Dissecting Four Correlated Growth Period Traits Using a Genome-Wide Association Study Approach in Soybean

doi:10.21203/rs.3.rs-188488/v1

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Dissecting Four Correlated Growth Period Traits Using a Genome-Wide Association Study Approach in Soybean

https://doi.org/10.21203/rs.3.rs-188488/v1

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published 15 Nov, 2021

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Soybean (Glycine max (L.) Merri) is a short-day, photoperiod-sensitive crop. The vegetative growth period (V), reproductive growth period (R), whole growth period (V + R) and the ratio of growth period structure (R/V) are photoperiod traits critical to adaptability and yield in soybean. To thoroughly dissect the genetic basis of these growth period traits and their correlations, a panel of 277 soybean accessions were genotyped with a single nucleotide polymorphisms (SNPs) chip and phenotyped at three field environments over three years. A genome-wide association study found 29 SNPs on fifteen chromosomes with 33 significant marker-trait associations, eleven, eight, four and ten for V, R, V + R and R/V, respectively. Two SNPs, Map-6077 and − 6502, showed pleiotropy in three and two traits, respectively. Map-6077, associated with V, R, and V + R, was a synonymous SNP of the GmMFT, a member of PEBP family that has been reported to inhibit seed germination and regulate oil content. These results provide candidate SNP markers and genes for further functional experiments to advance molecular marker-assisted breeding.

Plant Molecular Biology and Genetics

Genome-wide association study

Soybean

SNPs

Growth period traits

Photoperiod

As a typical short-day crop, soybean (Glycine max (L.) Merr.) is sensitive to changes in photoperiod, which varies with latitude and planting season (Liu et al. 2017). This sensitivity limits the introduction, cultivation and large-scale planting of elite soybean cultivars. Growth period traits are influenced by the response of the plant to the relative length of day and night. This is important not only in the context of extending the geographical range of cultivation of the crop but also to increase yield.

Although the whole growth period (V + R) includes vegetative growth (V) and reproductive growth (R), they are relatively independent developmental stages (Fehr and Caviness 1977) and are used as evaluation traits (Yang et al. 1994; Zhang et al. 2015). R is related to yield, quality and resistance to stresses from environment, and several genetic loci have been associated with R besides V and V + R (Cheng et al. 2011). Time to flower (R1), maturity (R8), and reproductive period (RP) have also been defined as three separate traits (Kong et al. 2018). Genetic loci differentiating DTF, days to flowering, (V) and DTM, days to maturity, (V + R) as well as DTFM, days from flowering to maturity, (R) have been found indicating that there may be major and minor-effect loci underlying V and V + R (Zhang et al. 2015). The ratio of R and V (R/V) is used as an index of growth period structure and the relationships between two stages (Jian 1990; Wang et al. 2015a). These traits are all photoperiod and important to the adaptability and yield of soybean (Garner and Allard 1927; Han et al. 2006). Pleiotropy is due to a QTP (SNP or other polymorphism affecting a quantitative trait) affecting two or more traits (Chen and Lubberstedt 2010). For example, a few genes have been found to associate with multiple agronomic traits in crops, for example, Ghd7 regulated heading date and yield potential in rice (Xue et al. 2008).

Growth period traits of soybean are complex quantitative traits, controlled by multiple genes, their interactions (Chen and Lubberstedt 2010), and the environment. Two major approaches, linkage and association analysis, have been used to dissect the genetic basis of complex traits (Purcell et al. 2003). With the development of high-throughput sequencing and SNP genotyping techniques, the genome-wide association study (GWAS) has become an efficient method to detect variants underlying complex traits. GWAS was used to identify ZmVPP1 for drought tolerance in maize seedlings (Wang et al. 2016) and OsSPL13 which controls grain size in cultivated rice (Si et al. 2016). Previously, GWAS on growth period traits in soybean was conducted using 113 wild accessions (Hu et al. 2014), 139 short-season accessions (Sonah et al. 2015), 309 early mature accessions (Zhang et al. 2015), 91 cultivars (Mao et al. 2017), and 421 landraces and 388 cultivars (Fang et al. 2017) planted at 32.20 to 45.57°N. However, growth period traits are sensitive to different environments and the genetic characteristics of the target population. This study selected accessions from the mini core collections of the Chinese cultivated soybean, which represent the 94.5 % phenotypic diversity and 63.5 % genetic diversity (Qiu et al. 2009; Qiu et al. 2013), that were planted in three locations with latitudes ranging from 18.25 to 40.22°N.

