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1

Isobe, N., V. Damotte, V. Lo Re, M. Ban, D. Pappas, L. Guillot-Noel, I. Rebeix et al. « Genetic burden in multiple sclerosis families ». Genes & ; Immunity 14, no 7 (1 août 2013) : 434–40. http://dx.doi.org/10.1038/gene.2013.37.

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Robinson, P. C., P. J. Leo, J. J. Pointon, J. Harris, K. Cremin, L. A. Bradbury, S. Stebbings et al. « The genetic associations of acute anterior uveitis and their overlap with the genetics of ankylosing spondylitis ». Genes & ; Immunity 17, no 1 (26 novembre 2015) : 46–51. http://dx.doi.org/10.1038/gene.2015.49.

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A, Shahin. « The Role of Genetic Mutations in Gene MTHFR in Anencephaly Syndrome ». Journal of Embryology & ; Stem Cell Research 3, no 1 (2019) : 1–5. http://dx.doi.org/10.23880/jes-16000117.

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McGEE, GLENN. « Gene Patents Can Be Ethical ». Cambridge Quarterly of Healthcare Ethics 7, no 4 (octobre 1998) : 417–21. http://dx.doi.org/10.1017/s0963180198004125.

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When one examines the emerging debate about genetic patenting, it becomes clear that those who oppose so-called “gene patents” misunderstand genetics or apply inappropriate moral and jurisprudential theory. In this brief essay I examine some arguments against gene patents of the “methods for detection” variety, and conclude that patents on methods for detecting the presence of a genetic correlation with disease-related (and other) phenotypes can be appropriate, and that with several precautions the U.S. Patent and Trademark Office should continue granting patent protection to investigators who generate genetic disease diagnostic innovations.
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Berghout, J., S. Higgins, C. Loucoubar, A. Sakuntabhai, K. C. Kain et P. Gros. « Genetic diversity in human erythrocyte pyruvate kinase ». Genes & ; Immunity 13, no 1 (11 août 2011) : 98–102. http://dx.doi.org/10.1038/gene.2011.54.

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Sabbagh, A., P. Luisi, E. C. Castelli, L. Gineau, D. Courtin, J. Milet, J. D. Massaro et al. « Worldwide genetic variation at the 3′ untranslated region of the HLA-G gene : balancing selection influencing genetic diversity ». Genes & ; Immunity 15, no 2 (19 décembre 2013) : 95–106. http://dx.doi.org/10.1038/gene.2013.67.

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Fang, Fang, Zhe Xu, Yue Suo, Hui Wang, Si Cheng, Hao Li, Wei Li et Yongjun Wang. « Gene panel for Mendelian strokes ». Stroke and Vascular Neurology 5, no 4 (26 avril 2020) : 416–21. http://dx.doi.org/10.1136/svn-2020-000352.

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BackgroundMendelian stroke causes nearly 7% of ischaemic strokes and is also an important aetiology of cryptogenic stroke. Identifying the genetic abnormalities in Mendelian strokes is important as it would facilitate therapeutic management and genetic counselling. Next-generation sequencing makes large-scale sequencing and genetic testing possible.MethodsA systematic literature search was conducted to identify causal genes of Mendelian strokes, which were used to construct a hybridization-based gene capture panel. Genetic variants for target genes were detected using Illumina HiSeq X10 and the Novaseq platform. The sensitivity and specificity were evaluated by comparing the results with Sanger sequencing.Results53 suspected patients of Mendelian strokes were analysed using the panel of 181 causal genes. According to the American College of Medical Genetics and Genomics standard, 16 likely pathogenic/variants of uncertain significance genetic variants were identified. Diagnostic testing was conducted by comparing the consistency between the results of panel and Sanger sequencing. Both the sensitivity and specificity were 100% for the panel.ConclusionThis panel provides an economical, time-saving and labour-saving method to detect causal mutations of Mendelian strokes.
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Kalefetoğlu Macar1, Tuğçe, Oksal Macar, Emine Yalçın et Kültiğin Çavuşoğlu. « Gene Technology and Plant Genetic Transformation Methods ». Afyon Kocatepe University Journal of Sciences and Engineering 17, no 2 (1 août 2017) : 377–92. http://dx.doi.org/10.5578/fmbd.58669.

