Genetic dissection of maize plant architecture using a novel nested association mapping population
Sheng ZhaoXueying LiJunfeng SongHuimin LiXiaodi ZhaoPeng ZhangZhimin LiZhiqiang TianMeng LvCe DengTangshun AiGengshen ChenHui ZhangJianlin HuZhijun XuJiafa ChenJunqiang DingWeibin SongYuxiao Chang
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Abstract The leaf angle (LA), plant height (PH), and ear height (EH) are key plant architectural traits influencing maize ( Zea mays L.) yield. However, their genetic determinants have not yet been well‐characterized. Here, we developed a maize advanced backcross‐nested association mapping population in Henan Agricultural University (HNAU‐NAM1) comprised of 1,625 BC 1 F 4 /BC 2 F 4 lines. These were obtained by crossing a diverse set of 12 representative inbred lines with the common GEMS41 line, which were then genotyped using the MaizeSNP9.4K array. Genetic diversity and phenotypic distribution analyses showed considerable levels of genetic variation. We obtained 18–88 quantitative trait loci (QTLs) associated with LA, PH, and EH by using three complementary mapping methods, named as separate linkage mapping, joint linkage mapping, and genome‐wide association studies. Our analyses enabled the identification of ten QTL hot‐spot regions associated with the three traits, which were distributed on nine different chromosomes. We further selected 13 major QTLs that were simultaneously detected by three methods and deduced the candidate genes, of which eight were not reported before. The newly constructed HNAU‐NAM1 population in this study will further broaden our insights into understanding of genetic regulation of plant architecture, thus will help to improve maize yield and provide an invaluable resource for maize functional genomics and breeding research.Keywords:
Genetic architecture
Association mapping
Genetic linkage
Candidate gene
Genome-wide Association Study
Plant Breeding
Ear length (EL) is an important trait in maize ( L.) because it is positively correlated with grain yield. To understand the genetic basis of natural EL variation, a F, a four-way cross and a genome-wide association study (GWAS) population were used to identify the quantitative trait loci (QTLs) and candidate EL genes. Linkage mapping identified 14 QTLs in two types of populations from multiple environments. Six of them were located in three common genomic regions considered "stable QTLs". Candidate genes for the three stable QTLs were identified by the GWAS results. These were related to auxin transport, cell proliferation, and developmental regulation. These results confirm that maize EL is under strong genetic control by many small-effect genes. They also improve our understanding of the genetic basis of maize EL.
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Genome-wide Association Study
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Family-based QTL mapping
Genetic linkage
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Genetic Association
Genetic architecture
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QTL regions associated with lactation persistency and protein yield in sheep have been identified on OAR3 and OAR20. Genotyping of 8 out of 15 existing microsatellite markers on OAR3, and 5 out of 9 on OAR20, in the QTL region of interest, over additional backcross and double backcross
daughters, resulted in high resolution mapping of these two QTL regions to 10 cM and 20cM intervals respectively using Linkage Analysis (LA). An additional 28 informative markers (12 on OAR3 and 16 on OAR20) have been genotyped and combined Linkage Analysis/ Linkage Disequilibrium analysis (LA/LD) will be used to obtain further resolution in these regions so that positional candidate genes can be identified. From standard QTL analyses animals with contrasting genotypes ‘Q-’ and ‘q-’ for desired QTLs have been identified for use in functional (transcriptome)
studies to obtain a list of differentially expressed genes. The transect of both positional and functional
(differentially expressed) genes should lead to a short list of positional functional candidate genes for further analyses and comparative mapping to dairy cows.
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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 quantitative genetic variation into genes at the molecular level has been recognized as the greatest challenge facing geneticists in the twenty-first century. Tremendous efforts in the last two decades were invested to map a wide spectrum of quantitative genetic variation in nearly all important organisms onto their genome regions that may contain genes underlying the variation, but the candidate regions predicted so far are too coarse for accurate gene targeting. In this article, the recurrent selection and backcross (RSB) schemes were investigated theoretically and by simulation for their potential in mapping quantitative trait loci (QTL). In the RSB schemes, selection plays the role of maintaining the recipient genome in the vicinity of the QTL, which, at the same time, are rapidly narrowed down over multiple generations of backcrossing. With a high-density linkage map of DNA polymorphisms, the RSB approach has the potential of dissecting the complex genetic architecture of quantitative traits and enabling the underlying QTL to be mapped with the precision and resolution needed for their map-based cloning to be attempted. The factors affecting efficiency of the mapping method were investigated, suggesting guidelines under which experimental designs of the RSB schemes can be optimized. Comparison was made between the RSB schemes and the two popular QTL mapping methods, interval mapping and composite interval mapping, and showed that the scenario of genomic distribution of QTL that was unlocked by the RSB-based mapping method is qualitatively distinguished from those unlocked by the interval mapping-based methods.
