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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Plot of log-likelihood for edge markers with maximum at (,).
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Figure 5: Plot of log-likelihood for edge markers with maximum at (,).

Mentions: For the genotype data, we estimated two recombination rate parameters, depending on whether the marker was on the edge or in the interior of a chromosome. The parameters α and β in probability models (3) and (5) were estimated using 165 children plants. The maximum pseudo log-likelihood estimates for these parameters were and (see Figures 4 and 5). The need for introducing a separate parameter for each chromosome is shown not to be necessary for interior markers (LRT=4.633, df=4, chi-square p-value=0.327, bootstrap p-value=0.456) and for edge markers (LRT=4.116, df=4, chi-square p-value=0.391). The goodness of fit of the proposed recombination models for interior and edge markers was tested using Pearson’s chi-squared statistic. The results suggest a good fit of both models (for interior marker: χ2=7458.23, df=9280, p-value=1; for edge markers: χ2=1210.51, df=1591, p-value=1), suggesting that we do not need more than just the flanking markers to infer the genotype of the missing marker. This is in agreement with the traditional meiosis model of recombination.


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

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

Plot of log-likelihood for edge markers with maximum at (,).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 5: Plot of log-likelihood for edge markers with maximum at (,).
Mentions: For the genotype data, we estimated two recombination rate parameters, depending on whether the marker was on the edge or in the interior of a chromosome. The parameters α and β in probability models (3) and (5) were estimated using 165 children plants. The maximum pseudo log-likelihood estimates for these parameters were and (see Figures 4 and 5). The need for introducing a separate parameter for each chromosome is shown not to be necessary for interior markers (LRT=4.633, df=4, chi-square p-value=0.327, bootstrap p-value=0.456) and for edge markers (LRT=4.116, df=4, chi-square p-value=0.391). The goodness of fit of the proposed recombination models for interior and edge markers was tested using Pearson’s chi-squared statistic. The results suggest a good fit of both models (for interior marker: χ2=7458.23, df=9280, p-value=1; for edge markers: χ2=1210.51, df=1591, p-value=1), suggesting that we do not need more than just the flanking markers to infer the genotype of the missing marker. This is in agreement with the traditional meiosis model of recombination.

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