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10.1016/j.ajhg.2020.03.013 http://scihub22266oqcxt.onion/10.1016/j.ajhg.2020.03.013 32330416!7212266!32330416     free     free     free Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Am+J+Hum+Genet 2020 ; 106 (5): 679-693 Nephropedia Template TP {{tp|p=32330416|t=2020. Accurate and Scalable Construction of Polygenic Scores in Large Biobank Data Sets |pdf=|usr=}}{{32330416}} gab.com Text Accurate and Scalable Construction of Polygenic Scores in Large Biobank Data Sets JO: Am J Hum Genet http://www.kidney.de/pget.php?&tw=1&pmid=32330416 #FOAvip Accurate construction of polygenic scores (PGS) can enable early diagnosis of diseases and facilitate the development of personalized medicine. Accurate PGS construction requires prediction models that are both adaptive to different genetic architectures and scalable to biobank scale datasets with millions of individuals and tens of millions of genetic variants. Here, we develop such a method called Deterministic Bayesian Sparse Linear Mixed Model (DBSLMM). DBSLMM relies on a flexible modeling assumption on the effect size distribution to achieve robust and accurate prediction performance across a range of genetic architectures. DBSLMM also relies on a simple deterministic search algorithm to yield an approximate analytic estimation solution using summary statistics only. The deterministic search algorithm, when paired with further algebraic innovations, results in substantial computational savings. With simulations, we show that DBSLMM achieves scalable and accurate prediction performance across a range of realistic genetic architectures. We then apply DBSLMM to analyze 25 traits in UK Biobank. For these traits, compared to existing approaches, DBSLMM achieves an average of 2.03%-101.09% accuracy gain in internal cross-validations. In external validations on two separate datasets, including one from BioBank Japan, DBSLMM achieves an average of 14.74%-522.74% accuracy gain. In these real data applications, DBSLMM is 1.03-28.11 times faster and uses only 7.4%-24.8% of physical memory as compared to other multiple regression-based PGS methods. Overall, DBSLMM represents an accurate and scalable method for constructing PGS in biobank scale datasets. Twit Text FOAVip Accurate and Scalable Construction of Polygenic Scores in Large Biobank Data Sets ????Am J Hum Genet http://www.kidney.de/pget.php?&tw=1&pmid=32330416 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32330416 #FOAvip English Wikipedia <ref>{{Cite pmid|32330416}}</ref> Accurate and Scalable Construction of Polygenic Scores in Large Biobank Data Sets #MMPMID32330416 Yang S; Zhou X Am J Hum Genet 2020[May]; 106 (5): 679-693 PMID32330416show ga Accurate construction of polygenic scores (PGS) can enable early diagnosis of diseases and facilitate the development of personalized medicine. Accurate PGS construction requires prediction models that are both adaptive to different genetic architectures and scalable to biobank scale datasets with millions of individuals and tens of millions of genetic variants. Here, we develop such a method called Deterministic Bayesian Sparse Linear Mixed Model (DBSLMM). DBSLMM relies on a flexible modeling assumption on the effect size distribution to achieve robust and accurate prediction performance across a range of genetic architectures. DBSLMM also relies on a simple deterministic search algorithm to yield an approximate analytic estimation solution using summary statistics only. The deterministic search algorithm, when paired with further algebraic innovations, results in substantial computational savings. With simulations, we show that DBSLMM achieves scalable and accurate prediction performance across a range of realistic genetic architectures. We then apply DBSLMM to analyze 25 traits in UK Biobank. For these traits, compared to existing approaches, DBSLMM achieves an average of 2.03%-101.09% accuracy gain in internal cross-validations. In external validations on two separate datasets, including one from BioBank Japan, DBSLMM achieves an average of 14.74%-522.74% accuracy gain. In these real data applications, DBSLMM is 1.03-28.11 times faster and uses only 7.4%-24.8% of physical memory as compared to other multiple regression-based PGS methods. Overall, DBSLMM represents an accurate and scalable method for constructing PGS in biobank scale datasets. |*Multifactorial Inheritance[MESH] |Bayes Theorem[MESH] |Databases, Factual/*standards[MESH] |Datasets as Topic/*standards[MESH] |Female[MESH] |Humans[MESH] |Linear Models[MESH] |Male[MESH] |Polymorphism, Single Nucleotide[MESH] |Reproducibility of Results[MESH] |Sample Size[MESH] |United Kingdom[MESH]Accurate and Scalable Construction of Polygenic Scores in Large Bioban(epmc).pdf DeepDyve Pubget Overpricing