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Results for “expression quantitative trait loci”

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PubMed2005

Association analysis of mild mental impairment using DNA pooling to screen 432 brain-expressed single-nucleotide polymorphisms.

We hypothesize that mild mental impairment (MMI) represents the low extreme of the same quantitative trait loci (QTLs) that operate throughout the distribution of intelligence. To detect QTLs of small effect size, we employed a direct association strategy by genotyping 432 presumably functional nonsynonymous single-nucleotide polymorphisms (nsSNPs) identified from public databases on DNA pools of 288 cases and 1025 controls. In total, 288 MMI cases were identified by in-home administration of McCarthy Scales of Children's Abilities to 836 twin pairs selected from a community sample of more than 14 000 children previously screened for nonverbal cognitive delay using parentally administered tests. Controls were selected from the community sample representing the full range of nonverbal intelligence. SNPs showing at least 7% allele frequency differences between case and control DNA pools were tested for their association with the full range of nonverbal intelligence using five DNA subpools, each representing quintiles of the normal quantitative trait scores from the 1025 controls. SNPs showing linear associations in the expected direction across quintiles using pooled DNA were individually genotyped for the 288 cases and 1025 controls and analyzed using standard statistical methods. One SNP (rs1136141) in HSPA8 met these criteria, yielding a significant (P=0.036) allelic frequency difference between cases and controls for individual genotyping and a significant (P=0.013) correlation within the control group that accounts for 0.5% of the variance. The present SNP strategy combined with DNA pooling and large samples represents a step towards identifying QTLs of small effect size associated with complex traits in the postgenomic era when all functional polymorphisms will be known.

Brain↗
PubMed2005

An application of Bayesian QTL mapping to early development in double haploid lines of rainbow trout including environmental effects.

A Bayesian model and variable dimensional parameter estimation based on Markov chain Monte Carlo was applied to map quantitative trait loci (QTLs) in a doubled haploid mapping population of rainbow trout. To increase power, the analysis was performed using the multiple-QTL model, which simultaneously accounted for all the environmental and genetic main effects that influence the expression of early development life history traits. By doing so we obtained the posterior estimated effects for the environmental factors as well as the number, positions, and the effects for the QTLs. The analyses revealed QTLs for time at hatching, embryonic length and weight at swim-up stage. The posterior expectation of the number of QTLs in different linkage groups shows that at least four QTLs are needed to explain the observed differences in early development between the clonal lines. The Bayesian method effectively combined all the information available to accurately position these QTLs in the rainbow trout genome.

Animals↗
PubMed2001

Improved power of sib-pair linkage analysis using measures of complex trait dynamics.

The influence of epistasis on a quantitative trait can reduce the power of linkage analysis to identify the underlying loci. In the present study, we simulated a complex trait derived from a dynamic one-locus gene expression system with epistasis arising from feedback regulation and tested the power of sib-pair linkage analysis methods for detecting the underlying quantitative trait locus (QTL). Using this simple genetic architecture, we demonstrate that the power of sib-pair linkage analysis can be greatly improved if measures of complex trait dynamics are considered.

Computer Simulation↗
PubMed2002

Marker-assisted selection of a neuro-behavioural trait related to behavioural inhibition in the SHR strain, an animal model of ADHD.

The search for the molecular bases of neuro-behavioural traits in Spontaneously Hypertensive Rats (SHR), an animal model of Attention Deficit Hyperactivity Disorder (ADHD), led to the discovery of two quantitative trait loci related to the locomotor activity in the centre of the open field. In the present study, rats from an F2 intercross between the SHR and Lewis strains were selected with markers on the basis of their genotype at these two loci. We obtained a 'high line' in which rats have the alleles increasing the trait, and a 'low line' with the lowering alleles. In activity cages with a dim light, the low line was more active than the high line. The reverse was found in the open field, and the inhibition of locomotor activity in the low line (as compared to the high line) was directly related to the aversiveness of the situation (larger in the centre than in the periphery, and in high light than in low light), and was more intense in males than in females. This inhibition is not attributable to a classical 'anxiety' factor as measured in the elevated plus maze, in which the open arms behaviours were not different between the lines. The high line also showed a deficit in prepulse inhibition of the acoustic startle reflex. The present data show that the two loci previously described in a SHR x Lewis intercross as related to the activity in the centre of the open field are indeed involved in a behavioural inhibition trait. The marker-based selected lines described here are unique tools for the study of the neurobiological bases of this trait and the molecular foundations of its variability of genetic origin.

