QTLminer: identifying genes regulating quantitative traits

Background  

Quantitative trait locus (QTL) mapping identifies genomic regions that likely contain genes regulating a quantitative trait. However, QTL regions may encompass tens to hundreds of genes. To find the most promising candidate genes that regulate the trait, the biologist typically collects information from multiple resources about the genes in the QTL interval. This process is very laborious and time consuming.     

solQTL: a tool for QTL analysis,visualization and linking to genomes at SGN database

Background  

A common approach to understanding the genetic basis of complex traits is through identification of associated quantitative trait loci (QTL). Fine mapping QTLs requires several generations of backcrosses and analysis of large populations, which is time-consuming and costly effort. Furthermore, as entire genomes are being sequenced and an increasing amount of genetic and expression data are being generated, a challenge remains: linking phenotypic variation to the underlying genomic variation. To identify candidate genes and understand the molecular basis underlying the phenotypic variation of traits, bioinformatic approaches are needed to exploit information such as genetic map, expression and whole genome sequence data of organisms in biological databases.     

Identification of quantitative trait loci and candidate genes for specific cellular resistance responses against Didymella pinodes in pea

Key message

Phenotyping of specific cellular resistance responses and improvement of previous genetic map allowed the identification of novel genomic regions controlling cellular mechanisms involved in pea resistance to ascochyta blight and provided candidate genes suitable for MAS.

Abstract

Didymella pinodes, causing ascochyta blight, is a major pathogen of the pea crop and is responsible for serious damage and yield losses. Resistance is inherited polygenically and several quantitative trait loci (QTLs) have been already identified. However, the position of these QTLs should be further refined to identify molecular markers more closely linked to the resistance genes. In previous works, resistance was scored visually estimating the final disease symptoms; in this study, we have conducted a more precise phenotyping of resistance evaluating specific cellular resistance responses at the histological level to perform a more accurate QTL analysis. In addition, P665 × Messire genetic map used to identify the QTLs was improved by adding 117 SNP markers located in genes. This combined approach has allowed the identification, for the first time, of genomic regions controlling cellular mechanisms directly involved in pea resistance to ascochyta blight. Furthermore, the inclusion of the gene-based SNP markers has allowed the identification of candidate genes co-located with QTLs and has provided robust markers for marker-assisted selection.     

Integration of gene-based markers in a pearl millet genetic map for identification of candidate genes underlying drought tolerance quantitative trait loci

Background  

Identification of genes underlying drought tolerance (DT) quantitative trait loci (QTLs) will facilitate understanding of molecular mechanisms of drought tolerance, and also will accelerate genetic improvement of pearl millet through marker-assisted selection. We report a map based on genes with assigned functional roles in plant adaptation to drought and other abiotic stresses and demonstrate its use in identifying candidate genes underlying a major DT-QTL.     

CASSIOPE: An expert system for conserved regions searches

Background  

Understanding genome evolution provides insight into biological mechanisms. For many years comparative genomics and analysis of conserved chromosomal regions have helped to unravel the mechanisms involved in genome evolution and their implications for the study of biological systems. Detection of conserved regions (descending from a common ancestor) not only helps clarify genome evolution but also makes it possible to identify quantitative trait loci (QTLs) and investigate gene function.     

Comparison of GWAS models to identify non-additive genetic control of flowering time in sunflower hybrids

Key message

This study compares five models of GWAS, to show the added value of non-additive modeling of allelic effects to identify genomic regions controlling flowering time of sunflower hybrids.

Abstract

Genome-wide association studies are a powerful and widely used tool to decipher the genetic control of complex traits. One of the main challenges for hybrid crops, such as maize or sunflower, is to model the hybrid vigor in the linear mixed models, considering the relatedness between individuals. Here, we compared two additive and three non-additive association models for their ability to identify genomic regions associated with flowering time in sunflower hybrids. A panel of 452 sunflower hybrids, corresponding to incomplete crossing between 36 male lines and 36 female lines, was phenotyped in five environments and genotyped for 2,204,423 SNPs. Intra-locus effects were estimated in multi-locus models to detect genomic regions associated with flowering time using the different models. Thirteen quantitative trait loci were identified in total, two with both model categories and one with only non-additive models. A quantitative trait loci on LG09, detected by both the additive and non-additive models, is located near a GAI homolog and is presented in detail. Overall, this study shows the added value of non-additive modeling of allelic effects for identifying genomic regions that control traits of interest and that could participate in the heterosis observed in hybrids.

