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Meta-Analysis of 100-Seed Weight QTLs in Soybean
Agricultural Sciences in China
March 2011
2011, 10(3): 327-334
Meta-Analysis of 100-Seed Weight QTLs in Soybean QI Zhao-ming1, SUN Ya-nan1,WANG Jia-lin1, ZHANG Da-wei1, LIU Chun-yan1, 2, HU Guo-hua2 and CHEN Qing-shan1 1 2
Northeast Agricultural University, Harbin 150030, P.R.China The Crop Research and Breeding Center of Land-Reclamation, Harbin 150090, P.R.China
Abstract 100-seed weight is a very complicated quantitative trait of yield. The study of gene mapping for yield trait in soybean is very important for application. However, the mapping result of 100-seed weight was dispersed, the public map should be chosen which was suitable for the published results integrated, and to improve yield. In this research, an integrated map of 100-seed weight QTLs in soybean had been established with soymap2 published in 2004 as a reference map. QTLs of 100seed weight in soybean were collected in recent 20 yr. With the software BioMercator 2.1, QTLs from their own maps were projected to the reference map. From published papers, 65 QTLs of 100-seed weight were collected and 53 QTLs were integrated, including 17 reductive effect QTLs and 36 additive effect QTLs. 12 clusters of QTLs were found in the integrated map. A method of meta-analysis was used to narrow down the confidence interval, and 6 additive QTLs and 6 reductive QTLs and their corresponding markers were obtained respectively. The minimum confidence interval (C.I.) was shrunk to 1.52 cM. These results would lay the foundation for marker-assisted selection and mapping QTL precisely, as well as QTL gene cloning in soybean. Key words: soybean, 100-seed weight, meta-analysis, consensus QTL, marker assisted selection
INTRODUCTION Soybean (Glycine max L. Merr), widely grown in the United States, Brazil, Argentina, and China, and so it is very important for us to improve the soybean yield. 100-seed weight of soybean is a quantitative trait controlled by multiple genes, and which are not identified easily by traditional methods (Main et al. 1996; Maughan et al. 1996; Li et al. 2008). Since the first publication of quantitative trait locus (QTL), a large number of QTLs have been identified in different genetic backgrounds and environments. At least 65 QTLs of 100seed weight had been mapped currently in soybean, but the utilization of these QTLs for soybean breeding
was confronted with many problems. The precision of mapping QTL was very low with primary population, and the gene of the QTL could not find easily. QTL locations among different researches could not be mapped in the same position. The LOD score of QTL was not over 2.5 (Lander and Kruglyak 1995). So for many experiments, the peak region of the was not the real QTL position because confidence interval (C.I.) of QTL was too long. So the confidence interval should be shrunk and to ensure where the consensus QTL position was. So the integration of known QTLs was an important method to mining the true and more effective QTLs. Recently, theory and application of bioinformatics was used in genetics successfully and widely. The International Maize and Wheat Improvement Center put
This paper is translated from its Chinese version in Scientia Agricultura Sinica. Correspondence HU Guo-hua, Professor, Tel: +86-451-55191945, E-mail: [email protected]; CHEN Qing-shan, Professor, Tel: +86-451-55191945, E-mail: [email protected] © 2011, CAAS. All rights reserved. Published by Elsevier Ltd. doi:10.1016/S1671-2927(11)60011-4
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forward a proposal to find the consensus QTL by building a consensus map. A total of 181 QTLs for agricultural and physiological traits in maize was integrated (Li et al. 2005), and 15 consensus drought QTLs were acquired. It offered a base for QTL analysis and marker assisted selection (MAS). Thomson et al. (2003) indicated that, QTL mapping affected by many factors, such as marker sets, experimental design, mapping populations, and statistical methods, the locations of QTLs varied in different studies, even if QTLs for the same trait were screened in the same population. The validity of locations of integrated QTLs generated only by maps comparison was hard to be ensured, and that there were biases when these integrated QTLs were explored by comparative analysis (Thomson et al. 2003). Fortunately, meta-analysis could narrow down the confidence interval of integrated QTLs to increase their precision and validity by using mathematical model to refine integrated QTLs (Goffinet and Gerber 2000). Meta-analysis, denominated by psychologist Glass in 1976 (Glass 1976), was combining results from different sources in a single study by statistical analysis. This technique is firstly widely used in medical, social, and behavioral sciences. The application of meta-analysis in genetics and evolution was illustrated in recent publications (Rudner et al. 2002; Hanocq et al. 2007; Shi et al. 2007). It can overcome the limits of individual studies and analyze generally all practical results to acquire a truthful conclusion. Meta-analysis was used and got an important breakthrough in human (Etzel and Guerra 2002). A total of 313 QTLs for flowering time in maize was integrated (Chardon et al. 2004). These QTLs were analyzed firstly with overview analysis that highlighted regions of key importance and then with a meta-analysis method that yielded a synthetic genetic model with 62 consensus QTLs. The meta-analysis led to a 2-fold increase in the precision in QTL position estimation, when compared with the most precise initial QTL position within the corresponding region. Until now, there was only little research in soybean by metaanalysis. A total of 62 marker-QTL associations for resistance to soybean cyst nematode in soybean were integrated (Guo et al. 2006). The aim of this study was designed to solve the dispersion of QTLs and can not be applied to practical problems. Sixty-five published 100-seed weight QTLs
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of soybean in recent 20 yr were collected to construct a 100-seed weight integrated QTL map with the software BioMercator 2.1 (Arcade et al. 2004), “consensus” QTLs were obtained by the method of meta-analysis, and should make sense in practice.
