Psychophysiological endophenotypes to characterize mechanisms of known schizophrenia genetic loci

BackgroundEndophenotypes are laboratory-based measures hypothesized to lie in the causal chain between genes and clinical disorder, and to serve as a more powerful way to identify genes associated with the disorder. One promise of endophenotypes is that they may assist in elucidating the neurobehavioral mechanisms by which an associated genetic polymorphism affects disorder risk in complex traits. We evaluated this promise by testing the extent to which variants discovered to be associated with schizophrenia through large-scale meta-analysis show associations with psychophysiological endophenotypes.MethodWe genome-wide genotyped and imputed 4905 individuals. Of these, 1837 were whole-genome-sequenced at 11× depth. In a community-based sample, we conducted targeted tests of variants within schizophrenia-associated loci, as well as genome-wide polygenic tests of association, with 17 psychophysiological endophenotypes including acoustic startle response and affective startle modulation, antisaccade, multiple frequencies of resting electroencephalogram (EEG), electrodermal activity and P300 event-related potential.ResultsUsing single variant tests and gene-based tests we found suggestive evidence for an association between contactin 4 (CNTN4) and antisaccade and P300. We were unable to find any other variant or gene within the 108 schizophrenia loci significantly associated with any of our 17 endophenotypes. Polygenic risk scores indexing genetic vulnerability to schizophrenia were not related to any of the psychophysiological endophenotypes after correction for multiple testing.ConclusionsThe results indicate significant difficulty in using psychophysiological endophenotypes to characterize the genetically influenced neurobehavioral mechanisms by which risk loci identified in genome-wide association studies affect disorder risk.

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Better estimation of SNP heritability from summary statistics provides a new understanding of the genetic architecture of complex traits

10.1101/284976 ◽

2018 ◽

Cited By ~ 6

Author(s):

Doug Speed ◽

David J Balding

Keyword(s):

Complex Traits ◽

Genetic Architecture ◽

Large Scale ◽

Association Studies ◽

Genome Wide Association Studies ◽

Summary Statistics ◽

Confounding Bias ◽

Conserved Regions ◽

Genome Wide ◽

Variation Explained

LD Score Regression (LDSC) has been widely applied to the results of genome-wide association studies. However, its estimates of SNP heritability are derived from an unrealistic model in which each SNP is expected to contribute equal heritability. As a consequence, LDSC tends to over-estimate confounding bias, under-estimate the total phenotypic variation explained by SNPs, and provide misleading estimates of the heritability enrichment of SNP categories. Therefore, we present SumHer, software for estimating SNP heritability from summary statistics using more realistic heritability models. After demonstrating its superiority over LDSC, we apply SumHer to the results of 24 large-scale association studies (average sample size 121 000). First we show that these studies have tended to substantially over-correct for confounding, and as a result the number of genome-wide significant loci has under-reported by about 20%. Next we estimate enrichment for 24 categories of SNPs defined by functional annotations. A previous study using LDSC reported that conserved regions were 13-fold enriched, and found a further twelve categories with above 2-fold enrichment. By contrast, our analysis using SumHer finds that conserved regions are only 1.6-fold (SD 0.06) enriched, and that no category has enrichment above 1.7-fold. SumHer provides an improved understanding of the genetic architecture of complex traits, which enables more efficient analysis of future genetic data.

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GWAS contribution to atrial fibrillation and atrial fibrillation-related stroke: pathophysiological implications

Pharmacogenomics ◽

10.2217/pgs-2019-0054 ◽

2019 ◽

Vol 20 (10) ◽

pp. 765-780 ◽

Cited By ~ 1

Author(s):

Diana Cruz ◽

Ricardo Pinto ◽

Margarida Freitas-Silva ◽

José Pedro Nunes ◽

Rui Medeiros

Keyword(s):

Atrial Fibrillation ◽

Genetic Variants ◽

Complex Traits ◽

Association Studies ◽

Cardioembolic Stroke ◽

Risk Scores ◽

Genome Wide Association Studies ◽

Genetic Determinants ◽

Genome Wide ◽

Genome Wide Significance

Atrial fibrillation (AF) and stroke are included in a group of complex traits that have been approached regarding of their study by susceptibility genetic determinants. Since 2007, several genome-wide association studies (GWAS) aiming to identify genetic variants modulating AF risk have been conducted. Thus, 11 GWAS have identified 26 SNPs (p < 5 × 10-2), of which 19 reached genome-wide significance (p < 5 × 10-8). From those variants, seven were also associated with cardioembolic stroke and three reached genome-wide significance in stroke GWAS. These associations may shed a light on putative shared etiologic mechanisms between AF and cardioembolic stroke. Additionally, some of these identified variants have been incorporated in genetic risk scores in order to elucidate new approaches of stroke prediction, prevention and treatment.

