Genotyping‐by‐sequencing approaches to characterize crop genomes: choosing the right tool for the right application

Abstract

SummaryIn the last decade, the revolution in sequencing technologies has deeply impacted crop genotyping practice. New methods allowing rapid, high‐throughput genotyping of entire crop populations have proliferated and opened the door to wider use of molecular tools in plant breeding. These new genotyping‐by‐sequencing (GBS) methods include over a dozen reduced‐representation sequencing (RRS) approaches and at least four whole‐genome resequencing (WGR) approaches. The diversity of methods available, each often producing different types of data at different cost, can make selection of the best‐suited method seem a daunting task. We review the most common genotyping methods used today and compare their suitability for linkage mapping, genomewide association studies (GWAS), marker‐assisted and genomic selection and genome assembly and improvement in crops with various genome sizes and complexity. Furthermore, we give an outline of bioinformatics tools for analysis of genotyping data. WGR is well suited to genotyping biparental cross populations with complex, small‐ to moderate‐sized genomes and provides the lowest cost per marker data point. RRS approaches differ in their suitability for various tasks, but demonstrate similar costs per marker data point. These approaches are generally better suited for de novo applications and more cost‐effective when genotyping populations with large genomes or high heterozygosity. We expect that although RRS approaches will remain the most cost‐effective for some time, WGR will become more widespread for crop genotyping as sequencing costs continue to decrease.

Linkage and association mapping reveals the genetic basis of brown fibre (Gossypium hirsutum)

“…Following the publication of several draft genomes of cotton (Li et al ., , ; Wang et al ., ; Yuan et al ., ; Zhang et al ., ) , the whole‐genome resequencing (WGR) approach has greatly facilitated cotton research via the genotyping of natural populations (Fang et al ., ; Scheben et al ., ; Wang et al ., ). Linkage mapping supports the precise study of genes but is time‐ and labour‐consuming.…”

“…It has been reported that the structural genes of the flavonoid pathway are significantly up-regulated in brown fibre (Feng et al, 2013(Feng et al, , 2014(Feng et al, , 2015Tan et al, 2013). Following the publication of several draft genomes of cotton Wang et al, 2012;Yuan et al, 2015;Zhang et al, 2015) , the whole-genome resequencing (WGR) approach has greatly facilitated cotton research via the genotyping of natural populations (Fang et al, 2017;Scheben et al, 2017;Wang et al, 2017). Linkage mapping supports the precise study of genes but is time-and labour-consuming.…”

Computational aspects underlying genome to phenome analysis in plants

“…Multiple approaches have been developed, including reduced representation libraries (RRL; Van Tassell et al ., ), restriction site associated DNA sequencing (RAD‐Seq; Baird et al ., ) and genotyping‐by‐sequencing (GBS; Elshire et al ., ). New variations of these techniques continue to be developed (He et al ., ; Scheben et al ., ).…”

“…Whole genome resequencing ranging from skim sequencing (approximately 1× coverage or below) to medium coverage resequencing (in the range of 20–40×) is increasingly common for small to mid‐size genomes (Scheben et al ., ), but remains so far prohibitively expensive for large genomes such as wheat. Using this resequencing approach, sequences from the whole genome are used, thereby offering more comprehensive SNP detection than reduced representation approaches.…”

Genetics and Genomics of Cultivated Eggplants and Wild Relatives

“…SSRs can be identified Goodwin et al 2016). However, these large-scale sequencing projects also allow the identification of thousands to millions of molecular markers of single nucleotide polymorphism (SNPs) markers with a reasonable amount of resources (Van Tassell et al 2008;Davey et al 2011;Scheben et al 2017). As occurs with the SSRs, SNPs are codominant, very reliable, ubiquitous and universal molecular markers, and although are generally less informative than SSRs (Yang et al 2011;Filippi et al 2015;Gonzaga, 2015), they are much more abundant and easy to automate (Thomson et al 2014;Kim et al 2016).…”