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PubMed2006

Discovery of quantitative trait loci for resistance to parasitic nematode infection in sheep: I. Analysis of outcross pedigrees.

BACKGROUND: Currently most pastoral farmers rely on anthelmintic drenches to control gastrointestinal parasitic nematodes in sheep. Resistance to anthelmintics is rapidly increasing in nematode populations such that on some farms none of the drench families are now completely effective. It is well established that host resistance to nematode infection is a moderately heritable trait. This study was undertaken to identify regions of the genome, quantitative trait loci (QTL) that contain genes affecting resistance to parasitic nematodes. RESULTS: Rams obtained from crossing nematode parasite resistant and susceptible selection lines were used to derive five large half-sib families comprising between 348 and 101 offspring per sire. Total offspring comprised 940 lambs. Extensive measurements for a range of parasite burden and immune function traits in all offspring allowed each lamb in each pedigree to be ranked for relative resistance to nematode parasites. Initially the 22 most resistant and 22 most susceptible progeny from each pedigree were used in a genome scan that used 203 microsatellite markers spread across all sheep autosomes. This study identified 9 chromosomes with regions showing sufficient linkage to warrant the genotyping of all offspring. After genotyping all offspring with markers covering Chromosomes 1, 3, 4, 5, 8, 12, 13, 22 and 23, the telomeric end of chromosome 8 was identified as having a significant QTL for parasite resistance as measured by the number of Trichostrongylus spp. adults in the abomasum and small intestine at the end of the second parasite challenge. Two further QTL for associated immune function traits of total serum IgE and T. colubiformis specific serum IgG, at the end of the second parasite challenge, were identified on chromosome 23. CONCLUSION: Despite parasite resistance being a moderately heritable trait, this large study was able to identify only a single significant QTL associated with it. The QTL concerned adult parasite burdens at the end of the second parasite challenge when the lambs were approximately 6 months old. Our failure to discover more QTL suggests that most of the genes controlling this trait are of relatively small effect. The large number of suggestive QTL discovered (more than one per family per trait than would be expected by chance) also supports this conclusion.

Animals↗
PubMed2001

Mapping quantitative trait Loci using generalized estimating equations.

A number of statistical methods are now available to map quantitative trait loci (QTL) relative to markers. However, no existing methodology can simultaneously map QTL for multiple nonnormal traits. In this article we rectify this deficiency by developing a QTL-mapping approach based on generalized estimating equations (GEE). Simulation experiments are used to illustrate the application of the GEE-based approach.

Algorithms↗
PubMed1999

High resolution mapping and identification of new quantitative trait loci (QTL) affecting susceptibility to Marek's disease.

Marek's disease (MD) is a lymphoproliferative disease of chickens that costs the poultry industry approximately $1 billion annually. Genetic resistance to MD is gaining increased attention to augment vaccinal control as disease outbreaks occur more frequently. Previously, analysis of a 272 F2 White Leghorn resource population measured for many MD traits and genotyped for 78 microsatellite markers revealed two and four quantitative trait loci (QTL) with significant and suggestive association, respectively, to one or more MD associated traits. Additional genetic markers have since been scored on the MD resource population to increase QTL resolution and genome coverage. Saturation of four of the QTL regions with 17 markers revealed five new QTL while 32 markers extended the genome coverage by 400 + CM and uncovered three more QTL. QTL analysis by single-point and interval mapping algorithms agreed well when marker saturation was approximately 20 CM or less. Currently 127 genetic markers cover approximately 68% of the genome that contain up to 14 MD QTL associated to one or more MD trait; seven at the significant level and seven at the suggestive level. Individually each QTL accounts for 2-10% of the variation and, in general, resistance was dominant although the resistant allele may come from either parental line. This study suggests that a limited number of genomic regions play a major role in the genetic control of MD resistance. Markers linked to these loci may be useful for selection of MD resistant stock by the poultry industry following verification of the association within their breeding populations.

Animals↗
PubMed2004

Zooming in on a quantitative trait for tomato yield using interspecific introgressions.

