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Recovering Independent Associations in Genetics: A Comparison

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Recovering Independent Associations in Genetics: A Comparison. / Sperrin, Matthew; Jaki, Thomas.

In: Journal of Computational Biology, Vol. 19, No. 8, 08.2012, p. 978–987.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Sperrin, M & Jaki, T 2012, 'Recovering Independent Associations in Genetics: A Comparison', Journal of Computational Biology, vol. 19, no. 8, pp. 978–987. https://doi.org/10.1089/cmb.2011.0141

APA

Sperrin, M., & Jaki, T. (2012). Recovering Independent Associations in Genetics: A Comparison. Journal of Computational Biology, 19(8), 978–987. https://doi.org/10.1089/cmb.2011.0141

Vancouver

Sperrin M, Jaki T. Recovering Independent Associations in Genetics: A Comparison. Journal of Computational Biology. 2012 Aug;19(8):978–987. https://doi.org/10.1089/cmb.2011.0141

Author

Sperrin, Matthew ; Jaki, Thomas. / Recovering Independent Associations in Genetics: A Comparison. In: Journal of Computational Biology. 2012 ; Vol. 19, No. 8. pp. 978–987.

Bibtex

@article{6eb5a58ad9e44e2f8c8ad331069d17cc,
title = "Recovering Independent Associations in Genetics: A Comparison",
abstract = "In genetics, it is often of interest to discover single nucleotide polymorphisms (SNPs) that are directly related to a disease, rather than just being associated with it. Few methods exist, however, for addressing this so-called “true sparsity recovery” issue. In a thorough simulation study, we show that for moderate or low correlation between predictors, lasso-based methods perform well at true sparsity recovery, despite not being specifically designed for this purpose. For large correlations, however, more specialized methods are needed. Stability selection and direct effect testing perform well in all situations, including when the correlation is large.",
author = "Matthew Sperrin and Thomas Jaki",
year = "2012",
month = aug,
doi = "10.1089/cmb.2011.0141",
language = "English",
volume = "19",
pages = "978–987",
journal = "Journal of Computational Biology",
issn = "1066-5277",
publisher = "Mary Ann Liebert Inc.",
number = "8",

}

RIS

TY - JOUR

T1 - Recovering Independent Associations in Genetics: A Comparison

AU - Sperrin, Matthew

AU - Jaki, Thomas

PY - 2012/8

Y1 - 2012/8

N2 - In genetics, it is often of interest to discover single nucleotide polymorphisms (SNPs) that are directly related to a disease, rather than just being associated with it. Few methods exist, however, for addressing this so-called “true sparsity recovery” issue. In a thorough simulation study, we show that for moderate or low correlation between predictors, lasso-based methods perform well at true sparsity recovery, despite not being specifically designed for this purpose. For large correlations, however, more specialized methods are needed. Stability selection and direct effect testing perform well in all situations, including when the correlation is large.

AB - In genetics, it is often of interest to discover single nucleotide polymorphisms (SNPs) that are directly related to a disease, rather than just being associated with it. Few methods exist, however, for addressing this so-called “true sparsity recovery” issue. In a thorough simulation study, we show that for moderate or low correlation between predictors, lasso-based methods perform well at true sparsity recovery, despite not being specifically designed for this purpose. For large correlations, however, more specialized methods are needed. Stability selection and direct effect testing perform well in all situations, including when the correlation is large.

U2 - 10.1089/cmb.2011.0141

DO - 10.1089/cmb.2011.0141

M3 - Journal article

VL - 19

SP - 978

EP - 987

JO - Journal of Computational Biology

JF - Journal of Computational Biology

SN - 1066-5277

IS - 8

ER -