Nontrivial Replication of Loci Detected by Multi-Trait Methods

The ever-growing genome-wide association studies (GWAS) have revealed widespread pleiotropy. To exploit this, various methods that jointly consider associations of a genetic variant with multiple traits have been developed. Most efforts have been made concerning improving GWAS discovery power. However, how to replicate these discovered pleiotropic loci has yet to be discussed thoroughly. Unlike a single-trait scenario, multi-trait replication is not trivial considering the underlying genotype-multi-phenotype map of the associations. Here, we evaluate four methods for replicating multi-trait associations, corresponding to four levels of replication strength. Weak replication cannot justify pleiotropic genetic effects, whereas strong replication using our developed correlation methods can inform consistent pleiotropic genetic effects across the discovery and replication samples. We provide a protocol for replicating multi-trait genetic associations in practice. The described methods are implemented in the free and open-source R package MultiABEL.

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Adjustment for index event bias in genome-wide association studies of subsequent events

10.1101/436063 ◽

2018 ◽

Cited By ~ 1

Author(s):

Frank Dudbridge ◽

Richard J. Allen ◽

Nuala A. Sheehan ◽

A. Floriaan Schmidt ◽

James C. Lee ◽

...

Keyword(s):

Association Studies ◽

Susceptibility Gene ◽

Disease Status ◽

Genetic Effects ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Genetic Associations ◽

Index Event ◽

Genome Wide ◽

Common Causes

AbstractFollowing numerous genome-wide association studies of disease susceptibility, there is increasing interest in genetic associations with prognosis, survival or other subsequent events. Such associations are vulnerable to index event bias, by which selection of subjects according to disease status creates biased associations if common causes of incidence and prognosis are not accounted for. We propose an adjustment for index event bias using the residuals from the regression of genetic effects on prognosis on genetic effects on incidence. Our approach eliminates this bias when direct genetic effects on incidence and prognosis are independent, and otherwise reduces bias in realistic situations. In a study of idiopathic pulmonary fibrosis, we reverse a paradoxical association of the strong susceptibility gene MUCSB with increased survival, suggesting instead a significant association with decreased survival. In re-analysis of a study of Crohn’s disease prognosis, four regions remain associated at genome-wide significance but with increased standard errors.

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prewas: Data pre-processing for more informative bacterial GWAS

10.1101/2019.12.20.873158 ◽

2019 ◽

Cited By ~ 1

Author(s):

Katie Saund ◽

Zena Lapp ◽

Stephanie N. Thiede ◽

Ali Pirani ◽

Evan S Snitkin

Keyword(s):

Association Studies ◽

Bacterial Genome ◽

R Package ◽

Genome Wide Association Studies ◽

Overlapping Genes ◽

Genetic Associations ◽

Genome Wide ◽

Minor Alleles ◽

Processing Steps ◽

Variant Identification

While variant identification pipelines are becoming increasingly standardized, less attention has been paid to the pre-processing of variants prior to their use in bacterial genome-wide association studies (bGWAS). Three nuances of variant pre-processing that impact downstream identification of genetic associations include the separation of variants at multiallelic sites, separation of variants in overlapping genes, and referencing of variants relative to ancestral alleles. Here we demonstrate the importance of these variant pre-processing steps on diverse bacterial genomic datasets and present prewas, an R package, that standardizes the pre-processing of multiallelic sites, overlapping genes, and reference alleles before bGWAS. This package facilitates improved reproducibility and interpretability of bGWAS results. Prewas enables users to extract maximal information from bGWAS by implementing multi-line representation for multiallelic sites and variants in overlapping genes. Prewas outputs a binary SNP matrix that can be used for SNP-based bGWAS and will prevent the masking of minor alleles during bGWAS analysis. The optional binary gene matrix output can be used for gene-based bGWAS which will enable users to maximize the power and evolutionary interpretability of their bGWAS studies. Prewas is available for download from GitHub.

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Beyond power: Multivariate discovery, replication, and interpretation of pleiotropic loci using summary association statistics

10.1101/022269 ◽

2015 ◽

Cited By ~ 5

Author(s):

Zheng Ning ◽

Yakov A. Tsepilov ◽

Sodbo Zh. Sharapov ◽

Alexander K. Grishenko ◽

Xiao Feng ◽

...

