Detection of Low Level Mixed Chimerism Using High Throughput SNP GenotypingAleksey Nakorchevsky1*, Eunice Flores1, Li Xiangyang2, Tao Hong2 and Anders Nygren1
- *Corresponding Author:
- Aleksey Nakorchevsky, Ph.D., M.Sc.
Sr. Scientist Bioinformatics, Agena Bioscience
3565 General Atomics Court
San Diego, CA-92121, USA
E-mail: aleksey.nakorchevsky@ agenabio.com
Received date: January 23, 2016; Accepted date: February 22, 2016; Published date: February 25, 2016
Citation: Nakorchevsky A, Flores E, Xiangyang L, Hong T, Nygren A (2016) Detection of Low Level Mixed Chimerism Using High Throughput SNP Genotyping. J Blood Disord Transfus 6:340. doi:10.4172/2155-9864.1000340
Copyright: © 2016 Nakorchevsky A, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Recipients of allogeneic bone marrow transplants (BMT) or stem cell transplants (SCT) require clinical monitoring to allow for early diagnosis of such post-transplant adverse effects as rejection, graft vs. host disease (GVHD), or a malignancy relapse. Triaging of the transplant recipients in clinical settings is achieved by monitoring the Minimal Residual Disease (MRD) and measuring the amount of mixed chimerism in peripheral blood lymphocytes (PBL). While MRD monitoring involves detection of the malignancy-specific markers, measuring the extent of mixed chimerism can be achieved via general PCR-based methods. We have developed a SNP genotyping method to detect low levels of mixed chimerism in PBL and genomic DNA. Sensitivity is achieved by measuring a cumulative skew in genotyping data across a cohort of 92 independent SNP markers. This method showed a sensitivity of 0.98 and a specificity of 0.90 for 10%, 5%, and 2% mixed chimerism samples. The overall specificity of the method is 0.98 and the accuracy is 0.95. The results show 100% concordance with the STR data for a set of clinical samples. The advantage of this method compared to already established methodologies is that it does not require disease-specific markers and can be multiplexed. The method and the analysis software can also be used with other genotyping and sequencing technologies.