Aspects of the design and analysis of high-dimensional SNP studies for disease risk estimation

The state of readiness for high-dimensional single nucleotide polymorphism (SNP) epidemiologic association studies is described, as background for a discussion of statistical aspects of case-control study design and analysis. Specifically, the important role that multistage designs can play in the elimination of false-positive associations and in the control of study costs will be noted. Also, the trade-offs associated with using pooled DNA at early design stages for additional important cost reductions will be discussed in some detail. An odds ratio approach to relating SNP alleles to disease risk using pooled DNA will be proposed, in conjunction with a simple empirical variance estimator, based on comparisons among log-odds ratio estimators from distinct pairs of case and control pools. Simulation studies will be presented to evaluate the moderate sample size properties of such multistage designs and estimation procedures. The design of an ongoing three-stage study in the Women's Health Initiative to relate 250,000 SNPs to the risk of coronary heart disease, stroke, and breast cancer will provide illustration, and will be used to motivate the choice of simulation configurations.     

La depresión: un predictor de demencia

Many studies suggest that in 10-25% of cases of Alzheimer's, the most common dementia in our society, can be prevented with the elimination of some risk factors. Barnes and Yaffe found that one-third of Alzheimer's cases are attributable to depression, but in the scientific literature it is not clear if it has a real causal effect on the development of dementia. The purpose of this study is to analyse the scientific evidence on the hypothesis that depression increases the risk of developing dementia.A systematic review and a meta-analysis were performed on the scientific literature published up until the present day, searching articles that were published between 1990 and 2014. Ten of the studies found met the selection criteria –similar to a) size and characteristics of the sample (origin, age…), b) process of gathering data c) method of studying the relationship (within and/or between group comparison), and d) statistical analysis of the results– and the previously established quality.The value of odds ratio varied from 1.72 to 3.59, and the hazard ratio from 1,72 to 5.44. This indicates that the subjects with a history of depression have a higher risk of developing dementia than others who did not suffer depression.     

Improving estimates of the basic reproductive ratio: using both the mean and the dispersal of transition times

In both within-host and epidemiological models of pathogen dynamics, the basic reproductive ratio, R(0), is a powerful tool for gauging the risk associated with an emerging pathogen, or for estimating the magnitude of required control measures. Techniques for estimating R(0), either from incidence data or in-host clinical measures, often rely on estimates of mean transition times, that is, the mean time before recovery, death or quarantine occurs. In many cases, however, either data or intuition may provide additional information about the dispersal of these transition times about the mean, even if the precise form of the underlying probability distribution remains unknown. For example, we may know that recovery typically occurs within a few days of the mean recovery time. In this paper we elucidate common situations in which R(0) is sensitive to the dispersal of transition times about their respective means. We then provide simple correction factors that may be applied to improve estimates of R(0) when not only the mean but also the standard deviation of transition times out of the infectious state can be estimated.     

Comments on Improving estimates of the basic reproductive ratio: using both the mean and the dispersal of transition times

Comments on Heffernan, J. M., Wahl, L. M. (2006). Improving estimates of the basic reproductive ratio: Using both the mean and the dispersal of transition times. Theoretical Population Biology, 70, 135-145.