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Pekmezović, Tatjana. « Gene-Environment Interaction : A Genetic-Epidemiological Approach ». Journal of Medical Biochemistry 29, no 3 (1 juillet 2010) : 131–34. http://dx.doi.org/10.2478/v10011-010-0021-z.

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Gene-Environment Interaction: A Genetic-Epidemiological ApproachClassical epidemiology addresses the distribution and determinants of diseases in populations, and the factors associated with disease causation, with the aim of preventing disease. Both genetic and environmental factors may contribute to susceptibility, and it is still unclear how these factors interact in their influence on risk. Genetic epidemiology is the field which incorporates concepts and methods from different disciplines including epidemiology, genetics, biostatistics, clinical and molecular medicine, and their interaction is crucial to understanding the role of genetic and environmental factors in disease processes. The study of gene-environment interaction is central in the field of genetic epidemiology. Gene-environment interaction is defined as »a different effect of an environmental exposure on disease risk in persons with different genotypes,« or, alternatively, »a different effect of a genotype on disease risk in persons with different environmental exposures.« Five biologically plausible models are described for the relations between genotypes and environmental exposures, in terms of their effects on disease risk. Therefore, the study of gene-environment interaction is important for improving accuracy and precision in the assessment of both genetic and environmental factors, especially in disorders of less defined etiology. Genetic epidemiology is also applied at the various levels of disease prevention.
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Lu, R., G. S. Vidal, J. A. Kelly, A. M. Delgado-Vega, X. K. Howard, S. R. Macwana, N. Dominguez et al. « Genetic associations of LYN with systemic lupus erythematosus ». Genes & ; Immunity 10, no 5 (16 avril 2009) : 397–403. http://dx.doi.org/10.1038/gene.2009.19.

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Capittini, C., C. Tinelli, M. Guarene, A. Pasi, C. Badulli, I. Sbarsi, F. Garlaschelli et al. « Possible KIR-driven genetic pressure on the genesis and maintenance of specific HLA-A,B haplotypes as functional genetic blocks ». Genes & ; Immunity 13, no 6 (10 mai 2012) : 452–57. http://dx.doi.org/10.1038/gene.2012.14.

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Saurabh, Prerna. « Study of Gene-Gene Interaction Networks ». Journal of Nature, Science & ; Technology 1, no 4 (21 octobre 2021) : 16–18. http://dx.doi.org/10.36937/janset.2021.004.004.

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Gene-Gene Interactions (GGI) Networks-Genomics essentially finds the driver node to understand the functional mechanism of Gene-Gene Interaction or GGI. This process can significantly improvise while examining molecular processes perturbed by genetics in human diseases. With Artificial Intelligence (AI) developments, pattern recognition and machine learning advancements can be exploited to automate from more superficial to handle any task in the medical field. In the past, deep learning-based methods have provided encouraging results in the medical field, such as breast cancer detection, skin cancer classification, brain disease classification, arrhythmia detection, pneumonia detection from X-Ray images, and lung segmentation. Interestingly, employing machine learning in the medical field has caught my great attention and motivated us to utilize machine learning-based algorithms to detect drive nodes. Several recent types of research have focused on identifying the bare minimum of driver nodes required to control underlying gene-gene interaction networks. This study is about analyzing gene interaction networks statistically. One or more abnormalities cause a genetic condition in the DNA. Disease-related genes play essential biological roles in the cell. Multiple genes often work together to cause complex genetic illnesses. So, the central concept is to create a system that can assess networks in various elements that influence them.
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Ghalandari, Vahab, Hamidreza Bagheri, Ali Mohebbi et Hadi Esmaeili. « Experimental Investigation and Multi-Gene Genetic Programming Simulation of Portland Clinker Burnability ». Chemistry & ; Chemical Technology 15, no 4 (25 novembre 2021) : 559–66. http://dx.doi.org/10.23939/chcht15.04.559.

