A versatile gene-based test for genome-wide association studies

Analysis of Evolutionary Conservation, Expression Level, and Genetic Association at a Genome-wide Scale Reveals Heterogeneity Across Polygenic Phenotypes

Understanding the expression level and evolutionary rate of associated genes with human polygenic diseases provides crucial insights into their disease-contributing roles. In this work, we leveraged genome-wide association studies (GWASs) to investigate the relationship between the genetic association and both the evolutionary rate (dN/dS) and expression level of human genes associated with the two polygenic diseases of schizophrenia and coronary artery disease. Our findings highlight a distinct variation in these relationships between the two diseases. Genes associated with both diseases exhibit a significantly greater variance in evolutionary rate compared to those implicated in monogenic diseases. Expanding our analyses to 4,756 complex traits in the GWAS atlas database, we unraveled distinct trait categories with a unique interplay among the evolutionary rate, expression level, and genetic association of human genes. In most polygenic traits, highly expressed genes were more associated with the polygenic phenotypes compared to lowly expressed genes. About 69% of polygenic traits displayed a negative correlation between genetic association and evolutionary rate, while approximately 30% of these traits showed a positive correlation between genetic association and evolutionary rate. Our results demonstrate the presence of a spectrum among complex traits, shaped by natural selection. Notably, at opposite ends of this spectrum, we find metabolic traits being more likely influenced by purifying selection, and immunological traits that are more likely shaped by positive selection. We further established the polygenic evolution portal (evopolygen.de) as a resource for investigating relationships and generating hypotheses in the field of human polygenic trait evolution.

From genetic associations to genes: methods, applications, and challenges

Genome-wide association studies (GWASs) have identified numerous genetic loci associated with human traits and diseases. However, pinpointing the causal genes remains a challenge, which impedes the translation of GWAS findings into biological insights and medical applications. In this review, we provide an in-depth overview of the methods and technologies used for prioritizing genes from GWAS loci, including gene-based association tests, integrative analysis of GWAS and molecular quantitative trait loci (xQTL) data, linking GWAS variants to target genes through enhancer-gene connection maps, and network-based prioritization. We also outline strategies for generating context-dependent xQTL data and their applications in gene prioritization. We further highlight the potential of gene prioritization in drug repurposing. Lastly, we discuss future challenges and opportunities in this field.

Gene-based association tests in family samples using GWAS summary statistics

Genome-wide association studies (GWAS) have led to rapid growth in detecting genetic variants associated with various phenotypes. Owing to a great number of publicly accessible GWAS summary statistics, and the difficulty in obtaining individual-level genotype data, many existing gene-based association tests have been adapted to require only GWAS summary statistics rather than individual-level data. However, these association tests are restricted to unrelated individuals and thus do not apply to family samples directly. Moreover, due to its flexibility and effectiveness, the linear mixed model has been increasingly utilized in GWAS to handle correlated data, such as family samples. However, it remains unknown how to perform gene-based association tests in family samples using the GWAS summary statistics estimated from the linear mixed model. In this study, we show that, when family size is negligible compared to the total sample size, the diagonal block structure of the kinship matrix makes it possible to approximate the correlation matrix of marginal Z scores by linkage disequilibrium matrix. Based on this result, current methods utilizing summary statistics for unrelated individuals can be directly applied to family data without any modifications. Our simulation results demonstrate that this proposed strategy controls the type 1 error rate well in various situations. Finally, we exemplify the usefulness of the proposed approach with a dental caries GWAS data set.

Multi-trait Analysis of GWAS for circulating FGF23 Identifies Novel Network Interactions Between HRG-HMGB1 and Cardiac Disease in CKD

Background

Genome-wide association studies (GWAS) have identified numerous genetic loci associated with mineral metabolism (MM) markers but have exclusively focused on single-trait analysis. In this study, we performed a multi-trait analysis of GWAS (MTAG) of MM, exploring overlapping genetic architecture between traits, to identify novel genetic associations for fibroblast growth factor 23 (FGF23).

Methods

We applied MTAG to genetic variants common to GWAS of 5 genetically correlated MM markers (calcium, phosphorus, FGF23, 25-hydroxyvitamin D (25(OH)D) and parathyroid hormone (PTH)) in European-ancestry subjects. We integrated information from UKBioBank GWAS for blood levels for phosphate, 25(OH)D and calcium (n=366,484), and CHARGE GWAS for PTH (n=29,155) and FGF23 (n=16,624). We then used functional genomics to model interactive and dynamic networks to identify novel associations between genetic traits and circulating FGF23.

Results

MTAG increased the effective sample size for all MM markers to n=50,325 for FGF23. After clumping, MTAG identified independent genome-wide significant SNPs for all traits, including 62 loci for FGF23. Many of these loci have not been previously reported in single-trait analyses. Through functional genomics we identified Histidine-rich glycoprotein (HRG) and high mobility group box 1(HMGB1) genes as master regulators of downstream canonical pathways associated with FGF23. HRG-HMGB1 network interactions were also highly enriched in left ventricular heart tissue of a cohort of deceased hemodialysis patients.

Conclusion

Our findings highlight the importance of MTAG analysis of MM markers to boost the number of genome-wide significant loci for FGF23 to identify novel genetic traits. Functional genomics revealed novel networks that inform unique cellular functions and identified HRG-HMGB1 as key master regulators of FGF23 and cardiovascular disease in CKD. Future studies will provide a deeper understanding of genetic signatures associated with FGF23 and its role in health and disease.

Identification of TACSTD2 as novel therapeutic targets for cisplatin-induced acute kidney injury by multi-omics data integration

Cisplatin-induced acute kidney injury (CP-AKI) is a common complication in cancer patients. Although ferroptosis is believed to contribute to the progression of CP-AKI, its mechanisms remain incompletely understood. In this study, after initially processed individual omics datasets, we integrated multi-omics data to construct a ferroptosis network in the kidney, resulting in the identification of the key driver TACSTD2. In vitro and in vivo results showed that TACSTD2 was notably upregulated in cisplatin-treated kidneys and BUMPT cells. Overexpression of TACSTD2 accelerated ferroptosis, while its gene disruption decelerated ferroptosis, likely mediated by its potential downstream targets HMGB1, IRF6, and LCN2. Drug prediction and molecular docking were further used to propose that drugs targeting TACSTD2 may have therapeutic potential in CP-AKI, such as parthenolide, progesterone, premarin, estradiol and rosiglitazone. Our findings suggest a significant association between ferroptosis and the development of CP-AKI, with TACSTD2 playing a crucial role in modulating ferroptosis, which provides novel perspectives on the pathogenesis and treatment of CP-AKI.

Network propagation for GWAS analysis: a practical guide to leveraging molecular networks for disease gene discovery

MOTIVATION

Genome-wide association studies (GWAS) have enabled large-scale analysis of the role of genetic variants in human disease. Despite impressive methodological advances, subsequent clinical interpretation and application remains challenging when GWAS suffer from a lack of statistical power. In recent years, however, the use of information diffusion algorithms with molecular networks has led to fruitful insights on disease genes.

RESULTS

We present an overview of the design choices and pitfalls that prove crucial in the application of network propagation methods to GWAS summary statistics. We highlight general trends from the literature, and present benchmark experiments to expand on these insights selecting as case study three diseases and five molecular networks. We verify that the use of gene-level scores based on GWAS P-values offers advantages over the selection of a set of 'seed' disease genes not weighted by the associated P-values if the GWAS summary statistics are of sufficient quality. Beyond that, the size and the density of the networks prove to be important factors for consideration. Finally, we explore several ensemble methods and show that combining multiple networks may improve the network propagation approach.

