Human sex ratio at birth and mother's birth season: multivariate analysis.

We used a population-based historical French Canadian database to examine the effects of mother's birth season on sex ratio at birth. Non-first births in the database (n = 127,658) were analyzed for their sex, parish size (2 large parishes of Montreal and Quebec or the other smaller parishes), time period (births up to 1719 or those from 1720), maternal age (< or = 24, 25-29, 30-34, 35+ years), sex of the preceding sibling (male or female), and birth seasons of the child and his or her parents (February-April, May-July, August-October, November-January). Season of child's birth significantly affected the sex ratio (chi 2 = 11.507, d.f. = 3, p = 0.009), with the births in February-April or May-July showing a lower sex ratio. Season of mother's birth also contributed highly significantly to the variation of sex ratio (chi 2 = 15.196, d.f. = 3, p = 0.002); mothers born in February-April had a low sex ratio among their children (sex ratio = 1.013). In contrast, season of father's birth did not affect the sex ratio (chi 2 = 0.618, d.f. = 3, p = 0.892). When a multiple logistic model was applied to the data, mother's birth season was the single most significant factor. The lower sex ratio from mothers born in February-April was observed consistently for every maternal age and delivery season. Seasonal influences on female fetuses seem to have changed their future reproductive characteristics.     

adegenet: a R package for the multivariate analysis of genetic markers

The package adegenet for the R software is dedicated to the multivariate analysis of genetic markers. It extends the ade4 package of multivariate methods by implementing formal classes and functions to manipulate and analyse genetic markers. Data can be imported from common population genetics software and exported to other software and R packages. adegenet also implements standard population genetics tools along with more original approaches for spatial genetics and hybridization. AVAILABILITY: Stable version is available from CRAN: http://cran.r-project.org/mirrors.html. Development version is available from adegenet website: http://adegenet.r-forge.r-project.org/. Both versions can be installed directly from R. adegenet is distributed under the GNU General Public Licence (v.2).     

Microevolutionary change in Arikara Crania: a multivariate analysis

Measurements of human crania from five archaeological sites were subjected to canonical analysis. The five sites form a temporal sequence, ranging from prehistoric (ca. 1600 A.D.) to late historic (1830 A.D.), and belonging archaeologically to the Coalescent Tradition, which in South Dakota encompasses the historic Arikara Indians and their prehistoric ancestors. One canonical variate in each sex arranges the sites in their appropriate temporal sequence, presumably reflecting systematic microevolutionary change. The hypothesis that the morphological change resulted from gene flow from either White or Mandan Indian sources was tested using a two-group discriminate function. The function is found to effectively discriminate a Mandan sample and prehistoric Arikara, but becomes progressively less effective on the historic Arikara crania. This would suggest that Mandan gene flow into Arikara populations is responsible for the observed temporal variation. A similar analysis using British White crania suggests the possibility of slight White influence, but the effect is minimal compared to Mandan.     

Integrated analysis of tropical trees growth: a multivariate approach

BACKGROUND AND AIMS: One of the problems analysing cause-effect relationships of growth and environmental factors is that a single factor could be correlated with other ones directly influencing growth. One attempt to understand tropical trees' growth cause-effect relationships is integrating research about anatomical, physiological and environmental factors that influence growth in order to develop mathematical models. The relevance is to understand the nature of the process of growth and to model this as a function of the environment. METHODS: The relationships of Aphananthe monoica, Pleuranthodendron lindenii and Psychotria costivenia radial growth and phenology with environmental factors (local climate, vertical strata microclimate and physical and chemical soil variables) were evaluated from April 2000 to September 2001. The association among these groups of variables was determined by generalized canonical correlation analysis (GCCA), which considers the probable associations of three or more data groups and the selection of the most important variables for each data group. KEY RESULTS: The GCCA allowed determination of a general model of relationships among tree phenology and radial growth with climate, microclimate and soil factors. A strong influence of climate in phenology and radial growth existed. Leaf initiation and cambial activity periods were associated with maximum temperature and day length, and vascular tissue differentiation with soil moisture and rainfall. The analyses of individual species detected different relationships for the three species. CONCLUSIONS: The analyses of the individual species suggest that each one takes advantage in a different way of the environment in which they are growing, allowing them to coexist.     

