Imputation strategies for genomic prediction using nanopore sequencing

Authors: LAMB, HARRY - University Of Queensland; NGUYEN, LOAN - University Of Queensland; COPLEY, JAMES - University Of Queensland; Engle, Bailey; HAYES, BEN - University Of Queensland; ROSS, ELIZABETH - University Of Queensland

Submitted to: BMC Biology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/27/2023
Publication Date: 12/8/2023
Citation: Lamb, H.J., Nguyen, L.T., Copley, J.P., Engle, B.N., Hayes, B.J., Ross, E.M. 2023. Imputation strategies for genomic prediction using nanopore sequencing. BMC Biology. 21. Article 286. https://doi.org/10.1186/s12915-023-01782-0.
DOI: https://doi.org/10.1186/s12915-023-01782-0

Interpretive Summary: With the advent and development of new genotyping techniques and technologies, many alternatives for SNP genotyping cattle currently exist. Oxford Nanopore Technologies’ MinION sequencer has now made sequencing portable and rapid, however, this technology has not yet been utilized to generate genotypes for the purpose of estimating genomic predictions. Here we evaluated the speed and accuracy of estimating genomic predictions using low coverage, Oxford Nanopore Technologies sequence data, in comparison to the current gold standard of genotyping, SNP microarrays. SNP array genotypes and Oxford Nanopore Technologies sequence data for 64 beef heifers were used to calculate genomic breeding values for four traits: body weight, hip height, heifer puberty, and body condition score. Factors such as the reference data set size for imputation, imputation program, DNA sequencing depth, and imputation parameters were tested and optimized. Here we demonstrated that accurate genomic prediction is possible with Oxford Nanopore Technologies sequence data, using very low sequencing depths and with the potential for very fast imputation time. Utilizing Oxford Nanopore Technologies sequence data is a viable alternative to SNP genotyping using microarrays for genomic prediction.

Technical Abstract: Background: Genomic prediction describes the use of SNP genotypes to predict complex traits and has been widely applied in humans and agricultural species. Genotyping-by-sequencing, a method which uses low-coverage sequence data paired with genotype imputation, is becoming an increasingly popular SNP genotyping method for genomic prediction. The development of Oxford Nanopore Technologies’ (ONT) MinION sequencer has now made genotyping-by-sequencing portable and rapid. Here we evaluate the speed and accuracy of genomic predictions using low-coverage ONT sequence data in a population of cattle using four imputation approaches. We also investigate the effect of SNP reference panel size on imputation performance. Results: SNP array genotypes and ONT sequence data for 62 beef heifers were used to calculate genomic estimated breeding values (GEBVs) from 641 k SNP for four traits. GEBV accuracy was much higher when genome-wide flanking SNP from sequence data were used to help impute the 641 k panel used for genomic predictions. Using the imputation package QUILT, correlations between ONT and low-density SNP array genomic breeding values were greater than 0.91 and up to 0.97 for sequencing coverages as low as 0.1×using a reference panel of 48 million SNP. Imputation time was significantly reduced by decreasing the number of flanking sequence SNP used in imputation for all methods. When compared to high-density SNP arrays, genotyping accuracy and genomic breeding value correlations at 0.5×coverage were also found to be higher than those imputed from low-density arrays. Conclusions: Here we demonstrated accurate genomic prediction is possible with ONT sequence data from sequencing coverages as low as 0.1×, and imputation time can be as short as 10 min per sample. We also demonstrate that in this population, genotyping-by-sequencing at 0.1×coverage can be more accurate than imputation from low-density SNP arrays.