Metodologias e estratégias de imputação de marcadores genéticos em bovinos da raça Cachim - Chud, Tatiane Cristina Seleguim [UNESP] (2014)
High-density panels (HD) have strong level linkage disequilibrium among genetic markers (i.e. single nucleotide polymorphism - SNP), which allows better predictions of genomic breeding values. However, HD genotyping still expensive and became a limitation for the quantity of candidate animals used in genomic studies. As an alternative to decrease costs, imputation methods are powerful tools to infer missing marker genotypes from low-density (LD) panels to HD. Imputation uses information from a reference population of animals genotyped with a HD panel to impute variants that are not directly genotyped in LD panels. The objective of this study was to compare different scenarios and methodologies of imputation for the Canchim cattle. Data set was provided by Embrapa Pecuária Sudeste and comprised 285 Canchim animals, 114 MA genetic group animals, and 1 ancestor Charolais bull. Animals born between 1999 and 2005 were genotyped with the Illumina BovineHD panel (786,799 SNP). Data editing was performed in the R software and in C ++ language. Multiple scenarios combining different minor allele frequencies (MAF) thresholds for SNPs were tested: no MAF filter (QC1), and exclusion of SNPs with MAF lower than 0.0025 (QC2) and MAF lower than 0.10 (QC3). LD panels were created by masking SNPs originally present in the HD panel, and then assigning markers into the Illumina Bovine3K (3K), Illumina BovineLD (6K), Beef LD GeneSeek Genomic Profiler (9K), Indicus LD GeneSeek Genomic Profiler (20K), Illumina BovineSNP50 (50K), GeneSeek Genomic Profiler Beef HD (80K) and GeneSeek Genomic Profiler Indicus HD (90K) panels. Reference and target populations were defined as scenario 1 (C1), reference animals were born up until 2004 and target animals were born in 2005; scenario 2A (C2A), reference animals from Canchim breed and target animals from MA genetic group; scenario 2B (C2B), reference animals from MA genetic ...