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At least 865 records · Page 48Linked to original sources
PubMed2000

Quantitative trait loci affecting components of wing shape in Drosophila melanogaster.

Two composite multiple regression-interval mapping analyses were performed to identify candidate quantitative trait loci (QTL) affecting components of wing shape in Drosophila melanogaster defined by eight relative warp-based measures. A recombinant inbred line design was used to map QTL for the shape of two intervein regions in the anterior compartment of the wing, using a high resolution map of retrotransposon insertion sites between Oregon-R and Russian 2b. A total of 35 QTL representing up to 23 different loci were identified, many of which are located near components of the epidermal growth factor-Ras signal transduction pathway that regulates vein vs. intervein decision making and vein placement. Over one-half of the loci were detected in both sexes, and just under one-half were detected at two different growth temperatures. Different loci were found to affect aspects of shape in each intervein region, confirming that the shape of the whole wing should be regarded as a compound trait composed of several developmental units. In addition, a reciprocal backcross design was used to map QTL affecting shape in the posterior compartment of the wings of 831 flies, using a molecular map of 16 allele-specific oligohybridization single nucleotide polymorphism (SNP) markers between two divergent inbred lines. A total of 13 QTL were detected and shown to have generally additive effects on separable components of shape, in both sexes. By contrast, 8 QTL that affected wing size in these backcrosses were nearly dominant in their effects. The results confirm at the genetic level that wing shape is regulated independent of wing size and set up the hypothesis that wing shape is regulated in part through the regulation of the length and positioning of wing veins, involving quantitative regulation of the activity of secreted growth factors.

Animals↗
PubMed1997

Mapping quantitative trait loci for open-field behavior in mice.

By performing a whole genome screen in an F2 intercross of two strains of mice (A/J and C57BL/6J), which differ markedly in their behavioral response to a brightly lit open field (O-F), we have mapped several quantitative trait loci (QTL) for this complex behavioral phenotype. QTL on chromosomes 1 and 10 were identified that affect both initial ambulation in the O-F (initial "response to novelty" ambulation) (lod of 7.1 and 8.8, respectively) and vertical rearings (lod of 4.5 and 8.5, respectively). For habituated O-F behavior, QTL were identified on chromosomes 3 and 10 for ambulation (lod of 4.1 and 14.7, respectively) and on chromosomes 1, 10, and 19 for vertical rearings (lod of 5.8, 6.0, and 4.7, respectively). The QTL on chromosome 1 (near D1Mit116; 101 cM) was specific for initial O-F ambulation behavior, whereas the QTL on chromosome 10 (near D10Mit237; 74 cM) affected both initial and habituated rearing behavior. Additional suggestive QTL (lod, > 2.8) were mapped to chromosomes 1, 8, 11, 15, and 19. The QTL on chromosomes 1, 10, and 19 individually explain from 3.2 to 12.7%. Collectively, the multiple independent QTL explain from 16.3 to 24.1% of the F2 population's phenotypic variance, depending on the trait. These identified QTL should prove useful for dissecting the genetic and behavioral dimensions of O-F behavior, fostering an understanding of individual differences.

Animals↗
PubMed2006

Bayesian method for gene detection and mapping, using a case and control design and DNA pooling.

Association mapping studies aim to determine the genetic basis of a trait. A common experimental design uses a sample of unrelated individuals classified into 2 groups, for example cases and controls. If the trait has a complex genetic basis, consisting of many quantitative trait loci (QTLs), each group needs to be large. Each group must be genotyped at marker loci covering the region of interest; for dense coverage of a large candidate region, or a whole-genome scan, the number of markers will be very large. The total amount of genotyping required for such a study is formidable. A laboratory effort efficient technique called DNA pooling could reduce the amount of genotyping required, but the data generated are less informative and require novel methods for efficient analysis. In this paper, a Bayesian statistical analysis of the classic model of McPeek and Strahs is proposed. In contrast to previous work on this model, I assume that data are collected using DNA pooling, so individual genotypes are not directly observed, and also account for experimental errors. A complete analysis can be performed using analytical integration, a propagation algorithm for a hidden Markov model, and quadrature. The method developed here is both statistically and computationally efficient. It allows simultaneous detection and mapping of a QTL, in a large-scale association mapping study, using data from pooled DNA. The method is shown to perform well on data sets simulated under a realistic coalescent-with-recombination model, and is shown to outperform classical single-point methods. The method is illustrated on data consisting of 27 markers in an 880-kb region around the CYP2D6 gene.

