Integrating eQTL data with GWAS summary statistics in pathway-based analysis with application to schizophrenia

Identifying risk loci for obsessive-compulsive disorder and shared genetic component with schizophrenia: A large-scale multi-trait association analysis with summary statistics

Due to limited samples, no genetic loci have been identified for obsessive-compulsive disorder (OCD) in genome-wide association studies. Additionally, although co-morbidities between OCD and schizophrenia (SCZ) were observed, their common genetic etiology was not completely known. Here, we conducted a comprehensive investigation regarding the genetic architecture of OCD and the common genetic foundation shared by OCD and SCZ using summary statistics data (2688 cases and 7037 controls for OCD; 53,386 cases and 77,258 controls for SCZ). We discovered significant genetic correlation between OCD and SCZ (r̂g=0.296, P = 2.82 × 10-11). We then performed two multi-trait association analyses to detect OCD-associated loci and colocalization analysis to detect causal variants. Parallel gene-level analyses were also implemented. We identified 323 OCD-relevant variants located within 12 loci, with four loci shared the same causal variants between OCD and SCZ. Further, the gene-level analyses discovered 8 OCD-associated genes. Finally, multiple functional analyses at both SNP and gene levels showed that these genetic association signals had significant enrichments in the regions of left ventricle and anterior cingulate cortex, and suggested an important role of pathways involving regulation of telomere maintenance, histone phosphorylation, and GnRH secretion. Overall, this study identified new genetic loci for OCD and provided substantial evidence supporting common genetic foundation underlying OCD and SCZ. The findings advanced our understanding of genetic architecture and pathophysiology of OCD as well as shedding light on shared genetic etiology of the two disorders.

Allelic variation in transcription factor PtoWRKY68 contributes to drought tolerance in Populus

Drought stress limits woody species productivity and influences tree distribution. However, dissecting the molecular mechanisms that underpin drought responses in forest trees can be challenging due to trait complexity. Here, using a panel of 300 Chinese white poplar (Populus tomentosa) accessions collected from different geographical climatic regions in China, we performed a genome-wide association study (GWAS) on seven drought-related traits and identified PtoWRKY68 as a candidate gene involved in the response to drought stress. A 12-bp insertion and/or deletion and three nonsynonymous variants in the PtoWRKY68 coding sequence categorized natural populations of P. tomentosa into two haplotype groups, PtoWRKY68hap1 and PtoWRKY68hap2. The allelic variation in these two PtoWRKY68 haplotypes conferred differential transcriptional regulatory activities and binding to the promoters of downstream abscisic acid (ABA) efflux and signaling genes. Overexpression of PtoWRKY68hap1 and PtoWRKY68hap2 in Arabidopsis (Arabidopsis thaliana) ameliorated the drought tolerance of two transgenic lines and increased ABA content by 42.7% and 14.3% compared to wild-type plants, respectively. Notably, PtoWRKY68hap1 (associated with drought tolerance) is ubiquitous in accessions in water-deficient environments, whereas the drought-sensitive allele PtoWRKY68hap2 is widely distributed in well-watered regions, consistent with the trends in local precipitation, suggesting that these alleles correspond to geographical adaptation in Populus. Moreover, quantitative trait loci analysis and an electrophoretic mobility shift assay showed that SHORT VEGETATIVE PHASE (PtoSVP.3) positively regulates the expression of PtoWRKY68 under drought stress. We propose a drought tolerance regulatory module in which PtoWRKY68 modulates ABA signaling and accumulation, providing insight into the genetic basis of drought tolerance in trees. Our findings will facilitate molecular breeding to improve the drought tolerance of forest trees.

