Analysis of the genetic architecture of maize kernel size traits by combined linkage and association mapping

Kernel size‐related traits are the most direct traits correlating with grain yield. The genetic basis of three kernel traits of maize, kernel length (KL), kernel width (KW) and kernel thickness (KT), was investigated in an association panel and a biparental population. A total of 21 single nucleotide polymorphisms (SNPs) were detected to be most significantly (P < 2.25 × 10^?6) associated with these three traits in the association panel under four environments. Furthermore, 50 quantitative trait loci (QTL) controlling these traits were detected in seven environments in the intermated B73 × Mo17 (IBM) Syn10 doubled haploid (DH) population, of which eight were repetitively identified in at least three environments. Combining the two mapping populations revealed that 56 SNPs (P < 1 × 10^?3) fell within 18 of the QTL confidence intervals. According to the top significant SNPs, stable‐effect SNPs and the co‐localized SNPs by association analysis and linkage mapping, a total of 73 candidate genes were identified, regulating seed development. Additionally, seven miRNAs were found to situate within the linkage disequilibrium (LD) regions of the co‐localized SNPs, of which zma‐miR164e was demonstrated to cleave the mRNAs of Arabidopsis CUC1, CUC2 and NAC6 in vitro. Overexpression of zma‐miR164e resulted in the down‐regulation of these genes above and the failure of seed formation in Arabidopsis pods, with the increased branch number. These findings provide insights into the mechanism of seed development and the improvement of molecular marker‐assisted selection (MAS) for high‐yield breeding in maize.     

The genetic architecture of amino acids dissection by association and linkage analysis in maize

Amino acids are both constituents of proteins, providing the essential nutrition for humans and animals, and signalling molecules regulating the growth and development of plants. Most cultivars of maize are deficient in essential amino acids such as lysine and tryptophan. Here, we measured the levels of 17 different total amino acids, and created 48 derived traits in mature kernels from a maize diversity inbred collection and three recombinant inbred line (RIL) populations. By GWAS, 247 and 281 significant loci were identified in two different environments, 5.1 and 4.4 loci for each trait, explaining 7.44% and 7.90% phenotypic variation for each locus in average, respectively. By linkage mapping, 89, 150 and 165 QTLs were identified in B73/By804, Kui3/B77 and Zong3/Yu87‐1 RIL populations, 2.0, 2.7 and 2.8 QTLs for each trait, explaining 13.6%, 16.4% and 21.4% phenotypic variation for each QTL in average, respectively. It implies that the genetic architecture of amino acids is relative simple and controlled by limited loci. About 43.2% of the loci identified by GWAS were verified by expression QTL, and 17 loci overlapped with mapped QTLs in the three RIL populations. GRMZM2G015534, GRMZM2G143008 and one QTL were further validated using molecular approaches. The amino acid biosynthetic and catabolic pathways were reconstructed on the basis of candidate genes proposed in this study. Our results provide insights into the genetic basis of amino acid biosynthesis in maize kernels and may facilitate marker‐based breeding for quality protein maize.     

Combined linkage mapping and association analysis reveals genetic control of maize kernel moisture content

The free moisture in crop kernels after being naturally dried is referred to as kernel moisture content (KMC). Maize KMC reflects grain quality and influences transportation and storage of seeds. We used an IBM Syn10 DH maize population consisting of 249 lines and an association panel comprising 310 maize inbred lines to identify the genetic loci affecting maize KMC in three environments. Using the IBM population detected 13 QTL on seven chromosomes, which were clustered into nine common QTL. Genome-wide association analysis (GWAS) identified 16 significant SNPs across the 3 environments, which were linked to 158 genes across the three environments. Combined QTL mapping and GWAS found two SNPs that were located in two of the mapped QTL, respectively. Twenty-three genes were linked with the loci co-localized in both populations. Of these 181 genes, five have previously been reported to be associated with KMC or to regulate seed development. These associations were verified by candidate gene association analysis. Two superior alleles and one favorable haplotype for Zm00001d007774 and Zm00001d047868 were found to influence KMC. These findings provide insights into molecular mechanisms underlying maize KMC and contribute to the use of marker-assisted selection for breeding low-KMC maize.     

