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Статті в журналах з теми "Genotype and Phenotype Relationship"

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Автор: Grafiati

Опубліковано: 10 грудня 2022

Оновлено: 29 січня 2023

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1

Wilson, David Sloan, and Alexandra Wells. "Radical Epistasis and the Genotype-Phenotype Relationship." Artificial Life 2, no. 1 (October 1994): 117–28. http://dx.doi.org/10.1162/artl.1994.2.1.117.

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Models of evolution often assume that the offspring of two genotypes, which are genetically intermediate by definition, are also phenotypically intermediate. The continuity between genotype and phenotype interferes with the process of evolution on multipeaked adaptive landscapes because the progeny of genotypes that lie on separate adaptive peaks fall into valleys of low fitness. This problem can be solved by epistasis, which disrupts the continuity between genotype and phenotype. In a five-locus sexual haploid model with maximum epistasis, natural selection in multipeak landscapes evolves a set of genotypes that a) occupy the adaptive peaks and b) give rise to each other by recombination. The epistatic genetic system therefore “molds” the phenotypic distribution to the adaptive landscape, without assortative mating or linkage disequilibrium. If the adaptive landscape is changed, a new set of genotypes quickly evolves that satisfies conditions a and b, above, for the new peaks. Our model may be relevant to a number of recalcitrant problems in biology and also stands in contrast to Kauffman's [3] NK model of evolution on rugged fitness surfaces, in which epistasis and recombination tend to constrain the evolutionary process.

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2

Spiegel, Ronen, Hanna Mandel, Ann Saada, Issy Lerer, Ayala Burger, Avraham Shaag, Stavit A. Shalev, et al. "Delineation of C12orf65-related phenotypes: a genotype–phenotype relationship." European Journal of Human Genetics 22, no. 8 (January 15, 2014): 1019–25. http://dx.doi.org/10.1038/ejhg.2013.284.

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3

Guilfoile, Patrick, and Stephen Plum. "The Relationship Between Phenotype & Genotype." American Biology Teacher 62, no. 4 (April 2000): 288–91. http://dx.doi.org/10.1662/0002-7685(2000)062[0288:trbpg]2.0.co;2.

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4

Romano, V., G. Anello, and S. Kaufman. "Genotype-phenotype relationship in PAH deficiency." Journal of Inherited Metabolic Disease 21 (August 1998): 13–16. http://dx.doi.org/10.1023/a:1005428215950.

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5

Li, Ruowang, Rui Duan, Rachel L. Kember, Daniel J. Rader, Scott M. Damrauer, Jason H. Moore, and Yong Chen. "A regression framework to uncover pleiotropy in large-scale electronic health record data." Journal of the American Medical Informatics Association 26, no. 10 (July 27, 2019): 1083–90. http://dx.doi.org/10.1093/jamia/ocz084.

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Abstract Objective Pleiotropy, where 1 genetic locus affects multiple phenotypes, can offer significant insights in understanding the complex genotype–phenotype relationship. Although individual genotype–phenotype associations have been thoroughly explored, seemingly unrelated phenotypes can be connected genetically through common pleiotropic loci or genes. However, current analyses of pleiotropy have been challenged by both methodologic limitations and a lack of available suitable data sources. Materials and Methods In this study, we propose to utilize a new regression framework, reduced rank regression, to simultaneously analyze multiple phenotypes and genotypes to detect pleiotropic effects. We used a large-scale biobank linked electronic health record data from the Penn Medicine BioBank to select 5 cardiovascular diseases (hypertension, cardiac dysrhythmias, ischemic heart disease, congestive heart failure, and heart valve disorders) and 5 mental disorders (mood disorders; anxiety, phobic and dissociative disorders; alcohol-related disorders; neurological disorders; and delirium dementia) to validate our framework. Results Compared with existing methods, reduced rank regression showed a higher power to distinguish known associated single-nucleotide polymorphisms from random single-nucleotide polymorphisms. In addition, genome-wide gene-based investigation of pleiotropy showed that reduced rank regression was able to identify candidate genetic variants with novel pleiotropic effects compared to existing methods. Conclusion The proposed regression framework offers a new approach to account for the phenotype and genotype correlations when identifying pleiotropic effects. By jointly modeling multiple phenotypes and genotypes together, the method has the potential to distinguish confounding from causal genotype and phenotype associations.