Alleles↗
PubMed2000

Fine mapping of quantitative trait loci using linkage disequilibria with closely linked marker loci.

A multimarker linkage disequilibrium mapping method was developed for the fine mapping of quantitative trait loci (QTL) using a dense marker map. The method compares the expected covariances between haplotype effects given a postulated QTL position to the covariances that are found in the data. The expected covariances between the haplotype effects are proportional to the probability that the QTL position is identical by descent (IBD) given the marker haplotype information, which is calculated using the genedropping method. Simulation results showed that a QTL was correctly positioned within a region of 3, 1.5, or 0.75 cM in 70, 62, and 68%, respectively, of the replicates using markers spaced at intervals of 1, 0.5, and 0.25 cM, respectively. These results were rather insensitive to the number of generations since the QTL occurred and to the effective population size, except that 10 generations yielded rather poor estimates of the QTL position. The position estimates of this multimarker disequilibrium mapping method were more accurate than those from a single marker transmission disequilibrium test. A general approach for identifying QTL is suggested, where several stages of disequilibrium mapping are used with increasingly dense marker spacing.

Chromosome Mapping↗
PubMed2000

Quantitative trait loci for life span in Drosophila melanogaster: interactions with genetic background and larval density.

The genetic architecture of variation in adult life span was examined for a population of recombinant inbred lines, each of which had been crossed to both inbred parental strains from which the lines were derived, after emergence from both high and low larval density. QTL affecting life span were mapped within each sex and larval density treatment by linkage to highly polymorphic roo-transposable element markers, using a composite interval mapping method. We detected a total of six QTL affecting life span; the additive effects and degrees of dominance for all were highly sex- and larval environment-specific. There were significant epistatic interactions between five of the life span QTL, the effects of which also differed according to genetic background, sex, and larval density. Five additional QTL were identified that contributed to differences among lines in their sensitivity to variation in larval density. Further fine-scale mapping is necessary to determine whether candidate genes within the regions to which the QTL map are actually responsible for the observed variation in life span.

Analysis of Variance↗
PubMed2005

Polygenic mutation in Drosophila melanogaster: Mapping spontaneous mutations affecting sensory bristle number.

Our ability to predict long-term responses to artificial and natural selection, and understand the mechanisms by which naturally occurring variation for quantitative traits is maintained, depends on detailed knowledge of the properties of spontaneous polygenic mutations, including the quantitative trait loci (QTL) at which mutations occur, mutation rates, and mutational effects. These parameters can be estimated by mapping QTL that cause divergence between mutation-accumulation lines that have been established from an inbred base population and selected for high and low trait values. Here, we have utilized quantitative complementation to deficiencies to map QTL at which spontaneous mutations affecting Drosophila abdominal and sternopleural bristle number have occurred in 11 replicate lines during 206 generations of divergent selection. Estimates of the numbers of mutations were consistent with diploid per-character mutation rates for bristle traits of 0.03. The ratio of the per-character mutation rate to total mutation rate (0.023) implies that >2% of the genome could affect just one bristle trait and that there must be extensive pleiotropy for quantitative phenotypes. The estimated mutational effects were not, however, additive and exhibited dependency on genetic background consistent with diminishing epistasis. However, these inferences must be tempered by the potential for epistatic interactions between spontaneous mutations and QTL affecting bristle number on the deficiency-bearing chromosomes, which could lead to overestimates in numbers of QTL and inaccurate inference of gene action.

