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PubMed1997

The use of selection experiments for detecting quantitative trait loci.

Gene frequency changes following selection may reveal the existence of gene effects on the trait selected. Loci for the selected quantitative trait (SQTL) may thus be detected. Additionally, one can estimate the average effect (alpha) of a marker allele associated with an SQTL from the allele frequency change (delta q) due to selection of given intensity (i). In a sample of unrelated individuals, it is optimal to select the upper and lower 27% for generating delta q in order to estimate alpha. For a given number of individuals genotyped, this estimator is 0.25i2 times more efficient than the classical estimator of alpha, based on the regression of the trait on the genotype at the marker locus. The method is extended to selection criteria using information from relatives, showing that combined selection considerably increases the efficiency of estimation for traits of low heritability. The method has been applied to the detection of SQTL in a selection experiment in which the trait selected was pig litter size averaged over the first four parities, with i = 3. Results for four genes are provided, one of which yielded a highly significant effect. The conditions required for valid application of the method are discussed, including selection experiments over several generations. Additional advantages of the method can be anticipated from determining gene frequencies on pooled samples of blood or DNA.

Animals↗
PubMed1999

Identifying influential individuals in linkage analysis: application to a quantitative trait locus detected in the COGA data.

Once linkage is detected to a quantitative trait locus (QTL), the next step towards localizing the gene involved may be to identify those families, or individuals, in whom the putative mutations are segregating. In this paper, we describe a jackknife procedure for identifying individuals (and families) who contribute disproportionately to the linkage. Following initial detection of linkage to a QTL, the strategy involves sequentially removing each individual (or each family) from the analysis and recomputing the lod score associated with the linked region using data from all remaining subjects (or families). This procedure can be used to determine if particular observations have substantial impact on evidence for linkage. Identification of such observations may provide insights for further efforts to localize the QTL.

Alcoholism↗
PubMed2005

Identification of quantitative trait loci across recombinant inbred lines and testcross populations for traits of agronomic importance in rice.

This study was conducted to determine whether quantitative trait loci (QTL) controlling traits of agronomic importance detected in recombinant inbred lines (RILs) are also expressed in testcross (TC) hybrids of rice. A genetic map was constructed using an RIL population derived from a cross between B5 and Minghui 63, a parent of the most widely grown hybrid rice cultivar in China. Four TC hybrid populations were produced by crossing the RILs with three maintaining lines for the widely used cytoplasmic male-sterile (CMS) lines and the genic male-sterile line Peiai64s. The mean values of the RILs for the seven traits investigated were significantly correlated to those of the F1 hybrids in the four TC populations. Twenty-seven main-effect QTL were identified in the RILs. Of these, the QTL that had the strongest effect on each of the seven traits in the RILs was detected in two or more of the TC populations, and six other QTL were detected in one TC population. Epistatic analysis revealed that the effect of epistatic QTL was relatively weak and cross combination specific. Searching publicly available QTL data in rice revealed the positional convergence of the QTL with the strongest effect in a wide range of populations and under different environments. Since the main-effect QTL is expressed across different testers, and in different genetic backgrounds and environments, it is a valuable target for gene manipulation and for further application in rice breeding. When a restorer line that expresses main-effect QTL is bred, it could be used in a number of cross combinations.

Chromosome Mapping↗
PubMed1998

Mapping of quantitative trait loci for body weight on chromosomes 1 and 4 in the rat.

Quantitative trait loci (QTLs) affecting body weight were investigated in the backcross population derived from diabetic BB/OK and spontaneously hypertensive rat (SHR). The F1 hybrids were backcrossed onto BB/OK rats, and QTL analysis was performed with the resulting backcross population on chromosomes 1, 3, 4, 10, 13 and 18. According to the stringent threshold for a lod score of 3.0, markers on chromosomes 1 and 4 were found to be linked with body weight. The QTL with a peak lod score (3.3) on chromosome 1 for a male population was located within the region flanked by loci Igf2 and D1Mgh12. On chromosome 4, linkage between the body weight and the region around the Npy locus was observed (lod score 3.1). The existence of the QTL on chromosome 4 affecting body weight was confirmed by congenic BB.LL rats, carrying chromosomal region of SHR (D4Mit6-Npy-Spr) on the genetic background of the BB/OK rat.