Evaluating 17 methods incorporating biological function with GWAS summary statistics to accelerate discovery demonstrates a tradeoff between high sensitivity and high positive predictive value

Where sufficiently large genome-wide association study (GWAS) samples are not currently available or feasible, methods that leverage increasing knowledge of the biological function of variants may illuminate discoveries without increasing sample size. We comprehensively evaluated 17 functional weighting methods for identifying novel associations. We assessed the performance of these methods using published results from multiple GWAS waves across each of five complex traits. Although no method achieved both high sensitivity and positive predictive value (PPV) for any trait, a subset of methods utilizing pleiotropy and expression quantitative trait loci nominated variants with high PPV (>75%) for multiple traits. Application of functionally weighting methods to enhance GWAS power for locus discovery is unlikely to circumvent the need for larger sample sizes in truly underpowered GWAS, but these results suggest that applying functional weighting to GWAS can accurately nominate additional novel loci from available samples for follow-up studies.

Dataset of single nucleotide polymorphisms of immune-associated genes in patients with SARS-CoV-2 infection

The SARS-CoV-2 pandemic has affected nations globally leading to illness, death, and economic downturn. Why disease severity, ranging from no symptoms to the requirement for extracorporeal membrane oxygenation, varies between patients is still incompletely understood. Consequently, we aimed at understanding the impact of genetic factors on disease severity in infection with SARS-CoV-2. Here, we provide data on demographics, ABO blood group, human leukocyte antigen (HLA) type, as well as next-generation sequencing data of genes in the natural killer cell receptor family, the renin-angiotensin-aldosterone and kallikrein-kinin systems and others in 159 patients with SARS-CoV-2 infection, stratified into seven categories of disease severity. We provide single-nucleotide polymorphism (SNP) data on the patients and a protein structural analysis as a case study on a SNP in the SIGLEC7 gene, which was significantly associated with the clinical score. Our data represent a resource for correlation analyses involving genetic factors and disease severity and may help predict outcomes in infections with future SARS-CoV-2 variants and aid vaccine adaptation.

Testing for pharmacogenomic predictors of ppRNFL thinning in individuals exposed to vigabatrin

Background

The anti-seizure medication vigabatrin (VGB) is effective for controlling seizures, especially infantile spasms. However, use is limited by VGB-associated visual field loss (VAVFL). The mechanisms by which VGB causes VAVFL remains unknown. Average peripapillary retinal nerve fibre layer (ppRNFL) thickness correlates with the degree of visual field loss (measured by mean radial degrees). Duration of VGB exposure, maximum daily VGB dose, and male sex are associated with ppRNFL thinning. Here we test the hypothesis that common genetic variation is a predictor of ppRNFL thinning in VGB exposed individuals. Identifying pharmacogenomic predictors of ppRNFL thinning in VGB exposed individuals could potentially enable safe prescribing of VGB and broader use of a highly effective drug.

Methods

Optical coherence topography (OCT) and GWAS data were processed from VGB-exposed individuals (n = 71) recruited through the EpiPGX Consortium. We conducted quantitative GWAS analyses for the following OCT measurements: (1) average ppRNFL, (2) inferior quadrant, (3) nasal quadrant, (4) superior quadrant, (5) temporal quadrant, (6) inferior nasal sector, (7) nasal inferior sector, (8) superior nasal sector, and (9) nasal superior sector. Using the summary statistics from the GWAS analyses we conducted gene-based testing using VEGAS2. We conducted nine different PRS analyses using the OCT measurements. To determine if VGB-exposed individuals were predisposed to having a thinner RNFL, we calculated their polygenic burden for retinal thickness. PRS alleles for retinal thickness were calculated using published summary statistics from a large-scale GWAS of inner retinal morphology using the OCT images of UK Biobank participants.

Results

The GWAS analyses did not identify a significant association after correction for multiple testing. Similarly, the gene-based and PRS analyses did not reveal a significant association that survived multiple testing.

Conclusion

We set out to identify common genetic predictors for VGB induced ppRNFL thinning. Results suggest that large-effect common genetic predictors are unlikely to exist for ppRNFL thinning (as a marker of VAVFL). Sample size was a limitation of this study. However, further recruitment is a challenge as VGB is rarely used today because of this adverse reaction. Rare variants may be predictors of this adverse drug reaction and were not studied here.

Multi-layered genetic approaches to identify approved drug targets

Drugs targeting genes linked to disease via evidence from human genetics have increased odds of approval. Approaches to prioritize such genes include genome-wide association studies (GWASs), rare variant burden tests in exome sequencing studies (Exome), or integration of a GWAS with expression/protein quantitative trait loci (eQTL/pQTL-GWAS). Here, we compare gene-prioritization approaches on 30 clinically relevant traits and benchmark their ability to recover drug targets. Across traits, prioritized genes were enriched for drug targets with odds ratios (ORs) of 2.17, 2.04, 1.81, and 1.31 for the GWAS, eQTL-GWAS, Exome, and pQTL-GWAS methods, respectively. Adjusting for differences in testable genes and sample sizes, GWAS outperforms e/pQTL-GWAS, but not the Exome approach. Furthermore, performance increased through gene network diffusion, although the node degree, being the best predictor (OR = 8.7), revealed strong bias in literature-curated networks. In conclusion, we systematically assessed strategies to prioritize drug target genes, highlighting the promises and pitfalls of current approaches.

Cross-phenotype association analysis of gastric cancer: in-silico functional annotation based on the disease-gene network

BACKGROUND

A gene or variant has pleiotropic effects, and genetic variant identification across multiple phenotypes can provide a comprehensive understanding of biological pathways shared among different diseases or phenotypes. Discovery of genetic loci associated with multiple diseases can simultaneously support general interventions. Several meta-analyses have shown genetic associations with gastric cancer (GC); however, no study has identified associations with other phenotypes using this approach.

METHODS

Here, we applied disease network analysis and gene-based analysis (GBA) to examine genetic variants linked to GC and simultaneously associated with other phenotypes. We conducted a single-nucleotide polymorphism (SNP) level meta-analysis and GBA through a systematic genome-wide association study (GWAS) linked to GC, to integrate published results for the SNP variants and group them into major GC-associated genes. We then performed disease network and expression quantitative trait loci (eQTL) analyses to evaluate cross-phenotype associations and expression levels of GC-related genes.

RESULTS

Seven genes (MTX1, GBAP1, MUC1, TRIM46, THBS3, PSCA, and ABO) were associated with GC as well as blood urea nitrogen (BUN), glomerular filtration rate (GFR), and uric acid (UA). In addition, 17 SNPs regulated the expression of genes located on 1q22, 24 SNPs regulated the expression of PSCA on 8q24.3, and rs7849820 regulated the expression of ABO on 9q34.2. Furthermore, rs1057941 and rs2294008 had the highest posterior causal probabilities of being a causal candidate SNP in 1q22, and 8q24.3, respectively.

CONCLUSIONS

These findings identified seven GC-associated genes exhibiting a cross-association with GFR, BUN, and UA.

Invited review: Good practices in genome-wide association studies to identify candidate sequence variants in dairy cattle

Genotype data from dairy cattle selection programs have greatly facilitated GWAS to identify variants related to economic traits. Results can enhance the accuracy of genomic prediction, analyze more complex models that go beyond additive effects, elucidate the genetic architecture of a trait, and finally, decipher the underlying biology of traits. The entire process, comprising data generation, quality control, statistical analyses, interpretation of association results, and linking results to biology should be designed and executed to minimize the generation of false-positive and false-negative associations and misleading links to biological processes. This review aims to provide general guidelines for data analysis that address data quality control, association tests, adjustment for population stratification, and significance evaluation to improve the reliability of conclusions. We also provide guidance on post-GWAS strategy and the interpretation of results. These guidelines are tailored to dairy cattle, which are characterized by long-range linkage disequilibrium, large half-sib families, and routinely collected phenotypes, requiring different approaches than those applied in human GWAS. We discuss common limitations and challenges that have been overlooked in the analysis and interpretation of GWAS to identify candidate sequence variants in dairy cattle.

