Welcome to Acta Agronomica Sinica，

Acta Agronomica Sinica ›› 2018, Vol. 44 ›› Issue (9): 1274-1289.doi: 10.3724/SP.J.1006.2018.01274

• RESEARCH PAPERS • Previous Articles     Next Articles

Characterization and Analytical Programs of the Restricted Two-stage Multi- locus Genome-wide Association Analysis

Jian-Bo HE(),Fang-Dong LIU,Guang-Nan XING,Wu-Bin WANG,Tuan-Jie ZHAO,Rong-Zhan GUAN,Jun-Yi GAI()   

  1. Soybean Research Institute / National Center for Soybean Improvement, Ministry of Agriculture / Key Laboratory of Biology and Genetic Improvement of Soybean (General), Ministry of Agriculture / State Key Laboratory for Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing 210095, Jiangsu, China
  • Received:2018-03-19 Accepted:2018-06-12 Online:2018-09-10 Published:2018-06-29
  • Contact: Jun-Yi GAI E-mail:hjbxyz@gmail.com;sri@njau.edu.cn
  • Supported by:
    This study was supported by the National Natural Science Foundation of China(31701447);This study was supported by the National Natural Science Foundation of China(31671718);National Key R&D Program for Crop Breeding in China(2017YFD0101500);National Key R&D Program for Crop Breeding in China the MOE 111 Project B08025((2017YFD0101500));MOE Program for Changjiang Scholars and Innovative Research Team in University(PCSIRT_17R55);China Agriculture Research System(CARS-04);the Jiangsu Higher Education PAPD Program, the Fundamental Research Funds for the Central Universities and the Jiangsu JCIC-MCP(CARS-04)

RichHTML336

25

PDF

1254

PDF

59

Abstract:

Genome-wide association studies (GWAS) have been widely used for genetic dissection of quantitative trait loci (QTL), and the previous GWAS procedures were concentrated on finding a handful of major loci, while the plant breeders are more likely interested in exploring the whole QTL system for both forward selection and background control. We proposed the restricted two-stage multi-locus genome-wide association analysis (RTM-GWAS, https://github.com/njau-sri/rtm-gwas/) for a relatively thorough detection of QTL and their multiple alleles. Firstly, RTM-GWAS groups the tightly linked sequential SNPs into linkage disequilibrium blocks (SNPLDBs) to form genomic markers with multiple haplotypes as alleles. Secondly, it utilizes two-stage association analysis based on a multi-locus multi-allele model to save computer space for focusing on genome-wide QTL identification along with their multiple alleles. Compared with the previous GWAS methods, RTM-GWAS takes the trait heritability as the upper limit of detected genetic contribution, which can avoid a large amount of false positives for a precise detection of the QTL system of the trait. The QTL-allele matrix as a compact form of the population genetic constitution can be used to design optimal genotypes, to predict optimal crosses in plant breeding, and to study the genetic properties of the population as well as the novel and newly emerged alleles. In the present study, we first introduced the function and usage of the RTM-GWAS analytical programs, and then used the experimental data from a research program on soybean to illustrate the application details of the RTM-GWAS.

Key words: restricted two-stage multi-locus genome-wide association study, SNP linkage disequilibrium block, multi-locus model, QTL-allele matrix, germplasm population, optimal cross design

Fig. 1

Graphical user interface of the RTM-GWAS analytical program"

Fig. 2

Framework of the RTM-GWAS analytical program The characters in italic type are names of binary program."

Fig. 3

File format of phenotype dataIndiv represents the name of column containing individual/ accession labels; SW, OC, PR are trait names; NaN represents missing value."

Fig. 4

Program dialog for SNPLDB construction VCF represents the VCF genotype file path; Min.: minimum;Max.: maximum; CI: confidence interval."

Fig. 5

Program dialog for genetic similarity coefficient calculationVCF represents the VCF genotype file path."

Fig. 6

Program dialog for association analysisVCF represents the VCF genotype file path; Max.: maximum; r-square: coefficient of determination."

