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Introduction: Observational studies highlight associations of common diseases with individual schizophrenia symptoms. However, it is unclear whether these diseases are associated with individual treatment-resistant schizophrenia (TRS). We aimed to explore the genetic associations between common immune diseases, metabolic diseases, psychiatric disorders, gut microbiota and TRS. Methods: Genome-wide association study (GWAS) summary data of European participants (n = ∼456,327) included TRS, 11 psychiatric disorders, 23 immune and metabolic diseases, body mass index, height, and 211 gut microbiota. In this genetic correlation and two-sample Mendelian randomization (MR) study, linkage disequilibrium score (LDSC) regression was applied to infer genetic correlation estimates. Two-sample MR tested potential causal associations of genetic variants associated with common immune diseases, metabolic diseases, psychiatric disorders, and gut microbiota with TRS. Results: LDSC revealed candidate associations between attention deficit/hyperactivity disorder (ADHD), schizophrenia, intestinal infectious diseases, obesity and TRS (genetic correlation range, 0.230–0.702; p < 0.05). Two-sample MR analyses suggested that ADHD was positively associated with TRS (estimate [SE] = 0.204 [0.073], p = 0.005), a finding that remained stable across statistical models. Besides, schizophrenia and genus Barnesiella levels were causally associated with TRS but not consistent across MR approaches. Conclusion: This study reports genetic correlations between ADHD, schizophrenia, intestinal infectious diseases, obesity and TRS. The study also found that genus Barnesiella was associated with TRS. These findings may have clinical implications, highlighting the possible strategy for TRS prevention.

Psychiatric disorders have great health hazards and the exact pathogeny remains elusive now. We aim to explore the potential interaction effects of mitochondrial function and human behavior on the risks of anxiety and depression.The genome-wide association study (GWAS) data of mitochondrial function (N = 383,476-982,072) were obtained from published studies. Individual level genotype and phenotype data of anxiety, depression and behavioral factors (including drinking, smoking and physical activity) were all from the UK Biobank (N = 84,805-85,164). We first calculated the polygenic risk scores (PRS) of mitochondrial function as the instrumental variables, and then constructed linear regression analyses to systematically explore the potential interaction effects of mitochondrial function and human behavior on anxiety and depression.In total samples, we observed mitochondrial heteroplasmy (MtHz) vs. Drinking (PConsidering all subjects were from UK Biobank, it should be careful to extrapolate our findings to other populations with different genetic background.Our results suggest the significant impacts of mitochondrial function and human behavior interactions on the development of anxiety and depression, providing new clues for clarifying the pathogenesis of anxiety and depression.

Previous studies have suggested a potential association between sleep and telomere length (TL), but its genetic basis remains unclear. In this study, we aimed to explore the genetic correlation and potential causal association between TL and insomnia.The genome-wide association study (GWAS) datasets of TL and insomnia-related traits were used, including insomnia, snoring, daytime dozing and napping. Based on the polygenic risk scores (PRS) of TL, linear regression and linkage disequilibrium score (LDSC) regression were used to preliminarily explore the association between TL and insomnia parameters in the UK Biobank cohort. Then, we investigated the causal association between TL and insomnia by mendelian randomization (MR) analysis and colocalization analysis.In the UK Biobank cohort, the association between TL and insomnia was observed in the female samples (t = 2.968, P = 3.00 × 10Our study found no evidence for causal association between insomnia and TL in individuals of European ancestry. We detected a candidate gene associated with both insomnia and TL, providing novel clues for understanding the roles of this association.

Disrupted brain structures and several life environmental factors have been shown to influence depression and anxiety, but their interactions with anxiety and depression remain elusive. Genome-wide association study datasets of 15 brain structure longitudinal changes (N = 15,640) were obtained from the published study. Genotype and phenotype-related data of depression, anxiety, and life environmental factors (including smoking, alcohol drinking, coffee intake, maternal smoking, physical activity, vitamin D, insomnia, sleep duration, and family satisfaction) were collected from UK Biobank. We calculated the polygenic risk scores (PRS) of 15 brain structure changes and then conducted linear regression analyses to explore the interactions of brain structure changes and life environmental factors on depression and anxiety using 15 brain structure change-related PRS, life environmental factors and interactions of them as instrumental variables, and depression score or anxiety score as outcomes. Sex stratification in all analyses was performed to reveal sex-specific differences in the interactions. We found 14 shared interactions related to both depression and anxiety in total sample, such as alcohol drinking × cerebellum white matter 3 (WM; beta = -.003, p = .018 for depression; beta = -003, p = .008 for anxiety) and maternal smoking × nucleus accumbens 2 (beta = .088, p = .002 for depression; beta = .070, p = .008 for anxiety). We also observed sex-specific differences in the interactions, for instance, alcohol drinking × cerebellum WM 3 was negatively associated with depression and anxiety in males (beta = -.004, p = .020 for depression; beta = -.005, p = .002 for anxiety). Our study results reveal the important interactions between brain structure changes and several life environmental factors on depression and anxiety, which may help to explore the pathogenesis of depression and anxiety.

