comprehensive-evaluation-genotype-imputation-tools-for-ultra-low

Comprehensive Evaluation of Genotype Imputation Tools for Ultra-low-depth Whole-Genome Sequencing Data

Ultra-low-depth sequencing (ULDS) is a cost-effective strategy for large-scale genomic studies, but its utility hinges on accurate genotype imputation. ...

BGI Research

Precision medicine depends on high-accuracy individual-level genotype data. However, the whole-genome sequencing (WGS) is still not suitable for gigantic studies due to budget constraints. It is particularly important to construct highly accurate haplotype reference panel for genotype imputation. In this study, we used 10 000 samples with medium-depth WGS to construct a reference panel that we named the CKB reference panel. By imputing microarray datasets, it showed that the CKB panel outperformed compared panels in terms of both the number of well-imputed variants and imputation accuracy. In addition, we have completed the imputation of 100 706 microarrays with the CKB panel, and the after-imputed data is the hitherto largest whole genome data of the Chinese population. Furthermore, in the GWAS analysis of real phenotype height, the number of tested SNPs tripled and the number of significant SNPs doubled after imputation. Finally, we developed an online server for offering free genotype imputation service based on the CKB reference panel (https://db.cngb.org/imputation/). We believe that the CKB panel is of great value for imputing microarray or low-coverage genotype data of Chinese population, and potentially mixed populations. The imputation-completed 100 706 microarray data are enormous and precious resources of population genetic studies for complex traits and diseases.

A high-resolution haplotype-resolved Reference panel constructed from the China Kadoorie Biobank Study.

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.

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

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.

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

Monitoring biochemical phenotypes during pregnancy is vital for maternal and fetal health, allowing early detection and management of pregnancy-related conditions to ensure safety for both. Here, we conducted a genetic analysis of 104 pregnancy phenotypes in 20,900 Chinese women. The genome-wide association study (GWAS) identified a total of 410 trait-locus associations, with 71.71% reported previously. Among the 116 novel hits for 45 phenotypes, 83 were successfully replicated. Among them, 31 were defined as potentially pregnancy-specific associations, including creatine and HELLPAR and neutrophils and ESR1, with subsequent analysis revealing enrichments in estrogen-related pathways and female reproductive tissues. The partitioning heritability underscored the significant roles of fetal blood, embryoid bodies, and female reproductive organs in pregnancy hematology and birth outcomes. Pathway analysis confirmed the intricate interplay of hormone and immune regulation, metabolism, and cell cycle during pregnancy. This study contributes to the understanding of genetic influences on pregnancy phenotypes and their implications for maternal health.

Genetic analyses of 104 phenotypes in 20,900 Chinese pregnant women reveal pregnancy-specific discoveries.

Non-invasive prenatal testing (NIPT) not only enables the detection of chromosomal anomalies in fetuses but also generates vast amounts of ultra-low-depth sequencing data, which can be leveraged for population genomic studies. Here, we present a protocol designed for massive ultra-low-depth sequencing datasets. We detail the steps for data processing, quality control, and genotype imputation, followed by genome-wide association study (GWAS) and post-GWAS analyses. This protocol applies to a wide range of ultra-low-depth sequencing studies, extending beyond data from NIPT. For complete details on the use and execution of this profile, please refer to Xiao et al..

Protocol for genetic analysis of population-scale ultra-low-depth sequencing data.

Next-generation sequencing has greatly advanced genomics, enabling large-scale studies of population genetics and complex traits. Genomic DNA (gDNA) from white blood cells has traditionally been the main data source, but cell-free DNA (cfDNA), found in bodily fluids as fragmented DNA, is increasingly recognized as a valuable biomarker in clinical and genetic studies. However, a direct comparison between cfDNA and gDNA has not been fully explored. In this study, we analyzed cfDNA and gDNA from 186 healthy individuals, using the same sequencing platform. We compared sequencing quality, variant detection, allele frequencies (AF), genotype concordance, population structure, and genomic association results (genome-wide association study and expression quantitative trait locus). While cfDNA showed higher duplication rates and lower effective sequencing depth, both DNA types displayed similar quality metrics at the same depth. We also observed that significant depth differences between cfDNA and gDNA were mainly found in centromeric regions. While gDNA identified more variants with more uniform coverage, AF spectra, population structure, and genomic associations were largely consistent between the two DNA types. This study provides a detailed comparison of cfDNA and gDNA, highlighting the potential of cfDNA as an alternative to gDNA in genomic research. Our findings could serve as a reference for future studies on cfDNA and gDNA.