A gene based combination test using GWAS summary data

BACKGROUND

Gene-based association tests provide a useful alternative and complement to the usual single marker association tests, especially in genome-wide association studies (GWAS). The way of weighting for variants in a gene plays an important role in boosting the power of a gene-based association test. Appropriate weights can boost statistical power, especially when detecting genetic variants with weak effects on a trait. One major limitation of existing gene-based association tests lies in using weights that are predetermined biologically or empirically. This limitation often attenuates the power of a test. On another hand, effect sizes or directions of causal genetic variants in real data are usually unknown, driving a need for a flexible yet robust methodology of gene based association tests. Furthermore, access to individual-level data is often limited, while thousands of GWAS summary data are publicly and freely available.

RESULTS

To resolve these limitations, we propose a combination test named as OWC which is based on summary statistics from GWAS data. Several traditional methods including burden test, weighted sum of squared score test [SSU], weighted sum statistic [WSS], SNP-set Kernel Association Test [SKAT], and the score test are special cases of OWC. To evaluate the performance of OWC, we perform extensive simulation studies. Results of simulation studies demonstrate that OWC outperforms several existing popular methods. We further show that OWC outperforms comparison methods in real-world data analyses using schizophrenia GWAS summary data and a fasting glucose GWAS meta-analysis data. The proposed method is implemented in an R package available at https://github.com/Xuexia-Wang/OWC-R-package CONCLUSIONS: We propose a novel gene-based association test that incorporates four different weighting schemes (two constant weights and two weights proportional to normal statistic Z) and includes several popular methods as its special cases. Results of the simulation studies and real data analyses illustrate that the proposed test, OWC, outperforms comparable methods in most scenarios. These results demonstrate that OWC is a useful tool that adapts to the underlying biological model for a disease by weighting appropriately genetic variants and combination of well-known gene-based tests.

A comprehensive comparison of multilocus association methods with summary statistics in genome-wide association studies

BACKGROUND

Multilocus analysis on a set of single nucleotide polymorphisms (SNPs) pre-assigned within a gene constitutes a valuable complement to single-marker analysis by aggregating data on complex traits in a biologically meaningful way. However, despite the existence of a wide variety of SNP-set methods, few comprehensive comparison studies have been previously performed to evaluate the effectiveness of these methods.

RESULTS

We herein sought to fill this knowledge gap by conducting a comprehensive empirical comparison for 22 commonly-used summary-statistics based SNP-set methods. We showed that only seven methods could effectively control the type I error, and that these well-calibrated approaches had varying power performance under the simulation scenarios. Overall, we confirmed that the burden test was generally underpowered and score-based variance component tests (e.g., sequence kernel association test) were much powerful under the polygenic genetic architecture in both common and rare variant association analyses. We further revealed that two linkage-disequilibrium-free P value combination methods (e.g., harmonic mean P value method and aggregated Cauchy association test) behaved very well under the sparse genetic architecture in simulations and real-data applications to common and rare variant association analyses as well as in expression quantitative trait loci weighted integrative analysis. We also assessed the scalability of these approaches by recording computational time and found that all these methods can be scalable to biobank-scale data although some might be relatively slow.

CONCLUSION

In conclusion, we hope that our findings can offer an important guidance on how to choose appropriate multilocus association analysis methods in post-GWAS era. All the SNP-set methods are implemented in the R package called MCA, which is freely available at https://github.com/biostatpzeng/ .

Towards the Genetic Architecture of Complex Gene Expression Traits: Challenges and Prospects for eQTL Mapping in Humans

The discovery of expression quantitative trait loci (eQTLs) and their target genes (eGenes) has not only compensated for the limitations of genome-wide association studies for complex phenotypes but has also provided a basis for predicting gene expression. Efforts have been made to develop analytical methods in statistical genetics, a key discipline in eQTL analysis. In particular, mixed model– and deep learning–based analytical methods have been extremely beneficial in mapping eQTLs and predicting gene expression. Nevertheless, we still face many challenges associated with eQTL discovery. Here, we discuss two key aspects of these challenges: 1, the complexity of eTraits with various factors such as polygenicity and epistasis and 2, the voluminous work required for various types of eQTL profiles. The properties and prospects of statistical methods, including the mixed model method, Bayesian inference, the deep learning method, and the integration method, are presented as future directions for eQTL discovery. This review will help expedite the design and use of efficient methods for eQTL discovery and eTrait prediction.

