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Fulltext Bidirectional crosstalk between the peripheral nervous system and lymphoid tissues/organs

Boahen A, Hu D, Adams MJ, Nicholls PK, Greene WK, Ma B

Front Immunol, 2023;14:1254054.
PMID: 37767094 DOI: 10.3389/fimmu.2023.1254054

The central nervous system (CNS) influences the immune system generally by regulating the systemic concentration of humoral substances (e.g., cortisol and epinephrine), whereas the peripheral nervous system (PNS) communicates specifically with the immune system according to local interactions/connections. An imbalance between the components of the PNS might contribute to pathogenesis and the further development of certain diseases. In this review, we have explored the "thread" (hardwiring) of the connections between the immune system (e.g., primary/secondary/tertiary lymphoid tissues/organs) and PNS (e.g., sensory, sympathetic, parasympathetic, and enteric nervous systems (ENS)) in health and disease in vitro and in vivo. Neuroimmune cell units provide an anatomical and physiological basis for bidirectional crosstalk between the PNS and the immune system in peripheral tissues, including lymphoid tissues and organs. These neuroimmune interactions/modulation studies might greatly contribute to a better understanding of the mechanisms through which the PNS possibly affects cellular and humoral-mediated immune responses or vice versa in health and diseases. Physical, chemical, pharmacological, and other manipulations of these neuroimmune interactions should bring about the development of practical therapeutic applications for certain neurological, neuroimmunological, infectious, inflammatory, and immunological disorders/diseases.
Fulltext A phenome-wide association and Mendelian Randomisation study of polygenic risk for depression in UK Biobank

Shen X, Howard DM, Adams MJ, Hill WD, Clarke TK, Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, et al.

Nat Commun, 2020 05 08;11(1):2301.
PMID: 32385265 DOI: 10.1038/s41467-020-16022-0

Depression is a leading cause of worldwide disability but there remains considerable uncertainty regarding its neural and behavioural associations. Here, using non-overlapping Psychiatric Genomics Consortium (PGC) datasets as a reference, we estimate polygenic risk scores for depression (depression-PRS) in a discovery (N = 10,674) and replication (N = 11,214) imaging sample from UK Biobank. We report 77 traits that are significantly associated with depression-PRS, in both discovery and replication analyses. Mendelian Randomisation analysis supports a potential causal effect of liability to depression on brain white matter microstructure (β: 0.125 to 0.868, pFDR
Fulltext Polygenic risk prediction: why and when out-of-sample prediction R2 can exceed SNP-based heritability

Wang X, Walker A, Revez JA, Ni G, Adams MJ, McIntosh AM, et al.

Am J Hum Genet, 2023 Jul 06;110(7):1207-1215.
PMID: 37379836 DOI: 10.1016/j.ajhg.2023.06.006

In polygenic score (PGS) analysis, the coefficient of determination (R2) is a key statistic to evaluate efficacy. R2 is the proportion of phenotypic variance explained by the PGS, calculated in a cohort that is independent of the genome-wide association study (GWAS) that provided estimates of allelic effect sizes. The SNP-based heritability (hSNP2, the proportion of total phenotypic variances attributable to all common SNPs) is the theoretical upper limit of the out-of-sample prediction R2. However, in real data analyses R2 has been reported to exceed hSNP2, which occurs in parallel with the observation that hSNP2 estimates tend to decline as the number of cohorts being meta-analyzed increases. Here, we quantify why and when these observations are expected. Using theory and simulation, we show that if heterogeneities in cohort-specific hSNP2 exist, or if genetic correlations between cohorts are less than one, hSNP2 estimates can decrease as the number of cohorts being meta-analyzed increases. We derive conditions when the out-of-sample prediction R2 will be greater than hSNP2 and show the validity of our derivations with real data from a binary trait (major depression) and a continuous trait (educational attainment). Our research calls for a better approach to integrating information from multiple cohorts to address issues of between-cohort heterogeneity.
Fulltext Association of Whole-Genome and NETRIN1 Signaling Pathway-Derived Polygenic Risk Scores for Major Depressive Disorder and White Matter Microstructure in the UK Biobank

Barbu MC, Zeng Y, Shen X, Cox SR, Clarke TK, Gibson J, et al.

Biol Psychiatry Cogn Neurosci Neuroimaging, 2019 01;4(1):91-100.
PMID: 30197049 DOI: 10.1016/j.bpsc.2018.07.006