To explore natural biodiversity we developed and examined introgression lines (ILs) containing chromosome segments of wild species (Solanum pennellii) in the background of the cultivated tomato (S. lycopersicum). We identified Brix9-2-5, which is a S. pennellii quantitative trait locus (QTL) that increases sugar yield of tomatoes and was mapped within a flower- and fruit-specific invertase (LIN5). QTL analysis representing five different tomato species delimited the functional polymorphism of Brix9-2-5 to an amino acid near the catalytic site of the invertase crystal, affecting enzyme kinetics and fruit sink strength. These results underline the power of diverse ILs for high-resolution perspectives on complex phenotypes.

Alleles↗
PubMed2003

Candidate gene association analysis for a quantitative trait, using parent-offspring trios.

With the increasing availability of genetic data, many studies of quantitative traits focus on hypotheses related to candidate genes, and also gene-environment (G x E) and gene-gene (G x G) interactions. In a population-based sample, estimates and tests of candidate gene effects can be biased by ethnic confounding, also known as population stratification bias. This paper demonstrates that even a modest degree of ethnic confounding can lead to unacceptably high type I error rates for tests of genetic effects. The parent-offspring trio design is reviewed, and several forms of the quantitative transmission disequilibrium test (QTDT) are summarized. A variation of the QTDT (QTDTM) is described that is based on a linear regression model with multiple intercepts, one per parental mating type. This and other models are expanded to allow testing of G x E and G x G interactions. A method for computing required sample sizes using direct computations is described. Sample size requirements for tests of genetic main effects and G x E and G x G interactions are compared across various QTDT approaches to infer their efficiencies relative to one another. The QTDTM is found to meet or exceed the efficiency of other QTDT approaches. For example, the QTDTM is approximately 3% more efficient than the QTDT of Rabinowitz ([1997] Hum. Hered. 47:342-350) for testing a genetic main effect, but can be as much as twice as efficient for testing G x E interaction, and three times more efficient for testing G x G interaction.

Algorithms↗
PubMed2004

Genome-scan analysis for quantitative trait loci in an F2 tilapia hybrid.

We searched for genetic linkage between DNA markers and quantitative trait loci (QTLs) for innate immunity, response to stress, biochemical parameters of blood, and fish size in an F2 population derived from an interspecific tilapia hybrid (Oreochromis mossambicusx O. aureus). A family of 114 fish was scanned for 40 polymorphic microsatellite DNA markers and two polymorphic genes, covering approximately 80% of the tilapia genome. These fish had previously been phenotyped for seven immune-response traits and six blood parameters. Critical values for significance were P <0.05 with the false discovery rate (FDR) controlled at 40%. The genome-scan analysis resulted in 35 significant marker-trait associations, involving 26 markers in 16 linkage groups. In a second experiment, nine markers were re-sampled in a second family of 79 fish of the same species hybrid. Seven markers (GM180, GM553, MHC-I, UNH848, UNH868, UNH898 and UNH925) in five linkage groups (LG 1, 3, 4, 22 and 23) were associated with stress response traits. An additional six markers (GM47, GM552, UNH208, UNH881, UNH952, UNH998) in five linkage groups (LG 4, 16, 19, 20 and 23) were verified for their associations with immune response traits, by linkage to several different traits. The portion of variance explained by each QTL was 11% on average, with a maximum of 29%. The average additive effect of QTLs was 0.2 standard deviation units of stress response traits and fish size, with a maximum of 0.33. In three linkage groups (LG 1, 3 and 23) markers were associated with stress response, body weight and sex determination, confirming the location of QTLs reported by several other studies.

Animals↗
PubMed2006

Quantitative trait loci for locomotor behavior in Drosophila melanogaster.

