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. ...

University of Chinese Academy of Sciences

BGI Research

The COVID-19 pandemic has caused over 220 million infections and 4.5 million deaths worldwide. Current risk factor cannot fully explain the diversity in disease severity. Here, we present a comprehensive analysis of a broad range of patients' laboratory and clinical assessments to investigate the genetic contributions to COVID-19 severity. By performing GWAS analysis, we discovered several concrete associations for laboratory traits and used Mendelian randomization (MR) analysis to further investigate the causality of traits on disease severity. Two causal traits, WBC counts and cholesterol levels, were identified based on MR study, and their functional genes are located at genes  complex and , respectively. Our gene-based analysis and GSEA revealed four interferon pathways, including  and . We hope that our work will contribute to studying the genetic mechanisms of disease and serve as a useful reference for COVID-19 diagnosis and treatment.

A Chinese host genetic study discovered IFNs and causality of laboratory traits on COVID-19 severity.

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.

Understanding tissue architecture and niche-specific microenvironments in spatially resolved transcriptomics (SRT) requires  annotation and labeling of cells. Effective spatial visualization of these data demands appropriate colorization of numerous cell types. However, current colorization frameworks often inadequately account for the spatial relationships between cell types. This results in perceptual ambiguity in neighboring cells of biological distinct types, particularly in complex environments such as brain or tumor. To address this, we introduce Spaco, a potent tool for spatially aware colorization. Spaco utilizes the Degree of Interlacement metric to construct a weighted graph that evaluates the spatial relationships among different cell types, refining color assignments. Furthermore, Spaco incorporates an adaptive palette selection approach to amplify chromatic distinctions. When benchmarked on four diverse datasets, Spaco outperforms existing solutions, capturing complex spatial relationships and boosting visual clarity. Spaco ensures broad accessibility by accommodating color vision deficiency and offering open-accessible code in both Python and R.

Spaco: A comprehensive tool for coloring spatial data at single-cell resolution.

Van der Waals (vdW) ferromagnetic materials have emerged as a promising platform for the development of 2D spintronic devices. However, studies to date are restricted to vdW ferromagnetic materials with low Curie temperature (T) and small magnetic anisotropy. Here, a chemical vapor transport method is developed to synthesize a high-quality room-temperature ferromagnet, FeGaTe (c-FeGaTe), which boasts a high T = 356 K and large perpendicular magnetic anisotropy. Due to the planar symmetry breaking, an unconventional room-temperature antisymmetric magnetoresistance (MR) is first observed in c-FeGaTe devices with step features, manifesting as three distinctive states of high, intermediate, and low resistance with the sweeping magnetic field. Moreover, the modulation of the antisymmetric MR is demonstrated by controlling the height of the surface steps. This work provides new routes to achieve magnetic random storage and logic devices by utilizing the room-temperature thickness-controlled antisymmetric MR and further design room-temperature 2D spintronic devices based on the vdW ferromagnet c-FeGaTe.

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Non-invasive prenatal testing (NIPT) is a quite popular approach for detecting fetal genomic aneuploidies. However, due to the limitations on sequencing read length and coverage, NIPT suffers a bottleneck on further improving performance and conducting earlier detection. The errors mainly come from reference biases and population polymorphism. To break this bottleneck, we proposed NIPT-PG, which enables the NIPT algorithm to learn from population data. A pan-genome model is introduced to incorporate variant and polymorphic loci information from tested population. Subsequently, we proposed a sequence-to-graph alignment method, which considers the read mis-match rates during the mapping process, and an indexing method using hash indexing and adjacency lists to accelerate the read alignment process. Finally, by integrating multi-source aligned read and polymorphic sites across the pan-genome, NIPT-PG obtains a more accurate z-score, thereby improving the accuracy of chromosomal aneuploidy detection. We tested NIPT-PG on two simulated datasets and 745 real-world cell-free DNA sequencing data sets from pregnant women. Results demonstrate that NIPT-PG outperforms the standard z-score test. Furthermore, combining experimental and theoretical analyses, we demonstrate the probably approximately correct learnability of NIPT-PG. In summary, NIPT-PG provides a new perspective for fetal chromosomal aneuploidies detection. NIPT-PG may have broad applications in clinical testing, and its detection results can serve as a reference for false positive samples approaching the critical threshold.

NIPT-PG: empowering non-invasive prenatal testing to learn from population genomics through an incremental pan-genomic approach.

Layered or chain materials have received significant research attention owing to their interesting physical properties, which can dramatically change when the material is thinned from bulk (three-dimensional) to thin two-dimensional sheet or one-dimensional (1D) chain form. Materials with the stoichiometry AX with A = Si or Ge and X = S or Se form an especially intriguing semiconducting class. For example, bulk silicon dichalcogenides (SiX) consist of 1D chains held together by van der Waals forces. Although this structural configuration has the potential to reveal interesting physical phenomena within the 1D limit, obtaining SiX single chains has been challenging. We here examine experimentally and theoretically SiX materials in the low chain number limit. Carbon nanotubes serve as growth templates and stabilize and protect the structures, and atomic-resolution scanning transmission electron microscopy directly identifies the atomic structure. Two distinct chain structures are observed for SiX. SiGeSSe quaternary alloy chains are also synthesized and characterized, demonstrating tunable semiconducting properties at the atomic-chain level. Density functional theory calculations reveal that the band gap of these alloy chains can be widely tuned through composition engineering. This work offers the possibilities for synthesizing and controlling semiconductor compositions at the single-chain limit to tailor material properties.

SiX (X = S, Se) Single Chains and (Si-Ge)X Quaternary Alloys.

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.

