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Genome-wide Analysis using ChIP to Identify Isoform-specific Gene Targets

Genome-wide Analysis using ChIP to Identify Isoform-specific Gene Targets

Published on: July 7, 2010

AUTALASSO: an automatic adaptive LASSO for genome-wide prediction.

Patrik Waldmann¹, Maja Ferenčaković², Gábor Mészáros³

¹Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Box 7023, Uppsala, 750 07, Sweden. Patrik.Waldmann@slu.se.

BMC Bioinformatics

|April 4, 2019

Summary

We developed AUTALASSO, an efficient method for genomic prediction, improving accuracy in animal and plant breeding. This approach enhances genetic gain and prediction accuracy, outperforming existing LASSO methods.

Keywords:

GWAS Genomic selection Mathematical optimization Proximal algorithms Regularization

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Area of Science:

Genomics
Animal Breeding
Plant Breeding

Background:

Genome-wide prediction (GWP) is crucial in breeding, utilizing large genomic datasets.
Model sparsity is a challenge when the number of individuals is less than the number of markers.
LASSO (Least Absolute Shrinkage and Selection Operator) is effective for sparse problems but hyper-parameter optimization is demanding.

Purpose of the Study:

To develop a novel, computationally efficient, and automatic adaptive LASSO (AUTALASSO) method for genomic prediction.
To optimize hyper-parameters for the ADMM algorithm used in AUTALASSO.

Main Methods:

Developed AUTALASSO using the alternating direction method of multipliers (ADMM) optimization algorithm.
Implemented automatic hyper-parameter tuning: learning rate with line search and regularization factor with Golden section search.

Main Results:

AUTALASSO demonstrated superior prediction accuracy on simulated and real data compared to adaptive LASSO, LASSO, and ridge regression.
AUTALASSO offers a flexible and computationally efficient approach to GWP.

Conclusions:

AUTALASSO enhances prediction accuracy and genetic gain in GWP.
AUTALASSO can perform Genome-Wide Association Studies (GWAS) for additive and dominance effects with reduced prediction error compared to standard LASSO.