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↗
PubMed2000

Quantitative trait loci mapping for cholesterol gallstones in AKR/J and C57L/J strains of mice.

Quantitative trait locus (QTL) mapping was used to locate genes that determine the difference in cholesterol gallstone disease between the gallstone-susceptible strain C57L/J and the gallstone-resistant strain AKR/J. Gallstone weight was determined in 231 male (AKR x C57L) F(1) x AKR backcross mice fed a lithogenic diet containing 1% cholesterol, 0.5% cholic acid, and 15% butterfat for 8 wk. Mice having no stones and mice having the largest stones were genotyped at approximately 20-cM intervals to find the loci determining cholesterol gallstone formation. The major locus, Lith1, mapped near D2Mit56 and was confirmed by constructing a congenic strain, AK. L-Lith1(s). Another locus, Lith2, mapped near D19Mit58 and was also confirmed by constructing a congenic strain AK.L-Lith2(s). Other suggestive, but not statistically significant, loci mapped to chromosomes 6, 7, 8, 10, and X. The identification of these Lith genes will elucidate the pathophysiology of cholesterol gallstone formation.

Animals↗
PubMed2000

Markov chain Monte Carlo for mapping a quantitative trait locus in outbred populations.

A Bayesian approach is presented for mapping a quantitative trait locus (QTL) using the 'Fernando and Grossman' multivariate Normal approximation to QTL inheritance. For this model, a Bayesian implementation that includes QTL position is problematic because standard Markov chain Monte Carlo (MCMC) algorithms do not mix, i.e. the QTL position gets stuck in one marker interval. This is because of the dependence of the covariance structure for the QTL effects on the adjacent markers and may be typical of the 'Fernando and Grossman' model. A relatively new MCMC technique, simulated tempering, allows mixing and so makes possible inferences about QTL position based on marginal posterior probabilities. The model was implemented for estimating variance ratios and QTL position using a continuous grid of allowed positions and was applied to simulated data of a standard granddaughter design. The results showed a smooth mixing of QTL position after implementation of the simulated tempering sampler. In this implementation, map distance between QTL and its flanking markers was artificially stretched to reduce the dependence of markers and covariance. The method generalizes easily to more complicated applications and can ultimately contribute to QTL mapping in complex, heterogeneous, human, animal or plant populations.

Animals↗
PubMed2001

Screening for highly heterozygous chickens in outbred commercial broiler lines to increase detection power for mapping quantitative trait loci.

Two commercial broiler lines were used in an experiment to map quantitative trait loci (QTL) affecting disease resistance. Chickens from these lines were genotyped with 27 microsatellite markers to estimate heterozygosity and polymorphism information content (PIC), the probability that one parent is heterozygous at a marker locus and the other has a different genotype. Heterozygosity estimated from allelic frequencies was 0.52 for the two lines; however, heterozygosity calculated from actual counts of heterozygous loci was much lower (0.36) than the estimated heterozygosity. The PIC was 0.45 in these lines, and average allele number per marker locus was about 3.5. Twenty-five males produced from a cross between these two lines were screened with the DNA markers to select birds with high heterozygosity at marker loci. Mating simulation showed that uninformative matings could be reduced by about 5% if 12 pairs of males and females with the highest heterozygosity at marker loci were selected, which was about a 25% reduction in total uninformative matings. This experiment demonstrated that the heterozygosity and PIC in commercial broiler lines were low and selection for the birds with high heterozygosity at marker loci could increase informative content in chickens used in the experiments of QTL mapping, thus increasing detection power for QTL mapping.

