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Gene-based association testing of dichotomous traits using generalized functional linear mixed models for family data

Jiang, Yingda (2015) Gene-based association testing of dichotomous traits using generalized functional linear mixed models for family data. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Gene-based association testing with rare variants requires arbitrarily aggregating or collapsing the information of the rare variants in genes into a single measure. As genotyping data can be viewed as a realization of a stochastic process that varies along the chromosome, it is more natural to summarize the genetic information using the approaches of functional data analysis. In functional data analysis, discrete genotypes are fitted by a continuous curve by using a collection of smooth basis functions. Existing generalized functional linear models (FLM) have been developed for unrelated samples to test for association between a dichotomous trait and genetic variants in a gene. In most situations, these models have higher power than well-known kernel-based methods (SKAT and SKAT-O). Here we extend this approach to accommodate family-based data using the GLOGS (genome-wide logistic mixed model/score test) approach developed by Stanhope and Abney, and develop family-based generalized functional linear mixed models (GFLMMs). This involves parallel computations to integrate out a multidimensional polygenic effect. Simulation results indicate that in most scenarios our new statistics are better than other similar statistics (famSKAT or F-SKAT), but not better than the retrospective kernel and burden statistics developed by Schaid and colleagues. We also embed FLM-smoothed genotypes in the retrospective statistics, improving the power of the kernel-based approach. We illustrate the behavior of these statistics by applying them to an age-related macular degeneration (AMD) family data set, where, as expected, we observe strong association between AMD and CFH and ARMS2, two known AMD susceptibility genes. Our proposed GFLMM provides a new tool for conducting family-based research studies in public health for complex or multifactorial diseases. The findings may improve the knowledge of existing AMD susceptibility genes and make a positive contribution to AMD treatment and prevention.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Jiang, Yingdayij5@pitt.eduYIJ50000-0001-5008-7982
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairWeeks, Daniel E.weeks@pitt.eduWEEKS0000-0001-9410-7228
Committee MemberChen, Weiwei.chen@chp.eduWEC47
Committee MemberTseng, George C.ctseng@pitt.eduCTSENG
Committee MemberZhou, Leminglzhou1@pitt.eduLZHOU1
Date: 28 September 2015
Date Type: Publication
Defense Date: 27 July 2015
Approval Date: 28 September 2015
Submission Date: 20 July 2015
Access Restriction: 5 year -- Restrict access to University of Pittsburgh for a period of 5 years.
Number of Pages: 153
Institution: University of Pittsburgh
Schools and Programs: School of Public Health > Biostatistics
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Functional linear model (FLM), GWAS, AMD, Linkage, Association
Date Deposited: 28 Sep 2015 18:59
Last Modified: 30 Jun 2022 15:23


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