Fig. 7

Quantile-Quantile plot of genome-wide association study of soybean plant height"

Table 1

Large effect SNPLDBs significantly associated with soybean plant height"

SNPLDB 染色体
Chromosome		 位置
Position		 Model a QTL QTL×Env. b
-lg P -lg P R2 (%) -lg P R2 (%)
LDB_19_44964630 19 44964630-45029584 58.26 206.01 7.361 52.86 1.693
LDB_6_44183574 6 44183574-44281248 46.46 199.50 7.359 11.64 0.495
LDB_16_8004288 16 8004288-8203845 42.01 171.86 6.140 6.15 0.280
LDB_13_11539212 13 11539212-11625990 28.64 120.36 4.091 0.66 0.053
LDB_17_36474880 17 36474880-36494652 22.81 96.33 3.229 0.80 0.059
LDB_4_37782684 4 37782684-37923093 22.06 83.36 2.809 3.39 0.170
LDB_8_7075139 8 7075139-7077091 26.19 81.67 2.616 16.09 0.505
LDB_3_26698545 3 26698545-26898267 17.24 68.85 2.423 4.56 0.261
LDB_15_16773982 15 16773982-16774010 22.09 72.65 2.273 5.22 0.154
LDB_16_28838874 16 28838874-28868118 16.44 56.11 1.887 1.01 0.077
LDB_4_11093449 4 11093449-11192120 13.61 51.18 1.800 3.13 0.195
LDB_2_5863888 2 5863888-5979031 14.88 47.63 1.493 1.06 0.042
LDB_16_7494681 16 7494681 16.21 48.32 1.440 1.04 0.018
LDB_1_50277902 1 50277902 17.34 47.47 1.413 8.98 0.239
LDB_14_2467475 14 2467475 15.57 45.66 1.356 0.88 0.014
LDB_4_29936477 4 29936477-29950803 14.74 38.98 1.217 5.63 0.186
LDB_3_22147965 3 22147965-22342699 11.98 32.08 1.209 11.34 0.516
LDB_7_20253563 7 20253563-20451607 13.83 36.68 1.174 2.68 0.108
LDB_14_46095634 14 46095634-46106570 12.67 35.50 1.076 0.28 0.008
LDB_3_8197776 3 8197776-8202466 11.51 32.01 1.026 1.05 0.052
LDB_6_22108685 6 22108685-22191360 10.97 28.35 1.004 5.03 0.241

Fig. 8

Manhattan plot of genome-wide association study of soybean plant height"

Fig. 9

QTL-allele matrix data file of main effectRows represent the 104 loci and columns represent the 723 accessions, the data are allele effects and presented in 104×723 matrix."

Fig. 10

Graphical representation of the QTL-allele matrix of soybean plant height"

Fig. 11

Prediction result file of plant height for all possible single crossesP1 and P2 are labels of parental accessions; MEAN and SD indicate the mean and standard deviation of homozygous progeny population; P10, P50, and P90 are 10-th, 50-th, and 90-th percentiles of homozygous progeny population."

Fig. 12

Graphical representation of the prediction result of plant height for all possible single crossesDotted lines indicate the range (15-165 cm) of plant height in parental population."

Supplementary table 1

SNPLDBs significantly associated with soybean plant height"

