Computational Prediction of Position Effects of Apparently Balanced Human Chromosomal Rearrangements.

Cinthya J Zepeda-Mendoza
Jonas Ibn-Salem
Tammy Kammin
David J Harris
Debra Rita
Karen W Gripp
Jennifer J MacKenzie
Andrea Gropman
Brett Graham
Ranad Shaheen
Fowzan S Alkuraya
Campbell K Brasington
Edward J Spence
Diane Masser-Frye
Lynne M Bird
Erica Spiegel
Rebecca L Sparkes
Zehra Ordulu
Michael E Talkowski
Miguel A Andrade-Navarro
Peter N Robinson, The Jackson Laboratory
Cynthia C Morton


Interpretation of variants of uncertain significance, especially chromosomal rearrangements in non-coding regions of the human genome, remains one of the biggest challenges in modern molecular diagnosis. To improve our understanding and interpretation of such variants, we used high-resolution three-dimensional chromosomal structural data and transcriptional regulatory information to predict position effects and their association with pathogenic phenotypes in 17 subjects with apparently balanced chromosomal abnormalities. We found that the rearrangements predict disruption of long-range chromatin interactions between several enhancers and genes whose annotated clinical features are strongly associated with the subjects' phenotypes. We confirm gene-expression changes for a couple of candidate genes to exemplify the utility of our analysis of position effect. These results highlight the important interplay between chromosomal structure and disease and demonstrate the need to utilize chromatin conformational data for the prediction of position effects in the clinical interpretation of non-coding chromosomal rearrangements.