Identification of additional QTLs for flowering time by removing the effect of the maturity gene E1 in soybean

Journal of Integrative Agriculture 2016, 15(1): 42–49 Available online at www.sciencedirect.com

ScienceDirect

RESEARCH ARTICLE

Identification of additional QTLs for flowering time by removing the effect of the maturity gene E1 in soybean LU Si-jia1, 2*, LI Ying3*, WANG Jia-lin1*, NAN Hai-yang1, CAO Dong1, LI Xiao-ming1, 2, SHI Dan-ning1, 2, FANG Chao1, 2, SHI Xin-yi1, 2, YUAN Xiao-hui1, Jun Abe4, LIU Bao-hui1, KONG Fan-jiang1 1

Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Harbin 150081, P.R.China 2 University of Chinese Academy of Sciences, Beijing 100049, P.R.China 3 State Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, Harbin 150040, P.R.China 4 Research Faculty of Agriculture, Hokkaido University, Sapporo 060-8589, Japan

Abstract The adaptability of soybean to be grown at a wide range of latitudes is attributed to natural variation in the major genes and quantitative trait loci (QTLs) that control flowering time and maturity. Thus, the identification of genes controlling flowering time and maturity and the understanding of their molecular basis are critical for improving soybean productivity. However, due to the great effect of the major maturity gene E1 on flowering time, it is difficult to detect other small-effect QTLs. In this study, aiming to reduce the effect of the QTL, associated with the E1 gene, on the detection of other QTLs, we divided a population of 96 recombinant inbred lines (RILs) into two sub-populations: one with the E1 allele and another with the e1nl allele. Compared with the results of using all 96 recombinant inbred lines, additional QTLs for flowering time were identified in the sub-populations, two (qFT-B1 and qFT-H) in RILs with the E1 allele and one (qFT-J-2) in the RILs with the e1nl allele, respectively. The three QTLs, qFT-B1, qFT-H and qFT-J-2 were true QTLs and played an important role in the regulation of growth period. Our data provides valuable information for the genetic mapping and gene cloning of traits controlling flowering time and maturity and will help a better understanding of the mechanism of photoperiod-regulated flowering and molecular breeding in soybean. Keywords: multiple QTL model (MQM), mixed model-based composite interval mapping (MCIM), photoperiod, maturity, productivity

1. Introduction Received 13 January, 2015 Accepted 16 April, 2015 LU Si-jia, E-mail: [email protected]; Correspondence KONG Fan-jiang, Tel/Fax: +86-451-86691226, E-mail: kongfj@iga. ac.cn; LIU Bao-hui, Tel/Fax: +86-451-86685735, E-mail: liubh@ iga.ac.cn * These authors contributed equally to this study. © 2016, CAAS. All rights reserved. Published by Elsevier Ltd. doi: 10.1016/S2095-3119(15)61046-2

Soybean is a facultative, short-day (SD) plant. It is grown at a wide range of latitudes, from at least 50°N to 35°S (McBlain and Bernard 1987); however, the cultivation area of each soybean cultivar is restricted to a very narrow range of latitudes. This wide adaptability attributed to a large number of the major genes and quantitative trait loci (QTLs) that control flowering behavior. In soybean, 10 maturity loci, E1 to E9

43

LU Si-jia et al. Journal of Integrative Agriculture 2016, 15(1): 42–49

and J, controlling flowering time and maturity have been identified and characterized at the phenotypic and genetic levels (Bernard 1971; Buzzell 1971; Buzzell and Voldeng 1980; McBlain and Bernard 1987; Ray et al. 1995; Bonato and Vello 1999; Cober and Voldeng 2001; Cober and Morrison 2010; Kong et al. 2014). Among these major genes, E1 was mapped to linkage group Gm 06 (LG C2). The E1 gene has the largest impact on flowering time in soybean, and the E1 protein contains a putative nuclear localization signal and a distantly related B3 domain (Xia et al. 2012). E2 is located on Gm 10 (LG O) and it has been identified as a homolog of Arabidopsis GIGANTIA (GI) (Watanabe et al. 2011). E3 and E4 are located on Gm 19 (LG L) and Gm 20 (LG I), respectively, and both of them have been identified as homologs of the Arabidopsis photoreceptor PHYTOCHROME A (PHYA) (Liu et al. 2008; Watanabe et al. 2009). Besides these cloned maturity genes, two homologs of FLOWERING LOCUS T (FT), GmFT2a and GmFT5a, were found to encode components of ‘florigen’, the mobile flowering promotion signal that is involved in the transition to flowering in soybean, and these two FT homologs were reported to coordinately control flowering in soybean (Kong et al. 2010). GmFT2a and GmFT5a redundantly and differentially regulate flowering through interactions with and up-regulation of the bZIP transcription factor GmFDL19 in soybean (Nan et al. 2014). Many QTLs have been reported to control the time for flowering. In SoyBase (http://soybase.org/), there are 61 Gm 06

Gm 10

Satt520 Sat_457 60.3 Sat_153 66.8 70.8 Satt170 76.7 82.1 Satt322 84.8 87.0 Satt450 92.8 Sat_076 E1 108.2 AGG/CGC380 111.6 ACG/CCG45 118.1 Satt307 Satt316

AGT/CAG115/125 0.0 0.0 Satt495 AW310961 0.0 Satt249 5.9 Satt182 Satt388 20.1 Satt143 Sct_046 21.2 AAC/CAC320 29.6 30.9 GmFT3a/GmFT5a 31.0 Satt462 39.8 Satt686 50.8 45.0 Satt529 54.8 52.4 51.0 AAC/CAG480 55.9 Satt076 Satt215 57.0 58.7 59.2 Satt166 ATG/CCG270 65.3 65.5 ATC/CCG315 GmFT2a/GmFT2b 65.8 Sat_099 AGT/CCA170 68.1 75.0 71.0 GmTFL1b ACT/CAC4 77.5 72.7 Satt006 GmBFTA 76.6 ATC/CCG215 Satt431 78.8 89.2 93.9 Satt229 AGT/CCA340 95.7 82.8 Satt712 88.1 104.2 101.6 Satt373 110.1 104.9 Sat_245 110.1 AGA/CAC240 116.6 123.8 121.2

131.3

AGC/CCG200

131.3

147.6 153.5

Satt357 Satt371

AW310961 0.0 Satt249 Sct_046 GmFT3a/GmFT5a 30.9 Satt686 Satt52945.0 AAC/CAG480 Satt21551.0 ATG/CCG270 59.2 GmFT2a/GmFT2b AGT/CCA170 71.0 ACT/CAC4 77.5 GmBFTA Satt431 89.2 AGT/CCA340 Satt71295.7 101.6 104.9 110.1 116.6 121.2

0.0 Sat_130 0.5 13.1 20.8 24.1 26.9 Sat_062 38.0

140.3

147.6 153.5

QTLs for R1 trait (days to flowering of the first flower) and more than 100 QTLs for R8 trait (full maturity). These QTLs are distributed throughout 20 chromosomes and each of them can explain 3.8–69.7% of the phenotypic variance. In soybeans, flowering time is sensitive to photoperiod. However, few genetic studies have been carried out to understand their molecular basis. In addition, because of the great effect of maturity loci on flowering time in soybean, minor QTLs involved in flowering time are difficult to detect. Therefore, the objectives of this study were to gain a better understanding of the genetic basis of flowering time and to develop an effective strategy for detecting minor QTLs in the presence of major QTLs by using recombinant inbred lines (RILs).