Animal Husbandry↗
PubMed1999

Bayesian mapping of multiple quantitative trait loci from incomplete outbred offspring data.

A general fine-scale Bayesian quantitative trait locus (QTL) mapping method for outcrossing species is presented. It is suitable for an analysis of complete and incomplete data from experimental designs of F2 families or backcrosses. The amount of genotyping of parents and grandparents is optional, as well as the assumption that the QTL alleles in the crossed lines are fixed. Grandparental origin indicators are used, but without forgetting the original genotype or allelic origin information. The method treats the number of QTL in the analyzed chromosome as a random variable and allows some QTL effects from other chromosomes to be taken into account in a composite interval mapping manner. A block-update of ordered genotypes (haplotypes) of the whole family is sampled once in each marker locus during every round of the Markov Chain Monte Carlo algorithm used in the numerical estimation. As a byproduct, the method gives the posterior distributions for linkage phases in the family and therefore it can also be used as a haplotyping algorithm. The Bayesian method is tested and compared with two frequentist methods using simulated data sets, considering two different parental crosses and three different levels of available parental information. The method is implemented as a software package and is freely available under the name Multimapper/outbred at URL http://www.rni.helsinki.fi/mjs/.

Algorithms↗
PubMed2001

A statistical framework for quantitative trait mapping.

We describe a general statistical framework for the genetic analysis of quantitative trait data in inbred line crosses. Our main result is based on the observation that, by conditioning on the unobserved QTL genotypes, the problem can be split into two statistically independent and manageable parts. The first part involves only the relationship between the QTL and the phenotype. The second part involves only the location of the QTL in the genome. We developed a simple Monte Carlo algorithm to implement Bayesian QTL analysis. This algorithm simulates multiple versions of complete genotype information on a genomewide grid of locations using information in the marker genotype data. Weights are assigned to the simulated genotypes to capture information in the phenotype data. The weighted complete genotypes are used to approximate quantities needed for statistical inference of QTL locations and effect sizes. One advantage of this approach is that only the weights are recomputed as the analyst considers different candidate models. This device allows the analyst to focus on modeling and model comparisons. The proposed framework can accommodate multiple interacting QTL, nonnormal and multivariate phenotypes, covariates, missing genotype data, and genotyping errors in any type of inbred line cross. A software tool implementing this procedure is available. We demonstrate our approach to QTL analysis using data from a mouse backcross population that is segregating multiple interacting QTL associated with salt-induced hypertension.

Algorithms↗
PubMed2004

Quantitative trait locus mapping for age-related cataract severity and synechia prevalence using four-way cross mice.

PURPOSE: The goal of this study was to map mouse quantitative trait loci (QTL) that influence the development of murine age-related cataract and synechia, by using a genetically heterogeneous mouse population bred by a four-way cross. METHODS: The test population consisted initially of 510 mice bred as the progeny of (BALB/cJ x C57BL/6J)F1 females and (C3H/HeJ x DBA/2J)F1 males. Each mouse was examined by slit lamp at 18 and 24 months of age and scored for degree of lens opacity on a 0 to 4+ scale, and the presence or absence of additional anterior chamber disease was noted. The presence of synechia was confirmed by histology. Each mouse was genotyped at 96 maternal and 92 paternal loci, and the significance of association between genotype and eye lesions was tested by permutation analysis. RESULTS: Significant QTL with effects on lens opacity at 24 months were detected on mouse chromosomes 4, 11, and 12. The effects were additive, and severe cataracts were seen in 80% of the mice with all three high-risk alleles, but in only 28% of the mice with all three low-risk alleles. The risk of synechia was associated with paternal chromosome 1 and on both the maternal and paternally inherited chromosome 4. Mice with all three high-risk alleles had a 68% risk of synechia, compared with a 0% incidence in mice with all three counteralleles. CONCLUSIONS: A four-way cross population of mice can be used to map polymorphic loci that influence cataract severity and synechia prevalence in late life. The results provide a first step toward identification of the individual genes involved and may help to guide the search for homologous human genes.

