Principal component analysis of gene frequencies and the origin of Basques

The genetic peculiarity of the Basque population has long been noted. We aim to describe Basque distinctiveness in space and assess the internal Basque heterogeneity. All these aspects are relevant to the question of the origin of Basques. After a thorough literature search, a data base was created containing all the available data on gene frequencies in the Iberian Peninsula and France. Twenty-nine systems, comprising 71 alleles, were used to carry out a principal component (PC) analysis. The results show a sharp peak in the first PC in the Basque area, which remains even when the geographic scope is widened to include western Europe. As demonstrated by “wombling” analysis, the steeper slope in the first PC is found to the east of the Basque area, along the Pyrenees. Measures of genetic heterogeneity (such as F_ST values) within the Basque country, as compared to those for non-Basques, do not show a particular internal substructuration in the Basque population. The genetic results support a scenario in which the Basques are the product of in situ differentiation around the time of the Last Glacial Maximum (18,000 B .P .), in agreement with archaeological and linguistic data. Isolation from the surrounding populations has allowed the differentiation to last for millennia, but has erased the differences existing among Basques. © 1994 Wiley-Liss, Inc.     

Principal component analysis for protein folding dynamics

Protein folding is considered here by studying the dynamics of the folding of the triple β-strand WW domain from the Formin-binding protein 28. Starting from the unfolded state and ending either in the native or nonnative conformational states, trajectories are generated with the coarse-grained united residue (UNRES) force field. The effectiveness of principal components analysis (PCA), an already established mathematical technique for finding global, correlated motions in atomic simulations of proteins, is evaluated here for coarse-grained trajectories. The problems related to PCA and their solutions are discussed. The folding and nonfolding of proteins are examined with free-energy landscapes. Detailed analyses of many folding and nonfolding trajectories at different temperatures show that PCA is very efficient for characterizing the general folding and nonfolding features of proteins. It is shown that the first principal component captures and describes in detail the dynamics of a system. Anomalous diffusion in the folding/nonfolding dynamics is examined by the mean-square displacement (MSD) and the fractional diffusion and fractional kinetic equations. The collisionless (or ballistic) behavior of a polypeptide undergoing Brownian motion along the first few principal components is accounted for.     

Principal component analysis of complex multijoint coordinative movements

Principal components analysis (PCA) has not been very much in vogue within the field of movement coordination even though it is useful to reduce data dimensionality and to reveal underlying data structures. Traditionally, studies of coordination between two joints have predominantly made use of relative phase analyses. This has resulted in the identification of principal constraints that govern the Central Nervous System’s organization and the control of coordination patterns. However, relative phase analyses on pairwise joints have some drawbacks because they are not optimal for revealing convergent patterns among multijoint coordination modes and for unraveling generic control strategies.In this paper, we present a method to analyze multijoint coordination based on the properties of PC, more specifically the eigenvalues and eigenvectors of the covariance matrix.The comparison between relative phase analysis and PCA shows that both provide similar and consistent results, underscoring the latter technique’s sensitivity to the study of coordination performance. In addition, it provides a method for automatic pattern detection as well as an index of performance for each joint within the context of the global coordination pattern.Finally, the merit of the PCA technique within the context of central pattern generators (CPG) will be discussed.     

Principal axis analysis of gene frequency data

When a set of populations are compared in respect of gene frequencies, and the chi-square test of heterogeneity is found to be significant, it is pertinent to find out whether the heterogeneity can be explained by a few linear combinations of the gene frequencies, and the total heterogeneity chi-square value can be partitioned as the sum of heterogeneity chi-square values contributed by the linear combinations. The present report describes such a method, and the linear combination that explains the maximum heterogeneity is called the principal axis. An application of this method is presented to find clusters of 31 Mongoloid tribal populations of eastern India using ABO gene frequency data.     

Principal component analysis for clustering gene expression data

MOTIVATION: There is a great need to develop analytical methodology to analyze and to exploit the information contained in gene expression data. Because of the large number of genes and the complexity of biological networks, clustering is a useful exploratory technique for analysis of gene expression data. Other classical techniques, such as principal component analysis (PCA), have also been applied to analyze gene expression data. Using different data analysis techniques and different clustering algorithms to analyze the same data set can lead to very different conclusions. Our goal is to study the effectiveness of principal components (PCs) in capturing cluster structure. Specifically, using both real and synthetic gene expression data sets, we compared the quality of clusters obtained from the original data to the quality of clusters obtained after projecting onto subsets of the principal component axes. RESULTS: Our empirical study showed that clustering with the PCs instead of the original variables does not necessarily improve, and often degrades, cluster quality. In particular, the first few PCs (which contain most of the variation in the data) do not necessarily capture most of the cluster structure. We also showed that clustering with PCs has different impact on different algorithms and different similarity metrics. Overall, we would not recommend PCA before clustering except in special circumstances.     

