SSR- and SNP-related QTL underlying linolenic acidand other fatty acid contents in soybean seedsacross multiple environments

Dongwei Xie • Yingpeng Han • Yuhong Zeng •

Wei Chang • Weili Teng • Wenbin Li

Received: 2 June 2010 / Accepted: 13 June 2011 / Published online: 6 July 2011

� Springer Science+Business Media B.V. 2011

Abstract Soybean fatty acids (FAs) are major

sources of vegetable oil in the world. The FA

composition of soybean is associated with the quality

and nutritional value of its oil and food products. The

polyunsaturated FAs, particularly linolenic acid (LN),

are prone to oxidation by lipoxygenase isozymes and

negatively affect the flavor and shelf-life of soybean

products. The improvement of FA composition and

the increase of oxidative stability has been a major

goal of soybean breeding for decades. The objective

of the present study was to identify quantitative trait

loci (QTL) associated with the low LN and other FA

contents in six environments. One hundred and

twenty-five recombinant inbred lines (RILs) of

F5:7, F5:8 and F5:9 generations derived by the

single-seed-descent method from the cross of Hefeng

25 [LN 6.20%; linoleic acid (LI) 53.06%; oleic acid

(OL) 19.75%; palmitic acid (PA) 12.16%; stearic acid

(ST) 4.97%] and Dongnong L-5 (LN 2.53%; LI

59.30%; OL 24.24%; PA 10.0%; ST 3.99%) were

used in this study. A total of 112 simple sequence

repeat markers were used to construct a genetic

linkage map. Six QTL associated with LN content,

four QTL associated with LI content, four QTL

associated with OL content, four QTL associated

with PA content and one QTL associated with ST

content were identified, and mapped onto different

linkage groups (LGs). The QTL detected here

explained 2.53–37.30% of phenotypic variation for

individual FA in different environments or individual

FA means. Of them, the beneficial alleles of

QLNB2_1 (close to Satt726), QLNB2_2 (close to

Fad3a-4) and QLND1b_1 (close to Satt701), which

were associated with low LN content across various

environments and LN means, were derived from

Dongnong L-5. These three QTL might have great

potential value in marker-assisted selection for low

LN content in soybean seed.

Keywords Soybean � SSR marker � SNP marker �Marker-assisted selection � Linolenic acid

Introduction

Soybean, the world’s leading oilseed crop, contains

fatty acids (FA) in seed at approximately 20% of the

seed mass (Yaklich and Vinyard 2004). Human

Dongwei Xie, Yingpeng Han, and Yuhong Zeng contributed

equally to the paper.

D. Xie � Y. Han � W. Chang � W. Teng � W. Li (&)

Soybean Research Institute (Key Laboratory of Soybean

Biology in Chinese Ministry of Education), Northeast

Agricultural University, Harbin 150030, China

e-mail: wenbinli@neau.edu.cn

Y. Zeng

Soybean Research Centre, Agricultural Academy

of Jilin Province, Jilin 130033, China

123

Mol Breeding (2012) 30:169–179

DOI 10.1007/s11032-011-9607-5

nutritional studies have suggested that FA might play

an important role in the prevention and treatment of a

number of diseases including certain forms of cancers

(Rose 1997), heart disease (Demaison and Moreau

2002) and myopia (Lauritzen et al. 2001). Soybean oil

consists of five major FAs including palmitic (16:0,

PA), stearic (18:0, ST), oleic (18:1, OL), linoleic

(18:2, LI), and linolenic acids (18:3, LN) (Wilson

2004). PA and ST are 16 and 18-carbon saturated FAs,

whereas OL, LI and LN are 18-carbon unsaturated

FAs with one, two and three double bonds, respec-

tively. Polyunsaturated FAs, such as LN, that contain a

cis,cis-1,4-pentadiene structure, are easily oxidized by

the catalysis of lipoxygenases (linoleate: oxygen

oxidoreductase; EC 1.13.11.12; Hildebrand et al.

1993), which reduces the food quality of soybean.

The breakdown of LN by oxidation causes accumu-

lations of undesirable odors and flavors in soybean oils

(Wilson et al. 1981). For good flavor stability or longer

storage life, the LN content in soybean seed should be

1% or less (Khanna and Singh 1991). The oil stability

problem can be solved by selecting cultivars with low

LN content (Hammond and Fehr 1975).

Traditionally, plant improvement has relied on the

phenotypic selection of populations from crosses

between commercial cultivars or experimental lines

(Stuber et al. 1992). However, the phenotypic selection

of quantitative traits, such as LN content, is compli-

cated by environmental effects that significantly

reduce the breeding gain (Panthee et al. 2006; Hyten

et al. 2004). Hence, selection for soybean cultivars with

low LN in seed requires evaluation in multiple

environments. Phenotypic selection is expensive,

time-consuming and labor-intensive. Molecular mar-

ker offers a faster and more accurate approach for

breeding, because selection can be based on genotype

rather than solely on phenotype. The use of molecular

markers for indirect selection or marker-assisted

selection (MAS) of important agronomic traits can

improve the efficiency of traditional plant breeding.

Cregan et al. (1999) and Song et al. (2004) developed

an integrated genetic linkage map of soybean contain-

ing 1,849 markers, including 1,015 simple sequence

repeat (SSR) markers in one or more of five different

populations, and aligned the molecular linkage groups

(LGs) into a consensus map of 20 LGs that correspond

to the 20 pairs of soybean chromosomes (Zou et al.

2003). This information has greatly facilitated MAS in

soybean breeding.

Few quantitative trait loci (QTL) associated with

seed FA contents, especially LN content in soybean

seed, have been reported. QTL associated with LN

content have been reported on LGs F and L in the

mapping population Essex 9 Williams (Hyten et al.

