Functional Linear Models for Association Analysis of Quantitative Traits

• Activity Number: 349
• Type: Contributed
• Date/Time: Tuesday, August 6, 2013 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Ruzong Fan*+ and Yifan Wang and Momiao Xiong and James L. Mills and Alexander F, Wilson and Joan E. Bailey-Wilson
• Companies: National Institutes of Health and National Institutes of Health and The University of Texas and NICHD, National Institutes of Health and NHGRI, National Institutes of Health and NHGRI, National Institutes of Health
• Keywords: association mapping ; functional data analysis ; disease mapping ; statistical genetics ; high dimensional data ; complex diseases

Abstract

Functional linear models are developed for testing association between complex traits and genetic variants. By treating multiple genetic variants of a human population as realizations of a stochastic process, the genome of an individual is a continuum of sequence data rather than discrete observations. The genome of an individual is viewed as a stochastic function which contains both linkage and linkage disequilibrium (LD) information. The functional linear models are built to test the association between complex traits and multiple single nucleotide polymorphism markers. After extensive simulation analysis, it is shown that the proposed models have high power for three scenarios: (1) the causal variants are all rare; (2) the causal variants are both rare and common; and (3) the causal variants are common. The superior performance of the functional linear models is likely due to its sufficient utilization both genetic LD information of multiple genetic variants in a genome and similarity among different individuals. The proposed models generate correct type I errors. The methods are applied to analyze Trinity Students Study data.