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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.


Dual admixture events at 160 and 10–80 generations. Gray bars represent the average over 50 simulations for each scenario; black bars represent the range for individual simulations. Blue bars show the average informative wavelet variance for a single admixture event at 160 generations, providing a reference point for comparison.
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fig8: Dual admixture events at 160 and 10–80 generations. Gray bars represent the average over 50 simulations for each scenario; black bars represent the range for individual simulations. Blue bars show the average informative wavelet variance for a single admixture event at 160 generations, providing a reference point for comparison.

Mentions: In the following dual-admixture scenarios, the first admixture event always occurs at 160 generations. To investigate the effect of separation between admixture events, the second admixture event varies between 10 and 80 generations. In the extreme case of admixture at 160 and 10 generations ago, the localized admixture signals contain two dominant frequencies. Single admixture events at 160 and 10 generations lead to peaks in the informative wavelet variance at wavelet scales of 9 and 13, respectively. When two admixture events occur, the informative wavelet variance is instead spread between these scales (Figure 8A). As the admixture events occur closer together, this spread in the observed informative wavelet variance decreases (Figures 8, B–D).


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

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

Dual admixture events at 160 and 10–80 generations. Gray bars represent the average over 50 simulations for each scenario; black bars represent the range for individual simulations. Blue bars show the average informative wavelet variance for a single admixture event at 160 generations, providing a reference point for comparison.
© Copyright Policy - open-access
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4492373&req=5

fig8: Dual admixture events at 160 and 10–80 generations. Gray bars represent the average over 50 simulations for each scenario; black bars represent the range for individual simulations. Blue bars show the average informative wavelet variance for a single admixture event at 160 generations, providing a reference point for comparison.
Mentions: In the following dual-admixture scenarios, the first admixture event always occurs at 160 generations. To investigate the effect of separation between admixture events, the second admixture event varies between 10 and 80 generations. In the extreme case of admixture at 160 and 10 generations ago, the localized admixture signals contain two dominant frequencies. Single admixture events at 160 and 10 generations lead to peaks in the informative wavelet variance at wavelet scales of 9 and 13, respectively. When two admixture events occur, the informative wavelet variance is instead spread between these scales (Figure 8A). As the admixture events occur closer together, this spread in the observed informative wavelet variance decreases (Figures 8, B–D).

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.