Animals↗
PubMed2003

Primary factors affecting the evaluation accuracy of animal model MBLUP.

Marker-assisted best linear unbiased prediction (MBLUP) is one important method of implementing marker-assisted selection (MAS). The computer stochastic simulation method was used to study the effects of heritability of trait, QTL variance and map distance between two adjacent markers on the evaluation accuracy of animal model MBLUP. With the higher heritability, the smaller QTL variance and the shorter map distance between two adjacent markers, the evaluation accuracy of animal model MBLUP would be increased. On the contrary, the evaluation accuracy of animal model MBLUP would be decreased when the heritability was lower, QTL variance was larger and map distance between two adjacent markers was longer.

Animals↗
PubMed2006

Integrating genetic and gene expression data: application to cardiovascular and metabolic traits in mice.

The millions of common DNA variations that occur in the human population, or among inbred strains of mice and rats, perturb the expression (transcript levels) of a large fraction of the genes expressed in a particular tissue. The hundreds or thousands of common cis-acting variations that occur in the population may in turn affect the expression of thousands of other genes by affecting transcription factors, signaling molecules, RNA processing, and other processes that act in trans. The levels of transcripts are conveniently quantitated using expression arrays, and the cis- and trans-acting loci can be mapped using quantitative trait locus (QTL) analysis, in the same manner as loci for physiologic or clinical traits. Thousands of such expression QTL (eQTL) have been mapped in various crosses in mice, as well as other experimental organisms, and less detailed maps have been produced in studies of cells from human pedigrees. Such an integrative genetics approach (sometimes referred to as "genetical genomics") is proving useful for identifying genes and pathways that contribute to complex clinical traits. The coincidence of clinical trait QTL and eQTL can help in the prioritization of positional candidate genes. More importantly, mathematical modeling of correlations between levels of transcripts and clinical traits in genetic crosses can allow prediction of causal interactions and the identification of "key driver" genes. An important objective of such studies will be to model biological networks in physiologic processes. When combined with high-density single nucleotide polymorphism (SNP) mapping, it should be feasible to identify genes that contribute to transcript levels using association analysis in outbred populations. In this review we discuss the basic concepts and applications of this integrative genomic approach to cardiovascular and metabolic diseases.

Animals↗
PubMed2005

A rice quantitative trait locus for salt tolerance encodes a sodium transporter.

Many important agronomic traits in crop plants, including stress tolerance, are complex traits controlled by quantitative trait loci (QTLs). Isolation of these QTLs holds great promise to improve world agriculture but is a challenging task. We previously mapped a rice QTL, SKC1, that maintained K(+) homeostasis in the salt-tolerant variety under salt stress, consistent with the earlier finding that K(+) homeostasis is important in salt tolerance. To understand the molecular basis of this QTL, we isolated the SKC1 gene by map-based cloning and found that it encoded a member of HKT-type transporters. SKC1 is preferentially expressed in the parenchyma cells surrounding the xylem vessels. Voltage-clamp analysis showed that SKC1 protein functions as a Na(+)-selective transporter. Physiological analysis suggested that SKC1 is involved in regulating K(+)/Na(+) homeostasis under salt stress, providing a potential tool for improving salt tolerance in crops.

Base Sequence↗
PubMed2004

Mouse inbred strain sequence information and yin-yang crosses for quantitative trait locus fine mapping.

