Global genetic architecture of an erythroid quantitative trait locus, HMIP-2.
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ABSTRACT:
SUBMITTER: Menzel S
PROVIDER: S-EPMC4303951 | biostudies-literature | 2014 Nov
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND: Proanthocyanidins (PAs), or condensed tannins, are flavonoid polymers, widespread throughout the plant kingdom, which provide protection against herbivores while conferring organoleptic and nutritive values to plant-derived foods, such as wine. However, the genetic basis of qualitative and quantitative PA composition variation is still poorly understood. To elucidate the genetic architecture of the complex grape PA composition, we first carried out quantitative trait locus (QTL) analysis on a 191-individual pseudo-F1 progeny. Three categories of PA variables were assessed: total content, percentages of constitutive subunits and composite ratio variables. For nine functional candidate genes, among which eight co-located with QTLs, we performed association analyses using a diversity panel of 141 grapevine cultivars in order to identify causal SNPs. RESULTS: Multiple QTL analysis revealed a total of 103 and 43 QTLs, respectively for seed and skin PA variables. Loci were mainly of additive effect while some loci were primarily of dominant effect. Results also showed a large involvement of pairwise epistatic interactions in shaping PA composition. QTLs for PA variables in skin and seeds differed in number, position, involvement of epistatic interaction and allelic effect, thus revealing different genetic determinisms for grape PA composition in seeds and skin. Association results were consistent with QTL analyses in most cases: four out of nine tested candidate genes (VvLAR1, VvMYBPA2, VvCHI1, VvMYBPA1) showed at least one significant association with PA variables, especially VvLAR1 revealed as of great interest for further functional investigation. Some SNP-phenotype associations were observed only in the diversity panel. CONCLUSIONS: This study presents the first QTL analysis on grape berry PA composition with a comparison between skin and seeds, together with an association study. Our results suggest a complex genetic control for PA traits and different genetic architectures for grape PA composition between berry skin and seeds. This work also uncovers novel genomic regions for further investigation in order to increase our knowledge of the genetic basis of PA composition.
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Project description:We conducted a comprehensive analysis of virulence in the fungal wheat pathogen Zymoseptoria tritici using quantitative trait locus (QTL) mapping. High-throughput phenotyping based on automated image analysis allowed the measurement of pathogen virulence on a scale and with a precision that was not previously possible. Across two mapping populations encompassing more than 520 progeny, 540 710 pycnidia were counted and their sizes and grey values were measured. A significant correlation was found between pycnidia size and both spore size and number. Precise measurements of percentage leaf area covered by lesions provided a quantitative measure of host damage. Combining these large and accurate phenotypic datasets with a dense panel of restriction site-associated DNA sequencing (RADseq) genetic markers enabled us to genetically dissect pathogen virulence into components related to host damage and those related to pathogen reproduction. We showed that different components of virulence can be under separate genetic control. Large- and small-effect QTLs were identified for all traits, with some QTLs specific to mapping populations, cultivars and traits and other QTLs shared among traits within the same mapping population. We associated the presence of four accessory chromosomes with small, but significant, increases in several virulence traits, providing the first evidence for a meaningful function associated with accessory chromosomes in this organism. A large-effect QTL involved in host specialization was identified on chromosome 7, leading to the identification of candidate genes having a large effect on virulence.
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Project description:Whether in natural populations or between two unrelated members of a species, most phenotypic variation is quantitative. To analyze such quantitative traits, one must first map the underlying quantitative trait loci. Next, and far more difficult, one must identify the quantitative trait genes (QTGs), characterize QTG interactions, and identify the phenotypically relevant polymorphisms to determine how QTGs contribute to phenotype. In this work, we analyzed three Saccharomyces cerevisiae high-temperature growth (Htg) QTGs (MKT1, END3, and RHO2). We observed a high level of genetic interactions among QTGs and strain background. Interestingly, while the MKT1 and END3 coding polymorphisms contribute to phenotype, it is the RHO2 3'UTR polymorphisms that are phenotypically relevant. Reciprocal hemizygosity analysis of the Htg QTGs in hybrids between S288c and ten unrelated S. cerevisiae strains reveals that the contributions of the Htg QTGs are not conserved in nine other hybrids, which has implications for QTG identification by marker-trait association. Our findings demonstrate the variety and complexity of QTG contributions to phenotype, the impact of genetic background, and the value of quantitative genetic studies in S. cerevisiae.
