Summary Clear evidence exists for heritability of human longevity, and much interest is focused on identifying genes associated with longer lives. To identify such longevity alleles, we performed the largest genome-wide linkage scan thus far reported. Linkage analyses included 2118 nonagenarian Caucasian sibling pairs that have been enrolled in fifteen study centers of eleven European countries as part of the Genetics of Healthy Ageing (GEHA) project. In the joint linkage analyses we observed four regions that show linkage with longevity; chromosome 14q11.2 (LOD=3.47), chromosome 17q12-q22 (LOD=2.95), chromosome 19p13.3-p13.11 (LOD=3.76) and chromosome 19q13.11-q13.32 (LOD=3.57). To fine map these regions linked to longevity, we performed association analysis using GWAS data in a subgroup of 1,228 unrelated nonagenarian and 1,907 geographically matched controls. Using a fixed effect meta-analysis approach, rs4420638 at the TOMM40/APOE/APOC1 gene locus showed significant association with longevity (p-value=9.6 × 10−8). By combined modeling of linkage and association we showed that association of longevity with APOEε4 and APOEε2 alleles explain the linkage at 19q13.11-q13.32 with p-value=0.02 and p-value=1.0 × 10−5, respectively. In the largest linkage scan thus far performed for human familial longevity, we confirm that the APOE locus is a longevity gene and that additional longevity loci may be identified at 14q11.2, 17q12-q22 and 19p13.3-p13.11. Since the latter linkage results are not explained by common variants, we suggest that rare variants play an important role in human familial longevity.

A challenge in microarray data analysis concerns discovering local structures composed by sets of genes that show homogeneous expression patterns across subsets of conditions. We present an extension of the mixture of factor analyzers model (MFA) allowing for simultaneous clustering of genes and conditions. The proposed model is rather flexible since it models the density of high-dimensional data assuming a mixture of Gaussian distributions with a particular omponent-specific covariance structure. Specifically, a binary and row stochastic matrix representing tissue membership is used to cluster tissues (experimental conditions), whereas the traditional mixture approach is used to define the gene clustering. An alternating expectation conditional maximization (AECM) algorithm is proposed for parameter estimation; experiments on simulated and real data show the efficiency of our method as a general approach to biclustering. The Matlab code of the algorithm is available upon request from authors.

The software of Ghahramani and Hinton is written in Matlab and available in 'Mixture of Factor Analyzers' on http://www.gatsby.ucl.ac.uk/~zoubin/software.html while the software of Rocci and Vichi is available upon request from the authors.

University evaluation is a topic of increasing concern in Italy as well as in other countries. In empirical analysis, university activities and performances are generally measured by means of indicator variables, summarizing the available information under different perspectives. In this paper, we argue that the evaluation process is a complex issue that can not be addressed by a simple descriptive approach and thus association between indicators and similarities among the observed universities should be accounted for. Particularly, we examine faculty-level data collected from different sources, covering 55 Italian Economics faculties in the academic year 2009/2010. Making use of a clustering framework, we introduce a biclustering model that accounts for both homogeneity/heterogeneity among faculties and correlations between indicators.Our results show that there are two substantial different performances between universities which can be strictly related to the nature of the institutions, namely the Private and Public profiles . Each of the two groups has its own peculiar features and its own group-specific list of priorities, strengths and weaknesses. Thus, we suggest that caution should be used in interpreting standard university rankings as they generally * Dipartimento di Scienze Statistiche, Sapienza Università di Roma, Roma, Italy † Dipartimento di Scienze Statistiche, Sapienza Università di Roma, Roma, Italy ‡ Southampton Statistical Sciences Research Institute, Southampton, UK 1 do not account for the complex structure of the data.

The present paper presents the application of a finite mixture model (FMM) to analyze spatially explicit data on forest composition and environmental variables to produce a high-resolution map of their current potential distribution. FMM provides a convenient yet formal setting for model-based clustering. Within this framework, forest data are assumed to come from an underlying FMM, where each mixture component corresponds to a cluster and each cluster is characterized by a different composition of tree species. An important extension of this model is based on including a set of covariates to predict class membership. These covariates can be climatic and topographical parameters as well as geographical coordinates and the class membership of neighbouring plots. FMM was applied to a national forest inventory of Italy consisting of 6,714 plots with a measure of abundance for 27 tree species. In this way, a map of potential forest types was produced. The limitations and usefulness of the proposed modelling approach were analyzed and discussed, comparing the results with an independently derived expert map