Locomotion is an integral component of most animal behaviors and many human diseases and disorders are associated with locomotor deficits, but little is known about the genetic basis of natural variation in locomotor behavior. Locomotion is a complex trait, with variation attributable to the joint segregation of multiple interacting quantitative trait loci (QTL), with effects that are sensitive to the environment. We assessed variation in a component of locomotor behavior (locomotor reactivity) in a population of 98 recombinant inbred lines of Drosophila melanogaster and mapped four QTL affecting locomotor reactivity by linkage to polymorphic roo transposable element insertion sites. We used complementation tests of deficiencies to fine map these QTL to 12 chromosomal regions and complementation tests of mutations to identify 13 positional candidate genes affecting locomotor reactivity, including Dopa decarboxylase (Ddc), which catalyzes the final step in the synthesis of serotonin and dopamine. Linkage disequilibrium mapping in a population of 164 second chromosome substitution lines derived from a single natural population showed that polymorphisms at Ddc were associated with naturally occurring genetic variation in locomotor behavior. These data implicate variation in the synthesis of bioamines as a factor contributing to natural variation in locomotor reactivity.

Animals↗
PubMed2003

FXR and ABCG5/ABCG8 as determinants of cholesterol gallstone formation from quantitative trait locus mapping in mice.

BACKGROUND & AIMS: Cholesterol gallstone formation is a complex genetic trait. To identify additional cholesterol gallstone susceptibility loci, we performed a quantitative trait locus analysis using an intercross of PERA/Ei and I/LnJ inbred strains of mice. METHODS: Mice of both sexes were examined for gallstone weight and evaluated according to a scoring system for the physical chemistry of cholelithiasis during feeding of a lithogenic diet. Intercross offspring were genotyped, and linkage analysis was performed by interval mapping. Differences in messenger RNA expression of positional candidate genes were determined using reverse-transcription and real-time polymerase chain reaction. RESULTS: We identified significant loci associated with gallstone weight on chromosomes 10 and 4, named Lith7 and Lith8, respectively (both susceptibility alleles conferred by strain I/LnJ). Positional candidate genes with higher expression in I/LnJ mice are Fxr (official symbol, Nr1h4), encoding the nuclear bile salt receptor, on chromosome 10 and Shp1 (official symbol, Nr0b2), encoding the small heterodimer partner 1, on chromosome 4. A significant locus associated with gallstone score on chromosome 17, named Lith9 (susceptibility allele conferred by strain PERA/Ei), colocalizes with the genes Abcg5 and Abcg8 that encode the canalicular cholesterol transporter. Higher hepatic messenger RNA expression of Abcg5 and Abcg8 in strain PERA/Ei correlates positively with higher biliary cholesterol levels. CONCLUSIONS: Our findings suggest a primary role of the nuclear bile salt receptor FXR and the canalicular cholesterol transporter ABCG5/ABCG8 in the genetic susceptibility and pathogenesis of cholesterol cholelithiasis in these strains of inbred mice.

ATP Binding Cassette Transporter, Subfamily G, Mem↗
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↗
PubMed2004

Linkage analysis: principles and methods for the analysis of human quantitative traits.

Currently, mapping genes for complex human traits relies on two complementary approaches, linkage and association analyses. Both suffer from several methodological and theoretical limitations, which can considerably increase the type-1 error rate and reduce the power to map human quantitative trait loci (QTL). This review focuses on linkage methods for QTL mapping. It summarizes the most common linkage statistics used, namely Haseman-Elston-based methods, variance components, and statistics that condition on trait values. Methods developed more recently that accommodate the X-chromosome, parental imprinting and allelic association in linkage analysis are also summarized. The type-I error rate and power of these methods are discussed. Finally, rough guidelines are provided to help guide the choice of linkage statistics.

Genetic Linkage↗
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↗
PubMed2003

Mapping of quantitative trait loci associated with ultraviolet-B resistance in rice (Oryza sativa L.).

