Inherited Genetic Risk in Stillbirth: A Shared Genomic Segments Analysis of High-Risk Pedigrees. 

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Tsegaselassie Workalemahu, Myke Madsen, Sarah Lopez, Jessica Page, and 15 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4858244/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Stillbirth is a devastating adverse pregnancy outcome affecting 2 million pregnancies worldwide every year. Though an etiology may be found in some, one-third of stillbirth cases remain unexplained. Stillbirth clusters in families and, apart from infrequent aneuploidies and balanced translocations, few underlying inherited genes associated with stillbirth are known. Well-characterized family-based studies may aid in identifying genetic contributors to unexplained stillbirth. Methods Using the Utah Population Database, we defined pedigrees with high familial risk of stillbirth. Comprehensive phenotyping with review of primary medical records was conducted to identify stillbirth cases without identifiable causes. We generated whole-genome sequencing in seven stillborn placentas from three pedigrees, referred to hereafter as Pedigree A, Pedigree B, and Pedigree C. We performed shared genomic segments analysis to identify evidence for segregating haplotypes shared by the stillbirths to provide evidence for inherited risk. Results A region at 15q26.3 was identified in two independent pedigrees with genome-wide significance in both (a 1.2 Mb segment shared by two stillbirths in Pedigree A, and a 1.8 Mb segment shared by two stillbirths in pedigree B). Four other regions reached genome-wide significance in single pedigrees at 16p13.13-p13.12, 9p13.3-p13.1, and 6p22.2-p22.1 (shared by the same two stillbirths in Pedigree B), and 0.8 Mb segment at 14q.32.2 shared by three stillbirths in Pedigree C. The identified regions are implicated in in utero and postnatal development, pregnancy loss, and infertility. Conclusions We identified evidence for inherited risk loci in stillbirth placental genes are implicated in in utero and postnatal development, pregnancy loss, and infertility. Identification of inherited genes in stillbirth risk may provide novel therapeutic targets for prevention and treatment to improve pregnancy outcomes. Stillbirth placenta shared genomic segments pedigree inherited Figures Figure 1 Background Stillbirth remains a tragic and distressing pregnancy outcome. Despite advances in obstetric care, there has been only modest reduction in incidence of stillbirth in recent years, with global estimates of 2 million stillbirths occurring after 28 weeks’ gestation each year [ 1 ]. Beyond the profound emotional toll on affected families, stillbirth poses a significant public health challenge that demands a better understanding of its multifactorial etiology. Individuals experiencing stillbirth are at increased risk of both its recurrence and other obstetric complications in subsequent pregnancies [ 2 , 3 ]. While some stillbirth cases can be attributed to known maternal, fetal, or placental factors, around one-third remain unexplained even after thorough investigation [ 4 ]. This lack of clarity surrounding the causes of stillbirth underscores the need for continued research. Apart from infrequent balanced parental translocations, few inherited genes are known to contribute to stillbirth [ 5 , 6 ]. Rare single-gene disorders, such as those caused by X-linked dominant mutations, have been implicated in stillbirth [ 5 , 7 , 8 ]. Recently, we showed that stillbirth aggregates in families [ 9 ], suggesting that genomic investigation in high-risk stillbirth pedigrees may reveal important insights into pathogenic heritable mechanisms. Using a population-based genealogical resource linked with medical records, we performed shared genomic segments (SGS) analyses to identify inherited regulatory and risk variants in stillbirth. Methods Study design and population We conducted SGS analysis in stillbirth cases using placental tissue banked from the Stillbirth Collaborative Research Network (SCRN) study. The SCRN study was a multi-site, case-control study designed to characterize and investigate stillbirth in the United States [ 6 , 10 ]. The sites included Brown University, Rhode Island; Emory University, Georgia; University of Texas Medical Branch at Galveston, Texas; University of Texas Health Sciences Center at San Antonio, Texas; and University of Utah Health Sciences Center, Utah. Stillbirth cases were defined as fetal death at ≥ 20 weeks’ gestation. Individuals underwent a standardized maternal interview, medical record abstraction, biospecimen collection, and postmortem examinations of the fetus and placenta. In total, the SCRN study had placental tissue available from 518 stillbirths and 1,200 live births [ 6 ]. Comprehensive phenotyping with review of primary medical records was conducted to identify stillbirths without identifiable causes, i.e., without infection, maternal medical conditions, or genetic abnormalities such as aneuploidy or copy number changes on microarray (based on the Initial Causes of Fetal Death Evaluation (INCODE) classification system [ 11 ]). The Utah Population Database (UPDB) is a data resource that stores administrative, health, and genealogical records for over 11 million people in Utah. Over 4 million people in the UPDB have at least three generations of pedigree data and some have many more [ 12 , 13 ]. Among 518 stillbirths with placental tissue, 11 from the Salt Lake County, Utah site that had stored DNA linked to the UPDB (all cases were related to at least one other sampled case). Of the 11 stillbirths that linked to the UPDB, 3 pedigrees with at least 2 stillbirth cases in each were eligible for SGS analysis, resulting a total of 7 stillbirths. The genealogical data in the UPDB was used to find distant relationships between the stillbirth cases and define multi-generational pedigrees that tied cases together across many generations. The parent SCRN study was approved by the institutional review board (IRB) at each participating institution, and written informed consent was obtained from all participants. Request for permission to use UPDB family structure data was approved by the Utah Resource for Genetic and Epidemiologic Research, the UPDB data use oversight committee. The secondary analysis of the parent SCRN study was approved by the IRB of the University of Utah (IRB application # 127960). Sample collection, genotyping and quality control Placental samples were collected at birth. Placental parenchymal tissue blocks were obtained from each placenta shortly after birth using a standardized sampling procedure [ 10 ]. Samples were frozen at -80°C [ 10 ]. DNA was purified and extracted using the Gentra PureGene tissue and blood kits (Qiagen Systems). Sample concentrations ranged from 50 ng/uL to 260 ng/uL. Whole-genome-sequencing (WGS) libraries were prepared for Illumina 150bp paired-end reads sequencing using the NEBNext Ultra II DNA Library Prep Kit protocols. All libraries were sequenced on the Novaseq 6000 platform (Illumina, San Diego, CA, USA) using standard protocols. Genotypes for SGS analysis were extracted from WGS sequencing at 120x coverage depth. Germline single-nucleotide variants (SNVs) for each sample were detected following a Genome Analysis Tool Kit best practices equivalent workflow for variant detection [ 14 ]. Raw data output short reads were aligned using the GRCh38 human reference genome, using alt-aware alignment and variant calling against the GRCh38 build with alt and decoy contigs. Because SGS analysis was optimized for genome-wide SNV panel data, a subset of all SNV calls based on the Illumina GSA24v3 was extracted to make computationally feasible simulations (see Statistical analysis section for genome-wide significant sharing simulations). SNVs were further checked for quality control which included duplicate check, sex check, SNV call-rate (≥ 95%), and sample call rate (≥ 95%). To check for genetic ancestry, we Peddy , software package that predicted ancestry of the samples using a support vector machine prediction probability [ 15 ]. After quality control, 495,253 autosomal SNVs were available for SGS analysis. We used the Utah Centre d’Etude du Polymorphisme Humain (CEPH) cohort to provide a matched population linkage disequilibrium map for SGS analysis [ 16 ]. Statistical analysis First, large (> 5 generations) high-risk pedigrees were identified as those containing more stillbirth cases than expected by chance. The familial standardized incidence ratio (FSIR) denotes the fold increase in stillbirth over population expectation, measuring the excess stillbirth risk for a pedigree, compared to population rates, accounting for age and sex [ 17 ]. To the best of our knowledge, the three pedigrees are independent, and cases were related only within pedigrees and not across pedigrees. We used the FSIR to confirm that the pedigrees are high-risk—with a statistically significant stillbirth risk in the pedigrees (FSIR > 2.0, P-value < 0.05). Additionally, we verified 12 meioses between genotyped stillbirth cases in each pedigree to ensure sufficient statistical power for genome-wide single pedigree SGS analysis [ 18 ]. The FSIR was used to confirm that the pedigrees we identified had a statistically significant increase in stillbirth. Next, we conducted single-pedigree SGS analysis that minimizes the effects of genetic heterogeneity [ 19 ]. This process resulted in three high-risk pedigrees containing seven stillbirth cases. In each of the three large pedigrees, we performed single-pedigree SGS to identify segregating shared regions among stillbirth cases. The detailed methodology has been previously described [ 18 ]. In brief, the SGS approach identifies significantly long DNA segments inherited across many meioses. The analysis assesses whether among sampled, distantly related cases, the length of consecutively shared loci (identified as identical-by-state, or IBS) is longer than expected by chance. IBS is established by determining if allelic types at sequential loci are consistent (phase is ignored). IBS does not infer identity-by-descent (IBD; the same inherited segment from a common ancestor) which is our true interest; however, if the length of SGS shared IBS is significantly longer than by chance (given the known relationships), then IBD is suggested. Chance IBD sharing in distant relatives is extremely improbable. Statistical significance for genome-wide sharing