Secondary analyses for genome-wide association studies using expression quantitative trait loci

Genome-wide association studies (GWAS) have successfully identified thousands of single nucleotide polymorphisms (SNPs) associated with complex traits; however, the identified SNPs account for a fraction of trait heritability, and identifying the functional elements through which genetic variants exert their effects remains a challenge. Recent evidence suggests that SNPs associated with complex traits are more likely to be expression quantitative trait loci (eQTL). Thus, incorporating eQTL information can potentially improve power to detect causal variants missed by traditional GWAS approaches. Using genomic, transcriptomic, and platelet phenotype data from the Genetic Study of Atherosclerosis Risk family-based study, we investigated the potential to detect novel genomic risk loci by incorporating information from eQTL in the relevant target tissues (i.e., platelets and megakaryocytes) using established statistical principles in a novel way. Permutation analyses were performed to obtain family-wise error rates for eQTL associations, substantially lowering the genome-wide significance threshold for SNP-phenotype associations. In addition to confirming the well known association between PEAR1 and platelet aggregation, our eQTL-focused approach identified a novel locus (rs1354034) and gene (ARHGEF3) not previously identified in a GWAS of platelet aggregation phenotypes. A colocalization analysis showed strong evidence for a functional role of this eQTL.

Integrative eQTL-weighted hierarchical Cox models for SNP-set based time-to-event association studies

BACKGROUND

Integrating functional annotations into SNP-set association studies has been proven a powerful analysis strategy. Statistical methods for such integration have been developed for continuous and binary phenotypes; however, the SNP-set integrative approaches for time-to-event or survival outcomes are lacking.

METHODS

We here propose IEHC, an integrative eQTL (expression quantitative trait loci) hierarchical Cox regression, for SNP-set based survival association analysis by modeling effect sizes of genetic variants as a function of eQTL via a hierarchical manner. Three p-values combination tests are developed to examine the joint effects of eQTL and genetic variants after a novel decorrelated modification of statistics for the two components. An omnibus test (IEHC-ACAT) is further adapted to aggregate the strengths of all available tests.

RESULTS

Simulations demonstrated that the IEHC joint tests were more powerful if both eQTL and genetic variants contributed to association signal, while IEHC-ACAT was robust and often outperformed other approaches across various simulation scenarios. When applying IEHC to ten TCGA cancers by incorporating eQTL from relevant tissues of GTEx, we revealed that substantial correlations existed between the two types of effect sizes of genetic variants from TCGA and GTEx, and identified 21 (9 unique) cancer-associated genes which would otherwise be missed by approaches not incorporating eQTL.

CONCLUSION

IEHC represents a flexible, robust, and powerful approach to integrate functional omics information to enhance the power of identifying association signals for the survival risk of complex human cancers.

An integrative multiomics analysis identifies putative causal genes for COVID-19 severity

Purpose

It is critical to identify putative causal targets for SARS coronavirus 2, which may guide drug repurposing options to reduce the public health burden of COVID-19.

Methods

We applied complementary methods and multiphased design to pinpoint the most likely causal genes for COVID-19 severity. First, we applied cross-methylome omnibus (CMO) test and leveraged data from the COVID-19 Host Genetics Initiative (HGI) comparing 9,986 hospitalized COVID-19 patients and 1,877,672 population controls. Second, we evaluated associations using the complementary S-PrediXcan method and leveraging blood and lung tissue gene expression prediction models. Third, we assessed associations of the identified genes with another COVID-19 phenotype, comparing very severe respiratory confirmed COVID versus population controls. Finally, we applied a fine-mapping method, fine-mapping of gene sets (FOGS), to prioritize putative causal genes.

