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Detecting genes that affect specific traits (such as human diseases and crop yields) is important for treating complex diseases and improving crop quality. A genome-wide association study (GWAS) provides new insights and directions for understanding complex traits by identifying important single nucleotide polymorphisms. Many GWAS summary statistics data related to various complex traits have been gathered recently. Studies have shown that GWAS risk loci and expression quantitative trait loci (eQTLs) often have a lot of overlaps, which makes gene expression gradually become an important intermediary to reveal the regulatory role of GWAS. In this review, we review three types of gene-trait association detection methods of integrating GWAS summary statistics and eQTLs data, namely colocalization methods, transcriptome-wide association study-oriented approaches, and Mendelian randomization-related methods. At the theoretical level, we discussed the differences, relationships, advantages, and disadvantages of various algorithms in the three kinds of gene-trait association detection methods. To further discuss the performance of various methods, we summarize the significant gene sets that influence high-density lipoprotein, low-density lipoprotein, total cholesterol, and triglyceride reported in 16 studies. We discuss the performance of various algorithms using the datasets of the four lipid traits. The advantages and limitations of various algorithms are analyzed based on experimental results, and we suggest directions for follow-up studies on detecting gene-trait associations., (© 2024. Science China Press.)

Phenotypic plasticity is the ability of a given genotype to produce multiple phenotypes in response to changing environmental conditions. Understanding the genetic basis of phenotypic plasticity and establishing a predictive model is highly relevant to future agriculture under a changing climate. Here we report findings on the genetic basis of phenotypic plasticity for 23 complex traits using a diverse maize population planted at five sites with distinct environmental conditions. We found that latitude-related environmental factors were the main drivers of across-site variation in flowering time traits but not in plant architecture or yield traits. For the 23 traits, we detected 109 quantitative trait loci (QTLs), 29 for mean values, 66 for plasticity, and 14 for both parameters, and 80% of the QTLs interacted with latitude. The effects of several QTLs changed in magnitude or sign, driving variation in phenotypic plasticity. We experimentally validated one plastic gene, ZmTPS14.1, whose effect was likely mediated by the compensation effect of ZmSPL6 from a downstream pathway. By integrating genetic diversity, environmental variation, and their interaction into a joint model, we could provide site-specific predictions with increased accuracy by as much as 9.9%, 2.2%, and 2.6% for days to tassel, plant height, and ear weight, respectively. This study revealed a complex genetic architecture involving multiple alleles, pleiotropy, and genotype-by-environment interaction that underlies variation in the mean and plasticity of maize complex traits. It provides novel insights into the dynamic genetic architecture of agronomic traits in response to changing environments, paving a practical way toward precision agriculture., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: Drought threatens the food supply of the world population. Dissecting the dynamic responses of plants to drought will be beneficial for breeding drought-tolerant crops, as the genetic controls of these responses remain largely unknown., Results: Here we develop a high-throughput multiple optical phenotyping system to noninvasively phenotype 368 maize genotypes with or without drought stress over a course of 98 days, and collected multiple optical images, including color camera scanning, hyperspectral imaging, and X-ray computed tomography images. We develop high-throughput analysis pipelines to extract image-based traits (i-traits). Of these i-traits, 10,080 were effective and heritable indicators of maize external and internal drought responses. An i-trait-based genome-wide association study reveals 4322 significant locus-trait associations, representing 1529 quantitative trait loci (QTLs) and 2318 candidate genes, many that co-localize with previously reported maize drought responsive QTLs. Expression QTL (eQTL) analysis uncovers many local and distant regulatory variants that control the expression of the candidate genes. We use genetic mutation analysis to validate two new genes, ZmcPGM2 and ZmFAB1A, which regulate i-traits and drought tolerance. Moreover, the value of the candidate genes as drought-tolerant genetic markers is revealed by genome selection analysis, and 15 i-traits are identified as potential markers for maize drought tolerance breeding., Conclusion: Our study demonstrates that combining high-throughput multiple optical phenotyping and GWAS is a novel and effective approach to dissect the genetic architecture of complex traits and clone drought-tolerance associated genes.