     

The Human Obesity Gene Map: The 2003 Update

This is the tenth update of the human obesity gene map, incorporating published results up to the end of October 2003 and continuing the previous format. Evidence from single‐gene mutation obesity cases, Mendelian disorders exhibiting obesity as a clinical feature, quantitative trait loci (QTLs) from human genome‐wide scans and animal crossbreeding experiments, and association and linkage studies with candidate genes and other markers is reviewed. Transgenic and knockout murine models relevant to obesity are also incorporated (N = 55). As of October 2003, 41 Mendelian syndromes relevant to human obesity have been mapped to a genomic region, and causal genes or strong candidates have been identified for most of these syndromes. QTLs reported from animal models currently number 183. There are 208 human QTLs for obesity phenotypes from genome‐wide scans and candidate regions in targeted studies. A total of 35 genomic regions harbor QTLs replicated among two to five studies. Attempts to relate DNA sequence variation in specific genes to obesity phenotypes continue to grow, with 272 studies reporting positive associations with 90 candidate genes. Fifteen such candidate genes are supported by at least five positive studies. The obesity gene map shows putative loci on all chromosomes except Y. Overall, more than 430 genes, markers, and chromosomal regions have been associated or linked with human obesity phenotypes. The electronic version of the map with links to useful sites can be found at http:obesitygene.pbrc.edu .     

Mapping and validation of quantitative trait loci associated with concentrations of 16 elements in unmilled rice grain

Key Message

QTLs controlling the concentrations elements in rice grain were identified in two mapping populations. The QTLs were clustered such that most genomic regions were associated with more than one element.

Abstract

In this study, quantitative trait loci (QTLs) affecting the concentrations of 16 elements in whole, unmilled rice (Oryza sativa L.) grain were identified. Two rice mapping populations, the ‘Lemont’ × ‘TeQing’ recombinant inbred lines (LT-RILs), and the TeQing-into-Lemont backcross introgression lines (TILs) were used. To increase opportunity to detect and characterize QTLs, the TILs were grown under two contrasting field conditions, flooded and irrigated-but-unflooded. Correlations between the individual elements and between each element with grain shape, plant height, and time of heading were also studied. Transgressive segregation was observed among the LT-RILs for all elements. The 134 QTLs identified as associated with the grain concentrations of individual elements were found clustered into 39 genomic regions, 34 of which were found associated with grain element concentration in more than one population and/or flooding treatment. More QTLs were found significant among flooded TILs (92) than among unflooded TILs (47) or among flooded LT-RILs (40). Twenty-seven of the 40 QTLs identified among the LT-RILs were associated with the same element among the TILs. At least one QTL per element was validated in two or more population/environments. Nearly all of the grain element loci were linked to QTLs affecting additional elements, supporting the concept of element networks within plants. Several of the grain element QTLs co-located with QTLs for grain shape, plant height, and days to heading; but did not always differ for grain elemental concentration as predicted by those traits alone. A number of interesting patterns were found, including a strong Mg–P–K complex.     

Genomic selection and complex trait prediction using a fast EM algorithm applied to genome-wide markers

Background  

The information provided by dense genome-wide markers using high throughput technology is of considerable potential in human disease studies and livestock breeding programs. Genome-wide association studies relate individual single nucleotide polymorphisms (SNP) from dense SNP panels to individual measurements of complex traits, with the underlying assumption being that any association is caused by linkage disequilibrium (LD) between SNP and quantitative trait loci (QTL) affecting the trait. Often SNP are in genomic regions of no trait variation. Whole genome Bayesian models are an effective way of incorporating this and other important prior information into modelling. However a full Bayesian analysis is often not feasible due to the large computational time involved.     

The genetics of domestication of rice bean, Vigna umbellata

Background and Aims

The Asian genus Vigna, to which four cultivated species (rice bean, azuki bean, mung bean and black gram) belong, is suitable for comparative genomics. The aims were to construct a genetic linkage map of rice bean, to identify the genomic regions associated with domestication in rice bean, and to compare these regions with those in azuki bean.

Methods

A genetic linkage map was constructed by using simple sequence repeat and amplified fragment length polymorphism markers in the BC₁F₁ population derived from a cross between cultivated and wild rice bean. Using this map, 31 domestication-related traits were dissected into quantitative trait loci (QTLs). The genetic linkage map and QTLs of rice bean were compared with those of azuki bean.

