Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “quantitative trait”

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 235 records · Page 13Linked to original sources
PubMed1998

Quantitative trait loci for compensatory renal hypertrophy in the mouse.

Reduction in numbers of nephrons or decrease in kidney function due to a variety of diseases results in compensatory renal hypertrophy (CRH). Recently, it has been proposed that CRH may be a prerequisite for progression of renal injury; genetic dissection of CRH may be therefore helpful in understanding the process whereby people with partial renal insufficiency progress to end-stage renal disease. Since genetic analysis of CRH in humans is quite limited, we searched for genetic determinants of CRH after unilateral nephrectomy using a total genome scan of the mouse BXD recombinant inbred strains. We demonstrated that CRH is a highly heritable trait and we identified a quantitative trait locus on mouse chromosome 11 near the D11Mit14 marker that exerts a major effect on CRH (lod score = 3.4) and is responsible for approximately 52% of genetic variation in CRH. This marker maps near Ace, Gh, and Ngfr positional candidate genes.

Animals↗
PubMed2003

Effect of Box-Cox transformation on power of Haseman-Elston and maximum-likelihood variance components tests to detect quantitative trait Loci.

Non-normality of the phenotypic distribution can affect power to detect quantitative trait loci in sib pair studies. Previously, we observed that Winsorizing the sib pair phenotypes increased the power of quantitative trait locus (QTL) detection for both Haseman-Elston (HE) least-squares tests [Hum Hered 2002;53:59-67] and maximum likelihood-based variance components (MLVC) analysis [Behav Genet (in press)]. Winsorizing the phenotypes led to a slight increase in type 1 error in H-E tests and a slight decrease in type I error for MLVC analysis. Herein, we considered transforming the sib pair phenotypes using the Box-Cox family of transformations. Data were simulated for normal and non-normal (skewed and kurtic) distributions. Phenotypic values were replaced by Box-Cox transformed values. Twenty thousand replications were performed for three H-E tests of linkage and the likelihood ratio test (LRT), the Wald test and other robust versions based on the MLVC method. We calculated the relative nominal inflation rate as the ratio of observed empirical type 1 error divided by the set alpha level (5, 1 and 0.1% alpha levels). MLVC tests applied to non-normal data had inflated type I errors (rate ratio greater than 1.0), which were controlled best by Box-Cox transformation and to a lesser degree by Winsorizing. For example, for non-transformed, skewed phenotypes (derived from a chi2 distribution with 2 degrees of freedom), the rates of empirical type 1 error with respect to set alpha level=0.01 were 0.80, 4.35 and 7.33 for the original H-E test, LRT and Wald test, respectively. For the same alpha level=0.01, these rates were 1.12, 3.095 and 4.088 after Winsorizing and 0.723, 1.195 and 1.905 after Box-Cox transformation. Winsorizing reduced inflated error rates for the leptokurtic distribution (derived from a Laplace distribution with mean 0 and variance 8). Further, power (adjusted for empirical type 1 error) at the 0.01 alpha level ranged from 4.7 to 17.3% across all tests using the non-transformed, skewed phenotypes, from 7.5 to 20.1% after Winsorizing and from 12.6 to 33.2% after Box-Cox transformation. Likewise, power (adjusted for empirical type 1 error) using leptokurtic phenotypes at the 0.01 alpha level ranged from 4.4 to 12.5% across all tests with no transformation, from 7 to 19.2% after Winsorizing and from 4.5 to 13.8% after Box-Cox transformation. Thus the Box-Cox transformation apparently provided the best type 1 error control and maximal power among the procedures we considered for analyzing a non-normal, skewed distribution (chi2) while Winzorizing worked best for the non-normal, kurtic distribution (Laplace). We repeated the same simulations using a larger sample size (200 sib pairs) and found similar results.

Chromosome Mapping↗
PubMed2004

Comparison of population genetic structures of common wild rice (Oryza rufipogon Griff.), as revealed by analyses of quantitative traits, allozymes, and RFLPs.

We investigated genetic diversity among and within natural populations of Asian common wild rice, Oryza rufipogon, from three different classes of data: quantitative traits, allozymes, and restriction fragment length polymorphisms (RFLPs). The seven populations examined showed polymorphism to varying degrees. The amount of intrapopulation variability appeared to be influenced not only by breeding system but also by the evolutionary history of each population. Interpopulation differentiation was clear, but different classes of data elucidated different patterns. Quantitative traits revealed ecotype differentiation into perennial and annual types of population, whereas allozyme and RFLP analyses revealed geographical differentiation among populations. These results suggest that the diversity patterns shown by quantitative trait analysis reflect mainly the occurrence of adaptive differentiation in response to habitat conditions and that those shown by allozyme and RFLP analyses reflect mainly the effect of isolation by distance. Population differentiation parameters (F(ST)) were highly variable among loci in allozymes as well as in RFLPs.

