Genomic prediction using low-coverage portable Nanopore sequencing

Abstract

Most traits in livestock, crops and humans are polygenic, that is, a large number of loci contribute to genetic variation. Effects at these loci lie along a continuum ranging from common low-effect to rare high-effect variants that cumulatively contribute to the overall phenotype. Statistical methods to calculate the effect of these loci have been developed and can be used to predict phenotypes in new individuals. In agriculture, these methods are used to select superior individuals using genomic breeding values; in humans these methods are used to quantitatively measure an individual’s disease risk, termed polygenic risk scores. Both fields typically use SNP array genotypes for the analysis. Recently, genotyping-by-sequencing has become popular, due to lower cost and greater genome coverage (including structural variants). Oxford Nanopore Technologies’ (ONT) portable sequencers have the potential to combine the benefits genotyping-by-sequencing with portability and decreased turn-around time. This introduces the potential for in-house clinical genetic disease risk screening in humans or calculating genomic breeding values on-farm in agriculture. Here we demonstrate the potential of the later by calculating genomic breeding values for four traits in cattle using low-coverage ONT sequence data and comparing these breeding values to breeding values calculated from SNP arrays. At sequencing coverages between 2X and 4X the correlation between ONT breeding values and SNP array-based breeding values was > 0.92 when imputation was used and > 0.88 when no imputation was used. With an average sequencing coverage of 0.5x the correlation between the two methods was between 0.85 and 0.92 using imputation, depending on the trait. This suggests that ONT sequencing has potential for in clinic or on-farm genomic prediction, however, further work to validate these findings in a larger population still remains.

Imputation of Low-density Marker Chip Data in Plant Breeding: Evaluation of Methods Based on Sugar Beet

“…This value is supported by a number of publications which estimate effective population sizes for crop and livestock populations much closer to 100 than to 1 million which is the default setting (Cowling, 2007; Gorssen et al, 2020; Leroy et al, 2013; Makanjuola et al, 2020; Saura et al, 2021; Zhao et al, 2021). After the publication of Pook et al (2020), some studies have already adopted lower values for ‘ne’ (Thorn et al, 2021; Whalen & Hickey, 2020) and a few are also increasing the window size (Arouisse et al, 2020; Geibel et al, 2021; Lamb et al, 2021).…”

“…This would lead to a biased comparison between approaches. Only a few studies use imputation parameters other than the default (Arouisse et al, 2020;Geibel et al, 2021;Lamb et al, 2021;Nyine et al, 2019;Thorn et al, 2021;.…”

“…This would lead to a biased comparison between approaches. Only a few studies use imputation parameters other than the default (Arouisse et al, 2020; Geibel et al, 2021; Lamb et al, 2021; Nyine et al, 2019; Thorn et al, 2021; Whalen & Hickey, 2020). These studies lower the parameter for the effective population size and some also increase the window size.…”

Imputation strategies for near real-time genomic prediction using nanopore sequencing

“…with the SNP from this project filtered to those segregating in Brahman, Droughtmaster, Santa Gertrudis and crossbred animals, which are the most relevant animals to Australia's northern beef industry. The first genotyping method used a minimum allele count (MAC) based on sequencing depth as described in Lamb, Hayes [31]. The second method, hereafter referred to as our Q-score method, used base qualities to assign genotype likelihoods using the original GATK method [32] and also incorporated a methylation masking approach to avoid calling genotypes at potential methylation sites.…”

“…All 641,163 SNP from the bovine HD SNP array used for calculating genomic breeding values (described below) were also retained in the reference SNP panels. The SNP effects used to calculate the GEBVs were derived from 28,000 animals with genotypes and phenotypes as described in Hayes, Fordyce [33] and Lamb, Hayes [31].…”