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PubMedDate not supplied

Genotypic selection provides experimental confirmation for an alcohol consumption quantitative trait locus in mouse.

Quantitative genetic research has produced a wealth of basic information concerning genetic influence on alcohol-related processes. Recent developments in quantitative trait locus (QTL) methodology were promptly applied to the task of individuating polygenes affecting alcohol-related attributes in animal models and a body of reliable data is gradually coming into focus as a result of replication and convergence of evidence from a variety of methods. A key issue in QTL research is the need to distinguish true positive results from the false positive results that are inherent in analytical procedures requiring large numbers of significance tests. One school of thought holds that stringent significance levels should be imposed; another suggests more modest criteria for QTL nomination, with subsequent confirmation trials with independent samples. Recombinant inbred strains and various types of intercrosses have been used in correlational designs, both for nomination and confirmation studies. Alternative experimental procedures include knockout preparations and short-term phenotypic selective breeding. We present here results from a third experimental method-that of marker-based genotypic selection--in evaluation of two nominated QTLs for alcohol acceptance in mice.

Alcohol Drinking↗
PubMed2000

Alcohol-induced locomotor activation in C57BL/6J, A/J, and AXB/BXA recombinant inbred mice: strain distribution patterns and quantitative trait loci analysis.

RATIONALE: Quantitative trait loci (QTLs) for initial sensitivity to alcohol have been identified in a number of mouse strains (e.g. BXD); however, confirmation is required. OBJECTIVES: The present paper aimed to characterize the C57BL/6J, A/J, and AXB/BXA recombinant inbred (RI) strains of mice for basal and ethanol-induced locomotor activation as measured in an open field and to provide provisional location of QTLs for these phenotypes. METHODS: A/J and C57BL/6J mice were habituated to handling and then randomly assigned to receive one of four alcohol doses (0, 0.5, 1.0, 2.0 g/kg). Subsequently, all available strains of the AXB/BXA RI were tested with the 2 g/kg dose of ethanol or vehicle control. RESULTS: Simple regression and interval mapping were used initially to identify significant gene markers associated with ethanol-induced activation (calculated as total activity on alcohol day-total activity on saline day). Subsequently, composite interval mapping (CIM) was used to increase the accuracy in mapping individual loci. Genetic markers on chromosomes 2, 3, 8, 13, 16, 18 and 19 were associated with ethanol-induced activation. CONCLUSIONS: Three significant markers identified through CIM accounted for 86% of the genetic variance in the ethanol-induced activation. QTLs on chromosome 16 (45.6 cM) and 19 (24 cM) previously associated with alcohol consumption in the AXB/BXA RI mice were found to overlap with QTLs for ethanol-induced activation identified in the present study.

Animals↗
PubMed2006

Pooled analysis of data from multiple quantitative trait locus mapping populations.

Quantitative trait locus (QTL) analysis on pooled data from multiple populations (pooled analysis) provides a means for evaluating, as a whole, evidence for existence of a QTL from different studies and examining differences in gene effect of a QTL among different populations. Objectives of this study were to: (1) develop a method for pooled analysis and (2) conduct pooled analysis on data from two soybean mapping populations. Least square interval mapping was extended for pooled analysis by inclusion of populations and cofactor markers as indicator variables and covariate variables separately in the multiple linear models. The general linear test approach was applied for detecting a QTL. Single population-based and pooled analyses were conducted on data from two F(2:3) mapping populations, Hamilton (susceptible) x PI 90763 (resistant) and Magellan (susceptible) x PI 404198A (resistant), for resistance to soybean cyst nematode (SCN) in soybean. It was demonstrated that where a QTL was shared among populations, pooled analysis showed increased LOD values on the QTL candidate region over single population analyses. Where a QTL was not shared among populations, however, the pooled analysis showed decreased LOD values on the QTL candidate region over single population analyses. Pooled analysis on data from genetically similar populations may have higher power of QTL detection than single population-based analyses. QTLs were identified by pooled analysis on linkage groups (LGs) G, B1 and J for resistance to SCN race 2 whereas QTLs on LGs G, B1 and E for resistance to SCN race 5 in soybean PI 90763 and PI 404198A. QTLs on LG G and B1 were identified in both PI 90763 and PI 404198A whereas QTLs on LG E and J were identified in PI 90763 only. QTLs on LGs G and B1 for resistance to race 2 may be the same or closely linked with QTLs on LG G and B1 for resistance to race 5, respectively. It was further demonstrated that QTLs on G and B1 carried by PI 90763 were not significantly different in gene effect from QTLs on LGs G and B1 in PI 404198A, respectively.