Results

Through analyses of the COVID-19 HGI using complementary CMO and S-PrediXcan methods along with fine-mapping, XCR1, CCR2, SACM1L, OAS3, NSF, WNT3, NAPSA, and IFNAR2 are identified as putative causal genes for COVID-19 severity.

Conclusion

We identified eight genes at five genomic loci as putative causal genes for COVID-19 severity.

Detection of Genetic Overlap Between Rheumatoid Arthritis and Systemic Lupus Erythematosus Using GWAS Summary Statistics

Background

Clinical and epidemiological studies have suggested systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA) are comorbidities and common genetic etiologies can partly explain such coexistence. However, shared genetic determinations underlying the two diseases remain largely unknown.

Methods

Our analysis relied on summary statistics available from genome-wide association studies of SLE (N = 23,210) and RA (N = 58,284). We first evaluated the genetic correlation between RA and SLE through the linkage disequilibrium score regression (LDSC). Then, we performed a multiple-tissue eQTL (expression quantitative trait loci) weighted integrative analysis for each of the two diseases and aggregated association evidence across these tissues via the recently proposed harmonic mean P-value (HMP) combination strategy, which can produce a single well-calibrated P-value for correlated test statistics. Afterwards, we conducted the pleiotropy-informed association using conjunction conditional FDR (ccFDR) to identify potential pleiotropic genes associated with both RA and SLE.

Results

We found there existed a significant positive genetic correlation (r_g = 0.404, P = 6.01E-10) via LDSC between RA and SLE. Based on the multiple-tissue eQTL weighted integrative analysis and the HMP combination across various tissues, we discovered 14 potential pleiotropic genes by ccFDR, among which four were likely newly novel genes (i.e., INPP5B, OR5K2, RP11-2C24.5, and CTD-3105H18.4). The SNP effect sizes of these pleiotropic genes were typically positively dependent, with an average correlation of 0.579. Functionally, these genes were implicated in multiple auto-immune relevant pathways such as inositol phosphate metabolic process, membrane and glucagon signaling pathway.

Conclusion

This study reveals common genetic components between RA and SLE and provides candidate associated loci for understanding of molecular mechanism underlying the comorbidity of the two diseases.

A gene-level methylome-wide association analysis identifies novel Alzheimer's disease genes

MOTIVATION

Transcriptome-wide association studies (TWAS) have successfully facilitated the discovery of novel genetic risk loci for many complex traits, including late-onset Alzheimer's disease (AD). However, most existing TWAS methods rely only on gene expression and ignore epigenetic modification (i.e., DNA methylation) and functional regulatory information (i.e., enhancer-promoter interactions), both of which contribute significantly to the genetic basis of AD.

RESULTS

We develop a novel gene-level association testing method that integrates genetically regulated DNA methylation and enhancer-target gene pairs with genome-wide association study (GWAS) summary results. Through simulations, we show that our approach, referred to as the CMO (cross methylome omnibus) test, yielded well controlled type I error rates and achieved much higher statistical power than competing methods under a wide range of scenarios. Furthermore, compared with TWAS, CMO identified an average of 124% more associations when analyzing several brain imaging-related GWAS results. By analyzing to date the largest AD GWAS of 71,880 cases and 383,378 controls, CMO identified six novel loci for AD, which have been ignored by competing methods.

