Biology subjects

Akdemir, D.

Publications and source records attributed to Akdemir, D..

4 recordsLinked to original sources

bioRxiv2019

Adventures in Multi-Omics I: Combining heterogeneous data sets via relationships matrices

Private and public breeding programs, as well as companies and universities, have developed different genomics technologies which have resulted in the generation of unprecedented amounts of sequence data, which bring new challenges in terms of data management, query, and analysis. The magnitude and complexity of these datasets bring new challenges but also an opportunity to use the data available as a whole. Detailed phenotype data, combined with increasing amounts of genomic data, have an enormous potential to accelerate the identification of key traits to improve our understanding of quantitative genetics. Data harmonization enables cross-national and international comparative research, facilitating the extraction of new scientific knowledge. In this paper, we address the complex issue of combining high dimensional and unbalanced omics data. More specifically, we propose a covariance-based method for combining partial datasets in the genotype to phenotype spectrum. This method can be used to combine partially overlapping relationship/covariance matrices. Here, we show with applications that our approach might be advantageous to feature imputation based approaches; we demonstrate how this method can be used in genomic prediction using heterogenous marker data and also how to combine the data from multiple phenotypic experiments to make inferences about previously unobserved trait relationships. Our results demonstrate that it is possible to harmonize datasets to improve available information across gene-banks, data repositories or other data resources. Key messageSeveral covariance matrices obtained from independent experiments can be combined as long as these matrices are partially overlapping. We demonstrate the usefulness of this methodology with applications in combining data from several partially linked genotypic and phenotypic experiments. Author contribution statement-DA: Conception or design of the work, statistics, R programs, simulations, drafting the article, and critical revision of the article. -JIS: R programs, graphs, drafting the article, critical revision of the article. -RK: Critical revision of the article.

genomics↗

bioRxiv2017

Multi-objective optimized breeding strategies

Multi-objective optimization is an emerging field in mathematical optimization which involves optimization a set of objective functions simultaneously. The purpose of most plant and animal breeding programs is to make decisions that will lead to sustainable genetic gains in more than one traits while controlling the amount of co-ancestry in the breeding population. The decisions at each cycle in a breeding program involve multiple, usually competing, objectives; these complex decisions can be supported by the insights that are gained by using the multi-objective optimization principles in breeding. The discussion here includes the definition of several multi-objective optimized breeding approaches and the comparison of these approaches with the standard multi-trait breeding schemes such as tandem selection, culling and index selection. We have illustrated the newly proposed methods with two empirical data sets and with simulations.

genetics↗

bioRxiv2017

Accuracies Of Univariate And Multivariate Genomic Prediction Models In African Cassava.

List of abbreviations\n\nAbstractO_ST_ABSBackgroundC_ST_ABSGenomic selection (GS) promises to accelerate genetic gain in plant breeding programs especially for long cycle crops like cassava. To practically implement GS in cassava breeding, it is useful to evaluate different GS models and to develop suitable models for an optimized breeding pipeline.\n\nMethodsWe compared prediction accuracies from a single-trait (uT) and a multi-trait (MT) mixed model for single environment genetic evaluation (Scenario 1) while for multi-environment evaluation accounting for genotype-by-environment interaction (Scenario 2) we compared accuracies from a univariate (uE) and a multivariate (ME) multi-environment mixed model. We used sixteen years of data for six target cassava traits for these analyses. All models for Scenario 1 and Scenario 2 were based on the one-step approach. A 5-fold cross validation scheme with 10-repeat cycles were used to assess model prediction accuracies.\n\nResultsIn Scenario 1, the MT models had higher prediction accuracies than the uT models for most traits and locations analyzed amounting to 32 percent better prediction accuracy on average. However for Scenario 2, we observed that the ME model had on average (across all locations and traits) 12 percent better predictive power than the uE model.\n\nConclusionWe recommend the use of multivariate mixed models (MT and ME) for cassava genetic evaluation. These models may be useful for other plant species.

genetics↗

bioRxiv2017

STPGA: Selection of training populations with a genetic algorithm

Optimal subset selection is an important task that has numerous algorithms designed for it and has many application areas. STPGA contains a special genetic algorithm supplemented with a tabu memory property (that keeps track of previously tried solutions and their fitness for a number of iterations), and with a regression of the fitness of the solutions on their coding that is used to form the ideal estimated solution (look ahead property) to search for solutions of generic optimal subset selection problems. I have initially developed the programs for the specific problem of selecting training populations for genomic prediction or association problems, therefore I give discussion of the theory behind optimal design of experiments to explain the default optimization criteria in STPGA, and illustrate the use of the programs in this endeavor. Nevertheless, I have picked a few other areas of application: supervised and unsupervised variable selection based on kernel alignment, supervised variable selection with design criteria, influential observation identification for regression, solving mixed integer quadratic optimization problems, balancing gains and inbreeding in a breeding population. Some of these illustrations pertain new statistical approaches.

genetics↗