Multivariate analysis of intraspecific responses to UV-B radiation in white clover (Trifolium repens L.)

White clover (Trifolium repens L.) is experiencing increased levels of ultraviolet‐B (UV‐B) radiation in temperate pastures due to the depletion of the stratospheric ozone layer. Based on 17 morphological, morphogenetic and physiological attributes, this study analysed the consequences of enhanced UV‐B on 26 white clover populations using principal components analysis (PCA). After 18 d of exposure to 13·3 kJ m ^{? 2} d ^{? 1} UV‐B in controlled environments, UV‐B significantly decreased above‐ground and below‐ground plant growth attributes, epidermal cell surface area and maximum quantum efficiency of photosystem II photochemistry (F_v/F_m). Aspects of cell division and cell expansion both were negatively affected by UV‐B. Stomatal density, specific leaf mass, root‐to‐shoot ratio and levels of UV‐B‐absorbing compounds increased in response to UV‐B. In the multivariate analysis, the main dimension of UV‐B sensitivity was characterized by changes in plant growth attributes. Alterations in partitioning within and between plant organs constituted a secondary tier of UV‐B responsiveness. Plant characteristics related to UV‐B tolerance included lower growth rate, smaller epidermal cell surface area and higher UV‐B‐induced levels of UV‐B‐absorbing compounds. The results suggest overall UV‐B tolerance for slower‐growing populations from less productive habitats with higher natural UV‐B irradiance.     

Melanocortin－1 receptor gene variants in four Chinese ethnic populations

INTRODUCTIONThe variation in human hall and skin color in~ geographic regions of the world is the result Of differences in two Principal forms Of melanin,the red-yellow phaeomelalilns and the bldebrowneUmelanins, which are present in the epidermallayer of hUman skin and hair[1, 2]. The type ofmelanin Produced is under the control of two genes,identified initially by the mouse mutation, extension and agouti. The eXtension gene is expressedin melanocytes, Producillg the melanocyte stimul…     

Principal components analysis of protein structure ensembles calculated using NMR data

One important problem when calculating structures of biomolecules from NMR data is distinguishing converged structures from outlier structures. This paper describes how Principal Components Analysis (PCA) has the potential to classify calculated structures automatically, according to correlated structural variation across the population. PCA analysis has the additional advantage that it highlights regions of proteins which are varying across the population. To apply PCA, protein structures have to be reduced in complexity and this paper describes two different representations of protein structures which achieve this. The calculated structures of a 28 amino acid peptide are used to demonstrate the methods. The two different representations of protein structure are shown to give equivalent results, and correct results are obtained even though the ensemble of structures used as an example contains two different protein conformations. The PCA analysis also correctly identifies the structural differences between the two conformations.     

Principal component analysis of nonlinear chromatography

Principal component analysis (PCA) has been used for the modeling of nonlinear chromatography under overload conditions. A 10-fold range of crude erythromycin samples were loaded onto columns with different stationary-phase chemistries (2 polystyrene, 1 methacrylate) in direct proportion to the bed volumes. The elution profiles indicated slightly concave isotherms for the polystyrene supports and a convex Langmuirian isotherm for the methacrylic support used. The principal component models accounted for over 98% of the original variance in the data for all three columns and were able to give excellent models of complete chromatograms in the absence of first-principle models or physicochemical data. Correlations between sample mass and the principal component scores were made for each that were consistent for the column types despite the different geometries and stationary phases. Linear relationships with high correlation coefficients were observed when the scores of the same principal component were compared between columns. Such correlations offer considerable potential for modeling of nonlinear chromatography.     