Previously, 362 bi-parental QTLs (quantitative trait loci) and 242 GWAS QTLs involved in growth period traits were identified by linkage and association analysis (summarized at: http://soybase.org). Eight major genes [E1 (Glyma.06g207800) (Bernard 1971; Xia et al. 2012), E2 (Glyma.10g221500, GmGIa) (Bernard 1971; Watanabe et al. 2011), E3 (Glyma.19g224200, GmPhyA3) (Buzzell 1971; Watanabe et al. 2009), E4 (Glyma.20g090000, GmPhyA2) (Buzzell and Voldeng 1980; Matsumura et al. 2008; Saindon et al. 1989a; Saindon et al. 1989b), E9 (Glyma.16g150700, GmFT2a) (Kong et al. 2014; Zhao et al. 2016), E10 (Glyma.08g363100, GmFT4) (Samanfar et al. 2017), J (Glyma.04g050200, GmELF3) (Lu et al. 2017; Ray et al. 1995; Yue et al. 2017), qDTF-J (Glyma.16g044100, GmFT5a) (Takeshima et al. 2016) have been reported to contribute to time of flowering and/or maturity through linkage analysis. We expect that there are many more loci or genes yet to be discovered.

Here, we conducted a genome-wide study of vegetative growth period (V), reproductive growth period (R), whole growth period (V + R), and the ratio of growth period structure (R/V) separately using 277 soybean accessions and 1161 SNPs, which found 32 significant marker-trait associations (MTAs). This study provides a better understanding of the underlying genetic basis of the growth period traits and provides functional SNPs as tools for molecular soybean breeding programs aimed at improving the adaptation and yield of soybean to each latitude.

Plant materials

The germplasm panel was comprised of 277 cultivated accessions with 192 landraces and 85 improved cultivars collected worldwide. Half (135) are members of the Chinese soybean mini-core collections (Qiu et al. 2009; Qiu et al. 2013) (Table S1). The remaining 142 originated in China (120), United States (11), Canada (1), Poland (1), North Korea (1), Denmark (1), Germany (1), Japan (1), India (1), Russia (1), France (1), Italy (1) and Romania (1) (Fig. 1; Table S1). All 277 accessions were collected between 18.15°N and 55.45°N (Fig. 1).

Measurement of phenotypes

The 277 accessions were planted in a randomized complete block design with three replications at three field environments (BJSp, Beijing Spring; BJSu, Beijing Summer; SY, Sanya) over two years 2011–2012 (BJSp, BJSu) or three years 2010-12 (SY). For BJSu, BJSp, and SY, the average photoperiod (day length) decreased gradually due to planting in the field without any treatment. The field environments were defined as combinations of each location and planting season. BJSp and BJSu are from the Changping and Shunyi district of Beijing (40.22°N, 116.2°E; 40.13 °N, 116.65°E) with two different planting seasons: Spring (planted in mid-May) and Summer (planted in mid-June). SY is from Sanya, Hainan Province (18.25°N, 109.5°E), nearly 22° south of Beijing, planted in early December. At the Beijing site, each accession was planted in a 1.5 meters long row with an interplant distance of ten centimeters (cm) and an inter-row distance of 55 cm. At Sanya, the row length was uniformly one meter, the interplant distance was six cm, and the inter-row distance was 45 cm. The following traits were scored: date of emergence of the cotyledons as VE, the date when 50% of the plants within the row first flowered as R1 and the date when 50% of the plants within the row had reached maturity (95% of pods maturity for one plant) as R8 (Fehr et al. 1971; Qiu et al. 2006). Four traits were analyzed: V = R1-VE, R = R8–R1, V + R = R8-VE, R/V = (R8 –R1) / (R1-VE). V and R represent the vegetative and reproductive stages as described previously (Fehr et al. 1971). Measurements were averaged over the three replicates, and subsequently over the three years in each environment, using a best linear unbiased prediction (BLUP) (Henderson 1975).

Phenotype data analysis and Heritability estimation

Plants that did not reach maturity were not included and treated as missing data. The field data was used to get V, R, V + R and R/V phenotype data, and extreme values that due to artificial error or plant died from virus or other reasons were filtered. BLUP values for each independent environment used in GWAS, phenotypic description analysis, and trait correlations analysis were estimated through the R environment package lem4 package (Bates et al. 2014) to evaluate the BLUPs with the linear mixed model, lmer(y ~ (1 | Accession) + (1 |Year) + (1|Accession:Year)). Descriptive analysis of the phenotypic data was performed using R software (https://www.r-project.org). The broad-sense heritability for each trait was evaluated by using QTL IciMapping V4.1 (Meng et al. 2015) using the formula H² = V_G / (V_G + V_E), where V_G and V_E represent estimates of the genetic and environmental variance, respectively.

SNP genotyping and filtering

A total of 1236 SNPs were used for genotyping 277 cultivated soybean accession. These SNPs included 1150 SNPs from a reported SNP genotyping array (Li et al. 2015) and an additional panel of 86 SNPs obtained by Sanger sequencing that distributed among chromosomes. The methods for SNP genotyping of 1150 SNPs and 86 SNPs were based on the Illumina BeadArray platform (Illumina Inc., San Diego, CA, USA) and Sequenom MassARRAY platform (Gabriel et al. 2009). After obtained genotype data, we filtered 75 SNPs with missing rate (> 10% of samples), or apparent heterozygosity (> 20%), or low minor allele frequency (< 5%). The final data set consisted of 1161 SNPs (Table S2).