Alleles↗
PubMedDate not supplied

[The ABO blood group system as genetic markers of quantitative traits in man].

A model of correlative variability of AB0 blood groups and a quantitative trait (mad-model) was analysed. Statistics for evaluation of additive and non-additive effects of alleles IA, IB and i on quantitative trait were developed. Restrictions of the model application are discussed. The obtained results may be used in genetic epidemiology for study of sensitivity or resistance to different diseases.

ABO Blood-Group System↗
PubMed2005

Semiparametric methods for mapping quantitative trait loci with censored data.

Statistical methods for the detection of genes influencing quantitative traits with the aid of genetic markers are well developed for normally distributed, fully observed phenotypes. Many experiments are concerned with failure-time phenotypes, which have skewed distributions and which are usually subject to censoring because of random loss to follow-up, failures from competing causes, or limited duration of the experiment. In this article, we develop semiparametric statistical methods for mapping quantitative trait loci (QTLs) based on censored failure-time phenotypes. We formulate the effects of the QTL genotype on the failure time through the Cox (1972, Journal of the Royal Statistical Society, Series B 34, 187-220) proportional hazards model and derive efficient likelihood-based inference procedures. In addition, we show how to assess statistical significance when searching several regions or the entire genome for QTLs. Extensive simulation studies demonstrate that the proposed methods perform well in practical situations. Applications to two animal studies are provided.

Animals↗
PubMed2003

[Mapping quantitative trait loci associated with rice grain shape based on an indica/japonica backcross population].

Appearance of rice grain represents a major character of rice quality in many rice-producing areas of the world, especially in hybrid rice production in China. In this study, we conducted a molecular marker-based genetic analysis of the traits that are determinants of the appearance quality of rice grains, including grain length, grain width and grain shape (measured as grain length to grain width ratio). Two typical indica/japonica varieties Balilla and Nantehao(NTH) were selected to construct Balilla/NTH//Balilla backcross population containing 142 individuals. In the population, grain length, grain width and grain shape all conform to the normal distribution with certain transgressive segregation. It can be deduced that all of three traits were controlled by some quantitative trait loci (QTLs). In order to explore the QTLs effect, number and location, a linkage map consisting of 108 SSR markers based on the backcross population was constructed, and QTLs mapping was carried out for grain length, grain width and grain shape. A QTL, qGL-12, was detected for grain length at the interval RM101-RM270 on chromosome 12, its additive effect was 0.26 mm, and can explain 16.7% genetic variation. As for grain width trait, two QTLs were found, qGW-2 located at RM154-RM211 interval on chromosome 2, and qGW-3 at interval RM257-RM175 on chromosome 3, accounting for 11.5% and 16.6% genetic variation, respectively. The alleles at qGW-2 and qGW-3 from parent Balilla can increase grain width by 0.10 mm and 0.12 mm. For grain shape, 3 QTLs, qLW-2, qLW-6 and qLW-7 were found, located on chromosome 2, 6, and 7, respectively. qLW-2 and qLW-7 had positive effect, and they can explain 12.7% and 18.3% genetic variation, while qLW-6 had negative effect and contributed 11.5% genetic variation to the backcross population. The prospects of application of linkage relationship between SSR marker and QTLs in marker based selection (MAS) in rice breeding, and the improvement of grain shape and rice appearance quality were discussed.

Chromosome Mapping↗
PubMed2006

Quantitative trait loci for callus initiation and totipotency in maize (Zea mays L.).

Induction of embryogenic callus in culture is an important step in plant transformation procedures, but response is genotype specific and the genetics of the trait are not well understood. Quantitative trait loci (QTL) were mapped in a set of 126 recombinant inbred lines (RILs) of inbred H99 (high Type I callus response) by inbred Mo17 (low Type I callus response) that were evaluated over two years for Type I callus response. QTL were observed in a total of eleven bins on eight chromosomes, including eight QTL with main effects and three epistatic interactions. Many of the QTL were mapped to the same or bordering chromosomal bins as candidate genes for abscisic acid metabolism, indicating a possible role for the hormone in the induction of embryogenic callus, as has previously been indicated in microspore embryo induction. Further examinations of allelic variability for known candidate genes located near the observed QTL could be useful for expanding the understanding of the genetic basis of induction embryogenic callus. The QTL observed herein could also be used in a marker assisted selection (MAS) program to improve the response of agronomically useful inbreds, but only if the resources required for MAS are lower than those required for phenotypic selection.

