Single trait versus principal component based association analysis for flowering related traits in pigeonpea

Abstract

Pigeonpea, a tropical photosensitive crop, harbors significant diversity for days to flowering, but little is known about the genes that govern these differences. Our goal in the current study was to use genome wide association strategy to discover the loci that regulate days to flowering in pigeonpea. A single trait as well as a principal component based association study was conducted on a diverse collection of 142 pigeonpea lines for days to first and fifty percent of flowering over 3 years, besides plant height and number of seeds per pod. The analysis used seven association mapping models (GLM, MLM, MLMM, CMLM, EMLM, FarmCPU and SUPER) and further comparison revealed that FarmCPU is more robust in controlling both false positives and negatives as it incorporates multiple markers as covariates to eliminate confounding between testing marker and kinship. Cumulatively, a set of 22 SNPs were found to be associated with either days to first flowering (DOF), days to fifty percent flowering (DFF) or both, of which 15 were unique to trait based, 4 to PC based GWAS while 3 were shared by both. Because PC1 represents DOF, DFF and plant height (PH), four SNPs found associated to PC1 can be inferred as pleiotropic. A window of ± 2 kb of associated SNPs was aligned with available transcriptome data generated for transition from vegetative to reproductive phase in pigeonpea. Annotation analysis of these regions revealed presence of genes which might be involved in floral induction like Cytochrome p450 like Tata box binding protein, Auxin response factors, Pin like genes, F box protein, U box domain protein, chromatin remodelling complex protein, RNA methyltransferase. In summary, it appears that auxin responsive genes could be involved in regulating DOF and DFF as majority of the associated loci contained genes which are component of auxin signaling pathways in their vicinity. Overall, our findings indicates that the use of principal component analysis in GWAS is statistically more robust in terms of identifying genes and FarmCPU is a better choice compared to the other aforementioned models in dealing with both false positive and negative associations and thus can be used for traits with complex inheritance.

Determination of traits responding to iron toxicity stress at different stages and genome-wide association analysis for iron toxicity tolerance in rice (Oryza sativa L.)

“…To identify QTLs associated with these evaluated traits, we performed GWAS using the FarmCPU model in GAPIT version 3. FarmCPU is a multilocus model that has been shown to be more robust in controlling for false positives and false negatives ( Kaler et al, 2019 ; Kumar et al, 2022 ). This was also evident in the Q-Q plots ( Supplementary Figure S3 ).…”

Genome-wide association study reveals the genetic basis of brace root angle and diameter in maize

“…MLM and BLINK are two different statistical methods for GWAS. MLM includes the kinship matrix (K) as an additional random effect component ( Kumar et al, 2022 ), whereas BLINK uses a multiple loci test method instead of a single loci test method, by combining a fixed effect model (FEM), Bayesian information criteria, and linkage disequilibrium information ( Huang et al, 2017 ). Due to the less false-negative rate, more significant SNPs had been revealed by BLINK model in the present study.…”

Single and multi‐trait genome‐wide association studies identify genomic regions associated with phenological traits in cowpea

“…As the multi‐trait analysis considered all three traits analyzed, we can suggest that the genes annotated in the genomic regions of SNPs identified in PC1 act together for the expression of those phenological traits. PCA‐based GWAS has been proven to be an efficient option for discovering pleiotropic QTLs (Kumar et al., 2022; Zhang et al., 2018). For example, VuUCR779.07G191600 was annotated as a U‐box protein and is homologous to PUB 13 and PUB14 genes, which acts in the control of flowering time and senescence in Arabidopsis thaliana (Feke et al., 2020; Li et al., 2012; Zhou et al., 2015).…”

“…The mtGWAS based on PCA was able to identify SNPs that could also be related to pleiotropic genes for phenological traits, including SNPs also observed by single trait analysis such as 2_55402, 2_31912, and 2_50960 (Table 4). The use of PCA as the dependent variable to run GWAS analyses is a powerful tool to dissect complex traits that are correlated (Kumar et al, 2022;Yano et al, 2019), which…”