Leveraging trans-ethnic genetic risk scores to improve association power for complex traits in underrepresented populations

Trans-ethnic genome-wide association studies have revealed that many loci identified in European populations can be reproducible in non-European populations, indicating widespread trans-ethnic genetic similarity. However, how to leverage such shared information more efficiently in association analysis is less investigated for traits in underrepresented populations. We here propose a statistical framework, trans-ethnic genetic risk score informed gene-based association mixed model (GAMM), by hierarchically modeling single-nucleotide polymorphism effects in the target population as a function of effects of the same trait in well-studied populations. GAMM powerfully integrates genetic similarity across distinct ancestral groups to enhance power in understudied populations, as confirmed by extensive simulations. We illustrate the usefulness of GAMM via the application to 13 blood cell traits (i.e. basophil count, eosinophil count, hematocrit, hemoglobin concentration, lymphocyte count, mean corpuscular hemoglobin, mean corpuscular hemoglobin concentration, mean corpuscular volume, monocyte count, neutrophil count, platelet count, red blood cell count and total white blood cell count) in Africans of the UK Biobank (n = 3204) while utilizing genetic overlap shared in Europeans (n = 746 667) and East Asians (n = 162 255). We discovered multiple new associated genes, which had otherwise been missed by existing methods, and revealed that the trans-ethnic information indirectly contributed much to the phenotypic variance. Overall, GAMM represents a flexible and powerful statistical framework of association analysis for complex traits in underrepresented populations by integrating trans-ethnic genetic similarity across well-studied populations, and helps attenuate health inequities in current genetics research for people of minority populations.

Exploring the relationship between socioeconomic deprivation index and Alzheimer's disease using summary-level data: From genetic correlation to causality

Patients with Alzheimer's disease (AD) are markedly increasing as population aging and no disease-modifying therapies are currently available for AD. Previous studies suggested a broad link between socioeconomic status and a variety of disorders, including mental illness and cognitive abilities. However, the association between socioeconomic deprivation and AD has been unknown. We here employed Townsend deprivation index (TDI) to explore such relation and found a positive genetic correlation (r̂_g=0.211, P = 8.00 × 10^-4) between the two traits with summary statistics data (N = 455,258 for TDI and N = 455,815 for AD). Then, we performed pleiotropy analysis at both variant and gene levels using a powerful method called PLACO and detected 87 distinct pleiotropic genes. Functional analysis demonstrated these genes were significantly enriched in pancreas, liver, heart, blood, brain, and muscle tissues. Using Mendelian randomization methods, we further found that one genetically predicted standard deviation elevation in TDI could lead to approximately 18.5% (95% confidence intervals 1.6- 38.2%, P = 0.031) increase of AD risk, and that the identified causal association was robust against used MR approaches, horizontal pleiotropy, and instrumental selection. Overall, this study provides deep insight into common genetic components underlying TDI and AD, and further reveals causal connection between them. It is also helpful to develop a more suitable plan for ameliorating inequities, hardship, and disadvantage, with the hope of improving health outcomes among economically disadvantaged people.

Genetic correlation and gene-based pleiotropy analysis for four major neurodegenerative diseases with summary statistics

Recent genome-wide association studies suggested shared genetic components between neurodegenerative diseases. However, pleiotropic association patterns among them remain poorly understood. We here analyzed 4 major neurodegenerative diseases including Alzheimer's disease (AD), Parkinson's disease (PD), frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS), and found suggestively positive genetic correlation. We next implemented a gene-centric pleiotropy analysis with a powerful method called PLACO and detected 280 pleiotropic associations (226 unique genes) with these diseases. Functional analyses demonstrated that these genes were enriched in the pancreas, liver, heart, blood, brain, and muscle tissues; and that 42 pleiotropic genes exhibited drug-gene interactions with 341 drugs. Using Mendelian randomization, we discovered that AD and PD can increase the risk of developing ALS, and that AD and ALS can also increase the risk of developing FTD, respectively. Overall, this study provides in-depth insights into shared genetic components and causal relationship among the 4 major neurodegenerative diseases, indicating genetic overlap and causality commonly drive their co-occurrence. It also has important implications on the etiology understanding, drug development and therapeutic targets for neurodegenerative diseases.

Genome-wide association study of handgrip strength in the Northern Chinese adult twins

PURPOSE

Currently, new loci related to handgrip strength have been identified in genome-wide association studies. However, this topic is an understudied area in the Chinese population.

MATERIALS AND METHODS

A total of 135 dizygotic twin pairs recruited from the Qingdao Twin Registry system were included in the present study. Using GEMMA, VEAGSE2, and PASCAL software for SNP-based analysis, gene-based analysis, and pathway-based analysis, respectively. The resulting SNPs were subjected to eQTL analysis.

RESULTS

Although none of the loci reach the statistically significant level (p < 5 × 10^-8), we found 19 SNPs exceeding the suggestive significant level (p < 1 × 10^-5). After imputation, 162 SNPs reached suggestive evidence level for handgrip strength. A total of 1,118 genes reached the nominal significance level (p < 0.05) in gene-based analysis. A total of 626 potential biological pathways were associated with handgrip strength (p < 0.05). The results of eQTL analysis were mainly enriched in tissues such as the muscle-skeletal, brain, visceral fat, and brain-cortical.

CONCLUSIONS

Genetic variants may involve in regulatory domains, functional genes, and biological pathways that mediate handgrip strength.

Cytokine- and Vascular Endothelial Growth Factor-Related Gene-Based Genome-Wide Association Study of Low-Dose Ketamine Infusion in Patients with Treatment-Resistant Depression

BACKGROUND AND OBJECTIVE

Ketamine may work as an anti-inflammatory agent, and it increases the levels of vascular endothelial growth factor (VEGF) in patients with treatment-resistant depression. However, whether genes related to pro-inflammatory and anti-inflammatory cytokines and VEGF may predict the treatment response to ketamine remains unknown.Therefore the aim of this study was to analyze whether specific genes related to inflammatory processes and VEGF were associated with treatment response to low-dose ketamine in patients with treatment-resistant depression.

METHODS

Based on the genome data from our clinical trial, this study was a secondary analysis of candidate genes correlated with different timepoints of depressive symptoms. In total, 65 patients with treatment-resistant depression (n = 21 for ketamine 0.5 mg/kg, 20 for ketamine 0.2 mg/kg, and 24 for normal saline) were genotyped for 684,616 single nucleotide polymorphisms. Genes associated with 80 cytokines (i.e., interleukin [IL]-1, IL-6, tumor necrosis factor-α, and adiponectin) and VEGF (i.e., VEGF and VEGF receptors) were selected for the gene-based genome-wide association study on the antidepressant effect of a ketamine infusion.

RESULTS

Specific single nucleotide polymorphisms, including rs2540315 and rs75746675 in IL1R1 and rs79568085 in VEGFC, were related to the rapid (within 240 min) antidepressant effect of a ketamine infusion; specific single nucleotide polymorphisms, such as Affx-20131665 in PIGF and rs8179353, rs8179353, and rs8179353 in TNFRSF8, were associated with the sustained (up to 2 weeks) antidepressant effect of low-dose (combined 0.5 mg/kg and 0.2 mg/kg) ketamine.

CONCLUSIONS

Our findings further revealed that genes related to both anti-inflammatory and pro-inflammatory cytokines (i.e., IL-1, IL-2, IL-6, tumor necrosis factor-α, C-reactive protein, and adiponectin) and VEGF-FLK signaling predicted the treatment response to a ketamine infusion in patients with treatment-resistant depression. The synergic modulation of inflammatory and VEGF systems may contribute to the antidepressant effect of ketamine.

