Results 31 to 40 of about 551,388 (334)
The big challenge for livestock genomics is to make sequence data pay [PDF]
This paper will argue that one of the biggest challenges for livestock genomics is to make whole-genome sequencing and functional genomics applicable to breeding practice. It discusses potential explanations for why it is so difficult to consistently improve the accuracy of genomic prediction by means of whole-genome sequence data, and three potential ...
arxiv
Haplotype prediction models open many possibilities to improve the accuracy of genomic selection but require more data processing and computing time than single-SNP prediction models. To facilitate haplotype analysis for genomic prediction and estimation
Dzianis Prakapenka+7 more
doaj +1 more source
Preselected variants associated with the trait of interest from genome-wide association studies (GWASs) are available to improve genomic prediction in pigs.
Chen Wei+11 more
doaj +1 more source
How predictable is genome evolution?
Similar patterns of genomic divergence have been observed in the evolution of plant species separated by oceans.
Matthew J Coathup+2 more
openaire +6 more sources
Background A better understanding of the genetic architecture underlying complex traits (e.g., the distribution of causal variants and their effects) may aid in the genomic prediction.
Lingzhao Fang+7 more
doaj +1 more source
Estimation of genomic prediction accuracy from reference populations with varying degrees of relationship. [PDF]
Genomic prediction is emerging in a wide range of fields including animal and plant breeding, risk prediction in human precision medicine and forensic.
S Hong Lee+2 more
doaj +1 more source
Computational methods for predicting genomic islands in microbial genomes
Clusters of genes acquired by lateral gene transfer in microbial genomes, are broadly referred to as genomic islands (GIs). GIs often carry genes important for genome evolution and adaptation to niches, such as genes involved in pathogenesis and antibiotic resistance.
Hon Wai Leong, Bingxin Lu
openaire +3 more sources
Enhancing Genome-Enabled Prediction by Bagging Genomic BLUP
We examined whether or not the predictive ability of genomic best linear unbiased prediction (GBLUP) could be improved via a resampling method used in machine learning: bootstrap aggregating sampling ("bagging"). In theory, bagging can be useful when the predictor has large variance or when the number of markers is much larger than sample size ...
Gianola D+4 more
openaire +5 more sources
Translating genomics into risk prediction [PDF]
Cigarette smoking is a leading risk factor in the development of cardiovascular, pulmonary and malignant diseases worldwide.1 Yet, it remains one of the most challenging environmental exposures to quantify. Rudimentary categorisations of ‘never’, ‘former’ and ‘current’ smoker capture only a fraction of the complexity associated with the exposure ...
Dawn L. DeMeo, Emily S. Wan
openaire +3 more sources
Predicting Genome Architecture: Challenges and Solutions [PDF]
Genome architecture plays a pivotal role in gene regulation. The use of high-throughput methods for chromatin profiling and 3-D interaction mapping provide rich experimental data sets describing genome organization and dynamics. These data challenge development of new models and algorithms connecting genome architecture with epigenetic marks.
Veniamin S. Fishman+3 more
openaire +4 more sources