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Background Depression is moderately heritable but there is no common genetic variant which has a major effect on susceptibility. It is possible that some very rare variants could have substantial effect sizes and these could be identified from exome sequence data.
Methods Data from 50,000 exome-sequenced UK Biobank participants was analysed. Subjects were treated as cases if they had reported having seen a psychiatrist for nerves, anxiety, tension or depression . Gene-wise weighted burden analysis was performed to see if there were any genes or sets of genes for which there was an excess of rare, functional variants in cases.
Results There were 5,872 cases and 43,862 controls. There were 22,028 informative genes but no gene or gene set produced a statistically significant result after correction for multiple testing. None of the genes or gene sets with the lowest p values appeared to be a biologically plausible candidate.
Limitations The phenotype is based on self-report and the cases are likely to be somewhat heterogeneous. Likewise, it is expected that some of the subjects classed as controls will in fact have suffered from depression or some other psychiatric diagnosis. The number of cases is on the low side for a study of exome sequence data.
Conclusions The results conform exactly with the expectation under the null hypothesis. It seems unlikely that depression genetics research will implicate specific genes having a substantial impact on the risk of developing psychiatric illness severe enough to merit referral to a specialist until far larger samples become available.
Study of effects of common and rare genetic variants on health-related phenotypes
Both common and rare genetic variants contribute to the heritability of complex traits. However, usually they are analysed separately using different analytical techniques, such as single variant regression versus burden methods. This fails to fully utilise the information contained in genomes. Here, we aim to apply computational methods we have developed which combine information from common and rare variants and which can utilise information about the predicted effects of the variants to identify genes influencing complex traits. We will use our results to assess how natural selection has shaped the genetic architecture of complex traits in modern humans. We will particularly focus on non-communicable diseases, such as cardiovascular disease, neurological disorders and cancer, and disease-related traits, such as obesity and cholesterol levels, as well as the susceptibility to infectious diseases. Differences in environments, natural selection and drift may have distinctly shaped the genetic architectures in some populations. Therefore, we will compare results for different ancestry groups.