i-GSEA4GWAS: a web server for identification of pathways/gene sets associated with traits by applying an improved gene set enrichment analysis to genome-wide association study

Reverse Phase Proteomic Array Profiling of Asparagine Synthetase Expression in Newly Diagnosed Acute Myeloid Leukemia

Asparaginase-based therapy is a cornerstone in acute lymphoblastic leukemia (ALL) treatment, capitalizing on the methylation status of the asparagine synthetase (ASNS) gene, which renders ALL cells reliant on extracellular asparagine. Contrastingly, ASNS expression in acute myeloid leukemia (AML) has not been thoroughly investigated, despite studies suggesting that AML with chromosome 7/7q deletions might have reduced ASNS levels. Here, we leverage reverse phase protein arrays to measure ASNS expression in 810 AML patients and assess its impact on outcomes. We find that AML with inv(16) has the lowest overall ASNS expression. While AML with deletion 7/7q had ASNS levels slightly lower than those of AML without deletion 7/7q, this observation was not significant. Low ASNS expression correlated with improved overall survival (46 versus 54 weeks, respectively, p = 0.011), whereas higher ASNS levels were associated with better response to venetoclax-based therapy. Protein correlation analysis demonstrated association between ASNS and proteins involved in methylation and DNA repair. In conclusion, while ASNS expression was not lower in patients with deletion 7/7q as initially predicted, ASNS levels were highly variable across AML patients. Further studies are needed to assess whether patients with low ASNS expression are susceptible to asparaginase-based therapy due to their inability to augment compensatory ASNS expression upon asparagine depletion.

Human lymphocytes mobilized with exercise have an anti-tumor transcriptomic profile and exert enhanced graft-versus-leukemia effects in xenogeneic mice

Background

Every bout of exercise mobilizes and redistributes large numbers of effector lymphocytes with a cytotoxic and tissue migration phenotype. The frequent redistribution of these cells is purported to increase immune surveillance and play a mechanistic role in reducing cancer risk and slowing tumor progression in physically active cancer survivors. Our aim was to provide the first detailed single cell transcriptomic analysis of exercise-mobilized lymphocytes and test their effectiveness as a donor lymphocyte infusion (DLI) in xenogeneic mice engrafted with human leukemia.

Methods

Peripheral blood mononuclear cells (PBMCs) were collected from healthy volunteers at rest and at the end of an acute bout of cycling exercise. Flow cytometry and single-cell RNA sequencing was performed to identify phenotypic and transcriptomic differences between resting and exercise-mobilized cells using a targeted gene expression panel curated for human immunology. PBMCs were injected into the tail vein of xenogeneic NSG-IL-15 mice and subsequently challenged with a luciferase tagged chronic myelogenous leukemia cell line (K562). Tumor growth (bioluminescence) and xenogeneic graft-versus-host disease (GvHD) were monitored bi-weekly for 40-days.

Results

Exercise preferentially mobilized NK-cell, CD8+ T-cell and monocyte subtypes with a differentiated and effector phenotype, without significantly mobilizing CD4+ regulatory T-cells. Mobilized effector lymphocytes, particularly effector-memory CD8+ T-cells and NK-cells, displayed differentially expressed genes and enriched gene sets associated with anti-tumor activity, including cytotoxicity, migration/chemotaxis, antigen binding, cytokine responsiveness and alloreactivity (e.g. graft-versus-host/leukemia). Mice receiving exercise-mobilized PBMCs had lower tumor burden and higher overall survival (4.14E+08 photons/s and 47%, respectively) at day 40 compared to mice receiving resting PBMCs (12.1E+08 photons/s and 22%, respectively) from the same donors (p<0.05). Human immune cell engraftment was similar for resting and exercise-mobilized DLI. However, when compared to non-tumor bearing mice, K562 increased the expansion of NK-cell and CD3+/CD4-/CD8- T-cells in mice receiving exercise-mobilized but not resting lymphocytes, 1-2 weeks after DLI. No differences in GvHD or GvHD-free survival was observed between groups either with or without K562 challenge.

Conclusion

Exercise in humans mobilizes effector lymphocytes with an anti-tumor transcriptomic profile and their use as DLI extends survival and enhances the graft-versus-leukemia (GvL) effect without exacerbating GvHD in human leukemia bearing xenogeneic mice. Exercise may serve as an effective and economical adjuvant to increase the GvL effects of allogeneic cell therapies without intensifying GvHD.

Modern drug discovery for inflammatory bowel disease: The role of computational methods

Inflammatory bowel diseases (IBDs) comprising ulcerative colitis, Crohn’s disease and microscopic colitis are characterized by chronic inflammation of the gastrointestinal tract. IBD has spread around the world and is becoming more prevalent at an alarming rate in developing countries whose societies have become more westernized. Cell therapy, intestinal microecology, apheresis therapy, exosome therapy and small molecules are emerging therapeutic options for IBD. Currently, it is thought that low-molecular-mass substances with good oral bio-availability and the ability to permeate the cell membrane to regulate the action of elements of the inflammatory signaling pathway are effective therapeutic options for the treatment of IBD. Several small molecule inhibitors are being developed as a promising alternative for IBD therapy. The use of highly efficient and time-saving techniques, such as computational methods, is still a viable option for the development of these small molecule drugs. The computer-aided (in silico) discovery approach is one drug development technique that has mostly proven efficacy. Computational approaches when combined with traditional drug development methodology dramatically boost the likelihood of drug discovery in a sustainable and cost-effective manner. This review focuses on the modern drug discovery approaches for the design of novel IBD drugs with an emphasis on the role of computational methods. Some computational approaches to IBD genomic studies, target identification, and virtual screening for the discovery of new drugs and in the repurposing of existing drugs are discussed.

Shared genetic mechanism between type 2 diabetes and COVID-19 using pathway-based association analysis

Recent studies have shown that, compared with healthy individuals, patients with type 2 diabetes (T2D) suffer a higher severity and mortality of COVID-19. When infected with this retrovirus, patients with T2D are more likely to face severe complications from cytokine storms and be admitted to high-dependency or intensive care units. Some COVID-19 patients are known to suffer from various forms of acute respiratory distress syndrome and have a higher mortality risk due to extreme activation of inflammatory cascades. Using a conditional false discovery rate statistical framework, an independent genome-wide association study data on individuals presenting with T2D (N = 62,892) and COVID-19 (N = 38,984) were analysed. Genome-wide association study data from 2,343,084 participants were analysed and a significant positive genetic correlation between T2D and COVID-19 was observed (T2D: r for genetic = 0.1511, p-value = 0.01). Overall, 2 SNPs (rs505922 and rs3924604) shared in common between T2D and COVID-19 were identified. Functional analyses indicated that the overlapping loci annotated into the ABO and NUS1 genes might be implicated in several key metabolic pathways. A pathway association analysis identified two common pathways within T2D and COVID-19 pathogenesis, including chemokines and their respective receptors. The gene identified from the pathway analysis (CCR2) was also found to be highly expressed in blood tissue via the GTEx database. To conclude, this study reveals that certain chemokines and their receptors, which are directly involved in the genesis of cytokine storms, may lead to exacerbated hyperinflammation in T2D patients infected by COVID-19.

Genome-wide association study of platelet factor 4/heparin antibodies in heparin-induced thrombocytopenia

Heparin, a widely used anticoagulant, carries the risk of an antibody mediated adverse drug reaction, heparin-induced thrombocytopenia (HIT). A subset of heparin-treated patients produces detectable levels of antibodies against complexes of heparin bound to circulating platelet factor 4 (PF4). Using a genome-wide association study (GWAS) approach, we aimed to identify genetic variants associated with anti-PF4/heparin antibodies that account for the variable antibody response seen in HIT. We performed a GWAS on anti-PF4/heparin antibody levels determined via polyclonal enzyme-linked immunosorbent assays (ELISA). Our discovery cohort (n=4237) and replication cohort (n=807) constituted patients with European ancestry and clinical suspicion of HIT with cases confirmed via functional assay. Genome-wide significance was considered at α=5x10-8. No variants were significantly associated with anti-PF4/heparin antibody levels in the discovery cohort at a genome-wide significant level. Secondary GWAS analyses included identification of variants with suggestive associations in the discovery cohort (α=1x10-4). The top variant in both cohorts was rs1555175145 (discovery β=-0.112[0.018], p=2.50x10-5; replication β=-0.104[0.051], p=0.041). In gene set enrichment analysis (GSEA), three gene sets reached false discovery rate-adjusted significance (q<0.05) in both discovery and replication cohorts: "Leukocyte Transendothelial Migration," "Innate Immune Response," and "Lyase Activity." Our results indicate that genomic variation is not significantly associated with anti-PF4/heparin antibody levels. Given our power to identify variants with moderate frequencies and effect sizes, this evidence suggests genetic variation is not a primary driver of variable antibody response in heparin-treated patients with European ancestry.

