Background: Genotype imputation has been used to increase genomic information, allow more animals in genome-wide analyses, and reduce genotyping costs. In Brazilian beef cattle production, many animals are resulting from crossbreeding and such an event may alter linkage disequilibrium patterns. Thus, the challenge is to obtain accurately imputed genotypes in crossbred animals. The objective of this study was to evaluate the best fitting and most accurate imputation strategy on the MA genetic group (the progeny of a Charolais sire mated with crossbred Canchim X Zebu cows) and Canchim cattle. The data set contained 400 animals (born between 1999 and 2005) genotyped with the Illumina BovineHD panel. Imputation accuracy of genotypes from the Illumina-Bovine3K (3K), Illumina-BovineLD (6K), GeneSeek-Genomic-Profiler (GGP) BeefLD (GGP9K), GGP-IndicusLD (GGP20Ki), Illumina-BovineSNP50 (50K), GGP-IndicusHD (GGP75Ki), and GGP-BeefHD (GGP80K) to Illumina-BovineHD (HD) SNP panels were investigated. Seven scenarios for reference and target populations were tested; the animals were grouped according with birth year (S1), genetic groups (S2 and S3), genetic groups and birth year (S4 and S5), gender (S6), and gender and birth year (S7). Analyses were performed using FImpute and BEAGLE software and computation run-time was recorded. Genotype imputation accuracy was measured by concordance rate (CR) and allelic R square (R(2)).
Results: The highest imputation accuracy scenario consisted of a reference population with males and females and a target population with young females. Among the SNP panels in the tested scenarios, from the 50K, GGP75Ki and GGP80K were the most adequate to impute to HD in Canchim cattle. FImpute reduced computation run-time to impute genotypes from 20 to 100 times when compared to BEAGLE.
Conclusion: The genotyping panels possessing at least 50 thousands markers are suitable for genotype imputation to HD with acceptable accuracy. The FImpute algorithm demonstrated a higher efficiency of imputed markers, especially in lower density panels. These considerations may assist to increase genotypic information, reduce genotyping costs, and aid in genomic selection evaluations in crossbred animals.
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http://dx.doi.org/10.1186/s12863-015-0251-7 | DOI Listing |
One of the major challenges in genomic data sharing is protecting participants' privacy in collaborative studies and when genomic data is outsourced to perform analysis tasks, e.g., genotype imputation services and federated collaborations genomic analysis.
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Plant Breeding Graduate Program, Horticultural Sciences Department, University of Florida, IFAS Gulf Coast Research and Education Center, Wimauma, Florida, USA.
Genomic selection is a widely used quantitative method of determining the genetic value of an individual from genomic information and phenotypic data. In this study, we used a large, multi-year training population of 3248 individuals from the University of Florida strawberry (Fragaria × ananassa Duchesne) breeding program. We coupled this training population with a test population of 1460 individuals derived from 20 biparental families.
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Confederación de Asociaciones de Frisona Española (CONAFE), Ctra. de Andalucía km 23600 Valdemoro, 28340 Madrid, Spain.
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College of Animal Science and Technology, Northwest A&F University, 22 nt, Xinong Road, Yangling, Shaanxi, China. Electronic address:
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Sow lameness results in premature culling, causing economic loss and well-being issues. A study, utilizing a pressure-sensing mat (GAIT4) and video monitoring system (NUtrack), was conducted to identify objective measurements on gilts that are predictive of future lameness. Gilts (N = 3656) were categorized to describe their lifetime soundness: SOUND, retained for breeding with no detected mobility issues; LAME_SOW, retained for breeding and detected lame as a sow; CULL_STR, not retained due to poor leg structure; LAME_GILT, not retained due to visible signs of lameness; and CULL, not retained due to reasons other than leg structure.
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