Title | Utilizing graph theory to select the largest set of unrelated individuals for genetic analysis. |
Publication Type | Journal Article |
Year of Publication | 2013 |
Authors | Staples, J, Nickerson, DA, Below, JE |
Journal | Genet Epidemiol |
Volume | 37 |
Issue | 2 |
Pagination | 136-41 |
Date Published | 2013 Feb |
ISSN | 1098-2272 |
Keywords | Algorithms, Genome-Wide Association Study, HapMap Project, Humans, Models, Theoretical |
Abstract | Many statistical analyses of genetic data rely on the assumption of independence among samples. Consequently, relatedness is either modeled in the analysis or samples are removed to "clean" the data of any pairwise relatedness above a tolerated threshold. Current methods do not maximize the number of unrelated individuals retained for further analysis, and this is a needless loss of resources. We report a novel application of graph theory that identifies the maximum set of unrelated samples in any dataset given a user-defined threshold of relatedness as well as all networks of related samples. We have implemented this method into an open source program called Pedigree Reconstruction and Identification of a Maximum Unrelated Set, PRIMUS. We show that PRIMUS outperforms the three existing methods, allowing researchers to retain up to 50% more unrelated samples. A unique strength of PRIMUS is its ability to weight the maximum clique selection using additional criteria (e.g. affected status and data missingness). PRIMUS is a permanent solution to identifying the maximum number of unrelated samples for a genetic analysis. |
DOI | 10.1002/gepi.21684 |
Alternate Journal | Genet. Epidemiol. |
PubMed ID | 22996348 |
PubMed Central ID | PMC3770842 |
Grant List | U54 HG006493 / HG / NHGRI NIH HHS / United States UC2 HL102926 / HL / NHLBI NIH HHS / United States RC2 HL102926 / HL / NHLBI NIH HHS / United States HG006493 / HG / NHGRI NIH HHS / United States T32 HG000035 / HG / NHGRI NIH HHS / United States HL102926 / HL / NHLBI NIH HHS / United States T32 HG00035 / HG / NHGRI NIH HHS / United States UM1 HG006493 / HG / NHGRI NIH HHS / United States |