MATERIALS AND METHODS Integration of mapping information of soybean 100-seed weight QTLs Mapping information of soybean 100-seed weight QTLs, including names, traits, chromosome, marker, and linkage group, were collected from recent research papers. Sixty-five QTLs could be projected from these papers. QTLs of one population for a given trait in a given environment were integrated as one experiment, and QTLs, analyzed with the mean of several locations, was seen as one experiment, too. Two important parameters of QTL were map position (most likely position and confidence interval) and the proportion of phenotypic variance explained, respectively. When the confidence interval for QTL position was not available in research papers, a 95% confidence interval was calculated with the formula proposed by Darvasi and Soller (1997a, b). (1) C.I.=530/(N×R2) (2) C.I.=163/(N×R2) 2 C.I. is short for confidence interval of QTL, R is the proportion of variance explained, and N is the size of the population. The formula (1) is suitable for both backcross population and F2 population. The formula (2) is suitable for RIL population.
Processing of the QTL information of soybean 100-seed weight The collecting of QTL additive effect will be divided into two classes. The first class was the QTLs with additive effect, the existence of which would increase the 100-seed weight. The second class was the QTLs with reductive effect, the existence of which would decrease the 100-seed weight. The original maps of QTLs were compared with reference map soymap2 (Song et al. 2004), which have many common markers with other maps. If one of flanking markers of a
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Meta-Analysis of 100-Seed Weight QTLs in Soybean
QTL in original map was same as reference map, the coordinate (left and right coordinates correspond to the left marker or right marker, respectively) of the common marker was written down in reference map; if none of flanking markers was in common, the coordinate (reference coordinate) of the nearest common marker of this QTL was written down in reference map. If flanking markers of a QTL was in reverse with reference map, the gradation of markers was changed to project in the precondition of no influence for the QTL. If some QTLs were linked with one marker, the formula proposed by Darvasi and Soller (1997) was used to calculate the confidence interval. If a QTL could not be projected, it would be discarded.
QTL projection The original maps of QTLs were compared with reference map soymap2 (Song et al. 2004), which have many common markers with other original maps. Projection based on the common marker between the original map and reference map. Although these QTLs from different populations has different backgrounds and research methods, but all QTLs were mapped in their own linkage map, and each QTL has contribution to the 100-seed weight, and the common markers in QTL mapping between each the original map. Therefore, soymap2 could be used to integrate QTLs from different populations easily. QTLs were projected from original map to reference map by most likely position and confidence interval with homothetic function. The 100-seed weight QTLs from original maps were projected to the reference map to build a 100-seed weight consensus map by the software BioMercator 2.1. Firstly, markers in each QTL map were input into the BioMercator 2.1 to construct a map database. QTLs information, which were mapname, QTL name, linkage group (LG), trait, LOD score, R2 (proportion of variance explained), SIM (indicate whether this QTL was detected by means of simple interval mapping or composite interval mapping), QTL position, QTL from, and QTL to, should be input into the corresponding QTL map as BioMercator 2.1 required. Secondly, QTLs of original map were projected on the reference map by map-projection function of BioMercator 2.1.
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Meta-analysis of the 100-seed weight in soybean Many projected QTLs were in a cluster in the integrated map. Based on the analysis principle of the software BioMercator 2.1, the QTL cluster was that the C.I. of original QTL covered half with each other, or the C.I. of original QTL covered another. Although these original QTLs were mapped in different genetic backgrounds and mapped with different methods, they were projected on the same regions by the common marker. If a QTL cluster contained more than 2 QTLs from different researches, this region could contain an allele in a high probability. Meta-analysis was used to estimate existence of the “consensus” QTL and to locate the confidence interval with the QTL cluster. The basic process of meta-analysis of BioMercator 2.1 is: QTLs from many independent experiments and associated on the same LG and at the neighboring interval to calculate a “consensus” QTL. This QTL will give 5 models, the minimum Akaike information criteria (AIC) value was the best model QTL, called “consensus” QTL. Each model give the most possible positions in the each linkage group (LG) with the maximum-likelihood function by the Gaussian function. The formula described by Goffinet and Gerber (2000) was to estimate, for LG, the number of QTLs underlying the results that were synthesized. In the model, the “consensus” QTL position depends on the position of each QTL in LG, its variance is calculated from the following formula: var (QTL) = 1/ 1/σi2 σi2 is the position of the variance for each QTL on LG, 95% confidence interval of the “consensus” QTL was calculated from the var (QTL): C.I.=3.92×var(QTL)1/2 AIC value depends on simulation of each model. The model of the smallest AIC model was close to “consensus” QTL. Mean R2 of original QTLs in that region was proportion of variance explained this “consensus” QTL. The “consensus” QTL was the “summarize” of original QTLs.
RESULTS Collection of 100-seed weight QTLs in soybean Sixty-five QTLs in recent 20 yr of 100-seed weight
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were collected from 9 research papers (Table 1). Fiftyfive QTLs have common markers with the reference map (soymap2), including 17 reductive QTLs of 100seed weight QTLs in soybean and 36 additive QTLs of 100-seed weight QTLs in soybean. LOD score of these QTLs were from 2.09 to 30.47. R2 of these QTLs were from 3.00 to 36.01%.