Key Results

A total of 326 markers converged into 11 linkage groups (LGs), corresponding to the haploid number of rice bean chromosomes. The domestication-related traits in rice bean associated with a few major QTLs distributed as clusters on LGs 2, 4 and 7. A high level of co-linearity in marker order between the rice bean and azuki bean linkage maps was observed. Major QTLs in rice bean were found on LG4, whereas major QTLs in azuki bean were found on LG9.

Conclusions

This is the first report of a genetic linkage map and QTLs for domestication-related traits in rice bean. The inheritance of domestication-related traits was so simple that a few major QTLs explained the phenotypic variation between cultivated and wild rice bean. The high level of genomic synteny between rice bean and azuki bean facilitates QTL comparison between species. These results provide a genetic foundation for improvement of rice bean; interchange of major QTLs between rice bean and azuki bean might be useful for broadening the genetic variation of both species.     

Genetic analysis of vegetative branching in sorghum

Key message

We identified quantitative trait loci influencing plant architecture that may be valuable in breeding of optimized genotypes for sustainable food and/or cellulosic biomass production, and advancing resilience to changing climates.

Abstract

We describe a 3-year study to identify quantitative trait loci (QTLs) for vegetative branching of sorghum in a recombinant inbred line population of 161 genotypes derived from two morphologically distinct parents, S. bicolor × S. propinquum. We quantify vegetative branching based on morphological position and physiological status. Different sets of QTLs for different levels of branching were identified. QTLs discovered on chromosomes 1, 3, 7 and 8 affect multiple vegetative branching variables, suggesting that these regions may contain genes that control general axillary meristem initiation. Other regions that only influence one vegetative branching trait could contain genes that influence developmental processes contributing to divergent patterns of plant architecture. We investigate the relationship between vegetative branching patterns and dry biomass, and conclude that tillers with mature panicles and immature secondary branches each show consistent positive correlation with dry biomass. Among 19 branching-related genes from rice, eight sorghum homologs of seven rice genes are in syntenic blocks within branching-related QTL likelihood intervals. Five of these eight genes are within 700 kb of SNPs significantly associated with differences in branching in genome-wide association study of a diversity panel of 377 sorghum accessions, and three contain striking allelic variations between S. bicolor and S. propinquum that are likely to impact gene functions. Unraveling genetic determinants for vegetative branching may contribute to deterministic breeding of optimized genotypes for sustainable food and cellulosic biomass production in both optimal and marginal conditions, which are resilient to future climates that are more volatile and more stressful.     

Integrated analysis of leaf morphological and color traits in different populations of Chinese cabbage (<Emphasis Type="Italic">Brassica rapa</Emphasis> ssp. <Emphasis Type="Italic">pekinensis</Emphasis>)

Key message

QTLs and candidate gene markers associated with leaf morphological and color traits were identified in two immortalized populations of Brassica rapa, which will provide genetic information for marker-assisted breeding.

Abstract

Brassica rapa is an important leafy vegetable consumed worldwide and morphology is a key character for its breeding. To enhance genetic control, quantitative trait loci (QTLs) for leaf color and plant architecture were identified using two immortalized populations with replications of 2 and 4 years. Overall, 158 and 80 QTLs associated with 23 and 14 traits were detected in the DH and RIL populations, respectively. Among them, 23 common robust-QTLs belonging to 12 traits were detected in common loci over the replications. Through comparative analysis, five crucifer genetic blocks corresponding to morphology trait (R, J&U, F and E) and color trait (F, E) were identified in three major linkage groups (A2, A3 and A7). These might be key conserved genomic regions involved with the respective traits. Through synteny analysis with Arabidopsis, 64 candidate genes involved in chlorophyll biosynthesis, cell proliferation and elongation were co-localized within QTL intervals. Among them, SCO3, ABI3, FLU, HCF153, HEMB1, CAB3 were mapped within QTLs for leaf color; and CYCD3;1, CYCB2;4, AN3, ULT1 and ANT were co-localized in QTL regions for leaf size. These robust QTLs and their candidate genes provide useful information for further research into leaf architecture with crop breeding.

