Human Genetics最新文献

{"title":"Structure-informed protein language models are robust predictors for variant effects.","authors":"Yuanfei Sun, Yang Shen","doi":"10.1007/s00439-024-02695-w","DOIUrl":"10.1007/s00439-024-02695-w","url":null,"abstract":"<p><p>Emerging variant effect predictors, protein language models (pLMs) learn evolutionary distribution of functional sequences to capture fitness landscape. Considering that variant effects are manifested through biological contexts beyond sequence (such as structure), we first assess how much structure context is learned in sequence-only pLMs and affecting variant effect prediction. And we establish a need to inject into pLMs protein structural context purposely and controllably. We thus introduce a framework of structure-informed pLMs (SI-pLMs), by extending masked sequence denoising to cross-modality denoising for both sequence and structure. Numerical results over deep mutagenesis scanning benchmarks show that our SI-pLMs, even when using smaller models and less data, are robustly top performers against competing methods including other pLMs, which shows that introducing biological context can be more effective at capturing fitness landscape than simply using larger models or bigger data. Case studies reveal that, compared to sequence-only pLMs, SI-pLMs can be better at capturing fitness landscape because (a) learned embeddings of low/high-fitness sequences can be more separable and (b) learned amino-acid distributions of functionally and evolutionarily conserved residues can be of much lower entropy, thus much more conserved, than other residues. Our SI-pLMs are applicable to revising any sequence-only pLMs through model architecture and training objectives. They do not require structure data as model inputs for variant effect prediction and only use structures as context provider and model regularizer during training.</p>","PeriodicalId":13175,"journal":{"name":"Human Genetics","volume":" ","pages":"209-225"},"PeriodicalIF":3.8,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12068927/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141906463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of enzyme activity predictions for variants of unknown significance in Arylsulfatase A.","authors":"Shantanu Jain, Marena Trinidad, Thanh Binh Nguyen, Kaiya Jones, Santiago Diaz Neto, Fang Ge, Ailin Glagovsky, Cameron Jones, Giankaleb Moran, Boqi Wang, Kobra Rahimi, Sümeyra Zeynep Çalıcı, Luis R Cedillo, Silvia Berardelli, Buse Özden, Ken Chen, Panagiotis Katsonis, Amanda Williams, Olivier Lichtarge, Sadhna Rana, Swatantra Pradhan, Rajgopal Srinivasan, Rakshanda Sajeed, Dinesh Joshi, Eshel Faraggi, Robert Jernigan, Andrzej Kloczkowski, Jierui Xu, Zigang Song, Selen Özkan, Natàlia Padilla, Xavier de la Cruz, Rocio Acuna-Hidalgo, Andrea Grafmüller, Laura T Jiménez Barrón, Matteo Manfredi, Castrense Savojardo, Giulia Babbi, Pier Luigi Martelli, Rita Casadio, Yuanfei Sun, Shaowen Zhu, Yang Shen, Fabrizio Pucci, Marianne Rooman, Gabriel Cia, Daniele Raimondi, Pauline Hermans, Sofia Kwee, Ella Chen, Courtney Astore, Akash Kamandula, Vikas Pejaver, Rashika Ramola, Michelle Velyunskiy, Daniel Zeiberg, Reet Mishra, Teague Sterling, Jennifer L Goldstein, Jose Lugo-Martinez, Sufyan Kazi, Sindy Li, Kinsey Long, Steven E Brenner, Constantina Bakolitsa, Predrag Radivojac, Dean Suhr, Teryn Suhr, Wyatt T Clark","doi":"10.1007/s00439-025-02731-3","DOIUrl":"10.1007/s00439-025-02731-3","url":null,"abstract":"<p><p>Continued advances in variant effect prediction are necessary to demonstrate the ability of machine learning methods to accurately determine the clinical impact of variants of unknown significance (VUS). Towards this goal, the ARSA Critical Assessment of Genome Interpretation (CAGI) challenge was designed to characterize progress by utilizing 219 experimentally assayed missense VUS in the Arylsulfatase A (ARSA) gene to assess the performance of community-submitted predictions of variant functional effects. The challenge involved 15 teams, and evaluated additional predictions from established and recently released models. Notably, a model developed by participants of a genetics and coding bootcamp, trained with standard machine-learning tools in Python, demonstrated superior performance among submissions. Furthermore, the study observed that state-of-the-art deep learning methods provided small but statistically significant improvement in predictive performance compared to less elaborate techniques. These findings underscore the utility of variant effect prediction, and the potential for models trained with modest resources to accurately classify VUS in genetic and clinical research.