2004). Panthee et al. (2006) analyzed FA content in

soybean seed using a set of recombinant inbred lines

(RILs) from the cross N87-984-16 9 TN93-99 in six

environments and found that SSR marker Satt263 of

LG E and Satt235 of LG G were associated with LN

content. Spencer et al. (2004) analyzed FA content

using a set of RILs from the cross N97-3708-

13 9 Anand. Their results showed that SSR markers

Satt534 and Satt560 on LG B2 were significantly

associated with LN content. However, these studies

focused on the North American soybean germplasm.

The QTL associated with certain FA contents,

especially LN content, in soybean seeds had not

been reported in Chinese germplasm by mid-2010.

The objective of the present study was to identify

the QTL associated with low LN content from

cultivar Dongnong L-5 and other FA contents in

soybean seeds across multiple environments using

SSR markers.

Materials and methods

Plant materials

The mapping population of 125 F5:7, F5: 8 and F5:9

RILs was advanced by single-seed descent from the

cross between HeFeng 25 (PA: 12.16%; ST: 4.97%;

OL: 19.75%; LI: 53.06%; LN: 6.20%) and Dongnong

L-5 (PA: 10.0%; ST: 3.99%; OL: 24.24%; LI:

59.30%; LN: 2.53%).

Field experiment

The RILs were grown together with the parents at

Harbin in 2006, 2007, 2008, and 2009 (45�N, fine-

mesic chernozen soil), at Hulan in 2007 (45.5�N,

fine-mesic chernozen soil) and at Suihua in 2007

(46�N, fine-mesic chernozen soil). Seeds were

planted 0.65 m apart in rows 3 m long. A randomized

complete block design was used with three replicates.

Each plot of a single genotype provided 10 plants as

seed donors that were later used to analyze FA

contents.

170 Mol Breeding (2012) 30:169–179

123

Fatty acid extraction and gas chromatography

analysis

FA composition was determined by gas chromatogra-

phy (GC-14C, Shimadzu Company, Japan). About

0.4–0.5 g of soybean flour was transferred to a test

tube. Extraction buffer (5 ml, anaesthetic grade ether)

was then added to the test tube containing the soybean

flour and closed with a stopper. After 6 h of extraction,

the upper phase transparent liquid was decanted into a

fresh test tube and the ether was removed by volatil-

ization overnight in an aerator. After volatilization,

100 ll of oil sample was placed in a clean test tube and

2 ml of a solvent mixture (anaesthetic ether:n-hexane

(2:1 v/v)) was added. Again, 2 ml of methanol was

mixed, then 2 ml of a second solvent mixture of

potassium hydroxide:methanol (0.8 mol/l) was put

into the test tube, mixed and incubated at room

temperature for 10–20 min. Finally, 2 ml of water

was added into the test tube, mixed and held for

10 min. This process liberated methyl groups which

could attach to form FA methyl esters. 1 ll of the upper

phase was used to detect the FA content by gas

chromatography (GC). The test columns all had

nominal dimensions of 30 m 9 0.125 m 9 0.13 lm

film thickness. Operating conditions were as follows:

carrier, hydric (40 ml/min) split injection, injection

temperature 250�C, detector temperature 250�C and

column temperature 210�C (modified method of

Panthee et al. 2006).

DNA extraction and SSR analyses

Genomic DNA of the parents and five plants of each

RIL were isolated from leaf tissue by the CTAB

method (Doyle and Doyle 1990). Polymerase chain

reaction (PCR) was performed in a 20-ll volume

containing 2 ll genomic DNA (25 ng/ll), 2 ll

MgCl2 (25 mM), 0.3 ll dNTP mixtures (10 mM),

2 ll 10 9 PCR buffer, 3 ll SSR primer (2 lM),

0.2 ll Taq polymerase (10 units/ll), and 11.5 ll

double-distilled water. PCR conditions were 5 min at

94�C, followed by 38 cycles of 30 s at 94�C, 30 s at

47�C, and 30 s at 72�C, then 5 min at 72�C. After

amplification, the PCR products were mixed with

loading buffer [2.5 mg/ml bromphenol blue, 2.5 mg/

ml diphenylamine blue, 10 mM EDTA, 95% (v/v)

formamide], and denatured for 10 min at 94�C, then

kept in 4�C. The denatured PCR products were

separated on 6% (w/v) denaturing polyacrylamide gel

and visualized by silver staining (Trigizano and

Caetano-Anolles 1998).

Single nucleotide polymorphism analyses

Single nucleotide polymorphism (SNP) primers were

designed based on the sequences deposited in Gen-

Bank to amplify soybean GmFad3a (AY204710),

GmFad3b (AY204711), and GmFad3c (AY204712)

cDNAs from HeFeng 25 and Dongnong L-5 (Table 1).

Reverse transcriptase reactions, PCR amplification,

isolation of products from agarose gels, and cloning

procession were as previously described by Bilyeu

et al. (2003). SNP for GmFad3a–3c of 125 RILs was

analyzed as previously described by Garritano et al.

(2009) through the LightScanner� instrument (Idaho

Technology, Salt Lake City, UT, USA).

Data analysis

The frequency distribution, broad-sense heritability

and statistical parameters for RILs were analyzed

using the SAS procedures (PROC Shapiro–Wilk

tests, line regression and GLM.SAS). A linkage

map was constructed by MapMaker/EXP version

3.0b as described by Primomo et al. (2005). The

commands ‘‘group’’, ‘‘map’’, ‘‘sequence’’, ‘‘lod

table’’, ‘‘try’’ and ‘‘compare’’ were used for creating

the linkage groups. The error detection ratio was set

at 1%. The Haldane mapping function was used with

a minimum LOD score of 3.0 and a maximum

distance of 50 cM.

