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Frontiers of Agriculture in China

ISSN 1673-7334

ISSN 1673-744X(Online)

CN 11-5729/S

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Front. Agric. China    2007, Vol. 1 Issue (1) : 1-7    https://doi.org/10.1007/s11703-007-0001-3
Research article
A comparative study on segregation analysis and QTL mapping of quantitative traits in plants—with a case in soybean
Junyi GAI1(),Yongjun WANG1,Xiaolei WU2,Shouyi CHEN2()
1.Soybean Research Institute of Nanjing Agricultural University, National Center for Soybean Improvement, National Key Laboratory for Crop Genetics and Germplasm Enhancement, Nanjing 210095, China E-mail: sri@njau.edu.cn
2. Institute of Genetics and Developmental Biology of Chinese Academy of Sciences, Beijing 100101, China E-mail: sychen@genetics.ac.cn
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Abstract

Two approaches of genetic analysis of quantitative traits were compared with a case study on soybean. One approach was the segregation analysis developed by Gai et al. (2003), which utilized information from individuals of one or multiple segregation populations as well as that from parents based on the principles of the major-gene plus polygene inheritance model, mixture distribution, joint maximum-likelihood function, IECM (Iterated Expectation and Conditional Maximization) algorithm, and Akaike’s information criterion and goodness of fit tests. Another approach was quantitative trait locus (QTL) mapping with molecular markers. A recombinant inbred line (RIL) population with 201 families derived from (Kefeng No.1x1138-2) F2:7:10 along with their parents were tested in a randomized block design experiment. The 171 RFLP, 60 SSR, and 79 AFLP molecular markers were used to mark the 201 families. The data of nine traits, i.e., number of days to flowering, number of days to maturity, plant height, number of nodes on main stem, number of pods per node, 100-seed weight, protein content, oil content, and plot yield, were analyzed with the segregation analysis procedure of RIL population with parents (Gai et al., 2003; Zhang and Gai, 2000; Zhang et al., 2001) to detect their genetic system, and those along with the molecular marker data were analyzed with WinQTL Cartographer (Basten et al., 1999; Zeng, 1993, 1994) to detect their QTL system. The results showed that both procedures could detect the main major genes or QTLs, and therefore, could be used as a mutual check and supplement. From the results that most of the traits were mainly controlled by three or four QTLs, it was impressed that the segregation analysis procedure of four major-gene plus polygene mixed inheritance model should be developed to fit the requirements. The results also showed that the QTLs of the involved traits concentrated on several linkage groups, such as C2, B1, F1, M, and N. Finally, the results showed that the experimental sample was not necessarily coincident with the theoretical population according to equality test, symmetry test, and representation test, and therefore, the sample should be checked, tested and then adjusted to fit the theoretical requirements through deleting the extra-biased families and markers.