Molecular dissection of QTL governing grain size traits employing association and linkage mapping in Basmati rice

Grain size is one of the key traits that determines the quality of Basmati rice from the consumers’ as well as the traders’ point of view. Though many genes governing grain size have been identified in indica and japonica, little work has been done in Basmati rice. The present study aims at dissection of a QTL region governing grain size traits in Basmati employing association and linkage mapping approaches. Association mapping revealed that three markers, i.e., RM 6024 (grain breadth), RM1237 and RM18582 (grain length-breadth ratio), which cover 889 kb in the targeted QTL region have been significantly associated with grain size traits. Using linkage mapping, the targeted QTL region has been further delimited to a physical distance of 268 kb that comprises 24 annotated genes. The gene expression analysis of parents, revealed 19 genes differentially expressing within the QTL. Of them, 15 genes showed high expression in Basmati370, while four were expressed in Jaya, and whereas five genes did not show any differential expression between parents. Among differentially expressed genes, a highly expressed gene in Basmati370, Os05g0374200 (Cytokinin dehydrogenase 1 precursor) seems to be involved in accumulation of cytokinins, thus affecting the grain size. Therefore, our findings demonstrated that by complimenting association and linkage mapping, it is likely to dissect a QTL governing grain size traits in Basmati rice and also the QTL could be a potential target for marker-assisted breeding and map-based cloning studies.     

Genetic Power Calculator: design of linkage and association genetic mapping studies of complex traits

SUMMARY: A website for performing power calculations for the design of linkage and association genetic mapping studies of complex traits. AVAILABILITY: The package is made available athttp://statgen.iop.kcl.ac.uk/gpc/.     

High-density linkage mapping of vitamin E content in maize grain

Vitamin E refers to eight distinct compounds collectively known as tocochromanols and can be further divided into two classes, tocotrienols and tocopherols. Tocochromanols are the major lipid-soluble antioxidants in maize (Zea mays L.) grain. Enhancing vitamin E content of maize through plant breeding has important implications for human and animal nutrition. Four inbred lines exhibiting unique variation for tocochromanol compounds were chosen from the Goodman maize diversity panel to construct two biparental mapping populations (N6xNC296 and E2558xCo125). The N6xNC296 population was developed to analyze segregation for α-tocopherol and α-tocotrienol content. The E2558WxCo125 population was developed to analyze segregation for the ratio of total tocotrienols to tocopherols. The tocochromanol variation in two replicates of each population was quantified using liquid chromatography-diode array detection. Using high-density linkage mapping, novel quantitative trait loci (QTL) in the N6xNC296 population were mapped using tocopherol ratio traits. These QTL contain the candidate gene homogentisate phytyltransferase (ZmVTE2) within the respective support intervals. This locus was not mapped in a previous genome-wide association study that analyzed tocochromanols in the Goodman diversity panel. Transgressive segregation was observed for γ- and α-tocochromanols in these populations, which facilitated QTL identification. These QTL and transgressive segregant families can be used in selection programs for vitamin E enhancement in maize. This work illustrates the complementary nature of biparental mapping populations and genome-wide association studies to further characterize genetic variation of tocochromanol content in maize grain.     

Genetic architecture of maize kernel composition in the nested association mapping and inbred association panels

The maize (Zea mays) kernel plays a critical role in feeding humans and livestock around the world and in a wide array of industrial applications. An understanding of the regulation of kernel starch, protein, and oil is needed in order to manipulate composition to meet future needs. We conducted joint-linkage quantitative trait locus mapping and genome-wide association studies (GWAS) for kernel starch, protein, and oil in the maize nested association mapping population, composed of 25 recombinant inbred line families derived from diverse inbred lines. Joint-linkage mapping revealed that the genetic architecture of kernel composition traits is controlled by 21-26 quantitative trait loci. Numerous GWAS associations were detected, including several oil and starch associations in acyl-CoA:diacylglycerol acyltransferase1-2, a gene that regulates oil composition and quantity. Results from nested association mapping were verified in a 282 inbred association panel using both GWAS and candidate gene association approaches. We identified many beneficial alleles that will be useful for improving kernel starch, protein, and oil content.     

Genetic basis of maize stalk strength decoded via linkage and association mapping

Stalk lodging is a severe problem that limits maize production worldwide, although little attention has been given to its genetic basis. Here we measured rind penetrometer resistance (RPR), an effective index for stalk lodging, in a multi-parent population of 1948 recombinant inbred lines (RILs) and an association population of 508 inbred lines (AMP508). Linkage and association mapping identified 53 and 29 single quantitative trait loci (QTLs) and 50 and 19 pairs of epistatic interactions for RPR in the multi-parent population and AMP508 population, respectively. Phenotypic variation explained by all identified epistatic QTLs (up to ~5%) was much less than that explained by all single additive QTLs (up to ~33% in the multi-parent population and ~ 60% in the AMP508 population). Among all detected QTLs, only eight single QTLs explained >10% of phenotypic variation in single RIL populations. Alleles that increased RPR were enriched in tropical/subtropical (TST) groups from the AMP508 population. Based on genome-wide association studies in both populations, we identified 137 candidate genes affecting RPR, which were assigned to multiple biological processes, such as the biosynthesis of cell wall components. Sixty-six candidate genes were cross-validated by multiple methods or populations. Most importantly, 23 candidate genes were upregulated or downregulated in high-RPR lines relative to low-RPR lines, supporting the associations between candidate genes and RPR. These findings reveal the complex nature of the genetic basis underlying RPR and provide loci or candidate genes for developing elite varieties that are resistant to stalk lodging via molecular breeding.     