CLINICAL TRIAL REGISTRATION

University Hospital Medical Information Network Clinical Trials Registry (UMIN-CTR) number: UMIN000016985.

mBAT-combo: A more powerful test to detect gene-trait associations from GWAS data

Gene-based association tests aggregate multiple SNP-trait associations into sets defined by gene boundaries and are widely used in post-GWAS analysis. A common approach for gene-based tests is to combine SNPs associations by computing the sum of χ² statistics. However, this strategy ignores the directions of SNP effects, which could result in a loss of power for SNPs with masking effects, e.g., when the product of two SNP effects and the linkage disequilibrium (LD) correlation is negative. Here, we introduce "mBAT-combo," a set-based test that is better powered than other methods to detect multi-SNP associations in the context of masking effects. We validate the method through simulations and applications to real data. We find that of 35 blood and urine biomarker traits in the UK Biobank, 34 traits show evidence for masking effects in a total of 4,273 gene-trait pairs, indicating that masking effects is common in complex traits. We further validate the improved power of our method in height, body mass index, and schizophrenia with different GWAS sample sizes and show that on average 95.7% of the genes detected only by mBAT-combo with smaller sample sizes can be identified by the single-SNP approach with a 1.7-fold increase in sample sizes. Eleven genes significant only in mBAT-combo for schizophrenia are confirmed by functionally informed fine-mapping or Mendelian randomization integrating gene expression data. The framework of mBAT-combo can be applied to any set of SNPs to refine trait-association signals hidden in genomic regions with complex LD structures.

Genetic and functional analyses implicate microRNA 499A in bipolar disorder development

Bipolar disorder (BD) is a complex mood disorder with a strong genetic component. Recent studies suggest that microRNAs contribute to psychiatric disorder development. In BD, specific candidate microRNAs have been implicated, in particular miR-137, miR-499a, miR-708, miR-1908 and miR-2113. The aim of the present study was to determine the contribution of these five microRNAs to BD development. For this purpose, we performed: (i) gene-based tests of the five microRNA coding genes, using data from a large genome-wide association study of BD; (ii) gene-set analyses of predicted, brain-expressed target genes of the five microRNAs; (iii) resequencing of the five microRNA coding genes in 960 BD patients and 960 controls and (iv) in silico and functional studies for selected variants. Gene-based tests revealed a significant association with BD for MIR499A, MIR708, MIR1908 and MIR2113. Gene-set analyses revealed a significant enrichment of BD associations in the brain-expressed target genes of miR-137 and miR-499a-5p. Resequencing identified 32 distinct rare variants (minor allele frequency < 1%), all of which showed a non-significant numerical overrepresentation in BD patients compared to controls (p = 0.214). Seven rare variants were identified in the predicted stem-loop sequences of MIR499A and MIR2113. These included rs142927919 in MIR2113 (p_nom = 0.331) and rs140486571 in MIR499A (p_nom = 0.297). In silico analyses predicted that rs140486571 might alter the miR-499a secondary structure. Functional analyses showed that rs140486571 significantly affects miR-499a processing and expression. Our results suggest that MIR499A dysregulation might contribute to BD development. Further research is warranted to elucidate the contribution of the MIR499A regulated network to BD susceptibility.

Genome-wide association study of SNP- and gene-based approaches to identify susceptibility candidates for lupus nephritis in the Han Chinese population

Background

Lupus nephritis (LN) is one of the most common and serious complications of systemic lupus erythaematosus (SLE). Genetic factors play important roles in the pathogenesis of LN and could be used to predict who might develop LN. The purpose of this study was to screen for susceptible candidates of LN across the whole genome in the Han Chinese population.

Methods

592 LN patients and 453 SLE patients without renal damage were genotyped at 492,970 single nucleotide polymorphisms (SNPs) in the genome-wide association study (GWAS). Fifty-six SNPs were selected for replication in an independent cohort of 188 LN and 171 SLE without LN patients. Further quantitative real-time (qRT) PCR was carried out in 6 LN patients and 6 healthy controls. Gene-based analysis was conducted using the versatile gene-based test for GWAS. Subsequently, enrichment and pathway analyses were performed in the DAVID database.

Results

The GWAS analysis and the following replication research identified 9 SNPs showing suggestive correlation with LN (P&lt;10-4). The most significant SNP was rs12606116 (18p11.32), at P=8.72×10−6. The qRT-PCR results verified the mRNA levels of LINC00470 and ADCYAP1, the closest genes to rs12606116, were significantly lower in LN patients. From the gene-based analysis, 690 genes had suggestive evidence of association (P&lt;0.05), including LINC00470. The enrichment analysis identified the involvement of transforming growth factor beta (TGF-β) signalings in the development of LN. Lower plasma level of TGF-β1 (P&lt;0.05) in LN patients and lower expression of transforming growth factor beta receptor 2 in lupus mice kidney (P&lt;0.05) futher indicate the involvement of TGF-β in LN.

Conclusions

Our analyses identified several promising susceptibility candidates involved in LN, and further verification of these candidates was necessary.

Pairwise genetic meta-analyses between schizophrenia and substance dependence phenotypes reveals novel association signals with pharmacological significance

Almost half of individuals diagnosed with schizophrenia also present with a substance use disorder, however, little is known about potential molecular mechanisms underlying this comorbidity. We used genetic analyses to enhance our understanding of the molecular overlap between these conditions. Our analyses revealed a positive genetic correlation between schizophrenia and the following dependence phenotypes: alcohol (r_g = 0.368, SE = 0.076, P = 1.61 × 10^-6), cannabis use disorder (r_g = 0.309, SE = 0.033, P = 1.97 × 10^-20) and nicotine (r_g = 0.117, SE = 0.043, P = 7.0 × 10^-3), as well as drinks per week (r_g = 0.087, SE = 0.021, P = 6.36 × 10^-5), cigarettes per day (r_g = 0.11, SE = 0.024, P = 4.93 × 10^-6) and life-time cannabis use (r_g = 0.234, SE = 0.029, P = 3.74 × 10^-15). We further constructed latent causal variable (LCV) models to test for partial genetic causality and found evidence for a potential causal relationship between alcohol dependence and schizophrenia (GCP = 0.6, SE = 0.22, P = 1.6 × 10^-3). This putative causal effect with schizophrenia was not seen using a continuous phenotype of drinks consumed per week, suggesting that distinct molecular mechanisms underlying dependence are involved in the relationship between alcohol and schizophrenia. To localise the specific genetic overlap between schizophrenia and substance use disorders (SUDs), we conducted a gene-based and gene-set pairwise meta-analysis between schizophrenia and each of the four individual substance dependence phenotypes in up to 790,806 individuals. These bivariate meta-analyses identified 44 associations not observed in the individual GWAS, including five shared genes that play a key role in early central nervous system development. The results from this study further supports the existence of underlying shared biology that drives the overlap in substance dependence in schizophrenia, including specific biological systems related to metabolism and neuronal function.

Detecting associated genes for complex traits shared across East Asian and European populations under the framework of composite null hypothesis testing

Background

Detecting trans-ethnic common associated genetic loci can offer important insights into shared genetic components underlying complex diseases/traits across diverse continental populations. However, effective statistical methods for such a goal are currently lacking.

Methods

By leveraging summary statistics available from global-scale genome-wide association studies, we herein proposed a novel genetic overlap detection method called CONTO (COmposite Null hypothesis test for Trans-ethnic genetic Overlap) from the perspective of high-dimensional composite null hypothesis testing. Unlike previous studies which generally analyzed individual genetic variants, CONTO is a gene-centric method which focuses on a set of genetic variants located within a gene simultaneously and assesses their joint significance with the trait of interest. By borrowing the similar principle of joint significance test (JST), CONTO takes the maximum P value of multiple associations as the significance measurement.

Results

Compared to JST which is often overly conservative, CONTO is improved in two aspects, including the construction of three-component mixture null distribution and the adjustment of trans-ethnic genetic correlation. Consequently, CONTO corrects the conservativeness of JST with well-calibrated P values and is much more powerful validated by extensive simulation studies. We applied CONTO to discover common associated genes for 31 complex diseases/traits between the East Asian and European populations, and identified many shared trait-associated genes that had otherwise been missed by JST. We further revealed that population-common genes were generally more evolutionarily conserved than population-specific or null ones.

Conclusion

Overall, CONTO represents a powerful method for detecting common associated genes across diverse ancestral groups; our results provide important implications on the transferability of GWAS discoveries in one population to others.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03637-8.

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/ .

Systematic review of gastric cancer-associated genetic variants, gene-based meta-analysis, and gene-level functional analysis to identify candidate genes for drug development

Objective: Despite being a powerful tool to identify novel variants, genome-wide association studies (GWAS) are not sufficient to explain the biological function of variants. In this study, we aimed to elucidate at the gene level the biological mechanisms involved in gastric cancer (GC) development and to identify candidate drug target genes.

