Bridging the genotyping gap: using genotyping by sequencing (GBS) to add high-density SNP markers and new value to traditional bi-parental mapping and breeding populations

Abstract

Genotyping by sequencing (GBS) is the latest application of next-generation sequencing protocols for the purposes of discovering and genotyping SNPs in a variety of crop species and populations. Unlike other high-density genotyping technologies which have mainly been applied to general interest "reference" genomes, the low cost of GBS makes it an attractive means of saturating mapping and breeding populations with a high density of SNP markers. One barrier to the widespread use of GBS has been the difficulty of the bioinformatics analysis as the approach is accompanied by a high number of erroneous SNP calls which are not easily diagnosed or corrected. In this study, we use a 384-plex GBS protocol to add 30,984 markers to an indica (IR64) x japonica (Azucena) mapping population consisting of 176 recombinant inbred lines of rice (Oryza sativa) and we release our imputation and error correction pipeline to address initial GBS data sparcity and error, and streamline the process of adding SNPs to RIL populations. Using the final imputed and corrected dataset of 30,984 markers, we were able to map recombination hot and cold spots and regions of segregation distortion across the genome with a high degree of accuracy, thus identifying regions of the genome containing putative sterility loci. We mapped QTL for leaf width and aluminum tolerance, and were able to identify additional QTL for both phenotypes when using the full set of 30,984 SNPs that were not identified using a subset of only 1,464 SNPs, including a previously unreported QTL for aluminum tolerance located directly within a recombination hotspot on chromosome 1. These results suggest that adding a high density of SNP markers to a mapping or breeding population through GBS has great value for numerous applications in rice breeding and genetics research.

The Tyrosine Aminomutase TAM1 Is Required for β-Tyrosine Biosynthesis in Rice

“…Genetic mapping using this population and scoring b-tyrosine as a binary trait (presence or absence) identified a highly significant QTL on chromosome 12 and a marginally significant QTL on chromosome 8 ( Figure 3A). Crosses between indica and japonica rice almost always result in segregation distortion because of the numerous sterility genes that segregate when these two varietal groups are crossed (Spindel et al, 2013). Higher prevalence of the IR64 allele on chromosome 12 in the current mapping population (Supplemental Figure 8) can account for the fact that only about one-third of the RILs, rather than the expected 50%, contain b-tyrosine.…”

“…The RILs and the two parental lines were genotyped using 96-plex genotyping-bysequencing (GBS) as described previously (Elshire et al, 2011;Spindel et al, 2013). The GBS data were analyzed using the TASSEL 3.0 GBS pipeline, and the results aligned to the MSU v.7.0 rice genome using Bowtie2, as described by Spindel et al (2013). Data were imputed using Plaid impute (-m 15, -n 60, -w 5).…”

“…PLUMAGE python scripts were used postimputation to perform a sequence error correction and remove singenucleotide polymorphisms (SNPs) with call rates #0.75, for a total SNP data set of 86,528 SNPs and a genetic map consisting of ;1417 centimorgans. See Spindel et al (2013) for details on the analysis pipeline and http://ricediversity.org/data for the molecular marker data set. A graphic representation of the genetic map, produced using r/qtl, confirmed that there were no distortions in the genetic map.…”

Genetic mapping in polyploids

“…The disadvantages of targeted approaches have been well explored (particularly regarding ascertainment bias, where the set of targeted SNPs on an array poorly represents the diversity in the samples under investigation due to biased methods of SNP discovery) (Albrechtsen et al, 2010;Moragues et al, 2010;Didion et al, 2012;Lachance and Tishkoff, 2013), although there are advantages and disadvantages to both methods (Mason et al, 2017). Apart from costs, differences exist in the ease of data analysis following genotyping, with sequencing data requiring greater curation and bioinformatics skills (Spindel et al, 2013;Bajgain et al, 2016) as well as potentially containing more erroneous and missing data (Spindel et al, 2013;Jones et al, 2017). In polyploids, SNP arrays have been developed in numerous species, which include both autopolyploid (or predominantly polysomic polyploids) and allopolyploid species.…”

Mapping resistance to the bird cherry-oat aphid and the greenbug in wheat using sequence-based genotyping

“…In the development of GBS libraries the genome complexity is reduced by the use of methylation-sensitive restriction enzymes and in combination with multiplex sequencing it is possible to genotype entire populations efficiently (Elshire et al 2011;Poland et al 2012). This is particularly useful for species with large genomes such as wheat, where sequencing needs to target non-duplicated regions of the genome in order to produce high quality maps (Spindel et al 2013). GBS methods can be applied in bi-parental populations to locate genomic regions associated to various agronomic traits of interest (Saintenac et al 2013).…”