The detection of quantitative trait loci (QTLs) associated with UV-B resistance in rice should allow their practical application in breeding for such a complex trait, and may lead to the identification of gene characteristics and functions. Considerable variation in UV-B resistance exists within cultivated rice (Oryza sativa L.), but its detailed genetic control mechanism has not been well elucidated. We detected putative QTLs associated with the resistance to enhanced UV-B radiation in rice, using 98 BC(1)F(5) (backcross inbred lines; BILs) derived from a cross between Nipponbare (a resistant japonica rice variety) and Kasalath (a sensitive indica rice variety). We used 245 RFLP markers to construct a framework linkage map. BILs and both parents were grown under visible light with or without supplemental UV-B radiation in a growth chamber. In order to evaluate UV-B resistance, we used the relative fresh weight of aerial parts (RFW) and the relative chlorophyll content of leaf blades (RCC). The BIL population exhibited a wide range of variation in RFW and RCC. Using composite interval mapping with a LOD threshold of 2.9, three putative QTLs associated with both RFW and RCC were detected on chromosomes 1, 3 and 10. Nipponbare alleles at the QTLs on chromosome 1 and 10 increased the RFW and RCC, while the Kasalath allele at the QTL on chromosome 3 increased both traits. Furthermore, the existence of both QTLs on chromosomes 1 and 10 for UV-B resistance was confirmed using chromosome segment substitution lines. Plants with Kasalath alleles at the QTL on chromosome 10 were more sensitive to UV-B radiation than plants with them on chromosome 1. These results also provide the information not only for the improvement of UV-B resistance in rice though marker-associated selection, but also for the identification of UV-B resistance mechanisms by using near-isogenic lines.

Chromosome Mapping↗
PubMed2000

Bayesian mapping of quantitative trait loci under the identity-by-descent-based variance component model.

Variance component analysis of quantitative trait loci (QTL) is an important strategy of genetic mapping for complex traits in humans. The method is robust because it can handle an arbitrary number of alleles with arbitrary modes of gene actions. The variance component method is usually implemented using the proportion of alleles with identity-by-descent (IBD) shared by relatives. As a result, information about marker linkage phases in the parents is not required. The method has been studied extensively under either the maximum-likelihood framework or the sib-pair regression paradigm. However, virtually all investigations are limited to normally distributed traits under a single QTL model. In this study, we develop a Bayes method to map multiple QTL. We also extend the Bayesian mapping procedure to identify QTL responsible for the variation of complex binary diseases in humans under a threshold model. The method can also treat the number of QTL as a parameter and infer its posterior distribution. We use the reversible jump Markov chain Monte Carlo method to infer the posterior distributions of parameters of interest. The Bayesian mapping procedure ends with an estimation of the joint posterior distribution of the number of QTL and the locations and variances of the identified QTL. Utilities of the method are demonstrated using a simulated population consisting of multiple full-sib families.

Alleles↗
PubMed2003

Genetic variability at neutral markers, quantitative trait land trait in a subdivided population under selection.

Genetic variability in a subdivided population under stabilizing and diversifying selection was investigated at three levels: neutral markers, QTL coding for a trait, and the trait itself. A quantitative model with additive effects was used to link genotypes to phenotypes. No physical linkage was introduced. Using an analytical approach, we compared the diversity within deme (H(S)) and the differentiation (F(ST)) at the QTL with the genetic variance within deme (V(W)) and the differentiation (Q(ST)) for the trait. The difference between F(ST) and Q(ST) was shown to depend on the relative amounts of covariance between QTL within and between demes. Simulations were used to study the effect of selection intensity, variance of optima among demes, and migration rate for an allogamous and predominantly selfing species. Contrasting dynamics of the genetic variability at markers, QTL, and trait were observed as a function of the level of gene flow and diversifying selection. The highest discrepancy among the three levels occurred under highly diversifying selection and high gene flow. Furthermore, diversifying selection might cause substantial heterogeneity among QTL, only a few of them showing allelic differentiation, while the others behave as neutral markers.

Computer Simulation↗
PubMed2005

Quantitative trait locus mapping in natural populations: progress, caveats and future directions.

