{"paper_id":"4e375e03-e114-4e1e-a384-f408b16bd6bf","body_text":"Human Brain Proteome-wide Association Study Provides Insights into the Genetic Components of Protein Abundance in Obesity | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Human Brain Proteome-wide Association Study Provides Insights into the Genetic Components of Protein Abundance in Obesity Yu-Fang Pei, Qi-Gang Zhao, Xin-Ling Ma, Qian Xu, ZiTong Song, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3975196/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Jul, 2024 Read the published version in International Journal of Obesity → Version 1 posted 9 You are reading this latest preprint version Abstract Backgrounds: Genome-wide association studies have identified multiple genetic variants associated with obesity. However, most obesity-associated loci were waiting to be translated into new biological insights. Given the critical role of brain in obesity development, we sought to explore whether obesity-associated genetic variants could be mapped to brain protein abundances. Methods: We performed proteome-wide association studies (PWAS) and colocalization analyses to identify genes whose cis-regulated brain protein abundances were associated with obesity-related traits, including body fat percentage, trunk fat percentage, body mass index, visceral adipose tissue, waist circumference, and waist-to-hip ratio. We then assessed the druggability of the identified genes and conducted pathway enrichment analysis to explore their functional relevance. Finally, we evaluated the effects of the significant PWAS genes at the brain transcriptional level. Results: By integrating human brain proteomes from discovery (ROSMAP, N = 376) and validation datasets (BANNER, N = 198) with genome-wide summary statistics of obesity-related phenotypes ( N ranged from 325 153 to 806 834), we identified 52 genes whose cis-regulated brain protein abundance was associated with obesity. These 52 genes were enriched in metabolic processes, e.g., small molecule metabolic process and metabolic pathways. Fourteen of the 52 genes had high drug repurposing value. Nine of the 52 genes were also associated with obesity at the transcriptome level, suggesting that genetic variants likely confer risk of obesity by regulating mRNA expression and protein abundance of these genes. Conclusions: Our study provides new insights into the genetic component of human brain protein abundance in obesity. The identified proteins represent promising therapeutic targets for future drug development. Health sciences/Endocrinology/Endocrine system and metabolic diseases/Obesity Biological sciences/Biochemistry/Proteins Biological sciences/Genetics body composition drug development obesity therapy proteome-wide association study Figures Figure 1 1. INTRODUCTION Obesity is a chronic disease characterized by the excessive accumulation of body fat or adipose tissue, which increases the risk of many diseases, such as type 2 diabetes, cardiovascular disease, hypertension, nonalcoholic fatty liver disease, and certain cancers. 1 Obesity is a highly genetic disease. Twin, family, and adoption studies have estimated the heritability of obesity to be between 40% and 70%. 2, 3 Genome-wide association studies (GWASs) over the past two decades have identified hundreds of genetic variants associated with obesity. 4-8 However, as the vast majority of the variants reported by GWASs lie in noncoding regions, translating genetic loci into biological mechanisms that underlie obesity and developing better preventive and therapeutic strategies has been one of the most arduous tasks. Increasingly, multiple omics data are being generated at the genome-scale. Integrating these multiple-omics data with GWAS data can help to determine a locus's regulatory impact and prioritize the likely causal variants or genes. For example, using the transcriptome-wide association study (TWAS), a powerful approach that integrates the gene expression reference panel and large-scale GWASs, Gusev A et al. prioritized ten genes, e.g., INO80E and FTSJ3 , whose cis-regulated expression abundance levels were associated with body mass index (BMI). 9 Similarly, Wingo AP et al. conducted a proteome-wide association study (PWAS) and identified 395 genes, e.g., ADCY3 and DOC2A , whose cis-regulated protein abundance levels were associated with BMI. 10 These results provided researchers with more information, from the genetic cause of obesity to the functional consequences or relevant interactions. Brain plays a critical role in obesity development. Some studies have suggested that most obesity-associated variants were near genes enriched or exclusively expressed in the brain. 6, 11 Dorsolateral prefrontal cortex (dPFC) is a critical brain region involved in appetite control, food craving, and executive function. 12 Furthermore, \"the prefrontal cortex model of obesity\" suggested that genetic factors may influence food reward sensitivity and self-control and, thus, the ability to self-regulate eating. 13 The above evidence suggested that investigating the relationship between obesity and molecules (mRNA and protein) in brain tissue, particularly in the dPFC, may improve our understanding of the neurobiological mechanisms of obesity. Most studies use BMI to measure obesity. However, BMI only accounts for weight and does not provide information about body fat distribution. Excess fat accumulation in some body regions, such as the abdomen, is associated with a higher risk of health conditions than excess fat accumulation in other areas. 14 Other measures, such as body fat percentage (BFP), trunk fat percentage (TFP), visceral adipose tissue (VAT), waist circumference (WC), and waist-to-hip ratio (WHR), are alternative measures of obesity and can provide more information about body fat distribution. In the present study, we first performed PWAS and colocalization analyses to identify genes whose cis-regulated brain protein abundances were associated with obesity. We then assessed the druggability of the identified genes and conducted pathway enrichment analysis to explore their functional relevance. Finally, we evaluated the effects of the significant PWAS genes at the brain transcriptional level. 2. MATERIALS AND METHODS We used a discovery and replication study design. In the discovery stage, we performed PWASs and colocalization analyses by integrating the Religious Orders Study and the Rush Memory and Aging Project (ROSMAP) human brain proteomes reference panel and GWASs of 6 obesity-related phenotypes, including BFP, TFP, BMI, VAT, WC, and WHR. Significant proteins were further replicated using the Banner Sun Health Research Institute (BANNER) human brain reference panel. 2.1. GWAS Summary Statistics for Obesity-related Phenotypes GWAS summary statistics of obesity-related phenotypes were obtained from the largest to-date GWAS. Specifically, summary statistics of BFP ( N = 452 264), TFP ( N = 452 264), and WC ( N = 452 264) were obtained from the UK Biobank (UKB) cohort through the Gene Atlas (http://geneatlas.roslin.ed.ac.uk/). 15 GWAS summary statistics of VAT ( N = 325 153) were obtained from the UKB cohort through the GWAS catalog (https://www.ebi.ac.uk/gwas/). 16 Summary statistics of BMI ( N = 806 834) and WHR ( N = 697 734) were obtained from a meta-analysis of the genetic investigation of the anthropometric traits (GIANT) consortium and the UK Biobank (UKB) cohort. 8, 17 Ethical approval for each GWAS can be obtained from the original publications. No new ethics approval or informed consent was needed. 2.2. Reference panels of protein abundance and mRNA abundance We used two human brain proteome reference panels. The first was profiled from the dPFC of 376 human subjects in the ROSMAP cohort. 10 All subjects in the ROSMAP cohort were of European origin, with a mean age of 89.4 years. In the ROSMAP reference panel, 1 761 proteins were heritable, and their protein weights were available for PWAS. The second human brain proteome reference panel for PWAS was derived from the dPFC of brain samples recruited by the BANNER cohort of 198 participants of European descent. 10 The median age for the BANNER cohort was 82 years. In the BANNER reference panel, weights of 1 147 heritable proteins were available for PWAS. Proteomics sequencing datasets were all marked with isotope tandem mass tagged peptides, which were then analyzed by liquid chromatography coupled to mass spectrometry. The human brain transcriptome reference panel was derived from the Common Mind Consortium (CMC) dPFC transcriptomic data ( N = 452), 18 which includes the weights of 5 419 transcripts. Transcriptome sequencing dataset was determined on Agilent Bioanalyzer and Qubit. 2.3. Proteome-wide association studies We performed PWAS by integrating the human brain reference panel and GWASs of six obesity phenotypes. Briefly, the single nucleotide polymorphism (SNP)-protein abundance associations obtained from the reference panel were used to calculate the protein expression weights. 9, 19 We first filtered the GWAS summary data using the LDSC munge_stats.py 20 utility to remove non-compliant SNPs with INFO ≤ 0.9, MAF ≤ 0.01, duplicate SNPs, non-SNPs or strand ambiguity, according to the default parameters. Then, the gene-obesity effect sizes were obtained by calculating the linear sum of GWAS Z score × weights of independent SNPs in the gene locus. To investigate whether causal variants are indeed shared between significant proteins and obesity rather than coincidentally shared through a correlation of linkage disequilibrium (LD), a further colocalization analysis was carried out. 9 Colocalization analysis assessed the posterior probability for the following five exclusive hypotheses: 1) no association with either trait; 2) functional association only; 3) GWAS association only; 4) independent functional/GWAS associations; and 5) colocalized functional/GWAS associations. A strict criterion of colocalization was defined as a posterior probability of H4 (PPH4) > 0.8. Protein-coding genes identified in the discovery stage were replicated using the replicated reference panel. 2.4. Pathway enrichment analysis We performed functional enrichment analysis of the replicated protein-coding genes using the g: Profiler ( https://biit.cs.ut.ee/gprofiler/ ). Multiple testing was corrected using the default g: SCS threshold method. The results were considered statistically significant at P adj values less than 0.05. For the database sources, in addition to the default Gene Ontology (GO) (http://geneontology.org/) database, we also selected the Kyoto Encyclopedia of Genes and Genomes (KEGG) (https://www.kegg.jp/kegg/pathway.html) and Reactome (https://reactome.org/) databases. 2.5. Druggability assessment of potential gene targets Successfully replicated protein-coding genes were assessed for their druggability based on the therapeutic targeting database (TTD) 21 and the druggability tiers database (conducted by Finan et al.). 22 The TTD database highlights the concept of therapeutic targets, and only targets with efficacy are retained in the database. There are four levels of therapeutic target types: successful target, clinical trial target, patent-recorded target, and literature-reported target. Discontinued, terminated, or withdrawn drugs were excluded. Unlike TTD, the druggability tier database is annotated by gene symbols and prioritizes the druggability of genes. There are three levels of druggability tier. Tier 1 incorporated the targets of approved drugs and drugs in clinical development. Tier 2 incorporated proteins closely related to drug targets or with associated drug-like compounds. Tier 3 incorporated extracellular proteins and members of key drug target families (3A: in proximity (±50 kbp) to a GWAS SNP and had an extracellular location; 3B: the remainder of Tier 3). 2.6. Effect at the transcriptional level Genetic central dogma indicates that genetic information is passed from DNA to mRNA to protein. TWAS could predict gene-obesity effect sizes by integrating the precomputed SNP-gene expression weights and obesity-related GWASs. We evaluated the effects of the successfully replicated PWAS genes at the transcriptional level in human brain. In addition, colocalization analysis was conducted to investigate whether there were shared causal variants between gene expression and obesity. 2.7. Sensitivity analysis Considering that the ROSMAP and BANNER cohorts contained 30-50% Alzheimer's patients, we excluded SNPs strongly associated with Alzheimer's disease (AD) ( P < 5 × 10 -8 ) derived from a large-scale GWAS meta-analysis of European populations (excluding 23andMe) (https://ctg.cncr.nl/software/summary_statistics) 23 and reperformed the PWAS. Furthermore, the CMC cohort included ~50% of cases with schizophrenia. 18 Therefore, we filtered SNPs strongly associated with schizophrenia ( P < 5 × 10 -8 ) derived from a large-scale GWAS summary statistics of European populations (https://www.med.unc.edu/pgc/download-results/scz/) 24 and reperformed transcriptional association analyses. The PWAS and association analyses at the transcriptional level were conducted using the fusion pipeline ( http://gusevlab.org/projects/fusion/ ) with the Elastic-net model, and a significant association was defined as P < 0.05/number of genes analyzed. The colocalization analyse were performed using fusion software with parameter-coloc_P 0.05/number of genes analyzed 9 . 3. RESULTS The flowchart of the study design is presented in Figure 1 . 3.1. Human brain proteomes PWAS of obesity We identified 253 genes associated with at least one obesity phenotype in the discovery stage. Specifically, 125, 120, 147, 60, 90, and 73 genes were identified for BFP, TFP, BMI, VAT, WC, and WHR, respectively (see Supplementary Tables 1-6). Further colocalization tests demonstrated that 138 (54.5%) of the above 253 genes provided evidence of colocalization (PPH4 > 0.8), including 60, 56, 68, 34, 46, and 35 genes for BFP, TFP, BMI, VAT, WC, and WHR, respectively. Seventy-six of the 138 protein-coding genes identified in the discovery stage were successfully replicated using the replication reference panel, among which 52 genes further passed the colocalization test (PPH4 > 0.8), including 24, 24, 27, 14, 19, and 11 genes for BFP, TFP, BMI, VAT, WC, and WHR, respectively (see Table 1 and Supplementary Tables 1-6). 3.2 Pathway enrichment analysis of potential gene targets Pathway enrichment analysis on the 52 replicated protein-coding genes identified four pathways (Table 2), including GO: 0044281 (small molecule metabolic process) ( P adj = 3.14 × 10 -4 ), GO: 0072521 (purine-containing compound metabolic process) ( P adj = 4.71 × 10 -2 ), KEGG: 01100 (metabolic pathways) ( P adj = 1.15 × 10 -2 ) and REAC: R-HSA-1430728 (metabolism) ( P adj = 1.70 × 10 -4 ). 