QTLs projection: a consensus map of 100-seed weight in soybean In total, 53 QTLs of 100-seed weight were projected
on the reference map (soymap2) and additive and reductive consensus map were obtained, respectively (Figs. 1 and 2). As shown in Fig. 1, there were 6 additive QTL clusters on B1, C2, D2, K, M, and O. As shown in Fig. 2, there were 6 reductive QTL clusters on B2, C1, E, H, and I. Two original QTLs were contained in QTL clusters on C1, D2, K, M, O, and B2. Three original QTLs were contained in QTL clusters on B1 and H. Four original QTLs were contained in QTL clusters on C2, E, and B2. Seven original QTLs were contained in QTL cluster on I.
Table 1 Information of 100-seed weight QTLs in soybean No. of QTLs 3 1 6 3 11 8 2 28 3 Total 65 1)
Parents Kefeng 1×Nannong 1138-2 Kefeng 1×Nannong 1138-2 Kefeng 1×Nannong 1138-2 Zheng 92116×Shang 951099 G. max ‘7499’×G. soja PI 245331 Charleston×Dongnong 594 Jin 23×Huibuzhiheidou Zhongdou 29×Zhongdou 32 Suinong 14×Suinong 20
Population size
Analysis method 1)
Population type
201 184 201 105 148 154 255 94
CIM CIM IM IM CIM CIM CIM CIM CIM
RIL RIL RIL F2 BIL (backcross inbred lines) RIL RIL RIL F2
Reference Gai et al. (2007) Zhang et al. (2004) Wu et al. (2001) Guan et al. (2004) Li et al. (2008) Chen et al. (2007) Wang et al. (2004) Wang et al. (2008) Zhu et al. (2006)
IM, interval mapping; CIM, composite interval mapping.
Fig. 1 Integrate map of 100-seed weight QTLs (additive). Vertical lines represent the confidence interval (C.I.) of QTL, horizontal lines represent the LOD score of QTL. The same as below.
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Meta-Analysis of 100-Seed Weight QTLs in Soybean
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Fig. 2 Integrate map of 100-seed weight QTLs (reductive).
Meta-analysis of QTLs Fifty-three original QTLs of all projected QTLs were analyzed by the meta-analysis of BioMercator 2.1. The “consensus” QTL is ensured by the minimum AIC value of each meta-analysis model. Six additive “consensus” QTLs and 6 reductive “consensus” QTLs were found by the meta-analysis (Tables 2 and 3).
By the meta-analysis of additive QTLs clusters, 3 QTLs were contained in the “consensus” QTL on B1, LOD score of QTLs were 2.25-3.98, map position was 25.01-46.39 cM, map position of the “consensus” QTL was 36.88-44.83 cM, mean R2 was 7.28%. Four QTLs were contained in the “consensus” QTL on C2, LOD score of QTLs were 3.15-4.11, map position was 105.89-120.89 cM, map position of the “consen-
Table 2 Meta-analysis of soybean 100-seed weight QTLs (additive) LG B1 C2 D2 K M O
AIC
MQTL
C.I.
Mean
Map distance
value
pos. (cM)
(95%)
R2 (%)
(cM)
27.34 16.89 6.52 14.51 8.80 14.37
40.85 110.19 25.28 15.68 9.28 40.19
36.88-44.83 108.64-111.75 24.52-26.04 14.33-17.04 6.25-12.31 38.71-41.67
7.28 3.95 8.50 11.39 12.29 6.50
7.95 3.11 1.52 2.71 6.06 2.96
L-marker
L-marker coordinate (cM)
Distance of MQTL to L-marker (cM)
R-marker
R-marker coordinate (cM)
Distance of MQTL to R-marker (cM)
Satt251 Satt277 Satt458 Satt242 Satt636 Satt653
36.48 107.59 24.52 14.35 5.00 38.10
4.37 2.60 0.76 1.33 4.28 2.09
Satt197 Satt557 Satt135 Sat_119 Satt201 Satt347
46.39 112.19 26.05 17.11 13.56 42.30
5.54 2.00 0.77 1.43 4.28 2.11
L-marker coordinate (cM) 43.58 59.82 71.08 21.05 46.95 51.99
Distance of MQTL to L-marker (cM) 6.78 5.74 1.36 3.44 3.77 3.11
Table 3 Meta-analysis of soybean 100-seed weight QTLs (reductive) LG B2 B2 C1 E H I
AIC
MQTL
C.I.
