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Genetic control of X chromosome inactivation in mice: definition of the Xce candidate interval.

In early mammalian development, one of the two X chromosomes is silenced in each female cell as a result of X chromosome inactivation, the mammalian dosage compensation mechanism. In the mouse epiblast, the choice of which chromosome is inactivated is essentially random, but can be biased by alleles at the X-linked X controlling element (Xce). Although this locus was first described nearly four decades ago, the identity and precise genomic localization of Xce remains elusive. Within the X inactivation center region of the X chromosome, previous linkage disequilibrium studies comparing strains of known Xce genotypes have suggested that Xce is physically distinct from Xist, although this has not yet been established by genetic mapping or progeny testing. In this report, we used quantitative trait locus (QTL) mapping strategies to define the minimal Xce candidate interval. Subsequent analysis of recombinant chromosomes allowed for the establishment of a maximum 1.85-Mb candidate region for the Xce locus. Finally, we use QTL approaches in an effort to identify additional modifiers of the X chromosome choice, as we have previously demonstrated that choice in Xce heterozygous females is significantly influenced by genetic variation present on autosomes (Chadwick and Willard 2005). We did not identify any autosomal loci with significant associations and thus show conclusively that Xce is the only major locus to influence X inactivation patterns in the crosses analyzed. This study provides a foundation for future analyses into the genetic control of X chromosome inactivation and defines a 1.85-Mb interval encompassing all the major elements of the Xce locus.

Alleles↗

Validation of quantitative trait loci for Ascochyta blight resistance in pea ( Pisum sativum L.), using populations from two crosses.

Resistance to Ascochyta blight of pea was genetically characterized by mapping quantitative trait loci (QTLs) using two crosses, 3147-A26 (A26, partially resistant) x cultivar Rovar (susceptible) and 3148-A88 (A88, partially resistant) x Rovar, with the aim of developing an increased understanding of the genetics of resistance and of identifying linked molecular markers that may be used to develop resistant germplasm. Molecular linkage maps for both crosses were aligned so that the results of QTL mapping could be compared. Ascochyta blight disease severity in response to natural epidemics was measured in field trials conducted in Western Australia and New Zealand. Eleven putative QTLs for Ascochyta blight resistance were identified from the A26 x Rovar population and 14 putative QTLs from the A88 x Rovar population. Six QTLs were associated with the same genomic regions in both populations. These QTLs reside on linkage groups II, III, IV, V, and VII (two QTLs). The severity of Ascochyta blight disease symptoms on pea increases during field epidemics as plants mature; therefore, QTLs for plant reproductive maturity were mapped. Six QTLs were detected for plant maturity in the A26 x Rovar population, while five plant maturity QTLs were mapped in the A88 x Rovar population. QTLs for plant maturity coincide with Ascochyta blight resistance QTLs in four genomic regions, on linkage groups II (two regions), III, and V. The plant maturity and Ascochyta blight resistance QTLs on III were linked in repulsion phase. Therefore, the coincidence of these QTLs may be explained by linkage of distinct loci for the two traits. The QTLs on linkage groups II and V were linked in coupling phase; therefore, linked QTLs for resistance and maturity may be present in these regions, or the Ascochyta blight resistance QTLs detected in these regions are the result of pleiotropic effects of plant-maturity genetic loci.

Ascomycota↗

[Progress on the sheep genome project].

During the last few years,advances in livestock genome projects have been remarkable. Species-specific genetic maps exist for pig, chicken, cattle, sheep, horse,and deer with marker intervals of 5 to 20 cM. These maps have been essential for the identification of genes and genetic markers associated with importantly economic traits in livestock. In this paper, advances of gene map, comparative map, the genes for importantly economic traits and quantitative trait loci (QTL) mapping were briefly introduced in sheep.

Animals↗

Molecular mapping of a novel gene, Grh5, conferring resistance to green rice leafhopper (Nephotettix cincticeps Uhler) in rice, Oryza sativa L.

