Istituto Dalle Molle di Studi sull'Intelligenza Artificiale, Manno-Lugano, Switzerland.
BMC Bioinformatics. 2010 Jun 15;11:321. doi: 10.1186/1471-2105-11-321.
Cancer and other disorders are due to genomic lesions. SNP-microarrays are able to measure simultaneously both genotype and copy number (CN) at several Single Nucleotide Polymorphisms (SNPs) along the genome. CN is defined as the number of DNA copies, and the normal is two, since we have two copies of each chromosome. The genotype of a SNP is the status given by the nucleotides (alleles) which are present on the two copies of DNA. It is defined homozygous or heterozygous if the two alleles are the same or if they differ, respectively. Loss of heterozygosity (LOH) is the loss of the heterozygous status due to genomic events. Combining CN and LOH data, it is possible to better identify different types of genomic aberrations. For example, a long sequence of homozygous SNPs might be caused by either the physical loss of one copy or a uniparental disomy event (UPD), i.e. each SNP has two identical nucleotides both derived from only one parent. In this situation, the knowledge of the CN can help in distinguishing between these two events.
To better identify genomic aberrations, we propose a method (called gBPCR) which infers the type of aberration occurred, taking into account all the possible influence in the microarray detection of the homozygosity status of the SNPs, resulting from an altered CN level. Namely, we model the distributions of the detected genotype, given a specific genomic alteration and we estimate the parameters involved on public reference datasets. The estimation is performed similarly to the modified Bayesian Piecewise Constant Regression, but with improved estimators for the detection of the breakpoints.Using artificial and real data, we evaluate the quality of the estimation of gBPCR and we also show that it outperforms other well-known methods for LOH estimation.
We propose a method (gBPCR) for the estimation of both LOH and CN aberrations, improving their estimation by integrating both types of data and accounting for their relationships. Moreover, gBPCR performed very well in comparison with other methods for LOH estimation and the estimated CN lesions on real data have been validated with another technique.
癌症和其他疾病是由于基因组病变引起的。SNP 微阵列能够同时测量基因组中几个单核苷酸多态性（SNP）的基因型和拷贝数（CN）。CN 定义为 DNA 拷贝数，正常情况下为 2，因为我们每条染色体都有两个拷贝。SNP 的基因型是指存在于 DNA 两个拷贝上的核苷酸（等位基因）的状态。如果两个等位基因相同或不同，则定义为纯合或杂合。杂合性丢失（LOH）是由于基因组事件导致杂合状态丢失。结合 CN 和 LOH 数据，可以更好地识别不同类型的基因组异常。例如，由于物理上丢失一个拷贝或单亲二倍体事件（UPD），即每个 SNP 都有两个相同的核苷酸，均来自一个亲本，可能会出现一系列长的纯合 SNP。在这种情况下，CN 的知识有助于区分这两种情况。
为了更好地识别基因组异常，我们提出了一种方法（称为 gBPCR），该方法考虑了 CN 水平改变对 SNP 纯合状态在微阵列检测中的所有可能影响，从而推断出发生的异常类型。即，我们对特定基因组改变下检测到的基因型分布进行建模，并在公共参考数据集上估计涉及的参数。估计与改进的贝叶斯分段常数回归类似，但改进了检测断点的估计器。使用人工和真实数据，我们评估了 gBPCR 估计的质量，还表明它在 LOH 估计方面优于其他知名方法。
我们提出了一种用于估计 LOH 和 CN 异常的方法（gBPCR），通过整合两种类型的数据并考虑它们之间的关系，改进了它们的估计。此外，gBPCR 在与其他 LOH 估计方法的比较中表现非常出色，并且在真实数据上估计的 CN 病变已通过另一种技术进行了验证。
