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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.


Informative wavelet variance for each time of admixture (10–320 generations using default thresholding ). Shaded bars represent the average over 50 simulations at each admixture time; black bars represent the range across individual simulations. The average block size metric for each scenario is indicated by a dotted blue line.
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fig2: Informative wavelet variance for each time of admixture (10–320 generations using default thresholding ). Shaded bars represent the average over 50 simulations at each admixture time; black bars represent the range across individual simulations. The average block size metric for each scenario is indicated by a dotted blue line.

Mentions: Because the ability of wavelet methods to calculate the time of admixture is well known from earlier work (Pugach et al. 2011), we explored this feature first. Simulations were performed for admixture times ranging from 10 to 320 generations (i.e., from the recent past to ∼10,000 years ago, using a generation interval of 30 years; Fenner 2005). Admixture at 10 generations shows the highest informative wavelet variance at scale 13, reflecting relatively few, long admixture blocks (Figure 2). As the time of admixture occurs further back in the past, the peak in wavelet variance shifts toward successively lower wavelet scales, reflecting ever-smaller admixture blocks driven by cumulative recombination along the chromosome. The average frequency content can be characterized by the average block size metric ABS, termed the “wavelet center” by Pugach et al. (2011), which as shown later, can be used to date the admixture event


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

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

Informative wavelet variance for each time of admixture (10–320 generations using default thresholding ). Shaded bars represent the average over 50 simulations at each admixture time; black bars represent the range across individual simulations. The average block size metric for each scenario is indicated by a dotted blue line.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig2: Informative wavelet variance for each time of admixture (10–320 generations using default thresholding ). Shaded bars represent the average over 50 simulations at each admixture time; black bars represent the range across individual simulations. The average block size metric for each scenario is indicated by a dotted blue line.
Mentions: Because the ability of wavelet methods to calculate the time of admixture is well known from earlier work (Pugach et al. 2011), we explored this feature first. Simulations were performed for admixture times ranging from 10 to 320 generations (i.e., from the recent past to ∼10,000 years ago, using a generation interval of 30 years; Fenner 2005). Admixture at 10 generations shows the highest informative wavelet variance at scale 13, reflecting relatively few, long admixture blocks (Figure 2). As the time of admixture occurs further back in the past, the peak in wavelet variance shifts toward successively lower wavelet scales, reflecting ever-smaller admixture blocks driven by cumulative recombination along the chromosome. The average frequency content can be characterized by the average block size metric ABS, termed the “wavelet center” by Pugach et al. (2011), which as shown later, can be used to date the admixture event

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.