Robust logistic regression to narrow down the winner's curse for rare and recessive susceptibility variants [Source Code] (doi:10.11588/data/0Z7H1X)

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Robust logistic regression to narrow down the winner's curse for rare and recessive susceptibility variants [Source Code]

doi:10.11588/data/0Z7H1X

heiDATA

2018-10-15

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Kesselmeier, Miriam; Lorenzo Bermejo, Justo, 2018, "Robust logistic regression to narrow down the winner's curse for rare and recessive susceptibility variants [Source Code]", https://doi.org/10.11588/data/0Z7H1X, heiDATA, V1

Robust logistic regression to narrow down the winner's curse for rare and recessive susceptibility variants [Source Code]

doi:10.11588/data/0Z7H1X

Kesselmeier, Miriam (Institute of Medical Biometry and Informatics, University Hospital Heidelberg, Heidelberg, Germany and Clinical Epidemiology, Center for Sepsis Control and Care (CSCC), Jena University Hospital, Jena, Germany)

Lorenzo Bermejo, Justo (Institute of Medical Biometry and Informatics, University of Heidelberg, Heidelberg, Germany)

R

heiDATA

Lorenzo Bermejo, Justo

2018-07-20

https://doi.org/10.11588/data/0Z7H1X

Medicine, Health and Life Sciences, robust logistic regression, re-descending Hampel function, winner’s curse, genetic association study

Logistic regression is the most common technique used for genetic case-control association studies. A disadvantage of standard maximum likelihood estimators of the genotype relative risk (GRR) is their strong dependence on outlier subjects, for example, patients diagnosed at unusually young age. Robust methods are available to constrain outlier influence, but they are scarcely used in genetic studies. This article provides a non-intimidating introduction to robust logistic regression, and investigates its benefits and limitations in genetic association studies. We applied the bounded Huber and extended the R package ‘robustbase’ with the re-descending Hampel functions to down-weight outlier influence. Computer simulations were carried out to assess the type I error rate, mean squared error (MSE) and statistical power according to major characteristics of the genetic study and investigated markers. Simulations were complemented with the analysis of real data. Both standard and robust estimation controlled type I error rates. Standard logistic regression showed the highest power but standard GRR estimates also showed the largest bias and MSE, in particular for associated rare and recessive variants. For illustration, a recessive variant with a true GRR=6.32 and a minor allele frequency=0.05 investigated in a 1000 case/1000 control study by standard logistic regression resulted in power=0.60 and MSE=16.5. The corresponding figures for Huber-based estimation were power=0.51 and MSE=0.53. Overall, Hampel- and Huber-based GRR estimates did not differ much. Robust logistic regression may represent a valuable alternative to standard maximum likelihood estimation when the focus lies on risk prediction rather than identification of susceptibility variants.

Miriam Kesselmeier, Justo Lorenzo Bermejo; Robust logistic regression to narrow down the winner’s curse for rare and recessive susceptibility variants, Briefings in Bioinformatics, Volume 18, Issue 6, 1 November 2017, Pages 962–972

10.1093/bib/bbw074

Miriam Kesselmeier, Justo Lorenzo Bermejo; Robust logistic regression to narrow down the winner’s curse for rare and recessive susceptibility variants, Briefings in Bioinformatics, Volume 18, Issue 6, 1 November 2017, Pages 962–972

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