To explore the roles of rare and high-impact variants in sleep duration.Based on the recently released UK Biobank 200k exome dataset, an exome-wide association study was conducted to detect rare variants (minor allele frequency0.01) contributing to sleep duration. Variant annotations were performed by the software tool ANNOVAR. Gene-based burden tests of sleep duration were conducted by the SKAT R-package. After quality control, 137,047 subjects were included in this study. CAUSALdb database was used to explore the related mental traits of identified genes.We detected 730,572 variants with MAF1%, including 3873 frameshift variants, 3977 nonframeshift variants, 449,632 nonsynonymous variants, 1293 startloss variants, 10,254 stopgain variants, 413 stoploss variants, 261,130 synonymous variants, and 3102 variants are annotated as unknown. The burden test of exonic variants detected two exome-wide significant associations for sleep duration including TMIE at 3p21.31 (PThis study reported 2 novel candidate genes for a sleep duration, supporting the roles of rare genetic variants in the regulation of sleep duration.

Despite thousands of common genetic loci of major depression disorders (MDD) have been identified by GWAS to date, a large proportion of genetic variation predisposing to MDD remains unaccounted for. By utilizing the newly released UK Biobank 200,643 exome dataset, we conducted an exome-wide association study to identify rare risk variants contributing to MDD. After quality control, 120,033 participants with MDD polygenic risk scores (PRS) values were included. The individuals with lower 30% quantile of the PRS value were filtered for case and control selecting. Then the cases were set as the individuals with upper 10% quantile of the PHQ depression score and lower 10% quantile were set as controls. Finally, 1612 cases and 1612 controls were included in this study. The variants were annotated by ANNOVRA software. After exclusions, 34,761 qualifying variants, including 148 frameshift variant, 335 non-frameshift variant, 33,758 nonsynonymous, 91 start-loss, 393 stop-gain, 36 stop-loss variants were imported into the SKAT R-package to perform single variants, gene-based burden and robust burden tests with minor allele frequency (MAF) 0.01. Single variant association testing identified one variant, rs4057749 (P = 5.39 × 10

Objectives:The role of neurological proteins in the development of bipolar disorder (BD) and schizophrenia (SCZ) remains elusive now. The current study aims to explore the potential genetic correlations of plasma neurological proteins with BD and SCZ.Methods:By using the latest genome-wide association study (GWAS) summary data of BD and SCZ (including 41,917 BD cases, 11,260 SCZ cases, and 396,091 controls) derived from the Psychiatric GWAS Consortium website (PGC) and a recently released GWAS of neurological proteins (including 750 individuals), we performed a linkage disequilibrium score regression (LDSC) analysis to detect the potential genetic correlations between the two common psychiatric disorders and each of the 92 neurological proteins. Two-sample Mendelian randomisation (MR) analysis was then applied to assess the bidirectional causal relationship between the neurological proteins identified by LDSC, BD and SCZ.Results:LDSC analysis identified one neurological protein, NEP, which shows suggestive genetic correlation signals for both BD (coefficient = −0.165, p value = 0.035) and SCZ (coefficient = −0.235, p value = 0.020). However, those association did not remain significant after strict Bonferroni correction. Two sample MR analysis found that there was an association between genetically predicted level of NEP protein, BD (odd ratio [OR] = 0.87, p value = 1.61 × 10−6) and SCZ (OR = 0.90, p value = 4.04 × 10−6). However, in the opposite direction, there is no genetically predicted association between BD, SCZ, and NEP protein level.Conclusion:This study provided novel clues for understanding the genetic effects of neurological proteins on BD and SCZ.

The effects of gene-by-environment (G×E) interactions on complex diseases are significant, especially the superimposed effects of multiple environmental factors. However, research on the multi-environments-gene interactions of anxiety, depression, and self-harm is still limited. This study included white individuals (N = 66,041-74,482) from the UK Biobank. We fitted all environmental factors to a single environmental score (ES), and the estimated ES was used to calculate the multiplicative interaction effects between ES and genome-wide SNPs. Heritability was stratified by minor allele frequency (MAF) and linkage disequilibrium (LD). Our research found 10 loci with significant interaction effects, such as rs114830993 (PRICKLE2, P = 2.30 × 10