Over the last 15 years quantitative trait locus (QTL) mapping has become a popular method for understanding the genetic basis of continuous variation in a variety of systems. For example, the technique is now an integral tool in medical genetics, livestock production, plant breeding and population genetics of model organisms. Ten years ago, it was suggested that the method could be used to understand continuous variation in natural populations. In this review I: (i) clarify what is meant by natural population in the QTL context, (ii) discuss whether evolutionary biologists have successfully mapped QTL in natural populations, (iii) highlight some of the questions that have been addressed by QTL mapping in natural populations, (iv) describe how QTL mapping can be conducted in unmanipulated natural populations, (v) highlight some of the limitations of QTL mapping and (vi) try to predict some future directions for QTL mapping in natural populations.

Biological Evolution↗
PubMed2004

Prediction of the confidence interval of quantitative trait Loci location.

In 1997, Darvasi and Soller presented empirical predictions of the confidence interval of quantitative trait loci (QTL) location for dense marker maps in experimental crosses. They showed from simulation results for backcross and F2 populations from inbred lines that the 95% confidence interval was a simple function of sample size and the effect of the QTL. In this study, we derive by theory simple equations that can be used to predict any confidence interval and show that for the 95% confidence interval, they are in good agreement with the empirical results given by Darvasi and Soller. A general form of the confidence interval is given that also applies to other population structures (e.g., collections of sib pairs). Furthermore, the expected shape of the likelihood-ratio-test around the true QTL location is derived, which is shown to be extremely leptokurtic. It is shown that this shape explains why confidence intervals from the Log of Odds (LOD) drop-off method and bootstrap results frequently differ for real data sets.

Animals↗
PubMed2003

Quantitative trait loci conditioning transovarial transmission of La Crosse virus in the eastern treehole mosquito, Ochlerotatus triseriatus.

Quantitative trait loci (QTL) affecting the ability of the Eastern treehole mosquito, Ochlerotatus triseriatus, to transovarially transmit (TOT) La Crosse virus (LAC) were mapped in an F1 intercross. The Holmen strain of O. triseriatus, previously selected for TOT refractoriness, was crossed to the AIDL strain that had been selected for TOT permissiveness. In P1 and F1 parents and 49 F2 individuals, regions of 10 cDNA loci were analysed with single strand conformation polymorphism (SSCP) analysis to identify and orientate linkage groups. Genotypes were also scored at fifty-six random amplified polymorphic DNA (RAPD)-SSCP loci. Twenty-eight F2 offspring were individually analysed for TOT. Three QTL for TOT were detected with standard interval mapping on chromosomes II and III. Alleles at the three loci contributed additively towards determining the overall TOT rate and cumulatively accounted for approximately 53% of the phenotypic variance in TOT.

Animals↗
PubMed2001

Should we consider gene x environment interaction in the hunt for quantitative trait loci?

We address the question of whether one can obtain increased power for finding a quantitative trait locus (QTL) if a gene x environment (G x E) interaction is incorporated directly into the linkage analysis. We consider both parametric and nonparametric analysis approaches to including G x E interaction. For the former, we utilize joint segregation and linkage analysis to estimate simultaneously the recombination fraction and a G x E interaction effect, as well as the remaining model parameters. The nonparametric approach is based on an extension of the Haseman-Elston method applied to sib pairs to include a regression of the squared trait difference on marker-identity-by-descent (IBD) probability (pi), the sibling covariate sum (z), and pi x z. We utilize 50 replicates of the simulated data and compare empirical power of the various approaches to detect MG4, a locus that is involved in a strong interaction with age for Q4 and in a weaker interaction with environmental factor E2 for Q3. Using the parametric approach, including a G x age effect does increase power for detecting linkage between MG4 and Q4 compared with ignoring the interaction (powers 58% and 38%, respectively, to exceed a lod score of 3.0). On the other hand, including a G x E2 interaction has little effect on the power to detect linkage between MG4 and Q3. The nonparametric approach leads to qualitatively similar findings. We conclude that it is beneficial to incorporate G x E interaction into a linkage analysis, provided the interaction effect is of sufficiently strong magnitude.

Chromosome Mapping↗