2. Results 2.1. Identification of QTLs for flowering time in 96 RILs by multiple QTL mapping (MQM) and mixed model-based composite interval mapping (MCIM) A major QTL, qFT-C2-1, as designated by Liu et al. (2007), was detected by MQM in all conditions (Fig. 1), explaining 57.1, 81.7, 62.9, and 68.7% of the phenotypic variance for Sapporo-2004 (the data of 2004 in Sapporo), Sapporo-2005 (the data of 2005 in Sapporo), Sapporo-C (the average data of 2004 and 2005 in Sapporo), and Harbin-2010 (the data of 2010 in Harbin), respectively (Table 1). This locus harbored Gm 16

Sat_196 0.0Sat_130 AGT/CAG115/125 0.0 Sat_130 0.0 0.0 Satt495 0.5 5.9 5.9 Satt182 13.1 Sat_321 Satt388 21.2 20.8 21.2 Satt143 24.1 BF008905 26.9Sat_062 AAC/CAC320 31.0 Sat_062 30.9 31.0 AAA/CCG260 Satt462 50.8 38.0 50.8 Satt520 Satt520 54.8 45.0 54.8 Satt420 Sat_457Sat_457 55.9 51.0 55.9 Satt262 58.7 60.3Sat_153 Satt076 58.7 Sat_153 59.2 GmNhxA Satt166 65.3 66.8 65.3 65.8 70.8Satt170 ATC/CCG315 65.8 Satt477 Satt170 71.0 Sat_099 68.1 76.7 68.1 GmTFL1b 72.7 82.1Satt322 Satt322 72.7 77.5 Satt006 76.6 84.8 76.6 78.8 ATC/CCG215 87.0Satt450 Satt450 78.8 Sat_274 89.2 Satt229 82.8 92.8Sat_076Sat_076 82.8 95.7 88.1 E1 88.1 E2 E1 101.6 Pgm1 Satt373 108.2AGG/CGC380 AGG/CGC380 104.9 Sat_245 111.6 ACG/CCG45 ACG/CCG45 110.1 AGA/CAC240 118.1Satt307 Satt307 116.6 Sat_108 Satt316 Satt316 121.2 AGC/CCG200 AGC/CCG200 131.3 Scaa001 Satt357 Satt357 147.6 Satt371 Satt371 153.5

Gm 19

AW310961 Sat_196 0.0 AW310961 Satt249 Satt249

0.0 0.5

AGT/CAG115/125 0.0 Satt495 AGT/CAG115/125 0.0 Satt495 0.5 Satt182 Satt182 13.1 Satt388 20.1 20.8 Satt143 Satt388 Satt143 24.1 AAC/CAC320 29.6 AAC/CAC320 26.9 Satt462 Satt462 39.8 38.0

13.1 Sat_321 20.8 20.1 Sct_046 Sct_046 24.1 BF008905 26.9 29.6 GmFT3a/GmFT5a GmFT3a/GmFT5a 38.0 AAA/CCG260 39.8 Satt686 Satt686 Satt529 Satt529 AAC/CAG480 Satt420 52.4 AAC/CAG480 Satt215 57.0 Satt215Satt262 60.3 Satt076 60.3 Satt166 Satt076 ATG/CCG270 GmNhxA 66.8 Satt166 65.5 ATG/CCG270 66.8 ATC/CCG315 GmFT2a/GmFT2b GmFT2a/GmFT2b70.8 70.8 AGT/CCA170 Sat_099ATC/CCG315 Satt477 76.7 75.0 AGT/CCA170 Sat_099 76.7 GmTFL1b ACT/CAC4 82.1 ACT/CAC4 82.1 Satt006 GmTFL1b GmBFTA 84.8 Satt006 84.8 Satt431 GmBFTA ATC/CCG215 87.0 Satt431 87.0 Satt229 ATC/CCG215 AGT/CCA340 Sat_274 92.8 93.9 Satt229 92.8 Satt712 AGT/CCA340 Satt712 E2 104.2 108.2 108.2 Satt373 Satt373 Pgm1 110.1 Sat_245 111.6 111.6 Sat_245 118.1 118.1 AGA/CAC240 AGA/CAC240 Sat_108 123.8 140.3

Scaa001

QTLs for Sapporo-2004 by MQM in 96 RILs

QTLs for Sapporo-2005 by MQM in 96 RILs

QTLs for Sapporo-C by MQM in 96 RILs

QTLs for Harbin-2010 by MQM in 96 RILs

QTLs for Sapporo-2004 by MCIM in 96 RILs

QTLs for Sapporo-C by MCIM in 96 RULs

52.4 57.0 65.5 75.0 93.9 104.2 110.1 123.8 140.3

QTLs for Harbin-2010 by MICM in 96 RILs

Fig. 1 Four linkage groups harboring QTLs (quantitative trait loci) in 96 RILs (recombinant inbred lines) for Sapporo-2004 (the data of 2004 in Sapporo), Sapporo-2005 (the data of 2005 in Sapporo), Sapporo-C (the average data of 2004 and 2005 in Sapporo), and Harbin-2010 (the data of 2010 in Harbin) by the MQM (multiple QTL mapping) and MCIM (mixed model-based composite interval mapping) models.

44

LU Si-jia et al. Journal of Integrative Agriculture 2016, 15(1): 42–49

the region of major maturity gene E1 (Watanabe et al. 2011). The second QTL, qFT-O, detected in Sapporo-2004, Sapporo-C and Harbin-2010, was found to be the same QTL as FT2, which harbored the E2 gene (Yamanaka et al. 2001). The E2 locus, located on Gm 10 (LG O), had a LOD score greater than 2.15 and explained 9.8 to 11.7% of the phenotypic variance under three conditions (Table 1). We also detected the third QTL for flowering, qFT-L, a minor-effect QTL, by MQM under two conditions: Sapporo-2004 and Sapporo-C (Table 1). The relationships among QTLs were complicated and MQM did not take it into account. This will mask some other QTLs. So the QTL analysis was also performed by MCIM to identify more QTLs for flowering time. As a result, an additional QTL, qFT-J-1, was detected under two conditions, Sapporo-C and Harbin-2010 (Table 2). These results suggested that the MQM and MCIM approaches can complement each other and identify more QTLs.

