Multivariate genome-wide covariance analyses of literacy, language and working memory skills reveal distinct etiologies

Several abilities outside literacy proper are associated with reading and spelling, both phenotypically and genetically, though our knowledge of multivariate genomic covariance structures is incomplete. Here, we introduce structural models describing genetic and residual influences between traits to study multivariate links across measures of literacy, phonological awareness, oral language, and phonological working memory (PWM) in unrelated UK youth (8–13 years, N = 6453). We find that all phenotypes share a large proportion of underlying genetic variation, although especially oral language and PWM reveal substantial differences in their genetic variance composition with substantial trait-specific genetic influences. Multivariate genetic and residual trait covariance showed concordant patterns, except for marked differences between oral language and literacy/phonological awareness, where strong genetic links contrasted near-zero residual overlap. These findings suggest differences in etiological mechanisms, acting beyond a pleiotropic set of genetic variants, and implicate variation in trait modifiability even among phenotypes that have high genetic correlations.

High-dimensional covariance matrices tests for analyzing multi-tumor gene expression data

By collecting multiple sets per subject in microarray data, gene sets analysis requires characterize intra-subject variation using gene expression profiling. For each subject, the data can be written as a matrix with the different subsets of gene expressions (e.g. multiple tumor types) indexing the rows and the genes indexing the columns. To test the assumption of intra-subject (tumor) variation, we present and perform tests of multi-set sphericity and multi-set identity of covariance structures across subjects (tumor types). We demonstrate by both theoretical and empirical studies that the tests have good properties. We applied the proposed tests on The Cancer Genome Atlas (TCGA) and tested covariance structures for the gene expressions across several tumor types.

lme4GS: An R-Package for Genomic Selection

Genomic selection (GS) is a technology used for genetic improvement, and it has many advantages over phenotype-based selection. There are several statistical models that adequately approach the statistical challenges in GS, such as in linear mixed models (LMMs). An active area of research is the development of software for fitting LMMs mainly used to make genome-based predictions. The lme4 is the standard package for fitting linear and generalized LMMs in the R-package, but its use for genetic analysis is limited because it does not allow the correlation between individuals or groups of individuals to be defined. This article describes the new lme4GS package for R, which is focused on fitting LMMs with covariance structures defined by the user, bandwidth selection, and genomic prediction. The new package is focused on genomic prediction of the models used in GS and can fit LMMs using different variance–covariance matrices. Several examples of GS models are presented using this package as well as the analysis using real data.

Troubles with teleconnections

<p>Rotated principal component analysis (RPCA) is a commonly used tool to detect modes of low-frequency atmospheric circulation variability, also referred to as teleconnections. Teleconnections manifest themselves as distant areas of high negative or positive correlations in sea level pressure, geopotential height, or another variable describing atmospheric circulation. For outputs of RPCA to be valid representations of teleconnections, their spatial patterns (loadings) must correspond to underlying correlation / covariance structures, that is, be in agreement with autocorrelation maps.</p><p>When comparing teleconnections identified in different datasets (e.g., between reanalyses, between outputs of climate models, between different periods, between different seasons), the spatial similarity of loadings is evaluated and quantified; if it is low, the datasets are said to disagree in the representation of a particular teleconnection. However, things appear to be less straightforward: It may happen that although the loadings pertaining to the same teleconnection differ, the maps of correlations with the action centres (i.e., points with highest positive or negative loadings) are identical. This may suggest that while the autocorrelation structures are the same in the two datasets, they appear with different weight (intensity). This issue appears to be unrelated to uncertainty due to the number of principal components to rotate; it typically occurs for various reasonable numbers of components.</p><p>In our contribution, we (i) introduce the above described issue on several examples (RPCA of different reanalyses, of sliding time periods, and of sliding 93-day seasons), (ii) discuss what is a correct interpretation of such cases (should we consider the teleconnections to be equal or different when the autocorrelation maps agree but the loadings disagree?), and (iii) suggest possible ways out of it (to use oblique instead of orthogonal rotation, to return back to autocorrelation maps).</p>

The role of flow-dependent oceanic background-error covariance information in air-sea coupled data assimilation during tropical cyclones: a case study

<p>In variational data assimilation, background-error covariance structures have the ability to spread information from an observed part of the system to unobserved parts.  Hence an accurate specification of these structures is crucially important for the success of assimilation systems and therefore of forecasts that their outputs initiate.  For oceanic models, background-error covariances have traditionally been modelled by parametrisations which mainly depend on macroscopic properties of the ocean and have limited dependence on local conditions.  This can be problematic during passage of tropical cyclones, when the spatial and temporal variability of the ocean state depart from their characteristic structures.  Furthermore, the traditional method of estimating oceanic background-error covariances could amplify imbalances across the air-sea interface when weakly coupled data assimilation is applied, thereby bringing a detrimental impact to forecasts of cyclones.  Using the case study of Cyclone Titli, which affected the Bay of Bengal in 2018, we explore hybrid methods that combine the traditional modelling strategy with flow-dependent estimates of the ocean's error covariance structures based on the latest-available short-range ensemble forecast.  This hybrid approach is investigated in the idealised context of a single-column model as well as in the UK Met Office’s state-of-the-art system.  The idealised model helps inform how the inclusion of ensemble information can improve coupled forecasts.  Different methods for producing the ensemble are explored, with the goal of generating a limited-sized ensemble that best represents the uncertainty in the ocean fields.  We then demonstrate the power of this hybrid approach in changing the analysed structure of oceanic fields in the Met Office system, and explain the difference between the traditional and hybrid approaches in light of the ways the assimilation systems respond to single synthetic observations.  Finally, we discuss the benefits that the hybrid approach in ocean data assimilation can bring to atmospheric forecasts of the cyclone.</p>