Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Trait mapping”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,351 records · Page 75Linked to original sources
PubMed2006

A quantitative linkage score for an association study following a linkage analysis.

BACKGROUND: Currently, a commonly used strategy for mapping complex quantitative traits is to use a genome-wide linkage analysis to narrow suspected genes to regions on a scale of centiMorgans (cM), followed by an association analysis to fine map the genetic variation in regions showing linkage. Two important questions arise in the design and the resulting inference at the association stage of this sequential procedure: (1) how should we design an efficient association study given the information provided by the previous linkage study? and (2) can an association in a linkage region explain, in part, the detected linkage signal? RESULTS: We derive a quantitative linkage score (QLS) based on Haseman-Elston regression (Haseman and Elston 1972) and make use of this score to address both questions. In designing an association study, the selection of a subsample from the linkage study sample can be guided by the linkage information summarized in the QLS. When heterogeneity exists, we show that selection based on the QLS can increase the proportion of sample individuals from the subpopulation affected by a disease allele and therefore greatly improves the power of the association study. For the resulting inference, we frame as a hypothesis test the question of whether a linkage signal in a region can be in part explained by a marker allele. A simple one sided paired t-statistic is defined by comparing the two sets of QLSs obtained with/without modeling a marker association: a significant difference indicates that the marker can at least partly account for the detected linkage. We also show that this statistic can be used to detect a spurious association. CONCLUSION: All our results suggest that a careful examination of QLSs should be helpful for understanding the results of both association and linkage studies.

Alleles↗
PubMed2001

Complex trait analysis of the mouse striatum: independent QTLs modulate volume and neuron number.

BACKGROUND: The striatum plays a pivotal role in modulating motor activity and higher cognitive function. We analyzed variation in striatal volume and neuron number in mice and initiated a complex trait analysis to discover polymorphic genes that modulate the structure of the basal ganglia. RESULTS: Brain weight, brain and striatal volume, neuron-packing density and number were estimated bilaterally using unbiased stereological procedures in five inbred strains (A/J, C57BL/6J, DBA/2J, BALB/cJ, and BXD5) and an F2 intercross between A/J and BXD5. Striatal volume ranged from 20 to 37 mm3. Neuron-packing density ranged from approximately 50,000 to 100,000 neurons/mm3, and the striatal neuron population ranged from 1.4 to 2.5 million. Inbred animals with larger brains had larger striata but lower neuron-packing density resulting in a narrow range of average neuron populations. In contrast, there was a strong positive correlation between volume and neuron number among intercross progeny. We mapped two quantitative trait loci (QTLs) with selective effects on striatal architecture. Bsc10a maps to the central region of Chr 10 (LRS of 17.5 near D10Mit186) and has intense effects on striatal volume and moderate effects on brain volume. Stnn19a maps to distal Chr 19 (LRS of 15 at D19Mit123) and is associated with differences of up to 400,000 neurons among animals. CONCLUSION: We have discovered remarkable numerical and volumetric variation in the mouse striatum, and we have been able to map two QTLs that modulate independent anatomic parameters.

Animals↗
PubMed2025

A spectral framework to map QTLs affecting joint differential networks of gene co-expression.

Studying the mechanisms underlying the genotype-phenotype association is crucial in genetics. Gene expression studies have deepened our understanding of the genotype  →  expression  →  phenotype mechanisms. However, traditional expression quantitative trait loci (eQTL) methods often overlook the critical role of gene co-expression networks in translating genotype into phenotype. This gap highlights the need for more powerful statistical methods to analyze genotype  →  network  →  phenotype mechanism. Here, we develop a network-based method, called spectral network quantitative trait loci analysis (snQTL), to map quantitative trait loci affecting gene co-expression networks. Our approach tests the association between genotypes and joint differential networks of gene co-expression via a tensor-based spectral statistics, thereby overcoming the ubiquitous multiple testing challenges in existing methods. We demonstrate the effectiveness of snQTL in the analysis of three-spined stickleback (Gasterosteus aculeatus) data. Compared to conventional methods, our method snQTL uncovers chromosomal regions affecting gene co-expression networks, including one strong candidate gene that would have been missed by traditional eQTL analyses. Our framework suggests the limitation of current approaches and offers a powerful network-based tool for functional loci discoveries.