is determined through simulations tailored to each pedigree's structure ( Supplementary Figs. 1–3 ). Here, we used 1 million simulations to establish pedigree-specific genome-wide thresholds, accounting for both the extensive testing across the genome and optimization over subsets. After genome-wide significance thresholds were determined, additional simulations were performed for precise resolution of very low p-values, if needed. In each pedigree, genome-wide significance was assigned at a false positive rate of 0.05 per genome (expected degree of sharing per genome [µ] ≤ 0.05), indicating the observation is expected to occur no more than 0.05 times per genome. Genome-wide significance thresholds and Manhattan plots are provided in Supplementary Figs. 1–3 . Analyses were conducted using the SGS analysis software that is freely available and can be accessed online: https://uofuhealth.utah.edu/huntsman/labs/camp/analysis-tool/shared-genomic-segment.php . Using the genome-wide significant SGS regions, we conducted Online Mendelian Inheritance in Man (OMIM) database and PubMed searches using genome-wide significant SGS regions we identified in our results to find reports of the regions with known disease etiologies relevant to pregnancy loss and fetal development [ 20 ]. Our database searches and discussion were focused on the SGS regions as a whole rather than restricting searches on specific genes because variants found in other studies may not exist in our data. Therefore, we considered the entirety of the SGS regions as defining our search space for further focused interrogation of variants using WGS data. Results The FSIR for the three pedigrees ranged between 2.4–30.9, suggesting a 2-to-31-fold increased stillbirth risk in the pedigrees compared with the background population risk (Table 1 ). Among the seven stillbirth cases included in our study, maternal and paternal ages at birth ranged between 25–42 and 25–43 years, respectively. The maternal and paternal self-reported race/ethnicity is consistent with Western European ancestry inferred from the genotypes of all stillbirths, as indicated by the support vector machine prediction probability for a particular ancestry. The gestational ages ranged between 20 weeks to 40 2/7 weeks. Birth weights ranged from 350–2635 in grams and 7th -77th in gestational age/fetal sex percentiles. Three out of the seven stillbirth cases were male. Three cases were from nulliparous women and four cases were from multiparous women with 3–5 total pregnancies. None of the cases had karyotype or chromosomal microarray abnormalities, prior history of stillbirth or identifiable causes according to INCODE classification. Table 1 Characteristics of pedigrees and stillbirths included in the study Pedigree Pedigree A Pedigree B Pedigree C FSIR (P-value) a 2.41 (< 0.001) 3.99 (< 0.001) 30.91 (< 0.001) Total number of stillbirth cases 6 5 3 Number of sampled stillbirth cases 2 2 3 Total number of meioses between sampled stillbirth cases 14 14 12 Shared genomic segments analysis genome-wide significance threshold b 3.1x10-4 2.9x10-4 5.1x10-5 Sampled stillbirth case Stillbirth_A1 Stillbirth_A2 Stillbirth_B1 Stillbirth_B2 Stillbirth_C1 Stillbirth_C2 Stillbirth_C3 Demographics Baby sex Female Female Female Male Male Female Male Gestational age at birth (weeks) 22 5/7 26 2/7 20 6/7 22 2/7 22 3/7 39 3/7 20 0/7 Maternal age at birth (years) 25 27 32 38 42 35 33 Paternal age at birth (years) 25 27 28 n/a 43 31 34 Baby genetic ancestry Western European Western European Western European Western European Western European Western European Western European Medical history Baby weight (g) 482 933 n/a 481 449 2635 350 Birthweight percentile 30th 77th n/a 30th 13th 7th 55th Gestational hypertension - - - - - - - Preeclampsia - - - - - - - Gestational diabetes - - - - - - - Any placental pathological lesion - - - - - Acute chorioamnionitis membrane - Prior history of stillbirth - - - - - - - Parity Multiparous Multiparous Nulliparous Nulliparous Multiparous Nulliparous Multiparous Total number of pregnancies 5 3 1 1 3 1 4 History of infertility treatment - - Invitro fertilization (sperm and egg donor) Invitro fertilization (sperm and egg donor) - - - Genetic testing Normal karyotype (XX) Normal karyotype (XX) - - Normal karyotype (XY) Normal karyotype (XX) Abnormal karyotype (XY) a FSIR (familial standardized incidence ratio) denotes the fold increase in stillbirth over population expectation, measuring the excess stillbirth risk for a pedigree. b denotes the pedigree-specific shared genomic segments genome-wide thresholds estimated using 1 million simulations, accounting for both the extensive testing across the genome and optimization over subsets We identified genome-wide significant SGS chromosomal regions at 15q26.3 (P-value = 2.7x10 − 5 ; µ = 0.01 and P-value = 1.4x10-4; µ = 0.02) in Pedigrees A and B, respectively (Table 2 and Fig. 1 ). In Pedigree B, we also identified genome-wide significant SGS chromosomal regions at 16p13.13-p13.12 (P-value = 1.3x10-4; µ = 0.02), 9p13.3-p13.1 (P-value = 2.1x10-4; µ = 0.03), and 6p22.2-p22.1 (P-value = 1.0x10-4; µ = 0.01). In addition, we identified one genome-wide significant SGS chromosomal region at 14q32.2 (P-value = 3.1x10 − 5 ; µ = 0.03) in Pedigree C. Table 2 Genome-wide significant shared genomic segments in stillbirths Pedigree Total number of meioses between sampled stillbirths Number of stillbirths in SGS analysis Locus Cytoband Region a Size in Mb Number of SNVs Number of stillbirths sharing P-value µ b A 14 2 15q26.3 98954683–100761210 1.81 532 2 2.7x10-5 0.01 B 14 2 15q26.3 99916971–101155417 1.23 411 2 1.4x10-4 0.02 16p13.13-p13.12 11425854–13456003 2.00 542 2 1.3x10-4 0.02 9p13.3-p13.1 36189000–38396005 2.20 382 2 2.1x10-4 0.03 6p22.2-p22.1 26106870–30106665 4.00 2078 2 1.0x10-4 0.01 C 12 3 14q32.2 98337930–99132229 0.79 211 3 3.1x10-5 0.03 a Genomic region according to the GRCh38/hg38 genome build position in the chromosome b Genome-wide false positive rate, indicating the expected degree of sharing per genome The 15q26.3 region in Pedigree A is a 1.81 Mb segment of 532 contiguous SNVs (chr15:98,954,683–100,761,210 bp) shared by two stillbirths (out of two genotyped stillbirths) (Table 2 and Supplementary Fig. 4 ). The 1.81 Mb region was shared through 14 meioses to both stillbirths which were females at 22 5/7 weeks’ and 26 2/7 weeks’ gestations. This region contains fifteen genes, including the IGF1R gene ( Supplementary Table 1 ). In another independent pedigree (Pedigree B), an overlapping 1.23 Mb segment of 411 contiguous SNVs (chr15:99,916,971–101,155,417 bp) in the same 15q26.3 region was shared by two stillbirths (out of two genotyped stillbirths). The 1.23 Mb region was shared through 14 meioses to both stillbirths which were a female at 20 6/7 weeks’ gestation and a male at 22 2/7 weeks’ gestation. The intersecting region identified in Pedigree A and Pedigree B is a 0.84 Mb segment (chr15:99,916,971 − 100,761,210) in 15q26.3. This region contains seven genes ( ADAMTS17 , ASB7 , CERS3 , CERS3-AS1 , LINS1 , LOC102723335 and PRKXP1 ; Supplementary Table 1 ). Additionally, a 2.0 Mb segment of 542 contiguous SNVs in 16p13.13-p13.12 region (chr16:11,425,854–13,456,003 bp), a 2.2 Mb segment of 382 contiguous SNVs in 9p13.3-p13.1 region (chr9:36,189,000–38,396,005 bp) and a 4.0 Mb segment of 2078 SNVs in 6p22.2-p22.1 region (chr6:26,106,870 − 30,106,665 bp) were shared through 14 meioses to both stillbirths in Pedigree B (Table 2 and Fig. 1 ). The 16p13.13-p13.12, 9p13.3-p13.1 and 6p22.2-p22.1 regions contain seventeen, twenty-four, and one hundred forty-three genes, respectively ( Supplementary Table 1 and Supplementary Fig. 5 ). Lastly, the 14q32.2 region in Pedigree C is a 0.79 Mb segment of 211 contiguous SNVs (chr14:98,337,930–99,132,229 bp) shared by three stillbirths (out of three genotyped stillbirths). The 0.79 Mb segment was shared through 12 meioses to two stillbirth males at 22 3/7 weeks’ and 20 0/7 weeks’ gestation, and one stillbirth female at 39 3/7 weeks’ gestation. This region contains the LINC02914 gene ( Supplementary Table 1 and Supplementary Fig. 6 ). Discussion By leveraging familial relationships with genetic data from three high-risk stillbirth pedigrees, we identified SGS chromosomal regions segregating with stillbirths, thereby highlighting the potential for inherited risk loci in stillbirth etiology. Specifically, we identified genome-wide significant chromosomal regions at 15q26.3 shared by two stillbirths in each of two independent pedigrees (Pedigree A and Pedigree B). Additionally, we identified four other regions: three at 16p13.13-p13.12, 9p13.3-p13.1 and 6p22.2-p22.1 that were shared by two stillbirths in Pedigree B, and one at 14q32.2 shared by three stillbirths in Pedigree C. Pathogenic and likely pathogenic variants in several genes account for 1.4 − 5.7% of stillbirths [ 5 , 6 ]. While single-gene and Mendelian disorders have been generally implicated in stillbirth [ 5 , 6 , 8 , 21 ], few inherited genes are known to be causal and one-third of stillbirths remain unexplained [ 4 ]. In a multicenter study of 148 pregnancies from 103 families with familial Long-QT syndrome (LQTS), a genetic disorder of cardiac ion channels, Cuneo et. al. identified known LQTS pathogenic variants in stillbirths [ 21 ]. That study indicated that parental LQTS is a risk factor for stillbirth. Furthermore, a genome wide-linkage analysis study of congenital heart disease in Spanish families identified the 15q26.3 region [ 22 ], which is a shared region identified in stillbirths in our study. Some congenital heart defects have been associated with fetal loss [ 23 ]. The 15q26.3 region is an OMIM region recognized for its role in Silver-Russell syndrome [ 24 ], a rare growth disorder that is associated with fetal and postnatal growth restriction and variable dysmorphisms [ 24 ]. Specifically, a heterozygous deletion in 15q26.3 is implicated in Silver-Russel syndrome [ 24 ]. This region harbors IGF1R (insulin-like growth factor receptor), a widely expressed, cell surface tyrosine kinase receptor gene, essential for normal human growth in utero and postnatally [ 25 ]. IGF1R is pivotal in regulating cellular growth, proliferation, and differentiation, which is particularly crucial during fetal development [ 26 , 27 ]. Inherited compound heterozygous IGF1R variants are associated with growth impairment in children [ 28 ]. In a three-generation Dutch family, microdeletion in IGF1R at 15q26.3 