Combining multivariate genomic approaches to elucidate the comorbidity between ASD and ADHD

ABSTRACTBackgroundSeveral lines of evidence point toward the presence of shared genetic factors underlying Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD). However, Genome-Wide Association Studies (GWAS) have yet to identify risk variants (i.e. Single-Nucleotide Polymorphisms, SNPs) shared by ADHD and ASD.MethodsTwo complementary multivariate analyses – genomic structural equation modelling (SEM) and colocalization analysis – were exploited to identify the shared SNPs for ASD and ADHD, using summary data from two independent GWAS of ASD (N=46,350) and ADHD individuals (N=55,374).ResultsGenomic SEM identified 7 novel SNPs shared between ASD and ADHD (pgenome-wide<5e-8), including three SNPs that were not identified in any of the original univariate GWAS of ASD and ADHD (rs227378, rs2391769 and rs325506). We also mapped 4 novel genes (MANBA, DPYD, INSM1, and PAX1) to SNPs shared by ASD and ADHD, as well as 4 genes that had already been mapped to SNPs identified in either ASD or ADHD GWAS (SORCS3, XRN2, PTBP2 and NKX2-4). All the shared genes between ADHD and ASD were more prominently expressed in the brain than the genes mapped to SNPs specific to ASD or ADHD. Colocalization analyses revealed that 44% percent of the SNPs associated with ASD (p<1e-6) colocalized with ADHD SNPs and 26% of the SNPs associated with ADHD (p<1e-6) colocalized with ASD SNPs.ConclusionsUsing multivariate genomic analyses, the present study reveals the shared genetic pathways that underlie ASD and ADHD. Further investigation of these pathways may help identify new targets for treatment of these disorders.

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Host Genetic Variants Potentially Associated with SARS-Cov-2: A Multi-Population Analysis

University of the Future: Re-Imagining Research and Higher Education ◽

10.29117/quarfe.2020.0298 ◽

2020 ◽

Author(s):

Maria K. Smatti ◽

Yasser Al-Sarraj ◽

Omar Albagha ◽

Hadi M. Yassine

Keyword(s):

Genetic Variants ◽

Association Studies ◽

Population Analysis ◽

Fold Change ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Risk Alleles ◽

Risk Variants ◽

Genome Wide ◽

Ifn Γ

Background: Clinical outcomes of Coronavirus Disease 2019 (COVID-19), caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) showed enormous inter-individual and interpopulation differences, possibly due to host genetics differences. Earlier studies identified single nucleotide polymorphisms (SNPs) associated with SARS-CoV-1 in Eastern Asian (EAS) populations. In this report, we aimed at exploring the frequency of a set of genetic polymorphisms that could affect SARS-CoV-2 susceptibility or severity, including those that were previously associated with SARS-CoV-1. Methods: We extracted the list of SNPs that could potentially modulate SARS-CoV-2 from the genome wide association studies (GWAS) on SARS-CoV-1 and other viruses. We also collected the expression data of these SNPs from the expression quantitative trait loci (eQTLs) databases. Sequences from Qatar Genome Programme (QGP, n=6,054) and 1000Genome project were used to calculate and compare allelic frequencies (AF). Results: A total of 74 SNPs, located in 10 genes: ICAM3, IFN-γ, CCL2, CCL5, AHSG, MBL, Furin, TMPRSS2, IL4, and CD209 promoter, were identified. Analysis of Qatari genomes revealed significantly lower AF of risk variants linked to SARS-CoV-1 severity (CCL2, MBL, CCL5, AHSG, and IL4) compared to that of 1000Genome and/or the EAS population (up to 25-fold change). Conversely, SNPs in TMPRSS2, IFN-γ, ICAM3, and Furin were more common among Qataris (average 2-fold change). Inter-population analysis showed that the distribution of risk alleles among Europeans differs substantially from Africans and EASs. Remarkably, Africans seem to carry extremely lower frequencies of SARS-CoV-1 susceptibility alleles, reaching to 32-fold decrease compared to other populations. Conclusion: Multiple genetic variants, which could potentially modulate SARS-CoV-2 infection, are significantly variable between populations, with the lowest frequency observed among Africans. Our results highlight the importance of exploring population genetics to understand and predict COVID-19 outcomes. Indeed, further studies are needed to validate these findings as well as to identify new genetic determinants linked to SARS-CoV-2.

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Pan-Cancer Study Detects Novel Genetic Risk Variants and Shared Genetic Basis in Two Large Cohorts

10.1101/635367 ◽

2019 ◽

Cited By ~ 3

Author(s):

Sara R. Rashkin ◽

Rebecca E. Graff ◽

Linda Kachuri ◽

Khanh K. Thai ◽

Stacey E. Alexeeff ◽

...

