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PubMed1999

Multiple-trait mapping of quantitative trait loci after selective genotyping using logistic regression.

Experiments to map QTL usually measure several traits, and not uncommonly genotype only those animals that are extreme for some trait(s). Analysis of selectively genotyped, multiple-trait data presents special problems, and most simple methods lead to biased estimates of the QTL effects. The use of logistic regression to estimate QTL effects is described, where the genotype is treated as the dependent variable and the phenotype as the independent variable. In this way selection on phenotype does not bias the results. If normally distributed errors are assumed, the logistic-regression analysis is almost equivalent to a maximum-likelihood analysis, but can be carried out with standard statistical packages. Analysis of a simulated half-sib experiment shows that logistic regression can estimate the effect and position of a QTL without bias and confirms the increased power achieved by multiple-trait analysis.

Genotype↗
PubMed2002

Modulation of multiple experimental arthritis models by collagen-induced arthritis quantitative trait loci isolated in congenic rat lines: different effects of non-major histocompatibility complex quantitative trait loci in males and females.

OBJECTIVE: Collagen-induced arthritis (CIA) is a model of inflammatory arthritis with many similarities to rheumatoid arthritis (RA). We previously mapped in F(2) offspring of CIA-susceptible DA and CIA-resistant F344 rats, 5 quantitative trait loci (QTLs) for which F344 alleles were associated with reduced CIA severity. In the present study, we sought to characterize the independent arthritis-modulating effects of these 5 QTLs. METHODS: CIA-regulatory regions were transferred from the F344 genome to the DA background or vice versa by repeated backcrossing. The arthritis-modulating effects of the transferred alleles were determined by comparing the severity of experimentally induced arthritis in congenic rats with that in DA rats. RESULTS: Congenic lines with either the F344 major histocompatibility complex (MHC) on the DA background or the DA MHC on the F344 background were resistant to CIA, confirming both MHC and non-MHC contributions to the genetic regulation of CIA. F344 alleles at the Cia3 and Cia5 regions of chromosomes 4 and 10 reduced CIA severity relative to that observed in DA rats. F344 Cia4 and Cia6 regions of chromosomes 7 and 8 failed to significantly alter CIA severity. Arthritis-modifying effects of Cia4 and Cia6 were, however, detected in pristane-induced and/or Freund's incomplete adjuvant oil-induced arthritis. The arthritis-modifying effects of the non-MHC CIA-regulatory loci differed in males and females. CONCLUSION: These congenic lines confirmed the existence and location of genes that regulate the severity of experimental arthritis in rats. Mechanisms responsible for the sex-specificity of individual arthritis-regulatory loci may explain some of the sex differences observed in RA and other autoimmune diseases in humans.

Animals↗
PubMed2006

Phenotypic variation and natural selection at catsup, a pleiotropic quantitative trait gene in Drosophila.

Quantitative traits are shaped by networks of pleiotropic genes . To understand the mechanisms that maintain genetic variation for quantitative traits in natural populations and to predict responses to artificial and natural selection, we must evaluate pleiotropic effects of underlying quantitative trait genes and define functional allelic variation at the level of quantitative trait nucleotides (QTNs). Catecholamines up (Catsup), which encodes a negative regulator of tyrosine hydroxylase , the rate-limiting step in the synthesis of the neurotransmitter dopamine, is a pleiotropic quantitative trait gene in Drosophila melanogaster. We used association mapping to determine whether the same or different QTNs at Catsup are associated with naturally occurring variation in multiple quantitative traits. We sequenced 169 Catsup alleles from a single population and detected 33 polymorphisms with little linkage disequilibrium (LD). Different molecular polymorphisms in Catsup are independently associated with variation in longevity, locomotor behavior, and sensory bristle number. Most of these polymorphisms are potentially functional variants in protein coding regions, have large effects, and are not common. Thus, Catsup is a pleiotropic quantitative trait gene, but individual QTNs do not have pleiotropic effects. Molecular population genetic analyses of Catsup sequences are consistent with balancing selection maintaining multiple functional polymorphisms.

Animals↗
PubMedDate not supplied

A transmission disequilibrium test for quantitative trait loci.

