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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.


PCA of autosomal SNP data from Indonesian populations, with Southern Han Chinese (blue circles) and Papua New Guinea Highlanders (green circles) employed as proxy ancestral populations. Numbers give calculated admixture proportions.
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fig6: PCA of autosomal SNP data from Indonesian populations, with Southern Han Chinese (blue circles) and Papua New Guinea Highlanders (green circles) employed as proxy ancestral populations. Numbers give calculated admixture proportions.

Mentions: The PCA for all individuals, where only the ancestral populations were used to define the axes, is shown in Figure 6. Admixed individuals dispersed along the first principal component illustrate the primary genomic signal, a strong gradient in Asian-Melanesian ancestry that has previously been observed across the region (Cox et al. 2010). The informative wavelet variance was computed separately for each chromosome and individual and subsequently combined to provide a single measure for each population (Figure S4). To combine information across chromosomes, which vary considerably in size, the raw admixture signals were windowed: all signals were reduced to the size of the smallest chromosome (importantly without discarding any data) by computing averages over a window of SNPs (details of the windowing procedure are provided in Supporting Information, Figure S5, Figure S6, Figure S7, Figure S8, Figure S9, and File S1). The SNP density and window size for each chromosome are shown in Table S1. This windowing procedure is used only to standardize chromosomes to the same length and utilizes very short windows of SNPs (unlike the approach of Pugach et al. 2011).


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

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

PCA of autosomal SNP data from Indonesian populations, with Southern Han Chinese (blue circles) and Papua New Guinea Highlanders (green circles) employed as proxy ancestral populations. Numbers give calculated admixture proportions.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig6: PCA of autosomal SNP data from Indonesian populations, with Southern Han Chinese (blue circles) and Papua New Guinea Highlanders (green circles) employed as proxy ancestral populations. Numbers give calculated admixture proportions.
Mentions: The PCA for all individuals, where only the ancestral populations were used to define the axes, is shown in Figure 6. Admixed individuals dispersed along the first principal component illustrate the primary genomic signal, a strong gradient in Asian-Melanesian ancestry that has previously been observed across the region (Cox et al. 2010). The informative wavelet variance was computed separately for each chromosome and individual and subsequently combined to provide a single measure for each population (Figure S4). To combine information across chromosomes, which vary considerably in size, the raw admixture signals were windowed: all signals were reduced to the size of the smallest chromosome (importantly without discarding any data) by computing averages over a window of SNPs (details of the windowing procedure are provided in Supporting Information, Figure S5, Figure S6, Figure S7, Figure S8, Figure S9, and File S1). The SNP density and window size for each chromosome are shown in Table S1. This windowing procedure is used only to standardize chromosomes to the same length and utilizes very short windows of SNPs (unlike the approach of Pugach et al. 2011).

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.