Biology subjects

Sha, Q.

Publications and source records attributed to Sha, Q..

11 recordsLinked to original sources

bioRxiv2025

Whole-genome DNA methylation profiling in COVID-19 positive patients reveals alterations in pathways linked to neurological dysfunction

BackgroundSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly transmissible RNA betacoronavirus, causing coronavirus disease-19 (COVID-19). Infection with SARS-CoV-2 can result in a broad spectrum of clinical outcomes, ranging from asymptomatic or mild to a severe, deadly illness. Emerging evidence suggests SARS-CoV-2 affects host gene regulation through epigenetic mechanisms, such as DNA methylation, potentially contributing to immune dysregulation and post-acute sequelae, including neurological and psychiatric disorders. However, the extent and functional relevance of these epigenetic changes remain uncertain. Methods and resultsWe employed whole-genome bisulfite sequencing to profile DNA methylation in peripheral blood from SARS-CoV-2-positive patients across a spectrum of symptom severity, ranging from asymptomatic to severe (n=101), in comparison to SARS-CoV-2-negative individuals (n=105). We observed a widespread hypomethylation in the genomes of infected individuals, which was more pronounced in severe cases. Notably, we identified differentially methylated genes in patients with mild (19 genes), moderate (19 genes), and severe (35 genes) symptoms. These genes included those involved in canonical immune responses as well as known to be linked to neurodegenerative diseases. Subsequent pathway enrichment analysis further supported the significant association between the differentially methylated genes and those implicated in Alzheimers and Parkinsons disease, as well as neuropsychiatric conditions, suggesting potential epigenetic links between acute SARS-CoV-2 infection and long-term neurological outcomes. This is one of the first studies to comprehensively map severity-stratified genome-wide DNA methylation changes in COVID-19 patients. ConclusionOur findings underscore the potential importance of epigenetic regulation in the acute responses to SARS-CoV-2 infection and highlight an overlap with epigenetic mechanisms relevant for neuropsychiatric disease processes.

neuroscience↗

bioRxiv2023

Single-cell transcriptomic analysis of immune cell dynamics in the healthy human endometrium

The microenvironment of the endometrial immune system is crucial to the success of placental implantation and healthy pregnancy. However, the functionalities of immune cells across various stages of the reproductive cycle have yet to be fully comprehended. To address this, we conducted advanced bioinformatic analyses on 230,049 high-quality single-cell transcriptomes from healthy endometrial samples obtained during the proliferative, secretory, early pregnancy, and late pregnancy stages. Our investigation revealed that proliferative natural killer (NK) cells, a potential source of endometrial NK cells, exhibit the most robust proliferative and differentiation potential during non-pregnant stages. During early pregnancy, NK cells display high oxidative phosphorylation metabolism activity, and together with macrophages and T cells, exhibit a strong type II interferon response. Based on our cell-cell interaction analyses, we identify a large majority of interaction pairs to occur in late pregnancy. Finally, we explored the correlation between stage-specific alterations in transcriptomics and the risk genes of common reproductive diseases, unveiling that MHC class I/II molecules, along with TGFBR1, exhibited the potential to serve as biomarkers. Our study provides insights into the dynamics of the endometrial immune microenvironment during different reproductive cycle stages, thus serving as a reference for detecting pathological changes during pregnancy.

bioinformatics↗

bioRxiv2023

LSM14B is essential for mitochondrial clustering in the oocyte meiosis

As oocyte meiotic maturation, they undergo two successive meiotic M phases, notably lacking an intervening interphase phase. During these M phases, oocytes remain transcriptionally quiescent, and we now know that "translational repressed mRNAs" are stored in a structure called the mitochondria associated ribonucleoprotein domain (MARDO). LSM14B is one of the abundant proteins of MARDO, and is predicted to bind mRNA, but its function(s) remain elusive. Here, we demonstrate that LSM14B functions to promote MARDO assembly in mouse oocytes. We also found that LSM14B knockout female mice are infertile, and show that the knockout oocytes fail to enter meiosis II, instead entering an aberrant interphase-like stage. Finally, we show that the failure of oocyte maturation results from decreased expression of Cyclin B1. Our study has revealed that the RNA-binding protein LSM14B modulates MARDO assembly and is essential for oocyte meiotic maturation.

developmental biology↗

bioRxiv2023

A novel method for multiple phenotype association studies based on genotype and phenotype network

