Application of DNA fingerprints for cell-line individualization.

Citation
A. Gilbert, Dennis et al., Application of DNA fingerprints for cell-line individualization., American journal of human genetics , 47-I(3), 1990, pp. 499-514
ISSN journal
00029297
Volume
47-I
Issue
3
Year of publication
1990
Pages
499 - 514
Database
ACNP
SICI code
Abstract
DNA fingerprints of 46 human cell lines were derived using minisatellite probes for hypervariable genetic loci.The incidence of 121 HaeIII DNA fragments among 33 cell lines derived from unrelated individuals was used to estimate allelic and genotypic frequencies for each fragment and for composite individual DNA fingerprints.We present a quantitative estimate of the extent of genetic difference between individuals, an estimate based on the percentage of restriction fragments at which they differ.The average percent difference (APD) among pairwise combinations from the population of 33 unrelated cell lines was 76.9%, compared with the APD in band sharing among cell lines derived from the same individual (less than or equal to 1.2%). Included in this survey were nine additional cell lines previously implicated as HeLa cell derivatives, and these lines were clearly confirmed as such by DNA fingerprints (APD less than or equal to 0.6%).On the basis of fragment frequencies in the tested cell line population, a simple genetic model was developed to estimate the frequencies of each DNA fingerprint in the population.The median incidence was 2.9 X 10(-17), and the range was 2.4 X 10(-21) to 6.6 X 10(-15).This value approximates the probability that a second cell line selected at random from unrelated individuals will match a given DNA fingerprint.Related calculations address the chance that any two DNA fingerprints would be identical among a large group of cell lines.This estimate is still very slight; for example, the chance of two or more common DNA fingerprints among 1 million distinct individuals is less than .001.The procedure provides a straightforward, easily interpreted, and statistically robust method for identification and individualization of human cells.