Meta-analysis of soybean amino acid QTLs and candidate gene mining

1. lntroduction

Soybean (Glycine max (L.) Merr), which is widely planted in the United States, Brazil, Argentina, China, and India(Thu et al. 2014), contains high levels of proteins, linoleic acid, phospholipids, and a variety of human essential amino acids and plays a significant role in global food security(Medic et al. 2014). There are 18 types of amino acids in soybean, such as Met (methionine) (1.8 g/16 g nitrogen(N) on average), Lys (lysine) (6.1 g/16 g N on average),Cys (cysteine) (1.2 g/16 g N on average), Thr (threonine)(4.0 g/16 g N on average), and Ala (alanine) (4.6 g/16 g N on average) (Kwanyuen and Burton 2010), and amino acid composition also had a crucial influence on the nutritional value of soybean. Therefore, obtaining an optimal proportion of amino acids that meets food nutrition requirements should be considered in cultivar breeding (Qiu et al. 2014).

Soybean amino acid content is an important but complex quantitative trait controlled by multiple genes (Qiu et al.2014). Many soybean quantitative trait loci (QTLs) have been mapped in various genetic backgrounds and environments.Panthee et al. (2006a) found four QTLs associated with Cys content and three QTLs related to Met content in soybean seeds. Warrington et al. (2015) mapped two Lys-related QTLs to Gm08 and Gm20, and three QTLs for Thr to Gm01,Gm09, and Gm17. Wang et al. (2015) detected eight QTLs for both Cys and Met contents over three environments.However, the complicated mapping methods, large number,and large confidence intervals of these QTLs make it difficult to use them directly in breeding. Thus, these “original”QTLs need to be further screened to identify QTLs with high heritability and short confidence intervals (CI) (Chardon et al. 2004). The meta-analysis method was first proposed by psychologist Glass (1976) and has been widely applied in the fields of medical science, sociology, and behavioral science. To date, meta-analysis has been widely used in maize (Hao et al. 2010; Verret et al. 2017), rice (Trijatmiko et al. 2014), wheat (Wang et al. 2017), potato (Yellareddygari et al. 2016), and soybean (Thilakarathna and Raizada 2017). The first time that meta-analysis and the overview method were used to integrate crop QTLs was in a study by Chardon et al. (2004), who identified 62 “real” QTLs with small CIs based on the integration of 313 maize flowering time QTLs. Guo et al. (2006) were the first to apply metaanalysis to soybean. They identified 17 “real” QTLs for soybean cyst nematode resistance from 62 original QTLs.Compared with the large amount of data required for metaanalysis, overview analysis requires less data and yields more accurate results (Liu et al. 2011). The integration of meta-analysis and overview analysis has been widely used in further QTL analysis. Since Chardon et al. (2004),meta-analysis and/or overview methods have been used in studies of many soybean agronomic traits, including 100-seed weight (Sun et al. 2012b), plant height (Sun et al.2012a), oil content (Qi et al. 2011), protein content (Qi et al.2011), fungal disease resistance (Wang et al. 2010), insect resistance (Wang et al. 2009), phosphorus efficiency (Huang et al. 2011), and growth (Wu et al. 2009). However, to date,meta-analysis and overview analysis have rarely been used in studies of soybean amino acid QTLs. Using metaanalysis, Qiu et al. (2015) integrated 33 sulfur-containing amino acid QTLs based on the Consensus Map 4.0 high density genetic map and identified eight real QTLs (2015).In our study, the physical map was applied innovatively to meta-analysis because the genetic map was used most until now, and we combined meta-analysis with overview analysis to obtain more accurate amino acid content QTLs than previous studies (Liu et al. 2011; Gao et al. 2013). A comprehensive map of 18 soybean amino acid contentrelated QTLs was constructed using the BioMercator ver.2.1 software, and the “real” large-effect QTLs with small CIs were obtained using meta-analysis and overview analysis.Furthermore, QTL candidate genes were annotated the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Swissprot databases. Our study lays the foundation for further fine mapping and analysis of gene functions.

2. Materials and methods

2.1. Collection of soybean amino acid QTL information

The soybean amino acid-related QTLs in this study were collected from previously published papers (Table 1). We obtained 140 original QTLs for 18 different types of amino acids that were originally mapped from recombination inbred line (RIL) populations. In addition, basic information, such as mapping method, flanking markers, most likely position,95% confidence interval (CI), LOD value, mean R2, were collected for use in meta-analysis and overview analysis.