Gene mapping by linkage and association analysis

Genetic analysis is used to map genes, including disease loci, to positions within the human genome. Linkage analysis depends on the co-segregation of a gene (locus) and a phenotype through a pedigree, while association analysis, or linkage disequilibrium mapping, depends on measuring deviation from the random occurrence of alleles in a haplotype in unrelated individuals or nuclear families. Complex computer programs may be used in both forms of analysis. In recent years most interest has focused on identifying genes involved in common, multifactorial diseases. Here I review some current and developing techniques of genetic analysis and give references to where further information can be obtained.     

Map-based molecular diversity, linkage disequilibrium and association mapping of fruit traits in melon

Melon has tremendous fruit diversity, the product of complex interactions of consumer preferences in different countries and a wide range of agro-climatic zones. Understanding footprints of divergence underlying formation of various morphotypes is important for developing sustainable and high-quality melons. Basic understanding of population structure and linkage disequilibrium (LD) is limited in melon and has lagged behind other crops. Characterization of population structure and LD are essential for carrying out association mapping of quantitative trait loci (QTL) underlying various complex traits. Mapped single-locus microsatellite markers are known to be very valuable for resolving the population structure and 268 such markers were used in the current study to resolve population structure and LD pattern using 87 accessions of melons belonging to Eastern European, Euro-North American and Asian types. A mixed linear model was implemented to detect QTL for various fruit traits. Various levels of QTL with high to moderate stringency were detected for fruit shape, fruit weight, soluble solids, and rind pressure and a majority of them was found to be in agreement with the previously published data, indicating that association mapping can be very useful for melon molecular breeding. Minor discrepancies in the position, strength and the variation explained by the QTL present between the methods of association and recombinant mapping approaches can be bridged if more melon groups and larger sets of accessions are involved in future studies, combined with high-throughput marker panels.     

QTLNetwork: mapping and visualizing genetic architecture of complex traits in experimental populations

SUMMARY: QTLNetwork is a software package for mapping and visualizing the genetic architecture underlying complex traits for experimental populations derived from a cross between two inbred lines. It can simultaneously map quantitative trait loci (QTL) with individual effects, epistasis and QTL-environment interaction. Currently, it is able to handle data from F(2), backcross, recombinant inbred lines and double-haploid populations, as well as populations from specific mating designs (immortalized F(2) and BC(n)F(n) populations). The Windows version of QTLNetwork was developed with a graphical user interface. Alternatively, the command-line versions have the facility to be run in other prevalent operating systems, such as Linux, Unix and MacOS. AVAILABILITY: http://ibi.zju.edu.cn/software/qtlnetwork.     

Genome-wide association study reveals the genetic architecture of 27 agronomic traits in tomato

Tomato (Solanum lycopersicum) is a highly valuable fruit crop, and yield is one of the most important agronomic traits. However, the genetic architecture underlying tomato yield-related traits has not been fully addressed. Based on ∼4.4 million single nucleotide polymorphisms obtained from 605 diverse accessions, we performed a comprehensive genome-wide association study for 27 agronomic traits in tomato. A total of 239 significant associations corresponding to 129 loci, harboring many previously reported and additional genes related to vegetative and reproductive development, were identified, and these loci explained an average of ∼8.8% of the phenotypic variance. A total of 51 loci associated with 25 traits have been under selection during tomato domestication and improvement. Furthermore, a candidate gene, Sl-ACTIVATED MALATE TRANSPORTER15, that encodes an aluminum-activated malate transporter was functionally characterized and shown to act as a pivotal regulator of leaf stomata formation, thereby affecting photosynthesis and drought resistance. This study provides valuable information for tomato genetic research and breeding.

A large-scale genome-wide association study sheds light on genetic and genomic bases underlying important yield-related traits in tomato.     

Ovine alpha-amylase genes: isolation, linkage mapping and association analysis with milk traits

On the basis of comparisons between cattle and sheep genome mapping information the ovine alpha-amylase gene was examined as a possible genetic marker for milk traits in sheep. The objective of the present study was to isolate, map and determine whether this gene is a candidate gene for milk traits. DNA fragments (832 and 2360 bp) corresponding to two different AMY genes were isolated, and one SNP in intron 3 and one GTG deletion in exon 3 of the 2360 bp DNA fragment were found. The 2360 bp ovine AMY DNA fragment was located on chromosome 1 by linkage mapping using the International Mapping Flock. No association was found between estimated breeding values for milk yield, protein and fat contents and AMY genotypes in a daughter design comprising 13 Manchega families with an average of 29 daughters (12-62) per sire.     

