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Genomic autopsy to identify underlying causes of pregnancy loss and perinatal death

Abstract

Pregnancy loss and perinatal death are devastating events for families. We assessed ‘genomic autopsy’ as an adjunct to standard autopsy for 200 families who had experienced fetal or newborn death, providing a definitive or candidate genetic diagnosis in 105 families. Our cohort provides evidence of severe atypical in utero presentations of known genetic disorders and identifies novel phenotypes and disease genes. Inheritance of 42% of definitive diagnoses were either autosomal recessive (30.8%), X-linked recessive (3.8%) or autosomal dominant (excluding de novos, 7.7%), with risk of recurrence in future pregnancies. We report that at least ten families (5%) used their diagnosis for preimplantation (5) or prenatal diagnosis (5) of 12 pregnancies. We emphasize the clinical importance of genomic investigations of pregnancy loss and perinatal death, with short turnaround times for diagnostic reporting and followed by systematic research follow-up investigations. This approach has the potential to enable accurate counseling for future pregnancies.

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Fig. 1: Diagnostic yield of pregnancy loss and perinatal death cohort with observed inheritance models.
Fig. 2: Diagnoses per major organ system affected.
Fig. 3

Data availability

Sequence data have been deposited at the European Genome-phenome Archive, which is hosted by the European Bioinformatics Institute and the Centre for Genomic Regulation under accession no. EGAS00001006295. Controlled access to primary data and/or material generated as part of this study may be requested from the corresponding author (Hamish.Scott@sa.gov.au), and will be shared in a nonidentifiable manner only where participants have consented to the sharing of data and samples for use in ethically approved future research studies. Studies requesting access must produce evidence of appropriate HREC permissions, and detailed description of how the data and samples will be used and stored. Once controlled data access approval has been granted (within 3 months of submission), a material transfer agreement between respective institutions will need to be established. The research team will not accept, or return to participants, any research findings unrelated to the referred condition. All reported variants will be submitted to ClinVar under Molecular Pathology Research Laboratory (organization ID: 507864, pre-2020 research sequenced) and Genetics and Molecular Pathology; SA Pathology (organization ID: 506043, post-2020 clinically sequenced).

Code availability

The VariantGrid code (www.variantgrid.com) generated during this study (by D.M.L.) is available for research use under business source license 1.1 on GitHub (https://github.com/SACGF/variantgrid). The code for the Seqr platform, used at the Broad Institute, is available at https://github.com/broadinstitute/seqr.

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Acknowledgements

First, we thank the families for their involvement in our project. We thank the Genomic Autopsy Study Research Network, which includes the referring clinical geneticists, genetic counselors, pathologists, colleagues at research and diagnostic laboratories and Australian Genomics, for their assistance with patient care, contributions and support. Additional thanks to the staff of the Kinghorn Centre for Clinical Genomics Sequencing Laboratory, the staff of the Centre for Cancer Biology ACRF Genomics Facility and the staff of the Broad Institute’s Genomic Platform. This research was supported by NHMRC (grant no. APP1123341), Genomics Health Futures Mission – Medical Research Futures Fund (no. GHFM76777) and the Australian Genomic Health Alliance NHMRC Targeted Call for Research into Preparing Australia for the Genomics Revolution in Healthcare (no. GNT1113531) to H.S.S. and C.P.B.; and by the ACRF to H.S.S. Sequencing provided by the Broad Institute of MIT and Harvard Center for Mendelian Genomics (Broad CMG) was funded by the National Human Genome Research Institute, the National Eye Institute and the National Heart, Lung and Blood Institute (grant no. UM1 HG008900 to D.G.M. and H.L.R). Additional support was provided by Cancer Council SA’s Beat Cancer Project on behalf of its donors and the State Government of South Australia through the Department of Health, and NHMRC Fellowship (no. APP1023059) to H.S.S.; by the Australian Government Research Training Program Scholarship, the Australian Genomics Health Alliance PhD Award & NHMRC (no. GNT1113531) and by the Maurice de Rohan International Scholarship (to A.B.B.). P.A.was supported by fellowships from the The Hospital Research Foundation and the Royal Adelaide Hospital Research Fund.

Author information

Authors and Affiliations

Authors

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Contributions

A.B.B., P.A. and T.H. drafted the manuscript. A.B.B., P.A., T.H., M.B., M.R.J., S.L.K-S., C.P.B. and H.S.S. coordinated the study. A.B.B., M.B. and H.N. managed samples. T.H., J.F., P.W., D.M.L., L.E. and L.A. processed genomic data. A.B.B., P.A., T.H., L.P., A.O.D-L., M.S.B.F. and R.M. performed data analyses. K.S.K., T.S.E.H. and H.S.S. implemented a clinically accredited pathway for analysis and reporting. T.H., L.E., L.A. and J.T. processed transcriptomic data. T.H. performed data analysis. A.W.S. supervised processing of genomic and transcriptomic data. P.A. performed phasing and ddPCR assays. R.M., J.L., N.M., T.Y.K. and L.M. performed routine autopsy investigations. G.M., J.P., F.M. T.S.E.H., J.E.L. and C.P.B. provided clinical care. A.B.B., P.A., T.H., K.S.K., M.R.J., A.O’D-L., R.M., T.S.E.H., S.L.K-S., C.P.B. and H.S.S. contributed to interpretation and discussion of results. Genomic Autopsy Study Research Network members assisted with national patient recruitment, routine autopsies, sample coordination and ethics. H.S.S. and C.P.B. jointly conceived and supervised the study. All authors read and approved the manuscript.

