Silander Kaisa, Komulainen Kati, Ellonen Pekka, Jussila Minttu, Alanne Mervi, Levander Minna, Tainola Päivi, Kuulasmaa Kari, Salomaa Veikko, Perola Markus, Peltonen Leena, Saarela Janna
Department of Molecular Medicine, National Public Health Institute, Helsinki, Finland.
Twin Res Hum Genet. 2005 Aug;8(4):368-75. doi: 10.1375/1832427054936664.
The amount of available DNA is often a limiting factor in pursuing genetic analyses of large-scale population cohorts. An association between higher DNA yield from blood and several phenotypes associated with inflammatory states has recently been demonstrated, suggesting that exclusion of samples with very low DNA yield may lead to biased results in statistical analyses. Whole genome amplification (WGA) could present a solution to the DNA concentration-dependent sample selection. The aim was to thoroughly assess WGA for samples with low DNA yield, using the multiply-primed rolling circle amplification method. Fifty-nine samples were selected with the lowest DNA yield (less than 7.5 microg) among 799 samples obtained for one population cohort. The genotypes obtained from two replicate WGA samples and the original genomic DNA were compared by typing 24 single nucleotide polymorphisms (SNPs). Multiple genotype discrepancies were identified for 13 of the 59 samples. The largest portion of discrepancies was due to allele dropout in heterozygous genotypes in WGA samples. Pooling the WGA DNA replicates prior to genotyping markedly improved genotyping reproducibility for the samples, with only 7 discrepancies identified in 4 samples. The nature of discrepancies was mostly homozygote genotypes in the genomic DNA and heterozygote genotypes in the WGA sample, suggesting possible allele dropout in the genomic DNA sample due to very low amounts of DNA template. Thus, WGA is applicable for low DNA yield samples, especially if using pooled WGA samples. A higher rate of genotyping errors requires that increased attention be paid to genotyping quality control, and caution when interpreting results.
在对大规模人群队列进行基因分析时,可用DNA的量常常是一个限制因素。最近已证实,血液中较高的DNA产量与几种与炎症状态相关的表型之间存在关联,这表明排除DNA产量极低的样本可能会导致统计分析结果出现偏差。全基因组扩增(WGA)可能为依赖DNA浓度的样本选择提供一种解决方案。本研究旨在使用多重引物滚环扩增方法,对低DNA产量的样本进行WGA的全面评估。在为一个人群队列获取的799个样本中,选择了59个DNA产量最低(低于7.5微克)的样本。通过对24个单核苷酸多态性(SNP)进行基因分型,比较了从两个重复的WGA样本和原始基因组DNA中获得的基因型。在59个样本中的13个样本中发现了多个基因型差异。差异的最大部分是由于WGA样本中杂合基因型的等位基因缺失。在基因分型前将WGA DNA重复样本合并,显著提高了样本的基因分型重复性,在4个样本中仅发现7个差异。差异的性质大多是基因组DNA中的纯合子基因型和WGA样本中的杂合子基因型,这表明由于DNA模板量极少,基因组DNA样本中可能存在等位基因缺失。因此,WGA适用于低DNA产量的样本,特别是在使用合并的WGA样本时。较高的基因分型错误率要求更加关注基因分型质量控制,并在解释结果时谨慎行事。