Incorporating regulatory interactions into gene-set analyses for GWAS data: A controlled analysis with the MAGMA tool

To date, genome-wide association studies have identified thousands of statistically-significant associations between genetic variants, and phenotypes related to a myriad of traits and diseases. A key goal for human-genetics research is to translate these associations into functional mechanisms. Popular gene-set analysis tools, like MAGMA, map variants to genes they might affect, and then integrate genome-wide association study data (that is, variant-level associations for a phenotype) to score genes for association with a phenotype. Gene scores are subsequently used in competitive gene-set analyses to identify biological processes that are enriched for phenotype association. By default, variants are mapped to genes in their proximity. However, many variants that affect phenotypes are thought to act at regulatory elements, which can be hundreds of kilobases away from their target genes. Thus, we explored the idea of augmenting a proximity-based mapping scheme with publicly-available datasets of regulatory interactions. We used MAGMA to analyze genome-wide association study data for ten different phenotypes, and evaluated the effects of augmentation by comparing numbers, and identities, of genes and gene sets detected as statistically significant between mappings. We detected several pitfalls and confounders of such “augmented analyses”, and introduced ways to control for them. Using these controls, we demonstrated that augmentation with datasets of regulatory interactions only occasionally strengthened the enrichment for phenotype association amongst (biologically-relevant) gene sets for different phenotypes. Still, in such cases, genes and regulatory elements responsible for the improvement could be pinpointed. For instance, using brain regulatory-interactions for augmentation, we were able to implicate two acetylcholine receptor subunits involved in post-synaptic chemical transmission, namely CHRNB2 and CHRNE, in schizophrenia. Collectively, our study presents a critical approach for integrating regulatory interactions into gene-set analyses for genome-wide association study data, by introducing various controls to distinguish genuine results from spurious discoveries.

Importance of SNP Dependency Correction and Association Integration for Gene Set Analysis in Genome-Wide Association Studies

A typical genome-wide association study (GWAS) analyzes millions of single-nucleotide polymorphisms (SNPs), several of which are in a region of the same gene. To conduct gene set analysis (GSA), information from SNPs needs to be unified at the gene level. A widely used practice is to use only the most relevant SNP per gene; however, there are other methods of integration that could be applied here. Also, the problem of nonrandom association of alleles at two or more loci is often neglected. Here, we tested the impact of incorporation of different integrations and linkage disequilibrium (LD) correction on the performance of several GSA methods. Matched normal and breast cancer samples from The Cancer Genome Atlas database were used to evaluate the performance of six GSA algorithms: Coincident Extreme Ranks in Numerical Observations (CERNO), Gene Set Enrichment Analysis (GSEA), GSEA-SNP, improved GSEA for GWAS (i-GSEA4GWAS), Meta-Analysis Gene-set Enrichment of variaNT Associations (MAGENTA), and Over-Representation Analysis (ORA). Association of SNPs to phenotype was calculated using modified McNemar's test. Results for SNPs mapped to the same gene were integrated using Fisher and Stouffer methods and compared with the minimum p-value method. Four common measures were used to quantify the performance of all combinations of methods. Results of GSA analysis on GWAS were compared to the one performed on gene expression data. Comparing all evaluation metrics across different GSA algorithms, integrations, and LD correction, we highlighted CERNO, and MAGENTA with Stouffer as the most efficient. Applying LD correction increased prioritization and specificity of enrichment outcomes for all tested algorithms. When Fisher or Stouffer were used with LD, sensitivity and reproducibility were also better. Using any integration method was beneficial in comparison with a minimum p-value method in specific combinations. The correlation between GSA results from genomic and transcriptomic level was the highest when Stouffer integration was combined with LD correction. We thoroughly evaluated different approaches to GSA in GWAS in terms of performance to guide others to select the most effective combinations. We showed that LD correction and Stouffer integration could increase the performance of enrichment analysis and encourage the usage of these techniques.

Transcriptomic signature of extinction learning in the brain of the fire-bellied toad, Bombina orientalis

Insight into the molecular and cellular mechanisms of learning and memory from a diverse array of taxa contributes to our understanding of the evolution of these processes. The fire-bellied toad, Bombina orientalis, is a basal anuran amphibian model species who could help us describe shared and divergent characteristics of learning and memory mechanisms between amphibians and other vertebrates, and hence answer questions about the evolution of learning. Utilizing next generation sequencing techniques, we profiled gene expression patterns associated with the extinction of prey-catching conditioning in the brain of the fire-bellied toad. For this purpose, gene expression was at first compared between toads sacrificed after acquisition and extinction of the conditioned response. A second comparison was done between toads submitted to extinction following either short or long acquisition training, which results in toads displaying response extinction or resistance to extinction, respectively. We analyzed brain tissue transcription profiles common to both acquisition and extinction learning, or unique to extinction learning and resistance to extinction, and found significant overlap in gene expression related to molecular pathways involving neuronal plasticity (e.g. structural modification, transcription). However, extinction learning induced a unique GABAergic transcriptomic signal, which may be responsible for suppression of the original response memory. Further, when comparing extinction learning in short- and long-trained groups, short training engaged many pathways related to neuronal plasticity, as expected, but long training engaged molecular pathways related to the suppression of learning through epigenetic mediated transcriptional suppression and inhibitory neurotransmission. Overall, gene expression patterns associated with extinction learning in the fire-bellied toad were similar to those found in mammals submitted to extinction, although some divergent profiles highlighted potential differences in the mechanisms of learning and memory among tetrapods.

Whole-genome sequencing analysis of semi-supercentenarians

Extreme longevity is the paradigm of healthy aging as individuals who reached the extreme decades of human life avoided or largely postponed all major age-related diseases. In this study, we sequenced at high coverage (90X) the whole genome of 81 semi-supercentenarians and supercentenarians [105+/110+] (mean age: 106.6 ± 1.6) and of 36 healthy unrelated geographically matched controls (mean age 68.0 ± 5.9) recruited in Italy. The results showed that 105+/110+ are characterized by a peculiar genetic background associated with efficient DNA repair mechanisms, as evidenced by both germline data (common and rare variants) and somatic mutations patterns (lower mutation load if compared to younger healthy controls). Results were replicated in a second independent cohort of 333 Italian centenarians and 358 geographically matched controls. The genetics of 105+/110+ identified DNA repair and clonal haematopoiesis as crucial players for healthy aging and for the protection from cardiovascular events.

Pathway analysis for genome-wide genetic variation data: Analytic principles, latest developments, and new opportunities

Pathway analysis, also known as gene-set enrichment analysis, is a multilocus analytic strategy that integrates a priori, biological knowledge into the statistical analysis of high-throughput genetics data. Originally developed for the studies of gene expression data, it has become a powerful analytic procedure for in-depth mining of genome-wide genetic variation data. Astonishing discoveries were made in the past years, uncovering genes and biological mechanisms underlying common and complex disorders. However, as massive amounts of diverse functional genomics data accrue, there is a pressing need for newer generations of pathway analysis methods that can utilize multiple layers of high-throughput genomics data. In this review, we provide an intellectual foundation of this powerful analytic strategy, as well as an update of the state-of-the-art in recent method developments. The goal of this review is threefold: (1) introduce the motivation and basic steps of pathway analysis for genome-wide genetic variation data; (2) review the merits and the shortcomings of classic and newly emerging integrative pathway analysis tools; and (3) discuss remaining challenges and future directions for further method developments.

Gene- and pathway-level analyses of iCOGS variants highlight novel signaling pathways underlying familial breast cancer susceptibility

Single‐nucleotide polymorphisms (SNPs) in over 180 loci have been associated with breast cancer (BC) through genome‐wide association studies involving mostly unselected population‐based case‐control series. Some of them modify BC risk of women carrying a BRCA1 or BRCA2 (BRCA1/2) mutation and may also explain BC risk variability in BC‐prone families with no BRCA1/2 mutation. Here, we assessed the contribution of SNPs of the iCOGS array in GENESIS consisting of BC cases with no BRCA1/2 mutation and a sister with BC, and population controls. Genotyping data were available for 1281 index cases, 731 sisters with BC, 457 unaffected sisters and 1272 controls. In addition to the standard SNP‐level analysis using index cases and controls, we performed pedigree‐based association tests to capture transmission information in the sibships. We also performed gene‐ and pathway‐level analyses to maximize the power to detect associations with lower‐frequency SNPs or those with modest effect sizes. While SNP‐level analyses identified 18 loci, gene‐level analyses identified 112 genes. Furthermore, 31 Kyoto Encyclopedia of Genes and Genomes and 7 Atlas of Cancer Signaling Network pathways were highlighted (false discovery rate of 5%). Using results from the “index case‐control” analysis, we built pathway‐derived polygenic risk scores (PRS) and assessed their performance in the population‐based CECILE study and in a data set composed of GENESIS‐affected sisters and CECILE controls. Although these PRS had poor predictive value in the general population, they performed better than a PRS built using our SNP‐level findings, and we found that the joint effect of family history and PRS needs to be considered in risk prediction models.

What's new?

Genetic studies have identified more than 180 single‐nucleotide polymorphisms (SNPs) associated with breast cancer susceptibility, but these studies are reaching their limits. Here, the authors evaluated SNPs in the iCOGS genotyping array using a multilevel approach, including single variant, gene, and pathway analyses. They measured the contribution of the SNPs to breast cancer in patients who have a sister with breast cancer but do not carry a BRCA1/2 mutation. They showed that a pathway‐derived polygenic risk score performed poorly in the general population, and that the best predictive model must include family history.

