No abstract

The literature on GWAS (genome-wide association studies) data suggests that very large sample sizes (for example, 50,000 cases and 50,000 controls) may be required to detect significant associations of genomic regions for complex disorders such as Alzheimer's disease (AD). Because of the challenges of obtaining such large cohorts, we describe here a novel sequential strategy that combines pooling of DNA and bootstrapping (pbGWAS) in order to significantly increase the statistical power and exponentially reduce expenses. We applied this method to a very homogeneous sample of patients belonging to a unique and clinically well-characterized multigenerational pedigree with one of the most severe forms of early onset AD, carrying the PSEN1 p.Glu280Ala mutation (often referred to as E280A mutation), which originated as a consequence of a founder effect. In this cohort, we identified novel loci genome-wide significantly associated as modifiers of the age of onset of AD (CD44, rs187116, P = 1.29 × 10–12; NPHP1, rs10173717, P = 1.74 × 10–12; CADPS2, rs3757536, P = 1.54 × 10–10; GREM2, rs12129547, P = 1.69 × 10–13, among others) as well as other loci known to be associated with AD. Regions identified by pbGWAS were confirmed by subsequent individual genotyping. The pbGWAS methodology and the genes it targeted could provide important insights in determining the genetic causes of AD and other complex conditions.

Background: Depression is associated with Alzheimer’s disease (AD). Objective: To evaluate the association between depressive symptoms and age of onset of cognitive decline in autosomal dominant AD, and to determine possible factors associated to early depressive symptoms in this population. Methods: We conducted a retrospective study to identify depressive symptoms among 190 presenilin 1 (PSEN1) E280A mutation carriers, subjected to comprehensive clinical evaluations in up to a 20-year longitudinal follow-up. We controlled for the following potential confounders: APOE, sex, hypothyroidism, education, marital status, residence, tobacco, alcohol, and drug abuse. Results: PSEN1 E280A carriers with depressive symptoms before mild cognitive impairment (MCI) develop dementia faster than E280A carriers without depressive symptoms (Hazard Ratio, HR = 1.95; 95% CI, 1.15–3.31). Not having a stable partner accelerated the onset of MCI (HR = 1.60; 95 % CI, 1.03–2.47) and dementia (HR = 1.68; 95 % CI, 1.09–2.60). E280A carriers with controlled hypothyroidism had later age of onset of depressive symptoms (HR = 0.48; 95 % CI, 0.25–0.92), dementia (HR = 0.43; 95 % CI, 0.21–0.84), and death (HR = 0.35; 95 % CI, 0.13–0.95). APOE ɛ2 significantly affected AD progression in all stages. APOE polymorphisms were not associate to depressive symptoms. Women had a higher frequency and developed earlier depressive symptoms than men throughout the illness (HR = 1.63; 95 % CI, 1.14–2.32). Conclusion: Depressive symptoms accelerated progress and faster cognitive decline of autosomal dominant AD. Not having a stable partner and factors associated with early depressive symptoms (e.g., in females and individuals with untreated hypothyroidism), could impact prognosis, burden, and costs.

Copulas have become a useful tool for modeling data when the dependence among random variables exists and the multivariate normality assumption is not fulfilled. The copulas have been applied in several fields. In finance, copulas are used in asset modeling and risk management. In biomedical studies, copulas are used to model correlated lifetimes and competitive risks [1]. In engineering, copulas are used in multivariate process control and hydrological modeling [2]. The interest in modeling multivariate problems involving dependent variables is generalized in several areas, making this methodology in a convenient way to model the dependence structure of random variables. However, in practice a first step before modeling phenomena through copulas is to assess whether there is dependence among the variables involved. In this paper some graphical methods to detect dependence are discussed and their performance will be evaluated through a simulation study. An application of graphical methods presented to insurance data is illustrated.

ResumenLas cópulas se han convertido en una herramienta popular para la construcción de modelos multivariados en campos donde la dependencia multivariada es de gran interés. El propósito de este trabajo es presentar las cópulas tanto en su concepto teórico, como en su implementación en el software estadístico R y profundizar en la construcción de distribuciones multivariadas con marginales dependientes, usando la clase mvdc del paquete copula, la cual permite utilizar varias y diferentes marginales ya implementadas. Además, se trabajará con métodos para dibujar representaciones de perspectiva y contorno para las funciones de distribución y densidad.Palabras clave: Análisis Multivariado, Cópulas en R, Paquete copula, Software R. AbstractThe copula has become a popular tool to build the multivariate models, in many fields where the multivariate dependence is of a great interest. This paper purpose is to present the copula both in their theoretical concept and its implementation in the R statistical software, and to deepen into the multivariate distributions? construction with the dependent marginal, by using the copula package's mvdc class, which allows to use the marginal in several and different types, that have been implemented already. In addition, to work with the methods for drawing the perspective and the contour representations for the distribution and the density functions.