Table 1 Information of soybean amino acid original QTLs

1) ANOVA, analysis of variance; GLM, general linear model; CIM, composite interval mapping; IM, interval mapping; PLABQTL, a computer program to map QTL. 2) RIL, recombinant inbred lines.

QTL no. Parents Population size Analysis method1) Population type2) Reference 1 N87-984-16×TN93-99 101 ANOVA RIL Panthee et al. (2004)3 N87-984-16×TN93-99 101 GLM RIL Panthee et al. (2006b)80 N87-984-16×TN93-99 101 ANOVA RIL Panthee et al. (2006a)4 Essex×PI437654 205 CIM RIL Gutierrez-Gonzalez et al. (2010)6 Essex×PI437654 196 IM RIL Gutierrez-Gonzalez et al. (2009)1 Minsoy×Archer 108 PLABQTL RIL Stombaugh et al. (2004)9 AC756×RCAT Angora 207 ANOVA RIL Primomo et al. (2005)3 Zhongdou 27×Jiunong 20 130 GLM RIL Zeng et al. (2009)3 Magellan×PI437654 188 CIM RIL Gutierrez-Gonzalez et al. (2011)5 Essex×Forrest 40 ANOVA RIL Kassem et al. (2004)13 Peking×Tamahomare 96 CIM RIL Yoshikawa et al. (2010)9 Hwangkeum×IT182932 113 CIM RIL Yang et al. (2011)3 Williams 82×Essex 92 CIM RIL Smallwood (2012)

2.2. QTL integration with a physical map

The Williams82 physical map obtained from Phytozome (https://phytozome.jgi.doe.gov/pz/portal.html#!info?alias=Org_Gmax) was used as a reference map. The Williams82 physical map could be easily used to integrate QTLs identified using different methods and genetic backgrounds. QTLs were projected from the original map onto the Williams82 physical map according to the most likely physical positions and CIs, and the projected QTLs were used to establish a consensus map using BioMercator ver. 2.1 software (Arcade et al. 2004).

2.3. Meta-analysis of amino acid content QTLs

There were numerous QTL clusters in the consensus map,laying a solid foundation for searches of consensus QTLs.By analyzing each QTL cluster using the tools-Meta-analysis function, we obtained the most likely positions, 95% CIs and average contribution rate of the consensus QTLs. The meta-analysis function calculates the positions of consensus QTLs by integrating several independent QTLs on the same chromosome. This analysis yielded four models, and the optimal model called the “consensus QTL” was determined based on the minimum Akaike Information Criteria (AIC)(Gof finet and Gerber 2000). According to the maximum likelihood function ratio, each model specified the most likely position on the chromosome obeying Gauss’s law.The formula referred from Goffinet and Gerber (2000) was applied to calculate.

2.4. Overview analysis of amino acid content QTLs using a physical map

A total of 33 clusters of consensus QTLs were identified based on the chromosomal locations of the 138 original QTLs (Table 2). Each QTL cluster was analyzed using the tools-Meta-analysis in BioMercator ver. 2.1. The original QTLs mapped to Gm10 and Gm12 showed a dispersed distribution, and there was no overlap between them. As a result, there were no available consensus QTLs from these chromosomes. R2 ranged from 0.2 to 32.7%. There were six consensus QTLs, located on chromosomes Gm04, Gm07,Gm09, Gm11, Gm14, and Gm18, with R2 values greater than 20.0%. The QTL CIs were clearly reduced after metaanalysis, and the average CI decreased from 4.90 to 3.80 Mb.Six original QTLs on Gm01, Gm13, and Gm19 were combined into one consensus QTL and five on Gm02 and Gm14 were combined into one by meta-analysis. The CIs of nine consensus QTLs were less than 1.00 Mb. The coordinates of the left and right markers were 1.14–1.21 Mb on Gm07, 4.21–4.40 Mb on Gm07, 5.67–5.84 Mb on Gm08, 6.48–7.14 Mb on Gm09, 5.12–5.44 Mb on Gm13,8.21–9.22 Mb on Gm14, 0.55–1.12 Mb on Gm16, 2.82–3.45 Mb on Gm18, and 0.69–0.77 Mb on Gm19. The minimum CI was 0.07 Mb for a QTL on Gm07. What’s more, two of the nine QTLs with the shortest CIs and highest contribution rates, which were 20.3 and 21.0% for the QTLs on Gm07 and Gm14, respectively (Table 2).

