Genetic studies of body mass index yield new insights for obesity biology.
- Authors
- Locke, Adam E; Kahali, Bratati; Berndt, Sonja I; Justice, Anne E; Pers, Tune H; Day, Felix R; Powell, Corey; Vedantam, Sailaja; Buchkovich, Martin L; Yang, Jian; Croteau-Chonka, Damien C; Esko, Tonu; Fall, Tove; Ferreira, Teresa; Gustafsson, Stefan; Kutalik, Zoltán; Luan, Jian'an; Mägi, Reedik; Randall, Joshua C; Winkler, Thomas W; Wood, Andrew R; Workalemahu, Tsegaselassie; Faul, Jessica D; Smith, Jennifer A; Zhao, Jing Hua; Zhao, Wei; Chen, Jin; Fehrmann, Rudolf; Hedman, Åsa K; Karjalainen, Juha; Schmidt, Ellen M; Absher, Devin; Amin, Najaf; Anderson, Denise; Beekman, Marian; Bolton, Jennifer L; Bragg-Gresham, Jennifer L; Buyske, Steven; Demirkan, Ayse; Deng, Guohong; Ehret, Georg B; Feenstra, Bjarke; Feitosa, Mary F; Fischer, Krista; Goel, Anuj; Gong, Jian; Jackson, Anne U; Kanoni, Stavroula; Kleber, Marcus E; Kristiansson, Kati; Lim, Unhee; Lotay, Vaneet; Mangino, Massimo; Leach, Irene Mateo; Medina-Gomez, Carolina; Medland, Sarah E; Nalls, Michael A; Palmer, Cameron D; Pasko, Dorota; Pechlivanis, Sonali; Peters, Marjolein J; Prokopenko, Inga; Shungin, Dmitry; Stančáková, Alena; Strawbridge, Rona J; Sung, Yun Ju; Tanaka, Toshiko; Teumer, Alexander; Trompet, Stella; van der Laan, Sander W; van Setten, Jessica; Van Vliet-Ostaptchouk, Jana V; Wang, Zhaoming; Yengo, Loïc; Zhang, Weihua; Isaacs, Aaron; Albrecht, Eva; Ärnlöv, Johan; Arscott, Gillian M; Attwood, Antony P; Bandinelli, Stefania; Barrett, Amy; Bas, Isabelita N; Bellis, Claire; Bennett, Amanda J; Berne, Christian; Blagieva, Roza; Blüher, Matthias; Böhringer, Stefan; Bonnycastle, Lori L; Böttcher, Yvonne; Boyd, Heather A; Bruinenberg, Marcel; Caspersen, Ida H; Chen, Yii-Der Ida; Clarke, Robert; Daw, E Warwick; de Craen, Anton J M; Delgado, Graciela; Dimitriou, Maria; Doney, Alex S F; Eklund, Niina; Estrada, Karol; Eury, Elodie; Folkersen, Lasse; Fraser, Ross M; Garcia, Melissa E; Geller, Frank; Giedraitis, Vilmantas; Gigante, Bruna; Go, Alan S; Golay, Alain; Goodall, Alison H; Gordon, Scott D; Gorski, Mathias; Grabe, Hans-Jörgen; Grallert, Harald; Grammer, Tanja B; Gräßler, Jürgen; Grönberg, Henrik; Groves, Christopher J; Gusto, Gaëlle; Haessler, Jeffrey; Hall, Per; Haller, Toomas; Hallmans, Goran; Hartman, Catharina A; Hassinen, Maija; Hayward, Caroline; Heard-Costa, Nancy L; Helmer, Quinta; Hengstenberg, Christian; Holmen, Oddgeir; Hottenga, Jouke-Jan; James, Alan L; Jeff, Janina M; Johansson, Åsa; Jolley, Jennifer; Juliusdottir, Thorhildur; Kinnunen, Leena; Koenig, Wolfgang; Koskenvuo, Markku; Kratzer, Wolfgang; Laitinen, Jaana; Lamina, Claudia; Leander, Karin; Lee, Nanette R; Lichtner, Peter; Lind, Lars; Lindström, Jaana; Lo, Ken Sin; Lobbens, Stéphane; Lorbeer, Roberto; Lu, Yingchang; Mach, François; Magnusson, Patrik K E; Mahajan, Anubha; McArdle, Wendy L; McLachlan, Stela; Menni, Cristina; Merger, Sigrun; Mihailov, Evelin; Milani, Lili; Moayyeri, Alireza; Monda, Keri L; Morken, Mario A; Mulas, Antonella; Müller, Gabriele; Müller-Nurasyid, Martina; Musk, Arthur W; Nagaraja, Ramaiah; Nöthen, Markus M; Nolte, Ilja M; Pilz, Stefan; Rayner, Nigel W; Renstrom, Frida; Rettig, Rainer; Ried, Janina S; Ripke, Stephan; Robertson, Neil R; Rose, Lynda M; Sanna, Serena; Scharnagl, Hubert; Scholtens, Salome; Schumacher, Fredrick R; Scott, William R; Seufferlein, Thomas; Shi, Jianxin; Smith, Albert Vernon; Smolonska, Joanna; Stanton, Alice V; Steinthorsdottir, Valgerdur; Stirrups, Kathleen; Stringham, Heather M; Sundström, Johan; Swertz, Morris A; Swift, Amy J; Syvänen, Ann-Christine; Tan, Sian-Tsung; Tayo, Bamidele O; Thorand, Barbara; Thorleifsson, Gudmar; Tyrer, Jonathan P; Uh, Hae-Won; Vandenput, Liesbeth; Verhulst, Frank C; Vermeulen, Sita H; Verweij, Niek; Vonk, Judith M; Waite, Lindsay L; Warren, Helen R; Waterworth, Dawn; Weedon, Michael N; Wilkens, Lynne R; Willenborg, Christina; Wilsgaard, Tom; Wojczynski, Mary K; Wong, Andrew; Wright, Alan F; Zhang, Qunyuan; LifeLines Cohort Study; Brennan, Eoin P; Choi, Murim; Dastani, Zari; Drong, Alexander W; Eriksson, Per; Franco-Cereceda, Anders; Gådin, Jesper R; Gharavi, Ali G; Goddard, Michael E; Handsaker, Robert E; Huang, Jinyan; Karpe, Fredrik; Kathiresan, Sekar; Keildson, Sarah; Kiryluk, Krzysztof; Kubo, Michiaki; Lee, Jong-Young; Liang, Liming; Lifton, Richard P; Ma, Baoshan; McCarroll, Steven A; McKnight, Amy J; Min, Josine L; Moffatt, Miriam F; Montgomery, Grant W; Murabito, Joanne M; Nicholson, George; Nyholt, Dale R; Okada, Yukinori; Perry, John R B; Dorajoo, Rajkumar; Reinmaa, Eva; Salem, Rany M; Sandholm, Niina; Scott, Robert A; Stolk, Lisette; Takahashi, Atsushi; Tanaka, Toshihiro; van 't Hooft, Ferdinand M; Vinkhuyzen, Anna A E; Westra, Harm-Jan; Zheng, Wei; Zondervan, Krina T; ADIPOGen Consortium; AGEN-BMI Working Group; CARDIOGRAMplusC4D Consortium; CKDGen Consortium; GLGC; ICBP; MAGIC Investigators; MuTHER Consortium; MIGen Consortium; PAGE Consortium; ReproGen Consortium; GENIE Consortium; International Endogene Consortium; Heath, Andrew C; Arveiler, Dominique; Bakker, Stephan J L; Beilby, John; Bergman, Richard N; Blangero, John; Bovet, Pascal; Campbell, Harry; Caulfield, Mark J; Cesana, Giancarlo; Chakravarti, Aravinda; Chasman, Daniel I; Chines, Peter S; Collins, Francis S; Crawford, Dana C; Cupples, L Adrienne; Cusi, Daniele; Danesh, John; de Faire, Ulf; den Ruijter, Hester M; Dominiczak, Anna F; Erbel, Raimund; Erdmann, Jeanette; Eriksson, Johan G; Farrall, Martin; Felix, Stephan B; Ferrannini, Ele; Ferrières, Jean; Ford, Ian; Forouhi, Nita G; Forrester, Terrence; Franco, Oscar H; Gansevoort, Ron T; Gejman, Pablo V; Gieger, Christian; Gottesman, Omri; Gudnason, Vilmundur; Gyllensten, Ulf; Hall, Alistair S; Harris, Tamara B; Hattersley, Andrew T; Hicks, Andrew A; Hindorff, Lucia A; Hingorani, Aroon D; Hofman, Albert; Homuth, Georg; Hovingh, G Kees; Humphries, Steve E; Hunt, Steven C; Hyppönen, Elina; Illig, Thomas; Jacobs, Kevin B; Jarvelin, Marjo-Riitta; Jöckel, Karl-Heinz; Johansen, Berit; Jousilahti, Pekka; Jukema, J Wouter; Jula, Antti M; Kaprio, Jaakko; Kastelein, John J P; Keinanen-Kiukaanniemi, Sirkka M; Kiemeney, Lambertus A; Knekt, Paul; Kooner, Jaspal S; Kooperberg, Charles; Kovacs, Peter; Kraja, Aldi T; Kumari, Meena; Kuusisto, Johanna; Lakka, Timo A; Langenberg, Claudia; Marchand, Loic Le; Lehtimäki, Terho; Lyssenko, Valeriya; Männistö, Satu; Marette, André; Matise, Tara C; McKenzie, Colin A; McKnight, Barbara; Moll, Frans L; Morris, Andrew D; Morris, Andrew P; Murray, Jeffrey C; Nelis, Mari; Ohlsson, Claes; Oldehinkel, Albertine J; Ong, Ken K; Madden, Pamela A F; Pasterkamp, Gerard; Peden, John F; Peters, Annette; Postma, Dirkje S; Pramstaller, Peter P; Price, Jackie F; Qi, Lu; Raitakari, Olli T; Rankinen, Tuomo; Rao, D C; Rice, Treva K; Ridker, Paul M; Rioux, John D; Ritchie, Marylyn D; Rudan, Igor; Salomaa, Veikko; Samani, Nilesh J; Saramies, Jouko; Sarzynski, Mark A; Schunkert, Heribert; Schwarz, Peter E H; Sever, Peter; Shuldiner, Alan R; Sinisalo, Juha; Stolk, Ronald P; Strauch, Konstantin; Tönjes, Anke; Trégouët, David-Alexandre; Tremblay, Angelo; Tremoli, Elena; Virtamo, Jarmo; Vohl, Marie-Claude; Völker, Uwe; Waeber, Gérard; Willemsen, Gonneke; Witteman, Jacqueline C; Zillikens, M Carola; Adair, Linda S; Amouyel, Philippe; Asselbergs, Folkert W; Assimes, Themistocles L; Bochud, Murielle; Boehm, Bernhard O; Boerwinkle, Eric; Bornstein, Stefan R; Bottinger, Erwin P; Bouchard, Claude; Cauchi, Stéphane; Chambers, John C; Chanock, Stephen J; Cooper, Richard S; de Bakker, Paul I W; Dedoussis, George; Ferrucci, Luigi; Franks, Paul W; Froguel, Philippe; Groop, Leif C; Haiman, Christopher A; Hamsten, Anders; Hui, Jennie; Hunter, David J; Hveem, Kristian; Kaplan, Robert C; Kivimaki, Mika; Kuh, Diana; Laakso, Markku; Liu, Yongmei; Martin, Nicholas G; März, Winfried; Melbye, Mads; Metspalu, Andres; Moebus, Susanne; Munroe, Patricia B; Njølstad, Inger; Oostra, Ben A; Palmer, Colin N A; Pedersen, Nancy L; Perola, Markus; Pérusse, Louis; Peters, Ulrike; Power, Chris; Quertermous, Thomas; Rauramaa, Rainer; Rivadeneira, Fernando; Saaristo, Timo E; Saleheen, Danish; Sattar, Naveed; Schadt, Eric E; Schlessinger, David; Slagboom, P Eline; Snieder, Harold; Spector, Tim D; Thorsteinsdottir, Unnur; Stumvoll, Michael; Tuomilehto, Jaakko; Uitterlinden, André G; Uusitupa, Matti; van der Harst, Pim; Walker, Mark; Wallaschofski, Henri; Wareham, Nicholas J; Watkins, Hugh; Weir, David R; Wichmann, H-Erich; Wilson, James F; Zanen, Pieter; Borecki, Ingrid B; Deloukas, Panos; Fox, Caroline S; Heid, Iris M; O'Connell, Jeffrey R; Strachan, David P; Stefansson, Kari; van Duijn, Cornelia M; Abecasis, Gonçalo R; Franke, Lude; Frayling, Timothy M; McCarthy, Mark I; Visscher, Peter M; Scherag, André; Willer, Cristen J; Boehnke, Michael; Mohlke, Karen L; Lindgren, Cecilia M; Beckmann, Jacques S; Barroso, Inês; North, Kari E; Ingelsson, Erik; Hirschhorn, Joel N; Loos, Ruth J F; Speliotes, Elizabeth K
- Year
- 2015
- Journal
- Nature
- PMID
- 25673413
- DOI
- 10.1038/nature14177
- PMCID
- PMC4382211
