Sequence imputation from low density single nucleotide polymorphism panel in a black poplar breeding population.

Area of Science:

• Plant genomics
• Forest tree breeding
• Quantitative genetics

Background:

• High single nucleotide polymorphism (SNP) coverage improves genomic selection accuracy but is costly.
• Low-density SNP panels imputed to higher densities offer a cost-effective alternative for genomic resources.
• This study explores SNP imputation in the tree species *Populus nigra* (black poplar).

Purpose of the Study:

• To assess the feasibility and accuracy of imputing low-density marker panels to whole-genome sequences in black poplar.
• To identify factors influencing imputation accuracy in a tree breeding population.
• To evaluate the impact of imputation on linkage disequilibrium and marker distribution.

Main Methods:

• Genotyping of ~1000 black poplar individuals using a 12K custom Infinium Bead-Chip.
• Full sequencing of 43 individuals as a reference panel.
• Imputation of 8K SNPs to 1.4 million SNPs using FImpute software.
• Leave-one-out cross-validation for SNP and individual imputation error evaluation.
• Analysis of factors like relatedness, SNP density, and minor allele frequency on imputation quality.

Main Results:

• High imputation accuracy at the individual level (0.84–0.99 proportion of correctly imputed SNPs), influenced by relatedness.
• SNP imputation quality depended on genotyped SNP density and minor allele frequency.
• Imputation did not increase linkage disequilibrium or introduce substantial bias in annotation categories.

Conclusions:

• Low-density marker panels can be accurately imputed to whole-genome sequence data in black poplar.
• Successful imputation is achievable under conditions applicable to many breeding populations.
• This method provides a cost-effective strategy for enhancing genomic selection in tree breeding programs.