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Li S, Liu L, Ahmed Z, Wang F, Lei C, Sun F. Identification of Heilongjiang crossbred beef cattle pedigrees and reveals functional genes related to economic traits based on whole-genome SNP data. Front Genet 2024; 15:1435793. [PMID: 39119576 PMCID: PMC11306169 DOI: 10.3389/fgene.2024.1435793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Accepted: 07/08/2024] [Indexed: 08/10/2024] Open
Abstract
Introduction: To enhance the beef cattle industry, Heilongjiang Province has developed a new Crossbred beef cattle variety through crossbreeding with exotic commercial breeds. This new variety exhibits relatively excellent meat quality, and efficient reproductive performance, catering to market demands. Method: This study employed whole genome resequencing technology to analyze the genetic pedigree and diversity of 19 Heilongjiang Crossbred beef cattle, alongside 59 published genomes from East Asian, Eurasian, and European taurine cattle as controls. In addition, genes related to production traits were also searched by identifying Runs of Homozygosity (ROH) islands and important fragments from ancestors. Results: A total of 14,427,729 biallelic SNPs were discovered, with the majority located in intergenic and intron regions and a small percentage in exon regions, impacting protein function. Population genetic analyses including Principal Component Analysis (PCA), Neighbor-Joining (NJ) tree, and ADMIXTURE identified Angus, Holstein, and Mishima as the main ancestors of Crossbred beef cattle. In genetic diversity analysis, nucleotide diversity, linkage disequilibrium, and inbreeding coefficient analysis reveal that the genetic diversity of Crossbred beef cattle is at a moderate level, and a higher inbreeding coefficient indicates the need for careful breeding management. In addition, some genes related to economic traits are identified through the identification of Runs of Homozygosity (ROH) islands and important fragments from ancestors. Conclusion: This comprehensive genomic characterization supports the targeted improvement of economically important traits in Crossbred beef cattle, facilitating advanced breeding strategies.
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Affiliation(s)
- Shuang Li
- Key Laboratory of Combining Farming and Animal Husbandry of Ministry of Agriculture, Institute of Animal Husbandry, Heilongjiang Academy of Agricultural Sciences, Harbin, China
- College of Animal Science and Technology, Northwest A&F University, Yangling, China
| | - Li Liu
- Key Laboratory of Combining Farming and Animal Husbandry of Ministry of Agriculture, Institute of Animal Husbandry, Heilongjiang Academy of Agricultural Sciences, Harbin, China
| | - Zulfiqar Ahmed
- Department of Livestock and Poultry Production, Faculty of Veterinary and Animal Sciences, University of Poonch Rawalakot, Azad Kashmir, Pakistan
| | - Fuwen Wang
- College of Animal Science and Technology, Northwest A&F University, Yangling, China
| | - Chuzhao Lei
- College of Animal Science and Technology, Northwest A&F University, Yangling, China
| | - Fang Sun
- Key Laboratory of Combining Farming and Animal Husbandry of Ministry of Agriculture, Institute of Animal Husbandry, Heilongjiang Academy of Agricultural Sciences, Harbin, China
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Song D, Yang F, Sun Y, Wu X, Zhou Q, Bi W, Sun J, Li S, Yu Y. Single-cell RNA sequencing reveals the heterogeneity of epithelial cell and fibroblast cells from non- to metastatic lymph node OTSCC. FASEB J 2024; 38:e23390. [PMID: 38169064 DOI: 10.1096/fj.202301724r] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Revised: 11/20/2023] [Accepted: 12/13/2023] [Indexed: 01/05/2024]
Abstract
Lymph node metastasis (LNM) is one of the common features of oral tongue squamous cell carcinoma (OTSCC). LNM is also taken as a sign of advanced OTSCC and poor survival rate. Recently, single-cell RNA sequencing has been applied in investigating the heterogeneity of tumor microenvironment and discovering the potential biomarkers for helping the diagnosis and prognosticating. Pathogenesis of LNM in OTSCC remains unknown. Specifically, cancer-associated fibroblasts (CAFs) and epithelial tumor cells could foster the progression of tumors. Thus, in this study, we aimed to comprehensively analyze the roles of subpopulations of CAFs and epithelial tumor cells in lymph node metastatic OTSCC using the integration of OTSCC single-cell RNA sequencing datasets. Four distinct subtypes of CAFs, namely vascular CAFs, myofibroblast CAFs, inflammatory CAFs, and growth arrest CAFs were successfully discovered in LNM tumor and confirmed the roles of GAS and PTN pathways in the progression of tumor metastasis. In addition, NKAIN2+ epithelial cells and FN1+ epithelial cells specifically exhibited an upregulation of PTN, NRG, MIF, and SPP1 signaling pathways in the metastatic OTSCC. In doing so, we put forth some potential biomarkers that could be utilized for the purpose of diagnosing and prognosticating OTSCC during its metastatic phase and tried to confirm by immunofluorescence assays.