2.2. Identification of QTLs of the E1 allele from 60 RILs by MQM and MCIM Previous studies showed that the maturity locus E1 has strong effects on flowering time and maturity (Bernard

1971; Yamanaka et al. 2001). We also demonstrated that the major QTL, qFT-C2-1, which associated with the E1 gene, showed high phenotypic variance (81.7%) in Sapporo-2005 and that other QTLs were hardly detectable (Table 1). Therefore, to identify more QTLs for flowering time, we reduced the influence of the QTL harboring the major maturity gene E1 by dividing the 96 RILs into two sub-populations: one group containing 60 RILs with the E1 allele and the other group containing 36 RILs with the e1nl allele. Using the genetic markers of the 60 RILs with the E1 allele, we constructed a genetic linkage map with 292 markers covering 2 255.4 cM. Because the population was small and the markers were limited, some linkage groups were divided into two parts: linkage groups Gm 02 (LG D1b), Gm 04 (LG C1), Gm 13 (LG F), and Gm 15 (LG E) were divided into Gm 02-1 and 02-2, Gm 04-1 and 04-2, Gm 13-1 and 13-2, Gm 15-1 and 15-2, respectively. Based on this genetic linkage map, we performed QTL detection using MQM and MCIM analyses, respectively (Fig. 2). First, we detected QTLs for the flowering times for Sapporo and Harbin using MQM (Table 3). Compared with the results of total 96 RILs (Table 1), some additional QTLs for flowering time were detected. The QTL qFT-B1 was consis-

Table 1 Identification of main effect QTLs (quantitative trait loci) in 96 RILs (recombinant inbred lines) by multiple QTL mapping (MQM) implemented by MapQTL 5.0 Environment1) Sapporo-2004

Sapporo-2005 Sapporo-C

Harbin-2010

QTL qFT-C2-1 qFT-L qFT-O qFT-C2-1 qFT-C2-1 qFT-L qFT-O qFT-C2-1 qFT-O

Linkage group Gm 06 Gm 19 Gm 10 Gm 06 Gm 06 Gm 19 Gm 10 Gm 06 Gm 10

Marker or interval2) E1-AGG/CGC380 Satt006 Sat_274 Sat_076-E1 Sat_076-E1 Satt006 Sat_274 E1-AGG/CGC380 Satt477-Sat_274

Position (cM)3) 102.6 84.8 93.9 99.7 100.7 84.8 93.9 102.6 92

LOD 14.67 2.54 2.25 25.33 18.61 2.07 2.15 22.99 2.17

A4) –11.37 5.41 –5.02 –12.76 –11.32 4.36 –4.36 –23.14 –9.29

R2 (%)5) 57.1 12.9 11.5 81.7 62.9 9.4 9.8 68.7 11.7

1)

Sapporo-2004, the data of 2004 in Sapporo; Sapporo-2005, the data of 2005 in Sapporo; Sapporo-C, the average data of 2004 and 2005 in Sapporo; Harbin-2010, the data of 2010 in Harbin. 2) Markers or support intervals on the linkage map in which the LOD (logarithm of odds) is the largest. 3) The LOD peak for candidate QTLs on the genetic linkage map in centimorgans. 4) A, the additive effects contributed by QTLs. A positive value (+) of the additive effect indicates the allele originating fromTK780; a negative value (–) of the additive effect indicates the allele originating from H4. 5) R2 (%), percentage of phenotypic variance explained by the QTL. The same as below.

Table 2 Identification of main effect QTLs in 96 RILs by MCIM implemented by QTLNetwork 2.1 Environment Sapporo-2004 Sapporo-C Harbin-2010

***

QTL qFT-C2-1*** qFT-C2-1*** qFT-J-1*** qFT-C2-1*** qFT-J-1*** qFT-O***

Linkage group Gm 06 Gm 06 Gm 16 Gm 06 Gm 16 Gm 10

Marker or interval AGG/CGC380-ACG/CCG45 E1-AGG/CGC380 GmFT3a/GmFT5a-Satt686 E1-AGG/CGC380 GmFT3a/GmFT5a-Satt686 E2-Pgm1

, significance at the 0.001 probability level. The same as below.

