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Results for “quantitative trait locus”

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At least 19 recordsLinked to original sources

PubMed2006

Dissecting the regulation of fructan metabolism in perennial ryegrass (Lolium perenne) with quantitative trait locus mapping.

Quantitative trait locus (QTL) mapping, which can be a useful tool for dissecting complex traits, has been used here to study the regulation of fructan metabolism in temperate forage grasses. An F2 mapping family, derived from a high water-soluble carbohydrate (WSC) x low WSC cross, was used to map fructans and the other components of WSC (sucrose, glucose and fructose) in leaves and tiller bases of perennial ryegrass (Lolium perenne) in spring and autumn. To characterize regions of the genome that control basic carbohydrate metabolism, a strategy to minimize the impact of genotype (G) x environment (E), and E-effects on the characterization of G-effects, was adopted. Most traits were highly variable within the family. There was also considerable year-to-year environmental variation. However, significant genetic effects were detected, and several traits had high broad-sense heritability. QTL were identified on chromosomes 1, 2, 5 and 6. Leaf and tiller base QTL did not coincide. Individual QTL explained between 8 and 59% of the total phenotypic variation in the traits. Fructan turnover, metabolism and their genetic control, and the effect of environment, are discussed in the context of the results.

Biomarkers↗

PubMed2005

Quantitative trait locus analysis of longitudinal quantitative trait data in complex pedigrees.

There is currently considerable interest in genetic analysis of quantitative traits such as blood pressure and body mass index. Despite the fact that these traits change throughout life they are commonly analyzed only at a single time point. The genetic basis of such traits can be better understood by collecting and effectively analyzing longitudinal data. Analyses of these data are complicated by the need to incorporate information from complex pedigree structures and genetic markers. We propose conducting longitudinal quantitative trait locus (QTL) analyses on such data sets by using a flexible random regression estimation technique. The relationship between genetic effects at different ages is efficiently modeled using covariance functions (CFs). Using simulated data we show that the change in genetic effects over time can be well characterized using CFs and that including parameters to model the change in effect with age can provide substantial increases in power to detect QTL compared with repeated measure or univariate techniques. The asymptotic distributions of the methods used are investigated and methods for overcoming the practical difficulties in fitting CFs are discussed. The CF-based techniques should allow efficient multivariate analyses of many data sets in human and natural population genetics.

Chromosome Mapping↗

PubMed2006

Pooled analysis of data from multiple quantitative trait locus mapping populations.

Quantitative trait locus (QTL) analysis on pooled data from multiple populations (pooled analysis) provides a means for evaluating, as a whole, evidence for existence of a QTL from different studies and examining differences in gene effect of a QTL among different populations. Objectives of this study were to: (1) develop a method for pooled analysis and (2) conduct pooled analysis on data from two soybean mapping populations. Least square interval mapping was extended for pooled analysis by inclusion of populations and cofactor markers as indicator variables and covariate variables separately in the multiple linear models. The general linear test approach was applied for detecting a QTL. Single population-based and pooled analyses were conducted on data from two F(2:3) mapping populations, Hamilton (susceptible) x PI 90763 (resistant) and Magellan (susceptible) x PI 404198A (resistant), for resistance to soybean cyst nematode (SCN) in soybean. It was demonstrated that where a QTL was shared among populations, pooled analysis showed increased LOD values on the QTL candidate region over single population analyses. Where a QTL was not shared among populations, however, the pooled analysis showed decreased LOD values on the QTL candidate region over single population analyses. Pooled analysis on data from genetically similar populations may have higher power of QTL detection than single population-based analyses. QTLs were identified by pooled analysis on linkage groups (LGs) G, B1 and J for resistance to SCN race 2 whereas QTLs on LGs G, B1 and E for resistance to SCN race 5 in soybean PI 90763 and PI 404198A. QTLs on LG G and B1 were identified in both PI 90763 and PI 404198A whereas QTLs on LG E and J were identified in PI 90763 only. QTLs on LGs G and B1 for resistance to race 2 may be the same or closely linked with QTLs on LG G and B1 for resistance to race 5, respectively. It was further demonstrated that QTLs on G and B1 carried by PI 90763 were not significantly different in gene effect from QTLs on LGs G and B1 in PI 404198A, respectively.

Chromosome Mapping↗

PubMed2004

Evidence for multiple alleles at the DGAT1 locus better explains a quantitative trait locus with major effect on milk fat content in cattle.