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In this study, the effect of chemical composition of the raw material on the clinker burnability was studied by determination of free CaO (wt %) content of clinker. The burnability of two types of Portland clinker was investigated for silica modules of 2.3, 2.5 and 2.7 and lime saturation factor of 0.88–0.98. In addition, using the Multi-gene genetic programming (MGGP) model, the burnability of clinker was predicted. The results of MGGP model indicated that the performance of the model for predicting the amount of free CaO (wt %) was acceptable. Moreover, using MGGP, a promising correlation was introduced for accurately calculating the amount of free CaO (wt %). The performance of this correlation was compared with FL-Smidth, and it was established that the average errors of MGGP correlation and FL-Smidth equation were 2.95 and 7.45 %, respectively.
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Asmamaw, Berhan. « Transferrin gene polymorphisms and population genetic studies of Atlantic cod (Gadus morhua) ». Journal of Coastal Life Medicine 4, no 1 (janvier 2016) : 1–7. http://dx.doi.org/10.12980/jclm.4.2016j5-196.

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Faltusová, Zuzana, Kateřina Vaculová, Jozef Pavel, Ilona Svobodová, Jana Hajšlová et Jaroslava Ovesná. « Fusarium culmorum Tri genes and barley Hvugt13248 gene transcription in infected barley cultivars ». Plant Protection Science 55, No. 3 (17 mai 2019) : 172–80. http://dx.doi.org/10.17221/21/2018-pps.

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The transcription activities of genes somehow associated with the mycotoxin deoxynivalenol (DON) biosynthesis, namely Fusarium Tri genes, and the barley gene coding for UDP-glycosyltransferase (HvUGT13248) on different genetic backgrounds were compared. Determining the amount of the pathogen DNA was used as a useful tool for evaluating the infestation of barley cultivars. Amounts of the pathogen DNA differed in six barley cultivars infected by F. culmorum. Transcription of HvUGT13248 was related to DON content in the samples. Low pathogenic infection and low DON content were accompanied by increased Fusarium Tri10 transcription in resistant cv. Amulet. This finding confirmed our recent results and makes us propose using this change as a possible marker of barley resistance against Fusarium.
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Rahnemoon, Ahmad Reza. « ETV6/RUNX1 fusion gene and its active role ». Cancer Research and Cellular Therapeutics 5, no 4 (30 août 2021) : 01–05. http://dx.doi.org/10.31579/2640-1053/091.

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Recent investigation successfully identified a pre leukemic ETV6/RUNX1-positive clone in the healthy twin of a patient diagnosed with ETV6/RUNXI-positive acute lymphoblastic leukemia (ALL) and also some studies with ETV6/RUNX1 knock in mice showed that the expression of the fusion gene is not sufficient for the invivo induction of ALL. Taken together, these data indicate that ETV6/RUNX1-positive leukemia is .generated through a multi-step mechanism, and that accumulation of additional genetic changes is necessary for the development of overt leukemia. Hence, to understand fully the genetic evolution of this disorder, identification of the complete spectrum of genetic changes that accompany the ETV6/RUNX1 fusion gene is necessary. Moreover, critical patho genetic insights may be gained from studying the correlation pattern of the different copy number changes.
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Amici, Serena, Maurizio Paciaroni, Giancarlo Agnelli et Valeria Caso. « Gene-Drug Interaction in Stroke ». Stroke Research and Treatment 2011 (2011) : 1–14. http://dx.doi.org/10.4061/2011/212485.