SNPLDB 染色体
Chromosome		 位置
Position		 Model a QTL QTL×Env. b
-lg P -lg P R2 (%) -lg P R2 (%)
LDB_19_44964630 19 44964630-45029584 58.26 206.01 7.361 52.86 1.693
LDB_6_44183574 6 44183574-44281248 46.46 199.50 7.359 11.64 0.495
LDB_16_8004288 16 8004288-8203845 42.01 171.86 6.140 6.15 0.280
LDB_13_11539212 13 11539212-11625990 28.64 120.36 4.091 0.66 0.053
LDB_17_36474880 17 36474880-36494652 22.81 96.33 3.229 0.80 0.059
LDB_4_37782684 4 37782684-37923093 22.06 83.36 2.809 3.39 0.170
LDB_8_7075139 8 7075139-7077091 26.19 81.67 2.616 16.09 0.505
LDB_3_26698545 3 26698545-26898267 17.24 68.85 2.423 4.56 0.261
LDB_15_16773982 15 16773982-16774010 22.09 72.65 2.273 5.22 0.154
LDB_16_28838874 16 28838874-28868118 16.44 56.11 1.887 1.01 0.077
LDB_4_11093449 4 11093449-11192120 13.61 51.18 1.800 3.13 0.195
LDB_2_5863888 2 5863888-5979031 14.88 47.63 1.493 1.06 0.042
LDB_16_7494681 16 7494681 16.21 48.32 1.440 1.04 0.018
LDB_1_50277902 1 50277902 17.34 47.47 1.413 8.98 0.239
LDB_14_2467475 14 2467475 15.57 45.66 1.356 0.88 0.014
LDB_4_29936477 4 29936477-29950803 14.74 38.98 1.217 5.63 0.186
LDB_3_22147965 3 22147965-22342699 11.98 32.08 1.209 11.34 0.516
LDB_7_20253563 7 20253563-20451607 13.83 36.68 1.174 2.68 0.108
LDB_14_46095634 14 46095634-46106570 12.67 35.50 1.076 0.28 0.008
LDB_3_8197776 3 8197776-8202466 11.51 32.01 1.026 1.05 0.052
LDB_6_22108685 6 22108685-22191360 10.97 28.35 1.004 5.03 0.241
LDB_6_1271502 6 1271502-1275159 12.04 32.96 0.997 0.61 0.018
LDB_12_34374105 12 34374105 12.35 32.82 0.956 0.40 0.005
LDB_5_27543725 5 27543725-27547377 11.25 27.31 0.851 1.50 0.056
LDB_5_4082209 5 4082209-4121721 10.60 26.74 0.834 2.20 0.079
LDB_4_16641482 4 16641482-16839772 8.72 22.21 0.786 2.84 0.150
LDB_19_39240844 19 39240844-39276967 11.01 23.49 0.759 5.16 0.188
LDB_5_15648588 5 15648588 10.85 25.48 0.731 0.14 0.001
LDB_4_12429588 4 12429588-12605113 9.93 20.00 0.715 6.46 0.276
LDB_9_45791340 9 45791340 10.33 22.42 0.638 0.56 0.008
LDB_3_36428638 3 36428638-36469336 8.56 19.15 0.624 1.44 0.066
LDB_16_2807415 16 2807415-2827492 9.48 17.29 0.588 6.02 0.231
SNPLDB 染色体
Chromosome		 位置
Position		 Model a QTL QTL×Env. b
-lg P -lg P R2 (%) -lg P R2 (%)
LDB_11_35694247 11 35694247-35698584 9.13 18.50 0.553 0.30 0.009
LDB_7_35303124 7 35303124-35327231 7.34 15.83 0.542 1.43 0.076
LDB_15_12007768 15 12007768-12011501 7.68 16.51 0.492 0.18 0.005
LDB_7_32985427 7 32985427-33184712 6.68 13.54 0.489 0.74 0.057
LDB_2_3911388 2 3911388-3928107 7.14 14.07 0.466 1.34 0.062
LDB_9_20673268 9 20673268-20692609 6.87 13.82 0.436 0.33 0.016
LDB_16_1151749 16 1151749 9.34 15.53 0.432 4.63 0.115
LDB_19_44669655 19 44669655-44754287 8.40 13.62 0.430 7.17 0.233
LDB_5_907001 5 907001-907042 7.84 15.21 0.423 0.13 0.001
LDB_9_8062600 9 8062600-8116659 6.60 11.79 0.374 1.12 0.044
LDB_8_19152075 8 19152075-19152100 7.48 13.34 0.367 0.92 0.015
LDB_10_5777915 10 5777915-5798626 7.46 10.73 0.362 5.67 0.204
LDB_19_44550587 19 44550587-44558289 7.30 11.07 0.352 3.42 0.117
LDB_20_34089188 20 34089188 7.57 12.42 0.340 1.11 0.020
LDB_5_2803587 5 2803587 6.90 12.30 0.336 0.35 0.004
LDB_8_16373446 8 16373446-16420876 4.96 10.50 0.334 0.32 0.016
LDB_18_57452705 18 57452705-57457239 6.26 10.95 0.325 0.33 0.010
LDB_7_25096830 7 25096830 6.89 11.75 0.320 0.36 0.004
LDB_9_38183930 9 38183930-38268800 5.66 7.74 0.319 4.07 0.194
LDB_12_9936416 12 9936416-9994538 4.45 8.40 0.307 0.79 0.050
LDB_3_33432777 3 33432777-33462071 6.40 9.52 0.305 1.19 0.046
LDB_5_1382138 5 1382138 6.66 10.46 0.283 0.70 0.010
