easyGWAS: A Cloud-based Platform for Comparing the Results of … · 2016. 12. 16. · information in species such as Arabidopsis thaliana (The 1001 Genomes Consortium, 2016). The

LARGE-SCALE BIOLOGY

easyGWAS: A Cloud-based Platform for Comparing the Results of Genome-wide Association Studies

Dominik G. Grimm1,2,3,4,*, Damian Roqueiro3,4, Patrice A. Salomé5,§, Stefan Kleeberger1, Bastian Greshake1,§, Wangsheng Zhu5, Chang Liu5,§, Christoph Lippert1,§, Oliver Stegle1,§, Bernhard Schölkopf6, Detlef Weigel5 and Karsten M. Borgwardt1,2,3,4,*

1 Machine Learning and Computational Biology Research Group, Max Planck Institute for Intelligent Systems and Max Planck Institute for Developmental Biology, Tübingen, Germany 2 Zentrum für Bioinformatik (ZBIT), Eberhard Karls Universität Tübingen, Tübingen, Germany 3 Department for Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland 4 Swiss Institute of Bioinformatics, Basel, Switzerland 5 Department of Molecular Biology, Max Planck Institute for Developmental Biology, Tübingen, Germany 6 Department of Empirical Inference, Max Planck Institute for Intelligent Systems, Tübingen, Germany

*Correspondence: [email protected] (Dominik G. Grimm),[email protected] (Karsten M. Borgwardt)

§ Current addresses: Johann Wolfgang Goethe University of Frankfurt am Main, Germany (BastianGreshake); Human Longevity, Inc., USA (Christoph Lippert); European Molecular Biology Laboratory,European Bioinformatics Institute, Genome Campus, Hinxton, Cambridge, UK (Oliver Stegle);Department of Chemistry and Biochemistry, UCLA, USA (Patrice A. Salomé); Center for PlantMolecular Biology (ZMBP), University of Tübingen, Germany (Chang Liu)

Short title: easyGWAS

One-sentence summary: The easyGWAS platform is a powerful online resource for computing, storing, sharing, and comparing the results of GWAS in both inbred and outbred plant and animal species.

AbstractThe ever-growing availability of high quality genotypes for a multitude of species has enabled researchers to explore the underlying genetic architecture of complex phenotypes at an unprecedented level of detail using genome-wide association studies (GWAS). The systematic comparison of results obtained from GWAS of different traits opens up new possibilities, including the analysis of pleiotropic effects. Other advantages that result from the integration of multiple GWAS are the ability to replicate GWAS signals and to increase statistical power to detect such signals through meta-analyses. In order to facilitate the simple comparison of GWAS results, we present easyGWAS, a powerful, species-independent online resource for computing, storing, sharing, annotating and comparing GWAS. The easyGWAS tool supports multiple species, the uploading of private genotype data and summary statistics of existing GWAS, as well as advanced methods for comparing GWAS results across different experiments and datasets in an interactive and user-friendly interface. easyGWAS is also a public data repository for GWAS data and summary statistics, and already includes published data and results from several major GWAS. We demonstrate the potential of easyGWAS with a case study of the model organism Arabidopsis thaliana, using flowering and growth-related traits.

Plant Cell Advance Publication. Published on December 16, 2016, doi:10.1105/tpc.16.00551

©2016 American Society of Plant Biologists. All Rights Reserved

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Introduction

The growing number of high-quality genotypes and phenotypes for many plant and animal

species has created a unique opportunity to improve our understanding of the genetic basis

of complex traits and diseases. Over the last decade, genome-wide association studies

(GWAS) have become a key technique for exploiting this wealth of data, by detecting

associations between a phenotype of interest and genetic variants present in a group of

individuals (Bush and Moore, 2012). Unlike classic linkage mapping, GWAS can survey

hundreds of thousands or even millions of single nucleotide polymorphisms (SNPs), which in

turn gives GWAS greater power to detect small effects (Cordell and Clayton, 2005).

Moreover, GWAS offer a higher resolution than linkage mapping because of the larger

number of recombination events that will have occurred in natural panels used for

association mapping (Nordborg and Weigel, 2008).

The utility of GWAS has been demonstrated in a variety of plants and crops, including

Arabidopsis thaliana (Atwell et al., 2010; Filiault and Maloof, 2012; Meijón et al., 2014), rice

(Zhao et al., 2011), wheat (Liu et al., 2015), and tomatoes (Lin et al., 2014), and in various

animal species, such as fruit flies (Mackay et al., 2012), mice (Kirby et al., 2010), and in

humans (Scott et al., 2007; Chasman et al., 2011; Freilinger et al., 2012). These studies are

accompanied by a steady improvement in the level of detail of the genotypic information.

