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

Authors: GYAWALI, ABISKAR - University Of Missouri; SHRESTHA, VIVEK - University Of Missouri; Guill, Katherine; Flint-Garcia, Sherry; Beissinger, Timothy

Submitted to: BMC Plant Biology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/29/2019
Publication Date: 10/8/2019
Citation: Gyawali, A., Shrestha, V., Guill, K.E., Flint Garcia, S.A., Beissinger, T.M. 2019. Single-plant GWAS coupled with bulk segregant analysis allows rapid identification and corroboration of plant-height candidate SNPs. Biomed Central (BMC) Plant Biology. 19:412. https://doi.org/10.1186/s12870-019-2000-y.
DOI: https://doi.org/10.1186/s12870-019-2000-y

Interpretive Summary: Researchers studying crop genetics seek to connect traits (observations or measurements) to the genes that control them. In this study we investigated the feasibility and statistical power of combining two statistical methods to link genes and traits. We evaluated this combined approach on a corn (maize) population for plant height, a well-studied and agriculturally-important trait. We identified 25 gene regions associated with plant height. Three regions were verified by both methods, demonstrating the robustness of our combined approach. These results are important for crop geneticists and breeders in their efforts to link traits to the genes that control them, in order to continue crop improvement.

Technical Abstract: 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. 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 306k 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. Overall, this study demonstrates that sp-GWAS coupled with BSA can be a useful tool in detecting significant SNPs and in identifying candidate genes. This result is particularly useful for species/populations where association panels are not readily available.