scholarly journals Prioritizing genetic variants in GWAS with lasso using permutation-assisted tuning

2020 ◽  
Vol 36 (12) ◽  
pp. 3811-3817 ◽  
Author(s):  
Songshan Yang ◽  
Jiawei Wen ◽  
Scott T Eckert ◽  
Yaqun Wang ◽  
Dajiang J Liu ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Complex Traits ◽  
Association Studies ◽  
Superior Performance ◽  
Supplementary Information ◽  
Tuning Parameter ◽  
Snp Analysis ◽  
Genome Wide Association Studies ◽  
Multiple Testing Correction ◽  
Wide Range

Abstract Motivation Large scale genome-wide association studies (GWAS) have resulted in the identification of a wide range of genetic variants related to a host of complex traits and disorders. Despite their success, the individual single-nucleotide polymorphism (SNP) analysis approach adopted in most current GWAS can be limited in that it is usually biologically simple to elucidate a comprehensive genetic architecture of phenotypes and statistically underpowered due to heavy multiple-testing correction burden. On the other hand, multiple-SNP analyses (e.g. gene-based or region-based SNP-set analysis) are usually more powerful to examine the joint effects of a set of SNPs on the phenotype of interest. However, current multiple-SNP approaches can only draw an overall conclusion at the SNP-set level and does not directly inform which SNPs in the SNP-set are driving the overall genotype–phenotype association. Results In this article, we propose a new permutation-assisted tuning procedure in lasso (plasso) to identify phenotype-associated SNPs in a joint multiple-SNP regression model in GWAS. The tuning parameter of lasso determines the amount of shrinkage and is essential to the performance of variable selection. In the proposed plasso procedure, we first generate permutations as pseudo-SNPs that are not associated with the phenotype. Then, the lasso tuning parameter is delicately chosen to separate true signal SNPs and non-informative pseudo-SNPs. We illustrate plasso using simulations to demonstrate its superior performance over existing methods, and application of plasso to a real GWAS dataset gains new additional insights into the genetic control of complex traits. Availability and implementation R codes to implement the proposed methodology is available at https://github.com/xyz5074/plasso. Supplementary information Supplementary data are available at Bioinformatics online.

Motif-Raptor: A Cell Type-Specific and Transcription Factor Centric Approach for Post-GWAS Prioritization of Causal Regulators

2021 ◽  
Author(s):  
Qiuming Yao ◽  
Paolo Ferragina ◽  
Yakir Reshef ◽  
Guillaume Lettre ◽  
Daniel E Bauer ◽  
...  
Keyword(s):  
Gene Expression ◽  
Genetic Variants ◽  
Complex Traits ◽  
Association Studies ◽  
Cell Types ◽  
Chromatin Accessibility ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Systematic Understanding ◽  
Coding Variants

Abstract Motivation Genome-wide association studies (GWAS) have identified thousands of common trait-associated genetic variants but interpretation of their function remains challenging. These genetic variants can overlap the binding sites of transcription factors (TFs) and therefore could alter gene expression. However, we currently lack a systematic understanding on how this mechanism contributes to phenotype. Results We present Motif-Raptor, a TF-centric computational tool that integrates sequence-based predictive models, chromatin accessibility, gene expression datasets and GWAS summary statistics to systematically investigate how TF function is affected by genetic variants. Given trait associated non-coding variants, Motif-Raptor can recover relevant cell types and critical TFs to drive hypotheses regarding their mechanism of action. We tested Motif-Raptor on complex traits such as rheumatoid arthritis and red blood cell count and demonstrated its ability to prioritize relevant cell types, potential regulatory TFs and non-coding SNPs which have been previously characterized and validated. Availability Motif-Raptor is freely available as a Python package at: https://github.com/pinellolab/MotifRaptor. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Statistical models and computational tools for predicting complex traits and diseases

2021 ◽  
Vol 19 (4) ◽  
pp. e36
Author(s):  
Wonil Chung
Keyword(s):  
Genetic Variants ◽  
Statistical Models ◽  
Complex Traits ◽  
Association Studies ◽  
Polygenic Risk Score ◽  
Genome Wide Association Studies ◽  
Computational Tools ◽  
Genome Wide ◽  
Individual Traits ◽  
Wide Range

Predicting individual traits and diseases from genetic variants is critical to fulfilling the promise of personalized medicine. The genetic variants from genome-wide association studies (GWAS), including variants well below GWAS significance, can be aggregated into highly significant predictions across a wide range of complex traits and diseases. The recent arrival of large-sample public biobanks enables highly accurate polygenic predictions based on genetic variants across the whole genome. Various statistical methodologies and diverse computational tools have been introduced and developed to computed the polygenic risk score (PRS) more accurately. However, many researchers utilize PRS tools without a thorough understanding of the underlying model and how to specify the parameters for the best performance. It is advantageous to study the statistical models implemented in computational tools for PRS estimation and the formulas of parameters to be specified. Here, we review a variety of recent statistical methodologies and computational tools for PRS computation.

