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PubMed2002

A note on estimating the posterior density of a quantitative trait locus from a Markov chain Monte Carlo sample.

We provide an overview of the use of kernel smoothing to summarize the quantitative trait locus posterior distribution from a Markov chain Monte Carlo sample. More traditional distributional summary statistics based on the histogram depend both on the bin width and on the sideway shift of the bin grid used. These factors influence both the overall mapping accuracy and the estimated location of the mode of the distribution. Replacing the histogram by kernel smoothing helps to alleviate these problems. Using simulated data, we performed numerical comparisons between the two approaches. The results clearly illustrate the superiority of the kernel method. The kernel approach is particularly efficient when one needs to point out the best putative quantitative trait locus position on the marker map. In such situations, the smoothness of the posterior estimate is especially important because rough posterior estimates easily produce biased mode estimates. Different kernel implementations are available from Rolf Nevanlinna Institute's web page (http://www.rni.helsinki.fi/;fjh).

Bayes Theorem↗
PubMed2003

[Estimating family correlation of quantitative traits using generalized estimating equation].

OBJECTIVE: To study the method for measuring familial correlations of quantitative trait and analyzing family data set of body height. METHODS: Generalized estimating equation 2 (GEE2) was employed to estimating both regression coefficients and the familial correlation. Analyses was carried out on software MAREG. A example from height pedigrees illustrated the method. RESULTS: GEE2 provided robust estimations of regression coefficients and familial correlations simultaneously. In body height the correlations between parents and offspring (r = 0.459) and between siblings (r = 0.671) were significantly higher than those between two parents (r = 0.184) after adjusting gender, residence and birth age. Of the same types of relative pairs, the correlation between pairs with individuals of the same gender (eg. father-son r = 0.603, mother-daughter r = 0.456, male sibling r = 0.947, female sibling r = 0.681) was higher than those individuals of different gender (eg father-daughter r = 0.431, mother-son r = 0.364, sibling with different gender r = 0.530). CONCLUSION: GEE2 should be considered a standard method for the investigation of familial aggregation due to its flexibility and robustness.

Adult↗
PubMed1997

[Changes in the intensity of effect and spectrum of action on quantitative traits of genes in Arabidopsis under varying conditions of cultivation].

Three experiments were performed in different years to study a pleiotropic effect of two marker genes A and B on quantitative traits in Arabidopsis thaliana L. (Heynh.) Experiments differed in their conditions for plant growth (light intensity and soil fertility). In experiment 1, substitution of B- by bb did not affect the duration of the sowing-flowering period, whereas substitution of A- by aa caused a 2-day delay in flowering. Experiment 2 showed that both genes affected this trait. The delay in flowering was one, two, or three days when B- was substituted by bb, A- by aa, or A-B- by aabb, respectively. Therefore, these genes were additive. Data of experiments 3 were opposite to those of experiment 1: substitution of A- by aa did not affect the trait studied, whereas substitution of B- by bb caused a 2-day elongation of the sowing-flowering period. Thus, variations in growth conditions transformed the effects of the marker genes duration of the sowing-flowering period and changed a set of genes that determined this trait. Note that effects of A and B genes on other qualitative and quantitative traits (for example, plant height) were constant in all experiments. Therefore, transformation of a gene set, which influenced the sowing-flowering period, was not related to the repression or derepression of A and B genes.

Arabidopsis↗
PubMed2005

Complete and overlapping congenics proving the existence of a quantitative trait locus for blood pressure on Dahl rat chromosome 17.

Linkage studies suggested that a quantitative trait locus (QTL) for blood pressure (BP) was present in a region on chromosome 17 (Chr 17) of Dahl salt-sensitive (DSS) rats. A subsequent congenic strain targeting this QTL, however, could not confirm it. These conflicting results called into question the validity of localization of a QTL by linkage followed by the use of a congenic strain made with an incomplete chromosome coverage. To resolve this issue, we constructed five new congenic strains, designated C17S.L1 to C17S.L5, that completely spanned the +/-2 LOD confidence interval supposedly containing the QTL. Each congenic strain was made by replacing a segment of the DSS rat by that of the normotensive Lewis (LEW) rat. The only section to be LL homozygous is the region on Chr 17 specified in a congenic strain, as evidenced by a total genome scan. The results showed that BPs of C17S.L1 and C17S.L2 were lower (P < 0.04) than that of DSS rats. In contrast, BPs of C17S.L3, C17S.L4, and C17S.L5 were not different (P > 0.6) from that of DSS rats. Consequently, a BP QTL must be located in an interval of approximately 15 cM shared between C17S.L1 and C17S.L2 and unique to them both, as opposed to C17S.L3, C17S.L4, and C17S.L5. The present study illustrates the importance of thorough chromosome coverage, the necessity for a genome-wide screening, and the use of "negative" controls in physically mapping a QTL by congenic strains.

