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Infinium Assay for Large-scale SNP Genotyping Applications
Published on: November 19, 2013
Technical note: Computing options for genetic evaluation with a large number of genetic markers
1Department of Animal and Dairy Science, University of Georgia, Athens 30602, USA.
Journal of Animal Science
|March 4, 2008
Summary
Genetic evaluation using quantitative trait loci (QTL) effects as covariables is feasible. Modifications to computing methods, particularly trait (BT) adjustments, significantly reduced analysis time for large animal datasets.
Area of Science:
- Animal Breeding and Genetics
- Quantitative Genetics
- Bioinformatics and Computational Biology
Background:
- Accurate genetic evaluation requires complex models, often including numerous effects.
- Fitting quantitative trait loci (QTL) effects as covariables presents computational challenges.
- Optimizing computational efficiency is crucial for large-scale genetic analyses.
Purpose of the Study:
- To evaluate computational options for genetic models incorporating QTL effects as covariables.
- To assess the impact of modified algorithms on computing time and convergence.
- To determine the feasibility of genetic evaluation with a large number of QTL covariables.
Main Methods:
- Utilized two simulated datasets (24,000 animals, 10 traits) and one commercial dataset (~110,000 animals, 11 traits).
- Employed the BLUP90IOD program with modifications: block preconditioners for QTL effects (BQ) and traits (BT).
- Compared computing times and convergence rates of the original program versus modified versions.
Main Results:
- The original program showed significant computing times (up to 1h 40m for simulated data).
- Trait (BT) modification decreased computing time by 1.5x to 7x, with increased memory needs.
- Block preconditioner for QTL (BQ) improved convergence but increased computing time; BT modification reduced commercial data analysis time from 10.3h to 6h.
Conclusions:
- Genetic evaluation incorporating a large number of QTL effects as covariables is computationally feasible.
- The trait (BT) modification offers substantial improvements in computing efficiency for large datasets.
- Further optimization may be needed, but current methods support the integration of QTL information into genetic evaluations.
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