Position (cM) 107.9 102.6 45 102.6 44.0 106.2

A –9.16 –11.40 4.43 –24.28 5.64 –6.70

R2 (%) 14.33 56.54 2.89 64.1 2.71 4.52

45

LU Si-jia et al. Journal of Integrative Agriculture 2016, 15(1): 42–49

B1 C2 I B1 C2 D2H I H I C2 D2 D2 H B1 I J 16 J Gm C2 Gm 06B1 C2 Gm 10 Gm 11HD2 I Gm 12 J Gm 17 Gm 20 C2 O D2 H Satt419 0.0 Satt666 BE806308 0.0 Sat_130 Sct008 0.0 Satt419 Satt419 0.0 0.0 0.0 0.0 0.0 Satt666 Satt666 BE806308 AW310961 BE806308 Sct008 Sat_130 Sct008 Sat_130 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Satt419 0.0 Satt666 AW310961 0.0 0.0 BE806308 0.0 Sat_1300.0 BE806308 0.0 Sct008 0.0 Satt419 Satt666 Sat_130 Satt3670.0 AW310961 0.0 0.0 0.0 0.0 Sct0085.6 Sat_296 Satt353 4.6 Sat_296 Satt367 2.7 Satt353 2.7 Satt367 Satt419 4.6 0.0AGC/CTG170/175 Satt353 Satt353 4.6 2.7 5.7 Satt666 4.6 Sat_196 5.6 0.0 2.7 Satt367 0.0 0.0 Sat_130 0.0 Sct008 0.0 AW310961 Sat_296 Satt249 5.6 2.7 Satt367 AGC/CTG170/175 Satt353 Sat_296 6.9 AGC/CTG170/175 Satt249 4.6 5.7 5.6 6.9 5.6 5.7Sat_296 5.7 Satt249 6.9 AGC/CTG170/175 2.7 Satt367 10.6 AGA/CCC260 6.9 AGC/CTG170/175 10.6 Satt353 4.6 AGA/CCC260 AGA/CCC260 Sat_296 10.6 Satt249 5.6 Sct_192 5.7 12.2 AGC/CTG170/175 10.6 AGA/CCC260 12.2 Sct_192 12.2 Sct_192 10.6 AGA/CCC260 Sct_192 12.2 10.6 12.2 Sct_192 AGA/CCC260 12.2 Sct_192 ACC/CAA207/210 19.9 Sat_127 20.3 ACC/CAA207/210 19.9 19.9 20.9 Sat_272 20.3 Sat_127 20.3 Sat_127 20.3 21.4 Sct_046 20.3 20.9 20.9ACC/CAA207/210 Sat_272 19.9 21.4 Sat_272 21.4Sat_127 Sct_046 19.9 ACC/CAA207/210 Sat_127 21.4 Sat_272 24.2 Sat_062 20.9 Sct_046 26.7 ATT/CGC350 21.4 Sat_27219.9 ACC/CAA207/210 24.2 Sat_062 24.2 Sat_062 ATT/CGC350 26.7 Sat_062 26.7 ATT/CGC350 21.5 Sat_32124.2 20.3 Sat_127 Sat_062 20.9 Sct_046 26.7 ATT/CGC350 24.2 27.9 Satt135 Satt442 26.7 ATT/CGC350 28.2 Satt135 ATT/CAG40 27.9 27.9 Satt135 Satt496 Satt442 Satt442 28.2 28.4 28.2 ATT/CAG40 27.9 Satt496 Satt496 Satt135 29.2 ATT/CAG40 29.2Satt442 24.2 Sat_062 28.4 28.4 30.2 27.929.2 ATT/CGC350 28.2 26.7 GmFT3a/GmFT5a Satt135 30.2 Satt442 Satt496 29.2 ATT/CAG40 28.2 30.2 28.4 GmFT3a/GmFT5a Satt496 29.2 ATT/CAG40 28.4 GmFT3a/GmFT5a E4 30.2 31.1 Satt135 Satt520 27.9 Satt442 34.4 28.2 E430.2 GmFT3a/GmFT5a 31.1 E4 Satt520 Satt496 31.1 Satt520 AGG/CGT7035.7 AGG/CGT70 34.4 Satt520 34.435.7 28.4Satt509 32.0 BF008905 31.1 E4 AGG/CGT70 35.737.3 35.7 Satt520 34.4 31.1 E4 34.7 Satt354 34.4 Satt509 37.3 Satt509 37.3 Satt354 AGG/CGT70 35.7 Satt354 34.7 34.7 37.3 Satt509 E4 31.1 34.4 Satt520 Satt509 Sat_457 37.3 Satt354 41.0 34.7 ACA/CCT2 45.9 AGG/CGT70 35.7 Sat_457 Sat_457 Satt354 41.0 41.042.3 Satt002 34.7 45.9 41.3 ACA/CCT2 45.9 Satt270 Satt002 42.3 Satt002 42.3 Sat_457 41.0 Satt270 Sat_457 41.3ACA/CCT2 41.3 Satt270 41.0 Satt002 Satt35445.9 42.3 ACA/CCT2 34.7 45.9 ACA/CCT2 Satt270 41.3 Satt002 42.3 42.6 AAA/CCG260 47.7 Sat_122 41.3 Satt270 41.0 Sat_457 47.7 Sat_153 47.7 Sat_122 47.7 Sat_122 ACA/CCT2 47.7 Sat_153 Satt154 47.9 Sat_122 51.7 42.3 Satt002 47.7 Sat_153 Satt154 47.7 Satt154 47.9 47.945.9 Satt27047.7 Satt686 Sat_122 41.3 51.7 47.7 51.7 Sat_153 Satt154 47.9 Satt253 Satt253 48.5 Satt686 Sat_153 Sat_104 47.7 Satt154 51.4 47.9 Satt253 Satt686 48.5 48.5 51.7 Sat_122 Sat_104 Sat_104 47.7 51.4 51.4 56.4 48.5 Satt253 47.7 Sat_153 Satt529 48.5 56.4 Satt253 47.9 Satt154 56.4Sat_104 51.7 Satt68651.4 51.4 Sat_104 51.1 Satt529 ATT/CAG140 ACC/CCG75/100 ACC/CCG75/100 51.1 55.9 51.1 56.0 56.4 Satt529 Satt197 ATT/CAG140 ATT/CAG140 55.9 Satt197 55.9ACC/CCG75/100 48.5 Satt253 56.0 56.0 57.3 Sat_104 51.4Satt197 51.1 55.9 Satt19755.4 Satt420 AAC/CAG480 ACC/CCG75/100 57.3ATT/CAG140 51.1 57.3 55.9 Satt197 56.4 Satt52956.0 56.0 ATT/CAG140 AAC/CAG480 Sat_128 60.7 57.3 AAC/CAG480 Sat_128 59.0 Satt262 51.1 ACC/CCG75/100 60.7 Sat_128 60.7 ATT/CAG140 56.0 62.0 Satt330 63.8 Satt170 60.7 Sat_128 63.4 Satt215 Satt330 62.0 Satt330 63.8 63.8 AAC/CAG480 57.3 63.4 Satt170 63.4 Satt170 Satt215 63.8 Satt330 Satt17060.7 Sat_128 63.4 62.0 Satt215 63.8 Satt330 67.0 Satt38962.0 67.0 66.4 Satt292 Satt389 ATG/CCG270 67.0 Satt389 Satt292 68.8 Satt292 66.4 Satt170 66.4 68.8 Satt215 Satt302 62.0 Satt330 63.4 Satt322 68.9 63.8 69.0 Satt389 ATG/CCG270 68.1 GmNhxA69.0 67.0 63.4 Satt170 69.0 Satt322 Satt292 68.8 Satt302 Satt302 66.4 Satt322 68.9 68.9 69.0 Satt389 ATG/CCG270 67.0 Satt292 68.8 66.4 Satt302 Satt322 68.9 69.7 Satt302 Satt322 68.9 69.7 69.0 GmFT2a/GmFT2b 69.7 Satt389 ATG/CCG270 67.0 68.8 Satt292 66.4 GmFT2a/GmFT2b Satt181 Satt519 74.6 74.7 68.9 Satt302 74.6 Satt181 69.0 Satt322 69.7 GmFT2a/GmFT2b Satt181 74.6 77.8 74.7 Satt519 74.7 Satt519 71.4 74.6 74.7 Satt519 AGT/CCA170 Satt148 Satt45071.4Satt148 77.9 GmFT2a/GmFT2b Satt181 69.7 74.6 71.4 74.7 Satt519 Satt148 Satt148 AGT/CCA170 77.8Satt181 77.8 76.0 Satt450 77.9 Satt450 77.9 78.7 Satt477 77.9 74.6 AGT/CCA170 71.4 77.8 Satt450 Satt181 Sat_218 ACT/CAC3 Satt148 81.2 81.2 77.8 Satt450 77.9 ACT/CAC4 ACT/CAC3 81.2 82.4 Sat_218 AAA/CCC320 76.0 81.2 81.2 ACT/CAC3 81.2 71.4 Sat_076 AAA/CCC320 AAA/CCC320 83.4 AGT/CCA170 ACT/CAC4 82.4Sat_218 82.4 80.6 ACT/CAC3 81.2 Sat_218 Satt14881.2 77.8 77.9 Satt450 83.4 Sat_076 Sat_076 83.4 ACT/CAC3 Sat_218 76.0 ACT/CAC4 AAA/CCC320 81.2 81.2 76.0 82.4 Sat_076 82.4 AAA/CCC320 83.4 81.2 GmBFTA Satt431 86.8 Satt434 Sat_076 80.6 83.4 Satt434 Sat_218 86.8 Satt434 76.0 ACT/CAC4 81.2 ACT/CAC3 Satt434 86.8 89.0 GmBFTA Satt431 82.4 AAA/CCC320 Satt440 Satt431 86.8 80.6 GmBFTA 83.4 Sat_076 Satt440 Satt434 89.0 Satt440 89.0 86.2 86.8 80.6 ATC/CCC180 AGT/CCA340 Satt440 91.4 86.2 89.0 GmBFTA Satt431 80.6 ATC/CCC180 Satt440 91.4 89.0 AGT/CCA340 Satt434 91.4 ATC/CCC180 86.2 86.8 91.4 ATC/CCC180 86.2 AGT/CCA340 Sat_001 94.5 AGG/CGC380 ATC/CCC180 94.9 91.4 91.9 Satt440 89.0 Sat_001 Sat_001 94.5 94.5 Satt712 94.9 AGG/CGC380 94.9 91.9 AGT/CCA340 86.2 Sat_001 94.599.6 AGG/CGC380 ATC/CCC180 91.9 Satt712 Sat_27494.9 91.4 94.5 Sat_001 98.8 Sat_001 AGG/CGC380 Sct_026 94.9 Satt712 91.9 Sct_026 99.6 Sct_026 99.6 94.5 Sct_026ACG/CCG45 99.6 102.6 94.9 AGG/CGC380 ACG/CCG45 91.9 102.6Satt712 99.6 Sct_026 ACG/CCG45 Satt430 102.3 ATT/CGA480 102.6 Satt430 ATT/CGA480 Satt430 102.6 102.3 ATT/CGA480 102.3 ACG/CCG45 Satt301 105.1 Satt301 102.6 105.1 ATT/CGA480 Satt430 102.3 106.2 Satt301 106.2 Satt307 105.1 ACG/CCG45 E2 107.9 102.3 ATT/CGA480 Satt307 Satt307 106.2 105.1 Satt301 102.6 ACG/CCG45 105.1 Satt301 106.2 Satt307 106.2 Satt307 109.8 Satt316 Satt301109.8 Satt316 Satt316 109.8 106.2 Satt307 109.8 105.1 Pgm1 114.2 Satt316 115.8 Satt186 114.2 Satt186 Satt316 114.2 109.8 Satt186 114.2 Satt186 109.8 Satt316 Satt186 114.2 Sat_022 121.2 Sat_022 121.2 119.9 AGC/CCG200 Satt186119.9 Sat_022 AGC/CCG200 AGC/CCG200 119.9 114.2 119.9 Sat_123 119.9 121.4 Sat_022 AGC/CCG200 121.2 Sat_123 121.4 Sat_123 121.4AGC/CCG200 121.2 Sat_022 Sat_108121.2 126.1 Sat_022 Sat_123 121.4 Satt386 123.1 Satt386 123.1 Sat_123 121.4 Satt386 123.1 119.9 AGC/CCG200 121.2 123.1 Satt386 123.1 Satt386 123.1 Satt386 Satt357 135.6 Satt357 135.6 Satt357 135.6 135.6 Satt357 138.7 Satt453 135.6 Satt453 139.2 Satt371 138.7 Satt453 138.7Satt357 139.2 Satt371 Satt453Satt371 138.7 139.2 135.6 Satt357 139.2 Satt371 138.7 Satt453 139.2 Satt371 149.7 Scaa001 139.2 Satt371 QTLs for Sapporo-2004 by MQM for 60 RILs

