The National Genomics Data Center (NGDC), part of the China National Center for Bioinformation (CNCB), provides a family of database resources to support global academic and industrial communities. With the explosive accumulation of multi-omics data generated at an unprecedented rate, CNCB-NGDC constantly expands and updates core database resources by big data archive, integrative analysis and value-added curation. In the past year, efforts have been devoted to integrating multiple omics data, synthesizing the growing knowledge, developing new resources and upgrading a set of major resources. Particularly, several database resources are newly developed for infectious diseases and microbiology (MPoxVR, KGCoV, ProPan), cancer-trait association (ASCancer Atlas, TWAS Atlas, Brain Catalog, CCAS) as well as tropical plants (TCOD). Importantly, given the global health threat caused by monkeypox virus and SARS-CoV-2, CNCB-NGDC has newly constructed the monkeypox virus resource, along with frequent updates of SARS-CoV-2 genome sequences, variants as well as haplotypes. All the resources and services are publicly accessible at https://ngdc.cncb.ac.cn.

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Novel coronary pneumonia (COVID-19) is a respiratory distress syndrome caused by a new type of coronavirus. The disease began to break out in Wuhan, China, at the end of 2019, and officially entered the global pandemic phase in April 2020. Until now, it is still in a pandemic stage in a number of countries. 1-3 So far, the cumulative number of deaths due to COVID-19 worldwide has reached 1 870 000. The COVID-19 is the direct cause of the current global political and economic turmoil. From an epidemiological perspective, an important feature of COVID-19 is that a considerable proportion of population can turn into a state of asymptomatic or mild illness for a long time after being infected with the virus, while a small proportion quickly enter the moderate or severe stage. 4

Amyotrophic lateral sclerosis (ALS) is a fatal progressive multisystem disorder with limited therapeutic options. Although genome-wide association studies (GWASs) have revealed multiple ALS susceptibility loci, the exact identities of causal variants, genes, cell types, tissues, and their functional roles in the development of ALS remain largely unknown. Here, we reported a comprehensive post-GWAS analysis of the recent large ALS GWAS (n = 80,610), including functional mapping and annotation (FUMA), transcriptome-wide association study (TWAS), colocalization (COLOC), and summary data-based Mendelian randomization analyses (SMR) in extensive multi-omics datasets. Gene property analysis highlighted inhibitory neuron 6, oligodendrocytes, and GABAergic neurons (Gad1/Gad2) as functional cell types of ALS and confirmed cerebellum and cerebellar hemisphere as functional tissues of ALS. Functional annotation detected the presence of multiple deleterious variants at three loci (9p21.2, 12q13.3, and 12q14.2) and highlighted a list of SNPs that are potentially functional. TWAS, COLOC, and SMR identified 43 genes at 24 loci, including 23 novel genes and 10 novel loci, showing significant evidence of causality. Integrating multiple lines of evidence, we further proposed that rs2453555 at 9p21.2 and rs229243 at 14q12 functionally contribute to the development of ALS by regulating the expression of C9orf72 in pituitary and SCFD1 in skeletal muscle, respectively. Together, these results advance our understanding of the biological etiology of ALS, feed into new therapies, and provide a guide for subsequent functional experiments.

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A broad range of complex phenotypes are related to dysfunctions in brain (hereafter referred to as brain-related traits), including various mental and behavioral disorders and diseases of the nervous system. These traits in general share overlapping symptoms, pathogenesis, and genetic components. Here, we present Brain Catalog (https://ngdc.cncb.ac.cn/braincatalog), a comprehensive database aiming to delineate the genetic components of more than 500 GWAS summary statistics datasets for brain-related traits from multiple aspects. First, Brain Catalog provides results of candidate causal variants, causal genes, and functional tissues and cell types for each trait identified by multiple methods using comprehensive annotation datasets (58 QTL datasets spanning 6 types of QTLs). Second, Brain Catalog estimates the SNP-based heritability, the partitioning heritability based on functional annotations, and genetic correlations among traits. Finally, through bidirectional Mendelian randomization analyses, Brain Catalog presents inference of risk factors that are likely causal to each trait. In conclusion, Brain Catalog presents a one-stop shop for the genetic components of brain-related traits, potentially serving as a valuable resource for worldwide researchers to advance the understanding of how GWAS signals may contribute to the biological etiology of brain-related traits.