A quantitative trait locus (QTL) for milk fat percentage has been mapped consistently to the centromeric region of bovine chromosome 14 (BTA14). Two independent studies have identified the nonconservative mutation K232A in the acylCoA-diacylglycerol-acyltransferase 1 (DGAT1) gene as likely to be causal for the observed variation. Here we provide evidence for additional genetic variability at the same QTL that is associated with milk fat percentage variation within the German Holstein population. Namely, we show that alleles of the DGAT1 promoter derived from the variable number of tandem repeat (VNTR) polymorphism are associated with milk fat content in animals homozygous for the allele 232A at DGAT1. Our results present another example for more than two trait-associated alleles being involved in a major gene effect on a quantitative trait. The segregation of multiple alleles affecting milk production traits at the QTL on BTA14 has to be considered whenever marker-assisted selection programs are implemented in dairy cattle. Due to the presence of a potential transcription factor binding site in the 18mer element of the VNTR, the variation in the number of tandem repeats of the 18mer element might be causal for the variability in the transcription level of the DGAT1 gene.

Acyltransferases↗

PubMed1996

Extensions to quantitative trait locus mapping in experimental organisms.

Quantitative trait locus (QTL) mapping studies involving inbred model organisms have become important and often-used investigative tools in contemporary genetics research. As a result, a number of statistical methods and study designs have been developed that have made it relatively easy for researchers to determine the rough locations of a number of QTLs that influence traits of all types, including a great many of relevance to hypertension and blood pressure regulation. However, as a whole, these methods and designs are limited in that they are not flexible enough to accommodate many of the complexities associated with quantitative trait expression. In this review, we describe a number of extensions to QTL mapping designs that should make QTL mapping experiments more insightful and compelling. These extensions include multiple QTL modeling with an emphasis on epistatic locus interaction and additive locus effects, the use of multiple phenotypic end points, the design of pharmacogenetic studies, the use of whole-genome parameters in quantitative trait investigations, and issues in mapping QTLs that influence traits that exhibit developmental trends. We use studies investigating the genetic basis of blood pressure regulation to exemplify relevant issues.

Animals↗

PubMedDate not supplied

Genotypic selection provides experimental confirmation for an alcohol consumption quantitative trait locus in mouse.

Quantitative genetic research has produced a wealth of basic information concerning genetic influence on alcohol-related processes. Recent developments in quantitative trait locus (QTL) methodology were promptly applied to the task of individuating polygenes affecting alcohol-related attributes in animal models and a body of reliable data is gradually coming into focus as a result of replication and convergence of evidence from a variety of methods. A key issue in QTL research is the need to distinguish true positive results from the false positive results that are inherent in analytical procedures requiring large numbers of significance tests. One school of thought holds that stringent significance levels should be imposed; another suggests more modest criteria for QTL nomination, with subsequent confirmation trials with independent samples. Recombinant inbred strains and various types of intercrosses have been used in correlational designs, both for nomination and confirmation studies. Alternative experimental procedures include knockout preparations and short-term phenotypic selective breeding. We present here results from a third experimental method-that of marker-based genotypic selection--in evaluation of two nominated QTLs for alcohol acceptance in mice.

Alcohol Drinking↗

PubMed1999

Congenic substitution mapping excludes Sa as a candidate gene locus for a blood pressure quantitative trait locus on rat chromosome 1.

Previously, linkage analysis in several experimental crosses between hypertensive rat strains and their contrasting reference strains have identified a major quantitative trait locus (QTL) for blood pressure on rat chromosome 1 (Chr 1) spanning the Sa gene locus. In this study, we report the further dissection of this Chr 1 blood pressure QTL with congenic substitution mapping. To address whether the Sa gene represents a candidate gene for the Chr 1 blood pressure QTL, congenic strains were developed by introgressing high blood pressure QTL alleles from the stroke-prone spontaneously hypertensive rat (SHRSP) into the normotensive Wistar-Kyoto (WKY-1) reference strain. Congenic animals carrying a chromosomal segment from stroke-prone spontaneously hypertensive rats between genetic markers Mt1pa and D1Rat200 (including the Sa gene locus) show a significant increase in basal systolic and diastolic blood pressure compared with their normotensive Wistar-Kyoto progenitors (P<0.001, respectively), whereas congenic animals carrying a subfragment of this Chr 1 region defined by markers Mt1pa and D1Rat57 (also spanning the Sa gene) do not show elevated basal blood pressure levels (P=0.83 and P=0.9, respectively). Similar results were obtained for NaCl-induced blood pressure values. Thus, the blood pressure QTL on Chr 1 is located centromeric to the Sa gene locus in a region that is syntenic to human chromosome 11p15.4-p15.3. This region excludes the Sa as a blood pressure-elevating candidate gene locus on the basis of congenic substitution mapping approaches.