Keyword(s):

Genetic Basis ◽

Association Studies ◽

Large Population ◽

Regulatory Elements ◽

European Ancestry ◽

Genome Wide Association Studies ◽

Risk Variants ◽

Genome Wide ◽

Pan Cancer ◽

Shared Genetic

AbstractDeciphering the shared genetic basis of distinct cancers has the potential to elucidate carcinogenic mechanisms and inform broadly applicable risk assessment efforts. However, no studies have investigated pan-cancer pleiotropy within single, well-defined populations unselected for phenotype. We undertook novel genome-wide association studies (GWAS) and comprehensive evaluations of heritability and pleiotropy across 18 cancer types in two large, population-based cohorts: the UK Biobank (413,870 European ancestry individuals; 48,961 cancer cases) and the Kaiser Permanente Genetic Epidemiology Research on Adult Health and Aging cohorts (66,526 European ancestry individuals; 16,001 cancer cases). The GWAS detected 21 novel genome-wide significant risk variants. In addition, numerous cancer sites exhibited clear heritability. Investigations of pleiotropy identified 12 cancer pairs exhibiting either positive or negative genetic correlations and 43 pleiotropic loci. We identified 158 pleiotropic variants, many of which were enriched for regulatory elements and influenced cross-tissue gene expression. Our findings demonstrate widespread pleiotropy and offer further insight into the complex genetic architecture of cross-cancer susceptibility.

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Embracing polygenicity: a review of methods and tools for psychiatric genetics research

Psychological Medicine ◽

10.1017/s0033291717002318 ◽

2017 ◽

Vol 48 (7) ◽

pp. 1055-1067 ◽

Cited By ~ 33

Author(s):

R. M. Maier ◽

P. M. Visscher ◽

M. R. Robinson ◽

N. R. Wray

Keyword(s):

Psychiatric Disorders ◽

Genetic Risk ◽

Complex Traits ◽

Association Studies ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Genetics Research ◽

Genome Wide ◽

Analyse Data ◽

Shared Genetic

AbstractThe availability of genome-wide genetic data on hundreds of thousands of people has led to an equally rapid growth in methodologies available to analyse these data. While the motivation for undertaking genome-wide association studies (GWAS) is identification of genetic markers associated with complex traits, once generated these data can be used for many other analyses. GWAS have demonstrated that complex traits exhibit a highly polygenic genetic architecture, often with shared genetic risk factors across traits. New methods to analyse data from GWAS are increasingly being used to address a diverse set of questions about the aetiology of complex traits and diseases, including psychiatric disorders. Here, we give an overview of some of these methods and present examples of how they have contributed to our understanding of psychiatric disorders. We consider: (i) estimation of the extent of genetic influence on traits, (ii) uncovering of shared genetic control between traits, (iii) predictions of genetic risk for individuals, (iv) uncovering of causal relationships between traits, (v) identifying causal single-nucleotide polymorphisms and genes or (vi) the detection of genetic heterogeneity. This classification helps organise the large number of recently developed methods, although some could be placed in more than one category. While some methods require GWAS data on individual people, others simply use GWAS summary statistics data, allowing novel well-powered analyses to be conducted at a low computational burden.

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Shared genetic influences on resting-state functional networks of the brain

10.1101/2021.02.15.431231 ◽

2021 ◽

Author(s):

JPOFT Guimaraes ◽

E Sprooten ◽

CF Beckmann ◽

B Franke ◽

J Bralten

Keyword(s):

Resting State ◽

Structural Equation ◽

Association Studies ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Resting State Networks ◽

Single Nucleotide ◽

Genome Wide ◽

The Uk ◽

Genetic Structures

AbstractThe amplitude of activation in brain resting state networks (RSNs), measured with resting-state functional MRI, is heritable and genetically correlated across RSNs, indicating pleiotropy. Recent univariate genome-wide association studies (GWAS) explored the genetic underpinnings of RSNs, but do not describe their pleiotropic nature. In this study, we used multivariate genomic structural equation modelling to model latent factors that capture shared genomic influences. Using summary statistics from GWAS of 21 RSNs in the UK Biobank (N = 21,081) sample, we show that their genetic organization can be best explained by two distinct but correlated genetic factors that divide multimodal association networks and sensory networks. We identify single-nucleotide polymorphisms and genes associated with the joint architecture of resting-state networks. We conclude that multivariate models of genetic structures can help us to learn more about the biological mechanisms involved in brain function.

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Genome-wide association studies identify 137 genetic loci for DNA methylation biomarkers of aging

Genome Biology ◽

10.1186/s13059-021-02398-9 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Daniel L. McCartney ◽

Josine L. Min ◽

Rebecca C. Richmond ◽

Ake T. Lu ◽

Maria K. Sobczyk ◽

...

