Browse
Submit Data
Databases
API
Help

Dataset Information

20 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

A quantitative trait locus for variation in dopamine metabolism mapped in a primate model using reference sequences from related species.

ABSTRACT: Non-human primates (NHP) provide crucial research models. Their strong similarities to humans make them particularly valuable for understanding complex behavioral traits and brain structure and function. We report here the genetic mapping of an NHP nervous system biologic trait, the cerebrospinal fluid (CSF) concentration of the dopamine metabolite homovanillic acid (HVA), in an extended inbred vervet monkey (Chlorocebus aethiops sabaeus) pedigree. CSF HVA is an index of CNS dopamine activity, which is hypothesized to contribute substantially to behavioral variations in NHP and humans. For quantitative trait locus (QTL) mapping, we carried out a two-stage procedure. We first scanned the genome using a first-generation genetic map of short tandem repeat markers. Subsequently, using >100 SNPs within the most promising region identified by the genome scan, we mapped a QTL for CSF HVA at a genome-wide level of significance (peak logarithm of odds score >4) to a narrow well delineated interval (<10 Mb). The SNP discovery exploited conserved segments between human and rhesus macaque reference genome sequences. Our findings demonstrate the potential of using existing primate reference genome sequences for designing high-resolution genetic analyses applicable across a wide range of NHP species, including the many for which full genome sequences are not yet available. Leveraging genomic information from sequenced to nonsequenced species should enable the utilization of the full range of NHP diversity in behavior and disease susceptibility to determine the genetic basis of specific biological and behavioral traits.

SUBMITTER: Freimer NB

PROVIDER: S-EPMC1987389 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Identification of Finely Mapped Quantitative Trait Locus and Candidate Gene Mining for the Three-Seeded Pod Trait in Soybean.

Project description:The three-seeded pod number is an important trait that positively influences soybean yield. Soybean variety with increased three-seeded pod number contributes to the seed number/plant and higher yield. The candidate genes of the three-seeded pod may be the key for improving soybean yield. In this study, identification and validation of candidate genes for three-seeded pod has been carried out. First, a total of 36 quantitative trait locus (QTL) were detected from the investigation of recombinant inbred lines including 147 individuals derived from a cross between Charleston and Dongning 594 cultivars. Five consensus QTLs were integrated. Second, an introgressed line CSSL-182 carrying the target segment for the trait from the donor parent was selected to verify the consensus QTL based on its phenotype. Third, a secondary group was constructed by backcrossing with CSSL-182, and two QTLs were confirmed. There were a total of 162 genes in the two QTLs. The mining of candidate genes resulted in the annotation of eight genes with functions related to pod and seed sets. Finally, haplotype analysis and quantitative reverse transcriptase real-time PCR were carried to verify the candidate genes. Four of these genes had different haplotypes in the resource group, and the differences in the phenotype were highly significant. Moreover, the differences in the expression of the four genes during pod and seed development were also significant. These four genes were probably related to the development process underlying the three-seeded pod in soybean. Herein, we discuss the past and present studies related to the three-seeded pod trait in soybean.

| S-EPMC8663486 | biostudies-literature

Conserved noncoding genomic sequences associated with a flowering-time quantitative trait locus in maize.

Project description:Flowering time is a fundamental trait of maize adaptation to different agricultural environments. Although a large body of information is available on the map position of quantitative trait loci for flowering time, little is known about the molecular basis of quantitative trait loci. Through positional cloning and association mapping, we resolved the major flowering-time quantitative trait locus, Vegetative to generative transition 1 (Vgt1), to an approximately 2-kb noncoding region positioned 70 kb upstream of an Ap2-like transcription factor that we have shown to be involved in flowering-time control. Vgt1 functions as a cis-acting regulatory element as indicated by the correlation of the Vgt1 alleles with the transcript expression levels of the downstream gene. Additionally, within Vgt1, we identified evolutionarily conserved noncoding sequences across the maize-sorghum-rice lineages. Our results support the notion that changes in distant cis-acting regulatory regions are a key component of plant genetic adaptation throughout breeding and evolution.

| S-EPMC2040906 | biostudies-literature

Small- and large-effect quantitative trait locus interactions underlie variation in yeast sporulation efficiency.

Project description:Quantitative trait loci (QTL) with small effects on phenotypic variation can be difficult to detect and analyze. Because of this a large fraction of the genetic architecture of many complex traits is not well understood. Here we use sporulation efficiency in Saccharomyces cerevisiae as a model complex trait to identify and study small-effect QTL. In crosses where the large-effect quantitative trait nucleotides (QTN) have been genetically fixed we identify small-effect QTL that explain approximately half of the remaining variation not explained by the major effects. We find that small-effect QTL are often physically linked to large-effect QTL and that there are extensive genetic interactions between small- and large-effect QTL. A more complete understanding of quantitative traits will require a better understanding of the numbers, effect sizes, and genetic interactions of small-effect QTL.

| S-EPMC3522155 | biostudies-literature

Automated quantitative trait locus analysis (AutoQTL).

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.

| S-EPMC10088184 | biostudies-literature

Combined expression trait correlations and expression quantitative trait locus mapping.

