Genomic prediction in a numerically small breed population using prioritized genetic markers from whole‐genome sequence data

Moghaddar, Nasir; Brown, D. J.; Swan, Andrew; Gurman, P. M.; Li, Li; Werf, J. H. J. van der

doi:10.1111/jbg.12638

Cited by 10 publications

(8 citation statements)

References 32 publications

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“…These findings are in line with results from previous studies, demonstrating increased accuracy when information from genotypes is included, such as for Manech Tête Rousse dairy sheep (Macedo et al, 2020 ), small population of Dorper sheep (Moghaddar et al, 2022 ) or chicken mortality (Bermann et al, 2020 ). Furthermore, several studies comparing accuracies obtained from various genomic models indicate single‐step BLUP as the best way for getting high accuracy values in sheep (Baloche et al, 2013 ) or dairy goats (Mucha, Bunger, & Conington, 2015 ; Mucha, Mrode, et al, 2015 ).…”

Section: Resultssupporting

confidence: 90%

Including genotypic information in genetic evaluations increases the accuracy of sheep breeding values

Kaseja

Mucha

Smith

et al. 2023

J Animal Breeding Genetics

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The impact of inclusion of genome‐wide genotypes into breeding value predictions for UK Texel sheep is addressed in this article. The main aim was to investigate the level of change in the accuracy values for EBVs when information from animal genotypes is incorporated into the genetic evaluations. New genetic parameters for a range of lamb growth, carcass composition and health traits are described and applied in the estimation of conventional breeding values (EBVs) for almost 822,000 animals as well as genomic breeding values (gEBVs) after adding 10,143 genotypes. Principal component analyses showed that there are no major distinct groups; hence, the population is mainly homogenous and genetically well‐linked. Results suggested that the highest change in accuracy was observed for the animals that are not phenotyped but have good links to the reference population. This was seen especially for the lowly heritable health traits thereby proving that the use of genotypes in breeding values estimation may accelerate the genetic gain by producing more accurate values especially for young, un‐phenotyped animals.

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Section: Resultssupporting

confidence: 90%

Including genotypic information in genetic evaluations increases the accuracy of sheep breeding values

Kaseja

Mucha

Smith

et al. 2023

J Animal Breeding Genetics

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“…Other studies have found small but often unstable improvements (e.g., from 1 to 5% or no improvement depending on the prediction method used [17][18][19], or trait-dependent results [19,20]). For genomic prediction across populations, the identification and inclusion of causal variants from WGS have been shown to improve prediction accuracy [21][22][23][24], especially for numerically small populations and for populations that are not represented in the training set [21,[23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Genomic prediction with whole-genome sequence data in intensely selected pig lines

et al. 2022

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Background Early simulations indicated that whole-genome sequence data (WGS) could improve the accuracy of genomic predictions within and across breeds. However, empirical results have been ambiguous so far. Large datasets that capture most of the genomic diversity in a population must be assembled so that allele substitution effects are estimated with high accuracy. The objectives of this study were to use a large pig dataset from seven intensely selected lines to assess the benefits of using WGS for genomic prediction compared to using commercial marker arrays and to identify scenarios in which WGS provides the largest advantage. Methods We sequenced 6931 individuals from seven commercial pig lines with different numerical sizes. Genotypes of 32.8 million variants were imputed for 396,100 individuals (17,224 to 104,661 per line). We used BayesR to perform genomic prediction for eight complex traits. Genomic predictions were performed using either data from a standard marker array or variants preselected from WGS based on association tests. Results The accuracies of genomic predictions based on preselected WGS variants were not robust across traits and lines and the improvements in prediction accuracy that we achieved so far with WGS compared to standard marker arrays were generally small. The most favourable results for WGS were obtained when the largest training sets were available and standard marker arrays were augmented with preselected variants with statistically significant associations to the trait. With this method and training sets of around 80k individuals, the accuracy of within-line genomic predictions was on average improved by 0.025. With multi-line training sets, improvements of 0.04 compared to marker arrays could be expected. Conclusions Our results showed that WGS has limited potential to improve the accuracy of genomic predictions compared to marker arrays in intensely selected pig lines. Thus, although we expect that larger improvements in accuracy from the use of WGS are possible with a combination of larger training sets and optimised pipelines for generating and analysing such datasets, the use of WGS in the current implementations of genomic prediction should be carefully evaluated against the cost of large-scale WGS data on a case-by-case basis.

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“…The design of biology‐informed custom SNP arrays may outperform the standard arrays, HD arrays and even WGS in terms of predicting genetic values (Brøndum et al, 2015; Moghaddar et al, 2022; Raymond et al, 2018; VanRaden et al, 2017; Xiang et al, 2021). A study by Brøndum et al (2015) showed that including 1623 QTL markers derived from WGS into a medium‐density chip increased prediction accuracies by 3–5 percentage points.…”

Section: Discussionmentioning

confidence: 99%

“…These variants can be identified through GWAS, gene expression studies, sequence data and other tools. This approach has been adopted and shown to be more accurate than WGS prediction in some scenarios (MacLeod et al, 2016; Moghaddar et al, 2022; Raymond et al, 2018). The limitation of including WGS data is likely due to the inclusion of a vast number of redundant SNPs with high LD (VanRaden et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Alternative SNP weighting for multi‐step and single‐step genomic BLUP in the presence of causative variants

Santana

Riser

Hay

et al. 2023

J Animal Breeding Genetics

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The accuracy of genetic selection in dairy can be increased by the adoption of new technologies, such as the inclusion of sequence data. In simulation studies, assigning different weights to causative single‐nucleotide polymorphism (SNP) markers led to better predictions depending on the genomic prediction method used. However, it is still not clear how the weights should be calculated. Our objective was to evaluate the accuracy of a multi‐step method (GBLUP) and single‐step GBLUP with simulated data using regular SNP, causatives variants (QTN) and the combination of both. Additionally, we compared the accuracies of all previous scenarios using alternatives for SNP weighting. The data were simulated assuming a single trait with a heritability of 0.3. The effective population size (Ne) was approximately 200. The pedigree contained 440,000 animals, and approximately 16,800 individuals were genotyped. A total of 49,974 SNP markers were evenly placed throughout the genome, and 100, 1000 and 2000 causative QTN were simulated. Both GBLUP and ssGBLUP were used in this study. We evaluated quadratic and nonlinear SNP weights in addition to the unweighted G. The inclusion of QTN to panels led to significant accuracy gains. Nonlinear A was demonstrated to be superior to quadratic weighting and unweighted approaches; however, results from Nonlinear A were dependent on the equation parameters. The unweighted approach was more suitable for less polygenic scenarios. Finally, SNP weighting might help elucidate trait architecture features based on changes in the accuracy of genomic prediction.

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Genomic prediction in a numerically small breed population using prioritized genetic markers from whole‐genome sequence data

Cited by 10 publications

References 32 publications

Including genotypic information in genetic evaluations increases the accuracy of sheep breeding values

Including genotypic information in genetic evaluations increases the accuracy of sheep breeding values

Genomic prediction with whole-genome sequence data in intensely selected pig lines

Alternative SNP weighting for multi‐step and single‐step genomic BLUP in the presence of causative variants

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