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Use of quantitative trait loci to map murine lung tumor susceptibility genes.

During the last decade new methods for mapping quantitative trait loci (QTLs) have helped geneticists uncover disease-associated genes. Genetic dissection of complex multigenic diseases such as cancer is being accomplished in part by mapping QTLs in experimental crosses of mice [1]. With the recent construction of dense genetic linkage maps for the mouse, mapping of quantitative trait loci has become practical [2]. Over 6000 polymorphic simple sequence length repeat markers (microsatellite markers) have been mapped in the mouse genome [3], and new analytical approaches to linkage analysis have made QTL mapping a powerful technique for identifying cancer genes [4-7]. In this overview we discuss the design of QTL mapping studies and some of the findings from studies on the mapping of murine lung tumor susceptibility loci.

Animals↗

PubMed2002

Novel linkage mapping approach using DNA pooling in human and animal genetics. II. Detection of quantitative traits loci in dairy cattle.

Selective DNA pooling is an advanced methodology for linkage mapping of quantitative trait loci (QTL) in farm animals. The principle is based on densitometric estimates of marker allele frequency in pooled DNA samples of phenotypically extreme individuals from half-sib, backcross and F(2) experimental designs in farm animals. This methodology provides a rapid and efficient analysis of a large number of individuals with short tandem repeat markers that are essential to detect QTL through the genome - wide searching approach. Several strategies involving whole genome scanning with a high statistical power have been developed for systematic search to detect the quantitative traits loci and linked loci of complex traits. In recent studies, greater success has been achieved in mapping several QTLs in Israel-Holstein cattle using selective DNA pooling. This paper outlines the currently emerged novel strategies of linkage mapping to identify QTL based on selective DNA pooling with more emphasis on its theoretical pre-requisite to detect linked QTLs, applications, a general theory for experimental half-sib designs, the power of statistics and its feasibility to identify genetic markers linked QTL in dairy cattle. The study reveals that the application of selective DNA pooling in dairy cattle can be best exploited in the genome-wide detection of linked loci with small and large QTL effects and applied to a moderately sized half-sib family of about 500 animals.

Animals↗

PubMed2004

Joint mapping of quantitative trait Loci for multiple binary characters.

Joint mapping for multiple quantitative traits has shed new light on genetic mapping by pinpointing pleiotropic effects and close linkage. Joint mapping also can improve statistical power of QTL detection. However, such a joint mapping procedure has not been available for discrete traits. Most disease resistance traits are measured as one or more discrete characters. These discrete characters are often correlated. Joint mapping for multiple binary disease traits may provide an opportunity to explore pleiotropic effects and increase the statistical power of detecting disease loci. We develop a maximum-likelihood method for mapping multiple binary traits. We postulate a set of multivariate normal disease liabilities, each contributing to the phenotypic variance of one disease trait. The underlying liabilities are linked to the binary phenotypes through some underlying thresholds. The new method actually maps loci for the variation of multivariate normal liabilities. As a result, we are able to take advantage of existing methods of joint mapping for quantitative traits. We treat the multivariate liabilities as missing values so that an expectation-maximization (EM) algorithm can be applied here. We also extend the method to joint mapping for both discrete and continuous traits. Efficiency of the method is demonstrated using simulated data. We also apply the new method to a set of real data and detect several loci responsible for blast resistance in rice.

Algorithms↗

PubMed2003

Effect of Box-Cox transformation on power of Haseman-Elston and maximum-likelihood variance components tests to detect quantitative trait Loci.