Keyword(s):

Open Source ◽

Genetic Variants ◽

Association Studies ◽

R Package ◽

Genetic Effects ◽

Pleiotropic Effects ◽

Genome Wide Association Studies ◽

Genetic Associations ◽

Study Results ◽

Wide Range

AbstractThe ever-growing genome-wide association studies (GWAS) have revealed widespread pleiotropy. To exploit this, various methods which consider variant association with multiple traits jointly have been developed. However, most effort has been put on improving discovery power: how to replicate and interpret these discovered pleiotropic loci using multivariate methods has yet to be discussed fully. Using only multiple publicly available single-trait GWAS summary statistics, we develop a fast and flexible multi-trait framework that contains modules for (i) multi-trait genetic discovery, (ii) replication of locus pleiotropic profile, and (iii) multi-trait conditional analysis. The procedure is able to handle any level of sample overlap. As an empirical example, we discovered and replicated 23 novel pleiotropic loci for human anthropometry and evaluated their pleiotropic effects on other traits. By applying conditional multivariate analysis on the 23 loci, we discovered and replicated two additional multi-trait associated SNPs. Our results provide empirical evidence that multi-trait analysis allows detection of additional, replicable, highly pleiotropic genetic associations without genotyping additional individuals. The methods are implemented in a free and open source R package MultiABEL.Author summaryBy analyzing large-scale genomic data, geneticists have revealed widespread pleiotropy, i.e. single genetic variation can affect a wide range of complex traits. Methods have been developed to discover such genetic variants. However, we still lack insights into the relevant genetic architecture - What more can we learn from knowing the effects of these genetic variants?Here, we develop a fast and flexible statistical analysis procedure that includes discovery, replication, and interpretation of pleiotropic effects. The whole analysis pipeline only requires established genetic association study results. We also provide the mathematical theory behind the pleiotropic genetic effects testing.Most importantly, we show how a replication study can be essential to reveal new biology rather than solely increasing sample size in current genomic studies. For instance, we show that, using our proposed replication strategy, we can detect the difference in genetic effects between studies of different geographical origins.We applied the method to the GIANT consortium anthropometric traits to discover new genetic associations, replicated in the UK Biobank, and provided important new insights into growth and obesity.Our pipeline is implemented in an open-source R package MultiABEL, sufficiently efficient that allows researchers to immediately apply on personal computers in minutes.

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Fast and Accurate Genome-Wide Association Test of Multiple Quantitative Traits

Computational and Mathematical Methods in Medicine ◽

10.1155/2018/2564531 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

Baolin Wu ◽

James S. Pankow

Keyword(s):

Quantitative Traits ◽

Association Studies ◽

R Package ◽

Genome Wide Association ◽

Type I ◽

Genome Wide Association Studies ◽

Competitive Performance ◽

Multiple Traits ◽

Atherosclerosis Risk In Communities ◽

Genome Wide

Multiple correlated traits are often collected in genetic studies. By jointly analyzing multiple traits, we can increase power by aggregating multiple weak effects and reveal additional insights into the genetic architecture of complex human diseases. In this article, we propose a multivariate linear regression-based method to test the joint association of multiple quantitative traits. It is flexible to accommodate any covariates, has very accurate control of type I errors, and offers very competitive performance. We also discuss fast and accurate significance p value computation especially for genome-wide association studies with small-to-medium sample sizes. We demonstrate through extensive numerical studies that the proposed method has competitive performance. Its usefulness is further illustrated with application to genome-wide association analysis of diabetes-related traits in the Atherosclerosis Risk in Communities (ARIC) study. We found some very interesting associations with diabetes traits which have not been reported before. We implemented the proposed methods in a publicly available R package.

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haploR: an R-package for querying web-based annotation tools

F1000Research ◽

10.12688/f1000research.10742.1 ◽

2017 ◽

Vol 6 ◽

pp. 97 ◽

Cited By ~ 1

Author(s):

Ilya Y. Zhbannikov ◽

Konstantin Arbeev ◽

Anatoliy I. Yashin

Keyword(s):

Genetic Variants ◽

Genome Annotation ◽

Association Studies ◽

R Package ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Genetic Associations ◽

Web Based ◽

Genome Wide

There exists a set of web-based tools for integration and exploring information linked to annotated genetic variants. We developed haploR, an R-package for querying such web-based genome annotation tools (currently implementing on HaploReg and RegulomeDB) and gathering information in a format suitable for downstream bioinformatic analyses. This will facilitate post-genome wide association studies streamline analysis for rapid discovery and interpretation of genetic associations.