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6

Letteboer, T. G. W. "Genotype-phenotype relationship in hereditary haemorrhagic telangiectasia." Journal of Medical Genetics 43, no. 4 (September 9, 2005): 371–77. http://dx.doi.org/10.1136/jmg.2005.035451.

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7

GALANELLO, RENZO, and ANTONIO CAO. "Relationship between Genotype and Phenotype: Thalassemia Intermediaa." Annals of the New York Academy of Sciences 850, no. 1 COOLEY'S ANEM (June 1998): 325–33. http://dx.doi.org/10.1111/j.1749-6632.1998.tb10489.x.

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8

Bronsveld, I., R. A. de Nooijer, H. G. M. Arets, F. Teding van Berkhout, C. K. van der Ent, Y. de Rijke, and H. R. de Jonge. "R117H homozygosity and the genotype–phenotype relationship." Journal of Cystic Fibrosis 9 (June 2010): S11. http://dx.doi.org/10.1016/s1569-1993(10)60042-2.

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9

Pera, Angelo, Raffaello Sostegni, Marco Daperno, Elena Ercole, Cristiana Laudi, Rodolfo Rocca, Caterina Rigazio, Marco Astegiano, and Giuseppe Rocca. "Genotype–phenotype relationship in inflammatory bowel disease." European Journal of Internal Medicine 11, no. 4 (August 2000): 204–9. http://dx.doi.org/10.1016/s0953-6205(00)00092-3.

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10

Sun, Yi-Min, Yan-Bin Zhang, and Zhi-Ying Wu. "Huntington’s Disease: Relationship Between Phenotype and Genotype." Molecular Neurobiology 54, no. 1 (January 7, 2016): 342–48. http://dx.doi.org/10.1007/s12035-015-9662-8.

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11

Go, Vay Liang W., Christine T. H. Nguyen, Diane M. Harris, and Wai-Nang Paul Lee. "Nutrient-Gene Interaction: Metabolic Genotype-Phenotype Relationship." Journal of Nutrition 135, no. 12 (December 1, 2005): 3016S—3020S. http://dx.doi.org/10.1093/jn/135.12.3016s.

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12

Raue, Friedhelm, and Karin Frank-Raue. "Genotype-phenotype relationship in multiple endocrine neoplasia type 2. Implications for clinical management." HORMONES 8, no. 1 (January 15, 2009): 23–28. http://dx.doi.org/10.14310/horm.2002.1218.

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13

Graham, Dustin M. "Generalizing genotype-phenotype relationships." Lab Animal 45, no. 11 (October 20, 2016): 411. http://dx.doi.org/10.1038/laban.1151.

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14

Kolmer, J. A., J. I. Mirza, M. Imtiaz, and S. J. A. Shah. "Genetic Differentiation of the Wheat Leaf Rust Fungus Puccinia triticina in Pakistan and Genetic Relationship to Other Worldwide Populations." Phytopathology® 107, no. 6 (June 2017): 786–90. http://dx.doi.org/10.1094/phyto-10-16-0388-r.

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Collections of Puccinia triticina, the wheat leaf rust pathogen, were obtained from Pakistan in 2008, 2010, 2011, 2013, and 2014. Collections were also obtained from Bhutan in 2013. Single uredinial isolates were derived and tested for virulence phenotype to 20 lines of Thatcher wheat that differ for single leaf rust resistance genes, and for molecular genotype with 23 simple-sequence repeat (SSR) primers. Twenty-four virulence phenotypes were described among the 89 isolates tested for virulence. None of the isolates had virulence to Thatcher lines with Lr9, Lr24, or Lr18. Virulence to most of the other Thatcher lines was over 50%. The two most common virulence phenotypes, FHPSQ and KHPQQ, had virulence to Lr16, Lr17, and Lr26. Twenty-seven SSR genotypes were found among the 38 isolates tested for molecular variation. The SSR genotypes had high levels of observed heterozygosity and significant correlation with virulence phenotype, which indicated clonal reproduction. Cluster analysis and principal component plots indicated three groups of SSR genotypes that also varied significantly for virulence. Isolates with MBDSS and MCDSS virulence phenotypes from Pakistan and Bhutan were highly related for SSR genotype and virulence to isolates from Turkey, Europe, Central Asia, the Middle East, North America and South America, indicating the possible migration of the rust fungus between continental regions.

