Bioinformatics Commons: The Cornerstone of Life and Health Sciences

正Bioinformatics, an interdisciplinary field that combines biology, mathematics, computer science, medicine, and health science, to integrate, analyze, and interpret biological data, is now becoming increasingly data-intensive. To dig out the treasure from big data powered by high-throughput sequencing technologies, it is highly dependent on Bioinformatics Commons that involves a variety of fundamental resources, includ-     

The Use of Multiplicity Corrections,Order Statistics and Generalized Family-Wise Statistics with Application to Genome-Wide Studies

The most important decision faced by large-scale studies, such as those presently encountered in human genetics, is to distinguish between those tests that are true positives from those that are not. In the context of genetics, this entails the determination of genetic markers that actually underlie medically-relevant phenotypes from a vast number of makers typically interrogated in genome-wide studies. A critical part of these decisions relies on the appropriate statistical assessment of data obtained from tests across numerous markers. Several methods have been developed to aid with such analyses, with family-wise approaches, such as the Bonferroni and Dunn-Šidàk corrections, being popular. Conditions that motivate the use of family-wise corrections are explored. Although simple to implement, one major limitation of these approaches is that they assume that p-values are i.i.d. uniformly distributed under the null hypothesis. However, several factors may violate this assumption in genome-wide studies including effects from confounding by population stratification, the presence of related individuals, the correlational structure among genetic markers, and the use of limiting distributions for test statistics. Even after adjustment for such effects, the distribution of p-values can substantially depart from a uniform distribution under the null hypothesis. In this work, I present a decision theory for the use of family-wise corrections for multiplicity and a generalization of the Dunn-Šidàk correction that relaxes the assumption of uniformly-distributed null p-values. The independence assumption is also relaxed and handled through calculating the effective number of independent tests. I also explicitly show the relationship between order statistics and family-wise correction procedures. This generalization may be applicable to multiplicity problems outside of genomics.     

The Concept of Data Utility in Health Risk Assessment: A Multi-Disciplinary Perspective

Human and ecological health risk assessments and the decisions that stem from them require the acquisition and analysis of data. In agencies that are responsible for health risk decision-making, data (and/or opinions/judgments) are obtained from sources such as scientific literature, analytical and process measurements, expert elicitation, inspection findings, and public and private research institutions. Although the particulars of conducting health risk assessments of given disciplines may be dramatically different, a common concern is the subjective nature of judging data utility. Often risk assessors are limited to available data that may not be completely appropriate to address the question being asked. Data utility refers to the ability of available data to support a risk-based decision for a particular risk assessment. This article familiarizes the audience with the concept of data utility and is intended to raise the awareness of data collectors (e.g., researchers), risk assessors, and risk managers to data utility issues in health risk assessments so data collection and use will be improved. In order to emphasize the cross-cutting nature of data utility, the discussion has not been organized into a classical partitioning of risk assessment concerns as being either human health- or ecological health-oriented, as per the U.S. Environmental Protection Agency's Superfund Program.     

Integrating Behavioral and Social Sciences with Public Health

Integrating Behavioral and Social Sciences with Public Health. Neil Schneiderman. Marjorie A. Speers. Julia M. Silva. Henry Tome. and Jacquelyn H. Gentry. eds. Washington, DC: American Psychological Association, 2001. xvi. 405 pp.