In maize ( Zea mays ), kernel weight is an important component of yield that has been selected during domestication. Many genes associated with kernel weight have been identified through mutant analysis. Most are involved in the biogenesis and functional maintenance of organelles or other fundamental cellular activities. However, few quantitative trait loci (QTLs) underlying quantitative variation in kernel weight have been cloned. Here, we characterize a QTL, qKW9 , associated with maize kernel weight. This QTL encodes a DYW motif pentatricopeptide repeat protein involved in C-to-U editing of ndhB , a subunit of the chloroplast NADH dehydrogenase-like complex. In a null qkw9 background, C-to-U editing of ndhB was abolished, and photosynthesis was reduced, resulting in less maternal photosynthate available for grain filling. Characterization of qKW9 highlights the importance of optimizing photosynthesis for maize grain yield production., (© 2020 American Society of Plant Biologists. All Rights Reserved.)

Background: Gene expression is a key determinant of cellular response. Natural variation in gene expression bridges genetic variation to phenotypic alteration. Identification of the regulatory variants controlling the gene expression in response to drought, a major environmental threat of crop production worldwide, is of great value for drought-tolerant gene identification., Results: A total of 627 RNA-seq analyses are performed for 224 maize accessions which represent a wide genetic diversity under three water regimes; 73,573 eQTLs are detected for about 30,000 expressing genes with high-density genome-wide single nucleotide polymorphisms, reflecting a comprehensive and dynamic genetic architecture of gene expression in response to drought. The regulatory variants controlling the gene expression constitutively or drought-dynamically are unraveled. Focusing on dynamic regulatory variants resolved to genes encoding transcription factors, a drought-responsive network reflecting a hierarchy of transcription factors and their target genes is built. Moreover, 97 genes are prioritized to associate with drought tolerance due to their expression variations through the Mendelian randomization analysis. One of the candidate genes, Abscisic acid 8'-hydroxylase, is verified to play a negative role in plant drought tolerance., Conclusions: This study unravels the effects of genetic variants on gene expression dynamics in drought response which allows us to better understand the role of distal and proximal genetic effects on gene expression and phenotypic plasticity. The prioritized drought-associated genes may serve as direct targets for functional investigation or allelic mining.

Background: Identifying genotype-phenotype links and causative genes from quantitative trait loci (QTL) is challenging for complex agronomically important traits. To accelerate maize gene discovery and breeding, we present the Complete-diallel design plus Unbalanced Breeding-like Inter-Cross (CUBIC) population, consisting of 1404 individuals created by extensively inter-crossing 24 widely used Chinese maize founders., Results: Hundreds of QTL for 23 agronomic traits are uncovered with 14 million high-quality SNPs and a high-resolution identity-by-descent map, which account for an average of 75% of the heritability for each trait. We find epistasis contributes to phenotypic variance widely. Integrative cross-population analysis and cross-omics mapping allow effective and rapid discovery of underlying genes, validated here with a case study on leaf width., Conclusions: Through the integration of experimental genetics and genomics, our study provides useful resources and gene mining strategies to explore complex quantitative traits.

The nutritional traits of maize kernels are important for human and animal nutrition, and these traits have undergone selection to meet the diverse nutritional needs of humans. However, our knowledge of the genetic basis of selecting for kernel nutritional traits is limited. Here, we identified both single and epistatic quantitative trait loci (QTLs) that contributed to the differences of oil and carotenoid traits between maize and teosinte. Over half of teosinte alleles of single QTLs increased the values of the detected oil and carotenoid traits. Based on the pleiotropism or linkage information of the identified single QTLs, we constructed a trait-locus network to help clarify the genetic basis of correlations among oil and carotenoid traits. Furthermore, the selection features and evolutionary trajectories of the genes or loci underlying variations in oil and carotenoid traits revealed that these nutritional traits produced diverse selection events during maize domestication and improvement. To illustrate more, a mutator distance-relative transposable element (TE) in intron 1 of DXS2, which encoded a rate-limiting enzyme in the methylerythritol phosphate pathway, was identified to increase carotenoid biosynthesis by enhancing DXS2 expression. This TE occurs in the grass teosinte, and has been found to have undergone selection during maize domestication and improvement, and is almost fixed in yellow maize. Our findings not only provide important insights into evolutionary changes in nutritional traits, but also highlight the feasibility of reintroducing back into commercial agricultural germplasm those nutritionally important genes hidden in wild relatives., (© 2019 The Authors The Plant Journal © 2019 John Wiley & Sons Ltd.)

