Biology subjects

Lin, X.

Publications and source records attributed to Lin, X..

At least 343 records · Page 19Linked to original sources

bioRxiv2019

Development of a potent and protective germline-like antibody lineage against Zika virus in a convalescent human

Zika virus (ZIKV) specific neutralizing antibodies hold a great promise for antibody-based interventions and vaccine design against ZIKV infection. However, their development in infected patients remain unknown. Here, we report on the dynamic development of a potent and protective ZIKV-specific human antibody ZK2B10 initially isolated from a ZIKV convalescent individual using next-generation sequencing (NGS). The unbiased repertoire analysis showed dramatic changes in many families of heavy and light chain variable regions. However, lineage tracing of ZK2B10 revealed limited somatic hypermutation throughout the 12 months since the onset of symptom. In particular, NGS-derived germline-like somatic variants neutralized and protected mice from lethal challenge of ZIKV without detectable cross-reactivity with Dengue virus (DENV). Site-directed mutagenesis identified two residues within {lambda} chain, N31 and S91 that are essential to the functional maturation. The dynamic features unveiled here will assist us to better understand the pathogenesis of ZIKV infection and inform rational design of vaccines.\n\nAuthor summaryRecently emerged ZIKV is associated with severe neurological complications such as Guillain-Barre syndrome in adults and congenital microcephaly in newborns. No ZIKV-specific therapeutics or vaccines are currently available. We and others have identified a number of neutralizing antibodies capable of protecting experimental animals from ZIKV infection. However, the development of these potent antibodies during ZIKV natural infection remains unknown. Here, we report on the longitudinal analysis of one such antibody ZK2B10 using next-generation sequencing (NGS), bioinformatics and functional analysis. We found that the ZK2B10 germline-like antibodies possess strong neutralizing activity in vitro and impressive protectivity against lethal ZIKV infection in vivo. These findings suggest that the potent and protective antibody response against ZIKV can be generated within relative short term with high germline identity which provide great hope and promise for successful vaccine development against ZIKV.

microbiology↗

bioRxiv2019

Host carbon dioxide concentration is an independent stress for Cryptococcus neoformans that affects virulence and antifungal susceptibility

The ability of Cryptococcus neoformans to cause disease in humans varies significantly among strains with highly related genotypes. In general, environmental isolates of pathogenic species such as C. neoformans var. grubii have reduced virulence relative to clinical isolates, despite having no differences in the expression of the canonical virulence traits (high temperature growth, melanization and capsule formation). In this observation, we report that environmental isolates of C. neoformans tolerate host CO2 concentrations poorly compared to clinical isolates and that CO2 tolerance correlates well with the ability of the isolates to cause disease in mammals. Initial experiments also suggest that CO2 tolerance is particularly important for dissemination of C. neoformans from the lung to the brain. Furthermore, CO2 concentrations affect the susceptibility of both clinical and environmental C. neoformans isolates to the azole class of antifungal drugs, suggesting that antifungal testing in the presence of CO2 may improve the correlation between in vitro azole activity and patient outcome.\n\nImportanceA number of studies comparing either patient outcomes or model system virulence across large collections of Cryptococcus isolates have found significant heterogeneity in virulence even among strains with highly related genotypes. Because this heterogeneity cannot be explained by variations in the three well-characterized virulence traits (growth at host body temperature; melanization; and polysaccharide capsule formation), it has been widely proposed that additional C. neoformans virulence traits must exist. C. neoformans natural niche is in the environment where the carbon dioxide concentration is very low ([~]0.04%); in contrast, mammalian host tissue carbon dioxide concentrations are 125-fold higher (5%). We have found that the ability to grow in the presence of 5% carbon dioxide distinguishes low virulence strains from high virulence strains, even those with a similar genotype. Our findings suggest that carbon dioxide tolerance is a previously un-recognized virulence trait for C. neoformans.

microbiology↗

bioRxiv2019

Whole-Genome Association Analyses of Sleep-disordered Breathing Phenotypes in the NHLBI TOPMed Program