Animals↗
PubMed2006

Cd14, Gbp1, and Pla2g2a: three major candidate genes for experimental IBD identified by combining QTL and microarray analyses.

Induction of inflammatory bowel (IBD)-like disease in mice by a targeted mutation in the Il10 gene (Il10(-/-)) is inbred strain dependent. C3H/HeJBir (C3) mice are colitis susceptible, whereas C57BL/6J (B6) mice are resistant. Genetic dissection of this susceptibility revealed 10 colitogenic quantitative trait loci (QTL). The aim of this study was to identify valuable candidate genes by a combination of QTL mapping and microarray analyses. Sixteen genes were differentially expressed between B6- and C3-Il10(-/-) mice and were located within the QTL intervals. Three major candidate genes (Cd14, Gbp1, Pla2g2a) showed prominent expression differences between B6- and C3-Il10(-/-) as well as between B6 and C3 wild-type mice, which was confirmed by semiquantitative or real-time RT-PCR. Because strain differences are known for Gbp1 and Pla2g2a, further analyses focused on Cd14. Western blot analysis revealed strain differences also on the protein level. Cd14 expression in animals with defective and intact Toll-like receptor (TLR)4 signaling (C3, C3H/HeN, B6, B6-Tlr4(tm1Aki)) make the TLR4 defect of C3 mice unlikely to be the reason for higher Cd14 expression. Less Cd14 expression in germ-free mice indicates a contribution of the microflora on Cd14 expression. Stimulation of naive peritoneal macrophages with bacterial antigens showed lower CD14 surface expression in B6 than in C3 mice. In conclusion, the large number of candidate genes was reduced to three major candidates that play an important role in inflammatory processes and immune response. Strain differences for them are already known or are shown in this study.

Animals↗
PubMed2006

Comparison of gene expression in segregating families identifies genes and genomic regions involved in a novel adaptation, zinc hyperaccumulation.

One of the challenges of comparative genomics is to identify specific genetic changes associated with the evolution of a novel adaptation or trait. We need to be able to disassociate the genes involved with a particular character from all the other genetic changes that take place as lineages diverge. Here we show that by comparing the transcriptional profile of segregating families with that of parent species differing in a novel trait, it is possible to narrow down substantially the list of potential target genes. In addition, by assuming synteny with a related model organism for which the complete genome sequence is available, it is possible to use the cosegregation of markers differing in transcription level to identify regions of the genome which probably contain quantitative trait loci (QTLs) for the character. This novel combination of genomics and classical genetics provides a very powerful tool to identify candidate genes. We use this methodology to investigate zinc hyperaccumulation in Arabidopsis halleri, the sister species to the model plant, Arabidopsis thaliana. We compare the transcriptional profile of A. halleri with that of its sister nonaccumulator species, Arabidopsis petraea, and between accumulator and nonaccumulator F(3)s derived from the cross between the two species. We identify eight genes which consistently show greater expression in accumulator phenotypes in both roots and shoots, including two metal transporter genes (NRAMP3 and ZIP6), and cytoplasmic aconitase, a gene involved in iron homeostasis in mammals. We also show that there appear to be two QTLs for zinc accumulation, on chromosomes 3 and 7.

Adaptation, Physiological↗
PubMed2004

Proteomic analysis of resistance mediated by Rcm 2.0 and Rcm 5.1, two loci controlling resistance to bacterial canker of tomato.