Population structure analysis

TASSEL 5.0 software was used to calculate the Minor Allele Frequency, Proportion Missing, and Proportion Heterozygous for each SNP locus (Bradbury et al. 2007). A subset of 800 SNPs distributed across twenty chromosomes were selected to determine population structure through the software package STRUCTURE 2.1 (Falush et al. 2007) using Bayesian Markov Chain Monte Carlo approach. The admixture and independent allele frequency model were adopted, with cluster number (K) from one to ten. Twenty runs performed with a 100,000 burn-in length and 100,000 iterations for each value of K. The most likely number of sub-clusters was determined by the derivative of log-likelihood (∆K) because estimated log-likelihood values increased as the values of K increased indicating no clear genetic structure (Li et al. 2010). The K value was obtained through the online Structure Harvester (Earl and vonHoldt 2011).

Genome-wide association study

GWAS was conducted using a developed model selection algorithm, FarmCPU (the fixed and random model circulating probability unification) in R package (Liu et al. 2016). The confounding problem between covariates and test SNP is considered in the algorithm by using both a Fixed Effect Model (FEM) and a Random Effect Model (REM). The first three calculated principal components were used as covariates. A sum of 1,161 SNPs, of which MAF > 5% and missing rate < 0.1, was used to perform GWAS. The parameter p.threshold = 0.05/number of markers with 1,000 permutations was used to calculate p-value threshold. The relationship with the genotypes was broken by the phenotypes. A vector of minimum p-value was produced the 95% quantile value of the vector is recommended. For the significantly associated MTAs, the allelic effects were determined by representing phenotype data carrying different alleles as box-plots and were confirmed through the Student’s t-test.

Phenotypic Evaluation

A total of 277 soybean accessions from worldwide (Fig. 1a) were phenotyped at three environments, including Sanya (SY), Beijing Spring (BJSp) and Beijing Summer (BJSu). BLUP values among two or three years were used in the following analysis as the variance of year was not significant. The descriptive statistics of V, R, V + R and R/V, revealed wide variation both within and between the three environments, providing a diverse phenotypic dataset with which to dissect the complex genetic architecture of the target traits. Among these three environments, SY had the smallest variation range from 1.29 (V) to 1.57 (R/V) while BJSp had the largest range from 1.99 (R) to 4.65 (V). For V, the mean value in BJSp was 0.25- and 1.08-fold higher than BJSu and SY, and the differences decreased for R and V + R as the fold differences were 0.19 and 0.38, 0.19 and 0.53, respectively. In contrast to R/V, the mean value in BJSp was the same as BJSu but 0.21-fold lower than SY. A different trend was observed for V, R, and V + R where they decreased from BJSp to BJSu, then to SY, while increasing for R/V (Table 1). Broad sense heritability estimates (h²) were calculated independently for each environment and varied from 0.8 to 0.98, 0.72 to 0.79, 0.68 to 0.95 and 0.77 to 0.93 for V, R, V + R and R/V, respectively. The higher h² was consistent with a previous study (Liu et al. 2017) indicating a predominant genetic underpinning for each trait (Table 1).

Table 1

Descriptive analysis of the phenotype data of 277 soybean accessions.
Trait	Location	Rang (days)	Variation fold	Mean ± SD (days)	CV (%)	Heritability
V	BJSp	26.2 ~ 121.8	4.65	56.6 ± 22.8	40.3	0.98
	BJSu	22.7 ~ 96.5	4.26	45.3 ± 16.0	35.3	0.97
	SY	24.6 ~ 31.9	1.29	27.2 ± 1.4	5	0.8
R	BJSp	52.5 ~ 155.9	2.97	76.9 ± 14.4	18.7	0.79
	BJSu	54.1 ~ 75.9	1.4	64.4 ± 4.2	6.5	0.78
	SY	45.5 ~ 62.7	1.38	55.6 ± 3.1	5.5	0.72
V + R	BJSp	80.6 ~ 160.7	1.99	126.3 ± 16.4	13	0.95
	BJSu	79.3 ~ 130.4	1.64	106.5 ± 10.4	9.7	0.95
	SY	71.6 ~ 88.3	1.23	82.8 ± 2.6	3.2	0.68
R/V	BJSp	0.74 ~ 3	4.05	1.7 ± 0.5	29.7	0.9
	BJSu	0.9 ~ 2.7	2.91	1.7 ± 0.4	24.9	0.93
	SY	1.6 ~ 2.5	1.57	2.1 ± 0.2	8.6	0.77
SD, Standard deviation. CV, the coefficient of variation. V, vegetative growth period; R, reproductive growth period; R/V, the value of growth period structure; V + R, whole growth period. BJSp, Beijing Spring; BJSu, Beijing Summer; SY, Sanya.