While early studies used single nucleotide polymorphisms to represent entire genomic

regions (Atwell et al., 2010), recent studies have provided large panels of whole-genome

information in species such as Arabidopsis thaliana (The 1001 Genomes Consortium, 2016).

The wealth of information provided by independent GWAS, many of which were conducted

on related traits, offers unique opportunities for comparing and integrating findings across

experiments. This integration makes it possible to detect genes with pleiotropic effects on

multiple traits and traits with a shared genetic basis, and to replicate findings or increase

statistical power in association mapping through meta-analyses of several studies (Franke et

al., 2010; Chasman et al., 2011; Andreassen et al., 2013; Evangelou and Ioannidis, 2013;

Pickrell et al., 2016).

To exploit the full potential of comparative GWAS analyses, we present easyGWAS, a

powerful platform for computing, storing, sharing, and comparing the results of GWAS in

both inbred and outbred plant and animal species. easyGWAS offers tools to conduct GWAS

and also, more importantly, makes available additional data and functionality to facilitate the

in-depth annotation, analysis, publishing, and comparison of GWAS results. There are three

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main aspects of easyGWAS, which together make it a unique online resource. The first

aspect is its use as a repository of results obtained from private and public GWAS, which are

either computed in easyGWAS or imported from external tools such as PLINK (Purcell et al.,

2007). The second key component is the functionality to conduct GWAS in a standardized

way to ensure a maximum degree of comparability between studies. This leads to

easyGWAS’s third key feature, which is its ability to compare the results of different GWAS

and conduct meta-analyses. While existing GWAS online resources, such as EMMA (Kang

et al., 2008), DGRP2 (Mackay et al., 2012), Matapax (Childs et al., 2012), and GWAPP

(Seren et al., 2012) allow for the computation, analysis, and annotation of GWAS, they focus

exclusively on a single species and, most importantly, do not provide functionality to

compare and integrate the results of already conducted GWAS.

A summary of the main contributions of easyGWAS is highlighted in Figure 1. The platform

is unique in five ways: (a) it is not limited to a certain species; (b) it allows users to upload

and manage their own genotype and gene annotation data for a species of choice; (c) it

supports the uploading of summary statistics of GWAS obtained from third-party tools; (d) it

provides a variety of different methods to correct for multiple hypothesis testing; most

importantly, (e) it integrates advanced methods for comparing results of GWAS across

different datasets.

Furthermore, easyGWAS supports state-of-the-art sharing and publishing functionalities:

users can choose between sharing the data and results associated to a genome-wide

association study, either with a restricted set of collaborators or with the entire scientific

community.

As a case study, we use easyGWAS on a large number of Arabidopsis thaliana accessions

from the latest efforts of the 1001 Genomes Project (The 1001 Genomes Consortium, 2016),

in which we measured several flowering time and growth related traits. We integrate the

results for different traits using the platform’s new comparison functionality. easyGWAS is

available online at: https://easygwas.ethz.ch.

Results

Overview

easyGWAS consists of two main web components: a Data Repository and the GWAS

Center. Each of them provides a user-friendly graphical front end that is divided into a

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publicly accessible area and a private area for registered users. The GWAS Center provides

state-of-the-art methods to perform GWAS and meta-analyses, as well as an interactive

results viewer for in-depth analysis of specific genomic regions. There is also an easy-to-use

interface for interactive comparisons of the results of already computed GWAS or uploaded

summary statistics.

Data Repository

The data repository comprises various functions related to data integration, storage,

management, and representation. As mentioned above, the data repository is divided into a

publicly accessible area and an area that is restricted to registered users. Published and

publicly available data can be accessed via the Public Data view, while users’ private data

are stored in a restricted and secure environment that can only be accessed through the

Private Data view (Supplemental Figure 1 or at

https://easygwas.ethz.ch/data/public/species/). Refer to the Methods section for details

about datasets already integrated into the Public Data view. Data shared between

collaborators or with subsets of other users will be displayed in the Private Data view. The

data repository contains general information, meta-information, and graphics about the

species, datasets, phenotypes, covariates, and samples. The Download Manager gives

access to all publicly available datasets in the widely used PLINK format (Purcell et al.,

2007). Users can easily make their private data available to the scientific community at a

later time point. The Upload Manager supports users in the secure integration of new

genotype, phenotype, covariate, or gene annotation data into easyGWAS. Furthermore,

easyGWAS supports the automatic import of public phenotypes from AraPheno

(https://arapheno.1001genomes.org), a central repository for population-scale phenotype

data from Arabidopsis thaliana (Seren, Grimm et al., 2016). Users can also upload their

custom summary statistics of GWAS performed in different environments – for example,

from offline analyses with PLINK (Purcell et al., 2007), or other third-party tools (Kang et al.,

2010; Lippert et al., 2011; Rakitsch et al., 2013; Azencott et al., 2013; Sugiyama et al., 2014;

Llinares-López et al., 2015) – for visualization, subsequent meta-analysis, or comparison

with GWAS results that have already been deposited in easyGWAS. Detailed descriptions of

the different views can be found in the Supplemental Materials (Supplemental Figures 2-6

and Supplemental Text 1).