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Affiliation(s)
- Dandan Song
- Department of Stomatology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Fei Yang
- Department of Stomatology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yang Sun
- Department of Stomatology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Xingwen Wu
- Department of Stomatology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Qianrong Zhou
- Department of Stomatology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Wei Bi
- Department of Stomatology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Jian Sun
- Department of Stomatology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Siyi Li
- Department of Oral Maxillofacial-Head and Neck Oncology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Youcheng Yu
- Department of Stomatology, Zhongshan Hospital, Fudan University, Shanghai, China
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Armstrong ND, Srinivasasainagendra V, Ammous F, Assimes TL, Beitelshees AL, Brody J, Cade BE, Ida Chen YD, Chen H, de Vries PS, Floyd JS, Franceschini N, Guo X, Hellwege JN, House JS, Hwu CM, Kardia SLR, Lange EM, Lange LA, McDonough CW, Montasser ME, O’Connell JR, Shuey MM, Sun X, Tanner RM, Wang Z, Zhao W, Carson AP, Edwards TL, Kelly TN, Kenny EE, Kooperberg C, Loos RJF, Morrison AC, Motsinger-Reif A, Psaty BM, Rao DC, Redline S, Rich SS, Rotter JI, Smith JA, Smith AV, Irvin MR, Arnett DK. Whole genome sequence analysis of apparent treatment resistant hypertension status in participants from the Trans-Omics for Precision Medicine program. Front Genet 2023; 14:1278215. [PMID: 38162683 PMCID: PMC10755672 DOI: 10.3389/fgene.2023.1278215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 11/24/2023] [Indexed: 01/03/2024] Open
Abstract
Introduction: Apparent treatment-resistant hypertension (aTRH) is characterized by the use of four or more antihypertensive (AHT) classes to achieve blood pressure (BP) control. In the current study, we conducted single-variant and gene-based analyses of aTRH among individuals from 12 Trans-Omics for Precision Medicine cohorts with whole-genome sequencing data. Methods: Cases were defined as individuals treated for hypertension (HTN) taking three different AHT classes, with average systolic BP ≥ 140 or diastolic BP ≥ 90 mmHg, or four or more medications regardless of BP (n = 1,705). A normotensive control group was defined as individuals with BP < 140/90 mmHg (n = 22,079), not on AHT medication. A second control group comprised individuals who were treatment responsive on one AHT medication with BP < 140/ 90 mmHg (n = 5,424). Logistic regression with kinship adjustment using the Scalable and Accurate Implementation of Generalized mixed models (SAIGE) was performed, adjusting for age, sex, and genetic ancestry. We assessed variants using SKAT-O in rare-variant analyses. Single-variant and gene-based tests were conducted in a pooled multi-ethnicity stratum, as well as self-reported ethnic/racial strata (European and African American). Results: One variant in the known HTN locus, KCNK3, was a top finding in the multi-ethnic analysis (p = 8.23E-07) for the normotensive control group [rs12476527, odds ratio (95% confidence interval) = 0.80 (0.74-0.88)]. This variant was replicated in the Vanderbilt University Medical Center's DNA repository data. Aggregate gene-based signals included the genes AGTPBP, MYL4, PDCD4, BBS9, ERG, and IER3. Discussion: Additional work validating these loci in larger, more diverse populations, is warranted to determine whether these regions influence the pathobiology of aTRH.