Mean
Map distance
value
pos. (cM)
(95%)
R2 (%)
(cM)
50.36 65.56 72.44 24.49 50.72 55.10
48.53-52.19 60.26-70.86 71.08-73.80 21.14-27.84 47.23-54.22 52.27-57.93
6.92 8.50 6.42 7.20 13.00 18.59
3.66 10.60 2.72 6.70 6.99 5.66
31.71 31.71 5.59 21.52 16.28 33.65
L-marker B142_1 BLT049_2 Dia A053_1 Satt442 A955_1
R-marker coordinate (cM) A108_1 53.54 G214_4 70.88 Satt718 73.79 Bng193_1 28.15 A404T_3 55.39 Satt049 58.82
R-marker
Distance of MQTL to R-marker (cM) 3.18 5.32 1.35 3.66 4.67 3.72
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sus” QTL was 108.64-111.75 cM, mean R2 was 3.95%. Two QTLs were contained in the “consensus” QTL on D2, LOD score of QTLs were 4.80-6.10, map position was 17.2-32.02 cM, map position of the “consensus” QTL was 24.52-26.04 cM, mean R2 was 8.50%. Two QTLs were contained in the “consensus” QTL of LG K, LOD score of QTLs were 7.98-8.17, map position was 6.85-21.85 cM, map position of the “consensus” QTL was 14.33-17.04 cM, mean R2 was 11.39%. Two QTLs were contained in the “consensus” QTL on M, LOD score of QTLs were 2.82-7.03, map position was 5.00-13.56 cM, map position of the “consensus” QTL was 6.25-12.31 cM, mean R2 was 12.29%. Two QTLs were contained in the “consensus” QTL on O, LOD score of QTLs was 5.12, map position was 38.0942.09 cM, map position of the “consensus” QTL was 38.71-41.67 cM, mean R 2 was 6.50%. By the meta-analysis of reductive QTLs clusters, 4 QTLs were contained in the “consensus” QTL on B2, LOD score of QTLs were 2.2-3.3, map position was 43.95-58.95 cM, map position of the “consensus” QTL was 48.53-52.19 cM, mean R2 was 6.92%. Two QTLs were contained in the “consensus” QTL on B2, LOD score of QTLs were 4.78-5.85, map position was 58.06-73.06 cM, map position of the “consensus” QTL was 60.26-70.86 cM, mean R2 was 8.5%. Two QTLs were contained in the “consensus” QTL on C1, LOD score of QTLs were 4.54-6.31, map position was 70.52-74.36 cM, map position of the “consensus” QTL was 71.08-73.8 cM, mean R2 was 6.42%. Four QTLs were contained in the “consensus” QTL on E, LOD score of QTLs were 2.97-8.55, map position was 105.89-120.89 cM, map position of the “consensus” QTL was 21.14-27.84 cM, mean R2 was 7.20%. Three QTLs were contained in the “consensus” QTL on H, LOD score of QTLs were 4.2-4.6, map position was 44.04-60.85 cM, map position of the “consensus” QTL was 47.23-54.22 cM, mean R2 was 13.00%. Seven QTLs were contained in the “consensus” QTL on I, LOD score of QTLs were 3.72-30.47, map position was 47.6-62.6 cM, map position of the “consensus” QTL was 52.27-57.93 cM, mean R2 was 18.59%.
DISCUSSION Soymap2, the reference map A new public map soymap2 has been constructed in
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2004 by Song et al. (2004). Five soybean genetic maps, including molecular genetic map of two F2 populations of A81-356022×PI468916 and Clark×Harosoy, 3 RIL populations of Minsoy×Noir1, Minsoy×Archer, and RILs of Noir 1×Archer, were integrated. There were 20 linkage groups and 1 849 markers, including 709 RFLP markers, 1 015 SSR markers, 73 RAPD markers, 6 AFLP markers, and 46 other types markers in the public map. This integrated map showed a very high density of SSR markers and RFLP markers, which were common technique for mapping QTLs, so QTLs of these maps could easily project on the public map.
Application of meta-analysis for marker-assisted selection (MAS) Marker-assisted selection (MAS) is an important strategy for crop improvement. Recently, MAS has been successfully used to increase quality and yield of wheat (Romagosa et al. 1999) and rice (Wang et al. 2004). However, MAS has not been widely used, mainly because of its validity, cost and practicability of markers. The accuracy of mapping QTL is on the base of gene discovering and cloning. A number of genes which often control many important traits located in the confidence interval of QTL, but it is difficult to find real QTL. Wang and Paigen (2002) found that 18 of the 22 human HDL-C QTLs were within the murine HDL-C QTL and indicated that murine QTL for HDL-C levels may predict their homologous locations in humans, and their underlying genes may be appropriate genes for the test in humans. Meta-analysis method was used in this study, advantages of meta-analysis was: (i) integrated lots of scattered QTLs to find major gene; (ii) the accuracy and validity of mapping QTL was improved, and reduced noise of QTLs analysis by software. In the basis of the integrated QTLs from previous studies, the integrated map has rich and high density markers. The “consensus QTL” was detected in different populations and environments to found appropriate genetic markers. For the QTL only detected in specific populations, the corresponding position of integrated map can be used to find molecular markers for MAS. Combined with R2, predict genotypes of the future generations in different groups and verification. It could increase the actual efficiency and improvement of MAS. © 2011, CAAS. All rights reserved. Published by Elsevier Ltd.
Meta-Analysis of 100-Seed Weight QTLs in Soybean
Wang et al. (2006) integrated 127 QTLs of plant height in maize, 40 “consensus QTLs” were acquired by meta-analysis method. In accordance with collinearity of rice and maize, 5 genes related plant height of rice were mapped on the “consensus QTL” of maize by CMap. Meta-analysis method had greatly narrowed down the confidence interval, the minimum 95% confidence interval of “consensus QTL” was 1.52 cM in this study. With the development of the information of soybean genome and bioinformatics, it was convenient to map the “consensus QTL” in the corresponding physical map, and find candidate gene by bioinformatics tools. It laid the foundation for the process from QTL to QTG. “Consensus QTL” laid foundation for QTL fine mapping of seed weight content and molecular assisted breeding.
CONCLUSION QTLs of 100-seed weight in soybean were collected, the integrated map was constructed. Twelve “consensus QTLs” were acquired by meta-analysis, the minimum 95% confidence interval of “consensus QTLs” was 1.52 cM in this study. It reduced noise of QTLs analysis by software and laid foundation for QTL fine mapping of seed weight content and molecular-assisted breeding.