was shown to segregate with short height [ 29 ]. In our data, none of the stillbirths sharing the IGF1R gene in the 15q26.3 region in Pedigree A are below the 10th percentile for birthweight, suggesting normal weight for gestation. However, the stillbirths were preterm (< 34 weeks’ gestation). Interestingly, another genome-wide linkage study using well-characterized Finnish families has shown that a 55 kb segment of the15q26.3 region within the IGF1R gene was shared by fetuses born preterm [ 30 ]. By using a combination of family-based and case-control designs, that study identified a low-frequency susceptibility haplotype in IGF1R in the fetal genome that is associated with spontaneous preterm birth risk. Additionally, polymorphisms in IGF1R (e.g. rs2229765) are associated with spontaneous preterm birth in Chinese women [ 31 ]. Together with previous findings, our data suggest the 15q26.3 region may harbor putative risk variants with shared etiology for limited fetal growth potential and prematurity, which are both recognized risk factors of stillbirth [ 32 ]. While an overlapping 0.84 Mb region of 15q26.3 was shared among stillbirths from two independent pedigrees in our data, this region contains seven genes but not the IGF1R gene (Table 2 ). Evidence of sharing in independent pedigrees in the same region suggests convergence for the 15q26.3 region in stillbirth risk, adding confidence to a risk locus in the 15q26.3 region. However, many of the high-risk pedigrees are sufficiently informative to provide significant evidence for SGS when analyzed alone [ 33 ]. Analyzing such pedigrees independently could allow identification of very rare or private (only occurring in a single individual or their close relatives) segregating variants [ 33 ]. Therefore, our data suggest the potential roles of very rare or private risk alleles for stillbirths in 16p13.13-p13.12, 9p13.3-p13.1 and 6p22.2-p22.1 SGS in Pedigree B and in 14q32.2 SGS in Pedigree C. The 16p13.13 region is linked with fetal hemoglobin Bart’s hydrops fetalis due to maternal uniparental disomy [ 34 ] and familial microhydranencephaly [ 35 ]. While variants in the 9p13.3-p13.1 region influencing stillbirth risk are unknown, variants in PAX5 (paired box 5), a transcription factor gene in 9p13.3, linked with abnormal posterior midbrain and cerebellum development in mice, is associated with neurodevelopmental disorders in children [ 36 ]. Furthermore, in the developing cerebral neocortex of human and mice fetuses, the 6p22.2 haplotype has been shown to downregulate KIAA0319 [ 37 ], a gene required for neuronal migration during the formation of the cerebral neocortex. In addition, the 6p22.2-p22.1 region contains a family of human leukocyte antigen genes, including the HLA-G histocompatibility antigen, class I, G gene. HLA-G is expressed in the placenta and plays a critical role in the maternal acceptance of the fetus [ 38 – 40 ]. Thus, HLA-G has been extensively studied in placentation disorders such as pre-eclampsia and fetal growth restriction [ 41 ]. Although spiral artery remodeling occurs in the first trimester during pregnancy, it is an essential process for a successful pregnancy outcome as it ensures that the placenta receives an adequate supply of oxygen and nutrients for the fetus. Finally, the 14q32.2 region is recognized for genomic alterations in nearby paternally imprinted DLK1 (delta-like homolog 1) and RTL1 (retrotransposon gag like 1) genes in several phenotypes [ 42 ]. Specifically, a paternally inherited 69 kb deletion of DLK1 in 14q32 is linked with Temple syndrome, a condition characterized by pre- and post-natal growth restriction [ 42 ]. The shared regions we identified in this study are also implicated in infertility and pregnancy loss in other studies [ 38 , 43 – 46 ]. For example, copy number variants in the 16p13.12 region are associated with spontaneous premature ovarian insufficiency [ 43 ]. In the 6p22.2 region, a genome-wide association study identified risk genes with large effects in the process of spermatogenesis in men [ 44 ], and chromosome 6 translocations in 6p22.1 increase miscarriage risk [ 45 ]. Furthermore, the HLA-G gene in the 6p22.2-p22.1 region is a clinical marker for adverse pregnancy outcomes, including recurrent implantation failure, miscarriage and recurrent pregnancy loss (RPL) [ 38 , 46 ]. Specifically, paternal HLA-G 5′ upstream regulatory region polymorphism (-725C > G/T vs. -725C > G/T genotype) is associated with a 4.3-fold increased RPL risk [ 47 ]. Its soluble isoform (sHLA-G) is produced in the very first stages of embryo development, and it can be detected in embryo culture medium, where its concentration seems to be predictive of successful implantation after in vitro fertilization (IVF) procedures [ 38 , 48 ]. In our data, the stillbirths from Pedigree B sharing 16q13.12 and 6p22.1 occurred in nulliparous women with a history of IVF, suggesting the potential role of pedigree-specific private segregating variants in these regions on infertility. One study found that stillbirth risk was higher among women using infertility treatment [ 49 ]. Lastly, in the 14q32 region, both DLK1 and RTL1 are highly conserved genes, and aberrant silencing of RTL1 gene is a principal epigenetic cause of pregnancy failure in pigs [ 50 ]. In contrast, restoration of RTL1 expression in pigs induced pluripotent stem cells and rescued fetal loss [ 50 ]. In humans, low RTL1 levels contribute to pregnancy loss [ 51 ]. Furthermore, DLK1 encodes an endocrine signaling molecule that reaches a high concentration in the maternal circulation during late pregnancy [ 51 ]. It is a paternally imprinted, fetus/placenta-derived gene that is required for maternal metabolic adaptations to pregnancy and associated with placental insufficiency [ 52 ], which is an established risk factor of stillbirth [ 32 , 53 , 54 ]. Previously, we demonstrated that there is stronger stillbirth risk in male relatives compared to female relatives [ 9 ], highlighting the potential role of paternal genes in placentation, a critical process for fetal development [ 55 ]. If data are validated, the SGS regions we identified in stillbirths may be used to provide prognoses based on data from other families with variants in the same regions [ 56 , 57 ]. By validating SGSs in stillbirth etiology, there is a potential to identify critical pathways, improved antenatal surveillance strategies and novel therapeutic targets for improving pregnancy outcomes. Our study has several limitations. The SGS method relies on accurate inference of familial relationships and may be sensitive to errors in pedigree structure or genetic data quality. Additionally, we cannot estimate interactive effects of genes with environmental risk factors due to the limited sample size of our study. While our study included individuals without known risk factors for stillbirth, they were primarily of European ancestry, limiting interpretability of our findings across other ancestral groups. Population-based family studies of stillbirth with individuals from diverse racial/ethnic backgrounds and studies that account for environmental (e.g., geographic) risk factors may be useful, informing prevention and intervention strategies. While shared segments analysis can prioritize candidate regions for further investigation, functional validation of identified variants and mechanistic studies are necessary to establish causal relationships with stillbirth. Furthermore, our ability to assess other sources of variation such as placental mosaicism, in which the placenta, but not the fetus, may harbor genetic abnormalities, is limited. One of the key strengths of the shared genomic segments approach is its ability to capture rare or low-frequency variants that may be missed by traditional single variant association analyses. stillbirth, like many complex outcomes, is likely influenced by a multitude of genetic variants, each conferring a small effect size. By focusing on SGSs, and using only few pedigrees, we aggregated information across multiple variants within a given region, thereby increasing statistical power to detect associations even in the presence of genetic heterogeneity. Moreover, the SGS approach provides valuable insights into the genetic architecture of stillbirth by identifying regions of the genome that are enriched for potentially causal variants. Furthermore, the SGS approach uncovers regions of the genome that may be shared by multiple affected individuals within a pedigree. By focusing on families with a high burden of stillbirth, genetic factors with large effect sizes contributing to disease susceptibility were enriched. Conclusion We identified shared genomic regions at 15q26.3, 16p13.13-p13.12, 9p13.3-p13.1, 6p22.2-p22.1 and 14q32.2 in stillbirth placentas that are implicated in in utero and postnatal development, pregnancy loss and infertility. These regions are likely to harbor genes and rare risk inherited loci in stillbirth, providing a framework for prioritizing functional follow-up studies to elucidate the biological mechanisms causing stillbirth. Future studies elucidating the functional consequences of the variants in the identified regions and their interactions with environmental factors may provide valuable insights into potential preventive strategies and therapeutic targets. Abbreviations SGS Shared genomic segment SCRN Stillbirth Collaborative Research Network UPDB Utah Population Database FSIR Familial Standardized Incidence Ratio INCODE Initial Causes of Fetal Death Evaluation IRB Institutional Review Board WGS Whole-genome-sequencing SNV Single-nucleotide Variant CEPH Centre d'Etude du Polymorphisme Humain IBS Identical-by-State IBD Identity-by-Descent OMIM Online Mendelian Inheritance in Man LQTS Long-QT syndrome IVF In Vitro Fertilization Declarations Ethics approval and consent to participate The parent SCRN study was approved by the institutional review board (IRB) at each participating institution, and written informed consent was obtained from all participants. Request for permission to use UPDB family structure data was approved by the Utah Resource for Genetic and Epidemiologic Research, the UPDB data use oversight committee. The secondary analysis of the parent SCRN study was approved by the IRB of the University of Utah (IRB application # 127960; date: 26 March 2020). Consent for publication Not applicable. Availability of data and materials: Data cannot be shared publicly because the clinical details associated