QTLs for Sapporo-2005 by MQM for 60 RILs

QTLs for Sapporo-C by MQM for 60 RILs

QTLs for Harbin-2010 by MQM for 60 RILs

Fig. 2 Seven linkage groups harboring QTLs in 60 RILs for Sapporo-2004, Sapporo-2005, Sapporo-C, and Harbin-2010 by the MQM model.

tently detected under all four conditions and explained 40.6,

to the two major QTLs qFT-B1 and qFT-H, we also identified

24.8, 38.6, and 30.4% of the phenotypic variation in Sap-

three additional minor QTLs, qFT-D2, qFT-I and qFT-C2-2,

poro-2004, Sapporo-2005, Sapporo-C, and Harbin-2010,

in different environments, which were not detected in the

respectively. The LODs of qFT-B1 for Sapporo-2004 and

96 RILs (Table 3). Among all QTLs detected in the 60 RILs,

Sapporo-C were higher than those for the genome-wide

only qFT-J-1 was contributed by the cultivar TK780, whereas

analyses, with permutation at the 0.05 probability level,

the others were derived from the wild accession H4. These

which suggested that qFT-B1 is a genome-wide major

results indicated that it is a sensible strategy to reduce the

QTL in Sapporo-2004 and Sapporo-C (Table 3). Another

effect of QTL, which harbored the major maturity gene E1,

major QTL, qFT-H, was not detected in Sapporo-2004, but

on flowering time to identify new QTLs for flowering time.

was detected under the 3 other conditions, Sapporo-2005,

Second, to test the additive effect and additive×envi-

Sapporo-C and Harbin-2010, accounting for 17.7, 15.2 and

ronment interaction effect relationships between the QTLs

15.9%, respectively, of the phenotypic variation. In addition

and the environments, we combined the phenotypic data of

Table 3 Identification of main effect QTLs in 60 RILs under the E1 allele by MQM implemented by MapQTL 5.0 Environment Sapporo-2004

Sapporo-2005

Sapporo-C

Harbin-2010

QTL qFT-B1 qFT-D2 qFT-I qFT-J-1 qFT-O qFT-B1 qFT-H qFT-J-1 qFT-O qFT-B1 qFT-H qFT-J-1 qFT-O qFT-B1 qFT-C2-2 qFT-H qFT-J-1 qFT-O

Linkage group Gm 11 Gm 17 Gm 20 Gm 16 Gm 10 Gm 11 Gm 12 Gm 16 Gm 10 Gm 11 Gm 12 Gm 16 Gm 10 Gm 11 Gm 06 Gm 12 Gm 16 Gm 10

Marker or interval Satt519 Satt154-Satt389 E4-Satt354 GmFT3a/GmFT5a-Satt686 Sat_274-E2 Satt519 Satt442 GmFT3a/GmFT5a-Satt686 E2-Pgm1 Satt519 Satt442 GmFT3a/GmFT5a-Satt686 E2-Pgm1 Sat_128-Satt519 Satt371 Satt442 GmFT3a/GmFT5a-Satt686 E2-Pgm1

Position (cM) 74.7 60.9 32.1 44.2 102.8 74.7 28.2 42.2 111.9 74.7 28.2 44.2 110.9 73.7 139.2 28.2 46.2 110.9

LOD 5.76 2.66 2.05 2.4 2.09 3.58 2.38 3.96 2.73 6.25 2.09 3.72 2.64 4.21 2.5 2.15 3.19 5.2

A –7.72 6.68 –4.82 8.63 –5.02 –3.40 –2.69 3.85 –2.96 –5.44 –3.23 5.36 –3.77 –10.82 –8.27 –7.34 10.32 –11.11

R2 (%) 40.6 35.8 18.7 59.9 20.3 24.8 17.7 36.2 20.6 38.6 15.2 41.8 20.5 30.4 17.4 15.9 31.3 35.8

46

LU Si-jia et al. Journal of Integrative Agriculture 2016, 15(1): 42–49

(Table 5). qFT-J-2 is located at 78.7 cM on Gm 16 (LG J), with LOD values of 11.21 and 5.11 for Sapporo-2004 and Harbin-2010, respectively, and it explained 79.1 and 48.0%, respectively, of the phenotypic variation. This QTL is located in the allele-specific DNA marker for the E9 gene (Bernard 1971; Yamanaka et al. 2001; Kong et al. 2014). The qFT-J-2 QTL was also detected when analyzing the additive effect and the additive×environment effect between QTLs and environments (Table 6). qFT-J-2 showed a larger phenotypic variation for the additive effect than that for the additive×environment effect, suggesting that genetic background had a greater effect on this QTL than the environment (Table 7).

