Associations between Attention-Deficit/Hyperactivity Disorder and various eating disorders: A Swedish nationwide population study using multiple genetically informative approaches

Background Although attention-deficit hyperactivity/impulsivity disorder (ADHD) and eating disorders (EDs) frequently co-occur, little is known about the shared etiology. In this study we comprehensively investigated the genetic association between ADHD and various EDs, including anorexia nervosa (AN) and other EDs (OED, including bulimia nervosa [BN]). Methods We applied different genetically informative designs to register-based information of a Swedish nationwide population (N=3,550,118). We first examined the familial co-aggregation of clinically diagnosed ADHD and EDs across multiple types of relatives. We then applied quantitative genetic modeling in full-sisters and maternal half-sisters to estimate the genetic correlations between ADHD and EDs. We further tested the associations between ADHD polygenic risk scores (PRS) and ED symptoms, and between AN PRS and ADHD symptoms, in a genotyped population-based sample (N=13,472). Results Increased risk of all types of EDs was found in individuals with ADHD (any ED: OR [95% CI]=3.97 [3.81-4.14], AN: 2.68 [2.15-2.86], OED: 4.66 [4.47-4.87], BN: 5.01 [4.63-5.41]) and their relatives compared to individuals without ADHD and their relatives. The magnitude of the associations reduced as the degree of relatedness decreased, suggesting shared familial liability between ADHD and EDs. Quantitative genetic models revealed stronger genetic correlation of ADHD with OED (0.37 [0.31-0.42]) than with AN (0.14 [0.05-0.22]). ADHD PRS correlated positively with ED symptom measures overall and sub-scales “drive for thinness” and “body dissatisfaction”, despite small effect sizes. Conclusions We observed stronger genetic association with ADHD for non-AN EDs than AN, highlighting specific genetic correlation beyond a general genetic factor across psychiatric disorders

The genetics of the mood disorder spectrum:genome-wide association analyses of over 185,000 cases and 439,000 controls

Background Mood disorders (including major depressive disorder and bipolar disorder) affect 10-20% of the population. They range from brief, mild episodes to severe, incapacitating conditions that markedly impact lives. Despite their diagnostic distinction, multiple approaches have shown considerable sharing of risk factors across the mood disorders. Methods To clarify their shared molecular genetic basis, and to highlight disorder-specific associations, we meta-analysed data from the latest Psychiatric Genomics Consortium (PGC) genome-wide association studies of major depression (including data from 23andMe) and bipolar disorder, and an additional major depressive disorder cohort from UK Biobank (total: 185,285 cases, 439,741 controls; non-overlapping N = 609,424). Results Seventy-three loci reached genome-wide significance in the meta-analysis, including 15 that are novel for mood disorders. More genome-wide significant loci from the PGC analysis of major depression than bipolar disorder reached genome-wide significance. Genetic correlations revealed that type 2 bipolar disorder correlates strongly with recurrent and single episode major depressive disorder. Systems biology analyses highlight both similarities and differences between the mood disorders, particularly in the mouse brain cell-types implicated by the expression patterns of associated genes. The mood disorders also differ in their genetic correlation with educational attainment – positive in bipolar disorder but negative in major depressive disorder. Conclusions The mood disorders share several genetic associations, and can be combined effectively to increase variant discovery. However, we demonstrate several differences between these disorders. Analysing subtypes of major depressive disorder and bipolar disorder provides evidence for a genetic mood disorders spectrum

Bipolar multiplex families have an increased burden of common risk variants for psychiatric disorders.

Multiplex families with a high prevalence of a psychiatric disorder are often examined to identify rare genetic variants with large effect sizes. In the present study, we analysed whether the risk for bipolar disorder (BD) in BD multiplex families is influenced by common genetic variants. Furthermore, we investigated whether this risk is conferred mainly by BD-specific risk variants or by variants also associated with the susceptibility to schizophrenia or major depression. In total, 395 individuals from 33 Andalusian BD multiplex families (166 BD, 78 major depressive disorder, 151 unaffected) as well as 438 subjects from an independent, BD case/control cohort (161 unrelated BD, 277 unrelated controls) were analysed. Polygenic risk scores (PRS) for BD, schizophrenia (SCZ), and major depression were calculated and compared between the cohorts. Both the familial BD cases and unaffected family members had higher PRS for all three psychiatric disorders than the independent controls, with BD and SCZ being significant after correction for multiple testing, suggesting a high baseline risk for several psychiatric disorders in the families. Moreover, familial BD cases showed significantly higher BD PRS than unaffected family members and unrelated BD cases. A plausible hypothesis is that, in multiplex families with a general increase in risk for psychiatric disease, BD development is attributable to a high burden of common variants that confer a specific risk for BD. The present analyses demonstrated that common genetic risk variants for psychiatric disorders are likely to contribute to the high incidence of affective psychiatric disorders in the multiplex families. However, the PRS explained only part of the observed phenotypic variance, and rare variants might have also contributed to disease development

