Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions

AbstractMajor depression is a debilitating psychiatric illness that is typically associated with low mood, anhedonia and a range of comorbidities. Depression has a heritable component that has remained difficult to elucidate with current sample sizes due to the polygenic nature of the disorder. To maximise sample size, we meta-analysed data on 807,553 individuals (246,363 cases and 561,190 controls) from the three largest genome-wide association studies of depression. We identified 102 independent variants, 269 genes, and 15 gene-sets associated with depression, including both genes and gene-pathways associated with synaptic structure and neurotransmission. Further evidence of the importance of prefrontal brain regions in depression was provided by an enrichment analysis. In an independent replication sample of 1,306,354 individuals (414,055 cases and 892,299 controls), 87 of the 102 associated variants were significant following multiple testing correction. Based on the putative genes associated with depression this work also highlights several potential drug repositioning opportunities. These findings advance our understanding of the complex genetic architecture of depression and provide several future avenues for understanding aetiology and developing new treatment approaches.

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Identification of Critical Core Genes of Sarcoma Based on Centrality Analysis of Networks Nodes

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2020.3080 ◽

2020 ◽

Vol 10 (7) ◽

pp. 1776-1784

Author(s):

Shudong Wang ◽

Jixiao Wang ◽

Xinzeng Wang ◽

Yuanyuan Zhang ◽

Tao Yi

Keyword(s):

Association Studies ◽

Meta Analysis ◽

Complex Diseases ◽

Enrichment Analysis ◽

Gene Interaction ◽

Core Gene ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Gene Set ◽

Genome Wide

Genome-wide association studies (GWAS) are powerful tools for identifying pathogenic genes of complex diseases and revealing genetic structure of diseases. However, due to gene-to-gene interactions, only a part of the hereditary factors can be revealed. The meta-analysis based on GWAS can integrate gene expression data at multiple levels and reveal the complex relationship between genes. Therefore, we used meta-analysis to integrate GWAS data of sarcoma to establish complex networks and discuss their significant genes. Firstly, we established gene interaction networks based on the data of different subtypes of sarcoma to analyze the node centralities of genes. Secondly, we calculated the significant score of each gene according to the Staged Significant Gene Network Algorithm (SSGNA). Then, we obtained the critical gene set HYC of sarcoma by ranking the scores, and then combined Gene Ontology enrichment analysis and protein network analysis to further screen it. Finally, the critical core gene set Hcore containing 47 genes was obtained and validated by GEPIA analysis. Our method has certain generalization performance to the study of complex diseases with prior knowledge and it is a useful supplement to genome-wide association studies.

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Capturing SNP Association across the NK Receptor and HLA Gene Regions in Multiple Sclerosis by Targeted Penalised Regression Models

Genes ◽

10.3390/genes13010087 ◽

2021 ◽

Vol 13 (1) ◽

pp. 87

Author(s):

Sean M. Burnard ◽

Rodney A. Lea ◽

Miles Benton ◽

David Eccles ◽

Daniel W. Kennedy ◽

...

Keyword(s):

Multiple Sclerosis ◽

Complex Traits ◽

Multiple Testing ◽

Large Scale ◽

Disease Risk ◽

Association Studies ◽

Meta Analysis ◽

Elastic Net ◽

Genome Wide Association Studies ◽

Multiple Testing Correction

Conventional genome-wide association studies (GWASs) of complex traits, such as Multiple Sclerosis (MS), are reliant on per-SNP p-values and are therefore heavily burdened by multiple testing correction. Thus, in order to detect more subtle alterations, ever increasing sample sizes are required, while ignoring potentially valuable information that is readily available in existing datasets. To overcome this, we used penalised regression incorporating elastic net with a stability selection method by iterative subsampling to detect the potential interaction of loci with MS risk. Through re-analysis of the ANZgene dataset (1617 cases and 1988 controls) and an IMSGC dataset as a replication cohort (1313 cases and 1458 controls), we identified new association signals for MS predisposition, including SNPs above and below conventional significance thresholds while targeting two natural killer receptor loci and the well-established HLA loci. For example, rs2844482 (98.1% iterations), otherwise ignored by conventional statistics (p = 0.673) in the same dataset, was independently strongly associated with MS in another GWAS that required more than 40 times the number of cases (~45 K). Further comparison of our hits to those present in a large-scale meta-analysis, confirmed that the majority of SNPs identified by the elastic net model reached conventional statistical GWAS thresholds (p < 5 × 10−8) in this much larger dataset. Moreover, we found that gene variants involved in oxidative stress, in addition to innate immunity, were associated with MS. Overall, this study highlights the benefit of using more advanced statistical methods to (re-)analyse subtle genetic variation among loci that have a biological basis for their contribution to disease risk.