Gene networks and pathways for plasma lipid traits via multitissue multiomics systems analysis

Genome-wide association studies (GWASs) have implicated ∼380 genetic loci for plasma lipid regulation. However, these loci only explain 17-27% of the trait variance, and a comprehensive understanding of the molecular mechanisms has not been achieved. In this study, we utilized an integrative genomics approach leveraging diverse genomic data from human populations to investigate whether genetic variants associated with various plasma lipid traits, namely, total cholesterol, high and low density lipoprotein cholesterol (HDL and LDL), and triglycerides, from GWASs were concentrated on specific parts of tissue-specific gene regulatory networks. In addition to the expected lipid metabolism pathways, gene subnetworks involved in "interferon signaling," "autoimmune/immune activation," "visual transduction," and "protein catabolism" were significantly associated with all lipid traits. In addition, we detected trait-specific subnetworks, including cadherin-associated subnetworks for LDL; glutathione metabolism for HDL; valine, leucine, and isoleucine biosynthesis for total cholesterol; and insulin signaling and complement pathways for triglyceride. Finally, by using gene-gene relations revealed by tissue-specific gene regulatory networks, we detected both known (e.g., APOH, APOA4, and ABCA1) and novel (e.g., F2 in adipose tissue) key regulator genes in these lipid-associated subnetworks. Knockdown of the F2 gene (coagulation factor II, thrombin) in 3T3-L1 and C3H10T1/2 adipocytes altered gene expression of Abcb11, Apoa5, Apof, Fabp1, Lipc, and Cd36; reduced intracellular adipocyte lipid content; and increased extracellular lipid content, supporting a link between adipose thrombin and lipid regulation. Our results shed light on the complex mechanisms underlying lipid metabolism and highlight potential novel targets for lipid regulation and lipid-associated diseases.

Genotype-environment correlation by intervention effects underlying middle childhood peer rejection and associations with adolescent marijuana use

Aggressive behavior in middle childhood can contribute to peer rejection, subsequently increasing risk for substance use in adolescence. However, the quality of peer relationships a child experiences can be associated with his or her genetic predisposition, a genotype-environment correlation (rGE). In addition, recent evidence indicates that psychosocial preventive interventions can buffer genetic predispositions for negative behavior. The current study examined associations between polygenic risk for aggression, aggressive behavior, and peer rejection from 8.5 to 10.5 years, and the subsequent influence of peer rejection on marijuana use in adolescence (n = 515; 256 control, 259 intervention). Associations were examined separately in control and intervention groups for children of families who participated in a randomized controlled trial of the family-based preventive intervention, the Family Check-Up . Using time-varying effect modeling (TVEM), polygenic risk for aggression was associated with peer rejection from approximately age 8.50 to 9.50 in the control group but no associations were present in the intervention group. Subsequent analyses showed peer rejection mediated the association between polygenic risk for aggression and adolescent marijuana use in the control group. The role of rGEs in middle childhood peer processes and implications for preventive intervention programs for adolescent substance use are discussed.

Primary cilia defects causing mitral valve prolapse

Mitral valve prolapse (MVP) affects 1 in 40 people and is the most common indication for mitral valve surgery. MVP can cause arrhythmias, heart failure, and sudden cardiac death, and to date, the causes of this disease are poorly understood. We now demonstrate that defects in primary cilia genes and their regulated pathways can cause MVP in familial and sporadic nonsyndromic MVP cases. Our expression studies and genetic ablation experiments confirmed a role for primary cilia in regulating ECM deposition during cardiac development. Loss of primary cilia during development resulted in progressive myxomatous degeneration and profound mitral valve pathology in the adult setting. Analysis of a large family with inherited, autosomal dominant nonsyndromic MVP identified a deleterious missense mutation in a cilia gene, DZIP1 A mouse model harboring this variant confirmed the pathogenicity of this mutation and revealed impaired ciliogenesis during development, which progressed to adult myxomatous valve disease and functional MVP. Relevance of primary cilia in common forms of MVP was tested using pathway enrichment in a large population of patients with MVP and controls from previously generated genome-wide association studies (GWAS), which confirmed the involvement of primary cilia genes in MVP. Together, our studies establish a developmental basis for MVP through altered cilia-dependent regulation of ECM and suggest that defects in primary cilia genes can be causative to disease phenotype in some patients with MVP.

Shared genetic etiology underlying late-onset Alzheimer's disease and posttraumatic stress syndrome

INTRODUCTION

Late-onset Alzheimer's disease (LOAD) manifests comorbid neuropsychiatric symptoms and posttraumatic stress disorder (PTSD) is associated with an increased risk for dementia in late life, suggesting the two disorders may share genetic etiologies.

METHODS

We performed genetic pleiotropy analysis using LOAD and PTSD genome-wide association study (GWAS) datasets from white and African-American populations, followed by functional-genomic analyses.

RESULTS

We found an enrichment for LOAD across increasingly stringent levels of significance with the PTSD GWAS association (LOAD|PTSD) in the discovery and replication cohorts and a modest enrichment for the reverse conditional association (PTSD|LOAD). LOAD|PTSD association analysis identified and replicated the MS4A genes region. These genes showed similar expression pattern in brain regions affected in LOAD, and across-brain-tissue analysis identified a significant association for MS4A6A. The African-American samples showed moderate enrichment; however, no false discovery rate-significant associations.

DISCUSSION

We demonstrated common genetic signatures for LOAD and PTSD and suggested immune response as a common pathway for these diseases.

Fifteen Years of Gene Set Analysis for High-Throughput Genomic Data: A Review of Statistical Approaches and Future Challenges

Over the last decade, gene set analysis has become the first choice for gaining insights into underlying complex biology of diseases through gene expression and gene association studies. It also reduces the complexity of statistical analysis and enhances the explanatory power of the obtained results. Although gene set analysis approaches are extensively used in gene expression and genome wide association data analysis, the statistical structure and steps common to these approaches have not yet been comprehensively discussed, which limits their utility. In this article, we provide a comprehensive overview, statistical structure and steps of gene set analysis approaches used for microarrays, RNA-sequencing and genome wide association data analysis. Further, we also classify the gene set analysis approaches and tools by the type of genomic study, null hypothesis, sampling model and nature of the test statistic, etc. Rather than reviewing the gene set analysis approaches individually, we provide the generation-wise evolution of such approaches for microarrays, RNA-sequencing and genome wide association studies and discuss their relative merits and limitations. Here, we identify the key biological and statistical challenges in current gene set analysis, which will be addressed by statisticians and biologists collectively in order to develop the next generation of gene set analysis approaches. Further, this study will serve as a catalog and provide guidelines to genome researchers and experimental biologists for choosing the proper gene set analysis approach based on several factors.

Quantitative Proteomics Reveals Distinct Molecular Signatures of Different Cerebellum-Dependent Learning Paradigms

The cerebellum improves motor performance by adjusting motor gain appropriately. As de novo protein synthesis is essential for the formation and retention of memories, we hypothesized that motor learning in the opposite direction would induce a distinct pattern of protein expression in the cerebellum. We conducted quantitative proteomic profiling to compare the level of protein expression in the cerebellum at 1 and 24 h after training from mice that underwent different paradigms of cerebellum-dependent oculomotor learning from specific directional changes in motor gain. We quantified a total of 43 proteins that were significantly regulated in each of the three learning paradigms in the cerebellum at 1 and 24 h after learning. In addition, functional enrichment analysis identified protein groups that were differentially enriched or depleted in the cerebellum at 24 h after the three oculomotor learnings, suggesting that distinct biological pathways may be engaged in the formation of three oculomotor memories. Weighted correlation network analysis discovered groups of proteins significantly correlated with oculomotor memory. Finally, four proteins (Snca, Sncb, Cttn, and Stmn1) from the protein group correlated with the learning amount after oculomotor training were validated by Western blot. This study provides a comprehensive and unbiased list of proteins related to three cerebellum-dependent motor learning paradigms, suggesting the distinct nature of protein expression in the cerebellum for each learning paradigm. The proteomics data have been deposited to the ProteomeXchange Consortium with identifiers <PXD008433>.

Genome-wide association study identifies genetic factors that modify age at onset in Machado-Joseph disease

Machado-Joseph disease (MJD/SCA3) is the most common form of dominantly inherited ataxia worldwide. The disorder is caused by an expanded CAG repeat in the ATXN3 gene. Past studies have revealed that the length of the expansion partly explains the disease age at onset (AO) variability of MJD, which is confirmed in this study (Pearson’s correlation coefficient R² = 0.62). Using a total of 786 MJD patients from five different geographical origins, a genome-wide association study (GWAS) was conducted to identify additional AO modifying factors that could explain some of the residual AO variability. We identified nine suggestively associated loci (P < 1 × 10⁻⁵). These loci were enriched for genes involved in vesicle transport, olfactory signaling, and synaptic pathways. Furthermore, associations between AO and the TRIM29 and RAG genes suggests that DNA repair mechanisms might be implicated in MJD pathogenesis. Our study demonstrates the existence of several additional genetic factors, along with CAG expansion, that may lead to a better understanding of the genotype-phenotype correlation in MJD.

