An IGHG1 variant exhibits polarized prevalence and confers enhanced IgG1 antibody responses against life-threatening organisms.

Evolutionary pressures sculpt population genetics, whereas immune adaptation fortifies humans against life-threatening organisms. How the evolution of selective genetic variation in adaptive immune receptors orchestrates the adaptation of human populations to contextual perturbations remains elusive. Here, we show that the G396R coding variant within the human immunoglobulin G1 (IgG1) heavy chain presents a concentrated prevalence in Southeast Asian populations. We uncovered a 190-kb genomic linkage disequilibrium block peaked in close proximity to this variant, suggestive of potential Darwinian selection. This variant confers heightened immune resilience against various pathogens and viper toxins in mice. Mechanistic studies involving severe acute respiratory syndrome coronavirus 2 infection and vaccinated individuals reveal that this variant enhances pathogen-specific IgG1+ memory B cell activation and antibody production. This G396R variant may have arisen on a Neanderthal haplotype background. These findings underscore the importance of an IGHG1 variant in reinforcing IgG1 antibody responses against life-threatening organisms, unraveling the intricate interplay between human evolution and immune adaptation.

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

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.

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

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 (R2>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 R2>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.

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.

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

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.

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

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.

Association between genetic predisposition and disease burden of stroke in China: a genetic epidemiological study.

Background: Stroke ranks second worldwide and first in China as a leading cause of death and disability. It has a polygenic architecture and is influenced by environmental and lifestyle factors. However, it remains unknown as to whether and how much the genetic predisposition of stroke is associated with disease burden. Methods: Allele frequency from the whole genome sequencing data in the Chinese Millionome Database of 141,418 individuals and trait-specific polygenic risk score models were applied to estimate the provincial genetic predisposition to stroke, stroke-related risk factors and stroke-related drug response. Disease burden including mortality, disability-adjusted life years (DALYs), years of life lost(YLLs), years lived with disability (YLDs) and prevalence in China was collected from the Global Burden Disease study. The association between stroke genetic predisposition and the epidemiological burden was assessed and then quantified in both regression-based models and machine learning-based models at a provincial resolution. Findings: Among the 30 administrative divisions in China, the genetic predisposition of stroke was characterized by a north-higher-than-south gradient (p < 0.0001). Genetic predisposition to stroke, blood pressure, body mass index, and alcohol use were strongly intercorrelated (rho >0.6; p < 0.05 after Bonferroni correction for each comparison). Genetic risk imposed an independent effect of approximately 1-6% on mortality, DALYs and YLLs. Interpretation: The distribution pattern of stroke genetic predisposition is different at a macroscopic level, and it subtly but significantly impacts the epidemiological burden. Further research is warranted to identify the detailed aetiology and potential translation into public health measures. Funding: Beijing Municipal Science and Technology Commission (Z191100006619106), CAMS Innovation Fund for Medical Sciences (CAMS-I2M, 2023-I2M-1-001), the National High Level Hospital Clinical Research Funding (2022-GSP-GG-17), National Natural Science Foundation of China (32000398, 32171441 to X.J.), Natural Science Foundation of Guangdong Province, China (2017A030306026 to X.J.), and National Key R&D Program of China (2022YFC2502402).

The trans-omics landscape of COVID-19.

The outbreak of coronavirus disease 2019 (COVID-19) is a global health emergency. Various omics results have been reported for COVID-19, but the molecular hallmarks of COVID-19, especially in those patients without comorbidities, have not been fully investigated. Here we collect blood samples from 231 COVID-19 patients, prefiltered to exclude those with selected comorbidities, yet with symptoms ranging from asymptomatic to critically ill. Using integrative analysis of genomic, transcriptomic, proteomic, metabolomic and lipidomic profiles, we report a trans-omics landscape for COVID-19. Our analyses find neutrophils heterogeneity between asymptomatic and critically ill patients. Meanwhile, neutrophils over-activation, arginine depletion and tryptophan metabolites accumulation correlate with T cell dysfunction in critical patients. Our multi-omics data and characterization of peripheral blood from COVID-19 patients may thus help provide clues regarding pathophysiology of and potential therapeutic strategies for COVID-19.