限制性两阶段多位点全基因组关联分析方法的特点与计算程序

全基因组关联分析(genome-wide association study, GWAS)的理论及应用是近十几年来国内外数量性状研究的热点, 但是以往GWAS方法注重于个别主要QTL/基因的检测与发掘。为了相对全面地解析全基因组QTL及其等位基因构成, 本研究提出了限制性两阶段多位点GWAS方法(RTM-GWAS, https://github.com/njau-sri/rtm-gwas)。RTM-GWAS首先将多个相邻且紧密连锁的SNP分组, 成为具有多个单倍型(复等位变异)的连锁不平衡区段(SNPLDB)标记, 然后采用两阶段分析策略, 基于多位点复等位变异遗传模型, 在节省计算空间的条件下保障全基因组QTL及其复等位变异检出的精确度。和以往GWAS方法相比, RTM-GWAS以性状遗传率为上限, 能够较充分地检测出QTL及其相应的复等位变异并能有效地控制假阳性的膨胀。由其结果建立的QTL-allele矩阵代表了群体中所研究性状的全部遗传组成。依据这种QTL-allele矩阵的信息, 可以设计最优基因型的遗传组成, 预测群体中最优化的杂交组合, 并用以进行群体遗传和特有与新生等位变异的研究。本研究首先对RTM-GWAS方法的特点和计算程序功能进行说明, 然后通过大豆试验数据说明RTM-GWAS计算程序的使用方法。

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

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.