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15

Turco, A., A. Renieri, and M. De Marchi. "Alport syndrome - is there a genotype-phenotype relationship?" Nephrology Dialysis Transplantation 12, no. 8 (August 1, 1997): 1551–53. http://dx.doi.org/10.1093/ndt/12.8.1551.

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16

Nademanee, Koonlawee. "Genotype–Phenotype Relationship in the Long QT Syndrome." Journal of the American College of Cardiology 54, no. 22 (November 2009): 2063–64. http://dx.doi.org/10.1016/j.jacc.2009.09.016.

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17

Courtet, Philippe, Fabrice Jollant, Didier Castelnau, Catherine Buresi, and Alain Malafosse. "Suicidal behavior: Relationship between phenotype and serotonergic genotype." American Journal of Medical Genetics Part C: Seminars in Medical Genetics 133C, no. 1 (2005): 25–33. http://dx.doi.org/10.1002/ajmg.c.30043.

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18

Vollebregt, Audrey A. M., Marianne Hoogeveen‐Westerveld, Marian A. Kroos, Esmee Oussoren, Iris Plug, George J. Ruijter, Ans T. van der Ploeg, and W. W. M. Pim Pijnappel. "Genotype–phenotype relationship in mucopolysaccharidosisII: predictive power ofIDSvariants for the neuronopathic phenotype." Developmental Medicine & Child Neurology 59, no. 10 (May 25, 2017): 1063–70. http://dx.doi.org/10.1111/dmcn.13467.

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19

Yip, Y. L. "Closing the Genotype-phenotype Gap." Yearbook of Medical Informatics 19, no. 01 (August 2010): 82–85. http://dx.doi.org/10.1055/s-0038-1638695.

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Summary Objectives: To summarize current excellent research in the field of bioinformatics. Method: Synopsis of the articles selected for the IMIA Yearbook 2010. Results: The selection process for this yearbook’s section on Bioinformatics results in five excellent articles highlighting the progress made in advancing the understanding of genotypephenotype relationship, and their concrete application in clinical settings. First, next generation sequencing techniques have allowed the discovery of an ever larger number of genetic variations at a greater resolution, and methods were developed to ensure accurate data analysis. Second, innovative approaches were applied to gene expression data to allow its link to a wider phenotypic spectrum and to enhance its use for disease understanding. Third, there is a notable trend in visualizing diseases as network rather than individual entities, and this has provided new insights for disease interpretation. The progress mentioned above is further aided by continual development in bio-ontologies which provide means for semantic, and thus phenotype, comparison. Conclusions: The current literature showed a tightening link between genotype and phenotype, placing us one step closer to a better disease classification, patient stratification as well as the development of personalized medicine.

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20

Sun, Y. M., C. Lu, and Z. Y. Wu. "Spinocerebellar ataxia: relationship between phenotype and genotype - a review." Clinical Genetics 90, no. 4 (June 30, 2016): 305–14. http://dx.doi.org/10.1111/cge.12808.

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21

Najafi, Mehri, Hosein Alimadadi, Pejman Rouhani, Mohammad Ali Kiani, Ahmad Khodadad, Farzaneh Motamed, Alireza Moraveji, Masoud Hooshmand, Mohammad Taghi Haghi Ashtiani, and Nima Rezaei. "Genotype-phenotype relationship in Iranian patients with cystic fibrosis." Turkish Journal of Gastroenterology 26, no. 3 (April 30, 2015): 241–43. http://dx.doi.org/10.5152/tjg.2015.5945.

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22

J??rvinen, Heikki J., and P??ivi Peltom??ki. "The complex genotype???phenotype relationship in familial adenomatous polyposis." European Journal of Gastroenterology & Hepatology 16, no. 1 (January 2004): 5–8. http://dx.doi.org/10.1097/00042737-200401000-00002.

