A multi-breed reference panel and additional rare variants maximize imputation accuracy in cattle

Rowan, Troy N.; Hoff, Jesse L.; Crum, Tamar E.; Jf, Taylor; Schnabel, Robert D.; Decker, Jared E.

doi:10.1186/s12711-019-0519-x

Cited by 46 publications

(69 citation statements)

References 47 publications

(49 reference statements)

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“…Prior to imputation, markers with a GenCall score lower than 0.15 were set to missing and individuals with Mendelian error rates higher than 2% had their parents set to missing in the pedigree. The GGP-F250 was designed to genotype functional variants and thus has more variants at low minor allele frequencies [ 26 ]. Therefore, no minor allele frequency filter was applied during or after imputation beyond the removal of monomorphic SNPs.…”

Section: Methodsmentioning

confidence: 99%

Development of a genetic evaluation for hair shedding in American Angus cattle to improve thermotolerance

Durbin

Lu²,

Yampara-Iquise

et al. 2020

Genet Sel Evol

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Background Heat stress and fescue toxicosis caused by ingesting tall fescue infected with the endophytic fungus Epichloë coenophiala represent two of the most prevalent stressors to beef cattle in the United States and cost the beef industry millions of dollars each year. The rate at which a beef cow sheds her winter coat early in the summer is an indicator of adaptation to heat and an economically relevant trait in temperate or subtropical parts of the world. Furthermore, research suggests that early-summer hair shedding may reflect tolerance to fescue toxicosis, since vasoconstriction induced by fescue toxicosis limits the ability of an animal to shed its winter coat. Both heat stress and fescue toxicosis reduce profitability partly via indirect maternal effects on calf weaning weight. Here, we developed parameters for routine genetic evaluation of hair shedding score in American Angus cattle, and identified genomic loci associated with variation in hair shedding score via genome-wide association analysis (GWAA). Results Hair shedding score was moderately heritable (h2 = 0.34 to 0.40), with different repeatability estimates between cattle grazing versus not grazing endophyte-infected tall fescue. Our results suggest modestly negative genetic and phenotypic correlations between a dam’s hair shedding score (lower score is earlier shedding) and the weaning weight of her calf, which is one metric of performance. Together, these results indicate that economic gains can be made by using hair shedding score breeding values to select for heat-tolerant cattle. GWAA identified 176 variants significant at FDR < 0.05. Functional enrichment analyses using genes that were located within 50 kb of these variants identified pathways involved in keratin formation, prolactin signalling, host-virus interaction, and other biological processes. Conclusions This work contributes to a continuing trend in the development of genetic evaluations for environmental adaptation. Our results will aid beef cattle producers in selecting more sustainable and climate-adapted cattle, as well as enable the development of similar routine genetic evaluations in other breeds.

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Section: Methodsmentioning

confidence: 99%

Development of a genetic evaluation for hair shedding in American Angus cattle to improve thermotolerance

Durbin

Lu²,

Yampara-Iquise

et al. 2020

Genet Sel Evol

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“…Phasing and imputation accuracies of high-density genotypes using the ARS-UCD1.2 bovine reference genome assembly have been investigated in different Bos taurus taurus breeds [ 11 ]. In Bos taurus indicus cattle, the accuracy of imputing 50 K genotypes to higher density has been investigated only using the UMD3.1 assembly of the bovine genome [ 23 , 24 ].…”

Section: Discussionmentioning

confidence: 99%

“…Large and informative reference panels are required to facilitate accurate imputation of rare and low-frequency variants [ 3 , 7 , 11 ]. The effect of the target panel size, on the other hand, has been barely studied.…”

Section: Introductionmentioning

confidence: 99%

Investigating the accuracy of imputing autosomal variants in Nellore cattle using the ARS-UCD1.2 assembly of the bovine genome

et al. 2020

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Background Imputation accuracy among other things depends on the size of the reference panel, the marker’s minor allele frequency (MAF), and the correct placement of single nucleotide polymorphism (SNP) on the reference genome assembly. Using high-density genotypes of 3938 Nellore cattle from Brazil, we investigated the accuracy of imputation from 50 K to 777 K SNP density using Minimac3, when map positions were 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 the empirical imputation accuracy. Results The overall accuracy of imputation measured as the squared correlation between true and imputed allele dosages (R2dose) was almost 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 increased from 0.903 to 0.913, and the marker density in the imputed data increased from 593,239 to 595,570 when haplotypes were inferred in 500 and 2900 target animals. The model-based imputation quality scores from Minimac3 (Rsq) were systematically higher than empirically estimated accuracies. However, both metrics were positively correlated and the correlation increased with the size of the reference panel and MAF of imputed variants. Conclusions Accurate 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 and MAF of the imputed variants.

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“…Phasing and imputation accuracies of high-density genotypes using the ARS-UCD1.2 bovine reference genome assembly have been investigated in different Bos taurus taurus breeds (11). In Bos taurus indicus cattle, the accuracy of imputing 50K genotypes to higher density has been investigated only using the UMD3.1 assembly of the bovine genome (23,24).…”

Section: Discussionmentioning

confidence: 99%

“…Large and informative reference panels are required to facilitate accurate imputation of rare and low-frequency variants (3,7,11). The effect of the target panel size, on the other hand, has been barely studied.…”

Section: Introductionmentioning

confidence: 99%

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

Hermisdorff

Costa

Albuquerque

et al. 2020

Preprint

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BackgroundImputation 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 modelbased imputation quality score (Rsq) from Minimac3 to empirical imputation accuracy.1 ResultsThe overall accuracy of imputation measured as the squared correlation between true and imputed allele dosages (R 2 dose) 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, R 2 dose 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.

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A multi-breed reference panel and additional rare variants maximize imputation accuracy in cattle

Cited by 46 publications

References 47 publications

Development of a genetic evaluation for hair shedding in American Angus cattle to improve thermotolerance

Development of a genetic evaluation for hair shedding in American Angus cattle to improve thermotolerance

Investigating the accuracy of imputing autosomal variants in Nellore cattle using the ARS-UCD1.2 assembly of the bovine genome

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

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