Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2011;12(2):R19.
doi: 10.1186/gb-2011-12-2-r19. Epub 2011 Feb 25.

Dating the Age of Admixture via Wavelet Transform Analysis of Genome-Wide Data

Affiliations
Free PMC article

Dating the Age of Admixture via Wavelet Transform Analysis of Genome-Wide Data

Irina Pugach et al. Genome Biol. .
Free PMC article

Abstract

We describe a PCA-based genome scan approach to analyze genome-wide admixture structure, and introduce wavelet transform analysis as a method for estimating the time of admixture. We test the wavelet transform method with simulations and apply it to genome-wide SNP data from eight admixed human populations. The wavelet transform method offers better resolution than existing methods for dating admixture, and can be applied to either SNP or sequence data from humans or other species.

Figures

Figure 1
Figure 1
Diagram giving an overview of the admixture process. When two populations admix, genetic recombination starts breaking ancestral genomes into blocks of different sizes, so that the genomes of the descendants of an admixture event are composed of different combinations of these ancestral blocks. The number and width of the admixture blocks contain information about the time since admixture, as more recombination events result in a greater number of blocks, which with time get progressively narrower and more evenly spread along and among chromosomes.
Figure 2
Figure 2
Data from 100 simulations for migration values of 1%, 5%, 10%, 20%, 30%, and 40%. Each curve represents a single admixed population. To generate the plots, 100 chromosomes were sampled from each population at exponentially growing time points, and the following statistics for each chromosome in each sampled generation were collected: (a) admixture rate; (b) number of breakpoints; black lines indicate the expected value, given by: Nbkpts = 2TgenR((1 - ) - var α); and (c) the WT centers. Inset: Average number of breakpoints for each simulation parameter. Black lines indicate the expected value.
Figure 3
Figure 3
Distributions of the WT levels, illustrating how the wavelet transform spectrum changes with time since admixture. For each illustrated time point, WT levels from 10 randomly chosen simulations are plotted (each bar represents one simulation, resulting in 10 bars for each level). The height of the columns indicate the abundance of wavelets of particular frequency present in the signal, starting with the lowest wave frequencies (widest recombination blocks) on the left and progressing towards the highest wave frequencies (narrowest recombination blocks) on the right. The WT centers in this plot are not adjusted for chromosome length, and thus appear to be higher than the values we present for genomewide data.
Figure 4
Figure 4
Performance of Hapmix and wavelet transform analysis in recovering the average number of recombination breakpoints per Morgan of genetic distance from simulated data. The two methods were applied to 20 artificially admixed individuals, created using a genomewide average of 20% European and 80% African ancestry. For simulated data the average number of ancestry switches (or breakpoints) was drawn from an exponential distribution with weight λ, such that the ancestry switch occurred with the probability 1 - e-λg for each distance of g Morgans. The following values of λ were simulated: 6, 10, 20, 40, 60, 100, 200, 400. Since in the simulated genomes the true number of breakpoints is known, we show the accuracy of both methods in recovering this information.
Figure 5
Figure 5
PCA and StepPCO results for chromosome 1. Solid lines centered around 1 and -1 indicate the mean PC1 coordinate for each parental population; progressively lighter shading surrounding the mean of each parental group indicate +/-1, +/-2 or +/-3 standard deviations from the mean. (a) Upper panel: PC1 vs PC2 for populations of CEU, YRI and ASW. Lower panel: Unphased chromosome 1 of an individual of African American ancestry; European (blue) and Yoruba (red) populations are used as parental groups. (b) Upper panel: PC1 vs PC2 for populations of MEL, BOR and PLY. Lower panel: Unphased chromosome 1 of an individual from Polynesia; Borneo (green) and New Guinean (orange) populations are used as parental groups. (c) Upper panel: PC1 vs PC2 for populations of MEL, PLY and FIJ. Lower panel: Unphased chromosome 1 of an individual from Fiji; Polynesia (brown) and New Guinean (orange) populations are used as parental groups.
Figure 6
Figure 6
Admixture estimates. (a) Genome-wide admixture estimates based on StepPCO for African-Americans, Polynesians and Fijians. (b) Comparison of admixture estimates obtained via StepPCO vs. Frappe, for chromosome 1 for 20 African-Americans.
Figure 7
Figure 7
Average centers of the WT coefficients, calculated for each individual.
Figure 8
Figure 8
Admixture time estimates for the African-Americans, Polynesians and Fijians. Simulated data from 100 simulations with a 20% and 40% migration rate are presented. Each curve represents a single admixed population. Average WT centers calculated for 100 chromosomes drawn at random from each population at exponentially growing time points are plotted as a function of time. Measurements obtained for the ASW, PLY and FIJ populations are shown by blue horizontal lines. Red vertical lines indicate the time estimate, and shaded boxes define the confidence intervals. Time estimate for ASW and PLY are based on simulations with a 20% admixture rate, while the time estimate for FIJ is based on simulations with a 40% admixture rate.

Similar articles

See all similar articles

Cited by 39 articles

See all "Cited by" articles

References

    1. Myles S, Davison D, Barrett J, Stoneking M, Timpson N. Worldwide population differentiation at disease-associated SNPs. BMC Medical Genomics. 2008;1:22. doi: 10.1186/1755-8794-1-22. - DOI - PMC - PubMed
    1. Choudhry S, Taub M, Mei R, Rodriguez-Santana J, Rodriguez-Cintron W, Shriver M, Ziv E, Risch N, Burchard E. Genome-wide screen for asthma in Puerto Ricans: evidence for association with 5q23 region. Human Genetics. 2008;123:455–468. doi: 10.1007/s00439-008-0495-7. - DOI - PMC - PubMed
    1. Chakraborty R, Weiss KM. Admixture as a tool for finding linked genes and detecting that difference from allelic association between loci. Proceedings of the National Academy of Sciences of the United States of America. 1988;85:9119–9123. doi: 10.1073/pnas.85.23.9119. - DOI - PMC - PubMed
    1. Patterson N, Hattangadi N, Lane B, Lohmueller KE, Hafler DA, Oksenberg JR, Hauser SL, Smith MW, OBrien SJ, Altshuler D, Daly MJ, Reich D. Methods for high-density admixture mapping of disease genes. American Journal of Human Genetics. 2004;74:979–1000. doi: 10.1086/420871. - DOI - PMC - PubMed
    1. Cheng CY, Kao WHL, Patterson N, Tandon A, Haiman CA, Harris TB, Xing C, John EM, Ambrosone CB, Brancati FL, Coresh J, Press MF, Parekh RS, Klag MJ, Meoni LA, Hsueh WC, Fejerman L, Pawlikowska L, Freedman ML, Jandorf LH, Bandera EV, Ciupak GL, Nalls MA, Akylbekova EL, Orwoll ES, Leak TS, Miljkovic I, Li R, Ursin G, Bernstein L. et al. Admixture mapping of 15,280 African Americans identifies obesity susceptibility loci on chromosomes 5 and X. PLoS Genetics. 2009;5:e1000490. doi: 10.1371/journal.pgen.1000490. - DOI - PMC - PubMed

Publication types

LinkOut - more resources

Feedback