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circVAR database: genome-wide archive of genetic variants for human circular RNAs

10.21203/rs.3.rs-48904/v2 ◽

2020 ◽

Author(s):

Min Zhao ◽

Hong Qu

Keyword(s):

Genetic Variants ◽

Complex Traits ◽

Large Scale ◽

Rna Binding ◽

Rna Binding Proteins ◽

Association Studies ◽

Chromosome 17 ◽

Circular Rnas ◽

Genome Wide Association Studies ◽

Genome Wide

Abstract Background: Circular RNAs (circRNAs) play important roles in regulating gene expression through binding miRNAs and RNA binding proteins. Genetic variation of circRNAs may affect complex traits/diseases by changing their binding efficiency to target miRNAs and proteins. There is a growing demand for investigations of the functions of genetic changes using large-scale experimental evidence. However, there is no online genetic resource for circRNA genes. Results: We performed extensive genetic annotation of 295,526 circRNAs integrated from circBase, circNet and circRNAdb. All pre-computed genetic variants were presented at our online resource, circVAR, with data browsing and search functionality. We explored the chromosome-based distribution of circRNAs and their associated variants. We found that, based on mapping to the 1000 Genomes and ClinVAR databases, chromosome 17 has a relatively large number of circRNAs and associated common and health-related genetic variants. Following the annotation of genome wide association studies (GWAS)-based circRNA variants, we found many non-coding variants within circRNAs, suggesting novel mechanisms for common diseases reported from GWAS studies. For cancer-based somatic variants, we found that chromosome 7 has many highly complex mutations that have been overlooked in previous research. Conclusion: We used the circVAR database to collect SNPs and small insertions and deletions (INDELs) in putative circRNA regions and to identify their potential phenotypic information. To provide a reusable resource for the circRNA research community, we have published all the pre-computed genetic data concerning circRNAs and associated genes together with data query and browsing functions at http://soft.bioinfo-minzhao.org/circvar .

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Transcriptome-wide association supplements genome-wide association in Zea mays

10.1101/363242 ◽

2018 ◽

Cited By ~ 4

Author(s):

Karl A. G. Kremling ◽

Christine H. Diepenbrock ◽

Michael A. Gore ◽

Edward S. Buckler ◽

Nonoy B. Bandillo

Keyword(s):

Gene Expression ◽

Complex Traits ◽

Large Scale ◽

New Technologies ◽

Seed Quality ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Biological Organization ◽

Genome Wide

AbstractModern improvement of complex traits in agricultural species relies on successful associations of heritable molecular variation with observable phenotypes. Historically, this pursuit has primarily been based on easily measurable genetic markers. The recent advent of new technologies allows assaying and quantifying biological intermediates (hereafter endophenotypes) which are now readily measurable at a large scale across diverse individuals. The potential of using endophenotypes for dissecting traits of interest remains underexplored in plants. The work presented here illustrated the utility of a large-scale (299 genotype and 7 tissue) gene expression resource to dissect traits across multiple levels of biological organization. Using single-tissue- and multi-tissue-based transcriptome-wide association studies (TWAS), we revealed that about half of the functional variation for agronomic and seed quality (carotenoid, tocochromanol) traits is regulatory. Comparing the efficacy of TWAS with genome-wide association studies (GWAS) and an ensemble approach that combines both GWAS and TWAS, we demonstrated that results of TWAS in combination with GWAS increase the power to detect known genes and aid in prioritizing likely causal genes. Using a variance partitioning approach in the independent maize Nested Association Mapping (NAM) population, we also showed that the most strongly associated genes identified by combining GWAS and TWAS explain more heritable variance for a majority of traits, beating the heritability captured by the random genes and the genes identified by GWAS or TWAS alone. This improves not only the ability to link genes to phenotypes, but also highlights the phenotypic consequences of regulatory variation in plants.Author summaryWe examined the ability to associate variability in gene expression directly with terminal phenotypes of interest, as a supplement linking genotype to phenotype. We found that transcriptome-wide association studies (TWAS) are a useful accessory to genome-wide association studies (GWAS). In a combined test with GWAS results, TWAS improves the capacity to re-detect genes known to underlie quantitative trait loci for kernel and agronomic phenotypes. This improves not only the capacity to link genes to phenotypes, but also illustrates the widespread importance of regulation for phenotype.