Obesity is heritable and predisposes to many diseases. To understand the genetic basis of obesity better, here we conduct a genome-wide association study and Metabochip meta-analysis of body mass index (BMI), a measure commonly used to define obesity and assess adiposity, in up to 339,224 individuals. This analysis identifies 97 BMI-associated loci (P < 5 × 10(-8)), 56 of which are novel. Five loci demonstrate clear evidence of several independent association signals, and many loci have significant effects on other metabolic phenotypes. The 97 loci account for ∼2.7% of BMI variation, and genome-wide estimates suggest that common variation accounts for >20% of BMI variation. Pathway analyses provide strong support for a role of the central nervous system in obesity susceptibility and implicate new genes and pathways, including those related to synaptic function, glutamate signalling, insulin secretion/action, energy metabolism, lipid biology and adipogenesis.
Cumulative variance explained and example of secondary signalsa, The estimated variance in BMI explained by SNPs selected at a range of P values using unrelated individuals from the QIMR (n = 3,924; purple) and TwinGene (n = 5,668; gold), their weighted average (cyan), inferred from within-family prediction (red; Extended Data Fig. 2), and by all HapMap phase III SNPs in 16,275 unrelated individuals from the QIMR, TwinGene and ARIC studies (orange). b, Plot of the region surrounding MC4R (ref. 36). SNP associations from the European sex-combined meta-analysis are plotted with joint conditional P values (Pj) indicated for the three conditionally significant signals. SNPs are shaded and shaped based on the index SNP with which they are in strongest LD (rs6567160 in blue, rs994545 in yellow and rs17066842 in green).
LLM interpretation
Figure a is a bar chart showing the estimated percentage of BMI variance explained across different P-value thresholds for four groups (Family-based prediction, QIMR, TwinGene, and Weighted average), with a separate bar for HapMap3 SNPs. Variance explained increases as the P-value threshold increases, with HapMap3 SNPs showing the highest variance (approximately 20%). Figure b is a Manhattan-style plot of the *MC4R* region on chromosome 18, plotting $\log_{10}(P\text{ value})$ against genomic position. It identifies three conditionally significant signals (rs6567160, rs9944545, and rs17066842), with SNPs color-coded by their linkage disequilibrium ($r^2$) with these index SNPs.
Tissues and reconstituted gene sets significantly enriched for genes within BMI-associated locia, DEPICT predicts genes within BMI-associated loci (P < 5 × 10−4) are enriched for expression in the brain and central nervous system. Tissues are sorted by physiological system and significantly enriched tissues are in black; the dotted line represents statistically significant enrichment. b, The gene sets most significantly enriched for BMI-associated loci by DEPICT (P < 10−6, FDR < 4 × 10−4). Nodes represent reconstituted gene sets and are colour-coded by P value. Edge thickness between nodes is proportional to degree of gene overlap as measured by the Jaccard index. Nodes with gene overlap greater than 25% were collapsed into a single ‘meta-node’ (blue border). c, The nodes contained within the most enriched meta-node, ‘clathrin-coated vesicle’, which shares genes with other gene sets relevant to glutamate signalling and synapse biology. d, The ‘generation of a signal involved in cell–cell signalling’ meta-node represents several overlapping gene sets relevant to obesity and energy metabolism (gene sets with P < 4 × 10−3, FDR < 0.05 shown). For the complete list of enriched gene sets refer to Supplementary Table 21a.
LLM interpretation
This figure consists of a bar chart and three network diagrams analyzing genes within BMI-associated loci. Panel **a** is a bar chart showing $-\log_{10}(P\text{ value})$ across various physiological systems, with the "Nervous" system showing the most significant enrichment above the dotted significance threshold. Panels **b, c, and d** are network diagrams where nodes represent gene sets (color-coded by $P$-value) and edges represent gene overlap (thickness proportional to the Jaccard index), highlighting clusters related to synapse biology, clathrin-coated vesicles, and cell-cell signaling.
Study design*The SNP counts reflect sample size filter of n ≥ 50,000. §Counts represent the primary European sex-combined analysis. Please see Extended Data Table 1 for counts for secondary analyses.
LLM interpretation
This figure is a study design diagram illustrating the workflow for combining two separate meta-analyses into a joint analysis. It shows a GWAS Meta-Analysis (80 studies, 234,069 subjects) and a Metabochip Meta-Analysis (34 studies, 88,137 subjects) undergoing within-study and among-studies genomic inflation ($\lambda_{GC}$) corrections. These paths converge into a Joint GWAS+MC Meta-Analysis comprising 322,154 subjects and 2,554,623 SNPs.
Genetic characterization of BMI-associated variantsa, Plot of the cumulative phenotypic variance explained by each locus ordered by decreasing effect size. b, The relationship between effect size and allele frequency. Previously identified loci are blue circles and novel loci are red triangles. c, Quantile–quantile (Q–Q) plot of meta-analysis P values for all 1,909 BMI-replication SNPs (blue) and after removing SNPs near the 97 associated loci (green). d, Histogram of cumulative effect of BMI risk alleles. Mean BMI for each bin is shown by the black dots (with standard deviation) and corresponds to the right-hand y axis.
LLM interpretation
This figure consists of four panels characterizing BMI-associated genetic variants. Panel **a** is a line plot showing cumulative phenotypic variance explained increasing with the number of loci, while panel **b** is a scatter plot comparing effect size to allele frequency for previously identified (blue circles) and novel (red triangles) loci. Panel **c** is a Q-Q plot comparing observed versus expected $-\log_{10}(P)$ values for all replication SNPs (blue) and those with significant SNPs removed (green). Panel **d** is a histogram of the number of BMI risk alleles per individual, with an overlaid line and black dots showing a positive correlation between the number of risk alleles and mean BMI.
Partitioning the variance in and risk prediction from SNP-derived predictora, b, The analyses were performed using 2,758 full sibling pairs from the TwinGene cohort (a) and 1,622 pairs from the QIMR cohort (b). The SNP-based predictor was adjusted for the first 20 principal components. The variance of the SNP-based predictor can be partitioned into four components (Vg, Ve, Cg and Ce) using the within-family prediction analysis, in which Vg is the variance explained by real SNP effects, Cg is the covariance between predictors attributed to the real effects of SNPs that are not in LD but correlated due to population stratification, Ve is the accumulated variance due to the errors in estimating SNP effects, and Ce is the covariance between predictors attributed to errors in estimating the effects of SNPs that are correlated due to population stratification. Error bars reflect s.e.m. of estimates. c, The prediction R2 shown on the y axis is the squared correlation between phenotype and SNP-based genetic predictor in unrelated individuals from the TwinGene (n = 5,668) and QIMR (n = 3,953) studies. The number shown in each column is the number of SNPs selected from the GCTA joint and conditional analysis at a range of P-value thresholds. In each case, the predictor was adjusted by the first 20 principal components. The column in orange is the average prediction R2 weighted by sample size over the two cohorts. The dashed grey line is the value inferred from the within-family prediction analyses using this equation R2 = (Vg + Cg)2/(Vg + Ve + Cg + Ce).
LLM interpretation
This figure consists of three panels: two grouped bar charts (a, b) and one grouped bar chart with an overlaid dashed line (c). Panels (a) and (b) show the partitioning of variance and covariance ($V_g, V_e, C_g, C_e$) across increasing p-value thresholds for the TwinGene and QIMR cohorts, with $V_g$ (blue) showing the most significant increase as the threshold relaxes. Panel (c) displays the prediction $R^2$ for both cohorts and their weighted average, showing a positive correlation between the p-value threshold and prediction accuracy, with the dashed grey line representing the value inferred from within-family analyses. All bar charts include error bars representing the standard error of the mean (s.e.m.).
Comparison of BMI-associated index SNPs across ethnicitiesa, b, BMI effects observed in European ancestry individuals (x axes) compared to African ancestry (a) or Asian ancestry (b) individuals (y axes). c, d, Allele frequencies between ancestry groups, as in a and b. e, f, Comparison of the estimates of explained variance. In all plots, novel loci are in red and previously identified loci are in blue.
