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Imputing genotypes using regularized generalized linear regression models

Author

Listed:
  • Wong William W.L.

    (Toronto Health Economics and Technology Assessment Collaborative, Leslie Dan Faculty of Pharmacy, University of Toronto, 6th Floor, Room 658, 144 College Street, Toronto M5S 3M2, ON, Canada)

  • Griesman Josh

    (Royal College of Surgeons in Ireland, 123 St. Stephens Green, Dublin 2, Ireland)

  • Feng Zeny Z.

    (Department of Mathematics and Statistics, University of Guelph, 50 Stone Road East, Guelph N1G2W1, ON, Canada)

Abstract

As genomic sequencing technologies continue to advance, researchers are furthering their understanding of the relationships between genetic variants and expressed traits. However, missing data can significantly limit the power of a genetic study. Here, the use of a regularized generalized linear model, denoted by GLMNET, is proposed to impute missing genotypes. The method aims to address certain limitations of earlier regression approaches in regards to genotype imputation, particularly the specification of the number of neighboring SNPs to be included for imputing the missing genotype. The performance of GLMNET-based method is compared to the conventional multinomial regression method and two phase-based methods: fastPHASE and BEAGLE. Two simulation scenarios are evaluated: a sparse-missing model, and a small-panel expansion model. The sparse-missing model simulates a scenario where SNPs were missing in a random fashion across the genome. In the small-panel expansion model, a set of individuals is only genotyped at a subset of the SNPs of the large panel. Each imputation method is tested in the context of two data-sets: Canadian Holstein cattle data and human HapMap CEU data. Results show that the proposed GLMNET method outperforms the other methods in the small panel expansion scenario and fastPHASE performs slightly better than the GLMNET method in the sparse-missing scenario.

Suggested Citation

  • Wong William W.L. & Griesman Josh & Feng Zeny Z., 2014. "Imputing genotypes using regularized generalized linear regression models," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 13(5), pages 1-11, October.
    • Handle: RePEc:bpj:sagmbi:v:13:y:2014:i:5:p:11:n:1
    DOI: 10.1515/sagmb-2012-0044
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    References listed on IDEAS

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    1. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    2. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    3. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
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