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Qtlizer: comprehensive QTL annotation of GWAS results

10.1101/495903 ◽

2018 ◽

Cited By ~ 4

Author(s):

Matthias Munz ◽

Inken Wohlers ◽

Eric Simon ◽

Tobias Reinberger ◽

Hauke Busch ◽

...

Keyword(s):

Genetic Variant ◽

Association Studies ◽

Housekeeping Genes ◽

R Package ◽

Genome Wide Association Studies ◽

Protein Abundance ◽

Base Pairs ◽

Genome Wide ◽

Wide Range ◽

Distance Limit

ABSTRACTExploration of genetic variant-to-gene relationships by quantitative trait loci such as expression QTLs is a frequently used tool in genome-wide association studies. However, the wide range of public QTL databases and the lack of batch annotation features complicate a comprehensive annotation of GWAS results. In this work, we introduce the tool “Qtlizer” for annotating lists of variants in human with associated changes in gene expression and protein abundance using an integrated database of published QTLs. Features include incorporation of variants in linkage disequilibrium and reverse search by gene names. Analyzing the database for base pair distances between best significant eQTLs and their affected genes suggests that the commonly used cis-distance limit of 1,000,000 base pairs might be too restrictive, implicating a substantial amount of wrongly and yet undetected eQTLs. We also ranked genes with respect to the maximum number of tissue-specific eQTL studies in which a most significant eQTL signal was consistent. For the top 100 genes we observed the strongest enrichment with housekeeping genes (P=2×10−6) and with the 10% highest expressed genes (P=0.005) after grouping eQTLs by r2>0.95, underlining the relevance of LD information in eQTL analyses. Qtlizer can be accessed via http://genehopper.de/qtlizer or by using the respective Bioconductor R-package (DOI: 10.18129/B9.bioc.Qtlizer).

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haploR: an R package for querying web-based annotation tools

F1000Research ◽

10.12688/f1000research.10742.2 ◽

2017 ◽

Vol 6 ◽

pp. 97 ◽

Cited By ~ 2

Author(s):

Ilya Y. Zhbannikov ◽

Konstantin Arbeev ◽

Svetlana Ukraintseva ◽

Anatoliy I. Yashin

Keyword(s):

Genome Annotation ◽

Association Studies ◽

R Package ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Genetic Associations ◽

Data Frame ◽

Web Based ◽

Genome Wide

We developed haploR, an R package for querying web based genome annotation tools HaploReg and RegulomeDB. haploR gathers information in a data frame which is suitable for downstream bioinformatic analyses. This will facilitate post-genome wide association studies streamline analysis for rapid discovery and interpretation of genetic associations.

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Heritability Informed Power Optimization (HIPO) Leads to Enhanced Detection of Genetic Associations Across Multiple Traits

10.1101/218404 ◽

2017 ◽

Author(s):

Guanghao Qi ◽

Nilanjan Chatterjee

Keyword(s):

Type I Error ◽

Power Optimization ◽

Association Studies ◽

Type I ◽

Genome Wide Association Studies ◽

Genetic Associations ◽

Multiple Traits ◽

Association Testing ◽

Genome Wide ◽

Individual Traits

AbstractGenome-wide association studies have shown that pleiotropy is a common phenomenon that can potentially be exploited for enhanced detection of susceptibility loci. We propose heritability informed power optimization (HIPO) for conducting powerful pleiotropic analysis using summary-level association statistics. We find optimal linear combinations of association coefficients across traits that are expected to maximize non-centrality parameter for the underlying test statistics, taking into account estimates of heritability, sample size variations and overlaps across the traits. Simulation studies show that the proposed method has correct type I error, robust to population stratification and leads to desired genome-wide enrichment of association signals. Application of the proposed method to publicly available data for three groups of genetically related traits, lipids (N=188,577), psychiatric diseases (Ncase=33,332, Ncontrol=27,888) and social science traits (N ranging between 161,460 to 298,420 across individual traits) increased the number of genome-wide significant loci by 12%, 200% and 50%, respectively, compared to those found by analysis of individual traits. Evidence of replication is present for many of these loci in subsequent larger studies for individual traits. HIPO can potentially be extended to high-dimensional phenotypes as a way of dimension reduction to maximize power for subsequent genetic association testing.