LLM interpretation
This figure consists of six scatter plots (a–f) comparing BMI-associated index SNPs between European ancestry individuals (x-axes) and either African (a, c, e) or Asian (b, d, f) ancestry individuals (y-axes). Plots a and b compare effect sizes, c and d compare allele frequencies, and e and f compare the percentage of variance explained, with data points color-coded as novel loci (red) or previously identified loci (blue). Most data points cluster along the diagonal identity line, though greater dispersion is visible in the allele frequency plots (c, d).
Effects of BMI-associated loci on related metabolic traitsUnsupervised hierarchical clustering of the 97 BMI-associated loci (y axis) on 23 related metabolic traits (x axis). The top row shows the a priori expected relationship with BMI (green is concordant effect direction, purple is opposite). Loci with statistically significant concordant direction of effect are highlighted in green, and significant but opposing effects are in purple. Grey indicates a non-significant relationship and those with no information are in white. The key in the top left corner also shows the count of gene–phenotype pairs in each category (cyan bars).
LLM interpretation
This figure is an unsupervised hierarchical clustering heatmap showing the effects of 97 BMI-associated loci (y-axis) across 23 related metabolic traits (x-axis). The heatmap uses green to indicate a concordant effect direction with BMI and purple for an opposite effect direction, with grey and white representing non-significant or missing data. A top row indicates a priori expected relationships, and a color key with a histogram in the top left shows the distribution of gene-phenotype pairs.
Bubble chart representing the genetic overlap across traits at BMI susceptibility lociEach bubble represents a trait for which association results were requested for the 97 GWS BMI loci. The size of the bubble is proportional to the number of BMI-increasing loci with a significant association. A line connects each pair of bubbles with thickness proportional to the number of significant loci shared between the traits. Traits tested include the current study BMI SNPs, African-American BMI (AA BMI), hip circumference (HIP), HIP adjusted for BMI (HIPadjBMI), waist circumference (WC), waist circumference adjusted for BMI (WCadjBMI), waist-to-hip ratio (WHR), waist-to-hip ratio adjusted for BMI (WHRadjBMI), height, adiponectin, coronary artery disease (CAD), diastolic blood pressure (DBP), systolic blood pressure (SBP), high-density lipoprotein (HDL), low-density lipoprotein (LDL), total cholesterol (TC), triglycerides (TG), type 2 diabetes (T2D), fasting glucose (FG), fasting insulin (FI), fasting insulin adjusted for BMI (FIadjBMI), two-hour glucose (Glu2hr), diabetic nephropathy (Diab_Neph), age at menopause (AgeMenopause), and age at menarche (AgeMenarche).
LLM interpretation
This is a bubble chart representing the genetic overlap across various traits at 97 genome-wide significant (GWS) BMI susceptibility loci. Bubble size is proportional to the number of BMI-increasing loci significantly associated with a trait, while the thickness of the connecting lines indicates the number of shared significant loci between trait pairs. The traits are categorized into groups including Anthropometrics, Obesity-Related, CVD-Related, Blood Pressure, Lipids, Diabetes, and Reproduction, with the largest bubbles and thickest connections observed among BMI and anthropometric traits.
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| Single-step genome-wide association study reveals candidate genes for body mass index trait in Yunong-black pigs. | Wu Z et al. | — | 2025 | → |
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| Sparse matrix factorization robust to sample sharing across GWASs reveals interpretable genetic components. | Omdahl AR et al. | — | 2025 | → |
| Splicing across adipocyte differentiation is highly dynamic and impacted by the metabolic phenotype. | Farris KM et al. | — | 2025 | → |
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| Sweet Taste Receptors' Genetic Variability in Advanced Potential Targets of Obesity. | Wagner-Reguero S et al. | — | 2025 | → |
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| The rs1558902 <i>FTO</i> Variant Is Associated with Body Mass Index but Not with Knee Osteoarthritis in a Mexican Mestizo Population. | Prone-Olazabal D et al. | — | 2025 | → |
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| Uncovering covariance patterns across energy balance traits enables the discovery of new obesity-related genes. | Gulisija D et al. | — | 2025 | → |
| Unraveling Mechanisms of Genetic Risks in Metabolic Dysfunction-Associated Steatotic Liver Diseases: A Pathway to Precision Medicine. | Zhang X et al. | — | 2025 | → |
| Unraveling the Genetic Architecture of Obesity: A Path to Personalized Medicine. | Kunnathodi F et al. | — | 2025 | → |
| Unveiling the genetic association between rheumatoid arthritis and four common hand pathologies. | Lv Z et al. | — | 2025 | → |
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| Whole brain gray matter volume may mediate the relationship between light physical activity and Body Mass Index in middle-aged Japanese adults. | Pineda JCD et al. | — | 2025 | → |
| Whole genome sequencing analysis of body mass index identifies novel African ancestry-specific risk allele. | Zhang X et al. | — | 2025 | → |
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| A multi-regional human brain atlas of chromatin accessibility and gene expression facilitates promoter-isoform resolution genetic fine-mapping. | Dong P et al. | — | 2024 | → |
| An abdominal obesity missense variant in the adipocyte thermogenesis gene TBX15 is implicated in adaptation to cold in Finns. | Deal M et al. | — | 2024 | → |
| Analyses of the autism-associated neuroligin-3 R451C mutation in human neurons reveal a gain-of-function synaptic mechanism. | Wang L et al. | — | 2024 | → |
| An insight into the causal relationship between sarcopenia-related traits and venous thromboembolism: A mendelian randomization study. | Du X et al. | — | 2024 | → |
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| Association between Body Mass Index and Early Renal Function after Kidney Transplantation: Observational and Mendelian Randomization Study. | Ming S et al. | — | 2024 | → |
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| Association between modifiable lifestyle factors and telomere length: a univariable and multivariable Mendelian randomization study. | Chen M et al. | — | 2024 | → |
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| Associations between fasting glucose rate-of-change and the missense variant, rs373863828, in an adult Samoan cohort. | Rivara AC et al. | — | 2024 | → |
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| Causal Relationship of Obstructive Sleep Apnea with Bone Mineral Density and the Role of BMI. | Xu F et al. | — | 2024 | → |
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| Deciphering the complex relationship between type 2 diabetes and fracture risk with both genetic and observational evidence. | Zhao P et al. | — | 2024 | → |
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| Different effects of 24 dietary intakes on gastroesophageal reflux disease: A mendelian randomization. | Liu YX et al. | — | 2024 | → |
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| Discrepancy Between Genetically Predicted and Observed BMI Predicts Incident Type 2 Diabetes. | Rhee TM et al. | — | 2024 | → |
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| Dissecting the association between gut microbiota, body mass index and specific depressive symptoms: a mediation Mendelian randomisation study. | Yu T et al. | — | 2024 | → |
| Dual and Triple Incretin-Based Co-agonists: Novel Therapeutics for Obesity and Diabetes. | Gutgesell RM et al. | — | 2024 | → |
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| Estimating causality between smoking and abdominal obesity by Mendelian randomization. | Carrasquilla GD et al. | — | 2024 | → |
| Evaluating the relationship between standing height, body mass index, body fat percentage with risk of inguinal hernia: a Mendelian randomization study. | Li Y et al. | — | 2024 | → |
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| Evidence of a Causal Relationship Between Body Mass Index and Immune-Mediated and Inflammatory Skin Diseases and Biomarkers: A Mendelian Randomization Study. | Li Z et al. | — | 2024 | → |
| Evidence of symptom specificity for depression in multiple sclerosis: A two sample Mendelian randomization study. | Hu C et al. | — | 2024 | → |
| Experimental obesity drug packs double punch to reduce weight. | Mullard A | — | 2024 | → |
| Exploring the causal relationship between body mass index and keratoconus: a Mendelian randomization study. | Wang J et al. | — | 2024 | → |
| Exploring the Causal Relationship Between Modifiable Exposures and Diabetes Mellitus: A Two-Sample Mendelian Randomization Analysis. | Muaddi MA | — | 2024 | → |
| Exploring the complex link between obesity and intelligence: Evidence from systematic review, updated meta-analysis, and Mendelian randomization. | Yun SY et al. | — | 2024 | → |
| Exploring the genetic causal relationship between physical activity and migraine in European population based on Mendelian randomization analysis. | Wang J et al. | — | 2024 | → |
| Exploring the Genetic Roles of Diet and Other Modifiable Risk Factors in the Risk of Angina: A Causal Investigation Using Mendelian Randomization in UK Biobank and FinnGen Cohorts. | Al Ageeli E | — | 2024 | → |
| Exploring the relationship between life course adiposity and sepsis: insights from a two-sample Mendelian randomization analysis. | Cheng Z et al. | — | 2024 | → |
| Exploring the therapeutic potential of precision medicine in rare genetic obesity disorders: a scientific perspective. | Collet TH et al. | — | 2024 | → |
| Factors associated with genetic markers for rotator cuff disease in patients with atraumatic rotator cuff tears. | Yanik EL et al. | — | 2024 | → |
| Fast and scalable ensemble learning method for versatile polygenic risk prediction. | Chen T et al. | — | 2024 | → |
| Female obesity: clinical and psychological assessment toward the best treatment. | Guglielmi V et al. | — | 2024 | → |
| Functional classes of SNPs related to psychiatric disorders and behavioral traits contrast with those related to neurological disorders. | Reimers MA et al. | — | 2024 | → |
| Functional genetics reveals the contribution of delta opioid receptor to type 2 diabetes and beta-cell function. | Meulebrouck S et al. | — | 2024 | → |
| Genetically Predicted Body Mass Index and Mortality in Chronic Obstructive Pulmonary Disease. | Zhang J et al. | — | 2024 | → |