The green rice leafhopper (GRH), Nephotettix cincticeps Uhler, is one of the most serious insect pests affecting cultivated rice (Oryza sativa L.) in temperate regions of East Asia. An accession of the wild rice species, Oryza rufipogon Griff. (W1962), was found to be highly resistant to GRH by an antibiosis test. To understand the genetic basis of the GRH resistance, a BC1F1 population derived from a cross between a susceptible Japonica variety, Taichung 65 (T65), and a highly resistant accession W1962 was analyzed by quantitative trait loci (QTL) mapping. A single major QTL for GRH resistance was detected on rice chromosome 8. A nearly isogenic population containing segments of the targeted QTL region derived from W1962 was then developed through advanced backcrossing with marker-assisted selection. Further molecular mapping using a BC4F2 population revealed that a new resistance gene, designated as Green rice leafhopper resistance 5 (Grh5), was located on the distal region of the long arm of chromosome 8 and tightly linked to the simple sequence repeat markers RM3754 and RM3761. A nearly isogenic line (NIL) carrying Grh5 was subsequently developed in the progeny of the mapping population. The resistance level of Grh5-NIL was compared with those of developed NILs for GRH resistance and was found to have the highest resistance. The DNA markers found to be closely linked to Grh5 would be useful for marker-assisted selection for the improvement of resistance to GRH in rice.

Animals↗

General formulas for obtaining the MLEs and the asymptotic variance-covariance matrix in mapping quantitative trait loci when using the EM algorithm.

We present in this paper general formulas for deriving the maximum likelihood estimates and the asymptotic variance-covariance matrix of the positions and effects of quantitative trait loci (QTLs) in a finite normal mixture model when the EM algorithm is used for mapping QTLs. The general formulas are based on two matrices D and Q, where D is the genetic design matrix, characterizing the genetic effects of the QTLs, and Q is the conditional probability matrix of QTL genotypes given flanking marker genotypes, containing the information on QTL positions. With the general formulas, it is relatively easy to extend QTL mapping analysis to using multiple marker intervals simultaneously for mapping multiple QTLs, for analyzing QTL epistasis, and for estimating the heritability of quantitative traits. Simulations were performed to evaluate the performance of the estimates of the asymptotic variances of QTL positions and effects.

Algorithms↗

Biased estimators of quantitative trait locus heritability and location in interval mapping.

In many empirical studies, it has been observed that genome scans yield biased estimates of heritability, as well as genetic effects. It is widely accepted that quantitative trait locus (QTL) mapping is a model selection procedure, and that the overestimation of genetic effects is the result of using the same data for model selection as estimation of parameters. There are two key steps in QTL modeling, each of which biases the estimation of genetic effects. First, test procedures are employed to select the regions of the genome for which there is significant evidence for the presence of QTL. Second, and most important for this demonstration, estimates of the genetic effects are reported only at the locations for which the evidence is maximal. We demonstrate that even when we know there is just one QTL present (ignoring the testing bias), and we use interval mapping to estimate its location and effect, the estimator of the effect will be biased. As evidence, we present results of simulations investigating the relative importance of the two sources of bias and the dependence of bias of heritability estimators on the true QTL heritability, sample size, and the length of the investigated part of the genome. Moreover, we present results of simulations demonstrating the skewness of the distribution of estimators of QTL locations and the resulting bias in estimation of location. We use computer simulations to investigate the dependence of this bias on the true QTL location, heritability, and the sample size.

Bias↗

Quantitative trait loci differentiating the outbreeding Mimulus guttatus from the inbreeding M. platycalyx.

Theoretical predictions about the evolution of selfing depend on the genetic architecture of loci controlling selfing (monogenic vs. polygenic determination, large vs. small effect of alleles, dominance vs. recessiveness), and studies of such architecture are lacking. We inferred the genetic basis of mating system differences between the outbreeding Mimulus guttatus and the inbreeding M. platycalyx by quantitative trait locus (QTL) mapping using random amplified polymorphic DNA and isozyme markers. One to three QTL were detected for each of five mating system characters, and each QTL explained 7.6-28.6% of the phenotypic variance. Taken together, QTL accounted for up to 38% of the variation in mating system characters, and a large proportion of variation was unaccounted for. Inferred QTL often affected more than one trait, contributing to the genetic correlation between those traits. These results are consistent with the hypothesis that quantitative variation in plant mating system characters is primarily controlled by loci with small effect.

Chromosome Mapping↗

Haplotype-specific expression of a terpene synthase underlies linalool variation in the grapevine cultivar Riesling.

