Genome-wide approaches including polygenic risk scores (PRSs) are now widely used in medical research; however, few studies have been conducted in low- and middle-income countries (LMICs), especially in South America. This study was designed to test the transferability of psychiatric PRSs to individuals with different ancestral and cultural backgrounds and to provide genome-wide association study (GWAS) results for psychiatric outcomes in this sample. The PrOMIS cohort (N = 3308) was recruited from prenatal care clinics at the Instituto Nacional Materno Perinatal (INMP) in Lima, Peru. Three major psychiatric outcomes (depression, PTSD, and suicidal ideation and/or self-harm) were scored by interviewers using valid Spanish questionnaires. Illumina Multi-Ethnic Global chip was used for genotyping. Standard procedures for PRSs and GWAS were used along with extra steps to rule out confounding due to ancestry. Depression PRSs significantly predicted depression, PTSD, and suicidal ideation/self-harm and explained up to 0.6% of phenotypic variation (minimum p = 3.9 × 10−6). The associations were robust to sensitivity analyses using more homogeneous subgroups of participants and alternative choices of principal components. Successful polygenic prediction of three psychiatric phenotypes in this Peruvian cohort suggests that genetic influences on depression, PTSD, and suicidal ideation/self-harm are at least partially shared across global populations. These PRS and GWAS results from this large Peruvian cohort advance genetic research (and the potential for improved treatments) for diverse global populations. / National Institutes of Health / Revisión por pares

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Alcohol use (AU) and alcohol use disorder (AUD) are leading causes of morbidity, premature death, and economic burden. They are also associated with high levels of disability and many other negative outcomes. Twin and family studies have consistently shown that AU and AUD are complex traits influenced by both genetic and environmental factors. Although much has been learned about the genetic and environmental etiology of AU and AUD, significant gaps remain. These include the need for a more comprehensive understanding of the roles of risk and protective factors, and the nature of developmental trajectories underpinning the progression from AU to AUD. The aims of this dissertation are: (1) to examine the roles of resilience and personality disorders in the etiology of AU and AUD; (2) to investigate the nature of longitudinal changes in genetic and environmental risk factors responsible for individual differences in AU; and (3) to determine the moderating roles of key environmental risk factors on the impact of aggregate molecular, or polygenic, risk for AU during adolescence. Using both biometrical behavioral genetic and molecular genetic methodologies, five key findings were observed: (1) Resilience is strongly associated with a reduction in risk for AUD, and this relationship appears to be the result of overlapping genetic and shared environmental influences; (2) Borderline and antisocial personality disorders are the strongest and most stable personality pathology predictors of the phenotypic and genotypic liability to AU and AUD across time; (3) Genetic influences on the development of AUD from early adulthood to mid-adulthood are dynamic, whereby two sets of genetic risk factors contribute to AUD risk; (4) The specific genetic influences on AU follow an unfolding pattern of growth over time, whereas unique environmental risk factors are consistent with an accumulation of environmental impacts and risks across time; and (5) High peer group deviance and low parental monitoring are associated with increased AU, while early parental monitoring moderates the polygenic risk for AU at age 20. The implications of these results with regard to prevention and intervention efforts are discussed.

