Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies

By Andrew D. Skol, L. Scott, Gonçalo R. Abecasis, Michael Boehnke. Nature Genetics, 2006

1,346 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/dec1ba636fe2c59afc45de95a2a8a305c5786924

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Citations in the corpus

SNPs in KCNQ1 are associated with susceptibility to type 2 diabetes in East Asian and European populations (2008, methodology): “With 207,097 tests performed in the initial genome-wide association study, these associations, except that for rs163171, remained significant after Bonferroni adjustmen”
The linkage disequilibrium and the selection of genetic markers for association studies in European populations (2007, background): “It has been shown that the joint analyzing the data from both stages almost always has more power to detect genetic association then the replication based analysis (Skol et al. 2006).”
Genome-wide association study identifies variants in the ABO locus associated with susceptibility to pancreatic cancer (2009, background): “In a combined analysis of individuals of European backgroun”
Covariate‐assisted ranking and screening for large‐scale two‐sample inference (2019, result): “For example, the empirical studies in Skol et al. (2006) concluded that a two-stage analysis is in general inferior compared with a naive joint analysis that combines the data from both stages.”
Family-based association tests for genomewide association scans. (2007, methodology): “It is important to note that a combined analysis of the stage 1 and stage 2 data, with a stringent significance threshold, often will provide more power than simply using the stage 2 data to replicate stage 1 findings.(41) In the results presented here, we have focused on imputing genotypes for all ”
PGA: power calculator for case-control genetic association analyses (2008, methodology): “Similarly to other popular software in this field [12-14], PGA incorporates basic power and sample size calculations for various genetic models and presents the results 'on the fly' in graphs and tables.”
Estimating Effects and Making Predictions from Genome-Wide Marker Data (2010, result): “In contrast, classical estimates of SNP effects combining the initial GWAS and the validation study (as in [39]) will still be biased for SNPs selected on the basis of the GWAS.”
Basic statistical analysis in genetic case-control studies (2011, methodology): “In stage two, the top signals are then followed up by genotyping them on the remaining samples while a joint analysis of data from both stages is conducted 29,30 .”
Estimation of the multiple testing burden for genomewide association studies of nearly all common variants (2008, methodology): “Such an evaluation is particularly critical to study designs that include a second stage of additional genotyping [Thomas et al., 2004; Skol et al., 2006] or analysis [Klein et al.”
Revisiting biomarker discovery by plasma proteomics (2017, background): “Researchers in this field found that joint analysis of as many samples as possible was superior to a sequential pipeline (Skol et al, 2006).”
Genome-wide association scan of tag SNPs identifies a susceptibility locus for lung cancer at 15q25.1 (2008, methodology): “We obtained P values combining data from the discovery phase as well as the two replication phases following a procedure outlined previousl”
Novel genes identified in a high-density genome wide association study for nicotine dependence. (2007, background): “We purposefully chose to examine the entire sample as the primary analysis, rather than use a split sample design because we felt that this had the greatest power to detect true findings (29).”
Meta-analysis of genome-wide association data identifies four new susceptibility loci for colorectal cancer (2008, methodology): “We based our estimate of study power on a joint analysis of phases 1 and 2 assuming a multiplicative model for each SN”
A variant in CDKAL1 influences insulin response and risk of type 2 diabetes (2007, background): “A comprehensive follow-up strategy would require genotyping a large number of SNP”