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Results for “quantitative trait locus”

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PubMed1996

Identification of genetic markers for initial sensitivity and rapid tolerance to ethanol-induced ataxia using quantitative trait locus analysis in BXD recombinant inbred mice.

Rapid tolerance to rotarod ataxia has previously been demonstrated in mice after sequential ethanol injections. Here we tested DBA/2J and C57BL/6J mice for initial ethanol sensitivity; DBA/2J mice were more sensitive (0.40 +/- 0.17 mg/g brain) than C57BL/6J mice (1.44 +/- 0.12 mg/g). We then monitored the development of tolerance by quantifying blood ethanol concentrations at the recovery from ataxia over five sequential injections; tolerance reached a plateau in about 5 hr. DBA/2J mice became very tolerant (final ethanol threshold 3.47 +/- 0.16 mg/ml, an increase of 3.07 mg/ml, or 8.7-fold above base line); B6 became slightly tolerant (final ethanol threshold 2.62 +/- 12 mg/ml, and increase of 1.18, or 1.8-fold above base line). Therefore, by the end of the treatment regimen, the rank order of sensitivity of the two strains had reversed. We then tested 25 recombinant inbred strains from among strains representing a cross between C57BL/6J and DBA/2J inbred strains, followed by a quantitative trait locus analysis with a database of 1522 markers to identify provisional loci. This procedure identified 19 markers on 11 chromosomes for initial sensitivity, 18 markers on 9 chromosomes for tolerance (delta) and 21 markers on 11 chromosomes for tolerance (fold-increase). Of these, 17 markers were in common, which suggests that initial sensitivity and tolerance share substantial genetic codetermination. Major candidate loci will be confirmed by genotyping B6D2F2 offspring that have been tested for initial sensitivity and tolerance.

Animals↗

PubMed1997

Genetic and molecular analysis of smooth, a quantitative trait locus affecting bristle number in Drosophila melanogaster.

A semi-lethal, sterile allele of the smooth locus (2-91.5), sm3, was discovered in an artificial selection line for low abdominal bristle number that had been started from a P-M dysgenic cross. The fitness effects and extremely low bristle number phenotype of the allele could not be separated by recombination from a P-element insertion at cytological location 56E, and precise excision of the P-element at this site was associated with reversion to wild type. The smooth gene was cloned using the P-element insertion as a tag. The gene encodes a 2.6-kb transcript derived from 10 exons and covers a genomic region of at least 80 kb. The Drosophila smooth gene shares substantial sequence identity with a group of RNA binding proteins, with the closest relationship being to the human heterogeneous nuclear ribonucleoprotein L gene. The smooth gene is by definition an abdominal bristle number quantitative trait locus, but further work is required to discern whether naturally occurring allelic variation at this locus is a source of genetic variation for abdominal bristle number in natural populations.

Abdomen↗

PubMed2005

Maize association population: a high-resolution platform for quantitative trait locus dissection.

Crop improvement and the dissection of complex genetic traits require germplasm diversity. Although this necessary phenotypic variability exists in diverse maize, most research is conducted using a small subset of inbred lines. An association population of 302 lines is now available--a valuable research tool that captures a large proportion of the alleles in cultivated maize. Provided that appropriate statistical models correcting for population structure are included, this tool can be used in association analyses to provide high-resolution evaluation of multiple alleles. This study describes the population structure of the 302 lines, and investigates the relationship between population structure and various measures of phenotypic and breeding value. On average, our estimates of population structure account for 9.3% of phenotypic variation, roughly equivalent to a major quantitative trait locus (QTL), with a high of 35%. Inclusion of population structure in association models is critical to meaningful analyses. This new association population has the potential to identify QTL with small effects, which will aid in dissecting complex traits and in planning future projects to exploit the rich diversity present in maize.

Chromosome Mapping↗

PubMed2006

The X chromosome in quantitative trait locus mapping.

The X chromosome requires special treatment in the mapping of quantitative trait loci (QTL). However, most QTL mapping methods, and most computer programs for QTL mapping, have focused exclusively on autosomal loci. We describe a method for appropriate treatment of the X chromosome for QTL mapping in experimental crosses. We address the important issue of formulating the null hypothesis of no linkage appropriately. If the X chromosome is treated like an autosome, a sex difference in the phenotype can lead to spurious linkage on the X chromosome. Further, the number of degrees of freedom for the linkage test may be different for the X chromosome than for autosomes, and so an X chromosome-specific significance threshold is required. To address this issue, we propose a general procedure to obtain chromosome-specific significance thresholds that controls the genomewide false positive rate at the desired level. We apply our methods to data on gut length in a large intercross of mice carrying the Sox10Dom mutation, a model of Hirschsprung disease. We identified QTL contributing to variation in gut length on chromosomes 5 and 18. We found suggestive evidence of linkage to the X chromosome, which would be viewed as strong evidence of linkage if the X chromosome was treated as an autosome. Our methods have been implemented in the package R/qtl.