BACKGROUND: Polygenic risk scores (PRS) have emerged as a promising tool for predicting the risk of developing a variety of illnesses, including psychiatric disorders. PRS are calculated by analyzing the genetic variants across the genome to assess an individual’s risk for developing a disorder. Family history (FHx) of psychiatric disorders has long been recognized as a valuable tool in assessing an individual’s risk in lieu of a genetic blood-based biomarker, like PRS. However, the accuracy of self-reported family history remains limited as a consequence of incomplete or unreliable information collected during a clinical interview. Existing risk factors for developing psychiatric disorders such as FHx tend to be non-specific in their prediction of outcome. Few research studies have evaluated the possibility of using PRS as a complement to FHx across psychosis spectrum disorders. The present study seeks to examine the relationship between the current standard indirect measure of inherited susceptibility being used, FHx, and an individual’s PRS to more directly predict risk of familial susceptibility in those diagnosed with schizophrenia (SZ) by comparing SZ probands based on their FHx of psychotic disorders diagnosis. METHODS: 396 SZ Probands with FHx data were identified. Data on polygenic risk scores for SZ (PRSSCZ) and FHx were obtained from the Bipolar-Schizophrenia Network on Intermediate Phenotypes consortium (B-SNIP 1). Genetic susceptibility was identified using PRSSCZ. FHx was established from detailed family interviews. SZ probands with only an affected first-degree relative (n= 42) or only an affected second-degree relative (n= 55) with history of a psychotic disorder diagnosis were included in the analyses. SZ probands without any affected relative (n=179) were used as a comparison group. Demographic information for all participant groups were compared using Chi-square for categorical variables, and ANOVA for continuous variables. ANCOVA was used to identify differences among relative proximity and PRSSCZ while accounting for covariates (age, sex, race). Multiple comparisons were adjusted for using Bonferroni correction. Healthy controls were added as a reference only. The significance level was set at p < 0.05. RESULTS: In SZ probands, there was a significant difference between those with an affected first-degree relative with non-affective psychosis and those without any affected relatives (p< 0.05). No significant difference was observed between those with an affected second-degree relative with non-affective psychosis and those without any affected relatives. Having only an affected first-degree relative with non-affective psychosis carries significantly more risk than having only an affected second-degree relative with non-affective psychosis (p< 0.05). CONCLUSIONS: These findings a) support the validity of taking careful family history of non-affective psychosis diagnosis when evaluating individuals with a psychotic disorder, b) suggest that PRSSCZ may be a useful complement to taking family history, and c) relative proximity is important in risk for SZ. The limitations of this study include lack of direct interviews of affected first- and second-degree relatives, and the lack of complete pedigree information that might allow for calculation of familial load.

Sleep disturbances and insomnia are prevalent, with around 33% of adults indicating that they experience at least one main symptom of insomnia, and bidirectional relationships exist with common psychopathology, particularly major depressive disorder (MDD). However, genetic and environmental (e.g., traumatic event exposure) contributions to the etiology of these phenotypes are not yet well understood. A genetically informative sample of approximately 12,000 Han Chinese women aged 30-60 (50% with recurrent MDD) was used to address several gaps within the sleep literature. Sleep disturbances were assessed in all individuals using a general item addressing sleeplessness (GS). A sleep within depression sum score (SDS) was also created in MDD cases, combining information from the GS and two insomnia items within MDD. A total of 11 traumatic events were assessed and additional information on childhood sexual abuse (CSA) was also obtained. First, factor analyses were conducted to determine trauma factor structure. The best-fit solution included 3 factors: interpersonal, child interpersonal, and non-assaultive, and composite variables were constructed accordingly. A series of hierarchical regressions were run to examine differential effects of trauma type and timing on sleeplessness. All traumatic events predicted sleeplessness at similar magnitudes, although population models indicated that childhood interpersonal trauma may be particularly potent. An association between CSA and sleeplessness was also replicated. A series of genetic analyses demonstrated that the single nucleotide polymorphism-based heritability of sleep phenotypes did not differ significantly from zero. Further, association analyses did not identify any genome-wide significant loci. However, using a liberal false discovery rate threshold of 0.5, two genes of interest, KCNK9 and ALDH1A2, emerged for the SDS. Polygenic risk score (PRS) analyses demonstrated genetic overlap between the SDS in MDD cases and GS in MDD controls, with PRSs explaining 0.2-0.3% of the variance. A final combined model of both genetic and environmental risk indicated that both PRS and traumatic events were significant predictors of sleeplessness. While genetic results should be interpreted with caution given the lack of heritability, additional research into the genetic and environmental contributions to insomnia, utilizing more standardized phenotypes and properly ascertained samples, is clearly warranted.