Joint analysis of multiple correlated phenotypes for genome-wide association studies (GWAS) can identify and interpret pleiotropic loci which are essential to understand pleiotropy in diseases and complex traits. Meanwhile, constructing a network based on associations between phenotypes and genotypes provides a new insight to analyze multiple phenotypes, which can explore whether phenotypes and genotypes might be related to each other at a higher level of cellular and organismal organization. In this paper, we first develop a bipartite signed network by linking phenotypes and genotypes into a Genotype and Phenotype Network (GPN). The GPN can be constructed by a mixture of quantitative and qualitative phenotypes and is applicable to binary phenotypes with extremely unbalanced case-control ratios in large-scale biobank datasets. We then apply a powerful community detection method to partition phenotypes into disjoint network modules based on GPN. Finally, we jointly test the association between multiple phenotypes in a network module and a single nucleotide polymorphism (SNP). Simulations and analyses of 72 complex traits in the UK Biobank show that multiple phenotype association tests based on network modules detected by GPN are much more powerful than those without considering network modules. The newly proposed GPN provides a new insight to investigate the genetic architecture among different types of phenotypes. Multiple phenotypes association studies based on GPN are improved by incorporating the genetic information into the phenotype clustering. Notably, it might broaden the understanding of genetic architecture that exists between diagnoses, genes, and pleiotropy.

genetics↗

bioRxiv2022

HCLC-FC: a novel statistical method for phenome-wide association studies

The emergence of genetic data coupled to longitudinal electronic medical records (EMRs) offers the possibility of phenome-wide association studies (PheWAS). In PheWAS, the whole phenome can be divided into numerous phenotypic categories according to the genetic architecture across phenotypes. Currently, statistical analyses for PheWAS are mainly univariate analyses, which test the association between one genetic variant and one phenotype at a time. In this article, we derived a novel and powerful multivariate method for PheWAS. The proposed method involves three steps. In the first step, we apply the bottom-up hierarchical clustering method to partition a large number of phenotypes into disjoint clusters within each phenotypic category. In the second step, the clustering linear combination method is used to combine test statistics within each category based on the phenotypic clusters and obtain p-values from each phenotypic category. In the third step, we propose a new false discovery rate (FDR) control approach. We perform extensive simulation studies to compare the performance of our method with that of other existing methods. The results show that our proposed method controls FDR very well and outperforms other methods we compared with. We also apply the proposed approach to a set of EMR-based phenotypes across more than 300,000 samples from UK Biobank. We find that the proposed approach not only can well-control FDR at a nominal level but also successfully identify 1,244 significant SNPs that are reported to be associated with some phenotypes in the GWAS catalog. Our open-access tools and instructions on how to implement HCLC-FC are available at https://github.com/XiaoyuLiang/HCLCFC. Author summaryAs a complementary approach to genome-wide association studies, phenome-wide association studies (PheWAS) have been an efficient tool for testing associations between genetic variations and a wide range of phenotypes utilizing all available phenotypic information. For instance, the first PheWAS has demonstrated that rs3135388 on HLA-DRB1 associated with atrial fibrillation and multiple sclerosis. A challenging step in performing large-scale multiple testing of PheWAS is to control the false discovery rate (FDR). In this work, we propose a novel and powerful multivariate method, HCLC-FC, to test the association between a genetic variant with a large number of phenotypes simultaneously controlling FDR. Within each phenotypic category, a newly proposed method clusters phenotypes into different groups and the combined test statistic within each category based on the phenotypic clusters has an asymptotic distribution which avoids the computational burden of simulation. Furthermore, the newly developed FDR controlling process is based on p-values and does not depend on test statistics. Therefore, it is more general and can be applied to other multiple testing procedures to control FDR.

bioinformatics↗

bioRxiv2022

Gene selection by incorporating genetic networks into case-control association studies

