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Statistical Approach for Improving Genomic Prediction Accuracy through Efficient Diagnostic Measure of Influential Observation.

ABSTRACT: It is expected the predictive performance of genomic prediction methods may be adversely affected in the presence of outliers. In agriculture science an outlier may arise due to wrong data imputation, outlying response, and in a series of trials over the time or location. Although several statistical procedures are already there in literature for identification of outlier but identification of true outlier is still a challenge especially in case of high dimensional genomic data. Here we have proposed an efficient approach for detecting outlier in high dimensional genomic data, our approach is p-value based combination methods to produce single p-value for detecting the outliers. Robustness of our approach has been tested using simulated data through the evaluation measures like precision, recall etc. It has been observed that significant improvement in the performance of genomic prediction has been obtained by detecting the outliers and handling them accordingly through our proposed approach using real data.

SUBMITTER: Budhlakoti N

PROVIDER: S-EPMC7242349 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Statistical Approach for Improving Genomic Prediction Accuracy through Efficient Diagnostic Measure of Influential Observation.

Budhlakoti Neeraj N Rai Anil A Mishra D C DC

Scientific reports 20200521 1

It is expected the predictive performance of genomic prediction methods may be adversely affected in the presence of outliers. In agriculture science an outlier may arise due to wrong data imputation, outlying response, and in a series of trials over the time or location. Although several statistical procedures are already there in literature for identification of outlier but identification of true outlier is still a challenge especially in case of high dimensional genomic data. Here we have pro ...[more]

PMID: 32439883

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Project description:BackgroundRecently, there has been a growing interest in the genetic improvement of body measurement traits in farm animals. They are widely used as predictors of performance, longevity, and production traits, and it is worthwhile to investigate the prediction accuracies of genomic selection for these traits. In genomic prediction, the single-step genomic best linear unbiased prediction (ssGBLUP) method allows the inclusion of information from genotyped and non-genotyped relatives in the analysis. Hence, we aimed to compare the prediction accuracy obtained from a pedigree-based BLUP only on genotyped animals (PBLUP-G), a traditional pedigree-based BLUP (PBLUP), a genomic BLUP (GBLUP), and a single-step genomic BLUP (ssGBLUP) method for the following 10 body measurement traits at yearling age of Hanwoo cattle: body height (BH), body length (BL), chest depth (CD), chest girth (CG), chest width (CW), hip height (HH), hip width (HW), rump length (RL), rump width (RW), and thurl width (TW). The data set comprised 13,067 phenotypic records for body measurement traits and 1523 genotyped animals with 34,460 single-nucleotide polymorphisms. The accuracy for each trait and model was estimated only for genotyped animals using five-fold cross-validations.ResultsThe accuracies ranged from 0.02 to 0.19, 0.22 to 0.42, 0.21 to 0.44, and from 0.36 to 0.55 as assessed using the PBLUP-G, PBLUP, GBLUP, and ssGBLUP methods, respectively. The average predictive accuracies across traits were 0.13 for PBLUP-G, 0.34 for PBLUP, 0.33 for GBLUP, and 0.45 for ssGBLUP methods. Our results demonstrated that averaged across all traits, ssGBLUP outperformed PBLUP and GBLUP by 33 and 43%, respectively, in terms of prediction accuracy. Moreover, the least root of mean square error was obtained by ssGBLUP method.ConclusionsOur findings suggest that considering the ssGBLUP model may be a promising way to ensure acceptable accuracy of predictions for body measurement traits, especially for improving the prediction accuracy of selection candidates in ongoing Hanwoo breeding programs.

Project description:Large-scale systematic analysis of gene essentiality is an important step closer toward unraveling the complex relationship between genotypes and phenotypes. Such analysis cannot be accomplished without unbiased and accurate annotations of essential genes. In current genomic databases, most of the essential gene annotations are derived from whole-genome transposon mutagenesis (TM), the most frequently used experimental approach for determining essential genes in microorganisms under defined conditions. However, there are substantial systematic biases associated with TM experiments. In this study, we developed a novel Poisson model-based statistical framework to simulate the TM insertion process and subsequently correct the experimental biases. We first quantitatively assessed the effects of major factors that potentially influence the accuracy of TM and subsequently incorporated relevant factors into the framework. Through iteratively optimizing parameters, we inferred the actual insertion events occurred and described each gene's essentiality on probability measure. Evaluated by the definite mapping of essential gene profile in Escherichia coli, our model significantly improved the accuracy of original TM datasets, resulting in more accurate annotations of essential genes. Our method also showed encouraging results in improving subsaturation level TM datasets. To test our model's broad applicability to other bacteria, we applied it to Pseudomonas aeruginosa PAO1 and Francisella tularensis novicida TM datasets. We validated our predictions by literature as well as allelic exchange experiments in PAO1. Our model was correct on six of the seven tested genes. Remarkably, among all three cases that our predictions contradicted the TM assignments, experimental validations supported our predictions. In summary, our method will be a promising tool in improving genomic annotations of essential genes and enabling large-scale explorations of gene essentiality. Our contribution is timely considering the rapidly increasing essential gene sets. A Webserver has been set up to provide convenient access to this tool. All results and source codes are available for download upon publication at http://research.cchmc.org/essentialgene/.

Dataset Information

Statistical Approach for Improving Genomic Prediction Accuracy through Efficient Diagnostic Measure of Influential Observation.

Publications

Statistical Approach for Improving Genomic Prediction Accuracy through Efficient Diagnostic Measure of Influential Observation.

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