Search results for: BLUP

Authors: Salim Omar Raoof

Abstract:

This study was conducted in a private local sheep herd at Gabaraka village-Kirkuk-Iraq. Analysis of 77 ewes recorded and 7 Rams of local sheep presented in Gabaraka village farm plain, the age of ewes ranged between (2-4) years. The aim of this study is to investigate the genetic and non-genetic factors (type of birth, sex, and age of dam) affecting daily milk yield (DMY), birth weight (BW), weaning weight (WW) and Gain characteristics of local sheep raised under Iraq conditions, and it also aims at estimating heritability’s, BLUP. The overall mean of daily milk yield, (BW), (WW), and gain. Was 444.15gm,4.92kg,43.08kg, and 38.16kg, respectively. The results showed there was a significant effect of the type of birth and sex on (BW) and (WW). Also, the age of the dam had a significant effect on daily milk yield (BW), (WW), and gain. Generally, the estimate of heritability of DMP, BWT, WWT, and Gain tend to be 0.22, 0.17, 0.27, and 0.22, respectively. The breeding value (BLUP) for rams ranged between (-0.1684 to 0.188), (-0.205 to 0.310), and ( -0.0171 to 0.029) according to growth traits of Lambs BW, WW, and Gain, respectively. It concluded that the selection of ewes and rams at the population level in planned selection schemes is based on BLUP value and heritability.

Keywords: locally sheep, milk yield, Genetic parameters, BLUP value

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Authors: E. Krishna

Abstract:

An Extended form of exponential distribution using Marshall and Olkin method is introduced.The location scale family of these distributions is considered. For location scale free family, exact expressions for single and product moments of upper record statistics are derived. The mean, variance and covariance of record values are computed for various values of the shape parameter. Using these the BLUE's of location and scale parameters are derived.The variances and covariance of estimates are obtained.Through Monte Carlo simulation the condence intervals for location and scale parameters are constructed.The Best liner unbiased Predictor (BLUP) of future records are also discussed.

Keywords: BLUE, BLUP, condence interval, Marshall-Olkin distribution, Monte Carlo simulation, prediction of future records, record statistics

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Authors: Sohyoung Won, Heebal Kim, Dajeong Lim

Abstract:

Genomic prediction is an effective way to measure the abilities of livestock for breeding based on genomic estimated breeding values, statistically predicted values from genotype data using best linear unbiased prediction (BLUP). Using haplotypes, clusters of linked single nucleotide polymorphisms (SNPs), as markers instead of individual SNPs can improve the reliability of genomic prediction since the probability of a quantitative trait loci to be in strong linkage disequilibrium (LD) with markers is higher. To efficiently use haplotypes in genomic prediction, finding optimal ways to define haplotypes is needed. In this study, 770K SNP chip data was collected from Hanwoo (Korean cattle) population consisted of 2506 cattle. Haplotypes were first defined in three different ways using 770K SNP chip data: haplotypes were defined based on 1) length of haplotypes (bp), 2) the number of SNPs, and 3) k-medoids clustering by LD. To compare the methods in parallel, haplotypes defined by all methods were set to have comparable sizes; in each method, haplotypes defined to have an average number of 5, 10, 20 or 50 SNPs were tested respectively. A modified GBLUP method using haplotype alleles as predictor variables was implemented for testing the prediction reliability of each haplotype set. Also, conventional genomic BLUP (GBLUP) method, which uses individual SNPs were tested to evaluate the performance of the haplotype sets on genomic prediction. Carcass weight was used as the phenotype for testing. As a result, using haplotypes defined by all three methods showed increased reliability compared to conventional GBLUP. There were not many differences in the reliability between different haplotype defining methods. The reliability of genomic prediction was highest when the average number of SNPs per haplotype was 20 in all three methods, implying that haplotypes including around 20 SNPs can be optimal to use as markers for genomic prediction. When the number of alleles generated by each haplotype defining methods was compared, clustering by LD generated the least number of alleles. Using haplotype alleles for genomic prediction showed better performance, suggesting improved accuracy in genomic selection. The number of predictor variables was decreased when the LD-based method was used while all three haplotype defining methods showed similar performances. This suggests that defining haplotypes based on LD can reduce computational costs and allows efficient prediction. Finding optimal ways to define haplotypes and using the haplotype alleles as markers can provide improved performance and efficiency in genomic prediction.

Keywords: best linear unbiased predictor, genomic prediction, haplotype, linkage disequilibrium

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Authors: Priscila A. Bernardes, Marcos E. Buzanskas, Luciana C. A. Regitano, Ricardo V. Ventura, Danisio P. Munari

Abstract:

The best linear unbiased predictor (BLUP) is a method commonly used in genetic evaluations of breeding programs. However, this approach can lead to higher inbreeding coefficients in the population due to the intensive use of few bulls with higher genetic potential, usually presenting some degree of relatedness. High levels of inbreeding are associated to low genetic viability, fertility, and performance for some economically important traits and therefore, should be constantly monitored. Unreliable pedigree data can also lead to misleading results. Genomic information (i.e., single nucleotide polymorphism – SNP) is a useful tool to estimate the inbreeding coefficient. Runs of homozygosity have been used to evaluate homozygous segments inherited due to direct or collateral inbreeding and allows inferring population selection history. This study aimed to evaluate runs of homozygosity (ROH) and inbreeding in a population of Nelore beef cattle. A total of 814 animals were genotyped with the Illumina BovineHD BeadChip and the quality control was carried out excluding SNPs located in non-autosomal regions, with unknown position, with a p-value in the Hardy-Weinberg equilibrium lower than 10⁻⁵, call rate lower than 0.98 and samples with the call rate lower than 0.90. After the quality control, 809 animals and 509,107 SNPs remained for analyses. For the ROH analysis, PLINK software was used considering segments with at least 50 SNPs with a minimum length of 1Mb in each animal. The inbreeding coefficient was calculated using the ratio between the sum of all ROH sizes and the size of the whole genome (2,548,724kb). A total of 25.711 ROH were observed, presenting mean, median, minimum, and maximum length of 3.34Mb, 2Mb, 1Mb, and 80.8Mb, respectively. The number of SNPs present in ROH segments varied from 50 to 14.954. The longest ROH length was observed in one animal, which presented a length of 634Mb (24.88% of the genome). Four bulls were among the 10 animals with the longest extension of ROH, presenting 11% of ROH with length higher than 10Mb. Segments longer than 10Mb indicate recent inbreeding. Therefore, the results indicate an intensive use of few sires in the studied data. The distribution of ROH along the chromosomes showed that chromosomes 5 and 6 presented a large number of segments when compared to other chromosomes. The mean, median, minimum, and maximum inbreeding coefficients were 5.84%, 5.40%, 0.00%, and 24.88%, respectively. Although the mean inbreeding was considered low, the ROH indicates a recent and intensive use of few sires, which should be avoided for the genetic progress of breed.

Keywords: autozygosity, Bos taurus indicus, genomic information, single nucleotide polymorphism

Procedia PDF Downloads 150