Analysis of quantitative trait loci for main plant traits in soybean

Yao, Dan; Liu, Z Z; Zhang, J; Liu, S Y; Qu, Junle; Guan, Shuyan; Pan, Liyuan; Wang, D; Liu, J W; Wang, P W

doi:10.4238/2015.june.8.8

Cited by 52 publications

(53 citation statements)

References 12 publications

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“…In previous linkage QTL mapping studies, approximately 93 QTLs were identified for branch number, which were distributed on all 19 chromosomes. Most of these QTLs showed relatively small effects, with only two major QTLs [17, 20], on the C1 and C2 chromosomes, respectively. Comparison of the regions of the QTLs detected in this study and meta-QTL for branch number reported by predecessor showed that qBN.A1 , qBN.C2 and qBN.F included meta-QTL, suggesting these three QTLs may have a robust effect on branch number in different genetic backgrounds if they are identical to these meta-QTL.…”

Section: Discussionmentioning

confidence: 99%

“…Previous studies have shown that branch number is controlled by multiple quantitative trait loci (QTLs), and almost 93 branch number QTLs have been identified from nearly 18 linkage mapping populations [9–20]. Interestingly, a few QTLs on the C2 chromosome were found to be concentrated in a region (103-121cM) derived from different parents and environments [4, 21–25].…”

Section: Introductionmentioning

confidence: 99%

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QTL Mapping and integration as well as candidate gene prediction for branch number in soybean

Yang

Yang²,

Bai³

et al. 2019

Preprint

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3Branch number is an important factor that affects crop plant architecture and yield in 4 soybean. With the aim of elucidating the genetic basis of branch number, we 5 identified 10 consensus quantitative trait loci (QTLs) through preliminary mapping, 6 which were on chromosome A1, B2, C1, C2, D1a, D1b, F, L and N, explained 7 0.3-33.3% of the phenotypic variance. Of these, three QTLs were identical to 8 previously identified ones, whereas the other seven were novel. In addition, one major 9 QTL-qBN.C2 (R 2 =33.3%) was detected in all three environments and another new 10 major QTL-qBN.N (R 2 =19.6%) was detected in two environments (Taiyuan 2017 and 11 Taiyuan 2018), but only in Taiyuan. Thus, the QTL × environment interaction 12 analysis confirmed that QTL-qBN.N was strongly affected by the environment. We 13 compared the physical positions of the QTL intervals of the candidate genes 14 potentially involved in branching development, and five orthologous genes were 15 ultimately selected and related to the establishment of axillae meristem organization 16 and lateral organs, qBN.A1 (SoyZH13_05G177000.m1), qBN.C2 17 (SoyZH13_06G176500.m1, SoyZH13_06G185600.m1), and qBN.D1b-1 18 (SoyZH13_02G035400.m1, SoyZH13_02G070000.m3). The results of our study 19 reveal a complex and relatively complete genetic architecture and can serve as a basis 20 for the positional gene cloning of branch number in soybean. 21 4 1 Introduction 2 Plant architecture is an extremely important role in plant yield, which can affect 3 light distribution in the canopy and photosynthesis. Modification of crop plant 4 architecture has been used to improve plant fitness and agricultural performance, and 5 these modifications are achieved through genetics and breeding [1-3]. Branching is a 6 major factor that affects plant architecture, together with plant height, main stem, leaf, 7 pod and others [2]. The number and distribution of branches determine the canopy 8 architecture, which influences light interception as well as lodging resistance and 9 ultimately seed yield [4]. Branch number is strongly dependent on aspects related to 10 the cultural practices and growth environment [5]. 11 Branch number is also the most complex trait and is very sensitive to the 12 environmental conditions. Because the environmental effects on branch number are so 13 prominent, there have been few studies on the genetics of this trait, although varietal 14 differences in branch number have been observed in the adaptation to row spacing in 15 accordance with its modest heritability observed in most studies [6, 7]. Two dominant 16 alleles at independent loci were found to be associated with a high-branch number 17 phenotype [8] but the loci and other genetic factors that determine branch number in 18 soybean remain unknown. However, branch number is difficult to improve efficiently 19by traditional breeding methods and the genetic and especially the molecular 20 mechanism involved in branch number in soybean are poorly understood. 21Previous studies have shown that b...