It is generally recognized that many favorable genes which were lost during domestication, including those related to both nutritional value and stress resistance, remain hidden in wild relatives. To uncover such genes in teosinte, an ancestor of maize, we conducted metabolite profiling in a BC 2 F 7 population generated from a cross between the maize wild relative (Zea mays ssp. mexicana) and maize inbred line Mo17. In total, 65 primary metabolites were quantified in four tissues (seedling-stage leaf, grouting-stage leaf, young kernel and mature kernel) with clear tissue-specific patterns emerging. Three hundred and fifty quantitative trait loci (QTLs) for these metabolites were obtained, which were distributed unevenly across the genome and included two QTL hotspots. Metabolite concentrations frequently increased in the presence of alleles from the teosinte genome while the opposite was observed for grain yield and shape trait QTLs. Combination of the multi-tissue transcriptome and metabolome data provided considerable insight into the metabolic variations between maize and its wild relatives. This study thus identifies favorable genes hidden in the wild relative which should allow us to balance high yield and quality in future modern crop breeding programs., (© 2019 The Authors The Plant Journal © 2019 John Wiley & Sons Ltd.)

Chromatin loops connect regulatory elements to their target genes. They serve as bridges between transcriptional regulation and phenotypic variation in mammals. However, spatial organization of regulatory elements and its impact on gene expression in plants remain unclear. Here, we characterize epigenetic features of active promoter proximal regions and candidate distal regulatory elements to construct high-resolution chromatin interaction maps for maize via long-read chromatin interaction analysis by paired-end tag sequencing (ChIA-PET). The maps indicate that chromatin loops are formed between regulatory elements, and that gene pairs between promoter proximal regions tend to be co-expressed. The maps also demonstrated the topological basis of quantitative trait loci which influence gene expression and phenotype. Many promoter proximal regions are involved in chromatin loops with distal regulatory elements, which regulate important agronomic traits. Collectively, these maps provide a high-resolution view of 3D maize genome architecture, and its role in gene expression and phenotypic variation.

Background: Root systems play important roles in crop growth and stress responses. Although genetic mechanism of root traits in maize (Zea mays L.) has been investigated in different mapping populations, root traits have rarely been utilized in breeding programs. Elucidation of the genetic basis of maize root traits and, more importantly, their connection to other agronomic trait(s), such as grain yield, may facilitate root trait manipulation and maize germplasm improvement. In this study, we analyzed genome-wide genetic loci for maize seedling root traits at three time-points after seed germination to identify chromosomal regions responsible for both seedling root traits and other agronomic traits in a recombinant inbred line (RIL) population (Zong3 × Yu87-1)., Results: Eight seedling root traits were examined at 4, 9, and 14 days after seed germination, and thirty-six putative quantitative trait loci (QTLs), accounting for 9.0-23.2% of the phenotypic variation in root traits, were detected. Co-localization of root trait QTLs was observed at, but not between, the three time-points. We identified strong or moderate correlations between root traits controlled by each co-localized QTL region. Furthermore, we identified an overlap in the QTL locations of seedling root traits examined here and six other traits reported previously in the same RIL population, including grain yield-related traits, plant height-related traits, and traits in relation to stress responses. Maize chromosomal bins 1.02-1.03, 1.07, 2.06-2.07, 5.05, 7.02-7.03, 9.04, and 10.06 were identified QTL hotspots for three or four more traits in addition to seedling root traits., Conclusions: Our identification of co-localization of root trait QTLs at, but not between, each of the three time-points suggests that maize seedling root traits are regulated by different sets of pleiotropic-effect QTLs at different developmental stages. Furthermore, the identification of QTL hotspots suggests the genetic association of seedling root traits with several other traits and reveals maize chromosomal regions valuable for marker-assisted selection to improve root systems and other agronomic traits simultaneously.

