Trio-R: a script for assessing maternity and paternity in trio studies performed on Agilent chromosomal microarrays
| Autor: | Va Lip, Daniel Xia, Marian H. Harris, Yiping Shen, Chen Zhang |
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| Rok vydání: | 2018 |
| Předmět: |
Male
Parents Non-paternity 0301 basic medicine Proband Paternity Health Informatics Single-nucleotide polymorphism Computational biology Biology lcsh:Computer applications to medicine. Medical informatics Polymorphism Single Nucleotide 03 medical and health sciences Control measure Chromosomal microarrays Humans SNP Computational analysis Copy-number variation Health Policy Genetic variants Single nucleotide polymorphisms Pre-analytical errors Microarray Analysis Computer Science Applications Trio studies 030104 developmental biology lcsh:R858-859.7 Female DNA microarray Software |
| Zdroj: | BMC Medical Informatics and Decision Making BMC Medical Informatics and Decision Making, Vol 18, Iss 1, Pp 1-5 (2018) |
| ISSN: | 1472-6947 |
| DOI: | 10.1186/s12911-018-0684-9 |
| Popis: | Background Trio studies, which involve the testing of samples from a proband and both parents, are often used by clinical laboratories to help with the classification of genetic variants, including copy number variants. In order for the results of the trio study to be valid, the mother and father must be the true biological parents of the proband. As such, non-paternity and sample mix-ups are potential sources of error. To address these potential issues, we developed a computer script to accurately assess maternity and paternity using single nucleotide polymorphism (SNP) data generated by Agilent chromosomal microarrays, a platform-of-choice for clinical copy number testing. Results We assessed the performance of the script on 10 putative trios tested at our laboratory, and found that the numbers and proportions of discordant SNPs were useful for determining parental relationships. The results of the assessment also confirmed maternity and paternity in the 10 trios tested, and by doing so essentially excluded pre-analytical sample switching in these 30 samples. Conclusions Computational analysis of SNP data can be implemented as a quality control measure for trio testing performed on Agilent microarrays. Electronic supplementary material The online version of this article (10.1186/s12911-018-0684-9) contains supplementary material, which is available to authorized users. |
| Databáze: | OpenAIRE |
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