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Stroke is the third cause of mortality and one of most frequent causes of long-term neurological disability, as well as a complex disease that results from the interaction of environmental and genetic factors. The focus on genetics has produced a large number of studies with the objective of revealing the genetic basis of cerebrovascular diseases. Furthermore, pharmacogenetic research has investigated the relation between genetic variability and drug effectiveness/toxicity. This review will examine the implications of pharmacogenetics of stroke; data on antihypertensives, statins, antiplatelets, anticoagulants, and recombinant tissue plasminogen activator will be illustrated. Several polymorphisms have been studied and some have been associated with positive drug-gene interaction on stroke, but the superiority of the genotype-guided approach over the clinical approach has not been proved yet; for this reason, it is not routinely recommended.
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Kohn, Donald B., W. French Anderson et R. Michael Blaese. « Gene Therapy for Genetic Diseases ». Cancer Investigation 7, no 2 (janvier 1989) : 179–92. http://dx.doi.org/10.3109/07357908909038283.

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Parry, James M. « Gene manipulation and genetic toxicology ». Mutagenesis 9, no 3 (1994) : 169. http://dx.doi.org/10.1093/mutage/9.3.169.

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Tennant, Raymond W., Laura Hansen et Judson Spalding. « Gene manipulation and genetic toxicology ». Mutagenesis 9, no 3 (1994) : 171–74. http://dx.doi.org/10.1093/mutage/9.3.171.

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Caskey, C. Thomas. « Genetic Therapy : Somatic Gene Transplants ». Hospital Practice 22, no 8 (15 août 1987) : 181–98. http://dx.doi.org/10.1080/21548331.1987.11703293.

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Dover, Gabriel A. « Genetic errors and gene redundancy ». Trends in Genetics 2 (janvier 1986) : 172. http://dx.doi.org/10.1016/0168-9525(86)90214-3.

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Smith, M. J., et P. N. Goodfellow. « Gene mapping and genetic diseases ». Current Opinion in Cell Biology 1, no 3 (juin 1989) : 460–65. http://dx.doi.org/10.1016/0955-0674(89)90006-9.

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Cremers, C. W. R. J., S. D. M. Brown, K. P. Steel, H. G. Brunner, A. P. Read et W. J. Kimberling. « Gene linkage and genetic deafness ». International Journal of Pediatric Otorhinolaryngology 32 (juin 1995) : S167—S174. http://dx.doi.org/10.1016/0165-5876(94)01154-p.

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Desnick, Robert J., et Edward H. Schuchman. « Gene therapy for genetic diseases ». Pediatrics International 40, no 3 (juin 1998) : 191–203. http://dx.doi.org/10.1111/j.1442-200x.1998.tb01912.x.

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Coenen, M. J. H., et P. K. Gregersen. « Rheumatoid arthritis : a view of the current genetic landscape ». Genes & ; Immunity 10, no 2 (6 novembre 2008) : 101–11. http://dx.doi.org/10.1038/gene.2008.77.

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Lu, R., G. S. Vidal, J. A. Kelly, A. M. Delgado-Vega, X. K. Howard, S. R. Macwana, N. Dominguez et al. « Erratum : Genetic associations of LYN with systemic lupus erythematosus ». Genes & ; Immunity 11, no 1 (janvier 2010) : 98. http://dx.doi.org/10.1038/gene.2009.102.

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Sardi, Maria, et Audrey P. Gasch. « Genetic background effects in quantitative genetics : gene-by-system interactions ». Current Genetics 64, no 6 (11 avril 2018) : 1173–76. http://dx.doi.org/10.1007/s00294-018-0835-7.

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FERREIRA, CÂNDIDA. « GENETIC REPRESENTATION AND GENETIC NEUTRALITY IN GENE EXPRESSION PROGRAMMING ». Advances in Complex Systems 05, no 04 (décembre 2002) : 389–408. http://dx.doi.org/10.1142/s0219525902000626.