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Affiliation(s)
- Nicole D. Armstrong
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, United States
| | | | - Farah Ammous
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, United States
- Survey Research Center, Institute for Social Research, Ann Arbor, MI, United States
| | - Themistocles L. Assimes
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Palo Alto, CA, United States
| | - Amber L. Beitelshees
- Division of Endocrinology, Diabetes, and Nutrition, Program for Personalized and Genomic Medicine, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Jennifer Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, United States
| | - Brian E. Cade
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, United States
| | - Yii-Der Ida Chen
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, United States
| | - Han Chen
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United States
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Paul S. de Vries
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - James S. Floyd
- Department of Medicine, University of Washington, Seattle, WA, United States
- Department of Epidemiology, University of Washington, Seattle, WA, United States
| | - Nora Franceschini
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, United States
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, United States
| | - Jacklyn N. Hellwege
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, United States
| | - John S. House
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Durham, NC, United States
| | - Chii-Min Hwu
- Section of Endocrinology and Metabolism, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Sharon L. R. Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, United States
| | - Ethan M. Lange
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Leslie A. Lange
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Caitrin W. McDonough
- Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, FL, United States
| | - May E. Montasser
- Division of Endocrinology, Diabetes, and Nutrition, Program for Personalized and Genomic Medicine, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, United States
| | | | - Megan M. Shuey
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Xiao Sun
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, United States
| | - Rikki M. Tanner
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Zhe Wang
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Wei Zhao
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, United States
- Survey Research Center, Institute for Social Research, Ann Arbor, MI, United States
| | - April P. Carson
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, United States
| | - Todd L. Edwards
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, United States
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Tanika N. Kelly
- Division of Nephrology, Department of Medicine, College of Medicine, University of Illinois Chicago, Chicago, IL, United States
| | - Eimear E. Kenny
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| | - Ruth J. F. Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Alanna C. Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Alison Motsinger-Reif
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Durham, NC, United States
| | - Bruce M. Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, United States
- Department of Medicine, University of Washington, Seattle, WA, United States
- Department of Epidemiology, University of Washington, Seattle, WA, United States
| | - Dabeeru C. Rao
- Division of Biostatistics, School of Medicine, Washington University in St. Louis, St. Louis, MO, United States
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, United States
| | - Stephen S. Rich
- Department of Public Health Sciences, Center for Public Health Genomics, University of Virginia, Charlottesville, VA, United States
| | - Jerome I. Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, United States
| | - Jennifer A. Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, United States
- Survey Research Center, Institute for Social Research, Ann Arbor, MI, United States
| | - Albert V. Smith
- Center for Statistical Genetics, Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, United States
| | - Marguerite R. Irvin
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Donna K. Arnett
- Office of the Provost, University of South Carolina, Columbia, SC, United States
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Ding R, Zhuang Z, Qiu Y, Ruan D, Wu J, Ye J, Cao L, Zhou S, Zheng E, Huang W, Wu Z, Yang J. Identify known and novel candidate genes associated with backfat thickness in Duroc pigs by large-scale genome-wide association analysis. J Anim Sci 2022; 100:6509022. [PMID: 35034121 PMCID: PMC8867564 DOI: 10.1093/jas/skac012] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 01/14/2022] [Indexed: 01/18/2023] Open
Abstract
Backfat thickness (BFT) is complex and economically important traits in the pig industry, since it reflects fat deposition and can be used to measure the carcass lean meat percentage in pigs. In this study, all 6,550 pigs were genotyped using the Geneseek Porcine 50K SNP Chip to identify SNPs related to BFT and to search for candidate genes through genome-wide association analysis in two Duroc populations. In total, 80 SNPs, including 39 significant and 41 suggestive SNPs, and 6 QTLs were identified significantly associated with the BFT. In addition, 9 candidate genes, including a proven major gene MC4R, 3 important candidate genes (RYR1, HMGA1, and NUDT3) which were previously described as related to BFT, and 5 novel candidate genes (SIRT2, NKAIN2, AMH, SORCS1, and SORCS3) were found based on their potential functional roles in BFT. The functions of candidate genes and gene set enrichment analysis indicate that most important pathways are related to energy homeostasis and adipogenesis. Finally, our data suggest that most of the candidate genes can be directly used for genetic improvement through molecular markers, except that the MC4R gene has an antagonistic effect on growth rate and carcass lean meat percentage in breeding. Our results will advance our understanding of the complex genetic architecture of BFT traits and laid the foundation for additional genetic studies to increase carcass lean meat percentage of pig through marker-assisted selection and/or genomic selection.