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23

Kerem, Eitan, and Batsheva Kerem. "The relationship between genotype and phenotype in cystic fibrosis." Current Opinion in Pulmonary Medicine 1, no. 6 (November 1995): 450–56. http://dx.doi.org/10.1097/00063198-199511000-00004.

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24

Moleri, E., S. Calandra, T. Fasano, S. Bertolini, G. Franceschini, and L. Calabresi. "Mo-P6:413 Genotype-phenotype relationship in LCAT deficiencies." Atherosclerosis Supplements 7, no. 3 (January 2006): 136–37. http://dx.doi.org/10.1016/s1567-5688(06)80543-x.

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25

Costanzo, Michael, Elena Kuzmin, Jolanda van Leeuwen, Barbara Mair, Jason Moffat, Charles Boone, and Brenda Andrews. "Global Genetic Networks and the Genotype-to-Phenotype Relationship." Cell 177, no. 1 (March 2019): 85–100. http://dx.doi.org/10.1016/j.cell.2019.01.033.

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26

El-Gawhary, S., M. Niazi, K. Abdelmeuid, S. Mekhemer, H. Makhlouf, H. Raslan, K. Eid, and A. El-Beshlawy. "O47 Genotype phenotype relationship in Gaucher's disease in Egypt." Blood Reviews 21 (August 2007): S81—S82. http://dx.doi.org/10.1016/s0268-960x(07)70063-2.

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27

Smits, J. P. P., and Arthur A. M. Wilde. "Brugada syndrome: in search of a genotype-phenotype relationship." Herzschrittmachertherapie und Elektrophysiologie 13, no. 3 (September 2002): 142–48. http://dx.doi.org/10.1007/s00399-002-0350-9.

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28

Fontaine, G. H., and L. Zhang. "Is the Phenotype-Genotype Relationship Necessary to Understand Cardiomyopathies?" Circulation: Cardiovascular Genetics 7, no. 4 (August 1, 2014): 405–6. http://dx.doi.org/10.1161/circgenetics.114.000743.

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29

Mansour, Lobna, Ezzat el Sobky, Solaf M. Mohamed, Huda Marzouk, and Lamia A. Tarek. "Genotype–phenotype relationship among Egyptian children with Rett syndrome." Journal of the Egyptian Public Health Association 90, no. 3 (September 2015): 133–37. http://dx.doi.org/10.1097/01.epx.0000469901.73624.7a.

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30

Iuhas, Alin Remus, Claudia Jurca, and Marius Bembea. "Genotype-phenotype correlation in phenylketonuria." Romanian Journal of Pediatrics 70, no. 4 (December 31, 2021): 215–18. http://dx.doi.org/10.37897/rjp.2021.4.2.

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Анотація:

Introduction. Phenylketonuria is an inborn metabolism error with a high phenotypical variability, due in part to the large number of implicated genetical variants (over 1200 reported) but also due to other factors. Establishing a genotype=phenotype correlation, accessible today through molecular testing, is an important instrument for diagnostical accuracy, personalized therapy, better evaluation of the prognostic and an optimal genetical advice. Objective. The article aims to make an analyze of the most recent progress made in the effort of increasing the predictive value of genotyping in establishing the evolution and the severity of the disease. Material and method. For this review there were analyzed article from specialty journals indexed to Pubmed database, published mainly in the last 10 years. Results. Genotype-phenotype correlations can be established in most patients, but in approximative 10% of cases there are discordances between previously reported data and the result found in some studies. This mismatch results from the allelic interaction in compound heterozygous, not yet fully understood, from the existence of variants with unpredictable evolution and from other, non-genetical factors. Conclusions. The genotype-phenotype relationship is increasingly better understood. Molecular testing in phenylketonuria and phenotypical predictions based on the genotype, obtained by comparison with international databases, have clinical importance for the genetic advice given to the family and for the therapeutic decision, among other reasons.

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31

Brennan, Steven K., Thomas W. Ferkol, and Stephanie D. Davis. "Emerging Genotype-Phenotype Relationships in Primary Ciliary Dyskinesia." International Journal of Molecular Sciences 22, no. 15 (July 31, 2021): 8272. http://dx.doi.org/10.3390/ijms22158272.