LDB_6_33023321 6 33023321 6.03 10.09 0.272 0.39 0.004
LDB_11_35162270 11 35162270 7.31 10.07 0.271 5.10 0.128
LDB_18_27478142 18 27478142 6.16 10.06 0.271 0.46 0.006
LDB_17_33708118 17 33708118-33815088 7.03 7.77 0.251 6.96 0.226
LDB_5_38350041 5 38350041-38432356 6.76 7.19 0.233 5.51 0.182
LDB_11_6370581 11 6370581-6486015 5.64 6.03 0.231 3.87 0.161
LDB_16_31959033 16 31959033-31999963 4.27 7.11 0.231 0.46 0.021
LDB_18_44942878 18 44942878-45064511 3.28 5.38 0.225 0.48 0.044
LDB_2_12669763 2 12669763-12684207 4.48 5.69 0.204 1.20 0.057
LDB_6_41523578 6 41523578 5.41 7.71 0.203 0.81 0.013
LDB_17_15782164 17 15782164-15845977 3.22 3.92 0.189 0.78 0.066
LDB_4_42809656 4 42809656-42809670 5.27 5.79 0.171 2.76 0.081
LDB_9_25244209 9 25244209 4.20 6.56 0.170 0.20 0.002
LDB_3_43245339 3 43245339 4.91 6.56 0.170 0.25 0.002
LDB_15_9202681 15 9202681 5.40 6.24 0.160 3.37 0.079
LDB_20_3498125 20 3498125-3691528 4.33 3.82 0.159 2.93 0.129
LDB_17_11367127 17 11367127-11382009 3.93 4.76 0.159 1.85 0.068
LDB_17_32520275 17 32520275-32552871 3.03 2.75 0.147 1.72 0.107
LDB_16_32918946 16 32918946 4.25 5.59 0.142 1.19 0.022
LDB_12_3456148 12 3456148 4.19 5.44 0.137 1.63 0.033
LDB_8_6056566 8 6056566 5.03 5.37 0.136 1.85 0.038
LDB_4_9550687 4 9550687-9550998 5.03 4.57 0.135 3.77 0.111
SNPLDB 染色体
Chromosome		 位置
Position		 Model a QTL QTL×Env. b
-lg P -lg P R2 (%) -lg P R2 (%)
LDB_5_2285265 5 2285265-2285296 3.31 4.50 0.133 1.00 0.030
LDB_15_27523398 15 27523398-27723343 3.84 3.02 0.132 3.47 0.147
LDB_13_4599736 13 4599736 3.30 4.78 0.119 0.10 0.000
LDB_9_10672160 9 10672160-10831207 5.87 4.01 0.118 6.77 0.200
LDB_1_50652928 1 50652928-50669137 3.70 2.97 0.117 2.84 0.113
LDB_18_52669179 18 52669179 3.92 4.71 0.117 1.55 0.031
LDB_6_18845190 6 18845190 3.30 4.49 0.111 1.13 0.020
LDB_13_17681170 13 17681170 3.51 4.26 0.104 0.25 0.002
LDB_4_15008106 4 15008106-15030261 2.72 2.85 0.099 1.58 0.059
LDB_20_39556580 20 39556580 2.34 3.88 0.094 0.61 0.009
LDB_16_20099141 16 20099141 2.27 3.87 0.093 0.18 0.001
LDB_14_20541667 14 20541667 3.05 3.80 0.091 0.56 0.008
LDB_15_3242880 15 3242880 8.41 3.74 0.090 13.78 0.380
LDB_1_52750312 1 52750312 2.93 3.72 0.089 0.25 0.002
LDB_16_21247996 16 21247996 3.85 3.59 0.085 1.90 0.040
LDB_3_28458666 3 28458666 2.67 3.26 0.076 0.89 0.015
LDB_18_3933946 18 3933946 3.53 3.14 0.073 1.65 0.033
LDB_7_15901391 7 15901391-15903281 4.33 2.93 0.067 4.15 0.101
LDB_19_2090261 19 2090261-2090611 4.43 2.92 0.067 4.40 0.108
LDB_7_27922333 7 27922333-28121779 3.45 1.47 0.067 3.33 0.129
LDB_5_41679020 5 41679020 4.15 2.90 0.067 3.78 0.091
LDB_18_57497434 18 57497434-57500329 3.29 2.79 0.064 1.07 0.019
LDB_4_571693 4 571693 2.40 2.63 0.059 1.66 0.034
LDB_6_46436793 6 46436793-46436833 2.30 2.56 0.057 1.05 0.018
LDB_20_32314703 20 32314703 3.45 2.40 0.053 2.98 0.069
LDB_8_42752500 8 42752500 4.23 2.40 0.053 3.74 0.090
LDB_4_43757706 4 43757706 2.86 2.40 0.053 2.38 0.052
LDB_11_31994374 11 31994374-31994436 2.28 2.31 0.051 0.76 0.012
LDB_4_1643625 4 1643625-1744093 2.39 1.65 0.049 3.06 0.090
LDB_16_8204099 16 8204099 2.68 2.08 0.044 1.63 0.033
LDB_13_33478164 13 33478164 2.85 1.79 0.037 3.21 0.075
LDB_14_48799491 14 48799491-48799496 4.12 1.20 0.035 4.55 0.134
LDB_7_16630442 7 16630442 3.58 1.55 0.031 3.76 0.090
LDB_3_5207322 3 5207322 4.23 1.45 0.028 4.94 0.123
LDB_7_23869970 7 23869970 3.28 1.26 0.024 3.41 0.081
LDB_1_3351751 1 3351751 3.42 0.89 0.015 3.41 0.081
LDB_7_30278846 7 30278846 4.23 0.60 0.008 6.12 0.157
LDB_1_24895878 1 24895878 2.40 0.36 0.004 3.14 0.073
Total 114 104 c 78.103 51 d 10.312

