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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.


Dating time of admixture for Bena (Flores, eastern Indonesia) using approximate Bayesian computation. (A) Relationship between admixture time and average block size metric for all simulations; (B) weighted posterior distribution of admixture time. Median estimated time of admixture, indicated by the blue line, is 147 generations (95% credible region: 122–178 generations).
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fig7: Dating time of admixture for Bena (Flores, eastern Indonesia) using approximate Bayesian computation. (A) Relationship between admixture time and average block size metric for all simulations; (B) weighted posterior distribution of admixture time. Median estimated time of admixture, indicated by the blue line, is 147 generations (95% credible region: 122–178 generations).

Mentions: The relationship between time of admixture and the ABS metric across all simulations is illustrated in Figure 7A. ABC was implemented using the R package abc (Csilléry et al. 2012), and the posterior distribution of admixture time was computed using a local linear regression (Beaumont et al. 2002) with a tolerance rate of 0.2. Cross validation was used to evaluate the accuracy of this estimate: the prediction error was low (0.038) and insensitive to the exact tolerance value. For future research focusing on parameter inference, this procedure could be modified to use a larger number of simulated data sets and a lower tolerance rate. However, this simple example clearly illustrates that the adwave method has good statistical power to date admixture using a relatively small number of simulations.


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

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

Dating time of admixture for Bena (Flores, eastern Indonesia) using approximate Bayesian computation. (A) Relationship between admixture time and average block size metric for all simulations; (B) weighted posterior distribution of admixture time. Median estimated time of admixture, indicated by the blue line, is 147 generations (95% credible region: 122–178 generations).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig7: Dating time of admixture for Bena (Flores, eastern Indonesia) using approximate Bayesian computation. (A) Relationship between admixture time and average block size metric for all simulations; (B) weighted posterior distribution of admixture time. Median estimated time of admixture, indicated by the blue line, is 147 generations (95% credible region: 122–178 generations).
Mentions: The relationship between time of admixture and the ABS metric across all simulations is illustrated in Figure 7A. ABC was implemented using the R package abc (Csilléry et al. 2012), and the posterior distribution of admixture time was computed using a local linear regression (Beaumont et al. 2002) with a tolerance rate of 0.2. Cross validation was used to evaluate the accuracy of this estimate: the prediction error was low (0.038) and insensitive to the exact tolerance value. For future research focusing on parameter inference, this procedure could be modified to use a larger number of simulated data sets and a lower tolerance rate. However, this simple example clearly illustrates that the adwave method has good statistical power to date admixture using a relatively small number of simulations.

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.