Aims: Despite the interest in the association of gut microbiota with bone health, limited population-based studies of gut microbiota and bone mineral density (BMD) have been made. Our aim is to explore the possible association between gut microbiota and BMD. Methods: A total of 3,321 independent loci of gut microbiota were used to calculate the individual polygenic risk score (PRS) for 114 gut microbiota-related traits. The individual genotype data were obtained from UK Biobank cohort. Linear regressions were then conducted to evaluate the possible association of gut microbiota with L1-L4 BMD (n = 4,070), total BMD (n = 4,056), and femur total BMD (n = 4,054), respectively. PLINK 2.0 was used to detect the single-nucleotide polymorphism (SNP) × gut microbiota interaction effect on the risks of L1-L4 BMD, total BMD, and femur total BMD, respectively. Results: We detected five, three, and seven candidate gut microbiota-related traits for L1-L4 BMD, total BMD, and femur BMD, respectively, such as genus Dialister (p = 0.004) for L1-L4 BMD, and genus Eisenbergiella (p = 0.046) for total BMD. We also detected two common gut microbiota-related traits shared by L1-L4 BMD, total BMD, and femur total BMD, including genus Escherichia Shigella and genus Lactococcus. Interaction analysis of BMD detected several genes that interacted with gut microbiota, such as phospholipase D1 ( PLD1) and endomucin ( EMCN) interacting with genus Dialister in total BMD, and COL12A1 and Discs Large MAGUK Scaffold Protein 2 ( DLG2) interacting with genus Lactococcus in femur BMD. Conclusion: Our results suggest associations between gut microbiota and BMD, which will be helpful to further explore the regulation mechanism and intervention gut microbiota of BMD. Cite this article: Bone Joint Res 2021;10(11):734–741.

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.

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 (p_PWAS-ROS/MAP = 3.00 × 10⁻⁵) and C3orf18 (p_PWAS-Banner = 7.07 × 10⁻³¹). Moreover, the TWAS also detected multiple candidate genes associated with PTSD in two different reference weight groups, including RIMS2 (p_{TWAS-Splicing} = 3.84 × 10⁻²), CHMP1A (p_TWAS-Rnaseq = 5.09 × 10⁻⁴), and SIRT5 (p_{TWAS-Splicing} = 4.81 × 10⁻³). Further comparison of the PWAS and TWAS results in different populations detected the overlapping genes: MADD (p_PWAS-Banner = 4.90 × 10⁻², p_{TWAS-Splicing} = 1.23 × 10⁻²) in the total population and GLO1(p_PWAS-Banner = 4.89 × 10⁻³, p_TWAS-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.

Objective: Genetic approaches are increasingly advantageous in characterizing treatment-resistant schizophrenia (TRS). We aimed to identify TRS-associated functional brain proteins, providing a potential pathway for improving psychiatric classification and developing better-tailored therapeutic targets. Methods: TRS-related proteome-wide association studies (PWAS) were conducted on genome-wide association studies (GWAS) from CLOZUK and the Psychiatric Genomics Consortium (PGC), which provided TRS individuals (n = 10,501) and non-TRS individuals (n = 20,325), respectively. The reference datasets for the human brain proteome were obtained from ROS/MAP and Banner, with 8,356 and 11,518 proteins collected, respectively. We then performed colocalization analysis and functional enrichment analysis to further explore the biological functions of the proteins identified by PWAS. Results: In PWAS, two statistically significant proteins were identified using the ROS/MAP and then replicated using the Banner reference dataset, including CPT2 (P_PWAS-ROS/MAP = 4.15 × 10⁻² and P_PWAS-Banner = 3.38 × 10⁻³) and APOL2 (P_PWAS-ROS/MAP = 4.49 × 10⁻³ and P_PWAS-Banner = 8.26 × 10⁻³). Colocalization analysis identified three variants that were causally related to protein expression in the human brain, including CCDC91 (PP4 = 0.981), PRDX1 (PP4 = 0.894), and WARS2 (PP4 = 0.757). We extended PWAS results from gene-based analysis to pathway-based analysis, identifying 14 gene ontology (GO) terms and the only candidate pathway for TRS, metabolic pathways (all P < 0.05). Conclusions: Our results identified two protein biomarkers, and cautiously support that the pathological mechanism of TRS is linked to lipid oxidation and inflammation, where mitochondria-related functions may play a role.

Dietary habits have considerable impact on brain development and mental health. Despite long-standing interest in the association of dietary habits with mental health, few population-based studies of dietary habits have assessed depression and fluid intelligence. Our aim is to investigate the association of dietary habits with depression and fluid intelligence. In total, 814 independent loci were utilized to calculate the individual polygenic risk score (PRS) for 143 dietary habit-related traits. The individual genotype data were obtained from the UK Biobank cohort. Regression analyses were then conducted to evaluate the association of dietary habits with depression and fluid intelligence, respectively. PLINK 2.0 was utilized to detect the single nucleotide polymorphism (SNP) × dietary habit interaction effect on the risks of depression and fluid intelligence. We detected 22 common dietary habit-related traits shared by depression and fluid intelligence, such as red wine glasses per month, and overall alcohol intake. For interaction analysis, we detected that OLFM1 interacted with champagne/white wine in depression, while SYNPO2 interacted with coffee type in fluid intelligence. Our study results provide novel useful information for understanding how eating habits affect the fluid intelligence and depression.

Aims: Osteoporosis (OP) is a metabolic bone disease, characterized by a decrease in bone mineral density (BMD). However, the research of regulatory variants has been limited for BMD. In this study, we aimed to explore novel regulatory genetic variants associated with BMD.Methods: We conducted an integrative analysis of BMD genome-wide association study (GWAS) and regulatory single nucleotide polymorphism (rSNP) annotation information. Firstly, the discovery GWAS dataset and replication GWAS dataset were integrated with rSNP annotation database to obtain BMD associated SNP regulatory elements and SNP regulatory element-target gene (E-G) pairs, respectively. Then, the common genes were further subjected to HumanNet v2 to explore the biological effects.Results: Through discovery and replication integrative analysis for BMD GWAS and rSNP annotation database, we identified 36 common BMD-associated genes for BMD irrespective of regulatory elements, such as FAM3C (p_{discovery GWAS} = 1.21 × 10^-25, p_{replication GWAS} = 1.80 × 10^-12), CCDC170 (p_{discovery GWAS} = 1.23 × 10^-11, p_{replication GWAS} = 3.22 × 10^-9), and SOX6 (p_{discovery GWAS} = 4.41 × 10^-15, p_{replication GWAS} = 6.57 × 10^-14). Then, for the 36 common target genes, multiple gene ontology (GO) terms were detected for BMD such as positive regulation of cartilage development (p = 9.27 × 10^-3) and positive regulation of chondrocyte differentiation (p = 9.27 × 10^-3).Conclusion: We explored the potential roles of rSNP in the genetic mechanisms of BMD and identified multiple candidate genes. Our study results support the implication of regulatory genetic variants in the development of OP.Cite this article: Bone Joint Res 2023;12(2):147–154.