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The neutral theory of molecular evolution states that the accumulation of neutral mutations in the genome is fundamental for evolution to occur. The genetic representation of gene expression programming, an artificial genotype/phenotype system, not only allows the existence of non-coding regions in the genome where neutral mutations can accumulate but also allows the controlled manipulation of both the number and the extent of these non-coding regions. Therefore, gene expression programming is an ideal artificial system where the neutral theory of evolution can be tested in order to gain some insights into the workings of artificial evolutionary systems. The results presented in this work show beyond any doubt that the existence of neutral regions in the genome is fundamental for evolution to occur efficiently.
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Iqbal, Rana Khalid. « Genetic Factors of Diabetes Mellitus ». Diabetes & ; Obesity International Journal 4, no 4 (2019) : 1–7. http://dx.doi.org/10.23880/doij-16000213.

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Diabetes mellitus is a disease in which pancreas fail to perform its work. In this condition, insulin is not produced in the amount as required for the normal function of the body. This led to different complication. And the results of many different diseases like kidney failure, high blood pressure, urination, blindness, stroke, heart attack, muscle dysfunction. Diabetes mellitus has two types: first is known as type 1 diabetes or Insulin-dependent diabetes. Second is known as type 2 diabetes or Non-insulin dependent diabetes mellitus. Most important is genetics which plays an important role in the development of diabetes. Other is environmental factors which are responsible for causing of disease by changing the gene patterns. In genetics, different genes are responsible for the causing of diabetes and these genes are present at a different position on a chromosome. In type 1 diabetes chromosome 6 and HLA complex, viral infection, physiological factors, environmental factors, and 60 genes are identified for causing of this disease. In type 2 diabetes environmental factor, 120 genetic loci, E23K polymorphism in the KCNG11 gene, Glucokinase gene mutation, epigenetics, TCF722, ABCC8, CAPN10, GIUT2, genes are responsible for the causing of disease of type 2 diabetes.
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Hunter, Jacqueline D., Eden G. Robertson, Kate Hetherington, David S. Ziegler, Glenn M. Marshall, Judy Kirk, Jonathan M. Marron et al. « What’s in a Name ? Parents’ and Healthcare Professionals’ Preferred Terminology for Pathogenic Variants in Childhood Cancer Predisposition Genes ». Journal of Personalized Medicine 12, no 8 (18 août 2022) : 1327. http://dx.doi.org/10.3390/jpm12081327.

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Current literature/guidelines regarding the most appropriate term to communicate a cancer-related disease-causing germline variant in childhood cancer lack consensus. Guidelines also rarely address preferences of patients/families. We aimed to assess preferences of parents of children with cancer, genetics professionals, and pediatric oncologists towards terminology to describe a disease-causing germline variant in childhood cancer. Using semi-structured interviews we asked participants their most/least preferred terms from; ‘faulty gene,’ ‘altered gene,’ ‘gene change,’ and ‘genetic variant,’ analyzing responses with directed content analysis. Twenty-five parents, 6 genetics professionals, and 29 oncologists participated. An equal number of parents most preferred ‘gene change,’ ‘altered gene,’ or ‘genetic variant’ (n = 8/25). Parents least preferred ‘faulty gene’ (n = 18/25). Half the genetics professionals most preferred ‘faulty gene’ (n = 3/6); however this was least preferred by the remaining genetics professionals (n = 3/6). Many oncologists most preferred ‘genetic variant’ (n = 11/29) and least preferred ‘faulty gene’ (n = 19/29). Participants across all groups perceived ‘faulty gene’ as having negative connotations, potentially placing blame/guilt on parents/children. Health professionals described challenges selecting a term that was scientifically accurate, easily understood and not distressing to families. Lack of consensus highlights the need to be guided by families’ preferred terminology, while providing accurate explanations regarding implications of genetic findings.
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Clarke, Angus. « Genetic imprinting in clinical genetics ». Development 108, Supplement (1 avril 1990) : 131–39. http://dx.doi.org/10.1242/dev.108.supplement.131.