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Reproducible Risk Loci and Psychiatric Comorbidities in Anxiety: Results from ~200,000 Million Veteran Program Participants

10.1101/540245 ◽

2019 ◽

Cited By ~ 2

Author(s):

Daniel F. Levey ◽

Joel Gelernter ◽

Renato Polimanti ◽

Hang Zhou ◽

Zhongshan Cheng ◽

...

Keyword(s):

Anxiety Disorder ◽

Psychiatric Disorders ◽

Complex Traits ◽

Association Studies ◽

Risk Scores ◽

Genome Wide Association Studies ◽

Uk Biobank ◽

Continuous Trait ◽

Genome Wide ◽

Program Sample

AbstractWe used GWAS in the Million Veteran Program sample (nearly 200,000 informative individuals) using a continuous trait for anxiety (GAD-2) to identify 5 genome-wide significant (GWS) signals for European Americans (EA) and 1 for African Americans. The strongest findings were on chromosome 3 (rs4603973, p=7.40×10−11) near the SATB1 locus, a global regulator of gene expression and on chromosome 6 (rs6557168, p=1.04×10−9) near ESR1 which encodes estrogen receptor α. A locus identified on chromosome 7 near MADIL1 (p=1.62×10−8) has been previously identified in GWAS of bipolar disorder and of schizophrenia and may represent a risk factor for psychiatric disorders broadly. SNP-based heritability was estimated to be ~6% for GAD-2. We also GWASed for self-reported anxiety disorder diagnoses (N=224,330) and identified two GWS loci, one (rs35546597, MAF=0.42, p=1.88×10−8) near the AURKB locus, and the other (rsl0534613, MAF=0.41, p=4.92×10−8) near the IQCHE and MADIL1 locus identified in the GAD-2 analysis. We demonstrate reproducibility by replicating our top findings in the summary statistics from the Anxiety NeuroGenetics Study (ANGST) and a UK Biobank neuroticism GWAS. We also replicated top findings from a large UK Biobank preprint, demonstrating stability of GWAS findings in complex traits once sufficient power is attained. Finally, we found evidence of significant genetic overlap between anxiety and major depression using polygenic risk scores, but also found that the main anxiety signals are independent of those for MDD. This work presents novel insights into the neurobiological risk underpinning anxiety and related psychiatric disorders.SignificanceAnxiety disorders are common and often disabling. They are also frequently co-morbid with other mental disorders such as major depressive disorder (MDD); these disorders may share commonalities in their underlying genetic architecture. Using one of the largest homogenously phenotyped cohorts available, the Million Veteran Program sample, we investigated common variants associated with anxiety in genome-wide association studies (GWASes), using survey results from the GAD-2 anxiety scale (as a continuous trait, n=199,611), and self-reported anxiety disorder diagnosis (as a binary trait, n=224,330). This largest GWAS to date for anxiety and related traits identified numerous novel significant associations, several of which are replicated in other datasets, and allows inference of underlying biology.