We aim to explore the potential interaction effects of brain aging and gut microbiota on the risks of sleep, anxiety and depression disorders. The genome-wide association study (GWAS) datasets of brain aging (N = 21,407) and gut microbiota (N = 3,890) were obtained from published studies. Individual level genotype and phenotype data of psychiatric traits (including sleep, anxiety and depression) were all from the UK Biobank (N = 107,947-374,505). We first calculated the polygenic risk scores (PRS) of 62 brain aging modes and 114 gut microbiota taxa as the instrumental variables, and then constructed linear and logistic regression analyses to systematically explore the potential interaction effects of brain aging and gut microbiota on psychiatric disorders. We observed the interaction effects of brain aging and gut microbiota on sleep, anxiety and depression disorders, such as Putamen/caudate T2* vs. Rhodospirillales (β = -0.012, P = 8.4 × 10

Biological geochemistry is a main suggested cause of Kashin-Beck disease (KBD), due to the absence or excess of elements in the environment. Initially, Se deficiency is regarded as the most key role in the etiology of KBD, and selenium supplementation effectively helps to prevent and control KBD. However, several elements are reported to be relevant to KBD or selenium in succession, which indicated selenium deficiency is not the original etiology of KBD. The study comprehensively analyzed the characteristics of the bio-element profile of KBD and further re-examined the unique role of selenium in etiology. The study measured 14 elements, including sodium, potassium, calcium, phosphorus, magnesium, copper, iron, zinc, selenium, iodine, manganese, lead, arsenic, and mercury, which were detected from hair samples collected from 150 boys. Research participants were separated based on whether they had received any preventative treatment (with and without selenium supplementation). From endemic areas, 30 KBD and 30 healthy children without any preventative treatment were selected alongside 30 KBD and 30 healthy children with selenium supplementation. The participants from endemic areas were then compared to 30 healthy children living in non-endemic areas. Compared to the non-endemic group, the levels of iron and manganese were all significantly higher in the endemic groups and were further elevated in KBD participants (p 0.05). In contrast, selenium and iodine levels in endemic areas were much lower than those of the control group (p 0.05). The proportions of selenium excess (p 0.05) and iodine deficiency (p 0.05) in endemic groups were significantly lower than participants from non-endemic areas. Meanwhile, excess levels of iron (p 0.05) and manganese (p 0.05) were higher in the endemic groups. Moreover, the proportions of Zn/Fe and Se/Mn were found to be significantly lower in endemic area participants than those in the control group (p 0.05). Three pairs of elements had a correlation coefficient value of more than 0.6: 0.7423 for manganese and calcium, 0.6446 for potassium and sodium, and 0.6272 for manganese and iron. The ratios of Se/Mn and Zn/Fe were associated with a correlation coefficient value of 0.8055. Magnesium, sodium, copper, and iodine levels were meticulously examined using binary regression analysis. This was also used to determine the ratios of Ca/Mg, Ca/P, Zn/Fe, Se/Mn, and Se/I. Thus, the study largely revealed the vital role of manganese, iron, and iodine (in conjunction with selenium) in KBD etiology and pathogenesis. High manganese and iron levels with low selenium and iodine levels were identified as characteristic features of the bio-element profile of KBD. The different element ratios reflect the interaction between several elements. The most significant of these were the proportions of Se/Mn and Zn/Fe, which may be significant in the occurrence and development of KBD.

There is a strong link between irritable bowel syndrome and brain volumes, yet, to date, research examining the mediators of this association has been little. Based on the phenotypic data of 15 248 participants from the UK Biobank, a two-stage mediation analysis was performed to assess the association among brain volumes, anxiety, and irritable bowel syndrome. In the first stage, we identified the candidate mediating role of anxiety for irritable bowel syndrome associated with brain volumes using regression models. Then, we quantified the magnitude of the mediation effects by evaluating the average causal-mediated effect and proportion of mediation through performing mediation analyses in the R package in the second stage. In the first stage, we identified the partly mediating role of anxiety in the association between irritable bowel syndrome and the volume of thalamus (Pleft = 1.16 × 10−4, Pright = 2.41 × 10−4), and grey matter (Pleft = 3.22 × 10−2, Pright = 1.18 × 10−2) in the VIIIa cerebellum. In the second stage, we observed that the proportion of the total effect of irritable bowel syndrome on volume of thalamus mediated by anxiety was 14.3% for the left region (βAverage causal-mediated effect = −0.008, PAverage causal-mediated effect = 0.004) and 14.6% for the right region (βAverage causal-mediated effect = −0.007, PAverage causal-mediated effect = 0.006). Anxiety mediated 30.8% for the left region (βAverage causal-mediated effect = −0.013, PAverage causal-mediated effect = 0.002) and 21.6% for the right region (βAverage causal-mediated effect = −0.010, PAverage causal-mediated effect x= 0.018) of the total effect of irritable bowel syndrome on the volume of grey matter in the VIIIa cerebellum. Our study revealed the indirect mediating role of anxiety in the association between irritable bowel syndrome and brain volumes, promoting our understanding of the functional mechanisms of irritable bowel syndrome and its related psychosocial factors.