with the small number of families involved in the study make it too easy to identify the families and would thus violate privacy. We will make data available to researchers who maintain confidentiality as mandated by our IRB. To request data access beyond that included in the manuscript and supplemental material, please contact Kathy Harvey, Associate Director of Research and Science ( [email protected] ), who can field data inquiries as part of ethics committee at the University of Utah. Competing interests The authors declare that they have no competing interests. Funding : This work was supported by grant funding from the Eunice Kennedy Shriver National Institute of Child Health and Development (grant R01HD112836) and in part by a grant from the Recurrent Pregnancy Loss Association in partnership with the American Society for Reproductive Medicine. Partial support for all datasets within the Utah Population Database (UPDB) is provided by the University of Utah Huntsman Cancer Institute and P30 CA2014. Record linking from the UPDB to enterprise data warehouses at the two healthcare systems was supported by the University of Utah’s Center for Clinical and Translational Science Institute, Information Technology Services and Biomedical Informatics core. LBJ is supported by National Institute of General Medical Sciences (grant R35GM118335). Authors' contributions TW, RMS, NJC and HC contributed to the concept and design of the study. MJM, CA, RS, ZY, EG and TW contributed to data analysis. All the authors contributed to the interpretation of findings, manuscript writing and discussion, and gave approval to the final version of the manuscript. Acknowledgements : We thank the participants and their families who make this research possible. The study uses data from the Utah Population Database linked to data from the University of Utah Health and Intermountain Health healthcare systems. Sequence alignment and variant calling were performed by the Utah Center for Genetic Discovery Core, part of the Health Sciences Center Cores at the University of Utah. This work utilized resources and support from the Center for High Performance Computing at the University of Utah, partially funded by NIH Shared Instrumentation Grant 1S10OD021644-01A1. This work also utilized resources and support from the Center for High Performance Computing at the University of Utah. The computational resources used were partially funded by the NIH Shared Instrumentation grant 1S10OD021644-01A1. The support and resources from the Center for High Performance Computing at the University of Utah are gratefully acknowledged. References Estimation UNI-aGfCM. Never Forgotten: the situation of stillbirth around the globe. United Nations Children’s Fund; 2023. Lamont K, Scott NW, Jones GT, Bhattacharya S. Risk of recurrent stillbirth: systematic review and meta-analysis. BMJ. 2015;350:h3080. https://doi.org/10.1136/bmj.h3080 . Nijkamp JW, Ravelli AC, Groen H, Erwich JJH, Mol BWJ. Stillbirth and neonatal mortality in a subsequent pregnancy following stillbirth: a population-based cohort study. BMC Pregnancy Childbirth. 2022;22(1):11. https://doi.org/10.1186/s12884-021-04355-7 . Reinebrant HE, Leisher SH, Coory M, Henry S, Wojcieszek AM, Gardener G, Lourie R, Ellwood D, Teoh Z, Allanson E. Making stillbirths visible: a systematic review of globally reported causes of stillbirth. BJOG: Int J Obstet Gynecol. 2018;125(2):212–24. https://doi.org/10.1111/1471-0528.14971 . 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Black-filled circles (Female) and squares (Male) indicate genotyped stillbirth cases. Each pedigree contains at least 12 meioses between genotyped stillbirth cases\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4858244/v1/2be93f0554fdde477f528e2d.jpeg"},{"id":63127896,"identity":"525b0537-5383-4c6c-92ad-94bd884b1293","added_by":"auto","created_at":"2024-08-23 12:34:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1085624,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4858244/v1/22bc0df8-d9eb-46d6-ab0d-cec27543dc9a.pdf"},{"id":63127043,"identity":"8d285768-a142-4fd1-b59b-802dea2e82db","added_by":"auto","created_at":"2024-08-23 12:26:58","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2592086,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-4858244/v1/9bf9ad113cb9fc288641a82b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Inherited Genetic Risk in Stillbirth: A Shared Genomic Segments Analysis of High-Risk Pedigrees. ","fulltext":[{"header":"Background","content":"\u003cp\u003eStillbirth remains a tragic and distressing pregnancy outcome. Despite advances in obstetric care, there has been only modest reduction in incidence of stillbirth in recent years, with global estimates of 2\u0026nbsp;million stillbirths occurring after 28 weeks\u0026rsquo; gestation each year [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Beyond the profound emotional toll on affected families, stillbirth poses a significant public health challenge that demands a better understanding of its multifactorial etiology.\u003c/p\u003e \u003cp\u003eIndividuals experiencing stillbirth are at increased risk of both its recurrence and other obstetric complications in subsequent pregnancies [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. While some stillbirth cases can be attributed to known maternal, fetal, or placental factors, around one-third remain unexplained even after thorough investigation [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This lack of clarity surrounding the causes of stillbirth underscores the need for continued research.\u003c/p\u003e \u003cp\u003eApart from infrequent balanced parental translocations, few inherited genes are known to contribute to stillbirth [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Rare single-gene disorders, such as those caused by X-linked dominant mutations, have been implicated in stillbirth [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Recently, we showed that stillbirth aggregates in families [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], suggesting that genomic investigation in high-risk stillbirth pedigrees may reveal important insights into pathogenic heritable mechanisms. Using a population-based genealogical resource linked with medical records, we performed shared genomic segments (SGS) analyses to identify inherited regulatory and risk variants in stillbirth.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and population\u003c/h2\u003e \u003cp\u003eWe conducted SGS analysis in stillbirth cases using placental tissue banked from the Stillbirth Collaborative Research Network (SCRN) study. The SCRN study was a multi-site, case-control study designed to characterize and investigate stillbirth in the United States [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The sites included Brown University, Rhode Island; Emory University, Georgia; University of Texas Medical Branch at Galveston, Texas; University of Texas Health Sciences Center at San Antonio, Texas; and University of Utah Health Sciences Center, Utah. Stillbirth cases were defined as fetal death at \u0026ge;\u0026thinsp;20 weeks\u0026rsquo; gestation. Individuals underwent a standardized maternal interview, medical record abstraction, biospecimen collection, and postmortem examinations of the fetus and placenta. In total, the SCRN study had placental tissue available from 518 stillbirths and 1,200 live births [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Comprehensive phenotyping with review of primary medical records was conducted to identify stillbirths without identifiable causes, i.e., without infection, maternal medical conditions, or genetic abnormalities such as aneuploidy or copy number changes on microarray (based on the Initial Causes of Fetal Death Evaluation (INCODE) classification system [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]).\u003c/p\u003e \u003cp\u003eThe Utah Population Database (UPDB) is a data resource that stores administrative, health, and genealogical records for over 11\u0026nbsp;million people in Utah. Over 4\u0026nbsp;million people in the UPDB have at least three generations of pedigree data and some have many more [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Among 518 stillbirths with placental tissue, 11 from the Salt Lake County, Utah site that had stored DNA linked to the UPDB (all cases were related to at least one other sampled case). Of the 11 stillbirths that linked to the UPDB, 3 pedigrees with at least 2 stillbirth cases in each were eligible for SGS analysis, resulting a total of 7 stillbirths. The genealogical data in the UPDB was used to find distant relationships between the stillbirth cases and define multi-generational pedigrees that tied cases together across many generations.\u003c/p\u003e \u003cp\u003eThe parent SCRN study was approved by the institutional review board (IRB) at each participating institution, and written informed consent was obtained from all participants. Request for permission to use UPDB family structure data was approved by the Utah Resource for Genetic and Epidemiologic Research, the UPDB data use oversight committee. The secondary analysis of the parent SCRN study was approved by the IRB of the University of Utah (IRB application # 127960).\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eSample collection, genotyping and quality control\u003c/h2\u003e \u003cp\u003ePlacental samples were collected at birth. Placental parenchymal tissue blocks were obtained from each placenta shortly after birth using a standardized sampling procedure [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Samples were frozen at -80\u0026deg;C [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. DNA was purified and extracted using the Gentra PureGene tissue and blood kits (Qiagen Systems). Sample concentrations ranged from 50 ng/uL to 260 ng/uL. Whole-genome-sequencing (WGS) libraries were prepared for Illumina 150bp paired-end reads sequencing using the NEBNext Ultra II DNA Library Prep Kit protocols. All libraries were sequenced on the Novaseq 6000 platform (Illumina, San Diego, CA, USA) using standard protocols. Genotypes for SGS analysis were extracted from WGS sequencing at 120x coverage depth.