A comprehensive comparison of high-density SNP panels and an alternative ultra-high-density panel for genomic analyses in Nellore cattle

2020 ◽  
Vol 60 (3) ◽  
pp. 333
Author(s):  
Ricardo V. Ventura ◽  
Luiz F. Brito ◽  
Gerson A. Oliveira ◽  
Hans D. Daetwyler ◽  
Flavio S. Schenkel ◽  
...  
Keyword(s):  
Imputation Accuracy ◽  
High Density ◽  
Zebu Cattle ◽  
Genomic Analyses ◽  
Nellore Cattle ◽  
Marker Distribution ◽  
Comprehensive Comparison ◽  
Snp Panels ◽  
Genomic Inbreeding ◽  
High Density Panel

There is evidence that some genotyping platforms might not work very well for Zebu cattle when compared with Taurine breeds. In addition, the availability of panels with low to moderate number of overlapping markers is a limitation for combining datasets for genomic evaluations, especially when animals are genotyped using different SNP panels. In the present study, we compared the performance of medium- and high-density (HD) commercially available panels and investigated the feasibility of developing an ultra-HD panel (SP) containing markers from an Illumina (HD_I) and an Affymetrix (HD_A) panels. The SP panel contained 1123442 SNPs. After performing SNP pruning on the basis of linkage disequilibrium, HD_A, HD_I and SP contained 429624, 365225 and 658770 markers distributed across the whole genome. The overall mean proportion of markers pruned out per chromosome for HD_A, HD_I and SP was 15.17%, 43.18%, 38.63% respectively. The HD_I panel presented the highest mean number of runs-of-homozygosity segments per animal (45.48%, an increment of 5.11% compared with SP) and longer segments, on average (3057.95 kb per segment), than did both HD_A and SP. HD_I also showed the highest mean number of SNPs per run-of-homozygosity segment. Consequently, the majority of animals presented the highest genomic inbreeding levels when genotyped using HD_I. The visual examination of marker distribution along the genome illustrated uncovered regions among the different panels. Haplotype-block comparison among panels and the average haplotype size constructed on the basis of HD_A were smaller than those from HD_I. The average number of SNPs per haplotype was different between HD_A and HD_I. Both HD_A and HD_I panels achieved high imputation accuracies when used as the lower-density panels for imputing to SP. However, imputation accuracy from HD_A to SP was greater than was imputation from HD_I to SP. Imputation from one HD panel to the other is also feasible. Low- and medium-density panels, composed of markers that are subsets of both HD_A and HD_I panels, should be developed to achieve better imputation accuracies to both HD levels. Therefore, the genomic analyses performed in the present study showed significant differences among the SNP panels used.

scholarly journals Genotype imputation accuracy in a F2 pig population using high density and low density SNP panels

BMC Genetics ◽  
2013 ◽  
Vol 14 (1) ◽  
pp. 38 ◽  
Author(s):  
Jose L Gualdrón Duarte ◽  
Ronald O Bates ◽  
Catherine W Ernst ◽  
Nancy E Raney ◽  
Rodolfo JC Cantet ◽  
...  
Keyword(s):  
Imputation Accuracy ◽  
High Density ◽  
Genotype Imputation ◽  
Low Density ◽  
Snp Panels

scholarly journals High-density marker imputation accuracy in sixteen French cattle breeds

2013 ◽  
Vol 45 (1) ◽  
Author(s):  
Chris Hozé ◽  
Marie-Noëlle Fouilloux ◽  
Eric Venot ◽  
François Guillaume ◽  
Romain Dassonneville ◽  
...  
Keyword(s):  
Imputation Accuracy ◽  
High Density ◽  
Cattle Breeds ◽  
Density Marker

Effect of quality control, density and allele frequency of markers on the accuracy of genomic prediction for complex traits in Nellore cattle

2019 ◽  
Vol 59 (1) ◽  
pp. 48 ◽  
Author(s):  
Tiago Bresolin ◽  
Guilherme Jordão de Magalhães Rosa ◽  
Bruno Dourado Valente ◽  
Rafael Espigolan ◽  
Daniel Gustavo Mansan Gordo ◽  
...  
Keyword(s):  
Quality Control ◽  
Allele Frequency ◽  
Minor Allele Frequency ◽  
Genomic Prediction ◽  
High Density ◽  
Minor Allele ◽  
Call Rate ◽  
Genotyping Assay ◽  
Nellore Cattle ◽  
Genomic Predictions