Longitudinal changes in brain structure and lifestyle can affect sleep phenotypes. However, the influence of the interaction between longitudinal changes in brain structure and lifestyle on sleep phenotypes remains unclear. Genome-wide association study dataset of longitudinal changes in brain structure was obtained from published study. Phenotypic data of lifestyles and sleep phenotypes were obtained from UK Biobank cohort. Using genotype data from UK Biobank, we calculated polygenetic risk scores of longitudinal changes in brain structure phenotypes. Linear/logistic regression analysis was conducted to evaluate interactions between longitudinal changes in brain structure and lifestyles on sleep duration, chronotype, insomnia, snoring and daytime dozing. Multiple lifestyle × longitudinal changes in brain structure interactions were detected for 5 sleep phenotypes, such as physical activity×caudate_age2 for daytime dozing (OR = 1.0389, P = 8.84 × 10−3) in total samples, coffee intake×cerebellar white matter volume_age2 for daytime dozing (OR = 0.9652, P = 1.13 × 10−4) in females. Besides, we found 4 overlapping interactions in different sleep phenotypes. We conducted sex stratification analysis and identified one overlapping interaction between female and male. Our results support the moderate effects of interaction between lifestyle and longitudinal changes in brain structure on sleep phenotypes, and deepen our understanding of the pathogenesis of sleep disorders.

Comparing chondrocytes derived from KBD-iPSCs and normal-iPSCs, we obtained 68 differential N-glycosylation proteins. The proteins were classified by GO, KEGG and so on. These proteins may lead to the abnormal function of KBD chondrocytes.

Correction for ‘Analysis of N-glycosylation protein of Kashin–Beck disease chondrocytes derived from induced pluripotent stem cells based on label-free strategies with LC-MS/MS’ by Sen Wang et al., Mol. Omics, 2023, https://doi.org/10.1039/d3mo00018d.

Vertigo is a leading symptom of various peripheral and central vestibular disorders. Although genome-wide association studies (GWASs) have identified multiple risk variants for vertigo, how these risk variants contribute to the risk of vertigo remains unknown. Discovery proteome-wide association study (PWAS) was first performed by integrating the protein quantitative trait loci from the dorsolateral prefrontal cortex (DLPFC) in the Banner Sun Health Research Institute dataset (n = 152) and GWAS summary of vertigo (n = 942 613), followed by replication PWAS using the protein quantitative trait loci from the DLPFC in Religious Orders Study or the Rush Memory and Aging Project dataset (n = 376). Transcriptome-wide association studies (TWASs) were then performed by integrating the same GWAS datasets of vertigo (n = 942 613) with mRNA expression reference from human fetal brain, and DLPFC. Chemical-related gene set enrichment analysis (GSEA) and Gene ontology/Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were finally conducted to further reveal the pathogenesis of vertigo. Permutation-based empirical P values were calculated in PWAS, TWAS, and GSEA. By integrating the GWAS of vertigo and two independent brain proteomes from human DLPFC, three genes were identified to genetically regulate protein abundance levels in vertigo, and were not previously implicated by GWAS, including MTERFD2 (PBanner = 0.045, PROSMAP = 0.031), MGST1 (PBanner = 0.014, PROSMAP = 0.018), and RAB3B (PBanner = 0.045, PROSMAP = 0.035). Compared with TWAS results, we identified overlapping genes RAB3B (PTWAS = 0.017) and MTERFD2 (PTWAS = 0.003) that showed significant associations with vertigo at both proteome-wide and transcriptome-wide levels. Chemical-related GSEA identified multiple chemicals that might be associated with vertigo, such as nickel (P = 0.007), glycidamide (P = 0.005), and proanthocyanidins (P = 0.015). Our study provides novel clues for understanding the biological mechanism of vertigo, and highlights several possible risks and therapeutic chemicals for vertigo.