Response of a weed community to nitrogen fertilization: a multivariate analysis

The effect of nitrogen fertilizers on the composition of a weed community was investigated in a barley field. Two doses (70 and 140 kg N/ha) of three fertilizers (ammonium sulphate, calcium-ammonium nitrate, and liquid urea) were used. The results were evaluated using the canonical correspondence analysis. Hybrid analyses and various combinations of environmental variables and co variables were used to separate the influence of the dose and the type of fertilizer and to separate the direct and indirect effects of fertilization. The results reveal that both the dose and type of fertilizer have a significant effect on the composition of the weed community. The results suggest that both the direct effect of fertilizer and an indirect effect, through increased competition of the crop, are important determinants of weed community composition.     

Quantitative genetic analysis of skin reflectance: a multivariate approach.

Skin color is a polygenically determined quantitative trait. Although it has been used extensively in studies of between-population variation, there have been relatively few studies of the inheritance of skin color. In this article we use measurements on 359 members of the Jirel population of eastern Nepal to assess the heritabilities and additive genetic correlations of three skin reflectance measures. Skin color was measured at the upper inner arm site at three wavelengths. A maximum likelihood approach was used to estimate sex and age effects on skin reflectance, heritabilities, and phenotypic variances at each wavelength and both additive genetic and environmental correlations between wavelengths. This technique incorporated information from 36 pedigrees with 2-25 members and 173 independent individuals. Likelihood ratio tests were used to assess the significance of specific variance/covariance components. The results indicate that skin reflectances are moderately heritable at all three wavelengths. The pairwise phenotypic correlations ranged from 0.76 to 0.88. The observed additive genetic correlations were not significantly different from 1.00, suggesting that the same loci influence variation at each wavelength. This evidence for relatively complete pleiotropy implies that measurements at multiple wavelengths yield little additional genetic information, although they may be useful for reducing measurement error. Based on estimates of the genetic and phenotypic covariance matrices, we determined that skin reflectance measurements are expected to provide only as much information for assessing local between-population genetic variation as a single two-allele polymorphic marker. Therefore microevolutionary studies based on skin color variation should be viewed with caution.     

Habitat selection and association of stream insects: a multivariate analysis

SUMMARY. Habitat selection and interspecies associations of stream insects were evaluated from a set of quantitative benthos samples and habitat measurements (velocity, depth, substrate, quantities of fine and leafy detritus) from a mountain stream. Substrate was characterized by an in situ grid method which effectively describes the size and distribution of the larger surficial materials within each sample unit. A principal components ordination of samples as characterized by abundance data over all species accounted for 54% of the total variance in three dimensions but implied continuous rather than discrete faunal change with habitat. Gradients in the habitat could not be identified with real confidence. Canonical correlation analysis identified three significant habitat-fauna interactions. The first corresponded to the gradient from eroding to depositing substrates and included detritus abundance, the second distinguished areas of fine from leafy detritus and the third appeared to include velocity-stone size interactions. A graphical technique based on ordination of physical data and clustering of associated species adequately summarized the distribution of five Ephemerella mayflies. The multivariate and interactive nature of stream bottom environments is discussed for problems involving prediction and experimental design and control.     

Coreferentiality: a new method for the hypothesis-based analysis of phenotypes characterized by multivariate data

Many multifactorial biologic effects, particularly in the context of complex human diseases, are still poorly understood. At the same time, the systematic acquisition of multivariate data has become increasingly easy. The use of such data to analyze and model complex phenotypes, however, remains a challenge. Here, a new analytic approach is described, termed coreferentiality, together with an appropriate statistical test. Coreferentiality is the indirect relation of two variables of functional interest in respect to whether they parallel each other in their respective relatedness to multivariate reference data, which can be informative for a complex effect or phenotype. It is shown that the power of coreferentiality testing is comparable to multiple regression analysis, sufficient even when reference data are informative only to a relatively small extent of 2.5%, and clearly exceeding the power of simple bivariate correlation testing. Thus, coreferentiality testing uses the increased power of multivariate analysis, however, in order to address a more straightforward interpretable bivariate relatedness. Systematic application of this approach could substantially improve the analysis and modeling of complex phenotypes, particularly in the context of human study where addressing functional hypotheses by direct experimentation is often difficult.     

Marginal regression analysis of a multivariate binary response

We propose the use of the mean parameter for regression analysisof a multivariate binary response. We model the associationusing dependence ratios defined in terms of the mean parameter,the components of which are the joint success probabilitiesof all orders. This permits flexible modelling of higher-orderassociations, using maximum likelihood estimation. We reanalysetwo data sets, one with variable cluster size and the othera longitudinal data set with constant cluster size.     