Sleep-disordered breathing (SDB) is a common disorder associated with significant morbidity. Through the NHLBI Trans-Omics for Precision Medicine (TOPMed) program we report the first whole-genome sequence analysis of SDB. We identified 4 rare gene-based associations with SDB traits in 7,988 individuals of diverse ancestry and 4 replicated common variant associations with inclusion of additional samples (n=13,257). We identified a multi-ethnic set-based rare-variant association (p = 3.48 x 10-8) on chromosome X with ARMCX3. Transcription factor binding site enrichment identified associations with genes implicated with respiratory and craniofacial traits. Results highlighted associations in genes that modulate lung development, inflammation, respiratory rhythmogenesis and HIF1A-mediated hypoxic response.

genetics↗

bioRxiv2019

CoMM-S2: a collaborative mixed model using summary statistics in transcriptome-wide association studies

MotivationAlthough genome-wide association studies (GWAS) have deepened our understanding of the genetic architecture of complex traits, the mechanistic links that underlie how genetic variants cause complex traits remains elusive. To advance our understanding of the underlying mechanistic links, various consortia have collected a vast volume of genomic data that enable us to investigate the role that genetic variants play in gene expression regulation. Recently, a collaborative mixed model (CoMM) [42] was proposed to jointly interrogate genome on complex traits by integrating both the GWAS dataset and the expression quantitative trait loci (eQTL) dataset. Although CoMM is a powerful approach that leverages regulatory information while accounting for the uncertainty in using an eQTL dataset, it requires individual-level GWAS data and cannot fully make use of widely available GWAS summary statistics. Therefore, statistically efficient methods that leverages transcriptome information using only summary statistics information from GWAS data are required.\n\nResultsIn this study, we propose a novel probabilistic model, CoMM-S2, to examine the mechanistic role that genetic variants play, by using only GWAS summary statistics instead of individual-level GWAS data. Similar to CoMM which uses individual-level GWAS data, CoMM-S2 combines two models: the first model examines the relationship between gene expression and genotype, while the second model examines the relationship between the phenotype and the predicted gene expression from the first model. Distinct from CoMM, CoMM-S2 requires only GWAS summary statistics. Using both simulation studies and real data analysis, we demonstrate that even though CoMM-S2 utilizes GWAS summary statistics, it has comparable performance as CoMM, which uses individual-level GWAS data.\n\nContactjin.liu@duke-nus.edu.sg\n\nAvailability and implementationThe implement of CoMM-S2 is included in the CoMM package that can be downloaded from https://github.com/gordonliu810822/CoMM.\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2019

Spectral clustering in regression-based biological networks

Biological networks often have complex structure consisting of meaningful clusters of nodes that are integral to understanding biological function. Community detection algorithms to identify the clustering, or community structure, of a network have been well established. These algorithms assume that data used in network construction is observed without error. However, oftentimes intermediary analyses such as regression are performed before constructing biological networks and the associated error is not propagated in community detection. In expression quantitative trait loci (eQTL) networks, one must first map eQTLs via linear regression in order to specify the matrix representation of the network. We study the effects of using estimates from regression models when applying the spectral clustering approach to community detection. We demonstrate the impacts on the affinity matrix and consider adjusted estimates of the affinity matrix for use in spectral clustering. We further provide a recommendation for selection of the tuning parameter in spectral clustering. We evaluate the proposed adjusted method for performing spectral clustering to detect gene clusters in eQTL data from the GTEx project and to assess the stability of communities in biological data.

systems biology↗

bioRxiv2019

Omnibus Inverse Normal Transformation Based Association Test Improves Power in Genome-Wide Association Studies of Quantitative Traits

SO_SCPLOWUMMARYC_SCPLOWQuantitative traits analyzed in Genome-Wide Association Studies (GWAS) are often non-normally distributed. For such traits, association tests based on standard linear regression are subject to reduced power and inflated type I error in finite samples. Applying the rank-based Inverse Normal Transformation (INT) to non-normally distributed traits has become common practice in GWAS. However, the different variations on INT-based association testing have not been formally defined, and guidance is lacking on when to use which approach. In this paper, we formally define and systematically compare the direct (D-INT) and indirect (I-INT) INT-based association tests. We discuss their assumptions, underlying generative models, and connections. We demonstrate that the relative powers of D-INT and I-INT depend on the underlying data generating process. Since neither approach is uniformly most powerful, we combine them into an adaptive omnibus test (O-INT). O-INT is robust to model misspecification, protects the type I error, and is well powered against a wide range of non-normally distributed traits. Extensive simulations were conducted to examine the finite sample operating characteristics of these tests. Our results demonstrate that, for non-normally distributed traits, INT-based tests outperform the standard untransformed association test (UAT), both in terms of power and type I error rate control. We apply the proposed methods to GWAS of spirometry traits in the UK Biobank. O-INT has been implemented in the R package RNOmni, which is available on CRAN.