A total of 600 SSR and eight Fad3 single SNP

markers were used to detect polymorphism between

the two parents. One hundred and twelve of the SSR

markers (18.67%) and three SNP markers were poly-

morphic between parental lines, which were mapped

onto 18 LGs (Cregan et al. 1999; Song et al. 2004). The

map spanned 2,718 cM with mean distance between

markers of 24.26 cM (data not shown).

QTL were identified by single-factor analysis of

variance (PROC GLM.SAS) as described by Primomo

et al. (2005), based on individual environment value.

Locus main effects were considered for linear models

if they were significant at P B 0.01. Significant loci on

the same LG were tested by two-factor analysis of

variance without interactions. If both loci were signif-

icant at P B 0.05 in the two-factor model, they would

Mol Breeding (2012) 30:169–179 171

123

be considered for both linear models. Otherwise, the

locus with the larger individual R value was chosen to

represent the effect of the putative QTL on the LG. The

nomenclature of the QTL included four parts following

the recommendations of the Soybean Germplasm

Coordination Committee.

GT (Genotype by Trait) biplot methodology (Yan

2001) was employed to analyze the interaction

between QTL and environments or individual FA

means, based on the formula: Tij-Tj/Sj = k1fi1sj1 ?

k2fi2sj2 ? eij, where Tij is the mean value of QTL i for

environment j; Tj is the mean value of environment

j over all QTL, Sj is the standard deviation of environ-

ment j among QTL means; fi1 and fi2 are the PC1 (first

principle component) and PC2 (second principle

component) scores, respectively, for QTL mean i; sj1

and sj2 are the PC1 and PC2 scores, respectively, for

environment j; and eij is the residual of the model

associated with QTL i, challenged with environment j.

Results

Gas chromatography analysis of linolenic acid

and other fatty acid contents

The ranges and means of individual FA contents

among the 125 RILs across six different environments

are shown in Table 2. There were significant difference

between the parents for each FA content under each of

the six environments. The LN content of Dongnong

L-5 was lower than that of Hefeng 25 at all six diverse

environments. The differences in the FA contents other

than LN between the parents were significant across all

six environments. In contrast, the variation of FA

contents for the 125 RILs across various environments

were not significant, with lower variation coefficients

(\0.30). Both skewness and kurtosis values for these

traits were less than 1.0 in the different environments

and individual FA means, suggesting that the segrega-

tion of these traits fits a normal distribution model.

Broad-sense heritability for 125 RILs across various

environments and individual FA means is listed in

Table 2.

QTL associated with linolenic acid and other fatty

acids

QTL associated with individual FA content were

identified by ANOVA (Table 3; Fig. 1). Six QTL

underlying LN content were identified and mapped

onto three LGs (Table 2) that explained 5.31–37.30%

of the phenotypic variation at three locations in

4 years. Of them, QLNB2_1 (Satt726), QLNB2_2

(Fad3a-4) and QLND1b_1 (Satt701) could be iden-

tified in five and three environments, respectively.

Table 1 SNP primers used in this study

Primer Forward primer Reverse primer

Fad3a-1 TACAATGACGAGAGGAGAAGAGAC TGAAACCACAAACAACAACAAC

GCGATTTGACCCCGTTCATACAT GCGGCAGAAATCCGCTCTCTTTA

Fad3a-2 GCGATGGCAATATGTTTTTGAGC GCGGCCTTGTAATTTTCCTTGTTAATGTG

GCGAAAGCACTTGGATGACACATAA GCGAGGGGTAGAGACTGGAGAGTA

Fad3a-3 GCGCCCATTATTATTTTCCTTGAATTGT GCGAATGCCTAATGTGATGATAAAAAAATA

GCGAAATTACCCAATAAGTTGATGAGAAAGT GCGGGTGAATCACTGATGTCTCTTGA

Fad3a-4 GCGCTACCGTGTGGTGGTGTGCTACCT GCGCAAGTGGCCAGCTCATCTATT

GCGGATAATTTTGCGTCATTATTAATTCAAT GCGGCAGGGAGTTGTTTTCATTCCAGT

Fad3b-1 GCGGTGTGGATCCAAAAACTCAAACTT GCGTGCTAGTTCGATCAGCTTAGTTTC

CCTCCACCCCCTTCCCACCCAAAA GGTGATATCGCGCTAAAATTA

Fad3b-2 GCGGATCCTATGAGCCTATCTGTATTT GCGGCAAGCATTATACTATTTATACGG

AGAATTAGAAAACCATAAATCTG GCGGAGGAATACAAGTCTCTATTCAA

Fad3c-1 CACTGCTTTTTCCCCTCTCT AAGATACCCCCAACATTATTTGTAA

GCGGGGCAGGATATGATATTGTT GCGTATTCGCCAAGCACTACTTTTT

Fad3c-2 GGGGTTTTGTTAATCATTGTCCTTAGAT GGGAGAAAATTTTTAATGCCAGTAGTTA

GCGGAGCAACCACTTGTGTCTTCCTGT GCGCGTAGTTGAATTAATTAAATTACT

172 Mol Breeding (2012) 30:169–179

123

Table 2 Ranges, means, standard deviations, coefficients of variation, skewnesses, kurtosis, and broad-sense heritability for five

