Genome-wide association studies and whole-genome prediction reveal the genetic architecture of KRN in maize

Abstract

Background Kernel row number (KRN) is an important trait for the domestication and improvement of maize. Exploring the genetic basis of KRN has great research significance and can provide valuable information for molecular assisted selection. Results In this study, one single-locus method (MLM) and six multilocus methods (mrMLM, FASTmrMLM, FASTmrEMMA, pLARmEB, pKWmEB and ISIS EM-BLASSO) of genome-wide association studies (GWASs) were used to identify significant quantitative trait nucleotides (QTNs) for KRN in an association panel including 639 maize inbred lines that were genotyped by the MaizeSNP50 BeadChip. In three phenotyping environments and with best linear unbiased prediction (BLUP) values, the seven GWAS methods revealed different numbers of KRN-associated QTNs, ranging from 11 to 177. Based on these results, seven important regions for KRN located on chromosomes 1, 2, 3, 5, 9, and 10 were identified by at least three methods and in at least two environments. Moreover, 49 genes from the seven regions were expressed in different maize tissues. Among the 49 genes, ARF29 (Zm00001d026540, encoding auxin response factor 29) and CKO4 (Zm00001d043293, encoding cytokinin oxidase protein) were significantly related to KRN, based on expression analysis and candidate gene association mapping. Whole-genome prediction (WGP) of KRN was also performed, and we found that the KRN-associated tagSNPs achieved a high prediction accuracy. The best strategy was to integrate all of the KRN-associated tagSNPs identified by all GWAS models. Conclusions These results aid in our understanding of the genetic architecture of KRN and provide useful information for genomic selection for KRN in maize breeding.

Multi-Locus Genome-Wide Association Study and Genomic Selection of Kernel Moisture Content at the Harvest Stage in Maize

“…The prediction accuracy was still high (0.26-0.56) when only 16 stable markers identified by at least four models were included. Similar findings were reported for maize kernel row number (An et al, 2020), resistance to maize southern leaf blight and gray leaf spot (Bian and Holland, 2017), and maize lowphosphorus tolerance (Xu et al, 2018). Therefore, using a small set of markers identified by multiple ML-GWAS methods as fixed effects in an rrBLUP model is a powerful tool for KMC prediction in maize molecular breeding and can effectively save time and costs.…”

“…Xu et al (2018) compared one SL-GWAS method (GEMMA) and three ML-GWAS methods (FASTmrEMMA, FarmCPU, and LASSO) for the genetic detection of maize starch pasting properties, and more QTNs were detected by individual ML-GWAS methods than by the SL-GWAS method. An et al (2020) used one SL-GWAS method (MLM) and six ML-GWAS methods (mrMLM, FASTmrMLM, FASTmrEMMA, pLARmEB, pKWmEB, and ISIS EMBLASSO) to dissect the genetic architecture of maize kernel row number. The largest number of QTNs were identified with the mrMLM method, and the most co-detected QTNs were identified with ISIS EM-BLASSO.…”

“…Controlling costs by using fewer markers while still achieving accurate predictions for complex quantitative traits remains a challenge (Xu et al, 2020). Recently, several studies reported that taking association markers for interesting traits detected by GWAS into account and including them as fixed effects in GS models resulted in higher accuracy than that achieved with GS models using genome-wide markers (Spindel et al, 2016;Qin et al, 2019;Ravelombola et al, 2019;An et al, 2020;Sehgal et al, 2020).…”

Genome wide association analysis for yield related traits in maize

“…We examined the LD in the genomic region around each significant SNP to establish a supporting interval for the significant association. That supporting interval would comprise the surrounding region in LD ( r 2 > 0.2) [ 57 ]. The candidate genes in the LD region around significant SNPs were identified based on the B73 reference genome V3 from MaizeGDB ( https://www.maizegdb.org/ ).…”

Multi-Locus GWAS for Grain Weight-Related Traits Under Rain-Fed Conditions in Common Wheat (Triticum aestivum L.)

“…Multi-locus genome-wide association study (ML-GWAS) is a powerful approach to deal with this problem. The approach has already been successfully utilized to dissect the genetic architecture associated with important agronomic and quality traits in several crops, such as maize (Zhang et al, 2018 ; Zhu et al, 2018 ; An et al, 2020 ), rice (Cui et al, 2018 ; Liu et al, 2020 ), barley (Hu et al, 2018 ), cotton (Li et al, 2018 ; Su et al, 2018 ), soybean (Ziegler et al, 2018 ), and foxtail millet (Jaiswal et al, 2019 ). In wheat also, ML-GWAS has been used to identify genomic regions associated with different agronomic and yield associated traits (Jaiswal et al, 2016 ; Ward et al, 2019 ; Hanif et al, 2021 ; Malik et al, 2021a ; Muhammad et al, 2021 ), grain architecture-related traits (Schierenbeck et al, 2021 ), spike-layer uniformity-related traits (Malik et al, 2021b ), potassium use efficiency (Safdar et al, 2020 ), nutrient accumulation (Bhatta et al, 2018 ; Kumar et al, 2018 ; Alomari et al, 2021 ), disease resistance (Cheng et al, 2020 ; Habib et al, 2020 ; Tomar et al, 2021 ), and salinity tolerance (Chaurasia et al, 2020 ).…”