Browsing by Author "Fernando RL"

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    Comparison of linkage disequilibrium estimated from genotypes versus haplotypes for crossbred populations
    (BioMed Central Ltd, 2022-02-08) Alemu SW; Bijma P; Calus MPL; Liu H; Fernando RL; Dekkers JCM
    Background Linkage disequilibrium (LD) is commonly measured based on the squared coefficient of correlation (r²) between the alleles at two loci that are carried by haplotypes. LD can also be estimated as the r² between unphased genotype dosage at two loci when the allele frequencies and inbreeding coefficients at both loci are identical for the parental lines. Here, we investigated whether r² for a crossbred population (F1) can be estimated using genotype data. The parental lines of the crossbred (F1) can be purebred or crossbred. Methods We approached this by first showing that inbreeding coefficients for an F1 crossbred population are negative, and typically differ in size between loci. Then, we proved that the expected r² computed from unphased genotype data is expected to be identical to the r² computed from haplotype data for an F1 crossbred population, regardless of the inbreeding coefficients at the two loci. Finally, we investigated the bias and precision of the r² estimated using unphased genotype versus haplotype data in stochastic simulation. Results Our findings show that estimates of r² based on haplotype and unphased genotype data are both unbiased for different combinations of allele frequencies, sample sizes (900, 1800, and 2700), and levels of LD. In general, for any allele frequency combination and r² value scenarios considered, and for both methods to estimate r², the precision of the estimates increased, and the bias of the estimates decreased as sample size increased, indicating that both estimators are consistent. For a given scenario, the r² estimates using haplotype data were more precise and less biased using haplotype data than using unphased genotype data. As sample size increased, the difference in precision and biasedness between the r² estimates using haplotype data and unphased genotype data decreased. Conclusions Our theoretical derivations showed that estimates of LD between loci based on unphased genotypes and haplotypes in F1 crossbreds have identical expectations. Based on our simulation results, we conclude that the LD for an F1 crossbred population can be accurately estimated from unphased genotype data. The results also apply for other crosses (F2, F3, Fn, BC1, BC2, and BCn), as long as (selected) individuals from the two parental lines mate randomly.
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    Deregressing estimated breeding values and weighting information for genomic regression analyses.
    (BIOMED CENTRAL LTD, 2009-12-31) Garrick DJ; Taylor JF; Fernando RL
    BACKGROUND: Genomic prediction of breeding values involves a so-called training analysis that predicts the influence of small genomic regions by regression of observed information on marker genotypes for a given population of individuals. Available observations may take the form of individual phenotypes, repeated observations, records on close family members such as progeny, estimated breeding values (EBV) or their deregressed counterparts from genetic evaluations. The literature indicates that researchers are inconsistent in their approach to using EBV or deregressed data, and as to using the appropriate methods for weighting some data sources to account for heterogeneous variance. METHODS: A logical approach to using information for genomic prediction is introduced, which demonstrates the appropriate weights for analyzing observations with heterogeneous variance and explains the need for and the manner in which EBV should have parent average effects removed, be deregressed and weighted. RESULTS: An appropriate deregression for genomic regression analyses is EBV/r2 where EBV excludes parent information and r2 is the reliability of that EBV. The appropriate weights for deregressed breeding values are neither the reliability nor the prediction error variance, two alternatives that have been used in published studies, but the ratio (1 - h2)/[(c + (1 - r2)/r2)h2] where c > 0 is the fraction of genetic variance not explained by markers. CONCLUSIONS: Phenotypic information on some individuals and deregressed data on others can be combined in genomic analyses using appropriate weighting.