\u003c/p\u003e \u003cp\u003eGermline single-nucleotide variants (SNVs) for each sample were detected following a Genome Analysis Tool Kit best practices equivalent workflow for variant detection [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Raw data output short reads were aligned using the GRCh38 human reference genome, using alt-aware alignment and variant calling against the GRCh38 build with alt and decoy contigs. Because SGS analysis was optimized for genome-wide SNV panel data, a subset of all SNV calls based on the Illumina GSA24v3 was extracted to make computationally feasible simulations (see \u003cspan refid=\"Sec5\" class=\"InternalRef\"\u003eStatistical analysis\u003c/span\u003e section for genome-wide significant sharing simulations). SNVs were further checked for quality control which included duplicate check, sex check, SNV call-rate (\u0026ge;\u0026thinsp;95%), and sample call rate (\u0026ge;\u0026thinsp;95%). To check for genetic ancestry, we \u003cem\u003ePeddy\u003c/em\u003e, software package that predicted ancestry of the samples using a support vector machine prediction probability [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. After quality control, 495,253 autosomal SNVs were available for SGS analysis. We used the Utah Centre d\u0026rsquo;Etude du Polymorphisme Humain (CEPH) cohort to provide a matched population linkage disequilibrium map for SGS analysis [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eFirst, large (\u0026gt;\u0026thinsp;5 generations) high-risk pedigrees were identified as those containing more stillbirth cases than expected by chance. The familial standardized incidence ratio (FSIR) denotes the fold increase in stillbirth over population expectation, measuring the excess stillbirth risk for a pedigree, compared to population rates, accounting for age and sex [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. To the best of our knowledge, the three pedigrees are independent, and cases were related only within pedigrees and not across pedigrees. We used the FSIR to confirm that the pedigrees are high-risk\u0026mdash;with a statistically significant stillbirth risk in the pedigrees (FSIR\u0026thinsp;\u0026gt;\u0026thinsp;2.0, P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Additionally, we verified 12 meioses between genotyped stillbirth cases in each pedigree to ensure sufficient statistical power for genome-wide single pedigree SGS analysis [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The FSIR was used to confirm that the pedigrees we identified had a statistically significant increase in stillbirth. Next, we conducted single-pedigree SGS analysis that minimizes the effects of genetic heterogeneity [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. This process resulted in three high-risk pedigrees containing seven stillbirth cases.\u003c/p\u003e \u003cp\u003eIn each of the three large pedigrees, we performed single-pedigree SGS to identify segregating shared regions among stillbirth cases. The detailed methodology has been previously described [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In brief, the SGS approach identifies significantly long DNA segments inherited across many meioses. The analysis assesses whether among sampled, distantly related cases, the length of consecutively shared loci (identified as identical-by-state, or IBS) is longer than expected by chance. IBS is established by determining if allelic types at sequential loci are consistent (phase is ignored). IBS does not infer identity-by-descent (IBD; the same inherited segment from a common ancestor) which is our true interest; however, if the length of SGS shared IBS is significantly longer than by chance (given the known relationships), then IBD is suggested. Chance IBD sharing in distant relatives is extremely improbable. Statistical significance for genome-wide sharing is determined through simulations tailored to each pedigree's structure (\u003cb\u003eSupplementary Figs.\u0026nbsp;1\u0026ndash;3\u003c/b\u003e). Here, we used 1\u0026nbsp;million simulations to establish pedigree-specific genome-wide thresholds, accounting for both the extensive testing across the genome and optimization over subsets. After genome-wide significance thresholds were determined, additional simulations were performed for precise resolution of very low p-values, if needed. In each pedigree, genome-wide significance was assigned at a false positive rate of 0.05 per genome (expected degree of sharing per genome [\u0026micro;]\u0026thinsp;\u0026le;\u0026thinsp;0.05), indicating the observation is expected to occur no more than 0.05 times per genome. Genome-wide significance thresholds and Manhattan plots are provided in \u003cb\u003eSupplementary Figs.\u0026nbsp;1\u0026ndash;3\u003c/b\u003e. Analyses were conducted using the SGS analysis software that is freely available and can be accessed online: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://uofuhealth.utah.edu/huntsman/labs/camp/analysis-tool/shared-genomic-segment.php\u003c/span\u003e\u003cspan address=\"https://uofuhealth.utah.edu/huntsman/labs/camp/analysis-tool/shared-genomic-segment.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Using the genome-wide significant SGS regions, we conducted Online Mendelian Inheritance in Man (OMIM) database and PubMed searches using genome-wide significant SGS regions we identified in our results to find reports of the regions with known disease etiologies relevant to pregnancy loss and fetal development [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Our database searches and discussion were focused on the SGS regions as a whole rather than restricting searches on specific genes because variants found in other studies may not exist in our data. Therefore, we considered the entirety of the SGS regions as defining our search space for further focused interrogation of variants using WGS data.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe FSIR for the three pedigrees ranged between 2.4\u0026ndash;30.9, suggesting a 2-to-31-fold increased stillbirth risk in the pedigrees compared with the background population risk (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Among the seven stillbirth cases included in our study, maternal and paternal ages at birth ranged between 25\u0026ndash;42 and 25\u0026ndash;43 years, respectively. The maternal and paternal self-reported race/ethnicity is consistent with Western European ancestry inferred from the genotypes of all stillbirths, as indicated by the support vector machine prediction probability for a particular ancestry. The gestational ages ranged between 20 weeks to 40 2/7 weeks. Birth weights ranged from 350\u0026ndash;2635 in grams and 7th -77th in gestational age/fetal sex percentiles. Three out of the seven stillbirth cases were male. Three cases were from nulliparous women and four cases were from multiparous women with 3\u0026ndash;5 total pregnancies. None of the cases had karyotype or chromosomal microarray abnormalities, prior history of stillbirth or identifiable causes according to INCODE classification.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of pedigrees and stillbirths included in the study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePedigree\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePedigree A\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003ePedigree B\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003ePedigree C\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFSIR (P-value) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2.41 (\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e3.99 (\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e30.91 (\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number of stillbirth cases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of sampled stillbirth cases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number of meioses between sampled stillbirth cases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShared genomic segments analysis genome-wide significance threshold \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e3.1x10-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2.9x10-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e5.1x10-5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSampled stillbirth case\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eStillbirth_A1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eStillbirth_A2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eStillbirth_B1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eStillbirth_B2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eStillbirth_C1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eStillbirth_C2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eStillbirth_C3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDemographics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaby sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGestational age at birth (weeks)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 5/7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 2/7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 6/7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22 2/7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22 3/7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39 3/7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20 0/7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaternal age at birth (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePaternal age at birth (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003en/a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaby genetic ancestry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWestern European\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWestern European\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWestern European\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWestern European\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWestern European\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWestern European\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eWestern European\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedical history\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaby weight (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en/a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBirthweight percentile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en/a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e55th\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGestational hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreeclampsia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGestational diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny placental pathological lesion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAcute chorioamnionitis membrane\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior history of stillbirth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMultiparous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMultiparous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNulliparous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNulliparous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMultiparous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNulliparous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMultiparous\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number of pregnancies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of infertility treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInvitro fertilization (sperm and egg donor)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eInvitro fertilization (sperm and egg donor)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenetic testing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal karyotype (XX)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNormal karyotype (XX)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNormal karyotype (XY)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNormal karyotype (XX)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAbnormal karyotype (XY)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003ea\u003c/sup\u003e FSIR (familial standardized incidence ratio) denotes the fold increase in stillbirth over population expectation, measuring the excess stillbirth risk for a pedigree.\u003c/p\u003e \u003cp\u003e \u003csup\u003eb\u003c/sup\u003e denotes the pedigree-specific shared genomic segments genome-wide thresholds estimated using 1\u0026nbsp;million simulations, accounting for both the extensive testing across the genome and optimization over subsets\u003c/p\u003e \u003cp\u003eWe identified genome-wide significant SGS chromosomal regions at 15q26.3 (P-value\u0026thinsp;=\u0026thinsp;2.7x10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e; \u0026micro;\u0026thinsp;=\u0026thinsp;0.01 and P-value\u0026thinsp;=\u0026thinsp;1.4x10-4; \u0026micro;\u0026thinsp;=\u0026thinsp;0.02) in Pedigrees A and B, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In Pedigree B, we also identified genome-wide significant SGS chromosomal regions at 16p13.13-p13.12 (P-value\u0026thinsp;=\u0026thinsp;1.3x10-4; \u0026micro;\u0026thinsp;=\u0026thinsp;0.02), 9p13.3-p13.1 (P-value\u0026thinsp;=\u0026thinsp;2.1x10-4; \u0026micro;\u0026thinsp;=\u0026thinsp;0.03), and 6p22.2-p22.1 (P-value\u0026thinsp;=\u0026thinsp;1.0x10-4; \u0026micro;\u0026thinsp;=\u0026thinsp;0.01). In addition, we identified one genome-wide significant SGS chromosomal region at 14q32.2 (P-value\u0026thinsp;=\u0026thinsp;3.1x10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e; \u0026micro;\u0026thinsp;=\u0026thinsp;0.03) in Pedigree C.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGenome-wide significant shared genomic segments in stillbirths\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePedigree\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal number of meioses between sampled stillbirths\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of stillbirths in SGS analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLocus Cytoband\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRegion \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSize in Mb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNumber of SNVs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNumber of stillbirths sharing\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026micro; \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15q26.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e98954683\u0026ndash;100761210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.7x10-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15q26.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e99916971\u0026ndash;101155417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.4x10-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16p13.13-p13.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11425854\u0026ndash;13456003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.3x10-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9p13.3-p13.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36189000\u0026ndash;38396005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.1x10-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6p22.2-p22.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26106870\u0026ndash;30106665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.0x10-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14q32.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e98337930\u0026ndash;99132229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.1x10-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003ea\u003c/sup\u003e Genomic region according to the GRCh38/hg38 genome build position in the chromosome\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003eb\u003c/sup\u003e Genome-wide false positive rate, indicating the expected degree of sharing per genome\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe 15q26.3 region in Pedigree A is a 1.81 Mb segment of 532 contiguous SNVs (chr15:98,954,683\u0026ndash;100,761,210 bp) shared by two stillbirths (out of two genotyped stillbirths) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cb\u003eSupplementary Fig.\u0026nbsp;4\u003c/b\u003e). The 1.81 Mb region was shared through 14 meioses to both stillbirths which were females at 22 5/7 weeks\u0026rsquo; and 26 2/7 weeks\u0026rsquo; gestations. This region contains fifteen genes, including the \u003cem\u003eIGF1R\u003c/em\u003e gene (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e). In another independent pedigree (Pedigree B), an overlapping 1.23 Mb segment of 411 contiguous SNVs (chr15:99,916,971\u0026ndash;101,155,417 bp) in the same 15q26.3 region was shared by two stillbirths (out of two genotyped stillbirths). The 1.23 Mb region was shared through 14 meioses to both stillbirths which were a female at 20 6/7 weeks\u0026rsquo; gestation and a male at 22 2/7 weeks\u0026rsquo; gestation. The intersecting region identified in Pedigree A and Pedigree B is a 0.84 Mb segment (chr15:99,916,971\u0026thinsp;\u0026minus;\u0026thinsp;100,761,210) in 15q26.3. This region contains seven genes (\u003cem\u003eADAMTS17\u003c/em\u003e, \u003cem\u003eASB7\u003c/em\u003e, \u003cem\u003eCERS3\u003c/em\u003e, \u003cem\u003eCERS3-AS1\u003c/em\u003e, \u003cem\u003eLINS1\u003c/em\u003e, \u003cem\u003eLOC102723335\u003c/em\u003e and \u003cem\u003ePRKXP1\u003c/em\u003e; \u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eAdditionally, a 2.0 Mb segment of 542 contiguous SNVs in 16p13.13-p13.12 region (chr16:11,425,854\u0026ndash;13,456,003 bp), a 2.2 Mb segment of 382 contiguous SNVs in 9p13.3-p13.1 region (chr9:36,189,000\u0026ndash;38,396,005 bp) and a 4.0 Mb segment of 2078 SNVs in 6p22.2-p22.1 region (chr6:26,106,870\u0026thinsp;\u0026minus;\u0026thinsp;30,106,665 bp) were shared through 14 meioses to both stillbirths in Pedigree B (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The 16p13.13-p13.12, 9p13.3-p13.1 and 6p22.2-p22.1 regions contain seventeen, twenty-four, and one hundred forty-three genes, respectively (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e and \u003cb\u003eSupplementary Fig.\u0026nbsp;5\u003c/b\u003e). Lastly, the 14q32.2 region in Pedigree C is a 0.79 Mb segment of 211 contiguous SNVs (chr14:98,337,930\u0026ndash;99,132,229 bp) shared by three stillbirths (out of three genotyped stillbirths). The 0.79 Mb segment was shared through 12 meioses to two stillbirth males at 22 3/7 weeks\u0026rsquo; and 20 0/7 weeks\u0026rsquo; gestation, and one stillbirth female at 39 3/7 weeks\u0026rsquo; gestation. This region contains the \u003cem\u003eLINC02914\u003c/em\u003e gene (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e and \u003cb\u003eSupplementary Fig.\u0026nbsp;6\u003c/b\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eBy leveraging familial relationships with genetic data from three high-risk stillbirth pedigrees, we identified SGS chromosomal regions segregating with stillbirths, thereby highlighting the potential for inherited risk loci in stillbirth etiology. Specifically, we identified genome-wide significant chromosomal regions at 15q26.3 shared by two stillbirths in each of two independent pedigrees (Pedigree A and Pedigree B). Additionally, we identified four other regions: three at 16p13.13-p13.12, 9p13.3-p13.1 and 6p22.2-p22.1 that were shared by two stillbirths in Pedigree B, and one at 14q32.2 shared by three stillbirths in Pedigree C.