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A COMPARISON OF PRINCIPLE COMPONENT ANALYSIS AND FACTOR ANALYSIS FOR QUANTITATIVE PHENOTYPES ON FAMILY DATA

Wang, Xiaojing (2007) A COMPARISON OF PRINCIPLE COMPONENT ANALYSIS AND FACTOR ANALYSIS FOR QUANTITATIVE PHENOTYPES ON FAMILY DATA. Master's Thesis, University of Pittsburgh. (Unpublished)

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Abstract

Background: Multivariate analysis, especially principal component analysis (PCA) and factor analysis (FA) is one of the effective methods by which to uncover the common factors (both genetic and environmental) that contribute to complex disease phenotypes, such as bone mineral density for osteoporosis. Although PCA and FA are widely used for this purpose, a formal evaluation of the performance of these two multivariate methodologies is lacking. Method: We conducted a comparison analysis using simulated data on 500 individuals from 250 nuclear families. We first simulated 7 underlying (unobserved) genetic and environmentally determined traits. Then we derived two sets of 50 complex (observed) traits using algebraic combinations of the underlying components plus an error term. We next performed PCA and FA on these complex traits and extracted the first factor/principal component. We studied three aspects of the performance of the methods: 1) the ability to detect the underlying genetic/environmental components; 2) whether the methods worked better when applied to raw traits or to residuals (that is, after regressing out potentially significant environmental covariates); and 3) whether heritabilities of composite PCA and FA phenotypes were higher than those of the original complex traits and/or underlying components. Results: Our results indicated that both multivariate analysis methods behave similarly in most cases, although FA is better able to detect predominant signals from underlying trait, which may improve the downstream QTL analysis. Using residuals (after regressing out potentially significant environmental covariates) in the PCA or FA analyses greatly increases the probability that PCs or factors detect common genetic components instead of common environmental factors, except if there is statistical interaction between genetic and environmental factors. Finally, although there is no predictable relationship between heritabilities obtained from composite phenotypes versus original complex traits, our results indicate that composite trait heritability generally reflects the genetic characteristics of the detectable underlying components. Public health significance: Understanding the strengths and weaknesses of multivariate analysis methods to detect underlying genetic and environmental factors for complex diseases will improve our identification of such factors. and this information may lead to better methods of treatment and prevention.


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Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Wang, Xiaojingxiaojing.wang@hgen.pitt.edu
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairFeingold, Eleanoreleanor.feingold@hgen.pitt.eduFEINGOLD
Committee MemberKammerer, Candaceckammerer@hgen.pitt.eduCMK3
Committee MemberAnderson, Stewartsja@pitt.eduSJA
Date: 28 June 2007
Date Type: Completion
Defense Date: 5 April 2007
Approval Date: 28 June 2007
Submission Date: 12 April 2007
Access Restriction: 5 year -- Restrict access to University of Pittsburgh for a period of 5 years.
Institution: University of Pittsburgh
Schools and Programs: Graduate School of Public Health > Biostatistics
Degree: MS - Master of Science
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: family data; Multivariate Analysis; quantitative genetics
Other ID: http://etd.library.pitt.edu/ETD/available/etd-04122007-133710/, etd-04122007-133710
Date Deposited: 10 Nov 2011 19:36
Last Modified: 15 Nov 2016 13:39
URI: http://d-scholarship.pitt.edu/id/eprint/7051

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