Two quantitative trait loci from Lycopersicon hirsutum, Rcm 2.0 and Rcm 5.1, control resistance to Clavibacter michiganensis subsp. michiganensis, the causal agent of bacterial canker of tomato. Lines containing Rcm 2.0 and Rcm 5.1 and a susceptible control line were compared at 72 and 144 h postinoculation, using 2-dimensional gel electrophoresis to identify proteins regulated in response to C. michiganensis subsp. michiganensis infection. A total of 47 proteins were subjected to tandem mass spectrometry. Database queries with resulting spectra identified tomato genes for 26 proteins. The remaining 21 proteins were either identified in other species or possessed no homology to known proteins. Spectra were interpreted to deduce peptide amino acid sequences that were then used to query publicly available data. This approach identified tomato genes or expressed sequence tags for 44 of the proteins analyzed. Three superoxide dismutase (SOD) enzymes were differentially regulated among genotypes, and patterns of hydrogen peroxide accumulation were genotype- and tissue-specific, indicating a role for oxidative stress in response to C. michiganensis subsp. michiganensis. Steady-state mRNA and protein levels for SOD, thioredoxin M-type, S-adenosylhomocysteine hydrolase, and pathogenesis-related proteins demonstrated similar patterns of differential regulation. Lines containing Rcm 2.0 and Rcm 5.1 accumulate different proteins and steady-state mRNAs in response to inoculation, suggesting that the two loci may confer resistance through distinct mechanisms.

Gene Expression Regulation, Plant↗
PubMed2026

Shared genetic architecture of smoking dependence and Crohn's disease: A cross-trait analysis of GWAS summary statistics.

INTRODUCTION: Smoking dependence (SD) and Crohn's disease (CD) are epidemiologically associated, but whether this relationship reflects shared genetic susceptibility remains unclear. METHODS: We conducted a cross-trait genetic analysis of SD and CD using publicly available genome-wide association study (GWAS) summary statistics from European-ancestry populations. Genome-wide genetic correlation was estimated using linkage disequilibrium score regression (LDSC) and high-definition likelihood (HDL). Pleiotropic variants were identified using PLACO and mapped to genomic loci using FUMA. Regional signal sharing was assessed by Bayesian colocalization. Functional analyses included stratified LDSC, Multi-marker Analysis of GenoMic Annotation (MAGMA), GTEx tissue analysis, and Metascape. Expression-linked candidate genes were prioritized using expression quantitative trait locus (eQTL)-based summary-data-based Mendelian randomization (SMR) with heterogeneity in dependent instruments (HEIDI) testing. Genetically informed spatial mapping of cells for complex traits (gsMap) was used for spatial mapping. RESULTS: SD and CD showed positive genetic correlation by LDSC (rg=0.2090, p=0.0008) and HDL (rg=0.3817, p=0.00106). PLACO identified 81 genome-wide significant pleiotropic SNPs, which were mapped by FUMA to three loci at 1p31.3, 5p13.1, and 12q12, represented by rs11209031, rs1395152, and rs17467116, respectively. MAGMA identified 22 FDR-significant genes, four of which remained Bonferroni significant: LRRK2, TNFRSF6B, ZGPAT, and RP4-583P15.15. Cross-trait tissue analysis showed significant enrichment of the shared genetic signal in whole blood and small intestine, while gene-set analysis highlighted inflammatory response (pbon=1.86×10-5) and T-helper 17 cell differentiation (pbon=7.37×10-4). SMR/HEIDI analysis further prioritized RPS6KB1 as a shared expression-linked candidate. Spatial mapping revealed a prominent signal in the embryonic gastrointestinal tract and gene-specific regional patterns involving LRRK2 and SLC2A13 in the adult mouse brain. CONCLUSIONS: SD and CD showed measurable shared genetic susceptibility, with convergent evidence from pleiotropic loci, immune-inflammatory pathway enrichment, tissue-level associations, and spatial transcriptomic mapping.

Crohn's disease↗
PubMed2002

Quantitative trait loci for bone density in mice: the genes determining total skeletal density and femur density show little overlap in F2 mice.