The correlation coefficients of the four traits were calculated for the three environments (BJSp, BJSu, and SY corresponding to Figs. 2a, b, c). Similar correlation patterns were observed for BJSp and BJSu, significant positive correlations between R and V + R, and R and R/V, significant negative correlations between V and R, V and R/V, and V + R and R/V, and little between V and V + R. However, SY differed in that V and R had a significant negative correlation, r = -0.47, and a significant positive correlation between R and V + R with r = 0.92, while other traits had no significant correlations. The high correlation coefficient between R and V + R in Sanya suggested that the length of V + R depended on R. The correlation coefficients between BJSp, BJSu, and SY for four traits were shown in Fig. S1. BJSp and BJSu had high positive correlation coefficients (r = 0.63 ~ 0.94) between them for the four traits. But SY showed lower positive coefficients with BJSp and BJSu for V and R (r = 0.38 ~ 0.45) and no relationship for V + R and R/V. In all, the Sanya environment exhibited a large difference from BJSp and BJSu while BJSp and BJSu had similar trends, which resulted from the very different photoperiodic environments (Beijing, 40.22°N; Sanya, 18.25°N).

Population structure

A total of 1,161 SNPs remaining after filtering (Table S2) were used in the ensuing analyses. Population structure analysis was conducted with STRUCTURE (Evanno et al. 2005; Kaeuffer et al. 2007) using 800 evenly distributed SNPs on the twenty chromosomes. The results indicated revealed three clusters, NR (Northern Region), CR (Central Region), and SR (Southern Region), concordant with a shared allele-based neighbor-joining tree and PCA (principal components analysis) (Figs. 1b-d). Subpopulation SR was the largest group with 105 accessions, containing 76 accessions from south China; subpopulation NR consisted of 90 accessions, dominated by the accessions of 63 from north and northeast and 16 from outside China; and subpopulation CR had 34 accessions, 25 of which were from North and Huanghuai (Table S1). The derivative clusters of these soybean accessions referred to Q value from population analysis consistent with the geographical origin (Fig. 1; Table S1).

Marker-trait association analysis for V, R, V + R and R/V

We used the fixed and random model through FarmCPU to conduct the GWAS and added the first three PCs in this model to avoid false positives from non-genetic effects associated with population structure (NR, CR, and SR). The GWAS revealed 29 SNP markers representing 33 significant MTAs above the threshold p < 6.70e-05 for V (10), R (8), V + R (4) and R/V (10) distributed across fourteen soybean chromosomes except for Gm01, 02, 04, 11, 12, and 19 (Fig. 3, Table 2). Two SNPs were associated with more than one trait and/or identified in two environments, for example Map-6502 associated with V (BJSp, p = 3.80E-07; BJSu, p = 9.05E-07) and R/V (BJSp, p = 5.46E-05). Most, 28 of the 29 SNPs were located in coding regions of annotated genes. The genetic effects of 27 SNPs were also determined: non-synonymous polymorphisms (17), synonymous polymorphisms (9) and a stop-gain codon (Table 2). Twelve of the 28 SNPs were close (300 kb, kilobase) or within (Map-6077) of reported flowering-related genes, 12 were in known QTLs related to growth period traits, and six matched both conditions (Table 2).

