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Navigating the Complexity of Genetic and Phenotypic Data : = Insights from Genomic Structural Equation Modeling and Assortative Mating Patterns.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Navigating the Complexity of Genetic and Phenotypic Data :/
其他題名:	Insights from Genomic Structural Equation Modeling and Assortative Mating Patterns.
作者:	Horwitz, Tanya Barbara.
面頁冊數:	1 online resource (200 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
Contained By:	Dissertations Abstracts International85-11B.
標題:	Behavioral sciences. -
電子資源:	click for full text (PQDT)
ISBN:	9798382717845

Navigating the Complexity of Genetic and Phenotypic Data : = Insights from Genomic Structural Equation Modeling and Assortative Mating Patterns.
Horwitz, Tanya Barbara.

Navigating the Complexity of Genetic and Phenotypic Data :Insights from Genomic Structural Equation Modeling and Assortative Mating Patterns. - 1 online resource (200 pages)

Source: Dissertations Abstracts International, Volume: 85-11, Section: B.

Thesis (Ph.D.)--University of Colorado at Boulder, 2024.

Includes bibliographical references

Statistical and behavioral geneticists continue to face hurdles in the quest to quantify the relationship between genes and phenotypes. For example, researchers who use factor analytic approaches to study abstract concepts must identify concrete ways to represent latent constructs using observed, quantitative data. Additionally, the phenomenon of non-random (or "assortative") coupling amongst co-parents and romantic/sexual partners-by virtue of violating assumptions in multiple genetically-informed designs--has hindered efforts to obtain accurate estimates of heritability and other related parameters.The first study in this dissertation uses factor analysis to assess the partitioning of variance and covariance of externalizing behaviors (those relating to risk-taking, self-regulation, aggression, sensation-/reward-seeking, and impulsivity) across three subfacets--substance use (SU), substance use disorder (SUD), and (non-SU/SUD) "behavioral disinhibition" (BD)--at the genome-wide latent level. Using European ancestry genome-wide association summary statistics for ten or twelve traits (depending on the model) and the software Genomic Structural Equational Modeling (Genomic SEM), we calculated proportions of shared and unshared genomic variance across these constructs.Past research has found widespread evidence of positive trait correlations in romantic/sexual partners for substance use behaviors, as well as for many other traits. Positive correlations between mates can increase trait variation and prevalence, as well as bias estimates from genetically informed study designs. The second analysis in this dissertation presents a set of systematic reviews and random-effects meta-analyses of human male-female phenotypic partner correlations across 22 commonly studied complex traits. For this dissertation's final project, I discuss an analysis of 133 phenotypic trait correlations based on up to 79,074 inferred British male-female couples in the UK Biobank.Our Genomic SEM study demonstrated high zero-order genetic correlations between the subfacets of externalizing we examined, yet a significant proportion of genomic BD variation was not shared with SU/SUD. Further research can better elucidate BD-specific genomic variation by exploring its genetic and molecular correlates. Meanwhile, we found widespread evidence of positive phenotypic partner correlations, with particularly high estimates for political and religious attitudes, some demographic traits, educational attainment, and some substance use traits. Overall, our findings address several gaps in the psychology and genetics literature.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798382717845Subjects--Topical Terms:

1148596
Behavioral sciences.
Subjects--Index Terms:

Assortative matingIndex Terms--Genre/Form:

554714
Electronic books.

Navigating the Complexity of Genetic and Phenotypic Data : = Insights from Genomic Structural Equation Modeling and Assortative Mating Patterns.
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