Background: Residual heterozygosity (RH) in advanced inbred lines of plants benefits quantitative trait locus (QTL) mapping studies. However, knowledge of factors affecting the genome-wide distribution of RH remains limited., Results: A set of 2196 heterogeneous inbred family (HIF) maize lines derived from 12 recombinant inbred line (RIL) populations was genotyped using the Maize50K SNP chip. A total of 18,615 unique RH intervals were identified, ranging from 505 to 2095 intervals per population, with average maize genome coverage of 94.8%. Across all populations, there were 8.6 RH intervals per HIF line on average, ranging from 1.8 to 14 intervals; the average size of an RH interval was approximately 58.7 Mb, ranging from 7.2 to 74.1 Mb. A given RH region was present in an average of 5 different individuals within a population. Seven RH hotspots, where RH segments were enriched in the genome, were found to be subject to selection during population development. The RH patterns varied significantly across populations, presumably reflecting differences in the genetic background of each population, and 8 QTLs were found to affect heterozygosity levels in the RH hotspots. The potential use of this HIF library for the fine mapping of QTLs was assessed based on publicly available QTL information, achieving a ≤ 1 Mb resolution on average., Conclusion: The examined library of HIF lines offers insight into the RH landscape and its intraspecific variation and provides a useful resource for the QTL cloning of important agronomic traits in maize.

Maize ( Zea mays ) is a major staple crop. Maize kernel size and weight are important contributors to its yield. Here, we measured kernel length, kernel width, kernel thickness, hundred kernel weight, and kernel test weight in 10 recombinant inbred line populations and dissected their genetic architecture using three statistical models. In total, 729 quantitative trait loci (QTLs) were identified, many of which were identified in all three models, including 22 major QTLs that each can explain more than 10% of phenotypic variation. To provide candidate genes for these QTLs, we identified 30 maize genes that are orthologs of 18 rice ( Oryza sativa ) genes reported to affect rice seed size or weight. Interestingly, 24 of these 30 genes are located in the identified QTLs or within 1 Mb of the significant single-nucleotide polymorphisms. We further confirmed the effects of five genes on maize kernel size/weight in an independent association mapping panel with 540 lines by candidate gene association analysis. Lastly, the function of ZmINCW1 , a homolog of rice GRAIN INCOMPLETE FILLING1 that affects seed size and weight, was characterized in detail. ZmINCW1 is close to QTL peaks for kernel size/weight (less than 1 Mb) and contains significant single-nucleotide polymorphisms affecting kernel size/weight in the association panel. Overexpression of this gene can rescue the reduced weight of the Arabidopsis ( Arabidopsis thaliana ) homozygous mutant line in the AtcwINV2 gene (Arabidopsis ortholog of ZmINCW1 ). These results indicate that the molecular mechanisms affecting seed development are conserved in maize, rice, and possibly Arabidopsis., (© 2017 American Society of Plant Biologists. All Rights Reserved.)

Plant architecture is a key factor affecting planting density and grain yield in maize ( Zea mays ). However, the genetic mechanisms underlying plant architecture in diverse genetic backgrounds have not been fully addressed. Here, we performed a large-scale phenotyping of 10 plant architecture-related traits and dissected the genetic loci controlling these traits in 10 recombinant inbred line populations derived from 14 diverse genetic backgrounds. Nearly 800 quantitative trait loci (QTLs) with major and minor effects were identified as contributing to the phenotypic variation of plant architecture-related traits. Ninety-two percent of these QTLs were detected in only one population, confirming the diverse genetic backgrounds of the mapping populations and the prevalence of rare alleles in maize. The numbers and effects of QTLs are positively associated with the phenotypic variation in the population, which, in turn, correlates positively with parental phenotypic and genetic variations. A large proportion (38.5%) of QTLs was associated with at least two traits, suggestive of the frequent occurrence of pleiotropic loci or closely linked loci. Key developmental genes, which previously were shown to affect plant architecture in mutant studies, were found to colocalize with many QTLs. Five QTLs were further validated using the segregating populations developed from residual heterozygous lines present in the recombinant inbred line populations. Additionally, one new plant height QTL, qPH3 , has been fine-mapped to a 600-kb genomic region where three candidate genes are located. These results provide insights into the genetic mechanisms controlling plant architecture and will benefit the selection of ideal plant architecture in maize breeding., (© 2017 American Society of Plant Biologists. All Rights Reserved.)