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Transethnic meta-analysis of genome-wide association studies identifies three new loci and characterizes population-specific differences for coronary artery disease

10.1101/739979 ◽

2019 ◽

Author(s):

Hiroshi Matsunaga ◽

Kaoru Ito ◽

Masato Akiyama ◽

Atsushi Takahashi ◽

Satoshi Koyama ◽

...

Keyword(s):

Adrenal Glands ◽

Association Studies ◽

Meta Analysis ◽

Enrichment Analysis ◽

Tissue Cell ◽

Genome Wide Association Studies ◽

Diverse Populations ◽

Cell Type ◽

Genome Wide ◽

Artery Disease

AbstractBackgroundGenome-wide association studies (GWAS) provided many biological insights into coronary artery disease (CAD), but these studies were mainly performed in Europeans. GWAS in diverse populations have the potential to advance our understanding of CAD.Methods and ResultsWe conducted two GWAS for CAD in the Japanese population, which included 12,494 cases and 28,879 controls, and 2,808 cases and 7,261 controls, respectively. Then, we performed transethnic meta-analysis using the results of the CARDIoGRAMplusC4D 1000 Genomes meta-analysis with UK Biobank. We identified 3 new loci on chromosome 1q21 (CTSS), 10q26 (WDR11-FGFR2), and 11q22 (RDX-FDX1). Quantitative trait locus analyses suggested the association of CTSS and RDX-FDX1 with atherosclerotic immune cells. Tissue/cell type enrichment analysis showed the involvement of arteries, adrenal glands and fat tissues in the development of CAD. Finally, we performed tissue/cell type enrichment analysis using East Asian-frequent and European-frequent variants according to the risk allele frequencies, and identified significant enrichment of adrenal glands in the East Asian-frequent group while the enrichment of arteries and fat tissues was found in the European-frequent group. These findings indicate biological differences in CAD susceptibility between Japanese and Europeans.ConclusionsWe identified 3 new loci for CAD and highlighted the genetic differences between the Japanese and European populations. Moreover, our transethnic analyses showed both shared and unique genetic architectures between the Japanese and Europeans. While most of the underlying genetic bases for CAD are shared, further analyses in diverse populations will be needed to elucidate variations fully.

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networkGWAS: A network-based approach for genome-wide association studies in structured populations

10.1101/2021.11.11.468206 ◽

2021 ◽

Author(s):

Giulia Muzio ◽

Leslie O'Bray ◽

Laetitia Meng-Papaxanthos ◽

Juliane Klatt ◽

Karsten Borgwardt

Keyword(s):

Genetic Markers ◽

Complex Traits ◽

Multiple Testing ◽

Association Studies ◽

Search Space ◽

Structured Populations ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Multiple Testing Correction ◽

Genome Wide

While the search for associations between genetic markers and complex traits has discovered tens of thousands of trait-related genetic variants, the vast majority of these only explain a tiny fraction of observed phenotypic variation. One possible strategy to detect stronger associations is to aggregate the effects of several genetic markers and to test entire genes, pathways or (sub)networks of genes for association to a phenotype. The latter, network-based genome-wide association studies, in particular suffers from a huge search space and an inherent multiple testing problem. As a consequence, current approaches are either based on greedy feature selection, thereby risking that they miss relevant associations, and/or neglect doing a multiple testing correction, which can lead to an abundance of false positive findings. To address the shortcomings of current approaches of network-based genome-wide association studies, we propose <tt>networkGWAS</tt>, a computationally efficient and statistically sound approach to gene-based genome-wide association studies based on mixed models and neighborhood aggregation. It allows for population structure correction and for well-calibrated p-values, which we obtain through a block permutation scheme. <tt>networkGWAS</tt> successfully detects known or plausible associations on simulated rare variants from H. sapiens data as well as semi-simulated and real data with common variants from A. thaliana and enables the systematic combination of gene-based genome-wide association studies with biological network information.

