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High resolution non‐invasive detection of a fetal microdeletion using the GCREM algorithm
Author(s) -
Chu Tianjiao,
Yeniterzi Suveyda,
Rajkovic Aleksandar,
Hogge W. Allen,
Dunkel Mary,
Shaw Patricia,
Bunce Kimberly,
Peters David G.
Publication year - 2014
Publication title -
prenatal diagnosis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.956
H-Index - 97
eISSN - 1097-0223
pISSN - 0197-3851
DOI - 10.1002/pd.4331
Subject(s) - algorithm , resolution (logic) , medicine , fetus , computer science , biology , pregnancy , artificial intelligence , genetics
Background/Objective The non‐invasive prenatal detection of fetal microdeletions becomes increasingly challenging as the size of the mutation decreases, with current practical lower limits in the range of a few megabases. Our goals were to explore the lower limits of microdeletion size detection via non‐invasive prenatal tests using Minimally Invasive Karyotyping (MINK) and introduce/evaluate a novel statistical approach we recently developed called the GC Content Random Effect Model (GCREM). Methods Maternal plasma was obtained from a pregnancy affected by a 4.2‐Mb fetal microdeletion and three normal controls. Plasma DNA was subjected to capture an 8‐Mb sequence spanning the breakpoint region and sequence. Data were analyzed with our published method, MINK, and a new method called GCREM. Results The 8‐Mb capture segment was divided into either 38 or 76 non‐overlapping regions of 200 and 100 Kb, respectively. At 200 Kb resolution, using GCREM (but not MINK), we obtained significant adjusted p ‐values for all 20 regions overlapping the deleted sequence, and non‐significant p ‐values for all 18 reference regions. At 100 Kb resolution, GCREM identified significant adjusted p ‐values for all but one 100‐Kb region located inside the deleted region. Conclusion Targeted sequencing and GCREM analysis may enable cost effective detection of fetal microdeletions and microduplications at high resolution. © 2014 John Wiley & Sons, Ltd.