genetics↗

bioRxiv2019

Single cell RNA sequencing reveals cellular diversity of trisomy 21 retina

Retina is a crucial tissue for the capturing and processing of light stimulus. Characterization of the retina at single cell level is essential for the understanding of its biological functions. A variety of abnormalities in terms of morphology and function were reported in T21 retina. To evaluate the effects of chromosome aneuploidy on retina development, we characterized single cell transcriptional profiles of a T21 fetus and performed comprehensive bioinformatic analyses. Our data revealed the diversity and heterogeneity of cellular compositions in T21 retina. In total, we identified seven major cell types, and detected several subtypes within each cell type, followed by the detection of corresponding molecular markers including previously reported ones and a series of novel markers. Our analyses identified extensive communication networks between distinct cellular types, among which a few ligand-receptor interactions were associated with the development of retina and immunoregulatory interactions. Taken together, our data provided the first single cell transcriptome profile for human T21 retina which facilitates our understanding on the dosage effects of chromosome 21 on the development of retina.

cell biology↗

bioRxiv2019

Projected changes of alpine grassland carbon dynamics in response to climate change and elevated CO2 concentrations under Representative Concentration Pathways (RCP) scenarios

The Tibetan Plateau is an important component of the global carbon cycle due to the large permafrost carbon pool and its vulnerability to climate warming. The Tibetan Plateau has experienced a noticeable warming over the past few decades and is projected to continue warming in the future. However, the direction and magnitude of carbon fluxes responses to climate change and elevated CO2 concentration under Representative Concentration Pathways (RCP) scenarios in the Tibetan Plateau grassland are poorly known. Here, we used a calibrated and validated biogeochemistry model, CENTURY, to quantify the contributions of climate change and elevated CO2 on the future carbon budget in the alpine grassland under three RCP scenarios. Though the Tibetan Plateau grassland was projected a net carbon sink of 16 ~ 25 Tg C yr-1 in the 21st century, the capacity of carbon sequestration was predicted to decrease gradually because climate-driven increases in heterotrophic respiration (Rh) (with linear slopes 0.49 ~ 1.62 g C m-2 yr-1) was greater than the net primary production (NPP) (0.35 ~ 1.52 g C m-2 yr-1). However, the elevated CO2 contributed more to plant growth (1.9% ~ 7.3%) than decomposition (1.7% ~ 6.1%), which could offset the warming-induced carbon loss. The interannual and decadal-scale dynamics of the carbon fluxes in the alpine grassland were primarily controlled by temperature, while the role of precipitation became increasingly important in modulating carbon cycle. The strengthened correlation between precipitation and carbon budget suggested that further research should consider the performance of precipitation in evaluating carbon dynamics in a warmer climate scenario.

ecology↗

bioRxiv2019

EPIC: MHC-I epitope prediction integrating mass spectrometry derived motifs and tissue-specific expression profiles