3.3 Druggability of potential protein target genes Fourteen of the 52 successfully replicated protein target genes have prospective drug repurposing value, as shown in Table 3. Nine genes were annotated as Tier 1, indicating that their protein productwas an approved or clinically developed drug. Three genes were annotated as Tier 2, suggesting that their protein product closely related to drug targets or associated drug-like compounds. In addition, twelve genes were annotated as therapeutic targets with proven efficacy, including four approved targets, two clinical targets, and six literature-reported targets. The specific drug information was shown in Supplementary Tables 7a and 7b. 3.4. Evidence at the transcriptional level We further evaluated whether the 52 potential protein target genes identified by PWASs were also associated with obesity at the transcriptome level. We identified 24 genes with gene expression levels associated with obesity, including 10, 11, 10, 8, 9, and 8 genes for BFP, TFP, BMI, VAT, WC, and WHR, respectively (see Supplementary Table 8). Of these 24 genes, 9 genes passed the colocalization test, including ADCY3, ANXA5, CCDC92, DAGLB, DPYSL4, HSD17B12, LSM6, NDUFAF1, and PIP4K2A , suggesting that genetic variants likely confer risk of obesity by regulating mRNA expression and protein abundance of these genes. 3.5 Sensitivity analysis Among 52 successfully replicated human brain proteins, the abundance of 10 proteins (ANXA5, ARL3, B3GAT3, COMT, DAGLB, FAM114A2, GPX1, PLCB3, PSMD9, SHMT1) increased and the abundance of 6 proteins (CCDC92, DPYSL4, LYRM4, MECR, SLC25A12, SNX32) decreased in AD patients. 25 For 9 genes with evidence at the at the brain transcriptome level, there was no evidence linking them to schizophrenia. 26, 27 After excluding 3 570 SNPs strongly associated with AD 23 ( P < 5 × 10 -8 ), almost no change was observed for the effect of the 52 proteins (see Supplementary Table 9a). Similar consistent results were also observed at the transcriptional level (see Supplementary Table 9b). The pre- and post-filtering consistency suggested that our results were slightly affected by AD or schizophrenia. 4. DISCUSSION In the present study, we sought to explore whether genetic variants associated with obesity could be mapped to brain protein concentrations. Using a combination of PWAS and colocalization analysis, we identified 52 potential protein target genes whose brain protein abundance changes were associated with obesity. Nine of these 52 genes showed similar evidence at the transcriptional level. The most important determinant of body weight is calorie intake over weeks and months. 28 The dPFC is an important brain region associated with appetite control, food cravings and executive functions. 12 Some neuroimaging studies showed lower activated neuronal activity in the left dPFC in obese compared to lean individuals. 29-31 This lower dPFC neuronal activity meant that they were less satiated. Therefore, exploring the relationship between protein abundance levels of dPFC and obesity may indicate a relationship between obesity and central nervous system (CNS) regulation. Among the 52 potential protein target genes identified by PWASs, some appear to be closely related to CNS regulation. Notably, ADCY3 showed the evidence of negative associations at both protein and transcript levels for multiple obesity phenotypes. ADCY3 encodes adenylyl cyclase three, which catalyzes the synthesis of cAMP, an essential second messenger in signaling pathways. Animal studies showed that ADCY3 knockout mice had increased susceptibility to obesity, insulin resistance, and dyslipidemia, which may be caused by disruption of cAMP signaling in the primary cilia of the hypothalamus. 32 In addition, we found that ADCY3 is involved in the biosynthetic pathway of some hormones related to lipid metabolism, including insulin, thyroid hormone, and cortisol, through the KEGG database. 33 This evidence suggests that ADCY3 is involved not only in the CNS regulation of obesity but also in many other mechanisms. COMT encodes catechol-o-methyltransferase protein, one of the major enzymes that degrades catecholamines such as dopamine and norepinephrine. 34 The theory of dopamine involvement in appetite regulation suggests that the mesocorticolimbic dopaminergic reward pathway in the brain plays a central role in the neuromodulation of appetite. 35 These evidences suggested that COMT may affect body weight by regulating the level of dopamine in the cerebral cortex, which further regulates appetite. EIF2AK4 encodes general control nonderepressible 2 (GCN2) protein, an amino acid sensor that is activated upon amino acid starvation and phosphorylates eukaryotic initiation factor 2α to maintain amino acid homeostasis. 36 An animal study showed that brain-specific inactivation of GCN2 affects feeding behaviour by activating neuronal circuits that bias feeding towards nutritionally unbalanced food sources (lacking leucine or threonine). 37 Another animal study showed that leucine deprivation activates GCN2/ATF4 signalling in PKC-δ neurons of the central amygdala, which in turn activates sympathetic nerves and promotes white lipid browning. 38 These evidences suggested that EIF2AK4 is involved in CNS regulation of body weight. SLC17A6 encodes Vesicular Glutamate Transporter 2 (VGLUT2), which can be expressed by a subset of dopamine (DA) neurons. 39, 40 The population of neurons in the lateral hypothalamic region upstream of the dopaminergic cells are glutamatergic neurons expressing VGLUT2, which regulates feeding, reward, and aversion. 41 In addition, glutaminergic neurons were responsive to a number of neuropeptides related to energy homeostasis; they have excitatory effects on anorexic peptide cholecystokinin, but inhibitory effects on anorexic neuropeptide Y, dynorphin and met‐enkephalin. 42 Furthermore, SULT1A1 encodes a Sulfotransferase family 1A member 1 (SULT1A1), which is widely distributed throughout the body, with high abundance in organs such as the liver, brain, skin, and gastrointestinal tissues. 43 A recent study has shown that tyrosine can be metabolised by the gut microbiota to 4-ethylphenol, which subsequently generates 4-ethylphenyl sulfate in response to host SULT1A1, which can modulate brain activity and anxiety-like behaviour in mice. 44 However, whether this association of SULT1A1 with the brain-gut axis contributes to obesity still requires experimental verification. In previous studies, Gagnon et al. explored whether genetic variants associated with BMI could influence brain protein concentrations using proteome-wide Mendelian randomization (MR) and colocalization methods. 45 Our study is more comprehensive in its investigation of obesity. In addition to BMI, we also analyzed some body fat distribution traits, including BFP, TFP, VAT, WC, and WHR. We noted that nearly half of the genes (67/138) identified in the discovery stage were found through associations with phenotype(s) other than BMI, suggesting that using multiple obesity-related phenotypes to explore obesity was necessary. We checked the replicability of proteins reported by Gagnon et al. in our discovery stage results. Of the 47 proteins reported by Gagnon et al., 13 proteins were not available in our results and all the other 34 proteins were replicated by our study at the nominal level 0.05 as well as PPH4 > 0.8 (see Supplementary Table 10). We also checked the replicability of our findings in Gagnon et al.’s study. Of the 52 identified genes, 21 genes were also identified by Gagnon et al. (see Supplementary Table 11). We observed that only ~17.3% (9/52) of proteins identified by PWASs were explained at the transcriptional level, implicating a possible inconsistency between the proteome and transcriptome. Indeed, several studies have reported inconsistencies in protein and mRNA levels, which may be caused by differential translation, protein degradation, contextual interfering factors, and the prevalence of protein level buffering. 46, 47 As seen, integrating transcriptomic and proteomic data can provide additional information on obesity-related gene expression control principles that cannot be obtained from either type of data alone. There are several limitations of the study. Firstly, the associations we identified between proteins and obesity-related phenotypes are not causal associations, and they may be affected by pleiotropy and reverse causation, although these effects may be small. 9 Second, our analyses focused on cis-regulated gene products (mRNAs and proteins), and we did not investigate trans-regulated mRNAs and proteins. Finally, obesity-related phenotypes GWASs and the proteome and transcriptome datasets were mainly based on European populations. Our findings need to be cautious in interpreting other races. In summary, we identified 52 potential protein target genes that contribute to the pathogenesis of obesity by regulating their protein abundance. Among the 52 genes, 9 ( ADCY3, ANXA5, CCDC92, DAGLB, DPYSL4, HSD17B12, LSM6, NDUFAF1, PIP4K2A ) also showed evidence at the transcriptome level. These genes provide novel, promising protein targets for further mechanistic and therapeutic studies. Declarations Acknowledgments We are grateful for all participants and investigators involved in the released GWAS summary statistics and human brain proteome and transcriptome weights. Author contributions Q-GZ and Y-FP designed the study. Q-GZ and Y-FP collected the data. Q-GZ and X-LM analyzed the data. X-LM, QX, Z-TS, FB, KL, and Q-GZ performed the literature search. Q-GZ drafted the early version of the manuscript. Y-FP and LZ jointly supervised the study. All authors revised the manuscript critically and approved the final version. Competing interests All authors declare that they have no competing interests Fundings: This study was partially supported by funding from the National Natural Science Foundation of China (32170670) and a project funded by the Priority Academic Program Development (PAPD) of Jiangsu higher education institutions. Data availability GWAS summary statistics data are available at http://geneatlas.roslin.ed.ac.uk/, https://portals.broadinstitute.org/collaboration/giant/index.php/GIANT_consortium_data_files, and https://www.ebi.ac.uk/gwas/. Human brain protein weights from Religious Orders Study and the Rush Memory and Aging Project (ROSMAP) and Banner Sun Health Research Institute (BANNER) are available at https://www.synapse.org/#!Synapse:syn23191787. Gene expression weights for the Common Mind Consortium (CMC) are available at http://gusevlab.org/projects/fusion/. Ethical approval Ethical approval for each GWAS and human brain reference panel can be obtained from the original publications. No new ethics approval or informed consent was needed. Supplementary material The following are the Supplementary data for this article: Supplementary Tables 1-11. References Wen X, Zhang B, Wu B, Xiao H, Li Z, Li R et al. Signaling pathways in obesity: mechanisms and therapeutic interventions. 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Genetic control of body weight by the human brain proteome. iScience 2023; 26 (4) : 106376. Buccitelli C, Selbach M. mRNAs, proteins and the emerging principles of gene expression control. Nature reviews. Genetics 2020; 21 (10) : 630-644. Vogel C, Marcotte EM. Insights into the regulation of protein abundance from proteomic and transcriptomic analyses. Nature reviews. Genetics 2012; 13 (4) : 227-32. Tables Table 1. Potential protein target genes for obesity Target Gene Related Phenotype(s) Lead Phenotype ROSMAP Cohort BANNER Cohort PWAS.Z PWAS.P PPH4 PWAS.Z PWAS.P PPH4 ABCG2 BMI, WC BMI 7.415 1.22E-13 0.998 7.217 5.31E-13 0.998 ACTR1B BMI BMI -4.315 1.60E-05 0.988 -4.843 1.28E-06 0.981 ADCY3 BFP, TFP, BMI, VAT, WC BMI -17.257 9.91E-67 0.948 -19.518 7.74E-85 0.952 ADPGK BFP, TFP, BMI, VAT, WC BFP 7.407 1.29E-13 0.821 5.423 5.85E-08 0.865 ANXA5 WHR WHR 4.669 3.03E-06 0.960 4.682 2.84E-06 0.964 ARL3 BMI BMI -5.678 1.36E-08 0.988 -4.763 1.91E-06 0.970 ASL BFP, TFP TFP -5.814 6.12E-09 0.926 -5.163 2.43E-07 0.884 B3GAT3 BFP, TFP, WHR TFP 7.314 2.60E-13 0.981 6.494 8.36E-11 0.976 C3orf18 BMI BMI -5.650 1.60E-08 0.970 -4.819 1.44E-06 0.951 C9orf64 BFP, TFP TFP 5.667 1.45E-08 0.907 4.686 2.79E-06 0.983 CAND2 WHR WHR -5.485 4.14E-08 0.949 -6.052 1.43E-09 0.970 CCDC92 BFP, WHR WHR 15.228 2.30E-52 0.952 13.968 2.44E-44 0.856 CNNM2 BMI BMI 10.230 1.46E-24 0.988 9.606 7.58E-22 0.987 COMT BMI, WC BMI -5.659 1.53E-08 0.998 -5.720 1.06E-08 0.998 CPNE1 WC WC -5.086 3.66E-07 0.848 -5.392 6.97E-08 0.861 DAGLB WHR WHR -4.589 4.45E-06 0.988 -4.493 7.03E-06 0.962 DCAKD TFP TFP -4.427 9.54E-06 0.832 -5.728 1.01E-08 0.957 DPYSL4 BFP, TFP, BMI, VAT, WC BMI 6.232 4.60E-10 0.999 7.099 1.26E-12 0.971 DPYSL5 BFP, TFP, VAT, WC VAT 5.067 4.05E-07 0.952 5.910 3.41E-09 0.979 EIF2AK4 TFP TFP 4.605 4.13E-06 0.956 4.086 4.39E-05 0.872 FAM114A2 BFP, TFP, VAT BFP 5.303 1.14E-07 0.941 4.444 8.81E-06 0.945 GBA2 BFP, TFP, BMI BFP 4.967 6.80E-07 0.988 4.710 2.48E-06 0.987 GPX1 BMI, VAT BMI 7.769 7.93E-15 0.992 8.035 9.32E-16 0.992 HINT1 BMI, WC WC -5.422 5.89E-08 0.990 -5.243 1.58E-07 0.990 HSD17B12 WC, WHR WC -7.016 2.28E-12 0.959 -5.928 3.07E-09 0.829 JMJD7 WHR WHR 4.679 2.89E-06 0.997 4.085 4.41E-05 0.892 LRP4 BFP BFP -4.282 1.85E-05 0.912 -4.316 1.59E-05 0.950 LSM6 WC WC 4.831 1.36E-06 0.967 4.969 6.72E-07 0.974 LYRM4 VAT, WC WC 4.940 7.80E-07 0.981 4.184 2.86E-05 0.953 MAP2K2 BFP, TFP, BMI, VAT, WC BMI -9.292 1.51E-20 0.950 -8.247 1.62E-16 0.848 MECR WHR WHR -4.703 2.56E-06 0.930 -3.727 1.93E-04 0.913 MRPL9 BFP, TFP BFP 5.430 5.63E-08 0.838 5.180 2.22E-07 0.898 MTERFD2 BFP, TFP TFP -5.790 7.05E-09 0.984 -6.148 7.82E-10 0.985 NDUFAF1 BFP, TFP, BMI, VAT BFP -6.087 1.15E-09 0.973 -5.929 3.05E-09 0.977 PANK4 BFP, BMI, VAT, WC BMI 6.843 7.77E-12 0.994 7.489 6.92E-14 1.000 PFKM BFP, TFP TFP -5.198 2.01E-07 0.964 -5.070 3.95E-07 0.986 PIP4K2A BMI BMI -4.767 1.87E-06 0.916 -3.249 1.16E-03 0.873 PLCB3 BFP, TFP, BMI, WC BMI -8.144 3.83E-16 0.980 -5.595 2.21E-08 0.864 PSMD9 BFP, TFP, BMI, WC BFP 5.798 6.72E-09 0.991 6.282 3.34E-10 0.980 RAB27B BFP, TFP, BMI, VAT, WC BMI -7.199 6.08E-13 0.988 -7.524 5.30E-14 0.960 SENP8 BMI BMI 5.117 3.10E-07 0.981 6.163 7.16E-10 0.937 SHMT1 WHR WHR -4.882 1.05E-06 0.993 -5.179 2.23E-07 0.980 SLC17A6 TFP TFP 4.885 1.04E-06 0.991 4.479 7.50E-06 0.937 SLC25A12 BFP, TFP, BMI, WC BMI -6.384 1.72E-10 0.969 -5.553 2.81E-08 0.939 SNX32 VAT, WC, WHR VAT 6.707 1.99E-11 0.998 6.919 4.56E-12 0.997 SPRYD4 BMI BMI -5.510 3.59E-08 0.997 -5.107 3.27E-07 0.975 SULT1A1 BMI, WHR BMI -13.013 1.03E-38 0.946 -14.854 6.58E-50 0.914 TMEM106B BMI BMI -4.464 8.03E-06 0.834 -4.021 5.81E-05 0.895 TYK2 BMI BMI -4.863 1.16E-06 0.981 -3.985 6.74E-05 0.980 UCHL3 BFP, TFP TFP -5.382 7.38E-08 0.989 -5.224 1.75E-07 0.991 ULK3 BFP, TFP, BMI, VAT, WC WC -5.947 2.73E-09 0.999 -5.796 6.79E-09 0.999 VKORC1 BFP, TFP, BMI, VAT BMI -9.588 9.00E-22 0.899 -11.537 8.61E-31 0.882 PWAS, proteome-wide association study; PPH4, colocalization posterior probability of H4; ROSMAP, Religious Orders Study and