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In eq. (3), nbQTL represents the total number of QTLs,and nbE is the sum of the number of experiments where a QTL was identified on a chromosome. The Pi values were plotted on the same graph to identify regions where there was a conspicuous peak in probability density to determine the location of “real” QTLs, chromosomal location was plotted on the horizontal axis, and P(x), U(x), and H(x) were plotted on the ordinate axis. The location of a “real” QTL was de fined as the peak value where P(x) is greater than U(x).

2.5. Gene mining from consensus QTL intervals

例(25)所处的语境：2018年4月2日8时至4月4日8时，全国大风降温，随着冷空气逐渐东进，华北地区最高气温纷纷降到15℃左右，山东、河南是降温最为猛烈的一带，累计降温可达到20℃。通过“确认过眼神X”构式用无奈的口吻表达了对严重降温的“斥责”。例(26)中，1994年的“柳倪之争”事件之后，联想和华为分别走上不同的道路，结果一直遥遥领先的联想逐步被华为赶超，并将优势保持至今，该例的表达委婉地讽刺了联想发展目光的短浅，以及发展战略的不足，从而导致逐渐衰弱。

白羽肉鸡传染性疾病的发生与流行，存在许多原因，受到传染性病原微生物、非传染性病原微生物、环境、饲养管理、气候、营素、病原等多种因素的影响。以上因素均能导致白羽肉鸡相关传染性疾病的发生与流行，因此，需要对白羽肉鸡相关传染性疾病的发生原因进行分类分析与总结。

3. Results

3.1. Analysis and integration of soybean amino acids amino acid-related QTLs

A total of 140 original QTLs related to 18 amino acids were obtained from literature searches (Appendix A). The numbers of the original amino acid-related QTLs were as follows: six Ala, one Arg (arginine), four Asp (aspartic acids),two Cys, eight Glu (glutamic acid), 61 Gly (glycine), one His(histidine), five Ile (isoleucine), seven Leu (leucine), three Lys, six Met, six Phe (phenylalanine), four Pro (proline), six Ser (serine),five Thr, six Trp (tryptophane), five Tyr (tyrosine), and four Val(valine). Two QTLs related to Gly haven’t been mapped to a specific chromosome, so a total of 138 QTLs were mapped onto the reference map. The maximum R2 was 56%, and LOD score ranged from 2 to 8.8. All of the amino acid-related QTLs were mapped using a RIL population.

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3.2. Meta-analysis of soybean amino acid-related QTLs

Normal distributions of the most likely QTL chromosomal locations were obtained from previously published papers.The most likely QTL position was calculated based on the CI (eq. (1)):

3.3. Overview analysis of soybean amino acid-related QTLs

With the exploration of molecular makers, it will complement the Williams-82 reference genome (Li et al.2016) and will eventually bring new insights to both soybean research and soybean production.

Table 2 Result of meta-analysis of soybean amino acids QTLs1)

1) AIC value, the Akaike Information Criteria (AIC) value of the meta-QTL; CI, confidence interval; mean R2 of the meta-QTL, it was calculated by the mean of the QTL of this cluster.

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3.4. Comparison between meta-analysis and overview analysis

QTLs identified using meta-analysis and overview analysis had similar CIs and chromosomal positions. Compared with meta-analysis, 24 more “real” QTLs were obtained with overview analysis. No “real” QTLs were identified on chromosomes Gm06, Gm07, Gm10, and Gm12 with either method. “Real” QTLs at 27 positions of 16 chromosomes were obtained using meta-analysis and overview analysis each separately. Compared with those obtained from metaanalysis, narrower CIs were obtained for QTLs located on Gm01 (44.50–45.50 Mb), Gm03 (37.00–39.00 Mb), Gm05(4.50–5.50 Mb), Gm09 (6.50–7.00 Mb and 43.00–44.00 Mb),Gm11 (8.00–9.00 Mb), Gm15 (19.50–27.50 Mb), Gm16 (0.50–1.00 Mb), Gm17 (10.00–11.00 Mb), Gm18 (3.00–3.50 Mb and 42.00–44.00 Mb), Gm19 (33.50–35.50 Mb) and Gm20(39.50–40.50) (Table 4). In Fig. 1, the positions and CIs of the QTLs on Gm03 and Gm09 were consistent between the two methods. A consensus QTL was mapped to 37 Mb on Gm03 using both meta-analysis and overview analysis.QTLs were mapped to coincident CIs on Gm09 for both meta-analysis and overview analysis (Table 4).

