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10.1093/bioinformatics/btu294 http://scihub22266oqcxt.onion/10.1093/bioinformatics/btu294 C4058916!4058916!24931985     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Bioinformatics 2014 ; 30 (12): i204-11 Nephropedia Template TP {{tp|p=24931985|t=2014. Privacy preserving protocol for detecting genetic relatives using rare variants |pdf=|usr=}}{{24931985}} gab.com Text Privacy preserving protocol for detecting genetic relatives using rare variants JO: Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=24931985 #FOAvip Motivation: High-throughput sequencing technologies have impacted many areas of genetic research. One such area is the identification of relatives from genetic data. The standard approach for the identification of genetic relatives collects the genomic data of all individuals and stores it in a database. Then, each pair of individuals is compared to detect the set of genetic relatives, and the matched individuals are informed. The main drawback of this approach is the requirement of sharing your genetic data with a trusted third party to perform the relatedness test.Results: In this work, we propose a secure protocol to detect the genetic relatives from sequencing data while not exposing any information about their genomes. We assume that individuals have access to their genome sequences but do not want to share their genomes with anyone else. Unlike previous approaches, our approach uses both common and rare variants which provide the ability to detect much more distant relationships securely. We use a simulated data generated from the 1000 genomes data and illustrate that we can easily detect up to fifth degree cousins which was not possible using the existing methods. We also show in the 1000 genomes data with cryptic relationships that our method can detect these individuals.Availability: The software is freely available for download at http://genetics.cs.ucla.edu/crypto/.Contact:fhormoz@cs.ucla.edu or eeskin@cs.ucla.eduSupplementary information:Supplementary data are available at Bioinformatics online Twit Text FOAVip Privacy preserving protocol for detecting genetic relatives using rare variants ????Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=24931985 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=24931985 #FOAvip English Wikipedia <ref>{{Cite pmid|24931985}}</ref> Privacy preserving protocol for detecting genetic relatives using rare variants #MMPMID24931985 Hormozdiari F; Joo JWJ; Wadia A; Guan F; Ostrosky R; Sahai A; Eskin E Bioinformatics 2014[Jun]; 30 (12): i204-11 PMID24931985show ga Motivation: High-throughput sequencing technologies have impacted many areas of genetic research. One such area is the identification of relatives from genetic data. The standard approach for the identification of genetic relatives collects the genomic data of all individuals and stores it in a database. Then, each pair of individuals is compared to detect the set of genetic relatives, and the matched individuals are informed. The main drawback of this approach is the requirement of sharing your genetic data with a trusted third party to perform the relatedness test.Results: In this work, we propose a secure protocol to detect the genetic relatives from sequencing data while not exposing any information about their genomes. We assume that individuals have access to their genome sequences but do not want to share their genomes with anyone else. Unlike previous approaches, our approach uses both common and rare variants which provide the ability to detect much more distant relationships securely. We use a simulated data generated from the 1000 genomes data and illustrate that we can easily detect up to fifth degree cousins which was not possible using the existing methods. We also show in the 1000 genomes data with cryptic relationships that our method can detect these individuals.Availability: The software is freely available for download at http://genetics.cs.ucla.edu/crypto/.Contact:fhormoz@cs.ucla.edu or eeskin@cs.ucla.eduSupplementary information:Supplementary data are available at Bioinformatics online äPrivacy preserving protocol for detecting genetic relatives using rare(epmc).pdf DeepDyve Pubget Overpricing