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Results for “QTL mapping”

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PubMed2002

Quantitative genetic analysis of natural variation in body size in Drosophila melanogaster.

Latitudinal, genetic variation in body size is a commonly observed phenomenon in many invertebrate species and is shaped by natural selection. In this study, we use a chromosome substitution and a quantitative trait locus (QTL) mapping approach to identify chromosomes and genomic regions associated with adaptive variation in body size in natural populations of Drosophila melanogaster from the extreme ends of clines in South America and Australia. Chromosome substitution revealed the largest effects on chromosome three in both continents, and minor effects on the X and second chromosome. Similarly, QTL analysis of the Australian cline identified QTL with largest effects on the third chromosome, with smaller effects on the second. However, no QTL were found on the X chromosome. We also compared the coincidence of locations of QTL with the locations of five microsatellite loci previously shown to vary clinally in Australia. Permutation tests using both the sum of the LOD scores and the sum distance to nearest QTL peak revealed there were no significant associations between locations of clinal markers and QTL's. The lack of significance may, in part, be due to broad QTL peaks identified in this study. Future studies using higher resolution QTL maps should reveal whether the degree of clinality in microsatellite allele frequencies can be used to identify QTL in traits that vary along an environmental gradient.

Animals↗

PubMed1998

A QTL with major effect on milk yield and composition maps to bovine chromosome 14.

A whole genome scan was undertaken in a granddaughter design comprising 1158 progeny-tested bulls in order to map QTL influencing milk yield and composition. In this paper we report the identification of a locus on the centromeric end of bovine Chromosome (Chr) 14, with major effect on fat and protein percentage as well as milk yield. The genuine nature of this QTL was verified using the grand2-daughter design, that is, by tracing the segregating QTL alleles from heterozygous grandsires to their maternal grandsons and confirming the predicted QTL allele substitution effect.

Alleles↗

PubMed2004

Multiple-interval mapping for quantitative trait loci controlling endosperm traits.

Endosperm traits are trisomic inheritant and are of great economic importance because they are usually directly related to grain quality. Mapping for quantitative trait loci (QTL) underlying endosperm traits can provide an efficient way to genetically improve grain quality. As the traditional QTL mapping methods (diploid methods) are usually designed for traits under diploid control, they are not the ideal approaches to map endosperm traits because they ignore the triploid nature of endosperm. In this article, a statistical method considering the triploid nature of endosperm (triploid method) is developed on the basis of multiple-interval mapping (MIM) to map for the underlying QTL. The proposed triploid MIM method is derived to broadly use the marker information either from only the maternal plants or from both the maternal plants and their embryos in the backcross and F2 populations for mapping endosperm traits. Due to the use of multiple intervals simultaneously to take multiple QTL into account, the triploid MIM method can provide better detection power and estimation precision, and as shown in this article it is capable of analyzing and searching for epistatic QTL directly as compared to the traditional diploid methods and current triploid methods using only one (or two) interval(s). Several important issues in endosperm trait mapping, such as the relation and differences between the diploid and triploid methods, variance components of genetic variation, and the problems if effects are present and ignored, are also addressed. Simulations are performed to further explore these issues, to investigate the relative efficiency of different experimental designs, and to evaluate the performance of the proposed and current methods in mapping endosperm traits. The MIM-based triploid method can provide a powerful tool to estimate the genetic architecture of endosperm traits and to assist the marker-assisted selection for the improvement of grain quality in crop science. The triploid MIM FORTRAN program for mapping endosperm traits is available on the worldwide web (http://www.stat.sinica.edu.tw/chkao/).

Chromosome Mapping↗

PubMed1997

Mapping-linked quantitative trait loci using Bayesian analysis and Markov chain Monte Carlo algorithms.

A Bayesian method for mapping linked quantitative trait loci (QTL) using multiple linked genetic markers is presented. Parameter estimation and hypothesis testing was implemented via Markov chain Monte Carlo (MCMC) algorithms. Parameters included were allele frequencies and substitution effects for two biallelic QTL, map positions of the QTL, and markers, allele frequencies of the markers, and polygenic and residual variances. Missing data were polygenic effects and multi-locus marker-QTL genotypes. Three different MCMC schemes for testing the presence of a single or two linked QTL on the chromosome were compared. The first approach includes a model indicator variable representing two unlinked QTL, affecting the trait, one linked and one unlinked QTL, or both QTL linked with the markers. The second approach incorporates an indicator variable for each QTL into the model for phenotype, allowing or not allowing for a substitution effect of a QTL, on phenotype, and the third approach is based on model determination by reversible jump MCMC. Methods were evaluated empirically by analyzing simulated granddaughter designs. All methods identified correctly a second, linked QTL and did not reject the one-QTL model when there was only a single QTL, and no additional or an unlinked QTL.