Acknowledgements This study was supported by the Chinese Transgenic Specific Technology Programs (2009ZX08009-013B).
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Meta-Analysis of 100-Seed Weight QTLs in Soybean QI Zhao-ming1, SUN Ya-nan1,WANG Jia-lin1, ZHANG Da-wei1, LIU Chun-yan1, 2, HU Guo-hua2 and CHEN Qing-shan1 1 2
Northeast Agricultural University, Harbin 150030, P.R.China The Crop Research and Breeding Center of Land-Reclamation, Harbin 150090, P.R.China
Abstract 100-seed weight is a very complicated quantitative trait of yield. The study of gene mapping for yield trait in soybean is very important for application. However, the mapping result of 100-seed weight was dispersed, the public map should be chosen which was suitable for the published results integrated, and to improve yield. In this research, an integrated map of 100-seed weight QTLs in soybean had been established with soymap2 published in 2004 as a reference map. QTLs of 100seed weight in soybean were collected in recent 20 yr. With the software BioMercator 2.1, QTLs from their own maps were projected to the reference map. From published papers, 65 QTLs of 100-seed weight were collected and 53 QTLs were integrated, including 17 reductive effect QTLs and 36 additive effect QTLs. 12 clusters of QTLs were found in the integrated map. A method of meta-analysis was used to narrow down the confidence interval, and 6 additive QTLs and 6 reductive QTLs and their corresponding markers were obtained respectively. The minimum confidence interval (C.I.) was shrunk to 1.52 cM. These results would lay the foundation for marker-assisted selection and mapping QTL precisely, as well as QTL gene cloning in soybean. Key words: soybean, 100-seed weight, meta-analysis, consensus QTL, marker assisted selection
INTRODUCTION Soybean (Glycine max L. Merr), widely grown in the United States, Brazil, Argentina, and China, and so it is very important for us to improve the soybean yield. 100-seed weight of soybean is a quantitative trait controlled by multiple genes, and which are not identified easily by traditional methods (Main et al. 1996; Maughan et al. 1996; Li et al. 2008). Since the first publication of quantitative trait locus (QTL), a large number of QTLs have been identified in different genetic backgrounds and environments. At least 65 QTLs of 100seed weight had been mapped currently in soybean, but the utilization of these QTLs for soybean breeding
was confronted with many problems. The precision of mapping QTL was very low with primary population, and the gene of the QTL could not find easily. QTL locations among different researches could not be mapped in the same position. The LOD score of QTL was not over 2.5 (Lander and Kruglyak 1995). So for many experiments, the peak region of the was not the real QTL position because confidence interval (C.I.) of QTL was too long. So the confidence interval should be shrunk and to ensure where the consensus QTL position was. So the integration of known QTLs was an important method to mining the true and more effective QTLs. Recently, theory and application of bioinformatics was used in genetics successfully and widely. The International Maize and Wheat Improvement Center put
This paper is translated from its Chinese version in Scientia Agricultura Sinica. Correspondence HU Guo-hua, Professor, Tel: +86-451-55191945, E-mail: [email protected]; CHEN Qing-shan, Professor, Tel: +86-451-55191945, E-mail: [email protected] © 2011, CAAS. All rights reserved. Published by Elsevier Ltd. doi:10.1016/S1671-2927(11)60011-4
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forward a proposal to find the consensus QTL by building a consensus map. A total of 181 QTLs for agricultural and physiological traits in maize was integrated (Li et al. 2005), and 15 consensus drought QTLs were acquired. It offered a base for QTL analysis and marker assisted selection (MAS). Thomson et al. (2003) indicated that, QTL mapping affected by many factors, such as marker sets, experimental design, mapping populations, and statistical methods, the locations of QTLs varied in different studies, even if QTLs for the same trait were screened in the same population. The validity of locations of integrated QTLs generated only by maps comparison was hard to be ensured, and that there were biases when these integrated QTLs were explored by comparative analysis (Thomson et al. 2003). Fortunately, meta-analysis could narrow down the confidence interval of integrated QTLs to increase their precision and validity by using mathematical model to refine integrated QTLs (Goffinet and Gerber 2000). Meta-analysis, denominated by psychologist Glass in 1976 (Glass 1976), was combining results from different sources in a single study by statistical analysis. This technique is firstly widely used in medical, social, and behavioral sciences. The application of meta-analysis in genetics and evolution was illustrated in recent publications (Rudner et al. 2002; Hanocq et al. 2007; Shi et al. 2007). It can overcome the limits of individual studies and analyze generally all practical results to acquire a truthful conclusion. Meta-analysis was used and got an important breakthrough in human (Etzel and Guerra 2002). A total of 313 QTLs for flowering time in maize was integrated (Chardon et al. 2004). These QTLs were analyzed firstly with overview analysis that highlighted regions of key importance and then with a meta-analysis method that yielded a synthetic genetic model with 62 consensus QTLs. The meta-analysis led to a 2-fold increase in the precision in QTL position estimation, when compared with the most precise initial QTL position within the corresponding region. Until now, there was only little research in soybean by metaanalysis. A total of 62 marker-QTL associations for resistance to soybean cyst nematode in soybean were integrated (Guo et al. 2006). The aim of this study was designed to solve the dispersion of QTLs and can not be applied to practical problems. Sixty-five published 100-seed weight QTLs
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of soybean in recent 20 yr were collected to construct a 100-seed weight integrated QTL map with the software BioMercator 2.1 (Arcade et al. 2004), “consensus” QTLs were obtained by the method of meta-analysis, and should make sense in practice.