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Affiliation(s)
- Rongrong Ding
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, 510642, P. R. China,Guangdong Wens Breeding Swine Technology Co., Ltd., Guangdong, 527400, P. R. China
| | - Zhanwei Zhuang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, 510642, P. R. China
| | - Yibin Qiu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, 510642, P. R. China
| | - Donglin Ruan
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, 510642, P. R. China
| | - Jie Wu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, 510642, P. R. China
| | - Jian Ye
- Guangdong Wens Breeding Swine Technology Co., Ltd., Guangdong, 527400, P. R. China
| | - Lu Cao
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, 510642, P. R. China
| | - Shenping Zhou
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, 510642, P. R. China
| | - Enqin Zheng
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, 510642, P. R. China,Lingnan Guangdong Laboratory of Modern Agriculture, Guangzhou, 510642, P. R. China
| | - Wen Huang
- Department of Animal Science, Michigan State University, East Lansing, MI, USA
| | - Zhenfang Wu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, 510642, P. R. China,Guangdong Wens Breeding Swine Technology Co., Ltd., Guangdong, 527400, P. R. China,Lingnan Guangdong Laboratory of Modern Agriculture, Guangzhou, 510642, P. R. China
| | - Jie Yang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, 510642, P. R. China,Lingnan Guangdong Laboratory of Modern Agriculture, Guangzhou, 510642, P. R. China,Corresponding authors:
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Asadollahpour Nanaei H, Esmailizadeh A, Ayatollahi Mehrgardi A, Han J, Wu DD, Li Y, Zhang YP. Comparative population genomic analysis uncovers novel genomic footprints and genes associated with small body size in Chinese pony. BMC Genomics 2020; 21:496. [PMID: 32689947 PMCID: PMC7370493 DOI: 10.1186/s12864-020-06887-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Accepted: 07/06/2020] [Indexed: 12/15/2022] Open
Abstract
Background Body size is considered as one of the most fundamental properties of an organism. Due to intensive breeding and artificial selection throughout the domestication history, horses exhibit striking variations for heights at withers and body sizes. Debao pony (DBP), a famous Chinese horse, is known for its small body size and lives in Guangxi mountains of southern China. In this study, we employed comparative population genomics to study the genetic basis underlying the small body size of DBP breed based on the whole genome sequencing data. To detect genomic signatures of positive selection, we applied three methods based on population comparison, fixation index (FST), cross population composite likelihood ratio (XP-CLR) and nucleotide diversity (θπ), and further analyzed the results to find genomic regions under selection for body size-related traits. Results A number of protein-coding genes in windows with the top 1% values of FST (367 genes), XP-CLR (681 genes), and log2 (θπ ratio) (332 genes) were identified. The most significant signal of positive selection was mapped to the NELL1 gene, probably underlies the body size and development traits, and may also have been selected for short stature in the DBP population. In addition, some other loci on different chromosomes were identified to be potentially involved in the development of body size. Conclusions Results of our study identified some positively selected genes across the horse genome, which are possibly involved in body size traits. These novel candidate genes may be useful targets for clarifying our understanding of the molecular basis of body size and as such they should be of great interest for future research into the genetic architecture of relevant traits in horse breeding program.
