ArticleStatistical applications in genetics and molecular biology2011
Accuracy and computational efficiency of a graphical modeling approach to linkage disequilibrium estimation.
Article in Statistical applications in genetics and molecular biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Family Study Designs Informed by Tumor Heterogeneity and Multi-Cancer Pleiotropies: The Power of the Utah Population Database.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2020Article
- Reparameterization of PAM50 Expression Identifies Novel Breast Tumor Dimensions and Leads to Discovery of a Genome-Wide Significant Breast Cancer Locus atCancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2018Article
- A method combining a random forest-based technique with the modeling of linkage disequilibrium through latent variables, to run multilocus genome-wide association studies.BMC bioinformatics · 2018Article
- Novel pedigree analysis implicates DNA repair and chromatin remodeling in multiple myeloma risk.PLoS genetics · 2018Article
- Modelling and visualizing fine-scale linkage disequilibrium structure.BMC bioinformatics · 2013Article
- Employing MCMC under the PPL framework to analyze sequence data in large pedigrees.Frontiers in genetics · 2013Article
- Pairwise shared genomic segment analysis in three Utah high-risk breast cancer pedigrees.BMC genomics · 2012Article
- Case-control association testing by graphical modeling for the Genetic Analysis Workshop 17 mini-exome sequence data.BMC proceedings · 2011Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
We develop recent work on using graphical models for linkage disequilibrium to provide efficient programs for model fitting, phasing, and imputation of missing data in large data sets. Two important features contribute to the computational efficiency: the separation of the model fitting and phasing-imputation processes into different programs, and holding in memory only the data within a moving window of loci during model fitting. Optimal parameter values were chosen by cross-validation to maximize the probability of correctly imputing masked genotypes. The best accuracy obtained is slightly below than that from the Beagle program of Browning and Browning, and our fitting program is slower. However, for large data sets, it uses less storage. For a reference set of n individuals genotyped at m markers, the time and storage required for fitting a graphical model are approximately O(nm) and O(n+m), respectively. To impute the phases and missing data on n individuals using an already fitted graphical model requires O(nm) time and O(m) storage. While the times for fitting and imputation are both O(nm), the imputation process is considerably faster; thus, once a model is estimated from a reference data set, the marginal cost of phasing and imputing further samples is very low.
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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.