Sapporo-2004, Sapporo-2005 and Harbin-2010 to perform the QTL analysis using the MCIM model (Table 4). In this analysis, three QTLs, qFT-B1, qFT-C1 and qFT-O, were detected. Only the P-value of qFT-B1 was significant at 0.001. This indicated that qFT-B1 was a major QTL, which was in accordance with the MQM analysis (Table 3). The explained phenotypic variation for these three QTLs ranged from 2.47 to 5.55% for the additive effect and from 2.42 to 4.65% for the additive×environment effect. The QTL qFT-B1 had the largest phenotypic variation by the additive effect and the minimum phenotypic variation by the additive×environment effect. The phenotypic variation for the additive effect was larger than that for the additive×environment effect, demonstrating that genetic background had a greater effect on qFT-B1 than the environment. The other two QTLs were detected at the 0.05 significant probability level, and both of them had greater phenotypic variation for the additive×environment effect than that for the additive effect, suggesting that the environment played a more important role than the genetic background for these two loci.

3. Discussion Flowering time and maturity in soybean is mainly regulated by photoperiod, and most soybean cultivars are agronomically adapted to a narrow range of latitudes. The identification of QTLs controlling flowering time and maturity and the understanding of the molecular mechanisms are critical to extend the growing region of cultivars and to make molecular breeding more efficient. In this study, 11 QTLs controlling flowering time were identified and were located on Gm 04 (LG C1), Gm 06 (LG C2), Gm 10 (LG O), Gm 11 (LG B1), Gm 12 (LG H), Gm 16 (LG J), Gm 17 (LG D2), Gm 19 (LG L) and Gm 20 (LG I). Among these identified flowering-time QTLs, qFT-C2-1 overlapped with the cleaved

2.3. Identification of QTLs for the e1nl allele of 36 RILs by MQM and MCIM Using 36 RILs, we constructed another genetic linkage map, and only one QTL, qFT-J-2, was detected in two environments, Sapporo-2004 and Harbin-2010, by MQM

Table 4 QTLs of 60 RILs under the E1 allele with additive effects and additive×environment interaction effects QTL

Maker or interval

Linkage group

Position (cM)

A

R(Ai)2 (%)1)

R(AEi)2 (%)2)

qFT-B1 qFT-C1 qFT-O

Satt519-Sct_026 Satt578-Satt646 E2-Pgm1

Gm 11 Gm 04 Gm 10

74.7 29.5 110.9

–4.57*** –2.00* –2.27*

5.55 2.45 3.47

2.42 4.63 4.65

1)

R(Ai)2 (%), phenotypic variation explained by additive effects. R(AEi)2 (%), phenotypic variation explained by additive effects by environment interactions. * , significance at the 0.05 probability level. The same as below. 2)

Table 5 Identification of main effect QTLs in 36 RILs under the e1nl allele by MQM implemented by MapQTL 5.0 Environment Sapporo-2004 Harbin-2010

QTL qFT-J-2 qFT-J-2

Linkage group Gm 16 Gm 16

Marker or interval GmFT2a/GmFT2b GmFT2a/GmFT2b

Position (cM) 78.7 78.7

LOD 11.21 5.11

A 5.92 4.64

R2 (%) 79.1 48

Table 6 Identification of main effect QTLs in 36 RILs under the e1nl allele by MCIM implemented by QTLNetwork 2.1 Environment Sapporo-2004

QTL qFT-J-2***

Linkage group Gm 16

Marker or interval ATG/CCG270-GmFT2a/GmFT2b

Position (cM) 79.7

A 9.26

R2 (%) 47.49

Table 7 QTLs of 36 RILs under the e1nl allele with additive effects and additive×environment interaction effects QTL qFT-J-2

Marker or interval

Linkage group

Position (cM)

ATG/CCG270-GmFT2a/GmFT2b

Gm 16

81.7

A 4.36

***

R(Ai)2 (%)

R(AEi)2 (%)

18.16

8.94

LU Si-jia et al. Journal of Integrative Agriculture 2016, 15(1): 42–49

amplified polymorphic sequences (CAPS) marker for the E1 gene (Xia et al. 2012), qFT-O was near the derived cleaved amplified polymorphic sequences (dCAPS) marker for the E2 gene (Watanabe et al. 2011), qFT-I was located around the allele-specific DNA marker for the E4 gene (Liu et al. 2008), and qFT-J-2 mapped near the maturity gene E9 (Kong et al. 2014), indicating that these four QTLs corresponded to the maturity genes E1, E2, E4, and E9, respectively. These results suggested that variations in these maturity genes significantly contribute to the adaptation of soybean to different latitudes. Among these genes, E1 had been cloned and demonstrated to be a legume-specific transcription factor that represses flowering and maturity (Xia et al. 2012). In our research, the major flowering-time QTL, qFT-C2-1, which harbored the E1 gene, was detected by both methods and in all test environments. It accounted for more than 51.7% of the flowering-time variation, which was consistent with the E1 locus having a large effect on flowering time and maturity (Bernard 1971; Yamanaka et al. 2001). The effect of the QTL for the E1 gene on flowering time was so great that the effects of other QTLs, which are influenced by E1, were small and difficult to detect. However, we reduced the effect of qFT-C2-1 by dividing the RIL population into E1 allele and e1nl allele backgrounds and were thus able to identify additional flowering-time QTLs. Our results suggested that removing the effect of a major QTL can be useful for identifying additional QTLs. As a significant QTL for 60 RILs, qFT-B1 was detected in all treatments. This QTL was flanked by Sat_128-Satt519. Some genomic regions associated with flowering time had been previously reported on Gm 11 (LG B1). In an F2 population of Misuzudaizu×Moshidou Gong 503 with RFLP markers, GM021 on Gm 11 (LG B1) was associated with flowering time (Yamanaka et al. 2001). Another study also found that the interval Sat_270-Satt509 was associated with maturity in the FAF population (Bachlava et al. 2009). Based on the integrated soybean genetic linkage map (Song et al. 2004), these intervals were close to qFT-B1 that they may be the same QTL. In our research, the QTLs qFT-B1 and qFT-H could be only detected in the dominant E1 allele background, which may suggest that the functions of qFT-B1 and qFT-H are dependent on a functional E1 gene or due to the limited sub-population under the e1nl allele background (Table 3). Using the RIL population with 201 families derived from Kefeng 1×1138-2, two QTLs associated with flowering time and maturity were previously identified and located near to qFT-B1 and qFT-H, respectively (Gai et al. 2007). Recent research based on whole-genome sequencing in wild soybean also identified two QTLs associated with the growth period, one each around qFT-B1 and qFT-H (Qi et al. 2014). As mentioned above, many QTLs associated with flowering time by different populations in different environ-