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Genetic, and indeed genomic, imprinting does occur in humans. This is manifest at the level of the genome, the individual chromosome, subchromosomal region or fragile site, or the single locus. The best evidence at the single gene level comes from a consideration of familial tumour syndromes. Chromosomal imprinting effects are revealed when uniparental disomy occurs, as in the Prader-Willi syndrome and doubtless other sporadic, congenital anomaly syndromes. Genomic imprinting is manifest in the developmental defects of hydatidiform mole, teratoma and triploidy. Fragile (X) mental retardation shows an unusual pattern of inheritance, and imprinting can account for these effects. Future work in clinical genetics may identify congenital anomalies and growth disorders caused by imprinting: the identification of imprinting effects for specific chromosomal regions in mice will allow the examination of the homologous chromosomal region in humans.
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Shi, T., Y. Xu, M. J. Yang, Y. Zhou, M. Liu, X. Y. Lan, C. Z. Lei et al. « Genetic variation, association analysis, and expression pattern of SMAD3 gene in Chinese cattle ». Czech Journal of Animal Science 61, No. 5 (15 juillet 2016) : 209–16. http://dx.doi.org/10.17221/34/2015-cjas.

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Sztankóová, Z., V. Mátlová et G. Malá. « Genetic polymorphism at the CSN1S1 gene in two Czech goat breeds ». Czech Journal of Animal Science 52, No. 7 (7 janvier 2008) : 199–202. http://dx.doi.org/10.17221/2276-cjas.

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The genetic polymorphism of the <i>CSN1S1</i> (casein <i>alpha-S1</i>) locus was investigated in two endangered Czech goat breeds (White Shorthair and Brown Shorthair). These breeds are kept mainly for their good dairy performance. Genetic characterization of the <i>CSN1S1</i> locus contributes to the knowledge of the genetic structure of these two endangered breeds. The study was performed on 498 goats (333 White and 165 Brown Shorthair goats) by means of different polymerase chain reactions (PCR). We detected <i>A</i>* (associated with normal content of protein), <i>E</i>, <i>F</i> and <i>01</i> alleles. The analysis showed a prevalence of <i>CSN1S1 F</i> (0.658; 0.597) and <i>CSN1S1 A</i>* (0.269; 0.303) alleles. In both breeds, the frequency of occurrence of <i>E</i> and <i>01</i> alleles was very low: <i>E</i> (0.054; 0.085) and <i>01</i> (0.019; 0.015), respectively. No population followed the Hardy-Weinberg equilibrium, the value of polymorphic information content (PIC) being 0.426 in White and 0.472 in Brown Shorthair goats. Moreover, the test of population differences (<i>P</i> = 0.130) showed no significant differences between White and Brown Shorthair goats. This genetic peculiarity makes the preservation of the population of both breeds worthwhile.
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Garcia, V. E., M. Chang, R. Brandon, Y. Li, N. Matsunami, K. P. Callis-Duffin, D. Civello et al. « Detailed genetic characterization of the interleukin-23 receptor in psoriasis ». Genes & ; Immunity 9, no 6 (24 juillet 2008) : 546–55. http://dx.doi.org/10.1038/gene.2008.55.

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Moser, K. L., J. A. Kelly, C. J. Lessard et J. B. Harley. « Recent insights into the genetic basis of systemic lupus erythematosus ». Genes & ; Immunity 10, no 5 (14 mai 2009) : 373–79. http://dx.doi.org/10.1038/gene.2009.39.

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Pattaradilokrat, S., J. Li, J. Wu, Y. Qi, R. T. Eastman, M. Zilversmit, S. C. Nair et al. « Plasmodium genetic loci linked to host cytokine and chemokine responses ». Genes & ; Immunity 15, no 3 (23 janvier 2014) : 145–52. http://dx.doi.org/10.1038/gene.2013.74.