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Primary ciliary dyskinesia (PCD) is a rare inherited condition affecting motile cilia and leading to organ laterality defects, recurrent sino-pulmonary infections, bronchiectasis, and severe lung disease. Research over the past twenty years has revealed variability in clinical presentations, ranging from mild to more severe phenotypes. Genotype and phenotype relationships have emerged. The increasing availability of genetic panels for PCD continue to redefine these genotype-phenotype relationships and reveal milder forms of disease that had previously gone unrecognized.

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32

Schupp, Jonas Christian, Sandra Freitag-Wolf, Elena Bargagli, Violeta Mihailović-Vučinić, Paola Rottoli, Aleksandar Grubanovic, Annegret Müller, et al. "Phenotypes of organ involvement in sarcoidosis." European Respiratory Journal 51, no. 1 (January 2018): 1700991. http://dx.doi.org/10.1183/13993003.00991-2017.

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Sarcoidosis is a highly variable, systemic granulomatous disease of hitherto unknown aetiology. The GenPhenReSa (Genotype–Phenotype Relationship in Sarcoidosis) project represents a European multicentre study to investigate the influence of genotype on disease phenotypes in sarcoidosis.The baseline phenotype module of GenPhenReSa comprised 2163 Caucasian patients with sarcoidosis who were phenotyped at 31 study centres according to a standardised protocol.From this module, we found that patients with acute onset were mainly female, young and of Scadding type I or II. Female patients showed a significantly higher frequency of eye and skin involvement, and complained more of fatigue. Based on multidimensional correspondence analysis and subsequent cluster analysis, patients could be clearly stratified into five distinct, yet undescribed, subgroups according to predominant organ involvement: 1) abdominal organ involvement, 2) ocular–cardiac–cutaneous–central nervous system disease involvement, 3) musculoskeletal–cutaneous involvement, 4) pulmonary and intrathoracic lymph node involvement, and 5) extrapulmonary involvement.These five new clinical phenotypes will be useful to recruit homogenous cohorts in future biomedical studies.

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33

Nichol, Daniel, Mark Robertson-Tessi, Alexander R. A. Anderson, and Peter Jeavons. "Model genotype–phenotype mappings and the algorithmic structure of evolution." Journal of The Royal Society Interface 16, no. 160 (November 2019): 20190332. http://dx.doi.org/10.1098/rsif.2019.0332.

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Cancers are complex dynamic systems that undergo evolution and selection. Personalized medicine approaches in the clinic increasingly rely on predictions of tumour response to one or more therapies; these predictions are complicated by the inevitable evolution of the tumour. Despite enormous amounts of data on the mutational status of cancers and numerous therapies developed in recent decades to target these mutations, many of these treatments fail after a time due to the development of resistance in the tumour. The emergence of these resistant phenotypes is not easily predicted from genomic data, since the relationship between genotypes and phenotypes, termed the genotype–phenotype (GP) mapping, is neither injective nor functional. We present a review of models of this mapping within a generalized evolutionary framework that takes into account the relation between genotype, phenotype, environment and fitness. Different modelling approaches are described and compared, and many evolutionary results are shown to be conserved across studies despite using different underlying model systems. In addition, several areas for future work that remain understudied are identified, including plasticity and bet-hedging. The GP-mapping provides a pathway for understanding the potential routes of evolution taken by cancers, which will be necessary knowledge for improving personalized therapies.

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34

Lacerra, Giuseppina, Clelia Scarano, Laura F. Lagona, Rosario Testa, Daniela G. Caruso, Emilia Medulla, Maria G. Friscia та ін. "Genotype-Phenotype Relationship of the δ-Thalassemia and Hb A2Variants: Observation of 52 Genotypes". Hemoglobin 34, № 5 (20 вересня 2010): 407–23. http://dx.doi.org/10.3109/03630269.2010.511586.

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35

Whittington, Joyce, and Anthony Holland. "Next Steps in Prader-Willi Syndrome Research: On the Relationship between Genotype and Phenotype." International Journal of Molecular Sciences 23, no. 20 (October 11, 2022): 12089. http://dx.doi.org/10.3390/ijms232012089.

