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Natural alleles at a tomato fruit size quantitative trait locus differ by heterochronic regulatory mutations.

fw2.2 is a major quantitative trait locus that accounts for as much as 30% of the difference in fruit size between wild and cultivated tomatoes. Evidence thus far indicates that fw2.2 alleles modulate fruit size through changes in gene regulation rather than in the FW2.2 protein itself. To investigate the nature of these regulatory changes and the manner in which they may affect fruit size, a pair of nearly isogenic lines has been subjected to detailed developmental, transcriptional, mitotic, and in situ hybridization studies. The results indicate that the large- and small-fruited alleles of fw2.2 differ in peak transcript levels by approximately 1 week. Moreover, this difference in timing of expression is associated with concomitant changes in mitotic activity in the early stage of fruit development. The changes in timing of gene expression (heterochronic allelic variation), combined with overall differences in total transcript levels, are sufficient to account for a large portion phenotypic differences in fruit weight associated with the two alleles.

Alleles↗

Epistasis in quantitative trait locus linkage analysis: interaction or main effect?

This paper explores a two-locus variance components model of quantitative trait locus (QTL) linkage for sib-pairs that incorporates epistasis. For a range of epistatic models the expected variance components and noncentrality parameter per sib-pair can be calculated, to indicate the power to detect epistasis. In QTL linkage analysis, additive and epistatic effects are in fact partially confounded; as a result, variance components under incorrect submodels can be distorted, with two main implications. First, the analysis of a single locus can in fact detect a QTL that has no main effect but interacts epistatically with another (unmeasured) locus. That is, single-locus approaches do not necessarily preclude the detection of purely epistatically interacting loci. Second, because the nonepistatic variance component estimates in submodels can partially absorb epistatic variance when it is not explicitly modeled, power to formally detect epistasis is low.

Alleles↗

Selective DNA pooling for determination of linkage between a molecular marker and a quantitative trait locus.

Selective genotyping is a method to reduce costs in marker-quantitative trait locus (QTL) linkage determination by genotyping only those individuals with extreme, and hence most informative, quantitative trait values. The DNA pooling strategy (termed: "selective DNA pooling") takes this one step further by pooling DNA from the selected individuals at each of the two phenotypic extremes, and basing the test for linkage on marker allele frequencies as estimated from the pooled samples only. This can reduce genotyping costs of marker-QTL linkage determination by up to two orders of magnitude. Theoretical analysis of selective DNA pooling shows that for experiments involving backcross, F2 and half-sib designs, the power of selective DNA pooling for detecting genes with large effect, can be the same as that obtained by individual selective genotyping. Power for detecting genes with small effect, however, was found to decrease strongly with increase in the technical error of estimating allele frequencies in the pooled samples. The effect of technical error, however, can be markedly reduced by replication of technical procedures. It is also shown that a proportion selected of 0.1 at each tail will be appropriate for a wide range of experimental conditions.

Chromosome Mapping↗

Quantitative trait locus analysis reveals two intragenic sites that influence O6-alkylguanine-DNA alkyltransferase activity in peripheral blood mononuclear cells.

The repair of specific types of DNA alkylation damage by O6-alkylguanine-DNA alkyltransferase (MGMT) is a major mechanism of resistance to the carcinogenic and chemotherapeutic effects of certain alkylating agents. MGMT expression levels vary widely between individuals but the underlying causes of this variability are not known. To address this, we used an expressed single nucleotide polymorphism (SNP) and demonstrated that the MGMT alleles are frequently expressed at different levels in peripheral blood mononuclear cells (PBMC). This suggests that there is a genetic component of inter-allelic variation of MGMT levels that maps close to or within the MGMT locus. We then used quantitative trait locus (QTL) analysis using intragenic SNPs and found that there are at least two sites influencing inter-individual variation in PBMC MGMT activity. One is characterized by an SNP at the 3' end of the first intron and the second by two SNPs in the last exon. The latter are in perfect disequilibrium and both result in amino acid substitutions-one of them, Ile143Val, affecting an amino acid close to the Cys145 residue at the active site of MGMT. Using in vitro assays, we further showed that while the Val143 variant did not affect the activity of the protein on methylated DNA substrate, it was more resistant to inactivation by the MGMT pseudosubstrate, O6-(4-bromothenyl)guanine. These findings suggest that further investigations of the potential epidemiological and clinical significance of inherited differences in MGMT expression and activity are warranted.

Base Sequence↗

The DGAT1 K232A mutation is not solely responsible for the milk production quantitative trait locus on the bovine chromosome 14.