Materials and methods: We conducted a systematic review for GWAS on GC following the PRISMA guidelines. Single nucleotide polymorphism (SNP)-level meta-analysis and gene-based analysis (GBA) were performed to identify SNPs and genes significantly associated with GC. Expression quantitative trait loci (eQTL), disease network, pathway enrichment, gene ontology, gene-drug, and chemical interaction analyses were conducted to elucidate the function of the genes identified by GBA.

Results: A review of GWAS on GC identified 226 SNPs located in 91 genes. In the comprehensive GBA, 44 genes associated with GC were identified, among which 12 genes (THBS3, GBAP1, KRTCAP2, TRIM46, HCN3, MUC1, DAP3, EFNA1, MTX1, PRKAA1, PSCA, and ABO) were eQTL. Using disease network and pathway analyses, we identified that PRKAA, THBS3, and EFNA1 were significantly associated with the PI3K-Alt-mTOR-signaling pathway, which is involved in various oncogenic processes, and that MUC1 acts as a regulator in both the PI3K-Alt-mTOR and P53 signaling pathways. Furthermore, RPKAA1 had the highest number of interactions with drugs and chemicals.

Conclusion: Our study suggests that PRKAA1, a gene in the PI3K-Alt-mTOR-signaling pathway, could be a potential target gene for drug development associated with GC in the future.

Systematic Review Registration: website, identifier registration number.

Identifying genes targeted by disease-associated non-coding SNPs with a protein knowledge graph

Genome-wide association studies (GWAS) have identified many single nucleotide polymorphisms (SNPs) that play important roles in the genetic heritability of traits and diseases. With most of these SNPs located on the non-coding part of the genome, it is currently assumed that these SNPs influence the expression of nearby genes on the genome. However, identifying which genes are targeted by these disease-associated SNPs remains challenging. In the past, protein knowledge graphs have often been used to identify genes that are associated with disease, also referred to as “disease genes”. Here, we explore whether protein knowledge graphs can be used to identify genes that are targeted by disease-associated non-coding SNPs by testing and comparing the performance of six existing methods for a protein knowledge graph, four of which were developed for disease gene identification. We compare our performance against two baselines: (1) an existing state-of-the-art method that is based on guilt-by-association, and (2) the leading assumption that SNPs target the nearest gene on the genome. We test these methods with four reference sets, three of which were obtained by different means. Furthermore, we combine methods to investigate whether their combination improves performance. We find that protein knowledge graphs that include predicate information perform comparable to the current state of the art, achieving an area under the receiver operating characteristic curve (AUC) of 79.6% on average across all four reference sets. Protein knowledge graphs that lack predicate information perform comparable to our other baseline (genetic distance) which achieved an AUC of 75.7% across all four reference sets. Combining multiple methods improved performance to 84.9% AUC. We conclude that methods for a protein knowledge graph can be used to identify which genes are targeted by disease-associated non-coding SNPs.

A Comparison of Methods for Gene-Based Testing That Account for Linkage Disequilibrium

Controlling the type I error rate while retaining sufficient power is a major concern in genome-wide association studies, which nowadays often examine more than a million single-nucleotide polymorphisms (SNPs) simultaneously. Methods such as the Bonferroni correction can lead to a considerable decrease in power due to the large number of tests conducted. Shifting the focus to higher functional structures (e.g., genes) can reduce the loss of power. This can be accomplished via the combination of p-values of SNPs that belong to the same structural unit to test their joint null hypothesis. However, standard methods for this purpose (e.g., Fisher’s method) do not account for the dependence among the tests due to linkage disequilibrium (LD). In this paper, we review various adjustments to methods for combining p-values that take LD information explicitly into consideration and evaluate their performance in a simulation study based on data from the HapMap project. The results illustrate the importance of incorporating LD information into the methods for controlling the type I error rate at the desired level. Furthermore, some methods are more successful in controlling the type I error rate than others. Among them, Brown’s method was the most robust technique with respect to the characteristics of the genes and outperformed the Bonferroni method in terms of power in many scenarios. Examining the genetic factors of a phenotype of interest at the gene-rather than SNP-level can provide researchers benefits in terms of the power of the study. While doing so, one should be careful to account for LD in SNPs belonging to the same gene, for which Brown’s method seems the most robust technique.

Enrichment analyses identify shared associations for 25 quantitative traits in over 600,000 individuals from seven diverse ancestries

Since 2005, genome-wide association (GWA) datasets have been largely biased toward sampling European ancestry individuals, and recent studies have shown that GWA results estimated from self-identified European individuals are not transferable to non-European individuals because of various confounding challenges. Here, we demonstrate that enrichment analyses that aggregate SNP-level association statistics at multiple genomic scales-from genes to genomic regions and pathways-have been underutilized in the GWA era and can generate biologically interpretable hypotheses regarding the genetic basis of complex trait architecture. We illustrate examples of the robust associations generated by enrichment analyses while studying 25 continuous traits assayed in 566,786 individuals from seven diverse self-identified human ancestries in the UK Biobank and the Biobank Japan as well as 44,348 admixed individuals from the PAGE consortium including cohorts of African American, Hispanic and Latin American, Native Hawaiian, and American Indian/Alaska Native individuals. We identify 1,000 gene-level associations that are genome-wide significant in at least two ancestry cohorts across these 25 traits as well as highly conserved pathway associations with triglyceride levels in European, East Asian, and Native Hawaiian cohorts.

Genome interpretation using in silico predictors of variant impact

Estimating the effects of variants found in disease driver genes opens the door to personalized therapeutic opportunities. Clinical associations and laboratory experiments can only characterize a tiny fraction of all the available variants, leaving the majority as variants of unknown significance (VUS). In silico methods bridge this gap by providing instant estimates on a large scale, most often based on the numerous genetic differences between species. Despite concerns that these methods may lack reliability in individual subjects, their numerous practical applications over cohorts suggest they are already helpful and have a role to play in genome interpretation when used at the proper scale and context. In this review, we aim to gain insights into the training and validation of these variant effect predicting methods and illustrate representative types of experimental and clinical applications. Objective performance assessments using various datasets that are not yet published indicate the strengths and limitations of each method. These show that cautious use of in silico variant impact predictors is essential for addressing genome interpretation challenges.

ExPheWas: a platform for cis-Mendelian randomization and gene-based association scans

Establishing the relationship between protein-coding genes and phenotypes has the potential to inform on the molecular etiology of diseases. Here, we describe ExPheWas (exphewas.ca), a gene-based phenome-wide association study browser and platform that enables the conduct of gene-based Mendelian randomization. The ExPheWas data repository includes sex-stratified and sex-combined gene-based association results from 26 616 genes with 1746 phenotypes measured in up to 413 133 individuals from the UK Biobank. Interactive visualizations are provided through a browser to facilitate data exploration supported by false discovery rate control, and it includes tools for enrichment analysis. The interactive Mendelian randomization module in ExPheWas allows the estimation of causal effects of a genetically predicted exposure on an outcome by using genetic variation in a single gene as the instrumental variable.

Potential Pleiotropic Genes and Shared Biological Pathways in Epilepsy and Depression Based on GWAS Summary Statistics

Current epidemiological and experimental studies have indicated the overlapping genetic foundation of epilepsy and depression. However, the detailed pleiotropic genetic etiology and neurobiological pathways have not been well understood, and there are many variants with underestimated effect on the comorbidity of the two diseases. Utilizing genome-wide association study (GWAS) summary statistics of epilepsy (15,212 cases and 29,677 controls) and depression (170,756 cases and 329,443 controls) from large consortia, we assessed the integrated gene-based association with both diseases by Multimarker Analysis of Genomic Annotation (MAGMA) and Fisher's meta-analysis. On the one hand, shared genes with significantly altered transcripts in Gene Expression Omnibus (GEO) data sets were considered as possible pleiotropic genes. On the other hand, the pathway enrichment analysis was conducted based on the gene lists with nominal significance in the gene-based association test of each disease. We identified a total of two pleiotropic genes (CD3G and SLCO3A1) with gene expression analysis validated and interpreted twenty-five common biological process supported with literature mining. This study indicates the potentially shared genes associated with both epilepsy and depression based on gene expression, meta-data analysis, and pathway enrichment strategy along with traditional GWAS and provides insights into the possible intersecting pathways that were not previously reported.