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Sarmiento, J., R. H. Wallis, T. Ning, L. Marandi, G. Y. C. Chao, A. D. Paterson et P. Poussier. « Genetic dissection of Iddm26 in the spontaneously diabetic BBDP rat ». Genes & ; Immunity 15, no 6 (12 juin 2014) : 378–91. http://dx.doi.org/10.1038/gene.2014.29.

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Wolff, A. S. B., A. L. Mitchell, H. J. Cordell, A. Short, B. Skinningsrud, W. Ollier, K. Badenhoop et al. « CTLA-4 as a genetic determinant in autoimmune Addison’s disease ». Genes & ; Immunity 16, no 6 (septembre 2015) : 430–36. http://dx.doi.org/10.1038/gene.2015.27.

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McDonald, B. A., et C. Linde. « Disease resistance and pathogen population genetic ». Plant Protection Science 38, SI 1 - 6th Conf EFPP 2002 (1 janvier 2002) : 245–48. http://dx.doi.org/10.17221/10375-pps.

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Plant pathologists have seen many boom-and-bust cycles following the deployment of resistant varieties. These cycles result when pathogen populations adapt to the presence of a major resistance gene by evolving a new population that can overcome this resistance gene. The breakdown of genetic resistance is due to the evolution of the local pathogen population because of selection for mutants, recombinants, or immigrants that are better adapted to the resistant cultivar. To understand the process that leads to breakdown of a resistance gene, we need to understand the processes that govern pathogen evolution. Population geneticists have identified five evolutionary forces that interact to affect the evolution of organisms. We ranked these risks and developed a quantitative framework to predict the risk that a pathogen will evolve to overcome major resistance genes. Our hypothesis is that much of the durability of resistance genes is due to the nature of the pathogen population rather than to the nature of the resistance gene. The framework we developed can be used as a hypothesis to test against a large number of plant pathosystems. The underlying principles of the framework can be tested individually or in combination according to the available knowledge of the population genetics for any pathogen. We propose that this framework can be used to design breeding strategies to break the boom-and-bust cycle and lead to durable resistance.
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De, Supriyo, Yongqing Zhang, John R. Garner, S. Alex Wang et Kevin G. Becker. « Disease and phenotype gene set analysis of disease-based gene expression in mouse and human ». Physiological Genomics 42A, no 2 (octobre 2010) : 162–67. http://dx.doi.org/10.1152/physiolgenomics.00008.2010.

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The genetic contributions to common disease and complex disease phenotypes are pleiotropic, multifactorial, and combinatorial. Gene set analysis is a computational approach used in the analysis of microarray data to rapidly query gene combinations and multifactorial processes. Here we use novel gene sets based on population-based human genetic associations in common human disease or experimental genetic mouse models to analyze disease-related microarray studies. We developed a web-based analysis tool that uses these novel disease- and phenotype-related gene sets to analyze microarray-based gene expression data. These gene sets show disease and phenotype specificity in a species-specific and cross-species fashion. In this way, we integrate population-based common human disease genetics, mouse genetically determined phenotypes, and disease or phenotype structured ontologies, with gene expression studies relevant to human disease. This may aid in the translation of large-scale high-throughput datasets into the context of clinically relevant disease phenotypes.
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Corvol, Harriet, Anthony De Giacomo, Celeste Eng, Max Seibold, Elad Ziv, Rocio Chapela, Jose R. Rodriguez-Santana et al. « Genetic ancestry modifies pharmacogenetic gene–gene interaction for asthma ». Pharmacogenetics and Genomics 19, no 7 (juillet 2009) : 489–96. http://dx.doi.org/10.1097/fpc.0b013e32832c440e.