Table 2

List of information of the SNP significantly associated with four growth period traits.
Marker name	Chr: position (bp)	MTAs	p Value	Wm82.a2.v1
V
Map-6173	7: 4378553	V_BJSp	5.53E-05	Glyma.07g050700	CDS: A<->S	QTL03; 23; 32; 37; 40	Glyma.07g049400 (GmPRR; 172; Watanabe et al., 2012)
Map-7132	7: 7749646	V_BJSp	4.83E-08	Glyma.07g084000	CDS	QTL01;30
Map-1899	10: 44738812	V_BJSp	5.17E-05	Glyma.10g215300	CDS: A<->S
Map-6384	10: 49716656	V_BJSp	1.16E-05	Glyma.10g274300	CDS		Glyma.10g274300 (GmCOL7a; within gene; Sedivy et al., 2017)
Map-6502	14: 10917924	V_BJSp; V_BJSu	3.80E-07; 9.05E-07	Glyma.14g105900	CDS: V<->A
Map-3175	16: 35908252	V_BJSp	2.24E-05	Glyma.16g197900	CDS: W<->X	QTL58	Glyma.16g196300 (GmTFL; 129; Suo et al., 2016)
Map-6646	18: 86492	V_BJSu	2.24E-05	Glyma.18g000500	CDS: S<->T
Map-6708	18: 2813504	V_BJSp; V_BJSu	1.37E-05; 5.41E-05	Glyma.18g036100	CDS: T<->N
Map-6743	18: 9082263	V_BJSp	2.71E-05	Glyma.18g091100	CDS: R<->S	QTL39
R
Map-6051	3: 42449846	R_BJSu	5.27E-06	Glyma.03g221400	CDS: S<->R		Glyma.03g219800 (130; Jung et al., 2012);
BARC-013665-01226	5: 33789326	R_SY	5.09E-05	Glyma.05g143900	CDS
Map-6077	5: 41853190	R_SY	5.17E-05	Glyma.05g244100	CDS		Glyma.05g244100 (GmMFT; within gene; Li et al., 2014)
Map-6214	7: 8323908	R_BJSu	6.14E-06	Glyma.07g089200	CDS: S<->N	QTL01; 28; 30	Glyma.07g089000 (GmVRN5; 19; Zeng et al., 2018); Glyma.07g090100 (-110; Jung et al., 2012)
Map-6285	8: 10308599	R_SY	4.76E-11	Glyma.08g134500	CDS: L<->P		Glyma.08g141000 (-69; Jung et al., 2012)
Map-6303	8: 11089154	R_SY	2.44E-05	Glyma.08g145800	CDS: H<->Y
Map-6383	10: 45026735	R_BJSu	1.58E-07	Glyma.10g218400	CDS	QTL17; 25; 26; 43	Glyma.10g221500 (E2/GmGIa; 268; Watanabe et al., 2011)
Map-3569	18: 25723766	R_BJSu	8.59E-08		Intergenic	QTL08; 20; 50; 55
V + R
Map-6312	8: 11647122	V + R_SY	2.16E-07	Glyma.08g151600	CDS: T<->A
Map-6541	15: 13587270	V + R_SY	1.79E-05	Glyma.15g160300	CDS	QTL09; 46; 51	Glyma15g17480 (GmZTL3; 262; Zeng et al., 2018)
Map-6733	18: 4240398	V + R_SY	5.00E-05	Glyma.18g049300	CDS: S<->N
Map-6803	20: 25300503	V + R_SY	3.29E-06	Glyma.20G071000	CDS	QTL11; 14; 18; 33; 34; 42; 53
R/V
Map-6143	6: 15873865	R/V_BJSu	2.02E-05	Glyma.06g183900	CDS	QTL49
Map-6146	6: 22813330	R/V_BJSu; R/V_BJSp	2.79E-09; 5.09E-06	Glyma.06g217000	CDS	QTL04; 06; 07; 13; 15; 16; 19; 27; 29; 35; 36; 38; 41; 47; 48; 52; 54; 56; 57
Map-6267	8: 9438024	R/V_BJSp	4.12E-06	Glyma.08g122400	CDS
Map-6293	8: 10706843	R/V_BJSp	6.20E-05	Glyma.08g139900	CDS: E<->Q		Glyma.08g120600 (GmMADS; 129; Shu et al., 2013)
Map-1722	9: 47977625	R/V_BJSu	2.49E-07	Glyma.09G261900	CDS: D<->G
Map-6413	13: 34928184	R/V_BJSp	1.08E-05	Glyma.13g239000	CDS: S<->R		Glyma.13g236800 (199; Jung et al., 2012); Glyma.13g237400 (164; Jung et al., 2012)
Map-6502	14: 10917924	R/V_BJSp	5.46E-05	Glyma.14g105900	CDS: V<->A
Map-7187	15: 16597249	R/V_BJSp	3.90E-05	Glyma.15g176000	CDS: Y<->F	QTL46; 51	Glyma.15g176300 (GmELF4-like; -31; Marcolino-Gomes et al., 2017)
Map-6640	17: 19289999	R/V_BJSp	7.53E-05	Glyma.17g178300	CDS: H<->P	QTL21; 44; 45
Chr, Chromsome. MTAs, marker trait associations. V, vegetative growth period; R, reproductive growth period; R/V, the ratio of growth period structure; V + R, whole growth period. BJSp, Beijing Spring; BJSu, Beijing Summer; SY, Sanya.
[a] Based on the QTL data on SoyBase (https://soybase.org).
QTL number and name: QTL01, First flower 2–2; QTL02, First flower 5 − 3; QTL03, First flower 6 − 1; QTL04, First flower 8 − 1; QTL05, First flower 8 − 3; QTL06, First flower 9 − 1; QTL07, First flower 10 − 1; QTL08, First flower 10 − 2; QTL09, First flower 12 − 3; QTL10, First flower 13 − 9; QTL11, First flower 16 − 3; QTL12, First flower 16 − 4; QTL13, First flower 20 − 1; QTL14, First flower 20 − 3; QTL15, First flower 22 − 2; QTL16, First flower 23 − 1; QTL17, First flower 24 − 4; QTL18, First flower 25 − 3; QTL19, Vegetative period 1–1; QTL20, Reproductive period 1–3; QTL21, Reproductive period 1–7; QTL22, Reproductive stage length 3–3; QTL23, Reproductive stage length 4 − 3; QTL24, Reproductive stage length 7 − 4; QTL25, Reproductive stage length 8 − 2; QTL26, Reproductive stage length 9 − 2; QTL27, Pod, beginning 1–1; QTL28, SeedFill 2–2; QTL29, Pod maturity, beginning 1–1; QTL30, Pod maturity 2 − 1; QTL31, Pod maturity 9 − 3; QTL32, Pod maturity 10 − 2; QTL33, Pod maturity 11 − 1; QTL34, Pod maturity 12 − 1; QTL35, Pod maturity 13 − 4; QTL36, Pod maturity 14 − 3; QTL37, Pod maturity 14 − 4; QTL38, Pod maturity 15 − 3; QTL39, Pod maturity 16 − 2; QTL40, Pod maturity 18 − 4; QTL41, Pod maturity 21 − 1; QTL42, Pod maturity 24 − 5; QTL43, Pod maturity 26 − 3; QTL44, Pod maturity 27 − 4; QTL45, Pod maturity 29 − 4; QTL46, Pod maturity 31 − 2; QTL47, Pod maturity 32 − 2; QTL48, Pod maturity 33 − 2; QTL49, Pod maturity 34 − 1; QTL50, Pod maturity 34 − 5; QTL51, Pod maturity 37 − 2; QTL52, Total growth duration 1–4; QTL53, cqR8Full maturity-001; QTL54, Photoperiod insensitivity 1–1; QTL55, Photoperiod insensitivity 2–2; QTL56, Photoperiod insensitivity 3 − 1; QTL57, Photoperiod insensitivity 4 − 1; QTL58, Photoperiod insensitivity 5 − 4