| Genetically predicted frailty index and risk of chronic kidney disease. | Chen HJ et al. | — | 2024 | → |
| Genetically predicted high serum sex hormone-binding globulin levels are associated with lower ischemic stroke risk: A sex-stratified Mendelian randomization study. | Sun W et al. | — | 2024 | → |
| Genetically supported causality between gut microbiota, immune cells and morphine tolerance: a two-sample Mendelian randomization study. | Han S et al. | — | 2024 | → |
| Genetic Analysis of Obstructive Sleep Apnea and Its Relationship with Severe COVID-19. | Strausz S et al. | — | 2024 | → |
| Genetic architecture reconciles linkage and association studies of complex traits. | Sidorenko J et al. | — | 2024 | → |
| Genetic associations with neural reward responsivity to food cues in children. | Yeum D et al. | — | 2024 | → |
| Genetic-by-age interaction analyses on complex traits in UK Biobank and their potential to identify effects on longitudinal trait change. | Winkler TW et al. | — | 2024 | → |
| Genetic determinants of obesity in Korean populations: exploring genome-wide associations and polygenic risk scores. | Jo J et al. | — | 2024 | → |
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| Genetic modifiers of body mass index in individuals with cystic fibrosis. | Ling H et al. | — | 2024 | → |
| Genetic prediction of modifiable lifestyle factors for erectile dysfunction. | Xi YJ et al. | — | 2024 | → |
| Genetic risk score based on obesity-related genes and progression in weight loss after bariatric surgery: a 60-month follow-up study. | Mas-Bermejo P et al. | — | 2024 | → |
| Genetics of Exercise and Diet-Induced Fat Loss Efficiency: A Systematic Review. | Bojarczuk A et al. | — | 2024 | → |
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| Genetic susceptibility to caffeine intake and metabolism: a systematic review. | Low JJ et al. | — | 2024 | → |
| Genetic variation in NOTCH1 is associated with overweight and obesity in Brazilian elderly. | Silva Barcelos EC et al. | — | 2024 | → |
| Genetic variation is a key determinant of chromatin accessibility and drives differences in the regulatory landscape of C57BL/6J and 129S1/SvImJ mice. | Mononen J et al. | — | 2024 | → |
| GENIUS-MAWII: for robust Mendelian randomization with many weak invalid instruments. | Ye T et al. | — | 2024 | → |
| Genome-wide association study based on clustering by obesity-related variables uncovers a genetic architecture of obesity in the Japanese and the UK populations. | Takahashi I et al. | — | 2024 | → |
| Genotype-by-Environment Interactions in Nonalcoholic Fatty Liver Disease and Chronic Illness among Mexican Americans: The Role of Acculturation Stress. | Manusov EG et al. | — | 2024 | → |
| Genotype-microbiome-metabolome associations in early childhood and their link to BMI. | Aparicio A et al. | — | 2024 | → |
| Genotypes of the UCP1 gene polymorphisms and cardiometabolic diseases: A multifactorial study of association with disease probability. | Pravednikova AE et al. | — | 2024 | → |
| GLP-1-directed NMDA receptor antagonism for obesity treatment. | Petersen J et al. | — | 2024 | → |
| Height of non-Hispanic white adults with homeostatic iron regulator HFE genotypes p.C282Y/p.C282Y and wt/wt. | Barton JC et al. | — | 2024 | → |
| Human brain proteome-wide association study provides insights into the genetic components of protein abundance in obesity. | Zhao QG et al. | — | 2024 | → |
| Hypothalamic control of body fat mass by food intake: The key to understanding why obesity should be treated as a disease. | Purnell JQ et al. | — | 2024 | → |
| Hypothalamic GABAergic Neurons Expressing Cellular Retinoic Acid Binding Protein 1 (CRABP1) Are Sensitive to Metabolic Status and Liraglutide in Male Mice. | Lavoie O et al. | — | 2024 | → |
| Identification of additional body weight QTLs in the Berlin Fat Mouse BFMI861 lines using time series data. | Delpero M et al. | — | 2024 | → |
| Identification of novel genes whose expression in adipose tissue affects body fat mass and distribution: an RNA-Seq and Mendelian Randomization study. | Konigorski S et al. | — | 2024 | → |
| Identifying BMI-associated genes via a genome-wide multi-omics integrative approach using summary data. | Tang J et al. | — | 2024 | → |
| Identifying latent genetic interactions in genome-wide association studies using multiple traits. | Bass AJ et al. | — | 2024 | → |
| <i>KCTD10</i> p.C124W variant contributes to schizophrenia by attenuating LLPS-mediated synapse formation. | Mu C et al. | — | 2024 | → |
| Impact of polygenic score for BMI on weight loss effectiveness and genome-wide association analysis. | Dashti HS et al. | — | 2024 | → |
| Implicating type 2 diabetes effector genes in relevant metabolic cellular models using promoter-focused Capture-C. | Wachowski NA et al. | — | 2024 | → |
| Improving fine-mapping by modeling infinitesimal effects. | Cui R et al. | — | 2024 | → |
| Improving on polygenic scores across complex traits using select and shrink with summary statistics (S4) and LDpred2. | Tyrer JP et al. | — | 2024 | → |
| Independent and Joint Effects of Prenatal Incense-Burning Smoke Exposure and Children's Early Outdoor Activity on Preschoolers' Obesity. | Chen M et al. | — | 2024 | → |
| Inflammatory Conditions During Pregnancy and Risk of Autism and Other Neurodevelopmental Disorders. | Croen LA et al. | — | 2024 | → |
| Influence of the Brain-Derived Neurotrophic Factor Gene Polymorphism on Weight Loss Following Intragastric Balloon Intervention: A Cross-Sectional Study. | Al-Serri A et al. | — | 2024 | → |
| Influence of the Interaction between Genetic Factors and Breastfeeding on Children's Weight Status: A Systematic Review. | Yang Z et al. | — | 2024 | → |
| Integrating multiple lines of evidence to assess the effects of maternal BMI on pregnancy and perinatal outcomes. | Borges MC et al. | — | 2024 | → |
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| Interactions between Polygenic Risk of Obesity and Dietary Factors on Anthropometric Outcomes: A Systematic Review and Meta-Analysis of Observational Studies. | Han HY et al. | — | 2024 | → |
| Interindividual differences of dietary fat-inducible Mest in white adipose tissue of C57BL/6J mice are not heritable. | Anunciado-Koza RVP et al. | — | 2024 | → |
| Is there a genetic relationship between blood glucose and osteoarthritis? A mendelian randomization study. | Wang J et al. | — | 2024 | → |
| KAT8 beyond Acetylation: A Survey of Its Epigenetic Regulation, Genetic Variability, and Implications for Human Health. | Yoo L et al. | — | 2024 | → |
| Large-scale genome-wide association studies reveal the genetic causal etiology between air pollutants and autoimmune diseases. | Wen J et al. | — | 2024 | → |
| Larval density can be used to predict genetic modifiers of glucagon signaling in Drosophila melanogaster. | Nicol A et al. | — | 2024 | → |
| Leukocyte Telomere Length and Cardiac Structure and Function: A Mendelian Randomization Study. | Salih AM et al. | — | 2024 | → |
| Lipotoxicity as a therapeutic target in obesity and diabetic cardiomyopathy. | Nakamura M | — | 2024 | → |
| Longitudinal analysis of epigenome-wide DNA methylation reveals novel loci associated with BMI change in East Asians. | Li W et al. | — | 2024 | → |
| Mapping biological influences on the human plasma proteome beyond the genome. | Carrasco-Zanini J et al. | — | 2024 | → |
| Maternal dietary fat during lactation shapes single nucleus transcriptomic profile of postnatal offspring hypothalamus in a sexually dimorphic manner in mice. | Huang Y et al. | — | 2024 | → |
| Mechanism of action of the bile acid receptor TGR5 in obesity. | Lun W et al. | — | 2024 | → |
| Mechanisms of Hypercoagulability Driving Stroke Risk in Obesity: A Mendelian Randomization Study. | Daghlas I et al. | — | 2024 | → |
| Mendelian randomization analysis separated the independent impact of childhood obesity and adult obesity on socioeconomic status, psychological status, and substance use. | Cai J et al. | — | 2024 | → |
| Meta-analysis reveals obesity associated gut microbial alteration patterns and reproducible contributors of functional shift. | Chanda D et al. | — | 2024 | → |
| Mice Lacking Mrs2 Magnesium Transporter are Hypophagic and Thin When Maintained on a High-Fat Diet. | Powell DR et al. | — | 2024 | → |
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| MUSSEL: Enhanced Bayesian polygenic risk prediction leveraging information across multiple ancestry groups. | Jin J et al. | — | 2024 | → |
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| Neurodevelopmental Programming of Adiposity: Contributions to Obesity Risk. | Skowronski AA et al. | — | 2024 | → |
| No evidence for a causal contribution of bioavailable testosterone to ADHD in sex-combined and sex-specific two-sample Mendelian randomization studies. | Dinkelbach L et al. | — | 2024 | → |
| No genetic causality between obesity and benign paroxysmal vertigo: A two-sample Mendelian randomization study. | Guo Z et al. | — | 2024 | → |
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| Obesity Variants in the GIPR Gene Are not Associated With Risk of Fracture or Bone Mineral Density. | Styrkarsdottir U et al. | — | 2024 | → |
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| Olanzapine Modulate Lipid Metabolism and Adipose Tissue Accumulation via Hepatic Muscarinic M3 Receptor-Mediated Alk-Related Signaling. | Su Y et al. | — | 2024 | → |
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| Protein-truncating variants in BSN are associated with severe adult-onset obesity, type 2 diabetes and fatty liver disease. | Zhao Y et al. | — | 2024 | → |
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| Unraveling the Role of the Human Gut Microbiome in Health and Diseases. | Khalil M et al. | — | 2024 | → |
| Using interpretable machine learning methods to identify the relative importance of lifestyle factors for overweight and obesity in adults: pooled evidence from CHNS and NHANES. | Sun Z et al. | — | 2024 | → |
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| Variant-to-function analysis of the childhood obesity chr12q13 locus implicates rs7132908 as a causal variant within the 3' UTR of FAIM2. | Littleton SH et al. | — | 2024 | → |
| 150 risk variants for diverticular disease of intestine prioritize cell types and enable polygenic prediction of disease susceptibility. | Wu Y et al. | — | 2023 | → |
| Abdominal obesity is a more important causal risk factor for pancreatic cancer than overall obesity. | Maina JG et al. | — | 2023 | → |
| A bidirectional Mendelian randomisation study to evaluate the relationship between body constitution and hearing loss. | He Y et al. | — | 2023 | → |
| Accounting for antihypertensive medication in Mendelian randomization studies of blood pressure: methodological considerations in the Canadian Longitudinal Study on Aging. | Mbutiwi FIN et al. | — | 2023 | → |