Large-scale genome-wide association studies (GWAS) have been successfully applied to a wide range of genetic variants underlying complex diseases. The network-based penalized regression approach has been developed to overcome the challenges caused by the computational efficiency for analyzing high-dimensional genomic data by incorporating a biological genetic network. In this paper, we propose a gene selection approach by incorporating genetic networks into case-control association studies for DNA sequence data or DNA methylation data. Instead of using traditional dimension reduction techniques such as principal component analyses and supervised principal component analyses, we use a linear combination of genotypes at SNPs or methylation values at CpG sites in each gene to capture gene-level signals. We develop three approaches for the linear combination: optimally weighted sum (OWS), LD-adjusted polygenic risk score (LD-PRS), and beta-based weighted sum (BWS). OWS and LD-PRS are supervised approaches that depend on the effect of each SNP or CpG site on the case-control status, while BWS can be extracted without using the case-control status. After using one of the linear combinations of genotypes or methylation values in each gene to capture gene-level signals, we regularize them to perform gene selection based on the biological network. Simulation studies show that the proposed approaches have higher true positive rates than using traditional dimension reduction techniques. We also apply our approaches to DNA methylation data and UK Biobank DNA sequence data for analyzing rheumatoid arthritis. The results show that the proposed methods can select potentially rheumatoid arthritis related genes that are missed by existing methods. Author SummaryThere is strong evidence showing that when genes are functionally related to each other in a genetic network, statistical methods utilizing prior biological network knowledge can outperform other methods that ignore genetic network structures. Therefore, statistical methods that can incorporate genetic network information into association analysis in human genetic association studies have been widely used since 2008. Here, we take advantage of recently developed methods to capture the gene-level signals in network-based penalized regression of high-dimensional genetic data. We have shown that the selection performance of our proposed methods can outperform three traditional principal component-based dimension reduction techniques in several simulation scenarios in terms of true positive rates. Meanwhile, by applying our methods in both DNA methylation data and DNA sequence data, the genes identified by our proposed methods can be significantly enriched into the rheumatoid arthritis pathway, such as genes HLA-DMA, HLA-DPB1, and HLA-DQA2 in the HLA region.

bioinformatics↗

bioRxiv2021

A computationally efficient clustering linear combination approach to jointly analyze multiple phenotypes for GWAS

There has been an increasing interest in joint analysis of multiple phenotypes in genome-wide association studies (GWAS) because jointly analyzing multiple phenotypes may increase statistical power to detect genetic variants associated with complex diseases or traits. Recently, many statistical methods have been developed for joint analysis of multiple phenotypes in genetic association studies, including the Clustering Linear Combination (CLC) method. The CLC method works particularly well with phenotypes that have natural groupings, but due to the unknown number of clusters for a given data, the final test statistic of CLC method is the minimum p-value among all p-values of the CLC test statistics obtained from each possible number of clusters. Therefore, a simulation procedure must be used to evaluate the p-value of the final test statistic. This makes the CLC method computationally demanding. We develop a new method called computationally efficient CLC (ceCLC) to test the association between multiple phenotypes and a genetic variant. Instead of using the minimum p-value as the test statistic in the CLC method, ceCLC uses the Cauchy combination test to combine all p-values of the CLC test statistics obtained from each possible number of clusters. The test statistic of ceCLC approximately follows a standard Cauchy distribution, so the p-value can be obtained from the cumulative density function without the need for the simulation procedure. Through extensive simulation studies and application on the COPDGene data, the results demonstrate that the type I error rates of ceCLC are effectively controlled in different simulation settings and ceCLC either outperforms all other methods or has statistical power that is very close to the most powerful method with which it has been compared.

bioinformatics↗

bioRxiv2019

Testing gene-environment interactions for rare and/or common variants in sequencing association studies

The risk of many complex diseases is determined by a complex interplay of genetic and environmental factors. Advanced next generation sequencing technology makes identification of gene-environment (GE) interactions for both common and rare variants possible. However, most existing methods focus on testing the main effects of common and/or rare genetic variants. There are limited methods developed to test the effects of GE interactions for rare variants only or rare and common variants simultaneously. In this study, we develop novel approaches to test the effects of GE interactions of rare and/or common risk, and/or protective variants in sequencing association studies. We propose two approaches: 1) testing the effects of an optimally weighted combination of GE interactions for rare variants (TOW-GE); 2) testing the effects of a weighted combination of GE interactions for both rare and common variants (variable weight TOW-GE, VW-TOW-GE). Extensive simulation studies based on the Genetic Analysis Workshop 17 data show that the type I error rates of the proposed methods are well controlled. Compared to the existing interaction sequence kernel association test (ISKAT), TOW-GE is more powerful when there are GE interactions effects for rare risk and/or protective variants; VW-TOW-GE is more powerful when there are GE interactions effects for both rare and common risk and protective variants. Both TOW-GE and VW-TOW-GE are robust to the directions of effects of causal GE interactions. We demonstrate the applications of TOW-GE and VW-TOW-GE using an imputed data from the COPDGene Study.

bioinformatics↗

bioRxiv2019

Test Gene-Environment Interactions for Multiple Traits in Sequencing Association Studies