Bone mineral density variation is a highly heritable trait and is the best predictor of skeletal fragility. Total skeletal density was determined by PIXIMUS, and femur density was determined by pQCT. The data were analyzed for quantitative trait loci (QTL) to determine if bone density at a specific skeletal site (femur) would identify new gene loci or the same gene loci as total body (PIXIMUS). In order to show concordance and differences in QTL for total body bone density versus femur bone density, we performed a genome-wide scan from 633 (MRL x SJL) F2 mice. The bone mineral density (BMD) data from pQCT were used to identify nine QTL on chromosomes 1, 3, 4, 9, 12, 17, and 18, while nine QTL on chromosomes 1, 2, 4, 9, 11, 14, and 15 were identified by PIXIMUSdata, accounting for 32.5% and 30.4% variation in F2 mice, respectively. QTL on chromosomes 1, 2, 3, 9, 11, 12, 14, 15, 17, and 18 are unique to our study, as they have never been described before. Chromosome 1 (D1Mit33 and D1Mit362) had similar QTL between pQCT and PIXIMUS. Several QTL were identified for both femur and total body BMD but only two QTL were common for both of these phenotypes. This suggests that genes regulating bone density differ depending on the skeletal site analyzed.

Animals↗
PubMed2005

Mapping genome-genome epistasis: a high-dimensional model.

MOTIVATION: The proper development of any organ or tissue requires the coordinated expression of its underlying genes that can be located on different genomes present in an organism. For instance, each step in the development of seed for a higher plant is the consequence of gene interactions from the maternal, embryo and endosperm genomes. RESULTS: We present a multivariate statistical model for mapping quantitative trait loci (QTL) by incorporating two important aspects of seed development in plants-QTL interactions derived from different genomes, the maternal, embryo and endosperm, and genetic correlations among phenotypic traits expressed in different genome-specific tissues. This model, which has a high dimensionality, is constructed within the maximum-likelihood context based on a finite mixture model. The implementation of the expectation-maximization algorithm allows for the efficient estimation of QTL positions, their action and interaction effects and pleiotropic effects. The application of this high-dimensional model to a real rice dataset has validated its usefulness. CONCLUSIONS: Our model was derived for self-pollinated plants, but it can be extended to cross-pollinated plants and to animals. With the burgeoning of genetic and genomic data, this high-dimensional model will have many implications for agricultural and evolutionary genetic research. AVAILABILITY: A package of software will be provided from the corresponding author upon request.

Chromosome Mapping↗
PubMed2006

Statistical epistasis is a generic feature of gene regulatory networks.

Functional dependencies between genes are a defining characteristic of gene networks underlying quantitative traits. However, recent studies show that the proportion of the genetic variation that can be attributed to statistical epistasis varies from almost zero to very high. It is thus of fundamental as well as instrumental importance to better understand whether different functional dependency patterns among polymorphic genes give rise to distinct statistical interaction patterns or not. Here we address this issue by combining a quantitative genetic model approach with genotype-phenotype models capable of translating allelic variation and regulatory principles into phenotypic variation at the level of gene expression. We show that gene regulatory networks with and without feedback motifs can exhibit a wide range of possible statistical genetic architectures with regard to both type of effect explaining phenotypic variance and number of apparent loci underlying the observed phenotypic effect. Although all motifs are capable of harboring significant interactions, positive feedback gives rise to higher amounts and more types of statistical epistasis. The results also suggest that the inclusion of statistical interaction terms in genetic models will increase the chance to detect additional QTL as well as functional dependencies between genetic loci over a broad range of regulatory regimes. This article illustrates how statistical genetic methods can fruitfully be combined with nonlinear systems dynamics to elucidate biological issues beyond reach of each methodology in isolation.

Epistasis, Genetic↗
PubMed2002

Mapping QTLs for root morphology of a rice population adapted to rainfed lowland conditions.