Animals↗
PubMed1994

Mapping quantitative trait loci for ethanol-induced anesthesia in LSxSS recombinant inbred and F2 mice: methodology and results.

QTL (quantitative trait locus) mapping is an exciting new technique currently being applied to many quantitative traits. We review the animal models and molecular genetic techniques of QTL mapping with particular reference to ethanol-induced anesthesia (sleep time). Sleep time is an easily obtained measure of initial sensitivity to ethanol. We are using three-stage strategy to map QTLs causing the difference in sleep time between selected strains of mice. This project has been expedited by the use of polymorphic genetic markers (simple sequence repeats) which are amplifiable by the polymerase chain reaction. Over 115 strain distribution patterns have been characterized in the 27 LSxSS recombinant inbred strains. Provisional QTLs have been identified in the RI strains and will be confirmed or rejected in an F2 population of 1063 mice. A new statistical technique, interval mapping, allows the use of all polymorphic markers simultaneously to locate QTLs.

Anesthesia↗
PubMed2000

[Heavy heat shock induces genetic variation in a polygenic system of a quantitative trait in Drosophila].

Results of two experiments dealing with positive and negative selection on the quantitative trait radius incompletus in an isogenic line of Drosophila melanogaster after heavy heat shock (HHS) are presented. Selection was not effective in the control without HHS. In experiment 1, in which offspring of HHS-exposed males lacked transposition induction, selection also was ineffective. By contrast, selection was highly effective in offspring of males that responded to HHS exposure by transposition induction. Thus, HHS, which is not mutagenic, generates genetic variation in a polygenic system of a quantitative trait via transpositions and excisions of mobile genetic elements. In experiment 2, positive and negative selection was conducted in three replicates, which showed concerted dynamics of the selected trait. This means that the trait dynamics is mainly related to the nearly deterministic process of accumulation of active polygenic alleles rather than to genetic drift. The induced variation of polygenic systems promotes rapid selection of "champion" genotypes. This variation is probably associated with "soft" modification of polygene expression by adjacent MGE copies.

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↗
PubMed2005

Epistasis and genotype-environment interaction for quantitative trait loci affecting flowering time in Arabidopsis thaliana.

A major goal of evolutionary biology is to understand the genetic architecture of the complex quantitative traits that may lead to adaptations in natural populations. Of particular relevance is the evaluation of the frequency and magnitude of epistasis (gene-gene and gene-environment interaction) as it plays a controversial role in models of adaptation within and among populations. Here, we explore the genetic basis of flowering time in Arabidopsis thaliana using a series of quantitative trait loci (QTL) mapping experiments with two recombinant inbred line (RIL) mapping populations [Columbia (Col) x Landsberg erecta (Ler), Ler x Cape Verde Islands (Cvi)]. We focus on the response of RILs to a series of environmental conditions including drought stress, leaf damage, and apical damage. These data were explicitly evaluated for the presence of epistasis using Bayesian based multiple-QTL genome scans. Overall, we mapped fourteen QTL affecting flowering time. We detected two significant QTL-QTL interactions and several QTL-environment interactions for flowering time in the Ler x Cvi population. QTL-environment interactions were due to environmentally induced changes in the magnitude of QTL effects and their interactions across environments--we did not detect antagonistic pleiotropy. We found no evidence for QTL interactions in the Ler x Col population. We evaluate these results in the context of several other studies of flowering time in Arabidopsis thaliana and adaptive evolution in natural populations.

Arabidopsis↗
PubMed2006

Statistical methods for expression quantitative trait loci (eQTL) mapping.