The transmission disequilibrium test uses association between marker alleles and dichotomous traits for precise genetic mapping while avoiding confounding due to population admixture. Here, the methodology is generalized from dichotomous traits to quantitative traits. The generalization is computationally straightforward and may be used with multiple alleles and with siblings. Parametric assumptions on the distribution of the quantitative traits are not needed. Environmental and demographic covariates may be incorporated into the analysis. The results of simulation studies that provide information about the power of the approach are reported.

Alleles↗
PubMed2006

Optimal multiple trait selection for multiple linked quantitative trait loci.

A method was developed to optimize selection on multiple traits with multiple quantitative trait loci (QTLs) over multiple generations. The basis of the method was to replace in the objective function the genotypic value of a single trait with an aggregate genotypic value of multiple traits weighted by their corresponding economic weight, and to maximize the weighted sum of the selection response in the planning horizon. The optimization was formulated as a multiple stage optimal control problem and solved by a forward and backward iteration cycle. The practical utility of this method was illustrated in an example of pig breeding population, in which the number born alive (NBA) and days to 100 kg (D100) were used as parameters. The selection response of this method was compared with standard QTL selection and regular best linear unbiased prediction (BLUP) selection. Results showed that optimal selection achieved greater selection response than either standard QTL or regular BLUP selections. The influence of economic weight to optimal selection was significant, and the optimization was better as the economic weight of D100 increased. Optimal selection increased the total selection response by two ways: 1) it sacrificed some QTL responses during early generations and 2) it put more emphasis on D100. Optimal cumulative discounted selection gave more weight to D100 than optimal terminal selection in the longer generations.

Algorithms↗
PubMed2003

Genetic linkage analysis of a dichotomous trait incorporating a tightly linked quantitative trait in affected sib pairs.

Many complex diseases are usually considered as dichotomous traits but are also associated with quantitative biological markers or quantitative risk factors. For such dichotomous traits, although their associated quantitative traits may not directly underly the diagnosis of the disease status, if the associated quantitative trait is also linked to the chromosomal regions linked to the dichotomous trait, then joint analysis of dichotomous and quantitative traits should be more efficient than consideration of them separately. Previous studies have focused on the situation when a dichotomous trait can be modeled by a threshold process acting on a single underlying normal liability distribution. However, for many complex disorders, including most psychiatric disorders, diagnosis is generally based on a set of binary or discrete criteria. These traits cannot be modeled on the basis of a threshold process acting on an underlying continuous trait. We propose a likelihood-based method that efficiently combines such a discrete trait and an associated quantitative trait in the analysis, using affected-sib-pair data. Our simulation studies suggest that joint analysis increases the power to detect linkage of dichotomous traits. We also apply the proposed new method to an asthma genome-scan data set and incorporate the total serum immunoglobulin E level in the analysis.

Asthma↗
PubMed2000

Accounting for variability in the use of permutation testing to detect quantitative trait loci.

Locating quantitative trait loci (QTL), or genomic regions associated with known molecular markers, is of increasing interest in a wide variety of applications ranging from human genetics to agricultural genetics. The hope of locating QTL (or genes) affecting a quantitative trait is that it will lead to characterization and possible manipulations of these genes. However, the complexity of both statistical and genetic issues surrounding the location of these regions calls into question the asymptotic statistical results supplying the distribution of the test statistics employed. Coupled with the power of current-day computing, permutation theory was reintroduced for the purpose of estimating the distribution of any test statistic used to test for the location of QTL. Permutation techniques have offered an attractive alternative to significance measures based on asymptotic theory. The ideas of permutation testing are extended in this application to include confidence intervals for the thresholds and p-values estimated in permutation testing procedures. The confidence intervals developed account for the Monte Carlo error associated with practical applications of permutation testing and lead to an effective method of determining an efficient permutation sample size.

Biometry↗
PubMed2002

Simultaneous mining of linkage and linkage disequilibrium to fine map quantitative trait loci in outbred half-sib pedigrees: revisiting the location of a quantitative trait locus with major effect on milk production on bovine chromosome 14.

