Despite the increasing global burden of neurological disorders, there is a lack of effective diagnostic and therapeutic biomarkers. Proteins are often dysregulated in disease and have a strong genetic component. Here, we carry out a protein quantitative trait locus analysis of 184 neurologically-relevant proteins, using whole genome sequencing data from two isolated population-based cohorts (N = 2893). In doing so, we elucidate the genetic landscape of the circulating proteome and its connection to neurological disorders. We detect 214 independently-associated variants for 107 proteins, the majority of which (76%) are cis-acting, including 114 variants that have not been previously identified. Using two-sample Mendelian randomisation, we identify causal associations between serum CD33 and Alzheimer’s disease, GPNMB and Parkinson’s disease, and MSR1 and schizophrenia, describing their clinical potential and highlighting drug repurposing opportunities.

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Abnormalities in social interaction are a common feature of several psychiatric disorders, aligning with the recent move towards using Research Domain Criteria (RDoC) to describe disorders in terms of observable behaviours rather than using specific diagnoses. Neuroeconomic games are an effective measure of social decision-making that can be adapted for use in neuroimaging, allowing investigation of the biological basis for behaviour. This review summarises findings of neuroeconomic gameplay studies in Axis 1 psychiatric disorders and advocates the use of these games as measures of the RDoC Affiliation and Attachment, Reward Responsiveness, Reward Learning and Reward Valuation constructs. Although research on neuroeconomic gameplay is in its infancy, consistencies have been observed across disorders, particularly in terms of impaired integration of social and cognitive information, avoidance of negative social interactions and reduced reward sensitivity, as well as a reduction in activity in brain regions associated with processing and responding to social information.

37Despite food choices being one of the most important factors influencing health, efforts to 38 identify individual food groups and dietary patterns that cause disease have been 39 challenging, with traditional nutritional epidemiological approaches plagued by biases and 40 confounding. After identifying 302 (289 novel) individual genetic determinants of dietary 41 intake in 445,779 individuals in the UK Biobank study, we develop a statistical genetics 42 framework that enables us, for the first time, to directly assess the impact of food choices 43 on health outcomes. We show that the biases which affect observational studies extend 44 also to GWAS, genetic correlations and causal inference through genetics, which can be 45 corrected by applying our methods. Finally, by applying Mendelian Randomization 46 approaches to the corrected results we identify some of the first robust causal associations 47 between eating patterns and risks of cancer, heart disease and obesity, distinguishing 48 between the effects of specific foods or dietary patterns. 49 50 Recently, causal inference has been improved by a large number of studies which use Mendelian 61 Randomization (MR) to assess the causal relationship between one or more exposures and 62 outcomes. In MR, genetic variants are used as instrumental variables to measure the "life-long 63 exposure" to a risk factor 5 . This technique has proven to be extremely powerful, not influenced by 64 represents the unadjusted GWAS associations while the lower panel represents the association with food choices, after 87 adjustment for mediating traits, such as health status. 88 89 90 91 92 Replication for 23 of the 29 traits was sought in two additional UK based cohorts (EPIC-Norfolk 15 93 and Fenland 16 ) totalling up to 32,779 subjects. Despite relatively limited power, we could nominally 94 replicate 104/325 associations at p<0.05 (one-sided test) (32%; p=9.47x10 -54 ). The direction of 95 effect was consistent with that for discovery in 268 of the 325 associations (82%; p=7.82x10 -35 , 96 Binomial test; see Table S5). After prioritization of the genes in each locus (see Methods for details 97 and Supp. Table S4 for the prioritized genes), we noticed that for many genes associated with 98 BMI, the BMI-raising allele was associated with lower reported consumption of energy-dense foods 99 such as meat or fat and with higher consumption of lower-calorie foods. Although the exact 100 mechanism of action of many of these genes is unknown, in the case of MC4R in mice loss-of-101 function K314X mutants show an increase in weight, higher intake of calories and higher 102 preference for a high fat diet 17 , while we observe a lower intake of fat and higher intake of fresh 103 fruit. We thus wondered if this could be due to the effect of higher BMI on food choices instead of 104 the reverse and if this effect might also occur for a broader range of health-related traits. 105 106 Detecting the effects of potential confounders on food frequency data 107To test this hypothesis, we first se...