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GWAS Meta-Analysis of Neuroticism (N=449,484) Identifies Novel Genetic Loci and Pathways

10.1101/184820 ◽

2017 ◽

Cited By ~ 8

Author(s):

Mats Nagel ◽

Philip R Jansen ◽

Sven Stringer ◽

Kyoko Watanabe ◽

Christiaan A de Leeuw ◽

...

Keyword(s):

Association Studies ◽

Meta Analysis ◽

Cell Types ◽

Brain Regions ◽

Specific Cell ◽

Medium Spiny Neurons ◽

Genome Wide Association Studies ◽

Genome Wide ◽

Genetic Signal

Neuroticism is an important risk factor for psychiatric traits including depression1, anxiety2,3, and schizophrenia4–6. Previous genome-wide association studies7–12 (GWAS) reported 16 genomic loci10–12. Here we report the largest neuroticism GWAS meta-analysis to date (N=449,484), and identify 136 independent genome-wide significant loci (124 novel), implicating 599 genes. Extensive functional follow-up analyses show enrichment in several brain regions and involvement of specific cell-types, including dopaminergic neuroblasts (P=3×10-8), medium spiny neurons (P=4×10-8) and serotonergic neurons (P=1×10-7). Gene-set analyses implicate three specific pathways: neurogenesis (P=4.4×10-9), behavioural response to cocaine processes (P=1.84×10-7), and axon part (P=5.26×10-8). We show that neuroticism’s genetic signal partly originates in two genetically distinguishable subclusters13 (depressed affect and worry, the former being genetically strongly related to depression, rg=0.84), suggesting distinct causal mechanisms for subtypes of individuals. These results vastly enhance our neurobiological understanding of neuroticism, and provide specific leads for functional follow-up experiments.

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Novel bioinformatics approach to investigate quantitative phenotype-genotype associations in neuroimaging studies

10.1101/015065 ◽

2015 ◽

Cited By ~ 1

Author(s):

Sejal Patel ◽

Min Tae M Park ◽

Mallar M Chakravarty ◽

Jo Knight

Keyword(s):

Multiple Testing ◽

Association Studies ◽

Genetic Research ◽

Hippocampal Volume ◽

Imaging Genetics ◽

Genome Wide Association Studies ◽

Multiple Testing Correction ◽

Novel Approach ◽

Genome Wide ◽

Novel Method

Imaging genetics is an emerging field in which the association between genes and neuroimaging-based quantitative phenotypes are used to explore the functional role of genes in neuroanatomy and neurophysiology in the context of healthy function and neuropsychiatric disorders. The main obstacle for researchers in the field is the high dimensionality of the data in both the imaging phenotypes and the genetic variants commonly typed. In this article, we develop a novel method that utilizes Gene Ontology, an online database, to select and prioritize certain genes, employing a stratified false discovery rate (sFDR) approach to investigate their associations with imaging phenotypes. sFDR has the potential to increase power in genome wide association studies (GWAS), and is quickly gaining traction as a method for multiple testing correction. Our novel approach addresses both the pressing need in genetic research to move beyond candidate gene studies, while not being overburdened with a loss of power due to multiple testing. As an example of our methodology, we perform a GWAS of hippocampal volume using the Alzheimer's Disease Neuroimaging Initiative sample.

