Fine-mapping of qGW4.05, a major QTL for kernel weight and size in maize
Lin ChenYongxiang LiChunhui LiXun WuWeiwei QinXin LiFuchao JiaoXiaojing ZhangDengfeng ZhangYunsu ShiYanchun SongLi YuTianyu Wang
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Abstract:
Kernel weight and size are important components of grain yield in cereals. Although some information is available concerning the map positions of quantitative trait loci (QTL) for kernel weight and size in maize, little is known about the molecular mechanisms of these QTLs. qGW4.05 is a major QTL that is associated with kernel weight and size in maize. We combined linkage analysis and association mapping to fine-map and identify candidate gene(s) at qGW4.05.QTL qGW4.05 was fine-mapped to a 279.6-kb interval in a segregating population derived from a cross of Huangzaosi with LV28. By combining the results of regional association mapping and linkage analysis, we identified GRMZM2G039934 as a candidate gene responsible for qGW4.05. Candidate gene-based association mapping was conducted using a panel of 184 inbred lines with variable kernel weights and kernel sizes. Six polymorphic sites in the gene GRMZM2G039934 were significantly associated with kernel weight and kernel size.The results of linkage analysis and association mapping revealed that GRMZM2G039934 is the most likely candidate gene for qGW4.05. These results will improve our understanding of the genetic architecture and molecular mechanisms underlying kernel development in maize.Keywords:
Candidate gene
Kernel (algebra)
Family-based QTL mapping
Genetic linkage
Association mapping
Linkage (software)
Genetic architecture
Trait
Inbred strain
Methods for mapping QTL are actively used in the chicken to identify chromosomal regions contributing to variation in traits related to growth, disease resistance, egg production, behavior, and metabolic parameters. However, higher-resolution mapping and better knowledge of the genetic architecture underlying QTL are needed for successful application of this information into breeding programs. Therefore, this paper summarizes and integrates original, primary QTL studies in the chicken to identify basic information on the genetic architecture of quantitative traits in chickens. The results of this review show several instances of consensus of QTL locations for similar traits from independent studies. Furthermore, the consensus of QTL location for different traits and evidence for QTL with parent-of-origin effect, transgressive alleles, epistatic QTL, and QTL × sex interaction in chicken are presented and discussed. This information can be helpful in identifying genes or mutations underlying the QTL and in the application of genomic information in marker-assisted breeding programs.
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Both modern molecular biology technique and new statistical methods greatly promoted the analysis of plant quantitative trait loci(QTL).Linkage mapping(LM)and association mapping(AM) are two important methods for QTL analysis.Both them have obvious complementarity in the accuracy and breadth of QTL mapping,the provided information,and the statistical analysis method.Linkage mapping can preliminary locate the target trait gene,while association mapping can quickly achieve the verification and fine mapping of target gene;moreover,it can also verify the candidate gene function according to a lot of information provided for a specific candidate gene.In this paper,the new advances were reviewed from the linkage mapping and association mapping of cotton quantitative traits.The research prospects of cotton QTL were analyzed by combining the two methods.
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Dissecting the genetic architecture of complex traits is an ongoing challenge for geneticists. Two complementary approaches for genetic mapping, linkage mapping and association mapping have led to successful dissection of complex traits in many crop species. Both of these methods detect quantitative trait loci (QTL) by identifying marker–trait associations, and the only fundamental difference between them is that between mapping populations, which directly determine mapping resolution and power. Based on this difference, we first summarize in this review the advances and limitations of family-based mapping and natural population-based mapping instead of linkage mapping and association mapping. We then describe statistical methods used for improving detection power and computational speed and outline emerging areas such as large-scale meta-analysis for genetic mapping in crops. In the era of next-generation sequencing, there has arisen an urgent need for proper population design, advanced statistical strategies, and precision phenotyping to fully exploit high-throughput genotyping.
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Understanding and estimating the structure and parameters associated with the genetic architecture of quantitative traits is a major research focus in quantitative genetics. With the availability of a well-saturated genetic map of molecular markers, it is possible to identify a major part of the structure of the genetic architecture of quantitative traits and to estimate the associated parameters. Multiple interval mapping, which was recently proposed for simultaneously mapping multiple quantitative trait loci (QTL), is well suited to the identification and estimation of the genetic architecture parameters, including the number, genomic positions, effects and interactions of significant QTL and their contribution to the genetic variance. With multiple traits and multiple environments involved in a QTL mapping experiment, pleiotropic effects and QTL by environment interactions can also be estimated. We review the method and discuss issues associated with multiple interval mapping, such as likelihood analysis, model selection, stopping rules and parameter estimation. The potential power and advantages of the method for mapping multiple QTL and estimating the genetic architecture are discussed. We also point out potential problems and difficulties in resolving the details of the genetic architecture as well as other areas that require further investigation. One application of the analysis is to improve genome-wide marker-assisted selection, particularly when the information about epistasis is used for selection with mating.
Genetic architecture
Epistasis
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Quantitative Genetics
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QTL mapping is an important step in gene fine mapping,map-based cloning,and the efficient use of gene information in molecular breeding.Questions are frequently met and asked in the application of QTL mapping in practical genetic populations.Questions related to statistical method of QTL mapping are:what does LOD score mean? What is the relationship between the reliability of detected QTL and the LOD threshold? How to evaluate different QTL mapping methods? How to improve the QTL detection power? Questions related to genetic parameter estimation are:how to calculate the phenotypic variance explained by each detected QTL? How to determine the source of favorable alleles at detected QTL? How efficient is the selective genotyping? Can composite traits be used in QTL mapping? Questions related to linkage map and mapping populations are:does the phenotype of a trait in interest have to follow a normal distribution? Does the increase in marker density greatly improve QTL mapping power? What effects will missing markers have in QTL mapping? What effects will segregation distortion have in QTL mapping? Our objective in this paper was to give an analysis and answer to each of the 12 frequently asked questions,based on our studies in past several years.
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Association mapping
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Association mapping, a high-resolution method for mapping quantitative trait loci based on linkage disequilibrium, holds great promise for the dissection of complex genetic traits. General understanding of association mapping has increased significantly since its debut in plants. We have seen a more concerted effort in assembling various association-mapping populations and initiating experiments through either candidate-gene or genome-wide approaches in different plant species. In this review, we describe the current status of association mapping in plants, Relation between LD and Association Mapping, QTL Mapping and Association Mapping, Types of Association mapping, Steps in Association Mapping and Benefits and limitations of association mapping.
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