Functional mapping - how to map and study the genetic architecture of dynamic complex traits

The development of any organism is a complex dynamic process that is controlled by a network of genes as well as by environmental factors. Traditional mapping approaches for analysing phenotypic data measured at a single time point are too simple to reveal the genetic control of developmental processes. A general statistical mapping framework, called functional mapping, has been proposed to characterize, in a single step, the quantitative trait loci (QTLs) or nucleotides (QTNs) that underlie a complex dynamic trait. Functional mapping estimates mathematical parameters that describe the developmental mechanisms of trait formation and expression for each QTL or QTN. The approach provides a useful quantitative and testable framework for assessing the interplay between gene actions or interactions and developmental changes.     

Two-trait-locus linkage analysis: a powerful strategy for mapping complex genetic traits.

Recent advances in molecular biology have provided geneticists with ever-increasing numbers of highly polymorphic genetic markers that have made possible linkage mapping of loci responsible for many human diseases. However, nearly all diseases mapped to date follow clear Mendelian, single-locus segregation patterns. In contrast, many common familial diseases such as diabetes, psoriasis, several forms of cancer, and schizophrenia are familial and appear to have a genetic component but do not exhibit simple Mendelian transmission. More complex models are required to explain the genetics of these important diseases. In this paper, we explore two-trait-locus, two-marker-locus linkage analysis in which two trait loci are mapped simultaneously to separate genetic markers. We compare the utility of this approach to standard one-trait-locus, one-marker-locus linkage analysis with and without allowance for heterogeneity. We also compare the utility of the two-trait-locus, two-marker-locus analysis to two-trait-locus, one-marker-locus linkage analysis. For common diseases, pedigrees are often bilineal, with disease genes entering via two or more unrelated pedigree members. Since such pedigrees often are avoided in linkage studies, we also investigate the relative information content of unilineal and bilineal pedigrees. For the dominant-or-recessive and threshold models that we consider, we find that two-trait-locus, two-marker-locus linkage analysis can provide substantially more linkage information, as measured by expected maximum lod score, than standard one-trait-locus, one-marker-locus methods, even allowing for heterogeneity, while, for a dominant-or-dominant generating model, one-locus models that allow for heterogeneity extract essentially as much information as the two-trait-locus methods. For these three models, we also find that bilineal pedigrees provide sufficient linkage information to warrant their inclusion in such studies. We also discuss strategies for assessing the significance of the two linkages assumed in two-trait-locus, two-marker-locus models.     

Beyond pathways: genetic dissection of tocopherol content in maize kernels by combining linkage and association analyses

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.     

Improvement of mapping accuracy by unifying linkage and association analysis

It is well known that pedigree/family data record information on the coexistence in founder haplotypes of alleles at nearby loci and the cotransmission from parent to offspring that reveal different, but complementary, profiles of the genetic architecture. Either conventional linkage analysis that assumes linkage equilibrium or family-based association tests (FBATs) capture only partial information, leading to inefficiency. For example, FBATs will fail to detect even very tight linkage in the case where no allelic association exists, while a violation of the assumption of linkage equilibrium will result in biased estimation and reduced efficiency in linkage mapping. In this article, by using a data augmentation technique and the EM algorithm, we propose a likelihood-based approach that embeds both linkage and association analyses into a unified framework for general pedigree data. Relative to either linkage or association analysis, the proposed approach is expected to have greater estimation accuracy and power. Monte Carlo simulations support our theoretical expectations and demonstrate that our new methodology: (1) is more powerful than either FBATs or classic linkage analysis; (2) can unbiasedly estimate genetic parameters regardless of whether association exists, thus remedying the bias and less precision of traditional linkage analysis in the presence of association; and (3) is capable of identifying tight linkage alone. The new approach also holds the theoretical advantage that it can extract statistical information to the maximum extent and thereby improve mapping accuracy and power because it integrates multilocus population-based association study and pedigree-based linkage analysis into a coherent framework. Furthermore, our method is numerically stable and computationally efficient, as compared to existing parametric methods that use the simplex algorithm or Newton-type methods to maximize high-order multidimensional likelihood functions, and also offers the computation of Fisher's information matrix. Finally, we apply our methodology to a genetic study on bone mineral density (BMD) for the vitamin D receptor (VDR) gene and find that VDR is significantly linked to BMD at the one-third region of the wrist.