Algorithms↗

PubMed2002

In silico discovery of gene-coding variants in murine quantitative trait loci using strain-specific genome sequence databases.

BACKGROUND: The identification of genes underlying complex traits has been aided by quantitative trait locus (QTL) mapping approaches, which in turn have benefited from advances in mammalian genome research. Most recently, whole-genome draft sequences and assemblies have been generated for mouse strains that have been used for a large fraction of QTL mapping studies. Here we show how such strain-specific mouse genome sequence databases can be used as part of a high-throughput pipeline for the in silico discovery of gene-coding variations within murine QTLs. As a test of this approach we focused on two QTLs on mouse chromosomes 1 and 13 that are involved in physical dependence on alcohol. RESULTS: Interstrain alignment of sequences derived from the relevant mouse strain genome sequence databases for 199 QTL-localized genes spanning 210,020 base-pairs of coding sequence identified 21 genes with different coding sequences for the progenitor strains. Several of these genes, including four that exhibit strong phenotypic links to chronic alcohol withdrawal, are promising candidates to underlie these QTLs. CONCLUSIONS: This approach has wide general utility, and should be applicable to any of the several hundred mouse QTLs, encompassing over 60 different complex traits, that have been identified using strains for which relatively complete genome sequences are available.

Alcohol Withdrawal Seizures↗

PubMed2005

Genetic architecture of qualitative and quantitative Melampsora larici-populina leaf rust resistance in hybrid poplar: genetic mapping and QTL detection.

In order to elucidate the genetic control of resistance to Melampsora larici-populina leaf rust in hybrid poplars, a Populus deltoides x P. trichocarpa F(1) progeny was analysed for qualitative and quantitative rust resistances. This progeny was evaluated for three components of quantitative resistance (latent period, uredinia number and uredinia size) to seven M. larici-populina strains in controlled conditions, and for one component of field susceptibility (rust colonization on the most infected leaf). One qualitative resistance locus inherited from P. deltoides, R(1), was localized on the genetic map. It segregates 1 : 1 in the F(1) progeny and is effective against four of the studied strains. QTL analysis was performed separately on R(1) and r(1) genotype subsets. An additional detection was conducted on the entire F(1) progeny for the three strains able to overcome R(1) and for MAX2. A total of nine QTLs were detected. Two had large, broad-spectrum effects. One (R(US)) is inherited from the P. trichocarpa parent; the other is inherited from P. deltoides and colocalized with R(1). Seven QTLs had only limited and specific effects. Significant interaction effects were detected mainly between the two major QTLs. Implications of these results for durable resistance breeding strategies, and possible benefits from the Populus genome sequence, are discussed.

Basidiomycota↗

PubMed2003

Genetic mapping and QTL analysis of horticultural traits in cucumber ( Cucumis sativus L.) using recombinant inbred lines.

A set of 171 recombinant inbred lines (RIL) were developed from a narrow cross in cucumber ( Cucumis sativus L.; 2n = 2 x = 14) using the determinate ( de), gynoecious ( F), standard-sized leaf line G421 and the indeterminate, monoecious, little-leaf ( ll) line H-19. A 131-point genetic map was constructed using these RILs and 216 F(2) individuals to include 14 SSRs, 24 SCARs, 27 AFLPs, 62 RAPDs, 1 SNP, and three economically important morphological [ F (gynoecy), de (determinate habit), ll (little leaf)] markers. Seven linkage groups spanned 706 cM with a mean marker interval of 5.6 cM. The location of F and de was defined by genetic linkage and quantitative trait locus (QTL) analysis to be associated with SSR loci CSWCT28 and CSWCTT14 at 5.0 cM and 0.8 cM, respectively. RIL-based QTL analysis of the number of lateral branches in three environments revealed four location-independent factors that cumulatively explained 42% of the observed phenotypic variation. QTLs conditioning lateral branching (mlb1.1), fruit length/diameter ratio (ldr1.2) and sex expression (sex1.2) were associated with de. Sex expression was influenced by three genomic regions corresponding to F and de both on linkage Group 1, and a third locus (sex6.1) on linkage Group 6. QTLs conditioning the number of fruit per plant (fpl1.2), the number of lateral branches (mlb1.4) and fruit length/diameter ratio (ldr1.3) were associated with ll. The potential value of these marker-trait associations (i.e., yield components) for plant improvement is portended by the relatively high LOD scores (2.6 to 13.0) and associated R(2) values (1.5% to 32.4%) that are affiliated with comparatively few genetic factors (perhaps 3 to 10).

