Using imputed whole-genome sequence data to improve the accuracy of genomic prediction for parasite resistance in Australian sheep

Kalaldeh, Mohammad Al; Gibson, John P.; Duijvesteijn, Naomi; Daetwyler, Hans D.; MacLeod, Iona M.; Moghaddar, Nasir; Lee, Sang; Werf, J. H. J. van der

doi:10.1186/s12711-019-0476-4

Cited by 38 publications

(61 citation statements)

References 33 publications

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“…They were then imputed to the sequence level one chromosome at a time, using whole genome sequence data of a reference population of 311 progeny tested Hanwoo bulls [1], resulting in a total of 27,980,473 SNPs; the imputation pipeline followed for imputing from 770K to the sequence level was the same as the one used for 50K to HD imputation. Following a previous study [32], only SNPs with Minimac3 imputation quality statistic (R 2 ) higher than 0.4 were retained for further analysis, resulting in a total of 12,980,473 SNPs. The overall average imputation accuracy, post quality control (R 2 ) was 0.76, which was similar to a previously reported study [33].…”

Section: Snp Genotyping Imputation and Filtering For Regulatory Snpsmentioning

confidence: 99%

A Gene-Set Enrichment and Protein–Protein Interaction Network-Based GWAS with Regulatory SNPs Identifies Candidate Genes and Pathways Associated with Carcass Traits in Hanwoo Cattle

Srikanth

Lee

Chung³

et al. 2020

Genes

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Non-synonymous SNPs and protein coding SNPs within the promoter region of genes (regulatory SNPs) might have a significant effect on carcass traits. Imputed sequence level data of 10,215 Hanwoo bulls, annotated and filtered to include only regulatory SNPs (450,062 SNPs), were used in a genome-wide association study (GWAS) to identify loci associated with backfat thickness (BFT), carcass weight (CWT), eye muscle area (EMA), and marbling score (MS). A total of 15, 176, and 1 SNPs were found to be significantly associated (p < 1.11 × 10 −7 ) with BFT, CWT, and EMA, respectively. The significant loci were BTA4 (CWT), BTA6 (CWT), BTA14 (CWT and EMA), and BTA19 (BFT). BayesR estimated that 1.1%~1.9% of the SNPs contributed to more than 0.01% of the phenotypic variance. So, the GWAS was complemented by a gene-set enrichment (GSEA) and protein-protein interaction network (PPIN) analysis in identifying the pathways affecting carcass traits. At p < 0.005 (~2,261 SNPs), 25 GO and 18 KEGG categories, including calcium signaling, cell proliferation, and folate biosynthesis, were found to be enriched through GSEA. The PPIN analysis showed enrichment for 81 candidate genes involved in various pathways, including the PI3K-AKT, calcium, and FoxO signaling pathways. Our finding provides insight into the effects of regulatory SNPs on carcass traits.Genes 2020, 11, 316 2 of 22 and eye muscle area (EMA) [4]. Though substantial improvement in carcass and meat quality have been achieved, due to market requirement for higher quality, and for improving the economic value of Hanwoo, continuous improvement of economically important trait is required [5,6]. A genome-wide association study (GWAS) is an affordable and powerful tool to discover candidate genes and loci associated with quantitative traits [7]. GWASs in livestock, including in Hanwoo [8][9][10][11], have resulted in remarkable insights into the genetic architecture of carcass traits. Genetic variation in complex traits such as carcass and meat quality traits are, however, due to the contribution of many mutations with small effects [1,12] (polygenic effect). Though some of these mutations have been successfully identified through GWASs, the high significance thresholds required to correct for the multiple testing problem results in the identification of only SNPs with a large effect size [7,12]. Further, a GWAS does not make use of the fact that genes work together in a network, and multi-allelic QTL might not be captured due to the bi-allelic nature of SNPs [13]. Moreover, epistasis is an important genetic component underlying phenotypic variation that also accounts for missing heritability [14]. Therefore, a GWAS alone might result in only limited understanding of the nature of complex traits [13]. Suggested solutions to overcome this limitation and understand the genetic complexities regulating complex traits are to complement a GWAS with gene-set enrichment, a protein-protein interaction network (PPIN), and pathway analyses [15][16][17][18]. In GSEA and pathway analysis, a g...