Use of multivariate linkage analysis for dissection of a complex cognitive trait

Replication of linkage results for complex traits has been exceedingly difficult, owing in part to the inability to measure the precise underlying phenotype, small sample sizes, genetic heterogeneity, and statistical methods employed in analysis. Often, in any particular study, multiple correlated traits have been collected, yet these have been analyzed independently or, at most, in bivariate analyses. Theoretical arguments suggest that full multivariate analysis of all available traits should offer more power to detect linkage; however, this has not yet been evaluated on a genomewide scale. Here, we conduct multivariate genomewide analyses of quantitative-trait loci that influence reading- and language-related measures in families affected with developmental dyslexia. The results of these analyses are substantially clearer than those of previous univariate analyses of the same data set, helping to resolve a number of key issues. These outcomes highlight the relevance of multivariate analysis for complex disorders for dissection of linkage results in correlated traits. The approach employed here may aid positional cloning of susceptibility genes in a wide spectrum of complex traits.     

Multiple regression on distance matrices: a multivariate spatial analysis tool

I explore the use of multiple regression on distance matrices (MRM), an extension of partial Mantel analysis, in spatial analysis of ecological data. MRM involves a multiple regression of a response matrix on any number of explanatory matrices, where each matrix contains distances or similarities (in terms of ecological, spatial, or other attributes) between all pair-wise combinations of n objects (sample units); tests of statistical significance are performed by permutation. The method is flexible in terms of the types of data that may be analyzed (counts, presence–absence, continuous, categorical) and the shapes of response curves. MRM offers several advantages over traditional partial Mantel analysis: (1) separating environmental distances into distinct distance matrices allows inferences to be made at the level of individual variables; (2) nonparametric or nonlinear multiple regression methods may be employed; and (3) spatial autocorrelation may be quantified and tested at different spatial scales using a series of lag matrices, each representing a geographic distance class. The MRM lag matrices model may be parameterized to yield very similar inferences regarding spatial autocorrelation as the Mantel correlogram. Unlike the correlogram, however, the lag matrices model may also include environmental distance matrices, so that spatial patterns in species abundance distances (community similarity) may be quantified while controlling for the environmental similarity between sites. Examples of spatial analyses with MRM are presented.     

A multivariate model for ordinal trait analysis

Many economically important characteristics of agricultural crops are measured as ordinal traits. Statistical analysis of the genetic basis of ordinal traits appears to be quite different from regular quantitative traits. The generalized linear model methodology implemented via the Newton-Raphson algorithm offers improved efficiency in the analysis of such data, but does not take full advantage of the extensive theory developed in the linear model arena. Instead, we develop a multivariate model for ordinal trait analysis and implement an EM algorithm for parameter estimation. We also propose a method for calculating the variance-covariance matrix of the estimated parameters. The EM equations turn out to be extremely similar to formulae seen in standard linear model analysis. Computer simulations are performed to validate the EM algorithm. A real data set is analyzed to demonstrate the application of the method. The advantages of the EM algorithm over other methods are addressed. Application of the method to QTL mapping for ordinal traits is demonstrated using a simulated baclcross (BC) population.     

GeoPCA: a new tool for multivariate analysis of dihedral angles based on principal component geodesics

The GeoPCA package is the first tool developed for multivariate analysis of dihedral angles based on principal component geodesics. Principal component geodesic analysis provides a natural generalization of principal component analysis for data distributed in non-Euclidean space, as in the case of angular data. GeoPCA presents projection of angular data on a sphere composed of the first two principal component geodesics, allowing clustering based on dihedral angles as opposed to Cartesian coordinates. It also provides a measure of the similarity between input structures based on only dihedral angles, in analogy to the root-mean-square deviation of atoms based on Cartesian coordinates. The principal component geodesic approach is shown herein to reproduce clusters of nucleotides observed in an η-θ plot. GeoPCA can be accessed via http://pca.limlab.ibms.sinica.edu.tw.     

Toward a computer assisted analysis of NOESY spectra: a multivariate data analysis of an RNA NOESY spectrum

A multivariate data-representation of a portion of the H-NOESY spectrum of an RNA octamer duplex was used to explore the possibility of using Principal Component Analysis and Partial Least Squares Discrimination for pattern recognition. In this case, it is found that the methods can: (i) distinguish slices containing signal from those containing only noise, (ii) locate slices containing overlapping signals, and (iii) in some cases to segregate slices with unique aspects such as those from terminal nucleotides, overlapping signals, purine-H8, pyrimidine-H6 and adenine-H2 containing slices. These properties can easily be included in a scheme to automate spectral analysis. The formulation described here does not distinguish patterns needed to automate sequential assignment of resonances in NOESY spectra of RNA.