MATERIALS AND METHODS Integration of mapping information of soybean 100-seed weight QTLs Mapping information of soybean 100-seed weight QTLs, including names, traits, chromosome, marker, and linkage group, were collected from recent research papers. Sixty-five QTLs could be projected from these papers. QTLs of one population for a given trait in a given environment were integrated as one experiment, and QTLs, analyzed with the mean of several locations, was seen as one experiment, too. Two important parameters of QTL were map position (most likely position and confidence interval) and the proportion of phenotypic variance explained, respectively. When the confidence interval for QTL position was not available in research papers, a 95% confidence interval was calculated with the formula proposed by Darvasi and Soller (1997a, b). (1) C.I.=530/(N×R2) (2) C.I.=163/(N×R2) 2 C.I. is short for confidence interval of QTL, R is the proportion of variance explained, and N is the size of the population. The formula (1) is suitable for both backcross population and F2 population. The formula (2) is suitable for RIL population.
Processing of the QTL information of soybean 100-seed weight The collecting of QTL additive effect will be divided into two classes. The first class was the QTLs with additive effect, the existence of which would increase the 100-seed weight. The second class was the QTLs with reductive effect, the existence of which would decrease the 100-seed weight. The original maps of QTLs were compared with reference map soymap2 (Song et al. 2004), which have many common markers with other maps. If one of flanking markers of a
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Meta-Analysis of 100-Seed Weight QTLs in Soybean
QTL in original map was same as reference map, the coordinate (left and right coordinates correspond to the left marker or right marker, respectively) of the common marker was written down in reference map; if none of flanking markers was in common, the coordinate (reference coordinate) of the nearest common marker of this QTL was written down in reference map. If flanking markers of a QTL was in reverse with reference map, the gradation of markers was changed to project in the precondition of no influence for the QTL. If some QTLs were linked with one marker, the formula proposed by Darvasi and Soller (1997) was used to calculate the confidence interval. If a QTL could not be projected, it would be discarded.
QTL projection The original maps of QTLs were compared with reference map soymap2 (Song et al. 2004), which have many common markers with other original maps. Projection based on the common marker between the original map and reference map. Although these QTLs from different populations has different backgrounds and research methods, but all QTLs were mapped in their own linkage map, and each QTL has contribution to the 100-seed weight, and the common markers in QTL mapping between each the original map. Therefore, soymap2 could be used to integrate QTLs from different populations easily. QTLs were projected from original map to reference map by most likely position and confidence interval with homothetic function. The 100-seed weight QTLs from original maps were projected to the reference map to build a 100-seed weight consensus map by the software BioMercator 2.1. Firstly, markers in each QTL map were input into the BioMercator 2.1 to construct a map database. QTLs information, which were mapname, QTL name, linkage group (LG), trait, LOD score, R2 (proportion of variance explained), SIM (indicate whether this QTL was detected by means of simple interval mapping or composite interval mapping), QTL position, QTL from, and QTL to, should be input into the corresponding QTL map as BioMercator 2.1 required. Secondly, QTLs of original map were projected on the reference map by map-projection function of BioMercator 2.1.
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Meta-analysis of the 100-seed weight in soybean Many projected QTLs were in a cluster in the integrated map. Based on the analysis principle of the software BioMercator 2.1, the QTL cluster was that the C.I. of original QTL covered half with each other, or the C.I. of original QTL covered another. Although these original QTLs were mapped in different genetic backgrounds and mapped with different methods, they were projected on the same regions by the common marker. If a QTL cluster contained more than 2 QTLs from different researches, this region could contain an allele in a high probability. Meta-analysis was used to estimate existence of the “consensus” QTL and to locate the confidence interval with the QTL cluster. The basic process of meta-analysis of BioMercator 2.1 is: QTLs from many independent experiments and associated on the same LG and at the neighboring interval to calculate a “consensus” QTL. This QTL will give 5 models, the minimum Akaike information criteria (AIC) value was the best model QTL, called “consensus” QTL. Each model give the most possible positions in the each linkage group (LG) with the maximum-likelihood function by the Gaussian function. The formula described by Goffinet and Gerber (2000) was to estimate, for LG, the number of QTLs underlying the results that were synthesized. In the model, the “consensus” QTL position depends on the position of each QTL in LG, its variance is calculated from the following formula: var (QTL) = 1/ 1/σi2 σi2 is the position of the variance for each QTL on LG, 95% confidence interval of the “consensus” QTL was calculated from the var (QTL): C.I.=3.92×var(QTL)1/2 AIC value depends on simulation of each model. The model of the smallest AIC model was close to “consensus” QTL. Mean R2 of original QTLs in that region was proportion of variance explained this “consensus” QTL. The “consensus” QTL was the “summarize” of original QTLs.
RESULTS Collection of 100-seed weight QTLs in soybean Sixty-five QTLs in recent 20 yr of 100-seed weight
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were collected from 9 research papers (Table 1). Fiftyfive QTLs have common markers with the reference map (soymap2), including 17 reductive QTLs of 100seed weight QTLs in soybean and 36 additive QTLs of 100-seed weight QTLs in soybean. LOD score of these QTLs were from 2.09 to 30.47. R2 of these QTLs were from 3.00 to 36.01%.
