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.

El objetivo de esta investigación fue analizar factores demográficos, socioeconómicos y académicos asociados con deserción y graduación, mediante un modelo de riesgos competitivos. Se incluyeron 639 estudiantes matriculados en 2009-2010, a quienes se les realizó seguimiento durante 14 periodos académicos. Los resultados mostraron que la probabilidad acumulada de deserción para el segundo periodo académico fue de 0.147. La probabilidad de graduarse en el tiempo estipulado por el programa fue del 0.187 y de 0.328 un año después. Para el periodo 14, el 49.0% de los estudiantes había obtenido su título y el 35.4% habían desertado. Variables socioeconómicas estuvieron asociadas a la probabilidad de deserción, mientras que ciertas condiciones demográficas se asociaron con la probabilidad de graduación. No obstante, las variables académicas tuvieron un efecto significativo en ambos desenlaces. Se concluye que las características asociadas a la deserción y la graduación se corresponden con aspectos que se pueden intervenir por las instituciones educativas para incrementar la permanencia y graduación.

Most survival analyzes are based on exact failure times and right censored observations, using methods widely known as the Kaplan-Meier (KM). When the data are interval censored is necessary to use the Turnbull's method to estimate the survival function, but in practice is often used the imputation of failure times in this kind of censorship through the midpoint of the interval, the right end of the interval or generating a random point within the interval using the uniform distribution. This paper studies through simulation the effect of three types of imputation on the estimates of the survival curve compared to the method developed by Turnbull. Different simulation scenarios based on the sample size and the time between visits were analyzed. In all scenarios simulation functions estimated using data imputation differ significantly from the true survival function S(t).Key words: Survival Analysis, Interval Censoring, Data Imputation. ResumenLa mayoría de los análisis de supervivencia se basan en tiempos de falla exactos y observaciones censuradas a la derecha, utilizándose métodos ampliamente difundidos como el método de Kaplan-Meier (KM). Cuando los datos presentan censura a intervalo es necesario utilizar el método de Turnbull para estimar la función de supervivencia, sin embargo en la práctica se usa con frecuencia la imputación del tiempo de falla en este tipo de censura a través del punto medio del intervalo (PM), el extremo derecho del intervalo (ED) o generando un punto aleatorio dentro del mismo a través de la distribución uniforme. Este trabajo estudia a través de simulación el efecto de los tres tipos de imputación sobre la estimación de la curva de supervivencia en comparación al método desarrollado por Turnbull. Se analizaron diferentes escenarios de simulación basados en el tamaño de muestra y el tiempo entre visitas. En todos los escenarios de simulación las funciones estimadas usando imputación de datos difieren significativamente de la verdadera función de supervivencia S(t).