Quantitative Trait Loci↗
PubMed2003

Genome-wide and fine-mapping linkage studies of type 2 diabetes and glucose traits in the Old Order Amish: evidence for a new diabetes locus on chromosome 14q11 and confirmation of a locus on chromosome 1q21-q24.

We conducted a genome scan using a 10-cM map to search for genes linked to type 2 diabetes in 691 individuals from a founder population, the Old Order Amish. We then saturated two regions on chromosomes 1 and 14 showing promising linkage signals with additional markers to produce a approximately 2-cM map for fine mapping. Analyses of both discrete traits (type 2 diabetes and the composite trait of type 2 diabetes and/or impaired glucose homeostasis [IGH]), and quantitative traits (glucose levels during a 75-g oral glucose challenge, designated glucose 0-180 and HbA(1c)) were performed. We obtained significant evidence for linkage to type 2 diabetes in a novel region on chromosome 14q11 (logarithm of odds [LOD] for diabetes = 3.48, P = 0.00005). Furthermore, we observed evidence for the existence of a diabetes-related locus on chromosome 1q21-q24 (LOD for type 2 diabetes/IGH = 2.35, P = 0.0008), a region shown to be linked to diabetes in several other studies. Suggestive evidence for linkage to glucose traits was observed on three other regions: 14q11-q13 (telomeric to that above with LOD = 1.82-1.85 for glucose 150 and 180), 1p31 (LOD = 1.28-2.30 for type 2 diabetes and glucose 120-180), and 18p (LOD = 3.07, P = 0.000085 for HbA(1c) and LOD = 1.50 for glucose 0). In conclusion, our findings provide evidence that type 2 diabetes susceptibility genes reside on chromosomes 1, 14, and 18.

Blood Glucose↗
PubMed2006

The p47 GTPases Igtp and Irgb10 map to the Chlamydia trachomatis susceptibility locus Ctrq-3 and mediate cellular resistance in mice.

Infections caused by the bacteria Chlamydia trachomatis contribute to diverse pathologies in a variety of human populations. We previously used a systemic model of C. trachomatis infection in mice to map three quantitative trait loci that influence in vivo susceptibility differences between the C57BL/6J and C3H/HeJ inbred strains of mouse. One of these quantitative trait loci, Ctrq-3, influences an IFN-gamma-dependent susceptibility difference in primary embryonic fibroblasts isolated from these strains. Here we use fine structure mapping in congenic fibroblasts carrying DNA from the susceptible parent to localize the effect of Ctrq-3 to a 1.2-megabase interval of genomic DNA that contains Irgb10 and Igtp, two members of the IFN-gamma-inducible p47 family of GTPases. This class of proteins has been widely implicated in resistance to intracellular pathogens in mice. We analyzed expression of Irgb10 and Igtp in parental and congenic embryonic fibroblasts treated with IFN-gamma and found that relatively resistant fibroblasts express more Irgb10 than relatively susceptible fibroblasts. However, we also found that abolishing the expression of either Irgb10 or Igtp increases susceptibility of embryonic fibroblasts to C. trachomatis. Thus, we conclude that, although a difference in Irgb10 expression is likely responsible for the effect of Ctrq-3 on susceptibility to C. trachomatis, both genes play a role in intracellular resistance to C. trachomatis.

Amino Acid Sequence↗
PubMed2002

Positional candidate cloning of a QTL in dairy cattle: identification of a missense mutation in the bovine DGAT1 gene with major effect on milk yield and composition.

We recently mapped a quantitative trait locus (QTL) with a major effect on milk composition--particularly fat content--to the centromeric end of bovine chromosome 14. We subsequently exploited linkage disequilibrium to refine the map position of this QTL to a 3-cM chromosome interval bounded by microsatellite markers BULGE13 and BULGE09. We herein report the positional candidate cloning of this QTL, involving (1) the construction of a BAC contig spanning the corresponding marker interval, (2) the demonstration that a very strong candidate gene, acylCoA:diacylglycerol acyltransferase (DGAT1), maps to that contig, and (3) the identification of a nonconservative K232A substitution in the DGAT1 gene with a major effect on milk fat content and other milk characteristics.

Acyltransferases↗
PubMed2002

Mapping multiple genes for quantitative or complex traits.