QTLs projection: a consensus map of 100-seed weight in soybean In total, 53 QTLs of 100-seed weight were projected
on the reference map (soymap2) and additive and reductive consensus map were obtained, respectively (Figs. 1 and 2). As shown in Fig. 1, there were 6 additive QTL clusters on B1, C2, D2, K, M, and O. As shown in Fig. 2, there were 6 reductive QTL clusters on B2, C1, E, H, and I. Two original QTLs were contained in QTL clusters on C1, D2, K, M, O, and B2. Three original QTLs were contained in QTL clusters on B1 and H. Four original QTLs were contained in QTL clusters on C2, E, and B2. Seven original QTLs were contained in QTL cluster on I.
Table 1 Information of 100-seed weight QTLs in soybean No. of QTLs 3 1 6 3 11 8 2 28 3 Total 65 1)
Parents Kefeng 1×Nannong 1138-2 Kefeng 1×Nannong 1138-2 Kefeng 1×Nannong 1138-2 Zheng 92116×Shang 951099 G. max ‘7499’×G. soja PI 245331 Charleston×Dongnong 594 Jin 23×Huibuzhiheidou Zhongdou 29×Zhongdou 32 Suinong 14×Suinong 20
Population size
Analysis method 1)
Population type
201 184 201 105 148 154 255 94
CIM CIM IM IM CIM CIM CIM CIM CIM
RIL RIL RIL F2 BIL (backcross inbred lines) RIL RIL RIL F2
Reference Gai et al. (2007) Zhang et al. (2004) Wu et al. (2001) Guan et al. (2004) Li et al. (2008) Chen et al. (2007) Wang et al. (2004) Wang et al. (2008) Zhu et al. (2006)
IM, interval mapping; CIM, composite interval mapping.
Fig. 1 Integrate map of 100-seed weight QTLs (additive). Vertical lines represent the confidence interval (C.I.) of QTL, horizontal lines represent the LOD score of QTL. The same as below.
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Meta-Analysis of 100-Seed Weight QTLs in Soybean
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Fig. 2 Integrate map of 100-seed weight QTLs (reductive).
Meta-analysis of QTLs Fifty-three original QTLs of all projected QTLs were analyzed by the meta-analysis of BioMercator 2.1. The “consensus” QTL is ensured by the minimum AIC value of each meta-analysis model. Six additive “consensus” QTLs and 6 reductive “consensus” QTLs were found by the meta-analysis (Tables 2 and 3).
By the meta-analysis of additive QTLs clusters, 3 QTLs were contained in the “consensus” QTL on B1, LOD score of QTLs were 2.25-3.98, map position was 25.01-46.39 cM, map position of the “consensus” QTL was 36.88-44.83 cM, mean R2 was 7.28%. Four QTLs were contained in the “consensus” QTL on C2, LOD score of QTLs were 3.15-4.11, map position was 105.89-120.89 cM, map position of the “consen-
Table 2 Meta-analysis of soybean 100-seed weight QTLs (additive) LG B1 C2 D2 K M O
AIC
MQTL
C.I.
Mean
Map distance
value
pos. (cM)
(95%)
R2 (%)
(cM)
27.34 16.89 6.52 14.51 8.80 14.37
40.85 110.19 25.28 15.68 9.28 40.19
36.88-44.83 108.64-111.75 24.52-26.04 14.33-17.04 6.25-12.31 38.71-41.67
7.28 3.95 8.50 11.39 12.29 6.50
7.95 3.11 1.52 2.71 6.06 2.96
L-marker
L-marker coordinate (cM)
Distance of MQTL to L-marker (cM)
R-marker
R-marker coordinate (cM)
Distance of MQTL to R-marker (cM)
Satt251 Satt277 Satt458 Satt242 Satt636 Satt653
36.48 107.59 24.52 14.35 5.00 38.10
4.37 2.60 0.76 1.33 4.28 2.09
Satt197 Satt557 Satt135 Sat_119 Satt201 Satt347
46.39 112.19 26.05 17.11 13.56 42.30
5.54 2.00 0.77 1.43 4.28 2.11
L-marker coordinate (cM) 43.58 59.82 71.08 21.05 46.95 51.99
Distance of MQTL to L-marker (cM) 6.78 5.74 1.36 3.44 3.77 3.11
Table 3 Meta-analysis of soybean 100-seed weight QTLs (reductive) LG B2 B2 C1 E H I
AIC
MQTL
C.I.