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Penalized partial least squares for pleiotropy

BMC Bioinformatics ◽

10.1186/s12859-021-03968-1 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Camilo Broc ◽

Therese Truong ◽

Benoit Liquet

Keyword(s):

Least Squares ◽

Partial Least Squares ◽

Association Studies ◽

A Priori ◽

Simulated Data ◽

Real Data ◽

Genome Wide Association Studies ◽

Genetic Associations ◽

Multiple Traits ◽

Application Fields

Abstract Background The increasing number of genome-wide association studies (GWAS) has revealed several loci that are associated to multiple distinct phenotypes, suggesting the existence of pleiotropic effects. Highlighting these cross-phenotype genetic associations could help to identify and understand common biological mechanisms underlying some diseases. Common approaches test the association between genetic variants and multiple traits at the SNP level. In this paper, we propose a novel gene- and a pathway-level approach in the case where several independent GWAS on independent traits are available. The method is based on a generalization of the sparse group Partial Least Squares (sgPLS) to take into account groups of variables, and a Lasso penalization that links all independent data sets. This method, called joint-sgPLS, is able to convincingly detect signal at the variable level and at the group level. Results Our method has the advantage to propose a global readable model while coping with the architecture of data. It can outperform traditional methods and provides a wider insight in terms of a priori information. We compared the performance of the proposed method to other benchmark methods on simulated data and gave an example of application on real data with the aim to highlight common susceptibility variants to breast and thyroid cancers. Conclusion The joint-sgPLS shows interesting properties for detecting a signal. As an extension of the PLS, the method is suited for data with a large number of variables. The choice of Lasso penalization copes with architectures of groups of variables and observations sets. Furthermore, although the method has been applied to a genetic study, its formulation is adapted to any data with high number of variables and an exposed a priori architecture in other application fields.

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Genetic association studies of alterations in protein function expose recessive effects on cancer predisposition

Scientific Reports ◽

10.1038/s41598-021-94252-y ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Nadav Brandes ◽

Nathan Linial ◽

Michal Linial

Keyword(s):

Cancer Risk ◽

Protein Function ◽

Association Studies ◽

Genetic Effects ◽

Cancer Predisposition ◽

Genome Wide Association Studies ◽

Genetic Associations ◽

Functional Interpretation ◽

Gene Damage ◽

Genomic Regions

AbstractThe characterization of germline genetic variation affecting cancer risk, known as cancer predisposition, is fundamental to preventive and personalized medicine. Studies of genetic cancer predisposition typically identify significant genomic regions based on family-based cohorts or genome-wide association studies (GWAS). However, the results of such studies rarely provide biological insight or functional interpretation. In this study, we conducted a comprehensive analysis of cancer predisposition in the UK Biobank cohort using a new gene-based method for detecting protein-coding genes that are functionally interpretable. Specifically, we conducted proteome-wide association studies (PWAS) to identify genetic associations mediated by alterations to protein function. With PWAS, we identified 110 significant gene-cancer associations in 70 unique genomic regions across nine cancer types and pan-cancer. In 48 of the 110 PWAS associations (44%), estimated gene damage is associated with reduced rather than elevated cancer risk, suggesting a protective effect. Together with standard GWAS, we implicated 145 unique genomic loci with cancer risk. While most of these genomic regions are supported by external evidence, our results also highlight many novel loci. Based on the capacity of PWAS to detect non-additive genetic effects, we found that 46% of the PWAS-significant cancer regions exhibited exclusive recessive inheritance. These results highlight the importance of recessive genetic effects, without relying on familial studies. Finally, we show that many of the detected genes exert substantial cancer risk in the studied cohort determined by a quantitative functional description, suggesting their relevance for diagnosis and genetic consulting.

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