Chromosome Mapping↗
PubMed1999

Quantitative trait locus analysis of plasma lipoprotein levels in an autoimmune mouse model : interactions between lipoprotein metabolism, autoimmune disease, and atherogenesis.

The autoimmune MRL/lpr mouse strain, a model for systemic lupus erythematosus, exhibited an unusual plasma lipoprotein profile, suggesting a possible interaction of autoimmune disease and lipoprotein metabolism. In an effort to examine the genetic basis of such interactions, and to study their relationship to atherogenesis, we performed a quantitative trait locus analysis using a total of 272 (MRL/lprxBALB/cJ) second generation (F2) intercross mice. These mice were examined for levels of total plasma cholesterol, HDL cholesterol, VLDL and LDL cholesterol, unesterified cholesterol, autoantibodies, and aortic fatty streak lesions. Using a genome scan approach, we identified 4 quantitative trait loci controlling plasma lipoprotein levels on chromosomes (Chrs) 5, 8, 15, and 19. The locus on Chr 15 exhibited lod scores of 11.1 for total cholesterol and 6.7 for VLDL and LDL cholesterol in mice fed an atherogenic diet, and it contains a candidate gene, the sterol regulatory element binding protein-2. The locus on Chr 5 exhibited lod scores of 3.8 for total cholesterol and 4.1 for unesterified cholesterol in mice fed an atherogenic diet, and this locus has been observed in 2 previous studies. The locus on Chr 8 exhibited a lod score of 3.1 for unesterified cholesterol in mice fed a chow diet. This locus contains the lecithin-cholesterol acyltransferase gene, and decreased activity of the enzyme in the MRL strain suggests that this gene underlies the quantitative-trait locus. The locus on Chr 19 exhibited a lod score of 8.4 for HDL cholesterol and includes the Fas gene, which is mutated in MRL/lpr mice and is primarily responsible for the autoimmune phenotype in this cross. That the Fas gene is responsible for the HDL quantitative-trait loci is supported by the finding that autoantibody levels were strongly correlated with HDL cholesterol levels (rho=-0.37, P<0.0001) among the F2 mice. HDL cholesterol levels were in turn significantly associated with aortic fatty streak lesions among the F2 mice (rho=-0.17, P=0.006). Further, there was a threshold effect of autoantibody levels on the development of fatty streak lesions (rho=0.45, P=0.004 for 42 F2 mice with anti-dsDNA Ab over 0.5 OD). Our results support the concept that the high prevalence of coronary artery disease in systemic lupus erythematosus is due in part to a reduction of HDL cholesterol levels resulting from the autoimmune disease.

Animals↗
PubMed2003

Linkage analysis of quantitative trait loci in the presence of heterogeneity.

Variance component modeling for linkage analysis of quantitative traits is a powerful tool for detecting and locating genes affecting a trait of interest, but the presence of genetic heterogeneity will decrease the power of a linkage study and may even give biased estimates of the location of the quantitative trait loci. Many complex diseases are believed to be influenced by multiple genes and therefore genetic heterogeneity is likely to be present for many real applications of linkage analysis. We consider a mixture of multivariate normals to model locus heterogeneity by allowing only a proportion of the sampled pedigrees to segregate trait-influencing allele(s) at a specific locus. However, for mixtures of normals the classical asymptotic distribution theory of the maximum likelihood estimates does not hold, so tests of linkage and/or heterogeneity are evaluated using resampling methods. It is shown that allowing for genetic heterogeneity leads to an increase in power to detect linkage. This increase is more prominent when the genetic effect of the locus is small or when the percentage of pedigrees not segregating trait-influencing allele(s) at the locus is high.

Chromosome Mapping↗
PubMed1998

Bayesian mapping of multiple quantitative trait loci from incomplete inbred line cross data.

A novel fine structure mapping method for quantitative traits is presented. It is based on Bayesian modeling and inference, treating the number of quantitative trait loci (QTLs) as an unobserved random variable and using ideas similar to composite interval mapping to account for the effects of QTLs in other chromosomes. The method is introduced for inbred lines and it can be applied also in situations involving frequent missing genotypes. We propose that two new probabilistic measures be used to summarize the results from the statistical analysis: (1) the (posterior) QTL intensity, for estimating the number of QTLs in a chromosome and for localizing them into some particular chromosomal regions, and (2) the locationwise (posterior) distributions of the phenotypic effects of the QTLs. Both these measures will be viewed as functions of the putative QTL locus, over the marker range in the linkage group. The method is tested and compared with standard interval and composite interval mapping techniques by using simulated backcross progeny data. It is implemented as a software package. Its initial version is freely available for research purposes under the name Multimapper at URL http://www.rni.helsinki.fi/mjs.