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Affiliation(s)
- Hojjat Asadollahpour Nanaei
- Department of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, PB, 76169-133, Iran
| | - Ali Esmailizadeh
- Department of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, PB, 76169-133, Iran. .,State Key Laboratory of Genetic Resources and Evolution and Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, No. 32 Jiaochang Donglu, Kunming, Yunnan, China.
| | - Ahmad Ayatollahi Mehrgardi
- Department of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, PB, 76169-133, Iran
| | - Jianlin Han
- CAAS-ILRI Joint Laboratory on Livestock and Forage Genetic Resources, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China.,Livestock Genetics Program, International Livestock Research Institute (ILRI), Nairobi, Kenya
| | - Dong-Dong Wu
- State Key Laboratory of Genetic Resources and Evolution and Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, No. 32 Jiaochang Donglu, Kunming, Yunnan, China.,State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan and Center for Life Sciences, School of Life Sciences, Yunnan University, Kunming, China
| | - Yan Li
- State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan and Center for Life Sciences, School of Life Sciences, Yunnan University, Kunming, China.
| | - Ya-Ping Zhang
- State Key Laboratory of Genetic Resources and Evolution and Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, No. 32 Jiaochang Donglu, Kunming, Yunnan, China. .,State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan and Center for Life Sciences, School of Life Sciences, Yunnan University, Kunming, China.
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Liu M, Jin HS, Park S. Protein and fat intake interacts with the haplotype of PTPN11_rs11066325, RPH3A_rs886477, and OAS3_rs2072134 to modulate serum HDL concentrations in middle-aged people. Clin Nutr 2020; 39:942-949. [PMID: 31006500 DOI: 10.1016/j.clnu.2019.03.039] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Revised: 03/25/2019] [Accepted: 03/26/2019] [Indexed: 12/14/2022]
Abstract
BACKGROUND & AIMS Low serum HDL cholesterol (HDL-C) concentration is a risk factor for cardiovascular diseases and it is influenced by genetic and environmental factors. We hypothesized that genetic variants that decrease serum HDL-C concentrations may interact with nutrient intakes in ways that increase or decrease the risk of cardiovascular disease. METHODS Candidate genetic variants that can lower serum HDL-C concentrations were explored by genome-wide association studies (GWAS), after adjusting for covariates, in the Ansan/Ansung cohort (n = 8842) from KoGES. The best genetic variants were selected and used to form a haplotype. According to the haplotype frequencies of SNPs, they were divided into major allele, heterozygote allele, and minor allele. The association of haplotype with serum HDL-C levels was determined using logistic regression after adjusting for confounding factors. Interaction of the haplotype with nutrient intake was also determined. RESULTS PTPN11_rs11066325, RPH3A_rs886477 and OAS3_rs2072134 were selected to modulate serum HDL-C levels from GWAS(P = 1.09E-09, 7.04E-10, and 1.27E-09, respectively). The adjusted odds ratios (ORs) for a decrease in serum HDL-C concentration in the minor-allele group of the haplotype were elevated by 1.534 fold, compared to the major-allele group of the haplotype. Furthermore, the adjusted ORs for serum LDL cholesterol and levels increased by 1.645 in the minor-alleles compared to the major-alleles of the haplotype without a significant change of serum cholesterol levels. Interestingly, the adjusted ORs for serum triglyceride were lower in the minor-alleles than in the major-alleles. The haplotype had a significant interaction with the intake of protein, fat, saturated fatty acids (SAF) and polyunsaturated fatty acids (PUFA; P < 0.05). In particular, the minor alleles of the haplotype decreased serum HDL-C levels compared to the major-alleles in the high intake of protein, fat, SFA, and PUFA, not in the low intake. CONCLUSIONS People carrying the minor-allele of haplotypes should avoid diets that are high in protein and fat, especially rich in SFA and PUFA.
