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. 2020 Jul 1;66(7):958-965.
doi: 10.1093/clinchem/hvaa104.

Noninvasive Fetal Genotyping by Droplet Digital PCR to Identify Maternally Inherited Monogenic Diabetes Variants

Richard C Caswell  1   2 , Tristan Snowsill  3 , Jayne A L Houghton  1   2 , Ali J Chakera  1   4 , Maggie H Shepherd  1   5 , Thomas W Laver  1 , Bridget A Knight  1   5 , David Wright  6 , Andrew T Hattersley  1   2 , Sian Ellard  1   2
Affiliations

Noninvasive Fetal Genotyping by Droplet Digital PCR to Identify Maternally Inherited Monogenic Diabetes Variants

Richard C Caswell et al. Clin Chem. .

Abstract

Background: Babies of women with heterozygous pathogenic glucokinase (GCK) variants causing mild fasting hyperglycemia are at risk of macrosomia if they do not inherit the variant. Conversely, babies who inherit a pathogenic hepatocyte nuclear factor 4α (HNF4A) diabetes variant are at increased risk of high birth weight. Noninvasive fetal genotyping for maternal pathogenic variants would inform pregnancy management.

Methods: Droplet digital PCR was used to quantify reference and variant alleles in cell-free DNA extracted from blood from 38 pregnant women heterozygous for a GCK or HNF4A variant and to determine fetal fraction by measurement of informative maternal and paternal variants. Droplet numbers positive for the reference/alternate allele together with the fetal fraction were used in a Bayesian analysis to derive probability for the fetal genotype. The babies' genotypes were ascertained postnatally by Sanger sequencing.

Results: Droplet digital PCR assays for GCK or HNF4A variants were validated for testing in all 38 pregnancies. Fetal fraction of ≥2% was demonstrated in at least 1 cell-free DNA sample from 33 pregnancies. A threshold of ≥0.95 for calling homozygous reference genotypes and ≤0.05 for heterozygous fetal genotypes allowed correct genotype calls for all 33 pregnancies with no false-positive results. In 30 of 33 pregnancies, a result was obtained from a single blood sample.

Conclusions: This assay can be used to identify pregnancies at risk of macrosomia due to maternal monogenic diabetes variants.

Keywords: Bayesian analysis; droplet digital PCR; monogenic diabetes; non-invasive prenatal diagnosis.

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Conflict of interest statement

Authors’ Disclosures or Potential Conflicts of Interest:

Upon manuscript submission, all authors completed the author disclosure form. Disclosures and/orpotential conflicts of interest:

Figures

Fig. 1
Fig. 1. Allele abundance and fetal fraction in cfDNA.
Fractional abundance of the GCK or HNF4A reference allele for each variant (y-axis) is plotted against fetal fraction (x-axis) for 55 cfDNA samples from 38 pregnancies. Data points from pregnancies in which fetal genotype was homozygous reference or heterozygous are shown as circles or squares, respectively; in both cases, filled symbols show samples meeting quality thresholds for ddPCR analysis, whereas open symbols show samples with fetal fraction <2% (threshold indicated by vertical broken line) or with <10 positive droplets for the paternal allele in fetal fraction assays (7 and 1 samples, respectively). Error bars show 95% Poisson confidence intervals, as calculated by QuantaSoft software. The horizontal broken line indicates 50% fractional abundance, along which samples derived from pregnancies with heterozygous fetal genotype would be expected to lie.
Fig. 2
Fig. 2. Probability of fetal genotype for homozygosity.
Probability scores for fetal homozygosity, derived from the custom Bayesian model, shown for 33 pregnancies in which at least 1 sample passed ddPCR quality thresholds. Pregnancies in which fetal genotype was homozygous reference or heterozygous are shown as circles or squares, respectively; in both cases, filled symbols show samples in which the model determined fetal genotype with ≥ 95% probability (i.e. P ≥ 0.95 for homozygous reference, P ≤ 0.05 for heterozygosity; n = 30), whereas open symbols show samples with intermediate probability scores (n = 3).
Fig. 3
Fig. 3. Summary of workflow and results.
See this image and copyright information in PMC

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