Keywords inheritance of quantitative trait      segregation analysis      QTL mapping      soybean     
Issue Date: 22 February 2016
 Cite this article:   
Junyi GAI,Yongjun WANG,Xiaolei WU, et al. A comparative study on segregation analysis and QTL mapping of quantitative traits in plants—with a case in soybean[J]. Front. Agric. China, 2007, 1(1): 1-7.
 URL:  
https://academic.hep.com.cn/fag/EN/10.1007/s11703-007-0001-3
https://academic.hep.com.cn/fag/EN/Y2007/V1/I1/1
Trait Best model Major gene Polygene AIC value Alternative model
Flowering G-0 3 Yes 1118.06 G-1
Maturity C-0 3 Yes 1395.22 G-1
Plant height E-2-5 2 linked Yes (additive) 1645.98 E-1-4,E-1-4
No. nodes G-0 3 Yes 942.28 G-1
100-seed weight E-1-1 2 Yes (additive) 902.68 E-1-4,E-1-5,E-1-6
Plot yield E-2-0 2 linked Yes 1903.58 E-2-4,E-2-5
Pods per node E-2-0 2 linked Yes 499.91 E-2-6
Protein content G-0 No Yes 739.36 No
Oil content E-2-0 2 linked Yes 690.38 E-1-0
Table 1  Genetic models of soybean traits detected from segregation analysis
Trait Additive AdditivexAdditive Major gene Polygene
da db dc iab iac ibc iabc σ2mg h2mg σ2pg h2pg
Flowering/d -0.89 -0.95 0.28 1.97 2.14 2.18 1.54 17.35 89.43 0.28 1.46
Maturity/d -3.66 -7.00 0.78 4.54 1.68 1.58 5.59 120.05 73.97 30.81 18.98
Plant height/cm 0.99 -16.11 - - - - - 130.75 76.85 7.93 4.66
No. nodes -0.58 -1.05 -0.50 1.04 0.51 0.96 1.08 5.13 76.84 0.66 9.90
100-seed weight /g -1.68 -0.32 - 0.90 - - - 3.72 70.03 0.39 7.41
Plot yield/g 12.20 12.14 - 12.14 - - - 221.89 41.48 6.19 1.16
Pods per node 0.43 0.43 - 0.43 - - - 0.28 41.79 0.40 11.76
Protein content /% - - - - - - - - - 1.83 60.60
Oil content /% -1.62 -0.89 - 0.21 - - - 1.73 56.35 1.34 26.06
Table 2  Estimates of genetic parameters in segregation analysis
Trait Linkage groupa) Locus Marker region cM LOD r2 Additive effect
1 Flowering N3-B1 fd1 Satt197―A118T 14.0-6.7 2.37 0.073 -1.667
fd2 A520T 0.0 3.35 0.058 -1.492
N6-C2 fd3 A397I―B131V 8.0-3.7 10.01 0.262 -3.149
fd4 AGCCAC10 0.0 12.94 0.322 -3.531
fd5 AGCCAC11 0.0 2.17 0.090 -1.836
N12-F1 fd6 Satt586 0.0 3.50 0.090 -1.842
fd7 ACGCCAC01―W 6.0-11.3 3.36 0.101 -1.948
2 Maturity N3-B1 md1 Satt509―Satt197 18.0-4.4 4.59 0.251 -5.234
md2 Satt197―A118T 14.0-6.7 12.88 0.497 -7.250
md3 A118T―A520T 2.0-4.7 11.46 0.282 -5.535
N6-C2 md4 A397I―B131V 2.0-9.7 2.55 0.078 -2.196
md5 AGCCAC10 0.0 2.30 0.077 -2.938
N12-F1 md6 Satt586 0.0 2.60 0.095 -3.210
md7 ACGCAC01―W 6.0-11.3 2.22 0.094 -3.201
N14-G md8 AGCCAC17―Satt472 10.0-3.5 2.19 0.130 3.816
md9 Satt472―K69T 14.0-17.3 3.23 0.221 4.923
md10 AACCAA04 0.00 2.81 0.091 3.139
N21-N md11 LBC―ABAB 2.0-9.9 2.69 0.129 -3.737
md12 AGGCTA03 0.0 2.05 0.139 -3.883
md13 AAGCAT12―AAGCAT10 10.0-3.1 2.00 0.187 -4.504