Models for complex and quantitative traits that involve multiple, possibly interacting, genes are described. Methods of linkage analysis are developed that utilize special features of these models, and their power is compared with that of simple genome scans that ignore these special features. Our calculations show that for family-based nonparametric linkage analysis in human genetics, in contrast to experimental genetics, there are limits to the increase in power that can be achieved by correctly modeling gene-gene interactions. In particular, the noncentrality parameter of likelihood-based statistics to detect single gene effects involves both single gene and interaction components of variance, so even when the interaction components of variance are relatively large, the incremental power from a statistic designed to detect both single gene and interaction effects is often quite modest. We carry out our analysis with the assistance of a parameterization that allows us to compute score statistics, noncentrality parameters, and Fisher information matrices reasonably explicitly.

Alleles↗
PubMed2006

Expression profiling identifies novel candidate genes for ethanol sensitivity QTLs.

The Inbred Long Sleep (ILS) and Inbred Short Sleep (ISS) mouse strains have a 16-fold difference in duration of loss of the righting response (LORR) following administration of a sedative dose of ethanol. Four quantitative trait loci (QTLs) have been mapped in these strains for this trait. Underlying each of these QTLs must be one or more genetic differences (polymorphisms in either gene coding or regulatory regions) influencing ethanol sensitivity. Because prior studies have tended to focus on differences in coding regions, genome-wide expression profiling in cerebellum was used here to identify candidate genes for regulatory region differences in these two strains. Fifteen differentially expressed genes were found that map to the QTL regions and polymorphisms were identified in the promoter regions of four of these genes by direct sequencing of ILS and ISS genomic DNA. Polymorphisms in the promoters of three of these genes, Slc22a4, Rassf2, and Tax1bp3, disrupt putative transcription factor binding sites. Slc22a4 and another candidate, Xrcc5, have human orthologs that map to genomic regions associated with human ethanol sensitivity in genetic linkage studies. These genes represent novel candidates for the LORR phenotype and provide new targets for future studies into the neuronal processes underlying ethanol sensitivity.

Animals↗
PubMed2006

A multivariate model for ordinal trait analysis.

Many economically important characteristics of agricultural crops are measured as ordinal traits. Statistical analysis of the genetic basis of ordinal traits appears to be quite different from regular quantitative traits. The generalized linear model methodology implemented via the Newton-Raphson algorithm offers improved efficiency in the analysis of such data, but does not take full advantage of the extensive theory developed in the linear model arena. Instead, we develop a multivariate model for ordinal trait analysis and implement an EM algorithm for parameter estimation. We also propose a method for calculating the variance-covariance matrix of the estimated parameters. The EM equations turn out to be extremely similar to formulae seen in standard linear model analysis. Computer simulations are performed to validate the EM algorithm. A real data set is analyzed to demonstrate the application of the method. The advantages of the EM algorithm over other methods are addressed. Application of the method to QTL mapping for ordinal traits is demonstrated using a simulated baclcross (BC) population.

Algorithms↗
PubMed2001

Fine chromosomal localization of the mouse Par2 gene that confers resistance against urethane-induction of pulmonary adenomas.

BALB/cByJ mice are 14 times more resistant to urethane-induction of pulmonary adenomas than the susceptible A/J strain. Our previous linkage analysis of (A/J x BALB/cByJ)F1 x A/J backcross mice provided statistical evidence that a major resistance locus of BALB/cByJ with a dominant effect, designated Par2 (Pulmonary adenoma resistance 2), exists within an approximately 25 cM section of distal chromosome 18. To facilitate molecular identification of the Par2 locus, the present study was conducted to finely localize its chromosomal position utilizing Par2-congenic mice. Male BALB/cByJ mice were mated with female C57BL/6J mice carrying recessive Par2 alleles and their male F1 progeny were backcrossed to female BALB/cByJ mice. A male backcross mouse heterozygous within the Par2 interval of 25 cM was randomly selected and again backcrossed to female BALB/cByJ mice. This backcross-selection cycle was simply repeated to produce semi-congenic mice with a general BALB/cByJ genetic background except for the Par2 interval, where the mice were heterozygous with paternal C57BL/6J alleles and maternal BALB/cByJ alleles. After the 6th or 7th backcross, nine male mice possessing a recombination within the paternal Par2 interval were retained and crossed to female A/J mice. Resultant progeny were treated with urethane and examined for lung tumor development in order to deduce the Par2 genotypes of the recombinants through linkage analysis. By comparing the deduced Par2 genotype of each recombinant with its recombinational breakpoint, the Par2 locus was confined to an approximately 0.5 cM region flanked by D18Mit103 and D18Mit188 loci. Our results indicate that fully congenic mice conventionally established by at least nine simple backcrosses or by the speed congenic method are not necessarily required for fine mapping of quantitative trait loci. In the case of the Par2 locus, we found that semi-congenic mice after as few as four simple backcrosses were useful for this purpose. The map information obtained in this study should enable subsequent positional cloning of the Par2 gene.

