Restricted two-stage multi-locus genome-wide association analysis (RTM-GWAS) is a novel GWAS procedure which provides a way to identify the QTL system with various multiple alleles in natural and bi- or multi-parental derived populations. The major purposes and its two major characteristics of the RTM-GWAS procedure were presented, including the establishment of the SNPLDB markers with multiple alleles fitting the property of the natural and bi- or multi-parental derived populations and the establishment of multi-locus model GWAS procedure with the total genetic contribution controlled within heritability value. Generally, the readers and editors do not doubt about the methods and principles, the multiple allele markers and the multi-locus model, but have questions and queries on the large amount of detected QTLs many more than those from single locus MLM-GWAS procedure and on the general significance level without correction used in RTM-GWAS. These doubts were carefully and seriously explained and relieved. Furthermore, the potential usefulness of the RTM-GWAS procedure in genetic and evolutionary studies were summarized, including usefulness in relatively thorough identification of the QTL-allele system in populations and major gene finding and cloning, usefulness in relatively thorough identification of the QTL-allele system in bi-and multi-parental derived populations, usefulness in studies on population genetic differentiation and evolution and usefulness in breeding by genetic design.
Genome-wide association studies (GWAS) take genome-wide high-density molecular markers to identify associations between genotype and phenotype, which have been widely used for genetic dissection of quantitative traits in plants and animals. However, previous GWAS methods focused on finding a handful of major loci and were not able to detect multi-allelic genetic variation in natural populations based on bi-allelic SNP marker, which caused limitations in extending application of GWAS. The restricted two-stage multi-locus genome-wide association analysis (RTM-GWAS) firstly groups multiple adjacent and tightly linked SNPs based on linkage disequilibrium to form multi-allelic SNPLDB markers with multiple haplotypes as alleles. Secondly, population structure bias is estimated using the genetic similarity coefficient matrix calculated from SNPLDB marker, and the eigenvectors of the similarity matrix are extracted and incorporated as model covariates to correct for population structure bias and to reduce false positives. Finally, RTM-GWAS utilizes two-stage association analysis to detect genome-wide QTLs and their multiple alleles efficiently based on the SNPLDB marker and multi-locus multi-allele model, and builds the final multi-QTL genetic model with the total QTL genetic contribution restricted to trait heritability. RTM-GWAS can also detect QTL-by-environment interaction effect using plot-based phenotype data, and can detect not only the main effect QTL, but also QTL with only interaction effect with environment. RTM-GWAS solves the issue that multiple alleles are not estimable in previous GWAS, and also improves the detection power and reduces the false positive rate by fitting multiple QTLs simultaneously in a multi-locus model. It provides a potential solution for a relatively thorough detection of genome-wide QTLs and their multiple alleles, and the allele effect and relative frequency can also be estimated. From RTM-GWAS results, a QTL-allele matrix can be constructed as a compact form of the population genetic constitution, and can be further used for gene discovery. QTL-allele matrix also provides a new tool for studies on the dynamic change of QTLs and their multiple alleles (genes and their multiple alleles), such as population genetic differentiation and population-specific and new alleles. According to QTL-allele matrix, the progeny genotype of cross between parental lines can be simulated by using computer simulation, and then the phenotype can be predicted to assist optimal cross design and molecular design breeding. In addition, RTM-GWAS is more efficient in QTL detection for bi-parental recombinant inbred line population and multi-parental nested association mapping population because the population structure bias can be well-controlled. The present paper presents the principles and procedures of the RTM-GWAS method at first, and then provides some potential applications of RTM-GWAS in plant genetic and breeding studies.