Habitual coffee consumption is an addictive behavior with unknown genetic variations and has raised public health issues about its potential health-related outcomes. We performed exome-wide association studies to identify rare risk variants contributing to habitual coffee consumption utilizing the newly released UK Biobank exome dataset (n = 200,643). A total of 34,761 qualifying variants were imported into SKAT to conduct gene-based burden and robust tests with minor allele frequency

Although previous genome-wide association studies (GWASs) on post-traumatic stress disorder (PTSD) have identified multiple risk loci, how these loci confer risk of PTSD remains unclear. Through the FUSION pipeline, we integrated two human brain proteome reference datasets (ROS/MAP and Banner) with the PTSD GWAS dataset, respectively, to conduct a proteome-wide association study (PWAS) analysis. Then two transcriptome reference weights (Rnaseq and Splicing) were applied to a transcriptome-wide association study (TWAS) analysis. Finally, the PWAS and TWAS results were investigated through brain imaging analysis. In the PWAS analysis, 8 and 13 candidate genes were identified in the ROS/MAP and Banner reference weight groups, respectively. Examples included ADK (pPWAS-ROS/MAP = 3.00 × 10−5) and C3orf18 (pPWAS-Banner = 7.07 × 10−31). Moreover, the TWAS also detected multiple candidate genes associated with PTSD in two different reference weight groups, including RIMS2 (pTWAS-Splicing = 3.84 × 10−2), CHMP1A (pTWAS-Rnaseq = 5.09 × 10−4), and SIRT5 (pTWAS-Splicing = 4.81 × 10−3). Further comparison of the PWAS and TWAS results in different populations detected the overlapping genes: MADD (pPWAS-Banner = 4.90 × 10−2, pTWAS-Splicing = 1.23 × 10−2) in the total population and GLO1(pPWAS-Banner = 4.89 × 10−3, pTWAS-Rnaseq = 1.41 × 10−3) in females. Brain imaging analysis revealed several different brain imaging phenotypes associated with MADD and GLO1 genes. Our study identified multiple candidate genes associated with PTSD in the proteome and transcriptome levels, which may provide new clues to the pathogenesis of PTSD.

Comparing with the general population, the pain in depression patients has more complex biological mechanism. We aim to explore the etiological mechanism of pain in depression patients from the perspective of genetics.Utilizing the UK Biobank samples with self-reported depression status or PHQ score ≥10, we conducted genome-wide association studies (GWAS) of seven pain traits (N = 1,133-58,349). Additionally, we used FUSION pipeline to perform proteome-wide association study (PWAS) and transcriptome-wide association study (TWAS) by integrating GWAS summary data with two different proteome reference weights (ROS/MAP and Banner) and Rnaseq gene expression reference weights, respectively.GWAS identified 3 significant genes associated with different pain traits in depression patients, including TRIOBP (P

Background/aim: We aimed to explore the roles of glycoprotein glycosylation in the pathogenesis of Kashin-Beck disease KBD , and evaluated the effectiveness of sodium hyaluronate treatment. Materials and methods: Blood and saliva were collected from KBD patients before and after the injection of sodium hyaluronate. Normal healthy subjects were included as controls. Saliva and serum lectin microarrays and saliva and serum microarray verifications were used to screen and confirm the differences in lectin levels among the three groups. Results: In saliva lectin microarray, bindings to Sophora japonica agglutinin SJA , Griffonia Bandeiraea simplicifolia lectin I GSL-I , Euonymus europaeus lectin EEL , Maackia amurensis lectin II MAL-II , Sambucus nigra lectin SNA , Hippeastrum hybrid lectin HHL , and Aleuria aurantia lectin AAL were higher in the untreated KBD patients than in the control group. Increased levels of HHL, MAL-II, and GSL-I in the untreated KBD patients discriminated them in particular from the treated ones. Jacalin was lower in the untreated KBD patients compared to the treated KBD and control groups. In serum lectin microarray, HHL and peanut agglutinin PNA were increased in the untreated KBD group in comparison to the control one. AAL, Phaseolus vulgaris agglutinin E+L PHA- E+L , and Psophocarpus tetragonolobus lectin I PTL-I were lower in the untreated KBD patients compared to the treated KBD and control groups. Hyaluronate treatment appeared to normalize SNA, AAL, and MAL-II levels in saliva, and HHL, PNA, AAL, PTL-I, and PHA-E+L levels in serum. Saliva reversed microarray verification confirmed significant differences between the groups in SNA and Jacalin, in particular for GSL-I levels, while serum reversed microarray verification indicated that HHL, PNA, and AAL levels returned to normal levels after the hyaluronate treatment. Lectin blot confirmed significant differences in HHL, AAL, and Jacalin in saliva, and HHL, PNA, PHA-E+L, and AAL in serum. Conclusion: HHL in saliva and serum may be a valuable diagnostic biomarker of KBD, and it may be used as follow-up for the hyaluronate treatment.