Shared genetic etiology underlying Alzheimer's disease and major depressive disorder

Patients with late-onset Alzheimer's disease (LOAD) frequently manifest comorbid neuropsychiatric symptoms with depression and anxiety being most frequent, and individuals with major depressive disorder (MDD) have an increased prevalence of LOAD. This suggests shared etiologies and intersecting pathways between LOAD and MDD. We performed pleiotropy analyses using LOAD and MDD GWAS data sets from the International Genomics of Alzheimer's Project (IGAP) and the Psychiatric Genomics Consortium (PGC), respectively. We found a moderate enrichment for SNPs associated with LOAD across increasingly stringent levels of significance with the MDD GWAS association (LOAD|MDD), of maximum four and eightfolds, including and excluding the APOE-region, respectively. Association analysis excluding the APOE-region identified numerous SNPs corresponding to 40 genes, 9 of which are known LOAD-risk loci primarily in chromosome 11 regions that contain the SPI1 gene and MS4A genes cluster, and others were novel pleiotropic risk-loci for LOAD conditional with MDD. The most significant associated SNPs on chromosome 11 overlapped with eQTLs found in whole-blood and monocytes, suggesting functional roles in gene regulation. The reverse conditional association analysis (MDD|LOAD) showed a moderate level, ~sevenfold, of polygenic overlap, however, no SNP showed significant association. Pathway analyses replicated previously reported LOAD biological pathways related to immune response and regulation of endocytosis. In conclusion, we provide insights into the overlapping genetic signatures underpinning the common phenotypic manifestations and inter-relationship between LOAD and MDD. This knowledge is crucial to the development of actionable targets for novel therapies to treat depression preceding dementia, in an effort to delay or ultimately prevent the onset of LOAD.

Hierarchical structural component model for pathway analysis of common variants

BACKGROUND

Genome-wide association studies (GWAS) have been widely used to identify phenotype-related genetic variants using many statistical methods, such as logistic and linear regression. However, GWAS-identified SNPs, as identified with stringent statistical significance, explain just a small portion of the overall estimated genetic heritability. To address this 'missing heritability' issue, gene- and pathway-based analysis, and biological mechanisms, have been used for many GWAS studies. However, many of these methods often neglect the correlation between genes and between pathways.

METHODS

We constructed a hierarchical component model that considers correlations both between genes and between pathways. Based on this model, we propose a novel pathway analysis method for GWAS datasets, Hierarchical structural Component Model for Pathway analysis of Common vAriants (HisCoM-PCA). HisCoM-PCA first summarizes the common variants of each gene, first at the gene-level, and then analyzes all pathways simultaneously by ridge-type penalization of both the gene and pathway effects on the phenotype. Statistical significance of the gene and pathway coefficients can be examined by permutation tests.

RESULTS

Using the simulation data set of Genetic Analysis Workshop 17 (GAW17), for both binary and continuous phenotypes, we showed that HisCoM-PCA well-controlled type I error, and had a higher empirical power compared to several other methods. In addition, we applied our method to a SNP chip dataset of KARE for four human physiologic traits: (1) type 2 diabetes; (2) hypertension; (3) systolic blood pressure; and (4) diastolic blood pressure. Those results showed that HisCoM-PCA could successfully identify signal pathways with superior statistical and biological significance.

CONCLUSIONS

Our approach has the advantage of providing an intuitive biological interpretation for associations between common variants and phenotypes, via pathway information, potentially addressing the missing heritability conundrum.

Gene set enrichment analysis to create polygenic scores: a developmental examination of aggression

Previous approaches for creating polygenic risk scores (PRSs) do not explicitly consider the biological or developmental relevance of the genetic variants selected for inclusion. We applied gene set enrichment analysis to meta-GWAS data to create developmentally targeted, functionally informed PRSs. Using two developmentally matched meta-GWAS discovery samples, separate PRSs were formed, then examined in time-varying effect models of aggression in a second, longitudinal sample of children (n = 515, 49% female) in early childhood (2-5 years old), and middle childhood (7.5-10.5 years old). Functional PRSs were associated with aggression in both the early and middle childhood models.

2018 George Lyman Duff Memorial Lecture: Genetics and Genomics of Coronary Artery Disease: A Decade of Progress

Recent studies have led to a broader understanding of the genetic architecture of coronary artery disease and demonstrate that it largely derives from the cumulative effect of multiple common risk alleles individually of small effect size rather than rare variants with large effects on coronary artery disease risk. The tools applied include genome-wide association studies encompassing over 200 000 individuals complemented by bioinformatic approaches including imputation from whole-genome data sets, expression quantitative trait loci analyses, and interrogation of ENCODE (Encyclopedia of DNA Elements), Roadmap Epigenetic Project, and other data sets. Over 160 genome-wide significant loci associated with coronary artery disease risk have been identified using the genome-wide association studies approach, 90% of which are situated in intergenic regions. Here, I will describe, in part, our research over the last decade performed in collaboration with a series of bright trainees and an extensive number of groups and individuals around the world as it applies to our understanding of the genetic basis of this complex disease. These studies include computational approaches to better understand missing heritability and identify causal pathways, experimental approaches, and progress in understanding at the molecular level the function of the multiple risk loci identified and potential applications of these genomic data in clinical medicine and drug discovery.

Efficient pathway enrichment and network analysis of GWAS summary data using GSA-SNP2

Pathway-based analysis in genome-wide association study (GWAS) is being widely used to uncover novel multi-genic functional associations. Many of these pathway-based methods have been used to test the enrichment of the associated genes in the pathways, but exhibited low powers and were highly affected by free parameters. We present the novel method and software GSA-SNP2 for pathway enrichment analysis of GWAS P-value data. GSA-SNP2 provides high power, decent type I error control and fast computation by incorporating the random set model and SNP-count adjusted gene score. In a comparative study using simulated and real GWAS data, GSA-SNP2 exhibited high power and best prioritized gold standard positive pathways compared with six existing enrichment-based methods and two self-contained methods (alternative pathway analysis approach). Based on these results, the difference between pathway analysis approaches was investigated and the effects of the gene correlation structures on the pathway enrichment analysis were also discussed. In addition, GSA-SNP2 is able to visualize protein interaction networks within and across the significant pathways so that the user can prioritize the core subnetworks for further studies. GSA-SNP2 is freely available at https://sourceforge.net/projects/gsasnp2.

GSEA-InContext: identifying novel and common patterns in expression experiments

Motivation

Gene Set Enrichment Analysis (GSEA) is routinely used to analyze and interpret coordinate pathway-level changes in transcriptomics experiments. For an experiment where less than seven samples per condition are compared, GSEA employs a competitive null hypothesis to test significance. A gene set enrichment score is tested against a null distribution of enrichment scores generated from permuted gene sets, where genes are randomly selected from the input experiment. Looking across a variety of biological conditions, however, genes are not randomly distributed with many showing consistent patterns of up- or down-regulation. As a result, common patterns of positively and negatively enriched gene sets are observed across experiments. Placing a single experiment into the context of a relevant set of background experiments allows us to identify both the common and experiment-specific patterns of gene set enrichment.

Results

We compiled a compendium of 442 small molecule transcriptomic experiments and used GSEA to characterize common patterns of positively and negatively enriched gene sets. To identify experiment-specific gene set enrichment, we developed the GSEA-InContext method that accounts for gene expression patterns within a background set of experiments to identify statistically significantly enriched gene sets. We evaluated GSEA-InContext on experiments using small molecules with known targets to show that it successfully prioritizes gene sets that are specific to each experiment, thus providing valuable insights that complement standard GSEA analysis.

Availability and implementation

GSEA-InContext implemented in Python, Supplementary results and the background expression compendium are available at: https://github.com/CostelloLab/GSEA-InContext.

Genetic analysis for cognitive flexibility in the trail-making test in attention deficit hyperactivity disorder patients from single nucleotide polymorphism, gene to pathway level

Objectives: Investigation of the genetic basis of endophenotype and analysis the pathways with multiple genes of small effects might increase the understanding of the genetic basis of attention deficit hyperactivity disorder (ADHD). Here we aimed to explore the genetic basis of cognitive flexibility in ADHD at the single nucleotide polymorphism (SNP), gene and pathway levels. Methods: The trail-making test was used to test the cognitive flexibility of 788 ADHD patients. A genome-wide association analysis of cognitive flexibility was conducted for 644,166 SNPs. Results: The top SNP rs2049161 (P = 5.08e-7) involved gene DLGAP1 and the top gene CADPS2 in the gene-based analysis resulted in much literature evidence of associations with psychiatric disorders. Gene expression and network analysis showed their contribution to cognition function. The interval-enrichment analysis highlighted a potential contribution of 'adenylate cyclase activity' and ADCY2 to cognitive flexibility. Candidate pathway-based analysis for all SNPs found that glutamate system-, neurite outgrowth- and noradrenergic system-related pathways were significantly associated with cognitive flexibility (FDR <0.05), among which the neurite outgrowth pathway was also associated with ADHD symptoms. Conclusions: This study provides evidence for the genes and pathways associated with cognitive flexibility and facilitate the uncovering of the genetic basis of ADHD.

