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LEA: An R package for landscape and ecological association studies
Abstract
Summary: Based on population genomic and environmental data, genomewide ecological association studies aim at detecting allele frequencies that exhibit significant statistical association with ecological gradients. Ecological association studies can provide lists of genetic polymorphisms that are potentially involved in local adaptation to environmental conditions through natural selection. Here, we present the R package LEA that enables users to run ecological association studies from the R command line. The package can perform analyses of population structure and genome scans for adaptive alleles from large genomic data sets. It derives advantages from R programming functionalities to adjust significance values for multiple testing issues and to visualize results. This note also illustrates the main steps of ecological association studies and the typical use of LEA for analysing data sets based on R commands.
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Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
Population Structure and Eigenanalysis
Patterson, N., Price, A. L., & Reich, D. (2006) · PLoS Genetics · doi:10.1371/journal.pgen.0020190 · Cited by 5,280
Genomic Control for Association Studies
Devlin, B., & Roeder, K. (1999) · Biometrics · doi:10.1111/j.0006-341x.1999.00997.x · Cited by 3,162
Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines
Atwell, S., Huang, Y. S., Vilhjálmsson, B. J., Willems, G., Horton, M., Li, Y., Meng, D., Platt, A., Tarone, A. M., Hu, T. T., et al. (2010) · Nature · doi:10.1038/nature08800 · Cited by 1,780
Fumagalli, M., Sironi, M., Pozzoli, U., Ferrer-Admettla, A., Pattini, L., & Nielsen, R. (2011) · PLoS Genetics · doi:10.1371/journal.pgen.1002355 · Cited by 600
Cited by
Finding the genomic basis of local adaptation: pitfalls, practical solutions, and future directions
Hoban, S., Kelley, J. L., Lotterhos, K. E., Antolin, M. F., Bradburd, G., Lowry, D. B., Poss, M. L., Reed, L. K., Storfer, A., & Whitlock, M. C. (2016) · The American Naturalist · doi:10.1086/688018 · Cited by 876
LFMM 2: Fast and Accurate Inference of Gene-Environment Associations in Genome-Wide Studies
Caye, K., Jumentier, B., Lepeule, J., & François, O. (2019) · Molecular Biology and Evolution · doi:10.1093/molbev/msz008 · Cited by 404
Self-citationA major locus controls local adaptation and adaptive life history variation in a perennial plant
Wang, J., Ding, J., Tan, B., Robinson, K. M., Michelson, I. H., Johansson, A., Nystedt, B., Scofield, D. G., Nilsson, O., Jansson, S., et al. (2018) · Genome Biology · doi:10.1186/s13059-018-1444-y · Cited by 145
The rise and fall of African rice cultivation revealed by analysis of 246 new genomes
Cubry, P., Tranchant-Dubreuil, C., Thuillet, A.-C., Monat, C., Ndjiondjop, M.-N., Labadie, K., Cruaud, C., Engelen, S., Scarcelli, N., Rhoné, B., et al. (2018) · Current Biology · doi:10.1016/j.cub.2018.05.066 · Cited by 142
Self-citationChang, J.-T., Chao, C.-T., Nakamura, K., Liu, H.-L., Luo, M.-X., & Liao, P.-C. (2022) · Frontiers in Plant Science · doi:10.3389/fpls.2022.824158 · Cited by 20
Metadata
- Authors
- Frichot, E., & François, O.
- Venue
- Methods in Ecology and Evolution
- Publisher
- Wiley
- Published
- 2015-05-13
- Volume
- 6
- Issue
- 8
- Pages
- 925-929
- ISSN
- 2041-210X (ISSN-L)
- OpenAlex
- W1976167484
- Cited by
- 1,674
- References
- 5
- FWCI
- 26.11
- Type
- journal article
- Language
- en
- Open access
- closed
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