Non-invasive prenatal testing as a valuable source of population specific allelic frequencies

Budis, J.a,b,1, Gazdarica, J.b,c,1, Radvanszky, J.b,d, Harsanyova, M.b,c, Gazdaricova, I.c, Strieskova, L.b,c, Frno, R.b,c, Duris, F.b,e, Minarik, G.f, Sekelska, M.f,g, Nagy, B.h, Szemes, T.b,c,i

aDepartment of Computer Science, Faculty of Mathematics, Physics and Informatics, Comenius University, Bratislava, Slovakia
bGeneton Ltd., Bratislava, Slovakia
cDepartment of Molecular Biology, Faculty of Natural Sciences, Comenius University, Bratislava, Slovakia
dInstitute for Clinical and Translational Research, Biomedical Research Center, Slovak Academy of Sciences, Bratislava, Slovakia
eSlovak Centre of Scientific and Technical Information, Bratislava, Slovakia
fMedirex Inc., Bratislava, Slovakia
gTrisomy Test Ltd., Bratislava, Slovakia
hDepartment of Human Genetics, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
iComenius University Science Park, Bratislava, Slovakia

Abstract

Low-coverage massively parallel genome sequencing for non-invasive prenatal testing (NIPT) of common aneuploidies is one of the most rapidly adopted and relatively low-cost DNA tests. Since aggregation of reads from a large number of samples allows overcoming the problems of extremely low coverage of individual samples, we describe the possible re-use of the data generated during NIPT testing for genome scale population specific frequency determination of small DNA variants, requiring no additional costs except of those for the NIPT test itself. We applied our method to a data set comprising of 1501 original NIPT test results and evaluated the findings on different levels, from in silico population frequency comparisons up to wet lab validation analyses using a gold-standard method based on Sanger sequencing. The revealed high reliability of variant calling and allelic frequency determinations suggest that these NIPT data could serve as valuable alternatives to large scale population studies even for smaller countries around the world.