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

QTL Mapping and integration as well as candidate gene prediction for branch number in soybean

Yang

Yang²,

Bai³

et al. 2019

Preprint

View full text Add to dashboard Cite

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“…Hwang (2010) Chapman et al 2003;Chung et al 2003;Nichols et al 2006;Bachlava et al 2009;Kuroda et al 2013;Hu et al 2014;Qi et al 2014a). Os QTLs identificados no GL I, por Hu et al 2014 (Mansur et al 1993;Lark et al 1994;Lark et al 1995;Mansur et al 1996;Orf et al 1999;Specht et al 2001;Wang et al 2004;Zhang et al 2004;Hwang 2010 (Lee et al 1996;Kim et al 2012), no GL C2 (Specht et al 2001;Wang et al 2004;Zhang et al 2004;Reinprecht et al 2006;Gai et al 2007;Palomeque et al 2009b;Liu et al 2011;Rossi et al 2013;Lee et al 2015), no GL D2 (Kim et al 2012), no GL I Chapman et al 2003), no GL J (Sun et al 2006) e no GL M (Specht et al 2001;Wang et al 2004;Zhang et al 2004;Pathan et al 2013;Yao et al 2015 (Mansur et al 1996;Zhang et al 2004;Sun et al 2006;Josie et al 2007;Palomeque et al 2009b;Hwang 2010;…”

Section: Mt-mim: Multiunclassified

“…Os QTLs descritos nessa região associados às pragas e doenças afetam a resistência da soja ao nematoide de cisto Heterodera glycines (Glover et al 2004;Guo et al 2006;Chang et al 2011;Ferreira et al 2011;Jiao et al 2015) , a Phytophthora sojae, (Weng et al 2007;Tucker et al 2010), ao vírus do mosaico comum (VMCS) (Yan et al 2015) e à podridão parda-da-haste (Phialophora gregata) (Lewers et al 1999;Patzoldt et al 2005a;Patzoldt et al 2005b (Wang et al 2004;Rossi et al 2013;Sun et al 2013;Yao et al 2015), um QTL no GL D2 (Kim et al 2012), e QTLs no GL M (Specht et al 2001;Wang et al 2004;Zhang et al 2004) (Keim et al 1990;Orf et al 1999;Kim et al 2012), quanto para a região revelada no subgrupo C1-2 (Orf et al 1999;Zhang et al 2004;Rossi et al 2013;Sun et al 2013). Kim et al 2012), e também para a região identificada no subgrupo C1-2 (Yuan et al 2002;Rossi et al 2013), no GL C2 (Zhang et al 2004;Reinprecht et al 2006;Gai et al 2007;Du et al 2009;Rossi et al 2013;Yao et al 2015), no GL D2 (Kazi et al 2010;Qi et al 2014a), no GL F (Reyna and Sneller 2001;Rossi et...…”

Section: Mt-mim: Multiunclassified

“…QTLs conferindo resistência das plantas aos afídeos e à lagarta H. zea (Rector et al 1999;Terry et al 2000;. No GL I, foram encontrados QTLs descritos afetando altura da planta Chapman et al 2003), número e massa de nódulos na raiz (Santos et al 2013;Yao et al 2015) e comprimento do interno na haste (Josie et al 2007). Já em relação à resistência a insetos, foram identificados QTLs conferindo resistência das plantas ao nematoide de cisto H. glycines (Qiu et al 1999;Winter et al 2007), a Phytophthora sojae, (Lee et al 2013), a Sclerotinia sclerotiorum (Arahana et al 2001;Huynh et al 2010) e à podridão vermelha da raiz (Fusarium solani) (Iqbal et al 2001;Swaminathan et al 2016).…”

Section: Mt-mim: Multiunclassified

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Identificação de QTLs em soja associados à resistência aos percevejos e a caracteres agronômicos utilizando a abordagem de mapeamento multivariado