Traditional genetic mapping has largely focused on the identification of loci affecting one, or at most a few, complex traits. Microarrays allow for measurement of thousands of gene expression abundances, themselves complex traits, and a number of recent investigations have considered these measurements as phenotypes in mapping studies. Combining traditional quantitative trait loci (QTL) mapping methods with microarray data is a powerful approach with demonstrated utility in a number of recent biological investigations. These expression quantitative trait loci (eQTL) studies are similar to traditional QTL studies, as a main goal is to identify the genomic locations to which the expression traits are linked. However, eQTL studies probe thousands of expression transcripts; and as a result, standard multi-trait QTL mapping methods, designed to handle at most tens of traits, do not directly apply. One possible approach is to use single-trait QTL mapping methods to analyze each transcript separately. This leads to an increased number of false discoveries, as corrections for multiple tests across transcripts are not made. Similarly, the repeated application, at each marker, of methods for identifying differentially expressed transcripts suffers from multiple tests across markers. Here, we demonstrate the deficiencies of these approaches and propose a mixture over markers (MOM) model that shares information across both markers and transcripts. The utility of all methods is evaluated using simulated data as well as data from an F(2) mouse cross in a study of diabetes. Results from simulation studies indicate that the MOM model is best at controlling false discoveries, without sacrificing power. The MOM model is also the only one capable of finding two genome regions previously shown to be involved in diabetes.

Animals↗
PubMed1998

Linkage of chromosome 5q and 11q gene markers to asthma-associated quantitative traits in Australian children.

Asthma is a genetically complex disease, and the investigation of putative linkages to candidate loci in independent populations is an important part of the gene discovery process. This study investigated the linkage of microsatellite markers in the 5q and 11q regions to asthma-associated quantitative traits in 121 Australian Caucasian nuclear families. The families were recruited on the basis of a child proband: a cohort of 95 randomly recruited families of unselected probands (n = 442 subjects) and a cohort of 26 families of probands selected on the basis of severe symptomatic asthma (n = 134 subjects). The quantitative traits assessed included serum levels of total IgE and specific IgE to house dust mite and mixed grass, blood eosinophil counts, and the dose-response slope (DRS) of FEV1 to histamine provocation. Multipoint linkage analysis using Haseman-Elston sib-pair methods provided evidence of significant linkage between the chromosome 5q markers and loge total serum IgE levels, specific serum IgE levels, and loge blood eosinophil counts. The chromosome 11q markers showed evidence of significant linkage to specific serum IgE levels. Neither region demonstrated significant linkage to the loge DRS to histamine. Phenotypes were residualized for age and sex. These data are consistent with the existence of loci regulating asthma-associated quantitative traits in both the 5q31-33 and 11q13 chromosomal regions.

Adolescent↗
PubMed1994

Locating human quantitative trait loci: guidelines for the selection of sibling pairs for genotyping.

Simulation studies were conducted to assess the relative merits of different nonrandom sampling strategies for the selection of sibling pairs for genotyping in the attempt to locate individual loci (QTLs) contributing to variation in human quantitative traits. For a constant amount of variation contributed by a QTL (25% of the total) the frequencies and dominance relationships of a trait increasing allele were varied. Three strategies for selection of pairs for genotyping were based on the phenotypic values of the siblings: "Concordant sib pairs" (CSP) are pairs in which both individuals exceed a given threshold value; "discordant sib pairs" (DSP) are pairs in which one member exceeds a given upper threshold and the other is below a specified lower threshold; and "most similar pairs" (MSP) are pairs selected for falling below a specified percentile ranking of the within-pair mean square for the quantitative trait. Tests for linkage with markers at 1, 2, 5, 10, and 20 cM from each of the QTLs were conducted for each of the selected samples and compared with tests based on the regression, in the entire sample, of within pair variation on the proportion of alleles identical by descent (IBD) at each marker locus. Tests for the effect of the increasing allele at the QTL ("candidate gene") were also conducted for the DSP pairs. No single nonrandom selection procedure yields as much as half the information realized in the total sample. However, a combined strategy which involves genotyping the 5% of MSP and DSP for the upper and lower quintiles of values of the quantitative trait (a further 3% of the sample approximately) yields lod scores which are usually more than 65% of the values realized for the entire sample. Tests comparing the proportion of increasing alleles in high- and low-scoring siblings from DSP samples are uniformly very powerful for detecting candidate loci. Even when it is not possible to measure the entire range of the phenotype with uniform precision, some attempt to differentiate among individuals in a common "unaffected" class of individuals can lead to considerable increase in power.

Alleles↗
PubMed2006

Short communication: quantitative trait loci affecting the somatic cell score on chromosomes 4 and 26 in Italian Holstein cattle.