Vegetative growth period (V)

For V (Fig. 3), 11 significant MTAs were identified on chromosomes Gm07 (2), 10 (2), 14 (2), 16, and 18 (4). Map-6173, was covered by five QTL of growth period traits including V (Suo et al. 2016; Wang et al. 2015b), was located at 172kb downstream of Glyma.07g049400, a homolog of APRR5 (pseudo-response regulator 5) belonging to the APRR1/TOC1 quintet implicated in circadian rhythm, control of flowering time, and early photomorphogenesis in Arabidopsis thaliana (Watanabe et al. 2012; Yamamoto et al. 2003). Map-6384 is a synonymous polymorphism within a B-box (BBX) protein gene GmCOL7a (Glyma.10g274300) (Sedivy et al. 2017). BBX proteins are key factors in regulatory networks controlling growth and developmental processes that included seedling photomorphogenesis, photoperiodic regulation of flowering (Gangappa and Botto 2014). Map-3175 was located at 129 kb downstream of Glyma.16g196300 (GmTFL), a member of PEBP (phosphatidylethanolamine binding protein) family protein, which participates in the photoperiod pathway to regulate flowering time as a floral repressor in soybean (Liu et al. 2010).

Reproductive growth period (R)

For R, we found eight significant MTAs on chromosomes Gm03, 05 (2), 07, 08 (2), 10, and 18. Map-6214 was located at 19 kb downstream of Glyma.07g089000, which was homologous to the A. thaliana VRN5 gene (At3g24440) (Suo et al. 2016) involved in both vernalization (Sung et al. 2006) and photoperiod pathways (Greb et al. 2007), and its expression is down-regulated significantly in the V4 stage in the photoperiod pathway in soybean (Zeng et al. 2018). The peak SNP, Map-6285, was associated with R by the most significant association (p = 4.76E-11) in BJSu. Glyma.08g141000, located at 69 kb upstream of the peak SNP Map-6285, is a homolog of the A. thaliana gene AT4G14540, the Nuclear Factor Y subunits NF-YB3, a candidate flowering gene in long-day photoperiod under the influence of E3 in soybean (Haider 2014). Map-6383 was located at 268 kb upstream of Glyma.10g221500 (E2/GmGIa), which was validated by functional experiments to regulate soybean maturity and flowering time (Tsubokura et al. 2014; Watanabe et al. 2012; Watanabe et al. 2011) and covered by four QTLs of growth period traits including two of R.

Whole growth period (V + R)

For V + R, we found four significant MTAs on chromosomes Gm08, 15, 18, and 20. Map-6541 was covered by three QTLs related to growth period traits (two of V + R). Glyma.15g162300 (GmZTL3) was located at 262 kb upstream of Map-6541 and has been proposed to be a photoreceptor and play a role in the control of flowering time in soybean (Jung et al. 2012; Zhou et al. 2016).

The ratio of growth period structure (R/V)

For R/V, we found 10 significant MTAs on chromosomes Gm06 (3), 08 (2), 09, 13, 14, 15 and 17. Map-6267 was located at 129 kb downstream of Glyma.08g120600 (GmMADS), one of the MADS-box gene family, which plays an important role in soybean growth and floral development (Shu et al. 2013). Glyma.08g148200 was located at 212 kb upstream of Map-6293 (R/V). It was a homolog to the A. thaliana gene AT1G08970, Nuclear Factor Y subunits NF-YC9, and related to seed development and maturation in soybean. Glyma.15g176300, a homolog of ELF4-L3 (EARLY FLOWERING 4-like3), functioned as a signaling intermediate in circadian clock function, and photoperiod perception in A. thaliana (Marcolino-Gomes et al. 2017), was located at 31 kb downstream of Map-7187. The second significant SNP Map-6146 was found in BJSu (p = 2.79E-09) and BJSp (p = 5.09E-06). Although it was covered by 19 growth period QTLs including seven of V + R, seven of V, and three of Photoperiod insensitivity (Table 2), no candidate genes with growth period traits were found nearby.