Chromosome Mapping↗
PubMed2004

Detection of quantitative trait loci affecting milk production, health, and reproductive traits in Holstein cattle.

We report putative quantitative trait loci affecting female fertility and milk production traits using the merged data from two research groups that conducted independent genome scans in Dairy Bull DNA Repository grandsire families to identify quantitative trait loci (QTL) affecting economically important traits. Six families used by both groups had been genotyped for 367 microsatellite markers covering 2713.5 cM of the cattle genome (90%), with an average spacing of 7.4 cM. Phenotypic traits included PTA for pregnancy rate and daughter deviations for milk, protein and fat yields, protein and fat percentages, somatic cell score, and productive life. Analysis of the merged dataset identified putative quantitative trait loci that were not detected in the separate studies, and the pregnancy rate PTA estimates that recently became available allowed detection of pregnancy rate QTL for the first time. Sixty-one putative significant marker effects were identified within families, and 13 were identified across families. Highly significant effects were found on chromosome 3 affecting fat percentage and protein yield, on chromosome 6 affecting protein and fat percentages, on chromosome 14 affecting fat percentage, on chromosome 18 affecting pregnancy rate, and on chromosome 20 affecting protein percentage. Within-family analysis detected putative QTL associated with pregnancy rate on six chromosomes, with the effect on chromosome 18 being the most significant statistically. These findings may help identify the most useful markers available for QTL detection and, eventually, for marker-assisted selection for improvement of these economically important traits.

Animals↗
PubMed2005

A method for using incomplete triads to test maternally mediated genetic effects and parent-of-origin effects in relation to a quantitative trait.

The authors recently developed a semiparametric family-based test for linkage and association between markers and quantitative traits. This quantitative polytomous logistic regression test allows for analysis of families with incomplete information on parental genotype. In addition, it is not necessary to assume normality of the quantitative trait. Previous simulations have shown that the new test is as powerful as the other widely used tests for linkage disequilibrium in relation to a quantitative trait. Here the authors propose an extension to quantitative polytomous logistic regression that allows testing for maternally mediated effects and parent-of-origin effects in the same framework. Missing data on parental genotype are accommodated through an expectation-maximization algorithm approach. Simulations show robustness of the new tests and good power for detecting effects in the presence or absence of offspring effects. Methods are illustrated with birth weight and gestational length, two quantitative outcomes for which data were collected in a Montreal, Canada, study of intrauterine growth restriction between May 1998 and June 2000.

Computer Simulation↗
PubMedDate not supplied

Teaching a new dog old tricks: identifying quantitative trait loci using lessons from plants.

Locating quantitative trait loci (QTL) in mammalian systems has proven difficult due to the lack of genetic control and reproducibility, as well as the expense of maintaining sufficiently large populations for genotyping and phenotyping. In plants, populations of recombinant inbred lines (progeny bred to homozygosity from a single cross) do not have these problems. Methods developed to identify QTL in a recombinant inbred soybean population provide a basis for analysis of a suitable mammalian population, such as Portuguese water dogs in the United States. The more than 6,000 dogs have accurate pedigrees, available phenotypic data and samples for genotyping, as well as interesting quantitative trait variation. The computer program Georgie allows us to choose large subpopulations with desirable characteristics such as high degrees of consanguinity that capture some of the benefits of recombinant inbred lines in plants. Computer simulations extending methods developed for simpler plant populations indicate that QTL with realistic effects can be identified from such subpopulations. Currently we are developing markers and collecting phenotypic and genotypic data from this population to begin the process of unraveling the genetic basis of quantitative traits in dogs.

Animals↗
PubMed2003

Combined autoimmune models of arthritis reveal shared and independent qualitative (binary) and quantitative trait loci.