</p>","PeriodicalId":13175,"journal":{"name":"Human Genetics","volume":" ","pages":"295-308"},"PeriodicalIF":3.8,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12122056/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143585545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An augmented transformer model trained on protein family specific variant data leads to improved prediction of variants of uncertain significance.","authors":"Dinesh Joshi, Swatantra Pradhan, Rakshanda Sajeed, Rajgopal Srinivasan, Sadhna Rana","doi":"10.1007/s00439-025-02727-z","DOIUrl":"10.1007/s00439-025-02727-z","url":null,"abstract":"<p><p>Variants of uncertain significance (VUS) represent variants that lack sufficient evidence to be confidently associated with a disease, thus posing a challenge in the interpretation of genetic testing results. Here we report an improved method for predicting the VUS of Arylsulfatase A (ARSA) gene as part of the Critical Assessment of Genome Interpretation challenge (CAGI6). Our method uses a transfer learning approach that leverages a pre-trained protein language model to predict the impact of mutations on the activity of the ARSA enzyme, whose deficiency is known to cause a rare genetic disorder, metachromatic leukodystrophy. Our innovative framework combines zero-shot log odds scores and embeddings from the ESM, an evolutionary scale model as features for training a supervised model on gene variants functionally related to the ARSA gene. The zero-shot log odds score feature captures the generic properties of the proteins learned due to its pre-training on millions of sequences in the UniProt data, while the ESM embeddings for the proteins in the ARSA family capture features specific to the family. We also tested our approach on another enzyme, N-acetyl-glucosaminidase (NAGLU), that belongs to the same superfamily as ARSA. Our results demonstrate that the performance of our family models (augmented ESM models) is either comparable or better than the ESM models. The ARSA model compares favorably with the majority of state-of-the-art predictors on area under precision and recall curve (AUPRC) performance metric. However, the NAGLU model outperforms all pathogenicity predictors evaluated in this study on AUPRC metric. The improved AUPRC has relevance in a diagnostic setting where variant prioritization generally entails identifying a small number of pathogenic variants from a larger number of benign variants. Our results also indicate that genes that have sparse or no experimental variant impact data, the family variant data can serve as a proxy training data for making accurate predictions. Attention analysis of active sites and binding sites in ARSA and NAGLU proteins shed light on probable mechanisms of pathogenicity for positions that are highly attended.</p>","PeriodicalId":13175,"journal":{"name":"Human Genetics","volume":" ","pages":"143-158"},"PeriodicalIF":3.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12166291/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143046678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the predicted impact of single amino acid substitutions in calmodulin for CAGI6 challenges.","authors":"Paola Turina, Giuditta Dal Cortivo, Carlos A Enriquez Sandoval, Emil Alexov, David B Ascher, Giulia Babbi, Constantina Bakolitsa, Rita Casadio, Piero Fariselli, Lukas Folkman, Akash Kamandula, Panagiotis Katsonis, Dong Li, Olivier Lichtarge, Pier Luigi Martelli, Shailesh Kumar Panday, Douglas E V Pires, Stephanie Portelli, Fabrizio Pucci, Carlos H M Rodrigues, Marianne Rooman, Castrense Savojardo, Martin Schwersensky, Yang Shen, Alexey V Strokach, Yuanfei Sun, Junwoo Woo, Predrag Radivojac, Steven E Brenner, Daniele Dell'Orco, Emidio Capriotti","doi":"10.1007/s00439-024-02720-y","DOIUrl":"10.1007/s00439-024-02720-y","url":null,"abstract":"<p><p>Recent thermodynamic and functional studies have been conducted to evaluate the impact of amino acid substitutions on Calmodulin (CaM). The Critical Assessment of Genome Interpretation (CAGI) data provider at University of Verona (Italy) measured the melting temperature (T_m) and the percentage of unfolding (%unfold) of a set of CaM variants (CaM challenge dataset). Thermodynamic measurements for the equilibrium unfolding of CaM were obtained by monitoring far-UV Circular Dichroism as a function of temperature. These measurements were used to determine the T_m and the percentage of protein remaining unfolded at the highest temperature. The CaM challenge dataset, comprising a total of 15 single amino acid substitutions, was used to evaluate the effectiveness of computational methods in predicting the T_m and unfolding percentages associated with the variants, and categorizing them as destabilizing or not. For the sixth edition of CAGI, nine independent research groups from four continents (Asia, Australia, Europe, and North America) submitted over 52 sets of predictions, derived from various approaches. In this manuscript, we summarize the results of our assessment to highlight the potential limitations of current algorithms and provide insights into the future development of more accurate prediction tools. By evaluating the thermodynamic stability of CaM variants, this study aims to enhance our understanding of the relationship between amino acid substitutions and protein stability, ultimately contributing to more accurate predictions of the effects of genetic variants.