PCGA: a comprehensive web server for phenotype-cell-gene association analysis

Shared genetic aetiology of respiratory diseases: a genome-wide multitraits association analysis

OBJECTIVE

This study aims to explore the common genetic basis between respiratory diseases and to identify shared molecular and biological mechanisms.

METHODS

This genome-wide pleiotropic association study uses multiple statistical methods to systematically analyse the shared genetic basis between five respiratory diseases (asthma, chronic obstructive pulmonary disease, idiopathic pulmonary fibrosis, lung cancer and snoring) using the largest publicly available genome wide association studies summary statistics. The missions of this study are to evaluate global and local genetic correlations, to identify pleiotropic loci, to elucidate biological pathways at the multiomics level and to explore causal relationships between respiratory diseases. Data were collected from 27 November 2022 to 30 March 2023 and analysed from 14 April 2023 to 13 July 2023.

MAIN OUTCOMES AND MEASURES

The primary outcomes are shared genetic loci, pleiotropic genes, biological pathways and estimates of genetic correlations and causal effects.

RESULTS

Significant genetic correlations were found for 10 paired traits in 5 respiratory diseases. Cross-Phenotype Association identified 12 400 significant potential pleiotropic single-nucleotide polymorphism at 156 independent pleiotropic loci. In addition, multitrait colocalisation analysis identified 15 colocalised loci and a subset of colocalised traits. Gene-based analyses identified 432 potential pleiotropic genes and were further validated at the transcriptome and protein levels. Both pathway enrichment and single-cell enrichment analyses supported the role of the immune system in respiratory diseases. Additionally, five pairs of respiratory diseases have a causal relationship.

CONCLUSIONS AND RELEVANCE

This study reveals the common genetic basis and pleiotropic genes among respiratory diseases. It provides strong evidence for further therapeutic strategies and risk prediction for the phenomenon of respiratory disease comorbidity.

A conserved protein tyrosine phosphatase, PTPN-22, functions in diverse developmental processes in C. elegans

Protein tyrosine phosphatases non-receptor type (PTPNs) have been studied extensively in the context of the adaptive immune system; however, their roles beyond immunoregulation are less well explored. Here we identify novel functions for the conserved C. elegans phosphatase PTPN-22, establishing its role in nematode molting, cell adhesion, and cytoskeletal regulation. Through a non-biased genetic screen, we found that loss of PTPN-22 phosphatase activity suppressed molting defects caused by loss-of-function mutations in the conserved NIMA-related kinases NEKL-2 (human NEK8/NEK9) and NEKL-3 (human NEK6/NEK7), which act at the interface of membrane trafficking and actin regulation. To better understand the functions of PTPN-22, we carried out proximity labeling studies to identify candidate interactors of PTPN-22 during development. Through this approach we identified the CDC42 guanine-nucleotide exchange factor DNBP-1 (human DNMBP) as an in vivo partner of PTPN-22. Consistent with this interaction, loss of DNBP-1 also suppressed nekl-associated molting defects. Genetic analysis, co-localization studies, and proximity labeling revealed roles for PTPN-22 in several epidermal adhesion complexes, including C. elegans hemidesmosomes, suggesting that PTPN-22 plays a broad role in maintaining the structural integrity of tissues. Localization and proximity labeling also implicated PTPN-22 in functions connected to nucleocytoplasmic transport and mRNA regulation, particularly within the germline, as nearly one-third of proteins identified by PTPN-22 proximity labeling are known P granule components. Collectively, these studies highlight the utility of combined genetic and proteomic approaches for identifying novel gene functions.

Dissecting the high-resolution genetic architecture of complex phenotypes by accurately estimating gene-based conditional heritability

Despite extensive research on global heritability estimation for complex traits, few methods accurately dissect local heritability. A precise local heritability estimate is crucial for high-resolution mapping in genetics. Here, we report the effective heritability estimator (EHE) that can use p values from genome-wide association studies (GWASs) for local heritability estimation by directly converting marginal heritability estimates of SNPs to a non-redundant heritability estimate of a gene or a small genomic region. EHE provides higher accuracy and precision for local heritability estimation among seven compared methods. Importantly, EHE can be applied to estimate the conditional heritability of nearby genes, where redundant heritability among the genes can also be removed further. The conditional estimation can be guided by tissue-specific expression profiles (or other functional scores) to prioritize and quantify more functionally important genes of complex phenotypes. Applying EHE to 42 complex phenotypes from the UK Biobank, we revealed the existence of two types of distinct genetic architectures for various complex phenotypes and found that highly pleiotropic genes are not enriched for more heritability compared to other candidate susceptibility genes. EHE provides an accurate and robust way to dissect the genetic architecture of complex phenotypes.

Causal effect of COVID-19 on Alzheimer's disease: A Mendelian randomization study

It was reported that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection may cause brain size reduction and cognitive decline. Whether COVID-19 may contribute to the development of Alzheimer's disease (AD) is not known. We conducted genetic correlation and Mendelian randomization (MR) analyses to assess genetic relationships and potential causal associations between AD and three COVID-19 outcomes (SARS-CoV-2 infection, COVID-19 hospitalization, and critical COVID-19) by utilizing genome-wide association study datasets on these traits. A map of COVID-19-driven molecular pathways was constructed to investigate potential mechanisms underlying the COVID-19 and AD connection. Genetic correlation analyses indicated that AD had a significant positive genetic correlation with hospitalized COVID-19 (rg  = 0.271). The MR analysis from the inverse-variance-weighted model showed that genetic liabilities to hospitalized COVID-19 (odds ratio: 1.02, 95% confidence interval: 1.01-1.03) and critical COVID-19 (1.01, 1.00-1.02) were associated with an increased risk for AD. However, no causal effect of genetic liability to SARS-CoV-2 infection on AD was detected (1.03, 0.97-1.09). A total of 60 functionally interconnected genes were reported to mediate the COVID-19-AD connection, which showed functional enrichment in immunity-related pathways and tissue enrichment in the lung and brain. Our study suggests that severe COVID-19 may contribute to the development of AD, while suffering a mild case of COVID-19 may not increase the risk for AD. The influence of COVID-19 on AD may be mediated by immunity-related pathways acting predominantly in the lung and brain.