In the rainfed lowlands, rice ( Oryza sativa L.) develops roots under anaerobic soil conditions with ponded water, prior to exposure to water stress and aerobic soil conditions that arise later in the season. Constitutive root system development in anaerobic soil conditions has been reported to have a positive effect on subsequent expression of adaptive root traits and water extraction during progressive water stress in aerobic soil conditions. We examined quantitative trait loci (QTLs) for constitutive root morphology traits using a mapping population derived from a cross between two rice lines which were well-adapted to rainfed lowland conditions. The effects of phenotyping environment and genetic background on QTLs identification were examined by comparing the experimental data with published results from four other populations. One hundred and eighty-four recombinant inbred lines (RILs) from a lowland indica cross (IR58821/IR52561) were grown under anaerobic conditions in two experiments. Seven traits, categorized into three groups (shoot biomass, deep root morphology, root thickness) were measured during the tillering stage. Though parental lines showed consistent differences in shoot biomass and root morphology traits across the two seasons, genotype-by-environment interaction (GxE) and QTL-by-environment interaction were significant among the progeny. Two, twelve, and eight QTLs for shoot biomass, deep root morphology, and root thickness, respectively, were identified, with LOD scores ranging from 2.0 to 12.8. Phenotypic variation explained by a single QTL ranged from 6% to 30%. Only two QTLs for deep root morphology, in RG256-RG151 in chromosome 2 and in PC75M3-PC11M4 in chromosome 4, were identified in both experiments. Comparison of positions of QTLs across five mapping populations (the current population plus populations from four other studies) revealed that these two QTLs for deep root morphology were only identified in populations that were phenotyped under anaerobic conditions. Fourteen and nine chromosome regions overlapped across different populations as putative QTLs for deep root morphology and root thickness, respectively. PC41M2-PC173M5 in chromosome 2 was identified as an interval that had QTLs for deep root morphology in four mapping populations. The PC75M3-PC11M4 interval in chromosome 4 was identified as a QTL for root thickness in three mapping populations with phenotypic variation explained by a single QTL consistently as large as 20-30%. Three QTLs for deep root morphology were found only in japonica/indica populations but not in IR58821/IR52561. The results identifying chromosome regions that had putative QTLs for deep root morphology and root thickness over different mapping populations indicate potential for marker-assisted selection for these traits.

Journal Article↗
PubMed1992

Evolution of multilocus genetic structure in an experimental barley population.

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.

Alleles↗
PubMed2004

Expression patterns of murine antichymotrypsin-like genes reflect evolutionary divergence at the Serpina3 locus.

Members of the serpin (serine protease inhibitor) superfamily of genes are well represented in both human and murine genomes. In many cases it is possible to identify a definite ortholog on the basis of sequence similarity and by examining the surrounding genes at syntenic loci. We have recently examined the murine serpin locus at 12F1 and observed that the single human alpha1-antichymotrypsin gene is represented by 14 paralogs. It is also known that the single human alpha1-antitrypsin gene has five paralogs in the mouse. The forces driving this gene multiplication are unknown and there are no data describing the function of the various serpin gene products at the alpha1-antichymotrypsin multigene locus. Examination of the predicted amino acid sequences shows that the serpins are likely to be functional protease inhibitors but with differing target protease specificities. In order to begin to address the question of the problem presented by the murine alpha1-antichymotrypsins, we have used RT-PCR to examine the expression pattern of these serpin genes. Our data show that the divergent reactive center loop sequence, and predictably variable target protease specificity, is reflected in tissue-specific expression for many of the family members. These observations add weight to the hypothesis that the antichymotrypsin-like serpins have an evolutionary importance which has led to their expansion and diversification in multiple species.

Animals↗
PubMed2006

Identification of a novel A1v-O1v hybrid allele with G829A mutation in a chimeric individual of AelBel phenotype.