the Rush Memory and Aging Project; BANNER, Banner Sun Health Research Institute; BFP, body fat percentage; TFP, trunk fat percentage; BMI, body mass index; WC, waist circumference; WHR, waist-hip ratio. Table 2. Pathway enrichment analysis of 52 PWAS potential protein target genes Source Term Name Term ID P adj Intersections GO: BP small molecule metabolic process GO: 0044281 3.14E-04 ADCY3, ADPGK, ASL, C9ORF64, GBA2, PANK4, PFKM, SLC25A12, VKORC1, ABCG2, GPX1, HINT1, SULT1A1, DCAKD, HSD17B12, DAGLB, MECR, SHMT1 GO: BP purine-containing compound metabolic process GO: 0072521 4.71E-02 ADCY3, PANK4, ABCG2, HINT1, SULT1A1, DCAKD, HSD17B12, SHMT1 KEGG Metabolic pathways KEGG: 01100 1.15E-02 ADCY3, ADPGK, ASL, B3GAT3, GBA2, PFKM, PLCB3, VKORC1, COMT, GPX1, PIP4K2A, HSD17B12, MECR, SHMT1 REAC Metabolism REAC: R-HSA-1430728 1.70E-04 ADCY3, ADPGK, ASL, B3GAT3, GBA2, NDUFAF1, PANK4, PFKM, PLCB3, PSMD9, SLC25A12, VKORC1, ABCG2, COMT, GPX1, PIP4K2A, SULT1A1, LYRM4, CPNE1, HSD17B12, MECR, SHMT1 GO, Gene Ontology; BP, Biological Process; KEGG, Kyoto Encyclopedia of Genes and Genomes; REAT, Reactome; P adj , P value adjusted by the G: SCS algorithm. Table 3. Druggability of 14 potential protein target genes Target Gene Target type (TTD) Target name (TTD) Target-related disease(s) (TTD) Druggability tier (Finan et al. 2017) Small mol druggable Bio druggable ADEM gene ABCG2 Successful target ATP-binding cassette transporter G2 (ABCG2) Irritable bowel syndrome; Rheumatoid arthritis Tier 1 Yes Yes Yes ANXA5 Clinical trial target Annexin A5 (ANXA5) Injury COMT Successful target Catechol-O-methyl-transferase (COMT) Parkinsonism Tier 1 Yes No No DAGLB Literature-reported target Diacylglycerol lipase beta (DAGLB) Tier 1 Yes No No EIF2AK4 Literature-reported target Eukaryotic translation initiation factor 2-alpha kinase 4 (EIF2AK4) Tier 1 Yes No No GBA2 Tier 2 Yes No No GPX1 Literature-reported target Glutathione peroxidase (GPX1) Tier 1 No No Yes MAP2K2 Clinical trial target ERK activator kinase 2 (MEK2) Melanoma; Thyroid cancer Tier 1 Yes No No SENP8 Literature-reported target Deneddylase-1 (SENP8) Tier 2 Yes No No SULT1A1 Tier 1 No No Yes TYK2 Successful target TYK2 tyrosine kinase (TYK2) Psoriasis UCHL3 Literature-reported target Ubiquitin thioesterase L3 (UCHL3) Tier 2 Yes No No ULK3 Literature-reported target Unc-51 like kinase 3 (ULK3) Tier 1 Yes No No VKORC1 Successful target Vitamin K epoxide reductase complex 1 (VKORC1) Bleeding disorder; Coagulation defect; Pulmonary thromboembolism; Supraventricular tachyarrhythmia; Thrombosis Tier 1 Yes No No TTD, therapeutic targeting database; Small mol druggable, the protein product of the gene was a small molecule target or predicted target; Bio druggable, the protein product of the gene was a target or predicted target for biologic therapies; ADEM gene, the protein product of the gene involved in absorption, distribution, metabolism, and excretion (ADME) of a compound; BFP, body fat percentage; TFP, trunk fat percentage; BMI, body mass index; VAT, visceral adipose tissue; WC, waist circumference; WHR, waist-hip ratio; Tier 1 incorporated the targets of approved drugs and drugs in clinical development. Additional Declarations There is NO conflict of interest to disclose Supplementary Files SupplementaryTable1.docx Supplementary Table 1 SupplementaryTable2.docx Supplementary Table 2 SupplementaryTable3.docx Supplementary Table 3 SupplementaryTable4.docx Supplementary Table 4 SupplementaryTable5.docx Supplementary Table 5 SupplementaryTable6.docx Supplementary Table 6 SupplementaryTable7a.docx Supplementary Table 7a SupplementaryTable7b.docx Supplementary Table 7b SupplementaryTable8.docx Supplementary Table 8 SupplementaryTable9a.docx Supplementary Table 9a SupplementaryTable9b.docx Supplementary Table 9b SupplementaryTable10.docx Supplementary Table 10 SupplementaryTable11.docx Supplementary Table 11 Cite Share Download PDF Status: Published Journal Publication published 18 Jul, 2024 Read the published version in International Journal of Obesity → Version 1 posted Editorial decision: revise 29 Apr, 2024 Review # 2 received at journal 07 Apr, 2024 Review # 1 received at journal 07 Apr, 2024 Reviewer # 2 agreed at journal 28 Mar, 2024 Reviewer # 1 agreed at journal 26 Mar, 2024 Reviewers invited by journal 02 Mar, 2024 Submission checks completed at journal 22 Feb, 2024 Editor assigned by journal 21 Feb, 2024 First submitted to journal 21 Feb, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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7b\",\"description\":\"\",\"filename\":\"SupplementaryTable7b.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3975196/v1/d02e623fe97845c51faf5a94.docx\"},{\"id\":51945798,\"identity\":\"db311d67-8bf9-45bf-bd90-4fcd1fee52b1\",\"added_by\":\"auto\",\"created_at\":\"2024-03-04 10:56:24\",\"extension\":\"docx\",\"order_by\":9,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":26897,\"visible\":true,\"origin\":\"\",\"legend\":\"Supplementary Table 8\",\"description\":\"\",\"filename\":\"SupplementaryTable8.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3975196/v1/9b5479e9ba37a2ff838f9605.docx\"},{\"id\":51945794,\"identity\":\"d0bc5970-8e30-4b6d-b108-cb16be998280\",\"added_by\":\"auto\",\"created_at\":\"2024-03-04 10:56:24\",\"extension\":\"docx\",\"order_by\":10,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":17584,\"visible\":true,\"origin\":\"\",\"legend\":\"Supplementary Table 9a\",\"description\":\"\",\"filename\":\"SupplementaryTable9a.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3975196/v1/a7f1fc6627eb1cd5736ad414.docx\"},{\"id\":51945800,\"identity\":\"a057c7db-d8f2-43af-8e52-1a93e59bdd98\",\"added_by\":\"auto\",\"created_at\":\"2024-03-04 10:56:24\",\"extension\":\"docx\",\"order_by\":11,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":16388,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSupplementary Table 9b\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"SupplementaryTable9b.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3975196/v1/77962c56c8d146b1d01e4c9b.docx\"},{\"id\":51945795,\"identity\":\"8ce38cd6-7eb6-4832-853d-b0dcd8c33b97\",\"added_by\":\"auto\",\"created_at\":\"2024-03-04 10:56:24\",\"extension\":\"docx\",\"order_by\":12,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":24506,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSupplementary Table 10\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"SupplementaryTable10.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3975196/v1/ccbccfe012ee7af1720a3b23.docx\"},{\"id\":51945792,\"identity\":\"8357a5ab-9cb8-4dad-b0d3-0a79ec2c25ce\",\"added_by\":\"auto\",\"created_at\":\"2024-03-04 10:56:23\",\"extension\":\"docx\",\"order_by\":13,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":30282,\"visible\":true,\"origin\":\"\",\"legend\":\"Supplementary Table 11\",\"description\":\"\",\"filename\":\"SupplementaryTable11.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3975196/v1/35d6bd2d1104c3468a12e53b.docx\"}],\"financialInterests\":\"There is \\u003cb\\u003eNO\\u003c/b\\u003e conflict of interest to disclose\",\"formattedTitle\":\"Human Brain Proteome-wide Association Study Provides Insights into the Genetic Components of Protein Abundance in Obesity\",\"fulltext\":[{\"header\":\"1. INTRODUCTION\",\"content\":\"\\u003cp\\u003eObesity is a chronic disease characterized by the excessive accumulation of body fat or adipose tissue, which increases the risk of many diseases, such as type 2 diabetes, cardiovascular disease, hypertension, nonalcoholic fatty liver disease, and certain cancers.\\u003csup\\u003e1\\u003c/sup\\u003e Obesity is a highly genetic disease. Twin, family, and adoption studies have estimated the heritability of obesity to be between 40% and 70%.\\u003csup\\u003e2, 3\\u003c/sup\\u003e Genome-wide association studies (GWASs) over the past two decades have identified hundreds of genetic variants associated with obesity.\\u003csup\\u003e4-8\\u003c/sup\\u003e However, as the vast majority of the variants reported by GWASs lie in noncoding regions, translating genetic loci into biological mechanisms that underlie obesity and developing better preventive and therapeutic strategies has been one of the most arduous tasks.\\u003c/p\\u003e\\n\\u003cp\\u003eIncreasingly, multiple omics data are being generated at the genome-scale. Integrating these multiple-omics data with GWAS data can help to determine a locus's regulatory impact and prioritize the likely causal variants or genes. For example, using the transcriptome-wide association study (TWAS), a powerful approach that integrates the gene expression reference panel and large-scale GWASs, Gusev A et al. prioritized ten genes, e.g., \\u003cem\\u003eINO80E\\u003c/em\\u003e and \\u003cem\\u003eFTSJ3\\u003c/em\\u003e, whose cis-regulated expression abundance levels were associated with body mass index (BMI).\\u003csup\\u003e9\\u003c/sup\\u003e Similarly, Wingo AP et al. conducted a proteome-wide association study (PWAS) and identified 395 genes, e.g., \\u003cem\\u003eADCY3\\u003c/em\\u003e and \\u003cem\\u003eDOC2A\\u003c/em\\u003e, whose cis-regulated protein abundance levels were associated with BMI.\\u003csup\\u003e10\\u003c/sup\\u003e These results provided researchers with more information, from the genetic cause of obesity to the functional consequences or relevant interactions.\\u003c/p\\u003e\\n\\u003cp\\u003eBrain plays a critical role in obesity development. Some studies have suggested that most obesity-associated variants were near genes enriched or exclusively expressed in the brain.\\u003csup\\u003e6, 11\\u003c/sup\\u003e Dorsolateral prefrontal cortex (dPFC) is a critical brain region involved in appetite control, food craving, and executive function.\\u003csup\\u003e12\\u003c/sup\\u003e Furthermore, \\\"the prefrontal cortex model of obesity\\\" suggested that genetic factors may influence food reward sensitivity and self-control and, thus, the ability to self-regulate eating.\\u003csup\\u003e13\\u003c/sup\\u003e The above evidence suggested that investigating the relationship between obesity and molecules (mRNA and protein) in brain tissue, particularly in the dPFC, may improve our understanding of the neurobiological mechanisms of obesity.\\u003c/p\\u003e\\n\\u003cp\\u003eMost studies use BMI to measure obesity. However, BMI only accounts for weight and does not provide information about body fat distribution. Excess fat accumulation in some body regions, such as the abdomen, is associated with a higher risk of health conditions than excess fat accumulation in other areas.\\u003csup\\u003e14\\u003c/sup\\u003e Other measures, such as body fat percentage (BFP), trunk fat percentage (TFP), visceral adipose tissue (VAT), waist circumference (WC), and waist-to-hip ratio (WHR), are alternative measures of obesity and can provide more information about body fat distribution.\\u003c/p\\u003e\\n\\u003cp\\u003eIn the present study, we first performed PWAS and colocalization analyses to identify genes whose cis-regulated brain protein abundances were associated with obesity. We then assessed the druggability of the identified genes and conducted pathway enrichment analysis to explore their functional relevance. Finally, we evaluated the effects of the significant PWAS genes at the brain transcriptional level.\\u003c/p\\u003e\"},{\"header\":\"2. MATERIALS AND METHODS \",\"content\":\"\\u003cp\\u003eWe used a\\u0026nbsp;discovery and replication study\\u0026nbsp;design. In the discovery stage,\\u0026nbsp;we performed PWASs and colocalization analyses by integrating the\\u0026nbsp;Religious Orders Study and the Rush Memory and Aging Project (ROSMAP)\\u0026nbsp;human brain proteomes reference panel and GWASs of 6 obesity-related phenotypes, including\\u0026nbsp;BFP, TFP, BMI, VAT, WC, and WHR. Significant proteins were further\\u0026nbsp;replicated using the Banner Sun Health Research Institute (BANNER) human brain reference panel.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003e2.1. GWAS Summary Statistics for Obesity-related Phenotypes\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eGWAS summary statistics of obesity-related phenotypes were obtained from the largest to-date GWAS. Specifically, summary statistics of BFP (\\u003cem\\u003eN\\u0026nbsp;\\u003c/em\\u003e= 452 264), TFP (\\u003cem\\u003eN\\u003c/em\\u003e = 452 264), and WC (\\u003cem\\u003eN\\u0026nbsp;\\u003c/em\\u003e= 452 264) were obtained from the UK Biobank (UKB) cohort through the Gene Atlas (http://geneatlas.roslin.ed.ac.uk/).\\u003csup\\u003e15\\u003c/sup\\u003e GWAS summary statistics of VAT (\\u003cem\\u003eN\\u0026nbsp;\\u003c/em\\u003e= 325 153) were obtained from the UKB cohort through the GWAS catalog (https://www.ebi.ac.uk/gwas/).\\u003csup\\u003e16\\u003c/sup\\u003e Summary statistics of BMI (\\u003cem\\u003eN\\u003c/em\\u003e = 806 834) and WHR (\\u003cem\\u003eN\\u003c/em\\u003e = 697 734) were obtained from a meta-analysis of the genetic investigation of the anthropometric traits (GIANT) consortium and the UK Biobank (UKB) cohort.\\u003csup\\u003e8, 17\\u003c/sup\\u003e \\u0026nbsp;Ethical\\u0026nbsp;approval for each GWAS can be obtained from the original publications. No new ethics approval\\u0026nbsp;or\\u0026nbsp;informed consent was needed.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003e2.2. Reference\\u0026nbsp;\\u003c/em\\u003e\\u003cem\\u003epanels\\u003c/em\\u003e\\u003cem\\u003e\\u0026nbsp;of protein abundance and mRNA abundance\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe used two human brain proteome reference panels. The first was profiled from the dPFC of 376 human subjects in the ROSMAP cohort.\\u003csup\\u003e10\\u003c/sup\\u003e All subjects in the ROSMAP cohort were of European origin, with a mean age of 89.4 years. In the ROSMAP reference panel, 1 761 proteins were heritable,\\u0026nbsp;and their protein weights were available for PWAS. The second human brain\\u0026nbsp;proteome\\u0026nbsp;reference panel for PWAS was derived from the dPFC of brain samples recruited by the BANNER cohort of 198 participants of European descent.\\u003csup\\u003e10\\u003c/sup\\u003e The median age for the BANNER cohort\\u0026nbsp;was\\u0026nbsp;82 years. In the BANNER reference panel, weights of 1 147 heritable proteins were available for PWAS. Proteomics sequencing datasets were all marked with isotope tandem mass tagged peptides,\\u0026nbsp;which were then analyzed by liquid chromatography coupled to mass spectrometry.\\u003c/p\\u003e\\n\\u003cp\\u003eThe human brain transcriptome reference panel was derived from the Common Mind Consortium (CMC) dPFC transcriptomic data (\\u003cem\\u003eN\\u003c/em\\u003e = 452),\\u003csup\\u003e18\\u003c/sup\\u003e which includes the weights of 5 419 transcripts. Transcriptome sequencing dataset was determined on Agilent Bioanalyzer and Qubit.