Relationships between soil bulk electrical conductivity and the principal component analysis of topography and soil fertility values

Measures of soil electrical conductivity (EC) and elevation are relatively inexpensive to collect and result in dense data sets which allow for mapping with limited interpolation. Conversely, soil fertility information is expensive to collect so that relatively few samples are taken and mapping requires extensive interpolation with large estimation errors, resulting in limited usefulness for site-specific applications in precision agriculture. Principal component (PC) analysis and cokriging can be applied to create meaningful field scale summaries of groups of attributes and to decrease the estimation error of maps of the summarized attributes. Deep (0–90 cm) and shallow (0–30 cm) EC, elevation, and soil fertility attributes were measured in fields under corn (Zea mays L.) and soybean (Glycine max L.) rotations, at two sites in Illinois (IL) and two sites in Missouri (MO). Soil fertility and topography attributes were summarized by PC analysis. The first topography PC (TopoPC1) contrasted flow accumulation against elevation and curvature, to describe the main topographic pattern of the fields. The first soil fertility PC (SoilPC1) consistently grouped together cation exchange capacity (CEC), Ca, Mg, and organic matter (OM). SoilPC1 was well correlated to soil EC for all sites and cokriging with EC had higher r ² in the crossvariogram models compared to ordinary kriging. The second and third soil fertility PCs (SoilPC2 and SoilPC3) were concerned with soil pH and P, and reflected historic land use patterns. Maps of SoilPC2 and SoilPC3 had little relationship to soil EC or topography and so could not be improved by cokriging.     

Principal directions of the evolution of Monimotrochida

Phylogenetic relations among the main groups of Monimotrochida are considered. The principal directions of monimotrochid evolution were defined by comparative investigations of mastax morphology (SEM), basic body structures, and general biology. On the basis of these results we propose a revision of previous rotifer taxonomy. We suggest to place the Monimotrochida in the order Protoramida divided into two suborders Flosculariina and Conochilina.     

Principal component analysis of DNA oligonucleotide structural data

The microstructure of a DNA helix is characterized by several base pair and base step parameters such as twist, rise, roll, propeller twist, etc., in addition to conformational parameters such as the backbone and the glycosidic torsion angles. Among these only a few, which are independent of all others and of each other, may be used to precisely characterize the helix. The problem however is to identify these independent parameters. We have used principal component analysis to identify a relatively small set of independent parameters, with which to characterize each DNA helix. We show that these principal components clearly discriminate between A and B DNA helical types. The calculations further suggest that the microstructure of a DNA helix is better characterized using dinucleotides.     

中国人群的等位基因地理分布图

发表了我国首批绘制的人类基因地理分布图,包括１２个等位基因,即ＡＢＯ系统的Ｉ＾Ｂ,Ｉ＾Ｏ,ＭＮＳ系统的ｍ,Ｒｈ－Ｄ,ＨＬＡ系统的Ａ１与Ａ１１,人体免疫球蛋白的Ｇｍ＾１;２１与Ｇｍ＾１,３;５,ＡＫ＾１,Ｇ６ＰＤ缺陷型,以及ＰＴＣ味盲基因。这些基因地理不仅显示了等位基因的地理分布状况,也可用于研究某些等位基因的起源与扩散,基因流动以及某些环境因子通过选择对某些等位基因频率的影响。     

Estimates of gene frequencies in two heterotic maize populations

Summary The present study was undertaken to obtain information on average gene frequency in two heterotic populations of maize (Zea mays L.), Mezcla Amarillo Selection (MAS) and J607. Sixty-four male plants were taken in each of the populations and each of these were crossed to a different set of eight plants, four of which belonged to the same population and four to the other population. This resulted in two groups of intra-population (within MAS and within J607) and two groups of inter-population (MAS X J607 and J607 X MAS) progenies. Each group consisted of 256 full-sib progenies on the pattern of the North Carolina Design I mating system. The male plants were selfed to produce 64 S₁ prgenies in each population. The materials were evaluated at two diverse locations, Ludhiana and Gurdaspur, for grain yield, ear length, ear girth, number of kernel rows, plant height, ear height and days to silk. An incomplete block design with two replications were used. The plot consisted of a 5 m long row. Ratios of estimated genetic components of variance and covariance were compared with corresponding theoretical ratios computed for a single locus for various gene frequencies and levels of dominance, and approximate ranges of the gene frequencies and their relative magnitude were worked out in the two populations. The average frequency of favourable genes for plant height was estimated as 0.6 in MAS and 0.8 in J607. For grain yield the average gene frequency was 0.8 to 0.9 in MAS and 0.7 to 0.8 in J607 whereas for ear height it was 0.5 to 0.7 in MAS and 0.4 to 0.6 in J607. The gene frequency in the two populations seemed to be similar for days to silk, ear length, ear girth and kernel rows.