| A comprehensive analysis of genetic risk for metabolic syndrome in the Egyptian population via allele frequency investigation and Missense3D predictions. | Bassyouni M et al. | — | 2023 | → |
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| A Rare Case of Postoperative Encephalopathy in Twin. | Huang CA et al. | — | 2023 | → |
| A scan of all coding region variants of the human genome, identifies 13q12.2-rs9579139 and 15q24.1-rs2277598 as novel risk loci for pancreatic ductal adenocarcinoma. | Giaccherini M et al. | — | 2023 | → |
| A sex- and site-specific relationship between body mass index and osteoarthritis: evidence from observational and genetic analyses. | Zhang L et al. | — | 2023 | → |
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| CADM2 is implicated in impulsive personality and numerous other traits by genome- and phenome-wide association studies in humans and mice. | Sanchez-Roige S et al. | — | 2023 | → |
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| Causal Association Between Obesity, Circulating Glutamine Levels, and Depression: A Mendelian Randomization Study. | He R et al. | — | 2023 | → |
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| Causal effects from tobacco smoking initiation on obesity-related traits: a Mendelian randomization study. | Park S et al. | — | 2023 | → |
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| Characterizing causal relationships of visceral fat and body shape on multiple sclerosis risk. | Misicka E et al. | — | 2023 | → |
| Chronic inflammation does not mediate the effect of adiposity on grip strength: results from a multivariable Mendelian randomization study. | Norris T et al. | — | 2023 | → |
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| Clinical and genetic associations of deep learning-derived cardiac magnetic resonance-based left ventricular mass. | Khurshid S et al. | — | 2023 | → |
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| Combinations of genes at the 16p11.2 and 22q11.2 CNVs contribute to neurobehavioral traits. | Vysotskiy M et al. | — | 2023 | → |
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| Construction and evaluation of the functional polygenic risk score for gastric cancer in a prospective cohort of the European population. | Gu Y et al. | — | 2023 | → |
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| Current Perspectives: Obesity and Neurodegeneration - Links and Risks. | Kueck PJ et al. | — | 2023 | → |
| Deciphering the genetic landscape of obesity: a data-driven approach to identifying plausible causal genes and therapeutic targets. | Ang MY et al. | — | 2023 | → |
| Development of a Polygenic Risk Score for BMI to Assess the Genetic Susceptibility to Obesity and Related Diseases in the Korean Population. | Yoon N et al. | — | 2023 | → |
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| Differences in Infant Diet Quality Index by Race and Ethnicity Predict Differences in Later Diet Quality. | Au LE et al. | — | 2023 | → |
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| Differentiation, Transcriptomic Profiling, and Calcium Imaging of Human Hypothalamic Neurons. | Chen HC et al. | — | 2023 | → |
| Discrimination Exposure and Polygenic Risk for Obesity in Adulthood: Testing Gene-Environment Correlations and Interactions. | Cuevas AG et al. | — | 2023 | → |
| Dissecting the Genetic Relationship Between Schizophrenia and Cardiovascular Disease. | Strawbridge RJ et al. | — | 2023 | → |
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| Early life adversity jointly regulates body-mass index and working memory development. | Farkas BC et al. | — | 2023 | → |
| Endocrine, genetic, and microbiome nexus of obesity and potential role of postbiotics: a narrative review. | Wu W et al. | — | 2023 | → |
| Energy Expenditure Homeostasis Requires ErbB4, an Obesity Risk Gene, in the Paraventricular Nucleus. | Santiago-Marrero I et al. | — | 2023 | → |
| Environmental carcinogens disproportionally mutate genes implicated in neurodevelopmental disorders. | Baker BH et al. | — | 2023 | → |
| Epigenome-wide association study of plasma lipids in West Africans: the RODAM study. | van der Linden EL et al. | — | 2023 | → |
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| Establishing the relationships between adiposity and reproductive factors: a multivariable Mendelian randomization analysis. | Prince C et al. | — | 2023 | → |
| Estimation of time-varying causal effects with multivariable Mendelian randomization: some cautionary notes. | Tian H et al. | — | 2023 | → |
| Evaluating 17 methods incorporating biological function with GWAS summary statistics to accelerate discovery demonstrates a tradeoff between high sensitivity and high positive predictive value. | Moore A et al. | — | 2023 | → |
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| Expanding the genetic landscape of obesity. | Yang J | — | 2023 | → |
| Exploring the associations between number of children, multi-partner fertility and risk of obesity at midlife: Findings from the 1970 British Cohort Study (BCS70). | Stannard S et al. | — | 2023 | → |
| Extremely sparse models of linkage disequilibrium in ancestrally diverse association studies. | Salehi Nowbandegani P et al. | — | 2023 | → |
| Fibrillin-1 and asprosin, novel players in metabolic syndrome. | Summers KM et al. | — | 2023 | → |
| FOXO3A-short is a novel regulator of non-oxidative glucose metabolism associated with human longevity. | Santo EE et al. | — | 2023 | → |
| FTO gene variants (rs9939609, rs8050136 and rs17817449) and type 2 diabetes mellitus risk: A Meta-Analysis. | Amine Ikhanjal M et al. | — | 2023 | → |
| Functional Enrichment Analysis Identifying Regulatory Information Associated with Human Fracture. | Meng XH et al. | — | 2023 | → |
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| Gene-by-Sex Interactions: Genome-Wide Association Study Reveals Five SNPs Associated with Obesity and Overweight in a Male Population. | Kyrgiafini MA et al. | — | 2023 | → |
| Gene-environment interaction explains a part of missing heritability in human body mass index. | Jung HU et al. | — | 2023 | → |
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| Genetically-predicted placental gene expression is associated with birthweight and adult body mass index. | Jasper EA et al. | — | 2023 | → |
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| Genetically supported causality between gut microbiota, gut metabolites and low back pain: a two-sample Mendelian randomization study. | Su M et al. | — | 2023 | → |
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| Genetic evidence that high BMI in childhood has a protective effect on intermediate diabetes traits, including measures of insulin sensitivity and secretion, after accounting for BMI in adulthood. | Hawkes G et al. | — | 2023 | → |
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| Genetics of sexually dimorphic adipose distribution in humans. | Hansen GT et al. | — | 2023 | → |
| Genetic variant panel allows predicting both obesity risk, and efficacy of procedures and diet in weight loss. | Mera-Charria A et al. | — | 2023 | → |
| Genetic variants of <i>ERBB4</i> gene and risk of gestational diabetes mellitus: a susceptibility and diagnostic nomogram study. | Li R et al. | — | 2023 | → |
| Genome Editing and Obesity. | Masi D et al. | — | 2023 | → |
| Genome-Wide Assessment of Shared Genetic Architecture Between Rheumatoid Arthritis and Cardiovascular Diseases. | Guo Y et al. | — | 2023 | → |
| Genome wide association joint analysis reveals 99 risk loci for pain susceptibility and pleiotropic relationships with psychiatric, metabolic, and immunological traits. | Mocci E et al. | — | 2023 | → |
| Genomic approaches to identify and investigate genes associated with atrial fibrillation and heart failure susceptibility. | Patel KK et al. | — | 2023 | → |
| Germline mechanisms of immunotherapy toxicities in the era of genome-wide association studies. | Gusev A | — | 2023 | → |
| Gut microbiota and risk of five common cancers: A univariable and multivariable Mendelian randomization study. | Wei Z et al. | — | 2023 | → |
| GWAS-Identified Variants for Obesity Do Not Influence the Risk of Developing Multiple Myeloma: A Population-Based Study and Meta-Analysis. | Sánchez-Maldonado JM et al. | — | 2023 | → |
| Heart Disease and Stroke Statistics-2023 Update: A Report From the American Heart Association. | Tsao CW et al. | — | 2023 | → |
| Hematologic traits and primary biliary cholangitis: a Mendelian randomization study. | Ke B et al. | — | 2023 | → |
| Heterogenous trajectories in physical, mental and cognitive health among older Americans: Roles of genetics and life course contextual factors. | Hoang CT et al. | — | 2023 | → |
| High-calorie diets uncouple hypothalamic oxytocin neurons from a gut-to-brain satiation pathway via κ-opioid signaling. | Gruber T et al. | — | 2023 | → |
| High-frequency variants in PKA signaling-related genes within a large pediatric cohort with obesity or metabolic abnormalities. | Bloyd M et al. | — | 2023 | → |
| High sucrose consumption decouples intrinsic and synaptic excitability of AgRP neurons without altering body weight. | Korgan AC et al. | — | 2023 | → |
| Identification of a Stress-Sensitive Anorexigenic Neurocircuit From Medial Prefrontal Cortex to Lateral Hypothalamus. | Clarke RE et al. | — | 2023 | → |
| Identification of a weight loss-associated causal eQTL in <i>MTIF3</i> and the effects of <i>MTIF3</i> deficiency on human adipocyte function. | Huang M et al. | — | 2023 | → |
| Identification of four novel loci associated with psychotropic drug-induced weight gain in a Swiss psychiatric longitudinal study: A GWAS analysis. | Sjaarda J et al. | — | 2023 | → |
| Identification of heel bone mineral density as a risk factor of Alzheimer's disease by analyzing large-scale genome-wide association studies datasets. | Gao F et al. | — | 2023 | → |
| Identification of potential genetic causal variants for obesity-related traits using statistical fine mapping. | Gong R et al. | — | 2023 | → |
| Identifying the potential causal role of insomnia symptoms on 11,409 health-related outcomes: a phenome-wide Mendelian randomisation analysis in UK Biobank. | Gibson MJ et al. | — | 2023 | → |
| Inborn Errors of Purine Salvage and Catabolism. | Camici M et al. | — | 2023 | → |
| Increased BMI and late-life mobility dysfunction; overlap of genetic effects in brain regions. | Chang X et al. | — | 2023 | → |
| Increased body mass index is linked to systemic inflammation through altered chromatin co-accessibility in human preadipocytes. | Garske KM et al. | — | 2023 | → |
| Independent relevance of adiposity measures to coronary heart disease risk among 0.5 million adults in UK Biobank. | Trichia E et al. | — | 2023 | → |