The risk of many complex diseases is determined by a complex interplay of genetic and environmental factors. Data on multiple traits is often collected for many complex diseases in order to obtain a better understanding of the diseases. Examination of gene-environment interactions (GxEs) for multiple traits can yield valuable insights about the etiology of the disease and increase power in detecting disease associated genes. Most existing methods focus on testing gene-environment interaction (GxE) for a single trait. In this study, we develop novel approaches to test GxEs for multiple traits in sequencing association studies. We first perform transformation of multiple traits by using either principle component analysis or standardization analysis. Then, we detect the effect of GxE for each transferred phenotypic trait using novel proposed tests: testing the effect of an optimallyweighted combination of GxE (TOW-GE) and/or variable weight TOW-GE (VW-TOW-GE). Finally, we employ the Fishers combination test to combine the p-values of TOW-GE and/or VW-TOW-GE. Extensive simulation studies based on the Genetic Analysis Workshop 17 data show that the type I error rates of the proposed methods are well controlled. Compared to the existing interaction sequence kernel association test (ISKAT), TOW-GE is more powerful when there are only rare risk and protective variants; VW-TOW-GE is more powerful when there are both rare and common risk and protective variants. Both TOW-GE and VW-TOW-GE are robust to directions of effects of causal GxEs. Application to the COPDGene Study demonstrates that our proposed methods are very powerful.

genetics↗

bioRxiv2019

A General Statistic to Test an Optimally WeightedCombination of Common and/or Rare Variants

Both genome-wide association study and next generation sequencing data analyses are widely employed in order to identify disease susceptible common and/or rare genetic variants in many large scale genetic studies. Rare variants generally have large effects though they are hard to detect due to their low frequency. Currently, many existing statistical methods for rare variants association studies employ a weighted combination scheme, which usually puts subjective weights or suboptimal weights based on some ad hoc assumptions (e.g. ignoring dependence between rare variants). In this study, we analytically derive optimal weights for both common and rare variants and propose a General and novel approach to Test association between an Optimally Weighted combination of variants (G-TOW) in a gene or pathway for a continuous or dichotomous trait while easily adjusting for covariates. We conduct extensive simulation studies to evaluate the performance of G-TOW. Results of the simulation studies show that G-TOW has properly controlled type I error rates and it is the most powerful test among the methods we compared, when testing effects of either both rare and common variants or rare variants only. We also illustrate the effectiveness of G-TOW using the Genetic Analysis Workshop 17 (GAW17) data. In addition, we applied G-TOW and other competitive methods to test association for schizophrenia. The G-TOW have successfully verified genes FYN and VPS39 which are associated with schizophrenia reported in existing publications. Both of these genes are missed by the weighted sum statistic (WSS) and the sequence kernel association test (SKAT). G-TOW also showed much stronger significance (p-value=0.0037) than our previously developed method named Testing the effect of an Optimally Weighted combination of variants (TOW) (p-value=0.0143) on gene FYN. FYN is a member of the protein-tyrosine kinase oncogene family that phosphorylates glutamate metabotropic receptors and ionotropic N-methyl-d-aspartate (NMDA) receptors. NMDA modulates trafficking, subcellular distribution and function. It is involved in neuronal apoptosis, brain development and synaptic transmission and lower expression, which has been observed in the platelets of schizophrenic patients compared with controls. The application for schizophrenia indicates that G-TOW is a powerful tool in genome-wide association studies.

genetics↗

bioRxiv2018

Testing an Optimally Weighted Combination of Common and/or Rare Variants with Multiple Traits

Joint analysis of multiple traits has recently become popular since it can increase statistical power to detect genetic variants and there is increasing evidence showing that pleiotropy is a widespread phenomenon in complex diseases. Currently, most of existing methods test the association between multiple traits and a single common variant. However, the variant-by-variant methods for common variant association studies may not be optimal for rare variant association studies due to the allelic heterogeneity as well as the extreme rarity of individual variants. In this article, we developed a statistical method by testing an optimally weighted combination of variants with multiple traits (TOWmuT) to test the association between multiple traits and a weighted combination of variants (rare and/or common) in a genomic region. TOWmuT is robust to the directions of effects of causal variants and is applicable to different types of traits. Using extensive simulation studies, we compared the performance of TOWmuT with the following five existing methods: gene association with multiple traits (GAMuT), multiple sequence kernel association test (MSKAT), adaptive weighting reverse regression (AWRR), single-TOW, and MANOVA. Our results showed that, in all of the simulation scenarios, TOWmuT has correct type I error rates and is consistently more powerful than the other five tests. We also illustrated the usefulness of TOWmuT by analyzing a whole-genome genotyping data from a lung function study.

bioinformatics↗