<p>Complex traits are influenced by genetic and environmental factors and their interactions. Most common human disorders such as cardiovascular, metabolic, autoimmune, and neurological diseases are complex. Understanding their genetic architecture and etiology is an important step to prevent, diagnose and treat these conditions. Genome Wide Association Studies (GWAS) have emerged as a powerful and widely used tool that can be used to explore and identify the genetic variants associated with complex traits. In this dissertation, we present some of the downstream applications of GWAS studies to analyze and understand the genetic risk and etiology of complex traits and provide important insights into the genetic architecture and background of several complex phenotypes. First, we examined whether prevalence of complex disorders around the world correlates to Polygenic Risk Scores (PRS). To do so, we determined the average PRS of 14 such complex disorders across 24 world populations using results of GWAS studies. We found variation in risk across populations and significant correlation was obtained between average disease risk and prevalence for seven of the studied disorders. Further exploring the power of PRS- based calculations, we performed a PRS - based phenome wide association study (PheWAS) for Tourette Syndrome (TS) and identified 57 phenotypic outcomes significantly associated with TS PRS. The strongest associations were found between TS PRS and mental health factors. Cross- disorder comparisons of phenotypic associations with genetic risk for other childhood-onset disorders (e.g.: attention deficit hyperactivity disorder [ADHD], autism spectrum disorder [ASD], and obsessive-compulsive disorder [OCD]) indicated an overlap in associations between TS and these disorders. Furthermore, we performed a sex specific PheWAS that highlighted differences in associations of complex disorders with TS PRS in males and females. Finally, we used large- scale GWAS results to identify causal associations between different biological markers (proteins, metabolites, and microbes) and subcortical brain structure volumes using Mendelian Randomization (MR) analysis. We identified eleven proteins and six metabolites to be significantly associated with subcortical brain volume structures. Enrichment analysis indicated that the associated proteins were enriched for proteolytic functions and regulation of apoptotic pathways. Overall, our work demonstrates the power of GWAS studies to help disentangle the genetic basis of complex diseases and also provides important insights into the etiology of the studied complex traits. </p>

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Introduction/Background: Guidelines from the World Health Organization currently recommend targeting a body mass index (BMI) between 18.5 and 24.9 kg/m2 based on the lowest risk of mortality observed in epidemiological studies. However, these recommendations are based on population observations and do not take into account potential inter-individual differences. We hypothesized that genetic and non-genetic differences in adiposity, anthropometric, and metabolic measures result in inter-individual variation in the optimal BMI. Methods: Genetic variants associated with BMI as well as related adiposity, anthropometric, and metabolic phenotypes (e.g. triglyceride (TG)) were combined into polygenic risk scores (PRS), cumulative risk scores derived from the weighted contributions of each variant. 387,692 participants in the UK Biobank were split by quantiles of PRS or clinical biomarkers such as C-reactive protein (CRP), and alanine aminotransferase (ALT). The BMI linked with the lowest risk of all-cause and cause-specific mortality outcomes (“nadir value”) was then compared across quantiles (“Cox meta-regression model”). Our results were replicated using the non-linear mendelian randomization (NLMR) model to assess causality. Results: The nadir value for the BMI–all-cause mortality relationship differed across percentiles of BMI PRS, suggesting inter-individual variation in optimal BMI based on genetics (p = 0.005). There was a difference of 1.90 kg/m2 in predicted optimal BMI between individuals in the top and bottom 5th BMI PRS percentile. Individuals having above and below median TG (p = 1.29×10-4), CRP (p = 7.92 × 10-5), and ALT (p = 2.70 × 10-8) levels differed in nadir for this relationship. There was no difference in the computed nadir between the Cox meta-regression or NLMR models (p = 0.102). Conclusions: The impact of BMI on mortality is heterogenous due to individual genetic and clinical biomarker level differences. Although we cannot confirm that are results are causal, genetics and clinical biomarkers have potential use for making more tailored BMI recommendations for patients. / Thesis / Master of Science (MSc) / The World Health Organization (WHO) recommends targeting a body mass index (BMI) between 18.5 - 24.9 kg/m2 for optimal health. However, this recommendation does not take into account individual differences in genetics or biology. Our project aimed to determine whether the optimal BMI, or the BMI associated with the lowest risk of mortality, varies due to genetic or biological variation. Analyses were conducted across 387,692 individuals. We divided participants into groups according to genetic risk for obesity or clinical biomarker profile. Our results show that the optimal BMI varies according to genetic or biomarker profile. WHO recommendations do not account for this variation, as the optimal BMI can fall under the normal 18.5 - 24.9 kg/m2 or overweight 25.0 – 29.0 kg/m2 WHO BMI categories depending on individual genetic or biomarker profile. Thus, there is potential for using genetic and/or biomarker profiles to make more precise BMI recommendations for patients.