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circVAR Database: Genome-Wide Archive of Genetic Variants for Human Circular RNAs

10.21203/rs.3.rs-48904/v1 ◽

2020 ◽

Author(s):

Min Zhao ◽

Hong Qu

Keyword(s):

Genetic Variants ◽

Complex Traits ◽

Large Scale ◽

Rna Binding ◽

Rna Binding Proteins ◽

Association Studies ◽

Chromosome 17 ◽

Circular Rnas ◽

Genome Wide Association Studies ◽

Genome Wide

Abstract Background: Circular RNAs (circRNAs) play important roles in regulating gene expression through binding miRNAs and RNA binding proteins. Genetic variation of circRNAs may affect complex traits/diseases by changing their binding efficiency to target miRNAs and proteins. There is a growing demand for investigations of the functions of genetic changes using large-scale experimental evidence. However, there is no online genetic resource for circRNA genes. Results: We performed extensive genetic annotation of 295,526 circRNAs integrated from circBase, circNet and circRNAdb. All pre-computed genetic variants were presented at our online resource, circVAR, with data browsing and search functionality. We explored the chromosome-based distribution of circRNAs and their associated variants. We found that, based on mapping to the 1000 Genomes and ClinVAR databases, chromosome 17 has a relatively large number of circRNAs and associated common and health-related genetic variants. Following the annotation of genome wide association studies (GWAS)-based circRNA variants, we found many non-coding variants within circRNAs, suggesting novel mechanisms for common diseases reported from GWAS studies. For cancer-based somatic variants, we found that chromosome 7 has many highly complex mutations that have been overlooked in previous research.Conclusion: We used the circVAR database to collect SNPs and small insertions and deletions (INDELs) in putative circRNA regions and to identify their potential phenotypic information. To provide a reusable resource for the circRNA research community, we have published all the pre-computed genetic data concerning circRNAs and associated genes together with data query and browsing functions at http://soft.bioinfo-minzhao.org/circvar.

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circVAR database: genome-wide archive of genetic variants for human circular RNAs

BMC Genomics ◽

10.1186/s12864-020-07172-y ◽

2020 ◽

Vol 21 (1) ◽

Cited By ~ 1

Author(s):

Min Zhao ◽

Hong Qu

Keyword(s):

Genetic Variants ◽

Complex Traits ◽

Large Scale ◽

Rna Binding ◽

Rna Binding Proteins ◽

Association Studies ◽

Chromosome 17 ◽

Circular Rnas ◽

Genome Wide Association Studies ◽

Genome Wide

Abstract Background Circular RNAs (circRNAs) play important roles in regulating gene expression through binding miRNAs and RNA binding proteins. Genetic variation of circRNAs may affect complex traits/diseases by changing their binding efficiency to target miRNAs and proteins. There is a growing demand for investigations of the functions of genetic changes using large-scale experimental evidence. However, there is no online genetic resource for circRNA genes. Results We performed extensive genetic annotation of 295,526 circRNAs integrated from circBase, circNet and circRNAdb. All pre-computed genetic variants were presented at our online resource, circVAR, with data browsing and search functionality. We explored the chromosome-based distribution of circRNAs and their associated variants. We found that, based on mapping to the 1000 Genomes and ClinVAR databases, chromosome 17 has a relatively large number of circRNAs and associated common and health-related genetic variants. Following the annotation of genome wide association studies (GWAS)-based circRNA variants, we found many non-coding variants within circRNAs, suggesting novel mechanisms for common diseases reported from GWAS studies. For cancer-based somatic variants, we found that chromosome 7 has many highly complex mutations that have been overlooked in previous research. Conclusion We used the circVAR database to collect SNPs and small insertions and deletions (INDELs) in putative circRNA regions and to identify their potential phenotypic information. To provide a reusable resource for the circRNA research community, we have published all the pre-computed genetic data concerning circRNAs and associated genes together with data query and browsing functions at http://soft.bioinfo-minzhao.org/circvar.

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A large-scale genome-wide enrichment analysis identifies new trait-associated genes, pathways and tissues across 31 human phenotypes*

10.1101/160770 ◽

2017 ◽

Cited By ~ 5

Author(s):

Xiang Zhu ◽

Matthew Stephens

Keyword(s):

