Protocols for study of Cross-ancestry genome-wide association meta-analyses of hippocampal and subfield volumes

Nana Liu

References:

1. Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81, 559-75 (2007).
2. Price, A.L. et al. Long-range LD can confound genome scans in admixed populations. Am J Hum Genet 83, 132-5; author reply 135-9 (2008).
3. Anderson, C.A. et al. Data quality control in genetic case-control association studies. Nat Protoc 5, 1564-73 (2010).
4. Delaneau, O., Zagury, J.F. & Marchini, J. Improved whole-chromosome phasing for disease and population genetic studies. Nat Methods 10, 5-6 (2013).
5. Howie, B.N., Donnelly, P. & Marchini, J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet 5, e1000529 (2009).
6. Auton, A. et al. A global reference for human genetic variation. Nature 526, 68-74 (2015).
7. Wu, D. et al. Large-Scale Whole-Genome Sequencing of Three Diverse Asian Populations in Singapore. Cell 179, 736-749.e15 (2019).
8. Fischl, B. et al. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33, 341-55 (2002).
9. Fortin, J.P. et al. Harmonization of multi-site diffusion tensor imaging data. Neuroimage 161, 149-170 (2017).
10. Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203-209 (2018).
11. Elliott, L.T. et al. Genome-wide association studies of brain imaging phenotypes in UK Biobank. Nature 562, 210-216 (2018).
12. Han, B. & Eskin, E. Random-effects model aimed at discovering associations in meta-analysis of genome-wide association studies. Am J Hum Genet 88, 586-98 (2011).
13. Bulik-Sullivan, B.K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet 47, 291-5 (2015).
14. Luo, Y. et al. Estimating heritability and its enrichment in tissue-specific gene sets in admixed populations. Hum Mol Genet 30, 1521-1534 (2021).
15. Kichaev, G. & Pasaniuc, B. Leveraging Functional-Annotation Data in Trans-ethnic Fine-Mapping Studies. Am J Hum Genet 97, 260-71 (2015).
16. Benner, C. et al. FINEMAP: efficient variable selection using summary data from genome-wide association studies. Bioinformatics 32, 1493-1501 (2016).
17. Choi, S.W. & O'Reilly, P.F. PRSice-2: Polygenic Risk Score software for biobank-scale data. Gigascience 8(2019).
18. Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet 10, e1004383 (2014).