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Mori, N. « A genetic fossil : Protamine gene as a primordial gene ». Naturwissenschaften 80, no 5 (mai 1993) : 222–24. http://dx.doi.org/10.1007/bf01175737.

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Blaese, R. Michael. « Challenges of Genetic Disease : Gene Addition or Gene Repair ». Nature Biotechnology 17, S4 (décembre 1999) : 8. http://dx.doi.org/10.1038/70117.

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Hannah-Shmouni, Fady, et Constantine A. Stratakis. « A Gene-Based Classification of Primary Adrenocortical Hyperplasias ». Hormone and Metabolic Research 52, no 03 (mars 2020) : 133–41. http://dx.doi.org/10.1055/a-1107-2972.

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AbstractPrimary or adrenocorticotropin-independent adrenocortical tumors and hyperplasias represent a heterogeneous group of adrenocortical neoplasms that arise from various genetic defects, either in isolation or familial. The traditional classification as adenomas, hyperplasias, and carcinomas is non-specific. The recent identification of various germline and somatic genes in the development of primary adrenocortical hyperplasias has provided important new insights into the molecular pathogenesis of adrenal diseases. In this new era of personalized care and genetics, a gene-based classification that is more specific is required to assist in the understanding of their disease processes, hormonal functionality and signaling pathways. Additionally, a gene-based classification carries implications for treatment, genetic counseling and screening of asymptomatic family members. In this review, we discuss the genetics of benign adrenocorticotropin-independent adrenocortical hyperplasias, and propose a new gene-based classification system and diagnostic algorithm that may aid the clinician in prioritizing genetic testing, screening and counseling of affected, at risk individuals and their relatives.
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Green, M. R., E. Camilleri, M. K. Gandhi, J. Peake et L. R. Griffiths. « A novel immunodeficiency disorder characterized by genetic amplification of interleukin 25 ». Genes & ; Immunity 12, no 8 (21 juillet 2011) : 663–66. http://dx.doi.org/10.1038/gene.2011.50.

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Tao, S. D., Y. M. He, Y. L. Ying, J. He, F. M. Zhu et H. J. Lv. « KIR3DL1 genetic diversity and phenotypic variation in the Chinese Han population ». Genes & ; Immunity 15, no 1 (31 octobre 2013) : 8–15. http://dx.doi.org/10.1038/gene.2013.55.

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Izraeli, Shai, Linda A. Lowe, Virginia L. Bertness, Stefano Campaner, Heidi Hahn, Ilan R. Kirsch et Michael R. Kuehn. « Genetic evidence thatSil is required for the sonic hedgehog response pathway ». genesis 31, no 2 (2001) : 72–77. http://dx.doi.org/10.1002/gene.10004.

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Christ, Nicole, et Peter Dröge. « Genetic manipulation of mouse embryonic stem cells by mutant λ integrase ». genesis 32, no 3 (27 février 2002) : 203–8. http://dx.doi.org/10.1002/gene.10031.

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Emerson, J. J., et Wen-Hsiung Li. « The genetic basis of evolutionary change in gene expression levels ». Philosophical Transactions of the Royal Society B : Biological Sciences 365, no 1552 (27 août 2010) : 2581–90. http://dx.doi.org/10.1098/rstb.2010.0005.

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The regulation of gene expression is an important determinant of organismal phenotype and evolution. However, the widespread recognition of this fact occurred long after the synthesis of evolution and genetics. Here, we give a brief sketch of thoughts regarding gene regulation in the history of evolution and genetics. We then review the development of genome-wide studies of gene regulatory variation in the context of the location and mode of action of the causative genetic changes. In particular, we review mapping of the genetic basis of expression variation through expression quantitative trait locus studies and measuring the cis / trans component of expression variation in allele-specific expression studies. We conclude by proposing a systematic integration of ideas that combines global mapping studies, cis / trans tests and modern population genetics methodologies, in order to directly estimate the forces acting on regulatory variation within and between species.
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