GWAS Center

The GWAS Center offers a variety of different methods related to computing, analyzing, and

managing GWAS; meta-analyses; and comparisons of results. Non-registered users can

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investigate and download the results of published and publicly available projects, while

registered users can perform their private analysis on data for a given species. As

mentioned above, a user can conduct (i) GWAS, (ii) meta-analyses, and (iii) comparisons of

results. For each of these tasks there are step-by-step workflows, also referred to as

Wizards, which facilitate and standardize their execution. Each wizard guides the user

through all necessary steps, such as selecting a species, phenotypes, transformations, and

appropriate algorithms. The wizard analyzes the user’s input at different steps and only

suggests suitable algorithms and transformations (for example, it excludes transformations

or algorithms that cannot be used for specific data types). To avoid an overload of the

easyGWAS server, a user is limited to five concurrent experiments. Submitted experiments

are distributed to different computation queues in the back-end and the user receives

automatic email notifications after the computations have finished (see the Runtime Analysis

Section for performance comparisons). Details about implemented genome-wide association

(GWA) mapping methods, meta-analysis methods, transformations, and genotype encodings

can be found in the Methods section. Please see the Supplemental Material for additional

details about the wizards (Supplemental Text 2, Supplemental Figure 7), as well as the

easyGWAS online FAQ (https://easygwas.ethz.ch/faq/).

By default, results are kept for 48 hours in the user’s profile and trashed afterwards. To store

experiments permanently, users can save and group individual experiments into GWAS

projects (Supplemental Text 3, Supplemental Figures 8-11). Private projects can be easily

shared with other collaborators or users using the project sharing functionality

(Supplemental Figure 10), which reduces the need to send large results and data files via

e-mail to collaborators. In addition to restricted data sharing with others, users can make

their private GWAS projects available to the scientific community. We have added a hand-

curated inquiry step to control the quality of publicly available projects and data

(Supplemental Figure 11).

Result Views

Results of computed GWAS and meta-analyses can be viewed by clicking on the experiment

name in either the temporary or permanent history. The results window is divided into

several panels, as illustrated in the screenshot shown in Figure 2 (Supplemental Text 4).

Figure 2A shows the general “GWAS Center” menu panel. To navigate through different

sections of the results, users can click on the different tabs shown in Figure 2B. The panels

in Figures 2C and 2D are the default panels when displaying the results of each experiment.

The panel in Figure 2D shows interactive Manhattan plots. The green line in each

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Manhattan plot illustrates the global multiple hypothesis correction threshold. Four widely

used correction methods can be applied: Bonferroni (Abdi, 2007), Benjamini and Hochberg

(1995), Benjamini and Yekutieli (2001), and Storey and Tibshirani (2003) (see Methods,

Supplemental Text 10). By default, the conservative Bonferroni correction controls the

family-wise error rate (FWER) with a significance level of α=5%, but the other methods also

provide less stringent False Discovery Rate (FDR) controls. The multiple hypothesis

correction method and the significance level α (1%, 5% or 10%) can be adjusted dynamically

in the plotting options, which can be found in the panel in Figure 2C. All Manhattan plots

contain interactive elements, such as the ability to zoom into regions of interest to display

further information about the local genes. Manhattan plots can be downloaded as PDF files

for use in manuscripts or presentations. The vertical panels on the left (Figure 2E and

Figure 2F) show a brief summary, including information about the selected species, dataset,

and selected settings. Additionally, the top 10 associated SNPs and their genes are ranked

(if a gene annotation set was selected for the experiment).

Moreover, each SNP in a Manhattan plot is clickable, such that users can obtain additional

information about the alleles at that position, boxplots about the phenotypic values for each

allele, as well as a detailed map of the linkage disequilibrium (LD) pattern around the focal

SNP (Figure 3, Supplemental Text 4). In addition, we also provide more detailed

information about the selected variant, such as whether the variant is a missense mutation,

frameshift, or stop codon. For this purpose, we automatically fetch the corresponding data

using the Ensemble REST interface for the Variant Effect Predictor (VEP) tool (Yates et al.,

2014, McLaren et al., 2016).

Figure 2B shows different clickable tabs that allow the user to explore all the results of the

experiment. The second tab, “QQ-Plots”, renders quantile-quantile plots (QQ-plots) and

provides information about the genomic control factor (Devlin and Roeder, 1999)

(Supplemental Text 5).