47

ments were detected on Gm 11 (LG B1) and Gm 12 (LG H), and they located very near our QTLs, qFT-B1 and qFT-H (Yamanaka et al. 2001; Gai et al. 2007; Bachlava et al. 2009; Qi et al. 2014). Moreover, the candidate genes for these two QTLs had been cloned and researched in our laboratory (not published). Therefore, although our population was small, these two QTLs were true QTLs and important for soybean flowering time (Yamanaka et al. 2001; Gai et al. 2007; Bachlava et al. 2009; Qi et al. 2014). Further determination of the molecular basis of qFT-B1 and qFT-H will help elucidate the molecular mechanism by which qFT-B1 and qFT-H control photoperiod-regulated flowering and their genetic relationship with the E1 gene. Among the 36 RILs, only one QTL, qFT-J-2, was detected on Gm 16 (LG J). This QTL originated from TK780 and was associated with early-flowering trait. In previous research, Tasma et al. (2001) used single-cross populations, IX32 (Sinshei×Corsoy) population to detect QTLs involved in days to flowering time in different environments. A QTL was found on Gm 16 (LG J) in different environments, which was located near to our QTL qFT-J-2 and also associated with early-flowering trait, so they may be the same QTL. In order to further research qFT-J-2, Kong et al. (2014) backcrossed between the parent TK780 and two early-flowering RILs (from our population of 96 RILs). The segregation patterns observed in the F2 and F3 progeny revealed that early-flowering was controlled by a single dominant gene, which was designated as E9 (Kong et al. 2014). By further research, the E9 gene was fine-mapped to a 245-kb interval on Gm 16 (LG J) (Kong et al. 2014). The QTL qFT-J-2 or the E9 gene can only be identified in the recessive e1nl allele background, which suggests that the function of qFT-J-2 or of the E9 gene is fully suppressed by a functional E1 gene, further confirming that E1 is a major flowering suppressor in soybean (Xia et al. 2012).

4. Conclusion A major flowering-time QTL, qFT-C2-1, corresponding to the E1 gene was identified, and it was found to account for more than 51.7% of the phenotypic variation and to mask many QTLs. However, by removing the effect of the E1 gene by dividing the RIL population into E1 allele and e1nl allele backgrounds, we were able to identify additional flowering-time QTLs, i.e., qFT-B1, qFT-H and qFT-J-2. In addition, the functions of the qFT-B1, qFT-H and qFT-J-2 were very much dependent on the function of E1 with regard to the photoperiod-regulated control of flowering-time. These three QTLs were detected using two small populations, but they were also detected in other researches (Yamanaka et al. 2001; Gai et al. 2007; Bachlava et al. 2009; Kong et al. 2014; Qi et al. 2014), indicating qFT-B1, qFT-H and qFT-J-2

48

LU Si-jia et al. Journal of Integrative Agriculture 2016, 15(1): 42–49

were true QTLs. Although many researches had detected these QTLs, no candidate genes have been found to date. Identifying the molecular basis of these QTLs will greatly facilitate the understanding of the molecular mechanism of photoperiod-regulated flowering and maturity in molecular breeding efforts for highly productive cultivars of soybean.

5. Materials and methods 5.1. Plant materials and field trials A population of 96 RILs was developed using a single-seed descendent (SSD) method from an F2 population of the cross between the max line Tokei 780 (TK780) and the soja accession Hidaka 4 (H4) line. The genotypes for TK780 and H4 were e1nle2E3e4 and E1E2E3E4, respectively (Kong et al. 2014). The flowering time was notably different for the two parents. The days to flowering in TK780 and H4 were 45.7 and 77.5 d for Sapporo-2004, 44.5 and 73 d for Sapporo-2005, 32.5 and 130 d for 2010-Harbin, respectively. The field trials in Sapporo (43°07´N, 141°39´E) were performed previously (Liu et al. 2007). Because flowering time is influenced by many factors, it is necessary to grow the same population in different locales, and this is helpful for finding stable QTLs. All of the 96 F10 RILs and two parental lines were grown randomly in the field of the Northeast Institute of Geography and Agroecology in Harbin (45°44´N, 126°36´E), China, during early May of 2010. The flowering time was recorded when the first flower emerged from each plant (Fehr et al. 1971). We used Sapporo-2004, Sapporo-2005, Sapporo-C, and Harbin-2010, which represented the flowering dates of 2004 in Sapporo and of 2005 in Sapporo, the average value of 2004 and 2005 in Sapporo (Liu et al. 2007), and the flowering date of 2010 in Harbin, respectively.

5.2. Linkage map construction and data analysis The linkage map for 96 RILs was constructed as before (Liu et al. 2007). We further enriched the map with allele-specific DNA markers including E1 (Xia et al. 2012), E2 (Watanabe et al. 2011), E4 (Liu et al. 2008), GmFT6/FT4 (Kong et al. 2010), GmFT2a/GmFT2b (Kong et al. 2010), GmFT3a/ GmFT5a (Kong et al. 2010), GmTFL1a and GmTFL1b (Liu et al. 2010). To exclude the influence of E1 on other QTLs, we divided the 96 RILs into two groups. One group harbored 60 RILs with the E1 allele, and the other group harbored 36 RILs with the e1nl allele. We used the Map Manager Program QTXb20 (http://mapmgr.roswellpark.org/mapmgr.html) with the Kosambi function and with a criterion of 0.001 probability to determine the marker order and distance for the 96 RIL

groups, the 60 RIL group with the E1 allele and the 36 RIL group with the e1nl allele. Then, we used Mapchart 2.1 to determine the linkage groups (Voorrips 2002). Two models were used to detect QTLs: multiple-QTL model (MQM) were implemented using MapQTL 5.0 (Van Ooijen 2004) and mixed model-based composite interval mapping (MCIM) implemented by QTLNetwork 2.1 (Yang et al. 2008). MCIM was also used to analyze the interactions between the QTLs and the environments. For the MQM, we first used a LOD score of 2.0 as a minimum to declare the presence of a QTL in a particular genomic region. Secondly, we also performed permutation with a total of 1 000 permutations at a probability of 0.05 to identify the genomic threshold of LOD. QTLs, which LOD score exceeded the genome wide LOD, were declared as genomic major QTL in MQM mapping, and the other QTLs were identified as putative small-effect QTL. For MCIM, the walking speed was 1 cM, the candidate interval was 0.05, and 1 000 permutation times was applied to calculate the critical significant thresholds.