Pleiotropy on V, R, V + R, and R/V in soybean

Two SNPs (Map-6502 and − 6077) showed pleiotropy on two or more traits of V, R, V + R, and R/V. Map-6502 was associated with two traits, V (BJSp, p = 3.80E-07; BJSu, p = 9.05E-07) and R/V (BJSp, p = 5.46E-05). Further significant analysis exhibited the significant difference between allele A and G in the defined cluster of SR for V and difference in the HR and SR for R/V but failed to calculate significance because the small number of statistics of 7 (allele A in HR) and 6 (allele G in SR) in Beijing Spring and 8 (allele A in HR) and 6 (allele G in SR) in Beijing Summer (Fig. S2). Map-6077 was related with R (SY, p = 5.17E-05), while it also appeared significant association with V (BJSu, p = 2.69E-03) and V + R (SY, p = 9.82E-04) where the p value below the threshold, and further confirmed by significant difference analysis (Fig. S3). The days significantly decreased for V and V + R while increased for R due to the favorable allele A of Map-6077 in BJSp and BJSu (Fig. S2). Map-6077 was a synonymous polymorphism within Glyma.05g244100, named as GmMFT in soybean, a member of the PEBP family that proposed to be a suppressor of seed germination (Li et al. 2014), also associated with seed oil content (Li et al. 2018; Zhang et al. 2018).

Soybean is a short-day crop and the timing of transition from vegetative to reproductive period is regulated by environmental cues. We explored the genetic basis for these developmental phases using an association approach of cultivated germplasm grown at different environments. It had been reported causal genes playing different roles on DPF (before flowering) and DFM (after flowering) in different planting seasons (Yang et al. 1994). Thus, we picked two locations (Beijing and Sanya) with a latitudinal difference of 21.97 degrees and two planting seasons (Spring and Summer) to form three diverse field environments (BJSp, BJSu, and SY). The major soybean loci (E1-8, and J) were reported to be related to both time of flowering and maturity (Watanabe et al. 2012) and were photoperiod-sensitive loci (Cober and Morrison 2010). Photoperiod effects on genes have been shown to be dependent on growth period stages, affecting E1 and E4 during V and R, respectively, and E3 during V + R (Wang et al. 2008).

The four traits, V, R, V + R, and R/V had relationship with each other. In BJSp and BJSu, a higher correlation was observed for V and V + R (r = 0.56, 55) as compared to R and V + R (r = 0.37, 0.39), indicating that V had greater impact on V + R than R. However, in Sanya where the average day length is seven hours, the correlation coefficient of R and V + R was large, r = 0.92, while V and V + R was negligible, r = 0.08, indicating that R had greater impact on V + R than V. The negative correlation between V and R (r = -0.47 ~ -0.07) indicating that a shorter V might imply a longer R, which may be beneficial to the accumulation of more dry matter in seeds while V, R, and V + R were all positively related with yield (Orf et al. 1999). SNP Map-6077 for V and R showed opposite effects, indicating an underlying genetic explanation for the negative correlation between V and R.

Several GWAS have been conducted to identify causal loci or genes. Mao et al using 91 cultivars through four photoperiod treatments (SD, LD, SP, and SU) taken at 39°54'N, 116°46' (Beijing China) identified six makers associated with flowering time close to (within 23 kb) or overlapping with other GWAS results (Fang et al. 2017; Hu et al. 2014; Mao et al. 2017; Sonah et al. 2015; Zhang et al. 2015). The first SSR marker was Satt564 identified in Mao’ result and Hu’s GWAS, which used 113 wild accessions planted in summer at Nanjing (32°12ʹN 118°37ʹE), and Nanyang (38°7ʹN 110°34ʹE), China (Hu et al. 2014). And the second SNP marker was ss715619034, 17.50 kb upstream of ss715619036 identified in Zhang’s GWAS, which collected phenotypic data of 309 early mature accessions in Aurora (44°17'N 96°41'W), Brookings (44°22'N, 96°47'W) and Watertown (44°54'N, 97°7'W), United States (Zhang et al. 2015). The other four SNP markers, Chr02_12316110, Chr05_40012587, Chr11_31718686, and Chr15_13133351, were 18.33, 22.11, 18.86, and 16.37 kb far from ss715581063, ss715591933, ss715610231, and ss715620540 that discovered in Fang’s research (Fang et al. 2017; Mao et al. 2017). Fang planted 421 landraces and 388 cultivars in six environments, including Beijing, (40°22ʹN, 116°23ʹE), Mudanjiang (44°58ʹN, 129°60ʹE), Zhoukou (33°62ʹN, 114°65ʹE), China, over three years. In total, there were six markers identified between Mao with Hu (one), with Zhang (one), and with Fang (four). The highly consistency in Mao and Fang’s GWAS could be caused by the similar photoperiodic environments.