A maximum-likelihood QTL mapping method that simultaneously exploits linkage and linkage disequilibrium and that is applicable in outbred half-sib pedigrees is described. The method is applied to fine map a QTL with major effect on milk fat content in a 3-cM marker interval on proximal BTA14. This proximal location is confirmed by applying a haplotype-based association method referred to as recombinant ancestral haplotype analysis. The origin of the discrepancy between the QTL position derived in this work and that of a previous analysis is examined and shown to be due to the existence of distinct marker haplotypes associated with QTL alleles having large substitution effects.

Animals↗
PubMed1995

A random model approach to interval mapping of quantitative trait loci.

Mapping quantitative trait loci in outbred populations is important because many populations of organisms are noninbred. Unfortunately, information about the genetic architecture of the trait may not be available in outbred populations. Thus, the allelic effects of genes can not be estimated with ease. In addition, under linkage equilibrium, marker genotypes provide no information about the genotype of a QTL (our terminology for a single quantitative trait locus is QTL while multiple loci are referred to as QTLs). To circumvent this problem, an interval mapping procedure based on a random model approach is described. Under a random model, instead of estimating the effects, segregating variances of QTLs are estimated by a maximum likelihood method. Estimation of the variance component of a QTL depends on the proportion of genes identical-by-descent (IBD) shared by relatives at the locus, which is predicted by the IBD of two markers flanking the QTL. The marker IBD shared by two relatives are inferred from the observed marker genotypes. The procedure offers an advantage over the regression interval mapping in terms of high power and small estimation errors and provides flexibility for large sibships, irregular pedigree relationships and incorporation of common environmental and fixed effects.

Alleles↗
PubMed2001

The genetic architecture of quantitative traits.

Phenotypic variation for quantitative traits results from the segregation of alleles at multiple quantitative trait loci (QTL) with effects that are sensitive to the genetic, sexual, and external environments. Major challenges for biology in the post-genome era are to map the molecular polymorphisms responsible for variation in medically, agriculturally, and evolutionarily important complex traits; and to determine their gene frequencies and their homozygous, heterozygous, epistatic, and pleiotropic effects in multiple environments. The ease with which QTL can be mapped to genomic intervals bounded by molecular markers belies the difficulty in matching the QTL to a genetic locus. The latter requires high-resolution recombination or linkage disequilibrium mapping to nominate putative candidate genes, followed by genetic and/or functional complementation and gene expression analyses. Complete genome sequences and improved technologies for polymorphism detection will greatly advance the genetic dissection of quantitative traits in model organisms, which will open avenues for exploration of homologous QTL in related taxa.

Animals↗
PubMed1999

Single- and multiple-trait mapping analysis of linked quantitative trait loci. Some asymptotic analytical approximations.

Estimating the resolution power of mapping analysis of linked quantitative trait loci (QTL) remains a difficult problem, which has been previously addressed mainly by Monte Carlo simulations. The analytical method of evaluation of the expected LOD developed in this article spreads the "deterministic sampling" approach for the case of two linked QTL for single- and two-trait analysis. Several complicated questions are addressed through this evaluation: the dependence of QTL detection power on the QTL effects, residual correlation between the traits, and the effect of epistatic interaction between the QTL for one or both traits on expected LOD (ELOD), etc. Although this method gives only an asymptotic estimation of ELOD, it allows one to get an approximate assessment of a broad spectrum of mapping situations. A good correspondence was found between the ELODs predicted by the model and LOD values averaged over Monte Carlo simulations.

Chromosome Mapping↗
PubMed2001

Quantitative trait loci in Drosophila.

Phenotypic variation for quantitative traits results from the simultaneous segregation of alleles at multiple quantitative trait loci. Understanding the genetic architecture of quantitative traits begins with mapping quantitative trait loci to broad genomic regions and ends with the molecular definition of quantitative trait loci alleles. This has been accomplished for some quantitative trait loci in Drosophila. Drosophila quantitative trait loci have sex-, environment- and genotype-specific effects, and are often associated with molecular polymorphisms in non-coding regions of candidate genes. These observations offer valuable lessons to those seeking to understand quantitative traits in other organisms, including humans.

Animals↗
PubMed2005

The effects of dominance, regular inbreeding and sampling design on Q(ST), an estimator of population differentiation for quantitative traits.