BackgroundAccurate prediction of epitopes presented by human leukocyte antigen (HLA) is crucial for personalized cancer immunotherapies targeting T cell epitopes. Mass spectrometry (MS) profiling of eluted HLA ligands, which provides high-throughput measurements of HLA associated peptides in vivo, can be used to faithfully model the presentation of epitopes on the cell surface. In addition, gene expression profiles measured by RNA-seq data in a specific cell/tissue type can significantly improve the performance of epitope presentation prediction. However, although large amount of high-quality MS data of HLA-bound peptides is being generated in recent years, few provide matching RNA-seq data, which makes incorporating gene expression into epitope prediction difficult. MethodsWe collected publicly available HLA peptidome and matching RNA-seq data of 34 cell lines derived from various sources. We built position score specific matrixes (PSSMs) for 21 HLA-I alleles based on these MS data, then used logistic regression (LR) to model the relationship among PSSM score, gene expression and peptide length to predict whether a peptide could be presented in each of the cell line. We further built a universal LR model, termed Epitope Presentation Integrated Prediction (EPIP), based on more than 180,000 unique HLA ligands collected from public sources and ~3,000 HLA ligands generated by ourselves, to predict epitope presentation for 66 common HLA-I alleles. ResultsWhen evaluating EPIP on large, independent HLA eluted ligand datasets, it performed substantially better than other popular methods, including MixMHCpred (v2.0), NetMHCpan (v4.0), and MHCflurry (v1.2.2), with an average 0.1% positive predictive value (PPV) of 52.01%, compared to 37.24%, 36.96%, 24.90% and 23.76% achieved by MixMHCpred, NetMHCpan-4.0 (EL), NetMHCpan-4.0 (BA) and MHCflurry, respectively. It is also comparable to EDGE, a recent deep learning-based model that is not publicly available, on predicting epitope presentation and selecting immunogenic cancer neoantigens. However, the simplicity and flexibility of EPIP makes it easier to be applied in diverse situations, and we demonstrated this by generating MS data for the HCC4006 cell line and adding the support of HLA-A*33:03 to EPIP. EPIP is publicly available as a web tool < http://epip.genomics.cn/>. Conclusionswe have developed an easy to use, publicly available epitope prediction tool, EPIP, that incorporates information from both MS and RNA-seq data, and demonstrated its superior performance over existing public methods.

bioinformatics↗

bioRxiv2019

Dynamic Scan Procedure for Detecting Rare-Variant Association Regions in Whole Genome Sequencing Studies

Whole genome sequencing (WGS) studies are being widely conducted to identify rare variants associated with human diseases and disease-related traits. Classical single-marker association analyses for rare variants have limited power, and variant-set based analyses are commonly used to analyze rare variants. However, existing variant-set based approaches need to pre-specify genetic regions for analysis, and hence are not directly applicable to WGS data due to the large number of intergenic and intron regions that consist of a massive number of non-coding variants. The commonly used sliding window method requires pre-specifying fixed window sizes, which are often unknown as a priori, are difficult to specify in practice and are subject to limitations given genetic association region sizes are likely to vary across the genome and phenotypes. We propose a computationally-efficient and dynamic scan statistic method (Scan the Genome (SCANG)) for analyzing WGS data that flexibly detects the sizes and the locations of rare-variants association regions without the need of specifying a prior fixed window size. The proposed method controls the genome-wise type I error rate and accounts for the linkage disequilibrium among genetic variants. It allows the detected rare variants association region sizes to vary across the genome. Through extensive simulated studies that consider a wide variety of scenarios, we show that SCANG substantially outperforms several alternative rare-variant association detection methods while controlling for the genome-wise type I error rates. We illustrate SCANG by analyzing the WGS lipids data from the Atherosclerosis Risk in Communities (ARIC) study.

genetics↗

bioRxiv2019

Non-coding Somatic Mutations Converge on the PAX8 Pathway in Epithelial Ovarian Cancer

Transcriptional regulation is highly disease and cell-type specific. We performed H3K27ac chromatin immunoprecipitation and transcriptomic sequencing in primary tumors for the four different subtypes of invasive epithelial ovarian cancer (OC). Histotype-specific regulatory elements (REs) were enriched in enhancers (P<0.001). In silico prediction of putative target genes for histotype-specific REs identified genes (WFDC2, P=5.5x10-5) and pathways (PI3K-Akt signaling, P<0.002) known to be involved in OC development. Some genes (e.g. PAX8 and CA125) are associated with super-enhancers (SEs) in all OCs, while others are histotype-specific, including PPP1R3B which is associated with SEs specific to clear cell OC. Integrated analysis of active chromatin landscapes with somatic single nucleotide variants (SNVs) from whole genome sequencing (WGS) of 232 primary OCs identified frequently mutated REs, including the KLF6 promoter (P=8.2x10-8) and a putative enhancer at chromosome 6p22.1 (P<0.05). In high-grade serous OCs, somatic SNVs clustered in binding sites for the PAX8 binding partner TEAD4 (P=6x10-11), while the collection of cis regulatory elements associated with PAX8 was the most frequently mutated set of enhancers in OC (P=0.003). Functional analyses supported our findings: Knockdown of PPP1R3B in clear cell OC cells significantly reduced intracellular glycogen content, a signature feature of this histotype; and stable knockout of a 635 bp region in the 6p22.1 enhancer induced downregulation of two predicted target genes, ZSCAN16 and ZSCAN12 (P=6.6 x 10-4 and P=0.02). In summary, we have characterized histotype-specific epigenomic and transcriptomic landscapes in OC and defined likely functional REs based on somatic mutation analysis of ovarian tumors.