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Abstract Fusarium verticillioides can be transmitted via seeds and cause systemic infection in maize ( Zea mays L.); its mycotoxin has harmful effects on animal and human health. We combined QTL mapping in recombinant inbred line (RIL) populations with a genome-wide association study (GWAS) of 217 diverse maize lines using 224,152 single nucleotide polymorphisms (SNPs) under controlled conditions to determine the genetic architecture of F. verticillioides seed rot (FSR) resistance. Our study identified 8 quantitative trait loci (QTLs) and 43 genes associated with 57 SNPs that were correlated with FSR resistance through linkage mapping and GWAS, respectively. Among these, there were three candidate genes, namely GRMZM2G0081223, AC213654.3_FG004 , and GRMZM2G099255 , which were detected in both linkage mapping and GWAS. Furthermore, the near-isogenic lines (NILs) containing GRMZM2G0081223 , which also had a susceptible parent background, were found to have a significantly improved level of resistance. In addition, the expression profile of the three candidate genes revealed that they all respond to the infection following inoculation with F. verticillioides . These genetic analyses indicate that FSR resistance is controlled by loci with minor effect, and the polymerization breeding of lines with beneficial alleles and candidate genes could improve FSR resistance in maize.
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Abstract Background Flowering time is an important agronomic trait of crops and significantly affects plant adaptation and seed production. Flowering time varies greatly among maize ( Zea mays ) inbred lines, but the genetic basis of this variation is not well understood. Here, we report the comprehensive genetic architecture of six flowering time-related traits using a recombinant inbred line (RIL) population obtained from a cross between two maize genotypes, B73 and Abe2, and combined with genome-wide association studies to identify candidate genes that affect flowering time. Results Our results indicate that these six traits showed extensive phenotypic variation and high heritability in the RIL population. The flowering time of this RIL population showed little correlation with the leaf number under different environmental conditions. A genetic linkage map was constructed by 10,114 polymorphic markers covering the whole maize genome, which was applied to QTL mapping for these traits, and identified a total of 82 QTLs that contain 13 flowering genes. Furthermore, a combined genome-wide association study and linkage mapping analysis revealed 17 new candidate genes associated with flowering time. Conclusions In the present study, by using genetic mapping and GWAS approaches with the RIL population, we revealed a list of genomic regions and candidate genes that were significantly associated with flowering time. This work provides an important resource for the breeding of flowering time traits in maize.
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Quantitative trait loci (QTL) mapping is an important approach for the study of the genetic architecture of quantitative traits. For perennial species, inbred lines cannot be obtained due to inbreed depression and a long juvenile period. Instead, linkage mapping can be performed by using a full-sib progeny. This creates a complex scenario because both markers and QTL alleles can have different segregation patterns as well as different linkage phases between them. We present a two-step method for QTL mapping using full-sib progeny based on composite interval mapping (i.e., interval mapping with cofactors), considering an integrated genetic map with markers with different segregation patterns and conditional probabilities obtained by a multipoint approach. The model is based on three orthogonal contrasts to estimate the additive effect (one in each parent) and dominance effect. These estimatives are obtained using the EM algorithm. In the first step, the genome is scanned to detect QTL. After, segregation pattern and linkage phases between QTL and markers are estimated. A simulated example is presented to validate the methodology. In general, the new model is more effective than existing approaches, because it can reveal QTL present in a full-sib progeny that segregates in any pattern present and can also identify dominance effects. Also, the inclusion of cofactors provided more statistical power for QTL mapping.
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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.
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Although tocopherols play an important role in plants and animals, the genetic architecture of tocopherol content in maize kernels has remained largely unknown. In this study, linkage and association analyses were conducted to examine the genetic architecture of tocopherol content in maize kernels. Forty-one unique quantitative trait loci (QTLs) were identified by linkage mapping in six populations of recombinant inbred lines (RILs). In addition, 32 significant loci were detected via genome-wide association study (GWAS), 18 of which colocalized with the QTLs identified by linkage mapping. Fine mapping of a major QTL validated the accuracy of GWAS and QTL mapping results and suggested a role for nontocopherol pathway genes in the modulation of natural tocopherol variation. We provided genome-wide evidence that genes involved in fatty acid metabolism, chlorophyll metabolism and chloroplast function may affect natural variation in tocopherols. These findings were confirmed through mutant analysis of a particular gene from the fatty acid pathway. In addition, the favourable alleles for many of the significant SNPs/QTLs represented rare alleles in natural populations. Together, our results revealed many novel genes that are potentially involved in the variation of tocopherol content in maize kernels. Pyramiding of the favourable alleles of the newly elucidated genes and the well-known tocopherol pathway genes would greatly improve tocopherol content in maize.
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