Linkage Analysis and Multi-Locus Genome-Wide Association Studies Identify QTNs Controlling Soybean Plant Height

Fang, Yanlong; Liu, Shulin; Dong, Quanzhong; Zhang, Kaixin; Tian, Zhixi; Li, Xiyu; Li, Wenbin; Qi, Zhengtang; Wang, Yue; Tian, Xiaocui; Song, Jie; Wang, Jiajing; Yang, Chaowu; Jiang, Sitong; Li, Wenxia; Ning, Hailong

doi:10.3389/fpls.2020.00009

Cited by 24 publications

(16 citation statements)

References 70 publications

(77 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A comparison of the four multi-locus methods used indicated that the ISIS-EM-BLASSO method identi ed the highest number of co-detected QTNs, corroborating the ndings of previous studies (Cui et al 2018;Ma et al 2018;Misra et al 2018;Zhang et al 2018b;Fang et al 2020). Although using the pLARmEB method initially detected the greatest number of QTNs, few of these contributed signi cantly to heritability, as previously reported by Li et al (2018) and Lü et al (2018).…”

Section: Discussionsupporting

confidence: 87%

Genome-wide association study for grain mineral content in a Brazilian common bean diversity panel

et al. 2021

View full text Add to dashboard Cite

Bioforti cation is one of the strategies developed to address malnutrition in developing countries, the aim of which is to improve the nutritional content of crops. The common bean (Phaseolus vulgaris L.), a staple food in several African and Latin American countries, has excellent nutritional attributes and is considered a strong candidate for bioforti cation. The objective of this study was to identify genomic regions associated with nutritional content in common bean grains using 178 Mesoamerican accessions belonging to a Brazilian Diversity Panel (BDP) and 25,011 good-quality single-nucleotide polymorphisms. The BDP was phenotyped in three environments for nine nutrients (phosphorus, potassium, calcium, magnesium, copper, manganese, sulfur, zinc, and iron) using four genome-wide association multi-locus methods. To obtain more accurate results, only Quantitative-Trait Nucleotides (QTNs) that showed repeatability (i.e., those detected at least twice using different methods or environments) were considered. Forty-eight QTNs detected for the nine minerals showed repeatability and were considered reliable. Pleiotropic QTNs and overlapping genomic regions surrounding the QTNs were identi ed, demonstrating the possible association between the deposition mechanisms of different nutrients in grains. The accumulation of favorable alleles in the same accession was associated with a gradually increasing nutrient content in the grain. The BDP proved to be a valuable source for association studies. The investigation of different methods and environments showed the reliability of markers associated with minerals. The loci identi ed in this study will potentially contribute to the improvement of Mesoamerican common beans, particularly carioca and black beans, the main groups consumed in Brazil.

show abstract

Section: Discussionsupporting

confidence: 87%

Genome-wide association study for grain mineral content in a Brazilian common bean diversity panel

et al. 2021

View full text Add to dashboard Cite

show abstract

“…On the other hand, the FASTmrMLM method, while it detected the lowest number of QTNs detected, was considered the most efficient of the methods evaluated. Several studies comparing the ISIS-EM-BLASSO, pLARmEB, mrMLM, and FASTmrMLM methods have already been performed in many crops and the results are similar to those observed in the present study (Ma et al, 2018;Misra et al, 2018;Zhang et al, 2018;Fang et al, 2020). Although the ML-GWAS methods have similar approaches, the differential identification of QTNs is related to different screening and estimation models of each method (Zhang et al, 2018).…”

Section: Discussionsupporting

confidence: 85%

“…Significant increases in productivity (YLD) and its primary components (NPP, SP, TSW, W100) were observed, and alleles that caused a significant PH reduction were identified. The PH is an essential factor in the formation of production components, and at the same time, it promotes or inhibits other components, affecting mainly the resistance to lodging, NN and NPP (Fang et al, 2020). Small-sized plants are associated with a determinate growth habit, less susceptibility to lodging, and shorter cycles (Chang et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