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003e2.3. Proteome-wide association studies\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe performed PWAS by integrating the human brain reference panel and GWASs of six obesity phenotypes.\\u0026nbsp;Briefly, the single nucleotide polymorphism (SNP)-protein abundance associations obtained from the reference panel were used to calculate the protein expression weights.\\u003csup\\u003e9, 19\\u003c/sup\\u003e We first filtered the GWAS summary data using the LDSC munge_stats.py\\u003csup\\u003e20\\u003c/sup\\u003e utility to remove non-compliant SNPs with INFO \\u0026le; 0.9, MAF \\u0026le; 0.01, duplicate SNPs, non-SNPs or strand ambiguity, according to the default parameters. Then, the gene-obesity effect sizes were obtained by calculating the linear sum of GWAS \\u003cem\\u003eZ\\u003c/em\\u003e score \\u0026times; weights of independent SNPs in the gene locus. To investigate whether causal variants are indeed shared between significant proteins and obesity rather than coincidentally shared through a correlation of linkage disequilibrium (LD), a further colocalization analysis was carried out.\\u003csup\\u003e9\\u003c/sup\\u003e Colocalization analysis assessed the posterior probability for the following five exclusive hypotheses: 1) no association with either trait; 2) functional association only; 3) GWAS association only; 4) independent functional/GWAS associations; and 5) colocalized functional/GWAS associations. A strict criterion of colocalization was defined as a posterior probability of H4 (PPH4) \\u0026gt; 0.8. Protein-coding genes identified in the discovery stage were replicated using the replicated reference panel.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003e2.4. Pathway enrichment analysis\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe performed functional enrichment analysis of the replicated protein-coding genes using the g: Profiler (\\u003ca href=\\\"https://biit.cs.ut.ee/gprofiler/\\\"\\u003ehttps://biit.cs.ut.ee/gprofiler/\\u003c/a\\u003e). Multiple testing was corrected using the default g: SCS threshold method. The results were considered statistically significant at \\u003cem\\u003eP\\u003csub\\u003eadj\\u003c/sub\\u003e\\u003c/em\\u003e values less\\u0026nbsp;than 0.05. For the database sources, in addition to the default\\u0026nbsp;Gene Ontology (GO) (http://geneontology.org/) database, we also selected the Kyoto Encyclopedia of Genes and Genomes (KEGG) (https://www.kegg.jp/kegg/pathway.html) and Reactome (https://reactome.org/) databases.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003e2.5. Druggability assessment of potential gene targets\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eSuccessfully replicated protein-coding genes were assessed for their druggability based on the therapeutic targeting database (TTD)\\u003csup\\u003e21\\u003c/sup\\u003e and the druggability tiers database (conducted by Finan et al.).\\u003csup\\u003e22\\u003c/sup\\u003e The TTD database highlights the concept of therapeutic targets, and only targets with efficacy are retained in the database. There are four levels of therapeutic target types: successful target, clinical trial target, patent-recorded target, and literature-reported target. Discontinued, terminated, or withdrawn drugs were excluded. Unlike TTD, the druggability\\u0026nbsp;tier\\u0026nbsp;database is annotated by gene symbols and prioritizes the druggability of genes. There are three levels of druggability tier. Tier 1 incorporated the targets of approved drugs and drugs in clinical development. Tier 2 incorporated proteins closely related to drug targets or with associated drug-like compounds. Tier 3 incorporated extracellular proteins and members of key drug target families (3A: in proximity (\\u0026plusmn;50 kbp) to a GWAS SNP and had an extracellular location; 3B: the remainder of Tier 3).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003e2.6. Effect at the transcriptional level\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eGenetic central dogma indicates that genetic information is passed from DNA to mRNA to protein.\\u0026nbsp;TWAS could predict gene-obesity effect sizes by integrating the precomputed SNP-gene expression weights and obesity-related GWASs.\\u0026nbsp;We evaluated the effects of the successfully replicated PWAS genes at the transcriptional level in human brain.\\u0026nbsp;In addition, colocalization analysis was conducted to investigate whether there were shared causal variants between gene expression and obesity.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003e2.7.\\u0026nbsp;\\u003c/em\\u003e\\u003cem\\u003eSensitivity analysis\\u0026nbsp;\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eConsidering that the ROSMAP and BANNER cohorts contained 30-50% Alzheimer\\u0026apos;s patients, we excluded SNPs strongly associated with Alzheimer\\u0026apos;s disease (AD) (\\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 5 \\u0026times; 10\\u003csup\\u003e-8\\u003c/sup\\u003e) derived from\\u0026nbsp;a large-scale GWAS meta-analysis of European populations (excluding 23andMe) (https://ctg.cncr.nl/software/summary_statistics)\\u003csup\\u003e23\\u003c/sup\\u003e and reperformed the PWAS. Furthermore,\\u0026nbsp;the CMC cohort included ~50% of cases with schizophrenia.\\u003csup\\u003e18\\u003c/sup\\u003e Therefore, we filtered SNPs strongly associated with schizophrenia (\\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 5 \\u0026times; 10\\u003csup\\u003e-8\\u003c/sup\\u003e) derived from a large-scale GWAS summary statistics of European populations (https://www.med.unc.edu/pgc/download-results/scz/)\\u003csup\\u003e24\\u003c/sup\\u003e and reperformed\\u0026nbsp;transcriptional\\u0026nbsp;association analyses.\\u003c/p\\u003e\\n\\u003cp\\u003eThe PWAS and association analyses\\u0026nbsp;at the transcriptional level\\u0026nbsp;were conducted using the fusion pipeline (\\u003ca href=\\\"http://gusevlab.org/projects/fusion/\\\"\\u003ehttp://gusevlab.org/projects/fusion/\\u003c/a\\u003e) with the Elastic-net model, and\\u0026nbsp;a\\u0026nbsp;significant association was defined as \\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.05/number of genes analyzed. The colocalization analyse were performed using fusion software with parameter-coloc_P 0.05/number of genes analyzed\\u003csup\\u003e9\\u003c/sup\\u003e.\\u0026nbsp;\\u003c/p\\u003e\"},{\"header\":\"3. RESULTS\",\"content\":\"\\u003cp\\u003eThe flowchart of the study design is presented in \\u003cstrong\\u003eFigure 1\\u003c/strong\\u003e.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003e3.1. Human brain proteomes PWAS of obesity\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe identified 253 genes associated with at least one obesity phenotype in the discovery stage. Specifically, 125, 120, 147, 60, 90, and 73 genes were identified for BFP, TFP, BMI, VAT, WC, and WHR, respectively (see Supplementary Tables 1-6). Further colocalization tests demonstrated that 138 (54.5%) of the above 253 genes provided evidence of colocalization (PPH4 \\u0026gt; 0.8), including 60, 56, 68, 34, 46, and 35 genes for BFP, TFP, BMI, VAT, WC, and WHR, respectively.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eSeventy-six of the 138 protein-coding genes identified in the discovery stage were successfully replicated\\u0026nbsp;using the replication reference panel, among which 52 genes further passed the colocalization test (PPH4 \\u0026gt; 0.8), including 24, 24, 27, 14, 19, and 11 genes for BFP, TFP, BMI, VAT, WC, and WHR, respectively (see Table 1 and Supplementary Tables 1-6).\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e3.2 \\u003cem\\u003ePathway enrichment analysis of\\u0026nbsp;\\u003c/em\\u003e\\u003cem\\u003epotential gene targets\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003ePathway enrichment analysis on the 52 replicated protein-coding genes identified four pathways (Table 2), including GO: 0044281 (small molecule metabolic process) (\\u003cem\\u003eP\\u003csub\\u003eadj\\u0026nbsp;\\u003c/sub\\u003e\\u003c/em\\u003e= 3.14 \\u0026times; 10\\u003csup\\u003e-4\\u003c/sup\\u003e), GO: 0072521 (purine-containing compound metabolic process) (\\u003cem\\u003eP\\u003csub\\u003eadj\\u003c/sub\\u003e\\u003c/em\\u003e= 4.71 \\u0026times; 10\\u003csup\\u003e-2\\u003c/sup\\u003e), KEGG: 01100 (metabolic pathways) (\\u003cem\\u003eP\\u003csub\\u003eadj\\u003c/sub\\u003e\\u003c/em\\u003e= 1.15 \\u0026times; 10\\u003csup\\u003e-2\\u003c/sup\\u003e) and REAC: R-HSA-1430728 (metabolism) (\\u003cem\\u003eP\\u003csub\\u003eadj\\u0026nbsp;\\u003c/sub\\u003e\\u003c/em\\u003e= 1.70 \\u0026times; 10\\u003csup\\u003e-4\\u003c/sup\\u003e).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003e3.3\\u0026nbsp;\\u003c/em\\u003e\\u003cem\\u003eDruggability of potential protein target genes\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eFourteen of the 52 successfully replicated protein target genes have prospective drug repurposing value, as shown in Table 3. Nine genes were annotated as Tier 1, indicating that their protein productwas an approved or clinically developed drug. Three genes were annotated as Tier 2, suggesting that\\u0026nbsp;their protein product\\u0026nbsp;closely related to drug targets or associated drug-like compounds.\\u0026nbsp;In addition, twelve genes were annotated as therapeutic targets with proven efficacy, including four approved targets, two clinical targets, and six literature-reported targets. The specific drug information was shown in Supplementary Tables 7a and 7b.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003e3.4. Evidence at the\\u0026nbsp;\\u003c/em\\u003e\\u003cem\\u003etranscriptional level\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe further evaluated whether the 52 potential\\u0026nbsp;protein\\u0026nbsp;target genes identified by PWASs were also associated with obesity at the transcriptome level. We identified 24 genes with gene expression levels associated with obesity, including 10, 11, 10, 8, 9, and 8 genes for BFP, TFP, BMI, VAT, WC, and WHR, respectively (see Supplementary Table 8). Of these 24 genes, 9 genes passed the colocalization test, including \\u003cem\\u003eADCY3, ANXA5, CCDC92, DAGLB, DPYSL4, HSD17B12, LSM6, NDUFAF1,\\u0026nbsp;\\u003c/em\\u003eand\\u003cem\\u003e\\u0026nbsp;PIP4K2A\\u003c/em\\u003e,\\u0026nbsp;suggesting that genetic variants likely confer risk of obesity by regulating mRNA expression and protein abundance of these genes.\\u003c/p\\u003e\\n\\u003cp\\u003e3.5 \\u003cem\\u003eSensitivity analysis\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAmong 52 successfully replicated human brain proteins, the abundance of 10 proteins (ANXA5, ARL3, B3GAT3, COMT, DAGLB, FAM114A2, GPX1, PLCB3, PSMD9, SHMT1) increased and the abundance of 6 proteins (CCDC92, DPYSL4, LYRM4, MECR, SLC25A12, SNX32) decreased in AD patients.\\u003csup\\u003e25\\u003c/sup\\u003e For 9 genes with evidence at the\\u0026nbsp;at the brain transcriptome level, there was no evidence linking them to schizophrenia.\\u003csup\\u003e26, 27\\u003c/sup\\u003e After excluding 3 570 SNPs strongly associated with AD\\u003csup\\u003e23\\u003c/sup\\u003e (\\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 5 \\u0026times; 10\\u003csup\\u003e-8\\u003c/sup\\u003e), almost no change was observed for the effect of the 52 proteins (see Supplementary Table 9a). Similar consistent results were also observed at the transcriptional level (see Supplementary Table 9b). The pre- and post-filtering consistency suggested that our results were slightly affected by AD or schizophrenia.\\u003c/p\\u003e\"},{\"header\":\"4. DISCUSSION\",\"content\":\"\\u003cp\\u003eIn the present study, we sought to explore whether genetic variants associated with obesity could be mapped to brain protein concentrations. Using a combination of PWAS and colocalization analysis, we identified 52 potential protein target genes whose brain protein abundance changes were associated with obesity. Nine of these 52 genes showed similar evidence at\\u0026nbsp;the\\u0026nbsp;transcriptional level.\\u003c/p\\u003e\\n\\u003cp\\u003eThe most important determinant of body weight is calorie intake over weeks and months.\\u003csup\\u003e28\\u003c/sup\\u003e The dPFC is an important brain region associated with appetite control, food cravings and executive functions.\\u003csup\\u003e12\\u003c/sup\\u003e Some neuroimaging studies showed lower activated neuronal activity in the left\\u0026nbsp;dPFC\\u0026nbsp;in obese compared to lean individuals.\\u003csup\\u003e29-31\\u003c/sup\\u003e This lower dPFC neuronal activity meant that they were less satiated. Therefore, exploring the relationship between protein abundance levels of dPFC and obesity may indicate a relationship between obesity and central nervous system (CNS) regulation.\\u003c/p\\u003e\\n\\u003cp\\u003eAmong the 52 potential\\u0026nbsp;protein\\u0026nbsp;target genes identified by PWASs, some appear to be closely related to CNS regulation. Notably, \\u003cem\\u003eADCY3\\u003c/em\\u003e showed the evidence of negative associations at both protein and transcript levels for multiple obesity phenotypes. \\u003cem\\u003eADCY3\\u003c/em\\u003e encodes adenylyl cyclase three, which catalyzes the synthesis of cAMP, an essential second messenger in signaling pathways. Animal studies showed that \\u003cem\\u003eADCY3\\u003c/em\\u003e knockout mice had increased susceptibility to obesity, insulin resistance, and dyslipidemia, which may be caused by disruption of cAMP signaling in the primary cilia of the hypothalamus.\\u003csup\\u003e32\\u003c/sup\\u003e In addition, we found that \\u003cem\\u003eADCY3\\u003c/em\\u003e is involved in the biosynthetic pathway of some hormones related to lipid metabolism, including insulin, thyroid hormone, and cortisol, through the KEGG database.\\u003csup\\u003e33\\u003c/sup\\u003e This evidence suggests that \\u003cem\\u003eADCY3\\u003c/em\\u003e is involved not only in the CNS regulation of obesity but also in many other mechanisms. \\u003cem\\u003eCOMT\\u003c/em\\u003e encodes catechol-o-methyltransferase protein, one of the major enzymes that degrades catecholamines such as dopamine and norepinephrine.\\u003csup\\u003e34\\u003c/sup\\u003e The theory of dopamine involvement in appetite regulation suggests that the mesocorticolimbic dopaminergic reward pathway in the brain plays a central role in the neuromodulation of appetite.\\u003csup\\u003e35\\u003c/sup\\u003e These evidences suggested that \\u003cem\\u003eCOMT\\u003c/em\\u003e may affect body weight by regulating the level of dopamine in the cerebral cortex, which further regulates appetite. \\u003cem\\u003eEIF2AK4\\u0026nbsp;\\u003c/em\\u003eencodes general control nonderepressible 2 (GCN2) protein, an amino acid sensor that is activated upon amino acid starvation and phosphorylates eukaryotic initiation factor 2\\u0026alpha; to maintain amino acid homeostasis.