Table 3 QTLs position after overview analysis1)

1) Original QTL no., number of original QTLs before overview analysis; “real” QTL no., number of “real” QTLs after overview analysis.

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Table 4 QTLs confidence intervals mapped by two methods

Quantitative trait locus Physical position/Confidence interval (Mb)Chromosome Contribution rate (%) Meta-analysis Overview analysis Gm01 17.5 44.87–46.22/1.35 44.50–45.50/1.00 Gm02 13.4 19.46–32.35/12.89 17.00–35.00/18.00 Gm02 15.8 47.24–48.55/1.31 46.50–48.50/2.00 Gm03 11.2 36.88–39.09/2.21 37.00–39.00/2.00 Gm04 21.3 26.09–39.69/13.60 23.50–42.50/19.00 Gm05 14.8 10.51–22.53/12.02 6.50–26.50/20.00 Gm05 8.1 4.45–5.59/1.14 4.50–5.50/1.00 Gm08 14.1 18.79–20.2/1.41 18.50–20.50/2.00 Gm09 11.0 6.46–7.14/0.68 6.50–7.00/0.50 Gm09 22.3 7.82–10.55/2.73 7.50–11.00/3.50 Gm09 1.9 15.03–34.88/19.85 15.00–38.50/23.50 Gm09 11.6 41.95–44.97/3.02 43.00–44.00/1.00 Gm11 31.7 7.66–8.97/1.31 8.00–9.00/1.00 Gm13 15.5 5.11–5.44/0.33 5.00–5.50/0.50 Gm14 11.7 8.24–9.22/0.98 8.00–9.50/1.50 Gm15 13.6 20.29–28.32/8.03 19.50–27.50/8.00 Gm16 21.0 0.53–1.18/0.65 0.50–1.00/0.50 Gm16 14.8 23.5–28.19/4.69 23.00–29.00/6.00 Gm17 12.3 9.02–11.08/2.06 10.00–11.00/1.00 Gm17 10.1 13.38–16.04/0.63 13.00–16.50/3.50 Gm18 11.7 2.82–3.45/2.15 3.00–3.50/0.50 Gm18 10.4 20.90–23.05/1.80 21.00–23.00/2.00 Gm18 11.2 41.32–43.12/3.12 42.00–44.00/2.00 Gm19 17.2 10.93–14.05/1.64 9.00–16.00/7.00 Gm19 14.1 33.82–35.46/34.64 33.50–35.50/2.00 Gm19 18.9 38.35–40.76/2.41 38.00–41.00/3.00 Gm20 16.2 39.25–40.29/1.04 39.50–40.50/1.00

3.5. Gene mining from consensus QTLs

The total number of candidate genes with annotations was 725. Of these, 225 had GO annotations, 73 had KEGG annotations, 260 had Swissprot annotation, 568 had nr annotation, and 585 had nt annotation (Appendix C). Finally,20 candidate genes were identified, of which four genes were published (Appendix D). SDD1 (Glyma.03G167600)encodes a subtilisin-like Ser protease (von Groll et al.2002). AtmBAC2 (Glyma.04G143500) is a mitochondrial Arg transporter (Catoni et al. 2003). Threonine residues in the activation loop of the catalytic subdomain VIII of AtSERK1 (Glyma.05G083100) are potential targets for phosphorylation (Shah et al. 2001; Lewis et al. 2010).PYRB (Glyma.09G110200) controls the amino acid binding(Hoover et al. 1983). The 16 candidate genes which control the contents of Ser, Thr, Tyr, Lys, and Asp are not published(Table 6).

Candidate genes from consensus QTL regions were obtained from Phytozome and annotated using gene annotation databases, including GO (http://www.geneontology.org/),KEGG (http://www.genome.jp/kegg/), Swissprot (http://www.gpmaw.com/html/swiss-prot.html), NT (https://blast.ncbi.nlm.nih.gov/Blast.cgi?PROGRAM=blastn&PAGE_TYPE=BlastSearch&LINK_LOC=blasthome),and NR (https://blast.ncbi.nlm.nih.gov/Blast.cgi?PROGRAM=blastp&PAGE_TYPE=BlastSearch&LINK_LOC=blasthome).

