scholarly journals GWIS: Genome Wide Inferred Statistics for non-linear functions of multiple phenotypes

2015 ◽  
Author(s):  
Harold Nieuwboer ◽  
Rene Pool ◽  
Conor V Dolan ◽  
Dorret I Boomsma ◽  
Michel G Nivard
Keyword(s):  
Genetic Architecture ◽  
Genome Wide Association Study ◽  
Mendelian Randomization ◽  
Linear Functions ◽  
Standard Errors ◽  
Summary Statistics ◽  
Polygenic Risk ◽  
Multiple Phenotypes ◽  
Genome Wide ◽  
Risk Scoring

Here we present a method of genome wide inferred study (GWIS) that provides an approximation of genome wide association study (GWAS) summary statistics for a variable that is a function of phenotypes for which GWAS summary statistics, phenotypic means and covariances are available. GWIS can be performed regardless of sample overlap between the GWAS of the phenotypes on which the function depends. As GWIS provides association estimates and their standard errors for each SNP, GWIS can form the basis for polygenic risk scoring, LD score regression, Mendelian randomization studies, biological annotation and other analyses. Here, we replicate a body mass index (BMI) GWAS using GWIS based on a height GWAS and a weight GWAS. We proceed to use a GWIS to further our understanding of the genetic architecture of schizophrenia and bipolar disorder.

scholarly journals JASS: command line and web interface for the joint analysis of GWAS results

2020 ◽  
Vol 2 (1) ◽  
Author(s):  
Hanna Julienne ◽  
Pierre Lechat ◽  
Vincent Guillemot ◽  
Carla Lasry ◽  
Chunzi Yao ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Large Scale ◽  
Genome Wide Association Study ◽  
Real Data ◽  
Joint Analysis ◽  
Summary Statistics ◽  
Web Interface ◽  
Multiple Phenotypes ◽  
Genome Wide ◽  
Secondary Analyses

Abstract Genome-wide association study (GWAS) has been the driving force for identifying association between genetic variants and human phenotypes. Thousands of GWAS summary statistics covering a broad range of human traits and diseases are now publicly available. These GWAS have proven their utility for a range of secondary analyses, including in particular the joint analysis of multiple phenotypes to identify new associated genetic variants. However, although several methods have been proposed, there are very few large-scale applications published so far because of challenges in implementing these methods on real data. Here, we present JASS (Joint Analysis of Summary Statistics), a polyvalent Python package that addresses this need. Our package incorporates recently developed joint tests such as the omnibus approach and various weighted sum of Z-score tests while solving all practical and computational barriers for large-scale multivariate analysis of GWAS summary statistics. This includes data cleaning and harmonization tools, an efficient algorithm for fast derivation of joint statistics, an optimized data management process and a web interface for exploration purposes. Both benchmark analyses and real data applications demonstrated the robustness and strong potential of JASS for the detection of new associated genetic variants. Our package is freely available at https://gitlab.pasteur.fr/statistical-genetics/jass.

scholarly journals Genome-wide Association Study identifies two novel loci for Gilles de la Tourette Syndrome

2021 ◽  
Author(s):  
Fotis Tsetsos ◽  
Apostolia Topaloudi ◽  
Pritesh Jain ◽  
Zhiyu Yang ◽  
Dongmei Yu ◽  
...  
Keyword(s):  
Tourette Syndrome ◽  
Genome Wide Association Study ◽  
Neurodevelopmental Disorder ◽  
Published Data ◽  
Volume Data ◽  
Polygenic Risk ◽  
Genome Wide ◽  
A Genome ◽  
Risk Scoring ◽  
Significant Locus