3 Plant height N3-B1 ht1 Satt197―A118 10.0-10.7 2.13 0.121 -5.207
ht2 TA520T 0.0 2.25 0.058 -3.613
N6-C2 ht3 STAS8_14T 0.0 2.32 0.059 3.802
ht4 A748V―A397I 16.0-6.3 10.77 0.371 -9.069
ht5 A397I―B131V 4.0-7.7 10.52 0.318 -8.393
ht6 AGCCAC10 0.00 12.54 0.360 -9.070
ht7 LI26T―AGCCAC02 30.0-17.7 4.42 0.486 -10.379
ht8 AGCCAC11 0.0 3.04 0.186 -6.422
N12-F1 ht9 Satt586 0.0 2.14 0.083 -4.278
ht10 ACGCAC01―W 12.0-5.3 2.19 0.097 -4.638
N13-F2 ht11 B174I 0.0 2.29 0.065 3.848
ht12 B174I―Satt335 4.0-3.9 2.48 0.084 4.368
4 Stem nodes N3-B1 Sn1 A520T 0.0 2.13 0.055 -0.699
N4-B2 sn2 A148I―AACCAT02 2.0-7.6 2.50 0.099 0.104
N6-C2 sn3 A748V―A397I 16.0-6.3 9.19 0.322 -1.679
sn4 A397I―B131V 6.0-5.7 9.77 0.299 -1.620
sn5 AGCCAC10 0.0 10.93 0.315 -1.686
sn6 LI26T―AGCCAC02 30.0-17.7 4.53 0.568 -2.231
sn7 AGCCAC11 0.0 2.74 0.143 -1.118
N12-F1 sn8 gmrvbp―Satt269 12.0-7.7 2.52 0.157 -1.174
sn9 Satt586 0.0 3.45 0.123 0.128
sn10 ACGCAC01―W 6.0-11.3 3.49 0.143 -1.118
N13-F2 sn11 L37_2I―Sat036 2.0-16.5 2.31 0.090 0.890
sn12 B174T―B174I 6.0-1.2 3.53 0.099 0.945
sn13 B174I―Satt335 4.0-3.9 4.23 0.149 1.159
5 100-seed weight N3-B1 sw1 Satt509 0.0 2.25 0.082 -0.468
N9-D2a sw2 B146H―A611D 2.0-8.8 4.26 0.128 -0.581
N18-K sw3 A199H―A64_3I 14.0-5.2 2.02 0.100 -0.514
6 Plot yield N6-C2 yd1 STAS8_14T 0 2.81 0.073 4.959
N20-M ol2 A60V―AACCAA08 16.0-0.8 2.38 0.133 -0.482
ol3 AACCAA08―AACCAA09 2.0-6.8 2.43 0.139 -0.491
ol4 AACCAA09―AAGCAT11 4.0-12.6 2.27 0.139 -0.491
Table 3  QTL mapping of agronomic traits of soybeans
Trait Segregation analysis QTL mapping
Major gene h2mg h2pg QTL Variation explainedb) Linkage group
Flowering 3 89.4 1.5 fd4, fd3 (fd7, fd6)a) 58.4% C2(1st two) F1(2nd two)
Maturity 3 74.0 19.0 md2, md3, md1, md9 125.1% B1(1st three)
Plant height 2, linked 76.9 4.7 ht6, ht4, ht5, ht7 153.5% C2(all four)
Stem nodes 3 76.9 9.9 sn5, sn4, sn3, sn6 150.4% C2(all four)
100-seed weight 2 70.0 7.4 sw2, (sw1, sw3) 12.8%
Plot yield 2, linked 41.5 1.2 yd4, yd3, yd2, yd8 81.2% C2(1st three)
Nodes per pod 2, linked 41.8 11.8 pn4, pn3, pn5, pn2 59.2% C2(all four)
Protein content No - 60.6 (pt1, pt3, pt2)
Oil content 2, linked 56.4 26.1 ol3, ol2, ol4 41.1% M(all three)
Table 4  Comparisons between the results from segregation analysis and QTL mapping
Test Before After
Equality Total number of effective loci Kefenf No. 1 14141 12113
1138-2 13225 12164
χ2 30.59 0.1
Symmetry Family number p>0.5, q<0.5 112 98
P<0.5, q>0.5 86 84
χ2 3.16 0.93
xC<3.84 p>0.5, q<0.5 75 75
P<0.5, q>0.5 64 62
χ2 0.72 1.05
xC>3.84 p>0.5 37 23
q>0.5 22 22
χ2 3.32 0
p=q=0.5 3 2
Test Before After Simulated critical value (a =0.05)
Representation RFLP number 171 166
Family number 201 184
Marker extra-biased rate 21.05% 19.28% 20.36%
Marker largest x2 C 38.56 23.08 23.75
Family extra-biased rate 29.35% 24.45% 24.47%
Family largest χ2 C 144.06 12.45 28.35
Table 5  Coincidence test of the NJRIKY population before and after adjustment
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