cancer biology↗

bioRxiv2019

Targeted antifungal liposomes

Aspergillus species cause pulmonary invasive aspergillosis resulting in nearly a hundred thousand deaths each year. Patients at the greatest risk of developing life-threatening aspergillosis have weakened immune systems and/or various lung disorders. Patients are treated with antifungals such as amphotericin B (AmB), casofungin acetate, or triazoles (itraconazole, voriconazole etc.), but these antifungal agents have serious limitations due to lack of sufficient fungicidal effect and human toxicity. Liposomes with AmB intercalated into the lipid membrane (AmBisomes, AmB-LLs), have several-fold reduced toxicity compared to detergent solubilized drug. However, even with the current antifungal therapies, one-year survival among patients is only 25 to 60%. Hence, there is a critical need for improved antifungal therapeutics.\n\nDectin-1 is a mammalian innate immune receptor in the membrane of some leukocytes that binds as a dimer to beta-glucans found in fungal cell walls, signaling fungal infection. Using a novel protocol, we coated AmB-LLs with Dectin-1s beta-glucan binding domain to make DEC-AmB-LLs. DEC-AmB-LLs bound rapidly, efficiently, and with great strength Aspergillus fumigatus and to Candida albicans and Cryptococcus neoformans, highly divergent fungal pathogens of global importance. By contrast, un-targeted AmB-LLs and BSA-coated BSA-AmB-LLs showed 200-fold lower affinity for fungal cells. DEC-AmB-LLs reduced the growth and viability of A. fumigatus an order of magnitude more efficiently than untargeted control liposomes delivering the same concentrations of AmB, in essence increasing the effective dose of AmB. Future efforts will focus on examining pan-antifungal targeted liposomal drugs in animal models of disease.\n\nTweetWe coated anti-fungal drug loaded liposomes to fungal cell walls with a beta-glucan binding protein and thereby increased drug effectiveness by an order of magnitude.\n\nImportanceThe fungus Aspergillus fumigatus causes pulmonary invasive aspergillosis resulting in nearly a hundred thousand deaths each year. Patients are often treated with antifungal drugs such as amphotericin B loaded into liposomes, AmB-LLs, but all antifungal drugs including AmB-LLs have serious limitations due to human toxicity and insufficient fungal cell killing. Even with the best current therapies, one-year survival among patients with invasive aspergillosis is only 25 to 60%. Hence, there is a critical need for improved antifungal therapeutics.\n\nDectin-1 is a mammalian protein that binds to beta-glucan polysaccharides found in nearly all fungal cell walls. We coated AmB-LLs with Dectin-1 to make DEC-AmB-LLs. DEC-AmB-LLs bond strongly to fungal cells, while AmB-LLs had little affinity. DEC-AmB-LLs killed or inhibited A. fumigatus ten times more efficiently than untargeted lipsomes, increasing the effective dose of AmB. Dectin-1 coated liposomes targeting fungal pathogens have the potential to greatly enhance antifungal therapeutics.

molecular biology↗

bioRxiv2019

Connectivity of variants in eQTL networks dictates reproducibility and functionality