Animals↗

PubMed2003

Power of linkage disequilibrium mapping to detect a quantitative trait locus (QTL) in selected samples of unrelated individuals.

We considered a strategy to map quantitative trait loci (QTLs) using linkage disequilibrium (LD) when the QTL and marker locus were multiallelic. The strategy involved phenotyping a large number of unrelated individuals and genotyping only selected individuals from the two tails of the trait distribution. Power to detect trait-marker association was assessed as a function of the number of QTL and marker alleles. Two patterns of LD were used to study their influence on power. When the frequency of the QTL allele with the largest effect and that of the marker allele linked in coupling were equal, power was maximum. In this case, increasing the number of QTL alleles reduced the power. The maximum difference in power between the two LD patterns studied was approximately 30%. For low QTL heritabilities (h2QTL<0.1) and single trait studies we recommend selecting around 5% of the upper and lower tails of the trait distribution.

Chromosome Mapping↗

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↗

PubMed1996

A quantitative trait locus in major histocompatibility complex determining latent period of mouse lymphomas.

The effects of two host genes on retrovirus-induced murine lymphoma were evaluated by studying 114 F2 intercross mice between SL/Kh and AKR/Ms mice. Out of 47 T-lymphoma-bearing F2 mice, 45 had the AKR-derived dominant allele at Tism-1. The length of the lymphoma latent period was not related to type of tumor. Instead, it was significantly shortened by a recessive SL/Kh-derived allele at a major histocompatibility complex (MHC)-linked locus on Chr. 17. A quantitative trait analysis of the latent period yielded a maximal logarithm of likelihood ratio for linkage (LOD) score of 7.06 at a class II gene within MHC. The SL/Kh-derived recessive gene was named lla (lymphoma latency acceleration).

Alleles↗

PubMed2005

A likelihood approach for quantitative-trait-locus mapping with selected pedigrees.

Selective sampling is a cost-effective design for mapping quantitative trait loci (QTLs). A unified framework, which naturally combines two complementary sources of linkage information in the data, is proposed for the mapping of QTLs using selected pedigrees. Score statistics for detecting linkage are introduced for single-locus models (univariate or bivariate phenotypes) and two-locus epistasis models. A computer implementation of the methods for single-locus univariate-phenotype models is provided for nuclear families with arbitrary number of sibs and is freely available.

Algorithms↗

PubMed2004

A quantitative trait locus mixture model that avoids spurious LOD score peaks.

In standard interval mapping of quantitative trait loci (QTL), the QTL effect is described by a normal mixture model. At any given location in the genome, the evidence of a putative QTL is measured by the likelihood ratio of the mixture model compared to a single normal distribution (the LOD score). This approach can occasionally produce spurious LOD score peaks in regions of low genotype information (e.g., widely spaced markers), especially if the phenotype distribution deviates markedly from a normal distribution. Such peaks are not indicative of a QTL effect; rather, they are caused by the fact that a mixture of normals always produces a better fit than a single normal distribution. In this study, a mixture model for QTL mapping that avoids the problems of such spurious LOD score peaks is presented.

Genetic Techniques↗

PubMed2006

Poor performance of bootstrap confidence intervals for the location of a quantitative trait locus.

The aim of many genetic studies is to locate the genomic regions (called quantitative trait loci, QTL) that contribute to variation in a quantitative trait (such as body weight). Confidence intervals for the locations of QTL are particularly important for the design of further experiments to identify the gene or genes responsible for the effect. Likelihood support intervals are the most widely used method to obtain confidence intervals for QTL location, but the nonparametric bootstrap has also been recommended. Through extensive computer simulation, we show that bootstrap confidence intervals behave poorly and so should not be used in this context. The profile likelihood (or LOD curve) for QTL location has a tendency to peak at genetic markers, and so the distribution of the maximum-likelihood estimate (MLE) of QTL location has the unusual feature of point masses at genetic markers; this contributes to the poor behavior of the bootstrap. Likelihood support intervals and approximate Bayes credible intervals, on the other hand, are shown to behave appropriately.

Bayes Theorem↗

PubMed2000

Selective genotyping with epistasis can be utilized for a major quantitative trait locus mapping in hypertension in rats.