Pathway analysis of a genome-wide gene by air pollution interaction study in asthmatic children

OBJECTIVES

We aimed to investigate the role of genetics in the respiratory response of asthmatic children to air pollution, with a genome-wide level analysis of gene by nitrogen dioxide (NO2) and carbon monoxide (CO) interaction on lung function and to identify biological pathways involved.

METHODS

We used a two-step method for fast linear mixed model computations for genome-wide association studies, exploring whether variants modify the longitudinal relationship between 4-month average pollution and post-bronchodilator FEV1 in 522 Caucasian and 88 African-American asthmatic children. Top hits were confirmed with classic linear mixed-effect models. We used the improved gene set enrichment analysis for GWAS (i-GSEA4GWAS) to identify plausible pathways.

RESULTS

Two SNPs near the EPHA3 (rs13090972 and rs958144) and one in TXNDC8 (rs7041938) showed significant interactions with NO2 in Caucasians but we did not replicate this locus in African-Americans. SNP-CO interactions did not reach genome-wide significance. The i-GSEA4GWAS showed a pathway linked to the HO-1/CO system to be associated with CO-related FEV1 changes. For NO2-related FEV1 responses, we identified pathways involved in cellular adhesion, oxidative stress, inflammation, and metabolic responses.

CONCLUSION

The host lung function response to long-term exposure to pollution is linked to genes involved in cellular adhesion, oxidative stress, inflammatory, and metabolic pathways.

The association of germline variants with chronic lymphocytic leukemia outcome suggests the implication of novel genes and pathways in clinical evolution

BACKGROUND

Chronic Lymphocytic Leukemia (CLL) is the most frequent lymphoproliferative disorder in western countries and is characterized by a remarkable clinical heterogeneity. During the last decade, multiple genomic studies have identified a myriad of somatic events driving CLL proliferation and aggressivity. Nevertheless, and despite the mounting evidence of inherited risk for CLL development, the existence of germline variants associated with clinical outcomes has not been addressed in depth.

METHODS

Exome sequencing data from control leukocytes of CLL patients involved in the International Cancer Genome Consortium (ICGC) was used for genotyping. Cox regression was used to detect variants associated with clinical outcomes. Gene and pathways level associations were also calculated.

RESULTS

Single nucleotide polymorphisms in PPP4R2 and MAP3K4 were associated with earlier treatment need. A gene-level analysis evidenced a significant association of RIPK3 with both treatment need and survival. Furthermore, germline variability in pathways such as apoptosis, cell-cycle, pentose phosphate, GNα13 and Nitric oxide was associated with overall survival.

CONCLUSION

Our results support the existence of inherited conditionants of CLL evolution and points towards genes and pathways that may results useful as biomarkers of disease outcome. More research is needed to validate these findings.

Exploring the underlying biology of intrinsic cardiorespiratory fitness through integrative analysis of genomic variants and muscle gene expression profiling

Intrinsic cardiorespiratory fitness (CRF) is defined as the level of CRF in the sedentary state. There are large individual differences in intrinsic CRF among sedentary adults. The physiology of variability in CRF has received much attention, but little is known about the genetic and molecular mechanisms that impact intrinsic CRF. These issues were explored in the present study by interrogating intrinsic CRF-associated DNA sequence variation and skeletal muscle gene expression data from the HERITAGE Family Study through an integrative bioinformatics guided approach. A combined analytic strategy involving genetic association, pathway enrichment, tissue-specific network structure, cis-regulatory genome effects, and expression quantitative trait loci was used to select and rank genes through a variation-adjusted weighted ranking scheme. Prioritized genes were further interrogated for corroborative evidence from knockout mouse phenotypes and relevant physiological traits from the HERITAGE cohort. The mean intrinsic V̇o2max was 33.1 ml O2·kg-1·min-1 (SD = 8.8) for the sample of 493 sedentary adults. Suggestive evidence was found for gene loci related to cardiovascular physiology (ATE1, CASQ2, NOTO, and SGCG), hematopoiesis (PICALM, SSB, CA9, and CASQ2), skeletal muscle phenotypes (SGCG, DMRT2, ADARB1, and CASQ2), and metabolism (ATE1, PICALM, RAB11FIP5, GBA2, SGCG, PRADC1, ARL6IP5, and CASQ2). Supportive evidence for a role of several of these loci was uncovered via association between DNA variants and muscle gene expression levels with exercise cardiovascular and muscle physiological traits. This initial effort to define the underlying molecular substrates of intrinsic CRF warrants further studies based on appropriate cohorts and study designs, complemented by functional investigations. NEW & NOTEWORTHY Intrinsic cardiorespiratory fitness (CRF) is measured in the sedentary state and is highly variable among sedentary adults. The physiology of variability in intrinsic cardiorespiratory fitness has received much attention, but little is known about the genetic and molecular mechanisms that impact intrinsic CRF. These issues were explored computationally in the present study, with further corroborative evidence obtained from analysis of phenotype data from knockout mouse models and human cardiovascular and skeletal muscle measurements.

Genome-Wide Association Study-Driven Gene-Set Analyses, Genetic, and Functional Follow-Up Suggest GLIS1 as a Susceptibility Gene for Mitral Valve Prolapse

Background Mitral valve prolapse (MVP) is a common heart valve disease, the most frequent indication for valve repair or replacement. MVP is characterized by excess extracellular matrix secretion and cellular disorganization, which leads to bulky valves that are unable to coapt correctly during ventricular systole resulting in mitral regurgitation, and it is associated with sudden cardiac death. Here we aim to characterize globally the biological mechanisms underlying genetic susceptibility to MVP to better characterize its triggering mechanisms. Methods We applied i-GSEA4GWAS and DEPICT, two pathway enrichment tools to MVP genome-wide association studies. We followed-up the association with MVP in an independent dataset of cases and controls. This research was conducted using the UK Biobank Resource. Immunohistochemistry staining for Glis1 (GLIS family zinc finger 1) was conducted in developing heart of mice. Knockdown of Glis1 using morpholinos was performed in zebrafish animals 72 hours postfertilization. Results We show that genes at risk loci are involved in biological functions relevant to actin filament organization, cytoskeleton biology, and cardiac development. The enrichment for positive regulation of transcription, cell proliferation, and migration motivated the follow-up of GLIS1, a transcription factor from the Krüppel-like zinc finger family. In combination with previously available data, we now report a genome-wide significant association with MVP (odds ratio, 1.20; P=4.36×10-10), indicating that Glis1 is expressed during embryonic development predominantly in nuclei of endothelial and interstitial cells of mitral valves in mouse. We also show that Glis1 knockdown causes atrioventricular regurgitation in developing hearts in zebrafish. Conclusions Our findings define globally molecular and cellular mechanisms underlying common genetic susceptibility to MVP and implicate established and unprecedented mechanisms. Through the GLIS1 association and function, we point at regulatory functions during cardiac development as common mechanisms to mitral valve degeneration.

Effect of Interaction between Early Menarche and Genetic Polymorphisms on Triglyceride

Early menarche has been associated with increased risk of metabolic syndrome. Therefore, investigating the association of each component of metabolic syndrome with age at menarche, and interactions between them, might lead to a better understanding of metabolic syndrome pathogenesis. In this study, we evaluated age at menarche for risk of metabolic syndrome and associations with its components. As a result, the risk of MetS incidence was significantly increased only at ≤12 years of age at menarche (OR = 1.91, P < 0.05). Women with early menarche (≤12 years) had significantly higher levels of triglycerides (β coefficient = 37.83, P = 0.02). In addition, hypertriglyceridemia was significantly increased at early menarche with 1.99 (95% CI: 1.16–3.41, P < 0.01). With GWAS-based pathway analysis, we found the type 2 diabetes mellitus, stress-activated protein kinase signaling, and Jun amino-terminal kinase cascade pathways (all nominal P < 0.001, all FDR < 0.05) to be significantly involved with early menarche on triglyceride levels. These findings may help us understand the role of early menarche on triglyceride and interaction between gene and early menarche on triglyceride for the development of metabolic syndrome.

A case-control genome wide association study of substance use disorder (SUD) identifies novel variants on chromosome 7p14.1 in patients from the United Arab Emirates (UAE)

Genome wide association studies (GWASs) have provided insights into the molecular basis of the disorder in different population. This study presents the first GWAS of substance use disorder (SUD) in patients from the United Arab Emirates (UAE). The aim was to identify genetic association(s) that may provide insights into the molecular basis of the disorder. The GWAS discovery cohort consisted of 512 (250 cases and 262 controls) male participants from the UAE. Controls with no prior history of SUD were available from the Emirates family registry. The replication cohort consisted of 520 (415 cases and 105 controls) Australian male Caucasian participants. The GWAS discovery samples were genotyped for 4.6 million single nucleotide polymorphism (SNP). The replication cohort was genotyped using TaqMan assay. The GWAS association analysis identified three potential SNPs rs118129027 (p-value = 6.24 × 10^-8 ), rs74477937 (p-value = 8.56 × 10^-8 ) and rs78707086 (p-value = 8.55 × 10^-8 ) on ch7p14.1, that did not meet the GWAS significance threshold but were highly suggestive. In the replication cohort, the association of the three top SNPs did not reach statistical significance. In a meta-analysis of the discovery and the replication cohorts, there were no strengthen evidence for association of the three SNPs. The top identified rs118129027 overlaps with a regulatory factor (enhancer) region that targets three neighboring genes LOC105375237, LOC105375240, and YAE1D1. The YAE1D1, which represents a potential locus that is involved in regulating translation initiation pathway. Novel associations that require further confirmation were identified, suggesting a new insight to the genetic basis of SUD.

