Carlson Christopher S, Smith Joshua D, Stanaway Ian B, Rieder Mark J, Nickerson Deborah A
Department of Genome Sciences, University of Washington, Seattle, WA 98195-7730, USA.
Hum Mol Genet. 2006 Jun 15;15(12):1931-7. doi: 10.1093/hmg/ddl115. Epub 2006 Apr 27.
Pinpointing genetic associations in the human genome relies heavily on the accuracy of the underlying genotype data. Null alleles can generate significant inaccuracies in genotype data and can negatively affect the statistical power of a study. Existing quality control (QC) tests, including tests of Hardy-Weinberg equilibrium, are not sensitive enough to detect the presence of even moderately frequent null alleles in the data. We show that direct analysis of raw data from a quantitative genotyping platform can detect up to 75% of null alleles, even at frequencies below the sensitivity of more traditional methods. Detecting unexpected null alleles not only has benefits in QC of genotype data but may also be valuable in detecting rare, functional null alleles that would otherwise be missed.
在人类基因组中精准定位基因关联在很大程度上依赖于基础基因型数据的准确性。无效等位基因会在基因型数据中产生显著的不准确，并可能对研究的统计效力产生负面影响。现有的质量控制（QC）测试，包括哈迪-温伯格平衡测试，对于检测数据中即使是中等频率的无效等位基因的存在也不够敏感。我们表明，对来自定量基因分型平台的原始数据进行直接分析能够检测出高达75%的无效等位基因，即使其频率低于更传统方法的灵敏度。检测意外的无效等位基因不仅对基因型数据的质量控制有益，而且在检测否则就会被遗漏的罕见功能性无效等位基因方面可能也很有价值。