fatty acid contents of seed in RILs under multiple environments

Trait Environments Parents RILs

Hefeng25 DongnongL-5 Range Means SDb CVc Sked Kure BSHf

Palmitic acid 06Harbin 11.39a 9.87 8.34–16.24 10.33 1.13 0.11 0.12 -0.38 0.45

07Harbin 12.43 9.34 8.23–17.01 10.96 1.31 0.12 0.17 0.22 0.52

07Suihua 13.65 9.87 8.22–15.27 10.35 1.24 0.12 0.28 0.19 0.47

07Hulan 10.98 10.07 8.24–16.05 10.67 1.49 0.14 0.11 0.18 0.40

08Harbin 12.86 10.76 9.26–13.44 10.79 1.40 0.13 0.30 0.29 0.56

09Harbin 11.65 10.89 10.01–12.71 11.19 1.45 0.13 0.19 -0.19 0.44

Meang 12.16 10.03 8.72–15.12 10.71 1.07 0.10 0.22 0.16 0.50

Stearic acid 06Harbin 5.32 3.99 3.27–6.41 4.71 1.55 0.33 0.21 -0.32 0.43

07Harbin 5.00 4.08 3.33–5.79 4.63 1.43 0.31 0.19 0.21 0.57

07Suihua 5.42 3.90 3.38–6.41 4.48 0.49 0.11 0.22 0.27 0.48

07Hulan 4.98 4.13 3.44–5.85 4.60 1.33 0.29 0.32 0.23 0.49

08Harbin 4.94 4.10 3.63–5.07 4.31 0.81 0.19 0.29 0.15 0.60

09Harbin 4.52 3.76 3.49–4.69 4.03 0.77 0.19 0.32 0.29 0.48

Meang 4.97 3.99 3.42–5.70 4.46 0.98 0.22 0.16 0.25 0.54

Oleic acid 06Harbin 17.89 24.86 16.96–34.55 22.16 3.54 0.16 0.25 0.16 0.46

07Harbin 19.86 23.63 17.00–28.42 21.22 3.61 0.17 0.18 0.24 0.48

07Suihua 18.76 26.75 17.62–34.44 22.78 2.51 0.11 0.31 0.30 0.55

07Hulan 19.49 25.43 17.67–26.49 21.69 3.25 0.15 0.12 0.19 0.49

08Harbin 22.29 24.20 19.28–25.12 22.01 3.08 0.14 0.35 0.16 0.50

09Harbin 20.20 21.21 17.64–23.64 20.25 3.03 0.15 0.15 0.24 0.47

Meang 19.75 24.24 17.65–28.87 21.68 4.12 0.19 0.32 0.25 0.53

Linoleic acid 06Harbin 49.08 59.92 46.90–66.14 57.61 4.61 0.08 0.29 0.17 0.58

07Harbin 53.27 60.86 51.26–65.12 58.23 4.08 0.07 0.31 0.19 0.61

07Suihua 49.76 58.83 46.44–64.06 57.15 2.86 0.05 0.26 0.18 0.50

07Hulan 54.54 59.06 52.29–66.14 57.85 5.21 0.09 0.38 0.21 0.46

08Harbin 55.89 60.87 53.60–61.33 57.51 5.17 0.09 0.19 0.22 0.58

09Harbin 55.87 56.31 54.48–62.91 59.04 6.49 0.11 0.12 0.23 0.40

Meang 53.06 59.30 50.82–64.28 57.89 7.53 0.13 0.23 0.21 0.46

Linolenic acid 06Harbin 7.19 2.29 2.53–7.26 4.98 1.19 0.24 0.11 -0.99 0.50

07Harbin 5.56 2.32 2.53–7.71 5.10 1.36 0.27 0.14 -0.83 0.44

07Suihua 5.9 2.52 2.57–5.83 4.35 0.87 0.20 -0.34 -0.51 0.40

07Hulan 5.52 2.68 2.63–5.77 4.43 0.82 0.19 -0.25 -0.43 0.51

08Harbin 5.77 2.81 2.61–5.46 4.17 0.75 0.18 -0.49 -0.52 0.53

09Harbin 7.25 2.56 2.60–5.99 4.41 0.86 0.19 -0.44 0.02 0.55

Meang 6.20 2.53 2.56–7.17 4.91 1.18 0.24 0.01 -0.82 0.51

Different environments are represented by year and location; for example, 06 Harbin means ‘at Harbin location in 2006’a % (w/w) of oilb Standard deviationc Coefficient of variationd Skewnesse Kurtosisf Broad-sense heritabilityg Means of all data from six different environments

Mol Breeding (2012) 30:169–179 173

123

Table 3 QTL associated with five fatty acid contents in seed using one-way ANOVA (P \ 0.01)

