows from research herds in Ireland, the Netherlands, Sweden, and the United Kingdom. In total, 44 significant SNP associated with 7 endocrine fertility traits were identified on Bos taurus autosome (BTA) 1-4, 6, 8-9, 11-12, 14-17, 19, 21-24, and 29. Three chromosomes, BTA8, BTA17, and BTA23, were imputed from 54,000 SNP genotypes to the whole-genome sequence level with Beagle version 4.1. The fine-mapping identified several significant associations with delayed cyclicity, cessation of cyclicity, commencement of luteal activity, and inter-ovulatory interval. These associations may contribute to an index of markers for genetic improvement of fertility. Several potential candidate genes reported to affect reproduction were also identified in the targeted genomic regions. However, due to high linkage disequilibrium, it was not possible to identify putative causal genes or polymorphisms for any of the regions.

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“…Contrary to those ndings, our study showed no signi cant increase in prediction accuracy when using WGS variants as opposed to SNP from the HD. Other authors have also observed lower or no signi cant bene ts in predictive ability gain using sequence data comparing with SNP arrays [6,9,10,12,45,49,50].…”

Section: Predictive Abilitymentioning

confidence: 98%

Estimation of Breeding Values Using Different Densities of Snp to Inform Kinship in Broiler Chickens

Salvian

Moreira

Reis

et al. 2020

Preprint

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Background: Traditionally, breeding values are estimated based on phenotypic and pedigree information using the numerator relationship (A) matrix. With the availability of genomic information, genome-wide markers can be included in the estimation of breeding values through genomic kinship. However, the density of genomic information used can impact the cost of implementation. The aim of this study was to compare the rank, accuracy, and bias of estimated breeding values (EBV) for organs [heart (HRT), liver (LIV), gizzard (GIZ), lungs (LUN)] and carcass [breast (BRST), drumstick (DRM) and thigh (THG)] weight traits in a broiler population using pedigree-based BLUP (PBLUP) and single-step genomic BLUP (ssGBLUP) methods using various densities of SNP and variants imputed from whole-genome sequence (WGS). Results: For both PBLUP and ssGBLUP, heritability estimates varied from low (LUN) to high (HRT, LIV, GIZ, BRST, DRM and THG). Regression coefficients values of EBV on genomic estimated breeding values (GEBV) were similar for both the high density (HD) and WGS sets of SNPs, ranging from 0.87 to 0.99 across scenarios. Conclusion: Results show no benefit of using WGS data compared to HD array data using an unweighted ssGBLUP. Our results suggest that 10% of the content of the HD array can yield unbiased and accurate EBV.

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Short communication: Genomic prediction using imputed whole-genome sequence variants in Brown Swiss Cattle

Cited by 40 publications

References 18 publications

Sequence Imputation from Low Density Single Nucleotide Polymorphism Panel in a Black Poplar Breeding population

Sequence Imputation from Low Density Single Nucleotide Polymorphism Panel in a Black Poplar Breeding population

Genome-wide association study of normal and atypical progesterone profiles in Holstein-Friesian dairy cows

Estimation of Breeding Values Using Different Densities of Snp to Inform Kinship in Broiler Chickens

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