Chromosome Mapping↗

PubMed1996

Use of AFLP markers for gene mapping and QTL detection in the rat.

The AFLP technique is a new DNA marker technology based on the selective amplification of restriction fragments. Multiple polymorphic markers are simultaneously produced and can be tested in one PCR. No prior information on genomic DNA sequences is needed. In the current study, we contribute 18 AFLP markers to the linkage map of the rat. Seven AFLP markers were assigned to specific chromosomes by analysis of a (BN x ACI)F1 x ACI backcross progeny. Another 11 AFLP markers were mapped by using a panel of the H x B/B x H recombinant inbred (RI) strains. Genotypes of these AFLP markers were also tested for correlations with some blood pressure phenotypes in the RI strains. Suggestive correlation was found between the mean arterial pressure and two closely linked AFLP markers located on chromosome 20. The current study illustrates the value of AFLP markers for the construction of linkage maps and the detection of quantitative trait loci.

Animals↗

PubMed1999

Mapping quantitative trait loci for complex binary traits in outbred populations.

Complex binary traits have a dichotomous phenotypic expression but do not show a simple Mendelian segregation ratio. These traits are considered to be jointly controlled by the actions of several genes and a random environmental effect. The binary phenotype and the underlying factor are assumed to be linked through a threshold model. The underlying factor, referred to as the liability, is treated as a regular but unobservable quantitative character. Mapping quantitative trait loci (QTL) can be performed directly on the liability. Methods of QTL mapping for the liability of a complex binary trait have been well developed in line-crossing experiments. However, such a method is not available in outbred populations which usually consist of many independent pedigrees (families). In this study, we develop a method to analyse jointly multiple families of an outbred population. The method is developed based on a fixed-model approach, i.e. the QTL effects, rather than the variance, are estimated and tested. After the test, the estimated effects are then converted into a single estimate of the QTL variance by taking into consideration errors in the estimated effects. The QTL effects and variance-covariance matrix of the estimates are obtained by a fast Fisher-scoring method. Monte Carlo simulations show that the method is not only powerful but also generates very accurate estimates of QTL variances.

Algorithms↗

PubMed1999

Quantitative trait loci affecting risk for pentobarbital withdrawal map near alcohol withdrawal loci on mouse chromosomes 1, 4, and 11.

Barbiturate dependence is associated with the development of physiological dependence (withdrawal), tolerance, or a maladaptive pattern of drug use. Analysis of strain and individual differences with animal models for physiological dependence liability are useful means to identify potential genetic determinants of liability in humans. Behavioral and quantitative trait locus (QTL) mapping analyses were conducted with mice that are resistant versus sensitive to pentobarbital withdrawal. With a multistage genetic mapping strategy, a pentobarbital withdrawal QTL (Pbw1) was mapped to the distal region of mouse Chromosome (Chr) 1 and may be identical to an alcohol withdrawal QTL mapped to this chromosomal region. Two suggestive QTLs for pentobarbital withdrawal, both in proximity to QTLs definitely mapped for alcohol withdrawal, were also tentatively identified. These were on Chr 11 in proximity to a gene cluster including several members of the GABAA receptor gene family, and on Chr 4 near a locus associated with beta-carboline-induced seizure severity. These data represent the first detection and mapping of loci influencing risk for physiological dependence on barbiturates, and suggest the involvement of common genes in physiological dependence on pentobarbital and alcohol.

Animals↗

PubMed2006

[Methodology of QTL's linkage analysis for discrete traits controlled by polygenes].

