Browsing by Author "Alemu SW"

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    Comparison of genomic prediction accuracies in dairy cattle lactation traits using five classes of functional variants versus generic SNP
    (BioMed Central Ltd, 2025-12) Alemu SW; Lopdell TJ; Trevarton AJ; Snell RG; Littlejohn MD; Garrick DJ
    Background: Genomic selection, typically employing genetic markers from SNP chips, is routine in modern dairy cattle breeding. This study assessed the impact of functional sequence variants on genomic prediction accuracy relative to 50 k SNP chip markers for fat percent, protein percent, milk volume, fat yield, and protein yield in lactating dairy cattle. The functional variants were identified through GWAS, RNA-seq, Histone modification ChIP-seq, ATAC-seq, or were coding variants. The genomic prediction accuracy obtained using each class of functional variants was compared with matched numbers of SNPs randomly selected from the Illumina 50 k SNP chip. Results: The investigation revealed that variants identified by GWAS or RNA-seq, significantly improved the prediction accuracy across all five traits. Contributions from ChIP-seq, ATAC-seq, and coding variants varied. Some variants identified using ChIP-seq showed marked improvements, while others reduced accuracy in protein yield predictions. Relative to a matched number of 32,595 SNPs from the SNP chip, pooling all the functional variants demonstrated prediction accuracy increases of 1.76% for fat percent, 2.97% for protein percent, 0.51% for milk volume, and 0.26% for fat yield, but with a slight decrease of 0.43% in protein yield. Conclusion: The study demonstrates that functional variants can improve prediction accuracy relative to equivalent numbers of variants from a generic SNP panel, with percent traits showing more significant gains than yield traits. The main advantage of using functional variants for genomic prediction was achievement of comparable accuracy using a smaller, more selective set of loci. This is particularly evident in trait-specific scenarios. Our findings indicate that specific combinations of functional variants comprising 16 k variants can achieve genomic prediction accuracy comparable to employing a standard panel of twice the size (32.6 k), especially for percent traits. This highlights the potential for the development of more efficient, trait-focused SNP panels utilizing functional variants.
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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.