Victoria Research Laboratory, National ICT Australia, Department of Computer Science and Software Engineering, University of Melbourne, Australia.
Bioinformatics. 2010 Apr 15;26(8):1007-14. doi: 10.1093/bioinformatics/btq088. Epub 2010 Feb 25.
High-density single nucleotide polymorphism (SNP) genotyping arrays are efficient and cost effective platforms for the detection of copy number variation (CNV). To ensure accuracy in probe synthesis and to minimize production costs, short oligonucleotide probe sequences are used. The use of short probe sequences limits the specificity of binding targets in the human genome. The specificity of these short probeset sequences has yet to be fully analysed against a normal reference human genome. Sequence similarity can artificially elevate or suppress copy number measurements, and hence reduce the reliability of affected probe readings. For the purpose of detecting narrow CNVs reliably down to the width of a single probeset, sequence similarity is an important issue that needs to be addressed.
We surveyed the Affymetrix Human Mapping SNP arrays for probeset sequence similarity against the reference human genome. Utilizing sequence similarity results, we identified a collection of fine-scaled putative CNVs between gender from autosomal probesets whose sequence matches various loci on the sex chromosomes. To detect these variations, we utilized our statistical approach, Detecting REcurrent Copy number change using rank-order Statistics (DRECS), and showed that its performance was superior and more stable than the t-test in detecting CNVs. Through the application of DRECS on the HapMap population datasets with multi-matching probesets filtered, we identified biologically relevant SNPs in aberrant regions across populations with known association to physical traits, such as height, covered by the span of a single probe. This provided empirical confirmation of the existence of naturally occurring narrow CNVs as well as the sensitivity of the Affymetrix SNP array technology in detecting them.
The MATLAB implementation of DRECS is available at http://ww2.cs.mu.oz.au/ approximately gwong/DRECS/index.html.
高密度单核苷酸多态性（SNP）基因分型阵列是检测拷贝数变异（CNV）的高效且具有成本效益的平台。为了确保探针合成的准确性并最大程度地降低生产成本，使用了短的寡核苷酸探针序列。短探针序列的使用限制了在人类基因组中结合靶标的特异性。这些短探针序列的特异性尚未针对正常参考人类基因组进行全面分析。序列相似性可能会人为地升高或抑制拷贝数测量值，从而降低受影响探针读数的可靠性。为了可靠地检测到单个探针宽度的狭窄 CNV，序列相似性是一个需要解决的重要问题。
我们调查了 Affymetrix Human Mapping SNP 阵列针对参考人类基因组的探针序列相似性。利用序列相似性结果，我们在常染色体探针中确定了一组来自性染色体的精细尺度的性别特异性潜在 CNV，其序列与性染色体上的各种基因座匹配。为了检测这些变化，我们利用了我们的统计方法，即使用秩次统计量检测重复拷贝数变化（DRECS），并表明其性能优于 t 检验，更稳定，更能检测 CNV。通过应用 DRECS 对经多匹配探针过滤的 HapMap 人群数据集，我们在已知与身高等物理特征相关的人群中，在异常区域鉴定出与已知关联的生物学相关 SNPs，这些特征由单个探针的跨度覆盖。这提供了对自然发生的狭窄 CNV 的存在以及 Affymetrix SNP 阵列技术检测它们的敏感性的经验证实。
DRECS 的 MATLAB 实现可在 http://ww2.cs.mu.oz.au/approximately gwong/DRECS/index.html 上获得。
