Asset Details
Do you wish to reserve the book?
Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies
Do you wish to request the book?
Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies
2006
Overview
Genome-wide association is a promising approach to identify common genetic variants that predispose to human disease
1
,
2
,
3
,
4
. Because of the high cost of genotyping hundreds of thousands of markers on thousands of subjects, genome-wide association studies often follow a staged design in which a proportion (π
samples
) of the available samples are genotyped on a large number of markers in stage 1, and a proportion (π
samples
) of these markers are later followed up by genotyping them on the remaining samples in stage 2. The standard strategy for analyzing such two-stage data is to view stage 2 as a replication study and focus on findings that reach statistical significance when stage 2 data are considered alone
2
. We demonstrate that the alternative strategy of jointly analyzing the data from both stages almost always results in increased power to detect genetic association, despite the need to use more stringent significance levels, even when effect sizes differ between the two stages. We recommend joint analysis for all two-stage genome-wide association studies, especially when a relatively large proportion of the samples are genotyped in stage 1 (π
samples
≥ 0.30), and a relatively large proportion of markers are selected for follow-up in stage 2 (π
markers
≥ 0.01).
Publisher
Nature Publishing Group US,Nature Publishing Group
Subject
/ Alleles
/ Animal Genetics and Genomics
/ Biological and medical sciences
/ Biomedical and Life Sciences
/ Disease
/ Fundamental and applied biological sciences. Psychology
/ Genetic Predisposition to Disease - genetics
/ Genetics of eukaryotes. Biological and molecular evolution
/ Genomics
/ Genotype
/ Heredity
/ Humans
/ Identification and classification
/ letter
/ Methods