Genome-Wide Association Study Identifies Genomic Regions for Important Morpho-Agronomic Traits in Mesoamerican Common Bean

et al. 2021

View full text Add to dashboard Cite

The population growth trend in recent decades has resulted in continuing efforts to guarantee food security in which leguminous plants, such as the common bean (Phaseolus vulgaris L.), play a particularly important role as they are relatively cheap and have high nutritional value. To meet this demand for food, the main target for genetic improvement programs is to increase productivity, which is a complex quantitative trait influenced by many component traits. This research aims to identify Quantitative Trait Nucleotides (QTNs) associated with productivity and its components using multi-locus genome-wide association studies. Ten morpho-agronomic traits [plant height (PH), first pod insertion height (FPIH), number of nodules (NN), pod length (PL), total number of pods per plant (NPP), number of locules per pod (LP), number of seeds per pod (SP), total seed weight per plant (TSW), 100-seed weight (W100), and grain yield (YLD)] were evaluated in four environments for 178 Mesoamerican common bean domesticated accessions belonging to the Brazilian Diversity Panel. In order to identify stable QTNs, only those identified by multiple methods (mrMLM, FASTmrMLM, pLARmEB, and ISIS EM-BLASSO) or in multiple environments were selected. Among the identified QTNs, 64 were detected at least thrice by different methods or in different environments, and 39 showed significant phenotypic differences between their corresponding alleles. The alleles that positively increased the corresponding traits, except PH (for which lower values are desired), were considered favorable alleles. The most influenced trait by the accumulation of favorable alleles was PH, showing a 51.7% reduction, while NN, TSW, YLD, FPIH, and NPP increased between 18 and 34%. Identifying QTNs in several environments (four environments and overall adjusted mean) and by multiple methods reinforces the reliability of the associations obtained and the importance of conducting these studies in multiple environments. Using these QTNs through molecular techniques for genetic improvement, such as marker-assisted selection or genomic selection, can be a strategy to increase common bean production.

show abstract

“…As mentioned above, research studies have already combined the two methods to conduct target trait location analysis. In soybean, the combination of the two methods was used in several traits, such as seed size and shape (Hu et al, 2013 ), seed protein and oil content (Zhang et al, 2019 ), number of pods (Song et al, 2020 ), and plant height (Fang et al, 2020 ). Similar to the MAGIC population, four parents carried multiple allelic genotypes in FW-RIL, so it could conduct linkage and association analysis (Zhang et al, 2018 ; Li et al, 2019 , 2020 , 2021 ; Liu et al, 2019 ; Qi et al, 2020 ; Song et al, 2020 ; Tian et al, 2020 ; Wang et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

QTL for Main Stem Node Number and Its Response to Plant Densities in 144 Soybean FW-RILs

Wang

Zhao

et al. 2021

Front. Plant Sci.

Self Cite

View full text Add to dashboard Cite

Although the main stem node number of soybean [Glycine max (L.) Merr. ] is an important yield-related trait, there have been limited studies on the effect of plant density on the identification of quantitative trait loci (QTL) for main stem node number (MSNN). To address this issue, here, 144 four-way recombinant inbred lines (FW-RILs) derived from Kenfeng 14, Kenfeng 15, Heinong 48, and Kenfeng 19 were used to identify QTL for MSNN with densities of 2.2 × 105 (D1) and 3 × 105 (D2) plants/ha in five environments by linkage and association studies. As a result, the linkage and association studies identified 40 and 28 QTL in D1 and D2, respectively, indicating the difference in QTL in various densities. Among these QTL, five were common in the two densities; 36 were singly identified for response to density; 12 were repeatedly identified by both response to density and phenotype of two densities. Thirty-one were repeatedly detected across various methods, densities, and environments in the linkage and association studies. Among the 24 common QTL in the linkage and association studies, 15 explained a phenotypic variation of more than 10%. Finally, Glyma.06G094400, Glyma.06G147600, Glyma.19G160800.1, and Glyma.19G161100 were predicted to be associated with MSNN. These findings will help to elucidate the genetic basis of MSNN and improve molecular assistant selection in high-yield soybean breeding.

show abstract

Linkage Analysis and Multi-Locus Genome-Wide Association Studies Identify QTNs Controlling Soybean Plant Height

Cited by 24 publications

References 70 publications

Genome-wide association study for grain mineral content in a Brazilian common bean diversity panel

Genome-wide association study for grain mineral content in a Brazilian common bean diversity panel

Genome-Wide Association Study Identifies Genomic Regions for Important Morpho-Agronomic Traits in Mesoamerican Common Bean

QTL for Main Stem Node Number and Its Response to Plant Densities in 144 Soybean FW-RILs

Contact Info

Product

Resources

About