Non-normality of the phenotypic distribution can affect power to detect quantitative trait loci in sib pair studies. Previously, we observed that Winsorizing the sib pair phenotypes increased the power of quantitative trait locus (QTL) detection for both Haseman-Elston (HE) least-squares tests [Hum Hered 2002;53:59-67] and maximum likelihood-based variance components (MLVC) analysis [Behav Genet (in press)]. Winsorizing the phenotypes led to a slight increase in type 1 error in H-E tests and a slight decrease in type I error for MLVC analysis. Herein, we considered transforming the sib pair phenotypes using the Box-Cox family of transformations. Data were simulated for normal and non-normal (skewed and kurtic) distributions. Phenotypic values were replaced by Box-Cox transformed values. Twenty thousand replications were performed for three H-E tests of linkage and the likelihood ratio test (LRT), the Wald test and other robust versions based on the MLVC method. We calculated the relative nominal inflation rate as the ratio of observed empirical type 1 error divided by the set alpha level (5, 1 and 0.1% alpha levels). MLVC tests applied to non-normal data had inflated type I errors (rate ratio greater than 1.0), which were controlled best by Box-Cox transformation and to a lesser degree by Winsorizing. For example, for non-transformed, skewed phenotypes (derived from a chi2 distribution with 2 degrees of freedom), the rates of empirical type 1 error with respect to set alpha level=0.01 were 0.80, 4.35 and 7.33 for the original H-E test, LRT and Wald test, respectively. For the same alpha level=0.01, these rates were 1.12, 3.095 and 4.088 after Winsorizing and 0.723, 1.195 and 1.905 after Box-Cox transformation. Winsorizing reduced inflated error rates for the leptokurtic distribution (derived from a Laplace distribution with mean 0 and variance 8). Further, power (adjusted for empirical type 1 error) at the 0.01 alpha level ranged from 4.7 to 17.3% across all tests using the non-transformed, skewed phenotypes, from 7.5 to 20.1% after Winsorizing and from 12.6 to 33.2% after Box-Cox transformation. Likewise, power (adjusted for empirical type 1 error) using leptokurtic phenotypes at the 0.01 alpha level ranged from 4.4 to 12.5% across all tests with no transformation, from 7 to 19.2% after Winsorizing and from 4.5 to 13.8% after Box-Cox transformation. Thus the Box-Cox transformation apparently provided the best type 1 error control and maximal power among the procedures we considered for analyzing a non-normal, skewed distribution (chi2) while Winzorizing worked best for the non-normal, kurtic distribution (Laplace). We repeated the same simulations using a larger sample size (200 sib pairs) and found similar results.

Chromosome Mapping↗

PubMed2006

Mixture cure model with an application to interval mapping of quantitative trait loci.

When censored time-to-event data are used to map quantitative trait loci (QTL), the existence of nonsusceptible subjects entails extra challenges. If the heterogeneous susceptibility is ignored or inappropriately handled, we may either fail to detect the responsible genetic factors or find spuriously significant locations. In this article, an interval mapping method based on parametric mixture cure models is proposed, which takes into consideration of nonsusceptible subjects. The proposed model can be used to detect the QTL that are responsible for differential susceptibility and/or time-to-event trait distribution. In particular, we propose a likelihood-based testing procedure with genome-wide significance levels calculated using a resampling method. The performance of the proposed method and the importance of considering the heterogeneous susceptibility are demonstrated by simulation studies and an application to survival data from an experiment on mice infected with Listeria monocytogenes.

Algorithms↗

PubMed2005

Semiparametric methods for mapping quantitative trait loci with censored data.

Statistical methods for the detection of genes influencing quantitative traits with the aid of genetic markers are well developed for normally distributed, fully observed phenotypes. Many experiments are concerned with failure-time phenotypes, which have skewed distributions and which are usually subject to censoring because of random loss to follow-up, failures from competing causes, or limited duration of the experiment. In this article, we develop semiparametric statistical methods for mapping quantitative trait loci (QTLs) based on censored failure-time phenotypes. We formulate the effects of the QTL genotype on the failure time through the Cox (1972, Journal of the Royal Statistical Society, Series B 34, 187-220) proportional hazards model and derive efficient likelihood-based inference procedures. In addition, we show how to assess statistical significance when searching several regions or the entire genome for QTLs. Extensive simulation studies demonstrate that the proposed methods perform well in practical situations. Applications to two animal studies are provided.

Animals↗

PubMed2005

Quantitative trait loci analysis for the developmental behavior of Soybean (Glycine max L. Merr.).