Keyword(s):

Dna Methylation ◽

Genome Wide Association Study ◽

Association Studies ◽

Genome Wide Association ◽

Biological Aging ◽

Genome Wide Association Studies ◽

Genetic Loci ◽

Biomarkers Of Aging ◽

Genome Wide ◽

Shared Genetic

Abstract Background Biological aging estimators derived from DNA methylation data are heritable and correlate with morbidity and mortality. Consequently, identification of genetic and environmental contributors to the variation in these measures in populations has become a major goal in the field. Results Leveraging DNA methylation and SNP data from more than 40,000 individuals, we identify 137 genome-wide significant loci, of which 113 are novel, from genome-wide association study (GWAS) meta-analyses of four epigenetic clocks and epigenetic surrogate markers for granulocyte proportions and plasminogen activator inhibitor 1 levels, respectively. We find evidence for shared genetic loci associated with the Horvath clock and expression of transcripts encoding genes linked to lipid metabolism and immune function. Notably, these loci are independent of those reported to regulate DNA methylation levels at constituent clock CpGs. A polygenic score for GrimAge acceleration showed strong associations with adiposity-related traits, educational attainment, parental longevity, and C-reactive protein levels. Conclusion This study illuminates the genetic architecture underlying epigenetic aging and its shared genetic contributions with lifestyle factors and longevity.

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The Genetic Basis of Hypertriglyceridemia

Current Atherosclerosis Reports ◽

10.1007/s11883-021-00939-y ◽

2021 ◽

Vol 23 (8) ◽

Author(s):

Germán D. Carrasquilla ◽

Malene Revsbech Christiansen ◽

Tuomas O. Kilpeläinen

Keyword(s):

Genetic Basis ◽

Association Studies ◽

Cumulative Effect ◽

Genome Wide Association Studies ◽

Genetic Loci ◽

Risk Variants ◽

Genome Wide ◽

Severe Hypertriglyceridemia ◽

Increased Risk ◽

Polygenic Scores

Abstract Purpose of Review Hypertriglyceridemia is a common dyslipidemia associated with an increased risk of cardiovascular disease and pancreatitis. Severe hypertriglyceridemia may sometimes be a monogenic condition. However, in the vast majority of patients, hypertriglyceridemia is due to the cumulative effect of multiple genetic risk variants along with lifestyle factors, medications, and disease conditions that elevate triglyceride levels. In this review, we will summarize recent progress in the understanding of the genetic basis of hypertriglyceridemia. Recent Findings More than 300 genetic loci have been identified for association with triglyceride levels in large genome-wide association studies. Studies combining the loci into polygenic scores have demonstrated that some hypertriglyceridemia phenotypes previously attributed to monogenic inheritance have a polygenic basis. The new genetic discoveries have opened avenues for the development of more effective triglyceride-lowering treatments and raised interest towards genetic screening and tailored treatments against hypertriglyceridemia. Summary The discovery of multiple genetic loci associated with elevated triglyceride levels has led to improved understanding of the genetic basis of hypertriglyceridemia and opened new translational opportunities.

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Pharmacogenomics of Lithium Response in Bipolar Disorder

Pharmaceuticals ◽

10.3390/ph14040287 ◽

2021 ◽

Vol 14 (4) ◽

pp. 287

Author(s):

Courtney M. Vecera ◽

Gabriel R. Fries ◽

Lokesh R. Shahani ◽

Jair C. Soares ◽

Rodrigo Machado-Vieira

Keyword(s):

Bipolar Disorder ◽

Association Studies ◽

Mood Stabilizer ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Non Coding Rna ◽

Genome Wide ◽

Lithium Response ◽

Genetic Loading ◽

Long Non Coding Rna

Despite being the most widely studied mood stabilizer, researchers have not confirmed a mechanism for lithium’s therapeutic efficacy in Bipolar Disorder (BD). Pharmacogenomic applications may be clinically useful in the future for identifying lithium-responsive patients and facilitating personalized treatment. Six genome-wide association studies (GWAS) reviewed here present evidence of genetic variations related to lithium responsivity and side effect expression. Variants were found on genes regulating the glutamate system, including GAD-like gene 1 (GADL1) and GRIA2 gene, a mutually-regulated target of lithium. In addition, single nucleotide polymorphisms (SNPs) discovered on SESTD1 may account for lithium’s exceptional ability to permeate cell membranes and mediate autoimmune and renal effects. Studies also corroborated the importance of epigenetics and stress regulation on lithium response, finding variants on long, non-coding RNA genes and associations between response and genetic loading for psychiatric comorbidities. Overall, the precision medicine model of stratifying patients based on phenotype seems to derive genotypic support of a separate clinical subtype of lithium-responsive BD. Results have yet to be expounded upon and should therefore be interpreted with caution.

