Procrustes analyses of genetic and geographic coordinates based on different numbers of loci.

<p>The same sets of randomly selected markers were used to generate PCA maps of genetic variation to compare with geographic maps for different regions. .</p

Procrustes analysis of genetic and geographic coordinates of worldwide populations.

<p>(A) Geographic coordinates of 53 populations. (B) Procrustes-transformed PCA plot of genetic variation. The Procrustes analysis is based on the Gall-Peters projected coordinates of geographic locations and PC1-PC2 coordinates of 938 individuals. The figures are plotted according to the Gall-Peters projection. PC1 and PC2 are indicated by dotted lines, crossing over the centroid of all individuals. PC1 and PC2 account for 6.22% and 4.72% of the total variance, respectively. The Procrustes similarity is (). The rotation angle of the PCA map is .</p

Relationship between and the proportion of genetic variation explained by the first two components of the PCA.

<p>Both the main analyses of the paper in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002886#pgen-1002886-t002" target="_blank">Table 2</a> and the supplementary analyses of Sub-Saharan Africa, in which certain populations excluded from the main analysis are included, are considered in obtaining the regression line. The values on the x-axis were obtained by summing the proportions of variance explained by PC1 and PC2 (columns 2 and 3 in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002886#pgen-1002886-t002" target="_blank">Table 2</a>, columns 6 and 7 in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002886#pgen.1002886.s016" target="_blank">Table S7</a>). values were estimated from the same datasets as used in the PCA (column 7 in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002886#pgen-1002886-t002" target="_blank">Table 2</a>, column 11 in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002886#pgen.1002886.s016" target="_blank">Table S7</a>). The dashed line indicates the linear least squares fit of . The Pearson correlation is .</p

Summary of the results for datasets from different geographic regions.

<p> is the rotation angle for the PCA map that optimizes the Procrustes similarity with the geographic map, and it is measured in degrees counterclockwise. -values are obtained from 100,000 permutations of population labels.</p

Procrustes analysis of genetic and geographic coordinates of East Asian populations.

<p>(A) Geographic coordinates of 23 populations. (B) Procrustes-transformed PCA plot of genetic variation. The Procrustes analysis is based on the unprojected latitude-longitude coordinates and PC1-PC2 coordinates of 334 individuals. PC1 and PC2 are indicated by dotted lines, crossing over the centroid of all individuals. PC1 and PC2 account for 1.58% and 0.98% of the total variance, respectively. The Procrustes similarity statistic is (). The rotation angle of the PCA map is .</p

Procrustes analysis of genetic and geographic coordinates of Central/South Asian populations.

<p>(A) Geographic coordinates of 18 populations. (B) Procrustes-transformed PCA plot of genetic variation. The Procrustes analysis is based on the unprojected latitude-longitude coordinates and PC1-PC2 coordinates of 362 individuals. PC1 and PC2 are indicated by dotted lines, crossing over the centroid of all individuals. PC1 and PC2 account for 1.59% and 1.31% of the total variance, respectively. The Procrustes similarity statistic is (). The rotation angle of the PCA map is .</p

Procrustes analysis of genetic and geographic coordinates of Sub-Saharan African populations, excluding hunter-gatherer populations and Mbororo Fulani.

<p>(A) Geographic coordinates of 23 populations. (B) Procrustes-transformed PCA plot of genetic variation. The Procrustes analysis is based on the unprojected latitude-longitude coordinates and PC1-PC2 coordinates of 348 individuals. PC1 and PC2 are indicated by dotted lines, crossing over the centroid of all individuals. PC1 and PC2 account for 1.34% and 0.69% of the total variance, respectively. The Procrustes similarity is (). The rotation angle of the PCA map is .</p

SNP datasets for different geographic regions.

<p>SNP datasets for different geographic regions.</p

Histograms of the Procrustes similarity of 100,000 permutations for analyses in Figure 1, Figure 2, Figure 3, Figure 4, Figure 5, and Figure 6<b>.</b>

<p>The blue vertical lines indicate the value of . (A) The worldwide dataset in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002886#pgen-1002886-g001" target="_blank">Figure 1</a> (, ). (B) The European dataset in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002886#pgen-1002886-g002" target="_blank">Figure 2</a> (, ). (C) The Sub-Saharan African dataset in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002886#pgen-1002886-g003" target="_blank">Figure 3</a> (, ). (D) The Asian dataset in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002886#pgen-1002886-g004" target="_blank">Figure 4</a> (, ). (E) The East Asian dataset in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002886#pgen-1002886-g005" target="_blank">Figure 5</a> (, ). (F) The Central/South dataset in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002886#pgen-1002886-g006" target="_blank">Figure 6</a> (, ).</p

Estimation of kinship coefficient in structured and admixed populations using sparse sequencing data

<div><p>Knowledge of biological relatedness between samples is important for many genetic studies. In large-scale human genetic association studies, the estimated kinship is used to remove cryptic relatedness, control for family structure, and estimate trait heritability. However, estimation of kinship is challenging for sparse sequencing data, such as those from off-target regions in target sequencing studies, where genotypes are largely uncertain or missing. Existing methods often assume accurate genotypes at a large number of markers across the genome. We show that these methods, without accounting for the genotype uncertainty in sparse sequencing data, can yield a strong downward bias in kinship estimation. We develop a computationally efficient method called SEEKIN to estimate kinship for both homogeneous samples and heterogeneous samples with population structure and admixture. Our method models genotype uncertainty and leverages linkage disequilibrium through imputation. We test SEEKIN on a whole exome sequencing dataset (WES) of Singapore Chinese and Malays, which involves substantial population structure and admixture. We show that SEEKIN can accurately estimate kinship coefficient and classify genetic relatedness using off-target sequencing data down sampled to ~0.15X depth. In application to the full WES dataset without down sampling, SEEKIN also outperforms existing methods by properly analyzing shallow off-target data (~0.75X). Using both simulated and real phenotypes, we further illustrate how our method improves estimation of trait heritability for WES studies.</p></div