A conditional gene-based association framework integrating isoform-level eQTL data reveals new susceptibility genes for schizophrenia

Linkage disequilibrium and disease-associated variants in the non-coding regions make it difficult to distinguish the truly associated genes from the redundantly associated genes for complex diseases. In this study, we proposed a new conditional gene-based framework called eDESE that leveraged an improved effective chi-squared statistic to control the type I error rates and remove the redundant associations. eDESE initially performed the association analysis by mapping variants to genes according to their physical distance. We further demonstrated that the isoform-level eQTLs could be more powerful than the gene-level eQTLs in the association analysis using a simulation study. Then the eQTL-guided strategies, that is, mapping variants to genes according to their gene/isoform-level variant-gene cis-eQTLs associations, were also integrated with eDESE. We then applied eDESE to predict the potential susceptibility genes of schizophrenia and found that the potential susceptibility genes were enriched with many neuronal or synaptic signaling-related terms in the Gene Ontology knowledgebase and antipsychotics-gene interaction terms in the drug-gene interaction database (DGIdb). More importantly, seven potential susceptibility genes identified by eDESE were the target genes of multiple antipsychotics in DrugBank. Comparing the potential susceptibility genes identified by eDESE and other benchmark approaches (i.e., MAGMA and S-PrediXcan) implied that strategy based on the isoform-level eQTLs could be an important supplement for the other two strategies (physical distance and gene-level eQTLs). We have implemented eDESE in our integrative platform KGGSEE (http://pmglab.top/kggsee/#/) and hope that eDESE can facilitate the prediction of candidate susceptibility genes and isoforms for complex diseases in a multi-tissue context.

Omnibus testing approach for gene-based gene-gene interaction

Genetic interaction is considered as one of the main heritable component of complex traits. With the emergence of genome-wide association studies (GWAS), a collection of statistical methods dedicated to the identification of interaction at the SNP level have been proposed. More recently, gene-based gene-gene interaction testing has emerged as an attractive alternative as they confer advantage in both statistical power and biological interpretation. Most of the gene-based interaction methods rely on a multidimensional modeling of the interaction, thus facing a lack of robustness against the huge space of interaction patterns. In this paper, we study a global testing approaches to address the issue of gene-based gene-gene interaction. Based on a logistic regression modeling framework, all SNP-SNP interaction tests are combined to produce a gene-level test for interaction. We propose an omnibus test that takes advantage of (1) the heterogeneity between existing global tests and (2) the complementarity between allele-based and genotype-based coding of SNPs. Through an extensive simulation study, it is demonstrated that the proposed omnibus test has the ability to detect with high power the most common interaction genetic models with one causal pair as well as more complex genetic models where more than one causal pair is involved. On the other hand, the flexibility of the proposed approach is shown to be robust and improves power compared to single global tests in replication studies. Furthermore, the application of our procedure to real datasets confirms the adaptability of our approach to replicate various gene-gene interactions.

Citrus-Gene interaction and melanoma risk in the UK Biobank

High citrus consumption may increase melanoma risk; however, little is known about the biological mechanisms of this association, or whether it is modified by genetic variants. We conducted a genome-wide analysis of gene-citrus consumption interactions on melanoma risk among 1563 melanoma cases and 193 296 controls from the UK Biobank. Both the 2-degrees-of-freedom (df) joint test of genetic main effect and gene-environment (G-E) interaction and the standard 1-df G-E interaction test were performed. Three index SNPs (lowest P-value SNP among highly correlated variants [r²  > .6]) were identified from among the 365 genome-wide significant 2-df test results (rs183783391 on chromosome 3 [MITF], rs869329 on chromosome 9 [MTAP] and rs11446223 on chromosome 16 [DEF8]). Although all three were statistically significant for the 2-df test (4.25e-08, 1.98e-10 and 4.93e-13, respectively), none showed evidence of interaction according to the 1-df test (P = .73, .24 and .12, respectively). Eight nonindex, 2-df test significant SNPs on chromosome 16 were significant (P < .05) according to the 1-df test, providing evidence of citrus-gene interaction. Seven of these SNPs were mapped to AFG3L1P (rs199600347, rs111822773, rs113178244, rs3803683, rs73283867, rs78800020, rs73283871), and one SNP was mapped to GAS8 (rs74583214). We identified several genetic loci that may elucidate the association between citrus consumption and melanoma risk. Further studies are needed to confirm these findings.

Integrative analysis prioritizes the relevant genes and risk factors for chronic venous disease

OBJECTIVES

Chronic venous disease (CVD) refers to a range of symptoms resulting from long-term morphological and functional abnormalities of the venous system. However, the mechanism of CVD development remains largely unknown. Here we aim to provide more information on CVD pathogenesis, prevention strategies and therapy development through the integrative analysis of large-scale genetic data.

METHODS

Genetic data were obtained from publicly accessible databases. We utilized different approaches, including FUMA, DEPICT, Sherlock, SMR/HEIDIS, DEPICT and NetWAS to identify possible causal genes for CVD. Candidate genes were prioritized to further literature-based review. The differential expression of prioritized genes was validated by microarray from the Gene Expression Omnibus (GEO), a public genomics data repository" and Real-time quantitative PCR (qPCR) of varicose veins (VVs) specimens. The causal relationships between risk factors and CVD were assessed using the Two-sample Mendelian randomization (MR) approach.

RESULTS

We identified 46 lead single-nucleotide polymorphisms (SNPs) and 26 plausible causal genes for CVD. Microarray data indicated differential expression of possible causal genes in CVD when compared to controls. The expression levels of WDR92, RSPO3, LIMA, ABCB10, DNAJC7, C1S, CXCL1 were significantly down-regulated (P<0.05). PHLDA1 and SERPINE1 were significantly upregulated (P<0.05). Dysregulated expression of WDR92, RSPO3 and CASZ1 was also found in varicose vein specimens by qPCR. Two-sample MR suggested causative effects of BMI (OR, 1.008, 95%CI, 1.005-1.010), standing height (OR, 1.009, 95%CI, 1.007-1.011), college degree (OR, 0.983, 95%CI, 0.991-0.976), insulin (OR, 0.858, 95%CI, 0.794-0.928) and metformin (OR, 0.944, 95%CI, 0.904-0.985) on CVD.

CONCLUSIONS

Our study integrates genetic and gene expression data to make an effective risk gene prediction and etiological inferences for CVD. Prioritized candidate genes provide more insights into CVD pathogenesis, and the causative effects of risk factors on CVD that deserve further investigation.

Gene-Based Analysis Reveals Sex-Specific Genetic Risk Factors of COPD

Sex-specific differences have been noted among people with chronic obstructive pulmonary disease (COPD), but whether these differences are attributable to genetic variation is poorly understood. The availability of large biobanks with deeply phenotyped subjects such as the UK Biobank enables the investigation of sex-specific genetic associations that may provide new insights into COPD risk factors. We performed sex-stratified genome-wide association studies (GWAS) of COPD (male: 12,958 cases and 95,631 controls; female: 11,311 cases and 123,714 controls) and found that while most associations were shared between sexes, several regions had sex-specific contributions, including respiratory viral infection-related loci in/near C5orf56 and PELI1. Using the newly developed R package 'snpsettest', we performed gene-based association tests and identified gene-level sex-specific associations, including C5orf56 on 5q31.1, CFDP1/TMEM170A/CHST6 on 16q23.1 and ASTN2/TRIM32 on 9q33.1. Our results identified promising genes to pursue in functional studies to better understand sexual dimorphism in COPD.

