Adapting Genotyping-by-Sequencing for Rice F2 Populations

Abstract

Rapid and cost-effective genotyping of large mapping populations can be achieved by sequencing a reduced representation of the genome of every individual in a given population, and using that information to generate genetic markers. A customized genotyping-by-sequencing (GBS) pipeline was developed to genotype a rice F2 population from a cross of Oryza sativa ssp. japonica cv. Nipponbare and the African wild rice species O. longistaminata. While most GBS pipelines aim to analyze mainly homozygous populations, we attempted to genotype a highly heterozygous F2 population. We show how species- and population-specific improvements of established protocols can drastically increase sample throughput and genotype quality. Using as few as 50,000 reads for some individuals (134,000 reads on average), we were able to generate up to 8154 informative SNP markers in 1081 F2 individuals. Additionally, the effects of enzyme choice, read coverage, and data postprocessing are evaluated. Using GBS-derived markers, we were able to assemble a genetic map of 1536 cM. To demonstrate the usefulness of our GBS pipeline, we determined quantitative trait loci (QTL) for the number of tillers. We were able to map four QTL to chromosomes 1, 3, 4, and 8, and partially confirm their effects using introgression lines. We provide an example of how to successfully use GBS with heterozygous F2 populations. By using the comparatively low-cost MiSeq platform, we show that the GBS method is flexible and cost-effective, even for smaller laboratories.

Linkage mapping and QTL analysis of flowering time using ddRAD sequencing with genotype error correction inBrassica napus

“…Because genotype errors in either of the compared duplicate samples can lead to discordance, the genotype error rate can be roughly estimated as half of the discordance (~6%). In line with GBS results in a rice F2 population (47), most errors can be attributed to undercalling of heterozygous alleles (97.51%). The parental lines are homozygous, though residual heterozygosity and mismapping can lead to heterozygous allele calls.…”

Genetic variation and population structure of maize inbred lines adapted to the mid-altitude sub-humid maize agro-ecology of Ethiopia using single nucleotide polymorphic (SNP) markers

“…One of the limitations of GBS markers was concerns on the reliability of allele calls on heterogeneous and highly heterozygous germplasm as compared with highly homozygous genotypes, which has been dealt through intensive post data correction, including implementation of reliable imputation methods [ 35 , 36 ]. Using 191 SNPs from Kompetitive Allele Specific PCR (KASP) and different number of GBS markers, we recently compared genetic purity of 80 maize samples (16 maize inbred lines, each represented from 2 to 9 seed sources).…”

Identification of QTLs and candidate genes for physiological traits associated with drought tolerance in cotton

“…GBS is applicable to GS, which predicts complex, economically important quantitative traits using genome-wide molecular markers at a lower cost than what is achieved through other methods (Poland et al 2012). The introduction of GBS has revolutionized the entire field owing to its specificity, simplicity, high reproducibility and increased speed owing to the simultaneous detection of single nucleotide polymorphisms (SNPs) and genotyping (Furuta et al 2017). Thus, the significance of GBS is the reduced sequencing steps, reduced cost, reduced sample handling, fewer polymerase chain reactions (PCR) and purification steps.…”