This study was designed to test the impact of quality control, density and allele frequency of single nucleotide polymorphisms (SNP) markers on the accuracy of genomic predictions, using three traits with different heritabilities and two methods of prediction in a Nellore cattle population genotyped with the Illumina Bovine HD Assay. A total of 1756; 3150 and 3119 records of age at first calving (AFC); weaning weight (WW) and yearling weight (YW), respectively, were used. Three scenarios with different exclusion thresholds for minor allele frequency (MAF), deviation from Hardy–Weinberg equilibrium (HWE) and correlation between SNP pairs (r2) were constructed for all traits: (1) high rigor (S1): call rate <0.98, MAF <0.05, HWE with P <10−5, and r2 >0.999; (2) Moderate rigor (S2): call rate <0.85 and MAF <0.01; (3) Low rigor (S3): only non-autosomal SNP and those mapped on the same position were excluded. Additionally, to assess the prediction accuracy from different markers density, six panels (10K, 50K, 100K, 300K, 500K and 700K) were customised using the high-density genotyping assay as reference. Finally, from the markers available in high-density genotyping assay, six groups (G) with different minor allele frequency bins were defined to estimate the accuracy of genomic prediction. The range of MAF bins was approximately equal for the traits studied: G1 (0.000–0.009), G2 (0.010–0.064), G3 (0.065–0.174), G4 (0.175–0.325), G5 (0.326–0.500) and G6 (0.000–0.500). The Genomic Best Linear Unbiased Predictor and BayesCπ methods were used to estimate the SNP marker effects. Five-fold cross-validation was used to measure the accuracy of genomic prediction for all scenarios. There were no effects of genotypes quality control criteria on the accuracies of genomic predictions. For all traits, the higher density panel did not provide greater prediction accuracies than the low density one (10K panel). The groups of SNP with low MAF (MAF ≤0.007 for AFC, MAF ≤0.009 for WW and MAF ≤0.008 for YW) provided lower prediction accuracies than the groups with higher allele frequencies.

scholarly journals Genetic and genomic analyses of testicular hypoplasia in Nellore cattle

PLoS ONE ◽  
2019 ◽  
Vol 14 (1) ◽  
pp. e0211159 ◽  
Author(s):  
Haroldo H. R. Neves ◽  
Giovana Vargas ◽  
Luiz F. Brito ◽  
Flavio S. Schenkel ◽  
Lucia G. Albuquerque ◽  
...  
Keyword(s):  
Genomic Analyses ◽  
Nellore Cattle

scholarly journals Investigating the accuracy of imputed genotypes in Nellore cattle using the ARS-UCD1.2 assembly of the bovine genome

2020 ◽  
Author(s):  
Isis da Costa Hermisdorff ◽  
Raphael Bermal Costa ◽  
Lucia Galvão de Albuquerque ◽  
Hubert Pausch ◽  
Naveen Kumar Kadri
Keyword(s):  
Genome Assembly ◽  
Reference Genome ◽  
Bovine Genome ◽  
Imputation Accuracy ◽  
Quality Score ◽  
Reference Panel ◽  
Marker Density ◽  
Model Based ◽  
Reference Genome Assembly ◽  
Nellore Cattle