Grapevine cultivars vary widely in monoterpenoid content, yet the genetic and regulatory mechanisms underlying this variation remain poorly characterized beyond highly aromatic Muscat types. We profiled free volatiles and monoterpenoid glycosides in a Riesling × Cabernet Sauvignon F1 mapping population, revealing extensive variation and transgressive segregation consistent with multigenic control. QTL mapping identified 70 significant loci associated with 48 volatile compounds and monoterpene glycosides, including two major QTLs explaining 33.6% and 33.4% of phenotypic variance in (3S)-linalool accumulation. Integration of haplotype-resolved transcriptomics with metabolite data, enabled by a chromosome-scale diploid Riesling genome assembly, resolved a (3S)-linalool/nerolidol synthase cluster on chromosome 10 and identified VviTPS54 as the strongest candidate underlying linalool variation. VviTPS54 exhibited haplotype-specific expression strongly correlated with (3S)-linalool accumulation across genotypes, while no QTL was detected at the 1-deoxy-D-xylulose-5-phosphate synthase 1 (VviDXS1) locus previously identified in Muscat cultivars. In addition, VviDXS1 expression was not correlated with terpene levels, indicating that regulatory variation within terpene synthase clusters, rather than methylerythritol phosphate (MEP) pathway flux, drives monoterpenoid composition in this population. These results establish regulatory variation of terpene synthases as a key mechanism underlying monoterpenoid diversity in grapevine and demonstrate that resolving such variation requires haplotype-phased genome assemblies coupled with haplotype-resolved transcriptomics to detect allele-specific expression differences at complex, heterozygous loci.

Grapevine↗

Quantitative trait loci mapping for dairy cattle production traits using a maximum likelihood method.

A maximum likelihood method was developed for QTL mapping in half-sib designs and compared to the regression method in analyses of both field and simulated data. The field data consisted of milk production evaluations of 433 progeny tested sons of 6 sires and 64 microsatellite markers distributed over 12 chromosomes. Based on permutation tests, 5 significant QTL were detected in the field data by the regression method compared with 10 by the maximum likelihood method (P < 0.05). In field data analysis, the maximum likelihood method detected more significant QTL and had a smaller residual variance than the regression method. The simulation included 9 scenarios differing in number of families, family size, QTL variance, and marker density, each replicated 100 times. The simulation results suggested that, as for the regression method, the precision of estimating QTL from the maximum likelihood method improves with increasing number of sons per sire, increasing the ratio of QTL to phenotypic variance, and decreasing marker interval. The maximum likelihood method had a smaller dispersion of estimated QTL positions than the regression method in 6 of 9 scenarios simulated. Overall, the maximum likelihood method shows potential advantage in QTL detection over the regression method, especially in the situations with less favorable conditions for QTL detection.

Algorithms↗

Identification and functional significance of polymorphisms in the mu-opioid receptor gene (Oprm) promoter of C57BL/6 and DBA/2 mice.

C57BL/6J and DBA/2J mice demonstrate differences in morphine preference when tested in a two-bottle choice paradigm. Quantitative trait loci (QTL) mapping suggested the proximal region of chromosome 10 was responsible for 41% of the observed genetic variance. The mu-opioid receptor (MOR) gene (Oprm) maps to this region and is a prime candidate for explaining the QTL. We hypothesized that variations in Oprm between these strains are responsible for differences in morphine preference. We identify five single nucleotide polymorphisms (SNPs) in the Oprm promoter; three within or near putative transcription factor binding sites. Promoter fragments were amplified from genomic DNA by polymerase chain reaction (PCR) and subcloned into luciferase reporter vectors. A significant difference in basal Oprm promoter activity was seen with C57BL/6 and DBA/2 approximately 1675 constructs in MOR-positive BE(2)-C cells, but not in MOR-negative Neuro-2a cells. In BE(2)-C cells, average DBA/2 approximately 1675 construct activity was 1.3-2.0x greater than average C57BL/6 activity suggesting that the SNPs might alter MOR expression in these two mouse strains. Significant differences in promoter activities between the two cell lines suggest that cell-type-specific transcription factors are involved. No significant differences in construct activity were found between untreated and morphine-treated BE(2)-C or Neuro-2a cells, suggesting that morphine does not regulate transcription of Oprm.

Animals↗

A genetic map in the Mimulus guttatus species complex reveals transmission ratio distortion due to heterospecific interactions.

As part of a study of the genetics of floral adaptation and speciation in the Mimulus guttatus species complex, we constructed a genetic linkage map of an interspecific cross between M. guttatus and M. nasutus. We genotyped an F(2) mapping population (N = 526) at 255 AFLP, microsatellite, and gene-based markers and derived a framework map through repeated rounds of ordering and marker elimination. The final framework map consists of 174 marker loci on 14 linkage groups with a total map length of 1780 cM Kosambi. Genome length estimates (2011-2096 cM) indicate that this map provides thorough coverage of the hybrid genome, an important consideration for QTL mapping. Nearly half of the markers in the full data set (49%) and on the framework map (48%) exhibited significant transmission ratio distortion (alpha = 0.05). We localized a minimum of 11 transmission ratio distorting loci (TRDLs) throughout the genome, 9 of which generate an excess of M. guttatus alleles and a deficit of M. nasutus alleles. This pattern indicates that the transmission ratio distortion results from particular interactions between the heterospecific genomes and suggests that substantial genetic divergence has occurred between these Mimulus species. We discuss possible causes of the unequal representation of parental genomes in the F(2) generation.