The shared ancestry of mouse inbred strains, together with the availability of sequence and phenotype information, is a resource that can be used to map quantitative trait loci (QTL). The difficulty in using only sequence information lies in the fact that in most instances the allelic state of the QTL cannot be unambiguously determined in a given strain. To overcome this difficulty, the performance of multiple crosses between various inbred strains has been proposed. Here we suggest and evaluate a general approach, which consists of crossing the two strains used initially to map the QTL and any new strain. We have termed these crosses "yin-yang," because they are complementary in nature as shown by the fact that the QTL will necessarily segregate in only one of the crosses. We used the publicly available SNP database of chromosome 16 to evaluate the mapping resolution achievable through this approach. Although on average the improvement of mapping resolution using only four inbred strains was relatively small (i.e., reduction of the QTL-containing interval by half at most), we found a great degree of variability among different regions of chromosome 16 with regard to mapping resolution. This suggests that with a large number of strains in hand, selecting a small number of strains may provide a significant contribution to the fine mapping of QTL.

Alleles↗
PubMed2002

Quantitative trait loci associated with leaf and neck blast resistance in recombinant inbred line population of rice (Oryza sativa).

Blast is an economically important disease of rice. To map genes controlling blast resistance, recombinant inbred lines (RIL) were developed from Khao Dawk Mali 105, an aromatic, blast-susceptible cultivar and the blast resistance donor, CT 9993-5-10-M (CT). A linkage map encompassing 2112 cM was constructed from 141 RILs using 90 restriction fragment length polymorphisms (RFLPs) and 31 simple sequence repeats (SSR). Virulent isolates of blast fungus were identified by screening differential host sets against 87 single-spore isolates collected from the north and northeast of Thailand. Fifteen virulent blast isolates were selected for leaf blast screening. Neck blast was evaluated both under natural conditions and controlled inoculations. Quantitative trait loci (QTLs) for broad resistance spectrum (BRS) to leaf blast were located on chromosomes 7 and 9. In particular, the QTL(ch9) was mapped near the Pi5(t) locus. The QTL(ch7) was located close to a previously mapped partial resistance QTL. Both loci showed significant allelic interaction. Genotypes having CT alleles at both QTL(ch7) and QTL(ch9) were the most resistant. Two neck-blast QTLs were mapped on chromosomes 5 and 6. The inconsistent map locations between the leaf and neck blast QTLs indicate the complexity of fixing both leaf and neck blast resistance. The coincidence of BRS and field resistance QTLs on chromosome 7 supports the idea that BRS may reflect the broad resistance spectrum to leaf blast in rice. These findings laid the foundation for the development of a marker-assisted scheme for improving Khoa Dawk Mali 105 and the majority of aromatic Thai rice varieties that are susceptible to blast.

Chromosome Mapping↗
PubMedDate not supplied

Statistical power for detecting epistasis QTL effects under the F-2 design.

Epistasis refers to gene interaction effect involving two or more genes. Statistical methods for mapping quantitative trait loci (QTL) with epistasis effects have become available recently. However, little is known about the statistical power and sample size requirements for mapping epistatic QTL using genetic markers. In this study, we developed analytical formulae to calculate the statistical power and sample requirement for detecting each epistasis effect under the F-2 design based on crossing inbred lines. Assuming two unlinked interactive QTL and the same absolute value for all epistasis effects, the heritability of additive x additive (a x a) effect is twice as large as that of additive x dominance (a x d) or dominance x additive (d x a) effect, and is four times as large as that of dominance x dominance (d x d) effect. Consequently, among the four types of epistasis effects involving two loci, ;a x a' effect is the easiest to detect whereas ;d x d' effect is the most difficult to detect. The statistical power for detecting ;a x a' effect is similar to that for detecting dominance effect of a single QTL. The sample size requirements for detecting ;a x d', ;d x a' and ;d x d' are highly sensitive to increased distance between the markers and the interacting QTLs. Therefore, using dense marker coverage is critical to detecting those effects.

Chromosome Mapping↗
PubMed2006

Identification of QTLs associated with citrus resistance to Phytophthora gummosis.

