Impact of genotypic errors with equal and unequal family contribution on accuracy of genomic prediction in aquaculture using simulation

N Khalilisamani,H W Raadsma,M S Khatkar,P C Thomson

doi:10.1038/s41598-021-97873-5

N Khalilisamani, H W Raadsma + Show 2 more

Open Access

https://doi.org/10.1038/s41598-021-97873-5

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Abstract

Genotypic errors, conflict between recorded genotype and the true genotype, can lead to false or biased population genetic parameters. Here, the effect of genotypic errors on accuracy of genomic predictions and genomic relationship matrix are investigated using a simulation study based on population and genomic structure comparable to black tiger prawn, Penaeus monodon. Fifty full-sib families across five generations with phenotypic and genotypic information on 53 K SNPs were simulated. Ten replicates of different scenarios with three heritability estimates, equal and unequal family contributions were generated. Within each scenario, four SNP densities and three genotypic error rates in each SNP density were implemented. Results showed that family contribution did not have a substantial impact on accuracy of predictions across different datasets. In the absence of genotypic errors, 3 K SNP density was found to be efficient in estimating the accuracy, whilst increasing the SNP density from 3 to 20 K resulted in a marginal increase in accuracy of genomic predictions using the current population and genomic parameters. In addition, results showed that the presence of even 10% errors in a 10 and 20 K SNP panel might not have a severe impact on accuracy of predictions. However, below 10 K marker density, even a 5% error can result in lower accuracy of predictions.

Highlights

Genotypic errors, conflict between recorded genotype and the true genotype, can lead to false or biased population genetic parameters
The detailed results of accuracy of genomic predictions, as measured by the correlation between true breeding values (TBV) and estimated breeding values (EBVs)/genomic estimated breeding values (GEBVs), for different generations, replicates, family type, SNP density and genotypic error rates are provided in Supplementary Table Scenario 1 (S1)
Using 0.5 K SNP density and without genotypic error, accuracy of GEBVs decreased across different trait heritabilities over five generations for equal family contribution whilst accuracy increased for unequal family contribution using medium (0.3) and high (0.5) heritability traits

Summary

Introduction

Conflict between recorded genotype and the true genotype, can lead to false or biased population genetic parameters. In the absence of genotypic errors, 3 K SNP density was found to be efficient in estimating the accuracy, whilst increasing the SNP density from 3 to 20 K resulted in a marginal increase in accuracy of genomic predictions using the current population and genomic parameters. This limits their use in routine agricultural applications. Application of low-density SNP panels combined with imputation methods to generate higher density SNP genotypes, usually based on a reference panel[25], can be a more cost-effective alternative. This approach has recently been extensively used in GS28–32. If the errors are known, imputation methods, based on, for example, application of maximum likelihood or Bayesian algorithms can be applied to estimate most probable g enotypes[40,41]

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