Statistical issues in genome-wide association studies
By: by David William Fardo
Published: 2008
Language: eng
Number of pages: 59
Description: The first replicable finding from a genome-wide association study was published in 2005 (Klein et al., 2005). Since then, genome-wide association has been responsible for the discovery of nearly 100 novel genetic loci conferring risk for 40 common diseases (Pearson and Manolio, 2008). Many similar studies have been conducted with varying degrees of success, and statistical advancements continue to enhance the ability of these studies to succeed. This dissertation presents original contributions to benefit the design and analysis of genome-wide association studies. Disease traits measured on a continuous scale generally provide greater study power than binary traits. However, these measurements can be difficult and costly to obtain and may need to be adjusted in the analysis by many other confounding factors which must also be collected. Chapter 1 details rules to analyze a dichotomized version of a quantitative trait in a family-based genome-wide association study while maintaining power levels comparable to that of analyzing the original trait. These rules are illustrated by an application to an asthma study.

Although the quality of the large-scale genotyping technologies is high, genotyping errors still occur. Testing for departures from Hardy-Weinberg equilibrium is a common quality control procedure used to detect these errors and subsequently remove poor data. The second Chapter focuses on population-based genome-wide association studies and the practice of testing for Hardy-Weinberg departure. An extensive simulation study is presented revealing that the practice of removing SNPs on the basis of this test can lead to an inability to discover true disease susceptibility loci. A higher-powered alternative approach is presented. Finally, the third Chapter introduces a new test for data quality in family-based genome-wide association studies. Some genotyping errors are not detectable by conventional quality control measures. Family data provides a unique way to assess and estimate the magnitude of these errors by examining parent-to-offspring transmissions. The importance of this new quality assessment tool is illustrated by estimating the genotyping error rate in several studies which employ the most commonly used genotyping platforms.
Notes: "May 2008."

Thesis (Ph.D., Dept. of Biostatistics)--Harvard University, 2008.

Includes bibliographical references.