Using feedback in pooled experiments augmented with imputation for high genotyping accuracy at reduced cost

Abstract

AbstractConducting genomic selection in plant breeding programs can substantially speed up the development of new varieties. Genomic selection provides more reliable insights when it is based on dense marker data, in which the rare variants can be particularly informative while they are delicate to capture with sufficient statistical power. Despite the release of new performing technologies, the cost of large-scale genotyping remains a major limitation to the implementation of genomic selection. We suggest to combine pooled genotyping with population-based imputation as a cost-effective computational strategy for genotyping SNPs. Pooling saves genotyping tests and has proven to accurately capture the rare variants that are usually missed by imputation. In this study, we investigate an extension to our joint model of pooling and imputation via iterative coupling. In each iteration, the imputed genotype probabilities serve as feedback input for rectifying the decoded data, before running a new imputation in these adjusted data. Such flexible set up indirectly imposes consistency between the imputed genotypes and the pooled observations. We demonstrate that repeated cycles of feedback can take full advantage of the strengths in both pooling and imputation. The iterations improve greatly upon the initial genotype predictions, achieving very high genotype accuracy for both low and high frequency variants. We enhance the average concordance from 94.5% to 98.4% at a very limited computational cost and without requiring any additional genotype testing. We believe that these results could be of interest for plant breeders and crop scientists.Author summaryIn applications such as large-scale population surveys or plant breeding, the cost of genetic testing can limit the number of samples that are genotyped, or force the reduction to more cost-effective low-density marker panels. A reduction in the number of samples or the number of variants surveyed can reduce the power to detect important genetic correlations. We propose a scheme of pooled genotype testing, which would allow for using half the number of test assays for the same number of individuals surveyed. The data from overlapping pool tests is augmented with genotype imputation. We have previously shown that this approach was competitive, but with some drawbacks. Most strikingly, the error rate for common variants could be in the range of 10%. Now, we propose a new computational method for reconstructing SNP genotypes with pooling and imputation, adding an iterative coupled model connecting the two. This model allows us to exploit the advantages of both methods and achieves consistently high genotype reconstruction accuracy. We demonstrate the performance of our approach on a hypothetical plant breeding application based on a public genetic dataset from wheat samples. However, main aspects of the methodology would translate to many other settings.