Metabolites are key indicators of health and therapeutic targets, but their genetic underpinnings during pregnancy-a critical period for human reproduction-are largely unexplored. Using genetic data from non-invasive prenatal testing, we performed a genome-wide association study on 84 metabolites, including 37 amino acids, 24 elements, 13 hormones, and 10 vitamins, involving 34,394 pregnant Chinese women, with sample sizes ranging from 6,394 to 13,392 for specific metabolites. We identified 53 metabolite-gene associations, 23 of which are novel. Significant differences in genetic effects between pregnant and non-pregnant women were observed for 16.7%-100% of these associations, indicating gene-environment interactions. Additionally, 50.94% of genetic associations exhibited pleiotropy among metabolites and between six metabolites and eight pregnancy phenotypes. Mendelian randomization revealed potential causal relationships between seven maternal metabolites and 15 human traits and diseases. These findings provide new insights into the genetic basis of maternal plasma metabolites during pregnancy.

Genome-wide association study of maternal plasma metabolites during pregnancy.

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) employs ultra-low-pass sequencing of maternal plasma cell-free DNA to detect fetal trisomy. Its global adoption has established NIPT as a large human genetic resource for exploring genetic variations and their associations with phenotypes. Here, we present methods for analyzing large-scale, low-depth NIPT data, including customized algorithms and software for genetic variant detection, genotype imputation, family relatedness, population structure inference, and genome-wide association analysis of maternal genomes. Our results demonstrate accurate allele frequency estimation and high genotype imputation accuracy (R>0.84) for NIPT sequencing depths from 0.1× to 0.3×. We also achieve effective classification of duplicates and first-degree relatives, along with robust principal-component analysis. Additionally, we obtain an R>0.81 for estimating genetic effect sizes across genotyping and sequencing platforms with adequate sample sizes. These methods offer a robust theoretical and practical foundation for utilizing NIPT data in medical genetic research.

Utilizing non-invasive prenatal test sequencing data for human genetic investigation.

As a noninvasive biomarker, cell-free DNA (cfDNA) has achieved remarkable success in clinical applications. Notably, cfDNA is essentially DNA, and conducting whole-genome sequencing (WGS) can yield a wealth of genetic information. These invaluable data should not be confined to one-time use; instead, they should be leveraged for more comprehensive population genetic analysis, including genetic variation spectrum, population structure and genetic selection, and genome-wide association studies (GWASs), among others. Such research findings can, in turn, facilitate clinical practice, enabling more advanced and accurate disease predictions. This review explores the advantages, challenges, and current research areas of cfDNA in population genetics. We hope that this review can serve as a new chapter in the repurposing of cfDNA sequence data generated from clinical testing in population genetics.

Cell-free DNA from clinical testing as a resource of population genetic analysis.

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.

Low-dimensional magnetic materials have garnered significant interest due to their unique physical properties and potential applications. Nevertheless, the synthesis of one-dimensional (1D) magnetic materials presents challenges, and the properties of these 1D materials at the single-chain limit have not been well investigated. We here explore experimentally and theoretically 1D CrX (X= Cl, Br, I) magnetic single chains residing within carbon nanotubes. Single chains of CrX are confirmed by the atomic-resolution scanning transmission electron microscopy (STEM) imaging and spectroscopy analysis. Electron energy loss spectroscopy clearly reveals the high-spin state of the Cr atoms within the chain. Notably, we present the first precise measurement and analysis of Cr spin state at the single-chain level, revealing that these spin states can be controlled by the local atomic bonding configuration (CrX versus CrX phases). Density functional theory (DFT) calculations support the structural stability and provide the magnetic and electronic properties of the 1D CrX chains.

Robust High-Spin State in One-Dimensional CrX (X = Cl, Br, I) at the Single-Chain Limit.

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.

Article	Year
A Chinese host genetic study discovered IFNs and causality of laboratory traits on COVID-19 severity. iScience\| PubMed ID: 34608450	2021
A high-resolution haplotype-resolved Reference panel constructed from the China Kadoorie Biobank Study. Nucleic acids research\| PubMed ID: 37870428	2023
Spaco: A comprehensive tool for coloring spatial data at single-cell resolution. Patterns (New York, N.Y.)\| PubMed ID: 38487801	2024
[Not Available]. Advanced materials (Deerfield Beach, Fla.)\| PubMed ID: 38631700	2024
NIPT-PG: empowering non-invasive prenatal testing to learn from population genomics through an incremental pan-genomic approach. Briefings in bioinformatics\| PubMed ID: 38836702	2024
SiX (X = S, Se) Single Chains and (Si-Ge)X Quaternary Alloys. ACS nano\| PubMed ID: 38920317	2024
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
Genome-wide association study of maternal plasma metabolites during pregnancy. Cell genomics\| PubMed ID: 39389015	2024
Genetic analyses of 104 phenotypes in 20,900 Chinese pregnant women reveal pregnancy-specific discoveries. Cell genomics\| PubMed ID: 39389017	2024
Utilizing non-invasive prenatal test sequencing data for human genetic investigation. Cell genomics\| PubMed ID: 39389018	2024
Cell-free DNA from clinical testing as a resource of population genetic analysis. Trends in genetics : TIG\| PubMed ID: 39578178	2025
Protocol for genetic analysis of population-scale ultra-low-depth sequencing data. STAR protocols\| PubMed ID: 39826115	2025
Robust High-Spin State in One-Dimensional CrX (X = Cl, Br, I) at the Single-Chain Limit. Journal of the American Chemical Society\| PubMed ID: 40663099	2025
Cell-free DNA as a potential alternative to genomic DNA in genetic studies. NAR genomics and bioinformatics\| PubMed ID: 40918064	2025

Linxuan Li

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