Abstract:Over the recent years, many advances have been made in the research of the genetic factors of pregnancy complications. In this work, we use publicly available data repositories, such as the National Human Genome Research Institute GWAS Catalog, HuGE Navigator, and the UK Biobank genetic and phenotypic dataset to gain insights into molecular pathways and individual genes behind a set of pregnancy-related traits, including the most studied ones—preeclampsia, gestational diabetes, preterm birth, and placental abr… Show more
“…This is the largest GWAS of PND conducted to date; however, still far below the sample size that identified genetic variants associated with MDD, it failed to identify any genome‐wide significant SNPs. Although the most significant SNP did not meet genome‐wide significance, its location within the KAZN gene may be of relevance to the etiology of PND as this gene has previously been associated with post‐traumatic stress disorder (Nievergelt et al, 2019), endometriosis (Christofolini et al, 2019), complications of pregnancy (Barbitoff et al, 2020), albumin‐to‐creatinine‐ratio (Haplotype Reference Consortium, 2016) (which has also been associated with depression; Martens et al, 2018), and a reproductive subtype of polycystic ovary syndrome (Dapas et al, 2020).…”
Background
Distinctions between major depressive disorder (MDD) and perinatal depression (PND) reflect varying views of PND, from a unique etiological subtype of MDD to an MDD episode that happens to coincide with childbirth. This case–control study investigated genetic differences between PND and MDD outside the perinatal period (non‐perinatal depression or NPD).
Methods
We conducted a genome‐wide association study using PND cases (Edinburgh Postnatal Depression Scale score ≥ 13) from the Australian Genetics of Depression Study 2018 data (n = 3804) and screened controls (n = 6134). Results of gene‐set enrichment analysis were compared with those of women with non‐PND. For six psychiatric disorders/traits, genetic correlations with PND were evaluated, and logistic regression analysis reported polygenic score (PGS) association with both PND and NPD.
Results
Genes differentially expressed in ovarian tissue were significantly enriched (stdBeta = 0.07, p = 3.3e−04), but were not found to be associated with NPD. The genetic correlation between PND and MDD was 0.93 (SE = 0.07; p = 3.5e−38). Compared with controls, PGS for MDD are higher for PND cases (odds ratio [OR] = 1.8, confidence interval [CI] = [1.7–1.8], p = 9.5e−140) than for NPD cases (OR = 1.6, CI = [1.5–1.7], p = 1.2e−49). Highest risk is for those reporting both antenatal and postnatal depression, irrespective of prior MDD history.
Conclusions
PND has a high genetic overlap with MDD, but points of distinction focus on differential expression in ovarian tissue and higher MDD PGS, particularly for women experiencing both antenatal and postpartum PND.
“…This is the largest GWAS of PND conducted to date; however, still far below the sample size that identified genetic variants associated with MDD, it failed to identify any genome‐wide significant SNPs. Although the most significant SNP did not meet genome‐wide significance, its location within the KAZN gene may be of relevance to the etiology of PND as this gene has previously been associated with post‐traumatic stress disorder (Nievergelt et al, 2019), endometriosis (Christofolini et al, 2019), complications of pregnancy (Barbitoff et al, 2020), albumin‐to‐creatinine‐ratio (Haplotype Reference Consortium, 2016) (which has also been associated with depression; Martens et al, 2018), and a reproductive subtype of polycystic ovary syndrome (Dapas et al, 2020).…”
Background
Distinctions between major depressive disorder (MDD) and perinatal depression (PND) reflect varying views of PND, from a unique etiological subtype of MDD to an MDD episode that happens to coincide with childbirth. This case–control study investigated genetic differences between PND and MDD outside the perinatal period (non‐perinatal depression or NPD).
Methods
We conducted a genome‐wide association study using PND cases (Edinburgh Postnatal Depression Scale score ≥ 13) from the Australian Genetics of Depression Study 2018 data (n = 3804) and screened controls (n = 6134). Results of gene‐set enrichment analysis were compared with those of women with non‐PND. For six psychiatric disorders/traits, genetic correlations with PND were evaluated, and logistic regression analysis reported polygenic score (PGS) association with both PND and NPD.