Alleles↗

Quantitative trait loci mapping for meat quality and muscle fiber traits in a Japanese wild boar x Large White intercross.

Three generations of a swine family produced by crossing a Japanese wild boar and three Large White female pigs were used to map QTL for various production traits. Here we report the results of QTL analyses for skeletal muscle fiber composition and meat quality traits based on phenotypic data of 353 F(2) animals and genotypic data of 225 markers covering almost the entire pig genome for all of the F(2) animals as well as their F(1) parents and F(0) grandparents. The results of a genome scan using least squares regression interval mapping provided evidence that QTL (<1% genome-wise error rate) affected the proportion of the number of type IIA muscle fibers on SSC2, the number of type IIB on SSC14, the relative area (RA) of type I on SSCX, the RA of type IIA on SSC6, the RA of type IIB on SSC6 and SSC14, the Minolta a* values of loin on SSC4 and SSC6, the Minolta b* value of loin on SSC15, and the hematin content of the LM on SSC6. Quantitative trait loci (<5% genome-wise error rate) were found for the number of type I on SSC1, SSC14, and SSCX, for the number of type IIA on SSC14, for the number of type IIB on SSC2, for the RA of type IIA on SSC2, for the Minolta b* value of loin on SSC3, for the pH of loin on SSC15, and for the i.m. fat content on SSC15. Twenty-four QTL were detected for 11 traits at the 5% genome-wise level. Some traits were associated with each other, so the 24 QTL were located on 11 genomic regions. In five QTL located on SSC2, SSC6, and SSC14, each wild boar allele had the effect of increasing types I and IIA muscle fibers and decreasing type IIB muscle fibers. These effects are expected to improve meat quality.

Animals↗

A new framework marker-based linkage map and SDPs for the rat HXB/BXH strain set.

A new contiguous genetic linkage map of the HXB/BXH set of rat recombinant inbred (RI) strains was constructed to enhance QTL mapping power and precision, and thereby make the RI strain set a better genomics resource. The HXB/BXH rat RI strains were developed from a cross between the hypertensive SHR/OlaIpcv and normotensive BN- Lx/Cub rat strains and have been shown useful for identifying quantitative trait loci (QTL) for a variety of cardiovascular, metabolic, and behavioral phenotypes. In the current analysis, the DNAs from 31 existing strains, 1 substrain, and 4 extinct strains were genotyped for a selection of polymorphic microsatellite marker loci, predominantly polymorphic framework markers from high-density integrated rat genome maps. The resulting linkage map consists of 245 microsatellite markers spanning a total length of 1789 cM with an average inter-marker distance of ~8.0 cM. This map covers the rat genome contiguously and completely with the exception of two locations on Chromosomes (Chrs) 11 and 16. The new genotypic information obtained also permitted further genetic characterization of the RI strain set including strain independence, genetic similarity among the individual strains, and non-syntenic associations between loci.

Alleles↗

Effect of within-strain sample size on QTL detection and mapping using recombinant inbred mouse strains.

Increasing the number of mice used to calculate recombinant inbred (RI) strain means increases the accuracy of determining the phenotype associated with each genotype (strain), which in turn enhances quantitative trait locus (QTL) detection and mapping. The purpose of this paper is to examine quantitatively the effect of within-strain sample size (n) on additive QTL mapping efficiency and to make comparisons with F2 and backcross (BC) populations where each genotype is represented by only a single mouse. When 25 RI strains are used, the estimated equivalent number of F2 mice yielding the same power to detect WTLs varies inversely as a function of the heritability of the trait in the RI population (hRI2). For example, testing 25 strains with n = 10 per strain is approximately equivalent to 160 F2 mice when hRI2 = 0.2, but only 55 when hRI2 = 0.6. While increasing n is always beneficial, the gain in power as n increases is greatest when hRI2 is low and is much diminished at high hRI2 values. Thus, hRI2 is high, there is little advantage of large n, even when n approaches infinity. A cost analysis suggested that RI populations are more cost-effective than conventional selectively genotyped F2 populations at hRI2 values likely to be seen in behavioral studies. However, with DNA pooling, this advantage is greatly reduced and may be reversed depending on the values of hRI2 and n.

Animals↗

A genetic linkage map for azuki bean [Vigna angularis (Willd.) Ohwi & Ohashi].