Acknowledgements We thank Dr. Xu Yunfeng, Chinese Academy of Sciences for instructing us in the use of the QTLNetwork software. We also appreciate Miss Kong Lingli and Mrs Liu Yafeng, Chinese Academy of Sciences, for the flowering time observations. This work was partially supported by the National Natural Science Foundation of China (31430065, 31571686, 31201222 and 31371643), the Open Foundation of the Key Laboratory of Soybean Molecular Design Breeding, Chinese Academy of Sciences, the “Hundred Talents” Program of the Chinese Academy of Sciences, the Strategic Action Plan for Science and Technology Innovation of the Chinese Academy of Sciences (XDA08030108), the Natural Science Foundation of Heilongjiang Province, China (ZD201001, JC201313), the Research and Development of Applied Technology Project, Harbin, China (2014RFQYJ055), the Scientific Research Foundation for Returned Chinese Scholars of Heilongjiang Province, China (LC201417), and the Science Foundation for Creative Research Talents of Harbin Science and Technology Bureau, China (2014RFQYJ046).

References Bachlava E, Dewey R E, Burton J W, Cardinal A J. 2009. Mapping and comparison of quantitative trait loci for oleic acid seed content in two segregating soybean populations. Crop Science, 49, 433–442. Bernard R. 1971. Two major genes for time of flowering and maturity in soybeans. Crop Science, 11, 242–244. Bonato E R, Vello N A. 1999. E6, a dominant gene conditioning early flowering and maturity in soybeans. Genetics and

LU Si-jia et al. Journal of Integrative Agriculture 2016, 15(1): 42–49

Molecular Biology, 22, 229–232. Buzzell R. 1971. Inheritance of a soybean flowering response to fluorescent-daylength conditions. Canadian Journal of Genetics and Cytology, 13, 703–707. Buzzell R, Voldeng H. 1980. Inheritance of insensitivity to long daylength. Soybean Genetics Newsletter, 7, 26–29. Cober E R, Morrison M J. 2010. Regulation of seed yield and agronomic characters by photoperiod sensitivity and growth habit genes in soybean. Theoretical and Applied Genetics, 120, 1005–1012. Cober E R, Voldeng H D. 2001. A new soybean maturity and photoperiod-sensitivity locus linked to E1 and T. Crop Science, 41, 698–701. Fehr W, Caviness C, Burmood D, Pennington J. 1971. Stage of development descriptions for soybeans, Glycine max (L.) Merrill. Crop Science, 11, 929–931. Gai J Y, Wang Y J, Wu X L, Chen S Y. 2007. A comparative study on segregation analysis and QTL mapping of quantitative traits in plants-with a case in soybean. Frontiers of Agriculture in China, 1, 1–7. Kong F J, Liu B H, Xia Z J, Sato S, Kim B M, Watanabe S, Yamada T, Tabata S, Kanazawa A, Harada K, Abe J. 2010. Two coordinately regulated homologs of FLOWERING LOCUS T are involved in the control of photoperiodic flowering in soybean. Plant Physiology, 154, 1220–1231. Kong F J, Nan H Y, Cao D, Li Y, Wu F F, Wang J L, Lu S J, Yuan X H, Cober E R, Abe J, Liu B H. 2014. A new dominant gene E9 conditions early flowering and maturity in soybean. Crop Science, 54, 1–7. Liu B H, Fujita T, Yan Z H, Sakamoto S, Xu D H, Abe J. 2007. QTL mapping of domestication-related traits in soybean (Glycine max). Annals of Botany, 100, 1027–1038. Liu B H, Kanazawa A, Matsumura H, Takahashi R, Harada K, Abe J. 2008. Genetic redundancy in soybean photoresponses associated with duplication of the phytochrome A gene. Genetics, 180, 995–1007. Liu B H, Watanabe S, Uchiyama T, Kong F J, Kanazawa A, Xia Z J, Nagamatsu A, Arai M, Yamada T, Kitamura K, Masuta C, Harada K, Abe J. 2010. The soybean stem growth habit gene Dt1 is an ortholog of Arabidopsis TERMINAL FLOWER 1. Plant Physiology, 153, 198–210. McBlain B, Bernard R. 1987. A new gene affecting the time of flowering and maturity in soybeans. Journal of Heredity, 78, 160–162. Nan H Y, Cao D, Zhang D Y, Li Y, Lu S J, Tang L L, Yuan X H, Liu B H, Kong F J. 2014. GmFT2a and GmFT5a redundantly and differentially regulate flowering through interaction with and upregulation of the bZIP transcription factor GmFDL19 in soybean. PLOS ONE, 9, e97669. Qi X P, Li M W, Xie M, Liu X, Ni M, Shao G H, Song C, Yim A K

49

Y, Tao Y, Wong F L, Isobe S, Wong C F, Wong K S, Xu C, Li C, Wang Y, Guan R, Sun F, Fan G, Xiao Z, et al. 2014. Identification of a novel salt tolerance gene in wild soybean by whole-genome sequencing. Nature Communications, 5, 4340. Ray J D, Hinson K, Mankono J, Malo M F. 1995. Genetic control of a long-juvenile trait in soybean. Crop Science, 35, 1001–1006. Song Q J, Marek L F, Shoemaker R C, Lark K G, Concibido V C, Delannay X, Specht J E, Cregan P B. 2004. A new integrated genetic linkage map of the soybean. Theoretical and Applied Genetics, 109, 122–128. Tasma I M, Lorenzen L L, Green D E, Shoemaker R C. 2001. Mapping genetic loci for flowering time, maturity, and photoperiod insensitivity in soybean. Molecular Breeding, 8, 25–35. Van Ooijen J. 2004. MapQTL®5. Software for the mapping of quantitative trait loci in experimental populations. Kyazma BV, Wageningen. Voorrips R. 2002. MapChart: Software for the graphical presentation of linkage maps and QTLs. Journal of Heredity, 93, 77–78. Watanabe S, Hideshima R, Xia Z, Tsubokura Y, Sato S, Nakamoto Y, Yamanaka N, Takahashi R, Ishimoto M, Anai T, Tabata S, Harada K. 2009. Map-based cloning of the gene associated with the soybean maturity locus E3. Genetics, 182, 1251–1262. Watanabe S, Xia Z, Hideshima R, Tsubokura Y, Sato S, Yamanaka N, Takahashi R, Anai T, Tabata Satoshi, Kitamura K, Harada K. 2011. A map-based cloning strategy employing a residual heterozygous line reveals that the GIGANTEA gene is involved in soybean maturity and flowering. Genetics, 188, 395–407. Xia Z J, Watanabe S, Yamada T, Tsubokura Y, Nakashima H, Zhai H, Anai T, Sato S, Yamazaki T, Lü S X, Wu H, Tabata S, Harada K. 2012. Positional cloning and characterization reveal the molecular basis for soybean maturity locus E1 that regulates photoperiodic flowering. Proceedings of the National Academy of Sciences of the United States of America, 109, E2155-E2164. Yamanaka N, Ninomiya S, Hoshi M, Tsubokura Y, Yano M, Nagamura Y, Sasaki T, Harada K. 2001. An informative linkage map of soybean reveals QTLs for flowering time, leaflet morphology and regions of segregation distortion. DNA Research, 8, 61–72. Yang J, Hu C C, Hu H, Yu R D, Xia Z, Ye X Z, Zhu J. 2008. QTLNetwork: Mapping and visualizing genetic architecture of complex traits in experimental populations. Bioinformatics, 24, 721–723. (Managing editor WANG Ning)