The third tab, “SNP Annotations”, lists gene annotations for the top associated SNPs per

chromosome. Users can dynamically change the number of top SNPs displayed or the

multiple hypothesis correction method used and search for genes upstream or downstream

of each SNP (Supplemental Text 5). For each SNP, we again redirect to the same detailed

SNP view as described above.

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The last tab in Figure 2B, “Summary/Downloads”, provides a detailed summary of the

experiment, including all selected settings, information about the experiment’s owner,

selected trait/covariate distributions, as well as associated publications (Supplemental Text

5). Adding too many covariates to regression-based models can easily lead to overfitting

(Hastie et al., 2009). Therefore, it is important to find a good balance between goodness of

fit R2 and model complexity. To this effect, three model selection parameters are

implemented to measure the relative quality of each model: Akaike information criterion

(AIC), Akaike information criterion with correction for finite sample sizes (AICc), and

Bayesian information criterion (BIC) (Schwarz, 1978). A general distinction between AIC and

AICc is that the latter gives preference to models with fewer parameters. The intuition behind

it is that a model with fewer parameters will generalize better. BIC is based on the

assumption that a true model exists and is present among the models to be compared

(Burnham and Anderson, 2002; Aho et al., 2014). It is important to note that neither of these

criteria is equivalent to hypothesis testing. Instead, they simply provide guidance on which

model to choose.

easyGWAS also provides variance-explained estimates for the null model when using linear

mixed models, such as EMMAX (Kang et al., 2010), or FaST-LMM (Lippert et al., 2011)


Comparative GWAS Intersection Analysis View

easyGWAS offers two types of comparative analyses of GWAS. The first type is the search

for associations at the SNP or gene level that were found to be significant in more than one

dataset; in the following we refer to this form of comparative search for intersecting

association hits as intersection analysis. The second type includes meta-analyses that

aggregate evidence from several datasets to find associations that are jointly supported by

these datasets. Here we describe easyGWAS’s Comparative Intersection Analysis View for

intersection analyses. More information on the available meta-analysis approaches can be

found in the “Meta-Analysis Methods” section in “Methods and Materials”.

easyGWAS enables intersection analyses on the results of a set of GWAS and provides

three comparison views: the “Pairwise Comparison View” for comparing results on pairs of

GWAS including overlaid Manhattan plots; the “Shared Associations View” which represents

shared SNPs among the top-x associated SNPs of all GWAS; and the “Shared Genes View”,

which lists all genes containing a significantly associated SNP in at least one GWAS. The

pairwise comparison provides several dynamic visualizations to support the investigation of

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results for all possible pairs from a set of GWAS results (see Figure 4, Supplemental Text

4).

Initial insights into whether traits might have a common genetic basis come from assessing

how different traits in a population correlate with each other, as shown in easyGWAS’s

phenotype-phenotype correlation plot. The cause for this correlation may be population

structure; that is, systematic ancestry differences between different phenotypic classes. To

make the user conscious of this source of confounding, easyGWAS highlights phenotypes

that are significantly associated to kinship in red (for computational details, see

Supplemental Text 7).

The phenotype-phenotype scatter plot (Figure 4B), sample overlap diagram (Figure 4C)

and the phenotype-phenotype Manhattan plot (Figure 4D) are dynamically updated

according to the position of the cursor on the correlation plot (Figure 4A). Users can

dynamically investigate results from different GWAS experiments.

The second tab at the top of Figure 4, “Shared Associations”, shows all shared associations

between selected experiments for the top x-associated SNPs per GWAS experiment as a

chord diagram, where x is a parameter that can be chosen by the user dynamically (see

Supplemental Text 5). The third window gives an overview of all genes across all

experiments that contained an associated hit (Supplemental Text 5). Again, users can

dynamically change the multiple hypothesis correction method in all windows.

Runtime Analysis

We compared the runtimes of four popular genome-wide association tools and methods – a

linear regression (PLINK v1.0.7; Purcell et al., 2007), logistic regression (PLINK v1.0.7;

Purcell et al., 2007), EMMAX (Kang et al., 2010), and FaST-LMM (Lippert et al., 2011) – to

those implemented in the easyGWASCore framework. We used real genotype data from the

1001 Genomes Project in A. thaliana (The 1001 Genomes Consortium, 2016) and random

continuous and binary phenotypes for the analysis. For all experiments, the number of SNPs

varied from 10,000 to five million and the number of samples from 100 to 500. Data in PLINK

format were used to conduct a fair comparison between all tools and methods. Real CPU

runtime in seconds were reported over a single AMD Opteron CPU (2048 KB, 2600MHz)

with 512 GB of memory, running Ubuntu 12.04.5 LTS (Supplemental Figure 13). We

observe that all algorithms implemented in easyGWAS, except for logistic regression, are at

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least as efficient as the tools to which we compared them. The results show that easyGWAS

can compute GWAS with standard models such as linear regression within a few minutes for

up to five million SNPs and up to 500 samples. More complex models, such as FaST-LMM

or EMMAX, only take minutes for approximately 100 samples and a few hours for up to 500

samples.

