ArticleGenetic epidemiology2013
Utilizing graph theory to select the largest set of unrelated individuals for genetic analysis.
Article in Genetic epidemiology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
43 citing papers in PubMed.
- Trial
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- Article
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- plmmr: an R package to fit penalized linear mixed models for genome-wide association data with complex correlation structure.Briefings in bioinformatics · 2026Article
- Short tandem repeats in populations of the Qinghai-Tibet Plateau and adjacent regions provide insights into high-altitude adaptation.Science advances · 2025Article
- Frequency enrichment of coding variants in a French-Canadian founder population and its implication for inflammatory bowel diseases.medRxiv : the preprint server for health sciences · 2025Article
- Multiomics reveal key inflammatory drivers of severe obesity: IL4R, LILRA5, and OSM.Cell genomics · 2025Article
- Detection of distant relatedness in biobanks to identify undiagnosed cases of Mendelian disease as applied to Long QT syndrome.Nature communications · 2024Article
- Polymorphic short tandem repeats make widespread contributions to blood and serum traits.Cell genomics · 2023Article
- Exome-wide assessment of isolated biliary atresia: A report from the National Birth Defects Prevention Study using child-parent trios and a case-control design to identify novel rare variants.American journal of medical genetics. Part A · 2023Article
- Article
- The female protective effect against autism spectrum disorder.Cell genomics · 2022Article
- A loss-of-function IFNAR1 allele in Polynesia underlies severe viral diseases in homozygotes.The Journal of experimental medicine · 2022Article
- Evaluating the utility of identity-by-descent segment numbers for relatedness inference via information theory and classification.G3 (Bethesda, Md.) · 2022Article
- The genomic basis of high-elevation adaptation in wild house mice (Mus musculus domesticus) from South America.Genetics · 2022Article
- Population-based genetic effects for developmental stuttering.HGG advances · 2022Article
- Population Histories and Genomic Diversity of South American Natives.Molecular biology and evolution · 2022Article
- Phenome risk classification enables phenotypic imputation and gene discovery in developmental stuttering.American journal of human genetics · 2021Article
- Broad Concordance in the Spatial Distribution of Adaptive and Neutral Genetic Variation across an Elevational Gradient in Deer Mice.Molecular biology and evolution · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.