Masters Theses

Abstract

"Autism spectrum disorder (ASD) refers to a set of developmental disorders with varied attributes. Due to its substantial heterogeneity in terms of behavioral and clinical phenotypes, it is challenging to discern the genetic biomarkers behind ASD, even though the disease is known to be genetic in nature. This serves as a motivation to detect relationships between single nucleotide polymorphisms (SNPs) and a causal autism disease susceptibility locus (DSL) within more homogeneous subgroups. Recently, clinically meaningful subclassifications of ASD have been discovered utilizing facial features of prepubescent boys. Therefore, through the employment of data from 44 prepubertal Caucasian boys with ASD belonging to one of the three facial-feature clusters and their immediate family, we attempt to identify possible genetic markers corresponding to the varying phenotypes of ASD. We utilize tools from family-based association studies for their ability to detect both linkage and association while being most powerful for rare diseases. The transmission disequilibrium test (TDT) and the family-based association test (FBAT) are implemented for the combined ASD and cluster-membership phenotypes; these tests use affected offspring and all offspring, respectively. We also carry out a screening method involving conditional power estimation and a rank-weighting step addressing the multiple testing problem. In each of the analyses conducted, there is not sufficient evidence to conclude that any of the 2828 SNPs included in the study are linked and associated with a DSL corresponding to the phenotype being tested. In order to increase the low statistical power due to small sample sizes, we recommend to recruit additional boys with ASD, determine the facial-feature cluster to which they belong, and genotype the boy and both his parents. There is no need to genotype any unaffected offspring, because their contributions to the test statistic are minor"--Abstract, page iii.

Advisor(s)

Olbricht, Gayla R.

Committee Member(s)

Paige, Robert L.
Wunsch, Donald C.

Department(s)

Mathematics and Statistics

Degree Name

M.S. in Applied Mathematics

Comments

Master of Science in Applied Mathematics with a Statistics emphasis

Publisher

Missouri University of Science and Technology

Publication Date

Spring 2017

Pagination

x, 106 pages

Note about bibliography

Includes bibliographical references (pages 102-105).

Rights

© 2017 Luke Andrew Settles, All rights reserved.

Document Type

Thesis - Open Access

File Type

text

Language

English

Thesis Number

T 11571

Electronic OCLC #

1105575679

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