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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.


Relationship between proportion of admixture and informative wavelet variance. For this example only, a nondefault value for the threshold  was used to account for increased noise in the admixture signals due to low proportions of admixture, as described in the text. The magnitude of the wavelet variance decreases with the admixture proportion, shown as colored bars from black (P = 0.50) to yellow (P = 0.025).
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fig3: Relationship between proportion of admixture and informative wavelet variance. For this example only, a nondefault value for the threshold was used to account for increased noise in the admixture signals due to low proportions of admixture, as described in the text. The magnitude of the wavelet variance decreases with the admixture proportion, shown as colored bars from black (P = 0.50) to yellow (P = 0.025).

Mentions: Admixture proportions were varied between 0.5 (equal ancestry from PA and PB) and 0.025 (ancestry predominately from PA). For this analysis, the time of admixture was fixed at 160 generations. As the proportion of admixture decreases, the raw wavelet variance exhibits increasing levels of noise relative to informative variation. This is shown by the reduced magnitude of the informative wavelet variance (Figure 3) and emphasizes that, as expected, it is increasingly difficult to extract informative variation at low admixture proportions (small p) even where the signal is technically present. In this example, informative estimates were obtained for admixture proportions as low as 2.5%, although in general, the range of p for which this method is applicable will also depend on other characteristics of the data, such as the SNP density and sample size, as considered in the next section.


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

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

Relationship between proportion of admixture and informative wavelet variance. For this example only, a nondefault value for the threshold  was used to account for increased noise in the admixture signals due to low proportions of admixture, as described in the text. The magnitude of the wavelet variance decreases with the admixture proportion, shown as colored bars from black (P = 0.50) to yellow (P = 0.025).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig3: Relationship between proportion of admixture and informative wavelet variance. For this example only, a nondefault value for the threshold was used to account for increased noise in the admixture signals due to low proportions of admixture, as described in the text. The magnitude of the wavelet variance decreases with the admixture proportion, shown as colored bars from black (P = 0.50) to yellow (P = 0.025).
Mentions: Admixture proportions were varied between 0.5 (equal ancestry from PA and PB) and 0.025 (ancestry predominately from PA). For this analysis, the time of admixture was fixed at 160 generations. As the proportion of admixture decreases, the raw wavelet variance exhibits increasing levels of noise relative to informative variation. This is shown by the reduced magnitude of the informative wavelet variance (Figure 3) and emphasizes that, as expected, it is increasingly difficult to extract informative variation at low admixture proportions (small p) even where the signal is technically present. In this example, informative estimates were obtained for admixture proportions as low as 2.5%, although in general, the range of p for which this method is applicable will also depend on other characteristics of the data, such as the SNP density and sample size, as considered in the next section.

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.