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This article reviews what we know of the phenotype and genotype of Prader-Willi syndrome and hypothesizes two possible paths from phenotype to genotype. It then suggests research that may strengthen the case for one or other of these hypotheses.

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36

Yaikhom, Gagarine, Hugh Morgan, Duncan Sneddon, Ahmad Retha, Julian Atienza-Herrero, Andrew Blake, James Brown, et al. "Comparative visualization of genotype-phenotype relationships." Nature Methods 12, no. 8 (July 30, 2015): 698–99. http://dx.doi.org/10.1038/nmeth.3477.

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37

Hubert, D., T. Bienvenu, N. Desmazes-Dufeu, I. Fajac, J. Lacronique, R. Matran, J. C. Kaplan, and D. J. Dusser. "Genotype-phenotype relationships in a cohort." European Respiratory Journal 9, no. 11 (November 1, 1996): 2207–14. http://dx.doi.org/10.1183/09031936.96.09112207.

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38

Mickle, John E., and Garry R. Cutting. "GENOTYPE-PHENOTYPE RELATIONSHIPS IN CYSTIC FIBROSIS." Medical Clinics of North America 84, no. 3 (May 2000): 597–607. http://dx.doi.org/10.1016/s0025-7125(05)70243-1.

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39

Pizzuti, Antonio. "Genotype-phenotype relationships in myotonic dystrophy." Neuromuscular Disorders 6, no. 2 (March 1996): S18. http://dx.doi.org/10.1016/0960-8966(96)88999-1.

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40

Sen, Śaunak, and Gary A. Churchill. "A Statistical Framework for Quantitative Trait Mapping." Genetics 159, no. 1 (September 1, 2001): 371–87. http://dx.doi.org/10.1093/genetics/159.1.371.

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AbstractWe describe a general statistical framework for the genetic analysis of quantitative trait data in inbred line crosses. Our main result is based on the observation that, by conditioning on the unobserved QTL genotypes, the problem can be split into two statistically independent and manageable parts. The first part involves only the relationship between the QTL and the phenotype. The second part involves only the location of the QTL in the genome. We developed a simple Monte Carlo algorithm to implement Bayesian QTL analysis. This algorithm simulates multiple versions of complete genotype information on a genomewide grid of locations using information in the marker genotype data. Weights are assigned to the simulated genotypes to capture information in the phenotype data. The weighted complete genotypes are used to approximate quantities needed for statistical inference of QTL locations and effect sizes. One advantage of this approach is that only the weights are recomputed as the analyst considers different candidate models. This device allows the analyst to focus on modeling and model comparisons. The proposed framework can accommodate multiple interacting QTL, nonnormal and multivariate phenotypes, covariates, missing genotype data, and genotyping errors in any type of inbred line cross. A software tool implementing this procedure is available. We demonstrate our approach to QTL analysis using data from a mouse backcross population that is segregating multiple interacting QTL associated with salt-induced hypertension.

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41

Gasparin, Maria Regina R., Felipe Crispim, Sílvia L. Paula, Maria Beatriz S. Freire, Ivaldir S. Dalbosco, Thais Della Manna, João Eduardo N. Salles, et al. "Identification of novel mutations of the WFS1 gene in Brazilian patients with Wolfram syndrome." European Journal of Endocrinology 160, no. 2 (February 2009): 309–16. http://dx.doi.org/10.1530/eje-08-0698.