【Objective】A genome-wide association study in the Northeast China soybean germplasm population was conducted for a relatively thorough detection of the QTL-allele constitution of 100-seed weight, which may provide a theoretical basis for soybean breeding for seed size improvement. 【Method】In the present study, a total of 290 soybean accessions that were frequently used for soybean breeding and production in the Northeast China were tested in 2013 and 2014 for 100-seed weight at four locations, including Keshan, Mudanjiang, Jiamusi and Changchun, which are all in the second sub-ecoregion of the Northeast China. RAD-seq (restriction site-associated DNA sequencing) was used for SNP genotyping, and 82 966 high-quality SNPs were obtained after filtering and imputation. According to the RTM-GWAS (restricted two-stage multi-locus genome-wide association analysis) method, firstly a total of 15 546 multi-allelic SNPLDBs were constructed, and then a multi-locus model was used for genome-wide association study of 100-seed weight. The genes near (within 50kb) the detected SNPLDBs were analyzed, and candidate genes for 100-seed weight were identified and annotated according to Chi-square test of independence between the SNPs within genes and the detected SNPLDBs. Finally, genetic differentiation among maturity groups were investigated based on the detected QTL-allele system of 100-seed weight. 【Result】The 100-seed weight of the present population ranged from 18.3 to 20.7 g, and the trait heritability was 92.3%. A total of 76 SNPLDBs were detected to be associated with 100-seed weight, among which there were 15 SNPLDBs with non-significant main effect and the 61 SNPLDBs with significant main effect explained 65.40% phenotypic variation. There were 68 SNPLDBs that had significant interaction effect with environment and explained 17.46% phenotypic variation. In addition, 34 out of 76 detected SNPLDBs overlapped 30 previously reported QTLs and 42 SNPLDBs were novel loci. A total of 137 candidate genes for 100-seed weight were annotated in the detected SNPLDB regions, and functional annotation showed that these genes were not only involved in regulation of 100-seed weight, but also involved in primary metabolism, translation, protein modification, material transport, stress response and signal transduction, etc. Although there was no obvious difference in the 100-seed weight among different maturity groups, genetic differentiation analysis showed varying changes of allele emergence and exclusion in QTL-allele structure of 100-seed weight among maturity groups. 【Conclusion】The RTM-GWAS method used in the present study provided a relatively thorough detection of genome-wide QTLs and their multiple alleles for 100-seed weight in the Northeast China soybean germplasm population. The 100-seed weight of the Northeast China soybean germplasm population was controlled by a large number of QTLs with large significant interaction effect with environment, and there was also abundant multiple allelic variation in these QTLs. The QTL-allele matrix established from RTM-GWAS provided an efficient tool for population genetics and evolution study.
【Objective】To thoroughly dissect the QTL system conferring 100-seed weight in a recombinant inbred lines population, the restricted two-stage multi-locus genome-wide association analysis (RTM-GWAS) method was compared with other mapping methods for method optimization, which will provides basis for further exploration of candidate gene system and molecular marker-assisted design breeding. 【Method】A recombinant inbred line population consisting of 427 lines derived from a cross between Kefeng-1 and NN1138-2 was tested for its 100-seed weight under three environments. A total of 3 683 SNPLDBs (SNP linkage disequilibrium blocks) composed of 39 353 SNPs were applied to QTL mapping using three different mapping procedures, including the composite interval mapping (CIM) method, the mixed linear model (MLM-GWAS) method and the RTM-GWAS method, and the best mapping procedure was selected for the analysis of the 100-seed weight genetic system in NJRIKY population through comparing their detection power, including the detected number of QTLs and total phenotypic variation explained. 【Result】The 100-seed weight of Kefeng-1 and NN1138-2 were 9.0 g and 17.9 g, respectively, showing significant difference. The genotypic coefficient of variation and heritability of the trait were 12.4% and 85.4%, respectively. These results indicated that the population was suitable for genetic analysis of 100-seed weight trait. The RTM-GWAS procedure performed the best with the largest number of QTLs (57) explaining the most phenotypic variation (PVE=70.78%), while a total of 14 and 6 QTLs contributing 56.47% and 18.47% phenotypic variation were detected using CIM and MLM-GWAS, respectively. The 57 QTLs detected from the RTM-GWAS distributed on 19 chromosomes, of which 41 QTLs overlapped with 81 QTLs identified from 30 bi-parental populations in the literature. Furthermore, the PVE of 57 QTLs ranged from 0.03% to 7.57%, of which 16 QTLs were novel ones, including one large contribution major QTL Sw-09-2 (PVE>3%). Furthermore, 20 QTLs had significant interaction effect with environment. A total of 36 candidate genes were annotated from 37 QTLs through χ2 test between SNPLDB markers and SNPs harboring in the predicted genes, of which 4 candidate genes were from the large contribution QTLs and other 32 candidate genes were from the small contribution QTLs. These candidate genes were included in different biological processes, of which 13 candidate genes were grouped in seed development directly, and the remaining candidate genes were grouped into different functions, such as transport, transcriptional regulators, etc., indicating that these genes from different biological pathways regulate the expression of 100-seed weight trait in NJRIKY together. 【Conclusion】Among the three different mapping procedures, RTM-GWAS procedure is the most powerful one which can provide a relatively thorough detection of 100-seed weight QTLs in NJRIKY population, therefore, it is more suitable for QTL mapping in bi-parental population such as RIL population. The candidate genes with various functions jointly regulated the complex expression of 100-seed weight trait.