\\u003csup\\u003e36\\u003c/sup\\u003e An animal study showed that brain-specific inactivation of GCN2 affects feeding behaviour by activating neuronal circuits that bias feeding towards nutritionally unbalanced food sources (lacking leucine or threonine).\\u003csup\\u003e37\\u003c/sup\\u003e Another animal study showed that leucine deprivation activates GCN2/ATF4 signalling in PKC-\\u0026delta; neurons of the central amygdala, which in turn activates sympathetic nerves and promotes white lipid browning.\\u003csup\\u003e38\\u003c/sup\\u003e These evidences suggested that EIF2AK4 is involved in CNS regulation of body weight. \\u003cem\\u003eSLC17A6\\u003c/em\\u003e encodes Vesicular Glutamate Transporter 2 (VGLUT2), which can be expressed by a subset of dopamine (DA) neurons.\\u003csup\\u003e39, 40\\u003c/sup\\u003e The population of neurons in the lateral hypothalamic region upstream of the dopaminergic cells are glutamatergic neurons expressing VGLUT2, which regulates feeding, reward, and aversion.\\u003csup\\u003e41\\u003c/sup\\u003e In addition, glutaminergic neurons were responsive to a number of neuropeptides related to energy homeostasis; they have excitatory effects on anorexic peptide cholecystokinin, but inhibitory effects on anorexic neuropeptide Y, dynorphin and met‐enkephalin.\\u003csup\\u003e42\\u003c/sup\\u003e Furthermore, \\u003cem\\u003eSULT1A1\\u003c/em\\u003e encodes a Sulfotransferase family 1A member 1 (SULT1A1), which is widely distributed throughout the body, with high abundance in organs such as the liver, brain, skin, and gastrointestinal tissues.\\u003csup\\u003e43\\u003c/sup\\u003e A recent study has shown that tyrosine can be metabolised by the gut microbiota to 4-ethylphenol, which subsequently generates 4-ethylphenyl sulfate in response to host SULT1A1, which can modulate brain activity and anxiety-like behaviour in mice.\\u003csup\\u003e44\\u003c/sup\\u003e However, whether this association of SULT1A1 with the brain-gut axis contributes to obesity still requires experimental verification.\\u003c/p\\u003e\\n\\u003cp\\u003eIn previous studies, Gagnon et al. explored whether genetic variants associated with BMI could influence brain protein concentrations using proteome-wide Mendelian randomization (MR) and colocalization methods.\\u003csup\\u003e45\\u003c/sup\\u003e Our study is more comprehensive in its investigation of obesity. In addition to BMI, we also analyzed some body fat distribution traits, including BFP, TFP, VAT, WC, and WHR. We noted that nearly half of the genes (67/138) identified in the discovery stage were found through associations with phenotype(s) other than BMI, suggesting that using multiple obesity-related phenotypes to explore obesity was necessary. We checked the replicability of proteins reported by Gagnon et al. in our discovery stage results. Of the 47 proteins reported by Gagnon et al., 13 proteins were not available in our results and all the other 34 proteins were replicated by our study at the nominal level 0.05 as well as PPH4 \\u0026gt; 0.8 (see Supplementary Table 10). We also checked the replicability of our findings in Gagnon et al.\\u0026rsquo;s study. Of the 52 identified genes, 21 genes were also identified by Gagnon et al. (see Supplementary Table 11).\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eWe observed that only ~17.3% (9/52) of proteins identified by PWASs were explained at the\\u0026nbsp;transcriptional\\u0026nbsp;level, implicating a possible inconsistency between the proteome and transcriptome. Indeed, several studies have reported inconsistencies in protein and mRNA levels, which may be caused by differential translation, protein degradation, contextual interfering factors, and the prevalence of protein level buffering.\\u003csup\\u003e46, 47\\u003c/sup\\u003e As seen, integrating transcriptomic and proteomic data can provide additional information on obesity-related gene expression control principles that cannot be obtained from either type of data alone.\\u003c/p\\u003e\\n\\u003cp\\u003eThere are several limitations of the study.\\u0026nbsp;Firstly, the associations we identified between proteins and obesity-related phenotypes are not causal associations, and they may be affected by pleiotropy and reverse causation, although these effects may be small.\\u003csup\\u003e9\\u003c/sup\\u003e Second, our analyses focused on cis-regulated gene products (mRNAs and proteins), and we did not investigate trans-regulated mRNAs and proteins. Finally, obesity-related phenotypes GWASs and the proteome and transcriptome datasets were mainly based on European populations. Our findings need to be cautious in interpreting other races.\\u003c/p\\u003e\\n\\u003cp\\u003eIn summary, we identified 52 potential\\u0026nbsp;protein\\u0026nbsp;target genes that contribute to the pathogenesis of obesity by regulating their protein abundance. Among the 52 genes, 9 (\\u003cem\\u003eADCY3, ANXA5, CCDC92, DAGLB, DPYSL4, HSD17B12, LSM6, NDUFAF1, PIP4K2A\\u003c/em\\u003e) also showed evidence at the transcriptome level. These genes provide novel, promising protein targets for further mechanistic and therapeutic studies.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgments\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe are grateful for all participants and investigators involved in the released GWAS summary statistics and human brain proteome and transcriptome weights.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthor contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eQ-GZ and Y-FP designed the study. Q-GZ and Y-FP collected the data. Q-GZ and X-LM analyzed the data. X-LM, QX, Z-TS, FB, KL, and Q-GZ performed the literature search. Q-GZ drafted the early version of the manuscript. Y-FP and LZ jointly supervised the study. All authors revised the manuscript critically and approved the final version.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll authors declare that they have no competing interests\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFundings:\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study was partially supported by funding from the National Natural Science Foundation of China (32170670) and a project funded by the Priority Academic Program Development (PAPD) of Jiangsu higher education institutions.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData availability\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eGWAS summary statistics data are available at http://geneatlas.roslin.ed.ac.uk/, https://portals.broadinstitute.org/collaboration/giant/index.php/GIANT_consortium_data_files, and https://www.ebi.ac.uk/gwas/. Human brain protein weights from Religious Orders Study and the Rush Memory and Aging Project (ROSMAP) and Banner Sun Health Research Institute (BANNER) are available at https://www.synapse.org/#!Synapse:syn23191787. Gene expression weights for the Common Mind Consortium (CMC) are available at http://gusevlab.org/projects/fusion/.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthical approval\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eEthical approval for each GWAS and human brain reference panel can be obtained from the original publications. No new ethics approval or informed consent was needed.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eSupplementary material\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe following are the Supplementary data for this article:\\u003c/p\\u003e\\n\\u003cp\\u003eSupplementary Tables 1-11.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eWen X, Zhang B, Wu B, Xiao H, Li Z, Li R\\u003cem\\u003e et al.\\u003c/em\\u003e Signaling pathways in obesity: mechanisms and therapeutic interventions. \\u003cem\\u003eSignal transduction and targeted therapy \\u003c/em\\u003e2022; \\u003cstrong\\u003e7\\u003c/strong\\u003e(1)\\u003cstrong\\u003e: \\u003c/strong\\u003e298.\\u003c/li\\u003e\\n\\u003cli\\u003eMaes HH, Neale MC, Eaves LJ. 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Genetics \\u003c/em\\u003e2020; \\u003cstrong\\u003e21\\u003c/strong\\u003e(10)\\u003cstrong\\u003e: \\u003c/strong\\u003e630-644.\\u003c/li\\u003e\\n\\u003cli\\u003eVogel C, Marcotte EM. Insights into the regulation of protein abundance from proteomic and transcriptomic analyses. \\u003cem\\u003eNature reviews. Genetics \\u003c/em\\u003e2012; \\u003cstrong\\u003e13\\u003c/strong\\u003e(4)\\u003cstrong\\u003e: \\u003c/strong\\u003e227-32.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"},{\"header\":\"Tables\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eTable 1. Potential\\u0026nbsp;\\u003c/strong\\u003e\\u003cstrong\\u003eprotein\\u003c/strong\\u003e\\u003cstrong\\u003e\\u0026nbsp;target genes for obesity\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003ctable border=\\\"0\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"87%\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"12.371134020618557%\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eTarget Gene\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.649484536082475%\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eRelated Phenotype(s)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.371134020618557%\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eLead Phenotype\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"26.804123711340207%\\\" colspan=\\\"3\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eROSMAP Cohort\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"26.804123711340207%\\\" colspan=\\\"3\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eBANNER Cohort\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"17.647058823529413%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003ePWAS.Z\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"17.647058823529413%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003ePWAS.P\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"15.686274509803921%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003ePPH4\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"17.647058823529413%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003ePWAS.Z\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"17.647058823529413%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003ePWAS.P\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.72549019607843%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003ePPH4\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eABCG2\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.875%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBMI, WC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBMI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e7.415\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1.22E-13\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.998\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e7.217\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e5.31E-13\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.291666666666667%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.998\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eACTR1B\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.875%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBMI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBMI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-4.315\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1.60E-05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.988\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-4.843\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1.28E-06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.291666666666667%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.981\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eADCY3\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.875%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBFP, TFP, BMI, VAT, WC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBMI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-17.257\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e9.91E-67\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.948\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-19.518\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e7.74E-85\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.291666666666667%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.952\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eADPGK\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.875%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBFP, TFP, BMI, VAT, WC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBFP\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e7.407\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1.29E-13\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.821\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e5.423\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e5.85E-08\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.291666666666667%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.865\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eANXA5\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.875%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eWHR\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eWHR\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e4.669\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e3.03E-06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.960\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e4.682\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e2.84E-06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.291666666666667%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.964\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eARL3\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.875%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBMI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBMI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-5.678\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1.36E-08\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.988\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-4.763\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1.91E-06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.291666666666667%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.970\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eASL\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.875%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBFP, TFP\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eTFP\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-5.814\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e6.12E-09\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.926\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-5.163\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e2.43E-07\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.291666666666667%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.884\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eB3GAT3\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.875%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBFP, TFP, WHR\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eTFP\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e7.314\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e2.60E-13\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.981\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e6.494\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e8.36E-11\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.291666666666667%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.976\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eC3orf18\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.875%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBMI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBMI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-5.650\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1.60E-08\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.970\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-4.819\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1.44E-06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.291666666666667%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.951\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eC9orf64\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.875%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBFP, TFP\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eTFP\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e5.667\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1.45E-08\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.907\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e4.686\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e2.79E-06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.291666666666667%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.983\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n 