Trait QTL Linkage group Marker Environment R2 (%)a P Allelic means ± SEMsb

Hefeng25 DongnongL-5

Palmitic acid QPAD2_1 D2 Satt389 07Harbin 6.36 \0.0001 c9.23 ± 1.02 15.48 ± 1.01

07Hulan 6.94 0.0010 9.43 ± 1.07 13.15 ± 1.10

08Habin 5.01 0.0005 10.04 ± 1.04 12.62 ± 1.05

09Harbin 2.53 \0.0001 10.41 ± 1.09 11.97 ± 1.08

Mean 3.67 0.0026 11.03 ± 1.16 13.30 ± 1.08

QPAJ_1 J Sat_144 07Harbin 7.78 \0.0001 11.09 ± 1.20 15.77 ± 1.13

07Hulan 6.20 0.0006 9.95 ± 1.06 15.08 ± 1.06

07Suihua 5.33 0.0009 9.32 ± 1.05 13.41 ± 1.06

QPAG_1 G Sat_164 06Harbin 15.23 \0.0001 9.17 ± 1.07 15.63 ± 1.06

08Harbin 9.38 0.0032 11.29 ± 1.03 13.08 ± 1.02

Mean 2.72 0.0008 9.32 ± 1.07 13.00 ± 1.05

QPAK_1 K Satt727 09Harbin 1.77 0.0032 9.01 ± 0.95 10.21 ± 1.00

Stearic acid QSTD1b_1 D1b Satt701 06Harbin 18.93 \0.0001 4.07 ± 0.32 5.76 ± 0.27

07Harbin 20.10 \0.0001 4.05 ± 0.30 5.42 ± 0.29

07Suihua 10.32 0.0009 3.98 ± 0.29 5.92 ± 0.29

Mean 13.48 0.0032 4.03 ± 0.31 5.26 ± 0.33

Oleic acid QOLJ_1 J Sat_144 06Harbin 8.87 0.0007 20.95 ± 2.03 27.96 ± 2.21

07Hulan 7.92 0.0009 20.94 ± 2.76 25.01 ± 2.64

08Harbin 16.96 0.0010 21.02 ± 2.14 24.79 ± 2.36

09Harbin 8.84 0.0001 18.98 ± 2.00 22.70 ± 2.06

Mean 10.29 \0.0001 18.93 ± 1.96 27.05 ± 2.00

QOLG_1 G Sat_164 06Harbin 5.79 \0.0001 17.29 ± 1.80 30.01 ± 1.83

07Suihua 17.35 0.0002 19.98 ± 1.85 30.02 ± 1.98

08Harbin 20.67 0.0009 20.56 ± 2.01 24.97 ± 2.15

Mean 13.91 \0.0001 19.04 ± 1.98 26.99 ± 2.01

QOLK_1 K Satt544 07Suihua 8.95 0.0013 24.04 ± 2.06 27.79 ± 2.12

QOLD2_1 D2 Satt389 08Harbin 7.80 0.0008 20.54 ± 2.07 23.98 ± 2.12

Linoleic acid QLIG_1 G Sat_164 07Harbin 27.49 \0.0001 54.00 ± 4.92 63.45 ± 5.07

07Hulan 19.06 0.0001 54.41 ± 5.31 63.99 ± 5.29

07Suihua 20.02 0.0001 50.17 ± 5.76 60.21 ± 5.30

09Harbin 16.20 0.0032 56.00 ± 5.28 60.03 ± 5.28

Mean 17.68 0.0020 53.47 ± 5.67 58.93 ± 5.67

QLID2_1 D2 Satt389 07Harbin 6.39 0.0012 55.87 ± 4.89 62.03 ± 5.05

07Suihua 5.22 0.0001 51.96 ± 5.13 58.77 ± 4.99

09Harbin 4.96 0.0026 56.31 ± 5.08 59.98 ± 5.13

Mean 5.34 0.0012 53.02 ± 4.84 60.00 ± 5.11

QLIJ_1 J Sat_144 06Harbin 3.23 \0.0001 48.02 ± 5.12 58.89 ± 5.09

07Harbin 6.98 0.0002 54.94 ± 5.35 63.01 ± 5.37

09Harbin 10.07 0.0028 56.00 ± 5.99 60.01 ± 6.02

Mean 6.29 0.0032 54.60 ± 5.96 59.96 ± 5.77

QLIB2_1 B2 Satt726 07Suihua 7.76 0.0001 51.23 ± 6.16 58.80 ± 5.88

174 Mol Breeding (2012) 30:169–179

123

However, QLNG_1 (Sat_164) was detected in only

one environment.

Four QTL, QLIG_1 (sat164), QLID2_1 (Satt389),

QLIJ_1 (Sat_144), and QLIB2_1 (Satt726), were

associated with LI content and were identified on LGs

G, D2, J, and B2, respectively. The phenotypic

variation ranged from 3.23 to 27.49% at three

locations in 3 years (Table 3). Of the 4 QTL,

QLIG_1, QLID2_1, and QLIJ_1 could be identified

in four environments and LI means, three environ-

ments and LI means, and three environments and LI

means, respectively. However, QLIB2_1 was

detected in only one environment.

Four QTL underlying OL content were detected

and mapped onto four LGs (Table 3), which

explained 5.79–20.67% of the phenotypic variation

at three locations in 3 years. Of them, QOLJ_1

(Sat_144) and QOLG_1 (Sat_164) could be identified

in four environments and OL means, and three

environments and OL means, respectively. However,

QOLK_1 (Satt544) and QOLD2_1 (Satt389) were

detected in only one environment.

Four QTL, QPAD2_1(Satt389), QPAJ_1(Sat_144),

QPAG_1(Sat_164), and QPAK_1 (Satt727), were

associated with PA content and were located in LGs

D2, J, G, and K, respectively (Table 3). Of them,

QPAD2_1 explained 2–7% of the phenotypic variation

at two locations in 3 years, QPAJ_1 explained 5–8% of

the phenotypic variation at three locations in 1 year,

QPAG_1 explained 2–16% of the phenotypic variation

at one location in 2 years, and QPAK_1 explained

1.77% of the phenotypic variation at one location.

Only one QTL, QSTD1b_1 (Satt701), underlying

ST content was detected and mapped onto LG D1b

(Table 3), which explained 10.3–20.1% of the phe-

notypic variation at two locations in 2 years.

