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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.


Comparing StepPCO and adwave showing the relationship between wavelet transform summaries and time of admixture. (A) Adwave using ; (B) StepPCO using , , threshold = 0.1, and maxlevel = 6. Numbers indicate the relative standard deviation (RSD, %) for each admixture time. Note the difference in discrimination power between the two methods for older admixture events (95% confidence intervals as dashed blue and green horizontal lines).
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fig5: Comparing StepPCO and adwave showing the relationship between wavelet transform summaries and time of admixture. (A) Adwave using ; (B) StepPCO using , , threshold = 0.1, and maxlevel = 6. Numbers indicate the relative standard deviation (RSD, %) for each admixture time. Note the difference in discrimination power between the two methods for older admixture events (95% confidence intervals as dashed blue and green horizontal lines).

Mentions: The original StepPCO method (Pugach et al. 2011) has already been tested extensively against other admixture detection methods, particularly HAPMIX (Price et al. 2009). We therefore focus here on comparing our improved wavelet method against the StepPCO procedure. Figure 5 shows that the summary measure (wavelet center) used in StepPCO is comparable to the adwave ABS metrics, as both exhibit a strong trend with time of admixture. However, the dispersion is consistently smaller for the adwave ABS metrics. For example, the wavelet centers (StepPCO) computed for and show substantial overlap, while the ABS metrics (adwave) for the same populations show only minimal overlap. This illustrates that adwave offers increased power to differentiate between older admixture scenarios, with substantially reduced uncertainty in dating.


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

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

Comparing StepPCO and adwave showing the relationship between wavelet transform summaries and time of admixture. (A) Adwave using ; (B) StepPCO using , , threshold = 0.1, and maxlevel = 6. Numbers indicate the relative standard deviation (RSD, %) for each admixture time. Note the difference in discrimination power between the two methods for older admixture events (95% confidence intervals as dashed blue and green horizontal lines).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig5: Comparing StepPCO and adwave showing the relationship between wavelet transform summaries and time of admixture. (A) Adwave using ; (B) StepPCO using , , threshold = 0.1, and maxlevel = 6. Numbers indicate the relative standard deviation (RSD, %) for each admixture time. Note the difference in discrimination power between the two methods for older admixture events (95% confidence intervals as dashed blue and green horizontal lines).
Mentions: The original StepPCO method (Pugach et al. 2011) has already been tested extensively against other admixture detection methods, particularly HAPMIX (Price et al. 2009). We therefore focus here on comparing our improved wavelet method against the StepPCO procedure. Figure 5 shows that the summary measure (wavelet center) used in StepPCO is comparable to the adwave ABS metrics, as both exhibit a strong trend with time of admixture. However, the dispersion is consistently smaller for the adwave ABS metrics. For example, the wavelet centers (StepPCO) computed for and show substantial overlap, while the ABS metrics (adwave) for the same populations show only minimal overlap. This illustrates that adwave offers increased power to differentiate between older admixture scenarios, with substantially reduced uncertainty in dating.

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.