Powers Scott, Gopalakrishnan Shyam, Tintle Nathan
Department of Statistics and Operations Research, University of North Carolina, Chapel Hill, NC, USA.
Hum Hered. 2011;72(3):153-60. doi: 10.1159/000332222. Epub 2011 Oct 15.
BACKGROUND/AIMS: We aim to quantify the effect of non-differential genotyping errors on the power of rare variant tests and identify those situations when genotyping errors are most harmful.
We simulated genotype and phenotype data for a range of sample sizes, minor allele frequencies, disease relative risks and numbers of rare variants. Genotype errors were then simulated using five different error models covering a wide range of error rates.
Even at very low error rates, misclassifying a common homozygote as a heterozygote translates into a substantial loss of power, a result that is exacerbated even further as the minor allele frequency decreases. While the power loss from heterozygote to common homozygote errors tends to be smaller for a given error rate, in practice heterozygote to homozygote errors are more frequent and, thus, will have measurable impact on power.
Error rates from genotype-calling technology for next-generation sequencing data suggest that substantial power loss may be seen when applying current rare variant tests of association to called genotypes.
背景/目的:我们旨在量化非差异性基因分型错误对罕见变异检测效能的影响,并确定基因分型错误最具危害性的情况。
我们针对一系列样本量、次要等位基因频率、疾病相对风险和罕见变异数量模拟了基因型和表型数据。然后使用涵盖广泛错误率的五种不同错误模型模拟基因分型错误。
即使在非常低的错误率下,将常见纯合子误分类为杂合子也会导致效能大幅损失,随着次要等位基因频率降低,这一结果会进一步加剧。对于给定的错误率,从杂合子到常见纯合子错误导致的效能损失往往较小,但在实际中,从杂合子到纯合子的错误更为频繁,因此会对效能产生可测量的影响。
下一代测序数据的基因分型技术的错误率表明,在将当前罕见变异关联检测应用于已分型的基因型时,可能会出现显著的效能损失。