We aim to systematically explore the potential genetic correlations between five major psychiatric disorders and gestational ages. Genome-wide association study (GWAS) summary datasets of attention deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), schizophrenia (SCZ) and major depressive disorder (MDD) in discovery were downloaded from the Psychiatric GWAS Consortium (PGC) website. Suggestive (Raw p 0.05) genetic associations in the discovery phrase were further replicated in independent GWASs which downloaded from PGC, the FinnGen study or Integrative Psychiatric Research (iPSYCH) website. GWASs of gestational duration, preterm and post-term birth were derived from previous studies of infants from the Early Growth Genetics (EGG) Consortium, the iPSYCH study, and the Genomic and Proteomic Network for Preterm Birth Research (GPN). We calculated genetic correlations using linkage disequilibrium score (LDSC) regression. Mendelian randomization (MR) analyses were performed to investigate the causal effects. We identified four suggestive genetic correlations between psychiatric disorders and gestational age factors in discovery LDSC and two replicated in a confirmation LDSC: gestational duration and ADHD (r

We aim to explore the combined effects of the smoking and breastfeeding on offspring mental health outcomes. We used data from UK biobank (N = 342,846) to evaluate joint effect of breastfeeding and maternal smoke during pregnancy (MSDP) on seven adult offspring mental health outcomes (self-reported depression, depression score, self-reported anxiety, anxiety score, neuroticism score, self-harm, suicide). We stratified individuals to MSDP group and non-MSDP group as well as breastfeeding group and non-breastfeeding group. Multiple linear regression and logistic regressions analysis were performed between independent variables (MSDP or breastfeeding) and dependent variables separately (seven mental health outcomes) in each stratum. Effect estimates were expressed as β values and OR values. Sex, age, 10 principle components of population structure, smoking, alcohol use, and Townsend deprivation index were examined as covariates. At MSDP grouping level, coefficients (odds ratio [OR]) for association of breastfed as a baby with self-reported anxiety (category variable) were 0.87 (95%CI, (0.82-0.93), P = 1.74 × 10

Great progress has been made in identifying risk loci for insomnia by genome-wide association studies (GWAS) analysis, but its association with human brain proteome is unclear. Two insomnia GWAS summary datasets were derived from deCODE (n = 113,006) and 23andMe (n = 1,331,010). Two human brain proteomic datasets were obtained from ROS/MAP and Banner, and two reference datasets were obtained from brain RNA-seq (CBR) and RNA-seq splicing (CBRS). Proteome-wide association study (PWAS) was first used to detect brain proteins associated with insomnia at the translation level. Transcriptome-wide association study (TWAS) was then used to verify the results at the DNA level. Finally, brain imaging GWAS was used to explore the brain functional areas related to the identified brain proteins and genes in insomnia. PWAS identified 4 and 1 common proteins shared by two human brain proteomic datasets in insomnia GWAS dataset 1 and 2, such as ME1 (PDataset1−Banner−full=1.08×10−2, PDataset1−ROS/MAP−full=8.21×10−3). Further TWAS identified 5 and 1 candidate genes shared by the two reference expression profiles in dataset 1 and 2, like ICA1L (PDataset2−CBR=3.01×10−2, PDataset2−CBRS=3.24×10−2). CAMLG was observed to associate with insomnia in both PWAS (PDataset2−ROS/MAP=2.94×10−2) and TWAS (PDataset2−CBR=1.11×10−2). Comparing the results of PWAS and TWAS, there are 9 common proteins and genes shared by both two datasets, such as INPP4A. Brain imaging analysis found that insomnia associated proteins and genes were functionally related to cortex. Our results may reinforce the understanding of the etiology and pathophysiology of insomnia and provide promising brain protein targets for further therapeutic and mechanistic studies.

Background Despite cumulative evidence showing obesity is associated with changes in sleep quality and quantity, the study about the relationships between sleep and body composition is scarce, and whether the relationship is causal remains unknown. In this study, we examined whether there are causal associations between sleep and body composition. Methods First, we estimated genetic correlations between sleep-related phenotypes and body composition using the linkage disequilibrium score regression (LDSC). Mendelian randomization (MR) analysis was then conducted to test 2-way causal relationships on phenotypes with significant genetic associations. Finally, Bayesian colocalization (COLOC) analysis was performed to calculate the posterior probability of causal variation and identify the common genes to verify the results of MR. Results For the LDSC analysis, we observed some significant genetic correlations (rG), such as snoring and right leg fat mass (rG = 0.376, P = 7.21 × 10−80). For the MR analysis, we identified some significant causal relationships, such as snoring is the causal risk factor for whole-body fat-free mass (Pweighted median = 1.28 × 10−6, PMR-PRESSO = 1.35 × 10−7), dozing is the causal risk factor for right leg fat mass (Pweighted median = 9.22 × 10−4, PMR-PRESSO = 9.55 × 10−4), and right arm fat mass (Pweighted median = 1.11 × 10−40, PMR-PRESSO = 4.93 × 10−55) is the causal risk factor for snoring. For the COLOC analysis, we identified rs143384 mapping on GDF5 and 6 overlapped single nucleotide polymorphisms (eg, rs1421085, rs11642015) mapping on FTO. Conclusion Our study identified the causal relationships between sleep-related phenotypes and body composition. These findings may give insights into the mechanism of sleep disturbances and provide novel therapeutic targets.