A detailed understanding of genetic architecture of mRNA expression by millions of genetic variants is important for studying quantitative trait variation. In this study, we identified 1.25M SNPs with a minor allele frequency greater than 0.05 by combining reduced genome sequencing (GBS), high-density array technologies (600K), and previous deep RNA-sequencing data from 368 diverse inbred lines of maize. The balanced allelic frequencies and distributions in a relatively large and diverse natural panel helped to identify expression quantitative trait loci (eQTLs) associated with more than 18 000 genes (63.4% of tested genes). We found that distant eQTLs were more frequent (∼75% of all eQTLs) across the whole genome. Thirteen novel associated loci affecting maize kernel oil concentration were identified using the new dataset, among which one intergenic locus affected the kernel oil variation by controlling expression of three other known oil-related genes. Altogether, this study provides resources for expanding our understanding of cellular regulatory mechanisms of transcriptome variation and the landscape of functional variants within the maize genome, thereby enhancing the understanding of quantitative variations., (Copyright © 2016 The Author. Published by Elsevier Inc. All rights reserved.)

With increasing demand for novel traits in crop breeding, the plant research community faces the challenge of quantitatively analyzing the structure and function of large numbers of plants. A clear goal of high-throughput phenotyping is to bridge the gap between genomics and phenomics. In this study, we quantified 106 traits from a maize ( Zea mays ) recombinant inbred line population ( n = 167) across 16 developmental stages using the automatic phenotyping platform. Quantitative trait locus (QTL) mapping with a high-density genetic linkage map, including 2,496 recombinant bins, was used to uncover the genetic basis of these complex agronomic traits, and 988 QTLs have been identified for all investigated traits, including three QTL hotspots. Biomass accumulation and final yield were predicted using a combination of dissected traits in the early growth stage. These results reveal the dynamic genetic architecture of maize plant growth and enhance ideotype-based maize breeding and prediction., (© 2017 American Society of Plant Biologists. All Rights Reserved.)

Key Message: Sixteen major QTLs regulating maize kernel traits were mapped in multiple environments and one of them, qKW - 9.2 , was restricted to 630 Kb, harboring 28 putative gene models. To elucidate the genetic basis of kernel traits, a quantitative trait locus (QTL) analysis was conducted in a maize recombinant inbred line population derived from a cross between two diverse parents Zheng58 and SK, evaluated across eight environments. Construction of a high-density linkage map was based on 13,703 single-nucleotide polymorphism markers, covering 1860.9 cM of the whole genome. In total, 18, 26, 23, and 19 QTLs for kernel length, width, thickness, and 100-kernel weight, respectively, were detected on the basis of a single-environment analysis, and each QTL explained 3.2-23.7 % of the phenotypic variance. Sixteen major QTLs, which could explain greater than 10 % of the phenotypic variation, were mapped in multiple environments, implying that kernel traits might be controlled by many minor and multiple major QTLs. The major QTL qKW-9.2 with physical confidence interval of 1.68 Mbp, affecting kernel width, was then selected for fine mapping using heterogeneous inbred families. At final, the location of the underlying gene was narrowed down to 630 Kb, harboring 28 putative candidate-gene models. This information will enhance molecular breeding for kernel traits and simultaneously assist the gene cloning underlying this QTL, helping to reveal the genetic basis of kernel development in maize.