Supplementary table 2

Optimal cross design of tall soybean breeding"

亲本 Parent 组合 Cross
P1 P2 Y1 Y2 平均数 Mean 标准差 SD P10 P50 P90
4L060 4L311 136.3 132.3 135.4 36.1 87.5 135.5 183.3
4L119 4L361 125.0 138.6 132.3 37.2 81.6 131.8 182.2
4L213 4L361 127.6 138.6 133.5 35.9 84.4 134.4 181.3
4L060 4L119 136.3 125.0 130.6 37.4 80.5 130.3 180.8
4L054 4L060 133.5 136.3 134.6 33.4 91.3 133.7 180.8
4L311 4L361 132.3 138.6 134.3 35.2 87.7 134.4 180.7
4L054 4L361 133.5 138.6 136.3 33.6 92.8 135.3 180.7
4L060 4L371 136.3 137.2 138.0 32.5 93.9 138.8 180.5
4L361 4L371 138.6 137.2 136.9 32.4 93.6 137.8 179.4
4L060 4L213 136.3 127.6 131.7 36.0 83.4 132.1 179.3
4L159 4L361 143.6 138.6 141.2 28.9 103.5 141.6 179.1
4L060 4L297 136.3 131.0 133.9 33.4 90.5 132.6 178.9
4L060 4L159 136.3 143.6 140.3 29.5 101.2 140.2 178.9
4L361 4L367 138.6 136.5 138.2 29.8 99.7 137.6 178.6
4L234 4L361 132.0 138.6 134.8 31.6 93.8 134.1 177.8
4L297 4L361 131.0 138.6 134.3 33.0 91.6 134.1 177.5
4L054 4L114 133.5 128.3 131.9 33.7 85.5 131.6 177.0
4L274 4L361 131.5 138.6 135.4 31.5 92.5 135.8 176.4
4L114 4L371 128.3 137.2 133.7 32.3 90.0 133.8 176.3
4L114 4L311 128.3 132.3 129.4 35.7 81.8 128.1 176.0
4L114 4L213 128.3 127.6 128.2 35.9 79.9 127.9 175.5
4L060 4L367 136.3 136.5 136.2 30.8 94.1 136.8 175.4
4L114 4L159 128.3 143.6 136.1 29.8 97.8 135.9 175.4
4L060 4L274 136.3 131.5 133.6 31.4 91.7 134.1 175.3
4L060 4L148 136.3 124.2 130.7 33.6 87.2 130.2 175.1
4L114 4L119 128.3 125.0 125.6 37.7 74.1 125.6 175.0
4L248 4L361 123.4 138.6 131.0 33.4 86.9 131.0 174.6
4L114 4L297 128.3 131.0 129.0 35.0 83.7 128.9 174.6
4L193 4L361 122.8 138.6 131.5 33.1 88.3 130.9 173.7
4L060 4L234 136.3 132.0 132.7 31.7 90.2 133.4 173.6
4L114 4L234 128.3 132.0 131.0 32.2 89.0 131.0 173.3
4L114 4L367 128.3 136.5 133.1 30.6 92.8 133.0 173.3
4L027 4L361 118.0 138.6 128.0 33.2 84.9 127.6 173.2
4L060 4L193 136.3 122.8 129.3 32.7 85.5 129.4 172.8
4L060 4L248 136.3 123.4 128.6 33.2 84.6 128.8 172.5
4L260 4L361 107.0 138.6 124.3 36.2 77.5 123.5 172.3
4L060 4L302 136.3 115.0 125.1 33.3 80.6 124.1 172.1
4L302 4L361 115.0 138.6 126.3 34.4 82.0 125.7 172.0
4L060 4L111 136.3 115.0 126.7 33.8 81.9 126.2 171.9
4L315 4L361 120.4 138.6 130.4 31.9 88.3 130.9 171.8
4L148 4L361 124.2 138.6 130.8 31.6 89.7 130.6 171.6
4L111 4L361 115.0 138.6 126.5 33.8 81.7 126.8 171.5
4L146 4L361 112.8 138.6 126.5 33.2 83.2 125.4 171.5
亲本 Parent 组合 Cross
P1 P2 Y1 Y2 平均数 Mean 标准差 SD P10 P50 P90
4L049 4L361 110.6 138.6 124.6 35.7 79.0 125.1 171.3
4B181 4L361 118.2 138.6 128.5 32.0 86.4 128.9 171.3
4L283 4L361 106.2 138.6 120.8 37.4 72.2 120.4 171.2
4L060 4L315 136.3 120.4 128.8 31.8 85.9 129.0 171.2
4L114 4L274 128.3 131.5 130.6 31.1 89.6 131.5 171.2
4L284 4L361 114.2 138.6 126.8 33.7 82.1 127.0 170.9
4L114 4L193 128.3 122.8 125.6 33.7 81.4 124.3 170.6
4L027 4L060 118.0 136.3 126.1 33.7 82.1 125.4 170.6
4B181 4L060 118.2 136.3 128.0 32.2 84.9 128.2 170.6
4L060 4L284 136.3 114.2 126.0 34.5 80.1 126.7 170.5
4L060 4L112 136.3 119.2 127.2 32.0 85.0 126.9 170.5
4L112 4L361 119.2 138.6 128.5 31.2 87.3 128.0 170.4
4L242 4L361 117.6 138.6 128.4 31.5 85.9 128.3 170.0
4L191 4L361 92.4 138.6 114.9 40.7 60.0 114.9 169.8