Aging↗
PubMed1998

Combining different line crosses for mapping quantitative trait loci using the identical by descent-based variance component method.

Mapping quantitative trait loci (QTLs) is usually conducted with a single line cross. The power of such QTL mapping depends highly on the two parental lines. If the two lines are fixed for the same allele at a putative QTL, the QTL is undetectable. On the other hand, if a QTL is segregating in the line cross and is detected, the estimated variance of the QTL cannot be extrapolated beyond the statistical inference space of the two parental lines. To reduce the likelihood of missing a QTL and to increase the statistical inference space of the estimated QTL variance, we present a consensus QTL mapping strategy. We adopt the identical by descent (IBD)-based variance component method originally applied to human linkage analysis by combining multiple line crosses as independent families. We explore the properties of consensus QTL mapping and demonstrate the method with F2, backcross (BC), and full-sib (FS) families. In addition, we examine the effects of the QTL heritability, marker informativeness, QTL position, the number of families, and family size. We show that F2 families notably outperform BC and FS families in detecting a QTL. There is a substantial reduction in the standard deviation of the estimated QTL position and the separation of the QTL and polygenic variance. Finally, we show that the power to detect a QTL is greater when using a small number of large families than a large number of small families.

Chromosome Mapping↗
PubMed2001

Sensitivity of AXB/BXA recombinant inbred lines of mice to the locomotor activating effects of cocaine: a quantitative trait loci analysis.

The present study was conducted in order to characterize putative quantitative trait loci (QTL) for cocaine-induced activation in the AXB/BXA recombinant inbred (RI) lines of mice. Locomotor activity was measured in the AXB/BXA and progenitor A/J and C57BL/6J strains using a computerized open-field apparatus following saline or cocaine (0, 5, 10, 20 mg/kg) administration (i.p.). Analyses were conducted on phenotypes including both novelty (responses under initial saline conditions) and cocaine-induced locomotor activity. Significant differences were observed across RI lines on all measures. Gender differences in sensitivity to the activating effects of cocaine were not observed. The wide and continuous distributions of phenotypic responses in the AXB/BXA RI lines suggested polygenic regulation. Initial basal locomotor activity was significantly correlated with cocaine-induced activation (raw scores) (r = 0.60, P = 0.0021) but not with cocaine difference scores (r = 0.370, P = 0.082). Simple regression and interval mapping were used to initially identify significant gene markers associated with novelty and cocaine-induced activation. Subsequently, composite interval mapping was used to increase the accuracy in mapping individual loci. QTL analysis of cocaine-induced activation (difference scores--20 mg/kg dose) identified significant loci on chromosomes 12 (23 cM), and 15 (46.8 cM). The significant QTLs were identified on chromosomes 12 and 15 map to regions in proximity to genes for the somatostatin 1 (Smstr1 -23 cM) and 3 (Smstr3 -46.3 cM) receptors, respectively. Further research employing AcB/BcA recombinant congenic lines of mice will be employed to confirm the QTL on chromosomes 12 and 15 identified in the present study.

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

Quantitative trait locus mapping and DNA array hybridization identify an FLM deletion as a cause for natural flowering-time variation.