Adenomatosis, Pulmonary↗
PubMed2005

A paternally imprinted QTL for mature body mass on mouse chromosome 8.

Body mass (BM) is a classic polygenic trait that has been extensively investigated to determine the underlying genetic architecture. Many previous studies looking at the genetic basis of variation in BM in murine animal models by quantitative trait loci (QTL) mapping have used crosses between two inbred lines. As a consequence it has not been possible to explore imprinting effects which have been shown to play an important role in the genetic basis of early growth with persistent effects throughout the growth curve. Here we use partially inbred mouse lines to identify QTL for mature BM by applying both Mendelian and Imprinting models. The analysis of an F2 population (n approximately 500) identified a number of QTL at 14, 16, and 18 weeks explaining in total 31.5%, 34.4%, and 30.5% of total phenotypic variation, respectively. On Chromosome 8 a QTL of large effect (14% of the total phenotypic variance at 14 weeks) was found to be explained by paternal imprinting. Although Chromosome 8 has not been previously associated with imprinting effects, features of candidate genes within the QTL confidence interval (CpG islands and direct clustered repeats) support the hypothesis that Insulin receptor substrate 2 may be associated with imprinting, but as yet is unidentified as being so.

Animals↗
PubMed2001

QTL analysis and genomewide mutagenesis in mice: complementary genetic approaches to the dissection of complex traits.

Quantitative genetics and quantitative trait locus (QTL) mapping have undergone a revolution in the last decade. Progress in the next decade promises to be at least as rapid, and strategies for fine-mapping QTLs and identifying underlying genes will be radically revised. In this Commentary we address several key issues: first, we revisit a perennial challenge--how to identify individual genes and allelic variants underlying QTLs. We compare current practice and procedures in QTL analysis with novel methods and resources that are just now being introduced. We argue that there is no one standard of proof for showing QTL = gene; rather, evidence from several sources must be carefully assembled until there is only one reasonable conclusion. Second, we compare QTL analysis with whole-genome mutagenesis in mice and point out some of the strengths and weakness of both of these phenotype-driven methods. Finally, we explore the advantages and disadvantages of naturally occurring vs mutagen-induced polymorphisms. We argue that these two complementary genetic methods have much to offer in efforts to highlight genes and pathways most likely to influence the susceptibility and progression of common diseases in human populations.

Alleles↗
PubMed2000

Mixed model analysis of quantitative trait loci.

We develop a mixed model approach of quantitative trait locus (QTL) mapping for a hybrid population derived from the crosses of two or more distinguished outbred populations. Under the mixed model, we treat the mean allelic value of each source population as the fixed effect and the allelic deviations from the mean as random effects so that we can partition the total genetic variance into between- and within-population variances. Statistical inference of the QTL parameters is obtained by using the Bayesian method implemented by Markov chain Monte Carlo (MCMC). This unified QTL mapping algorithm treats the fixed and random model approaches as special cases of the general mixed model methodology. Utility and flexibility of the method are demonstrated by using a set of simulated data.

Algorithms↗
PubMed2006

A quantitative trait locus for oleic fatty acid content on Sus scrofa chromosome 7.