\u003c/p\u003e \u003cp\u003ePathogenic and likely pathogenic variants in several genes account for 1.4\u0026thinsp;\u0026minus;\u0026thinsp;5.7% of stillbirths [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. While single-gene and Mendelian disorders have been generally implicated in stillbirth [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], few inherited genes are known to be causal and one-third of stillbirths remain unexplained [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In a multicenter study of 148 pregnancies from 103 families with familial Long-QT syndrome (LQTS), a genetic disorder of cardiac ion channels, Cuneo et. al. identified known LQTS pathogenic variants in stillbirths [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. That study indicated that parental LQTS is a risk factor for stillbirth. Furthermore, a genome wide-linkage analysis study of congenital heart disease in Spanish families identified the 15q26.3 region [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], which is a shared region identified in stillbirths in our study. Some congenital heart defects have been associated with fetal loss [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe 15q26.3 region is an OMIM region recognized for its role in Silver-Russell syndrome [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], a rare growth disorder that is associated with fetal and postnatal growth restriction and variable dysmorphisms [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Specifically, a heterozygous deletion in 15q26.3 is implicated in Silver-Russel syndrome [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This region harbors \u003cem\u003eIGF1R\u003c/em\u003e (insulin-like growth factor receptor), a widely expressed, cell surface tyrosine kinase receptor gene, essential for normal human growth \u003cem\u003ein utero\u003c/em\u003e and postnatally [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. \u003cem\u003eIGF1R\u003c/em\u003e is pivotal in regulating cellular growth, proliferation, and differentiation, which is particularly crucial during fetal development [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Inherited compound heterozygous \u003cem\u003eIGF1R\u003c/em\u003e variants are associated with growth impairment in children [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In a three-generation Dutch family, microdeletion in \u003cem\u003eIGF1R\u003c/em\u003e at 15q26.3 was shown to segregate with short height [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In our data, none of the stillbirths sharing the \u003cem\u003eIGF1R\u003c/em\u003e gene in the 15q26.3 region in Pedigree A are below the 10th percentile for birthweight, suggesting normal weight for gestation. However, the stillbirths were preterm (\u0026lt;\u0026thinsp;34 weeks\u0026rsquo; gestation). Interestingly, another genome-wide linkage study using well-characterized Finnish families has shown that a 55 kb segment of the15q26.3 region within the \u003cem\u003eIGF1R\u003c/em\u003e gene was shared by fetuses born preterm [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. By using a combination of family-based and case-control designs, that study identified a low-frequency susceptibility haplotype in \u003cem\u003eIGF1R\u003c/em\u003e in the fetal genome that is associated with spontaneous preterm birth risk. Additionally, polymorphisms in \u003cem\u003eIGF1R\u003c/em\u003e (e.g. rs2229765) are associated with spontaneous preterm birth in Chinese women [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Together with previous findings, our data suggest the 15q26.3 region may harbor putative risk variants with shared etiology for limited fetal growth potential and prematurity, which are both recognized risk factors of stillbirth [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile an overlapping 0.84 Mb region of 15q26.3 was shared among stillbirths from two independent pedigrees in our data, this region contains seven genes but not the \u003cem\u003eIGF1R\u003c/em\u003e gene (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Evidence of sharing in independent pedigrees in the same region suggests convergence for the 15q26.3 region in stillbirth risk, adding confidence to a risk locus in the 15q26.3 region. However, many of the high-risk pedigrees are sufficiently informative to provide significant evidence for SGS when analyzed alone [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Analyzing such pedigrees independently could allow identification of very rare or private (only occurring in a single individual or their close relatives) segregating variants [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Therefore, our data suggest the potential roles of very rare or private risk alleles for stillbirths in 16p13.13-p13.12, 9p13.3-p13.1 and 6p22.2-p22.1 SGS in Pedigree B and in 14q32.2 SGS in Pedigree C.\u003c/p\u003e \u003cp\u003eThe 16p13.13 region is linked with fetal hemoglobin Bart\u0026rsquo;s hydrops fetalis due to maternal uniparental disomy [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] and familial microhydranencephaly [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. While variants in the 9p13.3-p13.1 region influencing stillbirth risk are unknown, variants in \u003cem\u003ePAX5\u003c/em\u003e (paired box 5), a transcription factor gene in 9p13.3, linked with abnormal posterior midbrain and cerebellum development in mice, is associated with neurodevelopmental disorders in children [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Furthermore, in the developing cerebral neocortex of human and mice fetuses, the 6p22.2 haplotype has been shown to downregulate \u003cem\u003eKIAA0319\u003c/em\u003e [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], a gene required for neuronal migration during the formation of the cerebral neocortex. In addition, the 6p22.2-p22.1 region contains a family of human leukocyte antigen genes, including the \u003cem\u003eHLA-G\u003c/em\u003e histocompatibility antigen, class I, G gene. \u003cem\u003eHLA-G\u003c/em\u003e is expressed in the placenta and plays a critical role in the maternal acceptance of the fetus [\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Thus, \u003cem\u003eHLA-G\u003c/em\u003e has been extensively studied in placentation disorders such as pre-eclampsia and fetal growth restriction [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Although spiral artery remodeling occurs in the first trimester during pregnancy, it is an essential process for a successful pregnancy outcome as it ensures that the placenta receives an adequate supply of oxygen and nutrients for the fetus. Finally, the 14q32.2 region is recognized for genomic alterations in nearby paternally imprinted \u003cem\u003eDLK1\u003c/em\u003e (delta-like homolog 1) and \u003cem\u003eRTL1\u003c/em\u003e (retrotransposon gag like 1) genes in several phenotypes [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Specifically, a paternally inherited 69 kb deletion of \u003cem\u003eDLK1\u003c/em\u003e in 14q32 is linked with Temple syndrome, a condition characterized by pre- and post-natal growth restriction [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe shared regions we identified in this study are also implicated in infertility and pregnancy loss in other studies [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan additionalcitationids=\"CR44 CR45\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. For example, copy number variants in the 16p13.12 region are associated with spontaneous premature ovarian insufficiency [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. In the 6p22.2 region, a genome-wide association study identified risk genes with large effects in the process of spermatogenesis in men [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], and chromosome 6 translocations in 6p22.1 increase miscarriage risk [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Furthermore, the \u003cem\u003eHLA-G\u003c/em\u003e gene in the 6p22.2-p22.1 region is a clinical marker for adverse pregnancy outcomes, including recurrent implantation failure, miscarriage and recurrent pregnancy loss (RPL) [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Specifically, paternal \u003cem\u003eHLA-G\u003c/em\u003e 5\u0026prime; upstream regulatory region polymorphism (-725C\u0026thinsp;\u0026gt;\u0026thinsp;G/T vs. -725C\u0026thinsp;\u0026gt;\u0026thinsp;G/T genotype) is associated with a 4.3-fold increased RPL risk [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Its soluble isoform (sHLA-G) is produced in the very first stages of embryo development, and it can be detected in embryo culture medium, where its concentration seems to be predictive of successful implantation after in vitro fertilization (IVF) procedures [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. In our data, the stillbirths from Pedigree B sharing 16q13.12 and 6p22.1 occurred in nulliparous women with a history of IVF, suggesting the potential role of pedigree-specific private segregating variants in these regions on infertility. One study found that stillbirth risk was higher among women using infertility treatment [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Lastly, in the 14q32 region, both \u003cem\u003eDLK1\u003c/em\u003e and \u003cem\u003eRTL1\u003c/em\u003e are highly conserved genes, and aberrant silencing of \u003cem\u003eRTL1\u003c/em\u003e gene is a principal epigenetic cause of pregnancy failure in pigs [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. In contrast, restoration of \u003cem\u003eRTL1\u003c/em\u003e expression in pigs induced pluripotent stem cells and rescued fetal loss [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. In humans, low \u003cem\u003eRTL1\u003c/em\u003e levels contribute to pregnancy loss [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Furthermore, \u003cem\u003eDLK1\u003c/em\u003e encodes an endocrine signaling molecule that reaches a high concentration in the maternal circulation during late pregnancy [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. It is a paternally imprinted, fetus/placenta-derived gene that is required for maternal metabolic adaptations to pregnancy and associated with placental insufficiency [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], which is an established risk factor of stillbirth [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Previously, we demonstrated that there is stronger stillbirth risk in male relatives compared to female relatives [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], highlighting the potential role of paternal genes in placentation, a critical process for fetal development [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. If data are validated, the SGS regions we identified in stillbirths may be used to provide prognoses based on data from other families with variants in the same regions [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. By validating SGSs in stillbirth etiology, there is a potential to identify critical pathways, improved antenatal surveillance strategies and novel therapeutic targets for improving pregnancy outcomes.\u003c/p\u003e \u003cp\u003eOur study has several limitations. The SGS method relies on accurate inference of familial relationships and may be sensitive to errors in pedigree structure or genetic data quality. Additionally, we cannot estimate interactive effects of genes with environmental risk factors due to the limited sample size of our study. While our study included individuals without known risk factors for stillbirth, they were primarily of European ancestry, limiting interpretability of our findings across other ancestral groups. Population-based family studies of stillbirth with individuals from diverse racial/ethnic backgrounds and studies that account for environmental (e.g., geographic) risk factors may be useful, informing prevention and intervention strategies. While shared segments analysis can prioritize candidate regions for further investigation, functional validation of identified variants and mechanistic studies are necessary to establish causal relationships with stillbirth. Furthermore, our ability to assess other sources of variation such as placental mosaicism, in which the placenta, but not the fetus, may harbor genetic abnormalities, is limited.