Imputation strategies for genomic prediction using nanopore sequencing

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.

SARS-CoV-2 wastewater-based epidemiology in an enclosed compound: A 2.5-year survey to identify factors contributing to local community dissemination

Long-term (>2.5 years) surveillance of SARS-CoV-2 RNA concentrations in wastewater was conducted within an enclosed university compound. This study aims to demonstrate how coupling wastewater-based epidemiology (WBE) with meta-data can identify which factors contribute toward the dissemination of SARS-CoV-2 within a local community. Throughout the pandemic, the temporal dynamics of SARS-CoV-2 RNA concentrations were tracked by quantitative polymerase chain reaction and analyzed in the context of the number of positive swab cases, the extent of human movement, and intervention measures. Our findings suggest that during the early phase of the pandemic, when strict lockdown was imposed, the viral titer load in the wastewater remained below detection limits, with <4 positive swab cases reported over a 14-day period in the compound. After the lockdown was lifted and global travel gradually resumed, SARS-CoV-2 RNA was first detected in the wastewater on 12 August 2020 and increased in frequency thereafter, despite high vaccination rates and mandatory face-covering requirements in the community. Accompanied by a combination of the Omicron surge and significant global travel by community members, SARS-CoV-2 RNA was detected in most of the weekly wastewater samples collected in late December 2021 and January 2022. With the cease of mandatory face covering, SARS-CoV-2 was detected in at least two of the four weekly wastewater samples collected from May through August 2022. Retrospective Nanopore sequencing revealed the presence of the Omicron variant in the wastewater with a multitude of amino acid mutations, from which we could infer the likely geographical origins through bioinformatic analysis. This study demonstrated that long-term tracking of the temporal dynamics and sequencing of variants in wastewater would aid in identifying which factors contribute the most to SARS-CoV-2 dissemination within the local community, facilitating an appropriate public health response to control future outbreaks as we now live with endemic SARS-CoV-2.

Detection and Typing of a Fowl Adenovirus Type 1 Agent of Pancreatitis in Guinea Fowl

Adenoviral pancreatitis has been amply described for decades in guinea fowl. Although its pathologic picture has been characterized fairly well, its etiology still remains only partially clarified. Based on several outbreaks diagnosed on commercial guinea flocks raised in France since 2017, we performed direct whole-genome sequencing from pancreatic lesional tissue by using the Oxford Nanopore Technologies (ONT) sequencing method. We generated 4781 viral reads and assembled a whole genome of 43,509 bp, clustering within fowl adenovirus type 1 (FAdV-1). A phylogenetic analysis based on a partial sequence of the hexon and short fiber genes on viruses collected in France showed 98.7% and 99.8% nucleotide identity, respectively. Altogether, these results confirm that an FAdV-1 closely related to chicken and other avian strains is the agent of pancreatitis in guinea fowl. This study illustrates the potential of ONT sequencing method to achieve rapid whole-genome sequencing directly from pathologic material.