AVAILABILITY

Software: https://github.com/ChongWuLab/CMO.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Regional Variation of Splicing QTLs in Human Brain

A major question in human genetics is how sequence variants of broadly expressed genes produce tissue- and cell type-specific molecular phenotypes. Genetic variation of alternative splicing is a prevalent source of transcriptomic and proteomic diversity in human populations. We investigated splicing quantitative trait loci (sQTLs) in 1,209 samples from 13 human brain regions, using RNA sequencing (RNA-seq) and genotype data from the Genotype-Tissue Expression (GTEx) project. Hundreds of sQTLs were identified in each brain region. Some sQTLs were shared across brain regions, whereas others displayed regional specificity. These “regionally ubiquitous” and “regionally specific” sQTLs showed distinct positional distributions of single-nucleotide polymorphisms (SNPs) within and outside essential splice sites, respectively, suggesting their regulation by distinct molecular mechanisms. Integrating the binding motifs and expression patterns of RNA binding proteins with exon splicing profiles, we uncovered likely causal variants underlying brain region-specific sQTLs. Notably, SNP rs17651213 created a putative binding site for the splicing factor RBFOX2 and was associated with increased splicing of MAPT exon 3 in cerebellar tissues, where RBFOX2 was highly expressed. Overall, our study reveals a more comprehensive spectrum and regional variation of sQTLs in human brain and demonstrates that such regional variation can be used to fine map potential causal variants of sQTLs and their associated neurological diseases.

Gene mapping and functional annotation of GWAS of oral ulcers using FUMA software

Oral ulcers not only influence the physical health of patients, but they also interfere with their quality of life. However, the exact etiology of oral ulcers is not clear. To explore the roles of genetic factors in oral ulcers, a genome-wide association study of the condition in European individuals was re-evaluated by the FUMA v1.3.5e online tool. A total of 380 independent significant single nucleotide polymorphisms (SNPs) and 89 lead SNPs were identified in 34 genomic risk loci. Out of these identified genomic risk loci, 280 possible causal genes were pinpointed by positional mapping and expression quantitative trait locus mapping. Among these genes, 216 novel genes were identified. Furthermore, some genomic loci were mapped to a single gene. Functional annotation of these prioritized genes revealed that the immune response pathway was implicated in the onset of oral ulcers. Overall, our findings revealed novel possible causal genes and demonstrated that the immune response has a crucial role in the occurrence of oral ulcers.

A fast and powerful eQTL weighted method to detect genes associated with complex trait using GWAS summary data

Although genomewide association studies (GWASs) have identified many genetic variants underlying complex traits, a large fraction of heritability still remains unexplained. Integrative analysis that incorporates additional information, such as expression quantitativetrait locus (eQTL) data into sequencing studies (denoted as transcriptomewide association study [TWAS]), can aid the discovery of trait-associated genetic variants. However, general TWAS methods only incorporate one eQTL-derived weight (e.g., cis-effect), and thus can suffer a substantial loss of power when the single estimated cis-effect is not predictive for the effect size of a genetic variant or when there are estimation errors in the estimated cis-effect, or if the data are not consistent with the model assumption. In this study, we propose an omnibus test (OT) which utilizes a Cauchy association test to integrate association evidence demonstrated by three different traditional tests (burden test, quadratic test, and adaptive test) using GWAS summary data with multiple eQTL-derived weights. The p value of the proposed test can be calculated analytically, and thus it is fast and efficient. We applied our proposed test to two schizophrenia (SCZ) GWAS summary data sets and two lipids trait (HDL) GWAS summary data sets. Compared with the three traditional tests, our proposed OT can identify more trait-associated genes.

Incorporating external information to improve sparse signal detection in rare-variant gene-set-based analyses

Gene-set analyses are used to assess whether there is any evidence of association with disease among a set of biologically related genes. Such an analysis typically treats all genes within the sets similarly, even though there is substantial, external, information concerning the likely importance of each gene within each set. For example, for traits that are under purifying selection, we would expect genes showing extensive genic constraint to be more likely to be trait associated than unconstrained genes. Here we improve gene-set analyses by incorporating such external information into a higher-criticism-based signal detection analysis. We show that when this external information is predictive of whether a gene is associated with disease, our approach can lead to a significant increase in power. Further, our approach is particularly powerful when the signal is sparse, that is when only a small number of genes within the set are associated with the trait. We illustrate our approach with a gene-set analysis of amyotrophic lateral sclerosis (ALS) and implicate a number of gene-sets containing SOD1 and NEK1 as well as showing enrichment of small p values for gene-sets containing known ALS genes. We implement our approach in the R package wHC.