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Functional genomics of autoimmune diseases

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2019-216794 ◽

2021 ◽

pp. annrheumdis-2019-216794

Author(s):

Akari Suzuki ◽

Matteo Maurizio Guerrini ◽

Kazuhiko Yamamoto

Keyword(s):

Autoimmune Diseases ◽

Association Studies ◽

Linkage Disequilibrium Block ◽

Causal Variant ◽

Genome Wide Association Studies ◽

Coding Region ◽

Risk Variants ◽

Non Coding Rna ◽

Genome Wide ◽

Long Non Coding Rna

For more than a decade, genome-wide association studies have been applied to autoimmune diseases and have expanded our understanding on the pathogeneses. Genetic risk factors associated with diseases and traits are essentially causative. However, elucidation of the biological mechanism of disease from genetic factors is challenging. In fact, it is difficult to identify the causal variant among multiple variants located on the same haplotype or linkage disequilibrium block and thus the responsible biological genes remain elusive. Recently, multiple studies have revealed that the majority of risk variants locate in the non-coding region of the genome and they are the most likely to regulate gene expression such as quantitative trait loci. Enhancer, promoter and long non-coding RNA appear to be the main target mechanisms of the risk variants. In this review, we discuss functional genetics to challenge these puzzles.

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Investigation of gene–environment interactions in relation to tic severity

Journal of Neural Transmission ◽

10.1007/s00702-021-02396-y ◽

2021 ◽

Author(s):

Mohamed Abdulkadir ◽

Dongmei Yu ◽

Lisa Osiecki ◽

Robert A. King ◽

Thomas V. Fernandez ◽

...

Keyword(s):

Tourette Syndrome ◽

Association Studies ◽

Autism Spectrum ◽

Environment Interaction ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Linear Regression Models ◽

Compulsive Disorder ◽

Gene Environment ◽

Tic Severity

AbstractTourette syndrome (TS) is a neuropsychiatric disorder with involvement of genetic and environmental factors. We investigated genetic loci previously implicated in Tourette syndrome and associated disorders in interaction with pre- and perinatal adversity in relation to tic severity using a case-only (N = 518) design. We assessed 98 single-nucleotide polymorphisms (SNPs) selected from (I) top SNPs from genome-wide association studies (GWASs) of TS; (II) top SNPs from GWASs of obsessive–compulsive disorder (OCD), attention-deficit/hyperactivity disorder (ADHD), and autism spectrum disorder (ASD); (III) SNPs previously implicated in candidate-gene studies of TS; (IV) SNPs previously implicated in OCD or ASD; and (V) tagging SNPs in neurotransmitter-related candidate genes. Linear regression models were used to examine the main effects of the SNPs on tic severity, and the interaction effect of these SNPs with a cumulative pre- and perinatal adversity score. Replication was sought for SNPs that met the threshold of significance (after correcting for multiple testing) in a replication sample (N = 678). One SNP (rs7123010), previously implicated in a TS meta-analysis, was significantly related to higher tic severity. We found a gene–environment interaction for rs6539267, another top TS GWAS SNP. These findings were not independently replicated. Our study highlights the future potential of TS GWAS top hits in gene–environment studies.

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Transcriptional Regulation of RUNX1: An Informatics Analysis

Genes ◽

10.3390/genes12081175 ◽

2021 ◽

Vol 12 (8) ◽

pp. 1175

Author(s):

Amarni L. Thomas ◽

Judith Marsman ◽

Jisha Antony ◽

William Schierding ◽

Justin M. O’Sullivan ◽

...

Keyword(s):

Association Studies ◽

Regulatory Elements ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Genome Wide ◽

Runx1 Gene ◽

Gene Regulatory Elements ◽

Important Regulator ◽

Aml1 Gene ◽

Regulatory Landscape

The RUNX1/AML1 gene encodes a developmental transcription factor that is an important regulator of haematopoiesis in vertebrates. Genetic disruptions to the RUNX1 gene are frequently associated with acute myeloid leukaemia. Gene regulatory elements (REs), such as enhancers located in non-coding DNA, are likely to be important for Runx1 transcription. Non-coding elements that modulate Runx1 expression have been investigated over several decades, but how and when these REs function remains poorly understood. Here we used bioinformatic methods and functional data to characterise the regulatory landscape of vertebrate Runx1. We identified REs that are conserved between human and mouse, many of which produce enhancer RNAs in diverse tissues. Genome-wide association studies detected single nucleotide polymorphisms in REs, some of which correlate with gene expression quantitative trait loci in tissues in which the RE is active. Our analyses also suggest that REs can be variant in haematological malignancies. In summary, our analysis identifies features of the RUNX1 regulatory landscape that are likely to be important for the regulation of this gene in normal and malignant haematopoiesis.

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