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Reconstructing Past Admixture Processes from Local Genomic Ancestry Using Wavelet Transformation.

Sanderson J, Sudoyo H, Karafet TM, Hammer MF, Cox MP - Genetics (2015)

Bottom Line: Here, we describe an improved wavelet-based technique that better characterizes ancestry block structure from observed genomic patterns. principal components analysis is first applied to genomic data to identify the primary population structure, followed by wavelet decomposition to develop a new characterization of local ancestry information along the chromosomes.Time of admixture is inferred using an approximate Bayesian computation framework, providing robust estimates of both admixture times and their associated levels of uncertainty.Crucially, we demonstrate that this revised wavelet approach, which we have released as the R package adwave, provides improved statistical power over existing wavelet-based techniques and can be used to address a broad range of admixture questions.

View Article: PubMed Central - PubMed

Affiliation: Statistics and Bioinformatics Group, Institute of Fundamental Sciences, Massey University, Palmerston North 4442, New Zealand.

No MeSH data available.


Simulated example with 13,000 SNPs, 15 diploid individuals in ancestral populations (PA, PB), and 20 diploid individuals in the admixed population (PC). Populations are shown in green (PA), blue (PB), and red (PC). (A) PCA is used to describe the primary population structure; (B) raw wavelet variance for each population illustrates high frequency noise; (C) informative variation in the admixed population after standard correction for noise estimated from the ancestral populations. Note that this example uses the default threshold μ = 1.
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fig1: Simulated example with 13,000 SNPs, 15 diploid individuals in ancestral populations (PA, PB), and 20 diploid individuals in the admixed population (PC). Populations are shown in green (PA), blue (PB), and red (PC). (A) PCA is used to describe the primary population structure; (B) raw wavelet variance for each population illustrates high frequency noise; (C) informative variation in the admixed population after standard correction for noise estimated from the ancestral populations. Note that this example uses the default threshold μ = 1.

Mentions: This representation of admixed individuals in PCA space, as shown in Figure 1A, provides a genome-wide estimate of average ancestry, but does not indicate how admixture tracts are distributed along the chromosomes. To obtain localized estimates, the projection is performed at the SNP level rather than summing over the length of the genome as in Equation 1. The raw SNP-level admixture signals are given byFigure 1


Reconstructing Past Admixture Processes from Local Genomic Ancestry Using Wavelet Transformation.

Sanderson J, Sudoyo H, Karafet TM, Hammer MF, Cox MP - Genetics (2015)

Simulated example with 13,000 SNPs, 15 diploid individuals in ancestral populations (PA, PB), and 20 diploid individuals in the admixed population (PC). Populations are shown in green (PA), blue (PB), and red (PC). (A) PCA is used to describe the primary population structure; (B) raw wavelet variance for each population illustrates high frequency noise; (C) informative variation in the admixed population after standard correction for noise estimated from the ancestral populations. Note that this example uses the default threshold μ = 1.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4492373&req=5

fig1: Simulated example with 13,000 SNPs, 15 diploid individuals in ancestral populations (PA, PB), and 20 diploid individuals in the admixed population (PC). Populations are shown in green (PA), blue (PB), and red (PC). (A) PCA is used to describe the primary population structure; (B) raw wavelet variance for each population illustrates high frequency noise; (C) informative variation in the admixed population after standard correction for noise estimated from the ancestral populations. Note that this example uses the default threshold μ = 1.
Mentions: This representation of admixed individuals in PCA space, as shown in Figure 1A, provides a genome-wide estimate of average ancestry, but does not indicate how admixture tracts are distributed along the chromosomes. To obtain localized estimates, the projection is performed at the SNP level rather than summing over the length of the genome as in Equation 1. The raw SNP-level admixture signals are given byFigure 1

Bottom Line: Here, we describe an improved wavelet-based technique that better characterizes ancestry block structure from observed genomic patterns. principal components analysis is first applied to genomic data to identify the primary population structure, followed by wavelet decomposition to develop a new characterization of local ancestry information along the chromosomes.Time of admixture is inferred using an approximate Bayesian computation framework, providing robust estimates of both admixture times and their associated levels of uncertainty.Crucially, we demonstrate that this revised wavelet approach, which we have released as the R package adwave, provides improved statistical power over existing wavelet-based techniques and can be used to address a broad range of admixture questions.

View Article: PubMed Central - PubMed

Affiliation: Statistics and Bioinformatics Group, Institute of Fundamental Sciences, Massey University, Palmerston North 4442, New Zealand.

No MeSH data available.