Variant prioritization and selection of candidate genes relevant
to pregnancy loss
We used Slivar19 to prioritize and filter variants
based on modes of inheritance (e.g., compound heterozygous, de
novo , autosomal dominant and x-linked recessive). Slivar integrates
population allele frequencies from the Trans-Omics for Precision
Medicine (TopMED)20 and spliceAI scores into a
comprehensive variant filtering strategy to identify candidate
genes.19 Details on variant prioritization and
exploratory analyses of variants relevant to recurrent pregnancy loss
are provided in Online Supplement and Table S2 . We
evaluated SNVs across the families by modes of inheritance and highest
impact on genes (in-frame deletion/insertion, missense
[nonsynonymous], frameshift, stop gained, splice region).
Given the potential for identifying false positive germline SNVs due to
DNA quality (e.g., prioritization of false positive autosomal dominant
SNVs that differ by orders of magnitude from SNVs following other modes
of inheritance19) and overwhelming majority of
variants of unknown significance, we applied several approaches to
interpret our main findings. First, we selected SNVs identified in any
of the pregnancy losses but not live births within our data to interpret
candidate genes relevant to recurrent pregnancy loss. Second, we
interpreted rare (AF<.001) compound heterozygous SNVs,
autosomal recessive variants, where both parents are heterozygous for
the variant and the affected offspring receives two copies. We
prioritized compound heterozygous SNVs that were identified in losses
within our data but not observed in healthy controls
(gnomeAD21) to highlight variants in haplosufficient
genes relevant to embryonic/fetal lethality. Third, among SNVs that were
identified in any of the pregnancy losses, we selected pathogenic SNVs
(SNVs with pLI>0.90 and LOEUF<0.36) to highlight
potentially damaging variants in candidate genes. Finally, we selected
SNVs in genes that were involved in pregnancy loss-relevant
phenotypes/diseases (e.g., embryonic/fetal death and developmental
abnormalities22-24) to interpret candidate genes.
Analyses were performed using Slivar and R, utilizing resources and
support from the Center for High Performance Computing at the University
of Utah.