𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Multiple testing in the genomics era: Findings from Genetic Analysis Workshop 15, Group 15

✍ Scribed by Lisa J. Martin; Jessica G. Woo; Christy L. Avery; Huann-Sheng Chen; Kari E. North

Publisher
John Wiley and Sons
Year
2007
Tongue
English
Weight
135 KB
Volume
31
Category
Article
ISSN
0741-0395

No coin nor oath required. For personal study only.

✦ Synopsis

Recent advances in molecular technologies have resulted in the ability to screen hundreds of thousands of single nucleotide polymorphisms and tens of thousands of gene expression profiles. While these data have the potential to inform investigations into disease etiologies and advance medicine, the question of how to adequately control both type I and type II error rates remains. Genetic Analysis Workshop 15 datasets provided a unique opportunity for participants to evaluate multiple testing strategies applicable to microarray and single nucleotide polymorphism data. The Genetic Analysis Workshop 15 multiple testing and false discovery rate group (Group 15) investigated three general categories for multiple testing corrections, which are summarized in this review: statistical independence, error rate adjustment, and data reduction. We show that while each approach may have certain advantages, adequate error control is largely dependent upon the question under consideration and often requires the use of multiple analytic strategies.

📜 SIMILAR VOLUMES

Multiple testing in high-throughput sequ
Multiple testing in high-throughput sequence data: experiences from Group 8 of Genetic Analysis Workshop 17
✍ Inke R. König; Jeremie Nsengimana; Charalampos Papachristou; Matthew A. Simonson 📂 Article 📅 2011 🏛 John Wiley and Sons 🌐 English ⚖ 98 KB 👁 1 views

## Abstract The use of high‐throughput sequence data in genetic epidemiology allows the investigation of common and rare variants in the entire genome, thus increasing the amount of information and the potential number of statistical tests performed within one study. As a consequence, the problem o

Inflated type I error rates when using a
Inflated type I error rates when using aggregation methods to analyze rare variants in the 1000 Genomes Project exon sequencing data in unrelated individuals: summary results from Group 7 at Genetic Analysis Workshop 17
✍ Nathan Tintle; Hugues Aschard; Inchi Hu; Nora Nock; Haitian Wang; Elizabeth Pugh 📂 Article 📅 2011 🏛 John Wiley and Sons 🌐 English ⚖ 85 KB 👁 2 views

## Abstract As part of Genetic Analysis Workshop 17 (GAW17), our group considered the application of novel and standard approaches to the analysis of genotype‐phenotype association in next‐generation sequencing data. Our group identified a major issue in the analysis of the GAW17 next‐generation se