Background Sleep is a fundamental state of decreased activity and alertness conserved in human life. Maternal smoking during pregnancy may affect sleep duration female offspring, however the exact connection and mechanism still remain unclear. Methods Based on the UK Biobank data, sleep duration was categorized as short sleep (P ). GWGEIS analysis detected a significant association at ANKLE2 (rs78408032, P = 1.33×10− 8) and a suggestive association at MYT1L (rs76688230, P = 8.85×10− 7) in female subjects. Conclusion Our study results suggested a significant effect of maternal smoking during pregnancy on short sleep of female offspring.

Background Increasing evidence suggests the association between caffeine and the brain and nervous system. However, there is limited research on the genetic associations between coffee consumption subtypes and brain proteome, plasma proteomes, and peripheral metabolites. Methods First, proteome-wide association study (PWAS) of coffee consumption subtypes was performed by integrating two independent genome-wide association study (GWAS) datasets (91,462–502,650 subjects) with two reference human brain proteomes (ROS/MAP and Banner), by using the FUSION pipeline. Second, transcriptome-wide association study (TWAS) analysis of coffee consumption subtypes was conducted by integrating the two gene expression weight references (RNAseq and splicing) of brain RNA-seq and the two GWAS datasets (91,462–502,650 subjects) of coffee consumption subtypes. Finally, we used the LD Score Regression (LDSC) analysis to evaluate the genetic correlations of coffee consumption subtypes with plasma proteomes and peripheral metabolites. Results For the traits related to coffee consumption, we identified 3 common PWAS proteins, such as MADD (P PWAS−Banner−dis=0.0114, P PWAS−ROS/MAP−rep =0.0489). In addition, 11 common TWAS genes were found in two cohorts, such as ARPC2 (P TWAS−splicing−dis =2063×10− 12, P TWAS−splicing−dis =1.25×10− 10, P TWAS−splicing−dis =1.24e-08, P TWAS−splicing−rep =3.25×10− 9 and P TWAS−splicing−rep =3.42×10− 13). Importantly, we have identified 8 common genes between PWAS and TWAS, such as ALDH2 (P PWAS−banner−rep =1.22×10− 22, PTWAS− splicing−dis = 4.54×10− 92). For the LDSC analysis of human plasma proteome, we identified 11 plasma proteins, such as CHL1 (P dis = 0.0151, P rep =0.0438). For the LDSC analysis of blood metabolites, 5 metabolites have been found, such as myo-inositol (P dis = 0.0073, P dis = 0.0152, P dis =0.0414, P rep =0.0216). Conclusions We identified several brain proteins and genes associated with coffee consumption subtypes. In addition, we also detected several candidate plasma proteins and metabolites related to these subtypes.

Background: The association between physical activity (PA) and neuroticism has been established, but the genetic mechanism remains elusive now. Methods: In this study, 362,421 participants from UK Biobank were used to explore the associations between PA and neuroticism, using linear regression analysis. PA was measured as excess metabolic equivalent (MET)-hours per week, assessed from a combination of walking and moderate and vigorous PA. The Gene-environment-wide interaction analyses (GEWIS) was conducted by PLINK2.0 to evaluate gene×PA interacting effects on the risk of neuroticism. DAVID tool was then used to conduct gene ontology (GO) analysis of the observed PA-interacted genes. Results: We observed negative association between PA and neuroticism in total sample (b=−0.02, P=9.02×10−37), female sample (b=−0.02, P=5.74×10−28) and male sample (b=−0.02, P=6.17×10−14). GEWIS revealed multiple significant gene×PA interaction signals for neuroticism. 78 candidate genes were observed, such as ST18(rs76573348, P=6.45×10−16)，NRCAM (rs117655943, P=4.28×10-11) and RBFOX3(rs113329299, P=4.72×10-10) in total group, SYT1(rs151016298, P=1.56×10−11), S100B(rs80199493, P=8.56×10−11) and MYT1(rs111360063, P=1.53×10−10) in female group, and NOTCH3 (rs184928292, P=3.31×10-17), SLC24A2 (rs62563282, P=9.56×10-16), CSMD1(rs73177368, P=2.11×10-13) in male group. GO enrichment analysis identified 15, 11 and 38 GO terms for total, female and male samples, such as synapse assembly (Ptotal=1.37×10-2), calcium ion binding (Pfemale=3.33×10-2) and learning or memory (Pmale=1.14×10-2).Conclusion: This study suggested the influences of physical activity on neuroticism, and identified several neuroticism associated genes interacting with PA.

