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

## TL;DR

This paper introduces a cost-effective method for genotyping in plant breeding by combining pooled experiments with feedback-based imputation to achieve high accuracy.

## Contribution

The novel contribution is the use of iterative feedback to improve genotype imputation accuracy by aligning individual predictions with pooled observations.

## Key findings

- Iterative feedback improves genotype accuracy from 94.5% to 98.4%.
- The method achieves high accuracy for both low- and high-frequency variants.
- The approach is computationally efficient and does not require additional genotyping tests.

## Abstract

Conducting genomic selection (GS) in plant breeding programs can substantially speed up the development of new varieties. GS provides more reliable insights when it is based on dense marker data, in which the rare variants can be particularly informative. Despite the availability of new technologies, the cost of large-scale genotyping remains a major limitation to the implementation of GS. 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 adding iterative coupling to a joint model of pooling and imputation that we have previously proposed. In each iteration, the imputed genotype probabilities serve as feedback input for adjusting the per-sample prior genotype probabilities, before running a new imputation based on these adjusted data. This flexible setup indirectly imposes consistency between the imputed genotypes and the pooled observations. We demonstrate that repeated cycles of feedback can take advantage of the strengths in both pooling and imputation when an appropriate set of reference haplotypes is available for 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 limited computational cost and without requiring any additional genotype testing.

For large-scale settings, such as plant breeding, acquisition of dense genotypes can constitute a cost challenge. Sometimes, less dense SNP microarrays are used to reduce cost, augmented by imputation to a denser SNP panel. Clouard and Nettelblad instead propose to pool samples and test directly on fewer instances of a more dense array. The authors use a feedback approach to impute individuals, but then make sure that the imputation results are fully consistent with the observations on the pool level. If this does not happen, the prior probabilities are adjusted. They manage to increase accuracy from 94% to 98%, making pooling competitive.