Animals↗

PubMed2003

Quantitative trait locus analysis as a gene discovery tool.

Quantitative trait locus analysis has been a mainstay approach for obtaining a genetic description of complex agronomic traits for plants. What is sometimes overlooked is the role quantitative trait locus (QTL) analysis can play in identifying genes that underlay complex traits. In this chapter, I will describe the basic steps required to conduct QTL analysis in crop plants. This process involves choices by the investigator on type of population to be studied, molecular marker system to be used to genotype the population, and methods for QTL analysis. Examples of cloned genes, first identified as QTL, are also given to persuade the reader of the power of QTL analysis to discover genes controlling traits and phenotypes.

Chromosome Mapping↗

PubMed2000

Mapping a quantitative trait locus via the EM algorithm and Bayesian classification.

Mapping a locus controlling a quantitative genetic trait (e.g., blood pressure) to a specific genomic region is of considerable interest. Data on the quantitative trait under consideration and several codominant genetic markers with known genomic locations are collected from members of families and statistically analyzed to draw inferences on the genomic position of the trait locus. The vector of parameters of interest comprises the pairwise recombination fractions, theta, between the putative quantitative trait locus and the marker loci. One of the major complications in estimating theta for a quantitative trait in humans is the lack of haplotype information on members of families. The purpose of this study was to devise a computationally simple and efficient method of estimation of theta in the absence of haplotype information. We have proposed a two-stage estimation procedure using the expectation-maximization (EM) algorithm. In the first stage, parameters of the QTL are estimated based on data of a sample of unrelated individuals. From estimates thus obtained, we have used a Bayes' rule to infer QTL genotypes of parents in families. Finally, in the second stage of the procedure, we have proposed an EM algorithm for obtaining the maximum likelihood estimate of theta based on data of informative families (which are identified upon inferring parental QTL genotypes performed in the first stage). We have shown, using simulated data, that the proposed procedure is cost-effective, computationally simple, and statistically efficient. As expected, analysis of data on multiple markers jointly is more efficient than the analysis based on single markers.

Algorithms↗

PubMed1997

Congenic strains for the blood pressure quantitative trait locus on rat chromosome 2.

A quantitative trait locus (QTL) for blood pressure was previously detected by linkage analysis in a region of rat chromosome 2 using segregating populations derived from crosses of the Dahl salt-sensitive (Dahl S) rat with rats of the Wistar-Kyoto (WKY) strain or Milan normotensive strain (MNS). Two congenic strains, S.WKY-D2N35/Nep and S.MNS-Adh/D2Mit5, have been constructed by replacing a region of chromosome 2 of the Dahl S rat with the homologous region (ie, low blood pressure QTL allele) of either the WKY or MNS rat, respectively. Systolic pressures of congenic strains S.WKY-D2N35/Nep and S.MNS-Adh/D2Mit5 fed a 2% NaCl diet for 24 days were 44+/-4.6 and 29+/-4.5 mm Hg lower, respectively, than that of the comparably treated Dahl S rats. The differences between congenic and Dahl S rats in blood pressure were highly significant (P<.001) and were corroborated by significantly (P<.001) lower ratio of heart weight to body weight in the congenic strains compared with Dahl S rats. The data from two congenic strains combined unequivocally establish the existence of a blood pressure QTL on rat chromosome 2.

Alleles↗

PubMed2005

The F7 gene and clotting factor VII levels: dissection of a human quantitative trait locus.

Localization of human quantitative trait loci (QTLs) is now routine. However, identifying their functional DNA variants is still a formidable challenge. We present a complete dissection of a human QTL using novel statistical techniques to infer the most likely functional polymorphisms of a QTL that influence plasma levels of clotting factor VII (FVII), a risk factor for cardiovascular disease. Resequencing of 15 kb in and around the F7 gene identified 49 polymorphisms, which were then genotyped in 398 people. Using a Bayesian quantitative trait nucleotide (BQTN) method, we identified four to seven functional variants that completely account for this QTL. These variants include both rare coding variants and more common, potentially regulatory polymorphisms in intronic and promoter regions.

Bayes Theorem↗

PubMed2005

Quantitative trait locus analysis of aggressive behaviours in mice.