This work aimed to confirm previously reported quantitative trait loci (QTL) affecting the somatic cell score (SCS) in dairy cattle on Bos taurus autosomes (BTA) 4 and 26. A granddaughter design with selective genotyping was implemented that included half-sib families from 12 male lines of Italian Holstein cattle. The animals were genotyped for 5 microsatellite markers each on regions of BTA 4 (average marker spacing 9.42 cM) and BTA 26 (average marker spacing 5.26 cM), previously reported by other authors as carrying QTL for somatic cell count. Quantitative trait loci analyses were performed using interval mapping by regressing sire breeding values for SCS onto genotype probabilities at 1-cM intervals along the 2 chromosome regions. Breeding values for SCS were estimated for the whole population using a test-day repeatability animal model. Results were not significant on a chromosome basis, but a possible QTL was found at BM4505 on BTA 26, confirming this region for further studies of QTL affecting SCS in the Italian Holstein population.

Animals↗
PubMed1999

Joint multipoint linkage analysis of multivariate qualitative and quantitative traits. I. Likelihood formulation and simulation results.

We describe a variance-components method for multipoint linkage analysis that allows joint consideration of a discrete trait and a correlated continuous biological marker (e.g., a disease precursor or associated risk factor) in pedigrees of arbitrary size and complexity. The continuous trait is assumed to be multivariate normally distributed within pedigrees, and the discrete trait is modeled by a threshold process acting on an underlying multivariate normal liability distribution. The liability is allowed to be correlated with the quantitative trait, and the liability and quantitative phenotype may each include covariate effects. Bivariate discrete-continuous observations will be common, but the method easily accommodates qualitative and quantitative phenotypes that are themselves multivariate. Formal likelihood-based tests are described for coincident linkage (i.e., linkage of the traits to distinct quantitative-trait loci [QTLs] that happen to be linked) and pleiotropy (i.e., the same QTL influences both discrete-trait status and the correlated continuous phenotype). The properties of the method are demonstrated by use of simulated data from Genetic Analysis Workshop 10. In a companion paper, the method is applied to data from the Collaborative Study on the Genetics of Alcoholism, in a bivariate linkage analysis of alcoholism diagnoses and P300 amplitude of event-related brain potentials.

Chromosome Mapping↗
PubMed2002

Quantitative-trait homozygosity and association mapping and empirical genomewide significance in large, complex pedigrees: fasting serum-insulin level in the Hutterites.

We present methods for linkage and association mapping of quantitative traits for a founder population with a large, known genealogy. We detect linkage to quantitative-trait loci (QTLs) through a multipoint homozygosity-mapping method. We propose two association methods, one of which is single point and uses a general two-allele model and the other of which is multipoint and uses homozygosity by descent for a particular allele. In all three methods, we make extensive use of the pedigree and genotype information, while keeping the computations simple and efficient. To assess significance, we have developed a permutation-based test that takes into account the covariance structure due to relatedness of individuals and can be used to determine empirical genomewide and locus-specific P values. In the case of multivariate-normally distributed trait data, the permutation-based test is asymptotically exact. The test is broadly applicable to a variety of mapping methods that fall within the class of linear statistical models (e.g., variance-component methods), under the assumption of random ascertainment with respect to the phenotype. For obtaining genomewide P values, our proposed method is appropriate when positions of markers are independent of the observed linkage signal, under the null hypothesis. We apply our methods to a genome screen for fasting insulin level in the Hutterites. We detect significant genomewide linkage on chromosome 19 and suggestive evidence of QTLs on chromosomes 1 and 16.

Alleles↗
PubMed2002

High resolution mapping of quantitative trait loci by linkage disequilibrium analysis.

Two methods, linkage analysis and linkage disequilibrium (LD) mapping or association study, are usually utilised for mapping quantitative trait loci (QTL). Linkage mapping is appropriate for low resolution mapping to localise trait loci to broad chromosome regions within a few cM (<10 cM), and is based on family data. Linkage disequilibrium mapping, on the other hand, is useful in high resolution or fine mapping, and is based on both population and family data. Using only one marker, one may carry out single-point linkage analysis and linkage disequilibrium mapping. Using two or more markers, it is possible to flank the QTL by multipoint analysis. The development and thus availability of dense marker maps, such as single nucleotide polymorphisms (SNP) in human genome, presents a tremendous opportunity for multipoint fine mapping. In this article, we propose a regression approach of mapping QTL by linkage disequilibrium mapping based on population data. Assuming that two marker loci flank one quantitative trait locus, a two-point linear regression is proposed to analyse population data. We derive analytical formulas of parameter estimations, and non-centrality parameters of appropriate tests of genetic effects and linkage disequilibrium coefficients. The merit of the method is shown by the power calculation and comparison. The two-point regression model can capture much more linkage and linkage disequilibrium information than that derived when only one marker is used. For a complex disease with heritability h(2)> or =0.15, a study with sample size of 250 can provide high power for QTL detection under moderate linkage disequilibria.

