The Role of Theme Parks in Tourism

Although the theme park has been a major tourism destination in the United States, little research has been done in this industry. The growing economic significance and competition of the theme park industry ensure that the study of theme parks will emerge as a more popular research topic in the years to come. The authors review related articles and identify potential research topics in the theme park industry.

Book Review: Tourism in China: Exotic Land Revealed

There is no better way to lean about tourism in China than from renowned expert in the field. Alan Lew. PhD. and professor at Northern Arizona University, Lawrence Yu, Ph.D. and associate professor in the Department of Tourism and Hospitality Management at George Washington University. John Ap, Ph.D. and associate professor in tourism management at Hong Kong Polytechnic University and Zhang Guangrui, director of the Tourism Research Centre, Chinese Academy of Social Sciences in Beijing, China, have contributed to and edited a collection of writings detailing the development of tourism in this fascinating and exotic land.

Assessing Methods for Assigning SNPs to Genes in Gene-Based Tests of Association Using Common Variants

Gene-based tests of association are frequently applied to common SNPs (MAF>5%) as an alternative to single-marker tests. In this analysis we conduct a variety of simulation studies applied to five popular gene-based tests investigating general trends related to their performance in realistic situations. In particular, we focus on the impact of non-causal SNPs and a variety of LD structures on the behavior of these tests. Ultimately, we find that non-causal SNPs can significantly impact the power of all gene-based tests. On average, we find that the “noise” from 6–12 non-causal SNPs will cancel out the “signal” of one causal SNP across five popular gene-based tests. Furthermore, we find complex and differing behavior of the methods in the presence of LD within and between non-causal and causal SNPs. Ultimately, better approaches for a priori prioritization of potentially causal SNPs (e.g., predicting functionality of non-synonymous SNPs), application of these methods to sequenced or fully imputed datasets, and limited use of window-based methods for assigning inter-genic SNPs to genes will improve power. However, significant power loss from non-causal SNPs may remain unless alternative statistical approaches robust to the inclusion of non-causal SNPs are developed.