Analysis of quantitative trait loci using hybrid pedigrees: quantitative traits of animals

A method is proposed for analysis of quantitative traits in animal hybrid pedigrees formed by crosses between outbred lines differing in allele frequencies of the genes controlling the trait studied. The method is based on the decomposition of trait variances into components and uses maximization of the likelihood function for estimating model parameters, which allows the estimation of additive and dominance effects of the gene involved in trait determination and its allele frequencies, as well as determination of the chromosomal position of this gene relative to genotyped markers. To test the linkage of this gene with markers, a statistic with the noncentral chi(2) distribution has been chosen. Analytical expressions for the power of this method have been derived. The method has been tested on small model hybrid pedigrees. Phenotypic values of the trait and information on marker genotypes for each individual in hybrid pedigrees are original data for the analysis of a quantitative trait.     

Water and oleyl alcohol as stationary liquid phases in gas—liquid chromatography : Determination of partition coefficients of volatile substances for analysis of quantitative structure—activity relationships

Indirect determination of partition coefficients of volatile substances in the system oleyl alcohol—water for analysis of quantitative structure—activity relationships is discussed. Water and oleyl alcohol were used as stationary liquid phases in gas—liquid chromatography. In order to correct the results for adsorption effects, two new procedures using a standard substance were suggested. The procedures were compared with a procedure used by Conder et al. Comparison of molar hydration enthalpies and entropies derived from experimental results of a series of aliphatic alcohols with calorimetric data proved that all procedures under consideration yielded true partition coefficients.     

Three-dimensional ultrastructure and quantitative analysis of the human Sertoli cell nucleolus

The nucleolus of the human Sertoli cell displays a spontaneous segregation of its components and has only one or 2 large fibrillar centers. The 3-dimensional reconstruction and quantitative analysis of its components was undertaken using a Quantimet 900 image analysis system in order to define the spatial relationships between the dense fibrillar component and the fibrillar center and especially to investigate whether threads of dense fibrillar component exist independently, without being linked to a fibrillar center. Our 3D reconstructions demonstrated that the dense fibrillar threads or sheets were never independent of fibrillar centers. These structures belonged to a continuous network that joined the layer of dense fibrils surrounding the fibrillar center. When the nucleolus contained 2 different-sized fibrillar centers, quantitative analysis showed that there was a proportional relationship between the volume of the dense fibrillar component and the volume of the fibrillar center. These data, compared with those previously obtained by means of autoradiographic techniques, suggest that the rDNA-containing chromatin passes through the fibrillar center and unwinds from there into the dense fibrillar component.     

Intellectual activity during formation of quantitative concepts in the lower and higher monkeys

Results of study of formation of quantitative concepts in the lower and higher monkey are presented. Various authors used different methods of study of this issue. It has been established that not only the higher primates, such as chimpanzees and orangutans, but also the lower ones—macaques, capuchins, squirrel monkeys—have the “mathematical capabilities.” The detailed study of formation of quantitative concepts in primates of different levels of phylogenesis allows disclosing mechanisms of formation of this function in human.     

Variance components models for gene-environment interaction in quantitative trait locus linkage analysis.

Gene-environment interaction (G x E) is likely to be a common and important source of variation for complex behavioral traits. Gene-environment interaction, or genetic control of sensitivity to the environment, can be incorporated into variance components twin and sib-pair analyses by partitioning genetic effects into a mean part, which is independent of the environment, and a part that is a linear function of the environment. An approach described in a companion paper (Purcell, 2002) is applied to sib-pair variance components linkage analysis in two ways: allowing for quantitative trait locus by environment interaction and utilizing information on any residual interactions detected prior to analysis. As well as elucidating environmental pathways, consideration of G x E in quantitative and molecular studies will potentially direct and enhance gene-mapping efforts.     