Network analyses are a natural approach for identifying genetic variants and genes that work together to drive disease phenotypes. The relationship between SNPs and genes, captured in expression quantitative trait locus (eQTL) analysis, can be represented as a network with edges connecting SNPs and genes. Existing network methods treat such edges as fixed and known when they are most often thresholded estimates from eQTL regression. We consider various characterizations of an essential feature of nodes of eQTL networks, their degree centrality, that retains different data on eQTLs. We define the network metric of degree to be estimated by false discovery rates, test statistics, and p-values of the eQTL regressions in order to represent how central and potentially influential a SNP is to the network. We calculate degree metrics for data from 21 tissues from the GTEx project to assess the reproducibility, correlation across tissues, and, functional importance of degree.

systems biology↗

bioRxiv2018

Nongenic cancer-risk SNPs affect oncogenes, tumor suppressor genes, and immune function

Genome-wide associations studies (GWASes) have identified many germline genetic variants that are associated with an increased risk of developing cancer. However, how these single nucleotide polymorphisms (SNPs) alter biological function in a way that increases cancer risk is still largely unknown. We used a systems biology approach to analyze the regulatory role and functional associations of cancer-risk SNPs in thirteen distinct tissues. Using data from the Genotype-Tissue Expression (GTEx) project, we performed an expression quantitative trait locus (eQTL) analysis, keeping both cis- and trans-eQTLs, and representing those significant associations as edges in tissue-specific eQTL bipartite networks. We find that each network is organized into highly modular communities that group sets of SNPs together with functionally-related collections of genes. We mapped cancer-risk SNPs to each tissue-specific eQTL network. Although we find in each tissue that cancer-risk SNPs are distributed across the network, they are not uniformly distributed. Rather they are significantly over-represented in a small number of communities. This includes communities enriched for immune response processes as well as communities representing tissue-specific functions. Moreover, cancer-risk SNPs are over-represented in the central \"cores\" of communities, meaning they are more likely to influence the expression of many genes within the same community, thus affecting biological processes. And finally, we find that cancer-risk SNPs preferentially target oncogenes and tumor suppressor genes, suggesting non-genic mutations may still alter the effects of these key cancer-associated genes. This bipartite eQTL network approach provides a new way of understanding genetic effects on cancer risk and provides a biological context for interpreting the results of GWAS cancer studies.

cancer biology↗

bioRxiv2018

Retinoid X receptor alpha is a spatiotemporally-specific therapeutic target for doxorubicin-induced cardiomyopathy in adult zebrafish

While the genetic suppressor screen is efficient in suggesting therapeutic genes, this strategy has yet to be successful for cardiomyopathies in vertebrates. To develop such a strategy, we recently established a mutagenesis screen platform in zebrafish for systematic discovery of genetic modifiers of doxorubicin-induced cardiomyopathy (DIC). Here, we further revealed both molecular and cellular insights of the first salutary modifier emerged from the screen, i.e. gene-breaking transposon (GBT) 0419 that affects the retinoid X receptor alpha a (rxraa) gene. First, by rescuing the mutation in tissue-specific manner with multiple Cre-loxP systems, we demonstrated that the endothelial, but not myocardial or epicardial, function of rxraa is primary to this cardioprotective effects. Next, we showed that the rxraa-associated salutary effects on DIC were conferred partially by the activation of retinoid acid (RA) signaling. Finally, we identified isotretinoin and bexarotene, 2 US Food and Drug Administration-approved RXRA agonists that are effective in treating adult zebrafish DIC when administered during the early, but not the late, phase of DIC progression. Collectively, we provided the first in vivo genetic evidence in supporting RXRA as the therapeutic target for DIC, and uncovered a previously unrecognized spatiotemporally-restricted mechanism for this gene-based therapeutic strategy. Our study also justified that searching salutary modifiers via zebrafish mutagenesis screen can be effective in discovering new therapeutic targets for cardiomyopathies.

genetics↗

bioRxiv2018

Genome sequencing analysis identifies high-risk Epstein-Barr virus subtypes for nasopharyngeal carcinoma