Quantitative trait locus (QTL) analysis is a forward genetic strategy (phenotype to gene) that enables an investigator to start with a phenotype of interest (e.g. aggressive behaviours), and then identify chromosomal regions (QTLs) and, ultimately, specific gene variants (alleles) within those regions, that influence quantitative variation in the phenotype. QTL analysis is an important complement to reverse genetic strategies (gene to phenotype), such as the generation of knockout and transgenic mice. Although a propensity for aggressive behaviours is partially heritable in mice and other mammals, very few aggression QTLs have been mapped. This slow progress is likely due, in part, to the complexity of aggressive behaviours as phenotypes, which are affected by many nongenetic (environmental and random) factors and gene-environment interactions. This paper reviews the general principles of QTL analysis, as well as the non-genetic factors that can confound aggression QTL studies. Some examples of successfully mapped intermale mouse aggression QTLs are presented, such as QTLs on chromosomes 10 and X. Strategies for fine mapping these loci are discussed, and candidate genes are considered. Finally, newly available mouse genetic resources that may facilitate QTL analysis of aggressive behaviours are suggested, such as consomic mouse strains.

Aggression↗

PubMed2000

Removing the sampling restrictions from family-based tests of association for a quantitative-trait locus.

One strategy for localization of a quantitative-trait locus (QTL) is to test whether the distribution of a quantitative trait depends on the number of copies of a specific genetic-marker allele that an individual possesses. This approach tests for association between alleles at the marker and the QTL, and it assumes that association is a consequence of the marker being physically close to the QTL. However, problems can occur when data are not from a homogeneous population, since associations can arise irrespective of a genetic marker being in physical proximity to the QTL-that is, no information is gained regarding localization. Methods to address this problem have recently been proposed. These proposed methods use family data for indirect stratification of a population, thereby removing the effect of associations that are due to unknown population substructure. They are, however, restricted in terms of the number of children per family that can be used in the analysis. Here we introduce tests that can be used on family data with parent and child genotypes, with child genotypes only, or with a combination of these types of families, without size restrictions. Furthermore, equations that allow one to determine the sample size needed to achieve desired power are derived. By means of simulation, we demonstrate that the existing tests have an elevated false-positive rate when the size restrictions are not followed and that a good deal of information is lost as a result of adherence to the size restrictions. Finally, we introduce permutation procedures that are recommended for small samples but that can also be used for extensions of the tests to multiallelic markers and to the simultaneous use of more than one marker.

Alleles↗

PubMed2006

Quantitative-trait-locus mapping in the presence of locus heterogeneity.

Locus heterogeneity is a concern for quantitative trait locus mapping where phenotypes are likely to be influenced by more than one gene. We introduce a model which generalizes the locus heterogeneity model of Smith (1961) from dichotomous traits to quantitative traits and consider some test statistics for this model. The type I error rates and the power of these statistics are assessed through simulation studies. These statistics are applied to a linkage study of asthma genes.

Asthma↗

PubMed2005

Sympathetic hyperreactivity to air-jet stress in the chromosome 1 blood pressure quantitative trait locus congenic rats.

A chromosome 1 blood pressure quantitative trait locus (QTL) was introgressed from the stroke-prone spontaneously hypertensive rats (SHRSP) to Wistar-Kyoto (WKY) rats. This congenic strain (WKYpch1.0) showed an exaggerated pressor response to both restraint and cold stress. In this study, we evaluated cardiovascular and sympathetic response to an air-jet stress and also examined the role of the brain renin-angiotensin system (RAS) in the stress response of WKYpch1.0. We measured mean arterial pressure (MAP), heart rate (HR), and renal sympathetic nerve activity (RSNA) responses to air-jet stress in WKYpch1.0, WKY, and SHRSP. We also examined effects of intracerebroventricular administration of candesartan, an ANG II type 1 receptor blocker, on MAP and HR responses to air-jet stress. Baseline MAP in the WKYpch1.0 and WKY rats were comparable, while it was lower than that in SHRSP rats. Baseline HR did not differ among the strains. In WKYpch1.0, air-jet stress caused greater increase in MAP and RSNA than in WKY. The increase in RSNA was as large as that in SHRSP, whereas the increase in MAP was smaller than in SHRSP. Intracerebroventricular injection of a nondepressor dose of candesartan inhibited the stress-induced pressor response to a greater extent in WKYpch1.0 than in WKY. Intravenous injection of phenylephrine caused a presser effect comparable between WKYpch1.0 and WKY. These results suggest that the chromosome 1 blood pressure QTL congenic rat has a sympathetic hyperreactivity to an air-jet stress, which causes exaggerated pressor responses. The exaggerated response is at least partly mediated by the brain RAS.

