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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Arabidopsis RIL procedure. Left-RIL procedure with self-mating over 8 generations; right-Arabidopsis plant (source: http://www-ijpb.versailles.inra.fr/), namely left, the vegetative stage, before flowering and growth of the floral stalk (bottom left). On the center an adult plant at full flowering/seed set. On the rigth, flower, floral stem and seeds. White bars represent 1 cm, except for flower and seeds: 1 mm.
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Figure 1: Arabidopsis RIL procedure. Left-RIL procedure with self-mating over 8 generations; right-Arabidopsis plant (source: http://www-ijpb.versailles.inra.fr/), namely left, the vegetative stage, before flowering and growth of the floral stalk (bottom left). On the center an adult plant at full flowering/seed set. On the rigth, flower, floral stem and seeds. White bars represent 1 cm, except for flower and seeds: 1 mm.

Mentions: All RILs have the same parents. Each RIL has a unique combination of loci derived by recombination of the alleles present in the parents. Thus, each RIL has a unique genetic make-up. One traditional way of RIL construction is to cross two parental plants to produce an F1 generation, followed by several consecutive generations of self-mating. This results in a so called “core population”. These lines are practically homozygous and can be propagated indefinitely as clones. Biological and technical details of the RIL procedure are shown in Figure 1.


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

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

Arabidopsis RIL procedure. Left-RIL procedure with self-mating over 8 generations; right-Arabidopsis plant (source: http://www-ijpb.versailles.inra.fr/), namely left, the vegetative stage, before flowering and growth of the floral stalk (bottom left). On the center an adult plant at full flowering/seed set. On the rigth, flower, floral stem and seeds. White bars represent 1 cm, except for flower and seeds: 1 mm.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: Arabidopsis RIL procedure. Left-RIL procedure with self-mating over 8 generations; right-Arabidopsis plant (source: http://www-ijpb.versailles.inra.fr/), namely left, the vegetative stage, before flowering and growth of the floral stalk (bottom left). On the center an adult plant at full flowering/seed set. On the rigth, flower, floral stem and seeds. White bars represent 1 cm, except for flower and seeds: 1 mm.
Mentions: All RILs have the same parents. Each RIL has a unique combination of loci derived by recombination of the alleles present in the parents. Thus, each RIL has a unique genetic make-up. One traditional way of RIL construction is to cross two parental plants to produce an F1 generation, followed by several consecutive generations of self-mating. This results in a so called “core population”. These lines are practically homozygous and can be propagated indefinitely as clones. Biological and technical details of the RIL procedure are shown in Figure 1.

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