4L124 4L361 106.0 138.6 122.9 35.0 77.1 122.5 169.7
4L114 4L248 128.3 123.4 126.4 33.4 82.9 125.3 169.6
4L145 4L361 119.6 138.6 127.9 30.9 86.3 127.6 169.5
4L049 4L060 110.6 136.3 123.3 35.8 76.8 123.6 169.5
4L060 4L145 136.3 119.6 128.5 31.2 86.5 128.2 169.4
4L001 4L361 120.8 138.6 129.7 30.1 90.4 128.9 169.4
4L201 4L361 106.2 138.6 122.8 35.1 76.0 121.8 169.2
4L060 4L260 136.3 107.0 121.8 36.2 74.3 121.4 169.1
4L154 4L361 118.3 138.6 128.4 31.6 86.4 128.3 168.9
4L352 4L361 112.0 138.6 125.9 31.7 85.1 125.4 168.8
4L022 4L159 71.5 143.6 108.7 45.7 48.7 109.2 168.8
4L224 4L361 109.6 138.6 123.7 33.5 79.6 124.1 168.8
4L186 4L361 113.6 138.6 126.7 31.2 85.4 125.7 168.7
4L254 4L361 110.2 138.6 124.4 33.6 78.5 124.2 168.6
4L060 4L154 136.3 118.3 127.4 31.1 87.1 127.2 168.5
4L276 4L361 102.3 138.6 120.1 35.8 72.4 119.9 168.5
4L001 4L060 120.8 136.3 129.2 30.0 90.0 128.9 168.4
4L060 4L242 136.3 117.6 125.6 31.9 83.3 124.4 168.3
4B181 4L114 118.2 128.3 124.3 32.8 81.6 124.5 168.3
4L022 4L060 71.5 136.3 104.6 46.7 43.0 103.2 168.1
4L060 4L254 136.3 110.2 122.3 34.1 76.5 122.7 168.0
4L114 4L148 128.3 124.2 125.6 32.6 83.0 125.6 168.0
4L060 4L146 136.3 112.8 124.0 32.6 80.4 123.9 167.8
4L296 4L361 121.5 138.6 129.3 29.2 91.3 129.3 167.8
4L060 4L191 136.3 92.4 114.7 40.0 61.9 115.2 167.8
4L060 4L283 136.3 106.2 120.1 35.7 73.3 120.7 167.7
4L114 4L284 128.3 114.2 122.2 33.7 77.5 121.6 167.5
4L060 4L352 136.3 112.0 124.3 32.7 80.7 123.9 167.5
4L060 4L201 136.3 106.2 120.4 34.6 75.5 120.1 167.2
4L042 4L361 115.5 138.6 127.1 30.5 85.9 127.3 167.1
亲本 Parent 组合 Cross
P1 P2 Y1 Y2 平均数 Mean 标准差 SD P10 P50 P90
4L060 4L276 136.3 102.3 119.8 35.8 72.2 120.2 167.0
4L114 4L315 128.3 120.4 125.2 31.7 82.0 125.9 167.0
4L027 4L114 118.0 128.3 123.4 33.0 80.7 123.9 166.8
4L114 4L260 128.3 107.0 117.4 37.3 69.0 116.9 166.7
4L060 4L186 136.3 113.6 124.2 31.4 83.9 123.2 166.6
4L049 4L114 110.6 128.3 120.2 35.2 74.5 120.4 166.6
4L361 4L369 138.6 112.4 125.4 30.9 84.6 125.0 166.4
4L022 4L054 71.5 133.5 103.9 47.9 40.6 104.9 166.4
4L042 4L060 115.5 136.3 126.3 30.1 85.9 126.1 166.2
4L117 4L361 117.0 138.6 128.3 28.5 91.6 127.2 166.2
4L111 4L114 115.0 128.3 122.1 34.2 76.7 122.1 166.2
4L060 4L124 136.3 106.0 120.4 34.5 75.6 120.6 166.2
4L360 4L361 111.0 138.6 124.3 31.3 82.8 125.6 166.1
4L107 4L361 119.4 138.6 128.8 28.6 90.0 129.7 166.0
[1] Visscher P M, Wray N R, Zhang Q, Sklar P , McCarthy M I, Brown M A, Yang J . 10 Years of GWAS discovery: biology, function, and translation. Am J Hum Genet, 2017,101:5-22
[2] Pritchard J K, Stephens M, Rosenberg N A, Donnelly P . Association mapping in structured populations. Am J Hum Genet, 2000,67:170-181
doi: 10.1086/302959 pmid: 10827107
[3] Price A L, Patterson N J, Plenge R M, Weinblatt M E, Shadick N A, Reich D . Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet, 2006,38:904-909
[4] Yu J, Pressoir G, Briggs W H, Vroh Bi I, Yamasaki M, Doebley J F , McMullen M D, Gaut B S, Nielsen D M, Holland J B, Kresovich S, Buckler E S. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat Genet, 2006,38:203-208
