Samwald Matthias, Blagec Kathrin, Hofer Sebastian, Freimuth Robert R
Section for Medical Expert & Knowledge-Based Systems, Center for Medical Statistics, Informatics & Intelligent Systems, Medical University of Vienna, Spitalgasse 23, 1090 Vienna, Austria.
Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA.
Pharmacogenomics. 2015;16(15):1713-21. doi: 10.2217/pgs.15.108. Epub 2015 Sep 30.
Many currently available pharmacogenomic assays and algorithms interrogate a set of 'tag' polymorphisms for inferring haplotypes. We wanted to test the accuracy of such haplotype inferences across different populations.
MATERIALS & METHODS: We simulated haplotype inferences made by existing pharmacogenomic assays for seven important pharmacogenes based on full genome data of 2504 persons in the 1000 Genomes dataset.
A sizable fraction of samples did not match any of the haplotypes in the star allele nomenclature systems. We found no clear population bias in the accuracy of results of simulated assays.
Haplotype nomenclatures and inference algorithms need to be improved to adequately capture pharmacogenomic diversity in human populations.
目前许多可用的药物基因组学检测方法和算法会对一组“标签”多态性进行检测，以推断单倍型。我们希望测试这种单倍型推断在不同人群中的准确性。
我们基于千人基因组数据集中2504人的全基因组数据，模拟了现有药物基因组学检测方法对7个重要药物基因的单倍型推断。
相当一部分样本与星号等位基因命名系统中的任何单倍型都不匹配。我们发现模拟检测结果的准确性没有明显的人群偏差。
需要改进单倍型命名法和推断算法，以充分捕捉人类群体中的药物基因组学多样性。