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width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e13.968\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e2.44E-44\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.291666666666667%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.856\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eCNNM2\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.875%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBMI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBMI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e10.230\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1.46E-24\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.988\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e9.606\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e7.58E-22\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.291666666666667%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.987\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eCOMT\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.875%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBMI, WC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBMI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-5.659\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1.53E-08\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.998\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-5.720\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1.06E-08\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.291666666666667%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.998\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n 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width=\\\"7.291666666666667%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.861\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eDAGLB\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.875%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eWHR\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eWHR\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-4.589\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e4.45E-06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.988\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-4.493\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e7.03E-06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.291666666666667%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.962\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eDCAKD\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.875%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eTFP\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eTFP\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-4.427\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e9.54E-06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.832\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-5.728\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1.01E-08\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.291666666666667%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.957\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eDPYSL4\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.875%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBFP, TFP, BMI, VAT, WC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBMI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e6.232\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e4.60E-10\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.999\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e7.099\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1.26E-12\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.291666666666667%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.971\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n 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valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eGPX1\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.875%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBMI, VAT\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBMI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e7.769\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e7.93E-15\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.992\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e8.035\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n 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valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1.59E-05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.291666666666667%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.950\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eLSM6\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.875%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eWC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eWC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e4.831\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1.36E-06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.967\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e4.969\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e6.72E-07\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.291666666666667%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.974\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eLYRM4\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.875%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eVAT, WC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eWC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n 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width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1.93E-04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.291666666666667%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.913\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eMRPL9\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.875%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBFP, TFP\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBFP\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e5.430\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e5.63E-08\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.838\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e5.180\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e2.22E-07\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.291666666666667%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.898\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eMTERFD2\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.875%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBFP, TFP\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eTFP\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-5.790\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e7.05E-09\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.984\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-6.148\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e7.82E-10\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.291666666666667%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.985\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eNDUFAF1\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.875%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBFP, TFP, BMI, VAT\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBFP\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-6.087\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1.15E-09\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.973\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-5.929\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e3.05E-09\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.291666666666667%\\\" valign=\\\"top\\\"\\u003e\\n 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\\u003cp\\u003e7.489\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e6.92E-14\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.291666666666667%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1.000\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003ePFKM\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.875%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBFP, TFP\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eTFP\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-5.198\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e2.01E-07\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.964\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-5.070\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e3.95E-07\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.291666666666667%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.986\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003ePIP4K2A\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.875%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBMI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBMI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-4.767\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1.87E-06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.916\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-3.249\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1.16E-03\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.291666666666667%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.873\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003ePLCB3\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.875%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBFP, TFP, BMI, WC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBMI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-8.144\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e3.83E-16\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.980\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-5.595\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e2.21E-08\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.291666666666667%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.864\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003ePSMD9\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.875%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBFP, TFP, BMI, WC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBFP\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e5.798\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e6.72E-09\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.991\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e6.282\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e3.34E-10\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.291666666666667%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.980\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eRAB27B\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.875%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBFP, TFP, BMI, VAT, WC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBMI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-7.199\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e6.08E-13\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.988\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-7.524\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e5.30E-14\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.291666666666667%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.960\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eSENP8\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.875%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBMI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBMI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e5.117\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e3.10E-07\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.981\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e6.163\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e7.16E-10\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.291666666666667%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.937\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eSHMT1\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.875%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eWHR\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eWHR\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-4.882\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1.05E-06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.993\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-5.179\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n 