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An Enrichment Analysis for Cardiometabolic Traits Suggests Non-Random Assignment of Genes to microRNAs

International Journal of Molecular Sciences ◽

10.3390/ijms19113666 ◽

2018 ◽

Vol 19 (11) ◽

pp. 3666 ◽

Cited By ~ 1

Author(s):

Rima Mustafa ◽

Mohsen Ghanbari ◽

Marina Evangelou ◽

Abbas Dehghan

Keyword(s):

Multiple Testing ◽

Target Genes ◽

Association Studies ◽

Enrichment Analysis ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Random Assignment ◽

Genome Wide ◽

Cardiometabolic Disorders ◽

Cardiometabolic Traits

MicroRNAs (miRNAs) regulate the expression of the majority of genes. However, it is not known whether they regulate genes in random or are organized according to their function. To this end, we chose cardiometabolic disorders as an example and investigated whether genes associated with cardiometabolic disorders are regulated by a random set of miRNAs or a limited number of them. Single-nucleotide polymorphisms (SNPs) reaching genome-wide level significance were retrieved from most recent genome-wide association studies on cardiometabolic traits, which were cross-referenced with Ensembl to identify related genes and combined with miRNA target prediction databases (TargetScan, miRTarBase, or miRecords) to identify miRNAs that regulate them. We retrieved 520 SNPs, of which 355 were intragenic, corresponding to 304 genes. While we found a higher proportion of genes reported from all GWAS that were predicted targets for miRNAs in comparison to all protein-coding genes (75.1%), the proportion was even higher for cardiometabolic genes (80.6%). Enrichment analysis was performed within each database. We found that cardiometabolic genes were over-represented in target genes for 29 miRNAs (based on TargetScan) and 3 miRNAs (miR-181a, miR-302d and miR-372) (based on miRecords) after Benjamini-Hochberg correction for multiple testing. Our work provides evidence for non-random assignment of genes to miRNAs and supports the idea that miRNAs regulate sets of genes that are functionally related.

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Identifying insomnia-related chemicals through integrative analysis of genome-wide association studies and chemical–genes interaction information

SLEEP ◽

10.1093/sleep/zsaa042 ◽

2020 ◽

Vol 43 (9) ◽

Author(s):

Om Prakash Kafle ◽

Shiqiang Cheng ◽

Mei Ma ◽

Ping Li ◽

Bolun Cheng ◽

...

Keyword(s):

Association Studies ◽

Meta Analysis ◽

Enrichment Analysis ◽

Modern Society ◽

Environmental Chemicals ◽

Gene Set Enrichment Analysis ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Genome Wide ◽

Study Results

Abstract Study Objectives Insomnia is a common sleep disorder and constitutes a major issue in modern society. We provide new clues for revealing the association between environmental chemicals and insomnia. Methods Three genome-wide association studies (GWAS) summary datasets of insomnia (n = 113,006, n = 1,331,010, and n = 453,379, respectively) were driven from the UK Biobank, 23andMe, and deCODE. The chemical–gene interaction dataset was downloaded from the Comparative Toxicogenomics Database. First, we conducted a meta-analysis of the three datasets of insomnia using the METAL software. Using the result of meta-analysis, transcriptome-wide association studies were performed to calculate the expression association testing statistics of insomnia. Then chemical-related gene set enrichment analysis (GSEA) was used to explore the association between chemicals and insomnia. Results For GWAS meta-analysis dataset of insomnia, we identified 42 chemicals associated with insomnia in brain tissue (p < 0.05) by GSEA. We detected five important chemicals such as pinosylvin (p = 0.0128), bromobenzene (p = 0.0134), clonidine (p = 0.0372), gabapentin (p = 0.0372), and melatonin (p = 0.0404) which are directly associated with insomnia. Conclusion Our study results provide new clues for revealing the roles of environmental chemicals in the development of insomnia.

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Transethnic meta-analysis identifies GSDMA and PRDM1 as susceptibility genes to systemic sclerosis

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2016-210645 ◽

2017 ◽

Vol 76 (6) ◽

pp. 1150-1158 ◽

Cited By ~ 34

Author(s):

Chikashi Terao ◽

Takahisa Kawaguchi ◽

Philippe Dieude ◽

John Varga ◽

Masataka Kuwana ◽

...