Complex Traits ◽

Large Scale ◽

Late Onset ◽

Association Studies ◽

Low Density Lipoprotein ◽

Density Lipoprotein ◽

Enrichment Analysis ◽

Genome Wide Association Studies ◽

Genome Wide ◽

Artery Disease

Genome-wide association studies (GWAS) aim to identify genetic factors that are associated with complex traits. Standard analyses test individual genetic variants, one at a time, for association with a trait. However, variant-level associations are hard to identify (because of small effects) and can be difficult to interpret biologically. “Enrichment analyses” help address both these problems by focusing on sets of biologically-related variants. Here we introduce a new model-based enrichment analysis method that requires only GWAS summary statistics, and has several advantages over existing methods. Applying this method to interrogate 3,913 biological pathways and 113 tissue-based gene sets in 31 human phenotypes identifies many previously-unreported enrichments. These include enrichments of the endochondral ossification pathway for adult height, the NFAT-dependent transcription pathway for rheumatoid arthritis, brain-related genes for coronary artery disease, and liver-related genes for late-onset Alzheimer’s disease. A key feature of our method is that inferred enrichments automatically help identify new trait-associated genes. For example, accounting for enrichment in lipid transport genes yields strong evidence for association between MTTP and low-density lipoprotein levels, whereas conventional analyses of the same data found no significant variants near this gene.

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Could personalised risk prediction for type 2 diabetes using polygenic risk scores direct prevention, enhance diagnostics, or improve treatment?

Wellcome Open Research ◽

10.12688/wellcomeopenres.16251.1 ◽

2020 ◽

Vol 5 ◽

pp. 206

Author(s):

Mathilde Boecker ◽

Alvina G. Lai

Keyword(s):

Type 2 Diabetes ◽

Risk Prediction ◽

Large Scale ◽

Association Studies ◽

Genome Wide Association ◽

Risk Scores ◽

Genome Wide Association Studies ◽

Polygenic Risk ◽

Genome Wide

Over the past three decades, the number of people globally with diabetes mellitus has more than doubled. It is estimated that by 2030, 439 million people will be suffering from the disease, 90-95% of whom will have type 2 diabetes (T2D). In 2017, 5 million deaths globally were attributable to T2D, placing it in the top 10 global causes of death. Because T2D is a result of both genetic and environmental factors, identification of individuals with high genetic risk can help direct early interventions to prevent progression to more serious complications. Genome-wide association studies have identified ~400 variants associated with T2D that can be used to calculate polygenic risk scores (PRS). Although PRSs are not currently more accurate than clinical predictors and do not yet predict risk with equal accuracy across all ethnic populations, they have several potential clinical uses. Here, we discuss potential usages of PRS for predicting T2D and for informing and optimising interventions. We also touch on possible health inequality risks of PRS and the feasibility of large-scale implementation of PRS in clinical practice. Before PRSs can be used as a therapeutic tool, it is important that further polygenic risk models are derived using non-European genome-wide association studies to ensure that risk prediction is accurate for all ethnic groups. Furthermore, it is essential that the ethical, social and legal implications of PRS are considered before their implementation in any context.

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Phenotype-specific enrichment of Mendelian disorder genes near GWAS regions across 62 complex traits

10.1101/324558 ◽

2018 ◽

Author(s):

Malika Kumar Freund ◽

Kathryn Burch ◽

Huwenbo Shi ◽

Nicholas Mancuso ◽

Gleb Kichaev ◽

...

Keyword(s):

Complex Traits ◽

Large Scale ◽

Genetic Basis ◽

Association Studies ◽

Genome Wide Association Studies ◽

Mendelian Disorder ◽

Mendelian Disorders ◽

Gene Sets ◽

Genome Wide ◽

Significant Enrichment

ABSTRACTAlthough recent studies provide evidence for a common genetic basis between complex traits and Mendelian disorders, a thorough quantification of their overlap in a phenotype-specific manner remains elusive. Here, we quantify the overlap of genes identified through large-scale genome-wide association studies (GWAS) for 62 complex traits and diseases with genes known to cause 20 broad categories of Mendelian disorders. We identify a significant enrichment of phenotypically-matched Mendelian disorder genes in GWAS gene sets. Further, we observe elevated GWAS effect sizes near phenotypically-matched Mendelian disorder genes. Finally, we report examples of GWAS variants localized at the transcription start site or physically interacting with the promoters of phenotypically-matched Mendelian disorder genes. Our results are consistent with the hypothesis that genes that are disrupted in Mendelian disorders are dysregulated by noncoding variants in complex traits, and demonstrate how leveraging findings from related Mendelian disorders and functional genomic datasets can prioritize genes that are putatively dysregulated by local and distal non-coding GWAS variants.

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