To identify QTL candidate genes, we screened 725 genes located within the CIs of the 33 consensus QTLs (Appendix B). More candidate genes are located in QTLs on Gm09(15.03–34.88 Mb), Gm02 (19.46–32.35 Mb), and Gm06(22.51–39.34 Mb) (119, 62, and 55 genes, respectively)than in the other QTLs. Only one gene is located within the QTLs located at 1.14–1.21 Mb on Gm07, 5.67–5.83 Mb on Gm08, and 5.11–5.44 Mb on Gm13, and there is no gene within 4.20–4.40 Mb on Gm07 (Table 5).

Fig. 1 QTL locus control chart of meta-analysis and overview analysis.

4. Discussion

4.1. Physical map

With the completion of genomic sequencing, high-density physical molecular maps with higher accuracy compared with genetic maps have been constructed. Physical maps describe the exact location of the known DNA markers,although the genomes are large, complex, and polyploid(Chen et al. 2010). Use of these maps also reduces the risk of missing data, simplifies the data processing steps,and expedites the experimental process. Schmutz et al.(2010) integrated physical and high-density genetic maps with the polyploid soybean genome sequence to create a chromosome-scale draft sequence assembly. The soybean physical map was applied innovatively in our study. Use of a high-density physical map as the reference map, solved the problem of projection when there was not a sufficient number of common markers between the genetic or physical map and the mapping population.

中国高铁要走出国门，合作国目前的政治社会环境也是一个不可忽视的因素，复杂的地域政治因素同样会造成影响。例如我国在2015年印度高铁项目竞标中取得了胜利，然而同一年安倍出访印度，莫迪总统在12月13日选择了让日本修筑印度高铁，虽然日本提出的方案中贷款政策相较我国更加优惠，但其中也掺杂着政治因素。

Table 5 Genes of all meta-analysis QTL sections

Chromosome QTL section Gene Gm01 44.87–46.22 14 Gm02 19.46–32.35 62 Gm02 47.24–48.55 10 Gm03 36.88–39.09 28 Gm04 26.09–39.69 35 Gm05 10.51–22.53 39 Gm06 22.51–39.34 55 Gm07 1.14–1.21 1 Gm07 4.20–4.40 0 Gm07 4.45–5.59 13 Gm08 5.67–5.83 1 Gm08 18.79–20.20 9 Gm09 6.46–7.14 9 Gm09 7.82–10.55 28 Gm09 15.03–34.88 119 Gm09 41.95–44.97 26 Gm11 7.66–8.97 29 Gm11 35.33–38.55 0 Gm13 5.11–5.44 1 Gm14 8.24–9.22 14 Gm15 20.29–28.32 43 Gm16 0.53–1.18 10 Gm16 23.5–28.19 46 Gm17 9.02–11.08 19 Gm17 13.38–16.04 11 Gm18 2.82–3.45 5 Gm18 20.9–23.05 14 Gm18 41.32–43.12 12 Gm19 0.69–0.77 2 Gm19 10.93–14.05 23 Gm19 33.82–35.46 22 Gm19 38.35–40.76 19 Gm20 39.25–40.29 6

A total of 57 “real” QTL positions were mapped from the 138 original QTLs. The curve showed peaks on Gm08 and Gm18, where five QTLs were mapped respectively. There was one QTL each on chromosomes Gm02, Gm14, and Gm20. The number of QTLs on the other chromosomes ranged from two to four. We next used overview analysis, to further reduce the number of QTLs. Using overview analysis we integrated the original QTLs to obtain “real” QTLs. Using overview analysis, we obtained four chromosomes on which the number of QTLs reduced more than 6 from original QTLs respectively. The 10 original QTLs on Gm02 were reduced to one “real” QTL, the eight original QTLs on Gm07 werereduced to two “real” QTLs, the 11 original QTLs on Gm09 were reduced to four “real” QTLs, and the 15 original QTLs on Gm19 were reduced to three “real” QTLs (Table 3).

