Limits...
Probability genotype imputation method and integrated weighted lasso for QTL identification.

Demetrashvili N, Van den Heuvel ER, Wit EC - BMC Genet. (2013)

Bottom Line: The results confirm previously identified regions, however several new markers are also found.Our imputation method shows higher accuracy in terms of sensitivity and specificity compared to alternative imputation method.This means that under realistic missing data settings this methodology can be used for QTL identification.

View Article: PubMed Central - HTML - PubMed

Affiliation: Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, Groningen 9747 AG, The Netherlands. n.demetrashvili@rug.nl.

ABSTRACT

Background: 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.

Results: 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.

Conclusions: 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.

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LOD score for T50 vs. genetic distance; dashed lines separate 5 chromosomes.
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Figure 9: LOD score for T50 vs. genetic distance; dashed lines separate 5 chromosomes.

Mentions: For comparison with LRT given above, we computed the LOD-score for every marker across each trait. The LOD-score is essentially the LRT statistic on a 10-base logarithm scale. It measures the strength of evidence for the presence of a QTL at a particular location. The LOD-score for each marker is computed as the LRT under the full and a restricted models. The full model includes all 69 markers, while the restricted model includes all but the marker of interest. We present the LOD-scores for T50 in Figure 9 and it is clear that some of the peaks from the LRT are much sparser and that several large peaks have disappeared. This shows the instability of the ordinary multiple regression approach as compared to the stable lasso method.


Probability genotype imputation method and integrated weighted lasso for QTL identification.

Demetrashvili N, Van den Heuvel ER, Wit EC - BMC Genet. (2013)

LOD score for T50 vs. genetic distance; dashed lines separate 5 chromosomes.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4126192&req=5

Figure 9: LOD score for T50 vs. genetic distance; dashed lines separate 5 chromosomes.
Mentions: For comparison with LRT given above, we computed the LOD-score for every marker across each trait. The LOD-score is essentially the LRT statistic on a 10-base logarithm scale. It measures the strength of evidence for the presence of a QTL at a particular location. The LOD-score for each marker is computed as the LRT under the full and a restricted models. The full model includes all 69 markers, while the restricted model includes all but the marker of interest. We present the LOD-scores for T50 in Figure 9 and it is clear that some of the peaks from the LRT are much sparser and that several large peaks have disappeared. This shows the instability of the ordinary multiple regression approach as compared to the stable lasso method.

Bottom Line: The results confirm previously identified regions, however several new markers are also found.Our imputation method shows higher accuracy in terms of sensitivity and specificity compared to alternative imputation method.This means that under realistic missing data settings this methodology can be used for QTL identification.

View Article: PubMed Central - HTML - PubMed

Affiliation: Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, Groningen 9747 AG, The Netherlands. n.demetrashvili@rug.nl.

ABSTRACT

Background: 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.

Results: 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.

Conclusions: 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.

Show MeSH
Related in: MedlinePlus