Genetic and environmental predictors of psychiatric disorders and related traits

• Main Author: Power, Robert
• Other Authors: Lewis, Cathryn Mair ; Craig, Ian Watson ; McGuffin, Peter
• Published: King's College London (University of London) 2014
• Subjects: 616.89
• Online Access: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.677008

Psychiatric disorders pose a major economic and health burden worldwide. Family and twin studies indicate strong genetic influences, with estimates suggesting substantial variance in liability is heritable. However known genetic risk variants for psychiatric disorders explain only a small fraction of the heritability estimated from twin studies, leaving the underlying genetic aetiology largely unknown. Given the wide range in prevalence, age at onsets, and gender ratios seen across psychiatric disorders it is reasonable to expect that different genetic architectures exist for each disorder. Thus the underlying genetic architecture of psychiatric disorders remains an open question, with large potential implications for identifying predictive genetic risk variants, redefining diagnostic criteria, and developing novel drug targets. This thesis focuses on combining epidemiological, genetic and environmental data to answer the questions surrounding the genetic architectures of psychiatric disorders. Initial analysis of epidemiological measures surprisingly identified that major depression was not under negative selection, a finding that was then confirmed through analysis of genotypic data. These results suggested the potential role of gene-environment interactions as an adaptive mechanism for variants contributing risk to depression, and were followed up by studies focusing on identifying such interactions. The conclusion of the thesis was though no gene-environmental interaction could be found to explain how the risk variants for major depression avoided negative selection, this was likely due in part to substantial gene-environment correlation in the reporting and experiencing of environmental risk factors in psychiatric disorders which would confound such analyses. Further these gene-environment correlations likely confound epidemiological associations identifying ‘environmental’ risks, such as between cannabis and schizophrenia.