Collagen-induced arthritis (CIA) and proteoglycan-induced arthritis (PGIA) are murine models for rheumatoid arthritis both in terms of their pathology and genetics. Using the F(2) hybrids of the CIA-susceptible, but PGIA-resistant DBA/1 mice, and the CIA-resistant, but PGIA-susceptible BALB/c mice, our goals were to 1) identify both model-specific and shared loci that confer disease susceptibility, 2) determine whether any pathophysiological parameters could be used as markers that distinguish between nonarthritic and arthritic mice, and 3) analyze whether any immune subtraits showed colocalization with arthritis-related loci. To identify chromosomal loci, we performed a genome scan on 939 F(2) hybrid mice. For pathophysiological analyses, we measured pro- and anti-inflammatory cytokines (IL-1, IL-6, TNF-alpha, IFN-gamma, IL-4, IL-10, IL-12), Ag-specific T cell proliferation and IL-2 production, serum IgG1 and IgG2 levels of both auto- and heteroantibodies, and soluble CD44. In addition to multiple CIA- and PGIA-related loci identified in previous studies, we have identified nine new CIA- and eight new PGIA-linked loci. Comprehensive statistical analysis demonstrated that IL-2 production, T cell proliferation, and IFN-gamma levels differed significantly between arthritic and nonarthritic animals in both CIA and PGIA populations. High levels of TNF-alpha, IFN-gamma, IL-2, and Ab production were detected in F(2) hybrids with CIA, whereas T cell proliferation, IL-2 and IFN-gamma production, and a shift to IgG2a isotype were more characteristic of PGIA. Quantitative trait loci analysis demonstrated colocalization of numerous immune subtraits with arthritis-related traits. Quantitative trait loci on chromosomes 5, 10, 17, 18, and X were found to control arthritis in both models.

Acute Disease↗
PubMed2003

Quantitative traits in plants: beyond the QTL.

Phenotypic variation for quantitative traits results from segregation at multiple quantitative trait loci (QTL), the effects of which are modified by the internal and external environments. Because of their favorable genetic attributes (e.g. short generation time, large families and tolerance to inbreeding), plants are often used to test new concepts in quantitative trait analysis. Thus far, the molecular basis underlying allelic variation at QTL is similar to the identified variation for simple mendelian loci; namely, alterations in gene expression or protein function. Further comprehensive dissection of complex phenotypes will depend on our ability to link genetic components of the QTL variation to genomic databases.

Alleles↗
PubMed2006

[Construction of chromosome segment substitution lines carrying overlapping chromosome segments of the whole wild rice genome and identification of quantitative trait loci for rice quality].

Rice is one of the major staple cereal grains. Most of the important traits of crops are complex traits controlled by quantitative trait loci (QTL). Detection and genetic identification of QTLs can provide insights into molecular and biological mechanisms of development and physiology. Chromosome segment substitution lines (CSSLs) have been proposed as a simple and powerful way to identify QTLs. The demand for rice grain quality becomes increasingly important. The primary components of rice grain quality include appearance, milling and physico-chemical, cooking and eating quality. Most of these traits are complex and controlled by QTLs, so genetic characterization of these traits is more difficult than that of traits each controlled by a singular gene such as Waxy, which controls glutinousness of rice grain. We constructed 133 chromosome segment substitution lines (CSSLs) from backcross progenies (BC3F2) derived from a cross between Teqing (an O. sativa L. ssp. indica variety) as the recurrent parent and wild rice (O. rufipogon Griff.) as the donor parent. In this process, we carried out marker-assisted selection (MAS) by using 118 CAPS or SSR markers covering the whole rice genome (Figs.1, 2). Because phenotypic values of quality trait were significantly different between cultivar Teqing and wild rice, the CSSLs were used to identify QTLs for rice quality traits. Three appearance quality traits (head rice percentage, HRP; percentage of chalky rice grains, PCRG; transparency, TP) and five physico-chemical properties (gelatinization temperature, GT; gel consistency, GC; amylose content, AC; protein content, PC; fat content, FC) were scored using 133 CSSLs which substituted segments covering the whole genome of wild rice. A total of 15 QTLs for five of the eight traits mentioned above (HRP, PCRG, TP, PC and FC) were identified on nine chromosomes, and several QTLs affecting different quality traits were mapped in the same regions (Fig.3, Table 1).

Chromosomes, Plant↗
PubMed2004

The genetic architecture of quantitative traits: lessons from Drosophila.