【Objective】Soybean is an important cash crop, a major source of plant protein and oil for human diet. As a major objective of soybean breeding, protein content is a complex trait controlled by multiple genes with varying genetic effects interacting with environment. A genome-wide association study (GWAS) was conducted to dissect the genetic architecture of protein content in a soybean nested association mapping (NAM) population, and the detected genetic constitution can be further used for molecular design in soybean breeding for high protein content. 【Method】Four soybean recombinant inbred line (RIL) populations (Linhe×M8206, Zhengyang×M8206, M8206×Tongshan and M8206×WSB) with a common parent (M8206) as a NAM population were constructed, genotyped with RAD-seq (restriction-site-associated DNA sequencing) and tested under multiple locations in 2012 - 2014. Protein content was measured at full maturity (R8) stage. The restricted two-stage multi-locus GWAS (RTM-GWAS) procedure was used to dissect the genetic architecture of seed protein content of the population. 【Result】The protein content varied widely in the population with trait heritability estimated as high as 85.00%. The analysis of variance for protein content showed significant differences across genotypes, environments and genotype-by-environment interactions. A total of 90 QTLs were detected to be associated with protein content, with 20 loci being novel ones. The phenotypic variation explained by each QTL ranged from 0.06% to 3.99%, with a sum of 45.60%. The number of alleles at each locus ranged from 2 to 5, and the allele effects ranged from -2.434% to 2.845%, while most of them were between -1.000% and 1.000%. From the detected QTLs, 73 candidate genes were annotated. Among these candidate genes, Glyma18g03540 involved in cysteine biosynthetic process, and Glyma20g24830 involved in glycine and aromatic amino acid family metabolic process. The two genes may be selected for further functional study. Based on the QTL-allele matrix of protein content, the predicted transgressive potential of cross progeny was as high as 56.5%. 【Conclusion】A total of 90 QTLs for protein content were detected with 20 loci being novel, from which 73 candidate genes were annotated, indicating that protein content is a complex trait conferred by multiple genes or a gene network.
【Objective】Isoflavones are a group of phenolic secondary metabolites which are relatively abundant in soybean and some other legumes, and are important for food and healthcare industry. A total of 12 kinds of components are isolated from soybean seed, and can be grouped into three categories: daidzin group, genistin group and glycitin group. To understand the complex genetic constitutions of isoflavone content in soybean, the additive and epistatic quantitative trait loci (QTLs) conferring the total isoflavone content and its component contents were detected in the present study. 【Method】The NJRSXG recombinant inbred line (RIL) population derived from Xianjin 2 and Gantai-2-2 were used in this study. Four isoflavone content traits, i.e. the total seed isoflavone content (SIFC), the total daidzin group content (TDC), the total genistin group content (TGC) and the total glycitin group content (TGLC) were tested in five environments. The mixed model composite interval mapping (MCIM) and restricted two-stage multi-locus genome-wide association analysis (RTM-GWAS) were used for QTL detection. 【Result】There was a large difference in isoflavone content between the two parental lines of NJRSXG population, and transgressive segregations were observed in NJRSXG population while the transgressive trend in low isoflavone content direction were stronger than that in high isoflavone content direction. A total of 19 additive QTLs and 16 pairs of epistatic QTLs for the four isoflavone traits on 15 chromosomes were detected by MCIM. Three novel additive QTLs, i.e. qSifc-14-1, qTdc-14-2 and qTgc-14-1, were detected in the same important marker interval GNE186b-Sat020 on chromosome 14, and explained the highest phenotypic variation. A total of 51, 66, 42 and 36 significantly associated markers were detected by RTM-GWAS for SIFC, TDC, TGC and TGLC, respectively. The phenotypic variation explained by these markers was ranged from 39.7% to 52.5%, covering 11 additive QTLs and 11 epistatic QTLs detected by MCIM. Furthermore, a total of 93 and 100 candidate genes were annotated in the additive and epistatic QTL regions, respectively. Gene enrichment analysis indicated that three genes located in the important marker interval GNE186b-Satt020 on chromosome 14, i.e. Glyma14g33227, Glyma14g33244 and Glyma14g33715, were related to isoflavone metabolism. 【Conclusion】A relatively thorough detection of isoflavone content QTLs was achieved by using linkage and association mapping. Compared with the linkage mapping method MCIM, the association mapping method RTM-GWAS can detect more QTLs with larger total contribution to phenotypic variation, but cannot detect epistatic QTLs as in MCIM. The QTLs detected from the two methods can be used for complementary verification from each other. A large number of QTLs/genes are involved in the seed isoflavone contents of soybean.