Case Study in Arabidopsis thaliana

To demonstrate the usability of easyGWAS, we analyzed nine flowering time- and growth-

related traits of A. thaliana. Phenotypes were scored for up to 936 of 1135 accessions ((The

1001 Genomes Consortium, 2016); see Methods). We first used a standard linear mixed

model (FaST-LMM by Lippert et al., 2011) to perform GWAS on these nine phenotypes while

accounting for potential confounding effects due to population stratification (see Methods).

All results, including Manhattan plots and QQ-plots, are publicly available online at:

https://easygwas.ethz.ch/gwas/myhistory/public/14/.

Next, we used the comparison functions of easyGWAS to combine the results for all traits.

We identified a total of six significantly associated hits, when using Bonferroni correction to

control the FWER at α=5% (see Supplemental Table 1). Multiple testing correction methods

based on the false discovery rate (FDR; see, for example, Storey and Tibshirani (2003)) are

less conservative than Bonferroni correction and are often used in GWAS. Using Storey and

Tibshirani’s approach (α=5%), we identified a total of 254 significantly associated hits across

all experiments; that is, associated with at least one phenotype. A total of 87 significantly

associated hits are shared by at least two of the following five different phenotypes: flowering

time as days until emergence of visible flowering buds in the center of the rosette from time

of sowing (DTF1); flowering time as days until the inflorescence stem elongated to 1 cm

(DTF2); flowering time as days until first open flower (DTF3); rosette leaf number (RL); and

cauline leaf number (CL) (Supplemental Data Set 1).

This is not surprising given that these five phenotypes are highly correlated with each other

(Pearson's r2 between 0.67 and 0.99), as shown in Figure 5 or online at: https://easygwas.ethz.ch/comparison/results/manhattan/view/2c8da231-96ff-4f28-a17e-fd0e3510d8e1/.

Of the 254 significant hits across all experiments using Storey and Tibshirani’s multiple

hypothesis correction, 250 are located on chromosomes 4 and 5. Significantly associated

hits are distributed across 30 different genes on four different chromosomes (1, 2, 4, and 5),

as shown in the Shared Genes view in easyGWAS and in Supplemental Data Set 1. The

87 shared peaks include the dormancy regulator DOG1 (At5g45830), which has recently

been shown to affect flowering time (Huo et al., 2016); FLOWERING LOCUS C (FLC,

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At5G10140) and FRIGIDA (At4G00650), which are linked to flowering time variation

(Michaels and Amasino, 1999, 2001; Méndez-Vigo et al., 2013, 2016; Sanchez-Bermejo and

Balasubramanian, 2016); and ANTHOCYANINLESS 2 (ANL2, At4G00730), which is

involved in root development (Kubo and Hayashi, 2011).

Further, easyGWAS makes it possible to investigate the LD and gene information in close

proximity to a focal SNP. For example, three significantly associated hits for the RL

phenotype map to chromosome 1 and are in close proximity to the flowering regulator

FLOWERING LOCUS T (FT, AT1G65480), as shown in Figure 6 and online using the

easyGWAS Detailed SNP view: https://easygwas.ethz.ch/gwas/results/snp/detailed/57a7cf18-cb0f-408a-8954-49f94d1bfc47/Chr1/24338990/

FT shows remarkably little variation within its coding sequence. However, recent QTL fine-

mapping efforts have highlighted the contribution of cis-regulatory polymorphisms in natural

variation, including the flowering genes FT and FLC. For instance, a causal polymorphism

was mapped not to the FT coding region, but to the FT promoter of the Est-1 FT allele,

conferring delayed flowering relative to the Col-0 reference (Schwartz et al., 2009). A larger

study found that FT promoter length varies and correlates with flowering time, while the FT

coding sequence remains unchanged (Liu et al., 2014).

Similarly, FLC-dependent variation in flowering time often maps to promoter elements,

resulting in expression differences between alleles, both in A. thaliana and Brassica oleracea

(Irwin et al., 2016; Li et al., 2014). Such variation caused by cis-regulatory polymorphisms is

not limited to flowering time, as additional cases have been reported for (a) zinc homeostasis

conferred by expression changes in the FRD3 MATE transporter (Pineau et al., 2012), and

(b) sulfur homeostasis associated with the ATP sulfurylase ATPS1 (Koprivova et al., 2013).

These studies underscore the potential contribution that cis-regulatory changes can make to

natural variation, which is already reflected in the number of expression QTL (eQTL)

detected in selected recombinant populations (Cubillos et al., 2012).