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Correspondence to Christopher P. Barnett or Hamish S. Scott.

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Nature Medicine thanks Job Verdonschot and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: Anna Maria Ranzoni and Joao Monteiro, in collaboration with the Nature Medicine team.

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Extended data

Extended Data Fig. 1 Cohort distribution of (gestational) age, sex and classification of death.

Shows the distribution in (gestational) age, sex and death classification within the perinatal period of the 200 probands in our cohort. Only completed gestational weeks are displayed. Circle, female proband; square, male proband; red, termination of pregnancy; orange, miscarriage; light green, stillbirth; dark green, neonatal death; darker shading, LP/P variant; lighter shading, Variant or Gene of Uncertain Significance (VUS/GUS);, open box, no abnormality detected (NAD).

Extended Data Fig. 2 Sunburst diagram of the complete cohort.

A sunburst plot representing all 200 families separated by the type of pregnancy loss (2nd, most inner ring), across different organ systems (third ring), the genomic findings (fourth ring) and candidate disease genes (fifth, most outer ring). The genomic findings are separated by LP/P ((Likely) pathogenic), VUS (variant of uncertain significance), GUS (gene of uncertain significance), and NAD (no abnormalities detected). The percentages in the right-side legend represent the number of variants identified across the different pregnancy groups. This plot shows that the study cohort mostly consisted of termination of pregnancies. The input data for the sunburst plot was generated from Supplementary Table 2. All sunburst plots were generated using the sunburstR package (https://github.com/timelyportfolio/sunburstR).

Extended Data Fig. 3 Sunburst diagrams showing the characteristics of the terminated pregnancies and stillbirths.

(a) Sunburst plot representing the group of pregnancies terminated, due to in utero abnormalities, and the subsequent genomic findings. The five most affected organ systems within this subgroup are shown, with the neurological phenotypes yielding the highest number of diagnoses (36.6%). Multiple refers to two or more different organ systems affected. The genomic findings are separated by LP,P (Likely pathogenic, pathogenic), VUS (variant of uncertain significance), GUS (gene of uncertain significance), and NAD (no abnormalities detected). (b) Sunburst plot representing the group of pregnancies with stillbirth cases; pregnancy losses beyond 20 weeks of gestation. This plot shows that a majority of stillbirth cases with no genomic findings (NAD) are those with no congenital abnormalities (18.9%) identified, meanwhile stillbirth cases with affected respiratory or hematopoietic systems are more likely to yield a candidate disease gene; 7/7 POS (100%).

Extended Data Fig. 4 Sunburst diagrams showing the characteristics of the Neonatal deaths and miscarriages.

(a) Sunburst plot representing families with neonatal death; loss of an infant up to 28 days old. This plot shows that out of the four subgroups of fetal and neonatal loss, cases with neonatal deaths have the highest number of genomic findings (23/29 POS and 6/29 NAD). Neonates with respiratory and neurological abnormalities represent one third of this subgroup. Neonates with two or more major organ systems affected, and hematopoietic abnormalities, have 100% genomic findings in known and established disease genes (ADAMTSL2, FOXF1, PTPN11 and TPI1 respectively). (b) Sunburst plot representing the smallest subgroup within the study cohort, miscarriages; pregnancy losses before 20 weeks of gestation. This plot shows that there is a 66.7% chance of discovering candidate disease genes (6/9 POS) from early pregnancy loss, with and without congenital abnormalities detected in utero.

Extended Data Fig. 5 Workflow schematic of the first 200 families in the Genomic Autopsy study.

Schematic diagram of the workflow and analysis of the Genomic Autopsy Study with the goal to provide answers to families and prevent recurrence of pregnancy loss and perinatal death. Highlighting the separate clinical-grade analysis and integrated research follow-up. Mendeliome, OMIM morbid genes; AR:,Autosomal Recessive; hom, homozygous; CH, Compound Heterozygous,;XLR, X-Linked Recessive; AD, Autosomal Dominant,;VUS, Variant of Uncertain Significance; GUS, Gene of Uncertain Significance; ACMG, American College of Medical Genetics; P, Pathogenic; LP, Likely Pathogenic; PGD, Preimplantation Genetic Diagnosis; PND, Prenatal Diagnosis. Figure created using BioRender.com.

Extended Data Fig. 6 Evaluation of potential splice effects by RT-PCR and Sanger or Nanopore sequencing of PED002, PED013, PED017 and PED104.

