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QTL Express is the first application for Quantitative Trait Locus (QTL) mapping in outbred populations with a web-based user interface. User input of three files containing a marker map, trait data and marker genotypes allows mapping of single or multiple QTL by the regression approach, with the option to perform permutation or bootstrap tests.
We utilized maize (Zea mays L.) lines expressing the salmon silk (sm) phenotype, quantitative trait locus analysis, and analytical chemistry of flavone compounds to establish the order of undefined steps in the synthesis of the flavone maysin in maize silks. In addition to the previously described sm1 gene, we identified a second sm locus, which we designate sm2, located on the long arm of maize chromosome 2. Our data indicate that the sm1 gene encodes or controls a glucose modification enzyme and sm2 encodes or controls a rhamnosyl transferase. The order of intermediates in the late steps of maysin synthesis was established as luteolin --> isoorientin --> rhamnosylisoorientin --> maysin.
The ATP-binding cassette transporter G1 (ABCG1) catalyzes export of cellular cholesterol from macrophages and hepatocytes. Here we identify an additional function of ABCG1 in the regulation of adiposity in screens of the Drosophila melanogaster and the New Zealand obese (NZO) mouse genomes. Insertion of modified transposable elements of the P-family upstream of CG17646, the Drosophila ortholog of Abcg1, generated lines of flies with increased triglyceride stores. In NZO mice, an Abcg1 variant was identified in a suggestive adiposity quantitative trait locus and was associated with higher expression of the gene in white adipose tissue. Targeted disruption of Abcg1 in mice resulted in reduced body weight gain (8.42+/-0.6 g in Abcg1-/- vs. 13.07+/-1.1 g in Abcg1+/+ mice) and adipose tissue mass gain (3.78+/-1.3 g in Abcg1-/- vs. 9.39+/-1.6 g in Abcg1+/+ mice) detected over a period of 12 wk. The reduction of adipose tissue mass in Abcg1-/- mice was associated with markedly decreased size of the adipocytes. In contrast to their wild-type littermates, male Abcg1-/- mice exhibited no high-fat diet-induced impairment of glucose tolerance and fatty liver. Furthermore, Abcg1-/- mice possess decreased food intake and elevated total energy expenditure (Abcg1-/- mice, 748.1+/-5.4 kJ/kg metabolic body mass; Abcg1+/+ mice, 684.3+/-5.0 kJ/kg metabolic body mass; P=0.011), body temperature (Abcg1-/- mice, 37.82+/-0.29 C; Abcg1+/+ mice, 36.83+/-0.24 C; P<0.05), and locomotor activity (Abcg1-/- mice, 3655+/-189 counts/12 h during dark phase; Abcg1+/+ mice, 2445+/-235 counts/12 h during dark phase; P<0.01). Our data indicate a previously unrecognized role of ABCG1 in the regulation of energy balance and triglyceride storage.
Plastids of nongreen tissues import carbon as a source of biosynthetic pathways and energy, and glucose 6-phosphate is the preferred hexose phosphate taken up by nongreen plastids. A cDNA clone encoding glucose 6-phosphate/phosphate translocator (GPT) was isolated from a cDNA library of immature seeds of rice and named as OsGPT. The cDNA has one uninterrupted open reading frame encoding a 42 kDa polypeptide possessing transit peptide consisting of 70 amino acid residues. The OsGPT gene maps on chromosome 8 of rice and is linked to the quantitative trait locus for 1000-grain weight. The expression of OsCPT is mainly restricted to heterotrophic tissues. These results suggest that glucose 6-phosphate imported via GPT can be used for starch biosynthesis in rice nongreen plastids.
A key problem in developmental biology is understanding the origin of morphological innovations. Comparative studies in plants with different leaf morphologies indicate that the developmental pathway defined by KNOTTED1-type homeodomain proteins could be involved in generating different leaf forms. The differential expression of regulatory proteins has emerged as an important factor in driving morphological innovations in the plant kingdom--an idea that is well supported by quantitative trait locus analyses.
Highly parallel gene expression profiling has the potential to provide new insight into the molecular mechanisms of complex brain diseases and behavioral traits. We review how gene expression profiling in various brain regions of inbred mouse strains has been used to identify genes that may contribute to strain-specific phenotypes. New data, which demonstrate the use of gene expression profiling in combination with behavioral testing to identify candidate genes involved in mediating variation in running wheel activity, are also presented. These and other studies suggest that a combination of gene expression profiling and more traditional genetic approaches, such as quantitative trait locus analysis, can be used to identify genes responsible for specific neurobehavioral phenotypes.