Polymorphisms in non-coding regions can also result in phenotypic variation, as

demonstrated by a naturally occurring SNP affecting splicing of the FLC antisense transcript

COOLAIR in some A. thaliana accessions (Li, Tao and Dean, 2015). It is conceivable that

additional examples will be identified in the future, making full use of whole genome

sequences from thousands of accessions. Interestingly, the three SNPs reported by

easyGWAS for the RL phenotype are found to overlap within several non-coding RNAs

downstream of FT (At1NC09610, At1NC09620 and At1NC09630; see Supplemental Figure

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14) (Liu et al., 2012). This raises the possibility that these non-coding RNAs may act as

enhancer elements and play roles in modulating FT expression level, perhaps in a

mechanism similar to the PINOID/APOLO regulatory pair of loci, whereby the lncRNA

APOLO regulates the expression of PINOID via chromatin looping (Ariel et al., 2014). As in

any mapping project, of EMS-induced mutations or natural variation, further analysis will help

shed light on the mechanisms underlying the phenotype of interest.

Importantly, easyGWAS eliminates the need for complicated visualization scripts by

providing them automatically and interactively. All plots, including Manhattan plots, LD plots,

and the phenotype-phenotype correlation plot, can be explored by following the link to this

public easyGWAS project: https://easygwas.ethz.ch/gwas/myhistory/public/14/. We also

provide a list of links in Supplemental Table 2to access the individual experiments directly.

Discussion

We have introduced easyGWAS, a cloud platform that not only allows the computation,

annotation, and subsequent analysis of GWAS, but, most importantly, also offers the unique

feature of comparing results from GWAS across different experiments and datasets (Figure

1).

The constant increase of publicly available genotype and phenotype data (Li J.-Y. et al.,

2014; The 1001 Genomes Consortium, 2016) creates a demand for tools that enable

biologists to compare the results of multiple GWAS in order to facilitate the identification of

associations shared between related and/or correlated phenotypes. Such tools can lead to

new biological insights, such as a common genetic architecture of related phenotypes or the

seemingly unrelated functions of a gene due to pleiotropic effects. easyGWAS is currently

the only tool that offers a variety of methods to compare GWAS. We have also described the

publishing capabilities of easyGWAS that make it possible to share results between

collaborators. Another salient feature is the novel interactive visualizations that aim to

explore and compare the results of GWAS, not only for a single analysis but also across

different datasets and samples.

While current web applications for GWAS are typically limited to a single species, such as A.

thaliana (Childs et al., 2012; Seren et al., 2012), or Drosophila melanogaster (Mackay et al.,

2012), easyGWAS supports multiple species in a single platform. easyGWAS also provides

an added functionality that is absent in the above-mentioned web applications: users can

upload private GWAS datasets for any species of choice (genotype, phenotype and

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covariate data), custom gene annotation sets as well as summary statistics from offline

analyses; for example, from PLINK (Purcell et al., 2007) or from a custom user tool. While it

is technically possible to also analyze human data, human genotype data must only be

uploaded if this is explicitly allowed by the legal body governing data access.

We demonstrated some of the potential of easyGWAS in a case study in the model

organism Arabidopsis thaliana on nine newly measured phenotypes and a population of

1135 recently sequenced lines (The 1001 Genomes Consortium, 2016). Conducting such an

analysis without easyGWAS would be a time-consuming and cumbersome process. While

performing standard GWAS is nowadays facilitated by many tools, such as PLINK (Purcell et

al., 2007), EMMAX (Kang et al., 2010) and FaST-LMM (Lippert et al., 2011), these tools

often ignore the annotation of the results. Web applications such as GWAPP (Seren et al.,

2012) can be used to get on-the-fly annotations of GWAS results, but they are limited to a

specific species only.

easyGWAS simplifies the execution of GWAS and the comparative analysis of their results,

even for users who have never worked on GWAS before. It is important to note, however,

that there are several errors that can be made and biases that can be introduced

inadvertently when integrating results of different GWAS. For example, a common

assumption in a meta-analysis is the independence of samples in the studies to be

combined. If this assumption is violated – for example, because of overlap of a subset of

individuals across studies – this may result in spurious associations (Lin and Sullivan, 2009;

Zaykin and Kozbur, 2010). Similarly, in a comparative study, differences in sample size

between datasets can have an adverse effect in an intersection analysis. In this case, true

associations may be too weak to be detected in the smaller datasets, which will result in

false negative findings when the results of the studies are intersected. Therefore, it is

imperative that users of easyGWAS make themselves familiar with these pitfalls, which we

summarize in Supplemental Table 3. To increase awareness of these issues, easyGWAS

notifies each user of these potential problems before the submission of a comparative

analysis.

