Novel insights into the genetic architecture of pregnancy glycemic traits from 14,744 Chinese maternities.

Glycemic traits are critical indicators of maternal and fetal health during pregnancy. We performed genetic analysis for five glycemic traits in 14,744 Chinese pregnant women. Our genome-wide association study identified 25 locus-trait associations, including established links between gestational diabetes mellitus (GDM) and the genes CDKAL1 and MTNR1B. Notably, we discovered a novel association between fasting glucose during pregnancy and the ESR1 gene (estrogen receptor), which was validated by an independent study in pregnant women. The ESR1-GDM link was recently reported by the FinnGen project. Our work enhances the findings in East Asian populations and highlights the need for independent studies. Further analyses, including genetic correlation, Mendelian randomization, and transcriptome-wide association studies, provided genetic insights into the relationship between pregnancy glycemic traits and hypertension. Overall, our findings advance the understanding of genetic architecture of pregnancy glycemic traits, especially in East Asian populations.

Longitudinal integrative cell-free DNA analysis in gestational diabetes mellitus.

Gestational diabetes mellitus (GDM) presents varied manifestations throughout pregnancy and poses a complex clinical challenge. High-depth cell-free DNA (cfDNA) sequencing analysis holds promise in advancing our understanding of GDM pathogenesis and prediction. In 299 women with GDM and 299 matched healthy pregnant women, distinct cfDNA fragment characteristics associated with GDM are identified throughout pregnancy. Integrating cfDNA profiles with lipidomic and single-cell transcriptomic data elucidates functional changes linked to altered lipid metabolism processes in GDM. Transcription start site (TSS) scores in 50 feature genes are used as the cfDNA signature to distinguish GDM cases from controls effectively. Notably, differential coverage of the islet acinar marker gene PRSS1 emerges as a valuable biomarker for GDM. A specialized neural network model is developed, predicting GDM occurrence and validated across two independent cohorts. This research underscores the high-depth cfDNA early prediction and characterization of GDM, offering insights into its molecular underpinnings and potential clinical applications.

Non-Invasive Prenatal Diagnosis of Monogenic Disorders Through Bayesian- and Haplotype-Based Prediction of Fetal Genotype.

Background: Non-invasive prenatal diagnosis (NIPD) can identify monogenic diseases early during pregnancy with negligible risk to fetus or mother, but the haplotyping methods involved sometimes cannot infer parental inheritance at heterozygous maternal or paternal loci or at loci for which haplotype or genome phasing data are missing. This study was performed to establish a method that can effectively recover the whole fetal genome using maternal plasma cell-free DNA (cfDNA) and parental genomic DNA sequencing data, and validate the method's effectiveness in noninvasively detecting single nucleotide variations (SNVs), insertions and deletions (indels). Methods: A Bayesian model was developed to determine fetal genotypes using the plasma cfDNA and parental genomic DNA from five couples of healthy pregnancy. The Bayesian model was further integrated with a haplotype-based method to improve the inference accuracy of fetal genome and prediction outcomes of fetal genotypes. Five pregnancies with high risks of monogenic diseases were used to validate the effectiveness of this haplotype-assisted Bayesian approach for noninvasively detecting indels and pathogenic SNVs in fetus. Results: Analysis of healthy fetuses led to the following accuracies of prediction: maternal homozygous and paternal heterozygous loci, 96.2 ± 5.8%; maternal heterozygous and paternal homozygous loci, 96.2 ± 1.4%; and maternal heterozygous and paternal heterozygous loci, 87.2 ± 4.7%. The respective accuracies of predicting insertions and deletions at these types of loci were 94.6 ± 1.9%, 80.2 ± 4.3%, and 79.3 ± 3.3%. This approach detected pathogenic single nucleotide variations and deletions with an accuracy of 87.5% in five fetuses with monogenic diseases. Conclusions: This approach was more accurate than methods based only on Bayesian inference. Our method may pave the way to accurate and reliable NIPD.