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[Preprint]. 2023 Oct 2:2023.09.30.560263.

doi: 10.1101/2023.09.30.560263.

Admix-kit: An Integrated Toolkit and Pipeline for Genetic Analyses of Admixed Populations

Kangcheng Hou¹, Stephanie Gogarten², Joohyun Kim³, Xing Hua⁴, Julie-Alexia Dias⁵, Quan Sun⁶, Ying Wang⁷, Taotao Tan⁸; Polygenic Risk Methods in Diverse Populations (PRIMED) Consortium Methods Working Group; Elizabeth G Atkinson⁸, Alicia Martin⁷, Jonathan Shortt⁹, Jibril Hirbo¹⁰, Yun Li⁶, Bogdan Pasaniuc¹, Haoyu Zhang⁴

Affiliations

¹ Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, USA.
² Department of Biostatistics, University of Washington, Seattle, WA, USA.
³ Vanderbilt Genetics Institute and Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
⁴ Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
⁵ Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
⁶ Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
⁷ Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
⁸ Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.
⁹ Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
¹⁰ Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.

PMID: 37873338
PMCID: PMC10592849
DOI: 10.1101/2023.09.30.560263

Admix-kit: An Integrated Toolkit and Pipeline for Genetic Analyses of Admixed Populations

Kangcheng Hou et al. bioRxiv. 2023.

[Preprint]. 2023 Oct 2:2023.09.30.560263.

doi: 10.1101/2023.09.30.560263.

Authors

Affiliations

¹ Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, USA.
² Department of Biostatistics, University of Washington, Seattle, WA, USA.
³ Vanderbilt Genetics Institute and Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
⁴ Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
⁵ Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
⁶ Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
⁷ Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
⁸ Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.
⁹ Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
¹⁰ Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.

PMID: 37873338
PMCID: PMC10592849
DOI: 10.1101/2023.09.30.560263

Update in

Admix-kit: an integrated toolkit and pipeline for genetic analyses of admixed populations.
Hou K, Gogarten S, Kim J, Hua X, Dias JA, Sun Q, Wang Y, Tan T; Polygenic Risk Methods in Diverse Populations (PRIMED) Consortium Methods Working Group; Atkinson EG, Martin A, Shortt J, Hirbo J, Li Y, Pasaniuc B, Zhang H. Hou K, et al. Bioinformatics. 2024 Mar 29;40(4):btae148. doi: 10.1093/bioinformatics/btae148. Bioinformatics. 2024. PMID: 38490256 Free PMC article.

Abstract

Admixed populations, with their unique and diverse genetic backgrounds, are often underrepresented in genetic studies. This oversight not only limits our understanding but also exacerbates existing health disparities. One major barrier has been the lack of efficient tools tailored for the special challenges of genetic study of admixed populations. Here, we present admix-kit, an integrated toolkit and pipeline for genetic analyses of admixed populations. Admix-kit implements a suite of methods to facilitate genotype and phenotype simulation, association testing, genetic architecture inference, and polygenic scoring in admixed populations.

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Conflict of interest statement

Competing interests The authors declare no competing interests.

Figures

**Figure 1:. Overview of admix-kit’s data structure, functionality, and illustrative analyses using a simulated dataset**
**(a)** Local ancestry and phased genotypes are stored in matrix format. Individual-specific and SNP-specific covariates are stored as two tables with matching orders. **(b)** Analysis based on ancestry-specific genotype dosage. Starting with a phased genotype for an individual (0/1 denotes presence of minor allele, blue/red color denotes European/African local ancestry), genotypes are separated into ancestry-specific dosages. Local ancestry-informed downstream analyses can be subsequently performed. **(c)** visualization of local ancestry tracts. **(d)** Consistency of genome-wide genetic ancestry of simulated dataset **(e)** Consistency of allele frequencies from the simulated admixed genotypes.

See this image and copyright information in PMC

References

1. Atkinson E.G. et al. (2021) Tractor uses local ancestry to enable the inclusion of admixed individuals in GWAS and to boost power. Nat. Genet., 53, 195–204. - PMC - PubMed
1. Ding Y. et al. (2023) Polygenic scoring accuracy varies across the genetic ancestry continuum. Nature, 618, 774–781. - PMC - PubMed
1. Hou K. et al. (2023) Causal effects on complex traits are similar for common variants across segments of different continental ancestries within admixed individuals. Nat. Genet., 55, 549–558. - PMC - PubMed
1. Hou K. et al. (2021) On powerful GWAS in admixed populations. Nat. Genet., 53, 1631–1633. - PMC - PubMed
1. Kachuri L. et al. (2023) Principles and methods for transferring polygenic risk scores across global populations. Nat. Rev. Genet. - PMC - PubMed

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This is a preprint.

Admix-kit: An Integrated Toolkit and Pipeline for Genetic Analyses of Admixed Populations

Affiliations

Admix-kit: An Integrated Toolkit and Pipeline for Genetic Analyses of Admixed Populations

Authors

Affiliations

Update in

Abstract

Conflict of interest statement

Figures

References

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