Quantitative trait loci (QTLs) identified so far in soybean were mainly derived in the final stage of plant development, which did not apply to the exploitation of genetic effects that were expressed during a specific developmental stage. Thus, the aim of this study was to identify conditional QTLs associated with yield traits at a specific developmental interval of soybean plant. The 143 recombinant inbred lines developed from the cross of soybean cultivars 'Charleston' and 'Dongnong 594' were used for the developmental QTLs analysis of pod number in the main stem and plant height by composite interval mapping method combined with mixed genetic model. The results indicated that the number and type of QTLs and their genetic effects for the two agronomic traits were different in a series of measuring stages. A total of 10 unconditional QTLs in 6 linkage groups and 5 conditional QTLs in 3 linkage groups were identified for the pod number of the main stem, while 13 unconditional QTLs in 7 linkage groups and 12 conditional QTLs in 6 linkage groups were identified for plant height. Many QTLs that were detected in the early stages were different from those detected at the later stages. Some QTLs existed only at one stage and others existed across two or three stages. Five marker intervals (satt509-satt251, sat_099-sat_113, sat_113-OPAW19_4, satt457-OPC10_85, sat_095-OPBA08_5) were proven to be associated both with the development of pod number in the main stem and the development of plant height. The present study suggested that the development of pods and plant height in soybean were governed by time-dependent gene expression.

Chromosome Mapping↗

PubMed2005

Selection bias in quantitative trait loci mapping.

A simulation study was performed to see whether selection affected quantitative trait loci (QTL) mapping. Populations under random selection, under selection among full-sib families, and under selection within a full-sib family were simulated each with heritability of 0.3, 0.5, and 0.7. They were analyzed with the marker spacing of 10 cM and 20 cM. The accuracy for QTL detection decreased for the populations under selection within full-sib family. Estimates of QTL effects and positions differed (P < .05) from their input values. The problems could be ignored when mapping a QTL for the populations under selection among full-sib families. A large heritability helped reduction of such problems. When the animals were selected within a full-sib family, the QTL was detected for the populations with heritability of 0.5 or larger using the marker spacing of 10 cM, and with heritability of 0.7 using the marker spacing of 20 cM. This study implied that when selection was introduced, the accuracy for QTL detection decreased and the estimates of QTL effects were biased. A caution was warranted on the decision of data (including selected animals to be genotyped) for QTL mapping.

Animals↗

PubMed2005

Interaction between Rf-1 and Rf-4 quantitative trait loci increases susceptibility to renal damage in double congenic rats.

BACKGROUND: Five quantitative trait loci (QTLs), Rf-1 to Rf-5, were found in Fawn-Hooded hypertensive (FHH) rats influencing susceptibility to renal damage. Previously, we found that single transfer of the Rf-1 QTL from FHH rats onto the renal-resistant August x Copenhagen Irish (ACI) strain caused a small increase in renal susceptibility. To investigate the separate role of the Rf-4 QTL and its interaction with Rf-1, we generated a single congenic strain carrying Rf-4 and a double congenic carrying both Rf-1 and Rf-4. METHODS: Differences in renal susceptibility between ACI, Rf-1A, and Rf-4 single congenics and Rf-1A+4 double congenics were assessed using four different treatments: control (two-kidney), two-kidney with l-arginine analogue N-nitro-l-arginine methyl ester (L-NAME)-induced hypertension, unilateral nephrectomy, and unilateral nephrectomy + L-NAME. In separate experiments, renal blood flow (RBF) autoregulation was compared between two-kidney ACI and congenic rats. RESULTS: Compared to ACI, Rf-1A rats developed more renal damage, while Rf-4 rats did not. The most severe renal damage was found in the Rf-1A+4 double congenic rats. Analysis of variance (ANOVA) demonstrated a significant interaction between the Rf-1A and Rf-4 QTLs. The magnitude of the interaction varied with the type and duration of the treatment. The RBF autoregulation was impaired in Rf-1A single and Rf-1A+4 double congenics, while in Rf-4 single congenics it was similar to that of ACI controls. CONCLUSION: These findings indicate that the Rf-1 QTL directly influences renal susceptibility and autoregulation. In contrast, the Rf-4 QTL shows no direct effects, but significantly increases susceptibility to renal damage via an interaction with Rf-1.

