Open Access Open Badges Methodology article

Probability genotype imputation method and integrated weighted lasso for QTL identification

Nino Demetrashvili12*, Edwin R Van den Heuvel12 and Ernst C Wit1

Author Affiliations

1 Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, Groningen 9747 AG, The Netherlands

2 Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen 9700 RB, The Netherlands

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BMC Genetics 2013, 14:125  doi:10.1186/1471-2156-14-125

Published: 30 December 2013



Many QTL studies have two common features: (1) often there is missing marker information, (2) among many markers involved in the biological process only a few are causal. In statistics, the second issue falls under the headings “sparsity” and “causal inference”. The goal of this work is to develop a two-step statistical methodology for QTL mapping for markers with binary genotypes. The first step introduces a novel imputation method for missing genotypes. Outcomes of the proposed imputation method are probabilities which serve as weights to the second step, namely in weighted lasso. The sparse phenotype inference is employed to select a set of predictive markers for the trait of interest.


Simulation studies validate the proposed methodology under a wide range of realistic settings. Furthermore, the methodology outperforms alternative imputation and variable selection methods in such studies. The methodology was applied to an Arabidopsis experiment, containing 69 markers for 165 recombinant inbred lines of a F8 generation. The results confirm previously identified regions, however several new markers are also found. On the basis of the inferred ROC behavior these markers show good potential for being real, especially for the germination trait Gmax.


Our imputation method shows higher accuracy in terms of sensitivity and specificity compared to alternative imputation method. Also, the proposed weighted lasso outperforms commonly practiced multiple regression as well as the traditional lasso and adaptive lasso with three weighting schemes. This means that under realistic missing data settings this methodology can be used for QTL identification.

Arabidopsis; Germination traits; QTL mapping; Recombinant inbred line (RIL); Binary genotypes; Likelihood-based genotype imputation; Sparse variable selection; Weighted lasso