Improved laboratory methods allow one to investigate the contribution of measured allelic variability at a locus physiologically involved in determining the expression of a quantitative trait. We present statistical methods that incorporate measured genotype information into the analysis of a quantitative phenotype that allows one simultaneously to detect and estimate the effects of a measured single locus and residual polygenic effects. Likelihoods are presented for the joint distribution of the quantitative phenotype and a measured genotype that are appropriate when the data are collected as a sample of unrelated individuals or as a sample of nuclear families. Application of this method to the analysis of serum cholesterol levels and the concentration of the group specific component (Gc) are presented. The analysis of the contribution of the common Gc polymorphism to the determination of quantitative variability in Gc using samples of related and unrelated individuals presents, for the first time, the simultaneous estimation of the frequencies and the effects of the genotypes at a measured locus, and the contribution of residual unmeasured polygenes to phenotypic variability.
Isolates of Magnaporthe oryzae (the causal agent of rice blast disease) can infect a range of grass species, including barley. We report that barley Hordeum vulgare cv. Baronesse and an experimental line, BCD47, show a range of resistance reactions to infection with two rice blast isolates. The complete resistance of Baronesse to the isolate Ken 54-20 is controlled by a single dominant gene, designated RMo1. RMo1 mapped to the same linkage map position on chromosome 1H as the powdery mildew resistance locus Mla and an expressed sequence tag (k04320) that corresponds to the barley gene 711N16.16. A resistance quantitative trait locus (QTL), at which Baronesse contributed the resistance allele, to the isolate Ken 53-33 also mapped at the same position as RMo1. Synteny analysis revealed that a corresponding region on rice chromosome 5 includes the bacterial blight resistance gene xa5. These results indicate that a defined region on the short arm of barley chromosome 1H, including RMo1 and Mla, harbors genes conferring qualitative and quantitative resistance to multiple pathogens. The partial resistance of BCD47 to Ken53-33 is determined by alleles at three QTL, two of which coincide with the linkage map positions of the mildew resistance genes mlo and Mlf.
BACKGROUND: Quantitative Trait Locus (QTL) analysis of molecular data has identified genetic variants associated with traits such as gene expression, and colocalization of these functional QTL with GWAS risk loci has offered insights into the genetic basis of human disease. We employed gene expression (RNA-seq) and chromatin accessibility (ATAC-seq) obtained from human primary fibroblasts to investigate quantitative trait loci (QTLs) in cohorts ascertained for bipolar disorder of European (n = 150) and Latin American (n = 96) ancestries. RESULTS: Leveraging data from three countries of origin (The Netherlands, Colombia, Costa Rica) within our cohort, we characterized differences among individuals at the SNP, gene, and accessible-chromatin levels to compute ancestry-specific expression (e)QTLs and chromatin-accessibility (ca)QTLs. Across ancestries, we observed R2 ≥ 0.93 for eQTL effect sizes and R2 ≥ 0.95 for caQTLs, indicating a high degree of concordance. Integrating chromatin data with expression and genotype information enabled precise fine-mapping of eQTLs, yielding 203 genes with high-confidence (posterior probability > 90%) candidate regulatory pathways. In downstream analyses, transcriptome-wide (TWAS) and chromatin-wide (CWAS) association studies with brain- and skin-related GWAS identified 36 TWAS-significant genes and 77 CWAS-significant open chromatin regions. CONCLUSIONS: These findings underscore the shared genetic regulatory mechanisms across European and Latin American ancestries, while demonstrating that ancestry-specific reference panels enhance the accuracy of TWAS and CWAS in diverse populations. More broadly, this study highlights the value of paired multi-omic datasets from diverse cohorts for interpreting disease-associated genetic variation.
BACKGROUND: Correlations between polymorphic markers and observed phenotypes provide the basis for mapping traits in quantitative genetics. When the phenotype is gene expression, then loci involved in regulatory control can theoretically be implicated. Recent efforts to construct gene regulatory networks from genotype and gene expression data have shown that biologically relevant networks can be achieved from an integrative approach. In this paper, we consider the problem of identifying individual pairs of genes in a direct or indirect, causal, trans-acting relationship. RESULTS: Inspired by epistatic models of multi-locus quantitative trait (QTL) mapping, we propose a unified model of expression and genotype to identify quantitative trait genes (QTG) by extending the conventional linear model to include both genotype and expression of regulator genes and their interactions. The model provides mapping of specific genes in contrast to standard linkage approaches that implicate large QTL intervals typically containing tens of genes. In simulations, we found that the method can often detect weak trans-acting regulators amid the background noise of thousands of traits and is robust to transcription models containing multiple regulator genes. We reanalyze several pleiotropic loci derived from a large set of yeast matings and identify a likely alternative regulator not previously published. However, we also found that many regulators can not be so easily mapped due to the presence of cis-acting QTLs on the regulators, which induce close linkage among small neighborhoods of genes. QTG mapped regulator-target pairs linked to ARN1 were combined to form a regulatory module, which we observed to be highly enriched in iron homeostasis related genes and contained several causally directed links that had not been identified in other automatic reconstructions of that regulatory module. Finally, we also confirm the surprising, previously published results that regulators controlling gene expression are not enriched for transcription factors, but we do show that our more precise mapping model reveals functional enrichment for several other biological processes related to the regulation of the cell. CONCLUSION: By incorporating interacting expression and genotype, our QTG mapping method can identify specific regulator genes in contrast to standard QTL interval mapping. We have shown that the method can recover biologically significant regulator-target pairs and the approach leads to a general framework for inducing a regulatory module network topology of directed and undirected edges that can be used to identify leads in pathway analysis.
