Advanced Designs and Statistical Methods for Genetic and Genomic Studies of Complex Diseases

Call for Papers

The completion of the Human Genome Project and the International HapMap Project, coupled with rapid advancements of high-throughput biotechnology including next-generation sequencing (NGS), has facilitated the discovery of genetic and genomic variants linked to many human diseases. Massive amount of data from genetic and genomic studies provides a great opportunity for researchers to investigate and propose novel statistical methods and algorithms that can effectively identify disease-associated or causal genetic/genomic markers while avoiding an abundance of false positive results.

Despite many recent advances in statistical designs and methods for the analysis of genetic and genomic studies of complex diseases, numerous challenges still remain. For example, complex diseases including many cancers are heterogeneous in both disease phenotypes and disease etiology. The specification of disease phenotype and measurement of risk factors or environmental exposures are often subject to missing data, measurement errors, or oversimplification. Disease susceptibility is often affected by heterogeneous genetic/genomic factors including rare variants and further altered by various environmental exposures. Therefore, novel study designs and analysis methods are essential for proper adjustment of latent heterogeneity and for robust inferences using data with misspecification of disease phenotypes, incompletely measured exposures, or other complexities.

We invite investigators to submit original research articles as well as review articles that propose innovative study designs, novel probabilistic and statistical models, and analysis methods/algorithms for genetic and genomic studies of complex diseases. Potential topics include, but are not limited to:

• Novel study designs that can effectively control false negative and false positive rates
• Innovative models and methods to adjust for latent population heterogeneity
• Methods for analyzing genome-wide data and developing effective risk assessment models
• Design and analysis of high-throughput screening studies
• Methods and algorithms for robust analysis of data with measurement error or incompleteness
• Advanced methods for combining evidence from different types of data or studies

Before submission authors should carefully read over the journal's Author Guidelines, which are located at http://www.hindawi.com/journals/jps/guidelines/. Prospective authors should submit an electronic copy of their complete manuscript through the journal Manuscript Tracking System at http://mts.hindawi.com/ according to the following timetable:

Lead Guest Editor

• Yongzhao Shao, Division of Biostatistics, New York University School of Medicine, New York, NY, USA

Guest Editors

• Wei T. Pan, Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA
• Xiaohua Douglas Zhang, Biometrics Research, Merck Research Laboratories, Whitehouse Station, NJ, USA