A special feature of Probability and Statistics Day at UMBC 2015 is that the conference, including the workshop, is open to all statistics graduate students
from UMBC and local universites free of charge; however, REGISTRATION IS REQUIRED! The deadline to register is Friday,
April 3, 2015. // REGISTER NOW
For more information, contact any member of the organizing committee:
Bimal Sinha
Conference Chair
443.538.3012
Kofi Adragni
410.455.2406
Yvonne Huang
410.455.2422
Yaakov Malinovsky
410.455.2968
Thomas Mathew
410.455.2418
Nagaraj Neerchal
410.455.2437
DoHwan Park
410.455.2408
Junyong Park
410.455.2407
Anindya Roy
410.455.2435
Elizabeth Stanwyck
410.455.5731
Participant Information
QUAN ZOU
Poster: The generalized relative pairs IBD distribution: its use in the detection of linkage
In this research we adopt the incompletely penetrant model and develop the allelic identical by descent (IBD) distributions at marker locus given dichotomous disease affectional status for siblings, uncle-nephew, grandparent-grandchild, half-sibs, and first cousin pairs. We first show that the probabilities of dichotomous disease status given trait IBD score are independent of relative relationships through Li's ITO matrices. We then fully derive the marker IBD distributions given dichotomous disease affectional status for various relative relationships by utilizing the relative pairs' joint probabilities of IBD scores at both trait and marker loci. We also calculated the marker IBD distributions given extreme discordant relative pairs at a quantitative trait locus (QTL) for different relative relationships. Next, we examine the power to detect the presence of a significant disease susceptibility locus through linkage analysis by perturbing the conditional marker IBD distribution. Specifically, three tests, the proportion test, the mean test and the logarithm of odds (LOD) score test, were applied to obtain the sample size required to achieve significance level p with different power. Simulation studies have been conducted in order to evaluate the performance of our methods. Finally, the two-locus model are derived under the multiplicative, the additive and the genetic heterogeneity assumptions.