The gene, acyl-CoA:diacylglycerol acyltransferase1 (DGAT1), was recently identified as the one underlying the quantitative trait locus (QTL) for milk production traits in the centromeric region of the bovine chromosome 14. Until now, 2 alleles, the lysine variant (increasing fat yield, fat and protein percentage) and the alanine variant (increasing protein and milk yield), were postulated at DGAT1. This study investigated whether the diallelic DGAT1 polymorphism is responsible for all the genetic variation at the centromeric region of this chromosome for milk, fat, and protein yield and fat and protein percentage. A statistical model was applied to a granddaughter design to analyze 16 German Holstein families. The model included the diallelic DGAT1 effect and the QTL transition probability estimated for each chromosomal position by a multiple marker approach. Because the regression coefficient of this probability was corrected for the diallelic DGAT1 polymorphism, it represented a putative conditional QTL effect. The effect of the DGAT1 gene was always highly significant. The conditional QTL effect was significant genomewise for fat percentage at the proximal end of the chromosome and for protein percentage at a more distal chromosomal region. Additional chromosomewise significance was found for fat and protein yield. Our results suggest an additional source of genetic variance on this chromosome for these traits; either one or more additional alleles segregating at DGAT1 that were not previously detected, a second quantitative trait locus affecting these traits, or both.

Acyltransferases↗

A note on estimating the posterior density of a quantitative trait locus from a Markov chain Monte Carlo sample.

We provide an overview of the use of kernel smoothing to summarize the quantitative trait locus posterior distribution from a Markov chain Monte Carlo sample. More traditional distributional summary statistics based on the histogram depend both on the bin width and on the sideway shift of the bin grid used. These factors influence both the overall mapping accuracy and the estimated location of the mode of the distribution. Replacing the histogram by kernel smoothing helps to alleviate these problems. Using simulated data, we performed numerical comparisons between the two approaches. The results clearly illustrate the superiority of the kernel method. The kernel approach is particularly efficient when one needs to point out the best putative quantitative trait locus position on the marker map. In such situations, the smoothness of the posterior estimate is especially important because rough posterior estimates easily produce biased mode estimates. Different kernel implementations are available from Rolf Nevanlinna Institute's web page (http://www.rni.helsinki.fi/;fjh).

Bayes Theorem↗

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↗

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↗

Quantitative trait locus analysis of susceptibility to diet-induced atherosclerosis in recombinant inbred mice.

Quantitative trait locus (QTL) analysis is a statistical method that can be applied to identify loci making a significant impact on a phenotype. For the phenotype of susceptibility to diet-induced atherosclerosis in the mouse, we have studied four quantitative traits: area of aortic fatty streaks and serum concentrations of high-density lipoprotein-bound cholesterol (HDL-cholesterol), apolipoprotein A-I, and apolipoprotein A-II (apo A-II). QTL analysis revealed a significant locus on chromosome 1 distal impacting serum apo A-II concentration on a high-fat diet and serum HDL-cholesterol concentration on a chow diet. This locus is presumably Apoa-2, the structural gene for apo A-II. QTL analysis of aortic fatty streaks failed to reveal a significant locus.

Animals↗

PAPPA2, an enzyme that cleaves an insulin-like growth-factor-binding protein, is a candidate gene for a quantitative trait locus affecting body size in mice.

Identifying genes responsible for quantitative variation remains a major challenge. We previously identified a quantitative trait locus (QTL) affecting body size that segregated between two inbred strains of mice. By fine mapping, we have refined the location of this QTL to a genomic region containing only four protein-coding genes. One of these genes, PAPPA2, is a strong candidate because it codes for an enzyme that cleaves insulin-like growth-factor-binding protein 5 (IGFBP-5), an important stimulator of bone formation. Among littermates that segregate only for the four-gene region, we show that the QTL has a significant effect on the circulating levels of IGFBP-5 and IGFBP-3 (the latter subject to limited degradation by PAPPA2), but not on levels of IGFBP-2 and IGFBP-4, which are not cleaved by PAPPA2. There are 14 nonsynonymous SNPs among QTL alleles, which may affect the activity of the translated protein. The refinement of the target region to four genes and the finding that the QTL affects IGFBP-5 levels suggest that PAPPA2 may be involved with normal postnatal growth. Our mapping results also illustrate the potentially fractal nature of QTL: as we mapped our QTL with increasing resolution, what appeared to be a single QTL resolved into three closely linked QTL (previous work), and then one of these was further dissected into two in this study.

Animals↗

Quantitative trait locus mapping of airway responsiveness to chromosomes 6 and 7 in inbred mice.