Möller¹

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QTL identification in soybean related to stink bug resistance and agronomic traits using the multivariate multiple interval mapping approach Soybean is the Brazilian crop with the most expansion along the past three decades. Currently, it is a commodity commercialized in large scale as grain, bran and oil. Because it is an important source of protein, it plays a fundamental role in the food industry, both human and animal. Soybean monoculture in large areas has increased crop vulnerability to pathogens and insect pests, with significant consequences on grain production. Among such pest insects, stink bugs are considered a major pest of soybean crop, feeding directly on seeds, reducing yield and seed quality. Losses in crop yield due to stink bugs attack are greater than 30% and seed germination compromising can be greater than 50%. This study aimed to identify QTL (Quantitative Trait Loci), for stink bug resistance traits and agronomic traits using the multivariate multiple interval mapping. An F2 mapping population of 228 plants derived from a biparental cross between IAC-100 and CD-215 was used for genotyping. An F2:3 population was developed to evaluate eight stink bug resistant traits such as graining period, leaf retention, pod number per plant, percentage of pod damage, number of seeds, hundred seed weight, weight of healthy seeds and spotted seed weight. Other seven agronomic traits were evaluated such as number of days to flowering, plant height at flowering, number of days to maturity, plant height at maturity, lodging, agronomic value and grain yield. A total of 417 SNP (Single Nucleotide Polymorphism) markers, 61 SSR (Simple Sequence Repeat), 30 AFLP (Amplified Fragment Length Polymorphism) and 8 TRAP (Target Region Amplification Polymorphism) markers were mapped into 20 soybean linkage groups. The total map length was 2,814.82 cM with an average of 5.46 cM between markers. QTL mapping for those traits was performed using univariate (MIM) and multivariate (MT-MIM) multiple interval mapping, with main QTL effects estimates. MIM analysis identified a total of 60 QTL, through 13 soybean linkage groups, with 29 QTL related to stink bug resistant traits and 31 QTL related to agronomic traits. Phenotypic variation explained by QTL ranged from 14.27% for lodging to 65.45% for number of days to maturity. The traits were divided into nine groups for MT-MIM analysis considering their correlation coefficient. Twenty different genomic regions were identified showing a very high QTL clustering. For most of the traits phenotypic variation estimates for MT-MIM models were higher than MIM models, ranging from 27.98% to 65.30% for plant height at flowering and number of days to maturity, respectively. MT-MIM analysis showed 13 genomic regions controlling at least one of the evaluated traits which were not identified at MIM analysis. Comparison between identified QTL and QTL database at Soybase demonstrated that some QTL were similar to those described in different genetic background. However, 56 QTL detected in the pre...

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Exploring the QTL–allele constitution of main stem node number and its differentiation among maturity groups in a Northeast China soybean population

Wang

Haixiang

et al. 2020

Crop Science

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Northeast China (NEC) is a major soybean [Glycine max (L.) Merr.] production region in China, where the germplasm of American soybeans are mainly from. The main stem node number (MSN) is a trait related to plant type and yield potential. With the soybeans expanded to higher latitudes in NEC, earlier maturity groups (MG 0, MG 00, and MG 000) formed based on MG I + MG II (MG I+II), and correspondingly the MSN decreased. To explore the MSN quantitative trait locus (QTL)-allele constitution, 306 accessions from NEC were studied using the restricted two-stage multilocus genome-wide association study (RTM-GWAS) procedure. In total, 76 MSN QTLs and 183 alleles were identified, with their genetic contribution about 0.04-9.83% per locus for a total of 65.63% for all loci. With the MSN reduction from MG I+II to MG 0, MG 00, and MG 000 (17.89 to 13.11), the changed alleles accounted for 28.42% of all alleles (6.56% for new allele emergence plus 21.86% for old allele exclusion), whereas the major part of the alleles were those inherited from MG I+II (71.58%).Thus in the evolution of MSN in the NEC soybean population, inheritance is the first genetic motivation, exclusion and selection (positive allele exclusion, 65.00%) is the second, emergence and mutation (negative allele emergence, 95.67%) is the third, and recombination among retained alleles is the fourth. A potential of 2-5 MSN improvement keeping the MG earliness was predicted, and 49 candidate genes were identified.Abbreviations: GWAS, genome-wide association study; MG, maturity group; MSN, main stem node number; NCSP, Northeast China soybean population; NEC, Northeast China; PV, phenotypic variance; QEI, quantitative trait locus × environment interaction; QTL, quantitative trait locus; RIL, recombinant inbred line; RTM, restricted two-stage multilocus; SNP, single nucleotide polymorphism; SNPLDB, single nucleotide polymorphism linkage disequilibrium block.

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Analysis of quantitative trait loci for main plant traits in soybean

Cited by 52 publications

References 12 publications

QTL Mapping and integration as well as candidate gene prediction for branch number in soybean

QTL Mapping and integration as well as candidate gene prediction for branch number in soybean

Identificação de QTLs em soja associados à resistência aos percevejos e a caracteres agronômicos utilizando a abordagem de mapeamento multivariado

Exploring the QTL–allele constitution of main stem node number and its differentiation among maturity groups in a Northeast China soybean population

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