| Inferring feature importance with uncertainties with application to large genotype data. | Johnsen PV et al. | — | 2023 | → |
| Inflammatory bowel disease is causally related to irritable bowel syndrome: a bidirectional two-sample Mendelian randomization study. | Ke H et al. | — | 2023 | → |
| Integrated epigenome, whole genome sequence and metabolome analyses identify novel multi-omics pathways in type 2 diabetes: a Middle Eastern study. | Yousri NA et al. | — | 2023 | → |
| Integrative genomic analyses in adipocytes implicate DNA methylation in human obesity and diabetes. | McAllan L et al. | — | 2023 | → |
| Integrative polygenic analysis of the protective effects of fatty acid metabolism on disease as modified by obesity. | Astore C et al. | — | 2023 | → |
| Interaction between genetic susceptibility to obesity and food intake on BMI in Finnish school-aged children. | Viljakainen H et al. | — | 2023 | → |
| Interaction of environmental factors with the polygenic risk scores of thinness-related genes in preventing obesity risk in middle-aged adults: The KoGES. | Zhou JY et al. | — | 2023 | → |
| Investigating effect modification between childhood maltreatment and genetic risk for cardiovascular disease in the UK Biobank. | Urquijo H et al. | — | 2023 | → |
| Investigating the causal relationships between excess adiposity and cardiometabolic health in men and women. | Mutie PM et al. | — | 2023 | → |
| Investigating the shared genetic architecture between schizophrenia and body mass index. | Yu Y et al. | — | 2023 | → |
| Investigation of the association between habitual dietary FODMAP intake, metabolic parameters, glycemic status, and anthropometric features among apparently healthy overweight and obese individuals. | Hemami RM et al. | — | 2023 | → |
| Investigation on the association between serum lipid levels and periodontitis: a bidirectional Mendelian randomization analysis. | Chen Z et al. | — | 2023 | → |
| Iron status and obesity-related traits: A two-sample bidirectional Mendelian randomization study. | Zhou Z et al. | — | 2023 | → |
| Joint analysis of GWAS and multi-omics QTL summary statistics reveals a large fraction of GWAS signals shared with molecular phenotypes. | Wu Y et al. | — | 2023 | → |
| Large-scale exome sequence analysis identifies sex- and age-specific determinants of obesity. | Kaisinger LR et al. | — | 2023 | → |
| Large scale phenotype imputation and in vivo functional validation implicate ADAMTS14 as an adiposity gene. | Kentistou KA et al. | — | 2023 | → |
| LepRb+ cell-specific deletion of Slug mitigates obesity and nonalcoholic fatty liver disease in mice. | Kim MH et al. | — | 2023 | → |
| Leveraging Multi-Ancestry Polygenic Risk Scores for Body Mass Index to Predict Antiretroviral Therapy-Induced Weight Gain. | Keat K et al. | — | 2023 | → |
| Leveraging related health phenotypes for polygenic prediction of impulsive choice, impulsive action, and impulsive personality traits in 1534 European ancestry community adults. | Deng WQ et al. | — | 2023 | → |
| Lifestyle factors, metabolic factors and socioeconomic status for pelvic organ prolapse: a Mendelian randomization study. | Liu H et al. | — | 2023 | → |
| Lipid-induced transcriptomic changes in blood link to lipid metabolism and allergic response. | Dekkers KF et al. | — | 2023 | → |
| Mapping the transcriptional landscape of human white and brown adipogenesis using single-nuclei RNA-seq. | Gupta A et al. | — | 2023 | → |
| Maternal pre-pregnancy body mass index and offspring with overweight/obesity at preschool age: The possible role of epigenome-wide DNA methylation changes in cord blood. | Chang R et al. | — | 2023 | → |
| mBAT-combo: A more powerful test to detect gene-trait associations from GWAS data. | Li A et al. | — | 2023 | → |
| Mediation and moderation of genetic risk of obesity through eating behaviours in two UK cohorts. | Begum S et al. | — | 2023 | → |
| Mendelian randomization for cardiovascular diseases: principles and applications. | Larsson SC et al. | — | 2023 | → |
| Mendelian randomization on the association of obesity with vitamin D: Guangzhou Biobank Cohort Study. | Huang YY et al. | — | 2023 | → |
| Metabolic phenotyping of BMI to characterize cardiometabolic risk: evidence from large population-based cohorts. | Beyene HB et al. | — | 2023 | → |
| Metabolic Syndrome: A Narrative Review from the Oxidative Stress to the Management of Related Diseases. | Martemucci G et al. | — | 2023 | → |
| Metabolic Syndrome: An Overview on Its Genetic Associations and Gene-Diet Interactions. | Prone-Olazabal D et al. | — | 2023 | → |
| Metabolic Syndrome, Nonalcoholic Fatty Liver Disease, and Chronic Hepatitis B: A Narrative Review. | Diao Y et al. | — | 2023 | → |
| Modeling tissue co-regulation estimates tissue-specific contributions to disease. | Amariuta T et al. | — | 2023 | → |
| Multi-omics approaches for precision obesity management : Potentials and limitations of omics in precision prevention, treatment and risk reduction of obesity. | Woldemariam S et al. | — | 2023 | → |
| Multisite Pain and Myocardial Infarction and Stroke: A Prospective Cohort and Mendelian Randomization Analysis. | Tian J et al. | — | 2023 | → |
| Multi-view information fusion using multi-view variational autoencoder to predict proximal femoral fracture load. | Zhao C et al. | — | 2023 | → |
| Neuroanatomical correlates of genetic risk for obesity in children. | Morys F et al. | — | 2023 | → |
| Neurochemical Basis of Inter-Organ Crosstalk in Health and Obesity: Focus on the Hypothalamus and the Brainstem. | Haspula D et al. | — | 2023 | → |
| New insights from GWAS on BMI-related growth traits in a longitudinal cohort of admixed children with Native American and European ancestry. | Vicuña L et al. | — | 2023 | → |
| New Insights into Polygenic Score-Lifestyle Interactions for Cardiometabolic Risk Factors from Genome-Wide Interaction Analyses. | D'Urso S et al. | — | 2023 | → |
| Obesity and head and neck cancer risk: a mendelian randomization study. | Gui L et al. | — | 2023 | → |
| Obesity and risk of gestational diabetes mellitus: A two-sample Mendelian randomization study. | Song X et al. | — | 2023 | → |
| Obesity and the risk of cardiometabolic diseases. | Valenzuela PL et al. | — | 2023 | → |
| Obesity: A Review of Pathophysiology and Classification. | Busebee B et al. | — | 2023 | → |
| Obesity, birth weight, and lifestyle factors for frailty: a Mendelian randomization study. | Gu Y et al. | — | 2023 | → |
| Obesity increases heart failure incidence and mortality: observational and Mendelian randomization studies totalling over 1 million individuals. | Benn M et al. | — | 2023 | → |
| Obesity is a chronic progressive relapsing disease of particular interest for internal medicine. | Sbraccia P et al. | — | 2023 | → |
| Obesity Management in Children and Adolescents. | Wong G et al. | — | 2023 | → |
| Obesity-related parameters in carriers of some BDNF genetic variants may depend on daily dietary macronutrients intake. | Miksza U et al. | — | 2023 | → |
| Obesity-Related Single-Nucleotide Polymorphisms and Weight Gain Following First-Line Antiretroviral Therapy. | Berenguer J et al. | — | 2023 | → |
| Obesity Stigma: Causes, Consequences, and Potential Solutions. | Westbury S et al. | — | 2023 | → |
| Obesity wars: may the smell be with you. | López M et al. | — | 2023 | → |
| Outcomes and Trends of Endoscopic Bariatric Therapies (EBT) Among Minority Populations. | Ouni A et al. | — | 2023 | → |
| Outdoor light at night, genetic predisposition and type 2 diabetes mellitus: A prospective cohort study. | Xu Z et al. | — | 2023 | → |
| Overall Obesity Not Abdominal Obesity Has a Causal Relationship with Obstructive Sleep Apnea in Individual Level Data. | Li X et al. | — | 2023 | → |
| Overview of growth differentiation factor 15 in metabolic syndrome. | Asrih M et al. | — | 2023 | → |
| Parent-reported child appetite moderates relationships between child genetic obesity risk and parental feeding practices. | Jansen E et al. | — | 2023 | → |
| Periodontitis and metabolic diseases (diabetes and obesity): Tackling multimorbidity. | Marruganti C et al. | — | 2023 | → |
| Placental epigenetic marks related to gestational weight gain reveal potential genes associated with offspring obesity parameters. | Gómez-Vilarrubla A et al. | — | 2023 | → |
| Polygenic regression uncovers trait-relevant cellular contexts through pathway activation transformation of single-cell RNA sequencing data. | Ma Y et al. | — | 2023 | → |
| Polygenic risk scores analyses of psychiatric and metabolic traits with antipsychotic-induced weight gain in schizophrenia: an exploratory study. | Yoshida K et al. | — | 2023 | → |
| Polygenic risk scores for the prediction of cardiometabolic disease. | O'Sullivan JW et al. | — | 2023 | → |
| Polygenic scores of diabetes-related traits in subgroups of type 2 diabetes in India: a cohort study. | Yajnik CS et al. | — | 2023 | → |
| Potential Involvement of LncRNAs in Cardiometabolic Diseases. | Ilieva M et al. | — | 2023 | → |
| Prebiotic and Probiotic Modulation of the Microbiota-Gut-Brain Axis in Depression. | Radford-Smith DE et al. | — | 2023 | → |
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| Predicting mechanisms of action at genetic loci associated with discordant effects on type 2 diabetes and abdominal fat accumulation. | Aberra YT et al. | — | 2023 | → |
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| Processed meat, red meat, white meat, and digestive tract cancers: A two-sample Mendelian randomization study. | Yun Z et al. | — | 2023 | → |
| Proteome and genome integration analysis of obesity. | Zhao Q et al. | — | 2023 | → |
| Proteome-wide Mendelian randomization implicates nephronectin as an actionable mediator of the effect of obesity on COVID-19 severity. | Yoshiji S et al. | — | 2023 | → |
| PRSet: Pathway-based polygenic risk score analyses and software. | Choi SW et al. | — | 2023 | → |
| Quantifying factors that affect polygenic risk score performance across diverse ancestries and age groups for body mass index. | Hui D et al. | — | 2023 | → |
| Quantifying the Relationship Between Physical Activity Energy Expenditure and Incident Type 2 Diabetes: A Prospective Cohort Study of Device-Measured Activity in 90,096 Adults. | Strain T et al. | — | 2023 | → |
| Recent progress on action and regulation of anorexigenic adipokine leptin. | Nakagawa T et al. | — | 2023 | → |
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| Relevance of body composition in phenotyping the obesities. | Salmón-Gómez L et al. | — | 2023 | → |
| Repurposing antidiabetic drugs for rheumatoid arthritis: results from a two-sample Mendelian randomization study. | Qin C et al. | — | 2023 | → |
| Robust Bioinformatics Approaches Result in the First Polygenic Risk Score for BMI in Greek Adults. | Kafyra M et al. | — | 2023 | → |