AbstractBackgroundImputation accuracy among other things depends on the size of the reference panel, the marker’s minor allele frequency (MAF), and the correct placement of variants on the reference genome assembly. Using high-density genotypes of 3938 Nellore cattle from Brazil, we investigated the accuracy of imputation from 50K to 777K SNP density, using map positions determined according to the bovine genome assemblies UMD3.1 and ARS-UCD1.2. We assessed the effect of reference and target panel sizes on the pre-phasing-based imputation quality using ten-fold cross-validation. Further, we compared the reliability of the model-based imputation quality score (Rsq) from Minimac3 to empirical imputation accuracy.ResultsThe overall accuracy of imputation measured as the squared correlation between true and imputed allele dosages (R2dose) was virtually identical using either the UMD3.1 or ARS-UCD1.2 genome assembly. When the size of the reference panel increased from 250 to 2000, R2dose increased from 0.845 to 0.917, and the number of polymorphic markers in the imputed data set increased from 586,701 to 618,660. Advantages in both accuracy and marker density were also observed when larger target panels were imputed, likely resulting from more accurate haplotype inference. Imputation accuracy and the marker density in the imputed data increased from 0.903 to 0.913 and from 593,239 to 595,570 when haplotypes were inferred in 500 and 2900 target animals, respectively. The model-based imputation quality scores from Minimac3 (Rsq) were highly correlated to but systematically higher than empirically estimated accuracies. The correlation between these metrics increased with the size of the reference panel and MAF of imputed variants.ConclusionsAccurate imputation of BovineHD BeadChip markers is possible in Nellore cattle using the new bovine reference genome assembly ARS-UCD1.2. The use of large reference and target panels improves the accuracy of the imputed genotypes and provides genotypes for more markers segregating at low frequency for downstream genomic analyses. The model-based imputation quality score from Minimac3 (Rsq) can be used to detect poorly imputed variants but its reliability depends on the size of the reference panel used and MAF of the imputed variants.

scholarly journals Optimizing Low-Cost Genotyping and Imputation Strategies for Genomic Selection in Atlantic Salmon

2019 ◽  
Vol 10 (2) ◽  
pp. 581-590 ◽  
Author(s):  
Smaragda Tsairidou ◽  
Alastair Hamilton ◽  
Diego Robledo ◽  
James E. Bron ◽  
Ross D. Houston
Keyword(s):  
Atlantic Salmon ◽  
Genomic Selection ◽  
Environmental Sustainability ◽  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Imputation Accuracy ◽  
Cost Effective ◽  
High Density ◽  
Genotype Imputation ◽  
Breeding Programs

Genomic selection enables cumulative genetic gains in key production traits such as disease resistance, playing an important role in the economic and environmental sustainability of aquaculture production. However, it requires genome-wide genetic marker data on large populations, which can be prohibitively expensive. Genotype imputation is a cost-effective method for obtaining high-density genotypes, but its value in aquaculture breeding programs which are characterized by large full-sibling families has yet to be fully assessed. The aim of this study was to optimize the use of low-density genotypes and evaluate genotype imputation strategies for cost-effective genomic prediction. Phenotypes and genotypes (78,362 SNPs) were obtained for 610 individuals from a Scottish Atlantic salmon breeding program population (Landcatch, UK) challenged with sea lice, Lepeophtheirus salmonis. The genomic prediction accuracy of genomic selection was calculated using GBLUP approaches and compared across SNP panels of varying densities and composition, with and without imputation. Imputation was tested when parents were genotyped for the optimal SNP panel, and offspring were genotyped for a range of lower density imputation panels. Reducing SNP density had little impact on prediction accuracy until 5,000 SNPs, below which the accuracy dropped. Imputation accuracy increased with increasing imputation panel density. Genomic prediction accuracy when offspring were genotyped for just 200 SNPs, and parents for 5,000 SNPs, was 0.53. This accuracy was similar to the full high density and optimal density dataset, and markedly higher than using 200 SNPs without imputation. These results suggest that imputation from very low to medium density can be a cost-effective tool for genomic selection in Atlantic salmon breeding programs.

Accuracy of genomic prediction using mixed low-density marker panels

2020 ◽  
Vol 60 (8) ◽  
pp. 999
Author(s):  
Lianjie Hou ◽  
Wenshuai Liang ◽  
Guli Xu ◽  
Bo Huang ◽  
Xiquan Zhang ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Imputation Accuracy ◽  
Simulated Data ◽  
Real Data ◽  
Substantial Effect ◽  
Low Density ◽  
Snp Panel ◽  
Snp Panels ◽  
The Cost ◽  
Better Than

