An atlas of genetic associations in UK Biobank

By O. Canela‐Xandri, Konrad Rawlik, A. Tenesa. Nature Genetics, 2017

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webTWAS: a resource for disease candidate susceptibility genes identified by transcriptome-wide association study (2021, unknown): “UKBB cohort data is collected from Neale Lab UKBB v3 (http://www.nealelab.is/uk-biobank), Gene ATLAS (35) and GWAS ATLAS (30).”
Mixed model association for biobank-scale data sets (2018, methodology): “) Our new release of BOLT-LMM makes mixed model association computationally efficient even on extremely large data sets without the need for distributed computing [9].”
Fine-mapping from summary data with the “Sum of Single Effects” model (2021, methodology): “Following [49], we took steps to filter out genotype samples, resulting in a candidate set of 274,549 samples.”
An integrated framework for local genetic correlation analysis (2022, background): “As expected, many of these were located in the MHC, likely owing to the number of immune-and health-related phenotypes analyzed 29,30 and the MHC having been flagged as a pleiotropy hotspot previously 2,28 .”
A resource-efficient tool for mixed model association analysis of large-scale data (2019, methodology): “We compared the results from GeneATLAS to those from the Neale Lab and our fastGWA analysis for 10 traits available in all the three sets ( Supplementary Table 4 and 5 ).”