RNA analysis for interpretation of variant effect in PED002, PED013, PED017 and PED104. (a) RT-PCR and nanopore sequencing results for PED002A (mother; blue) and PED002B (father; green) versus a control blood sample (red). The Sashimi plot shows retention of 8 intronic bases (black arrow) of the 8th intron of DNAJB11 as a result from the intronic c.853-10 G > A variant in both parents. (b) RT-PCR and Sanger sequencing of paternal cDNA shows the synonymous PIBF1 c.954 G > A p.(Lys318 = ) variant identified in PED013 causes skipping of exon 8, predicted to result in a downstream frameshift and premature termination. The right (cDNA) figure shows the initiation of a heteroduplex after exon 7, with the mutant allele continuing to exon 9. (c) RT-PCR and Sanger sequencing of fetal cDNA shows the TPI1 c.544-1 G > C variant identified in PED017 alters the canonical splice acceptor site of exon 6, resulting in an in-frame deletion of 2 amino acids. The right (cDNA) figure shows the start of a heteroduplex after exon 5, with the mutant sequence displaying a 6 bp deletion from the start of exon 6. (d) RT-PCR and nanopore sequencing on a maternal blood sample of PED104 A shows skipping of the (out-of-frame) exon 9 in MECOM as a result from the intronic c.2208 + 4A > T variant (red). The long reads included a synonymous SNP (c.2667 G > A) in exon 14, which allowed separation and comparison of the mutant (red) versus wildtype (blue) alleles.

Extended Data Fig. 7 Interpretation of Poly(A) RNA sequencing data for PED005, PED013 and PED024.

RNA sequencing analysis for interpretation of variant effect in PED005, PED013 and PED024 (a) RNA-seq confirmed that the paternal MKS1 c.1408-34_1408-6del variant results in skipping of exon 16 in the proband (red) versus control (blue) sample. The novel maternal c.1024 + 1 G > T variant revealed altered splicing downstream of exon 11, resulting in partial inclusion of intron 12. (b) RNA-seq for PED013, showing skipping of exon 8 in the proband sample (red) versus control (blue) as a result from the synonymous PIBF1 c.954 G > A p.(Lys318=) variant. (c) RNA-seq results for PED024, showing retention of intron 2 (yellow box) in the proband sample (red) verus control samples (blue and green), and much lower expression values which are likely due to nonsense mediated decay as a result from the intronic EIF2B2 c.284 + 5 G > T variant.

Extended Data Fig. 8 Relatedness and quad analysis diagrams.

(a) Boxplot shows the relatedness coefficients (y-axis) of all families (dots) and their diagnostic findings based on mode of inheritance (x-axis). The relatedness coefficients were calculated using Peddy48, and required a minimum of 1000 shared heterozygous alternate calls per sample. This boxplot indicates that families that are related are more likely to yield a (candidate) diagnosis (12/16, 75%), compared to unrelated families (93/184, 50.5%), not statistically significant (P value = 0.0710, Two-sided Fisher’s exact test). (b) Diagnostic yield from families with two affected individuals. This bar plot shows the sex of the two affected individuals per family and the exome findings categorised by their ACMG classifications. There is a slightly higher proportion of families, where both affected individuals are males, with no diagnostic finding (8/14, 57.1%) versus mixed sex siblings (2/5, 40%). Abbreviations: ACMG, American College of Medical Genetics; AD, Autosomal dominant; AR, Autosomal recessive; XLR, X-linked recessive; NAD, no abnormalities detected; LP/P, likely pathogenic or pathogenic; VUS, variant of uncertain significance; GUS, Gene of uncertain significance.

Extended Data Fig. 9 Interpretation of droplet digital PCR results for parents with mosaicism >1%.

(a) ddPCR shows the PBX1 p.Arg107Trp variant in PED043 is present at different allelic ratios in paternal sperm (20.1%) compared to paternal blood (10.4%). The mother does not carry the variant and s and the proband is heterozygous. (b) ddPCR of the TUBA1A p.Arg64Trp variant in PED084 is present at an allelic ratio of 2.9% in paternal sperm and at 2.3% in paternal blood. The maternal sample is negative for the mutation, and the proband is heterozygous. Figures are generated by the original QuantasoftTM software for ddPCR analysis (BioRad). The concentration (copies/µl) of the WT and the Mutant allele are used to calculate the fractional abundance (mean with 95% CI).

Supplementary information

Reporting Summary

Supplementary Tables 1–7.

Supplementary Table 1: Overview of genetic outcomes per subgroup (sex, classification, reason for referral, family structure tested and major affected organ system. Supplementary Table 2: Clinical and parental information and genomic findings (including classification) of all pedigrees in the Genomic Autopsy Study. Supplementary Table 3: Functional follow-up studies to confirm causality of candidate variants. Supplementary Table 4: Candidate variants shared on matchmaking platforms. Supplementary Table 5: Samples used for nucleic acid isolation. Supplementary Table 6: Results from phasing and ddPCR of de novo variants. Supplementary Table 7: Sequences of primers used for long-range PCR and RT–PCR.

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Byrne, A.B., Arts, P., Ha, T.T. et al. Genomic autopsy to identify underlying causes of pregnancy loss and perinatal death. Nat Med 29, 180–189 (2023). https://doi.org/10.1038/s41591-022-02142-1

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