Tourette Syndrome (TS) is a childhood-onset neurodevelopmental disorder of complex genetic architecture, characterized by multiple motor tics and at least one vocal tic persisting for more than one year. We performed a genome-wide meta-analysis integrating a novel TS cohort with previously published data, resulting in a sample size of 6,133 TS individuals and 13,565 ancestry-matched controls. We identified a genome-wide significant locus on chromosome 5q15 and one array-wide significant locus on chromosome 2q24.2. Integration of eQTL, Hi-C and GWAS data implicated the NR2F1 gene and associated lncRNAs within the 5q15 locus, and the RBMS1 gene within the 2q24.2 locus. Polygenic risk scoring using previous GWAS results demonstrated statistically significant ability to predict TS status in the novel cohort. Heritability partitioning identified statistically significant enrichment in brain tissue histone marks, while polygenic risk scoring on brain volume data identified statistically significant associations with right and left putamen volumes. Our work presents novel insights in the neurobiology of TS opening up new directions for future studies.

scholarly journals Genomic and multi-tissue proteomic integration for understanding the biology of disease and other complex traits

2020 ◽  
Author(s):  
Chengran Yang ◽  
Fabiana G. Farias ◽  
Laura Ibanez ◽  
Brooke Sadler ◽  
Maria Victoria Fernandez ◽  
...  
Keyword(s):  
Gene Expression ◽  
Complex Traits ◽  
Drug Targets ◽  
Genetic Architecture ◽  
Genome Wide Association Study ◽  
Mendelian Randomization ◽  
Protein Levels ◽  
Genome Wide ◽  
Novel Biomarkers ◽  
Potential Drug Targets

AbstractExpression quantitative trait loci (eQTL) mapping has successfully resolved some genome-wide association study (GWAS) loci for complex traits1–6. However, there is a need for implementing additional “omic” approaches to untangle additional loci and provide a biological context for GWAS signals. We generated a detailed landscape of the genomic architecture of protein levels in multiple neurologically relevant tissues (brain, cerebrospinal fluid (CSF) and plasma), by profiling thousands of proteins in a large and well-characterized cohort. We identified 274, 127 and 32 protein quantitative loci (pQTL) for CSF, plasma and brain respectively. We demonstrated that cis-pQTL are more likely to be shared across tissues but trans-pQTL are tissue-specific. Between 78% to 87% of pQTL are not eQTL, indicating that protein levels have a different genetic architecture than gene expression. By combining our pQTL with Mendelian Randomization approaches we identified potential novel biomarkers and drug targets for neurodegenerative diseases including Alzheimer disease and frontotemporal dementia. In the context of personalized medicine, these results highlight the need for implementing additional functional genomic approaches beyond gene expression in order to understand the biology of complex traits, and to identify novel biomarkers and potential drug targets for those traits.

scholarly journals The genetic architecture of Parkinson disease in Spain: characterizing population-specific risk, differential haplotype structures, and providing etiologic insight

2019 ◽  
Author(s):  
Sara Bandres-Ciga ◽  
Sarah Ahmed ◽  
Marya S. Sabir ◽  
Cornelis Blauwendraat ◽  
Astrid D. Adarmes-Gómez ◽  
...  
Keyword(s):  
Parkinson Disease ◽  
Genetic Architecture ◽  
Genome Wide Association Study ◽  
Mendelian Randomization ◽  
Age At Onset ◽  
Genetic Correlations ◽  
Risk Haplotype ◽  
Specific Risk ◽  
Genome Wide ◽  
Gwas Signal

ABSTRACTBackgroundThe Iberian Peninsula stands out as having variable levels of population admixture and isolation, making Spain an interesting setting for studying the genetic architecture of neurodegenerative diseases.ObjectivesTo perform the largest Parkinson disease (PD) genome-wide association study (GWAS) restricted to a single country.MethodsWe performed a GWAS for both risk of PD and age-at-onset (AAO) in 7,849 Spanish individuals. Further analyses included population-specific risk haplotype assessments, polygenic risk scoring through machine learning, Mendelian randomization of expression and methylation data to gain insight into disease-associated loci, heritability estimates, genetic correlations and burden analyses.ResultsWe identified a novel population-specific GWAS signal atPARK2associated with AAO. We replicated four genome-wide independent signals associated with PD risk, includingSNCA, LRRK2, KANSL1/MAPTandHLA-DQB1. A significant trend for smaller risk haplotypes at known loci was found compared to similar studies of non-Spanish origin. Seventeen PD-related genes showed functional consequence via two-sample Mendelian randomization in expression and methylation datasets. Long runs of homozygosity at 28 known genes/loci were found to be enriched in cases versus controls.ConclusionsOur data demonstrate the utility of the Spanish risk haplotype substructure for future fine-mapping efforts, showing how leveraging unique and diverse population histories can benefit genetic studies of complex diseases. The present study points toPARK2as a major hallmark of PD etiology in Spain.

