Applications of genotyping-by-sequencing (GBS) in maize genetics and breeding

TLDR

Genotyping‑by‑Sequencing (GBS) is a low‑cost, high‑throughput method that uses restriction enzymes to reduce genome complexity and is widely applied in genetic and breeding studies. In this study, 2,240 maize individuals from eight diverse populations were genotyped by GBS, yielding ~955,120 SNPs per individual with an average genotyping error of 0.70%. Missing genotype rates varied with multiplex level (~25% for 96‑plex, ~55% for 384‑plex), but imputation reduced missingness to 12.65% and 3.72% for association and bi‑parental populations, respectively, while increasing error; unimputed data are preferable for diversity and linkage mapping, whereas imputed data enhance marker density and map resolution for association mapping, and both data types yield comparable genomic‑prediction accuracy, underscoring GBS as a versatile SNP discovery tool for maize genetics and breeding.

Abstract

Abstract Genotyping-by-Sequencing (GBS) is a low-cost, high-throughput genotyping method that relies on restriction enzymes to reduce genome complexity. GBS is being widely used for various genetic and breeding applications. In the present study, 2240 individuals from eight maize populations, including two association populations (AM), backcross first generation (BC1), BC1F2, F2, double haploid (DH), intermated B73 × Mo17 (IBM), and a recombinant inbred line (RIL) population, were genotyped using GBS. A total of 955,120 of raw data for SNPs was obtained for each individual, with an average genotyping error of 0.70%. The rate of missing genotypic data for these SNPs was related to the level of multiplex sequencing: ~ 25% missing data for 96-plex and ~ 55% for 384-plex. Imputation can greatly reduce the rate of missing genotypes to 12.65% and 3.72% for AM populations and bi-parental populations, respectively, although it increases total genotyping error. For analysis of genetic diversity and linkage mapping, unimputed data with a low rate of genotyping error is beneficial, whereas, for association mapping, imputed data would result in higher marker density and would improve map resolution. Because imputation does not influence the prediction accuracy, both unimputed and imputed data can be used for genomic prediction. In summary, GBS is a versatile and efficient SNP discovery approach for homozygous materials and can be effectively applied for various purposes in maize genetics and breeding.