[5] Kang H M, Zaitlen N A, Wade C M, Kirby A, Heckerman D, Daly M J, Eskin E . Efficient control of population structure in model organism association mapping. Genetics, 2008,178:1709-1723
doi: 10.1534/genetics.107.080101 pmid: 18385116
[6] Kang H M, Sul J H, Service S K, Zaitlen N A, Kong S Y, Freimer N B, Sabatti C, Eskin E . Variance component model to account for sample structure in genome-wide association studies. Nat Genet, 2010,42:348-354
doi: 10.1038/ng.548 pmid: 20208533
[7] Zhang Z, Ersoz E, Lai C Q, Todhunter R J, Tiwari H K, Gore M A, Bradbury P J, Yu J, Arnett D K, Ordovas J M, Buckler E S . Mixed linear model approach adapted for genome-wide association studies. Nat Genet, 2010,42:355-360
doi: 10.1038/ng.546 pmid: 20208535
[8] Pritchard J K, Stephens M, Donnelly P . Inference of population structure using multilocus genotype data. Genetics, 2000,155:945-959
pmid: 10835412
[9] Alexander D H, Novembre J, Lange K . Fast model-based estimation of ancestry in unrelated individuals. Genome Res, 2009,19:1655-1664
[10] Atwell S, Huang Y S, Vilhjalmsson B J, Willems G, Horton M, Li Y, Meng D, Platt A, Tarone A M, Hu T T, Jiang R, Muliyati N W, Zhang X, Amer M A, Baxter I, Brachi B, Chory J, Dean C , Debieu M, de Meaux J, Ecker J R, Faure N, Kniskern J M, Jones J D, Michael T, Nemri A, Roux F, Salt D E, Tang C, Todesco M, Traw M B, Weigel D, Marjoram P, Borevitz J O, Bergelson J, Nordborg M . Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines. Nature, 2010,465:627-631
[11] Huang X, Wei X, Sang T, Zhao Q, Feng Q, Zhao Y, Li C, Zhu C, Lu T, Zhang Z, Li M, Fan D, Guo Y, Wang A, Wang L, Deng L, Li W, Lu Y, Weng Q, Liu K, Huang T, Zhou T, Jing Y, Li W, Lin Z, Buckler E S, Qian Q, Zhang Q F, Li J, Han B . Genome-wide association studies of 14 agronomic traits in rice landraces. Nat Genet, 2010,42:961-967
doi: 10.1038/ng.695 pmid: 20972439
[12] Li H, Peng Z, Yang X, Wang W, Fu J, Wang J, Han Y, Chai Y, Guo T, Yang N, Liu J, Warburton M L, Cheng Y, Hao X, Zhang P, Zhao J, Liu Y, Wang G, Li J, Yan J . Genome-wide association study dissects the genetic architecture of oil biosynthesis in maize kernels. Nat Genet, 2013,45:43-50
[13] Fang C, Ma Y M, Wu S W, Liu Z, Wang Z, Yang R, Hu G H, Zhou Z K, Yu H, Zhang M, Pan Y, Zhou G A, Ren H X, Du W Q, Yan H R, Wang Y P, Han D Z, Shen Y T, Liu S L, Liu T F, Zhang J X, Qin H, Yuan J, Yuan X H, Kong F J, Liu B H, Li J Y, Zhang Z W, Wang G D, Zhu B G, Tian Z X . Genome-wide association studies dissect the genetic networks underlying agronomical traits in soybean. Genome Biol, 2017,18:161
[14] Peleman J D , van der Voort J R. Breeding by design. Trends Plant Sci, 2003,8:330-334
[15] Huang X, Wei X, Sang T, Zhao Q, Feng Q, Zhao Y, Li C, Zhu C, Lu T, Zhang Z, Li M, Fan D, Guo Y, Wang A, Wang L, Deng L, Li W, Lu Y, Weng Q, Liu K, Huang T, Zhou T, Jing Y, Li W, Lin Z, Buckler E S, Qian Q, Zhang Q F, Li J, Han B . Genome-wide association studies of 14 agronomic traits in rice landraces. Nat Genet, 2010,42:961-967
doi: 10.1038/ng.695 pmid: 20972439
[16] Zhao K, Tung C W, Eizenga G C, Wright M H, Ali M L, Price A H, Norton G J, Islam M R, Reynolds A, Mezey J , McClung A M, Bustamante C D, McCouch S R . Genome-wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa. Nat Commun, 2011,2:467
