Prioritizing GWAS results: A review of statistical methods and recommendations for their application.

By R. Cantor, Kenneth L. Lange, Janet S. Sinsheimer. American Journal of Human Genetics, 2010

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Privacy-Preserving Statistical Analysis by Exact Logistic Regression (2015, methodology): “If the case and control sets have identical distribution of all clinical variables, simple correlation-based statistics such as chi-squared statistics can be used [8].”
Meta-analysis methods for genome-wide association studies and beyond (2013, background): “These typically include thresholds of statistical significance, but additional information may also be incorporated (for example, biological plausibility, evidence from other GWASs and other meta-analyses in the same field, text-mining-based methods or information on pathways 17 ).”
Integrating predicted transcriptome from multiple tissues improves association detection (2018, background): “However, understanding the mechanism underlying these associations remains a challenging 38 problem, requiring follow-up studies and a wide array of techniques such as prioritization [4] and pathway 39 analysis [5].”
GAPIT Version 2: An Enhanced Integrated Tool for Genomic Association and Prediction (2016, methodology): “These large datasets require increasingly greater computing capabilities for performing analyses such as GWAS (Cantor et al., 2010) and genomic prediction of phenotypes from genotypes (VanRaden et al.”
Using bioinformatics to predict the functional impact of SNVs (2011, background): “Received on October 20, 2010 ; revised on November 21, 2010; accepted on December 12, 2010”