To test whether quantitative traits are under directional or homogenizing selection, it is common practice to compare population differentiation estimates at molecular markers (F(ST)) and quantitative traits (Q(ST)). If the trait is neutral and its determinism is additive, then theory predicts that Q(ST) = F(ST), while Q(ST) > F(ST) is predicted under directional selection for different local optima, and Q(ST) < F(ST) is predicted under homogenizing selection. However, nonadditive effects can alter these predictions. Here, we investigate the influence of dominance on the relation between Q(ST) and F(ST) for neutral traits. Using analytical results and computer simulations, we show that dominance generally deflates Q(ST) relative to F(ST). Under inbreeding, the effect of dominance vanishes, and we show that for selfing species, a better estimate of Q(ST) is obtained from selfed families than from half-sib families. We also compare several sampling designs and find that it is always best to sample many populations (>20) with few families (five) rather than few populations with many families. Provided that estimates of Q(ST) are derived from individuals originating from many populations, we conclude that the pattern Q(ST) > F(ST), and hence the inference of directional selection for different local optima, is robust to the effect of nonadditive gene actions.

Analysis of Variance↗
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↗
PubMed1998

Iteratively reweighted least squares mapping of quantitative trait loci.

Mapping quantitative trait loci (QTL) is a typical problem of regression with uncertain independent variables because the genotype of a putative QTL is not observed. Rather, the genotype is inferred from marker information. The method of maximum likelihood (ML) methods is considered to be the optimal solution for this problem because the distribution of the unobserved QTL genotype is fully taken into account. The simple linear regression method (REG) is a first-order approximation to ML and usually performs very well. In this study, an iteratively reweighted least squares method (IRWLS) is proposed. The new method is a second-order approximation to ML because both the expectation and the variance of the unobserved QTL genotype are taken into consideration. The IRWLS is developed in the context of a single large outbred family. The properties of IRWLS are demonstrated and compared with REG and ML via replicated Monte Carlo simulations. The conclusions are: (1) when marker information content is high, the three methods perform equally well, but ML and IRWLS outperform REG when marker information content is low and the variance explained by the QTL is high; (2) when the residual distribution is not normal, ML can fail or have low power to detect small QTLs, but REG and IRWLS are robust to non-normality; and (3) when the residual distribution is normal, the performance of IRWLS is almost identical to ML, but the computational speed of IRWLS is many times faster than that of ML.

Chromosome Mapping↗
PubMed2003

Using advanced intercross lines for high-resolution mapping of HDL cholesterol quantitative trait loci.

Mapping quantitative trait loci (QTLs) with high resolution facilitates identification and positional cloning of the underlying genes. The novel approach of advanced intercross lines (AILs) generates many more recombination events and thus can potentially narrow QTLs significantly more than do conventional backcrosses and F2 intercrosses. In this study, we carried out QTL analyses in (C57BL/6J x NZB/BlNJ) x C57BL/6J backcross progeny fed either chow or an atherogenic diet to detect QTLs that regulate high-density lipoprotein cholesterol (HDL)concentrations, and in (C57BL/6J x NZB/BlNJ) F11 AIL progeny to confirm and narrow those QTLs. QTLs for HDL concentrations were found on chromosomes 1, 5, and 16. AIL not only narrowed the QTLs significantly more than did a conventional backcross but also resolved a chromosome 5 QTL identified in the backcross into two QTLs, the peaks of both being outside the backcross QTL region. We tested 27 candidate genes and found significant mRNA expression differences for 12 (Nr1i3, Apoa2, Sap, Tgfb2, Fgfbp1, Prom, Ppargc1, Tcf1, Ncor2, Srb1, App, and Ifnar). Some of these underlay the same QTL, indicating that expression differences are common and not sufficient to identify QTL genes. All the major HDL QTLs in our study had homologous counterparts in humans, implying that their underlying genes regulate HDL in humans.

Animals↗
PubMed1994

Individual genes underlying quantitative traits: molecular and analytical methods.

Because of innovations in quantitative and molecular genetics, it is becoming possible to study the individual loci affecting quantitative traits, quantitative trait loci (QTL). We review a number of recently developed molecular and analytical techniques that allow the identification and localization of QTL and the quantification of their effects, and present an example. We suggest that this area of investigation, already in widespread use in the agricultural and medical sciences, will become increasingly important in evolutionary biology as techniques continue to improve.

Animals↗