Previous studies have suggested that vitamin D (VD) was associated with psychiatric diseases, but efforts to elucidate the functional relevance of VD with depression and anxiety from genetic perspective have been limited. Based on the UK Biobank cohort, we first calculated polygenic risk score (PRS) for VD from genome-wide association study (GWAS) data of VD. Linear and logistic regression analysis were conducted to evaluate the associations of VD traits with depression and anxiety traits, respectively. Then, using individual genotype and phenotype data from the UK Biobank, genome-wide environment interaction studies (GWEIS) were performed to identify the potential effects of gene × VD interactions on the risks of depression and anxiety traits. In the UK Biobank cohort, we observed significant associations of blood VD level with depression and anxiety traits, as well as significant associations of VD PRS and depression and anxiety traits. GWEIS identified multiple candidate loci, such as rs114086183 (p = 4.11 × 10−8, LRRTM4) for self-reported depression status and rs149760119 (p = 3.88 × 10−8, GNB5) for self-reported anxiety status. Our study results suggested that VD was negatively associated with depression and anxiety. GWEIS identified multiple candidate genes interacting with VD, providing novel clues for understanding the biological mechanism potential associations between VD and psychiatric disorders.

Background: Rheumatoid arthritis (RA) is one of the chronic inflammatory diseases that primarily influences the joints, and its prevalence is 0.5-1.0%. Previous studies have shown that there are differences in the genetic structure of RA between European and Asian populations, and most of the studies have been conducted using meta-analysis. This study analyzed the genetic structure of rheumatoid arthritis in European and Asian populations using a new method. Methods: The Genome-Wide Association Study (GWAS) summary statistics of RA from Europe (N=8383) and Asia (N=19190) were derived from an article published in Nature. First, the GWAS data was divided into 1368 blocks, in which SNPs were approximately independent of linkage disequilibrium (LD). Second, we calculated the LD matrix of SNP in each block by using PLINK 1.9. Then, PESCA analysis was performed to detect population-specific/shared risk genes of RA. Finally, Metascape platform was used to perform gene set enrichment analysis. Results: In European population, we found multiple genes which were associated with RA, including HLA-DPA1, HLA-DPB1 (rs2856822, PTP=1.000), MICA (rs2844518, PTP=1.000). In Asian population, C6orf10 (rs3129915, PTP=1.000), PTPN2 (rs2847288, PTP=0.995), were significant related to RA. The population-shared genes included PADI2 (rs2235920, PTP=1.000), STAT4 (rs12612769, PTP=1.000). Furthermore, gene sets enrichment analysis reported population-specific/shared pathway terms, such as interferon-gamma-mediated signaling pathway (P=2.884×10-9), negative regulation of innate immune response (P=1.841×10-7), protein-arginine deiminase activity (P=7.047×10-8). Conclusions: The results of our study indicate differences in risk loci between Asian and European populations, which provided clues for exploring the population-specific/shared genetics and pathogenesis of RA.

Background: This study aims to investigate the altered expression of a disintegrin and metalloproteinase with thrombospondin motifs 4（ADAMTS4）and a disintegrin and metalloproteinase with thrombospondin motifs 5（ADAMTS5）in the human articular cartilage between osteoarthritis(OA) and Kashin-Beck disease(KBD) and compare their roles in the cartilage injury. Methods: Articular samples were collected from confirmed OA patients and KBD patients then divided into three groups, and the articular cartilages from the normal donors were used as controls. The morphology，location and expression of ADAMTS4 and ADAMTS5 as well as aggrecan were detected by histochemical staining and immunohistochemical staining. Results: Compared to the control, the number of living cells in OA and KBD groups declined at three zones. Meanwhile, the results of toluidine blue staining showed that there was a loss of aggrecan in extracellular matrix of KBD and OA cartilages. The amounts of chondrocytes positively stained for ADAMTS4 were lower in the middle and deep zones of OA and KBD cartilages than those in the control samples. On the contrary, the immunostaining for ADAMTS5 significantly increased in OA and KBD group compared with control group at all three zones. Notably, although there was no statistical significance, the expression of ADAMST5 in KBD was slightly higher than the ones in OA in superficial, middle and deep zones. Conclusions: The role ADAMTS4 acted in aggrecan degradation was seemingly minor. It could be inferred that ADAMTS5 is the key enzyme in the cartilage destruction, particularly in KBD cartilage, which could explain the complex pathogenesis of KBD and provide a potential therapeutic target for KBD patients.