Evaluation and Recommendations for Routine Genotyping Using Skim Whole Genome Re-sequencing in Canola

Abstract

Whole genome sequencing offers genome wide, unbiased markers, and inexpensive library preparation. With the cost of sequencing decreasing rapidly, many plant genomes of modest size are amenable to skim whole genome resequencing (skim WGR). The use of skim WGR in diverse sample sets without the use of imputation was evaluated in silico in 149 canola samples representative of global diversity. Fastq files with an average of 10x coverage of the reference genome were used to generate skim samples representing 0.25x, 0.5x, 1x, 2x, 3x, 4x, and 5x sequencing coverage. Applying a pre-defined list of SNPs versus de novo SNP discovery was evaluated. As skim WGR is expected to result in some degree of insufficient allele sampling, all skim coverage levels were filtered at a range of minimum read depths from a relaxed minimum read depth of 2 to a stringent read depth of 5, resulting in 28 list-based SNP sets. As a broad recommendation, genotyping pre-defined SNPs between 1x and 2x coverage with relatively stringent depth filtering is appropriate for a diverse sample set of canola due to a balance between marker number, sufficient accuracy, and sequencing cost, but depends on the intended application. This was experimentally examined in two sample sets with different genetic backgrounds: 1x coverage of 1,590 individuals from 84 Australian spring type four-parent crosses aimed at maximizing diversity as well as one commercial F1 hybrid, and 2x coverage of 379 doubled haploids (DHs) derived from a subset of the four-parent crosses. To determine optimal coverage in a simpler genetic background, the DH sample sequence coverage was further down sampled in silico. The flexible and cost-effective nature of the protocol makes it highly applicable across a range of species and purposes.

Assessment of low-coverage nanopore long read sequencing for SNP genotyping in doubled haploid canola (Brassica napus L.)

“…Although heterozygous genotype calls in DHs can be indicative of misalignment, there are other possible causes. Firstly, sequencing error can cause heterozygous genotypes, particularly in low coverage samples lacking sufficient reads to drown out noise 35,53 , and is especially relevant for minION, which has a higher error rate than current SRS technologies 30,31 . Secondly, sequence collapse, incomplete or missing regions of the reference genome resulting in the absence of one or more duplicated regions may lead to heterozygous genotype calls.…”

“…However, the current error rate of c. 10% 22,30,31 , requires the use of more accurate short read sequencing (SRS) to polish such assemblies to ensure the final sequence is correct and has likely prevented the assessment of LRS for routine SNP genotyping purposes. Since skim whole genome re-sequencing (skim WGR) using SRS has been found to be highly cost-effective, high-throughput and a relatively accurate genotyping-by-sequencing method 32–35 , LRS must be sufficiently accurate at skim levels without correction in order to compete with current SRS technology. The SRS data generated for error correction would likely in itself be sufficient for genotyping purposes in many species.…”

Evaluation of variant calling tools for large plant genome re-sequencing

“…There are three basic approaches for generating sequence data for genome wide variant detection against a genome reference including whole genome sequencing (WGS), genotype-by-sequencing (GBS), and whole exome capture (WEC) sequencing, each with different strengths and applications. WGS covers the whole genome including the large non-coding genomic sequence regions [ 1 , 2 ] while WEC focuses on the coding exome [ 3 , 4 ]. GBS applies specific restriction enzymes for genome reduction followed by barcoding samples, and works for both large and small genomes [ 5 – 10 ].…”

Construction of a High-Density Genetic Map and Mapping of Firmness in Grapes (Vitis vinifera L.) Based on Whole-Genome Resequencing

“…In recent years, several genetic maps for grapevine have been constructed on the basis of high-throughput sequencing technology; these maps have increased marker density and have led to the identification of some new QTLs [10,24,28,33,46]. Nearly all of the genetic maps were constructed using reduced representation methods, such as restriction site-associated DNA sequencing and genotyping-by-sequencing, which are relatively cheap as they sample only a fraction of the genome, but they also produce incomplete data [60]. With the development of high-throughput sequencing technologies and the availability of a reference genome for grapevines [61], whole-genome resequencing (WGR) allows the identification of whole genome differences between individuals and large numbers of SNPs, and has become one of the most rapid and effective methods used in QTL mapping and breeding research [62,63].…”