</p>","PeriodicalId":13175,"journal":{"name":"Human Genetics","volume":" ","pages":"113-125"},"PeriodicalIF":3.8,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11975486/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142876860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating predictors of kinase activity of STK11 variants identified in primary human non-small cell lung cancers.","authors":"Yile Chen, Kyoungyeul Lee, Junwoo Woo, Dong-Wook Kim, Changwon Keum, Giulia Babbi, Rita Casadio, Pier Luigi Martelli, Castrense Savojardo, Matteo Manfredi, Yang Shen, Yuanfei Sun, Panagiotis Katsonis, Olivier Lichtarge, Vikas Pejaver, David J Seward, Akash Kamandula, Constantina Bakolitsa, Steven E Brenner, Predrag Radivojac, Anne O'Donnell-Luria, Sean D Mooney, Shantanu Jain","doi":"10.1007/s00439-025-02726-0","DOIUrl":"10.1007/s00439-025-02726-0","url":null,"abstract":"<p><p>Critical evaluation of computational tools for predicting variant effects is important considering their increased use in disease diagnosis and driving molecular discoveries. In the sixth edition of the Critical Assessment of Genome Interpretation (CAGI) challenge, a dataset of 28 STK11 rare variants (27 missense, 1 single amino acid deletion), identified in primary non-small cell lung cancer biopsies, was experimentally assayed to characterize computational methods from four participating teams and five publicly available tools. Predictors demonstrated a high level of performance on key evaluation metrics, measuring correlation with the assay outputs and separating loss-of-function (LoF) variants from wildtype-like (WT-like) variants. The best participant model, 3Cnet, performed competitively with well-known tools. Unique to this challenge was that the functional data was generated with both biological and technical replicates, thus allowing the assessors to realistically establish maximum predictive performance based on experimental variability. Three out of the five publicly available tools and 3Cnet approached the performance of the assay replicates in separating LoF variants from WT-like variants. Surprisingly, REVEL, an often-used model, achieved a comparable correlation with the real-valued assay output as that seen for the experimental replicates. Performing variant interpretation by combining the new functional evidence with computational and population data evidence led to 16 new variants receiving a clinically actionable classification of likely pathogenic (LP) or likely benign (LB). Overall, the STK11 challenge highlights the utility of variant effect predictors in biomedical sciences and provides encouraging results for driving research in the field of computational genome interpretation.</p>","PeriodicalId":13175,"journal":{"name":"Human Genetics","volume":" ","pages":"127-142"},"PeriodicalIF":3.8,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11976797/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143399119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrative analysis of transcriptome and proteome wide association studies prioritized functional genes for obesity.","authors":"Qi-Gang Zhao, Xin-Ling Ma, Qian Xu, Zi-Tong Song, Fan Bu, Kuan Li, Bai-Xue Han, Shan-Shan Yan, Lei Zhang, Yuan Luo, Yu-Fang Pei","doi":"10.1007/s00439-024-02714-w","DOIUrl":"10.1007/s00439-024-02714-w","url":null,"abstract":"<p><strong>Background: </strong>Genome-wide association studies have identified dozens of genomic loci for obesity. However, functional genes and their detailed genetic mechanisms underlying these loci are mainly unknown. In this study, we conducted an integrative study to prioritize plausibly functional genes by combining information from genome-, transcriptome- and proteome-wide association analyses.</p><p><strong>Methods: </strong>We first conducted proteome-wide association analyses and transcriptome-wide association analyses for the six obesity-related traits. We then performed colocalization analysis on the identified loci shared between the proteome- and transcriptome-association analyses. Finally, we validated the identified genes with other plasma/blood reference panels. The highlighted genes were assessed for expression of other tissues, single-cell and tissue specificity, and druggability.</p><p><strong>Results: </strong>We prioritized 4 high-confidence genes (FASN, ICAM1, PDCD6IP, and YWHAB) by proteome-wide association studies, transcriptome-wide association studies, and colocalization analyses, which consistently influenced the variation of obesity traits at both mRNA and protein levels. These 4 genes were successfully validated using other plasma/blood reference panels. These 4 genes shared regulatory structures in obesity-related tissues. Single-cell and tissue-specific analyses showed that FASN and ICAM1 were explicitly expressed in metabolism- and immunity-related tissues and cells. Furthermore, FASN and ICAM1 had been developed as drug targets.