Sequence Imputation from Low Density Single Nucleotide Polymorphism Panel in a Black Poplar Breeding population

Abstract

Background: Genomic selection accuracy increases with the use of high SNP (single nucleotide polymorphism) coverage. However, such gains in coverage come at high costs, preventing their operational implementation by breeders. Low density panels imputed to higher densities offer a cheaper alternative. Our study is one of the first to explore the imputation in a tree species: black poplar. About 1000 pure-breed Populus nigra trees corresponding to a subsample of the French breeding population were selected and genotyped with a 12K custom Infinium Bead-Chip. Forty-three of those individuals corresponding mostly to nodal trees in the pedigree were fully sequenced (reference), while the remaining majority (target) was imputed from 8K to 1.4 million SNPs using FImpute. Each SNP and individual was evaluated for imputation errors by leave-one-out cross validation in the training sample of 43 sequenced trees. Some summary statistics such as Hardy Weinberg Equilibrium exact test p-value, quality of sequencing, depth of sequencing per site and per individual, minor allele frequency, marker density ratio or SNP information redundancy were calculated. Principal component and Boruta analyses were used on all these parameters to rank the factors affecting the quality of imputation. Additionally, we characterize the impact of the relatedness between reference population and target population.Results: During the imputation process, we used 7,540 SNPs from the chip to impute 1,438,827 SNPs from sequences along the 19 Chromosomes. At the individual level, imputation accuracy was very high with a proportion of SNPs correctly imputed between 0.84 and 0.99. The variation in accuracies was mostly due to differences in relatedness between individuals. At a SNP level, the imputation quality strongly depended on genotyped SNP density and to a lesser extent on the original minor allele frequency. The imputation did not appear to result in an increase of linkage disequilibrium. The genotype densification not only brought a better distribution of markers all along the genome, but also we did not detect any substantial bias in annotation categories.Conclusions: This study shows that it is possible to impute low-density marker panels to whole genome sequence with good accuracy under certain conditions that could be common to many breeding populations.

The apple REFPOP—a reference population for genomics-assisted breeding in apple

“…The imputation has multiplied the amount of marker information by ~43× for the progeny group at relatively low cost. A high imputation accuracy was achieved, which is comparable to a similar study in poplar 24 . To evaluate the imputation accuracy, two parental combinations ‘Fuji’ × ‘Pinova’ and ‘Golden Delicious’ × ‘Renetta Grigia di Torriana’ consisting together of 86 progeny with known high-density marker data were used.…”

Favorable Conditions for Genomic Evaluation to Outperform Classical Pedigree Evaluation Highlighted by a Proof-of-Concept Study in Poplar

“…The population under study corresponds to a pedigree of 24 parents and 1,011 offspring, structured into 35 full-sib cohorts, and involving a 4 by 4 factorial mating design together with a series of multiple pair-mating designs (Pegard et al, 2018). Most of the parents were sampled from natural populations or were high-performance trees already used in the breeding program.…”

“…The set of unrelated individuals were used to assess the imputation ability under challenging conditions. In a previous study (Pegard et al, 2018) genotype imputation from 7K (effective SNPs out of 12K in array) to 1,466,586 SNPs was performed attaining imputation qualities higher than 0.84 per individual, and evaluated by a leave-oneout cross-validation scheme (CV). Resulting imputation was used in the present study to constitute alternative sets of selected markers for genotyping.…”

“…One of the main objectives of the study was to compare GS to pedigreebased traditional evaluation, by assessing different modeling options including non-additive genetic effects and multipletrait evaluation. The study also considered the role of marker densification in the performance of GS, by benefiting from a recent imputation study (Pegard et al, 2018). The potential benefits of shortening the breeding cycle, although of importance, were not evaluated but only discussed in present work, because of the relatively late sexual maturity in the species.…”