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Affiliation(s)
- Meiling Liu
- Dept. of Food and Nutrition, Institue of Basic Science, Obesity/Diabetes Research Center, Hoseo University, Asan, Chungnam, 31499, South Korea
| | - Hyun Seok Jin
- Department of Biomedical Laboratory Science, College of Life and Health Sciences, Hoseo University, Asan, Chungnam, 31499, South Korea
| | - Sunmin Park
- Dept. of Food and Nutrition, Institue of Basic Science, Obesity/Diabetes Research Center, Hoseo University, Asan, Chungnam, 31499, South Korea.
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7
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Yasukochi Y, Sakuma J, Takeuchi I, Kato K, Oguri M, Fujimaki T, Horibe H, Yamada Y. Evolutionary history of disease-susceptibility loci identified in longitudinal exome-wide association studies. Mol Genet Genomic Med 2019; 7:e925. [PMID: 31402603 PMCID: PMC6732299 DOI: 10.1002/mgg3.925] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2018] [Revised: 06/12/2019] [Accepted: 07/26/2019] [Indexed: 12/17/2022] Open
Abstract
Background Our longitudinal exome‐wide association studies previously detected various genetic determinants of complex disorders using ~26,000 single‐nucleotide polymorphisms (SNPs) that passed quality control and longitudinal medical examination data (mean follow‐up period, 5 years) in 4884–6022 Japanese subjects. We found that allele frequencies of several identified SNPs were remarkably different among four ethnic groups. Elucidating the evolutionary history of disease‐susceptibility loci may help us uncover the pathogenesis of the related complex disorders. Methods In the present study, we conducted evolutionary analyses such as extended haplotype homozygosity, focusing on genomic regions containing disease‐susceptibility loci and based on genotyping data of our previous studies and datasets from the 1000 Genomes Project. Results Our evolutionary analyses suggest that derived alleles of rs78338345 of GGA3, rs7656604 at 4q13.3, rs34902660 of SLC17A3, and six SNPs closely located at 12q24.1 associated with type 2 diabetes mellitus, obesity, dyslipidemia, and three complex disorders (hypertension, hyperuricemia, and dyslipidemia), respectively, rapidly expanded after the human dispersion from Africa (Out‐of‐Africa). Allele frequencies of GGA3 and six SNPs at 12q24.1 appeared to have remarkably changed in East Asians, whereas the derived alleles of rs34902660 of SLC17A3 and rs7656604 at 4q13.3 might have spread across Japanese and non‐Africans, respectively, although we cannot completely exclude the possibility that allele frequencies of disease‐associated loci may be affected by demographic events. Conclusion Our findings indicate that derived allele frequencies of nine disease‐associated SNPs (rs78338345 of GGA3, rs7656604 at 4q13.3, rs34902660 of SLC17A3, and six SNPs at 12q24.1) identified in the longitudinal exome‐wide association studies largely increased in non‐Africans after Out‐of‐Africa.
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Affiliation(s)
- Yoshiki Yasukochi
- Department of Human Functional Genomics, Advanced Science Research Promotion Center, Mie University, Tsu, Japan.,CREST, Japan Science and Technology Agency, Kawaguchi, Japan
| | - Jun Sakuma
- CREST, Japan Science and Technology Agency, Kawaguchi, Japan.,Computer Science Department, College of Information Science, University of Tsukuba, Tsukuba, Japan.,RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
| | - Ichiro Takeuchi
- CREST, Japan Science and Technology Agency, Kawaguchi, Japan.,RIKEN Center for Advanced Intelligence Project, Tokyo, Japan.,Department of Computer Science, Nagoya Institute of Technology, Nagoya, Japan
| | - Kimihiko Kato
- Department of Human Functional Genomics, Advanced Science Research Promotion Center, Mie University, Tsu, Japan.,Department of Internal Medicine, Meitoh Hospital, Nagoya, Japan
| | - Mitsutoshi Oguri
- Department of Human Functional Genomics, Advanced Science Research Promotion Center, Mie University, Tsu, Japan.,Department of Cardiology, Kasugai Municipal Hospital, Kasugai, Japan