To better understand the adaptation of soybean to more extreme latitudinal differences, we used GWAS to validate and find genes or loci during the growth period. We selected 277 accessions including 135 from Chinese mini core collections (Qiu et al. 2009; Qiu et al. 2013) and planted them in three locations with latitudes from 18.25 to 40.22°N. Three SNPs, Map-6077, Map-1899, and Map-6383, from our study were close to previously reported loci. Map-6077 related with V, R, and V + R in our study was 1.23 kb upstream of Chr05_41854422 associated with V + R (Fang et al. 2017) and 140.77 kb downstream of ss715599242 (Gm05_38636402, Glyma1.1) (Fang et al. 2017; Mao et al. 2017). Map-1899 and Map-6383, associated with V and R individually, were 27.87, and 1.58 kb downstream of Chr10_44710944 and Chr10_45025153 from Fang et al. (Fang et al. 2017). The reminding 26 SNP loci were not found in other reported GWAS studies.

The pleiotropy and trait correlations in this study are different growth stages, different from the general pleiotropy, such as the correlation between seed oil content and protein content. The last two traits (V + R and R/V) are derived from the first two and their relationships with other traits are determined by the variance and covariance of V and R and the calculation formula. Deciphering the correlations among V, R, V + R, and R/V could facilitate soybean improvement. A positive correlation could simultaneously increase the trait values, and negative correlation raised the trait value for one while decreasing other traits.

Map-6077 exhibited extreme significant difference between phenotypic variations carrying A and G alleles in all three environments (BJSp, BJSu, and SY) for V, R, and V + R (Fig. S2). Improved cultivars had a higher proportion of favorable allele A which had higher R, shorter V and V + R, as compared to landraces with 0.84 to 0.51, implying that the soybean accessions with early flowering (V) and maturity (R) and a comparatively longer reproductive period (R) were selected during improvement (Fig. S2). Map-6077 was a synonymous SNV in GmMFT, expressed at the stage of seed 10DAF and increased at seed 42DAF stage. It has been reported to be a negative regulator of seed germination (Li et al. 2014) and a potential causal candidate gene controlling oil content (Li et al. 2018) in soybean. There was another SNP in GmMFT associated with R8 full maturity as shown by GWAS (Fang et al. 2017). We proposed that GmMFT is a pleiotropic gene and Map-6077 is a pleiotropic SNP associated with V, R, and V + R. The analysis above implied that Map-6077 and − 6502 as the pleiotropic SNP could be used via molecular breeding for new modern cultivars.

We identified 28 SNPs related to V, R, V + R or R/V. Fourteen candidate genes were found, including the E2 gene. Although the number of SNPs (1161) used in this study was not large, the low LD decay rate in soybean and phenotypic variations in multiple environments made it feasible for us to discover loci and candidate genes. Because of the decrease in costs associated with obtaining high-density genome-wide marker sets and improvements of statistical methods for GWAS, future challenges lay in innovation of accurate, low-cost, high-throughput, and reproducible phenotyping, which together may launch a new wave of marker-assisted breeding for crops. It is straightforward for crop breeders to provide the pleiotropic SNP allele with desirable effects on two or more correlated traits. In this study, two SNPs (Map-6077 and − 6502) have pleiotropic effects the measured traits, but further functional experiments are needed. It was exciting to find that GmMFT, reported to inhibit the seed germination and related with seed oil content, was associated with V, R, and V + R in this study. Our critical mission in the future is to provide candidate genes and favorable alleles to produce better soybean cultivars more rapidly and efficiently.

GWAS

Genome-wide association study; SNP:Single nucleotide polymorphism; QTL:Quantitative trait loci

Acknowledgments

This research was supported by the National Key R & D Program for Crop Breeding (2016YFD0100304 and 2016YFD0100201), the Platform of National Crop Germplasm Resources of China (no. 2016-004 and 2017-004) and the Agricultural Science and Technology Innovation Program (ASTIP) of Chinese Academy of Agricultural Sciences.

Competing interests

The authors declare that they have no competing interests.

Availability of data and materials

The datasets analyzed during the current study are available in this article in Additional Table S1 and S2.

Code availability

Not applicable.

Authors' Contributions

Y-F.L., Y-H.L., S.J., and L.Q. conceived the study and jointly wrote the paper; Y-F.L. performed the GWAS, genotype data processing, population structure analysis and relative analyses. H.H. and Z.L. carried out the phenotypic evaluation; Y.M. analyzed the phenotypic data; All authors read and approved the final manuscript.

Ethics approval and consent to participate

Not applicable.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

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Dissecting Four Correlated Growth Period Traits Using a Genome-Wide Association Study Approach in Soybean

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Abstract

Figures

Introduction

Materials And Methods

Plant materials

Measurement of phenotypes

Phenotype data analysis and Heritability estimation

SNP genotyping and filtering

Population structure analysis

Genome-wide association study

Results

Phenotypic Evaluation

Population structure

Marker-trait association analysis for V, R, V + R and R/V

Vegetative growth period (V)

Reproductive growth period (R)

Whole growth period (V + R)

The ratio of growth period structure (R/V)

Pleiotropy on V, R, V + R, and R/V in soybean

Discussion

Conclusions

Abbreviations

Declarations

References

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