     

The Human Obesity Gene Map: The 2002 Update

This is the ninth update of the human obesity gene map, incorporating published results through October 2002 and continuing the previous format. Evidence from single‐gene mutation obesity cases, Mendelian disorders exhibiting obesity as a clinical feature, quantitative trait loci (QTLs) from human genome‐wide scans and various animal crossbreeding experiments, and association and linkage studies with candidate genes and other markers is reviewed. For the first time, transgenic and knockout murine models exhibiting obesity as a phenotype are incorporated (N = 38). As of October 2002, 33 Mendelian syndromes relevant to human obesity have been mapped to a genomic region, and the causal genes or strong candidates have been identified for 23 of these syndromes. QTLs reported from animal models currently number 168; there are 68 human QTLs for obesity phenotypes from genome‐wide scans. Additionally, significant linkage peaks with candidate genes have been identified in targeted studies. Seven genomic regions harbor QTLs replicated among two to five studies. Attempts to relate DNA sequence variation in specific genes to obesity phenotypes continue to grow, with 222 studies reporting positive associations with 71 candidate genes. Fifteen such candidate genes are supported by at least five positive studies. The obesity gene map shows putative loci on all chromosomes except Y. More than 300 genes, markers, and chromosomal regions have been associated or linked with human obesity phenotypes. The electronic version of the map with links to useful sites can be found at http:obesitygene.pbrc.edu .     

Using a system of differential equations that models cattle growth to uncover the genetic basis of complex traits

The interplay between dynamic models of biological systems and genomics is based on the assumption that genetic variation of the complex trait (i.e., outcome of model behavior) arises from component traits (i.e., model parameters) in lower hierarchical levels. In order to provide a proof of concept of this statement for a cattle growth model, we ask whether model parameters map genomic regions that harbor quantitative trait loci (QTLs) already described for the complex trait. We conducted a genome-wide association study (GWAS) with a Bayesian hierarchical LASSO method in two parameters of the Davis Growth Model, a system of three ordinary differential equations describing DNA accretion, protein synthesis and degradation, and fat synthesis. Phenotypic and genotypic data were available for 893 Nellore (Bos indicus) cattle. Computed values for parameter k₁ (DNA accretion rate) ranged from 0.005 ± 0.003 and for α (constant for energy for maintenance requirement) 0.134 ± 0.024. The expected biological interpretation of the parameters is confirmed by QTLs mapped for k₁ and α. QTLs within genomic regions mapped for k₁ are expected to be correlated with the DNA pool: body size and weight. Single nucleotide polymorphisms (SNPs) which were significant for α mapped QTLs that had already been associated with residual feed intake, feed conversion ratio, average daily gain (ADG), body weight, and also dry matter intake. SNPs identified for k₁ were able to additionally explain 2.2% of the phenotypic variability of the complex ADG, even when SNPs for k₁ did not match the genomic regions associated with ADG. Although improvements are needed, our findings suggest that genomic analysis on component traits may help to uncover the genetic basis of more complex traits, particularly when lower biological hierarchies are mechanistically described by mathematical simulation models.     

A genome-wide association study of seed protein and oil content in soybean

Background

Association analysis is an alternative to conventional family-based methods to detect the location of gene(s) or quantitative trait loci (QTL) and provides relatively high resolution in terms of defining the genome position of a gene or QTL. Seed protein and oil concentration are quantitative traits which are determined by the interaction among many genes with small to moderate genetic effects and their interaction with the environment. In this study, a genome-wide association study (GWAS) was performed to identify quantitative trait loci (QTL) controlling seed protein and oil concentration in 298 soybean germplasm accessions exhibiting a wide range of seed protein and oil content.

Results

A total of 55,159 single nucleotide polymorphisms (SNPs) were genotyped using various methods including Illumina Infinium and GoldenGate assays and 31,954 markers with minor allele frequency >0.10 were used to estimate linkage disequilibrium (LD) in heterochromatic and euchromatic regions. In euchromatic regions, the mean LD (r ² ) rapidly declined to 0.2 within 360 Kbp, whereas the mean LD declined to 0.2 at 9,600 Kbp in heterochromatic regions. The GWAS results identified 40 SNPs in 17 different genomic regions significantly associated with seed protein. Of these, the five SNPs with the highest associations and seven adjacent SNPs were located in the 27.6-30.0 Mbp region of Gm20. A major seed protein QTL has been previously mapped to the same location and potential candidate genes have recently been identified in this region. The GWAS results also detected 25 SNPs in 13 different genomic regions associated with seed oil. Of these markers, seven SNPs had a significant association with both protein and oil.