Citrus gummosis, caused by Phytophthora spp., is an important citrus disease in Brazil. Almost all citrus rootstock varieties are susceptible to it to some degree, whereas resistance is present in Poncirus trifoliata, a closely related species. The objective of this study was to detect QTLs linked to citrus Phytophthora gummosis resistance. Eighty individuals of the F1 progeny, obtained by controlled crosses between Sunki mandarin Citrus sunki (susceptible) and Poncirus trifoliata cv. Rubidoux (resistant), were evaluated. Resistance to Phytophthora parasitica was evaluated by inoculating stems of young plants with a disc of fungal mycelia and measuring lesion lengths a month later. Two QTLs linked to gummosis resistance were detected in linkage groups 1 and 5 of the P. trifoliata map, and one QTL in linkage group 2 of the C. sunki map. The phenotypic variation explained by individual QTLs was 14% for C. sunki and ranged from 16 to 24% for P. trifoliata. The low character heritability (h2 = 18.7%) and the detection of more than one QTL associated with citrus Phytophthora gummosis resistance showed that inheritance of the resistance is quantitative.

Chromosome Mapping↗
PubMed2005

Genetic mapping of maize stripe disease resistance from the Mascarene source.

Maize stripe virus (MStV) is a potentially threatening virus disease of maize in the tropics. We mapped quantitative trait loci (QTLs) controlling resistance to MStV in a maize population of 157 F(2:3) families derived from the cross between two maize lines, Rev81 (tropical resistant) and B73 (temperate susceptible). Resistance was evaluated under artificial inoculations in replicated screenhouse trials across different seasons in Réunion Island, France. Composite interval mapping was employed for QTL detection with a linkage map of 143 microsatellite markers. Heritability estimates across seasons were 0.96 and 0.90 for incidence and severity, respectively, demonstrating a high genotypic variability and a good control of the environment. Three regions on chromosomes 2L, 3 and 5, with major effects, and another region on chromosome 2S, with minor effects, provided resistance to MStV in Rev81. In individual seasons, the chr2L QTL explained 60-65% of the phenotypic variation for disease incidence and 21-42% for severity. The chr3 QTL, mainly associated with incidence and located near centromere, explained 42-57% of the phenotypic variation, whereas the chr5 QTL, mainly associated with severity, explained 26-53%. Overall, these QTLs explained 68-73% of the phenotypic variance for incidence and 50-59% for severity. The major QTLs on chr2 and 3 showed additive gene action and were found to be stable over time and across seasons. They also were found to be included in genomic regions with important clusters of resistance genes to diseases and pests. The major QTL on chr5 appeared to be partially dominant in favour of resistance. It was stable over time but showed highly significant QTL x season interactions. Possible implications of these QTLs in different mechanisms of resistance against the virus or the insect vector are discussed. The prospects for transferring these QTLs in susceptible maize cultivars and combining them with other resistances to virus diseases by conventional or marker-assisted breeding are promising.

Chromosome Mapping↗
PubMed2002

Multiple cross mapping (MCM) markedly improves the localization of a QTL for ethanol-induced activation.

This study examines the use of multiple cross mapping (MCM) to reduce the interval for an ethanol response QTL on mouse chromosome 1. The phenotype is the acute locomotor response to a 1.5-g/kg i.p. dose of ethanol. The MCM panel consisted of the six unique intercrosses that can be obtained from the C57BL/6J (B6), DBA/2J (D2), BALB/cJ (C) and LP/J (LP) inbred mouse strains (N > or = 600/cross). Ethanol response QTL were detected only with the B6xD2 and B6xC intercrosses. For both crosses, the D2 and C alleles were dominant and decreased ethanol response. The QTL information was used to develop an algorithm for sorting and editing the chromosome 1 Mit microsatellite marker set (http://www.jax.org). This process yielded a cluster of markers between 82 and 85cM (MGI). Evidence that the QTL was localized in or near this interval was obtained by the analysis of a sample (n = 550) of advanced cross heterogenous stock animals. In addition, it was observed that one of the BXD recombinant inbred strains (BXD-32) had a recombination in the interval of interest which produced the expected change in behavior. Overall, the data obtained suggest that the information available within existing genetic maps coupled with MCM data can be used to reduce the QTL interval. In addition, the MCM data set can be used to interrogate gene expression data to estimate which polymorphisms within the interval of interest are relevant to the QTL.

