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Exploring the genetic characteristics underlying a multidimensional latent chemotherapy symptom burden

Eng, Winston (2018) Exploring the genetic characteristics underlying a multidimensional latent chemotherapy symptom burden. Master's Thesis, University of Pittsburgh. (Unpublished)

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Abstract

The incidence rate of cancer is expected to increase within the coming decades. While related mortality is expected to decrease with improving treatments, oncology patients are still expected to experience harmful physiological and psychological symptoms. This “symptom burden” has been shown to permanently extend into the patients’ lives, and researchers have hypothesized that a genetic component may dictate the severity of its presentation. From a public health perspective, sustaining an expanding concentration of those considered “symptom burdened” will create unsustainable stress on the current healthcare system. Hoping to describe the heterogeneity in this oncology experience, researchers have relied on statistical clustering to generate patient subgroups differing in quality-of-life. This study’s objective is to assess if there exists any association between Single Nucleotide Polymorphisms (SNP) and oncology “symptom burden” following subcategorization; more specifically, it aims to compare analyses of the latent class phenotypes using the “default” method of Multinomial Logistic Regression with those of the “novel” method of Dirichlet Regression.
A four category latent class was generated while adjusting for site from symptom clusters measured on 2111 subjects from sites UCSF_total and TOR_1 . Genotyping occurred using two versions of the Illumina exome chip: HumanCoreExome-24v1-0 (Group A) and HumanExome-12v1-1 A (Group B). Group A had 944 UCSF_total individuals, while Group B had 669 UCSF_total and 498 TOR_1 subjects. Following quality control, there were 1272 and 415 for the UCSF_total and TOR_1 cohorts respectively. Covariates included within the regression models were total number of comorbidities, Karnofsky Performance Status (KPS), sex, and the first four principal components for population substructure.
After applying both the Multinomial Logistic Regression and Dirichlet Regression approaches, neither method demonstrated statistically significant genetic association (P < 5 × 10 −8 ). However, issues concerning SNPs with very low minor allele frequencies appeared to plague both approaches, indicating that methodological corrections may be necessary in future studies. Additionally, covariate selection, sample size, and imputation may be areas of future inquiry with regards to rectifying some of the issues presented. Future aims should involve simulation and power calculations to determine how appropriate these proposed methods are for assessing genetic association in relation to oncology “symptom burden.”


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Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Eng, Winstonwie4@pitt.eduwie4
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairWeeks, Danielweeks@pitt.eduweeks
Committee MemberLin, Yanyal14@pitt.eduyal14
Committee MemberAnderson, Stewartsja@pitt.edusja
Date: 28 June 2018
Date Type: Publication
Defense Date: 13 April 2018
Approval Date: 28 June 2018
Submission Date: 6 April 2018
Access Restriction: 5 year -- Restrict access to University of Pittsburgh for a period of 5 years.
Number of Pages: 64
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: GWAS, symptom burden, latent class analysis, dirichlet
Date Deposited: 28 Jun 2018 20:05
Last Modified: 28 Jun 2018 20:05
URI: http://d-scholarship.pitt.edu/id/eprint/34168

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