The gene SNCA (or NACP), which codes for alpha-synuclein, a small synaptic protein involved in dopaminergic neurotransmission, maps to a quantitative trait locus for alcohol preference and is differentially expressed in specific brain regions in alcohol-preferring versus -nonpreferring rats. Moreover, elevated alpha-synuclein messenger RNA (mRNA) and protein levels in peripheral blood have been shown to be associated with craving in patients with alcoholism. The focus of this study was to evaluate gene expression, including the levels of alpha-synuclein mRNA, in peripheral blood in nonhuman primates that were induced to drink ethanol (4 months) and then allowed 14 months of 22-h/day access to ethanol (4% wt/vol) or water compared to alcohol-naïve controls. Differential gene expression, including alpha-synuclein mRNA levels, was measured in 18 cynomolgus macaque monkeys, 8 that had been chronically self-administering ethanol for 18 months and 10 that were alcohol naïve. Cynomolgus monkeys in this study self-administered ethanol at average rates of between 1.2 and 4.2g/kg/day. This group of ethanol-drinking monkeys had a highly significant 3.21-fold higher level of alpha-synuclein mRNA in peripheral blood than alcohol-naïve controls. These data agree with recent reports of elevated alpha-synuclein mRNA and protein in the blood of human alcoholics, support the concept of an association between alpha-synuclein and alcoholism, and demonstrate, for the first time, a biomarker present in rats, monkeys, and humans for the consumption of ethanol.
Data from 311 selfed families isolated from four generations (F8, F13, F23, F45) of an experimental barley population were analyzed to determine patterns of change in character expression for seven quantitative traits, and in single-locus allelic frequencies, and multilocus genetic structure, for 16 Mendelian loci that code for discretely recognizable variants. The analyses showed that large changes in single-locus allelic frequencies and major reorganizations in multilocus genetic structure occurred in each of the generation-to-generation transitions examined. Although associations among a few traits persisted over generations, dynamic dissociations and reassociations occurred among several traits in each generation-transition period. Overall, the restructuring that occurred was characterized by gradual decreases in the number of clusters of associated traits and increases in the number of traits within each cluster. The observed changes in single-locus frequencies and in multilocus genetic structure were attributed to interplay among various evolutionary factors among which natural selection acting in a temporally heterogeneous environment was the guiding force.
Genetic linkage analysis for complex diseases offers a major challenge to geneticists. In these complex diseases multiple genetic loci are responsible for the disease and they may vary in the size of their contribution; the effect of any single one of them is likely to be small. In many situations, like in extensive twin registries, trait values have been recorded for a large number of individuals, and preliminary studies have revealed summary measures for those traits, like mean, variance and components of variance, including heritability. Given the small effect size, a random sample of twins will require a prohibitively large sample size. It is well known that selective sampling is far more efficient in terms of genotyping effort. In this paper we derive easy expressions for the information contributed by sib pairs for the detection of linkage to a quantitative trait locus (QTL). We consider random samples as well as samples of sib pairs selected on the basis of their trait values. These expressions can be rapidly computed and do not involve simulation. We extend our results for quantitative traits to dichotomous traits using the concept of a liability threshold model. We present tables with required sample sizes for height, insulin levels and migraine, three of the traits studied in the GenomEUtwin project.
We performed a strain intercross between two apoE-deficient mouse strains with a large difference in lesion susceptibility and measured aortic root lesion area in 98 female F(2) progeny. Total RNA was prepared from bone marrow-derived macrophages, and RNA from the five mice with the smallest and largest lesions were used for microarray gene expression profiling. Remarkably, approximately 5% of the 12,288 expressed transcripts were differentially expressed in the atherosclerosis-susceptible and atherosclerosis-resistant bone marrow-derived macrophages (unadjusted p < 0.05), thus defining the transcriptome of macrophages associated with atherosclerosis susceptibility. Using more stringent criteria of twofold or greater change and p < 0.01, 116 and 70 transcripts were overexpressed in lesion-prone and lesion-resistant bone marrow-derived macrophages, respectively. Transcription factor binding site analysis identified two promoter elements that were found more often in the genes overexpressed in the large-lesion group, and one promoter element that was found more often in the small-lesion group. The combination of this expression profiling data with the genetic method of quantitative trait locus mapping should give powerful insights into the genes that affect atherosclerosis susceptibility in mice.