QPAD2_1 was associated with PA content across

four environments, and was found in the same region

Table 3 continued

Trait QTL Linkage group Marker Environment R2 (%)a P Allelic means ± SEMsb

Hefeng25 DongnongL-5

Linolenic acid QLNB2_1 B2 Satt726 06Harbin 10.26 0.0001 3.02 ± 0.43 5.98 ± 0.44

07Harbin 8.30 0.0003 3.12 ± 0.37 6.87 ± 0.37

07Hulan 7.29 0.0009 3.09 ± 0.37 5.48 ± 0.37

07Suihua 5.31 0.0008 3.10 ± 0.29 5.63 ± 0.31

09Harbin 6.78 0.0001 2.81 ± 0.45 5.61 ± 0.48

Mean 5.80 \0.0001 3.05 ± 0.29 6.89 ± 0.32

QLNB2_2 Fad3a-4 06Harbin 37.30 0.0001 2.98 ± 0.34 6.89 ± 0.33

08Harbin 33.58 \0.0001 3.23 ± 0.41 7.05 ± 0.41

09Harbin 25.75 \0.0001 2.97 ± 0.44 5.79 ± 0.44

07Suihua 27.20 0.0006 2.76 ± 0.31 5.59 ± 0.33

QLNB2_3 Fad3b-1 08Harbin 9.70 0.0009 3.04 ± 0.40 5.67 ± 0.40

09Harbin 11.20 0.0032 2.67 ± 0.36 5.78 ± 0.40

QLNB2_4 Fad3c-2 07Suihua 7.20 0.0001 2.86 ± 0.32 5.83 ± 0.33

QLND1b_1 D1b Satt701 07Harbin 9.24 \0.0001 3.09 ± 0.34 6.92 ± 0.32

08Harbin 8.41 0.0014 3.21 ± 0.41 4.87 ± 0.40

09Harbin 7.60 0.0001 2.90 ± 0.36 5.57 ± 0.36

Mean 6.67 0.0007 3.12 ± 0.35 6.90 ± 0.36

QLNG_1 G Sat_164 06Harbin 15.42 \0.0001 3.19 ± 0.39 6.00 ± 0.40

Different environments are represented by year and location; for example, 06 Harbin means ‘at Harbin location in 2006’a Proportion of the phenotypic data explained by the marker locusb Allelic means: average value of each LN content from six environments; SEM: means ± SD HN, where N is the number of each

allelec % (w/w) of oil

Mol Breeding (2012) 30:169–179 175

123

as QOLD2_1 related to OL content across one

environment. QLID2_1 also related to LI content

across three environments. These QTL were all

linked to the same SSR marker (Satt389) on linkage

group D2 with a beneficial allele from Dongnong

L-5. Additionally, QPAJ_1 was associated with PA

content across three environments, and was found in

the same region as QOLJ_1 associated with OL

content across four environments. QLIJ_1 was also

associated with LI content across three environments.

These QTL were all linked with the same SSR

marker (Sat_144) on linkage group J. QPAG_1

associated with PA content across two environments,

QOLG_1 underlying OL content across three envi-

ronments, and QLIG_1 related to LI content across

four environments were found to be linked with the

same SSR marker (Sat_164) on linkage group G with

a beneficial allele from Dongnong L-5.

Analyses of QTL by environment interaction

GT biplot analysis (Yan 2001) of all QTL with

individual FA content against six environments and

individual FA means showed that the detected QTL

explained 63% of the total variation in individual FA

content (Fig. 2). The performance of different QTL in

each environment was evaluated. When QPAD2_1,

QLNB2_1, QLNB2_2, QLIG_1, and QOLJ_1 were

set as the corner QTL, individual FA means fell into

the sector in which QPAD2_1 were the best QTL for

Fig. 1 QTL associated

with individual fatty acids

Fig. 2 GT biplot analysis for the association of QTL and

environment or individual fatty acid means. PC1 first principle

component, PC2 second principle component. Different envi-

ronments are represented by year and location; for example, 06Harbin means ‘at Harbin location in 2006’

176 Mol Breeding (2012) 30:169–179

123

two environments (at Harbin in 2009 and at Hulan in

2007) and PA means. QLNB2_1 and QLNB2_2 was

the best QTL at Harbin in 2006 and 2007, and at

Suihua in 2007 and LN means. QLIG_1 was the best

QTL at Harbin in 2008 and LI means. QOLJ_1 was

the best QTL for OL means.

Discussion

Soybean oil is used for a wide array of food and

industrial applications such as cooking, margarine,

lubricants, inks and biodiesel fuel (Panthee et al.

2006). Low linolenic acid is beneficial for human

health and for cooking taste, but shortens the shelf-

life of oil. FA content can be manipulated in a

breeding program if the genetics of these traits are

well known and desired sources of germplasm are

available. Dongnong L-5 (LN: 2.53%) has proved to

have lower LN content in China over several years,

and hence there is interest in transferring low LN

content from Dongnong L-5 into other cultivars in

Northeastern China to decrease their LN content.

Because LN and other FA contents in soybean are

difficult to evaluate by phenotype, increasing the

genotype selection intensity by MAS might improve

the selection efficiency.

In soybean, LN content is often inherited as a

quantitative trait (White et al. 1961). Both major and

minor loci contribute to FA composition (Hyten et al.

2004). Many studies have demonstrated that the

interactions between environmental factors, such as

temperature (Wolf et al. 1982), and genotypes are the

primary sources of variation for FA in soybean seeds.

The results of the present study corresponded well to

those previous studies (Table 2).

Six QTL associated with LN content, four QTL

associated with LI content, four QTL associated with

OL content, four QTL associated with PA content

and one QTL associated with ST content were

mapped onto LGs. These QTL explained 2.5–37.3%

of phenotypic variation for individual FA in different

environments or individual FA means, and most of

the variation was less than 10.0%. The low propor-

tions of phenotypic variation explained by these QTL

indicated the quantitative genetic nature of individual

FA in soybean seeds. These results are consistent

with the fact that genetic improvement of soybean FA

by phenotype selection is less efficient.

QTL specific to one environment have also been

reported by other studies for different traits (Rauh

et al. 2002; Price et al. 2002; Li et al. 2003).

Inconsistent QTL detection across multi-environ-

ments could be due to no or weak expression of the

QTL, QTL 9 environment interaction in the opposite

direction to the main QTL effects, and/or epistasis.

The results of our study demonstrate that

QTL 9 environment interaction is an important

property of many QTL for individual FA content.