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ObjectiveWolfram syndrome (WS) is a rare, progressive, neurodegenerative disorder with an autosomal recessive pattern of inheritance. The gene for WS, WFS1, was identified on chromosome 4p16 and most WS patients carry mutations in this gene. However, some studies have provided evidence for genetic heterogeneity and the genotype–phenotype relationships are not clear. Our aim was to ascertain the spectrum of WFS1 mutations in Brazilian patients with WS and to examine the phenotype–genotype relationships in these patients.Design and methodsClinical characterization and analyses of the WFS1 gene were performed in 27 Brazilian patients with WS from 19 families.ResultsWe identified 15 different mutations in the WFS1 gene in 26 patients, among which nine are novel. All mutations occurred in exon 8, except for one missense mutation which was located in exon 5. Although we did not find any clear phenotype–genotype relationship in patients with mutations in exon 8, the homozygous missense mutation in exon 5 was associated with a mild phenotype: onset of diabetes mellitus and optic atrophy during adulthood with good metabolic control being achieved with low doses of sulfonylurea.ConclusionsOur data show that WFS1 is the major gene involved in WS in Brazilian patients and most mutations are concentrated in exon 8. Also, our study increases the spectrum of WFS1 mutations. Although no clear phenotype–genotype relationship was found for mutations in exon 8, a mild phenotype was associated with a homozygous missense mutation in exon 5.

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Magaji, H. S., M. M. Musa, and I. M. Badamasi. "Relationship between finger print patterns with blood group and genotype among basic medical science, students of Bayero University, Kano." Bayero Journal of Pure and Applied Sciences 12, no. 2 (February 15, 2021): 182–90. http://dx.doi.org/10.4314/bajopas.v12i2.27.

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Fingerprint patterns and the blood genotypes of individuals are chiefly determined by genetic factors during in-utero development. Evaluating genotypes requires expertise and facilities which could relatively be difficult to obtain and operate in some setting. The aim of the current study was to determine the correlation between hand finger print patterns and the common blood typing phenotype (genotype and blood group) among agroup of consenting adult population in Nigeria. Four hundred students (217 males and 183 females) of the Faculty of Basic Medical Sciences in Bayero University Kano had their total hand fingerprints captured using a scanner / computer set up. Data regarding common blood phenotypes was also determined for all participants from the blood phenotype information on their University identification cards. The mean age of theparticipants was 21.86±3.37 years. Loop finger prints patternwas the most common identified in the participants (58.4%), followed by whorls (27.9%), and then the least was arches (13.7%). There was a significant association between the finger print patternon the left thumb (p=0.012) as well as right thumb (p=0.013) withblood groups, while the print in the right index (p=0.042) and left little finger (p= 0.024) were associated with genotypes in the participants respectively. There was a relationship between the finger prints patterns the thumb, index finger and little finger with the common blood typing phenotypes. Thus, finger print patterns on the right index and left little fingercorrelates with blood genotypes.

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HAYES, B. J., P. M. VISSCHER, and M. E. GODDARD. "Increased accuracy of artificial selection by using the realized relationship matrix." Genetics Research 91, no. 1 (February 2009): 47–60. http://dx.doi.org/10.1017/s0016672308009981.

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SummaryDense marker genotypes allow the construction of the realized relationship matrix between individuals, with elements the realized proportion of the genome that is identical by descent (IBD) between pairs of individuals. In this paper, we demonstrate that by replacing the average relationship matrix derived from pedigree with the realized relationship matrix in best linear unbiased prediction (BLUP) of breeding values, the accuracy of the breeding values can be substantially increased, especially for individuals with no phenotype of their own. We further demonstrate that this method of predicting breeding values is exactly equivalent to the genomic selection methodology where the effects of quantitative trait loci (QTLs) contributing to variation in the trait are assumed to be normally distributed. The accuracy of breeding values predicted using the realized relationship matrix in the BLUP equations can be deterministically predicted for known family relationships, for example half sibs. The deterministic method uses the effective number of independently segregating loci controlling the phenotype that depends on the type of family relationship and the length of the genome. The accuracy of predicted breeding values depends on this number of effective loci, the family relationship and the number of phenotypic records. The deterministic prediction demonstrates that the accuracy of breeding values can approach unity if enough relatives are genotyped and phenotyped. For example, when 1000 full sibs per family were genotyped and phenotyped, and the heritability of the trait was 0·5, the reliability of predicted genomic breeding values (GEBVs) for individuals in the same full sib family without phenotypes was 0·82. These results were verified by simulation. A deterministic prediction was also derived for random mating populations, where the effective population size is the key parameter determining the effective number of independently segregating loci. If the effective population size is large, a very large number of individuals must be genotyped and phenotyped in order to accurately predict breeding values for unphenotyped individuals from the same population. If the heritability of the trait is 0·3, and Ne=1000, approximately 5750 individuals with genotypes and phenotypes are required in order to predict GEBVs of un-phenotyped individuals in the same population with an accuracy of 0·7.