Quantitative trait locus (QTL) mapping was used to identify chromosomal regions contributing to airway hyperresponsiveness in mice. Airway responsiveness to methacholine was measured in A/J and C3H/HeJ parental strains as well as in progeny derived from crosses between these strains. QTL mapping of backcross [(A/J x C3H/HeJ) x C3H/HeJ] progeny (n = 137-227 informative mice for markers tested) revealed two significant linkages to loci on chromosomes 6 and 7. The QTL on chromosome 6 confirms the previous report by others of a linkage in this region in the same genetic backgrounds; the second QTL, on chromosome 7, represents a novel locus. In addition, we obtained suggestive evidence for linkage (logarithm of odds ratio = 1.7) on chromosome 17, which lies in the same region previously identified in a cross between A/J and C57BL/6J mice. Airway responsiveness in a cross between A/J and C3H/HeJ mice is under the control of at least two major genetic loci, with evidence for a third locus that has been previously implicated in an A/J and C57BL/6J cross; this indicates that multiple genetic factors control the expression of this phenotype.

Animals↗

Fine mapping of Lvm1: a quantitative trait locus controlling heart size independently of blood pressure.

We have previously reported a quantitative trait locus (QTL) on rat chromosome 2 that influences heart size independently of blood pressure (Left Ventricular Mass Locus 1; Lvm1). The recent release of the rat genome sequence allowed us to retest and refine this relatively broad QTL with a view to identifying within it candidate genes worthy of structural investigation. We sought to achieve this 'fine mapping' by increasing the marker density within the interval and undertaking a linkage analysis in a previously defined population of F2 hybrids generated from inbred spontaneously hypertensive rats (SHR) of the Okamoto strain and Fischer rat (F344) progenitors. We were able to reconfirm and resolve Lvm1 from its original width of approximately 45 to 15 cM. By reference to the ENSEBL rat genome data bank, we identified within Lvm1 27 known genes, 109 predicted genes and 7 pseudogenes. Of the known genes, candidates include potential regulators of cardiac growth, a sodium channel and calcium channel as well as the fibroblast growth factor 2 gene. Located nearby the Lvm1 locus was the gene for the angiotensin Type 1B receptor. Given the evidence that the ligand for the angiotensin Type 1B receptor-angiotensin II-is a potent cardiotroph, we also consider this gene a potential candidate. The identification of the precise allelic variant(s) within Lvm1 involved in the control of pressure-independent cardiac growth awaits further molecular studies.

Animals↗

Characterization of a major X-linked quantitative trait locus influencing body weight of mice.

Growth rate in mice is an archetypal quantitative trait that has long been studied genetically, physiologically, and metabolically, but its genetic basis is still poorly understood due to its complex inheritance and the influence of environment. We measured differences in 17 growth-related traits between a pair of partially congenic lines that differ for a segment of the X chromosome containing a quantitative trait locus (QTL) that we identified in a genomewide QTL scan. The QTL has a large effect on mean body weight of approximately 20% at all ages, and affects early growth rate to a greater extent than late growth rate. Feed is converted to body mass more efficiently in the high chromosome segment-bearing line than the low line. The weights of various internal organs are affected to a somewhat greater extent by the QTL than body weight. The proportional change in body length is smaller than body weight, but this may be an effect of scale. Body weight at late ages appears to allow the most efficient detection of allelic differences at the QTL, although assignment of genotypic state based on phenotype is never unambiguous.

Animals↗

Association of hypertension with single nucleotide polymorphisms in the quantitative trait locus for abdominal obesity-metabolic syndrome on chromosome 17.

Genome scan in Chinese revealed an association of blood pressure with the microsatellite marker D17S1303, which lies in a quantitative trait locus for the abdominal obesity-metabolic syndrome (AOMS2) at 17p12 on chromosome 17. We previously reported that D17S1303 was associated with hypertension and obesity. Therefore, we studied 10 single nucleotide polymorphisms (SNP) within 3 kb of D17S1303. One hundred and eighty hypertensive subjects (91 men, 89 women, age 53+/-12 years) and 180 normotensive matched controls (91 men, 89 women, age 52+/-11) were genotyped using the Sequenom genotyping platform. Allelic frequencies in these Chinese subjects differed from those reported for Caucasians. Three SNPs (rs11656507, rs1357926, rs852319) were homozygous in our subjects. The genotype frequencies of rs852320, rs852321 and rs852322 did not differ between hypertensive and normotensive subjects. However, there were significant differences for rs1525402 (P=0.048), rs2692343 (P=0.022), rs2692344 (P=0.017) and rs2321313 (P=0.028). A four-locus haplotype comprising G at rs1525402, C at rs2692343, C at rs2692344 and G at rs2321313 was associated with lower systolic blood pressure (P=0.023) and normotension (P=0.048). Our results provide further evidence that there is a gene, as yet unidentified, influencing blood pressure in the vicinity of D17S1303 in a quantitative trait locus for abdominal obesity-metabolic syndrome at 17p12.

