Stillbirth affects around 2 million pregnancies worldwide each year and remains a major public health problem despite advances in obstetric care. It is devastating for affected families and represents a persistent public health burden driven by multiple interacting factors. Even after a detailed clinical investigation, roughly one-third of stillbirth cases have no identifiable cause, which limits prevention strategies and counseling for affected families. Familial clustering has been reported, but few inherited genetic risk factors have been clearly established.
One reason progress has been slow is the central role of the placenta. The placenta is biologically transient and exists only for the duration of the pregnancy. It is also comparatively undercharacterised. Its short lifespan and routine disposal after birth have limited systematic study, particularly at the genetic and molecular level, despite placental dysfunction being a leading contributor to stillbirth.
Most genetic studies of stillbirth have focused on fetal aneuploidy, rare Mendelian disorders, or sporadic variants identified in population cohorts. These approaches are less effective for detecting rare inherited risk that segregates within families. In a recent study, Workalemahu et al. (2026) combined multigenerational genealogical data with whole-genome sequencing (WGS) of placental tissue to test whether shared inherited genomic regions contribute to stillbirth risk in high-risk pedigrees, using a family-based design to capture signals that standard case-control studies may miss1.
Building on their previous work that suggested stillbirth aggregates in families2, the authors identified extended families with an excess burden of stillbirth using the Utah Population Database, a linked resource combining genealogical records with medical data. Three independent pedigrees met criteria for high familial risk, each showing a significantly elevated familial standardized incidence ratio for stillbirth. Across these pedigrees, seven stillbirths without an identifiable clinical cause were selected for genetic analysis.
WGS was performed on placental tissue from each stillbirth at high coverage. Rather than testing individual variants, the study used shared genomic segment (SGS) analysis, which detects long chromosomal regions shared across distantly related individuals more often than what is expected by chance. This approach is designed to identify inherited haplotypes that segregate within families, even when causal variants are rare, regulatory, or largely confined to a pedigree. Statistical significance was assessed using pedigree-specific simulations to account for family structure and multiple testing.
Five genome-wide significant shared regions were identified across the three pedigrees. A region on chromosome 15q26.3 was detected independently in two unrelated pedigrees, making convergence by chance unlikely. Additional significant regions were found at 16p13.13 to p13.12, 9p13.3 to p13.1, and 6p22.2 to p22.1 in one pedigree, and at 14q32.2 in another. These regions contain genes previously linked to placental development, immune tolerance, growth regulation, and early neurodevelopment. The authors did not assign causality to specific genes or variants; instead, they treated each shared segment as a candidate risk locus for further study.
This investigation suggests that inherited genetic factors may contribute to stillbirth risk through placental pathways, particularly in families with recurrent unexplained cases. By identifying long shared chromosomal regions across extended relatives, the findings support a model in which rare or low-frequency inherited haplotypes influence susceptibility, even when no single causal variant is obvious.
The convergence on 15q26.3 across two independent pedigrees is notable. Independent replication within a family-based design strengthens confidence that this region contains biologically relevant risk, even though the study does not resolve specific variants or causal mechanisms. The additional pedigree-specific regions point to genetic heterogeneity, suggesting that genetic stillbirth risk may arise through multiple pathways.
These results highlight the value of pedigree-based approaches for studying complex outcomes where population studies have limited power. SGS analysis aggregates risk across regions rather than individual variants, making it well-suited to outcomes such as stillbirth, where effects may be regulatory, context-dependent, or placental-specific. However, the authors are careful to frame their findings as hypothesis-generating. Functional validation and replication in other cohorts will be required to determine whether these regions directly influence placental function or fetal survival.
This study adds to a growing body of evidence that stillbirth is not exclusively driven by sporadic or environmental factors, but can involve inherited genetic risk that is difficult to detect with standard study designs. By focusing on extended families and placental genomes, the authors identify candidate regions that would likely be missed in population-based analyses, particularly where risk is rare or confined to specific lineages.
The findings also underline broader gaps in stillbirth research. Placental biology remains less well characterized than many other organ systems, and genetic reference data for placental tissues are limited, especially across diverse populations. Expanding family-based studies, integrating functional genomics in placental models, and increasing ancestral diversity will be necessary to assess how widely these findings apply.
Importantly, the authors do not propose immediate clinical applications. Instead, they provide a framework for prioritizing regions for follow-up, linking inherited risk to placental development, immune regulation, and pregnancy maintenance. In a field where a substantial fraction of cases remain unexplained, approaches that combine genealogy, genomics, and placental biology may help shift stillbirth research from descriptive classification toward mechanistic understanding.