Faivre-Rampant, P. | bioRxiv Science

bioRxiv2018

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

BackgroundGenomic 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.\n\nResultsDuring 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.\n\nConclusionsThis 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.

genomics↗

bioRxiv2018

Phenomic selection: a low-cost and high-throughput alternative to genomic selection

Genomic selection - the prediction of breeding values using DNA polymorphisms - is a disruptive method that has widely been adopted by animal and plant breeders to increase productivity. It was recently shown that other sources of molecular variations such as those resulting from transcripts or metabolites could be used to accurately predict complex traits. These endophenotypes have the advantage of capturing the expressed genotypes and consequently the complex regulatory networks that occur in the different layers between the genome and the phenotype. However, obtaining such omics data at very large scales, such as those typically experienced in breeding, remains challenging. As an alternative, we proposed using near-infrared spectroscopy (NIRS) as a high-throughput, low cost and non-destructive tool to indirectly capture endophenotypic variants and compute relationship matrices for predicting complex traits and coined this new approach \"phenomic selection\" (PS). We tested PS on two species of economic interest (Triticum aestivum L. and Populus nigra L.) using NIRS on various tissues (grains, leaves, wood). We showed that one could reach predictions as accurate as with molecular markers, for developmental, tolerance and productivity traits, even in environments radically different from the one in which NIRS were collected. Our work constitutes a proof of concept and provides new perspectives for the breeding community, as PS is theoretically applicable to any organism at low cost and does not require any molecular information.\n\nARTICLE SUMMARYDespite its widely adopted interest in breeding, genomic selection - the prediction of breeding values using DNA polymorphisms - remains difficult to implement for many species because of genotyping costs. As an alternative or complement depending on the context, we propose \"phenomic selection\" (PS) as the use of low-cost and high-throughput phenotypic records to reconstruct similarities between genotypes and predict their performances. As a proof of concept of PS, we made use of near infrared spectroscopy applied to different tissues in poplar and wheat to predict various key traits and showed that PS could reach predictions as accurate as with molecular markers.

genetics↗