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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.


Related in: MedlinePlus

Sensitivity to a range of realistic data limitations. Comparison to reference data (condition 1) simulated with T = 13,000 SNPs, populations sizes , , and ancestral population divergence at  generations. The gray area shows the range of ABS metrics observed under the standard reference condition. (A) Potential sources of error (conditions 2–5); (B) varying SNP densities (conditions 6–8). Note that the decline in absolute values of the ABS metrics in B is expected; these are easily accounted for in an inference setting because the SNP density is always a known variable. Condition descriptions and numeric values are presented in Table 2.
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fig4: Sensitivity to a range of realistic data limitations. Comparison to reference data (condition 1) simulated with T = 13,000 SNPs, populations sizes , , and ancestral population divergence at generations. The gray area shows the range of ABS metrics observed under the standard reference condition. (A) Potential sources of error (conditions 2–5); (B) varying SNP densities (conditions 6–8). Note that the decline in absolute values of the ABS metrics in B is expected; these are easily accounted for in an inference setting because the SNP density is always a known variable. Condition descriptions and numeric values are presented in Table 2.

Mentions: The sensitivity of the method to a wide range of data characteristics was considered by repeating the results of the admixture time example with a large number of simulated data sets. Results are summarized in Table 2 and Figure 4.


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

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

Sensitivity to a range of realistic data limitations. Comparison to reference data (condition 1) simulated with T = 13,000 SNPs, populations sizes , , and ancestral population divergence at  generations. The gray area shows the range of ABS metrics observed under the standard reference condition. (A) Potential sources of error (conditions 2–5); (B) varying SNP densities (conditions 6–8). Note that the decline in absolute values of the ABS metrics in B is expected; these are easily accounted for in an inference setting because the SNP density is always a known variable. Condition descriptions and numeric values are presented in Table 2.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig4: Sensitivity to a range of realistic data limitations. Comparison to reference data (condition 1) simulated with T = 13,000 SNPs, populations sizes , , and ancestral population divergence at generations. The gray area shows the range of ABS metrics observed under the standard reference condition. (A) Potential sources of error (conditions 2–5); (B) varying SNP densities (conditions 6–8). Note that the decline in absolute values of the ABS metrics in B is expected; these are easily accounted for in an inference setting because the SNP density is always a known variable. Condition descriptions and numeric values are presented in Table 2.
Mentions: The sensitivity of the method to a wide range of data characteristics was considered by repeating the results of the admixture time example with a large number of simulated data sets. Results are summarized in Table 2 and Figure 4.

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.


Related in: MedlinePlus