Albuminuria↗

PubMed2005

Attack behaviors in mice: from factorial structure to quantitative trait loci mapping.

The emergence or non-emergence of attack behavior results from interaction between the genotype and the conditions under which the mice are tested. Inbred mice of the same strain reared or housed under conditions do not react the same way; reactions also vary according to the place selected for testing and the different opponents. A factor analysis showed that the attack behavior in non-isolated males, tested in neutral area covaried with high testosterone and steroid sulfatase and low brain 5-hydroxytriptamine (5-HT), beta-endorphin and Adrenocorticotropic Hormone (ACTH) concentration, whereas, for isolated males tested in their own housing cage, it covaried with high testosterone activity and low brain 5-HT concentration. A wide genome scan was performed with two independent populations derived from C57BL/6J and NZB/BlNJ, each being reared, housed and tested under highly contrasting conditions, as described above, and confronted with A/J standard males. Common Quantitative Trait Loci emerged for two rearing/testing conditions. For rattling latency we detected Quantitative Trait Loci on Mus musculus chromosome 8 (MMU8) (at 44, LOD score=3.51 and 47 cM, LOD score=6.22, for the first and the second conditions) and on MMU12 (at 39 cM, LOD score=3.69 and at 41 cM, LOD score=2.99, respectively). For the number of attacks, Quantitative Trait Loci were common: on MMU11 at 39 cM LOD score=4.51 and 45 cM, LOD score=3.05, respectively, and on MMU12 (17 cM, LOD score=2.71 and 24 cM, LOD score=3.10). The steroid sulfatase gene (Sts), located on the X-Y pairing region, was linked, but only in non-isolated males, tested in neutral area for rattling latency, first attack latency, and number of attacks (LOD scores=4.9, 4.79 and 3.57, respectively). We found also that the Quantitative Trait Locus encompassing Sts region interacted with other Quantitative Trait Loci. These results indicate that attack behavior measured in different rearing and testing conditions have different biological and genetic correlates. This suggests that further explorations should be done with standardized tests and, in addition, with a wide range of tests, so as to gain an understanding of the true impact of genes or pharmacological treatments on specific categories of aggressive behavior.

Aggression↗

PubMed2000

A method for fine mapping quantitative trait loci in outbred animal stocks.

High-resolution mapping of quantitative trait loci (QTL) in animals has proved to be difficult because the large effect sizes detected in crosses between inbred strains are often caused by numerous linked QTLs, each of small effect. In a study of fearfulness in mice, we have shown it is possible to fine map small-effect QTLs in a genetically heterogeneous stock (HS). This strategy is a powerful general method of fine mapping QTLs, provided QTLs detected in crosses between inbred strains that formed the HS can be reliably detected in the HS. We show here that single-marker association analysis identifies only two of five QTLs expected to be segregating in the HS and apparently limits the strategy's usefulness for fine mapping. We solve this problem with a multipoint analysis that assigns the probability that an allele descends from each progenitor in the HS. The analysis does not use pedigrees but instead requires information about the HS founder haplotypes. With this method we mapped all three previously undetected loci [chromosome (Chr.) 1 logP 4.9, Chr. 10 logP 6.0, Chr. 15 logP 4.0]. We show that the reason for the failure of single-marker association to detect QTLs is its inability to distinguish opposing phenotypic effects when they occur on the same marker allele. We have developed a robust method of fine mapping QTLs in genetically heterogeneous animals and suggest it is now cost effective to undertake genomewide high-resolution analysis of complex traits in parallel on the same set of mice.

Animals↗

PubMed1996

Quantitative trait loci in genetically hypertensive rats. Possible sex specificity.