The normal function value of every position in 0.5 Mb steps from x to x+0.5 was calculated using the function NORMDIST (Pi, QTL position; Si, false=0) in Excel. Pi refers to the most likely location of the chromosome. Si, variance,is calculated from eq. (1). QTL position refers to the genetic QTL location on the chromosome (Chardon et al. 2004).False=0 returns the probability density function. Function P(x) is set as the probability density function indicating the likelihood of a QTL being real in every experiment (eq.(2)).U(x), as a ruler of P(x), realizes its function by estimating the unified possibility of QTL existence in every unit of chromosomes in single experiment.

4.2. Meta-analysis and overview analysis

Although the locations of QTLs can be determined efficiently based on the results of previous studies, the application of QTLs to crop improvement is still limited (Bolger et al. 2014).The fact that the true chromosomal locations are unclear limits their use in breeding currently. It is important to have a complete view of a polygenic trait to optimize its use(Stock et al. 2016). Therefore, meta-analysis and overview analysis were used in this study to integrate published results. Overview analysis and meta-analysis have been used to obtain the physical QTL positions and accurate CIs (Gao et al. 2013). The gene mapping could be greatly facilitated by the QTL meta-analysis and overview analysis(Danan et al. 2011a, b).

In recent years, meta-analysis and overview methods have been frequently used in soybeanin recent years. For example, meta-analysis and/or overview methods have been utilized to analyze QTLs for many soybean agronomic traits, such as oil content (Qi et al. 2011), protein content(Qi et al. 2011), plant height (Sun et al. 2012a), 100-seed weight (Sun et al. 2012b), fungal disease resistance (Wang et al. 2010), insect resistance (Wang et al. 2009), and phosphorus efficiency (Huang et al. 2011). Qiu et al. (2015)used software BioMercator ver. 2.1 to map 113 genes relatedto sulfur-containing amino acid enzymes and 33 QTLs controlling sulfur-containing amino acid content onto the Consensus Map 4.0, which was obtained by integrating the genetic and physical maps of soybean. Based on synteny between gene loci and QTLs and the QTL effect sizes, 16 candidate genes relating to the synthesis of sulfur-containing amino acids were identified (Qiu et al. 2014). In this study,33 consensus QTLs were obtained from 138 original QTLs using meta-analysis. When using another statistical method,overview analysis, the smaller CIs indicates that it is more accurate than meta-analysis. Qiu et al. (2014) previously used bioinformatic analysis of candidate gene copy number,SNP information and expression pro file to identify 12 genes involved in sulfur-containing amino acid metabolism on eight chromosomes. Compared with their study, our findings gave a more comprehensive picture of the loci involved in soybean amino acid metabolism. We identified 16 unpublished candidate genes controlling Ser, Thr, Tyr, Lys, and Asp contents based on GO annotation, as well as four candidate genes that have been identified by other studies (Table 6).In addition, some concrete gene functions and pathways were ascertained by gene annotation for the first time in this study. For instance, Glyma.05G091300 controls Thrtype endopeptidase activity within the proteasome pathway,and Glyma.17G157500 controls Asp-type endopeptidase activity in the Ribosome. Gene mining raises the potential for targeted interventions in soybean breeding.

Table 6 Candidate genes from meta-analysis results1)

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Gene ID Associate gene2) GO GO annotation information Pathway Reference Glyma.03G167600 SDD1 GO:0004252 Molecular function: serine-type endopeptidase activity von Groll et al.(2002)Glyma.04G143500 ATMBAC1,MBAC1 GO:0005290 Molecular function: L-histidine transmembrane transporter activity Catoni et al. (2003)GO:0015189 Molecular function: L-lysine transmembrane transporter activity GO:0015817 Biological process: histidine transport GO:0015819 Biological process: lysine transport Glyma.04G151300 NA GO:0004674 Molecular function: protein serine/threonine kinase activity Unpublished GO:0004713 Molecular function: protein tyrosine kinase activity GO:0007169 Biological process: transmembrane receptor protein tyrosine kinase signaling pathway Glyma.05G083100 ATSERK1,SERK1 GO:0004675 Molecular function: transmembrane receptor protein serine/threonine kinase activity Shah et al. (2001)GO:0004715 Molecular function: non-membrane spanning protein tyrosine kinase activity GO:0007178 Biological process: transmembrane receptor protein serine/threonine kinase signaling pathway Lewis et al. (2010)

Table 6 (Continued from preceding page)

1) GO, Gene Ontology. 2) NA, not applicable.

