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10.1534/genetics.115.175174 http://scihub22266oqcxt.onion/10.1534/genetics.115.175174 C4423382!4423382!25745024     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Genetics 2015 ; 200 (1): 69-78 Nephropedia Template TP {{tp|p=25745024|t=2015. A Powerful Nonparametric Statistical Framework for Family-Based Association Analyses |pdf=|usr=}}{{25745024}} gab.com Text A Powerful Nonparametric Statistical Framework for Family-Based Association Analyses JO: Genetics http://www.kidney.de/pget.php?&tw=1&pmid=25745024 #FOAvip Family-based study design is commonly used in genetic research. It has many ideal features, including being robust to population stratification (PS). With the advance of high-throughput technologies and ever-decreasing genotyping cost, it has become common for family studies to examine a large number of variants for their associations with disease phenotypes. The yield from the analysis of these family-based genetic data can be enhanced by adopting computationally efficient and powerful statistical methods. We propose a general framework of a family-based U-statistic, referred to as family-U, for family-based association studies. Unlike existing parametric-based methods, the proposed method makes no assumption of the underlying disease models and can be applied to various phenotypes (e.g., binary and quantitative phenotypes) and pedigree structures (e.g., nuclear families and extended pedigrees). By using only within-family information, it can offer robust protection against PS. In the absence of PS, it can also utilize additional information (i.e., between-family information) for power improvement. Through simulations, we demonstrated that family-U attained higher power over a commonly used method, family-based association tests, under various disease scenarios. We further illustrated the new method with an application to large-scale family data from the Framingham Heart Study. By utilizing additional information (i.e., between-family information), family-U confirmed a previous association of CHRNA5 with nicotine dependence. Twit Text FOAVip A Powerful Nonparametric Statistical Framework for Family-Based Association Analyses ????Genetics http://www.kidney.de/pget.php?&tw=1&pmid=25745024 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=25745024 #FOAvip English Wikipedia <ref>{{Cite pmid|25745024}}</ref> A Powerful Nonparametric Statistical Framework for Family-Based Association Analyses #MMPMID25745024 Li M; He Z; Schaid DJ; Cleves MA; Nick TG; Lu Q Genetics 2015[May]; 200 (1): 69-78 PMID25745024show ga Family-based study design is commonly used in genetic research. It has many ideal features, including being robust to population stratification (PS). With the advance of high-throughput technologies and ever-decreasing genotyping cost, it has become common for family studies to examine a large number of variants for their associations with disease phenotypes. The yield from the analysis of these family-based genetic data can be enhanced by adopting computationally efficient and powerful statistical methods. We propose a general framework of a family-based U-statistic, referred to as family-U, for family-based association studies. Unlike existing parametric-based methods, the proposed method makes no assumption of the underlying disease models and can be applied to various phenotypes (e.g., binary and quantitative phenotypes) and pedigree structures (e.g., nuclear families and extended pedigrees). By using only within-family information, it can offer robust protection against PS. In the absence of PS, it can also utilize additional information (i.e., between-family information) for power improvement. Through simulations, we demonstrated that family-U attained higher power over a commonly used method, family-based association tests, under various disease scenarios. We further illustrated the new method with an application to large-scale family data from the Framingham Heart Study. By utilizing additional information (i.e., between-family information), family-U confirmed a previous association of CHRNA5 with nicotine dependence. äA Powerful Nonparametric Statistical Framework for Family-Based Associ(epmc).pdf DeepDyve Pubget Overpricing