Project description:Coordinated regulation of gene expression levels across a series of experimental conditions provides valuable information about the functions of correlated transcripts. The consideration of gene expression correlation over a time or tissue dimension has proved valuable in predicting gene function. Here, we consider correlations over a genetic dimension. In addition to identifying coregulated genes, the genetic dimension also supplies us with information about the genomic locations of putative regulatory loci. We calculated correlations among approximately 45,000 expression traits derived from 60 individuals in an F2 sample segregating for obesity and diabetes. By combining the correlation results with linkage mapping information, we were able to identify regulatory networks, make functional predictions for uncharacterized genes, and characterize novel members of known pathways. We found evidence of coordinate regulation of 174 G protein-coupled receptor protein signaling pathway expression traits. Of the 174 traits, 50 had their major LOD peak within 10 cM of a locus on Chromosome 2, and 81 others had a secondary peak in this region. We also characterized a Riken cDNA clone that showed strong correlation with stearoyl-CoA desaturase 1 expression. Experimental validation confirmed that this clone is involved in the regulation of lipid metabolism. We conclude that trait correlation combined with linkage mapping can reveal regulatory networks that would otherwise be missed if we studied only mRNA traits with statistically significant linkages in this small cross. The combined analysis is more sensitive compared with linkage mapping alone.

| S-EPMC1331977 | biostudies-literature

Combined Expression Trait Correlations and Expression Quantitative Trait Locus Mapping

Project description:Coordinated regulation of gene expression levels across a series of experimental conditions provides valuable information about the functions of correlated transcripts. To map gene regulatory pathways, we used microarray-derived gene expression measurements in 60 individuals of an F2 sample segregating for diabetes. We performed correlation analysis among ~40,000 expression traits. By combining correlation among expression traits and linkage mapping information, we were able to identify regulatory networks, make functional predictions to uncharacterized genes, and characterize novel members of known pathways. Using 36 seed traits, we found evidence of coordinate regulation of 160 G-protein coupled receptor (GPCR) pathway expression traits. Of the 160 traits, 50 had their major LOD peak within 8 cM of a locus on chromosome 2, and 81 others had a secondary peak in this region. A previously uncharacterized Riken cDNA clone, which showed strong correlation with stearoyl CoA desaturase 1 expression, was experimentally validated to be responsive to conditions that regulate lipid metabolism. Using linkage mapping, we identified multiple genes whose expression is under the control of transcription regulatory loci. Trait-correlation combined with linkage mapping can reveal regulatory networks that would otherwise be missed if we only studied mRNA traits with statistically significant linkages in this small cross. The combined analysis is more sensitive compared with linkage mapping only. References: Kendziorski C., M. Chen, M. Yuan, H. Lan, and A.D. Attie. Statistical Methods for Expression Quantitative Trait Loci (eQTL) Mapping. Biometrics, to appear, 2005. Lan H, Chen M, Flowers JB, Yandell BS, Stapleton DS, et al. (2006) Combined Expression Trait Correlations and Expression Quantitative Trait Locus Mapping. PLoS Genet 2(1): e6. Keywords: Genetics of gene expression

2005-09-21 | GSE3330 | GEO

Expression quantitative trait locus analysis of chicken liver

Project description:This data originates from an expression quantitative trait locus analysis of liver in an advanced intercross of Red Jungefowl and White Leghorn chickens. The aim of the study was to map the genetic basis of growth traits and transcript abundance traits in the liver, and use the latter to search for candidate causative genes for chicken growth.

2018-01-25 | E-MTAB-5572 | biostudies-arrayexpress

Expression quantitative trait locus analysis of chicken cerebrum

Project description:This data originates from an expression quantitative trait locus analysis of cerebrum in an advanced intercross of Red Jungefowl and White Leghorn chickens. The aim of the study was to map the genetic basis of cerebrum and body mass, and idenifiy transcriptional differences within the intercross to assess any candidate genes for cerebrum and body mass.

2020-07-07 | E-MTAB-9313 | biostudies-arrayexpress

Quantitative trait locus mapping and identification of Zhx2 as a novel regulator of plasma lipid metabolism.

Project description:We previously mapped a quantitative trait locus on chromosome 15 in mice contributing to high-density lipoprotein cholesterol and triglyceride levels and now report the identification of the underlying gene.We first fine-mapped the locus by studying a series of congenic strains derived from the parental strains BALB/cJ and MRL/MpJ. Analysis of gene expression and sequencing followed by transgenic complementation led to the identification of zinc fingers and homeoboxes 2 (Zhx2), a transcription factor previously implicated in the developmental regulation of alpha-fetoprotein. Reduced expression of the protein in BALB/cJ mice resulted in altered hepatic transcript levels for several genes involved in lipoprotein metabolism. Most notably, the Zhx2 mutation resulted in a failure to suppress expression of lipoprotein lipase, a gene normally silenced in the adult liver, and this was normalized in BALB/cJ mice carrying the Zhx2 transgene.We identified the gene underlying the chromosome 15 quantitative trait locus, and our results show that Zhx2 functions as a novel developmental regulator of key genes influencing lipoprotein metabolism.

| S-EPMC2846770 | biostudies-literature

Expression quantitative trait locus analysis for translational medicine.

Project description:Expression quantitative trait locus analysis has emerged as an important component of efforts to understand how genetic polymorphisms influence disease risk and is poised to make contributions to translational medicine. Here we review how expression quantitative trait locus analysis is aiding the identification of which gene(s) within regions of association are causal for a disease or phenotypic trait; the narrowing down of the cell types or regulators involved in the etiology of disease; the characterization of drivers and modifiers of cancer; and our understanding of how different environments and cellular contexts can modify gene expression. We also introduce the concept of transcriptional risk scores as a means of refining estimates of individual liability to disease based on targeted profiling of the transcripts that are regulated by polymorphisms jointly associated with disease and gene expression.

| S-EPMC4479075 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data