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Using penalized regression to predict phenotype from SNP data.

Svetlana Cherlin¹, Richard A J Howey¹, Heather J Cordell¹

¹Institute of Genetic Medicine, Newcastle University, International Centre for Life, Central Parkway, Newcastle upon Tyne, NE1 3BZ UK.

BMC Proceedings

|October 3, 2018

Summary

This summary is machine-generated.

Penalized regression, like LASSO, struggles with small sample sizes in genome prediction. Large sample sizes (thousands of individuals) are crucial for improving prediction accuracy by reducing shrinkage of single nucleotide polymorphism effects.

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Area of Science:

Genetics
Statistical Genomics
Bioinformatics

Background:

Genome-enabled prediction often involves more predictor variables (SNPs) than response variables, precluding standard multiple linear regression.
Penalized regression methods are used to address this by shrinking regression coefficients towards zero.

Purpose of the Study:

To evaluate the effectiveness of penalized regression (LASSO) for phenotype prediction using SNP data.
To determine the impact of sample size on prediction accuracy in genome-enabled studies.

Main Methods:

Utilized the LASSO (least absolute shrinkage and selection operator) regression approach for prediction.
Employed 10-fold cross-validation for performance assessment and nested cross-validation for penalty parameter selection on the GAW20 dataset.

Main Results:

With sample sizes of a few hundred individuals, approximately 600,000 SNPs showed heavily penalized effects, leading to poor predictive performance.
Increasing sample size to several thousand individuals significantly reduced penalization of true SNP effects, substantially improving prediction accuracy.

Conclusions:

LASSO regression inherently involves substantial shrinkage of regression coefficients.
Achieving good prediction accuracy with LASSO regression necessitates large sample sizes, typically in the thousands.