Animals↗

PubMed2002

Models and tests of linkage and association studies of quantitative trait locus for multi-allele marker Loci.

In this paper, we explore models and tests for association and linkage studies of a quantitative trait locus (QTL) linked to a multi-allele marker locus. Based on the difference between an offspring's conditional trait means of receiving and not receiving an allele from a parent at marker locus, we propose three statistics T(m), T(m,row) and T(m,col) to test association or linkage disequilibrium between the marker locus and the QTL. These tests are composite tests, and use the offspring marginal sample means including offspring data of both homozygous and heterozygous parents. For the linkage study, we calculate the offspring's conditional trait mean given the allele transmission status of a heterozygous parent at the marker locus. Based on the difference between the conditional means of a transmitted and a nontransmitted allele from a heterozygous parent, we propose statistics T(parsi), T(satur), T(gen) and T(m,het) to perform composite tests of linkage between the marker locus and the quantitative trait locus in the presence of association. These tests only use the offspring data that are related to the heterozygous parents at the marker locus. T(parsi) is a parsimonious or allele-wise statistic, T(satur) and T(gen )are satured or genotype-wise statistics, and T(m,het) compares the row and column sample means for offspring data of heterozygous parents. After comparing the powers and the sample sizes, we conclude that T(parsi) has higher power than those of the bi-allele tests, T(satur), T(gen), and T(m,het). If there is tight linkage between the marker and the trait locus, T(parsi) is powerful in detecting linkage between the marker and the trait locus in the presence of association. By investigating the goodness-of-fit of T(parsi), we find that T(satur) does not gain much power compared to that of T(parsi). Moreover, T(parsi) takes into account the pattern of the data that is consistent with linkage and linkage disequilibrium. As the number of alleles at the marker locus increases, T(parsi) is very conservative, and can be useful even for sparse data. To illustrate the usefulness and the power of the methods proposed in this paper, we analyze the chromosome 6 data of the Oxford asthma data, Genetic Analysis Workshop 12.

Asthma↗

PubMed2001

Fine mapping of Ath6, a quantitative trait locus for atherosclerosis in mice.

Ath6 is a novel quantitative trait locus associated with differences in susceptibility to atherosclerosis between C57BL/6J (B6) and C57BLKS/J (BKS) inbred mouse strains. Combining data from an intercross and a backcross (1593 meioses) between mice from B6 and BKS strains and from The Jackson Laboratory interspecific backcross panels, (C57BL/6J x Mus spretus) F1 x C57BL/6J and (C57BL/6J x SPRET/Ei) F1 x SPRET/Ei, we constructed a consensus genetic map and narrowed Ath6 to a 1.07 +/- 0.26 cM interval between the anonymous DNA marker D12Pgn4 and the gene Nmyc1. This region is near the proximal end of murine Chromosome (Chr) 12, which is homologous to the human chromosomal region 2p24-p25. Marker order in the Ath6 region was concordant among the two crosses and The Jackson Laboratory interspecific backcross panels. This high resolution map rules out candidate genes encoding apolipoprotein B, syndecan 1, and Adam17. The two Ath6 crosses have a combined potential resolution of 0.06 cM.

Alleles↗

PubMed2001

Hd6, a rice quantitative trait locus involved in photoperiod sensitivity, encodes the alpha subunit of protein kinase CK2.

Hd6 is a quantitative trait locus involved in rice photoperiod sensitivity. It was detected in backcross progeny derived from a cross between the japonica variety Nipponbare and the indica variety Kasalath. To isolate a gene at Hd6, we used a large segregating population for the high-resolution and fine-scale mapping of Hd6 and constructed genomic clone contigs around the Hd6 region. Linkage analysis with P1-derived artificial chromosome clone-derived DNA markers delimited Hd6 to a 26.4-kb genomic region. We identified a gene encoding the alpha subunit of protein kinase CK2 (CK2 alpha) in this region. The Nipponbare allele of CK2 alpha contains a premature stop codon, and the resulting truncated product is undoubtedly nonfunctional. Genetic complementation analysis revealed that the Kasalath allele of CK2 alpha increases days-to-heading. Map-based cloning with advanced backcross progeny enabled us to identify a gene underlying a quantitative trait locus even though it exhibited a relatively small effect on the phenotype.

Amino Acid Sequence↗