Gene ID Associate gene2) GO GO annotation information Pathway Reference Glyma.05G091300 ATCDK8, CDKE;1, HEN3 Proteasome Unpublished Glyma.06G236800 LON1, LON_ARA_ARA GO:0004298 Molecular function: threonine-type endopeptidase activity Unpublished Glyma.08G237200 NA GO:0004674 Molecular function: protein serine/threonine kinase activity GO:0004252 Molecular function: serine-type endopeptidase activity Unpublished Glyma.09G066600 ATMKK2, MK1,MKK2 GO:0004674 Molecular function: protein serine/threonine kinase activity Plantpathogen interaction Unpublished Glyma.09G099200 NA GO:0004674 Molecular function: protein serine/threonine kinase activity Unpublished Glyma.09G110200 PYRB GO:0006520 Biological process: cellular amino acid metabolic process Pyrimidine metabolism Hoover et al.(1983)GO:0016597 Molecular function: amino acid binding Glyma.15G199100 IBS1 GO:0004674 Molecular function: protein serine/threonine kinase activity Unpublished Glyma.16G008900 NA GO:0004252 Molecular function: serine-type endopeptidase activity Unpublished Glyma.16G117100 APK1, APK1A GO:0004674 Molecular function: protein serine/threonine kinase activity Unpublished Glyma.16G117200 NA GO:0004674 Molecular function: protein serine/threonine kinase activity Unpublished GO:0004715 Molecular function: non-membrane spanning protein tyrosine kinase activity Glyma.17G157500 NA GO:0004190 Molecular function: aspartic-type endopeptidase activity Ribosome Unpublished Glyma.17G158700 ATKRS-1 GO:0004824 Molecular function: lysine-tRNA ligase activity AminoacyltRNA biosynthesis Unpublished Glyma.18G036500 NA GO:0004674 Molecular function: protein serine/threonine kinase activity Unpublished Glyma.19G059500 AME3 GO:0004674 Molecular function: protein serine/threonine kinase activity Unpublished GO:0004713 Molecular function: protein tyrosine kinase activity Unpublished Glyma.19G099300 NA GO:0004674 Molecular function: protein serine/threonine kinase activity Unpublished Glyma.19G128700 NA GO:0004674 Molecular function: protein serine/threonine kinase activity Unpublished

5. Conclusion

Meta-analysis and overview analysis were conducted in this research. The consensus QTLs and “real” QTLs with good reproducibility were obtained from 138 original QTLs by two analysis methods. Thirty-three consensus QTLs were screened out by meta-analysis, of which the minimum CI was 0.07 Mb, the maximum average contribution rate was 32.7%. Fifty-seven “real” QTL positions were analyzed by overview analysis. And 16 unpublished candidate genes related with amino acids metabolism were identified. The results laid a foundation for fine mapping of soybean amino acid related QTLs and marker assisted selection.

Acknowledgements

This study was financially supported by the National Key R&D Program of China (2016YFD0100500, 2016YFD0100300,2016YFD0100201-21), the “Challenge Cup” National College Student Curricular Academic Science and Technology Works Competition of Ministry of Education of China (to Gong Qianchun, guided by Qi Zhaoming), the National Natural Science Foundation of China (31701449,31471516, 31401465, 31400074, 31501332), the China Post Doctoral Project (2015M581419), the Dongnongxuezhe Project (to Chen Qingshan), China, the Young Talent Project (to Qi Zhaoming) of Northeast Agriculture University,China (518062), the Heilongjiang Funds for Distinguished Young Scientists, China (JC2016004), and the Outstanding Academic Leaders Projects of Harbin, China(2015RQXXJ018).

在本例中，英国移民官试图确定一个难民身份申请者的年龄。该申请人声称自己是个未成年人。当出现“虚岁”这个词的时候，译员自己没有做出解释，而是提示移民官由申请人来解释。这在许多时候是比较安全的调解策略。如果译员根据自己的理解进行解释，有可能其解释与说话者的看法并不相同。许多文化差异导致的障碍，实际上可以通过让各方直接交流来得以化解。

Appendices associated with this paper can be available on http://www.ChinaAgriSci.com/V2/En/appendix.htm

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