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Project description:Association mapping focused on 36 genes involved in branch development was used to identify candidate genes for variation in shoot branching in Arabidopsis thaliana. The associations between four branching traits and moderate-frequency haplogroups at the studied genes were tested in a panel of 96 accessions from a restricted geographic range in Central Europe. Using a mixed-model association-mapping method, we identified three loci--MORE AXILLARY GROWTH 2 (MAX2), MORE AXILLARY GROWTH 3 (MAX3), and SUPERSHOOT 1 (SPS1)--that were significantly associated with branching variation. On the basis of a more extensive examination of the MAX2 and MAX3 genomic regions, we find that linkage disequilibrium in these regions decays within approximately 10 kb and trait associations localize to the candidate genes in these regions. When the significant associations are compared to relevant quantitative trait loci (QTL) from previous Ler x Col and Cvi x Ler recombinant inbred line (RIL) mapping studies, no additive QTL overlapping these candidate genes are observed, although epistatic QTL for branching, including one that spans the SPS1, are found. These results suggest that epistasis is prevalent in determining branching variation in A. thaliana and may need to be considered in linkage disequilibrium mapping studies of genetically diverse accessions.
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Project description:Genome-wide association study (GWAS) can be used to identify genes that increase the risk of psychiatric diseases. However, much of the disease heritability is still unexplained, suggesting that there are genes to be discovered. Functional annotation of the genetic variants may increase the power of GWAS to identify disease genes, by providing prior information that can be used in Bayesian analysis or in reducing the number of tests. Expression quantitative trait loci (eQTLs) are genomic loci that regulate gene expression. Genetic mapping of eQTLs can help reveal novel functional effects of thousands of single nucleotide polymorphisms (SNPs). The present review mainly focused on the current knowledge on brain eQTL mapping, and discussed some major methodological issues and their possible solutions. The frequently ignored problems of batch effects, covariates, and multiple testing were emphasized, since they can lead to false positives and false negatives. The future application of eQTL data in GWAS analysis was also discussed.
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Project description:Osteoarthritis (OA) is a common, painful and debilitating disease of articulating joints resulting from the age-associated loss of cartilage. Well-powered genetic studies have identified a number of DNA polymorphisms that are associated with OA susceptibility. Like most complex trait loci, these OA loci are thought to influence disease susceptibility through the regulation of gene expression, so-called expression quantitative loci, or eQTLs. One mechanism through which eQTLs act is epigenetic, by modulating DNA methylation. In such cases, there are quantitative differences in DNA methylation between the two alleles of the causal polymorphism, with the association signal referred to as a methylation quantitative trait locus, or meQTL. In this study, we aimed to investigate whether the OA susceptibility loci identified to date are functioning as meQTLs by integrating genotype data with whole genome methylation data of cartilage DNA. We investigated potential genotype-methylation correlations within a 1.0-1.5 Mb region surrounding each of 16 OA-associated single-nucleotide polymorphisms (SNPs) in 99 cartilage samples and identified four that function as meQTLs. Three of these replicated in an additional cohort of up to 62 OA patients. These observations suggest that OA susceptibility loci regulate the level of DNA methylation in cis and provide a mechanistic explanation as to how these loci impact upon OA susceptibility, further increasing our understanding of the role of genetics and epigenetics in this common disease.
