Abstract
BackgroundWith the emphasis on analysing genotype-by-environment interactions within the framework of genomic selection and genome-wide association analysis, there is an increasing demand for reliable tools that can be used to simulate large-scale genomic data in order to assess related approaches.ResultsWe proposed a theory to simulate large-scale genomic data on genotype-by-environment interactions and added this new function to our developed tool GPOPSIM. Additionally, a simulated threshold trait with large-scale genomic data was also added. The validation of the simulated data indicated that GPOSPIM2.0 is an efficient tool for mimicking the phenotypic data of quantitative traits, threshold traits, and genetically correlated traits with large-scale genomic data while taking genotype-by-environment interactions into account.ConclusionsThis tool is useful for assessing genotype-by-environment interactions and threshold traits methods.
Highlights
With the emphasis on analysing genotype-by-environment interactions within the framework of genomic selection and genome-wide association analysis, there is an increasing demand for reliable tools that can be used to simulate large-scale genomic data in order to assess related approaches
Access to dense single nucleotide polymorphism (SNP) markers across the genome has created the opportunity for finely identifying quantitative trait loci (QTLs) through genome-wide association studies (GWASs) and accurately predicting genetic values through genomic selection (GS) for economically important traits in animal and plant breeding [1,2,3]
The true breeding value (TBV) of one individual is defined as the cumulative effect across all true Quantitative trait loci (QTLs), while the phenotypic value is generated by adding the TBV with a random residual error
Summary
With the emphasis on analysing genotype-by-environment interactions within the framework of genomic selection and genome-wide association analysis, there is an increasing demand for reliable tools that can be used to simulate large-scale genomic data in order to assess related approaches. An increasing number of investigations on the detection of G-by-E interactions has been carried out in GWASs, detecting such interactions is inherently more difficult than determining additive genetic effects [7, 8]. Compared to those needed for traditional GWASs, a larger sample size and more environmental levels for individual records are required to interpret G-by-E interactions, and it is obviously challenging to find such samples. Simulation is a key step in providing simulated data with large-scale genome SNP markers for assessing algorithms and methods for
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