</p><p><strong>Conclusion: </strong>Our study provided novel promising protein targets for further mechanistic and therapeutic studies of obesity.</p>","PeriodicalId":13175,"journal":{"name":"Human Genetics","volume":" ","pages":"31-41"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142568057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating transcriptomic and polygenic risk scores to enhance predictive accuracy for ischemic stroke subtypes.","authors":"Xuehong Cai, Haochang Li, Xiaoxiao Cao, Xinyan Ma, Wenhao Zhu, Lei Xu, Sheng Yang, Rongbin Yu, Peng Huang","doi":"10.1007/s00439-024-02717-7","DOIUrl":"10.1007/s00439-024-02717-7","url":null,"abstract":"<p><p>Ischemic stroke (IS), characterized by complex etiological diversity, is a significant global health challenge. Recent advancements in genome-wide association studies (GWAS) and transcriptomic profiling offer promising avenues for enhanced risk prediction and understanding of disease mechanisms. GWAS summary statistics from the GIGASTROKE Consortium and genetic and phenotypic data from the UK Biobank (UKB) were used. Transcriptome-Wide Association Studies (TWAS) were conducted using FUSION to identify genes associated with IS and its subtypes across eight tissues. Colocalization analysis identified shared genetic variants influencing both gene expression and disease risk. Sum Transcriptome-Polygenic Risk Scores (STPRS) models were constructed by combining polygenic risk scores (PRS) and polygenic transcriptome risk scores (PTRS) using logistic regression. The predictive performance of STPRS was evaluated using the area under the curve (AUC). A Phenome-wide association study (PheWAS) explored associations between STPRS and various phenotypes. TWAS identified 34 susceptibility genes associated with IS and its subtypes. Colocalization analysis revealed 18 genes with a posterior probability (PP) H4 > 75% for joint expression quantitative trait loci (eQTL) and GWAS associations, highlighting their genetic relevance. The STPRS models demonstrated superior predictive accuracy compared to conventional PRS, showing significant associations with numerous UKB phenotypes, including atrial fibrillation and blood pressure. Integrating transcriptomic data with polygenic risk scores through STPRS enhances predictive accuracy for IS and its subtypes. This approach refines our understanding of the genetic and molecular landscape of stroke and paves the way for tailored preventive and therapeutic strategies.</p>","PeriodicalId":13175,"journal":{"name":"Human Genetics","volume":" ","pages":"43-54"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142647531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genetic landscape in undiagnosed patients with syndromic hearing loss revealed by whole exome sequencing and phenotype similarity search.","authors":"Hideki Mutai, Fuyuki Miya, Kiyomitsu Nara, Nobuko Yamamoto, Satomi Inoue, Haruka Murakami, Kazunori Namba, Hiroshi Shitara, Shujiro Minami, Atsuko Nakano, Yukiko Arimoto, Noriko Morimoto, Taiji Kawasaki, Koichiro Wasano, Masato Fujioka, Yasue Uchida, Kimitaka Kaga, Kazuki Yamazawa, Yoshiaki Kikkawa, Kenjiro Kosaki, Tatsuhiko Tsunoda, Tatsuo Matsunaga","doi":"10.1007/s00439-024-02719-5","DOIUrl":"10.1007/s00439-024-02719-5","url":null,"abstract":"<p><p>There are hundreds of rare syndromic diseases involving hearing loss, many of which are not targeted for clinical genetic testing. We systematically explored the genetic causes of undiagnosed syndromic hearing loss using a combination of whole exome sequencing (WES) and a phenotype similarity search system called PubCaseFinder. Fifty-five families with syndromic hearing loss of unknown cause were analyzed using WES after prescreening of several deafness genes depending on patient clinical features. Causative genes were identified in 22 families, including both established genes associated with syndromic hearing loss (PTPN11, CHD7, KARS1, OPA1, DLX5, MITF, SOX10, MYO7A, and USH2A) and those associated with nonsyndromic hearing loss (STRC, EYA4, and KCNQ4). Association of a DLX5 variant with incomplete partition type I (IP-I) anomaly of the inner ear was identified in a patient with cleft lip and palate and acetabular dysplasia. The study identified COL1A1, CFAP52, and NSD1 as causative genes through phenotype similarity search or by analogy. ZBTB10 was proposed as a novel candidate gene for syndromic hearing loss with IP-I. A mouse model with homozygous Zbtb10 frameshift variant resulted in embryonic lethality, suggesting the importance of this gene for early embryonic development. Our data highlight a wide spectrum of rare causative genes in patients with syndromic hearing loss, and demonstrate that WES analysis combined with phenotype similarity search is a valuable approach for clinical genetic testing of undiagnosed disease.