scholarly journals Concordance of Genetic Variation that Increases Risk for Tourette Syndrome and that Influences its Underlying Neurocircuitry

2018 ◽  
Author(s):  
Mary Mufford ◽  
Josh Cheung ◽  
Neda Jahanshad ◽  
Celia van der Merwe ◽  
Linda Ding ◽  
...  
Keyword(s):  
Tourette Syndrome ◽  
Genetic Architecture ◽  
Genome Wide Association Study ◽  
Brain Regions ◽  
Genetic Variations ◽  
Summary Statistics ◽  
Brain Volumes ◽  
Subcortical Brain ◽  
Genome Wide ◽  
A Genome

ABSTRACTBACKGROUNDThere have been considerable recent advances in understanding the genetic architecture of Tourette Syndrome (TS) as well as its underlying neurocircuitry. However, the mechanisms by which genetic variations that increase risk for TS - and its main symptom dimensions - influence relevant brain regions are poorly understood. Here we undertook a genome-wide investigation of the overlap between TS genetic risk and genetic influences on the volume of specific subcortical brain structures that have been implicated in TS.METHODSWe obtained summary statistics for the most recent TS genome-wide association study (GWAS) from the TS Psychiatric Genomics Consortium Working Group (4,644 cases and 8,695 controls) and GWAS of subcortical volumes from the ENIGMA consortium (30,717 individuals). We also undertook analyses using GWAS summary statistics of key symptom factors in TS, namely social disinhibition and symmetry behaviour. SNP Effect Concordance Analysis (SECA) was used to examine genetic pleiotropy - the same SNP affecting two traits - and concordance - the agreement in SNP effect directions across these two traits. In addition, a conditional false discovery rate (FDR) analysis was performed, conditioning the TS risk variants on each of the seven subcortical and the intracranial brain volume GWAS. Linkage Disequilibrium Score Regression (LDSR) was used as validation of SECA.RESULTSSECA revealed significant pleiotropy between TS and putaminal (p=2×10−4) and caudal (p=4×10−4) volumes, independent of direction of effect, and significant concordance between TS and lower thalamic volume (p=1×10−3). LDSR lent additional support for the association between TS and thalamic volume (p=5.85×10−2). Furthermore, SECA revealed significant evidence of concordance between the social disinhibition symptom dimension and lower thalamic volume (p=1×10−3), as well as concordance between symmetry behaviour and greater putaminal volume (p=7×10−4). Conditional FDR analysis further revealed novel variants significantly associated with TS (p<8×10−7) when conditioning on intracranial (rs2708146, q=0.046; and rs72853320, q=0.035 and hippocampal (rs1922786, q=0.001 volumes respectively.CONCLUSIONThese data indicate concordance for genetic variations involved in disorder risk and subcortical brain volumes in TS. Further work with larger samples is needed to fully delineate the genetic architecture of these disorders and their underlying neurocircuitry.