In future releases we will extend the application programming interface (API) of easyGWAS

that currently uses the Representational State Transfer (REST) web service to facilitate the

exchange of phenotypic and genotypic data between different web platforms (Supplemental

Text 11). This is especially important in order to keep not only public phenotypes but also

genotypes synchronized between easyGWAS and other species-specific web applications,

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such as GWAPP (Seren et al., 2012) or AraPheno (Seren, Grimm et al., 2016). In addition,

this API will allow users to acquire additional information and meta-information using their

custom scripts or analysis pipelines. A similar feature that is already available is

easyGWAS’s ability to automatically import public phenotypes from AraPheno, a central

repository for population scale phenotype data from Arabidopsis thaliana (Seren, Grimm et

al. 2016). easyGWAS currently supports comparative analyses on one-dimensional

phenotypes through intersection analyses and meta-analyses. In future work, we plan to

integrate methods for mapping several phenotypes simultaneously in multi-trait analyses

(Korte et al., 2012, Lippert et. al., 2014). Further plans include adding methods for multi-

locus mapping (Azencott et al., 2013; Llinares-López et al., 2015), and providing support for

replicated phenotypic measurements as well as complex and high dimensional data, such as

methylome and transcriptome data.

Methods and Materials

Genome-Wide Association Mapping Methods

The goal of a genome-wide association study is to detect genomic regions that are

associated with a trait in a cohort of individuals. This is achieved by computing statistical

associations between the trait and genetic variants in the form of SNPs. Of the many

methods used to compute this statistical association (see Bush and Moore (2012) for a

complete review) five have been implemented in easyGWAS: a Wilcoxon rank-sum test for

homozygous genotype data; a linear regression and logistic regression for homozygous and

heterozygous genotype data; and two linear mixed models, EMMAX (Kang et al., 2010), and

FaST-LMM (Lippert et al., 2011) to account for confounding due to population stratification,

family structure, and cryptic relatedness. The remainder of this subsection discusses these

methods in detail.

As mentioned above, the Wilcoxon rank-sum test can only be used with biallelic SNPs.

The easyGWAS wizard will not offer this method when the genotype data do not meet this

requirement. This test measures the difference in the distributions of phenotypic values

between the alleles, under the null hypothesis of equal distributions for both alleles.

In a linear regression there is an implicit assumption that the phenotype is normally

(Gaussian) distributed and that it can be modeled as a linear (additive) combination of a set

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of terms, where one term is the list of genotypes of a given SNP and other (optional) terms

may include covariates to correct for confounders.

Logistic regression makes a similar assumption about the additive effects of genotype and

covariates, but unlike linear regression (which can be used for continuous phenotypes),

logistic regression is applied in analyses where the phenotype is binary; for example, in

case/control studies.

The default method for association testing in easyGWAS is the linear mixed model, which

results in an improvement over linear regression in that the phenotype is measured as a sum

of fixed and random effects. The linear mixed model (LMM) has historically been used to

identify genetic associations between a phenotype of interest and a group of individuals

when the relationship between the individuals is known. This model can effectively quantify

the association of a genetic variant to the phenotype (fixed effect) while correcting for the

familial structure in the data (random effect). However, the LMM has gained popularity in

recent years in GWAS when there is no pedigree of the individuals. Although it may be

tempting to assume that the individuals in a study are unrelated – that is, it may be a

requirement in the collection or recruiting process – it is still possible that cryptic relatedness

may be present in the data due to the fact that some individuals may share a common

ancestry. If this relatedness is not taken into account, spurious associations may arise (Price

et al., 2006). To correct for such confounding structure in the data, a LMM will estimate the

genetic similarity between all pairs of individuals in the study. In easyGWAS, this similarity is

modeled with the kinship matrix, which is estimated by computing the realized relationship

matrix (Hayes et al., 2009) for both EMMAX (Kang et al., 2010) and FaST-LMM (Lippert et

al., 2011). easyGWAS also enables users to include principal components as part of a linear

regression, logistic regression or LMM. When only population stratification is present, this

strategy of adding principal components into the model has been shown to yield more power

than a standard LMM (Zhao et al., 2007; Widmer et al., 2014).

In addition to the standard implementation of the above-mentioned algorithms, easyGWAS

offers permutation-based versions of linear regression, logistic regression, and EMMAX. The

added value of these additional functionality is that it allows for an empirical estimation of the

true null distribution. Covariates can be easily added to any model (except for the Wilcoxon

Rank-Sum Test) to account for confounding effects, such as environmental factors or sex.

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Finally, easyGWAS provides several means of encoding genotype data. Four genotype

encodings have been implemented for heterozygous phenotypes to allow for the testing of

different allelic effects. The standard encoding is based on what is known as the “additive

model” where the major allele is encoded as 0, the heterozygous allele as 1, and the minor

allele as 2 (Supplemental Table 4). However, the recessive genotype encoding encodes

the major and heterozygous allele as 0 and the minor allele as 1. The dominant genotype

encoding encodes the major allele as 0 and the remaining two alleles as 1, whereas the

overdominant (or co-dominant) encoding encodes the major and minor allele as 0 and the

heterozygous allele as 1 (Supplemental Table 4). All algorithms have been implemented in

a custom C/C++ framework called easyGWASCore, which comes with Python interfaces to

allow for easy integration into our web framework.