The generalized Fisher's combination and accurate p-value calculation under dependence

Combining dependent tests of significance has broad applications but the related p-value calculation is challenging. For Fisher's combination test, current p-value calculation methods (e.g., Brown's approximation) tend to inflate the type I error rate when the desired significance level is substantially less than 0.05. The problem could lead to significant false discoveries in big data analyses. This paper provides two main contributions. First, it presents a general family of Fisher type statistics, referred to as the GFisher, which covers many classic statistics, such as Fisher's combination, Good's statistic, Lancaster's statistic, weighted Z-score combination, etc. The GFisher allows a flexible weighting scheme, as well as an omnibus procedure that automatically adapts proper weights and the statistic-defining parameters to a given data. Second, the paper presents several new p-value calculation methods based on two novel ideas: moment-ratio matching and joint-distribution surrogating. Systematic simulations show that the new calculation methods are more accurate under multivariate Gaussian, and more robust under the generalized linear model and the multivariate t-distribution. The applications of the GFisher and the new p-value calculation methods are demonstrated by a gene-based SNP-set association study. Relevant computation has been implemented to an R package GFisher available on the Comprehensive R Archive Network. This article is protected by copyright. All rights reserved.

A flexible summary statistics-based colocalization method with application to the mucin cystic fibrosis lung disease modifier locus

Mucus obstruction is a central feature in the cystic fibrosis (CF) airways. A genome-wide association study (GWAS) of lung disease by the CF Gene Modifier Consortium (CFGMC) identified a significant locus containing two mucin genes, MUC20 and MUC4. Expression quantitative trait locus (eQTL) analysis using human nasal epithelia (HNE) from 94 CF-affected Canadians in the CFGMC demonstrated MUC4 eQTLs that mirrored the lung association pattern in the region, suggesting that MUC4 expression may mediate CF lung disease. Complications arose, however, with colocalization testing using existing methods: the locus is complex and the associated SNPs span a 0.2 Mb region with high linkage disequilibrium (LD) and evidence of allelic heterogeneity. We previously developed the Simple Sum (SS), a powerful colocalization test in regions with allelic heterogeneity, but SS assumed eQTLs to be present to achieve type I error control. Here we propose a two-stage SS (SS2) colocalization test that avoids a priori eQTL assumptions, accounts for multiple hypothesis testing and the composite null hypothesis, and enables meta-analysis. We compare SS2 to published approaches through simulation and demonstrate type I error control for all settings with the greatest power in the presence of high LD and allelic heterogeneity. Applying SS2 to the MUC20/MUC4 CF lung disease locus with eQTLs from CF HNE revealed significant colocalization with MUC4 (p = 1.31 × 10⁻⁵) rather than with MUC20. The SS2 is a powerful method to inform the responsible gene(s) at a locus and guide future functional studies. SS2 has been implemented in the application LocusFocus.

Computational Genomics in the Era of Precision Medicine: Applications to Variant Analysis and Gene Therapy

Rapid methodological advances in statistical and computational genomics have enabled researchers to better identify and interpret both rare and common variants responsible for complex human diseases. As we continue to see an expansion of these advances in the field, it is now imperative for researchers to understand the resources and methodologies available for various data types and study designs. In this review, we provide an overview of recent methods for identifying rare and common variants and understanding their roles in disease etiology. Additionally, we discuss the strategy, challenge, and promise of gene therapy. As computational and statistical approaches continue to improve, we will have an opportunity to translate human genetic findings into personalized health care.

Association of genetic variants in autophagy-lysosome pathway genes with susceptibility and survival to prostate cancer

BACKGROUND

Previous studies have indicated the connections between autophagy-lysosome pathway genes dysfunction and prostate cancer, but few studies have investigated whether single nucleotide polymorphisms (SNPs) in autophagy-lysosome pathway genes are implicated in prostate cancer risk and survival.

MATERIALS AND METHODS

Logistic regression analysis and stepwise Cox regression analysis were conducted in 4,662 cases and 3,114 controls from the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial. The false positive rate probability (FPRP) method was applied to correct for multiple comparisons. Gene-based analysis was calculated by versatile gene-based association study approach.

RESULTS

We found that SLC11A1 rs7573065 significantly increased the risk of prostate cancer [adjusted odds ratio (OR) = 1.24, 95% confidence interval (CI) = 1.06-1.46, P = 7.02 × 10^-3, FPRP = 0.082]. Furthermore, rs7573065 was confirmed as the independent predicator of overall survival (OS) for prostate cancer patients [Hazard ratio (HR) = 1.30, 95% CI = 1.01-1.66, P = 0.041]. The significant association between SLC11A1 and prostate cancer risk was calculated by gene-based analysis (P = 0.030). We also observed that the mRNA of SLC11A1 in prostate tumor tissues was significantly over-expressed than that in normal tissues.

CONCLUSION

This study suggested that rs7573065 in SLC11A1 was associated with an increased risk and poor OS of prostate cancer. Our findings may provide evidence for genetic variants in autophagy-lysosome pathway as the risk and prognostic biomarkers for prostate cancer.

Integrated transcriptome and small RNA sequencing in revealing miRNA-mediated regulatory network of floral bud break in Prunus mume

MicroRNAs is one class of small non-coding RNAs that play important roles in plant growth and development. Though miRNAs and their target genes have been widely studied in many plant species, their functional roles in floral bud break and dormancy release in woody perennials is still unclear. In this study, we applied transcriptome and small RNA sequencing together to systematically explore the transcriptional and post-transcriptional regulation of floral bud break in P. mume. Through expression profiling, we identified a few candidate genes and miRNAs during different developmental stage transitions. In total, we characterized 1,553 DEGs associated with endodormancy release and 2,084 DEGs associated with bud flush. Additionally, we identified 48 known miRNAs and 53 novel miRNAs targeting genes enriched in biological processes such as floral organ morphogenesis and hormone signaling transudation. We further validated the regulatory relationship between differentially expressed miRNAs and their target genes combining computational prediction, degradome sequencing, and expression pattern analysis. Finally, we integrated weighted gene co-expression analysis and constructed miRNA-mRNA regulatory networks mediating floral bud flushing competency. In general, our study revealed the miRNA-mediated networks in modulating floral bud break in P. mume. The findings will contribute to the comprehensive understanding of miRNA-mediated regulatory mechanism governing floral bud break and dormancy cycling in wood perennials.

Testing Gene-Gene Interactions Based on a Neighborhood Perspective in Genome-wide Association Studies

Unexplained genetic variation that causes complex diseases is often induced by gene-gene interactions (GGIs). Gene-based methods are one of the current statistical methodologies for discovering GGIs in case-control genome-wide association studies that are not only powerful statistically, but also interpretable biologically. However, most approaches include assumptions about the form of GGIs, which results in poor statistical performance. As a result, we propose gene-based testing based on the maximal neighborhood coefficient (MNC) called gene-based gene-gene interaction through a maximal neighborhood coefficient (GBMNC). MNC is a metric for capturing a wide range of relationships between two random vectors with arbitrary, but not necessarily equal, dimensions. We established a statistic that leverages the difference in MNC in case and in control samples as an indication of the existence of GGIs, based on the assumption that the joint distribution of two genes in cases and controls should not be substantially different if there is no interaction between them. We then used a permutation-based statistical test to evaluate this statistic and calculate a statistical p-value to represent the significance of the interaction. Experimental results using both simulation and real data showed that our approach outperformed earlier methods for detecting GGIs.

Gene-Based Testing of Interactions Using XGBoost in Genome-Wide Association Studies

Among the myriad of statistical methods that identify gene–gene interactions in the realm of qualitative genome-wide association studies, gene-based interactions are not only powerful statistically, but also they are interpretable biologically. However, they have limited statistical detection by making assumptions on the association between traits and single nucleotide polymorphisms. Thus, a gene-based method (GGInt-XGBoost) originated from XGBoost is proposed in this article. Assuming that log odds ratio of disease traits satisfies the additive relationship if the pair of genes had no interactions, the difference in error between the XGBoost model with and without additive constraint could indicate gene–gene interaction; we then used a permutation-based statistical test to assess this difference and to provide a statistical p-value to represent the significance of the interaction. Experimental results on both simulation and real data showed that our approach had superior performance than previous experiments to detect gene–gene interactions.