Many characters of biological interest and economical importance that are not inherited in a simple Mendelian fashion vary in a discrete form. These traits are called complex discrete traits. A complex discrete trait is presumably controlled by several genes, and characteristic of the trait is modified by environmental effects. Although methods of mapping QTL for continuous quantitative characters have been well developed, such methods for discrete characters are not available yet. So many non-linear methods were developed under the framework of the generalized linear model. In this paper, we reviewed the recent progress and improvement of the methods for QTL mapping in this field.

Animals↗

PubMed2003

Theoretical basis of the Beavis effect.

The core of statistical inference is based on both hypothesis testing and estimation. The use of inferential statistics for QTL identification thus includes estimation of genetic effects and statistical tests. Typically, QTL are reported only when the test statistics reach a predetermined critical value. Therefore, the estimated effects of detected QTL are actually sampled from a truncated distribution. As a result, the expectations of detected QTL effects are biased upward. In a simulation study, William D. Beavis showed that the average estimates of phenotypic variances associated with correctly identified QTL were greatly overestimated if only 100 progeny were evaluated, slightly overestimated if 500 progeny were evaluated, and fairly close to the actual magnitude when 1000 progeny were evaluated. This phenomenon has subsequently been called the Beavis effect. Understanding the theoretical basis of the Beavis effect will help interpret QTL mapping results and improve success of marker-assisted selection. This study provides a statistical explanation for the Beavis effect. The theoretical prediction agrees well with the observations reported in Beavis's original simulation study. Application of the theory to meta-analysis of QTL mapping is discussed.

Chromosome Mapping↗

PubMedDate not supplied

Detection and parameter estimation for quantitative trait loci using regression models and multiple markers.

A strategy of multi-step minimal conditional regression analysis has been developed to determine the existence of statistical testing and parameter estimation for a quantitative trait locus (QTL) that are unaffected by linked QTLs. The estimation of marker-QTL recombination frequency needs to consider only three cases: 1) the chromosome has only one QTL, 2) one side of the target QTL has one or more QTLs, and 3) either side of the target QTL has one or more QTLs. Analytical formula was derived to estimate marker-QTL recombination frequency for each of the three cases. The formula involves two flanking markers for case 1), two flanking markers plus a conditional marker for case 2), and two flanking markers plus two conditional markers for case 3). Each QTL variance and effect, and the total QTL variance were also estimated using analytical formulae. Simulation data show that the formulae for estimating marker-QTL recombination frequency could be a useful statistical tool for fine QTL mapping. With 1,000 observations, a QTL could be mapped to a narrow chromosome region of 1.5 cM if no linked QTL is present, and to a 2.8 cM chromosome region if either side of the target QTL has at least one linked QTL.

Journal Article↗

PubMed1994

Genetic aspects and risk factors in alcoholism and alcoholic liver disease.

There is a great deal of evidence for genetic predisposition to alcoholism; considerably less is known regarding predisposition to alcoholic liver disease. The specific genes involved in either disorder are not well understood, although the enzymes of alcohol metabolism appear to play some role. It will be interesting to determine whether genetic factors that alter the expression of these enzymes, in addition to altering the kinetics of the enzymes, could modify responses to drinking. Work in the next few years will include determination of which responses to alcohol are indeed genetically influenced in twin studies, testing additional candidate genes for alcohol-related traits in populations and families, as well as the application of genomic mapping methodologies to alcoholic pedigrees. The latter strategy will be integrated into the larger number of studies that will grow from the Human Genome Project. Animal studies with selectively bred lines of rodents that differ in voluntary alcohol consumption will lead the way to define the neuronal and behavioral substrates responsible for differences in alcohol-drinking behavior. The use of the quantitative trait locus (QTL) mapping approach in F2 intercross between two inbred strains of rodents with opposite alcohol-response characteristics and in recombinant inbred strains derived from F2 intercross already has and will continue to help identify chromosomal locations of genes relevant to voluntary alcohol consumption. Perhaps in the future selective breeding of rodents and QTL mapping strategies can also be used to determine the biology and genetics of alcohol-induced liver injury.

Adenylyl Cyclases↗

PubMed2000

Power of quantitative trait locus mapping for polygenic binary traits using generalized and regression interval mapping in multi-family half-sib designs.