Much of the flowering time variation in wild strains of Arabidopsis thaliana is due to allelic variation at two epistatically acting loci, FRIGIDA (FRI) and FLOWERING LOCUS C (FLC). FLC encodes a MADS (MCM1/AGAMOUS/DEFICIENS/SRF1) domain transcription factor that directly represses a series of flowering-promoting genes. FRI and FLC, however, do not explain all of the observed variation, especially when plants are grown in short days. To identify loci that act in addition to FRI and FLC in controlling flowering of natural accessions, we have analyzed a recombinant inbred line population derived from crosses of accession Niederzenz (Nd) to Columbia, both of which contain natural FRI lesions. Quantitative trait locus mapping and genomic DNA analysis by microarray hybridization were used to identify candidate genes affecting variation in flowering behavior. In both long and short days, the quantitative trait locus of largest effect, termed FLOWERING 1 (FLW1), was found to be associated with a Nd-specific deletion of FLOWERING LOCUS M (FLM), which encodes a floral repressor closely related to FLC. Analysis of near isogenic lines and quantitative transgenic complementation experiments confirmed that the FLM deletion is, in large part, responsible for the early flowering of the Nd accession.

Alleles↗
PubMed2005

Identification of quantitative trait loci for susceptibility to mouse adenovirus type 1.

Adult SJL/J mice are highly susceptible to mouse adenovirus type 1 (MAV-1) infections, whereas other inbred strains, including BALB/cJ, are resistant (K. R. Spindler, L. Fang, M. L. Moore, C. C. Brown, G. N. Hirsch, and A. K. Kajon, J. Virol. 75:12039-12046, 2001). Using congenic mouse strains, we showed that the H-2(s) haplotype of SJL/J mice is not associated with susceptibility to MAV-1. Susceptibility of MAV-1-infected (BALB/cJ x SJL/J)F(1) mice was intermediate between that of SJL/J mice and that of BALB/cJ mice, indicating that susceptibility is a genetically controlled quantitative trait. We mapped genetic loci involved in mouse susceptibility to MAV-1 by analysis of 192 backcross progeny in a genome scan with 65 simple sequence length polymorphic markers. A major quantitative trait locus (QTL) was detected on chromosome 15 (Chr 15) with a highly significant logarithm of odds score of 21. The locus on Chr 15 alone accounts for 40% of the total trait variance between susceptible and resistant strains. QTL modeling of the data indicated that there are a number of other QTLs with small effects that together with the major QTL on Chr 15 account for 54% of the trait variance. Identification of the major QTL is the first step in characterizing host genes involved in susceptibility to MAV-1.

Adenoviridae↗
PubMed2005

Assessing the power of tag SNPs in the mapping of quantitative trait loci (QTL) with extremal and random samples.

BACKGROUND: Recent studies have indicated that the human genome could be divided into regions with low haplotype diversity interspersed with regions of high haplotype diversity. In regions of low haplotype diversity, a small fraction of SNPs (tag SNPs) are sufficient to account for most of the haplotype diversity of the human genome. These tag SNPs can be extremely useful for testing the association of a marker locus with a qualitative or quantitative trait locus in that it may not be necessary to genotype all the SNPs. When tag SNPs are used to reduce the genotyping effort in association studies, it is important to know how much power is lost. It is also important to know how much power is gained when tag SNPs instead of the same number of randomly chosen SNPs are used. RESULTS: We design a simulation study to tackle these problems for a variety of quantitative association tests using either case-parent samples or unrelated population samples. First, the samples are generated based on the quantitative trait model with the assumption of either an extremal sampling scheme or a random sampling scheme. Second, a small number of samples are selected to determine the haplotype blocks and the tag SNPs. Third, the statistical power of the tests is evaluated using four kinds of data: (1) all the SNPs and the corresponding haplotypes, (2) the tag SNPs and the corresponding haplotypes, (3) the same number of evenly spaced SNPs with minor allele frequency greater than a threshold and the corresponding haplotypes, (4) the same number of randomly chosen SNPs and their corresponding haplotypes. CONCLUSION: Our results suggest that in most situations genotyping efforts can be significantly reduced by using tag SNPs for mapping the QTL in association studies without much loss of power, which is consistent with previous studies on association mapping of qualitative traits. For all situations considered, two-locus haplotype analysis using tag SNPs are more powerful than those using the same number of randomly selected SNPs, but the degree of such power differences depends upon the sampling scheme and the population history.

Chromosome Mapping↗