We performed a total genome screen in an F2 cross derived from the stroke-prone spontaneously hypertensive rat and the normotensive Wistar-Kyoto rat. Blood pressure at baseline and after 1% NaCl was measured by radiotelemetry; other phenotypes included heart rate, motor activity, left ventricle weight to body weight ratio, and vascular smooth muscle cell polyploidy, a measure of vascular hypertrophy. Quantitative trait loci affecting a given phenotype were mapped relative to microsatellite markers by using the MAPMAKER/QTL 1.1 computer package. We identified three blood pressure quantitative trait loci, two on rat chromosome 2 and one on rat chromosome 3. The quantitative trait loci close to genetic markers D2Mgh12 ("suggestive" linkage, with a maximal logarithm of the odds [LOD] score of 3.1) and D3Mgh16 (significant linkage, with a maximal LOD score of 5.6) showed possible sex specificity in the male F2 cohort only. This was confirmed by the likelihood ratio test for the difference in locus effects between the sexes. We also identified a new quantitative trait locus for LV hypertrophy on rat chromosome 14 ("suggestive" linkage, with a maximal LOD score of 3.1). The sex specificity of blood pressure quantitative trait loci will be important in designing congenic strains and substrains for fine genetic mapping and for identifying genes that regulate blood pressure.

Animals↗

PubMed2002

Accelerated congenics for mapping two blood pressure quantitative trait loci on chromosome 10 of Dahl rats.

OBJECTIVE: To localize quantitative trait loci (QTL) in an animal model that is potentially relevant to human hypertension. DESIGN AND METHODS: Four congenic strains have been constructed by replacing various segments of the Dahl salt-sensitive (S) rat by those of the Lewis (LEW) rat. A marker-assisted approach was employed to facilitate this process. When these congenic strains were established, their blood pressures (BPs) were measured by telemetry and compared with that of the S rat. Moreover, a search was conducted to find possible intermediate phenotypes linking the BP effects of the QTL and other physiological traits. RESULTS: Two BP QTL, designated as QTL1 and QTL2, have been mapped to the regions of 4.2 centiMorgans (cM) and less than 12.1 cM respectively on rat chromosome 10. The effects of both QTL correlate with cardiac, left ventricular and aortic hypertrophy. The effect of QTL1 is also associated with renal hypertrophy. CONCLUSION: The current study proved that multiple QTL exist in the region of Dahl rat chromosome 10. The identification of these QTL may help unravel the mechanisms underlying the pathogenesis of certain QTL in humans.

Animals↗

PubMed2002

Mapping epigenetic quantitative trait loci (QTL) altering a developmental trajectory.

Genetic variation in a quantitative trait that changes with age is important to both evolutionary biologists and breeders. A traditional analysis of the dynamics of genetic variation is based on the genetic variance-covariance matrix among different ages estimated from a quantitative genetic model. Such an analysis, however, cannot reveal the mechanistic basis of the genetic variation for a growth trait during ontogeny. Age-specific genetic variance at time t conditional on the causal genetic effect at time t - 1 implies the generation of episodes of new genetic variation arising during the interval t - 1 to t. In the present paper, the conditional genetic variance estimated from Zhu's (1995) conditional model was partitioned into its underlying individual quantitative trait loci (QTL) using molecular markers in an F2 progeny of poplars (Populus trichocarpa and Populus deltoides). These QTL, defined as epigenetic QTL, govern the alterations of growth trajectory in a population. Three epigenetic QTL were detected to contribute significantly to variation in growth trajectory during the period from the establishment year to the subsequent year in the field. It is suggested that the activation and expression of epigenetic QTL are influenced by the developmental status of trees and the environment in which they are grown.

Analysis of Variance↗

PubMed2002

Improving the power of sib pair quantitative trait loci detection by phenotype winsorization.