| | - Tetsuo Fujimaki
- Department of Cardiovascular Medicine, Inabe General Hospital, Inabe, Japan
| | - Hideki Horibe
- Department of Cardiovascular Medicine, Gifu Prefectural Tajimi Hospital, Tajimi, Japan
| | - Yoshiji Yamada
- Department of Human Functional Genomics, Advanced Science Research Promotion Center, Mie University, Tsu, Japan.,CREST, Japan Science and Technology Agency, Kawaguchi, Japan
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8
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Lv P, Yang S, Wu F, Liu W, Qin H, Tang X, Liu Z, Gao H. Single-nucleotide polymorphisms (rs342275, rs342293, rs7694379, rs11789898, and rs17824620) showed significant association with lobaplatin-induced thrombocytopenia. Gene 2019; 713:143964. [PMID: 31279707 DOI: 10.1016/j.gene.2019.143964] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2019] [Revised: 06/17/2019] [Accepted: 07/03/2019] [Indexed: 12/20/2022]
Abstract
This study aimed to investigate single-nucleotide polymorphisms (SNPs) associated with lobaplatin-induced thrombocytopenia in patients with advanced lung cancer in China. Thirty-nine patients who received lobaplatin-based chemotherapy in the 307 Hospitals of Chinese People's Liberation Army from April 2017 to March 2018 were enrolled as study subjects. Peripheral blood DNA was extracted, and 79 candidate SNP positions were selected. A Sanger sequencing platform was employed to measure genotypes for locating the SNP positions associated with lobaplatin-induced thrombocytopenia. Of the 79 candidate genes, SNPs rs342275 and rs7694379 were significantly associated with lobaplatin-induced decrease in platelet (PLT) count (P < 0.05). SNPs rs342275, rs342293, rs11789898, and rs17824620 showed significant association with lobaplatin-induced lowest PLT counts (P < 0.05). SNPs rs342275, rs342293, rs11789898, rs17824620, and rs7694379 can be used as predictors of thrombocytopenia induced by lobaplatin-based chemotherapy in patients with advanced lung cancer in China.
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Affiliation(s)
- Panpan Lv
- Department of Pulmonary Oncology, The Fifth Medical Centre, Chinese PLA General Hospital, Beijing 100071, China
| | - Shaoxing Yang
- Department of Pulmonary Oncology, The Fifth Medical Centre, Chinese PLA General Hospital, Beijing 100071, China
| | - Fangfang Wu
- Department of Pulmonary Oncology, The Fifth Medical Centre, Chinese PLA General Hospital, Beijing 100071, China; Department of Pulmonary Oncology, Clinical College of 307th Hospital of PLA, Anhui Medical University, Beijing 100071, China
| | - Wenjing Liu
- Department of Pulmonary Oncology, The Fifth Medical Centre, Chinese PLA General Hospital, Beijing 100071, China
| | - Haifeng Qin
- Department of Pulmonary Oncology, The Fifth Medical Centre, Chinese PLA General Hospital, Beijing 100071, China
| | - Xiuhua Tang
- Department of Pulmonary Oncology, The Fifth Medical Centre, Chinese PLA General Hospital, Beijing 100071, China
| | - Zeyuan Liu
- Department of Pulmonary Oncology, The Fifth Medical Centre, Chinese PLA General Hospital, Beijing 100071, China
| | - Hongjun Gao
- Department of Pulmonary Oncology, The Fifth Medical Centre, Chinese PLA General Hospital, Beijing 100071, China; Department of Pulmonary Oncology, Clinical College of 307th Hospital of PLA, Anhui Medical University, Beijing 100071, China.
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9
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Rodríguez-Pardo C, Segura A, Zamorano-León JJ, Martínez-Santos C, Martínez D, Collado-Yurrita L, Giner M, García-García JM, Rodríguez-Pardo JM, López-Farre A. Decision tree learning to predict overweight/obesity based on body mass index and gene polymporphisms. Gene 2019; 699:88-93. [DOI: 10.1016/j.gene.2019.03.011] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2018] [Revised: 03/06/2019] [Accepted: 03/07/2019] [Indexed: 12/26/2022]
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