Epistasis used to be considered an obstacle in mapping quantitative trait loci (QTL) despite its significance. Numerous epistases have proved to be involved in quantitative genetics. We established a backcross model that demonstrates a major QTL for hypertension (Ht). Seventy-eight backcrossed rats (BC), derived from spontaneously hypertensive rats (SHR) and normotensive Fischer 344 rats, showed bimodal distribution of systolic blood pressure (BP) values and a phenotypic segregation ratio consistent with 1:1. In this backcross analysis, sarco(endo)plasmic reticulum Ca(2+)-dependent ATPase (Serca) II heterozygotes showed widespread bimodality in frequency distribution of BP values and obviously demonstrated Ht. First, in genome-wide screening, Mapmaker/QTL analysis mapped Ht at a locus between D1Mgh8 and D1Mit4 near Sa in all 78 BC. The peak logarithm of the odds (LOD) score reached 5.3. Second, Serca II heterozygous and homozygous BC were analyzed separately using Mapmaker/QTL. In the 35 Serca II heterozygous BC, the peak LOD score was 3.8 at the same locus whereas it did not reach statistical significance in the 43 Serca II homozygotes. Third, to map Ht efficiently, we selected 18 Serca II heterozygous BC with 9 highest and 9 lowest BP values. In these 18 BC, the peak LOD score reached 8.1. In 17 of the 18, D1Mgh8 genotypes (homo or hetero) qualitatively cosegregated with BP phenotypes (high or low) (P < 0.0001, by chi-square analysis). In conclusion, selective genotyping with epistasis can be utilized for a major QTL mapping near Sa on chromosome 1 in SHR.

Animals↗

PubMed2000

More about quantitative trait locus mapping with diallel designs.

We present a general regression-based method for mapping quantitative trait loci (QTL) by combining different populations derived from diallel designs. The model expresses, at any map position, the phenotypic value of each individual as a function of the specific-mean of the population to which the individual belongs, the additive and dominance effects of the alleles carried by the parents of that population and the probabilities of QTL genotypes conditional on those of neighbouring markers. Standard linear model procedures (ordinary or iteratively reweighted least-squares) are used for estimation and test of the parameters.

Alleles↗

PubMed2006

The candidate gene, Clock, localizes to a strong spawning time quantitative trait locus region in rainbow trout.

We applied a candidate gene mapping approach to an existing quantitative trait loci (QTL) data set for spawning date in rainbow trout (Oncorynchus mykiss) to ascertain whether these genes could potentially account for any observed QTL effects. Several genes were chosen for their known or suspected roles in reproduction, circadian, or circannual timing, including salmon-type gonadotropin-releasing hormone 3A and 3B (GnRH3A and GnRH3B), Clock, Period1, and arylalkylamine N-acetlytransferase-1 and -2 (AANAT-1 and AANAT-2). Genes were sequenced, and polymorphisms were identified in parents of two rainbow trout mapping families, one of which was used previously to detect spawn timing QTL. Interval mapping was used to identify associations between genetic markers and spawning date effects. Using a genetic map that was updated with 574 genetic markers (775 total), we found evidence for 11 significant or suggestive QTL regions. Most QTL were only localized within one of the parents; however, a strong QTL region was identified in both female and male parents on linkage group RT-8 that explained 20% and 50% of trait variance, respectively. The Clock gene mapped to this region. Period1 mapped to a region in the female parent associated with a marginal effect (P = .056) on spawn timing. Other candidate genes were not associated with significant QTL effects.

Animals↗

PubMed2006

Identifying genes associated with a quantitative trait or quantitative trait locus via selective transcriptional profiling.

Genetical genomics is an approach that blends the mapping of quantitative trait loci (QTL) with microarray analysis. The approach can be used to identify associations between the allelic state of a genomic region and a gene's transcript abundance. However, the large number of microarrays required for adequate power results in high material and labor costs that prevent wide adoption of the genetical genomics strategy outside of some well-funded laboratories. We present a method called selective transcriptional profiling that involves selecting an optimal subset of individuals to microarray from a larger set of individuals for which relatively inexpensive quantitative trait and molecular marker data are available. We show how to use microarray data from the selected individuals, along with the trait and marker data from all individuals, to identify genes whose transcript abundance is associated with a quantitative trait of interest through linkage to a trait QTL or correlation with the trait. Our methods for selection and analysis are derived within a missing data framework.

Alleles↗

PubMed2004

Evidence of a novel quantitative-trait locus for obesity on chromosome 4p in Mexican Americans.