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Section: Snp Genotyping Imputation and Filtering For Regulatory Snpsmentioning

confidence: 99%

A Gene-Set Enrichment and Protein–Protein Interaction Network-Based GWAS with Regulatory SNPs Identifies Candidate Genes and Pathways Associated with Carcass Traits in Hanwoo Cattle

Srikanth

Lee

Chung³

et al. 2020

Genes

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“…This is also in line with results of GP in this thesis using the variants pre-selected based on a WGS-based GWAS , and other results in literature e.g. Ober et al 2015;Van Den Berg et al 2016;Al Kalaldeh et al 2019). For example, Al Kalaldeh et al (2019) showed an improvement of between 5% to 9% in the accuracy of multi-breed GP of parasite resistance in Australian sheep, when variants were carefully pre-selected based on their -log10(p) value from a WGS-based GWAS, as compared to scenarios with no variants pre-selection.…”

Section: Using Qtl From a Snp-chip Based Gwassupporting

confidence: 91%

“…Ober et al 2015;Van Den Berg et al 2016;Al Kalaldeh et al 2019). For example, Al Kalaldeh et al (2019) showed an improvement of between 5% to 9% in the accuracy of multi-breed GP of parasite resistance in Australian sheep, when variants were carefully pre-selected based on their -log10(p) value from a WGS-based GWAS, as compared to scenarios with no variants pre-selection. Furthermore, they showed that the improvement in accuracy was more pronounced when variants were pre-selected from WGS-based GWAS as compared to when variants were pre-selected from a GWAS based on a high density (HD) SNP panel.…”

Section: Using Qtl From a Snp-chip Based Gwasmentioning

confidence: 99%

Use of whole-genome sequence data for genomic prediction across populations and species

Raymond¹

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Raymond, B. (2020). Use of whole-genome sequence data for genomic prediction across populations and species. PhD thesis, Wageningen University, the Netherlands The availability of whole genome sequence data presents an opportunity to improve the accuracy of genomic prediction (GP), given the expectation that the data contains the causal variants that underlie any given complex trait. This thesis investigated the benefit of using whole genome sequence data for GP across populations and species. Results show that the accuracy of GP across populations does not improve by simply increasing marker density up to whole genome sequence level (Chapter 2). However, accuracy can be improved if GP across populations is based on a few variants that are pre-selected from whole genome sequence based on the significance level of their effect on the trait from a genomewide association study (GWAS). The result highlights the relevance of GWAS for GP. In Chapter 3, it was demonstrated that the accuracy of prediction can further be improved using a multi-population GP model in which important pre-selected variants are used to create a genomic relationship matrix (GRM), other available and unselected variants are used to create a second GRM, and both GRMs are fitted simultaneously. While the pre-selected variants in the first GRM are isolated from the noise effect of neutral variants, and as such their effects are more accurately estimated, the variants in the second GRM captures genetic variance for the trait that cannot be captured by the pre-selected variants in the first GRM. In terms of accuracy, it was shown that the multi-population, multiple GRMs (MPMG) GP model outperforms within-population and multi-population GP models in which either the pre-selected or all available variants are equally weighted in a single GRM. In Chapter 4, the predictive performance of the MPMG model was theoretically underpinned by deriving and validating a deterministic prediction equation for its accuracy. Using the derived prediction equation, it was found that the predictive performance of the model is due to its ability to benefit from the low number of effective chromosomal segments () represented by the few preselected variants in the first GRM. However, the low values for due to the preselected variants is an advantage for the MPMG model only if variant pre-selection is accurate, such that the pre-selected variants explain some genetic variance for the trait of interest. In addition to its use as a tool to gain theoretical insights into the performance of biology-informed GP models, the derived prediction equation can be used to, a priori, estimate expected accuracy if the MPMG model were to be implemented for GP. In Chapter 5, the usefulness of summary-level GWAS result for human height as prior information for identifying genes and gene-associated variants that affect stature in cattle, was investigated. Results show that in some cases, for example in the absence of stature GWAS, human height GWAS results can be useful in identifying cattle...

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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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Using imputed whole-genome sequence data to improve the accuracy of genomic prediction for parasite resistance in Australian sheep

Cited by 38 publications

References 33 publications

A Gene-Set Enrichment and Protein–Protein Interaction Network-Based GWAS with Regulatory SNPs Identifies Candidate Genes and Pathways Associated with Carcass Traits in Hanwoo Cattle

A Gene-Set Enrichment and Protein–Protein Interaction Network-Based GWAS with Regulatory SNPs Identifies Candidate Genes and Pathways Associated with Carcass Traits in Hanwoo Cattle

Use of whole-genome sequence data for genomic prediction across populations and species

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

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