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\\u003cp\\u003e0.991\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e4.479\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e7.50E-06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.291666666666667%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.937\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eSLC25A12\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.875%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBFP, TFP, BMI, WC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBMI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-6.384\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1.72E-10\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.969\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-5.553\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e2.81E-08\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.291666666666667%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.939\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eSNX32\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.875%\\\" valign=\\\"top\\\"\\u003e\\n 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valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e3.27E-07\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.291666666666667%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.975\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eSULT1A1\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.875%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBMI, WHR\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBMI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-13.013\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1.03E-38\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.946\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-14.854\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e6.58E-50\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.291666666666667%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.914\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eTMEM106B\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.875%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBMI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBMI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-4.464\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e8.03E-06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.834\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-4.021\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e5.81E-05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.291666666666667%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.895\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eTYK2\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.875%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBMI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBMI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-4.863\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1.16E-06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.981\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-3.985\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e6.74E-05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.291666666666667%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.980\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eUCHL3\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.875%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBFP, TFP\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eTFP\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-5.382\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e7.38E-08\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.989\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-5.224\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1.75E-07\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.291666666666667%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.991\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eULK3\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.875%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBFP, TFP, BMI, VAT, WC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eWC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-5.947\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e2.73E-09\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.999\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-5.796\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e6.79E-09\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.291666666666667%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.999\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eVKORC1\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.875%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBFP, TFP, BMI, VAT\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.5%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBMI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-9.588\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e9.00E-22\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.333333333333334%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.899\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-11.537\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.375%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e8.61E-31\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.291666666666667%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.882\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003ePWAS, proteome-wide association study;\\u0026nbsp;PPH4, colocalization posterior probability of H4; ROSMAP, Religious Orders Study and the Rush Memory and Aging Project; BANNER, Banner Sun Health Research Institute;\\u0026nbsp;BFP, body fat percentage; TFP, trunk fat percentage; BMI, body mass index;\\u0026nbsp;WC, waist circumference; WHR, waist-hip ratio.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\u003cp\\u003e\\u003cstrong\\u003eTable 2. Pathway enrichment analysis of 52\\u0026nbsp;\\u003c/strong\\u003e\\u003cstrong\\u003ePWAS potential protein target genes\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"790\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"10.786802030456853%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eSource\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"20.050761421319798%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eTerm Name\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"11.928934010152284%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eTerm ID\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"11.928934010152284%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eP\\u003csub\\u003eadj\\u003c/sub\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"45.30456852791878%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eIntersections\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"10.786802030456853%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eGO: BP\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"20.050761421319798%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003esmall molecule metabolic process\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"11.928934010152284%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eGO: 0044281\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"11.928934010152284%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e3.14E-04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"45.30456852791878%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eADCY3, ADPGK, ASL, C9ORF64, GBA2, PANK4, PFKM, SLC25A12, VKORC1, ABCG2, GPX1, HINT1, SULT1A1, DCAKD, HSD17B12, DAGLB, MECR, SHMT1\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"10.786802030456853%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eGO: BP\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"20.050761421319798%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003epurine-containing compound metabolic process\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"11.928934010152284%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eGO: 0072521\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"11.928934010152284%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e4.71E-02\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"45.30456852791878%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eADCY3, PANK4, ABCG2, HINT1, SULT1A1, DCAKD, HSD17B12, SHMT1\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"10.786802030456853%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eKEGG\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"20.050761421319798%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eMetabolic pathways\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"11.928934010152284%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eKEGG: 01100\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"11.928934010152284%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1.15E-02\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"45.30456852791878%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eADCY3, ADPGK, ASL, B3GAT3, GBA2, PFKM, PLCB3, VKORC1, COMT, GPX1, PIP4K2A, HSD17B12, MECR, SHMT1\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"10.786802030456853%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eREAC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"20.050761421319798%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eMetabolism\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"11.928934010152284%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eREAC: R-HSA-1430728\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"11.928934010152284%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1.70E-04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"45.30456852791878%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eADCY3, ADPGK, ASL, B3GAT3, GBA2, NDUFAF1, PANK4, PFKM, PLCB3, PSMD9, SLC25A12, VKORC1, ABCG2, COMT, GPX1, PIP4K2A, SULT1A1, LYRM4, CPNE1, HSD17B12, MECR, SHMT1\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003eGO, Gene Ontology; BP, Biological Process; KEGG, Kyoto Encyclopedia of Genes and Genomes; REAT,\\u0026nbsp;Reactome; P\\u003csub\\u003eadj\\u003c/sub\\u003e, P value adjusted by the G: SCS algorithm.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\u003cp\\u003e\\u003cstrong\\u003eTable 3. Druggability of 14 potential\\u0026nbsp;\\u003c/strong\\u003e\\u003cstrong\\u003eprotein\\u003c/strong\\u003e\\u003cstrong\\u003e\\u0026nbsp;target genes\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003ctable border=\\\"0\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"973\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"7.810894141829394%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eTarget Gene\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"17.471736896197328%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eTarget type (TTD)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"17.471736896197328%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eTarget name (TTD)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"17.471736896197328%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eTarget-related disease(s) (TTD)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.566289825282631%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eDruggability tier (Finan et al. 2017)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.735868448098664%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eSmall mol druggable\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.735868448098664%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eBio druggable\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.735868448098664%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eADEM gene\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"7.810894141829394%\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eABCG2\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"17.471736896197328%\\\"\\u003e\\n \\u003cp\\u003eSuccessful target\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"17.471736896197328%\\\"\\u003e\\n \\u003cp\\u003eATP-binding cassette transporter G2 (ABCG2)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"17.471736896197328%\\\"\\u003e\\n \\u003cp\\u003eIrritable bowel syndrome; Rheumatoid arthritis\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.566289825282631%\\\"\\u003e\\n \\u003cp\\u003eTier 1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.735868448098664%\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.735868448098664%\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.735868448098664%\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"7.810894141829394%\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eANXA5\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"17.471736896197328%\\\"\\u003e\\n \\u003cp\\u003eClinical trial target\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"17.471736896197328%\\\"\\u003e\\n \\u003cp\\u003eAnnexin A5 (ANXA5)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"17.471736896197328%\\\"\\u003e\\n \\u003cp\\u003eInjury\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.566289825282631%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.735868448098664%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.735868448098664%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.735868448098664%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"7.810894141829394%\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eCOMT\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"17.471736896197328%\\\"\\u003e\\n \\u003cp\\u003eSuccessful target\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"17.471736896197328%\\\"\\u003e\\n \\u003cp\\u003eCatechol-O-methyl-transferase (COMT)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"17.471736896197328%\\\"\\u003e\\n \\u003cp\\u003eParkinsonism\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.566289825282631%\\\"\\u003e\\n \\u003cp\\u003eTier 1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.735868448098664%\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.735868448098664%\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.735868448098664%\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"7.810894141829394%\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eDAGLB\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"17.471736896197328%\\\"\\u003e\\n \\u003cp\\u003eLiterature-reported target\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"17.471736896197328%\\\"\\u003e\\n \\u003cp\\u003eDiacylglycerol lipase beta (DAGLB)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"17.471736896197328%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.566289825282631%\\\"\\u003e\\n \\u003cp\\u003eTier 