Access the full text.
Sign up today, get DeepDyve free for 14 days.
P. Armitage (1955)
Tests for Linear Trends in Proportions and FrequenciesBiometrics, 11
Lihan Yan, G. Zheng, Z. Li (2008)
Two‐Stage Group Sequential Robust Tests in Family‐Based Association Studies: Controlling Type I ErrorAnnals of Human Genetics, 72
S. Slager, Daniel Schaid (2001)
Case-Control Studies of Genetic Markers: Power and Sample Size Approximations for Armitage’s Test for TrendHuman Heredity, 52
(2008)
Infinium R © HD DNA Analysis BeadChips
Hansong Wang, D. Thomas, I. Pe’er, D. Stram (2006)
Optimal two‐stage genotyping designs for genome‐wide association scansGenetic Epidemiology, 30
(2006)
Golden Gate R Assay Workflow
B. Freidlin, G. Zheng, Zhaohai Li, J. Gastwirth (2002)
Trend Tests for Case-Control Studies of Genetic Markers: Power, Sample Size and RobustnessHuman Heredity, 53
H. Müller, R. Pahl, H. Schäfer (2007)
Including sampling and phenotyping costs into the optimization of two stage designs for genome wide association studiesGenetic Epidemiology, 31
Andrew Skol, L. Scott, G. Abecasis, M. Boehnke (2007)
Optimal designs for two‐stage genome‐wide association studiesGenetic Epidemiology, 31
J. Satagopan, E. Venkatraman, C. Begg (2004)
Two‐Stage Designs for Gene–Disease Association Studies with Sample Size ConstraintsBiometrics, 60
J. Satagopan, D. Verbel, E. Venkatraman, K. Offit, C. Begg (2002)
Two‐Stage Designs for Gene–Disease Association StudiesBiometrics, 58
P. Sasieni (1997)
From genotypes to genes: doubling the sample size.Biometrics, 53 4
Hansong Wang, D. Stram (2006)
Optimal two-stage genome-wide association designs based on false discovery rateComput. Stat. Data Anal., 51
A. Scherag, J. Hebebrand, H. Schäfer, H. Müller (2009)
Flexible Designs for Genomewide Association StudiesBiometrics, 65
J. Satagopan, R. Elston (2003)
Optimal two‐stage genotyping in population‐based association studiesGenetic Epidemiology, 25
Duncan Thomas, Rongrong Xie, M. Gebregziabher (2004)
Two‐Stage sampling designs for gene association studiesGenetic Epidemiology, 27
B. Han, H. Kang, E. Eskin (2009)
Rapid and Accurate Multiple Testing Correction and Power Estimation for Millions of Correlated MarkersPLoS Genetics, 5
Gang Zheng, B. Freidlin, J. Gastwirth (2002)
Robust TDT‐type candidate‐gene association testsAnnals of Human Genetics, 66
R. Pahl, H. Schäfer, H. Müller (2009)
Optimal multistage designs--a general framework for efficient genome-wide association studies.Biostatistics, 10 2
G. Zheng, J. Gastwirth (2006)
On estimation of the variance in Cochran–Armitage trend tests for genetic association using case–control studiesStatistics in Medicine, 25
Danyu Lin (2006)
Evaluating statistical significance in two-stage genomewide association studies.American journal of human genetics, 78 3
Andrew Skol, L. Scott, G. Abecasis, M. Boehnke (2006)
Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studiesNature Genetics, 38
Qizhai Li, Kai Yu, Z. Li, G. Zheng (2008)
MAX-rank: a simple and robust genome-wide scan for case-control association studiesHuman Genetics, 123
F. Dudbridge (2006)
A note on permutation tests in multistage association scans.American journal of human genetics, 78 6
Qizhai Li, G. Zheng, X. Liang, Kai Yu (2009)
Robust Tests for Single‐marker Analysis in Case‐Control Genetic Association StudiesAnnals of Human Genetics, 73
R. Elston, D. Lin, G. Zheng (2007)
Multistage sampling for genetic studies.Annual review of genomics and human genetics, 8
Optimal robust two‐stage designs for genome‐wide association studies are proposed using the maximum of the recessive, additive and dominant linear trend test statistics. These designs combine cost‐saving two‐stage genotyping with robustness against misspecification of the genetic model and are much more efficient than designs based on a single model specific test statistic in detecting multiple loci with different modes of inheritance. For given power of 90%, typical cost savings of 34% can be realised by increasing the total sample size by about 13% but genotyping only about half of the sample for the full marker set in the first stage and carrying forward about 0.06% of the markers to the second stage analysis. We also present robust two‐stage designs providing optimal allocation of a limited budget for pre‐existing samples. If a sample is available which would yield a power of 90% when fully genotyped, genotyping only half of the sample due to a limited budget will typically cause a loss of power of more than 55%. Using an optimal two‐stage approach in the same sample under the same budget restrictions will limit the loss of power to less than 10%. In general, the optimal proportion of markers to be followed up in the second stage strongly depends on the cost ratio for chips and individual genotyping, while the design parameters of the optimal designs (total sample size, first stage proportion, first and second stage significance limit) do not much depend on the genetic model assumptions.
Annals of Human Genetics – Wiley
Published: Jan 1, 2009
Keywords: ; ; ; ; ; ;
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.