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作物学报 ›› 2023, Vol. 49 ›› Issue (9): 2321-2330.doi: 10.3724/SP.J.1006.2023.23072

• 作物遗传育种·种质资源·分子遗传学 •    下一篇

基于Adaptive Lasso的两阶段全基因组关联分析方法

杨文宇1,2(), 吴成秀1, 肖英杰1,3,*(), 严建兵1,3   

  1. 1作物遗传改良全国重点实验室, 湖北武汉 430070
    2华中农业大学理学院, 湖北武汉 430070
    3湖北洪山实验室, 湖北武汉 430070
  • 收稿日期:2022-10-28 接受日期:2023-02-21 出版日期:2023-09-12 网络出版日期:2023-03-03
  • 通讯作者: *肖英杰, E-mail: yxiao25@mail.hzau.edu.cn
  • 作者简介:杨文宇, E-mail: yangwenyurain@126.com
  • 基金资助:
    国家自然科学基金项目(32201855);国家自然科学基金项目(32122066)

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 Published:2023-09-12 Published online:2023-03-03
  • Supported by:
    National Natural Science Foundation of China(32201855);National Natural Science Foundation of China(32122066)

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摘要：

作为进行全基因组关联分析的主流方法, 混合线性模型类方法得到了广泛的应用。但是, 现有方法仍存在检测功效不高的问题。本文提出一种基于Adaptive Lasso的2阶段全基因组关联分析方法(two-stage Adaptive Lasso-based genome-wide association analysis, ALGWAS), 该方法在第1阶段通过变量选择方法Adaptive Lasso筛选出与目标性状相关联的单核苷酸多态性位点(single nucleotide polymorphism, SNP), 第2阶段将第1阶段筛选出的SNP作为协变量放入线性模型中进行全基因组扫描。在模拟实验中, ALGWAS方法与3种常用的全基因组关联分析方法fastGWA、GEMMA和EMMAX相比具有最高的检测功效, 同时具有较低的错误发现率(false discovery rate, FDR)。将以上4种方法应用到包含1341份材料的玉米CUBIC (Complete-diallel plus Unbalanced Breeding-like Inter-Cross)群体的全基因组关联分析中, ALGWAS方法可检测到与开花期相关基因ZmMADS69、ZmMADS15/31、ZmZCN8和ZmRAP2.7, 与株高相关基因ZmBRD1和ZmBR2, 与产量相关基因ZmUB2、ZmKRN2和ZmCLE7等, 而其他3种常用的全基因组关联分析方法检测功效较低。本研究提出了一种非混合线性模型类的全基因组关联分析方法, 对解析微效多基因决定的复杂遗传性状具有更高的检测效率, 为基因挖掘提供了新的途径。

关键词: 玉米, 全基因组关联分析, 变量选择, Adaptive Lasso

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

表1

基于从头模拟表型的不同全基因组关联分析方法的平均检测功效和错误发现率"

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

图1

基于真实性状遗传结构模拟表型的不同全基因组关联分析方法的检测功效 A: 抽雄期; B: 株高; C: 穗重。"

图2

CUBIC群体抽雄期的曼哈顿图"

图3

CUBIC群体株高的曼哈顿图"

图4

CUBIC群体穗重的曼哈顿图"

图5

不同全基因组关联分析方法的QQ图 A: 抽雄期; B: 株高; C: 穗重。"

表2

ALGWAS方法检测到的已知基因位置及其对应的peakSNP位置"

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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