Chi-Square Distribution↗

The impact of marker allele frequency misspecification in variance components quantitative trait locus analysis using sibship data.

In cases where sibship data are collected for a quantitative trait locus (QTL) linkage study without access to parental genotypes, the proportion of genes shared identical by descent must be estimated using the marker allele frequencies. No systematic study has been conducted to date to evaluate the effect of misspecification of these frequencies on a test of quantitative trait linkage. Analysis of both simulated and actual data on quantitative traits was carried out under various sets of allele frequency estimates. While correctly specifying the allele frequency distribution led to a slightly more powerful test and higher lod scores, the differences were small and would not likely alter the conclusion of a study. These results suggest that, at least for QTL analysis, there is a great deal of tolerance for misspecifying marker allele frequencies with little, if any, appreciable effect on the linkage test. However, the observed variations may be sufficiently large to alter the priority on might give to a positive finding for follow up.

Alcoholism↗

Quantitative trait locus mapping of pyrethroid resistance in Colorado potato beetle, Leptinotarsa decemlineata (Say) (Coleoptera: Chrysomelidae).

Quantitative trait locus (QTL) mapping is a valuable new tool for locating genomic regions that underlie variation in important traits such as insecticide resistance. Because QTL mapping complements a candidate gene strategy for understanding the genetic architecture of important traits, it may also facilitate the identification of genes causing important variation. After mapping the QTL locations, markers closely linked to QTL can be used for genetic analysis of population structure and to measure the spread and increase of resistance-causing QTL alleles. In this study, QTL influencing resistance to the pyrethroid insecticide esfenvalerate were mapped in the Colorado potato beetle Leptinotarsa decemlineata (Say) (CPB). Three QTL contributing to esfenvalerate resistance were identified from a mapping population of 79 individuals analyzed at 90 marker loci. One QTL had a large effect and two QTL had smaller effects. The major QTL occurs on the X chromosome, overlapping the position of a candidate gene (Leptinotarsa decemlineata Voltage sensitive sodium channel [LdVssc1]) previously implicated in pyrethroid resistance. Resistance-increasing alleles at the two minor-effect QTL originated with the susceptible parent, suggesting that alleles of small effect may be segregating in susceptible populations. Comparison of the New York population from which the susceptible parent originated with a more-susceptible population from North Carolina suggests that the minor-effect loci identified here may explain some of the variation in tolerance observed among susceptible populations. DNA sequencing of a portion of LdVssc1 shows that the resistance-conferring allele from the resistant parent does not contain the kdr mutation previously found in CPB and typically observed in other insects that are resistant to pyrethroid insecticides because of changes in this gene.

Animals↗

Substitution mapping of dth1.1, a flowering-time quantitative trait locus (QTL) associated with transgressive variation in rice, reveals multiple sub-QTL.

A quantitative trait locus (QTL), dth1.1, was associated with transgressive variation for days to heading in an advanced backcross population derived from the Oryza sativa variety Jefferson and an accession of the wild rice relative Oryza rufipogon. A series of near-isogenic lines (NILs) containing different O. rufipogon introgressions across the target region were constructed to dissect dth1.1 using substitution mapping. In contrast to the late-flowering O. rufipogon parent, O. rufipogon alleles in the substitution lines caused early flowering under both short- and long-day lengths and provided evidence for at least two distinct sub-QTL: dth1.1a and dth1.1b. Potential candidate genes underlying these sub-QTL include genes with sequence similarity to Arabidopsis GI, FT, SOC1, and EMF1, and Pharbitis nil PNZIP. Evidence from families with nontarget O. rufipogon introgressions in combination with dth1.1 alleles also detected an early flowering QTL on chromosome 4 and a late-flowering QTL on chromosome 6 and provided evidence for additional sub-QTL in the dth1.1 region. The availability of a series of near-isogenic lines with alleles introgressed from a wild relative of rice provides an opportunity to better understand the molecular basis of transgressive variation in a quantitative trait.

Alleles↗

Localization of a quantitative trait locus via a Bayesian approach.

A Bayesian approach to the direct mapping of a quantitative trait locus (QTL), fully utilizing information from multiple linked gene markers, is presented in this paper. The joint posterior distribution (a mixture distribution modeling the linkage between a biallelic QTL and N gene markers) is computationally challenging and invites exploration via Markov chain Monte Carlo methods. The parameter's complete marginal posterior densities are obtained, allowing a diverse range of inferences. Parameters estimated include the QTL genotype probabilities for the sires and the offspring, the allele frequencies for the QTL, and the position and additive and dominance effects of the QTL. The methodology is applied through simulation to a half-sib design to form an outbred pedigree structure where there is an entire class of missing information. The capacity of the technique to accurately estimate parameters is examined for a range of scenarios.

Algorithms↗