Browsing by Subject "heritability"
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Item Adolescent Behavioral Disinhibition And Its Relationship To Marijuana Use Development(2019-03) Zellers, StephanieBehavioral disinhibition is a highly heritable risk factor for drug use, yet how it relates to marijuana use development is under-studied. We addressed this using independent twin samples from Colorado (N=2608) and Minnesota (N=3630), assessed from adolescence to early adulthood. We fit a biometric latent growth model of marijuana use using data from up to four waves of assessment between ages 14-24, to examine change in marijuana use and its relationship with a factor model of behavioral disinhibition. The factor structure of behavioral disinhibition, as well as its association with early marijuana use (r~.8) and increase in use (r~.3), was similar in both states. Early use was moderately heritable in both states. Increase in use was highly heritable in Minnesota (h2 =.81) but not Colorado (h2 =.14), and shared environmental effects were larger in Colorado (c2=.53) than Minnesota (c2=0). State differences in variance components could reflect state differences in culture or legal landscape. We found significant genetic correlations between disinhibition and early use in both states, as well as between disinhibition and increase in use in Minnesota (rg=.37). Lastly, exploratory analyses in Minnesota indicate that marijuana use decreases across the late 20s. This decline is strongly heritable (h2=.79) and moderately, negatively correlated with adolescent disinhibition (r=-.54). We conclude that adolescent behavioral disinhibition is positively related to early marijuana use and increase in use and negatively related to decrease in use in adulthood. This study broadens our understanding of adolescent risk and later marijuana use.Item Genetic And Environmental Influences On DSM-5 Maladaptive Personality Traits And Their Connections With Normative Personality Traits(2016-12) Wright, ZaraThe Diagnostic and Statistical Manual for Mental Disorders, 5th Edition (DSM-5) proposes an alternative model for personality disorders, a key element of which is pathological traits. These traits can be operationalized by the Personality Inventory for the DSM-5 (PID-5). Although there has been extensive research on genetic and environmental influences on normative personality, the heritability of the DSM-5 traits, and maladaptive personality in general, remains understudied. The present study addresses this gap in the literature by assessing traits indexed by the PID-5 and the International Personality Item Pool NEO (IPIP-NEO) in adult twins (N = 1,812 individuals). Research aims included 1) replicating past findings of heritability of normative personality as measured by the IPIP-NEO as a benchmark for studying maladaptive traits, 2) ascertaining univariate heritability estimates of maladaptive personality traits as measured by the PID-5, 3) establishing how much variation in maladaptive personality can be attributed to the same genetic components affecting variation in normative personality, and 4) determining residual variance in maladaptive personality after variance attributable to genetic and environmental components of normative personality has been removed. Results revealed that maladaptive personality traits reflect similar levels of heritability to that of normative personality. Further, maladaptive and normative personality traits that correlate at the phenotypic level also correlate at the genotypic level, indicating overlapping genetic components contribute to variance in both. Nevertheless, we also found evidence for genetic and environmental components unique to maladaptive personality traits, not shared with normative personality.Item Robust Variance Component Models and Powerful Variable Selection Methods for Addressing Missing Heritability(2018-08) Arbet, JaronThe development of a complex human disease is an intricate interplay of genetic and environmental factors. Broadly speaking, “heritability” is defined as the proportion of total trait variance due to genetic factors within a given population. Over the past 50 years, studies involving monozygotic and dizygotic twins have estimated the heritability of over 17,800 human traits [1]. Genetic association studies that measure thousands to millions of genetic “markers” have attempted to determine the exact markers that explain a given trait’s heritability. However, often the identified set of “statistically-significant” markers fails to explain more than 10% of the estimated heritability of a trait [2], which has been defined as the “missing heritability” problem [3][4]. “Missing heritability’ implies that many genetic markers that contribute to disease risk are still waiting to be discovered. Identification of the exact genetic markers associated with a disease is important for the development of pharmaceutical drugs that may target these markers (see [5] for recent examples). Additionally, “missing heritability” may imply that we are inaccurately estimating heritability in the first place [3, 4, 6], thus motivating the development of more robust models for estimating heritability. This dissertation focuses on two objectives that attempt to address the missing heritability problem: (1) develop a more robust framework for estimating heritability; and (2) develop powerful association tests in attempt to find more genetic markers associated with a given trait. Specifically: in Chapter 2, robust variance component models are developed for estimating heritability in twin studies using second-order generalized estimating equations (GEE2). We demonstrate that GEE2 can improve coverage rates of the true heritability parameter for non-normally distributed outcomes, and can easily incorporate both mean and variance-level covariate effects (e.g. let heritability vary by sex or age). In Chapter 3, penalized regression is used to jointly model all genetic markers. It is demonstrated that jointly modeling all markers can improve power to detect individual associated markers compared to conventional methods that model each marker “one-at-a-time.” Chapter 4 expands on this work by developing a more flexible nonparametric Bayesian variable selection model that can account for non-linear or non-additive effects, and can also test biologically meaningful groups of markers for an association with the outcome. We demonstrate how the nonparametric Bayesian method can detect markers with complex association structures that more conventional models might miss.