Epstein-Barr virus (EBV) infection is ubiquitous worldwide and associated with multiple cancers including nasopharyngeal carcinoma (NPC). The role of EBV viral genomic variation in NPC development and its striking endemicity in southern China has been poorly explored. Through large-scale genome sequencing and association study of EBV isolates from China, we identified two non-synonymous EBV variants within BALF2 strongly associated with NPC risk (conditional P value 1.75x10-6 for SNP162476_C and 3.23x10-13 for SNP163364_T), whose cumulative effects contributed to 83% of the overall risk in southern China. Phylogenetic analysis of the risk variants revealed a unique origin in southern China followed by clonal expansion. EBV BALF2 haplotype carrying the risk variants were shown to reduce viral lytic DNA replication, as a result potentially promoting viral latency. Our discovery has not only provided insight to the unique endemic pattern of NPC occurrence in southern China, but also paved the way for the identification of individuals at high risk of NPC and effective intervention program to reduce the disease burden in southern China.

genetics↗

bioRxiv2018

ACAT: A Fast and Powerful P-value Combination Method for Rare-variant Analysis in Sequencing Studies

Set-based analysis that jointly tests the association of variants in a group has emerged as a popular tool for analyzing rare and low-frequency variants in sequencing studies. The existing set-based tests can suffer significant power loss when only a small proportion of variants are causal, and their powers can be sensitive to the number, effect sizes and effect directions of the causal variants and the choices of weights. Here we propose an Aggregated Cauchy Association Test (ACAT), a general, powerful and computationally efficient p-value combination method to boost power in sequencing studies. First, by combining variant-level p-values, we use ACAT to construct a set-based test (ACAT-V) that is particularly powerful in the presence of only a small number of casual variants in a variant set. Second, by combining different variant set-level p-values, we use ACAT to construct an omnibus test (ACAT-O) that combines the strength of multiple complimentary set-based tests including the burden test, Sequence Kernel Association Test (SKAT) and ACAT-V. Through analysis of extensively simulated data and the whole-genome sequencing data from the Atherosclerosis Risk in Communities (ARIC) study, we demonstrate that ACAT-V complements the SKAT and burden test, and that ACAT-O has a substantially more robust and higher power than the alternative tests.

genetics↗

bioRxiv2018

Epigenome-wide association analysis of daytime sleepiness in the Multi-Ethnic Study of Atherosclerosis reveals African-American specific associations

Study ObjectivesExcessive daytime sleepiness (EDS) is a consequence of inadequate sleep, or of a primary disorder of sleep-wake control. Population variability in prevalence of EDS and susceptibility to EDS are likely due to genetic and biological factors as well as social and environmental influences. Epigenetic modifications (such as DNA methylation-DNAm) are potential influences on a range of health outcomes. Here, we explored the association between DNAm and daytime sleepiness quantified by the Epworth Sleepiness Scale (ESS).\n\nMethodsWe performed multi-ethnic and ethnic-specific epigenome-wide association studies for DNAm and ESS in 619 individuals from the Multi-Ethnic Study of Atherosclerosis. Replication was assessed in the Cardiovascular Health Study (CHS). Genetic variants in genes proximal to ESS-associated DNAm were analyzed to identify methylation quantitative trait loci and followed with replication of genotype-sleepiness associations in the UK Biobank.\n\nResults61 methylation sites were associated with ESS (FDR [≤] 0.1) in African Americans only, including an association in KCTD5, a gene strongly implicated in sleep. One association (cg26130090) replicated in CHS African Americans (p-value 0.0004). We identified a sleepiness-associated methylation site in the gene RAI1, a gene associated with sleep and circadian phenotypes. In a follow-up analysis, a genetic variant within RAI1 associated with both DNAm and sleepiness score. The variants association with sleepiness was replicated in the UK Biobank.\n\nConclusionsOur analysis identified methylation sites in multiple genes that may be implicated in EDS. These sleepiness-methylation associations were specific to African Americans. Future work is needed to identify mechanisms driving ancestry-specific methylation effects.\n\nStatement of SignificanceExcessive daytime sleepiness is associated with negative health outcomes such as reduction in quality of life, increased workplace accidents, and cardiovascular mortality. There are race/ethnic disparities in excessive daytime sleepiness, however, the environmental and biological mechanisms for these differences are not yet understood. We performed an association analysis of DNA methylation, measured in monocytes, and daytime sleepiness within a racially diverse study population. We detected numerous DNA methylation markers associated with daytime sleepiness in African Americans, but not in European and Hispanic Americans. Future work is required to elucidate the pathways between DNA methylation, sleepiness, and related behavioral/environmental exposures.

genomics↗