1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.735868448098664%\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.735868448098664%\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.735868448098664%\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"7.810894141829394%\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eEIF2AK4\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"17.471736896197328%\\\"\\u003e\\n \\u003cp\\u003eLiterature-reported target\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"17.471736896197328%\\\"\\u003e\\n \\u003cp\\u003eEukaryotic translation initiation factor 2-alpha kinase 4 (EIF2AK4)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"17.471736896197328%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.566289825282631%\\\"\\u003e\\n \\u003cp\\u003eTier 1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.735868448098664%\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.735868448098664%\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.735868448098664%\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"7.810894141829394%\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eGBA2\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"17.471736896197328%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"17.471736896197328%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"17.471736896197328%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.566289825282631%\\\"\\u003e\\n \\u003cp\\u003eTier 2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.735868448098664%\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.735868448098664%\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.735868448098664%\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"7.810894141829394%\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eGPX1\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"17.471736896197328%\\\"\\u003e\\n \\u003cp\\u003eLiterature-reported target\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"17.471736896197328%\\\"\\u003e\\n \\u003cp\\u003eGlutathione peroxidase (GPX1)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"17.471736896197328%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.566289825282631%\\\"\\u003e\\n \\u003cp\\u003eTier 1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.735868448098664%\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.735868448098664%\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.735868448098664%\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"7.810894141829394%\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eMAP2K2\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"17.471736896197328%\\\"\\u003e\\n \\u003cp\\u003eClinical trial target\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"17.471736896197328%\\\"\\u003e\\n \\u003cp\\u003eERK activator kinase 2 (MEK2)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"17.471736896197328%\\\"\\u003e\\n \\u003cp\\u003eMelanoma; Thyroid cancer\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.566289825282631%\\\"\\u003e\\n \\u003cp\\u003eTier 1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.735868448098664%\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.735868448098664%\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.735868448098664%\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"7.810894141829394%\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eSENP8\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"17.471736896197328%\\\"\\u003e\\n \\u003cp\\u003eLiterature-reported target\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"17.471736896197328%\\\"\\u003e\\n \\u003cp\\u003eDeneddylase-1 (SENP8)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"17.471736896197328%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.566289825282631%\\\"\\u003e\\n \\u003cp\\u003eTier 2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.735868448098664%\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.735868448098664%\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.735868448098664%\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"7.810894141829394%\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eSULT1A1\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"17.471736896197328%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"17.471736896197328%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"17.471736896197328%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.566289825282631%\\\"\\u003e\\n \\u003cp\\u003eTier 1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.735868448098664%\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.735868448098664%\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.735868448098664%\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"7.810894141829394%\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eTYK2\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"17.471736896197328%\\\"\\u003e\\n \\u003cp\\u003eSuccessful target\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"17.471736896197328%\\\"\\u003e\\n \\u003cp\\u003eTYK2 tyrosine kinase (TYK2)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"17.471736896197328%\\\"\\u003e\\n \\u003cp\\u003ePsoriasis\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.566289825282631%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.735868448098664%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.735868448098664%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.735868448098664%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"7.810894141829394%\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eUCHL3\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"17.471736896197328%\\\"\\u003e\\n \\u003cp\\u003eLiterature-reported target\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"17.471736896197328%\\\"\\u003e\\n \\u003cp\\u003eUbiquitin thioesterase L3 (UCHL3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"17.471736896197328%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.566289825282631%\\\"\\u003e\\n \\u003cp\\u003eTier 2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.735868448098664%\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.735868448098664%\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.735868448098664%\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"7.810894141829394%\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eULK3\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"17.471736896197328%\\\"\\u003e\\n \\u003cp\\u003eLiterature-reported target\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"17.471736896197328%\\\"\\u003e\\n \\u003cp\\u003eUnc-51 like kinase 3 (ULK3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"17.471736896197328%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.566289825282631%\\\"\\u003e\\n \\u003cp\\u003eTier 1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.735868448098664%\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.735868448098664%\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.735868448098664%\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"7.810894141829394%\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eVKORC1\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"17.471736896197328%\\\"\\u003e\\n \\u003cp\\u003eSuccessful target\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"17.471736896197328%\\\"\\u003e\\n \\u003cp\\u003eVitamin K epoxide reductase complex 1 (VKORC1)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"17.471736896197328%\\\"\\u003e\\n \\u003cp\\u003eBleeding disorder; Coagulation defect; Pulmonary thromboembolism; Supraventricular tachyarrhythmia; Thrombosis\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.566289825282631%\\\"\\u003e\\n \\u003cp\\u003eTier 1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.735868448098664%\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.735868448098664%\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.735868448098664%\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003eTTD, therapeutic targeting database;\\u0026nbsp;Small mol druggable,\\u0026nbsp;the protein product of the gene was a small molecule target or predicted target;\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003eBio druggable,\\u0026nbsp;the protein product of the gene was a target or predicted target for biologic therapies;\\u0026nbsp;ADEM gene, the protein product of the gene involved in absorption, distribution, metabolism, and excretion (ADME) of a compound;\\u0026nbsp;BFP, body fat percentage; TFP, trunk fat percentage; BMI, body mass index; VAT, visceral adipose tissue;\\u0026nbsp;WC, waist circumference; WHR, waist-hip ratio;\\u0026nbsp;Tier 1 incorporated the targets of approved drugs and drugs in clinical development.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"international-journal-of-obesity\",\"isNatureJournal\":false,\"hasQc\":false,\"allowDirectSubmit\":false,\"externalIdentity\":\"ijo\",\"sideBox\":\"Learn more about [International Journal of Obesity](http://www.nature.com/ijo/)\",\"snPcode\":\"41366\",\"submissionUrl\":\"https://mts-ijo.nature.com/cgi-bin/main.plex\",\"title\":\"International Journal of Obesity\",\"twitterHandle\":\"@intjobesity\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"ejp\",\"reportingPortfolio\":\"Nature AJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false},\"keywords\":\"body composition, drug development, obesity therapy, proteome-wide association study\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-3975196/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-3975196/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e\\u003cstrong\\u003eBackgrounds:\\u003c/strong\\u003eGenome-wide association studies have identified multiple genetic variants associated with obesity. However, most obesity-associated loci were waiting to be translated into new biological insights. Given the critical role of brain in obesity development, we sought to explore whether obesity-associated genetic variants could be mapped to brain protein abundances.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMethods:\\u003c/strong\\u003eWe performed proteome-wide association studies (PWAS) and colocalization analyses to identify genes whose cis-regulated brain protein abundances were associated with obesity-related traits, including body fat percentage, trunk fat percentage, body mass index, visceral adipose tissue, waist circumference, and waist-to-hip ratio. We then assessed the druggability of the identified genes and conducted pathway enrichment analysis to explore their functional relevance. Finally, we evaluated the effects of the significant PWAS genes at the brain transcriptional level.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eResults:\\u003c/strong\\u003eBy integrating human brain proteomes from discovery (ROSMAP, \\u003cem\\u003eN \\u003c/em\\u003e= 376) and validation datasets (BANNER, \\u003cem\\u003eN \\u003c/em\\u003e= 198) with genome-wide summary statistics of obesity-related phenotypes (\\u003cem\\u003eN\\u003c/em\\u003e ranged from 325 153 to 806 834), we identified 52 genes whose cis-regulated brain protein abundance was associated with obesity. These 52 genes were enriched in metabolic processes, e.g., small molecule metabolic process and metabolic pathways. Fourteen of the 52 genes had high drug repurposing value. Nine of the 52 genes were also associated with obesity at the transcriptome level, suggesting that genetic variants likely confer risk of obesity by regulating mRNA expression and protein abundance of these genes.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConclusions:\\u003c/strong\\u003eOur study provides new insights into the genetic component of human brain protein abundance in obesity. The identified proteins represent promising therapeutic targets for future drug development.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Human Brain Proteome-wide Association Study Provides Insights into the Genetic Components of Protein Abundance in Obesity\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-03-04 10:56:11\",\"doi\":\"10.21203/rs.3.rs-3975196/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"revise\",\"date\":\"2024-04-29T16:22:06+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"This content is not available.\",\"date\":\"2024-04-07T09:30:55+00:00\",\"index\":2,\"fulltext\":\"This content is not available.\"},{\"type\":\"editorInvitedReview\",\"content\":\"This content is not available.\",\"date\":\"2024-04-07T05:54:17+00:00\",\"index\":1,\"fulltext\":\"This content is not available.\"},{\"type\":\"reviewerAgreed\",\"content\":\"This content is not available.\",\"date\":\"2024-03-28T15:04:42+00:00\",\"index\":2,\"fulltext\":\"This content is not available.\"},{\"type\":\"reviewerAgreed\",\"content\":\"This content is not available.\",\"date\":\"2024-03-26T23:53:50+00:00\",\"index\":1,\"fulltext\":\"This content is not available.\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2024-03-02T19:54:34+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2024-02-22T11:56:51+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2024-02-21T10:17:46+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"International Journal of Obesity\",\"date\":\"2024-02-21T10:17:45+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"international-journal-of-obesity\",\"isNatureJournal\":false,\"hasQc\":false,\"allowDirectSubmit\":false,\"externalIdentity\":\"ijo\",\"sideBox\":\"Learn more about [International Journal of Obesity](http://www.nature.com/ijo/)\",\"snPcode\":\"41366\",\"submissionUrl\":\"https://mts-ijo.nature.com/cgi-bin/main.plex\",\"title\":\"International Journal of Obesity\",\"twitterHandle\":\"@intjobesity\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"ejp\",\"reportingPortfolio\":\"Nature AJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false}}],\"origin\":\"\",\"ownerIdentity\":\"8745c391-aec5-4bbf-8aa1-9fc4825bcaf3\",\"owner\":[],\"postedDate\":\"March 4th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[{\"id\":29094856,\"name\":\"Health sciences/Endocrinology/Endocrine system and metabolic diseases/Obesity\"},{\"id\":29094857,\"name\":\"Biological sciences/Biochemistry/Proteins\"},{\"id\":29094858,\"name\":\"Biological sciences/Genetics\"}],\"tags\":[],\"updatedAt\":\"2024-07-19T07:07:56+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-3975196\",\"link\":\"https://doi.org/10.1038/s41366-024-01592-6\",\"journal\":{\"identity\":\"international-journal-of-obesity\",\"isVorOnly\":false,\"title\":\"International Journal of Obesity\"},\"publishedOn\":\"2024-07-18 04:00:00\",\"publishedOnDateReadable\":\"July 18th, 2024\"},\"versionCreatedAt\":\"2024-03-04 10:56:11\",\"video\":\"\",\"vorDoi\":\"10.1038/s41366-024-01592-6\",\"vorDoiUrl\":\"https://doi.org/10.1038/s41366-024-01592-6\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-3975196\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-3975196\",\"identity\":\"rs-3975196\",\"version\":[\"v1\"]},\"buildId\":\"qtupq5eGEP_6zYnWcrvyt\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}