ArticleHuman heredity2012
On the meta-analysis of genome-wide association studies: a robust and efficient approach to combine population and family-based studies.
Article in Human heredity, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- FARVATX: Family-Based Rare Variant Association Test for X-Linked Genes.Genetic epidemiology · 2016Article
- LEVERAGING LOCAL IDENTITY-BY-DESCENT INCREASES THE POWER OF CASE/CONTROL GWAS WITH RELATED INDIVIDUALS.The annals of applied statistics · 2014Article
- Statistical Approaches to Combine Genetic Association Data.Journal of biometrics & biostatistics · 2013Article
Corrections and comments
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Authors and funding
5 authors.
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Abstract
For the meta-analysis of genome-wide association studies, we propose a new method to adjust for the population stratification and a linear mixed approach that combines family-based and unrelated samples. The proposed approach achieves similar power levels as a standard meta-analysis which combines the different test statistics or p values across studies. However, by virtue of its design, the proposed approach is robust against population admixture and stratification, and no adjustments for population admixture and stratification, even in unrelated samples, are required. Using simulation studies, we examine the power of the proposed method and compare it to standard approaches in the meta-analysis of genome-wide association studies. The practical features of the approach are illustrated with a meta-analysis of three genome-wide association studies for Alzheimer's disease. We identify three single nucleotide polymorphisms showing significant genome-wide association with affection status. Two single nucleotide polymorphisms are novel and will be verified in other populations in our follow-up study.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.