Strategies for Pathway Analysis Using GWAS and WGS Data

Single-allele study designs, commonly used in genome-wide association studies (GWAS) as well as the more recently developed whole genome sequencing (WGS) studies, are a standard approach for investigating the relationship of common variation within the human genome to a given phenotype of interest. However, single-allele association results published for many GWAS studies represent only the tip of the iceberg for the information that can be extracted from these datasets. The primary analysis strategy for GWAS entails association analysis in which only the single nucleotide polymorphisms (SNPs) with the strongest p-values are declared statistically significant due to issues arising from multiple testing and type I errors. Factors such as locus heterogeneity, epistasis, and multiple genes conferring small effects contribute to the complexity of the genetic models underlying phenotype expression. Thus, many biologically meaningful associations having lower effect sizes at individual genes are overlooked, making it difficult to separate true associations from a sea of false-positive associations. Organizing these individual SNPs into biologically meaningful groups to look at the overall effects of minor perturbations to genes and pathways is desirable. This pathway-based approach provides researchers with insight into the functional foundations of the phenotype being studied and allows testing of various genetic scenarios. © 2018 by John Wiley & Sons, Inc.

Optimizing gene set annotations combining GO structure and gene expression data

Background

With the rapid accumulation of genomic data, it has become a challenge issue to annotate and interpret these data. As a representative, Gene set enrichment analysis has been widely used to interpret large molecular datasets generated by biological experiments. The result of gene set enrichment analysis heavily relies on the quality and integrity of gene set annotations. Although several methods were developed to annotate gene sets, there is still a lack of high quality annotation methods. Here, we propose a novel method to improve the annotation accuracy through combining the GO structure and gene expression data.

Results

We propose a novel approach for optimizing gene set annotations to get more accurate annotation results. The proposed method filters the inconsistent annotations using GO structure information and probabilistic gene set clusters calculated by a range of cluster sizes over multiple bootstrap resampled datasets. The proposed method is employed to analyze p53 cell lines, colon cancer and breast cancer gene expression data. The experimental results show that the proposed method can filter a number of annotations unrelated to experimental data and increase gene set enrichment power and decrease the inconsistent of annotations.

Conclusions

A novel gene set annotation optimization approach is proposed to improve the quality of gene annotations. Experimental results indicate that the proposed method effectively improves gene set annotation quality based on the GO structure and gene expression data.

Integrative system biology analyses of CRISPR-edited iPSC-derived neurons and human brains reveal deficiencies of presynaptic signaling in FTLD and PSP

Mutations in the microtubule-associated protein tau (MAPT) gene cause autosomal dominant frontotemporal lobar degeneration with tau inclusions (FTLD-tau). MAPT p.R406W carriers present clinically with progressive memory loss and neuropathologically with neuronal and glial tauopathy. However, the pathogenic events triggered by the expression of the mutant tau protein remain poorly understood. To identify the genes and pathways that are dysregulated in FTLD-tau, we performed transcriptomic analyses in induced pluripotent stem cell (iPSC)-derived neurons carrying MAPT p.R406W and CRISPR/Cas9-corrected isogenic controls. We found that the expression of the MAPT p.R406W mutation was sufficient to create a significantly different transcriptomic profile compared with that of the isogeneic controls and to cause the differential expression of 328 genes. Sixty-one of these genes were also differentially expressed in the same direction between MAPT p.R406W carriers and pathology-free human control brains. We found that genes differentially expressed in the stem cell models and human brains were enriched for pathways involving gamma-aminobutyric acid (GABA) receptors and pre-synaptic function. The expression of GABA receptor genes, including GABRB2 and GABRG2, were consistently reduced in iPSC-derived neurons and brains from MAPT p.R406W carriers. Interestingly, we found that GABA receptor genes, including GABRB2 and GABRG2, are significantly lower in symptomatic mouse models of tauopathy, as well as in brains with progressive supranuclear palsy. Genome wide association analyses reveal that common variants within GABRB2 are associated with increased risk for frontotemporal dementia (P < 1 × 10^-3). Thus, our systems biology approach, which leverages molecular data from stem cells, animal models, and human brain tissue can reveal novel disease mechanisms. Here, we demonstrate that MAPT p.R406W is sufficient to induce changes in GABA-mediated signaling and synaptic function, which may contribute to the pathogenesis of FTLD-tau and other primary tauopathies.

Identification of susceptibility pathways for the role of chromosome 15q25.1 in modifying lung cancer risk

Genome-wide association studies (GWAS) identified the chromosome 15q25.1 locus as a leading susceptibility region for lung cancer. However, the pathogenic pathways, through which susceptibility SNPs within chromosome 15q25.1 affects lung cancer risk, have not been explored. We analyzed three cohorts with GWAS data consisting 42,901 individuals and lung expression quantitative trait loci (eQTL) data on 409 individuals to identify and validate the underlying pathways and to investigate the combined effect of genes from the identified susceptibility pathways. The KEGG neuroactive ligand receptor interaction pathway, two Reactome pathways, and 22 Gene Ontology terms were identified and replicated to be significantly associated with lung cancer risk, with P values less than 0.05 and FDR less than 0.1. Functional annotation of eQTL analysis results showed that the neuroactive ligand receptor interaction pathway and gated channel activity were involved in lung cancer risk. These pathways provide important insights for the etiology of lung cancer.

Sex Differences in Genetic Associations With Longevity

IMPORTANCE

Sex differences in genetic associations with human longevity remain largely unknown; investigations on this topic are important for individualized health care.

OBJECTIVE

To explore sex differences in genetic associations with longevity.

DESIGN SETTING AND PARTICIPANTS

This population-based case-control study used sex-specific genome-wide association study and polygenic risk score (PRS) analyses to examine sex differences in genetic associations with longevity. Five hundred sixty-four male and 1614 female participants older than 100 years were compared with a control group of 773 male and 1526 female individuals aged 40 to 64 years. All were Chinese Longitudinal Healthy Longevity Study participants with Han ethnicity who were recruited in 1998 and 2008 to 2014.

MAIN OUTCOMES AND MEASURES

Sex-specific loci and pathways associated with longevity and PRS measures of joint effects of sex-specific loci.

RESULTS

Eleven male-specific and 11 female-specific longevity loci (P < 10-5) and 35 male-specific and 25 female-specific longevity loci (10-5 ≤ P < 10-4) were identified. Each of these loci's associations with longevity were replicated in north and south regions of China in one sex but were not significant in the other sex (P = .13-.97), and loci-sex interaction effects were significant (P < .05). The associations of loci rs60210535 of the LINC00871 gene with longevity were replicated in Chinese women (P = 9.0 × 10-5) and US women (P = 4.6 × 10-5) but not significant in Chinese and US men. The associations of the loci rs2622624 of the ABCG2 gene were replicated in Chinese women (P = 6.8 × 10-5) and European women (P = .003) but not significant in both Chinese and European men. Eleven male-specific pathways (inflammation and immunity genes) and 34 female-specific pathways (tryptophan metabolism and PGC-1α induced) were significantly associated with longevity (P < .005; false discovery rate < 0.05). The PRS analyses demonstrated that sex-specific associations with longevity of the 4 exclusive groups of 11 male-specific and 11 female-specific loci (P < 10-5) and 35 male-specific and 25 female-specific loci (10-5 ≤P < 10-4) were jointly replicated across north and south discovery and target samples. Analyses using the combined data set of north and south showed that these 4 groups of sex-specific loci were jointly and significantly associated with longevity in one sex (P = 2.9 × 10-70 to 1.3 × 10-39) but not jointly significant in the other sex (P = .11 to .70), while interaction effects between PRS and sex were significant (P = 4.8 × 10-50 to 1.2 × 10-16).

CONCLUSION AND RELEVANCE

The sex differences in genetic associations with longevity are remarkable, but have been overlooked by previously published genome-wide association studies on longevity. This study contributes to filling this research gap and provides a scientific basis for further investigating effects of sex-specific genetic variants and their interactions with environment on healthy aging, which may substantially contribute to more effective and targeted individualized health care for male and female elderly individuals.