| Robust multivariable Mendelian randomization based on constrained maximum likelihood. | Lin Z et al. | — | 2023 | → |
| Role of pancreatic ductal adenocarcinoma risk factors in intraductal papillary mucinous neoplasm progression. | Gentiluomo M et al. | — | 2023 | → |
| Schizophrenia and cardiometabolic abnormalities: A Mendelian randomization study. | Saadullah Khani N et al. | — | 2023 | → |
| SDPRX: A statistical method for cross-population prediction of complex traits. | Zhou G et al. | — | 2023 | → |
| Self-regulation linking the quality of early parent-child relationship to adolescents' obesity risk and food consumption. | Kim JH et al. | — | 2023 | → |
| Sex hormones in the risk of breast cancer: a two-sample Mendelian randomization study. | Ke B et al. | — | 2023 | → |
| Sex modifies the effect of genetic risk scores for polycystic ovary syndrome on metabolic phenotypes. | Actkins KV et al. | — | 2023 | → |
| Sex-Related Aspects in Diabetic Kidney Disease-An Update. | Loeffler I et al. | — | 2023 | → |
| Sex-Specific Features of the Correlation between GWAS-Noticeable Polymorphisms and Hypertension in Europeans of Russia. | Ivanova T et al. | — | 2023 | → |
| SH2B1 Tunes Hippocampal ERK Signaling to Influence Fluid Intelligence in Humans and Mice. | Du X et al. | — | 2023 | → |
| Shared Activities With Parents During Adolescence Predicts Health Risk Across Multiple Biological Systems 22 Years Later. | Manczak EM | — | 2023 | → |
| Single nucleus multiomics identifies ZEB1 and MAFB as candidate regulators of Alzheimer's disease-specific <i>cis</i>-regulatory elements. | Anderson AG et al. | — | 2023 | → |
| Sleep traits, fat accumulation, and glycemic traits in relation to gastroesophageal reflux disease: A Mendelian randomization study. | Zhao X et al. | — | 2023 | → |
| Surrogate Adiposity Markers and Mortality. | Khan I et al. | — | 2023 | → |
| The association between body fatness and mortality among breast cancer survivors: results from a prospective cohort study. | Bonet C et al. | — | 2023 | → |
| The associations between asthma and common comorbidities: a comprehensive Mendelian randomization study. | Wang X et al. | — | 2023 | → |
| The Associations of Genetically Predicted Plasma Alanine with Coronary Artery Disease and its Risk Factors: A Mendelian Randomization Study. | Huang X et al. | — | 2023 | → |
| The Big (Genetic) Sort? A Research Note on Migration Patterns and Their Genetic Imprint in the United Kingdom. | Furuya S et al. | — | 2023 | → |
| The case for precision medicine in the prevention, diagnosis, and treatment of cardiometabolic diseases in low-income and middle-income countries. | Misra S et al. | — | 2023 | → |
| The Casual Association Inference for the Chain of Falls Risk Factors-Falls-Falls Outcomes: A Mendelian Randomization Study. | Wu JX et al. | — | 2023 | → |
| The Causal Effect of Obesity on the Risk of 15 Autoimmune Diseases: A Mendelian Randomization Study. | Li X et al. | — | 2023 | → |
| The causal relationship between air pollution, obesity, and COVID-19 risk: a large-scale genetic correlation study. | Zhang J et al. | — | 2023 | → |
| The causal relationship between risk of developing bronchial asthma and frailty: a bidirectional two-sample Mendelian randomization study. | Ma X et al. | — | 2023 | → |
| The Effects of Food Advertisements on Food Intake and Neural Activity: A Systematic Review and Meta-Analysis of Recent Experimental Studies. | Arrona-Cardoza P et al. | — | 2023 | → |
| The Emerging Role of Circular RNA Homeodomain Interacting Protein Kinase 3 and Circular RNA 0046367 through Wnt/Beta-Catenin Pathway on the Pathogenesis of Nonalcoholic Steatohepatitis in Egyptian Patients. | Abdelgwad M et al. | — | 2023 | → |
| The Genetic Basis of Childhood Obesity: A Systematic Review. | Vourdoumpa A et al. | — | 2023 | → |
| The genetics of falling susceptibility and identification of causal risk factors. | Smith MC et al. | — | 2023 | → |
| The HIF1α polymorphism rs2301104 is associated with obesity and obesity-related cytokines in Han Chinese population. | Zheng X et al. | — | 2023 | → |
| The impact of age-specific childhood body-mass index on adult cardiometabolic traits: a Mendelian randomization study. | Yang J et al. | — | 2023 | → |
| The impact of obesity on lung function measurements and respiratory disease: A Mendelian randomization study. | Liu J et al. | — | 2023 | → |
| The impact of sociodemographic status on the association of classical cardiovascular risk factors with coronary artery disease: a stratified Mendelian randomization study. | Martens LG et al. | — | 2023 | → |
| The Influence of Single Nucleotide Polymorphisms On Body Weight Trajectory After Bariatric Surgery: A Systematic Review. | Duarte ACS et al. | — | 2023 | → |
| The influence of six polymorphisms of uncoupling protein 3 (UCP3) gene and childhood obesity: a case-control study. | Fortes JS et al. | — | 2023 | → |
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| The prediction of Alzheimer's disease through multi-trait genetic modeling. | Clark K et al. | — | 2023 | → |
| The relationship between adiposity and cognitive function: a bidirectional Mendelian randomization study in UK Biobank. | Norris T et al. | — | 2023 | → |
| The relationship between education attainment and gout, and the mediating role of modifiable risk factors: a Mendelian randomization study. | Huang X et al. | — | 2023 | → |
| The relationship of trait-like compassion with epigenetic aging: The population-based prospective Young Finns Study. | Dobewall H et al. | — | 2023 | → |
| The rs1421085 variant within FTO promotes brown fat thermogenesis. | Zhang Z et al. | — | 2023 | → |
| TMT-Based Proteomics Analysis Revealed the Protein Changes in Perirenal Fat from Obese Rabbits. | Jiang G et al. | — | 2023 | → |
| Toward Precision Weight-Loss Dietary Interventions: Findings from the POUNDS Lost Trial. | Qi L et al. | — | 2023 | → |
| Trans-ethnic polygenic risk scores for body mass index: An international hundred K+ cohorts consortium study. | Qu HQ et al. | — | 2023 | → |
| Transferability of the PRS estimates for height and BMI obtained from the European ethnic groups to the Western Russian populations. | Albert EA et al. | — | 2023 | → |
| TREM2 has a significant, gender-specific, effect on human obesity. | Reich T et al. | — | 2023 | → |
| TT Genotype of <i>TLR4 rs1928295</i> Is a Risk Factor of Overweight/Obesity in Han Chinese Children Aged 7-12 Years and Can Interact with Dietary Patterns to Affect the Incidence of Central Obesity and Lipid Profile, Systolic Blood Pressure Levels. | Zhu Q et al. | — | 2023 | → |
| Unappreciated subcontinental admixture in Europeans and European Americans and implications for genetic epidemiology studies. | Gouveia MH et al. | — | 2023 | → |
| Understanding the contemporary high obesity rate from an evolutionary genetic perspective. | Wu T et al. | — | 2023 | → |
| Unraveling the Complex Relationship Between Gastroesophageal Reflux Disease, Lifestyle Factors, and Interstitial Lung Disease: Insights From Two-Sample Mendelian Randomization Analyses. | Jareebi MA et al. | — | 2023 | → |
| Using allele scores to identify confounding by reverse causation: studies of alcohol consumption as an exemplar. | Sallis HM et al. | — | 2023 | → |
| Using genetics to examine the overall and sex-specific associations of branch-chain amino acids and the valine metabolite, 3-hydroxyisobutyrate, with ischemic heart disease and diabetes: A two-sample Mendelian randomization study. | Zhao JV et al. | — | 2023 | → |
| Whole-exome sequence analysis of anthropometric traits illustrates challenges in identifying effects of rare genetic variants. | Young KL et al. | — | 2023 | → |
| Whole genome sequencing of simmental cattle for SNP and CNV discovery. | Sun T et al. | — | 2023 | → |
| A comparison of the genes and genesets identified by GWAS and EWAS of fifteen complex traits. | Battram T et al. | — | 2022 | → |
| Acute and long-term effects of psilocybin on energy balance and feeding behavior in mice. | Fadahunsi N et al. | — | 2022 | → |
| Adiposity and the risk of dementia: mediating effects from inflammation and lipid levels. | Karlsson IK et al. | — | 2022 | → |
| Advances in multi-omics study of biomarkers of glycolipid metabolism disorder. | Fang X et al. | — | 2022 | → |
| A Guide for Understanding and Designing Mendelian Randomization Studies in the Musculoskeletal Field. | Hartley AE et al. | — | 2022 | → |
| Altered macronutrient composition and genetics influence the complex transcriptional network associated with adiposity in the Collaborative Cross. | Yam P et al. | — | 2022 | → |
| Are Ingested or Inhaled Microplastics Involved in Nonalcoholic Fatty Liver Disease? | Auguet T et al. | — | 2022 | → |
| Association between trauma exposure and respiratory disease-A Mendelian randomization study. | Ma Y et al. | — | 2022 | → |
| Association of Alcohol Use Disorder Risk With <i>ADH1B, DRD2, FAAH, SLC39A8, GCKR</i>, and <i>PDYN</i> Genetic Polymorphisms. | Legaki E et al. | — | 2022 | → |
| Association of cannabis use disorder with cardiovascular diseases: A two-sample Mendelian randomization study. | Chen M et al. | — | 2022 | → |
| A Systems Analysis of Phenotype Heterogeneity in APOE*3Leiden.CETP Mice Induced by Long-Term High-Fat High-Cholesterol Diet Feeding. | Paalvast Y et al. | — | 2022 | → |
| Body mass index and incidence of lung cancer in the HUNT study: using observational and Mendelian randomization approaches. | Jiang L et al. | — | 2022 | → |
| Body shape and risk of glaucoma: A Mendelian randomization. | Yuan R et al. | — | 2022 | → |
| Both indirect maternal and direct fetal genetic effects reflect the observational relationship between higher birth weight and lower adult bone mass. | Xia JW et al. | — | 2022 | → |
| Causal relationships between metabolic-associated fatty liver disease and iron status: Two-sample Mendelian randomization. | He H et al. | — | 2022 | → |
| Causal Relationships of General and Abdominal Adiposity on Osteoarthritis: A Two-Sample Mendelian Randomization Study. | Lyu L et al. | — | 2022 | → |
| Causal relationships of obesity on musculoskeletal chronic pain: A two-sample Mendelian randomization study. | Chen X et al. | — | 2022 | → |
| Changing genetic architecture of body mass index from infancy to early adulthood: an individual based pooled analysis of 25 twin cohorts. | Silventoinen K et al. | — | 2022 | → |
| Cigarette Smoking and Endometrial Cancer Risk: Observational and Mendelian Randomization Analyses. | Dimou N et al. | — | 2022 | → |
| Cohort Profile Update: The Study of Health in Pomerania (SHIP). | Völzke H et al. | — | 2022 | → |
| Contributions of obesity to kidney health and disease: insights from Mendelian randomization and the human kidney transcriptomics. | Xu X et al. | — | 2022 | → |
| Cross-fitted instrument: A blueprint for one-sample Mendelian randomization. | Denault WRP et al. | — | 2022 | → |