To make progress in genome analysis of azuki bean (Vigna angularis) a genetic linkage map was constructed from a backcross population of (V. nepalensis x V. angularis) x V.angularis consisting of 187 individuals. A total of 486 markers-205 simple sequence repeats (SSRs), 187 amplified fragment length polymorphisms (AFLPs) and 94 restriction fragment length polymorphisms (RFLPs) -were mapped onto 11 linkage groups corresponding to the haploid chromosome number of azuki bean. This map spans a total length of 832.1 cM with an average marker distance of 1.85 cM and is the most saturated map for a Vigna species to date. In addition, RFLP markers from other legumes facilitated finding several orthologous linkage groups based on previously published RFLP linkage maps. Most SSR primers that have been developed from SSR-enriched libraries detected a single locus. The SSR loci identified are distributed throughout the azuki bean genome. This moderately dense linkage map equipped with many SSR markers will be useful for mapping a range of useful traits such as those related to domestication and stress resistance. The mapping population will be used to develop advanced backcross lines for high resolution QTL mapping of these traits.

Chromosome Mapping↗

The genetic basis of emotional behaviour in mice.

The last decade has witnessed a steady expansion in the number of quantitative trait loci (QTL) mapped for complex phenotypes. However, despite this proliferation, the number of successfully cloned QTL has remained surprisingly low, and to a great extent limited to large effect loci. In this review, we follow the progress of one complex trait locus; a low magnitude moderator of murine emotionality identified some 10 years ago in a simple two-strain intercross, and successively resolved using a variety of crosses and fear-related phenotypes. These experiments have revealed a complex underlying genetic architecture, whereby genetic effects fractionate into several separable QTL with some evidence of phenotype specificity. Ultimately, we describe a method of assessing gene candidacy, and show that given sufficient access to genetic diversity and recombination, progression from QTL to gene can be achieved even for low magnitude genetic effects.

Animals↗

Durability of marker-quantitative trait loci haplotypes in structured populations.

Given the relative ease of identifying genetic markers linked to QTL (compared to finding the loci themselves), it is natural to ask whether linked markers can be used to address questions concerning the contemporary dynamics and recent history of the QTL. In particular, can a marker allele found associated with a QTL allele in a QTL mapping study be used to track population dynamics or the history of the QTL allele? For this strategy to succeed, the marker-QTL haplotype must persist in the face of recombination over the relevant time frame. Here we investigate the dynamics of marker-QTL haplotype frequencies under recombination, population structure, and divergent selection to assess the potential utility of linked markers for a population genetic study of QTL. For two scenarios, described as "secondary contact" and "novel allele," we use both deterministic and stochastic methods to describe the influence of gene flow between habitats, the strength of divergent selection, and the genetic distance between a marker and the QTL on the persistence of marker-QTL haplotypes. We find that for most reasonable values of selection on a locus (s < or = 0.5) and migration (m > 1%) between differentially selected populations, haplotypes of typically spaced markers (5 cM) and QTL do not persist long enough (>100 generations) to provide accurate inference of the allelic state at the QTL.

Alleles↗

Modulation of retinal cell populations and eye size in retinoic acid receptor knockout mice.

PURPOSE: The retinoic acid receptors are expressed from early stages of development in the diverse tissues that make up the vertebrate eye. Their loss has subtle effects on eye development. We adapted sensitive quantitative trait locus (QTL) mapping methods to assess consequences of inactivating alleles of the alpha and beta receptors, Rara and Rarb, on eye and retinal development. Rara is of particular interest because this gene is a candidate for Nnc1, a QTL that controls retinal ganglion cell proliferation. METHODS: We studied lines of mice in which expression of the a1 isoform of Rara or all isoforms of Rarb had been disrupted by gene targeting. We measured eye weight, lens weight, retinal area, and retinal ganglion cell number in each of six genotypes (Rara and Rarb -/-, +/-, +/+; 10-25 cases/genotype). RESULTS: Loss of either protein is associated with a small but significant loss of eye weight and retinal area. However, only the Rarb knockout has a significant effect on the ganglion cell population and the loss of both wildtype alleles leads to an 8,000 cell deficit. Surprisingly, loss of the Rara a1 isoform that is expressed in this cell population from early stages has no effect on number. Null alleles of both genes have little if any effect on lens growth. CONCLUSIONS: Despite its expression in embryonic retina, Rara is unlikely to be the Nnc1 QTL. In contrast, Rarb, a gene that maps to Chr 14 and which is not an Nnc1 candidate gene, has a significant effect on cell number and is therefore a QTL controlling this key population. This raises the intriguing possibility that normal allelic variants of Rarb modulate the ganglion cell population in other vertebrates, including humans.

Animals↗