Key Message: Maize kernel row number might be dominated by a set of large additive or partially dominant loci and several small dominant loci and can be accurately predicted by fewer than 300 top KRN-associated SNPs. Kernel row number (KRN) is an important yield component in maize and directly affects grain yield. In this study, we combined linkage and association mapping to uncover the genetic architecture of maize KRN and to evaluate the phenotypic predictability using these detected loci. A genome-wide association study revealed 31 associated single nucleotide polymorphisms (SNPs) representing 17 genomic loci with an effect in at least one of five individual environments and the best linear unbiased prediction (BLUP) over all environments. Linkage mapping in three F2:3 populations identified 33 KRN quantitative trait loci (QTLs) representing 21 QTLs common to several population/environments. The majority of these common QTLs that displayed a large effect were additive or partially dominant. We found 70% KRN-associated genomic loci were mapped in KRN QTLs identified in this study, KRN-associated SNP hotspots detected in NAM population and/or previous identified KRN QTL hotspots. Furthermore, the KRN of inbred lines and hybrids could be predicted by the additive effect of the SNPs, which was estimated using inbred lines as a training set. The prediction accuracy using the top KRN-associated tag SNPs was obviously higher than that of the randomly selected SNPs, and approximately 300 top KRN-associated tag SNPs were sufficient for predicting the KRN of the inbred lines and hybrids. The results suggest that the KRN-associated loci and QTLs that were detected in this study show great potential for improving the KRN with genomic selection in maize breeding.

Head smut is a systemic disease in maize caused by the soil-borne fungus Sporisorium reilianum that poses a grave threat to maize production worldwide. A major head smut quantitative resistance locus, qHSR1, has been detected on maize chromosome bin2.09. Here we report the map-based cloning of qHSR1 and the molecular mechanism of qHSR1-mediated resistance. Sequential fine mapping and transgenic complementation demonstrated that ZmWAK is the gene within qHSR1 conferring quantitative resistance to maize head smut. ZmWAK spans the plasma membrane, potentially serving as a receptor-like kinase to perceive and transduce extracellular signals. ZmWAK was highly expressed in the mesocotyl of seedlings where it arrested biotrophic growth of the endophytic S. reilianum. Impaired expression in the mesocotyl compromised ZmWAK-mediated resistance. Deletion of the ZmWAK locus appears to have occurred after domestication and spread among maize germplasm, and the ZmWAK kinase domain underwent functional constraints during maize evolution.

Association mapping is a powerful approach for dissecting the genetic architecture of complex quantitative traits using high-density SNP markers in maize. Here, we expanded our association panel size from 368 to 513 inbred lines with 0.5 million high quality SNPs using a two-step data-imputation method which combines identity by descent (IBD) based projection and k-nearest neighbor (KNN) algorithm. Genome-wide association studies (GWAS) were carried out for 17 agronomic traits with a panel of 513 inbred lines applying both mixed linear model (MLM) and a new method, the Anderson-Darling (A-D) test. Ten loci for five traits were identified using the MLM method at the Bonferroni-corrected threshold -log10 (P) >5.74 (α=1). Many loci ranging from one to 34 loci (107 loci for plant height) were identified for 17 traits using the A-D test at the Bonferroni-corrected threshold -log10 (P) >7.05 (α=0.05) using 556809 SNPs. Many known loci and new candidate loci were only observed by the A-D test, a few of which were also detected in independent linkage analysis. This study indicates that combining IBD based projection and KNN algorithm is an efficient imputation method for inferring large missing genotype segments. In addition, we showed that the A-D test is a useful complement for GWAS analysis of complex quantitative traits. Especially for traits with abnormal phenotype distribution, controlled by moderate effect loci or rare variations, the A-D test balances false positives and statistical power. The candidate SNPs and associated genes also provide a rich resource for maize genetics and breeding.

Kernel oil content in maize is a complex quantitative trait. Phenotypic variation in kernel oil content can be dissected into its component traits such as oil metabolism and physical characteristics of the kernel, including embryo size and embryo-to-endosperm weight ratio (EEWR). To characterize quantitative trait loci (QTL) for kernel oil content, a recombinant inbred population derived from a cross between normal line B73 and high-oil line By804 was genotyped using 228 molecular markers and phenotyped for kernel oil content and its component traits [embryo oil content, embryo oil concentration, EEWR, embryo volume, embryo width, embryo length, and embryo width-to-length ratio (EWLR)]. A total of 58 QTL were identified for kernel oil content and its component traits in 26 genomic regions across all chromosomes. Eight main-effect QTL were identified for kernel oil content, embryo oil content, embryo oil concentration, EEWR, embryo weight, and EWLR, each accounting for over 10 % of the phenotypic variation in six genomic regions. Over 90 % of QTL identified for kernel oil content co-localized with QTL for component traits, validating their molecular contribution to kernel oil content. On chromosome 1, the QTL that had the largest effect on kernel oil content (qKO1-1) was associated with embryo width; on chromosome 9, the QTL for kernel oil content (qKO9) was related to EEWR (qEEWR9). Embryo oil concentration and embryo width were identified as the most important component traits controlling the second largest QTL for kernel oil content on chromosome 6 (qKO6) and a minor QTL for kernel oil content on chromosome 5 (qKO5-2), respectively. The dissection of kernel oil QTL will facilitate future cloning and/or functional validation of kernel oil content, and help to elucidate the genetic basis of kernel oil content in maize.