| Deconstructing a Syndrome: Genomic Insights Into PCOS Causal Mechanisms and Classification. | Dapas M et al. | — | 2022 | → |
| Development of a genetic risk score for obesity predisposition evaluation. | Damavandi N et al. | — | 2022 | → |
| Dietary carbohydrates: Pathogenesis and potential therapeutic targets to obesity-associated metabolic syndrome. | Akter S et al. | — | 2022 | → |
| Disentangling Genetic Risks for Metabolic Syndrome. | van Walree ES et al. | — | 2022 | → |
| Distinct factors associated with short-term and long-term weight loss induced by low-fat or low-carbohydrate diet intervention. | Li X et al. | — | 2022 | → |
| Effects of Pre-Pregnancy Overweight/Obesity on the Pattern of Association of Hypertension Susceptibility Genes with Preeclampsia. | Abramova M et al. | — | 2022 | → |
| Estimating the causal effect of frailty index on vestibular disorders: A two-sample Mendelian randomization. | Xiao G et al. | — | 2022 | → |
| Evidence of genetic overlap and causal relationships between blood-based biochemical traits and human cortical anatomy. | Kiltschewskij DJ et al. | — | 2022 | → |
| Examination on the risk factors of cholangiocarcinoma: A Mendelian randomization study. | Chen L et al. | — | 2022 | → |
| Exploring Lead loci shared between schizophrenia and Cardiometabolic traits. | He Q et al. | — | 2022 | → |
| Folliculin-interacting protein FNIP2 impacts on overweight and obesity through a polymorphism in a conserved 3' untranslated region. | Fernández LP et al. | — | 2022 | → |
| Genetic and clinical determinants of abdominal aortic diameter: genome-wide association studies, exome array data and Mendelian randomization study. | Portilla-Fernandez E et al. | — | 2022 | → |
| Genetic and phenotypic links between obesity and extracellular vesicles. | Zhai R et al. | — | 2022 | → |
| Genetic architecture of heart failure with preserved versus reduced ejection fraction. | Joseph J et al. | — | 2022 | → |
| Genetic association-based functional analysis detects <i>HOGA1</i> as a potential gene involved in fat accumulation. | Kim M et al. | — | 2022 | → |
| Genetic evidence for a causal relationship between type 2 diabetes and peripheral artery disease in both Europeans and East Asians. | Xiu X et al. | — | 2022 | → |
| Genetic liability to obesity and peptic ulcer disease: a Mendelian randomization study. | Li Z et al. | — | 2022 | → |
| Genetic regulation of serum IgA levels and susceptibility to common immune, infectious, kidney, and cardio-metabolic traits. | Liu L et al. | — | 2022 | → |
| Genetics, genomics, and diet interactions in obesity in the Latin American environment. | Guevara-Ramírez P et al. | — | 2022 | → |
| Genome-wide association analyses of physical activity and sedentary behavior provide insights into underlying mechanisms and roles in disease prevention. | Wang Z et al. | — | 2022 | → |
| Genome-Wide Association Analysis of Over 170,000 Individuals from the UK Biobank Identifies Seven Loci Associated with Dietary Approaches to Stop Hypertension (DASH) Diet. | Mompeo O et al. | — | 2022 | → |
| Genome-Wide Association Study of Exercise-Induced Fat Loss Efficiency. | Bojarczuk A et al. | — | 2022 | → |
| Genome-wide rare variant score associates with morphological subtypes of autism spectrum disorder. | Chan AJS et al. | — | 2022 | → |
| Genomics and phenomics of body mass index reveals a complex disease network. | Huang J et al. | — | 2022 | → |
| Healthy beverages may reduce the genetic risk of abdominal obesity and related metabolic comorbidities: a gene-diet interaction study in Iranian women. | Gholami F et al. | — | 2022 | → |
| Higher Waist Hip Ratio Genetic Risk Score Is Associated with Reduced Weight Loss in Patients with Severe Obesity Completing a Meal Replacement Programme. | Handley D et al. | — | 2022 | → |
| How does age determine the development of human immune-mediated arthritis? | Degboe Y et al. | — | 2022 | → |
| How dysregulation of the immune system promotes diabetes mellitus and cardiovascular risk complications. | Girard D et al. | — | 2022 | → |
| Identification of hub genes and candidate herbal treatment in obesity through integrated bioinformatic analysis and reverse network pharmacology. | Tai Y et al. | — | 2022 | → |
| Identification of potentially common loci between childhood obesity and coronary artery disease using pleiotropic approaches. | Wang L et al. | — | 2022 | → |
| <i>dSec16</i> Acting in Insulin-like Peptide Producing Cells Controls Energy Homeostasis in <i>Drosophila</i>. | Zhang RX et al. | — | 2022 | → |
| Influence of CLOCK Gene Variants on Weight Response after Bariatric Surgery. | Torrego-Ellacuría M et al. | — | 2022 | → |
| Insights into the constellating drivers of satiety impacting dietary patterns and lifestyle. | Rakha A et al. | — | 2022 | → |
| Interaction-based Mendelian randomization with measured and unmeasured gene-by-covariate interactions. | Spiller W et al. | — | 2022 | → |
| Leveraging global multi-ancestry meta-analysis in the study of idiopathic pulmonary fibrosis genetics. | Partanen JJ et al. | — | 2022 | → |
| Leveraging pleiotropy for joint analysis of genome-wide association studies with per trait interpretations. | Taraszka K et al. | — | 2022 | → |
| <i>Keratinocyte-associated protein 3</i> plays a role in body weight and adiposity with differential effects in males and females. | Szalanczy AM et al. | — | 2022 | → |
| Mendelian randomization analysis of the causal association of bone mineral density and fracture with multiple sclerosis. | Yao Y et al. | — | 2022 | → |
| Mendelian randomization prioritizes abdominal adiposity as an independent causal factor for liver fat accumulation and cardiometabolic diseases. | Gagnon E et al. | — | 2022 | → |
| Meta-analysis fine-mapping is often miscalibrated at single-variant resolution. | Kanai M et al. | — | 2022 | → |
| Microbiota Modulation in Patients with Metabolic Syndrome. | Araujo R et al. | — | 2022 | → |
| Modelling metabolic diseases and drug response using stem cells and organoids. | Hu W et al. | — | 2022 | → |
| Multiancestry exome sequencing reveals INHBE mutations associated with favorable fat distribution and protection from diabetes. | Akbari P et al. | — | 2022 | → |
| Multigenerational metabolic disruption: Developmental origins and mechanisms of propagation across generations. | Davis DD et al. | — | 2022 | → |
| Next-Generation Sequencing of a Large Gene Panel for Outcome Prediction of Bariatric Surgery in Patients with Severe Obesity. | Bonetti G et al. | — | 2022 | → |
| Nicotine dose-dependent epigenomic-wide DNA methylation changes in the mice with long-term electronic cigarette exposure. | Peng G et al. | — | 2022 | → |
| Obesity, Fat Distribution and Risk of Cancer in Women and Men: A Mendelian Randomisation Study. | Loh NY et al. | — | 2022 | → |
| Obesity-related biomarkers underlie a shared genetic architecture between childhood body mass index and childhood asthma. | Han X et al. | — | 2022 | → |
| Phenotype-informed polygenic risk scores are associated with worse outcome in individuals at risk of Alzheimer's disease. | Nordengen K et al. | — | 2022 | → |
| Physical activity and risk of multiple sclerosis: A Mendelian randomization study. | Li C et al. | — | 2022 | → |
| Polygenic power calculator: Statistical power and polygenic prediction accuracy of genome-wide association studies of complex traits. | Wu T et al. | — | 2022 | → |
| Population-level variation in enhancer expression identifies disease mechanisms in the human brain. | Dong P et al. | — | 2022 | → |
| Prenatal ozone exposure programs a sexually dimorphic susceptibility to high-fat diet in adolescent Long Evans rats. | Stewart EJ et al. | — | 2022 | → |
| Quality Control Procedures for Genome-Wide Association Studies. | Truong VQ et al. | — | 2022 | → |
| Quantifying the phenome-wide disease burden of obesity using electronic health records and genomics. | Robinson JR et al. | — | 2022 | → |
| Risk variants of obesity associated genes demonstrate BMI raising effect in a large cohort. | Saqlain M et al. | — | 2022 | → |
| scGWAS: landscape of trait-cell type associations by integrating single-cell transcriptomics-wide and genome-wide association studies. | Jia P et al. | — | 2022 | → |
| Sex Differences in Adiposity and Cardiovascular Diseases. | Li H et al. | — | 2022 | → |
| Sex-specific epigenetic development in the mouse hypothalamic arcuate nucleus pinpoints human genomic regions associated with body mass index. | MacKay H et al. | — | 2022 | → |
| Simulated distributions from negative experiments highlight the importance of the body mass index distribution in explaining depression-body mass index genetic risk score interactions. | Casanova F et al. | — | 2022 | → |
| Single-Cell Chromatin Accessibility Data Combined with GWAS Improves Detection of Relevant Cell Types in 59 Complex Phenotypes. | Das AC et al. | — | 2022 | → |
| Single nucleotide polymorphism genes and mitochondrial DNA haplogroups as biomarkers for early prediction of knee osteoarthritis structural progressors: use of supervised machine learning classifiers. | Bonakdari H et al. | — | 2022 | → |
| Strategies to identify causal common genetic variants and corresponding effector genes for paediatric obesity. | Littleton SH et al. | — | 2022 | → |
| The association between adiposity and atypical energy-related symptoms of depression: a role for metabolic dysregulations | Alshehri T et al. | — | 2022 | — |
| The association of obesity-related traits on COVID-19 severity and hospitalization is affected by socio-economic status: a multivariable Mendelian randomization study. | Cabrera-Mendoza B et al. | — | 2022 | → |
| The effect of heteroscedasticity on the prediction efficiency of genome-wide polygenic score for body mass index. | Baek EJ et al. | — | 2022 | → |
| The HUNT study: A population-based cohort for genetic research. | Brumpton BM et al. | — | 2022 | → |
| The impact of SNP density on quantitative genetic analyses of body size traits in a wild population of Soay sheep. | James C et al. | — | 2022 | → |
| The link between liver fat and cardiometabolic diseases is highlighted by genome-wide association study of MRI-derived measures of body composition. | van der Meer D et al. | — | 2022 | → |
| The Role of Molecular and Hormonal Factors in Obesity and the Effects of Physical Activity in Children. | Aragón-Vela J et al. | — | 2022 | → |
| The Sexual Dimorphism of Human Adipose Depots. | Boulet N et al. | — | 2022 | → |
| Transcriptomic Differences Between Monozygotic Adolescent Twins Discordant For Metabolic Syndrome Following Weight Loss: A Case Study. | Day K et al. | — | 2022 | → |
| Triglyceride-glucose index and the risk of heart failure: Evidence from two large cohorts and a mendelian randomization analysis. | Li X et al. | — | 2022 | → |