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Sweadner, Kathleen J., Elena Arystarkhova, John T. Penniston, Kathryn J. Swoboda, Allison Brashear, and Laurie J. Ozelius. "Genotype-structure-phenotype relationships diverge in paralogs ATP1A1, ATP1A2, and ATP1A3." Neurology Genetics 5, no. 1 (February 2019): e303. http://dx.doi.org/10.1212/nxg.0000000000000303.

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ObjectiveWe tested the assumption that closely related genes should have similar pathogenic variants by analyzing >200 pathogenic variants in a gene family with high neurologic impact and high sequence identity, the Na,K-ATPases ATP1A1, ATP1A2, and ATP1A3.MethodsData sets of disease-associated variants were compared. Their equivalent positions in protein crystal structures were used for insights into pathogenicity and correlated with the phenotype and conservation of homology.ResultsRelatively few mutations affected the corresponding amino acids in 2 genes. In the membrane domain of ATP1A3 (primarily expressed in neurons), variants producing milder neurologic phenotypes had different structural positions than variants producing severe phenotypes. In ATP1A2 (primarily expressed in astrocytes), membrane domain variants characteristic of severe phenotypes in ATP1A3 were absent from patient data. The known variants in ATP1A1 fell into 2 distinct groups. Sequence conservation was an imperfect indicator: it varied among structural domains, and some variants with demonstrated pathogenicity were in low conservation sites.ConclusionsPathogenic variants varied between genes despite high sequence identity, and there is a genotype-structure-phenotype relationship in ATP1A3 that correlates with neurologic outcomes. The absence of “severe” pathogenic variants in ATP1A2 patients predicts that they will manifest either in a different tissue or by death in utero and that new ATP1A1 variants will produce additional phenotypes. It is important that some variants in poorly conserved amino acids are nonetheless pathogenic and could be incorrectly predicted to be benign.

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Ji, Wan-Sheng. "Relationship between genotype and phenotype of flagellin C in Salmonella." World Journal of Gastroenterology 7, no. 6 (2001): 864. http://dx.doi.org/10.3748/wjg.v7.i6.864.

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Schorge, S., and D. M. Kullmann. "Sodium channelopathy of peripheral nerve: tightening the genotype-phenotype relationship." Brain 132, no. 7 (May 8, 2009): 1690–92. http://dx.doi.org/10.1093/brain/awp120.

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Enkhmaa, Byambaa, Erdembileg Anuurad, Wei Zhang, Tina Tran, and Lars Berglund. "Lipoprotein(a): Genotype–Phenotype Relationship and Impact on Atherogenic Risk." Metabolic Syndrome and Related Disorders 9, no. 6 (December 2011): 411–18. http://dx.doi.org/10.1089/met.2011.0026.

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Pehlivan, Davut, Kivanc Cefle, Anja Raams, Sukru Ozturk, Can Baykal, Wim J. Kleijer, Sukru Palanduz, and Nicolaas G. J. Jaspers. "A Turkish trichothiodystrophy patient with homozygousXPDmutation and genotype-phenotype relationship." Journal of Dermatology 39, no. 12 (October 5, 2012): 1016–21. http://dx.doi.org/10.1111/j.1346-8138.2012.01662.x.

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Woods, KA, AJL Clark, S. Amselem, and MO Savage. "Relationship between phenotype and genotype in growth hormone insensitivity syndrome." Acta Paediatrica 88, s428 (February 1999): 158–62. http://dx.doi.org/10.1111/j.1651-2227.1999.tb14376.x.

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Wright, J. T., P. S. Hart, M. J. Aldred, K. Seow, P. J. M. Crawford, S. P. Hong, C. W. Gibson, and T. C. Hart. "Relationship of Phenotype and Genotype in X-Linked Amelogenesis Imperfecta." Connective Tissue Research 44, no. 1 (2003): 72–78. http://dx.doi.org/10.1080/713713640.

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