Discovering genetic ancestry using spectral graph theory.
paper
Cited
Public
- Authors
- Lee, Ann B; Luca, Diana; Klei, Lambertus; Devlin, Bernie; Roeder, Kathryn
- Year
- 2010
- Journal
- Genetic epidemiology
- PMID
- 19455578
- DOI
- 10.1002/gepi.20434
- PMCID
- PMC4610359
As one approach to uncovering the genetic underpinnings of complex disease, individuals are measured at a large number of genetic variants (usually SNPs) across the genome and these SNP genotypes are assessed for association with disease status. We propose a new statistical method called Spectral-GEM for the analysis of genome-wide association studies; the goal of Spectral-GEM is to quantify the ancestry of the sample from such genotypic data. Ignoring structure due to differential ancestry can lead to an excess of spurious findings and reduce power. Ancestry is commonly estimated using the eigenvectors derived from principal component analysis (PCA). To develop an alternative to PCA we draw on connections between multidimensional scaling and spectral graph theory. Our approach, based on a spectral embedding derived from the normalized Laplacian of a graph, can produce more meaningful delineation of ancestry than by using PCA. Often the results from Spectral-GEM are straightforward to interpret and therefore useful in association analysis. We illustrate the new algorithm with an analysis of the POPRES data [Nelson et al., 2008].
Principal components from PCA for Scenario 1. Subjects are self-identified as UK (black), Italian (red), and non-European (blue).
Principal components from the Spectral-GEM analysis of data from Scenario 1. Subjects are self-identified as UK (black), Italian (red), and non-European (blue).
Principal components 3β6 for data from Scenario 2. PC 1 and PC 2 are quite similar to the eigenvectors shown in Fig. 4. Subjects are self-identified as UK (black), Italian (red), Iberian Peninsula (green), African American (blue), and Indian (orange).
Principal components from the spectral graph approach for Scenario 2. Subjects are self-identified as UK (black), Italian (red), Iberian Peninsula (green), African American (blue), and Indian (orange).
Country membership by cluster for Scenario 3. Cluster labels and country groupings are defined in Table I. Cluster labels were derived from the majority country or country grouping membership.
Dendrogram for European clusters from Scenario 3.
1β20 of 51
Next β
| Name | Type |
|---|---|
| African | cohort |
| African American | cohort |
| African American cluster local | cohort |
| ancestry | phenotype |
| Ashkenazi Jews local | cohort |
| Asian Indian local | phenotype |
| Asian-Indian local | cohort |
| Asian-Indians local | cohort |
| Asian sample local | cohort |
| Australia | cohort |
| Belgium | cohort |
| Bosnia local | cohort |
| British local | cohort |
| British cluster local | cohort |
| British Isles | cohort |
| Canada | cohort |
| case-control sample | cohort |
| Central local | cohort |
| Central cluster local | cohort |
| Cluster A local | cohort |
| Cluster B local | cohort |
| Cluster C local | cohort |
| Cluster D local | cohort |
| Cluster E local | cohort |
| Cluster F local | cohort |
| Cluster G local | cohort |
| Cluster H local | cohort |
| Cluster I local | cohort |
| Cluster J local | cohort |
| Cluster K local | cohort |
| Cluster L local | cohort |
| Cochran-Mantel-Haenszel test local | drug |
| Continental clusters local | cohort |
| Country of origin local | phenotype |
| Cyprus local | cohort |
| dimensions of ancestry local | phenotype |
| disease status | phenotype |
| East Asian | cohort |
| Euclidean distance m(i,j) local | drug |
| European ancestry | cohort |
| European cluster local | cohort |
| European population | cohort |
| Europeans | cohort |
| France | cohort |
| genetic ancestry | phenotype |
| Germany | cohort |
| Greece local | cohort |
| HapMap | cohort |
| HapMap core samples local | cohort |
| Hispanic | phenotype |
| human population structure local | cohort |
| Iberian peninsula | cohort |
| Indian | cohort |
| Indian cluster local | cohort |
| individuals | cohort |
| Italian A local | cohort |
| Italian A cluster local | cohort |
| Italian B local | cohort |
| Italian B cluster local | cohort |
| Italian clusters local | cohort |
| Italy | cohort |
| kernel H local | drug |
| major continental samples local | cohort |
| Mexican local | phenotype |
| Mexican American | cohort |
| Mexicans local | cohort |
| non-European ancestry | cohort |
| North East local | cohort |
| outliers local | cohort |
| outliers local | phenotype |
| PCA | drug |
| Poland | cohort |
| POPRES local | cohort |
| POPRES database local | cohort |
| population-based genetic association study local | cohort |
| Portuguese local | cohort |
| Romania local | cohort |
| Russia | cohort |
| Scenario 1 local | cohort |
| self-identified British and Italian samples local | cohort |
| six unusual subjects local | cohort |
| Small cluster local | cohort |
| smartpca local | drug |
| SNP | cohort |
| South Asian local | phenotype |
| South East local | cohort |
| Spain | cohort |
| Spectral-GEM local | drug |
| subjects | cohort |
| subpopulations local | cohort |
| Swiss local | cohort |
| Switzerland | cohort |
| Turkey local | cohort |
| United Kingdom | cohort |
| United States | cohort |
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