Association mapping based on the linkage disequilibrium provides a promising tool to identify genes responsible for quantitative variations underlying complex traits. Presented here is a maize association mapping panel consisting of 155 inbred lines with mainly temperate germplasm, which was phenotyped for 34 traits and genotyped using 82 SSRs and 1,536 SNPs. Abundant phenotypic and genetic diversities were observed within the panel based on the phenotypic and genotypic analysis. A model-based analysis using 82 SSRs assigned all inbred lines to two groups with eight subgroups. The relative kinship matrix was calculated using 884 SNPs with minor allele frequency > or = 20% indicating that no or weak relationships were identified for most individual pairs. Three traits (total tocopherol content in maize kernel, plant height and kernel length) and 1,414 SNPs with missing data < 20% were used to evaluate the performance of four models for association mapping analysis. For all traits, the model controlling relative kinship (K) performed better than the model controlling population structure (Q), and similarly to the model controlling both population structure and relative kinship (Q + K) in this panel. Our results suggest this maize panel can be used for association mapping analysis targeting multiple agronomic and quality traits with optimal association model.

Lipids play an important role in plants due to their abundance and their extensive participation in many metabolic processes. Genes involved in lipid metabolism have been extensively studied in Arabidopsis and other plant species. In this study, a total of 1003 maize lipid-related genes were cloned and annotated, including 42 genes with experimental validation, 732 genes with full-length cDNA and protein sequences in public databases and 229 newly cloned genes. Ninety-seven maize lipid-related genes with tissue-preferential expression were discovered by in silico gene expression profiling based on 1984483 maize Expressed Sequence Tags collected from 182 cDNA libraries. Meanwhile, 70 QTL clusters for maize kernel oil were identified, covering 34.5% of the maize genome. Fifty-nine (84%) QTL clusters co-located with at least one lipid-related gene, and the total number of these genes amounted to 147. Interestingly, thirteen genes with kernel-preferential expression profiles fell within QTL clusters for maize kernel oil content. All the maize lipid-related genes identified here may provide good targets for maize kernel oil QTL cloning and thus help us to better understand the molecular mechanism of maize kernel oil accumulation.

High-oil maize is a useful genetic resource for genomic investigation in plants. To determine the genetic basis of oil concentration and composition in maize grain, a recombinant inbred population derived from a cross between normal line B73 and high-oil line By804 was phenotyped using gas chromatography, and genotyped with 228 molecular markers. A total of 42 individual QTL, associated with fatty acid compositions and oil concentration, were detected in 21 genomic regions. Five major QTL were identified for measured traits, one each of which explained 42.0% of phenotypic variance for palmitic acid, 15.0% for stearic acid, 27.7% for oleic acid, 48.3% for linoleic acid, and 15.7% for oil concentration in the RIL population. Thirty-six loci were involved in 24 molecular marker pairs of epistatic interactions across all traits, which explained phenotypic variances ranging from 0.4 to 6.1%. Seven of 18 mapping candidate genes related to lipid metabolism were localized within or were close to identified individual QTL, explaining 0.7-13.2% of the population variance. These results demonstrated that a few major QTL with large additive effects could play an important role in attending fatty acid compositions and increasing oil concentration in used germplasm. A larger number of minor QTL and a certain number of epistatic QTL, both with additive effects, also contributed to fatty acid compositions and oil concentration.