Biostatistics and Bioinformatics

Background Climate change is taking a toll on human health, and some leaders in the public health community have urged their colleagues to give voice to its health implications. Previous research has shown that Americans are only dimly aware of the health implications of climate change, yet the literature on issue framing suggests that providing a novel frame - such as human health - may be potentially useful in enhancing public engagement. We conducted an exploratory study in the United States of people's reactions to a public health-framed short essay on climate change. Methods U.S. adult respondents (n = 70), stratified by six previously identified audience segments, read the essay and were asked to highlight in green or pink any portions of the essay they found "especially clear and helpful" or alternatively "especially confusing or unhelpful." Two dependent measures were created: a composite sentence-specific score based on reactions to all 18 sentences in the essay; and respondents' general reactions to the essay that were coded for valence (positive, neutral, or negative). We tested the hypothesis that five of the six audience segments would respond positively to the essay on both dependent measures. Results There was clear evidence that two of the five segments responded positively to the public health essay, and mixed evidence that two other responded positively. There was limited evidence that the fifth segment responded positively. Post-hoc analysis showed that five of the six segments responded more positively to information about the health benefits associated with mitigation-related policy actions than to information about the health risks of climate change. Conclusions Presentations about climate change that encourage people to consider its human health relevance appear likely to provide many Americans with a useful and engaging new frame of reference. Information about the potential health benefits of specific mitigation-related policy actions appears to be particularly compelling. We believe that the public health community has an important perspective to share about climate change, a perspective that makes the problem more personally relevant, significant, and understandable to members of the public.

Objective. To examine the relationship between depressive symptoms and cardiovascular disease (CVD) risk factors in a group of African American individuals. Design. A nonrandom sample of 253 (age 43.7 ± 11.6 years; 37% male) African American individuals was recruited by advertisements. Data were obtained by validated questionnaires, anthropometric, blood pressure, and blood sample measurements. Results. Regression analyses were performed to assess the relationship between depressive symptoms and CVD risk factors controlling for socioeconomic status indicators. These analyses demonstrated that those with higher levels of depressive symptoms had larger waist-to-hip ratios, higher percent body fat, higher triglycerides, and were more likely to be smokers. Conclusions. It has been well documented that higher levels of depressive symptoms are associated with higher CVD risk. However, this evidence is derived primarily from samples of predominantly Caucasian individuals. The present investigation demonstrates that depressive symptoms are related to CVD risk factors in African American individuals.

Assessing genome-wide statistical significance is an important issue in genetic studies. We describe a new resampling approach for determining the appropriate thresholds for statistical significance. Our simulation results demonstrate that the proposed approach accurately controls the genome-wide type I error rate even under the large p small n situations. Q UANTITATIVE trait loci (QTL) mapping plays an important role in understanding the genetic variations in experimental crosses. A critical issue concerns assessing the genome-wide significance (GWS), since statistical tests are performed at many putative loci. Analytic methods to determine GWS have been investigated by several authors including Lander and Kruglyak (1995) and Zou et al. (2004) and these involve specific assumptions on the experimental design and genetic map density. Churchill and Doerge (1994) proposed a permutation test to address these issues. However, their method is computationally intensive due to repeated analyses of the permuted data sets, and its validity relies on the assumption of complete exchangeability under the null hypothesis, which can frequently be violated (see, for instance, Manichaikul et al. 2007). To overcome the limitations of the permutation methods, Zou et al. (2004) proposed a resampling procedure requiring one analysis of the data set only, thereby reducing the computational complexity. In this note, we propose a further modification of the resampling approach of Zou et al. (2004) to improve the power of the tests while retaining the same computational complexity. We begin by considering n independent subjects from an experimental cross and statistical testing at p putative loci. Let b j be a vector of the genetic effects at the jth location and H 0 : b 1 =. .. = b p = 0 denote the null hypothesis of no genetic effects at all loci. It is well known that given a statistical model, the likelihood-ratio test for testing the hypothesis H 0j : b j = 0 can be approximated by the score statistic