scholarly journals A transcriptome-wide Mendelian randomization study to uncover tissue-dependent regulatory mechanisms across the human phenome

2019 ◽  
Author(s):  
Tom G Richardson ◽  
Gibran Hemani ◽  
Tom R Gaunt ◽  
Caroline L Relton ◽  
George Davey Smith
Keyword(s):  
Gene Expression ◽  
Genetic Variants ◽  
Complex Traits ◽  
Mendelian Randomization ◽  
Drug Repositioning ◽  
Association Studies ◽  
Thyroid Tissue ◽  
Genome Wide Association Studies ◽  
Tissue Specific ◽  
Genome Wide

AbstractBackgroundDeveloping insight into tissue-specific transcriptional mechanisms can help improve our understanding of how genetic variants exert their effects on complex traits and disease. By applying the principles of Mendelian randomization, we have undertaken a systematic analysis to evaluate transcriptome-wide associations between gene expression across 48 different tissue types and 395 complex traits.ResultsOverall, we identified 100,025 gene-trait associations based on conventional genome-wide corrections (P < 5 × 10−08) that also provided evidence of genetic colocalization. These results indicated that genetic variants which influence gene expression levels in multiple tissues are more likely to influence multiple complex traits. We identified many examples of tissue-specific effects, such as genetically-predicted TPO, NR3C2 and SPATA13 expression only associating with thyroid disease in thyroid tissue. Additionally, FBN2 expression was associated with both cardiovascular and lung function traits, but only when analysed in heart and lung tissue respectively.We also demonstrate that conducting phenome-wide evaluations of our results can help flag adverse on-target side effects for therapeutic intervention, as well as propose drug repositioning opportunities. Moreover, we find that exploring the tissue-dependency of associations identified by genome-wide association studies (GWAS) can help elucidate the causal genes and tissues responsible for effects, as well as uncover putative novel associations.ConclusionsThe atlas of tissue-dependent associations we have constructed should prove extremely valuable to future studies investigating the genetic determinants of complex disease. The follow-up analyses we have performed in this study are merely a guide for future research. Conducting similar evaluations can be undertaken systematically at http://mrcieu.mrsoftware.org/Tissue_MR_atlas/.

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

2019 ◽  
Vol 20 (10) ◽  
pp. 765-780 ◽  
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.

scholarly journals A global overview of pleiotropy and genetic architecture in complex traits

2018 ◽  
Author(s):  
Kyoko Watanabe ◽  
Sven Stringer ◽  
Oleksandr Frei ◽  
Maša Umićević Mirkov ◽  
Tinca J.C. Polderman ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Complex Traits ◽  
Genetic Architecture ◽  
Human Genetics ◽  
Association Studies ◽  
Regulatory Elements ◽  
Genome Wide Association Studies ◽  
Multiple Trait ◽  
Current Availability ◽  
Flanking Regions

ABSTRACTAfter a decade of genome-wide association studies (GWASs), fundamental questions in human genetics are still unanswered, such as the extent of pleiotropy across the genome, the nature of trait-associated genetic variants and the disparate genetic architecture across human traits. The current availability of hundreds of GWAS results provide the unique opportunity to gain insight into these questions. In this study, we harmonized and systematically analysed 4,155 publicly available GWASs. For a subset of well-powered GWAS on 558 unique traits, we provide an extensive overview of pleiotropy and genetic architecture. We show that trait associated loci cover more than half of the genome, and 90% of those loci are associated with multiple trait domains. We further show that potential causal genetic variants are enriched in coding and flanking regions, as well as in regulatory elements, and how trait-polygenicity is related to an estimate of the required sample size to detect 90% of causal genetic variants. Our results provide novel insights into how genetic variation contributes to trait variation. All GWAS results can be queried and visualized at the GWAS ATLAS resource (http://atlas.ctglab.nl).

scholarly journals CoMM-S2: a collaborative mixed model using summary statistics in transcriptome-wide association studies

2019 ◽  
Author(s):  
Yi Yang ◽  
Xingjie Shi ◽  
Yuling Jiao ◽  
Jian Huang ◽  
Min Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Genetic Variants ◽  
Complex Traits ◽  
Mixed Model ◽  
Association Studies ◽  
Gwas Data ◽  
Supplementary Information ◽  
Summary Statistics ◽  
Individual Level ◽  
The Relationship