Keyword(s):

Systemic Sclerosis ◽

T Cell ◽

Autoimmune Diseases ◽

Association Studies ◽

Meta Analysis ◽

Enrichment Analysis ◽

Susceptibility Genes ◽

Genome Wide Association Studies ◽

Hla Association ◽

Genome Wide

ObjectivesSystemic sclerosis (SSc) is an autoimmune disease characterised by skin and systemic fibrosis culminating in organ damage. Previous genetic studies including genome-wide association studies (GWAS) have identified 12 susceptibility loci satisfying genome-wide significance. Transethnic meta-analyses have successfully expanded the list of susceptibility genes and deepened biological insights for other autoimmune diseases.MethodsWe performed transethnic meta-analysis of GWAS in the Japanese and European populations, followed by a two-staged replication study comprising a total of 4436 cases and 14 751 controls. Associations between significant single nuclear polymorphisms (SNPs) and neighbouring genes were evaluated. Enrichment analysis of H3K4Me3, a representative histone mark for active promoter was conducted with an expanded list of SSc susceptibility genes.ResultsWe identified two significant SNP in two loci, GSDMA and PRDM1, both of which are related to immune functions and associated with other autoimmune diseases (p=1.4×10−10 and 6.6×10−10, respectively). GSDMA also showed a significant association with limited cutaneous SSc. We also replicated the associations of previously reported loci including a non-GWAS locus, TNFAIP3. PRDM1 encodes BLIMP1, a transcription factor regulating T-cell proliferation and plasma cell differentiation. The top SNP in GSDMA was a missense variant and correlated with gene expression of neighbouring genes, and this could explain the association in this locus. We found different human leukocyte antigen (HLA) association patterns between the two populations. Enrichment analysis suggested the importance of CD4-naïve primary T cell.ConclusionsGSDMA and PRDM1 are associated with SSc. These findings provide enhanced insight into the genetic and biological basis of SSc.

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Genome-wide associations for udder and teat conformational risk factors for mastitis in Holstein cows

10.21203/rs.2.16763/v1 ◽

2019 ◽

Author(s):

Asha M. Miles ◽

Christian Posbergh ◽

Heather Jay Huson

Keyword(s):

Cell Proliferation ◽

Multiple Testing ◽

Association Studies ◽

Principal Component ◽

Special Focus ◽

Genome Wide Association Studies ◽

Multiple Testing Correction ◽

Genome Wide ◽

Length Width ◽

Mastitis Susceptibility

Abstract BACKGROUND The objective of our study was to conduct high-density genome-wide association studies of dairy cow udder and teat conformation with direct phenotyping. We identified and compared quantitative trait loci ( QTL ) for a novel composite mastitis risk trait and considered environmental impact of milking by comparing primiparous cows only. Cows (N = 471) were genotyped on the Illumina BovineHD 777K beadchip and scored for front and rear teat length, width, end shape, and placement, fore udder attachment, udder cleft, udder depth, rear udder height, and rear udder width. Principal component analysis was performed on fore udder attachment, rear teat end shape, rear teat width, and rear udder height, to create a single new phenotype describing mastitis susceptibility based on these high-risk traits.RESULTS Over all 14 traits of interest, a total of 56 genome-wide associations were performed and 28 significantly associated (Bonferroni multiple testing correction < 0.05) QTL were identified. The linkage disequilibrium ( LD ) block surrounding the associated QTL or a 1 Mb window in the absence of LD was interrogated for candidate genes, resulting in the identification of genes with functions related to both cell proliferation and immune signaling, including ZNF683, DHX9, CUX1, TNNT1 , and SPRY1 . We assessed a primiparous only subset of cows (n = 144) to account for the possibility that the genetic variance component of the phenotype is greater for cows who have had less exposure to the environment, and observed different associated QTL and inheritance patterns for udder depth in primiparous cows compared to the total cohort.CONCLUSION Special focus was given to the aforementioned mastitis risk traits, and candidate gene investigation revealed both immune function and cell proliferation related genes in the areas surrounding significantly associated QTL, suggesting that selecting for mastitis resistant cows based on these traits would be an effective method for increasing mastitis resiliency in a herd.

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