Animals↗
PubMed2005

A chicken linkage map based on microsatellite markers genotyped on a Japanese Large Game and White Leghorn cross.

A detailed linkage map is necessary for efficient detection of quantitative trait loci (QTL) in chicken resource populations. In this study, microsatellite markers isolated from a (CA)n-enriched library (designated as ABR Markers) were mapped using a population developed from a cross between Japanese Game and White Leghorn chickens. In total, 296 markers including 193 ABR, 43 MCW, 31 ADL, 22 LEI, 3 HUJ, 2 GCT, 1 UMA and 1 ROS were mapped by linkage to chicken chromosomes 1-14, 17-21, 23, 24, 26-28 and Z. In addition, five markers were assigned to the map based on the chicken draft genomic sequence, bringing the total number of markers on the map to 301. The resulting linkage map will contribute to QTL mapping in chicken.

Animals↗
PubMed2006

QTL and candidate genes for fecundity in sows.

Fecundity in pigs is a trait of major economic interest but low heritability. For the improvement of fecundity, genetic markers for selection are desirable and therefore, several searches for genetic variation influencing fecundity have been performed. The aim of this review is to compare and to evaluate all published QTL analyses and candidate gene approaches concerning reproductive traits in sows. For this purpose, we present a comprehensive cytogenetic map comprising 54 QTL and 11 candidate genes with influence on reproductive traits. The evaluation and comparison of the results showed similarities, but also marked differences among studies. Reasons for different results are multicausal and are due to differences between resource populations, number of evaluated animals, mating systems, measured phenotypical traits and environmental influences. We could show that chromosome 8 and to a lower extend chromosome 7 are the most important chromosomes with regard to reproductive traits in pigs. For further research, fine mapping of the identified QTL regions is necessary in order to confirm and to narrow the most likely chromosomal intervals. Although difficult to perform, an advance would be a standardization of the experimental setup in particular, in respect to the collection of phenotypic data. Furthermore, we suggest to publish the information on further identified QTL and candidate genes as comprehensive and accurate as possible in order to allow a more transparent comparison and collation of the results.

Animals↗
PubMed2006

Quantitative trait loci for thermotolerance phenotypes in Drosophila melanogaster.

For insects, temperature is a major environmental variable that can influence an individual's behavioral activities and fitness. Drosophila melanogaster is a cosmopolitan species that has had great success in adapting to and colonizing diverse thermal niches. This adaptation and colonization has resulted in complex patterns of genetic variation in thermotolerance phenotypes in nature. Although extensive work has been conducted documenting patterns of genetic variation, substantially less is known about the genomic regions or genes that underlie this ecologically and evolutionarily important genetic variation. To begin to understand and identify the genes controlling thermotolerance phenotypes, we have used a mapping population of recombinant inbred (RI) lines to map quantitative trait loci (QTL) that affect variation in both heat- and cold-stress resistance. The mapping population was derived from a cross between two lines of D. melanogaster (Oregon-R and 2b) that were not selected for thermotolerance phenotypes, but exhibit significant genetic divergence for both phenotypes. Using a design in which each RI line was backcrossed to both parental lines, we mapped seven QTL affecting thermotolerance on the second and third chromosomes. Three of the QTL influence cold-stress resistance and four affect heat-stress resistance. Most of the QTL were trait or sex specific, suggesting that overlapping but generally unique genetic architectures underlie resistance to low- and high-temperature extremes. Each QTL explained between 5 and 14% of the genetic variance among lines, and degrees of dominance ranged from completely additive to partial dominance. Potential thermotolerance candidate loci contained within our QTL regions are identified and discussed.

Adaptation, Physiological↗