Genome-wide association study of borderline personality disorder reveals genetic overlap with bipolar disorder, major depression and schizophrenia

Borderline personality disorder (BOR) is determined by environmental and genetic factors, and characterized by affective instability and impulsivity, diagnostic symptoms also observed in manic phases of bipolar disorder (BIP). Up to 20% of BIP patients show comorbidity with BOR. This report describes the first case–control genome-wide association study (GWAS) of BOR, performed in one of the largest BOR patient samples worldwide. The focus of our analysis was (i) to detect genes and gene sets involved in BOR and (ii) to investigate the genetic overlap with BIP. As there is considerable genetic overlap between BIP, major depression (MDD) and schizophrenia (SCZ) and a high comorbidity of BOR and MDD, we also analyzed the genetic overlap of BOR with SCZ and MDD. GWAS, gene-based tests and gene-set analyses were performed in 998 BOR patients and 1545 controls. Linkage disequilibrium score regression was used to detect the genetic overlap between BOR and these disorders. Single marker analysis revealed no significant association after correction for multiple testing. Gene-based analysis yielded two significant genes: DPYD (P=4.42 × 10^−7) and PKP4 (P=8.67 × 10^−7); and gene-set analysis yielded a significant finding for exocytosis (GO:0006887, P_FDR=0.019; FDR, false discovery rate). Prior studies have implicated DPYD, PKP4 and exocytosis in BIP and SCZ. The most notable finding of the present study was the genetic overlap of BOR with BIP (r_g=0.28 [P=2.99 × 10^−3]), SCZ (r_g=0.34 [P=4.37 × 10^−5]) and MDD (r_g=0.57 [P=1.04 × 10^−3]). We believe our study is the first to demonstrate that BOR overlaps with BIP, MDD and SCZ on the genetic level. Whether this is confined to transdiagnostic clinical symptoms should be examined in future studies

Torrefaction of biomass pellets using the thermogravimetric analyser

Greater heating values, greater energy density and improved physical properties such as shape stability, homogeneity and hydrophobic behaviour are advantages of torrefied biomass. All this leads to an overall reduction in transport costs, storage capacity and to lower requirements for factory equipment. The properties of the different types of biomass used before and after torrefaction and the effect of torrefaction at the different process conditions were studied. For the laboratory tests of torrefaction, wood and grass waste biomass were used. For these selected materials, a number of measurements were performed to verify the most suitable torrefaction conditions (heating temperature and retention time). Experiments were carried out on a small scale on TGA 701 (LECO). Waste biomass was heated to a final temperature of 200, 225, 250, 275 and 300 degrees C with a retention time at these temperatures of 10, 20 and 45 min. The heating rate was set up to 15 degrees C min(-1). The determination of the appropriate temperature depended on the optimum ratio between mass loss and higher heating values (in case of grassy material from 200 to 225 degrees C and for woody material at 250 degrees C). From the results we can state that it is possible to do fast and exact test in TGA before the torrefaction process on the pilot unit to shorten the whole process.Web of Scienc

Improving genetic prediction by leveraging genetic correlations among human diseases and traits

Genomic prediction has the potential to contribute to precision medicine. However, to date, the utility of such predictors is limited due to low accuracy for most traits. Here theory and simulation study are used to demonstrate that widespread pleiotropy among phenotypes can be utilised to improve genomic risk prediction. We show how a genetic predictor can be created as a weighted index that combines published genome-wide association study (GWAS) summary statistics across many different traits. We apply this framework to predict risk of schizophrenia and bipolar disorder in the Psychiatric Genomics consortium data, finding substantial heterogeneity in prediction accuracy increases across cohorts. For six additional phenotypes in the UK Biobank data, we find increases in prediction accuracy ranging from 0.7% for height to 47% for type 2 diabetes, when using a multi-trait predictor that combines published summary statistics from multiple traits, as compared to a predictor based only on one trait