OBJECTIVES: In sib pair studies, quantitative trait loci (QTL) identification may be adversely affected by non-normality in the phenotypic distribution, particularly when subjects falling in the tails of the distribution bias the trait mean or variance. We evaluated the robustness and power of reducing the influence of subjects with extreme phenotypic values by Winsorizing non-normal distributions in three versions of Haseman-Elston regression-based methods of QTL linkage analysis. METHODS: Data were simulated for normal and non-normal distributions. Phenotypic values that correspond to cutoff points at the omega and 1 - omega percentiles of the distribution were identified, and phenotypic values falling outside the boundaries of the omega and 1 - omega cutoff points were replaced by the omega and 1 - omega values, respectively. One million replications were performed for the three tests of linkage for Winsorized and non-Winsorized data. RESULTS: Winsorization reduced conservatism in the tails of the empirical type I error rate for the vast majority of the tests of linkage, increased the power of QTL detection in non-normal data and created a slight negative bias in symmetrical phenotypic distributions. CONCLUSIONS: Winsorizing can improve the power of QTL detection with certain non-normal distributions but can also introduce bias into the estimate of the QTL effect.

Genetic Predisposition to Disease↗

PubMed2003

Advanced backcross QTL analysis for the identification of quantitative trait loci alleles from wild relatives of wheat ( Triticum aestivum L.).

Advanced backcross QTL (AB-QTL) analysis was used to identify quantitative trait loci (QTLs) for yield and yield components in a BC(2)F(2) population derived from a cross between the German winter wheat variety 'Prinz' and the synthetic wheat line W-7984 developed by CIMMYT. Two hundred and ten microsatellite markers were employed to genotype 72 pre-selected BC(2)F(2) plants and phenotypic data were collected for five agronomic traits from corresponding BC(2)F(3) families that were grown at four locations in Germany. Using single-marker regression and interval mapping, a total of 40 putative QTLs derived from W-7984 were detected, of which 11 were for yield, 16 for yield components, eight for ear emergence time and five for plant height. For 24 (60.0%) of them, alleles from the synthetic wheat W-7984 were associated with a positive effect on agronomic traits, despite the fact that synthetic wheat was overall inferior with respect to agronomic appearance and performance. The present study indicated that favorable QTL alleles could be transferred from wild relatives of wheat into an elite wheat variety for improvement of quantitative trait loci like yield by the advanced backcross QTL strategy and molecular breeding. To our knowledge, the results presented here were the first report on AB-QTL analysis in wheat.

Alleles↗

PubMed1998

Identification of two quantitative trait loci involved in antibody production on mouse chromosome 8.

Several quantitative trait loci (QTLs) contributing to the extreme phenotypes of the selected high (H) and low (L) antibody-responder lines of mice were mapped on distinct chromosomes. Successive backcrosses were bred to reduce the length of the QTL-bearing segment detected on chromosome 8 and to produce congenic lines to test gene effect independently of the other QTLs. An increase in antibody responses was repeatedly found to be associated with inheritance of the H-line allele at two markers separated by 30 cM on that chromosome. In the successive backcrosses, background and unlinked involved genes of H-line origin were progressively eliminated; however, unexpected within-progeny variations persisted in the third and even fourth backcross. Nevertheless, the presence of two QTLs within the considered interval was definitely demonstrated in distinct progenies of the fourth backcross which separately inherited one of the two gene-marker H-line alleles. The previously identified chromosome 8 segment therefore contains at least two QTLs involved in antibody responsiveness.

Animals↗

PubMed1994

Mapping quantitative trait loci with DNA microsatellites in a commercial dairy cattle population.

Individual loci affecting economically important traits can be located using genetic linkage between quantitative trait loci and genetic markers. In the 'granddaughter' experimental design, heterozygous grandsires and their sons are genotyped for the genetic marker, while the quantitative trait records of the granddaughters are used for statistical analysis. Ten DNA microsatellite markers were used to look for associations with quantitative trait loci affecting milk production traits in seven Israeli Holstein grandsire families. At least 60% more grandsires were heterozygous, and 40% fewer individuals were discarded because of unknown paternal allele origin, as compared with diallelic markers. The effects of paternal alleles for locus D21S4 on kg milk and protein were significant (P < 0.025). The allele substitution effects for sire 783 were 283 kg milk and 5.7 kg protein. For both traits, progeny of sire 783 that inherited allele '18' had higher evaluations than progeny that inherited allele '21'. These results were verified by genotyping 151 of his daughters. Thus, the rate of genetic gain for protein production can be increased by selecting progeny of sire 783 carrying allele '18' at this locus.

Animals↗