Chromosome Mapping↗
PubMed2001

Trans-ethnic fine mapping of a quantitative trait locus for circulating angiotensin I-converting enzyme (ACE).

Circulating angiotensin I-converting enzyme (ACE) levels are influenced by a major quantitative trait locus (QTL) that maps to the ACE gene. Phylogenetic and measured haplotype analyses have suggested that the ACE-linked QTL lies downstream of a putative ancestral breakpoint located near to position 6435. However, strong linkage disequilibrium between markers in the 3' portion of the gene has prevented further resolution of the QTL in Caucasian subjects. We have examined 10 ACE gene polymorphisms in Afro-Caribbean families recruited in JAMAICA: Variance components analyses showed strong evidence of linkage and association to circulating ACE levels. When the linkage results were contrasted with those from a set of British Caucasian families, there was no evidence for heterogeneity between the samples. However, patterns of allelic association between the markers and circulating ACE levels differed significantly in the two data sets. In the British families, three markers [G2215A, Alu insertion/deletion and G2350A] were in complete disequilibrium with the ACE-linked QTL. In the Jamaican families, only marker G2350A showed strong but incomplete disequilibrium with the ACE-linked QTL. These results suggest that additional unobserved polymorphisms have an effect on circulating ACE levels in Jamaican families. Furthermore, our results show that a variance components approach combined with structured, quantitative comparisons between families from different ethnic groups may be a useful strategy for helping to determine which, if any, variants in a small genomic region directly influence a quantitative trait.

Black People↗
PubMed2000

The Finland-United States investigation of non-insulin-dependent diabetes mellitus genetics (FUSION) study. II. An autosomal genome scan for diabetes-related quantitative-trait loci.

Type 2 diabetes mellitus is a complex disorder encompassing multiple metabolic defects. We report results from an autosomal genome scan for type 2 diabetes-related quantitative traits in 580 Finnish families ascertained for an affected sibling pair and analyzed by the variance components-based quantitative-trait locus (QTL) linkage approach. We analyzed diabetic and nondiabetic subjects separately, because of the possible impact of disease on the traits of interest. In diabetic individuals, our strongest results were observed on chromosomes 3 (fasting C-peptide/glucose: maximum LOD score [MLS] = 3.13 at 53.0 cM) and 13 (body-mass index: MLS = 3.28 at 5.0 cM). In nondiabetic individuals, the strongest results were observed on chromosomes 10 (acute insulin response: MLS = 3.11 at 21.0 cM), 13 (2-h insulin: MLS = 2.86 at 65.5 cM), and 17 (fasting insulin/glucose ratio: MLS = 3.20 at 9.0 cM). In several cases, there was evidence for overlapping signals between diabetic and nondiabetic individuals; therefore we performed joint analyses. In these joint analyses, we observed strong signals for chromosomes 3 (body-mass index: MLS = 3.43 at 59.5 cM), 17 (empirical insulin-resistance index: MLS = 3.61 at 0.0 cM), and 19 (empirical insulin-resistance index: MLS = 2.80 at 74.5 cM). Integrating genome-scan results from the companion article by Ghosh et al., we identify several regions that may harbor susceptibility genes for type 2 diabetes in the Finnish population.

Age Factors↗
PubMed1999

Comparison of variance components and sibpair-based approaches to quantitative trait linkage analysis in unselected samples.

We compared the statistical performance of sibpair-based and variance components approaches to multipoint linkage analysis of a quantitative trait in unselected samples. As a benchmark dataset, we used the simulated family data from Genetic Analysis Workshop 10 [Goldin et al., 1997], and each method was used to screen all 200 replications of the GAW10 genome for evidence of linkage to quantitative trait Q1. The sibpair and variance components methods were each applied to datasets comprising single-sibpairs and complete sibships, and for further comparison we also applied the variance components method to the nuclear family and extended pedigree datasets. For each analysis, the unbiasedness and efficiency of parameter estimation, the power to detect linkage, and the Type I error rate were estimated empirically. Sibpair and variance components methods exhibited comparable performance in terms of the unbiasedness of the estimate of QTL location and the Type I error rate. Within the single-sibpair and sibship sampling units, the variance components approach gave consistently superior power and efficiency of parameter estimation. Within each method, the statistical performance was improved by the use of the larger and more informative sampling units.

Chromosome Mapping↗