Mean
Map distance
value
pos. (cM)
(95%)
R2 (%)
(cM)
50.36 65.56 72.44 24.49 50.72 55.10
48.53-52.19 60.26-70.86 71.08-73.80 21.14-27.84 47.23-54.22 52.27-57.93
6.92 8.50 6.42 7.20 13.00 18.59
3.66 10.60 2.72 6.70 6.99 5.66
31.71 31.71 5.59 21.52 16.28 33.65
L-marker B142_1 BLT049_2 Dia A053_1 Satt442 A955_1
R-marker coordinate (cM) A108_1 53.54 G214_4 70.88 Satt718 73.79 Bng193_1 28.15 A404T_3 55.39 Satt049 58.82
R-marker
Distance of MQTL to R-marker (cM) 3.18 5.32 1.35 3.66 4.67 3.72
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sus” QTL was 108.64-111.75 cM, mean R2 was 3.95%. Two QTLs were contained in the “consensus” QTL on D2, LOD score of QTLs were 4.80-6.10, map position was 17.2-32.02 cM, map position of the “consensus” QTL was 24.52-26.04 cM, mean R2 was 8.50%. Two QTLs were contained in the “consensus” QTL of LG K, LOD score of QTLs were 7.98-8.17, map position was 6.85-21.85 cM, map position of the “consensus” QTL was 14.33-17.04 cM, mean R2 was 11.39%. Two QTLs were contained in the “consensus” QTL on M, LOD score of QTLs were 2.82-7.03, map position was 5.00-13.56 cM, map position of the “consensus” QTL was 6.25-12.31 cM, mean R2 was 12.29%. Two QTLs were contained in the “consensus” QTL on O, LOD score of QTLs was 5.12, map position was 38.0942.09 cM, map position of the “consensus” QTL was 38.71-41.67 cM, mean R 2 was 6.50%. By the meta-analysis of reductive QTLs clusters, 4 QTLs were contained in the “consensus” QTL on B2, LOD score of QTLs were 2.2-3.3, map position was 43.95-58.95 cM, map position of the “consensus” QTL was 48.53-52.19 cM, mean R2 was 6.92%. Two QTLs were contained in the “consensus” QTL on B2, LOD score of QTLs were 4.78-5.85, map position was 58.06-73.06 cM, map position of the “consensus” QTL was 60.26-70.86 cM, mean R2 was 8.5%. Two QTLs were contained in the “consensus” QTL on C1, LOD score of QTLs were 4.54-6.31, map position was 70.52-74.36 cM, map position of the “consensus” QTL was 71.08-73.8 cM, mean R2 was 6.42%. Four QTLs were contained in the “consensus” QTL on E, LOD score of QTLs were 2.97-8.55, map position was 105.89-120.89 cM, map position of the “consensus” QTL was 21.14-27.84 cM, mean R2 was 7.20%. Three QTLs were contained in the “consensus” QTL on H, LOD score of QTLs were 4.2-4.6, map position was 44.04-60.85 cM, map position of the “consensus” QTL was 47.23-54.22 cM, mean R2 was 13.00%. Seven QTLs were contained in the “consensus” QTL on I, LOD score of QTLs were 3.72-30.47, map position was 47.6-62.6 cM, map position of the “consensus” QTL was 52.27-57.93 cM, mean R2 was 18.59%.
DISCUSSION Soymap2, the reference map A new public map soymap2 has been constructed in
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2004 by Song et al. (2004). Five soybean genetic maps, including molecular genetic map of two F2 populations of A81-356022×PI468916 and Clark×Harosoy, 3 RIL populations of Minsoy×Noir1, Minsoy×Archer, and RILs of Noir 1×Archer, were integrated. There were 20 linkage groups and 1 849 markers, including 709 RFLP markers, 1 015 SSR markers, 73 RAPD markers, 6 AFLP markers, and 46 other types markers in the public map. This integrated map showed a very high density of SSR markers and RFLP markers, which were common technique for mapping QTLs, so QTLs of these maps could easily project on the public map.
Application of meta-analysis for marker-assisted selection (MAS) Marker-assisted selection (MAS) is an important strategy for crop improvement. Recently, MAS has been successfully used to increase quality and yield of wheat (Romagosa et al. 1999) and rice (Wang et al. 2004). However, MAS has not been widely used, mainly because of its validity, cost and practicability of markers. The accuracy of mapping QTL is on the base of gene discovering and cloning. A number of genes which often control many important traits located in the confidence interval of QTL, but it is difficult to find real QTL. Wang and Paigen (2002) found that 18 of the 22 human HDL-C QTLs were within the murine HDL-C QTL and indicated that murine QTL for HDL-C levels may predict their homologous locations in humans, and their underlying genes may be appropriate genes for the test in humans. Meta-analysis method was used in this study, advantages of meta-analysis was: (i) integrated lots of scattered QTLs to find major gene; (ii) the accuracy and validity of mapping QTL was improved, and reduced noise of QTLs analysis by software. In the basis of the integrated QTLs from previous studies, the integrated map has rich and high density markers. The “consensus QTL” was detected in different populations and environments to found appropriate genetic markers. For the QTL only detected in specific populations, the corresponding position of integrated map can be used to find molecular markers for MAS. Combined with R2, predict genotypes of the future generations in different groups and verification. It could increase the actual efficiency and improvement of MAS. © 2011, CAAS. All rights reserved. Published by Elsevier Ltd.
Meta-Analysis of 100-Seed Weight QTLs in Soybean
Wang et al. (2006) integrated 127 QTLs of plant height in maize, 40 “consensus QTLs” were acquired by meta-analysis method. In accordance with collinearity of rice and maize, 5 genes related plant height of rice were mapped on the “consensus QTL” of maize by CMap. Meta-analysis method had greatly narrowed down the confidence interval, the minimum 95% confidence interval of “consensus QTL” was 1.52 cM in this study. With the development of the information of soybean genome and bioinformatics, it was convenient to map the “consensus QTL” in the corresponding physical map, and find candidate gene by bioinformatics tools. It laid the foundation for the process from QTL to QTG. “Consensus QTL” laid foundation for QTL fine mapping of seed weight content and molecular assisted breeding.
CONCLUSION QTLs of 100-seed weight in soybean were collected, the integrated map was constructed. Twelve “consensus QTLs” were acquired by meta-analysis, the minimum 95% confidence interval of “consensus QTLs” was 1.52 cM in this study. It reduced noise of QTLs analysis by software and laid foundation for QTL fine mapping of seed weight content and molecular-assisted breeding.
Acknowledgements This study was supported by the Chinese Transgenic Specific Technology Programs (2009ZX08009-013B).
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