Review: How to improve genomic predictions in small dairy cattle populations

Lund, Mogens Sandø; Berg, I. van den; Ma, Peipei; Brøndum, Rasmus Froberg; Su, Guosheng

doi:10.1017/s1751731115003031

Cited by 38 publications

(33 citation statements)

References 37 publications

(42 reference statements)

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“…This confirms the expectation that, within the Holstein population, it is probably difficult to increase the accuracy of prediction, due to the small size of the effective population and the long-range LD in the population, as was previously demonstrated in a simulation study by MacLeod [30]. Hence, using ISQ and selected variants might be especially beneficial for across-breed prediction in small populations [31] or when traits are used with some QTL having large effects, as for fat percentage [32]. …”

Section: Discussionsupporting

confidence: 86%

Genomic prediction using preselected DNA variants from a GWAS with whole-genome sequence data in Holstein–Friesian cattle

et al. 2016

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BackgroundWhole-genome sequence data is expected to capture genetic variation more completely than common genotyping panels. Our objective was to compare the proportion of variance explained and the accuracy of genomic prediction by using imputed sequence data or preselected SNPs from a genome-wide association study (GWAS) with imputed whole-genome sequence data.MethodsPhenotypes were available for 5503 Holstein–Friesian bulls. Genotypes were imputed up to whole-genome sequence (13,789,029 segregating DNA variants) by using run 4 of the 1000 bull genomes project. The program GCTA was used to perform GWAS for protein yield (PY), somatic cell score (SCS) and interval from first to last insemination (IFL). From the GWAS, subsets of variants were selected and genomic relationship matrices (GRM) were used to estimate the variance explained in 2087 validation animals and to evaluate the genomic prediction ability. Finally, two GRM were fitted together in several models to evaluate the effect of selected variants that were in competition with all the other variants.ResultsThe GRM based on full sequence data explained only marginally more genetic variation than that based on common SNP panels: for PY, SCS and IFL, genomic heritability improved from 0.81 to 0.83, 0.83 to 0.87 and 0.69 to 0.72, respectively. Sequence data also helped to identify more variants linked to quantitative trait loci and resulted in clearer GWAS peaks across the genome. The proportion of total variance explained by the selected variants combined in a GRM was considerably smaller than that explained by all variants (less than 0.31 for all traits). When selected variants were used, accuracy of genomic predictions decreased and bias increased.ConclusionsAlthough 35 to 42 variants were detected that together explained 13 to 19% of the total variance (18 to 23% of the genetic variance) when fitted alone, there was no advantage in using dense sequence information for genomic prediction in the Holstein data used in our study. Detection and selection of variants within a single breed are difficult due to long-range linkage disequilibrium. Stringent selection of variants resulted in more biased genomic predictions, although this might be due to the training population being the same dataset from which the selected variants were identified.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-016-0274-1) contains supplementary material, which is available to authorized users.

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Section: Discussionsupporting

confidence: 86%

Genomic prediction using preselected DNA variants from a GWAS with whole-genome sequence data in Holstein–Friesian cattle

et al. 2016

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“…Wiggans et al [26] reported that during the genomic selection of cattle conducted in 2011 in the USA, the reliability of the selection of milk yield increased by 34.0% over the parent average, and that of fat and protein yields increased by 33.8% and 24.9%, respectively, indicating that reliabilities can be increased even more than those we obtained in our study. The smaller improvements we found might have been due to the relatively very small reference population we used [29,30]. When genomic selection is applied in the selection of dairy cattle in the domestic population, the size of the reference population will increase continuously and potentially result in greater improvements, but this will take time.…”

Section: Discussionmentioning

confidence: 96%

The effectiveness of genomic selection for milk production traits of Holstein dairy cattle