Using a cDNA array-based functional genomics approach in barley, several candidate genes for malting quality including serine carboxypeptidase I (Cxp1) were previously identified (Potokina et al. in Mol Breed 14:153, 2004). The gene was mapped as a single nucleotide polymorphism (SNP) marker on chromosome 3H using the Steptoe (feeding grade)xMorex (malting grade) mapping population. Subsequently, the relative level of Cxp1 expression was determined by real-time RT-PCR for each of the 134 progeny lines and mapped as a quantitative trait. Only one quantitative trait locus (QTL) could be identified that significantly influenced the level of the Cxp1 expression. The expressed QTL maps to the same region on chromosome 3H as does the structural gene and corresponds to a QTL for "diastatic power," one among several traits measured to assess malting quality. An analysis of 90 barley cultivars sampled from a worldwide collection revealed six SNPs at the Cxp1 locus, three of which display complete linkage disequilibrium and define two haplotypes. The Cxp1 expression level in a set of barley accessions showing haplotype I was significantly higher than that of accessions displaying haplotype II. The data provide evidence that (1) the expression of Cxp1 is regulated in cis and that (2) the level of diastatic power in the barley seed is influenced by the level of Cxp1 expression.
There are differences between human individuals and between mouse strains in levels of mu opiate receptor (muOR) expression, responses to painful stimuli, and responses to opiate drugs. One of the best candidates for contributing to these differences is variation at the muOR gene locus. Support for this idea comes from analyses of the human and murine muOR genes. Assessments of individual differences in human muOR expression add further support. Studies with mice, including knockout-transgenic, quantitative trait locus, and strain-comparison studies, also strongly support the possibility that muOR gene alleles would be strong candidates for contributing to individual differences in human nociception and opiate drug responses. This paper reviews current analyses of the murine and human muOR genes, their important variants, and correlations between these variants and opiate influences on pain.
The millions of common DNA variations that occur in the human population, or among inbred strains of mice and rats, perturb the expression (transcript levels) of a large fraction of the genes expressed in a particular tissue. The hundreds or thousands of common cis-acting variations that occur in the population may in turn affect the expression of thousands of other genes by affecting transcription factors, signaling molecules, RNA processing, and other processes that act in trans. The levels of transcripts are conveniently quantitated using expression arrays, and the cis- and trans-acting loci can be mapped using quantitative trait locus (QTL) analysis, in the same manner as loci for physiologic or clinical traits. Thousands of such expression QTL (eQTL) have been mapped in various crosses in mice, as well as other experimental organisms, and less detailed maps have been produced in studies of cells from human pedigrees. Such an integrative genetics approach (sometimes referred to as "genetical genomics") is proving useful for identifying genes and pathways that contribute to complex clinical traits. The coincidence of clinical trait QTL and eQTL can help in the prioritization of positional candidate genes. More importantly, mathematical modeling of correlations between levels of transcripts and clinical traits in genetic crosses can allow prediction of causal interactions and the identification of "key driver" genes. An important objective of such studies will be to model biological networks in physiologic processes. When combined with high-density single nucleotide polymorphism (SNP) mapping, it should be feasible to identify genes that contribute to transcript levels using association analysis in outbred populations. In this review we discuss the basic concepts and applications of this integrative genomic approach to cardiovascular and metabolic diseases.
To identify novel genes regulating the biologic response to lipopolysaccharide (LPS), we used a combination of quantitative trait locus (QTL) analysis and microarray-based gene expression studies of C57BL/6J x DBA/2J(BXD) F2 and recombinant inbred (RI) mice. A QTL affecting pulmonary TNF-alpha production was identified on chromosome 2, and a region affecting both polymorphonuclear leukocyte recruitment and TNF-alpha levels was identified on chromosome 11. Microarray analyses of unchallenged and LPS-challenged BXD RI strains identified approximately 500 genes whose expression was significantly changed by inhalation of LPS. Of these genes, 28 reside within the chromosomal regions identified by the QTL analyses, implicating these genes as high priority candidates for functional studies. Additional high priority candidate genes were identified based on their differential expression in mice having high and low responses to LPS. Functional studies of these genes are expected to reveal important molecular mechanisms regulating the magnitude of biologic responses to LPS.