Collaborative meta-analysis finds no evidence of a strong interaction between stress and 5-HTTLPR genotype contributing to the development of depression

The hypothesis that the S allele of the 5-HTTLPR serotonin transporter promoter region is associated with increased risk of depression, but only in individuals exposed to stressful situations, has generated much interest, research, and controversy since first proposed in 2003. Multiple meta-analyses combining results from heterogeneous analyses have not settled the issue. To determine the magnitude of the interaction and the conditions under which it might be observed, we performed new analyses on 31 datasets containing 38 802 European-ancestry subjects genotyped for 5-HTTLPR and assessed for depression and childhood maltreatment or other stressful life events, and meta-analyzed the results. Analyses targeted two stressors (narrow, broad) and two depression outcomes (current, lifetime). All groups that published on this topic prior to the initiation of our study and met the assessment and sample size criteria were invited to participate. Additional groups, identified by consortium members or self-identified in response to our protocol (published prior to the start of analysis1) with qualifying unpublished data were also invited to participate. A uniform data analysis script implementing the protocol was executed by each of the consortium members. Our findings do not support the interaction hypothesis. We found no subgroups or variable definitions for which an interaction between stress and 5-HTTLPR genotype was statistically significant. In contrast, our findings for the main effects of life stressors (strong risk factor) and 5-HTTLPR genotype (no impact on risk) are strikingly consistent across our contributing studies, the original study reporting the interaction, and subsequent meta-analyses. Our conclusion is that if an interaction exists in which the S allele of 5-HTTLPR increases risk of depression only in stressed individuals, then it is not broadly generalizable, but must be of modest effect size and only observable in limited situations

Illuminating The Genetic Basis Of Complex Liver Traits In Humans Via Computational Genomics

This dissertation uses statistical and computational methods in human genetics to further our understanding of the genetic basis of liver traits. Genome-wide association studies (GWAS) have provided researchers with many genomic regions associated with liver phenotypes. However, GWA studies do not directly identify causal risk factors, variants, and genes – therefore, in this dissertation, I use complementary computational and statistical approaches to help identify genes, variants, molecular processes, and risk factors for liver traits with cardiometabolic implications. In the first chapter of this dissertation, I examine the role of genetically-driven differences in alternative splicing in blood lipid level variation by mapping splicing quantitative trait loci (sQTL) and integrating these data with lipid GWAS through colocalization analysis. We find that sQTLs provide information as to the causal variants and genes driving variation and provide a level of granularity that cannot be captured by total gene expression measurements. In the second chapter of this dissertation, I use GWAS data in the recently developed framework of Mendelian Randomization (MR) to better understand the causal risk factors for non-alcoholic fatty liver disease (NAFLD) a complex disease of increasing prevalence and limited treatment options. We find that body mass index and central adiposity have independent effects on NAFLD risk, as does birthweight. We are also the first to show that both causal relationships for body mass index and central adiposity replicate in African American and Hispanic populations, which in turn suggests that the underlying genetics of NAFLD are similar across ancestry groups. In sum, this dissertation improves our understanding of complex liver phenotypes by identifying underlying molecular mechanisms and genes (Chapter 1) and genetically determined risk factors (Chapter 2). Importantly, the results of this report provide researchers and clinicians with new targets for pharmacological and behavioral interventions