Traditional selection methods, such as sib and best linear unbiased prediction (BLUP) selection, which increased genetic gain by increasing accuracy of evaluation have also led to an increased rate of inbreeding per generation (DeltaFG). This is not necessarily the case with genome-wide selection, which also increases genetic gain by increasing accuracy. This paper explains why genome-wide selection reduces DeltaFG when compared with sib and BLUP selection. Genome-wide selection achieves high accuracies of estimated breeding values through better prediction of the Mendelian sampling term component of breeding values. This increases differentiation between sibs and reduces coselection of sibs and DeltaFG. The high accuracy of genome-wide selection is expected to reduce the between family variance and reweigh the emphasis of estimated breeding values of individuals towards the Mendelian sampling term. Moreover, estimation induced intraclass correlations of sibs are expected to be lower in genome-wide selection leading to a further decrease of coselection of sibs when compared with BLUP. Genome-wide prediction of breeding values, therefore, enables increased genetic gain while at the same time reducing DeltaFG when compared with sib and BLUP selection.
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Genome
January 2025
ICAR - National Bureau of Animal Genetic Resources, Karnal, Haryana, India;
India harbours a substantial population of 9.43 million dogs, showcasing diverse phenotypes and utility. Initiatives focusing on awareness, conservation and informed breeding can greatly enhance the recognition and welfare of the unique Indian canine heritage.
View Article and Find Full Text PDFPlant Genome
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Department of Soil, Plant and Food Sciences, Genetics and Plant Breeding Section, University of Bari Aldo Moro, Bari, Italy.
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View Article and Find Full Text PDFProbl Endokrinol (Mosk)
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Background: Osteoporosis is a common age-related disease with disabling consequences, the early diagnosis of which is difficult due to its long and hidden course, which often leads to diagnosis only after a fracture. In this regard, great expectations are placed on advanced developments in machine learning technologies aimed at predicting osteoporosis at an early stage of development, including the use of large data sets containing information on genetic and clinical predictors of the disease. Nevertheless, the inclusion of DNA markers in prediction models is fraught with a number of difficulties due to the complex polygenic and heterogeneous nature of the disease.
View Article and Find Full Text PDFThe expression of genomically-encoded information is not error-free. Transcript-error rates are dramatically higher than DNA-level mutation rates, and despite their transient nature, the steady-state load of such errors must impose some burden on cellular performance. However, a broad perspective on the degree to which transcript-error rates are constrained by natural selection and diverge among lineages remains to be developed.
View Article and Find Full Text PDFNat Commun
January 2025
Department of Biochemistry and Molecular Biology, Thomas Jefferson University, Philadelphia, PA, USA.
While all native tRNAs undergo extensive post-transcriptional modifications as a mechanism to regulate gene expression, mapping these modifications remains challenging. The critical barrier is the difficulty of readthrough of modifications by reverse transcriptases (RTs). Here we use Induro-a new group-II intron-encoded RT-to map and quantify genome-wide tRNA modifications in Induro-tRNAseq.
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