A generalized interval mapping (GIM) method to map quantitative trait loci (QTL) for binary polygenic traits in a multi-family half-sib design is developed based on threshold theory and implemented using a Newton-Raphson algorithm. Statistical power and bias of QTL mapping for binary traits by GIM is compared with linear regression interval mapping (RIM) using simulation. Data on 20 paternal half-sib families were simulated with two genetic markers that bracketed an additive QTL. Data simulated and analysed were: (1) data on the underlying normally distributed liability (NDL) scale, (2) binary data created by truncating NDL data based on three thresholds yielding data sets with three different incidences, and (3) NDL data with polygenic and QTL effects reduced by a proportion equal to the ratio of the heritabilities on the binary versus NDL scale (reduced-NDL). Binary data were simulated with and without systematic environmental (herd) effects in an unbalanced design. GIM and RIM gave similar power to detect the QTL and similar estimates of QTL location, effects and variances. Presence of fixed effects caused differences in bias between RIM and GIM, where GIM showed smaller bias which was affected less by incidence. The original NDL data had higher power and lower bias in QTL parameter estimates than binary and reduced-NDL data. RIM for reduced-NDL and binary data gave similar power and estimates of QTL parameters, indicating that the impact of the binary nature of data on QTL analysis is equivalent to its impact on heritability.

Algorithms↗

PubMed2006

Gene flow's effect on the genetic architecture of a local adaptation and its consequences for QTL analyses.

This paper uses computer simulations to determine how gene flow between populations affects (1) the genetic architecture of a local adaptation and (2) properties of alleles segregating in quantitative trait locus (QTL) mapping populations. Results suggest that the average magnitude of an allele that causes a phenotypic difference between populations declines as the migration rate increases, but with an increase in migration, alleles of larger magnitude cause proportionally more of the phenotypic difference between populations. Gene flow between populations that are used in a QTL study tends to cause the average magnitude and percent variance explained (PVE) of an allele in a mapping population to increase. Thus, although the average magnitude of an allele causing a difference declines with migration the average magnitude or PVE of an allele in a QTL mapping population may increase. The reason is that the probability an allele is sampled for a QTL mapping population is in direct proportion to its frequency and alleles of larger magnitude tend to segregate at relatively higher frequencies than alleles of smaller effect with an increased migration. As the rate of gene flow between populations increases, the proportion of the phenotypic difference explained by alleles that are segregating in a QTL mapping population (and therefore potentially detected) decreases. Lastly, results suggest QTL alleles of large effect (>20% PVE) should be commonly found, provided the divergence time between populations is not too long or optima of populations are not too far apart.

Crosses, Genetic↗

PubMed2000

Mixed model analysis of quantitative trait loci.

We develop a mixed model approach of quantitative trait locus (QTL) mapping for a hybrid population derived from the crosses of two or more distinguished outbred populations. Under the mixed model, we treat the mean allelic value of each source population as the fixed effect and the allelic deviations from the mean as random effects so that we can partition the total genetic variance into between- and within-population variances. Statistical inference of the QTL parameters is obtained by using the Bayesian method implemented by Markov chain Monte Carlo (MCMC). This unified QTL mapping algorithm treats the fixed and random model approaches as special cases of the general mixed model methodology. Utility and flexibility of the method are demonstrated by using a set of simulated data.

Algorithms↗

PubMed2006

Mapping quantitative trait loci for longitudinal traits in line crosses.

Quantitative traits whose phenotypic values change over time are called longitudinal traits. Genetic analyses of longitudinal traits can be conducted using any of the following approaches: (1) treating the phenotypic values at different time points as repeated measurements of the same trait and analyzing the trait under the repeated measurements framework, (2) treating the phenotypes measured from different time points as different traits and analyzing the traits jointly on the basis of the theory of multivariate analysis, and (3) fitting a growth curve to the phenotypic values across time points and analyzing the fitted parameters of the growth trajectory under the theory of multivariate analysis. The third approach has been used in QTL mapping for longitudinal traits by fitting the data to a logistic growth trajectory. This approach applies only to the particular S-shaped growth process. In practice, a longitudinal trait may show a trajectory of any shape. We demonstrate that one can describe a longitudinal trait with orthogonal polynomials, which are sufficiently general for fitting any shaped curve. We develop a mixed-model methodology for QTL mapping of longitudinal traits and a maximum-likelihood method for parameter estimation and statistical tests. The expectation-maximization (EM) algorithm is applied to search for the maximum-likelihood estimates of parameters. The method is verified with simulated data and demonstrated with experimental data from a pseudobackcross family of Populus (poplar) trees.

Chromosome Mapping↗