Advances in qualitative and quantitative plant membrane proteomics

The membrane proteome consists of integral and membrane-associated proteins that are involved in various physiological and biochemical functions critical for cellular function. It is also dynamic in nature, where many proteins are only expressed during certain developmental stages or in response to environmental stress. These proteins can undergo post-translational modifications in response to these different conditions, allowing them to transiently associate with the membrane or other membrane proteins. Along with their increased size, hydrophobicity, and the additional organelle and cellular features of plant cells relative to mammalian systems, the characterization of the plant membrane proteome presents unique challenges for effective qualitative and quantitative analysis using mass spectrometry (MS) analysis. Here, we present the latest advancements developed for the isolation and fractionation of plant organelles and their membrane components amenable to MS analysis. Separations of membrane proteins from these enriched preparations that have proven effective are discussed for both gel- and liquid chromatography-based MS analysis. In this context, quantitative membrane proteomic analyses using both isotope-coded and label-free approaches are presented and reveal the potential to establish a wider-biological interpretation of the function of plant membrane proteins that will ultimately lead to a more comprehensive understanding of plant physiology and their response mechanisms.     

Advances in quantitative proteomics via stable isotope tagging and mass spectrometry

The high-throughput identification and accurate quantification of proteins are essential components of proteomic strategies for studying cellular functions and processes. Techniques that are largely based on stable isotope protein or peptide labeling and automated tandem mass spectrometry are increasingly being applied in quantitative proteomic studies. Over the past year, significant progress has been made toward improving and diversifying these technologies with respect to the methods for stable isotope labeling, process automation and data processing and analysis. Advances in stable isotope protein labeling and recent biological studies that used stable isotope based quantitative proteomics techniques are reviewed.     

气象因素对芒果蓟马种群数量动态的影响

明确芒果园中蓟马复合种种群动态与气象因子之间的关系。用粘虫板定期定点监测芒果园中蓟马的种群消长情况;通过相关分析、主成分分析和灰色关联分析,分析其与气象因素的关系。芒果花期蓟马种群数量为全年发生高峰。相关分析结果表明,监测日蓟马种群数量与日平均气温(℃)、日最大风速(m/s)显著正相关,与日最低气温(℃)显著负相关。芒果花期蓟马种群数量与2月降水量(mm)、3月平均风速(m/s)、1-2月平均气温(℃)、2月月均最高、最低气温(℃)、2-3月平均相对湿度(%)及1-3月平均最小相对湿度(%)显著相关。主成分分析表明,月平均温度(℃)和月最低气温(℃)是影响蓟马种群动态的主要因素,对蓟马种群动态的影响达56.0%。灰色关联分析表明,月均最低气温(℃)对芒果蓟马种群数量影响最大。月最低气温(℃)和月平均温度(℃)是影响芒果园中蓟马复合种种群数量动态的主要气象因素。芒果蓟马复合种种群数量受食物、种群扩散、天敌、自身繁殖和气候因素的共同影响。     

Qualitative and quantitative analysis of reducing carbohydrates by radiochromatography on ion-exchange papers

A highly specific method for qualitative and quantitative analysis of reducing carbohydrates has been described. The method involves the measurement of the amount of tritium found in [³H]sodium borohydridereduced sugars following their paper chromatographic separation on ion-exchange papers. These procedures may be used to quantitate all of those components in 5 μl of a mixture which are present in amounts ranging from 0.1 to 10,000 nmoles.The quantitation gives approximately 10% accuracy at the lower end of the sensitivity range.     

Streptavidin-based quantitative staining of intracellular antigens for flow cytometric analysis.

A streptavidin-biotin-based three-step immunolabeling protocol for quantitative staining of intracellular antigens for flow cytometric analysis was evaluated using simian virus 40 (SV40) large T antigen. The concentration as well as the quantity of antibody used required optimization. The optimum labeling conditions varied moderately with cell lines that express T antigen levels over a 40-50-fold range. The procedure resulted in specific fluorescence 2.4 times higher than that using a comparable two-step indirect immunofluorescence technique. The gain in resolution was shown to be greater when staining cells with lower antigen levels. In the analysis of background fluorescence, the principal components were, as for the two-step technique, autofluorescence and propidium spectral overlap. While streptavidin does add to the background, the increase is relatively small. Decreasing the propidium concentration from 50 micrograms/ml to 5 micrograms/ml was found to reduce significantly the level of background from this source. Theoretical aspects of quantitative staining and of resolution versus quantification are discussed.     