AbstractMotivationAlthough genome-wide association studies (GWAS) have deepened our understanding of the genetic architecture of complex traits, the mechanistic links that underlie how genetic variants cause complex traits remains elusive. To advance our understanding of the underlying mechanistic links, various consortia have collected a vast volume of genomic data that enable us to investigate the role that genetic variants play in gene expression regulation. Recently, a collaborative mixed model (CoMM) [42] was proposed to jointly interrogate genome on complex traits by integrating both the GWAS dataset and the expression quantitative trait loci (eQTL) dataset. Although CoMM is a powerful approach that leverages regulatory information while accounting for the uncertainty in using an eQTL dataset, it requires individual-level GWAS data and cannot fully make use of widely available GWAS summary statistics. Therefore, statistically efficient methods that leverages transcriptome information using only summary statistics information from GWAS data are required.ResultsIn this study, we propose a novel probabilistic model, CoMM-S2, to examine the mechanistic role that genetic variants play, by using only GWAS summary statistics instead of individual-level GWAS data. Similar to CoMM which uses individual-level GWAS data, CoMM-S2 combines two models: the first model examines the relationship between gene expression and genotype, while the second model examines the relationship between the phenotype and the predicted gene expression from the first model. Distinct from CoMM, CoMM-S2 requires only GWAS summary statistics. Using both simulation studies and real data analysis, we demonstrate that even though CoMM-S2 utilizes GWAS summary statistics, it has comparable performance as CoMM, which uses individual-level GWAS [email protected] and implementationThe implement of CoMM-S2 is included in the CoMM package that can be downloaded from https://github.com/gordonliu810822/CoMM.Supplementary informationSupplementary data are available at Bioinformatics online.

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

Genes ◽  
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.

scholarly journals Extensive impact of low-frequency variants on the phenotypic landscape at population-scale

2019 ◽  
Author(s):  
T. Fournier ◽  
O. Abou Saada ◽  
J. Hou ◽  
J. Peter ◽  
E. Caudal ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Complex Traits ◽  
Growth Traits ◽  
Association Studies ◽  
Significant Proportion ◽  
Low Frequency ◽  
Population Level ◽  
Initial Population ◽  
Phenotypic Variance ◽  
Genome Wide Association Studies

AbstractGenome-wide association studies (GWAS) allows to dissect the genetic basis of complex traits at the population level1. However, despite the extensive number of trait-associated loci found, they often fail to explain a large part of the observed phenotypic variance2–4. One potential source of this discrepancy could be the preponderance of undetected low-frequency genetic variants in natural populations5,6. To increase the allele frequency of those variants and assess their phenotypic effects at the population level, we generated a diallel panel consisting of 3,025 hybrids, derived from pairwise crosses between a subset of natural isolates from a completely sequenced 1,011 Saccharomyces cerevisiae population. We examined each hybrid across a large number of growth traits, resulting in a total of 148,225 cross/trait combinations. Parental versus hybrid regression analysis showed that while most phenotypic variance is explained by additivity, a significant proportion (29%) is governed by non-additive effects. This is confirmed by the fact that a majority of complete dominance is observed in 25% of the traits. By performing GWAS on the diallel panel, we detected 1,723 significantly associated genetic variants, with 16.3% of them being low-frequency variants in the initial population. These variants, which would not be detected using classical GWAS, explain 21% of the phenotypic variance on average. Altogether, our results demonstrate that low-frequency variants should be accounted for as they contribute to a large part of the phenotypic variation observed in a population.

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

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 .

scholarly journals emeraLD: Rapid Linkage Disequilibrium Estimation with Massive Data Sets

2018 ◽  
Author(s):  
Corbin Quick ◽  
Christian Fuchsberger ◽  
Daniel Taliun ◽  
Gonçalo Abecasis ◽  
Michael Boehnke ◽  
...  
Keyword(s):  
Linkage Disequilibrium ◽  
Association Studies ◽  
Random Access ◽  
Supplementary Information ◽  
Data Sets ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Genome Wide ◽  
Wide Range ◽  
Supplementary Material

AbstractSummaryEstimating linkage disequilibrium (LD) is essential for a wide range of summary statistics-based association methods for genome-wide association studies (GWAS). Large genetic data sets, e.g. the TOPMed WGS project and UK Biobank, enable more accurate and comprehensive LD estimates, but increase the computational burden of LD estimation. Here, we describe emeraLD (Efficient Methods for Estimation and Random Access of LD), a computational tool that leverages sparsity and haplotype structure to estimate LD orders of magnitude faster than existing tools.Availability and ImplementationemeraLD is implemented in C++, and is open source under GPLv3. Source code, documentation, an R interface, and utilities for analysis of summary statistics are freely available at http://github.com/statgen/[email protected] informationSupplementary data are available at Bioinformatics online.

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