Conclusions

This research indicated that GWAS not only identified most of the previously reported QTL controlling seed protein and oil, but also resulted in narrower genomic regions than the regions reported as containing these QTL. The narrower GWAS-defined genome regions will allow more precise marker-assisted allele selection and will expedite positional cloning of the causal gene(s).     

Multi-year linkage and association mapping confirm the high number of genomic regions involved in oilseed rape quantitative resistance to blackleg

Key message

A repertoire of the genomic regions involved in quantitative resistance to Leptosphaeria maculans in winter oilseed rape was established from combined linkage-based QTL and genome-wide association (GWA) mapping.

Abstract

Linkage-based mapping of quantitative trait loci (QTL) and genome-wide association studies are complementary approaches for deciphering the genomic architecture of complex agronomical traits. In oilseed rape, quantitative resistance to blackleg disease, caused by L. maculans, is highly polygenic and is greatly influenced by the environment. In this study, we took advantage of multi-year data available on three segregating populations derived from the resistant cv Darmor and multi-year data available on oilseed rape panels to obtain a wide overview of the genomic regions involved in quantitative resistance to this pathogen in oilseed rape. Sixteen QTL regions were common to at least two biparental populations, of which nine were the same as previously detected regions in a multi-parental design derived from different resistant parents. Eight regions were significantly associated with quantitative resistance, of which five on A06, A08, A09, C01 and C04 were located within QTL support intervals. Homoeologous Brassica napus genes were found in eight homoeologous QTL regions, which corresponded to 657 pairs of homoeologous genes. Potential candidate genes underlying this quantitative resistance were identified. Genomic predictions and breeding are also discussed, taking into account the highly polygenic nature of this resistance.

     

Quantitative trait loci of stripe rust resistance in wheat

Key message

Over 140 QTLs for resistance to stripe rust in wheat have been published and through mapping flanking markers on consensus maps, 49 chromosomal regions are identified.

Abstract

Over thirty publications during the last 10 years have identified more than 140 QTLs for stripe rust resistance in wheat. It is likely that many of these QTLs are identical genes that have been spread through plant breeding into diverse backgrounds through phenotypic selection under stripe rust epidemics. Allelism testing can be used to differentiate genes in similar locations but in different genetic backgrounds; however, this is problematic for QTL studies where multiple loci segregate from any one parent. This review utilizes consensus maps to illustrate important genomic regions that have had effects against stripe rust in wheat, and although this methodology cannot distinguish alleles from closely linked genes, it does highlight the extent of genetic diversity for this trait and identifies the most valuable loci and the parents possessing them for utilization in breeding programs. With the advent of cheaper, high throughput genotyping technologies, it is envisioned that there will be many more publications in the near future describing ever more QTLs. This review sets the scene for the coming influx of data and will quickly enable researchers to identify new loci in their given populations.     

Exploring the quantitative resistance to <Emphasis Type="Italic">Pseudomonas syringae</Emphasis> pv. <Emphasis Type="Italic">phaseolicola</Emphasis> in common bean (<Emphasis Type="Italic">Phaseolus vulgaris</Emphasis> L.)

Pseudomonas syringae pv. phaseolicola is an important disease that causes halo blight in common bean. The genetic mechanisms underlying quantitative halo blight resistance are poorly understood in this species, as most disease studies have focused on qualitative resistance. The present work examines the genetic basis of quantitative resistance to the nine halo blight races in different organs (primary and trifoliate leaf, stem and pod) of an Andean recombinant inbred line (RIL) progeny. Using a multi-environment quantitative trait locus (QTL) mapping approach, 76 and 101 main-effect and epistatic QTLs were identified, respectively. Most of the epistatic interactions detected were due to loci without detectable QTL additive main effects. Main and epistatic QTLs detected were mainly consistent across the environment conditions. The homologous genomic regions corresponding to 26 of the 76 main-effect detected QTLs were positive for the presence of resistance-associated gene cluster encoding nucleotide-binding and leucine-rich repeat (NL) proteins and known defence genes. Main-effect QTLs for resistance to races 3, 4 and 5 in leaf, stem and pod were located on chromosome 2 within a 3.01-Mb region, where a cluster of nine NL genes was detected. The NL gene Phvul.002G323300 is located in this region, which can be considered an important putative candidate gene for the non-organ-specific QTL identified here. The present research provides essential information not only for the better understanding of the plant-pathogen interaction but also for the application of genomic assisted breeding for halo blight resistance in common bean.