</p>","PeriodicalId":13175,"journal":{"name":"Human Genetics","volume":" ","pages":"93-112"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142927239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The obesity-related mutation gene on nonalcoholic fatty liver disease.","authors":"Yen-Yu Chen, Chi-Sheng Chen, Jee-Fu Huang, Wen-Hsiu Su, Chia-Yang Li, Wei-Shiun Chen, En-Sheng Lin, Wan-Long Chuang, Ming-Lung Yu, Shu-Chi Wang","doi":"10.1007/s00439-024-02686-x","DOIUrl":"10.1007/s00439-024-02686-x","url":null,"abstract":"<p><p>The prevalence of overweight and obesity is increasing, leading to metabolic-associated fatty liver disease (MAFLD) characterized by excessive accumulation of liver fat and a risk of developing hepatocellular carcinoma (HCC). The driver gene mutations may play the roles of passengers that occur in single 'hotspots' and can promote tumorigenesis from benign to malignant lesions. We investigated the impact of high body weight and BMI on HCC survival using The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) dataset. To explore the effects of obesity-related gene mutations on HCC, we collected driver mutation genes in 34 TCGA patients with BMI ≥ 27 and 23 TCGA patients with BMI < 27. The digital PCR performing the PBMC samples for the variant rate by clinical cohort of 96 NAFLD patients. Our analysis showed that obesity leads to significantly worse survival outcomes in HCC. Using cbioportal, we identified 414 driver mutation genes in patients with obesity and 127 driver mutation genes in non-obese patients. Functional analysis showed that obese-related genes significantly enriched the regulated lipid and insulin pathways in HCC. The insulin secretion pathway in patients with obesity HCC-specific survival identified ABCC8 and PRKCB as significant genes (p < 0.001). It revealed significant differences in gene mutation and gene expression profiles compared to non-obese patients. The digital PCR test ABCC8 variants were detected in PBMC samples and caused a 14.5% variant rate, significantly higher than that of non-obese NAFLD patients. The study findings showed that the gene ABCC8 was a patient with the obesity-related gene in NAFLD, which provides the probability that ABCC8 mutation contributes to the pre-cancer lesion biomarker for HCC.</p>","PeriodicalId":13175,"journal":{"name":"Human Genetics","volume":" ","pages":"1-14"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141563310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"T1R2/T1R3 polymorphism affects sweet and fat perception: Correlation between SNP and BMI in the context of obesity development.","authors":"Vinithra Ponnusamy, Gowtham Subramanian, Keerthana Vasanthakumar, Karthi Muthuswamy, Prabha Panneerselvan, Vasanth Krishnan, Selvakumar Subramaniam","doi":"10.1007/s00439-024-02690-1","DOIUrl":"10.1007/s00439-024-02690-1","url":null,"abstract":"<p><p>Genetic variations in taste receptors are associated with gustatory perception and obesity, which in turn affects dietary preferences. Given the increasing tendency of people with obesity choosing sweet, high-fat meals, the current study assessed the cross-regulation of two polymorphisms of the sweet taste receptor (T1R2/T1R3), rs35874116 and rs307355, on fat sensitivity in Indian adults. We investigated the association between taste sensitivity and BMI in the T1R2, T1R3, and CD36 polymorphic and non-polymorphic groups. The general labelled magnitude scale (gLMS) was used to assess the taste sensitivity of 249 participants in addition to anthropometric data. TaqMan Probe-based RT-PCR was employed to determine the polymorphisms. Additionally, the colorimetric method utilizing 3, 5-dinitro salicylic acid was used to evaluate the participants' salivary amylase activity. The mean detection thresholds for linoleic acid (LA) and sucrose were greater in individuals with obesity (i.e., 0.97 ± 0.08 mM and 0.22 ± 0.02 M, respectively) than in healthy adults (p < 0.0001), indicating lower sensitivity. Moreover, it was found that a greater proportion of persons with obesity fall into the polymorphic groups (i.e., 52% with genotype CD36 AA, 44% with genotype T1R2 CC, and 40% with genotype T1R3 TT). All three single nucleotide polymorphisms support the Hardy-Weinberg equilibrium (p = 0.78). The Pearson correlation analysis between LA and the sucrose detection threshold revealed a significant (p < 0.0001) positive relationship with an r value of 0.5299. Moreover, salivary amylase activity was significantly (p < 0.05) higher in the polymorphic sub-groups. The results of our study imply that genetic variations in T1R2/T1R3 receptors affect perception of both sweetness and fat, which may have an effect on obesity.</p>","PeriodicalId":13175,"journal":{"name":"Human Genetics","volume":" ","pages":"15-30"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141897334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}