Single-plant GWAS coupled with bulk segregant analysis allows rapid identification and corroboration of plant-height candidate SNPs

Abstract

Background Genome wide association studies (GWAS) are a powerful tool for identifying quantitative trait loci (QTL) and causal single nucleotide polymorphisms (SNPs)/genes associated with various important traits in crop species. Typically, GWAS in crops are performed using a panel of inbred lines, where multiple replicates of the same inbred are measured and the average phenotype is taken as the response variable. Here we describe and evaluate single plant GWAS (sp-GWAS) for performing a GWAS on individual plants, which does not require an association panel of inbreds. Instead sp-GWAS relies on the phenotypes and genotypes from individual plants sampled from a randomly mating population. Importantly, we demonstrate how sp-GWAS can be efficiently combined with a bulk segregant analysis (BSA) experiment to rapidly corroborate evidence for significant SNPs. Results In this study we used the Shoepeg maize landrace, collected as an open pollinating variety from a farm in Southern Missouri in the 1960’s, to evaluate whether sp-GWAS coupled with BSA can efficiently and powerfully used to detect significant association of SNPs for plant height (PH). Plant were grown in 8 locations across two years and in total 768 individuals were genotyped and phenotyped for sp-GWAS. A total of 306 k polymorphic markers in 768 individuals evaluated via association analysis detected 25 significant SNPs (P ≤ 0.00001) for PH. The results from our single-plant GWAS were further validated by bulk segregant analysis (BSA) for PH. BSA sequencing was performed on the same population by selecting tall and short plants as separate bulks. This approach identified 37 genomic regions for plant height. Of the 25 significant SNPs from GWAS, the three most significant SNPs co-localize with regions identified by BSA. Conclusion Overall, this study demonstrates that sp-GWAS coupled with BSA can be a useful tool for detecting significant SNPs and identifying candidate genes. This result is particularly useful for species/populations where association panels are not readily available.

Advances in omics technology for improving crop yield and stress resilience

“…It facilitates mapping in pooled population through bulk segregant analysis (BSA) or selective DNA pooling. Moreover, combining high‐throughput genotyping methods with BSA has been successfully used to map both monogenic as well as complex traits in many species (Gyawali et al, 2019; Mansfeld & Grumet, 2018; Martinez et al, 2020; Ramirez‐Gonzalez et al, 2015).…”

The reuse of public datasets in the life sciences: potential risks and rewards

“…GWAS to associate variants (QTLs, SNPs) with traits, for example single-plant GWAS for identification of plant height candidate SNPs (Gyawali et al, 2019) A large number of false positives requires large datasets, their sharing and compulsory replication (Marigorta et al, 2018). One possibility to check for sufficient sample size in for example genetic association studies is the random division of the study population by two and the requirement that any results have to be detected in both subsets (Hirschhorn et al, 2002).…”

Prospects for the Study and Improvement of Abiotic Stress Tolerance in Date Palms in the Post-genomics Era

“…These approaches therefore require phenotyping single samples as is common in animal and human genetics, but less common in plants. Such single plant linkage mapping or GWAS ("sp-GWAS, " Gyawali et al, 2019) suffers from increased error in phenotypic measurements owing to lack of replication and the inability to phenotype a seedling genotype in multiple environments or treatments (Figure 1).…”