Genomewide Linkage Scan of 409 European-ancestry and African American Families with Schizophrenia: Suggestive Evidence of Linkage at 8p23.3-p21.2 and 11p13.1-q14.1 in the Combined Sample

We report the clinical characteristics of a schizophrenia sample of 409 pedigrees-263 of European ancestry ( EA) and 146 of African American ancestry ( AA)-together with the results of a genome scan ( with a simple tandem repeat polymorphism interval of 9 cM) and follow-up fine mapping. A family was required to have a proband with schizophrenia ( SZ) and one or more siblings of the proband with SZ or schizoaffective disorder. Linkage analyses included 403 independent full-sibling affected sibling pairs ( ASPs) ( 279 EA and 124 AA) and 100 all-possible half-sibling ASPs ( 15 EA and 85 AA). Nonparametric multipoint linkage analysis of all families detected two regions with suggestive evidence of linkage at 8p23.3-q12 and 11p11.2-q22.3 ( empirical Z likelihood-ratio score [ Z(lr)] threshold >= 2.65) and, in exploratory analyses, two other regions at 4p16.1-p15.32 in AA families and at 5p14.3-q11.2 in EA families. The most significant linkage peak was in chromosome 8p; its signal was mainly driven by the EA families. Z(lr) scores >= 2.0 in 8p were observed from 30.7 cM to 61.7 cM ( Center for Inherited Disease Research map locations). The maximum evidence in the full sample was a multipoint Z(lr) of 3.25 ( equivalent Kong-Cox LOD of 2.30) near D8S1771 ( at 52 cM); there appeared to be two peaks, both telomeric to neuregulin 1 ( NRG1). There is a paracentric inversion common in EA individuals within this region, the effect of which on the linkage evidence remains unknown in this and in other previously analyzed samples. Fine mapping of 8p did not significantly alter the significance or length of the peak. We also performed fine mapping of 4p16.3-p15.2, 5p15.2-q13.3, 10p15.3-p14, 10q25.3-q26.3, and 11p13-q23.3. The highest increase in Z(lr) scores was observed for 5p14.1-q12.1, where the maximum Z(lr) increased from 2.77 initially to 3.80 after fine mapping in the EA families.

Small-scale randomized controlled trials need more powerful methods of mediational analysis than the Baron-Kenny method

Objective: To devise more-effective physical activity interventions, the mediating mechanisms yielding behavioral change need to be identified. The Baron-Kenny method is most commonly used. but has low statistical power and May not identify mechanisms of behavioral change in small-to-medium size Studies. More powerful statistical tests are available, Study Design and Setting: Inactive adults (N = 52) were randomized to either a print or a print-plus-telephone intervention. Walking and exercise-related social support Were assessed at baseline, after file intervention, and 4 weeks later. The Baron-Kenny and three alternative methods of mediational analysis (Freedman-Schatzkin; MacKinnon et al.: bootstrap method) were used to examine the effects of social support on initial behavior change and maintenance. Results: A significant mediational effect of social support on initial behavior change was indicated by the MacKinnon et al., bootstrap. and. marginally. Freedman-Schatzkin methods, but not by the Baron-Kenny method. No significant mediational effecl of social support on maintenance of walking was found. Conclusions: Methodologically rigorous intervention studies to identify mediators of change in physical activity are costly and labor intensive, and may not be feasible with large samples. The Use of statistically powerful tests of mediational effects in small-scale studies can inform the development of more effective interventions. (C) 2006 Elsevier Inc. All rights reserved.

Motor unit number estimation - A Bayesian approach

All muscle contractions are dependent on the functioning of motor units. In diseases such as amyotrophic lateral sclerosis (ALS), progressive loss of motor units leads to gradual paralysis. A major difficulty in the search for a treatment for these diseases has been the lack of a reliable measure of disease progression. One possible measure would be an estimate of the number of surviving motor units. Despite over 30 years of motor unit number estimation (MUNE), all proposed methods have been met with practical and theoretical objections. Our aim is to develop a method of MUNE that overcomes these objections. We record the compound muscle action potential (CMAP) from a selected muscle in response to a graded electrical stimulation applied to the nerve. As the stimulus increases, the threshold of each motor unit is exceeded, and the size of the CMAP increases until a maximum response is obtained. However, the threshold potential required to excite an axon is not a precise value but fluctuates over a small range leading to probabilistic activation of motor units in response to a given stimulus. When the threshold ranges of motor units overlap, there may be alternation where the number of motor units that fire in response to the stimulus is variable. This means that increments in the value of the CMAP correspond to the firing of different combinations of motor units. At a fixed stimulus, variability in the CMAP, measured as variance, can be used to conduct MUNE using the "statistical" or the "Poisson" method. However, this method relies on the assumptions that the numbers of motor units that are firing probabilistically have the Poisson distribution and that all single motor unit action potentials (MUAP) have a fixed and identical size. These assumptions are not necessarily correct. We propose to develop a Bayesian statistical methodology to analyze electrophysiological data to provide an estimate of motor unit numbers. Our method of MUNE incorporates the variability of the threshold, the variability between and within single MUAPs, and baseline variability. Our model not only gives the most probable number of motor units but also provides information about both the population of units and individual units. We use Markov chain Monte Carlo to obtain information about the characteristics of individual motor units and about the population of motor units and the Bayesian information criterion for MUNE. We test our method of MUNE on three subjects. Our method provides a reproducible estimate for a patient with stable but severe ALS. In a serial study, we demonstrate a decline in the number of motor unit numbers with a patient with rapidly advancing disease. Finally, with our last patient, we show that our method has the capacity to estimate a larger number of motor units.