The numerous QTL with small effects detected in this

study and their instability across environments sup-

ports the ‘‘buffering mechanism’’ model of gene

expression for typical polygenic inheritance (Mather

1943). By averaging trait values over environments

(locations and years), specific environmental effects

would likely be avoided (Orf et al. 1999a). The

inconsistency of QTL over environments may be a

major limitation in using QTL for MAS of pheno-

types in environments that differ from the environ-

ment in which the QTL were detected. However, the

interaction between QTL and environment suggests

that QTL QLNB2_1 and QLNB2_2 (associated with

LN in this study) possess great potential for MAS

across different environments (Table 3; Fig. 2).

Therefore, information on the QTL 9 environment

interaction should be considered if MAS is to be

applied to the manipulation of quantitative traits.

Significant epistatic interactions have been reported

in soybean for other traits including plant height

(Lark et al. 1995), yield (Orf et al. 1999b) and

isoflavone content of seed (Primomo et al. 2005).

Significant (P \ 0.001) two-way epistatic interac-

tions were identified by running EPISTACY 2.0

(Holland et al. 1997) with SAS software in individual

environment values; however, no epistatic interac-

tions were found in the present study (data not

shown). It is possible that longer distances between

markers and the smaller population size made the

detection of epistatic interactions difficult. Resolution

of this problem may rely on fine mapping of linkage

groups.

In this study, QPAD2_1, QOLD2_1 and QLID2_1

were linked with the same SSR marker (Satt389) in

LG D2. Equally, QPAJ_1 and QLIJ_1 were linked

with the same SSR marker (Sat_144) in linkage group

J, and QPAG_1, QOLG_1 and QLIG_1 were linked

with the same SSR marker (Sat_164) in LG G

(Table 2). These three SSR markers were associated

Mol Breeding (2012) 30:169–179 177

123

with multiple FA contents in seed across multi-

environment conditions. Genes underlying the QTL

might be linked in clusters or be pleiotropic. Never-

theless, the markers have great potential for applica-

tion in MAS for developing varieties with reasonable

FA contents.

LN content in soybean seed is controlled by three

microsomal x–3 fatty acid desaturase (Fad3) genes

(GmFad3a, GmFad3b and GmFad3c; Bilyeu et al.

2003). Some studies have showed that the low level of

LN in soybean is a result of mutations in Fad3 genes

(Reinprecht et al. 2009; Chappell and Bilyeu 2006). In

this study, the mutation points of these three genes of

Dongnong L-5 were analyzed using the LightScanner�

instrument; the results showed that SNP marker Fad3a-

4 could explain higher phenotypic variation, indicating

that the low LN content of Dongnong L-5 was the result

of Fad3a mutants. Moreover, another three QTL

(QLNB2_1, QLND1b_1 and QLNG_1) associated

with LN content were found in this study. These

QTL were different from those detected by other

studies (Hyten et al. 2004; Panthee et al. 2006; Spencer

et al. 2004), suggesting that QTL associated with LN

content are strongly influenced by different genetic

backgrounds.

The availability of QTL associated with individual

FA content in soybean seed could facilitate MAS in

breeding programs aiming to transfer low LN content

from soybean cultivar DongnongL-5 to other elite

breeding lines in Northeastern China. Knowledge of

QTL controlling individual FA content in soybean

would allow the design and implementation of more

efficient selection schemes for developing soybean

cultivars with lower LN content and reasonable

content of other FA. So far, progress in breeding

soybean cultivars with lower LN has been slow due to

large environmental effects on this trait. However,

MAS may be an efficient breeding strategy for

developing soybean cultivars with low LN content

and a reasonable FA composition.

Acknowledgments This study was conducted in the Key

Laboratory of Soybean Biology of Chinese Education Ministry

and Soybean Development Centre of Agricultural Ministry,

financially supported by National High Technology Project

(2006AA100104-4), National Nature Science Foundation

projects (30971810, 60932008), National 973 Project (2009CB

118400), Foundation projects of Northeast Agricultural Univer-

sity, Technology project of education ministry of Heilongjiang

province (11541025), Provincial Education Ministry for soybean

molecular design.

References

Bilyeu KD, Palavalli L, Sleper DA, Beuselinck PR (2003)

Three microsomal omega-3 fatty-acid desaturase genes

contribute to soybean linolenic acid levels. Crop Sci

43:1833–1838

Chappell AS, Bilyeu KD (2006) A GmFAD3A mutation in the

low linolenic acid soybean mutant C1640. Plant Breed

125:535–536

Cregan PB, Jarvik T, Bush AL, Shoemaker RC, Lark KG,

Kahler AL, VanToai TT, Lohnes DG, Chung J, Specht JE

(1999) An integrated genetic linkage map of the soybean

genome. Crop Sci 39:1464–1490

Demaison L, Moreau D (2002) Dietary n-3 polyunsaturated fatty

acids and coronary heart disease–related mortality: a possi-

ble mechanism of action. Cell Mol Life Sci 59(3):463–477

Doyle JJ, Doyle JL (1990) Isolation of plant DNA from fresh

tissue. Focus 12:13–15

Garritano S, Gemignani F, Voegele C, Dumont TN, Calvez-

Kelm FL, De Silva D, Lesueur F, Landi S, Tavtigian SV

(2009) Determining the effectiveness of High Resolution

Melting analysis for SNP genotyping and mutation scan-

ning at the TP53 locus. BMC Genet 10:5

Hammond EG, Fehr WR (1975) Oil quality improvement in

soybeans, Glycine max (L.) Merr. Fette Seifen Anstrichm

77:97–101

Hildebrand DF, Deng W, Grayburn WS (1993) Use of genetic

transformation to alter fatty acid metabolism in plants. In:

Hiatt A (ed) Transgenic plants: fundamentals and appli-

cations. Marcel Dekker, Inc., New York, pp 3–21

Holland JB, Moser HS, O’Donoughue LS, Lee M (1997) QTLs

and epistasis associated with vernalization in oat. Crop Sci

37:1306–1316

Hyten DL, Pantalonea VR, Saxtonb AM, Schmidt ME, Sams

CE (2004) Molecular mapping and identification of soy-

bean fatty acid modifier quantitative trait loci. J Am Oil

Chem Soc 81(12):1115–1118

Khanna KR, Singh SP (1991) Genetics of fatty acid compo-

sition and breeding for modified seed oils. In: Khanna KR

(ed) Biochemical aspects of crop improvement. CRC

Press, Boca Raton, pp 255–281

Lark KG, Chase K, Adler F, Mansur LM, Orf JH (1995)

Interactions between quantitative trait loci in soybean in

which trait variation at one locus is conditional upon a

specific allele at another. Proc Natl Acad Sci USA 92:

4656–4660

Lauritzen L, Hansen HS, Jorgensen MH (2001) The essenti-

ality of long-chain-3 fatty acids in relation to development

and function of the brain and retina. Prog Lipid Res 40:

1–94

Li ZK, Yu SB, Lafitte HR, Huang N, Courtois B, Hittalmani S,

Vijayakumar CHM, Liu GF, Wang GC, Shashidhar HE,

ZHuang JY, Zheng KL, Singh VP, Sidhu JS, Srivanta-

neeyakul S, Khush GS (2003) QTL by environment

interactions in rice I. heading date and plant height. Theor

Appl Genet 108:141–153

Mather K (1943) Polygenic balance in the canalization of

development. Nature (London) 16:68–71

Orf JH, Chase K, Jarvik T, Mansur LM, Cregan PB, Adler FR,

Lark KG (1999a) Genetics of soybean agronomic traits: I.

178 Mol Breeding (2012) 30:169–179

123

Comparison of three related recombinant inbred popula-

tions. Crop Sci 39:1642–1651

Orf JH, Chase K, Adler FR, Mansur LM, Lark KG (1999b)

Genetics of soybean agronomic traits: II. Interaction

between yield quantitative trait loci in soybean. Crop Sci

39:1652–1657

Panthee DR, Pantalone VR, Saxton AM (2006) Modifier QTL

for fatty acid composition in soybean oil. Euphytica

152:67–73

Price AH, Towhend J, Jones MP, Audebert A, Courtois B

(2002) Mapping QTL associated with drought avoidance

in upland rice grown in the Philippines and West Africa.

Plant Mol Biol 48:683–695

Primomo VS, Poysa V, Ablett GR, Jackson CJ, Gijzen M, Rajcan

I (2005) Mapping QTL for individual and total isoflavone

content in soybean seeds. Crop Sci 45:2454–2464

Rauh BL, Basten C, Buckler ES IV (2002) Quantitative trait

loci analysis of growth response to varying nitrogen

sources in Arabidopsis thaliana. Theor Appl Genet 104:

743–750

Reinprecht Y, Luk-labey S, Larsen J, Poysa VW, Yu K, Rajcan

I, Ablett GR, Pauls KP (2009) Molecular basis of the low

linolenic acid trait in soybean EMS mutant line RG10.

Plant Breed 128:253–258

Rose D (1997) Effects of dietary fatty acids on breast and

prostate cancer: evidence from in vitro experiments and

animal studies. J Am Clin Nutr 66:1513s–1522s

Song QJ, Marek LF, Shoemaker RC, Lark KG, Concibido VC,

Delannay X, Specht JE, Cregan PB (2004) A new inte-

grated genetic linkage map of the soybean. Theor Appl

Genet 109:122–128

Spencer MM, Landau-Ellis D, Meyer EJ, Pantalone VR (2004)

Molecular markers associated with linolenic acid content

in soybean. J Am Oil Chem Soc 81(6):559–562

Stuber CW, Lincoln SE, Wolff DW, Helentjaris T, Lander ES

(1992) Identification of genetic factors contributing to

heterosis in a hybrid from two elite maize inbred lines

using molecular markers. Genetics 132:832–839

Trigizano RN, Caetano-Anolles G (1998) Laboratory exercises

on DNA amplification fingerprinting for evaluating the

molecular diversity of horticultural species. Hort Technol

8:413–423

White HB, Quackenbush FW, Probst AH (1961) Occurrence

and inheritance of linolenic and linoleic acids in soybean

seeds. J Am Oil Chem Soc 38:113–117

Wilson RF (2004) Seed composition. In: Boerma HR, Specht

JE (eds) Soybean: improvement, production and users, 3rd

edn. ASA, CSSA, SSA, Madison

Wilson RF, Burton JW, Brim CA (1981) Progress in the

selection for altered fatty acid composition in soybeans.

Crop Sci 21:788–791

Wolf RB, Cavins JF, Kleiman R, Black LT (1982) Effect of

temperature on soybean seed constituents: oil, protein,

moisture, fatty acids, amino acids and sugars. J Am Oil

Chem Soc 59:230–232

Yaklich RW, Vinyard BT (2004) Estimating soybean seed

protein and oil concentration before harvest. J Am Oil

Chem Soc 81:189–194

Yan W (2001) GGE biplot-a windows application for grahical

analysis of multienvironment trial data and other types of

two way data. Agron J 93:1111–1117

Zou J, Lee J, Singh R, Xu SS, Cregan PB, Hymowitz T (2003)

Assignment of molecular linkage groups to the soybean

chromosomes by primary trisomics. Theor Appl Genet

107:745–750

Mol Breeding (2012) 30:169–179 179

123