Although several genomewide scans have identified quantitative-trait loci influencing several obesity-related traits in humans, genes influencing normal variation in obesity phenotypes have not yet been identified. We therefore performed a genome scan of body mass index (BMI) on Mexican Americans, a population prone to obesity and diabetes, using a variance-components linkage analysis to identify loci that influence BMI. We used phenotypic data from 430 individuals (26% diabetics, 59% females, mean age +/- SD = 43 +/- 17 years, mean BMI +/- SD = 30.0 +/- 6.7, mean leptin (ng/ml) +/- SD = 22.1 +/- 17.1) distributed across 27 low-income Mexican American pedigrees who participated in the San Antonio Family Diabetes Study (SAFDS) for whom a 10-15-cM map is available. In this genomewide search, after accounting for the covariate effects of age, sex, diabetes, and leptin, we identified a genetic region exhibiting the most highly significant evidence for linkage (LOD 4.5) with BMI on chromosome 4p (4p15.1) at 42 cM, near marker D4S2912. This linkage result has been confirmed in an independent linkage study of severe obesity in Utah pedigrees. Two strong positional candidates, the human peroxisome proliferator-activated receptor gamma coactivator 1 (PPARGC1) and cholecystokinin A receptor (CCKAR) with major roles in the development of obesity, are located in this region. In conclusion, we identified a major genetic locus influencing BMI on chromosome 4p in Mexican Americans.

Body Mass Index↗

PubMed2003

Rank-based statistical methodologies for quantitative trait locus mapping.

This article addresses the identification of genetic loci (QTL and elsewhere) that influence nonnormal quantitative traits with focus on experimental crosses. QTL mapping is typically based on the assumption that the traits follow normal distributions, which may not be true in practice. Model-free tests have been proposed. However, nonparametric estimation of genetic effects has not been studied. We propose an estimation procedure based on the linear rank test statistics. The properties of the new procedure are compared with those of traditional likelihood-based interval mapping and regression interval mapping via simulations and a real data example. The results indicate that the nonparametric method is a competitive alternative to the existing parametric methodologies.

Crosses, Genetic↗

PubMed1999

Identification of an acute ethanol response quantitative trait locus on mouse chromosome 2.

A two-stage strategy was used to identify and confirm quantitative trait loci (QTLs) associated with the changes in locomotor activity induced by a 1.5 gm/kg ethanol challenge. For stage 1, putative QTLs were identified by analysis of the strain means for 25 strains of the BXD recombinant inbred (RI) series (males only). QTLs were identified on chromosomes 1, 2, 4, and 6. The activity response to chlordiazepoxide generated similar QTLs on chromosomes 2 and 6. None of the QTLs were similar to those generated from analysis of the saline response data. For stage 2, 900 male C57BL/6J (B6) x DBA/2J (D2) F2 intercross animals were phenotyped for ethanol response, and the phenotypic extremes (those animals > and <1 SD from the mean) were identified. These extremes differed by >10,000 cm/15 min in their response to ethanol. The extreme progeny were used for a genome-wide scan both to confirm the putative RI-generated QTLs and to detect new QTLs. The F2 analysis generated no new QTLs with logarithm of the likelihood for linkage (LOD) scores >3. For RI-generated QTLs, only the QTL on chromosome 2 was confirmed (LOD = 5.3). The position of the peak LOD was estimated to be 47 cM with a 20 cM 1 LOD support interval; this QTL accounted for 6% of the phenotypic variance. The 1 LOD support interval overlaps with QTLs previously identified for alcohol preference and acute ethanol withdrawal (;; ).

Animals↗

PubMed2002

Quantitative trait locus mapping of genes that regulate HDL cholesterol in SM/J and NZB/B1NJ inbred mice.

To investigate the quantitative trait loci (QTL) regulating plasma cholesterol, the female progeny of an (SMxNZB/ B1NJ)xNZB/B1NJ backcross were fed an atherogenic diet. After 18 wk, plasma total cholesterol and high-density lipoprotein cholesterol (HDL-C) was measured. HDL-C concentrations were greater in NZB than in SM mice. For standard chow-fed mice, QTL were found near D5Mit370 and D18Mit34. For mice fed an atherogenic diet, a QTL was found near D5Mit239. The QTL for chow-fed and atherogenic-fed mice on chromosome 5 seem to be two different loci. We used a multitrait analysis to rule out pleiotropy in favor of a two-QTL hypothesis. Furthermore, the HDL-C in these strains was induced by the high-fat diet. For inducible HDL-C, one significant locus was found near D15Mit39. The gene for an HDL receptor, Srb1, maps close to the HDL-C QTL at D5Mit370, but the concentrations of Srb1 mRNA and SR-B1 protein and the gene sequence of NZB/B1NJ and SM/J did not support Srb1 as a candidate gene. With these QTL, we have identified chromosomal regions that affect lipoprotein profiles in these strains.

Animals↗