Understanding the genetic architecture of quantitative traits begins with identifying the genes regulating these traits, mapping the subset of genetically varying quantitative trait loci (QTLs) in natural populations, and pinpointing the molecular polymorphisms defining QTL alleles. Studies in Drosophila have revealed large numbers of pleiotropic genes that interact epistatically to regulate quantitative traits, and large numbers of QTLs with sex-, environment- and genotype-specific effects. Multiple molecular polymorphisms in regulatory regions of candidate genes are often associated with variation for complex traits. These observations offer valuable lessons for understanding the genetic basis of variation for complex traits in other organisms, including humans.

Animals↗
PubMed2004

A statistical model for functional mapping of quantitative trait loci regulating drug response.

Differential drug response, that is, pharmacodynamics, is most often likely to be a complex trait, controlled by the combined influences of multiple genes and environmental influences. Genetic mapping has proven to be a powerful tool for detecting and identifying specific genes affecting complex traits, that is, quantitative trait loci (QTL), based on polymorphic markers. In this article, we present a novel statistical model for genetic mapping of QTL governing pharmacodynamic processes. In principle, this model is a combination of functional mapping proposed to map function-valued traits and linkage disequilibrium mapping designed to provide high-resolution mapping of QTL by making use of recombination events created at a historic time. We implement a closed-form solution for the Expectation-Maximization algorithm to estimate the population genetic parameters of QTL and the simplex algorithm to estimate the curve parameters describing the pharmacodynamic changes of different QTL genotypes in response to drug dose or concentrations. Extensive simulations are performed to investigate the statistical properties of our model. The implications of our model in pharmacogenetic and pharmacogenomic research are discussed.

Algorithms↗
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↗
PubMed2003

Quantitative trait loci analysis for plasma HDL-cholesterol concentrations and atherosclerosis susceptibility between inbred mouse strains C57BL/6J and 129S1/SvImJ.

OBJECTIVE: The C57BL/6 (B6) and 129 mouse inbred strains differ markedly in plasma HDL-cholesterol concentrations and atherosclerosis susceptibility after a high-fat diet consumption. To identify loci controlling these traits, we performed quantitative trait loci (QTL) analysis. METHODS AND RESULTS: We fed a high-fat diet to 294 (B6x129S1/SvImJ)F2 females for 14 weeks, measured plasma HDL concentrations and size of aortic fatty-streak lesions, genotyped F2 females, and performed QTL analysis. HDL concentrations were affected by six loci: Hdlq14 and Hdlq15 on chromosome 1 (peaks cM 80 and cM 104, logarithm of odds [LOD] 5.3 and 9.7, respectively); Hdlq16 on chromosome 8 (cM 44, LOD 2.6); Hdlq17 on chromosome 9 (cM 24, LOD 2.9); Hdlq18 on chromosome 12 (cM 20, LOD 5.9); and Hdlq19 on chromosome 2 (cM 90), which interacted with Hdlq15. Atherosclerosis susceptibility was affected by five loci: Ath17 on chromosome 10 (cM 34, LOD 6.6); Ath18 on chromosome 12 (cM 16, LOD 3.7); Ath19 (chromosome 11, cM 60), which interacted with Ath18; and Ath20 (chromosome 10, cM 10), which interacted with Ath21 (chromosome 12, cM 50). CONCLUSIONS: We identified six loci for HDL and five loci for atherosclerosis susceptibility in a (B6x129S1/SvImJ)F2 intercross.

Animals↗
PubMed1998

Mapping quantitative trait loci for ordered categorical traits in four-way crosses.

Many quantitative traits of economical importance are ordinal in nature. Although methods of mapping quantitative trait loci (QTLs) for continuous quantitative characters are well developed, such methods for ordinal characters are generally lacking. In this paper, we develop a method based on the framework of a generalized linear model using four-way cross populations. The method estimates and tests the average effects of a gene substitution in the parents. All markers in the same linkage group are simultaneously used to infer the allelic transmission of a putative QTL. General results of the method are demonstrated by a few simulation experiments. We discuss extensions of the method to QTL mapping in full-sib families.

Animals↗
PubMed2000

Whole genome scan to detect quantitative trait loci for conformation and functional traits in dairy cattle.