A partial genome scan using microsatellite markers was conducted to detect quantitative trait loci (QTLs) for 10 fatty acid contents of backfat on 15 chromosomes in a porcine resource population. Two QTLs were discovered on Sus scrofa chromosome 4 (SSC4) and SSC7. The QTL on SSC4 was located between marker loci sw1336 and sw512, and this QTL was detected (P < 0.05) only for linoleic acid. Its position was in proximity of those mapped for linoleic acid content in previous studies. The QTL on SSC7 was mapped between markers swr1343 and sw2155, and it was significant (P < 0.05) only for oleic acid. A novelty of the QTL for oleic acid was suggested because the QTL was located far from any other QTLs previously mapped for fatness traits. The QTL on SSC7 explained 19% of phenotypic variation for oleic acid content. Further studies on fine mapping and positional comparative candidate gene analysis would be the next step toward better understanding of the genetic architecture of fatty acid contents.

Animals↗
PubMed2006

Quantitative trait analysis of seed yield and other complex traits in hybrid spring rapeseed (Brassica napus L.): 2. Identification of alleles from unadapted germplasm.

Unadapted germplasm may contain alleles that could improve hybrid cultivars of spring oilseed Brassica napus. Quantitative trait loci (QTL) mapping was used to identify potentially useful alleles from two unadapted germplasm sources, a Chinese winter cultivar and a re-synthesized B. napus, that increase seed yield when introgressed into a B. napus spring hybrid combination. Two populations of 160 doubled haploid (DH) lines were created from crosses between the unadapted germplasm source and a genetically engineered male-fertility restorer line (P1804). A genetically engineered male-sterile tester line was used to create hybrids with each DH line (testcrosses). The two DH line populations were evaluated in two environments and the two testcross populations were evaluated in three or four environments for seed yield and other agronomic traits. Several genomic regions were found in the two testcross populations which contained QTL for seed yield. The map positions of QTL for days to flowering and resistance to a bacterial leaf blight disease coincided with QTL for seed yield and other agronomic traits, suggesting the occurrence of pleiotropic or linked effects. For two hybrid seed yield QTL, the favorable alleles increasing seed yield originated from the unadapted parents, and one of these QTL was detected in multiple environments and in both populations. In this QTL region, a chromosome rearrangement was identified in P1804, which may have affected seed yield.

Alleles↗
PubMed2007

The quest for genes causing complex traits in ocular medicine: successes, interpretations, and challenges.

Gene mapping and positional cloning have gained acceptance as state-of-the-art methods to identify molecules that cause common complex diseases. However, the use of specialized technology, varying study designs, and misconceptions about the role of novel findings in genetics has led to confusion among basic scientists and health care professionals alike regarding the importance of these findings in molecular diagnostics and individualized medicine. To alleviate this confusion, the successes achieved in the past few years in mapping of genes for complex traits such as age-related macular degeneration and glaucoma are interpreted in the context of the appropriate population biology framework. The current article veers away from propagating the overly simplistic belief of a linear relationship between a specific gene and age-related macular degeneration, particularly one that equates possession of a specific risk allele as the only precursor to end-stage disease. Ascribing predictive properties to a single gene without consideration of its network partners, timing of action, or environmental correlates argues for a static view of gene action. Modern viewpoints of the mechanisms of action of a gene are contextual and encompass more cohesive frameworks, ranging from the developmental timing of action, to the genomic and environmental milieu. In this regard, gene mapping studies that have been so immensely successful in the gene detection phase of a study provide biased perspectives on the importance of these genes and the corresponding risk alleles in the general population because of their limited sample size and constrained design. To move the field of gene-based diagnosis forward, it will be necessary to conduct additional cohort and longitudinal studies using the original gene finding studies as a knowledge base to develop predictive models. In summary, while we have achieved great successes in finding genes for complex traits, the application of these findings to clinical medicine is not straightforward. The key question of who will develop disease in the future remains.

Chromosome Mapping↗
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↗
PubMed1993

Genomic mismatch scanning: a new approach to genetic linkage mapping.

Genomic mismatch scanning (GMS) is a new method of genetic linkage analysis that does not require conventional polymorphic markers or gel electrophoresis. GMS is ideally suited to affected-relative-pair mapping. DNA fragments from all regions of identity-by-descent between two relatives are isolated based on their ability to form extensive mismatch-free hybrid molecules. The genomic origin of this selected pool of DNA fragments is then mapped in a single hybridization step. Here we demonstrate the practicality of GMS in a model organism, Saccharomyces cerevisiae. GMS is likely to be applicable to other organisms, including humans, and may be of particular value in mapping complex genetic traits.

Chromosome Mapping↗