Random-effects model aimed at discovering associations in meta-analysis of genome-wide association studies.

By B. Han, Eleazar Eskin. American Journal of Human Genetics, 2011

633 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/1988d2e2e69325e6522abd906e3c2c3c3b3c1ceb

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In Genetics

Citations in the corpus

Meta-Analysis in Clinical Trials Revisited (2015, methodology): “In particular, this method is sometimes criticized as ‘anti-conservative’ [25] or as being ‘too-conservative’ [26].”
Rare-variant association analysis: study designs and statistical tests. (2014, methodology): “88,89 For example, Morris 88 developed a single-variant transethnic meta-analysis method by using a Bayesian partition model that takes into account the expected heterogeneity be-tween diverse ancestry groups.”
Genome-wide association studies in ancestrally diverse populations: opportunities, methods, pitfalls, and recommendations. (2019, methodology): “When cross-cohort heterogeneity is present, using random-effects meta-analysis allows for the true marginal effect size of each variant to vary across studies, testing the null hypothesis that the mean true effect size across cohorts is zero (Han and Eskin, 2011).”
Large-scale meta-analysis of genome-wide association data identifies six new risk loci for Parkinson’s disease (2014, positive): “In addition, we tested novel random-effects approaches from Han and Eskin, 2011; however, this method did not identify any novel loci and since the results across both methodologies were near identical, thus only fixed-effect results are reported here.”
Genetic mapping of quantitative trait loci in crops (2017, methodology): “Although random-effects models have been used to deal with this problem [96], some studies with high heterogeneity should be removed from a metaanalysis [97].”
Genome-wide association analysis identifies 30 new susceptibility loci for schizophrenia (2017, neutral): “19 2 225334070, 133771; 224391301, 1996805; (1) (10) 20 2 233562197, 244575; 232749825, 1993197; (4) (39) 21 3 2495202, 66490; 1515988, 1999847; (1) (5) 22 3 17220098, 668159; 16859710, 1999180; (1) (5) 23 3 31058620, 1999878; 31058620, 1998976; (13) (13) 24 3 36843149, 52593; 35872376, 1999364; (1)”