IGESS: a statistical approach to integrating individual-level genotype data and summary statistics in genome-wide association studies

Motivation

Results from genome-wide association studies (GWAS) suggest that a complex phenotype is often affected by many variants with small effects, known as 'polygenicity'. Tens of thousands of samples are often required to ensure statistical power of identifying these variants with small effects. However, it is often the case that a research group can only get approval for the access to individual-level genotype data with a limited sample size (e.g. a few hundreds or thousands). Meanwhile, summary statistics generated using single-variant-based analysis are becoming publicly available. The sample sizes associated with the summary statistics datasets are usually quite large. How to make the most efficient use of existing abundant data resources largely remains an open question.

Results

In this study, we propose a statistical approach, IGESS, to increasing statistical power of identifying risk variants and improving accuracy of risk prediction by i ntegrating individual level ge notype data and s ummary s tatistics. An efficient algorithm based on variational inference is developed to handle the genome-wide analysis. Through comprehensive simulation studies, we demonstrated the advantages of IGESS over the methods which take either individual-level data or summary statistics data as input. We applied IGESS to perform integrative analysis of Crohns Disease from WTCCC and summary statistics from other studies. IGESS was able to significantly increase the statistical power of identifying risk variants and improve the risk prediction accuracy from 63.2% ( ±0.4% ) to 69.4% ( ±0.1% ) using about 240 000 variants.

Availability and implementation

The IGESS software is available at https://github.com/daviddaigithub/IGESS .

Contact

zbxu@xjtu.edu.cn or xwan@comp.hkbu.edu.hk or eeyang@hkbu.edu.hk.

Supplementary information

Supplementary data are available at Bioinformatics online.

Genetic variation in 117 myelination-related genes in schizophrenia: Replication of association to lipid biosynthesis genes

Schizophrenia is a serious psychotic disorder with high heritability. Several common genetic variants, rare copy number variants and ultra-rare gene-disrupting mutations have been linked to disease susceptibility, but there is still a large gap between the estimated and explained heritability. Since several studies have indicated brain myelination abnormalities in schizophrenia, we aimed to examine whether variants in myelination-related genes could be associated with risk for schizophrenia. We established a set of 117 myelination genes by database searches and manual curation. We used a combination of GWAS (SCZ_N = 35,476; CTRL_N = 46,839), exome chip (SCZ_N = 269; CTRL_N = 336) and exome sequencing data (SCZ_N = 2,527; CTRL_N = 2,536) from schizophrenia cases and healthy controls to examine common and rare variants. We found that a subset of lipid-related genes was nominally associated with schizophrenia (p = 0.037), but this signal did not survive multiple testing correction (FWER = 0.16) and was mainly driven by the SREBF1 and SREBF2 genes that have already been linked to schizophrenia. Further analysis demonstrated that the lowest nominal p-values were p = 0.0018 for a single common variant (rs8539) and p = 0.012 for burden of rare variants (LRP1 gene), but none of them survived multiple testing correction. Our findings suggest that variation in myelination-related genes is not a major risk factor for schizophrenia.

A sibling method for identifying vQTLs

The propensity of a trait to vary within a population may have evolutionary, ecological, or clinical significance. In the present study we deploy sibling models to offer a novel and unbiased way to ascertain loci associated with the extent to which phenotypes vary (variance-controlling quantitative trait loci, or vQTLs). Previous methods for vQTL-mapping either exclude genetically related individuals or treat genetic relatedness among individuals as a complicating factor addressed by adjusting estimates for non-independence in phenotypes. The present method uses genetic relatedness as a tool to obtain unbiased estimates of variance effects rather than as a nuisance. The family-based approach, which utilizes random variation between siblings in minor allele counts at a locus, also allows controls for parental genotype, mean effects, and non-linear (dominance) effects that may spuriously appear to generate variation. Simulations show that the approach performs equally well as two existing methods (squared Z-score and DGLM) in controlling type I error rates when there is no unobserved confounding, and performs significantly better than these methods in the presence of small degrees of confounding. Using height and BMI as empirical applications, we investigate SNPs that alter within-family variation in height and BMI, as well as pathways that appear to be enriched. One significant SNP for BMI variability, in the MAST4 gene, replicated. Pathway analysis revealed one gene set, encoding members of several signaling pathways related to gap junction function, which appears significantly enriched for associations with within-family height variation in both datasets (while not enriched in analysis of mean levels). We recommend approximating laboratory random assignment of genotype using family data and more careful attention to the possible conflation of mean and variance effects.

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

Many genetic variants affect complex traits through gene expression, which can be exploited to boost statistical power and enhance interpretation in genome-wide association studies (GWASs) as demonstrated by the transcriptome-wide association study (TWAS) approach. Furthermore, due to polygenic inheritance, a complex trait is often affected by multiple genes with similar functions as annotated in gene pathways. Here, we extend TWAS from gene-based analysis to pathway-based analysis: we integrate public pathway collections, expression quantitative trait locus (eQTL) data and GWAS summary association statistics (or GWAS individual-level data) to identify gene pathways associated with complex traits. The basic idea is to weight the SNPs of the genes in a pathway based on their estimated cis-effects on gene expression, then adaptively test for association of the pathway with a GWAS trait by effectively aggregating possibly weak association signals across the genes in the pathway. The P values can be calculated analytically and thus fast. We applied our proposed test with the KEGG and GO pathways to two schizophrenia (SCZ) GWAS summary association data sets, denoted by SCZ1 and SCZ2 with about 20,000 and 150,000 subjects, respectively. Most of the significant pathways identified by analyzing the SCZ1 data were reproduced by the SCZ2 data. Importantly, we identified 15 novel pathways associated with SCZ, such as GABA receptor complex (GO:1902710), which could not be uncovered by the standard single SNP-based analysis or gene-based TWAS. The newly identified pathways may help us gain insights into the biological mechanism underlying SCZ. Our results showcase the power of incorporating gene expression information and gene functional annotations into pathway-based association testing for GWAS.

Nuclear receptor and VEGF pathways for gene-blood lead interactions, on bone mineral density, in Korean smokers

Osteoporosis has a complex etiology and is considered a multifactorial polygenic disease, in which genetic determinants are modulated by hormonal, lifestyle, environmental, and nutritional factors. Therefore, investigating these multiple factors, and the interactions between them, might lead to a better understanding of osteoporosis pathogenesis, and possible therapeutic interventions. The objective of this study was to identify the relationship between three blood metals (Pb, Cd, and Al), in smoking and nonsmoking patients' sera, and prevalence of osteoporosis. In particular, we focused on gene-environment interactions of metal exposure, including a dataset obtained through genome-wide association study (GWAS). Subsequently, we conducted a pathway-based analysis, using a GWAS dataset, to elucidate how metal exposure influences susceptibility to osteoporosis. In this study, we evaluated blood metal exposures for estimating the prevalence of osteoporosis in 443 participants (aged 53.24 ± 8.29), from the Republic of Korea. Those analyses revealed a negative association between lead blood levels and bone mineral density in current smokers (p trend <0.01). By further using GWAS-based pathway analysis, we found nuclear receptor (FDR<0.05) and VEGF pathways (FDR<0.05) to be significantly upregulated by blood lead burden, with regard to the prevalence of osteoporosis, in current smokers. These findings suggest that the intracellular pathways of angiogenesis and nuclear hormonal signaling can modulate interactions between lead exposure and genetic variation, with regard to susceptibility to diminished bone mineral density. Our findings may provide new leads for understanding the mechanisms underlying the development of osteoporosis, including possible interventions.

Genome-wide association study identifies ERBB4 on 2q34 as a novel locus associated with sperm motility in Japanese men

Background

The decrease in sperm motility has a potent influence on fertilisation. Sperm motility, represented as the percentage of motile sperm in ejaculated sperms, is influenced by lifestyle habits or environmental factors and by inherited factors. However, genetic factors contributing to individual differences in sperm motility remain unclear. To identify genetic factors that influence human sperm motility, we performed a genome-wide association study (GWAS) of sperm motility.

Methods

A two-stage GWAS was conducted using 811 Japanese men in a discovery stage, followed by a replication study using an additional 779 Japanese men.

Results

In the two-staged GWAS, a single nucleotide polymorphism rs3791686 in the intron of gene for erb-b2 receptor tyrosine kinase 4 (ERBB4) on chromosome 2q34 was identified as a novel locus for sperm motility, as evident from the discovery and replication results using meta-analysis (β=−4.01, combined P=5.40×10⁻⁹).

Conclusions

Together with the previous evidence that Sertoli cell-specific Erbb4-knockout mice display an impaired ability to produce motile sperm, this finding provides the first genetic evidence for further investigation of the genome-wide significant association at the ERBB4 locus in larger studies across diverse human populations.

Neuroanatomical phenotypes in mental illness: identifying convergent and divergent cortical phenotypes across autism, ADHD and schizophrenia

BACKGROUND

There is evidence suggesting neuropsychiatric disorders share genomic, cognitive and clinical features. Here, we ask if autism-spectrum disorders (ASD), attention-deficit/hyperactivity disorder (ADHD) and schizophrenia share neuroanatomical variations.

METHODS

First, we used measures of cortical anatomy to estimate spatial overlap of neuroanatomical variation using univariate methods. Next, we developed a novel methodology to determine whether cortical deficits specifically target or are "enriched" within functional resting-state networks.