Bayes Theorem↗
PubMed2002

Dissecting the architecture of a quantitative trait locus in yeast.

Most phenotypic diversity in natural populations is characterized by differences in degree rather than in kind. Identification of the actual genes underlying these quantitative traits has proved difficult. As a result, little is known about their genetic architecture. The failures are thought to be due to the different contributions of many underlying genes to the phenotype and the ability of different combinations of genes and environmental factors to produce similar phenotypes. This study combined genome-wide mapping and a new genetic technique named reciprocal-hemizygosity analysis to achieve the complete dissection of a quantitative trait locus (QTL) in Saccharomyces cerevisiae. A QTL architecture was uncovered that was more complex than expected. Functional linkages both in cis and in trans were found between three tightly linked quantitative trait genes that are neither necessary nor sufficient in isolation. This arrangement of alleles explains heterosis (hybrid vigour), the increased fitness of the heterozygote compared with homozygotes. It also demonstrates a deficiency in current approaches to QTL dissection with implications extending to traits in other organisms, including human genetic diseases.

Alleles↗
PubMed2005

On normality, ethnicity, and missing values in quantitative trait locus mapping.

BACKGROUND: This paper deals with the detection of significant linkage for quantitative traits using a variance components approach. Microsatellite markers were obtained for the Genetic Analysis Workshop 14 Collaborative Study on the Genetics of Alcoholism data. Ethnic heterogeneity, highly skewed quantitative measures, and a high rate of missing values are all present in this dataset and well known to impact upon linkage analysis. This makes it a good candidate for investigation. RESULTS: As expected, we observed a number of changes in LOD scores, especially for chromosomes 1, 7, and 18, along with the three factors studied. A dramatic example of such changes can be found in chromosome 7. Highly significant linkage to one of the quantitative traits became insignificant when a proper normalizing transformation of the trait was used and when analysis was carried out on an ethnically homogeneous subset of the original pedigrees. CONCLUSION: In agreement with existing literature, transforming a trait to ensure normality using a Box-Cox transformation is highly recommended in order to avoid false-positive linkages. Furthermore, pedigrees should be sorted by ethnic groups and analyses should be carried out separately. Finally, one should be aware that the inclusion of covariates with a high rate of missing values reduces considerably the number of subjects included in the model. In such a case, the loss in power may be large. Imputation methods are then recommended.

Chromosome Mapping↗
PubMed2006

Heritability and tissue specificity of expression quantitative trait loci.

Variation in gene expression is heritable and has been mapped to the genome in humans and model organisms as expression quantitative trait loci (eQTLs). We applied integrated genome-wide expression profiling and linkage analysis to the regulation of gene expression in fat, kidney, adrenal, and heart tissues using the BXH/HXB panel of rat recombinant inbred strains. Here, we report the influence of heritability and allelic effect of the quantitative trait locus on detection of cis- and trans-acting eQTLs and discuss how these factors operate in a tissue-specific context. We identified several hundred major eQTLs in each tissue and found that cis-acting eQTLs are highly heritable and easier to detect than trans-eQTLs. The proportion of heritable expression traits was similar in all tissues; however, heritability alone was not a reliable predictor of whether an eQTL will be detected. We empirically show how the use of heritability as a filter reduces the ability to discover trans-eQTLs, particularly for eQTLs with small effects. Only 3% of cis- and trans-eQTLs exhibited large allelic effects, explaining more than 40% of the phenotypic variance, suggestive of a highly polygenic control of gene expression. Power calculations indicated that, across tissues, minor differences in genetic effects are expected to have a significant impact on detection of trans-eQTLs. Trans-eQTLs generally show smaller effects than cis-eQTLs and have a higher false discovery rate, particularly in more heterogeneous tissues, suggesting that small biological variability, likely relating to tissue composition, may influence detection of trans-eQTLs in this system. We delineate the effects of genetic architecture on variation in gene expression and show the sensitivity of this experimental design to tissue sampling variability in large-scale eQTL studies.

Alleles↗
PubMed1998

Geographical divergence for quantitative traits in colonising populations of Drosophila kikkawai from India.