A granddaughter design was used to locate quantitative trait loci determining conformation and functional traits in dairy cattle. In this granddaughter design, consisting of 20 Holstein Friesian grandsires and 833 sons, genotypes were determined for 277 microsatellite markers covering the whole genome. Breeding values for 27 traits, regarding conformation (18), fertility (2), birth (4), workability (2), and udder health (1), were evaluated in an across-family analysis using multimarker regression. Significance thresholds were determined using a permutation test. The across-family analysis suggested the presence of 61 quantitative trait loci when 27 (i.e., one for each trait) were expected by chance. The test statistic exceeded the genomewise significance threshold for the following traits and chromosomes: chest width on chromosome 2; gestation length on chromosome 4; stature, body capacity, and size on chromosome 5; dairy character on chromosome 6; angularity on chromosome 12; fore udder attachment on chromosome 13; and fore udder attachment and front teat placement on chromosome 19. The quantitative trait loci for size traits on chromosomes 2, 5, and 6 may also have an effect on calving ease. The quantitative trait loci for udder traits on chromosomes 13 and 19 may also affect somatic cell score and mastitis resistance. If there are no negative effects on other economically important traits, marker assisted selection using markers associated with these quantitative trait loci can be applied.

Animals↗
PubMed2005

The power to detect genetic linkage for quantitative traits in the Utah CEPH pedigrees.

Quantitative trait phenotypes and linked marker genotypes were simulated for a range of models with different sets of assumptions based on displacement, prevalence, and heritability of the trait in 30 Utah Centre d'Etude du Polymorphisme Humain (CEPH) families. The gain in power by the addition of 15 families was also estimated by extrapolation. Power was evaluated using both parametric single locus (PSL) models and variance components (VC) methods for two situations: (1) a single marker with 75% heterozygosity and a recombination fraction of 0.05, and (2) a fully informative marker as an approximation to multipoint analysis. When the simulation and analysis models were both dominant with the same prevalence, power > or =80% for lod >3 was estimated when quantitative trait locus variance was > or =40% with a displacement of 2.5 or 3. Power was 5-15% lower for recessive models compared to dominant models. With the addition of 15 families, an average increase in power of 17% and 22% was estimated for the dominant and recessive models, respectively. In PSL analyses, power was estimated at < or =20% when the dominance was misspecified. This investigation delineates parameter conditions under which this unique sample affords adequate power to detect linkage using both PSL and VC methods.

Genetic Linkage↗
PubMed2005

Quantitative trait loci mapped to single-nucleotide resolution in yeast.

Identifying the genetic variation underlying quantitative trait loci remains problematic. Consequently, our molecular understanding of genetically complex, quantitative traits is limited. To address this issue directly, we mapped three quantitative trait loci that control yeast sporulation efficiency to single-nucleotide resolution in a noncoding regulatory region (RME1) and to two missense mutations (TAO3 and MKT1). For each quantitative trait locus, the responsible polymorphism is rare among a diverse set of 13 yeast strains, suggestive of genetic heterogeneity in the control of yeast sporulation. Additionally, under optimal conditions, we reconstituted approximately 92% of the sporulation efficiency difference between the two genetically distinct parents by engineering three nucleotide changes in the appropriate yeast genome. Our results provide the highest resolution to date of the molecular basis of a quantitative trait, showing that the interaction of a few genetic variants can have a profound phenotypic effect.

Adaptor Proteins, Signal Transducing↗
PubMed1998

Restricted maximum likelihood analysis of linkage between genetic markers and quantitative trait loci for a granddaughter design.

A REML for the estimation of location and variance of a single quantitative trait locus, together with polygenic and residual variance, is described for the analysis of a granddaughter design. The method is based on a mixed linear model that includes the allelic effects of the quantitative trait locus, which are assumed to be normally distributed. Information from four marker loci situated on a single chromosome was available to derive the covariances at the linked quantitative trait locus. A derivative-free algorithm is described that makes use of the specific structure of the granddaughter design. The procedure has been applied to simulated data for a granddaughter design with 50 grandsire families of 40 sires each. Error variance was 60, and total additive genetic variance equaled 40; the quantitative trait locus explained either 10 or 25% of the latter variance. The size of the marker bracket containing the quantitative trait locus was either 10 or 30 cM. The power of detecting the quantitative trait locus ranged from 19 to 99%. Estimates of total genetic variance and variance explained by the quantitative trait locus were found to be empirically unbiased. A small bias was found in location estimates, especially when markers were not fully informative. The accuracy of parameter estimates was greatly improved by the use of information from individual daughters.

Animals↗