[17] Zeng Z B . Precision mapping of quantitative trait loci. Genetics, 1994,136:1457-1468
doi: 10.1007/s00122-012-2032-2. pmid: 8013918
[18] He J, Meng S, Zhao T, Xing G, Yang S, Li Y, Guan R, Lu J, Wang Y, Xia Q, Yang B, Gai J . An innovative procedure of genome-wide association analysis fits studies on germplasm population and plant breeding. Theor Appl Genet, 2017,130:2327-2343
[19] Zhang Y, He J, Wang Y, Xing G, Zhao J, Li Y, Yang S, Palmer R G, Zhao T, Gai J . Establishment of a 100-seed weight quantitative trait locus-allele matrix of the germplasm population for optimal recombination design in soybean breeding programmes. J Exp Bot, 2015,66:6311-6325
[20] Meng S, He J, Zhao T, Xing G, Li Y, Yang S, Lu J, Wang Y, Gai J . Detecting the QTL-allele system of seed isoflavone content in Chinese soybean landrace population for optimal cross design and gene system exploration. Theor Appl Genet, 2016,129:1557-1576
[21] Li S, Cao Y, He J, Zhao T, Gai J . Detecting the QTL-allele system conferring flowering date in a nested association mapping population of soybean using a novel procedure. Theor Appl Genet, 2017,130:2297-2314
[22] Gabriel S B, Schaffner S F, Nguyen H, Moore J M, Roy J, Blumenstiel B, Higgins J , DeFelice M, Lochner A, Faggart M, Liu-Cordero S N, Rotimi C, Adeyemo A, Cooper R, Ward R, Lander E S, Daly M J, Altshuler D . The structure of haplotype blocks in the human genome. Science, 2002,296:2225-2229
[23] Patterson N, Price A L, Reich D . Population structure and eigenanalysis. PLoS Genet, 2006,2:e190
[24] Price A L, Patterson N J, Plenge R M, Weinblatt M E, Shadick N A, Reich D . Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet, 2006,38:904-909
[25] VanRaden P M . Efficient methods to compute genomic predictions. J Dairy Sci, 2008,91:4414-4423
doi: 10.3168/jds.2007-0980 pmid: 18946147
[26] Scheet P, Stephens M . A fast and flexible statistical model for large-scale population genotype data: applications to inferring missing genotypes and haplotypic phase. Am J Hum Genet, 2006,78:629-644
[27] Buckler E S, Holland J B, Bradbury P J, Acharya C B, Brown P J, Browne C, Ersoz E, Flint-Garcia S, Garcia A, Glaubitz J C, Goodman M M, Harjes C, Guill K, Kroon D E, Larsson S, Lepak N K, Li H, Mitchell S E, Pressoir G, Peiffer J A, Rosas M O, Rocheford T R, Romay M C, Romero S, Salvo S , Sanchez Villeda H, da Silva H S, Sun Q, Tian F, Upadyayula N, Ware D, Yates H, Yu J, Zhang Z, Kresovich S, McMullen M D. The genetic architecture of maize flowering time. Science, 2009,325:714-718
doi: 10.1126/science.1174276 pmid: 19661422
[28] Meuwissen T H, Hayes B J, Goddard M E . Prediction of total genetic value using genome-wide dense marker maps. Genetics, 2001,157:1819-1829
doi: 10.1017/S0016672301004931 pmid: 11290733
[1] FENG Jian-Ying,WEN Yang-Jun,ZHANG Jin,ZHANG Yuan-Ming. Advances on Methodologies for Genome-wide Association Studies in Plants [J]. Acta Agron Sin, 2016, 42(07): 945-956.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Add: No. 80 Xueyuan South Rd, Beijing 100081, P. R. China E-mail: zwxb301@caas.cn
Supported by Beijing Magtech Co.Ltd