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Acta Agronomica Sinica ›› 2023, Vol. 49 ›› Issue (9): 2321-2330.doi: 10.3724/SP.J.1006.2023.23072

• CROP GENETICS & BREEDING·GERMPLASM RESOURCES·MOLECULAR GENETICS •     Next Articles

ALGWAS: two-stage Adaptive Lasso-based genome-wide association study

YANG Wen-Yu1,2(), WU Cheng-Xiu1, XIAO Ying-Jie1,3,*(), YAN Jian-Bing1,3   

  1. 1National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, Hubei, China
    2College of Science, Huazhong Agricultural University, Wuhan 430070, Hubei, China
    3Hubei Hongshan Laboratory, Wuhan 430070, Hubei, China
  • Received:2022-10-28 Accepted:2023-02-21 Online:2023-09-12 Published:2023-03-03
  • Supported by:
    National Natural Science Foundation of China(32201855);National Natural Science Foundation of China(32122066)

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Abstract:

As mainstream methods for genome-wide association analysis, mixed linear model methods have been widely used. However, the existing methods still have the problem of low detection power. In this study, a two-stage Adaptive Lasso-based genome-wide association analysis (ALGWAS) method was proposed. In the first stage, single nucleotide polymorphism (SNP) associated with target traits were screened by Adaptive Lasso, a variable selection method. In the second stage, SNPs selected from the first stage were put into the linear model as the covariates for genome-wide scanning. Compared with fastGWA, GEMMA and EMMAX, the ALGWAS method had the highest detection power and lower false discovery rate (FDR) in the simulation experiments. The above four methods were applied to genome-wide association analysis of Complete-diallel plus Unbalanced Breeding-like Inter-Cross (CUBIC) population of 1341 individuals in maize. ALGWAS method can detect the genes (ZmMADS69, ZmMADS15/31, ZmZCN8, and ZmRAP2.7) related to days to tasseling, the genes (ZmBRD1 and ZmBR2) related to plant height, and the genes (ZmUB2, ZmKRN2, and ZmCLE7) related to yield, while the other three commonly used genome-wide association analysis methods had low detection efficiency. In this study, a non-mixed linear model class of genome-wide association analysis method was proposed, which had higher detection advantage for microeffect polygenes and provided a new way for genetic analysis of complex traits.

Key words: maize, genome-wide association study, variable selection, Adaptive Lasso

Table 1

Average detection power and false discovery rate of genome-wide association study methods"

遗传力Heritability QTN数目
Number of QTN		 方法
Method		 检测功效
Detection power		 高效应QTN
检测功效
Detection power of QTN with high effect		 中等效应QTN
检测功效
Detection power of QTN with moderate effect		 低效应QTN
检测功效
Detection power of QTN with low effect		 错误发现率
False discovery rate
0.8
0.8
0.8
0.8
0.8
0.5
0.5
0.5
0.5
0.5
0.8
0.8
0.8
0.8
0.8
0.5
0.5
0.5
0.5
0.5		 20
20
20
20
20
20
20
20
20
20
50
50
50
50
50
50
50
50
50
50		 LM
ALGWAS
GEMMA
fastGWA
EMMAX
LM
ALGWAS
GEMMA
fastGWA
EMMAX
LM
ALGWAS
GEMMA
fastGWA
EMMAX
LM
ALGWAS
GEMMA
fastGWA
EMMAX		 0.811
0.802
0.482
0.457
0.446
0.808
0.562
0.442
0.382
0.370
0.763
0.513
0.270
0.250
0.244
0.600
0.208
0.158
0.134
0.129		 0.997
1.000
0.987
0.983
0.983
0.947
0.793
0.777
0.697
0.683
0.937
0.911
0.729
0.699
0.684
0.843
0.561
0.479
0.416
0.404		 0.838
0.895
0.440
0.375
0.358
0.825
0.565
0.423
0.353
0.338
0.769
0.520
0.128
0.101
0.095
0.571
0.094
0.036
0.022
0.020		 0.590
0.480
0.033
0.040
0.027
0.647
0.327
0.133
0.107
0.100
0.581
0.107
0.001
0.001
0.003
0.397
0.008
0.000
0.000
0.000		 0.853
0.040
0.066
0.074
0.050
0.811
0.105
0.070
0.074
0.069
0.780
0.065
0.025
0.030
0.020
0.708
0.118
0.045
0.060
0.063

Fig. 1

Average detection power of genome-wide association study methods based on simulated phenotype on realistic genetic structure A: days to tasseling; B: plant height; C: ear weight."

Fig. 2

Manhattan plots for days to tasseling of CUBIC population"

Fig. 3

Manhattan plots for plant height of CUBIC population"

Fig. 4

Manhattan plots for ear weight of CUBIC population"

Fig. 5

Quantitle-quantitle plot of genome-wide association study methods A: days to tasseling; B: plant height; C: ear weight."

Table 2

ALGWAS detected the known gene location and its corresponding peakSNP location"

基因名称
Gene name		 基因 ID
Gene ID		 基因位置
Gene location		 peakSNP位置
peakSNP location		 P值
P-value
ZmMADS69 GRMZM2G171650 Chr. 3: 159,022,119..159,050,063 Chr. 3: 158,013,626 8.434874E-09
ZmMADS15/31 GRMZM2G553379 Chr. 5: 6,993,294..7,011,505 Chr. 5: 6,684,616 1.667779E-08
ZmZCN8 GRMZM2G179264 Chr. 8: 123,028,887..123,033,675 Chr. 8: 121,955,506 1.638071E-12
ZmRAP2.7 GRMZM2G700665 Chr. 8: 131,575,389..131,581,816 Chr. 8: 131,013,374 6.050159E-10
ZmBR2 GRMZM2G315375 Chr. 1: 20,2334,824..202,342,008 Chr. 1: 202,814,745 1.595160E-09
ZmBRD1 GRMZM2G103773 Chr. 1: 249,371,977..249,376,239 Chr. 1: 249,462,103 3.165227E-21
ZmZCN8 GRMZM2G107829 Chr. 8: 123,028,887..123,033,675 Chr. 8: 124,009,899 2.306359E-10
ZmBAM1d GRMZM2G043584 Chr. 1: 30,516,319..30,522,796 Chr. 1: 27,500,593 1.373829E-04
ZmYIGE1 GRMZM2G008490 Chr. 1: 51,075,171..51,161,917 Chr. 1: 58,798,749 4.593378E-05
ZmUB2 GRMZM2G160917 Chr. 1: 188,213,876..188,220,983 Chr. 1: 192,566,425 1.078588E-05
ZmKRN2 GRMZM2G125656 Chr. 2: 17,742,986..17,750,216 Chr. 2: 17,280,224 6.421449E-06
ZmCLE7 GRMZM2G372364 Chr. 4: 7,568,824..7,572,604 Chr. 4: 16,087,155 7.774088E-06
ZmKRN6 GRMZM2G119714 Chr. 6: 94,188,754..94,201,186 Chr. 6: 90,084,654 8.809443E-05
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