Understanding the genetic basis of complex polygenic traits through Bayesian model selection of multiple genetic models and network modeling of family-based genetic data

The global aim of this dissertation is to develop advanced statistical modeling to understand the genetic basis of complex polygenic traits. In order to achieve this goal, this dissertation focuses on the development of (i) a novel methodology to detect genetic variants with different inheritance patterns formulated as a Bayesian model selection problem, (ii) integration of genetic data and non-genetic data to dissect the genotype-phenotype associations using Bayesian networks with family-based data, and (iii) an efficient technique to model the family-based data in the Bayesian framework. In the first part of my dissertation, I present a coherent Bayesian framework for selection of the most likely model from the five genetic models (genotypic, additive, dominant, co-dominant, and recessive) used in genetic association studies. The approach uses a polynomial parameterization of genetic data to simultaneously fit the five models and save computations. I provide a closed-form expression of the marginal likelihood for normally distributed data, and evaluate the performance of the proposed method and existing methods through simulated and real genome-wide data sets. The second part of this dissertation presents an integrative analytic approach that utilizes Bayesian networks to represent the complex probabilistic dependency structure among many variables from family-based data. I propose a parameterization that extends mixed effects regression models to Bayesian networks by using random effects as additional nodes of the networks to model the between-subjects correlations. I also present results of simulation studies to compare different model selection metrics for mixed models that can be used for learning BNs from correlated data and application of this methodology to real data from a large family-based study. In the third part of this dissertation, I describe an efficient way to account for family structure in Bayesian inference Using Gibbs Sampling (BUGS). In linear mixed models, a random effects vector has a variance-covariance matrix whose dimension is as large as the sample size. However, a direct handling of this multivariate normal distribution is not computationally feasible in BUGS. Therefore, I propose a decomposition of this multivariate normal distribution into univariate normal distributions using singular value decomposition, and implementation in BUGS is presented

A genome-wide association study of 2304 extreme longevity cases identifies novel longevity variants

We performed a genome-wide association study (GWAS) of human extreme longevity (EL), defined as surviving past the 99th survival percentile, by aggregating data from four centenarian studies. The combined data included 2304 EL cases and 5879 controls. The analysis identified a locus in CDKN2B-AS1 (rs6475609

Definition, reporting, and interpretation of composite outcomes in clinical trials: systematic review

Objective To study how composite outcomes, which have combined several components into a single measure, are defined, reported, and interpreted

Changes in undergraduates’ marijuana, heavy alcohol and cigarette use following legalization of recreational marijuana use in Oregon

Background and AimsRecreational marijuana legalization (RML) went into effect in Oregon in July 2015. RML is expected to influence marijuana use by adolescents and young adults in particular, and by those with a propensity for substance use. We sought to quantify changes in rates of marijuana use among college students in Oregon from pre‐ to post‐RML relative to college students in other states across the same time period.DesignRepeated cross‐sectional survey data from the 2012–16 administrations of the Healthy Minds Study.SettingSeven 4‐year universities in the United States.ParticipantsThere were 10 924 undergraduate participants. One large public Oregon university participated in 2014 and 2016 (n = 588 and 1115, respectively); six universities in US states where recreational marijuana use was illegal participated both in 2016 and at least once between 2012 and 2015.MeasurementsSelf‐reported marijuana use in the past 30 days (yes/no) was regressed on time (pre/post 2015), exposure to RML (i.e. Oregon students in 2016) and covariates using mixed‐effects logistic regression. Moderation of RML effects by recent heavy alcohol use was examined.FindingsRates of marijuana use increased from pre‐ to post‐2015 at six of the seven universities, a trend that was significant overall. Increases in rates of marijuana use were significantly greater in Oregon than in comparison institutions, but only among students reporting recent heavy alcohol use.ConclusionsRates of Oregon college students’ marijuana use increased (relative to that of students in other states) following recreational marijuana legislation in 2015, but only for those who reported recent heavy use of alcohol. Such alcohol misuse may be a proxy for vulnerabilities to substance use or lack of prohibitions (e.g. cultural) against it.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138912/1/add13906_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/138912/2/add13906.pd