A powerful fine-mapping method for transcriptome-wide association studies

Transcriptome-wide association studies (TWAS) have been recently applied to successfully identify many novel genes associated with complex traits. While appealing, TWAS tend to identify multiple significant genes per locus, and many of them may not be causal due to confounding through linkage disequilibrium (LD) among SNPs. Here we introduce a powerful fine-mapping method that prioritizes putative causal genes by accounting for local LD. We apply a weighted adaptive test with eQTL-derived weights to maintain high power across various scenarios. Through simulations, we show that our new approach yielded a well-controlled Type I error rate while achieving higher power and AUC than competing methods. We applied our approach to a schizophrenia GWAS summary dataset and successfully prioritized some well-known schizophrenia-related genes, such as C4A. Importantly, our approach identified some putative causal genes (e.g., B3GAT1 and RGS6) that were missed by competing methods and TWAS. Our results suggest that our approach is a useful tool to prioritize putative causal genes, gaining insights into the mechanisms of complex traits.

Postmortem brain tissue as an underutilized resource to study the molecular pathology of neuropsychiatric disorders across different ethnic populations

In recent years, large scale meta-analysis of genome-wide association studies (GWAS) have reliably identified genetic polymorphisms associated with neuropsychiatric disorders such as schizophrenia (SCZ), bipolar disorder (BPD) and major depressive disorder (MDD). However, the majority of disease-associated single nucleotide polymorphisms (SNPs) appear within functionally ambiguous non-coding genomic regions. Recently, increased emphasis has been placed on identifying the functional relevance of disease-associated variants via correlating risk polymorphisms with gene expression levels in etiologically relevant tissues. For neuropsychiatric disorders, the etiologically relevant tissue is brain, which requires robust postmortem sample sizes from varying genetic backgrounds. While small sample sizes are of decreasing concern, postmortem brain databases are composed almost exclusively of Caucasian samples, which significantly limits study design and result interpretation. In this review, we highlight the importance of gene expression and expression quantitative loci (eQTL) studies in clinically relevant postmortem tissue while addressing the current limitations of existing postmortem brain databases. Finally, we introduce future collaborations to develop postmortem brain databases for neuropsychiatric disorders from Chinese and Asian subpopulations.

Integration of Enhancer-Promoter Interactions with GWAS Summary Results Identifies Novel Schizophrenia-Associated Genes and Pathways

It remains challenging to boost statistical power of genome-wide association studies (GWASs) to identify more risk variants or loci that can account for "missing heritability." Furthermore, since most identified variants are not in gene-coding regions, a biological interpretation of their function is largely lacking. On the other hand, recent biotechnological advances have made it feasible to experimentally measure the three-dimensional organization of the genome, including enhancer-promoter interactions in high resolutions. Due to the well-known critical roles of enhancer-promoter interactions in regulating gene expression programs, such data have been applied to link GWAS risk variants to their putative target genes, gaining insights into underlying biological mechanisms. However, their direct use in GWAS association testing is yet to be exploited. Here we propose integrating enhancer-promoter interactions into GWAS association analysis to both boost statistical power and enhance interpretability. We demonstrate that through an application to two large-scale schizophrenia (SCZ) GWAS summary data sets, the proposed method could identify some novel SCZ-associated genes and pathways (containing no significant SNPs). For example, after the Bonferroni correction, for the larger SCZ data set with 36,989 cases and 113,075 controls, our method applied to the gene body and enhancer regions identified 27 novel genes and 11 novel KEGG pathways to be significant, all missed by the transcriptome-wide association study (TWAS) approach. We conclude that our proposed method is potentially useful and is complementary to TWAS and other standard gene- and pathway-based methods.