Lee

Dang

Alam

et al. 2020

Asian-Australas J Anim Sci

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Objective: This study was conducted to test the efficiency of genomic selection for milk production traits in a Korean Holstein cattle population. Methods: A total of 506,481 milk production records from 293,855 animals (2,090 heads with single nucleotide polymorphism information) were used to estimate breeding value by single step best linear unbiased prediction. Results: The heritability estimates for milk, fat, and protein yields in the first parity were 0.28, 0.26, and 0.23, respectively. As the parity increased, the heritability decreased for all milk production traits. The estimated generation intervals of sire for the production of bulls (L SB ) and that for the production of cows (L SC ) were 7.9 and 8.1 years, respectively, and the estimated generation intervals of dams for the production of bulls (L DB ) and cows (L DC ) were 4.9 and 4.2 years, respectively. In the overall data set, the reliability of genomic estimated breeding value (GEBV) increased by 9% on average over that of estimated breeding value (EBV), and increased by 7% in cows with test records, about 4% in bulls with progeny records, and 13% in heifers without test records. The difference in the reliability between GEBV and EBV was especially significant for the data from young bulls, i.e. 17% on average for milk (39% vs 22%), fat (39% vs 22%), and protein (37% vs 22%) yields, respectively. When selected for the milk yield using GEBV, the genetic gain increased about 7.1% over the gain with the EBV in the cows with test records, and by 2.9% in bulls with progeny records, while the genetic gain increased by about 24.2% in heifers without test records and by 35% in young bulls without progeny records. Conclusion: More genetic gains can be expected through the use of GEBV than EBV, and genomic selection was more effective in the selection of young bulls and heifers without test records.a These authors contributed equally to this work.

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“…This is likely because the Red Holstein is much more closely related to the Holstein individuals in the reference populations. Because LD is conserved over much shorter distances across breeds than within breeds, sequence data is thought to be especially beneficial for multi-breed and across-breed prediction [ 2 ].…”

Section: Discussionmentioning

confidence: 99%

“…In dairy cattle, LD is extensive within a breed but the phase of LD varies between breeds [ 1 ], which is expected to decrease across-breed prediction. Use of sequence data is expected to increase the accuracy of multi-breed and across-breed prediction, which would be beneficial for breeds with small reference population sizes [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-breed genomic prediction using Bayes R with sequence data and dropping variants with a small effect

et al. 2017

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BackgroundThe increasing availability of whole-genome sequence data is expected to increase the accuracy of genomic prediction. However, results from simulation studies and analysis of real data do not always show an increase in accuracy from sequence data compared to high-density (HD) single nucleotide polymorphism (SNP) chip genotypes. In addition, the sheer number of variants makes analysis of all variants and accurate estimation of all effects computationally challenging. Our objective was to find a strategy to approximate the analysis of whole-sequence data with a Bayesian variable selection model. Using a simulated dataset, we applied a Bayes R hybrid model to analyse whole-sequence data, test the effect of dropping a proportion of variants during the analysis, and test how the analysis can be split into separate analyses per chromosome to reduce the elapsed computing time. We also investigated the effect of imputation errors on prediction accuracy. Subsequently, we applied the approach to a dataset that contained imputed sequences and records for production and fertility traits for 38,492 Holstein, Jersey, Australian Red and crossbred bulls and cows.ResultsWith the simulated dataset, we found that prediction accuracy was highly increased for a breed that was not represented in the training population for sequence data compared to HD SNP data. Either dropping part of the variants during the analysis or splitting the analysis into separate analyses per chromosome decreased accuracy compared to analysing whole-sequence data. First, dropping variants from each chromosome and reanalysing the retained variants together resulted in an accuracy similar to that obtained when analysing whole-sequence data. Adding imputation errors decreased prediction accuracy, especially for errors in the validation population. With real data, using sequence variants resulted in accuracies that were similar to those obtained with the HD SNPs.ConclusionsWe present an efficient approach to approximate analysis of whole-sequence data with a Bayesian variable selection model. The lack of increase in prediction accuracy when applied to real data could be due to imputation errors, which demonstrates the importance of developing more accurate methods of imputation or directly genotyping sequence variants that have a major effect in the prediction equation.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-017-0347-9) contains supplementary material, which is available to authorized users.

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Review: How to improve genomic predictions in small dairy cattle populations

Cited by 38 publications

References 37 publications

Genomic prediction using preselected DNA variants from a GWAS with whole-genome sequence data in Holstein–Friesian cattle

Genomic prediction using preselected DNA variants from a GWAS with whole-genome sequence data in Holstein–Friesian cattle

The effectiveness of genomic selection for milk production traits of Holstein dairy cattle

Multi-breed genomic prediction using Bayes R with sequence data and dropping variants with a small effect

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