Characterization of global yeast quantitative proteome data generated from the wild-type and glucose repression saccharomyces cerevisiae strains: the comparison of two quantitative methods

The quantitative proteomic analysis of complex protein mixtures is emerging as a technically challenging but viable systems-level approach for studying cellular function. This study presents a large-scale comparative analysis of protein abundances from yeast protein lysates derived from both wild-type yeast and yeast strains lacking key components of the Snf1 kinase complex. Four different strains were grown under well-controlled chemostat conditions. Multidimensional protein identification technology followed by quantitation using either spectral counting or stable isotope labeling approaches was used to identify relative changes in the protein expression levels between the strains. A total of 2388 proteins were relatively quantified, and more than 350 proteins were found to have significantly different expression levels between the two strains of comparison when using the stable isotope labeling strategy. The stable isotope labeling based quantitative approach was found to be highly reproducible among biological replicates when complex protein mixtures containing small expression changes were analyzed. Where poor correlation between stable isotope labeling and spectral counting was found, the major reason behind the discrepancy was the lack of reproducible sampling for proteins with low spectral counts. The functional categorization of the relative protein expression differences that occur in Snf1-deficient strains uncovers a wide range of biological processes regulated by this important cellular kinase.     

Segregation analysis comparing liability and quantitative trait models for hypertension using the Genetic Analysis Workshop 13 simulated data

Discrete (qualitative) data segregation analysis may be performed assuming the liability model, which involves an underlying normally distributed quantitative phenotype. The appropriateness of the liability model for complex traits is unclear. The Genetic Analysis Workshop 13 simulated data provides measures on systolic blood pressure, a highly complex trait, which may be dichotomized into a discrete trait (hypertension). We perform segregation analysis under the liability model of hypertensive status as a qualitative trait and compare this with results using systolic blood pressure as a quantitative trait (without prior knowledge at that stage of the true underlying simulation model) using 1050 pedigrees ascertained from four replicates on the basis of at least one affected member. Both analyses identify models with major genes and polygenic components to explain the family aggregation of systolic blood pressure. Neither of the methods estimates the true parameters well (as the true model is considerably more complicated than those considered for the analysis), but both identified the most complicated model evaluated as the preferred model. Segregation analysis of complex diseases using relatively simple models is unlikely to provide accurate parameter estimates but is able to indicate major gene and/or polygenic components in familial aggregation of complex diseases.     

A proteome catalog of Drosophila melanogaster: an essential resource for targeted quantitative proteomics

Proteomic analyses are critically important for systems biology because important aspects related to the structure, function and control of biological systems are only amenable by direct protein measurements. It has become apparent that the current proteomics technologies are unlikely to allow routine, quantitative measurements of whole proteomes. We have therefore suggested and largely implemented a two-step strategy for quantitative proteome analysis. In a first step, the discovery phase, the proteome observable by mass spectrometry is extensively analyzed. The resulting proteome catalog can then be used to select peptides specific to only one protein, so-called proteotypic peptides (PTPs). It represents the basis to realize sensitive, robust and reproducible measurements based on targeted mass spectrometry of these PTPs in a subsequent scoring phase. In this Extra View we describe the need for such proteome catalogs and their multiple benefits for catalyzing the shift towards targeted quantitative proteomic analysis and beyond. We use the Insulin signaling cascade as a representative example to illustrate the limitations of currently used proteomics approaches for the specific analysis of individual pathway components, and describe how the recently published Drosophila proteome catalog already helped to overcome many of these limitations.     

Benchmark dose profiles for joint‐action continuous data in quantitative risk assessment

Benchmark analysis is a widely used tool in biomedical and environmental risk assessment. Therein, estimation of minimum exposure levels, called benchmark doses (BMDs), that induce a prespecified benchmark response (BMR) is well understood for the case of an adverse response to a single stimulus. For cases where two agents are studied in tandem, however, the benchmark approach is far less developed. This paper demonstrates how the benchmark modeling paradigm can be expanded from the single‐agent setting to joint‐action, two‐agent studies. Focus is on continuous response outcomes. Extending the single‐exposure setting, representations of risk are based on a joint‐action dose–response model involving both agents. Based on such a model, the concept of a benchmark profile—a two‐dimensional analog of the single‐dose BMD at which both agents achieve the specified BMR—is defined for use in quantitative risk characterization and assessment.     