BACKGROUND: Many A and B suballeles responsible for ABO subgroup formation have been identified. Some of these minor alleles have mutations in the ABO gene coding sequence. Most of these mutations are due to single-nucleotide substitution and lead to amino acid alteration. Several alleles at the ABO locus appear to be caused by crossing over between dissimilar alleles. STUDY DESIGN AND METHODS: Blood samples were collected from an individual with AelBel phenotype and her family members. Sequencing of the seven ABO exons was performed on these samples. The following was performed for the samples from the AelBel proposita: cloning and sequencing of the genomic DNA of the ABO gene, reverse transcription-polymerase chain reaction (PCR) analysis of cDNA transcript of the ABO gene, sequence-specific priming (SSP)-PCR analysis and direct sequencing of the ABO gene, SSP-PCR DNA typing of generic HLA-ABC and HLA-DRB, and short-tandem repeat (STR)-PCR typing on 15 autosomal, 2 X-chromosomal, and 6 Y-chromosomal loci. RESULTS: The proposita with AelBel phenotype has blood group chimerism with a major group of A1v-O1v/O1(O01) and a minor group of B(B101)/O1v(O02). Additional haplotypes on HLA-ABC, HLA-DR-B, STR loci, and Y-chromosome STR loci were present on the proposita. The paternal genotype is B(B101)/O1(O01) and the maternal genotype is A1v(A102)/O1v(O02). No other siblings have the A1v-O1v hybrid allele. Parentage was confirmed with the paternity STR-PCR test. Full-length cDNA transcripts of the B(B101) allele and alternately spliced cDNA transcripts of the hybrid A1v-O1v, O(1), and O1v alleles were cloned from the proposita. The A1v-O1v hybrid gene contains two missense mutations: C467T and G829A, resulting in Pro156Leu and Val277Met substitution. CONCLUSION: Formation of the A1v-O1v hybrid allele appears to result from de novo recombination in the germ line of the mother during meiosis. G829A with Val277Met appears to be responsible for the decrease in A-transferase activity and Ael phenotypic expression in the proposita. The chimeric minor population of B(B101)/O1v is responsible for Bel phenotypic expression in the proposita.

ABO Blood-Group System↗
PubMed1997

Exploiting pleiotropy to map genes for oligogenic phenotypes using extended pedigree data.

We investigated the utility of two approaches for exploiting pleiotropy to search for genes influencing related traits. To do this we first assessed the genetic correlations among a set of five closely related quantitative traits (Q1, Q2, Q3, Q4, Q5). We then used the genetic correlations among these five traits both to remove the common genetic effects of the four remaining traits, thereby identifying the unique genetic contribution to each trait, and to extract a synthetic phenotype which exploits the shared genetic information (pleiotropy) among these five traits. After obtaining these conditional traits, we then searched for evidence of quantitative trait loci (QTLs) (using variance component linkage) influencing the unique residual genetic component for each trait as well as those influencing the expression of the synthetic traits. From this work, we conclude that the removal of the common genetic effects of other traits in a group may be of greater utility when the majority of the pleiotropy initially detected between traits is attributable to the shared additive effects of polygenes, rather than to those of major loci. By contrast, decomposition of the genetic covariance matrix to its principal components is a greater utility when the majority of pleiotropy is attributable to major loci.

Chromosome Mapping↗
PubMed2025

The proteogenomic landscape of the human kidney and implications for cardio-kidney-metabolic health.

Nearly one-third of the global population is affected by cardio-kidney-metabolic (CKM) diseases; however, the molecular mechanisms underlying CKM diseases are poorly understood. Here we show that tissue proteomics provide critical insights not captured by tissue gene expression or blood proteomics information by performing whole-genome and RNA sequencing and proteomics analysis of human kidney samples (n = 337), and we generated a publicly available database. Via Bayesian co-localization and Mendelian randomization analyses of kidney protein quantitative trait loci and 36 CKM genome-wide association studies, we prioritized 89 proteins for CKM traits. We prioritized relationships that could underlie the interconnectedness of CKM traits and discovered multiple and targetable mechanisms for CKM diseases, including the potential role of kidney angiopoietin-like protein 3 (ANGPTL3) in serum lipid levels and kidney function as well as the role of charged multivesicular body protein 1A in kidney function and hypertension. Notably, we identify pathways with confluence of evidence from genetic loci, tissue gene expression and protein levels for CKM traits. In summary, our large-scale kidney proteomics study uncovers proteins and targetable mechanisms prioritized for CKM diseases.

Humans↗