Abstract
Genome-wide association studies (GWAS) have been extensively used to estimate the signed effects of trait-associated alleles. One of the key challenges in GWAS are confounding factors, such as population stratification, which can lead to spurious genotype-trait associations. Recent independent studiesĀ [1, 8, 10] failed to replicate the strong evidence of previously reported signals of directional selection on height in Europeans in the UK Biobank cohort, and attributed the loss of signal to cryptic relatedness in populations.
Supported by NSF IIS 1715202 and NFS DMS 1760353 awarded to PD and PP.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Berg, J.J., Harpak, A., Sinnott-Armstrong, N., et al.: Reduced signal for polygenic adaptation of height in UK Biobank. eLife 8, e39725 (2019)
Bose, A., Kalantzis, V., Kontopoulou, E.M., et al.: TeraPCA: a fast and scalable software package to study genetic variation in tera-scale genotypes. Bioinformatics 35, 3679ā3683 (2019)
Ewens, W.J., Spielman, R.S.: The transmission/disequilibrium test: history, subdivision, and admixture. Am. J. Hum. Genet. 57(2), 455 (1995)
Kang, H.M., Sul, J.H., Service, S.K., et al.: Variance component model to account for sample structure in genome-wide association studies. Nat. Genet. 42(4), 348 (2010)
Mahalanobis, P.C.: On the generalized distance in statistics. National Institute of Science of India (1936)
Mathew, B., LƩon, J., SillanpƤƤ, M.J.: A novel linkage-disequilibrium corrected genomic relationship matrix for SNP-heritability estimation and genomic prediction. Heredity 120(4), 356 (2018)
Price, A.L., Patterson, N.J., Plenge, R.M., et al.: Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38(8), 904 (2006)
Sohail, M., Maier, R.M., Ganna, A., et al.: Polygenic adaptation on height is overestimated due to uncorrected stratification in genome-wide association studies. eLife 8, e39702 (2019)
Song, M., Hao, W., Storey, J.D.: Testing for genetic associations in arbitrarily structured populations. Nat. Genet. 47(5), 550 (2015)
Uricchio, L.H., Kitano, H.C., Gusev, A., et al.: An evolutionary compass for detecting signals of polygenic selection and mutational bias. Evol. Lett. 3(1), 69ā79 (2019)
Zhou, X., Stephens, M.: Genome-wide efficient mixed-model analysis for association studies. Nat. Genet. 44(7), 821 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
Ā© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Bose, A., Burch, M.C., Chowdhury, A., Paschou, P., Drineas, P. (2020). CluStrat: A Structure Informed Clustering Strategy for Population Stratification. In: Schwartz, R. (eds) Research in Computational Molecular Biology. RECOMB 2020. Lecture Notes in Computer Science(), vol 12074. Springer, Cham. https://doi.org/10.1007/978-3-030-45257-5_19
Download citation
DOI: https://doi.org/10.1007/978-3-030-45257-5_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-45256-8
Online ISBN: 978-3-030-45257-5
eBook Packages: Computer ScienceComputer Science (R0)