\u003c/p\u003e \u003cp\u003eOne of the key strengths of the shared genomic segments approach is its ability to capture rare or low-frequency variants that may be missed by traditional single variant association analyses. stillbirth, like many complex outcomes, is likely influenced by a multitude of genetic variants, each conferring a small effect size. By focusing on SGSs, and using only few pedigrees, we aggregated information across multiple variants within a given region, thereby increasing statistical power to detect associations even in the presence of genetic heterogeneity. Moreover, the SGS approach provides valuable insights into the genetic architecture of stillbirth by identifying regions of the genome that are enriched for potentially causal variants. Furthermore, the SGS approach uncovers regions of the genome that may be shared by multiple affected individuals within a pedigree. By focusing on families with a high burden of stillbirth, genetic factors with large effect sizes contributing to disease susceptibility were enriched.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe identified shared genomic regions at 15q26.3, 16p13.13-p13.12, 9p13.3-p13.1, 6p22.2-p22.1 and 14q32.2 in stillbirth placentas that are implicated in \u003cem\u003ein utero\u003c/em\u003e and postnatal development, pregnancy loss and infertility. These regions are likely to harbor genes and rare risk inherited loci in stillbirth, providing a framework for prioritizing functional follow-up studies to elucidate the biological mechanisms causing stillbirth. Future studies elucidating the functional consequences of the variants in the identified regions and their interactions with environmental factors may provide valuable insights into potential preventive strategies and therapeutic targets.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSGS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eShared genomic segment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSCRN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStillbirth Collaborative Research Network\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUPDB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUtah Population Database\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFSIR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFamilial Standardized Incidence Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eINCODE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInitial Causes of Fetal Death Evaluation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIRB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInstitutional Review Board\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWGS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWhole-genome-sequencing\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSNV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSingle-nucleotide Variant\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCEPH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCentre d'Etude du Polymorphisme Humain\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIBS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIdentical-by-State\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIBD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIdentity-by-Descent\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOMIM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOnline Mendelian Inheritance in Man\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLQTS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLong-QT syndrome\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIVF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIn Vitro Fertilization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe parent SCRN study was approved by the institutional review board (IRB) at each participating institution, and written informed consent was obtained from all participants. Request for permission to use UPDB family structure data was approved by the Utah Resource for Genetic and Epidemiologic Research, the UPDB data use oversight committee. The secondary analysis of the parent SCRN study was approved by the IRB of the University of Utah (IRB application # 127960; date: 26 March 2020).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData cannot be shared publicly because the clinical details associated with the small number of families involved in the study make it too easy to identify the families and would thus violate privacy. We will make data available to researchers who maintain confidentiality as mandated by our IRB. To request data access beyond that included in the manuscript and supplemental material, please contact Kathy Harvey, Associate Director of Research and Science ([email protected]), who can field data inquiries as part of ethics committee at the University of Utah.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eThis work was supported by grant funding from the \u003cem\u003eEunice Kennedy Shriver\u003c/em\u003e National Institute of Child Health and Development (grant R01HD112836) and in part by a grant from the Recurrent Pregnancy Loss Association in partnership with the American Society for Reproductive Medicine. Partial support for all datasets within the Utah Population Database (UPDB) is provided by the University of Utah Huntsman Cancer Institute and P30 CA2014. Record linking from the UPDB to enterprise data warehouses at the two healthcare systems was supported by the University of Utah’s Center for Clinical and Translational Science Institute, Information Technology Services and Biomedical Informatics core. LBJ is supported by National Institute of General Medical Sciences (grant R35GM118335).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTW, RMS, NJC and HC contributed to the concept and design of the study. MJM, CA, RS, ZY, EG and TW contributed to data analysis. All the authors contributed to the interpretation of findings, manuscript writing and discussion, and gave approval to the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eWe thank the participants and their families who make this research possible. The study uses data from the Utah Population Database linked to data from the University of Utah Health and Intermountain Health healthcare systems. Sequence alignment and variant calling were performed by the Utah Center for Genetic Discovery Core, part of the Health Sciences Center Cores at the University of Utah. This work utilized resources and support from the Center for High Performance Computing at the University of Utah, partially funded by NIH Shared Instrumentation Grant 1S10OD021644-01A1. This work also utilized resources and support from the Center for High Performance Computing at the University of Utah. The computational resources used were partially funded by the NIH Shared Instrumentation grant 1S10OD021644-01A1. The support and resources from the Center for High Performance Computing at the University of Utah are gratefully acknowledged.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eEstimation UNI-aGfCM. Never Forgotten: the situation of stillbirth around the globe. United Nations Children\u0026rsquo;s Fund; 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLamont K, Scott NW, Jones GT, Bhattacharya S. Risk of recurrent stillbirth: systematic review and meta-analysis. 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Cell. 2014;156(5):872\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cell.2014.02.002\u003c/span\u003e\u003cspan address=\"10.1016/j.cell.2014.02.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Stillbirth, placenta, shared genomic segments, pedigree, inherited","lastPublishedDoi":"10.21203/rs.3.rs-4858244/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4858244/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eStillbirth is a devastating adverse pregnancy outcome affecting 2\u0026nbsp;million pregnancies worldwide every year. Though an etiology may be found in some, one-third of stillbirth cases remain unexplained. Stillbirth clusters in families and, apart from infrequent aneuploidies and balanced translocations, few underlying inherited genes associated with stillbirth are known. Well-characterized family-based studies may aid in identifying genetic contributors to unexplained stillbirth.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eUsing the Utah Population Database, we defined pedigrees with high familial risk of stillbirth. Comprehensive phenotyping with review of primary medical records was conducted to identify stillbirth cases without identifiable causes. We generated whole-genome sequencing in seven stillborn placentas from three pedigrees, referred to hereafter as Pedigree A, Pedigree B, and Pedigree C. We performed shared genomic segments analysis to identify evidence for segregating haplotypes shared by the stillbirths to provide evidence for inherited risk.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA region at 15q26.3 was identified in two independent pedigrees with genome-wide significance in both (a 1.2 Mb segment shared by two stillbirths in Pedigree A, and a 1.8 Mb segment shared by two stillbirths in pedigree B). Four other regions reached genome-wide significance in single pedigrees at 16p13.13-p13.12, 9p13.3-p13.1, and 6p22.2-p22.1 (shared by the same two stillbirths in Pedigree B), and 0.8 Mb segment at 14q.32.2 shared by three stillbirths in Pedigree C. The identified regions are implicated in \u003cem\u003ein utero\u003c/em\u003e and postnatal development, pregnancy loss, and infertility.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eWe identified evidence for inherited risk loci in stillbirth placental genes are implicated in \u003cem\u003ein utero\u003c/em\u003e and postnatal development, pregnancy loss, and infertility. Identification of inherited genes in stillbirth risk may provide novel therapeutic targets for prevention and treatment to improve pregnancy outcomes.\u003c/p\u003e","manuscriptTitle":"Inherited Genetic Risk in Stillbirth: A Shared Genomic Segments Analysis of High-Risk Pedigrees. 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