Burden of disease variants in participants of the Long Life Family Study

Case control studies of nonagenarians and centenarians provide evidence that long-lived individuals do not differ in the rate of disease associated variants compared to population controls. These results suggest that an enrichment of novel protective variants, rather than a lack of disease associated variants, determine the genetic predisposition to exceptionally long lives. Using data from the Long Life Family Study (LLFS), we sought to replicate these findings and extend them to include a larger number of disease-specific risk alleles. To accomplish this goal, we built a genetic risk score for each of four age-related disease groups: Alzheimer's disease, cardiovascular disease and stroke, type 2 diabetes, and various cancers and compared the distribution of these scores between older participants of the LLFS, their offspring and their spouses. The analyses showed no significant differences in distribution of the genetic risk scores for cardiovascular disease and stroke, type 2 diabetes, or cancer between the groups, while participants of the LLFS appeared to carry an average 1% fewer risk alleles for Alzheimer's disease compared to spousal controls and, while the difference may not be clinically relevant, it was statistically significant. However, the statistical significance between familial longevity and the Alzheimer's disease genetic risk score was lost when a more stringent linkage disequilibrium threshold was imposed to select independent genetic variants

Extra-ocular movement restriction and diplopia following orbital fracture repair

Purpose To report a series of patients with extra-ocular movement restriction and diplopia after orbital fracture repair, and determine the effect of timing of repair and the type of implant used. Methods A chart review was conducted identifying all patients > 18 years of age at our institution between June 2005 and June 2008 who underwent orbital fracture repair, and presented with clinically significant diplopia and extra-ocular movement restriction persisting longer than one month after repair. Data collected included timing of repair, implant used within the orbit, and need for revision. Results Ten patients were identified with a mean time to primary orbital fracture repair at 9 days (range 1–48). Seven patients underwent revision of their orbital fracture repair with removal of the previously placed implant and replacement with non-porous 0.4 mm Supramid Foil, whereas one patient underwent lateral and inferior rectus recessions without revision of primary fracture repair. Titanium mesh was the intra-orbital implant found in all patients requiring revision of orbital fracture repair. All revisions resulted in resolution of clinically significant diplopia. Conclusions Clinically significant diplopia and extra-ocular movement restriction is not an uncommon complication after orbital fracture repair. In our series, there was a strong association between these complications and the use of porous titanium mesh implants. Revision of fractures significantly improved diplopia in all but one patient. This suggests that meticulous fracture repair and the use of non-porous implants primarily or secondarily may preclude the need for strabismus surgery after orbital trauma

Learning Bayesian Networks from Correlated Data

Bayesian networks are probabilistic models that represent complex distributions in a modular way and have become very popular in many fields. There are many methods to build Bayesian networks from a random sample of independent and identically distributed observations. However, many observational studies are designed using some form of clustered sampling that introduces correlations between observations within the same cluster and ignoring this correlation typically inflates the rate of false positive associations. We describe a novel parameterization of Bayesian networks that uses random effects to model the correlation within sample units and can be used for structure and parameter learning from correlated data without inflating the Type I error rate. We compare different learning metrics using simulations and illustrate the method in two real examples: an analysis of genetic and non-genetic factors associated with human longevity from a family-based study, and an example of risk factors for complications of sickle cell anemia from a longitudinal study with repeated measures.This is the publisher’s final pdf. The published article is copyrighted by the author(s) and published by Nature Publishing Group. The published article can be found at: http://www.nature.com/articles/srep2515

Burden of disease variants in participants of the long life family study

Case control studies of nonagenarians and centenarians provide evidence that long‐lived individuals do not differ in the rate of disease associated variants compared to population controls. These results suggest that an enrichment of novel protective variants, rather than a lack of disease associated variants, determine the genetic predisposition to exceptionally long lives. Using data from the Long Life Family Study (LLFS), we sought to replicate these findings and extend them to include a larger number of disease‐specific risk alleles. To accomplish this goal, we built a genetic risk score for each of four age‐related disease groups: Alzheimer’s disease, cardiovascular disease and stroke, type 2 diabetes, and various cancers and compared the distribution of these scores between older participants of the LLFS, their offspring and their spouses. The analyses showed no significant differences in distribution of the genetic risk scores for cardiovascular disease and stroke, type 2 diabetes, or cancer between the groups, while participants of the LLFS appeared to carry an average 1% fewer risk alleles for Alzheimer’s disease compared to spousal controls and, while the difference may not be clinically relevant, it was statistically significant. However, the statistical significance between familial longevity and the Alzheimer’s disease genetic risk score was lost when a more stringent linkage disequilibrium threshold was imposed to select independent genetic variants.This is the publisher’s final pdf. The published article is copyrighted by the author(s) and published by Impact Journals, LLC. The published article can be found at: http://www.ncbi.nlm.nih.gov/pmc/journals/1105/.Keywords: Exceptional longevity, Alzheimer’s disease, Disease variants, Genetic risk scor