RESULTS

We found cortical anomalies were preferentially enriched across functional networks rather than clustering spatially. Specifically, cortical thickness showed significant enrichment between patients with ASD and those with ADHD in the default mode network, between patients with ASD and those with schizophrenia in the frontoparietal and limbic networks, and between patients with ADHD and those with schizophrenia in the ventral attention network. Networks enriched in cortical thickness anomalies were also strongly represented in functional MRI results (Neurosynth; r = 0.64, p = 0.032).

LIMITATIONS

We did not account for variable symptom dimensions and severity in patient populations, and our cross-sectional design prevented longitudinal analyses of developmental trajectories.

CONCLUSION

These findings suggest that common deficits across neuropsychiatric disorders cannot simply be characterized as arising out of local changes in cortical grey matter, but rather as entities of both local and systemic alterations targeting brain networks.

The phosphatidylinositide 3-kinase (PI3K) signaling pathway is a determinant of zileuton response in adults with asthma

Variable responsiveness to zileuton, a leukotriene antagonist used to treat asthma, may be due in part to genetic variation. While individual SNPs were previously associated with zileuton-related lung function changes, specific quantitative trait loci (QTLs) and biological pathways that may contribute have not been identified. In this study, we investigated the hypothesis that genetic variation within biological pathways is associated with zileuton response. We performed an integrative QTL mapping and pathway enrichment study to investigate data from a GWAS of zileuton response, in addition to mRNA expression profiles and leukotriene production data from lymphoblastoid cell lines (LCLs) (derived from asthmatics) that were treated with zileuton or ethanol (control). We identified 1060 QTLs jointly associated with zileuton-related differential LTB₄ production in LCLs and lung function change in patients taking zileuton, of which eight QTLs were also significantly associated with persistent LTB₄ production in LCLs following zileuton treatment (i.e., ‘poor’ responders). Four nominally significant trans-eQTLs were predicted to regulate three candidate genes (SELL, MTF2, and GAL), the expression of which was significantly reduced in LCLs following zileuton treatment. Gene and pathway enrichment analyses of QTL associations identified multiple genes and pathways, predominantly related to phosphatidyl inositol signaling via PI3K. We validated the PI3K pathway activation status in a subset of LCLs demonstrating variable zileuton-related LTB₄ production, and show that in contrast to LCLs that responded to zileuton, the PI3K pathway was activated in poor responder LCLs. Collectively, these findings demonstrate a role for the PIK3 pathway and its targets as important determinants of differential responsiveness to zileuton.

An exploratory genome-wide analysis of genetic risk for alcoholic hepatitis

OBJECTIVES

To elucidate the genetic variability between heavy drinkers with and without alcoholic hepatitis (AH).

MATERIALS AND METHODS

An exploratory genome-wide association study (GWAS; NCT02172898) was conducted comparing 90 AH cases with 93 heavy drinking matched controls without liver disease in order to identify variants or genes associated with risk for AH. Individuals were genotyped using the multi-ethnic genotyping array, after which the data underwent conventional quality control. Using bioinformatics tools, pathways associated with AH were explored on the basis of individual variants, and based on genes with a higher 'burden' of functional variation.

RESULTS

Although no single variant reached genome-wide significance, an association signal was observed for PNPLA3 rs738409 (p = .01, OR 1.9, 95% CI 1.1-3.1), a common single nucleotide polymorphism that has been associated with a variety of liver-related pathologies including alcoholic cirrhosis. Using the improved gene set enrichment analysis for GWAS tool, it was shown that, based on the single variants' trait-association p-values, multiple pathways were associated with risk for AH with high confidence (false discovery rate [FDR] < 0.05), including several pathways involved in lymphocyte activation and chemokine signaling, which coincides with findings from other research groups. Several Tox Functions and Canonical Pathways were highlighted using Ingenuity Pathway Analysis, with an especially conspicuous role for pathways related to ethanol degradation, which is not surprising considering the phenotype of the genotyped individuals.

CONCLUSION

This preliminary analysis suggests a role for PNPLA3 variation and several gene sets/pathways that may influence risk for AH among heavy drinkers.

Genome-wide association study of Parkinson's disease in East Asians

Genome-wide association studies (GWAS) on Parkinson's disease (PD) have mostly been done in Europeans and Japanese. No study has been done in Han Chinese, which make up nearly a fifth of the world population. We conducted the first Han Chinese GWAS analysing a total of 22,729 subjects (5,125 PD cases and 17,604 controls) from Singapore, Hong Kong, Malaysia, Korea, mainland China and Taiwan. We performed imputation, merging and logistic regression analyses of 2,402,394 SNPs passing quality control filters in 779 PD cases, 13,227 controls, adjusted for the first three principal components. 90 SNPs with association P < 10-4 were validated in 9 additional sample collections and the results were combined using fixed-effects inverse-variance meta-analysis. We observed strong associations reaching genome-wide significance at SNCA, LRRK2 and MCCC1, confirming their important roles in both European and Asian PD. We also identified significant (P < 0.05) associations at 5 loci (DLG2, SIPA1L2, STK39, VPS13C and RIT2), and observed the same direction of associations at 9 other loci including BST1 and PARK16. Allelic heterogeneity was observed at LRRK2 while European risk SNPs at 6 other loci including MAPT and GBA-SYT11 were non-polymorphic or very rare in our cohort. Overall, we replicate associations at SNCA, LRRK2, MCCC1 and 14 other European PD loci but did not identify Asian-specific loci with large effects (OR > 1.45) on PD risk. Our results also demonstrate some differences in the genetic contribution to PD between Europeans and Asians. Further pan-ethnic meta-analysis with European GWAS cohorts may unravel new PD loci.

The genomic landscape of human cellular circadian variation points to a novel role for the signalosome

The importance of natural gene expression variation for human behavior is undisputed, but its impact on circadian physiology remains mostly unexplored. Using umbilical cord fibroblasts, we have determined by genome-wide association how common genetic variation impacts upon cellular circadian function. Gene set enrichment points to differences in protein catabolism as one major source of clock variation in humans. The two most significant alleles regulated expression of COPS7B, a subunit of the COP9 signalosome. We further show that the signalosome complex is imported into the nucleus in timed fashion to stabilize the essential circadian protein BMAL1, a novel mechanism to oppose its proteasome-mediated degradation. Thus, circadian clock properties depend in part upon a genetically-encoded competition between stabilizing and destabilizing forces, and genetic alterations in these mechanisms provide one explanation for human chronotype.

Identification of additional loci associated with antibody response to Mycobacterium avium ssp. Paratuberculosis in cattle by GSEA-SNP analysis

Mycobacterium avium subsp. paratuberculosis: (MAP) causes a contagious chronic infection results in Johne's disease in a wide range of animal species, including cattle. Several genome-wide association studies (GWAS) have been carried out to identify loci putatively associated with MAP susceptibility by testing each marker separately and identifying SNPs that show a significant association with the phenotype, while SNP with modest effects are usually ignored. The objective of this study was to identify modest-effect genes associated with MAP susceptibility using a pathway-based approach. The Illumina BovineSNP50 BeadChip was used to genotype 966 Holstein cows, 483 positive and 483 negative for antibody response to MAP, data were then analyzed using novel SNP-based Gene Set Enrichment Analysis (GSEA-SNP) and validated with Adaptive Rank Truncated Product methodology. An allele-based test was carried out to estimate the statistical association for each marker with the phenotype, subsequently SNPs were mapped to the closest genes, considering for each gene the single variant with the highest value within a window of 50 kb, then pathway-statistics were tested using the GSEA-SNP method. The GO biological process "embryogenesis and morphogenesis" was most highly associated with antibody response to MAP. Within this pathway, five genes code for proteins which play a role in the immune defense relevant to response to bacterial infection. The immune response genes identified would not have been considered using a standard GWAS, thus demonstrating that the pathway approach can extend the interpretation of genome-wide association analyses and identify additional candidate genes for target traits.

Glucocorticoid therapy regulates podocyte motility by inhibition of Rac1

Nephrotic syndrome (NS) occurs when the glomerular filtration barrier becomes excessively permeable leading to massive proteinuria. In childhood NS, immune system dysregulation has been implicated and increasing evidence points to the central role of podocytes in the pathogenesis. Children with NS are typically treated with an empiric course of glucocorticoid (Gc) therapy; a class of steroids that are activating ligands for the glucocorticoid receptor (GR) transcription factor. Although Gc-therapy has been the cornerstone of NS management for decades, the mechanism of action, and target cell, remain poorly understood. We tested the hypothesis that Gc acts directly on the podocyte to produce clinically useful effects without involvement of the immune system. In human podocytes, we demonstrated that the basic GR-signalling mechanism is intact and that Gc induced an increase in podocyte barrier function. Defining the GR-cistrome identified Gc regulation of motility genes. These findings were functionally validated with live-cell imaging. We demonstrated that treatment with Gc reduced the activity of the pro-migratory small GTPase regulator Rac1. Furthermore, Rac1 inhibition had a direct, protective effect on podocyte barrier function. Our studies reveal a new mechanism for Gc action directly on the podocyte, with translational relevance to designing new selective synthetic Gc molecules.