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10.1016/j.ajhg.2016.01.008 http://scihub22266oqcxt.onion/10.1016/j.ajhg.2016.01.008 C4827100!4827100!26924528     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=26924528&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Am+J+Hum+Genet 2016 ; 98 (3): 490-9 Nephropedia Template TP {{tp|p=26924528|t=2016. Phenotype Similarity Regression for Identifying the Genetic Determinants of Rare Diseases |pdf=|usr=}}{{26924528}} gab.com Text Phenotype Similarity Regression for Identifying the Genetic Determinants of Rare Diseases JO: Am J Hum Genet http://www.kidney.de/pget.php?&tw=1&pmid=26924528 #FOAvip Rare genetic disorders, which can now be studied systematically with affordable genome sequencing, are often caused by high-penetrance rare variants. Such disorders are often heterogeneous and characterized by abnormalities spanning multiple organ systems ascertained with variable clinical precision. Existing methods for identifying genes with variants responsible for rare diseases summarize phenotypes with unstructured binary or quantitative variables. The Human Phenotype Ontology (HPO) allows composite phenotypes to be represented systematically but association methods accounting for the ontological relationship between HPO terms do not exist. We present a Bayesian method to model the association between an HPO-coded patient phenotype and genotype. Our method estimates the probability of an association together with an HPO-coded phenotype characteristic of the disease. We thus formalize a clinical approach to phenotyping that is lacking in standard regression techniques for rare disease research. We demonstrate the power of our method by uncovering a number of true associations in a large collection of genome-sequenced and HPO-coded cases with rare diseases. Twit Text FOAVip Phenotype Similarity Regression for Identifying the Genetic Determinants of Rare Diseases ????Am J Hum Genet http://www.kidney.de/pget.php?&tw=1&pmid=26924528 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26924528 #FOAvip English Wikipedia <ref>{{Cite pmid|26924528}}</ref> Phenotype Similarity Regression for Identifying the Genetic Determinants of Rare Diseases #MMPMID26924528 Greene D; Richardson S; Turro E Am J Hum Genet 2016[Mar]; 98 (3): 490-9 PMID26924528show ga Rare genetic disorders, which can now be studied systematically with affordable genome sequencing, are often caused by high-penetrance rare variants. Such disorders are often heterogeneous and characterized by abnormalities spanning multiple organ systems ascertained with variable clinical precision. Existing methods for identifying genes with variants responsible for rare diseases summarize phenotypes with unstructured binary or quantitative variables. The Human Phenotype Ontology (HPO) allows composite phenotypes to be represented systematically but association methods accounting for the ontological relationship between HPO terms do not exist. We present a Bayesian method to model the association between an HPO-coded patient phenotype and genotype. Our method estimates the probability of an association together with an HPO-coded phenotype characteristic of the disease. We thus formalize a clinical approach to phenotyping that is lacking in standard regression techniques for rare disease research. We demonstrate the power of our method by uncovering a number of true associations in a large collection of genome-sequenced and HPO-coded cases with rare diseases. äPhenotype Similarity Regression for Identifying the Genetic Determinan(epmc).pdf DeepDyve Pubget Overpricing