RT Journal
A1 Peters, Brock A.
A1 Kermani, Bahram G.
A1 Alferov, Oleg
A1 Agarwal, Misha R.
A1 McElwain, Mark A.
A1 Gulbahce, Natali
A1 Hayden, Daniel M.
A1 Tang, Y. Tom
A1 Zhang, Rebecca Yu
A1 Tearle, Rick
A1 Crain, Birgit
A1 Prates, Renata
A1 Berkeley, Alan
A1 Munné, Santiago
A1 Drmanac, Radoje
T1 Detection and phasing of single base de novo mutations in biopsies from human in vitro fertilized embryos by advanced whole-genome sequencing
JF Genome Research 
JO Genome Research 
YR 2015 
FD March 01 
VO 25 
IS 3 
SP 426 
OP 434 
DO 10.1101/gr.181255.114 
UL http://genome.cshlp.org/content/25/3/426.abstract 
AB Currently, the methods available for preimplantation genetic diagnosis (PGD) of in vitro fertilized (IVF) embryos do not detect de novo single-nucleotide and short indel mutations, which have been shown to cause a large fraction of genetic diseases. Detection of all these types of mutations requires whole-genome sequencing (WGS). In this study, advanced massively parallel WGS was performed on three 5- to 10-cell biopsies from two blastocyst-stage embryos. Both parents and paternal grandparents were also analyzed to allow for accurate measurements of false-positive and false-negative error rates. Overall, >95% of each genome was called. In the embryos, experimentally derived haplotypes and barcoded read data were used to detect and phase up to 82% of de novo single base mutations with a false-positive rate of about one error per Gb, resulting in fewer than 10 such errors per embryo. This represents a ∼100-fold lower error rate than previously published from 10 cells, and it is the first demonstration that advanced WGS can be used to accurately identify these de novo mutations in spite of the thousands of false-positive errors introduced by the extensive DNA amplification required for deep sequencing. Using haplotype information, we also demonstrate how small de novo deletions could be detected. These results suggest that phased WGS using barcoded DNA could be used in the future as part of the PGD process to maximize comprehensiveness in detecting disease-causing mutations and to reduce the incidence of genetic diseases.