Genotype imputation in a tropical crossbred dairy cattle population

Abstract

The objective of this study was to investigate different strategies for genotype imputation in a population of crossbred Girolando (Gyr × Holstein) dairy cattle. The data set consisted of 478 Girolando, 583 Gyr, and 1,198 Holstein sires genotyped at high density with the Illumina BovineHD (Illumina, San Diego, CA) panel, which includes ∼777K markers. The accuracy of imputation from low (20K) and medium densities (50K and 70K) to the HD panel density and from low to 50K density were investigated. Seven scenarios using different reference populations (RPop) considering Girolando, Gyr, and Holstein breeds separately or combinations of animals of these breeds were tested for imputing genotypes of 166 randomly chosen Girolando animals. The population genotype imputation were performed using FImpute. Imputation accuracy was measured as the correlation between observed and imputed genotypes (CORR) and also as the proportion of genotypes that were imputed correctly (CR). This is the first paper on imputation accuracy in a Girolando population. The sample-specific imputation accuracies ranged from 0.38 to 0.97 (CORR) and from 0.49 to 0.96 (CR) imputing from low and medium densities to HD, and 0.41 to 0.95 (CORR) and from 0.50 to 0.94 (CR) for imputation from 20K to 50K. The CORR exceeded 0.96 (for 50K and 70K panels) when only Girolando animals were included in RPop (S1). We found smaller CORR when Gyr (S2) was used instead of Holstein (S3) as RPop. The same behavior was observed between S4 (Gyr + Girolando) and S5 (Holstein + Girolando) because the target animals were more related to the Holstein population than to the Gyr population. The highest imputation accuracies were observed for scenarios including Girolando animals in the reference population, whereas using only Gyr animals resulted in low imputation accuracies, suggesting that the haplotypes segregating in the Girolando population had a greater effect on accuracy than the purebred haplotypes. All chromosomes had similar imputation accuracies (CORR) within each scenario. Crossbred animals (Girolando) must be included in the reference population to provide the best imputation accuracies.

Single-step genome-wide association studies (GWAS) and post-GWAS analyses to identify genomic regions and candidate genes for milk yield in Brazilian Girolando cattle

“…A total of 6,760 animals were genotyped using different SNP chip densities: 4,403 Girolando cattle genotyped with the Illumina Bovine SNP50 BeadChip v. 2 (50K; Illumina, San Diego, CA), and 1,256 Holstein, 624 Gir, and 477 Girolando cattle genotyped at high density with the Illumina Bovine HD BeadChip (777K). Genotypes generated with 20K, 50K, or 70K chips were imputed to the HD v. 2 panel using FImpute software (Sargolzaei et al, 2014) with the default parameter settings and using pedigree information (Oliveira Júnior et al, 2017). In total, 413K SNP were imputed.…”

The relationship between semen seminal plasma ions and sperm cell velocities of wild-caught longspine scraper, <i>Capoeta trutta</i>

“…In this study, we analyzed the relationships between sperm cell velocities and semen seminal plasma contents in Capoeta trutta. Thereby, the values of VSL, VCL, and VAP were higher than those for Barbus sharpeyi, Catla catla, and Labeo rohita (Kalbassi et al, 2013); they were similar to Labeo calbasu, Cirrhinus mrigala, Hypophthalmichthys molitrix, and Ctenopharyngodon idella (Verma et al, 2009). However, the sperm cell density of Capoeta trutta (9.44 × 10 9 ) was found to be more than the sperm cell density of Liza abu (4.27×10 9 ) (Şahinöz et al, 2008) and Capoeta damascina (∼ 5 × 10 9 ) (Zadmajid et al, 2018), while it was found to be less for Barbus grypus (18.8 × 10 9 ) (Khodadadi et al, 2016), Cyprinus carpio (16.96-18.81 × 10 9 ) (Cejko et al, 2014), and Ctenopharyngodon idella (15.43 × 10 9 ) (Bozkurt et al, 2008).…”

Comparison of Genotype Imputation for SNP Array and Low-Coverage Whole-Genome Sequencing Data

“…Another factor that affected the imputation error rate was the difference in MAF, which at first sight may be an unexpected indicator for imputation, especially since haplotypes are used for imputation. However, as shown in other studies (Huang et al, 2009;Oliveira Júnior et al, 2017), since the process of imputation first calculated correlation between reference and target haplotypes and then considered the consistency between the haplotypes, when imputing markers with a higher allele frequency can maintain high correlations, if the frequency between the two genotypes were similar, the marker may not be imputed correctly.…”