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Articles 31 - 32 of 32
Full-Text Articles in Medicine and Health Sciences
Identity-By-Descent Estimation With Population- And Pedigree-Based Imputation In Admixed Family Data, Mohamad Saad, Alejandro Q. Nato Jr., Fiona L. Grimson, Steven M. Lewis, Lisa A. Brown, Elizabeth M. Blue, Timothy A. Thornton, Elizabeth A. Thompson, Ellen M. Wijsman
Identity-By-Descent Estimation With Population- And Pedigree-Based Imputation In Admixed Family Data, Mohamad Saad, Alejandro Q. Nato Jr., Fiona L. Grimson, Steven M. Lewis, Lisa A. Brown, Elizabeth M. Blue, Timothy A. Thornton, Elizabeth A. Thompson, Ellen M. Wijsman
Alejandro Nato
Background: In the past few years, imputation approaches have been mainly used in population-based designs of genome-wide association studies, although both family- and population-based imputation methods have been proposed. With the recent surge of family-based designs, family-based imputation has become more important. Imputation methods for both designs are based on identity-by-descent (IBD) information. Apart from imputation, the use of IBD information is also common for several types of genetic analysis, including pedigree-based linkage analysis.
Methods: We compared the performance of several family- and population-based imputation methods in large pedigrees provided by Genetic Analysis Workshop 19 (GAW19). We also evaluated the …
Mapping Genes With Longitudinal Phenotypes Via Bayesian Posterior Probabilities, Anthony Musolf, Alejandro Q. Nato Jr., Douglas Londono, Lisheng Zhou, Tara C. Matise, Derek Gordon
Mapping Genes With Longitudinal Phenotypes Via Bayesian Posterior Probabilities, Anthony Musolf, Alejandro Q. Nato Jr., Douglas Londono, Lisheng Zhou, Tara C. Matise, Derek Gordon
Alejandro Nato
Most association studies focus on disease risk, with less attention paid to disease progression or severity. These phenotypes require longitudinal data. This paper presents a new method for analyzing longitudinal data to map genes in both population-based and family-based studies. Using simulated systolic blood pressure measurements obtained from Genetic Analysis Workshop 18, we cluster the phenotype data into trajectory subgroups. We then use the Bayesian posterior probability of being in the high subgroup as a quantitative trait in an association analysis with genotype data. This method maintains high power (>80%) in locating genes known to affect the simulated phenotype …