Two-Stage Testing Strategies for Genome-Wide Association Studies in Family-Based Designs

2009 ◽  
pp. 485-496 ◽  
Author(s):  
Amy Murphy ◽  
Scott T. Weiss ◽  
Christoph Lange
Keyword(s):  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Two Stage ◽  
Testing Strategies ◽  
Genome Wide ◽  
Family Based

scholarly journals A modified two-stage approach for family-based genome-wide association studies

2013 ◽  
Vol 22 (1) ◽  
pp. 148-151 ◽  
Author(s):  
Weijun Ma ◽  
Ying Zhou ◽  
Yajing Zhou ◽  
Lili Chen ◽  
Zhen Gu
Keyword(s):  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Two Stage ◽  
Genome Wide ◽  
Family Based

scholarly journals One-stage design is empirically more powerful than two-stage design for family-based genome-wide association studies

2007 ◽  
Vol 1 (S1) ◽  
Author(s):  
Rori V Rohlfs ◽  
Chelsea Taylor ◽  
Lucia Mirea ◽  
Shelley B Bull ◽  
Mary Corey ◽  
...  
Keyword(s):  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Two Stage ◽  
Stage Design ◽  
One Stage ◽  
Genome Wide ◽  
Two Stage Design ◽  
Family Based

A Robust Test for Two-Stage Design in Genome-Wide Association Studies

Biometrics ◽  
2009 ◽  
Vol 65 (4) ◽  
pp. 1288-1295 ◽  
Author(s):  
Minjung Kwak ◽  
Jungnam Joo ◽  
Gang Zheng
Keyword(s):  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Two Stage ◽  
Stage Design ◽  
Robust Test ◽  
Genome Wide ◽  
Two Stage Design

scholarly journals Joint Analysis for Genome-Wide Association Studies in Family-Based Designs

PLoS ONE ◽  
2011 ◽  
Vol 6 (7) ◽  
pp. e21957 ◽  
Author(s):  
Qiuying Sha ◽  
Zhaogong Zhang ◽  
Shuanglin Zhang
Keyword(s):  
Association Studies ◽  
Genome Wide Association ◽  
Joint Analysis ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Family Based

scholarly journals Non-random error in genotype calling procedures: Implications for family-based and case-control genome-wide association studies

2008 ◽  
Vol 147B (8) ◽  
pp. 1379-1386 ◽  
Author(s):  
Richard J.L. Anney ◽  
Elaine Kenny ◽  
Colm T. O'Dushlaine ◽  
Jessica Lasky-Su ◽  
Barbara Franke ◽  
...  
Keyword(s):  
Random Error ◽  
Association Studies ◽  
Case Control ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genotype Calling ◽  
Genome Wide ◽  
Family Based

scholarly journals Application of seventeen two-locus models in genome-wide association studies by two-stage strategy

2009 ◽  
Vol 3 (Suppl 7) ◽  
pp. S26 ◽  
Author(s):  
Adan Niu ◽  
Zhaogong Zhang ◽  
Qiuying Sha
Keyword(s):  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Two Stage ◽  
Genome Wide

Jointly determining significance levels of primary and replication studies by controlling the false discovery rate in two-stage genome-wide association studies

2017 ◽  
Vol 27 (9) ◽  
pp. 2795-2808 ◽  
Author(s):  
Wei Jiang ◽  
Weichuan Yu
Keyword(s):  
False Discovery Rate ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Two Stage ◽  
Traditional Methods ◽  
Replication Studies ◽  
False Discovery ◽  
Genome Wide ◽  
Significance Levels

In genome-wide association studies, we normally discover associations between genetic variants and diseases/traits in primary studies, and validate the findings in replication studies. We consider the associations identified in both primary and replication studies as true findings. An important question under this two-stage setting is how to determine significance levels in both studies. In traditional methods, significance levels of the primary and replication studies are determined separately. We argue that the separate determination strategy reduces the power in the overall two-stage study. Therefore, we propose a novel method to determine significance levels jointly. Our method is a reanalysis method that needs summary statistics from both studies. We find the most powerful significance levels when controlling the false discovery rate in the two-stage study. To enjoy the power improvement from the joint determination method, we need to select single nucleotide polymorphisms for replication at a less stringent significance level. This is a common practice in studies designed for discovery purpose. We suggest this practice is also suitable in studies with validation purpose in order to identify more true findings. Simulation experiments show that our method can provide more power than traditional methods and that the false discovery rate is well-controlled. Empirical experiments on datasets of five diseases/traits demonstrate that our method can help identify more associations. The R-package is available at: http://bioinformatics.ust.hk/RFdr.html .

scholarly journals Statistical challenges for genome-wide association studies of suicidality using family data

2010 ◽  
Vol 25 (5) ◽  
pp. 307-309 ◽  
Author(s):  
J. Lasky-Su ◽  
C. Lange
Keyword(s):  
Association Studies ◽  
Statistical Tests ◽  
Genetic Association Studies ◽  
Genetic Effect ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genetic Etiology ◽  
Genome Wide ◽  
Family Based ◽  
Study Designs

AbstractThe etiology of suicide is complex in nature with both environmental and genetic causes that are extremely diverse. This extensive heterogeneity weakens the relationship between genotype and phenotype and as a result, we face many challenges when studying the genetic etiology of suicide. We are now in the midst of a genetics revolution, where genotyping costs are decreasing and genotyping speed is increasing at a fast rate, allowing genetic association studies to genotype thousands to millions of SNPs that cover the entire human genome. As such, genome-wide association studies (GWAS) are now the norm. In this article we address several statistical challenges that occur when studying the genetic etiology of suicidality in the age of the genetics revolution. These challenges include: (1) the large number of statistical tests; (2) complex phenotypes that are difficult to quantify; and (3) modest genetic effect sizes. We address these statistical issues in the context of family-based study designs. Specifically, we discuss several statistical extensions of family-based association tests (FBATs) that work to alleviate these challenges. As our intention is to describe how statistical methodology may work to identify disease variants for suicidality, we avoid the mathematical details of the methodologies presented.

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