Resistance toMelampsora larici-epitea leaf rust inSalix: analyses of quantitative trait loci

Quantitative resistance ofSalix toMelampsora larici-epitea leaf rust was studied in 2Salix mapping populations. One population was a backcross between aS. schwerinii ×S. viminalis hybrid andS. viminalis, and the other was an F₂ population betweenS. viminalis andS. dasyclados. A leaf disc bioassay was used to study the components of quantitative resistance (latent period, uredinia number, and uredinia size) to 3 isolates of the leaf rust. The analysis of quantitative trait loci (QTLs) revealed 9 genomic regions in the backcross population and 7 genomic regions in the F₂ population that were important for rust resistance, with QTLs explaining 8–26% of the phenotypic variation. An important genomic region was identified for the backcross population in linkage group 2, where QTLs were identified for all resistance components for 2 of the rust isolates. Four of the QTLs had overlapping mapping intervals, demonstrating a common genetic background for latent period, uredinia diameter, and uredinia number. QTLs specific to some rust isolates and to some resistance components were also found, indicating a combination of common and specific mechanisms involved in the various resistance components. Breeding implications in relation to these findings are discussed.     

Reproducible quantitative proteotype data matrices for systems biology

Historically, many mass spectrometry–based proteomic studies have aimed at compiling an inventory of protein compounds present in a biological sample, with the long-term objective of creating a proteome map of a species. However, to answer fundamental questions about the behavior of biological systems at the protein level, accurate and unbiased quantitative data are required in addition to a list of all protein components. Fueled by advances in mass spectrometry, the proteomics field has thus recently shifted focus toward the reproducible quantification of proteins across a large number of biological samples. This provides the foundation to move away from pure enumeration of identified proteins toward quantitative matrices of many proteins measured across multiple samples. It is argued here that data matrices consisting of highly reproducible, quantitative, and unbiased proteomic measurements across a high number of conditions, referred to here as quantitative proteotype maps, will become the fundamental currency in the field and provide the starting point for downstream biological analysis. Such proteotype data matrices, for example, are generated by the measurement of large patient cohorts, time series, or multiple experimental perturbations. They are expected to have a large effect on systems biology and personalized medicine approaches that investigate the dynamic behavior of biological systems across multiple perturbations, time points, and individuals.     

Gas—liquid chromatography of isobutyl ester,N(O)-heptafluorobutyrate derivatives of amino acids on a glass capillary column for quantitative separation in clinical biology

A gas chromatographic method adapted to routine analysis has been developed for quantitative separation on glass capillary columns for free proteic and other known amino acids normally or abnormally found in physiological fluids. The procedure involves ion-exchange chromatography and isobutyl ester, N(O)-heptafluorobutyrate derivatization of free plasma and urine amino acid samples. Derivatized components were ascertained by combined gas chromatography—mass spectrometry. The use of glass for the capillary column is mandatory to achieve qualitative and quantitative analysis of the known occurring amino acids in urine and small plasma samples. Quantitative analysis of several types of human amino acid disorders are presented.     

Estimation of variance components of quantitative traits in inbred populations

Use of variance-component estimation for mapping of quantitative-trait loci in humans is a subject of great current interest. When only trait values, not genotypic information, are considered, variance-component estimation can also be used to estimate heritability of a quantitative trait. Inbred pedigrees present special challenges for variance-component estimation. First, there are more variance components to be estimated in the inbred case, even for a relatively simple model including additive, dominance, and environmental effects. Second, more identity coefficients need to be calculated from an inbred pedigree in order to perform the estimation, and these are computationally more difficult to obtain in the inbred than in the outbred case. As a result, inbreeding effects have generally been ignored in practice. We describe here the calculation of identity coefficients and estimation of variance components of quantitative traits in large inbred pedigrees, using the example of HDL in the Hutterites. We use a multivariate normal model for the genetic effects, extending the central-limit theorem of Lange to allow for both inbreeding and dominance under the assumptions of our variance-component model. We use simulated examples to give an indication of under what conditions one has the power to detect the additional variance components and to examine their impact on variance-component estimation. We discuss the implications for mapping and heritability estimation by use of variance components in inbred populations.