Background and aims: Carotid artery plaque is an established marker of subclinical atherosclerosis and common patho-mechanisms with coronary artery disease (CAD) are hypothesized. We aimed to identify genetic variants associated with carotid plaque and examine the potential shared genetic basis with CAD. Methods: After investigating the reliability of plaque detection, we performed a genome-wide metaassociation study in two independent cohorts (LIFE-Adult, n=4,037 and LIFE-Heart, n=3,152) for carotid plaque score (PS), defined as the sum of the plaque load of common carotid artery and carotid bulb. Further, we analyzed whether previously reported CAD and stroke loci were also associated with PS. Results: We identified two loci with genome-wide significance for PS. One locus is the known CADlocus at chromosome 9p21 (lead SNP rs9644862, p=8.73x10-12). We also describe a novel locus at on chromosome 10q24 within the SFXN2 gene as the most probable candidate (lead SNP rs2902548, p=1.97x10-8). In addition, 17 out of 58 known CAD loci and six of 17 known stroke loci were associated with PS at a nominal level of significance. Conclusion: We showed that PS is a reliable trait to analyze genetics of atherosclerosis. Two new loci of genome-wide significant association with PS were found. The observed non-random overlap of CAD and PS associations strengthens the hypothesis of a shared genetic basis for these atherosclerotic manifestations.

No description available.

Indiana University-Purdue University Indianapolis (IUPUI) / New neurons are generated throughout adulthood in two regions of the brain, the dentate gyrus of the hippocampus, which is important for memory formation and cognitive functions, and the sub-ventricular zone of the olfactory bulb, which is important for the sense of smell, and are incorporated into hippocampal network circuitry. Disruption of this process has been postulated to contribute to neurodegenerative disorders including Alzheimer’s disease [1]. AD is the most common form of adult-onset dementia and the number of patients with AD escalates dramatically each year. The generation of new neurons in the dentate gyrus declines with age and in AD. Many of the molecular players in AD are also modulators of adult neurogenesis, but the genetic mechanisms influencing adult neurogenesis in AD are unclear. The overall goal of this project is to identify candidate genes and pathways that play a role in neurogenesis in the adult brain and to test the hypotheses that 1) hippocampal neurogenesis-related genes and pathways are significantly perturbed in AD and 2) neurogenesis-related pathways are significantly associated with hippocampal volume and other AD-related biomarker endophenotypes including brain deposition of amyloid-β and tau pathology. First, potential modulators of adult neurogenesis and their roles in neurodegenerative diseases were evaluated. Candidate genes that control the turnover process of neural stem cells/precursors to new functional neurons during adult neurogenesis were manually curated using a pathway-based systems biology approach. Second, a targeted neurogenesis pathway-based gene analysis was performed resulting in the identification of ADORA2A as associated with hippocampal volume and memory performance in mild cognitive impairment and AD. Third, a genome-wide gene-set enrichment analysis was conducted to discover associations between hippocampal volume and AD related endophenotypes and neurogenesis-related pathways. Within the discovered neurogenesis enriched pathways, a gene-based association analysis identified TESC and ACVR1 as significantly associated with hippocampal volume and APOE and PVLR2 as significantly associated with tau and amyloid beta levels in cerebrospinal fluid. This project identifies new genetic contributions to hippocampal neurogenesis with translational implications for novel therapeutic targets related to learning and memory and neuroprotection in AD.

<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>

Background: Imputation of partially missing or unobserved genotypes is an indispensable tool for SNP data analyses. However, research and understanding of the impact of initial SNP-data quality control on imputation results is still limited. In this paper, we aim to evaluate the effect of different strategies of pre-imputation quality filtering on the performance of the widely used imputation algorithms MaCH and IMPUTE. Results: We considered three scenarios: imputation of partially missing genotypes with usage of an external reference panel, without usage of an external reference panel, as well as imputation of ompletely un-typed SNPs using an external reference panel. We first created various datasets applying different SNP quality filters and masking certain percentages of randomly selected high-quality SNPs. We imputed these SNPs and compared the results between the different filtering scenarios by using established and newly proposed measures of imputation quality. While the established measures assess certainty of imputation results, our newly proposed measures focus on the agreement with true genotypes. These measures showed that pre-imputation SNP-filtering might be detrimental regarding imputation quality. Moreover, the strongest drivers of imputation quality were in general the burden of missingness and the number of SNPs used for imputation. We also found that using a reference panel always improves imputation quality of partially missing genotypes. MaCH performed slightly better than IMPUTE2 in most of our scenarios. Again, these results were more pronounced when using our newly defined measures of imputation quality. Conclusion: Even a moderate filtering has a detrimental effect on the imputation quality. Therefore little or no SNP filtering prior to imputation appears to be the best strategy for imputing small to moderately sized datasets. Our results also showed that for these datasets, MaCH performs slightly better than IMPUTE2 in most scenarios at the cost of increased computing time.

Steroid hormones act as important regulators of physiological processes including gene expression. They provide possible mechanistic explanations of observed sex-dimorphisms in obesity and coronary artery disease (CAD). Here, we aim to unravel causal relationships between steroid hormones, obesity, and CAD in a sex-specific manner. In genome-wide meta-analyses of four steroid hormone levels and one hormone ratio, we identified 17 genome-wide significant loci of which 11 were novel. Among loci, seven were female-specific, four male-specific, and one was sex-related (stronger effects in females). As one of the loci was the human leukocyte antigen (HLA) region, we analyzed HLA allele counts and found four HLA subtypes linked to 17-OH-progesterone (17-OHP), including HLA-B*14*02. Using Mendelian randomization approaches with four additional hormones as exposure, we detected causal effects of dehydroepiandrosterone sulfate (DHEA-S) and 17-OHP on body mass index (BMI) and waist-to-hip ratio (WHR). The DHEA-S effect was stronger in males. Additionally, we observed the causal effects of testosterone, estradiol, and their ratio on WHR. By mediation analysis, we found a direct sex-unspecific effect of 17-OHP on CAD while the other four hormone effects on CAD were mediated by BMI or WHR. In conclusion, we identified the sex-specific causal networks of steroid hormones, obesity-related traits, and CAD.

Background: Imputation of partially missing or unobserved genotypes is an indispensable tool for SNP data analyses. However, research and understanding of the impact of initial SNP-data quality control on imputation results is still limited. In this paper, we aim to evaluate the effect of different strategies of pre-imputation quality filtering on the performance of the widely used imputation algorithms MaCH and IMPUTE. Results: We considered three scenarios: imputation of partially missing genotypes with usage of an external reference panel, without usage of an external reference panel, as well as imputation of ompletely un-typed SNPs using an external reference panel. We first created various datasets applying different SNP quality filters and masking certain percentages of randomly selected high-quality SNPs. We imputed these SNPs and compared the results between the different filtering scenarios by using established and newly proposed measures of imputation quality. While the established measures assess certainty of imputation results, our newly proposed measures focus on the agreement with true genotypes. These measures showed that pre-imputation SNP-filtering might be detrimental regarding imputation quality. Moreover, the strongest drivers of imputation quality were in general the burden of missingness and the number of SNPs used for imputation. We also found that using a reference panel always improves imputation quality of partially missing genotypes. MaCH performed slightly better than IMPUTE2 in most of our scenarios. Again, these results were more pronounced when using our newly defined measures of imputation quality. Conclusion: Even a moderate filtering has a detrimental effect on the imputation quality. Therefore little or no SNP filtering prior to imputation appears to be the best strategy for imputing small to moderately sized datasets. Our results also showed that for these datasets, MaCH performs slightly better than IMPUTE2 in most scenarios at the cost of increased computing time.