A comprehensive gene-centric pleiotropic association analysis for 14 psychiatric disorders with GWAS summary statistics

BACKGROUND

Recent genome-wide association studies (GWASs) have revealed the polygenic nature of psychiatric disorders and discovered a few of single-nucleotide polymorphisms (SNPs) associated with multiple psychiatric disorders. However, the extent and pattern of pleiotropy among distinct psychiatric disorders remain not completely clear.

METHODS

We analyzed 14 psychiatric disorders using summary statistics available from the largest GWASs by far. We first applied the cross-trait linkage disequilibrium score regression (LDSC) to estimate genetic correlation between disorders. Then, we performed a gene-based pleiotropy analysis by first aggregating a set of SNP-level associations into a single gene-level association signal using MAGMA. From a methodological perspective, we viewed the identification of pleiotropic associations across the entire genome as a high-dimensional problem of composite null hypothesis testing and utilized a novel method called PLACO for pleiotropy mapping. We ultimately implemented functional analysis for identified pleiotropic genes and used Mendelian randomization for detecting causal association between these disorders.

RESULTS

We confirmed extensive genetic correlation among psychiatric disorders, based on which these disorders can be grouped into three diverse categories. We detected a large number of pleiotropic genes including 5884 associations and 2424 unique genes and found that differentially expressed pleiotropic genes were significantly enriched in pancreas, liver, heart, and brain, and that the biological process of these genes was remarkably enriched in regulating neurodevelopment, neurogenesis, and neuron differentiation, offering substantial evidence supporting the validity of identified pleiotropic loci. We further demonstrated that among all the identified pleiotropic genes there were 342 unique ones linked with 6353 drugs with drug-gene interaction which can be classified into distinct types including inhibitor, agonist, blocker, antagonist, and modulator. We also revealed causal associations among psychiatric disorders, indicating that genetic overlap and causality commonly drove the observed co-existence of these disorders.

CONCLUSIONS

Our study is among the first large-scale effort to characterize gene-level pleiotropy among a greatly expanded set of psychiatric disorders and provides important insight into shared genetic etiology underlying these disorders. The findings would inform psychiatric nosology, identify potential neurobiological mechanisms predisposing to specific clinical presentations, and pave the way to effective drug targets for clinical treatment.

TIGA: target illumination GWAS analytics

MOTIVATION

Genome-wide association studies can reveal important genotype-phenotype associations; however, data quality and interpretability issues must be addressed. For drug discovery scientists seeking to prioritize targets based on the available evidence, these issues go beyond the single study.

RESULTS

Here, we describe rational ranking, filtering and interpretation of inferred gene-trait associations and data aggregation across studies by leveraging existing curation and harmonization efforts. Each gene-trait association is evaluated for confidence, with scores derived solely from aggregated statistics, linking a protein-coding gene and phenotype. We propose a method for assessing confidence in gene-trait associations from evidence aggregated across studies, including a bibliometric assessment of scientific consensus based on the iCite relative citation ratio, and meanRank scores, to aggregate multivariate evidence.This method, intended for drug target hypothesis generation, scoring and ranking, has been implemented as an analytical pipeline, available as open source, with public datasets of results, and a web application designed for usability by drug discovery scientists.

AVAILABILITY AND IMPLEMENTATION

Web application, datasets and source code via https://unmtid-shinyapps.net/tiga/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

An approach to gene-based testing accounting for dependence of tests among nearby genes

In genome-wide association studies (GWAS), it has become commonplace to test millions of single-nucleotide polymorphisms (SNPs) for phenotypic association. Gene-based testing can improve power to detect weak signal by reducing multiple testing and pooling signal strength. While such tests account for linkage disequilibrium (LD) structure of SNP alleles within each gene, current approaches do not capture LD of SNPs falling in different nearby genes, which can induce correlation of gene-based test statistics. We introduce an algorithm to account for this correlation. When a gene's test statistic is independent of others, it is assessed separately; when test statistics for nearby genes are strongly correlated, their SNPs are agglomerated and tested as a locus. To provide insight into SNPs and genes driving association within loci, we develop an interactive visualization tool to explore localized signal. We demonstrate our approach in the context of weakly powered GWAS for autism spectrum disorder, which is contrasted to more highly powered GWAS for schizophrenia and educational attainment. To increase power for these analyses, especially those for autism, we use adaptive $P$-value thresholding, guided by high-dimensional metadata modeled with gradient boosted trees, highlighting when and how it can be most useful. Notably our workflow is based on summary statistics.

Performing post-genome-wide association study analysis: overview, challenges and recommendations

Genome-wide association studies (GWAS) provide  huge information on statistically significant single-nucleotide polymorphisms (SNPs) associated with various human complex traits and diseases. By performing GWAS studies, scientists have successfully identified the association of hundreds of thousands to  millions of SNPs to a single phenotype. Moreover, the association of some SNPs with rare diseases has been intensively tested. However, classic GWAS studies have not yet provided solid, knowledgeable insight into functional and biological mechanisms underlying phenotypes or mechanisms of diseases. Therefore, several post-GWAS (pGWAS) methods have been recommended. Currently, there is no simple scientific document to provide a quick guide for performing pGWAS analysis. pGWAS is a crucial step for a better understanding of the biological machinery beyond the SNPs. Here, we provide an overview to performing pGWAS analysis and demonstrate the challenges behind each method. Furthermore, we direct readers to key articles for each pGWAS method and present the overall issues in pGWAS analysis.  Finally, we include a custom pGWAS pipeline to guide new users when performing their research.

Advancing the use of genome-wide association studies for drug repurposing

Genome-wide association studies (GWAS) have revealed important biological insights into complex diseases, which are broadly expected to lead to the identification of new drug targets and opportunities for treatment. Drug development, however, remains hampered by the time taken and costs expended to achieve regulatory approval, leading many clinicians and researchers to consider alternative paths to more immediate clinical outcomes. In this Review, we explore approaches that leverage common variant genetics to identify opportunities for repurposing existing drugs, also known as drug repositioning. These approaches include the identification of compounds by linking individual loci to genes and pathways that can be pharmacologically modulated, transcriptome-wide association studies, gene-set association, causal inference by Mendelian randomization, and polygenic scoring.

Heritability and genome-wide association study of blood pressure in Chinese adult twins

BACKGROUND

Blood pressure (BP) is an independent and important factor for chronic diseases such as cardiovascular diseases and diabetes.

METHODS

We firstly conducted twin modeling analyses to explore the heritability of BP, including systolic blood pressure (SBP), diastolic blood pressure (DBP), pulse pressure (PP) and mean arterial pressure (MAP), and then performed genome-wide association studies to explore the associated genomic loci, genes, and pathways.

RESULTS

A total of 380 Chinese twin pairs were included. The AE model containing additive genetic parameter (A) and unique/non-shared environmental parameter (E) was the best fit model, with A accounting for 53.7%, 50.1%, 48.1%, and 53.3% for SBP, DBP, PP and MAP, respectively. No SNP was found to reach the genome-wide significance level (p < 5 × 10^-8 ), however, three, four, 14 and nine SNPs were found to exceed suggestive significance level (p < 1 × 10^-5 ) for SBP, DBP, PP, and MAP, respectively. And after imputation, 46, 37, 91 and 61 SNPs were found to exceed the suggestive significance level for SBP, DBP, PP, and MAP, respectively. In gene-based analysis, 53 common genes were found among SBP, DBP, PP, and MAP. In pathway enrichment analysis, 672, 706, 701, and 596 biological pathways were associated with SBP, DBP, PP, and MAP, respectively (p < 0.05).

CONCLUSION

Our study suggests that BP is moderately heritable in the Chinese population and could be mediated by a series of genomic loci, genes, and pathways. Future larger-scale studies are needed to confirm our findings.