Identification of Potential Pleiotropic Genes for Immune and Skeletal Diseases Using Multivariate MetaCCA Analysis

2021 ◽  
Vol 23 ◽  
Author(s):  
Pei He ◽  
Rong- Rong Cao ◽  
Fei- Yan Deng ◽  
Shu- Feng Lei
Keyword(s):  
Association Study ◽  
Canonical Correlation ◽  
Genetic Factors ◽  
Genome Wide Association Study ◽  
Summary Statistics ◽  
Immune Disease ◽  
Histocompatibility Complex ◽  
Genome Wide ◽  
Genetic Mechanisms ◽  
Shared Genetic

Background: Immune and skeletal systems physiologically and pathologically interact with each other. The immune and skeletal diseases may share potential pleiotropic genetics factors, but the shared specific genes are largely unknown Objective: This study aimed to investigate the overlapping genetic factors between multiple diseases (including rheumatoid arthritis (RA), psoriasis, osteoporosis, osteoarthritis, sarcopenia and fracture) Methods: The canonical correlation analysis (metaCCA) approach was used to identify the shared genes for six diseases by integrating genome-wide association study (GWAS)-derived summary statistics. Versatile Gene-based Association Study (VEGAS2) method was further applied to refine and validate the putative pleiotropic genes identified by metaCCA. Results: About 157 (p<8.19E-6), 319 (p<3.90E-6) and 77 (p<9.72E-6) potential pleiotropic genes were identified shared by two immune disease, four skeletal diseases, and all of the six diseases, respectively. The top three significant putative pleiotropic genes shared by both immune and skeletal diseases, including HLA-B, TSBP1 and TSBP1-AS1 (p<E-300) were located in the major histocompatibility complex (MHC) region. Nineteen of 77 putative pleiotropic genes identified by metaCCA analysis were associated with at least one disease in the VEGAS2 analysis. Specifically, majority (18) of these 19 putative validated pleiotropic genes were associated with RA. Conclusion: The metaCCA method identified some pleiotropic genes shared by the immune and skeletal diseases. These findings help to improve our understanding of the shared genetic mechanisms and signaling pathways underlying immune and skeletal diseases.

scholarly journals Genetic architecture of common bunt resistance in winter wheat using genome-wide association study

2018 ◽  
Vol 18 (1) ◽  
Author(s):  
Amira M. I. Mourad ◽  
Ahmed Sallam ◽  
Vikas Belamkar ◽  
Ezzat Mahdy ◽  
Bahy Bakheit ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Association Study ◽  
Genetic Architecture ◽  
Genome Wide Association Study ◽  
Genome Wide Association ◽  
Common Bunt ◽  
Genome Wide ◽  
Bunt Resistance

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

2018 ◽  
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.

scholarly journals Alcohol consumption and telomere length: observational and Mendelian randomization approaches

2021 ◽  
Author(s):  
Anya Topiwala ◽  
Bernd Taschler ◽  
Klaus P Ebmeier ◽  
Steve Smith ◽  
Hang Zhou ◽  
...  
Keyword(s):  
Alcohol Consumption ◽  
Telomere Length ◽  
Alcohol Use Disorder ◽  
Genome Wide Association Study ◽  
Mendelian Randomization ◽  
Prevention Strategies ◽  
Genome Wide ◽  
Non Linear ◽  
Alcoholic Drinks ◽  
Potential Threshold

Alcohols impact on telomere length, a proposed marker of biological age, is unclear. We performed the largest observational study to date and compared findings with Mendelian randomization (MR) estimates. Two-sample MR used data from a recent genome-wide association study (GWAS) of telomere length. Genetic variants were selected on the basis of associations with alcohol consumption and alcohol use disorder (AUD). Non-linear MR employed UK Biobank individual data. MR analyses suggest a causal relationship between alcohol and telomere length: both genetically predicted alcohol traits were inversely associated with telomere length. 1 S.D. higher genetically-predicted log-transformed alcoholic drinks weekly had a -0.07 S.D. effect on telomere length (95% confidence interval [CI]:-0.14 to -0.01); genetically-predicted AUD -0.06 S.D. effect (CI:-0.10 to -0.02). Results were consistent across methods and independent from smoking. Non-linear analyses indicated a potential threshold relationship between alcohol and telomere length. Our findings have implications for potential aging-related disease prevention strategies.

Sign in / Sign up

Export Citation Format

Share Document