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

Branch number is an important factor that affects crop plant architecture and yield in soybean. With the aim of elucidating the genetic basis of branch number, we identified 10 consensus quantitative trait loci (QTLs) through preliminary mapping, which were on chromosome A1, B2, C1, C2, D1a, D1b, F, L and N, explained 0.3-33.3% of the phenotypic variance. Of these, three QTLs were identical to previously identified ones, whereas the other seven were novel. In addition, one major QTL-qBN.C2 (R2=33.3%) was detected in all three environments and another new major QTL-qBN.N (R2=19.6%) was detected in two environments (Taiyuan 2017 and Taiyuan 2018), but only in Taiyuan. Thus, the QTL × environment interaction analysis confirmed that QTL-qBN.N was strongly affected by the environment. We compared the physical positions of the QTL intervals of the candidate genes potentially involved in branching development, and five orthologous genes were ultimately selected and related to the establishment of axillae meristem organization and lateral organs, qBN.A1 (SoyZH13_05G177000.m1), qBN.C2 (SoyZH13_06G176500.m1, SoyZH13_06G185600.m1), and qBN.D1b-1 (SoyZH13_02G035400.m1, SoyZH13_02G070000.m3). The results of our study reveal a complex and relatively complete genetic architecture and can serve as a basis for the positional gene cloning of branch number in soybean.

1 quantitative trait loci (QTLs), and almost 93 branch number QTLs have been 2 identified from nearly 18 linkage mapping populations [9][10][11][12][13][14][15][16][17][18][19][20]. Interestingly, a few 3 QTLs on the C2 chromosome were found to be concentrated in a region (103-121cM) 4 derived from different parents and environments [4,[21][22][23][24][25]. It is indicated that this 5 region on the C2 chromosome is a hotspot for branch number in soybean. However, 6 only a few of these QTLs have been repeatedly detected, in accordance with the 7 modest heritability of branch number. In addition, almost all these branch number 8 QTLs were found to have a moderate effect. Therefore, these data suggest that the 9 existence of varietal differences in branch number among soybean cultivars and it is 10 difficult to narrow down these QTLs and identify the underlying candidate genes.

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The objectives of the present study were to (a) identify the genetic mechanism 12 that controls branch number in soybean by performing quantitative trait locus (QTL) 13 analysis using F 2 population derived from two soybean lines, C025 and JD18, that 14 have showed consistent significant differences in branch number in different 15 environments, to (b) dissect the relationship between major QTLs and the 16 environment, to (c) integrate QTLs associated with branch number in soybean using 17 meta-analysis and to (d) predict the potential candidate genes for further fine-mapping 18 and mechanism studies.

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Plant materials, field experiments and trait investigation 21 The F 2 population included 109 individuals, and was derived from two soybean lines, 6 1 C025 (high branch number) and JD18 (low branch number). The F 2 individuals were 2 planted in Hainan from Nov. 2016to Feb. 2017(code Hainan 2016, the F 2:3 and F 2:4 3 lines were planted in Taiyuan from May 2017 to Oct. 2017 (code Taiyuan 2017) and 4 May 2018 to Oct. 2018(code Taiyuan 2018. The F 2 , F 2:3 and F 2:4 populations and 5 two parents were arranged in a randomized complete block design with two 6 replications. Each block contained two rows with a space of 50 cm between rows and 7 13.5 cm between individual plants. The seeds were sown by hand, and the field 8 management followed standard agriculture practices. In each block, 10 representative 9 individuals from the two rows were harvested by hand at maturity. The branch 10 number was measured based on a previously described method [4]. 12 The SSR markers from the SoyBase database (https://soybase.org/) were used for 13 polymorphism screening between the two parents and used to genotype the F 2 14 individuals. Leaf tissue was collected from seedlings of the parents and F 2 populations. 15 Genomic DNA was extracted according to the CTAB method [26]. The PCR, 16 electrophoresis, and silver staining procedures were performed as described initials "cq", followed by the name of the trait abbreviation and linkage group.

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The broad-sense heritability was calculated as h 2 = σ2 g/( σ2 g + σ2 ge / n + σ2 e / nr), 19 as described previously, where σ2 g, σ2 ge, and σ2 e are the variance of genotype, 20 genotype×environment, and error, respectively, and n and r are the number of 8 1 environments and replications, respectively. The values of σ2 g, σ2 ge, and σ2 e were 2 estimated using the SAS ANOVA procedure. 3

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Phenotypic variation of the parents and F 2 population 5 The two parents, C025 and JD18, showed extremely significant differences in branch 6 number in all three investigated environments. The branch number of C025 was 4.1± 7 0.83, which was approximately twice the branch number of JD18 (1.6±0.79) ( Table   8 1). The branch number of the F 2 population showed more or less normal distributions 9 in all three investigated environments (Fig 1), indicating a quantitative inheritance 10 suitable for QTL identification. In addition, the branch number of the F 2 population 11 exhibited transgressive segregation but to a small degree, indicating that the favorable 12 alleles were mainly distributed in one of the two parents.

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The analysis of variance revealed that the genotypic, environmental and genotype × 14 environment effects were all extremely significant for branch number ( variance (S1 Table). Ten QTLs were integrated by the meta-analysis, and these were 2 distributed on nine (A1, B2, C1, C2, D1a, D1b, F, L and N) of the 20 chromosomes. Interestingly, one major QTL-qBN.N was a novel branch number QTL in soybean, 12 and was detected only in Taiyuan. Thus, to determine the relationship between 13 QTL-qBN.N and the environment, a QTL × environment interaction analysis was 14 performed (Fig 2). The results showed that the branch numbers of QQ and 15 Qq-genotype in Hainan were lower than those in Taiyuan, but the branch number of 16 the qq-genotype was higher, thus, these results indicated that QTL-qBN.N was 17 strongly affected by the environment.

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A total of 93 QTLs were identified in the reported QTL mapping (S2 Table), and 63 20 consensus QTLs (including current studies) were successfully anchored to a complete 21 genetic map, which were subjected to meta-analysis (Table 4). These consensus QTLs 10 1 were distributed on all of the 20 chromosomes. The R 2 of these consensus QTLs 2 ranged from 0.1 to 50.9%. About 80% QTLs showed small effects (R 2 < 10%), and 3 only four QTLs had large effects (R 2 > 30%), which should be considered as major 4 QTLs.

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Identification of candidate genes 6 To identify candidate genes for branch number in soybean, we extracted the gene 7 sequences of 10 QTLs regions identified in the current study by blasting the bilateral 8 sequence of SSR markers of QTLs to the Gmax_ZH13, which is the third generation 9 sequencing of the soybean genome. After performing BLASTN searches against all of 10 the genes in A.thaliana, we found five orthologous genes, which were involved in the 11 establishment of axillae meristem organization and lateral organs and were possible 12 candidate genes for branch number ( to these meta-QTL. These "repeatable" QTLs found across the current and previous 21 studies should be potential targets for marker-assisted selection and map-based 12 1 cloning. These results showed that branch number was controlled by a large number 2 of loci, mostly with small effects, which strongly suggested the complexity of the 3 genetic basis of branch number traits in soybean.

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The effect of the QTL× environment interaction has also been addressed in several performing high-resolution mapping. We expect that the use of these branch 20 number-related genes may allow us to improve the soybean branch number system, 21 resulting in high yields.

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In the future, as data will be continually obtained through QTL mapping analyses [46, 13 1 47], it will be possible to integrate the data from this study into further meta-analyses 2 using this approach to provide a better understanding of complex traits for crop 3 improvement. These data will provide an important theoretical and applied resource 4 for studies on soybean genetics and genomics.