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Project description:BackgroundQuantitative Trait Locus (QTL) analysis and Genome-Wide Association Studies (GWAS) have the power to identify variants that capture significant levels of phenotypic variance in complex traits. However, effort and time are required to select the best methods and optimize parameters and pre-processing steps. Although machine learning approaches have been shown to greatly assist in optimization and data processing, applying them to QTL analysis and GWAS is challenging due to the complexity of large, heterogenous datasets. Here, we describe proof-of-concept for an automated machine learning approach, AutoQTL, with the ability to automate many complicated decisions related to analysis of complex traits and generate solutions to describe relationships that exist in genetic data.ResultsUsing a publicly available dataset of 18 putative QTL from a large-scale GWAS of body mass index in the laboratory rat, Rattus norvegicus, AutoQTL captures the phenotypic variance explained under a standard additive model. AutoQTL also detects evidence of non-additive effects including deviations from additivity and 2-way epistatic interactions in simulated data via multiple optimal solutions. Additionally, feature importance metrics provide different insights into the inheritance models and predictive power of multiple GWAS-derived putative QTL.ConclusionsThis proof-of-concept illustrates that automated machine learning techniques can complement standard approaches and have the potential to detect both additive and non-additive effects via various optimal solutions and feature importance metrics. In the future, we aim to expand AutoQTL to accommodate omics-level datasets with intelligent feature selection and feature engineering strategies.
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Project description:We have previously identified a locus on rat chromosome 10 as carrying a major hypertension gene, BP/SP-1. The 100:1 odds support interval for this gene extended over a 35-centimorgan (cM) region of the chromosome that included the angiotensin I-converting enzyme (ACE) locus as demonstrated in a cross between the stroke-prone spontaneously hypertensive rat (SHRSPHD) and the normotensive Wistar-Kyoto (WKY-0HD) rat. Here we report on the further characterization of BP/SP-1, using a congenic strain, WKY-1HD. WKY-1HD animals carry a 6-cM chromosomal fragment genotypically identical with SHRSPHD on chromosome 10, 26 cM away from the ACE locus. Higher blood pressures in the WKY-1HD strain compared with the WKY-0HD strain, as well as absence of linkage of the chromosome 10 region to blood pressure in an F2 (WKY-1HD x SHRSPHD) population suggested the existence of a quantitative trait locus, termed BP/SP-1a, that lies within the SHRSP-congenic region in WKY-1HD. Linkage analysis in the F2 (WKY-0HD x SHRSPHD) cross revealed that BP/SP-1a is linked to basal blood pressure, whereas a second locus on chromosome 10, termed BP/SP-1b, that maps closer to the ACE locus cosegregates predominantly with blood pressure after exposure to excess dietary NaCl. Thus, we hypothesize that the previously reported effect of BP/SP-1 represents a composite phenotype that can be dissected into at least two specific components on the basis of linkage data and congenic experimentation. One of the loci identified, BP/SP-1a, represents the most precisely mapped locus affecting blood pressure that has so far been characterized by random-marker genome screening.
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Project description:Detecting quantitative trait loci (QTL) and estimating QTL variances (represented by the squared QTL effects) are two main goals of QTL mapping and genome-wide association studies (GWAS). However, there are issues associated with estimated QTL variances and such issues have not attracted much attention from the QTL mapping community. Estimated QTL variances are usually biased upwards due to estimation being associated with significance tests. The phenomenon is called the Beavis effect. However, estimated variances of QTL without significance tests can also be biased upwards, which cannot be explained by the Beavis effect; rather, this bias is due to the fact that QTL variances are often estimated as the squares of the estimated QTL effects. The parameters are the QTL effects and the estimated QTL variances are obtained by squaring the estimated QTL effects. This square transformation failed to incorporate the errors of estimated QTL effects into the transformation. The consequence is biases in estimated QTL variances. To correct the biases, we can either reformulate the QTL model by treating the QTL effect as random and directly estimate the QTL variance (as a variance component) or adjust the bias by taking into account the error of the estimated QTL effect. A moment method of estimation has been proposed to correct the bias. The method has been validated via Monte Carlo simulation studies. The method has been applied to QTL mapping for the 10-week-body-weight trait from an F2 mouse population.
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