Low-density single-nucleotide polymorphism (LD-SNP) panel is one effective way to reduce the cost of genomic selection in animal breeding. The present study proposes a new type of LD-SNP panel called mixed low-density (MLD) panel, which considers SNPs with a substantial effect estimated by Bayes method B (BayesB) from many traits and evenly spaced distribution simultaneously. Simulated and real data were used to compare the imputation accuracy and genomic-selection accuracy of two types of LD-SNP panels. The result of genotyping imputation for simulated data showed that the number of quantitative trait loci (QTL) had limited influence on the imputation accuracy only for MLD panels. Evenly spaced (ELD) panel was not affected by QTL. For real data, ELD performed slightly better than did MLD when panel contained 500 and 1000 SNP. However, this advantage vanished quickly as the density increased. The result of genomic selection for simulated data using BayesB showed that MLD performed much better than did ELD when QTL was 100. For real data, MLD also outperformed ELD in growth and carcass traits when using BayesB. In conclusion, the MLD strategy is superior to ELD in genomic selection under most situations.

scholarly journals SNP panels for the estimation of dairy breed proportion and parentage assignment in African crossbred dairy cattle

2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Netsanet Z. Gebrehiwot ◽  
Eva M. Strucken ◽  
Karen Marshall ◽  
Hassan Aliloo ◽  
John P. Gibson
Keyword(s):  
Allele Frequency ◽  
Bos Taurus ◽  
Low Cost ◽  
Bos Indicus ◽  
High Density ◽  
Indigenous Populations ◽  
Genetic Admixture ◽  
Genotype Data ◽  
Parentage Assignment ◽  
Snp Panels

Abstract Background Understanding the relationship between genetic admixture and phenotypic performance is crucial for the optimization of crossbreeding programs. The use of small sets of informative ancestry markers can be a cost-effective option for the estimation of breed composition and for parentage assignment in situations where pedigree recording is difficult. The objectives of this study were to develop small single nucleotide polymorphism (SNP) panels that can accurately estimate the total dairy proportion and assign parentage in both West and East African crossbred dairy cows. Methods Medium- and high-density SNP genotype data (Illumina BovineSNP50 and BovineHD Beadchip) for 4231 animals sampled from African crossbreds, African Bos taurus, European Bos taurus, Bos indicus, and African indigenous populations were used. For estimating breed composition, the absolute differences in allele frequency were calculated between pure ancestral breeds to identify SNPs with the highest discriminating power, and different combinations of SNPs weighted by ancestral origin were tested against estimates based on all available SNPs. For parentage assignment, informative SNPs were selected based on the highest minor allele frequency (MAF) in African crossbred populations assuming two Scenarios: (1) parents were selected among all the animals with known genotypes, and (2) parents were selected only among the animals known to be a parent of at least one progeny. Results For the medium-density genotype data, SNPs selected for the largest differences in allele frequency between West African indigenous and European Bos taurus breeds performed best for most African crossbred populations and achieved a prediction accuracy (r2) for breed composition of 0.926 to 0.961 with 200 SNPs. For the high-density dataset, a panel with 70% of the SNPs selected on their largest difference in allele frequency between African and European Bos taurus performed best or very near best across all crossbred populations with r2 ranging from 0.978 to 0.984 with 200 SNPs. In all African crossbred populations, unambiguous parentage assignment was possible with ≥ 300 SNPs for the majority of the panels for Scenario 1 and ≥ 200 SNPs for Scenario 2. Conclusions The identified low-cost SNP assays could overcome incomplete or inaccurate pedigree records in African smallholder systems and allow effective breeding decisions to produce progeny of desired breed composition.

scholarly journals Magnetic Assembly of High-Density DNA Arrays for Genomic Analyses

2008 ◽  
Vol 80 (6) ◽  
pp. 2149-2154 ◽  
Author(s):  
Kristopher D. Barbee ◽  
Xiaohua Huang
Keyword(s):  
High Density ◽  
Dna Arrays ◽  
Magnetic Assembly ◽  
Genomic Analyses

scholarly journals Linkage Disequilibrium Estimation of Chinese Beef Simmental Cattle Using High-density SNP Panels

2013 ◽  
Vol 26 (6) ◽  
pp. 772-779 ◽  
Author(s):  
M. Zhu ◽  
B. Zhu ◽  
Y. H. Wang ◽  
Y. Wu ◽  
L. Xu ◽  
...  
Keyword(s):  
Linkage Disequilibrium ◽  
High Density ◽  
Simmental Cattle ◽  
Snp Panels
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