There are significant geographical variations for four quantitative traits among eight natural populations of Drosophila kikkawai along the Indian latitudinal transect (8.29 to 33 degrees N). Body weight, wing length, thorax length, and ovariole number exhibit significant clinal variation with increase in latitutde. Genetic correlations between all the four traits are significantly higher. Slope values for body weight and wing length are higher (2.32 per degree latitude) while lower for thorax length (0.70) and ovariole number (0.56). South Indian populations are characterised by lower mean values but higher variances as well as CV values as compared with northern populations. Multiple regression analyses (on the basis of temperature related climatic variables) evidence significantly higher association between all the four traits and coefficient of variation of mean annual temperature (seasonal thermal amplitude; TCV). Thus, genetic differentiations for quantitative traits in D. kikkawai are due to selective pressure from variable seasonal environmental conditions occurring on the southern tropical versus northern subtropical regions of the Indian subcontinent.

Adaptation, Physiological↗
PubMed2004

Identification of quantitative trait loci for growth and carcass composition in cattle.

A genomic screening to detect quantitative trait loci (QTL) affecting growth, carcass composition and meat quality traits was pursued. Two hundred nineteen microsatellite markers were genotyped on 176 of 620 (28%) progeny from a Brahman x Angus sire mated to mostly MARC III dams. Selective genotyping, based on retail product yield (%) and fat yield (%), was used to select individuals to be genotyped. Traits included in the study were birth weight (kg), hot carcass weight (kg), retail product yield, fat yield, marbling score (400 = slight00 and 500 = small00), USDA yield grade, and estimated kidney, heart and pelvic fat (%). The QTL were classified as significant when the expected number of false positives (ENFP) was less than 0.05 (F-statistic greater than 17.3), and suggestive when the ENFP was <1 (F-statistic between 10.2 and 17.3). A significant QTL (F = 19; ENFP = 0.02) was detected for marbling score at centimorgan (cM) 54 on chromosome 2. Suggestive QTL were detected for fat yield at 50 cM, for retail product yield at 53 cM, and for USDA yield grade at 63 cM on chromosome 1, for marbling score at 56 cM, for retail product yield at 70 cM, and for estimated kidney, heart and pelvic fat at 79 cM on chromosome 3, for marbling score at 44 cM, for hot carcass weight at 49 cM, and for estimated kidney, heart and pelvic fat at 62 cM on chromosome 16, and for fat yield at 35 cM on chromosome 17. Two suggestive QTL for birth weight were identified, one at 12 cM on chromosome 20 and the other at 56 cM on chromosome 21. An additional suggestive QTL was detected for retail product yield, for fat yield, and for USDA yield grade at 26 cM on chromosome 26. Results presented here represent the initial search for quantitative trait loci in this family. Validation of detected QTL in other populations will be necessary.

Agriculture↗
PubMed2005

Genomics of the future: identification of quantitative trait loci in the mouse.

Positional cloning of quantitative trait loci in rodents is a common approach to identify genes involved in complex phenotypes, including genes important to human disease. However, cloning the causative genes has proved to be more difficult than determining their positions. New tools such as genomic sequence, clone libraries, and new genomic-based methods offer new approaches to identify these genes. Here we review how these new tools and approaches will improve our ability to discover the genes important in complex traits.

Animals↗
PubMed1997

The effect of selective sampling on mapping quantitative trait loci.

Sib pairs were selectively sampled for extreme concordance or discordance for the quantitative trait Q1, a simulated phenotype (GAW10). Two selective sampling criteria were used (SC1 and SC2), and results for these were compared to linkage analyses using all pairs (ALL). In total 773 sib pairs were available, which reduced to an average of 59.7 pairs under SC1, and 134.1 pairs under SC2. Whole genome screens were performed on 10 different data replicates for each selection criterion (ALL, SC1, and SC2). Fine screens were then performed over regions which indicated at least suggestive linkage, and these regions were also fine screened in an independent data replicate in an attempt to repeat any areas found. The results for the coarse genome screens were similar under each of the criteria, although in general lower maxima and slightly more erratic lods were found under the stricter selection methods. The correct region on chromosome 5 (responsible for approximately 22% of the variance of Q1) was detected (p < 0.0001) in 6/10 of the data replicates using ALL, and 4/10 using SC1 and SC2. The second quantitative trait locus (QTL) on chromosome 8 (only 0.5% of the variance of Q1) was detected in only a single data replicate using SC1. False positive rates were similar for each criterion, whereas power decreased using selective sampling compared to ALL, although this was probably due to an insufficient initial sample size.

Chromosome Mapping↗
PubMed2005

Mapping quantitative trait loci in humans: achievements and limitations.

Recent advances in statistical methods and genomic technologies have ushered in a new era in mapping clinically important quantitative traits. However, many refinements and novel statistical approaches are required to enable greater successes in this mapping. The possible impact of recent findings pertaining to the structure of the human genome on efforts to map quantitative traits is yet unclear.

Genome, Human↗