Results
Genes differentially expressed in ovarian tissue were significantly enriched (stdBeta = 0.07, p = 3.3e−04), but were not found to be associated with NPD. The genetic correlation between PND and MDD was 0.93 (SE = 0.07; p = 3.5e−38). Compared with controls, PGS for MDD are higher for PND cases (odds ratio [OR] = 1.8, confidence interval [CI] = [1.7–1.8], p = 9.5e−140) than for NPD cases (OR = 1.6, CI = [1.5–1.7], p = 1.2e−49). Highest risk is for those reporting both antenatal and postnatal depression, irrespective of prior MDD history.
Conclusions
PND has a high genetic overlap with MDD, but points of distinction focus on differential expression in ovarian tissue and higher MDD PGS, particularly for women experiencing both antenatal and postpartum PND.
“…Although extensive studies have been conducted, the etiology of PE remains unknown. However, multiple factors have been implicated, including endothelial 4 , 5 , immunological 6 , and genetic factors 7 , 8 . It has been suggested that an inadequate maternal immunological response to the semiallogenic fetus and consequently abnormal placentation could cause PE.…”
Preeclampsia is a pregnancy-induced disorder that is characterized by hypertension and is a leading cause of perinatal and maternal–fetal morbidity and mortality. HLA-G is thought to play important roles in maternal–fetal immune tolerance, and the associations between HLA-G gene polymorphisms and the onset of pregnancy-related diseases have been explored extensively. Because contiguous genomic sequencing is difficult, the association between the HLA-G genotype and preeclampsia onset is controversial. In this study, genomic sequences of the HLA-G region (5.2 kb) from 31 pairs of mother–offspring genomic DNA samples (18 pairs from normal pregnancies/births and 13 from preeclampsia births) were obtained by single-molecule real-time sequencing using the PacBio RS II platform. The HLA-G alleles identified in our cohort matched seven known HLA-G alleles, but we also identified two new HLA-G alleles at the fourth-field resolution and compared them with nucleotide sequences from a public database that consisted of coding sequences that cover the 3.1-kb HLA-G gene span. Intriguingly, a potential association between preeclampsia onset and the poly T stretch within the downstream region of the HLA-G*01:01:01:01 allele was found. Our study suggests that long-read sequencing of HLA-G will provide clues for characterizing HLA-G variants that are involved in the pathophysiology of preeclampsia.
“…Many PE-associated genes and pathways have been reported [ 52 , 53 ], but it is unlikely that the etiology of PE would be explained by any one of these factors; it is highly probable that multiple associated genes act together. Expanding our knowledge of the inter-connectivity of PE-associated genes in functional pathways is thus crucial to fully elucidate the mechanisms underlying PE.…”
Preeclampsia (PE) is the serious obstetric-related disease characterized by newly onset hypertension and causes damage to the kidneys, brain, liver, and more. To investigate genes with key roles in PE’s pathogenesis and their contributions, we used a microarray dataset of normotensive and PE patients and conducted a weighted gene co-expression network analysis (WGCNA). Cyan and magenta modules that are highly enriched with differentially expressed genes (DEGs) were revealed. By using the molecular complex detection (MCODE) algorithm, we identified five significant clusters in the cyan module protein–protein interaction (PPI) network and nine significant clusters in the magenta module PPI network. Our analyses indicated that (i) human accelerated region (HAR) genes are enriched in the magenta-associated C6 cluster, and (ii) positive selection (PS) genes are enriched in the cyan-associated C3 and C5 clusters. We propose these enriched HAR and PS genes, i.e., EIF4E, EIF5, EIF3M, DDX17, SRSF11, PSPC1, SUMO1, CAPZA1, PSMD14, and MNAT1, including highly connected hub genes, HNRNPA1, RBMX, PRKDC, and RANBP2, as candidate key genes for PE’s pathogenesis. A further clarification of the functions of these PPI clusters and key enriched genes will contribute to the discovery of diagnostic biomarkers for PE and therapeutic intervention targets.
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