Cell-free DNA as a potential alternative to genomic DNA in genetic studies.

In population-scale genomic variation studies based on shallow whole genome sequencing, pangenomes have become an effective tool for identifying population-specific single-nucleotide polymorphisms and indels. Extending these advantages to copy number variation (CNV), however, remains challenging due to two unresolved issues. First, current pangenome frameworks exhibit pronounced population-representation bias arising from uneven sampling across populations. As the number of samples increases, the pangenome tends to capture variations primarily from majority populations while suppressing signals from minority populations. Second, in population-scale genomic variation analyses, common but benign population-specific copy number polymorphisms (CNPs) frequently obscure pathogenic CNVs. Existing pangenome frameworks lack dedicated mechanisms for representing CNPs and CNVs, limiting their ability to distinguish pathogenic CNVs from benign, population-specific CNPs. In this study, we present PangenomeX, a graph-convolutional pangenome framework tailored for low-coverage, population-scale CNV analysis. To address CNP representation, we embed known CNPs as prior knowledge into the pangenome graph and construct a CNV relationship network guided by a phylogenetic tree. A graph convolutional network (GCN) then learns the interactions between CNV and CNP nodes. To mitigate population-representation bias, the GCN aggregates information from only one- and two-hop neighborhoods, preserving local population context while preventing majority group signals from dominating. Evaluation on simulated cohorts and 561 real samples shows that PangenomeX distinguishes pathogenic CNVs from common population CNPs markedly better than existing methods. Overall, PangenomeX offers a methodological blueprint for large-cohort variant screening and provides a practical path for bringing graph-based genomics into clinical practice.

PangenomeX: a graph convolutional network-based pangenome framework for unbiased population-scale genomic variation analysis.

Shallow whole-genome sequencing (sWGS), a rapid and cost-effective sequencing technology, has gradually been widely adopted for CNV analyses. However, with genome‑wide coverage of only 0.1-5×, sWGS data display a pronounced zero‑inflation phenomenon-a large fraction of loci has zero sequencing reads. Zero inflation causes read counts to fluctuate by several‑fold between adjacent windows. As a result, random upward blips in coverage can be misinterpreted as copy‑number gains (false positives), and true deletions often become indistinguishable from pervasive zero‑coverage noise. In addition, existing CNV detection tools developed for sWGS data often struggle to adapt across different CNV sizes. These combined effects severely constrain the accuracy of CNV inference.

Article	Year
A high-resolution haplotype-resolved Reference panel constructed from the China Kadoorie Biobank Study. Nucleic acids research\| PubMed ID: 37870428	2023
Longitudinal integrative cell-free DNA analysis in gestational diabetes mellitus. Cell reports. Medicine\| PubMed ID: 39059385	2024
Novel insights into the genetic architecture of pregnancy glycemic traits from 14,744 Chinese maternities. Cell genomics\| PubMed ID: 39389014	2024
Genetic analyses of 104 phenotypes in 20,900 Chinese pregnant women reveal pregnancy-specific discoveries. Cell genomics\| PubMed ID: 39389017	2024
Protocol for genetic analysis of population-scale ultra-low-depth sequencing data. STAR protocols\| PubMed ID: 39826115	2025
Cell-free DNA as a potential alternative to genomic DNA in genetic studies. NAR genomics and bioinformatics\| PubMed ID: 40918064	2025
PangenomeX: a graph convolutional network-based pangenome framework for unbiased population-scale genomic variation analysis. Briefings in bioinformatics\| PubMed ID: 41115209	2025
ZIPcnv: accurate and efficient inference of copy number variations from shallow whole-genome sequencing. Bioinformatics (Oxford, England)\| PubMed ID: 41138169	2025

Jingyu Zeng

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