Common Treatment, Common Variant: Evolutionary Prediction of Functional Pharmacogenomic Variants.
Common Treatment, Common Variant: Evolutionary Prediction of Functional Pharmacogenomic Variants.
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常见治疗，常见变异：功能性药物基因组学变异的进化预测。
DOI：
10.3390/jpm11020131
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发表时间：
2021-02-16
影响因子：
--
通讯作者：
Gharani N
中科院分区：
文献类型：
--
作者：
Scheinfeldt LB;Brangan A;Kusic DM;Kumar S;Gharani N
药物基因组学有望实现个性化药物疗效优化和药物毒性最小化。然而，迄今为止进行的大部分研究都存在对欧洲参与者的确定偏见。在这里，我们利用从全球人群中收集的公开可用的全基因组测序数据，进化特征和注释的蛋白质特征来构建一种新的计算机机器学习药物遗传学鉴定方法，称为XGB-PGX。当应用于药物遗传学数据时，XGB-PGX优于所有现有的预测方法，并确定了2000多个新的药物遗传学变体。虽然在全球人群样本中存在适度的药物遗传学等位基因频率分布差异，但最显著的区别是相对罕见的中性药物基因变体与相对常见的已确定和新预测的功能性药物遗传学变体之间的差异。因此，我们的研究结果支持专注于个体患者药物遗传学检测，而不是对患者种族，民族或祖先地理居住地的临床假设。我们进一步鼓励更多地关注常见变异对药物反应的影响，并提出了一个新的“常见治疗，常见变异”的药物遗传学预测的角度，这是不同于复杂和孟德尔疾病的变异类型。XGB-PGX已经发现了许多新的药物变体，这些变体存在于全球所有社区;然而，在基因组研究中代表性不足的社区可能从XGB-PGX的计算机预测中受益最多。
Pharmacogenomics holds the promise of personalized drug efficacy optimization and drug toxicity minimization. Much of the research conducted to date, however, suffers from an ascertainment bias towards European participants. Here, we leverage publicly available, whole genome sequencing data collected from global populations, evolutionary characteristics, and annotated protein features to construct a new in silico machine learning pharmacogenetic identification method called XGB-PGX. When applied to pharmacogenetic data, XGB-PGX outperformed all existing prediction methods and identified over 2000 new pharmacogenetic variants. While there are modest pharmacogenetic allele frequency distribution differences across global population samples, the most striking distinction is between the relatively rare putatively neutral pharmacogene variants and the relatively common established and newly predicted functional pharamacogenetic variants. Our findings therefore support a focus on individual patient pharmacogenetic testing rather than on clinical presumptions about patient race, ethnicity, or ancestral geographic residence. We further encourage more attention be given to the impact of common variation on drug response and propose a new ‘common treatment, common variant’ perspective for pharmacogenetic prediction that is distinct from the types of variation that underlie complex and Mendelian disease. XGB-PGX has identified many new pharmacovariants that are present across all global communities; however, communities that have been underrepresented in genomic research are likely to benefit the most from XGB-PGX’s in silico predictions.
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DOI：
10.1146/annurev-pharmtox-010814-124835
发表时间：
2015
影响因子：
12.5
作者：
Dunnenberger HM;Crews KR;Hoffman JM;Caudle KE;Broeckel U;Howard SC;Hunkler RJ;Klein TE;Evans WE;Relling MV
通讯作者：
Relling MV
DOI：
10.1056/nejmoa1310669
发表时间：
2013-12-12
期刊：
The New England journal of medicine
影响因子：
--
作者：
Kimmel SE;French B;Kasner SE;Johnson JA;Anderson JL;Gage BF;Rosenberg YD;Eby CS;Madigan RA;McBane RB;Abdel-Rahman SZ;Stevens SM;Yale S;Mohler ER 3rd;Fang MC;Shah V;Horenstein RB;Limdi NA;Muldowney JA 3rd;Gujral J;Delafontaine P;Desnick RJ;Ortel TL;Billett HH;Pendleton RC;Geller NL;Halperin JL;Goldhaber SZ;Caldwell MD;Califf RM;Ellenberg JH;COAG Investigators
通讯作者：
COAG Investigators
影响因子：
3.9
作者：
Devarajan, Sandhya;Moon, Irene;Reid, Joel M.
通讯作者：
Reid, Joel M.
影响因子：
30.8
作者：
Kircher, Martin;Witten, Daniela M.;Jain, Preti;O'Roak, Brian J.;Cooper, Gregory M.;Shendure, Jay
通讯作者：
Shendure, Jay
影响因子：
6.7
作者：
通讯作者：
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