Medicine and Health Sciences Commons^™

Estimating Relationships Between Phenotypes And Subjects Drawn From Admixed Families., Elizabeth M. Blue, Lisa A. Brown, Matthew P. Conomos, Jennifer L. Kirk, Alejandro Q. Nato Jr., Alice B. Popejoy, Jesse Raffa, John Ranola, Ellen M. Wijsman, Timothy Thornton

Alejandro Nato

Background: Estimating relationships among subjects in a sample, within family structures or caused by population substructure, is complicated in admixed populations. Inaccurate allele frequencies can bias both kinship estimates and tests for association between subjects and a phenotype. We analyzed the simulated and real family data from Genetic Analysis Workshop 19, and were aware of the simulation model.

Results: We found that kinship estimation is more accurate when marker data include common variants whose frequencies are less variable across populations. Estimates of heritability and association vary with age for longitudinally measured traits. Accounting for local ancestry identified different true associations …

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

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 …