@article {320,
	title = {Utilizing graph theory to select the largest set of unrelated individuals for genetic analysis.},
	journal = {Genet Epidemiol},
	volume = {37},
	year = {2013},
	month = {2013 Feb},
	pages = {136-41},
	abstract = {<p>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.</p>},
	keywords = {Algorithms, Genome-Wide Association Study, HapMap Project, Humans, Models, Theoretical},
	issn = {1098-2272},
	doi = {10.1002/gepi.21684},
	author = {Staples, Jeffrey and Nickerson, Deborah A and Below, Jennifer E}
}
@article {329,
	title = {Human genome sequencing in health and disease.},
	journal = {Annu Rev Med},
	volume = {63},
	year = {2012},
	month = {2012},
	pages = {35-61},
	abstract = {<p>Following the "finished," euchromatic, haploid human reference genome sequence, the rapid development of novel, faster, and cheaper sequencing technologies is making possible the era of personalized human genomics. Personal diploid human genome sequences have been generated, and each has contributed to our better understanding of variation in the human genome. We have consequently begun to appreciate the vastness of individual genetic variation from single nucleotide to structural variants. Translation of genome-scale variation into medically useful information is, however, in its infancy. This review summarizes the initial steps undertaken in clinical implementation of personal genome information, and describes the application of whole-genome and exome sequencing to identify the cause of genetic diseases and to suggest adjuvant therapies. Better analysis tools and a deeper understanding of the biology of our genome are necessary in order to decipher, interpret, and optimize clinical utility of what the variation in the human genome can teach us. Personal genome sequencing may eventually become an instrument of common medical practice, providing information that assists in the formulation of a differential diagnosis. We outline herein some of the remaining challenges.</p>},
	keywords = {Genetic Diseases, Inborn, Genetic Privacy, Genome, Human, HapMap Project, Human Genome Project, Humans},
	issn = {1545-326X},
	doi = {10.1146/annurev-med-051010-162644},
	author = {Gonzaga-Jauregui, Claudia and Lupski, James R and Gibbs, Richard A}
}