BACKGROUND: Major depressive disorder is a clinically heterogeneous psychiatric disorder with a polygenic architecture. Genome-wide association studies have identified a number of risk-associated variants across the genome and have reported growing evidence of NETRIN1 pathway involvement. Stratifying disease risk by genetic variation within the NETRIN1 pathway may provide important routes for identification of disease mechanisms by focusing on a specific process, excluding heterogeneous risk-associated variation in other pathways. Here, we sought to investigate whether major depressive disorder polygenic risk scores derived from the NETRIN1 signaling pathway (NETRIN1-PRSs) and the whole genome, excluding NETRIN1 pathway genes (genomic-PRSs), were associated with white matter microstructure.
METHODS: We used two diffusion tensor imaging measures, fractional anisotropy (FA) and mean diffusivity (MD), in the most up-to-date UK Biobank neuroimaging data release (FA: n = 6401; MD: n = 6390).
RESULTS: We found significantly lower FA in the superior longitudinal fasciculus (β = -.035, pcorrected = .029) and significantly higher MD in a global measure of thalamic radiations (β = .029, pcorrected = .021), as well as higher MD in the superior (β = .034, pcorrected = .039) and inferior (β = .029, pcorrected = .043) longitudinal fasciculus and in the anterior (β = .025, pcorrected = .046) and superior (β = .027, pcorrected = .043) thalamic radiation associated with NETRIN1-PRS. Genomic-PRS was also associated with lower FA and higher MD in several tracts.
CONCLUSIONS: Our findings indicate that variation in the NETRIN1 signaling pathway may confer risk for major depressive disorder through effects on a number of white matter tracts.
Correction: Genome-wide gene-environment analyses of major depressive disorder and reported lifetime traumatic experiences in UK Biobank

Coleman JRI, Peyrot WJ, Purves KL, Davis KAS, Rayner C, Choi SW, et al.

Mol Psychiatry, 2021 Sep;26(9):5465.
PMID: 32424234 DOI: 10.1038/s41380-020-0779-4
Fulltext Genome-wide gene-environment analyses of major depressive disorder and reported lifetime traumatic experiences in UK Biobank

Coleman JRI, Peyrot WJ, Purves KL, Davis KAS, Rayner C, Choi SW, et al.

Mol Psychiatry, 2020 Jul;25(7):1430-1446.
PMID: 31969693 DOI: 10.1038/s41380-019-0546-6

Depression is more frequent among individuals exposed to traumatic events. Both trauma exposure and depression are heritable. However, the relationship between these traits, including the role of genetic risk factors, is complex and poorly understood. When modelling trauma exposure as an environmental influence on depression, both gene-environment correlations and gene-environment interactions have been observed. The UK Biobank concurrently assessed Major Depressive Disorder (MDD) and self-reported lifetime exposure to traumatic events in 126,522 genotyped individuals of European ancestry. We contrasted genetic influences on MDD stratified by reported trauma exposure (final sample size range: 24,094-92,957). The SNP-based heritability of MDD with reported trauma exposure (24%) was greater than MDD without reported trauma exposure (12%). Simulations showed that this is not confounded by the strong, positive genetic correlation observed between MDD and reported trauma exposure. We also observed that the genetic correlation between MDD and waist circumference was only significant in individuals reporting trauma exposure (rg = 0.24, p = 1.8 × 10-7 versus rg = -0.05, p = 0.39 in individuals not reporting trauma exposure, difference p = 2.3 × 10-4). Our results suggest that the genetic contribution to MDD is greater when reported trauma is present, and that a complex relationship exists between reported trauma exposure, body composition, and MDD.
Fulltext Identifying the Common Genetic Basis of Antidepressant Response

Pain O, Hodgson K, Trubetskoy V, Ripke S, Marshe VS, Adams MJ, et al.

Biol Psychiatry Glob Open Sci, 2022 Apr;2(2):115-126.
PMID: 35712048 DOI: 10.1016/j.bpsgos.2021.07.008

BACKGROUND: Antidepressants are a first-line treatment for depression. However, only a third of individuals experience remission after the first treatment. Common genetic variation, in part, likely regulates antidepressant response, yet the success of previous genome-wide association studies has been limited by sample size. This study performs the largest genetic analysis of prospectively assessed antidepressant response in major depressive disorder to gain insight into the underlying biology and enable out-of-sample prediction.
METHODS: Genome-wide analysis of remission (n remit = 1852, n nonremit = 3299) and percentage improvement (n = 5218) was performed. Single nucleotide polymorphism-based heritability was estimated using genome-wide complex trait analysis. Genetic covariance with eight mental health phenotypes was estimated using polygenic scores/AVENGEME. Out-of-sample prediction of antidepressant response polygenic scores was assessed. Gene-level association analysis was performed using MAGMA and transcriptome-wide association study. Tissue, pathway, and drug binding enrichment were estimated using MAGMA.
RESULTS: Neither genome-wide association study identified genome-wide significant associations. Single nucleotide polymorphism-based heritability was significantly different from zero for remission (h 2 = 0.132, SE = 0.056) but not for percentage improvement (h 2 = -0.018, SE = 0.032). Better antidepressant response was negatively associated with genetic risk for schizophrenia and positively associated with genetic propensity for educational attainment. Leave-one-out validation of antidepressant response polygenic scores demonstrated significant evidence of out-of-sample prediction, though results varied in external cohorts. Gene-based analyses identified ETV4 and DHX8 as significantly associated with antidepressant response.
CONCLUSIONS: This study demonstrates that antidepressant response is influenced by common genetic variation, has a genetic overlap schizophrenia and educational attainment, and provides a useful resource for future research. Larger sample sizes are required to attain the potential of genetics for understanding and predicting antidepressant response.