Transformation Methods

Several transformation methods have been implemented in easyGWAS to improve the

normality of phenotypic measurements and covariates. Supplemental Text 8 provides

additional details about the motivation behind the use of transformation methods, as well as

detailed descriptions of each method. A summary of all possible transformations in

easyGWAS and the type of measurements to which they can be applied is shown in

Supplemental Table 5. In addition, a Shapiro-Wilk test is provided to test the null

hypothesis if the data were drawn from a normal distribution (Shapiro and Wilk, 1965).

Meta-Analysis Methods

Historically, meta-analysis arose due to the need to pool results from different studies.

Although its origins are rooted in combining the results from independent clinical trials, meta-

analysis has been successfully used as a tool to combine or integrate the results from

different GWAS. The rationale behind this is that, due to their limited sample size, GWAS are

underpowered and the meta-analysis is a means to increase the overall power and to reduce

false positives (Evangelou and Ioannidis, 2013).

easyGWAS implements a variety of different meta-analysis methods that can be used to

combine results from several conducted GWAS on distinct sets of samples/cohorts. Five

state-of-the-art algorithms are part of the easyGWASCore framework: (1) Fisher’s method to

combine p-values from several studies (Fisher, 1925); (2) Stouffer’s Z to combine Z-scores

derived from the studies’ p-values (Stouffer et al., 1949); (3) Stouffer’s weighted Z to weight

each study based on the square root of the number of samples (Stouffer et al., 1949); (4) a

fixed-effect model to combine effect estimates while assuming that they are fixed for each

15

study (Borenstein et al., 2010, 2011); and (5) a random effect model which assumes that

they arise randomly (Borenstein et al., 2010, 2011). See Supplemental Text 9 for more

details about these methods, their assumptions, and guidelines on how to apply them.

Web Application Details

The easyGWAS web application is written in Python and builds upon the Python Django

(https://www.djangoproject.com) and easyGWASCore frameworks running on an open-

source Apache HTTP server (http://httpd.apache.org). Bootstrap (http://getbootstrap.com), a

popular HTML5, CSS and JavaScript (JS) framework, provides a modern and state-of-the-

art front end. To create dynamic and interactive visualizations, we used the JS library D3.js

(http://d3js.org). Asynchronous JavaScript and XML (AJAX) allows dynamic interactions and

updates to the front end without reloading the whole webpage. To schedule and distribute

long-running and file-system-intensive tasks across different computation nodes, such as

performing GWAS or generating data for dynamic visualizations, the message-passing

system RabbitMQ (http://rabbitmq.com) is used together with the asynchronous task queue

Celery (http://celeryproject.org). Thus, easyGWAS is highly scalable and can be quickly

extended to a larger number of users by adding additional computation nodes.

Reliable and fast data storage is essential for handling user-generated data as well as

genotype datasets with hundreds of samples and potentially hundreds of thousands of

genetic markers. Therefore, we developed a hybrid database model that is a combination of

a PostgreSQL database, user-specific SQLite databases, and HDF5 files. General

information about users, datasets, or GWA projects is stored in the PostgreSQL database.

Genotype, phenotypes, covariate data, and results are stored in HDF5 files and linked to the

user profiles in the PostgreSQL database. User-specific gene annotation sets are stored in

user specific SQLite database files such that efficient queries for different sets are ensured

(Supplemental Figure 12). Currently, easyGWAS is running on an Ubuntu-based machine

with 64 CPUs and 512GB of memory.

Data Download, Upload and Sharing

To simplify data handling, all publicly available datasets can be easily downloaded in the

commonly used PLINK format (Purcell et al., 2007) using the easyGWAS download

manager. To integrate newly sequenced genotype data, phenotypic measurements (in

PLINK format), or custom gene annotation sets (in GFF format), the easyGWAS upload

manager can be used by registered users (Supplemental Text 1 or online FAQ). Users

can also create new species that are not already available in easyGWAS. Furthermore,

16

easyGWAS supports the automatic import of public phenotypes from AraPheno, a central

repository for population scale phenotype data from Arabidopsis thaliana (Seren, Grimm et

al. 2016). In addition, GWA projects can be easily shared with other registered users

(Supplemental Text 3 or online FAQ).

Furthermore, we provide a REST web service that allows users to query and get data/meta-

information from easyGWAS in a programmatic way, or simply via URLs in a web browser


Publicly Available Data

As of December 2016, easyGWAS provides publicly available genotype, phenotype, and

gene annotation data for the species Arabidopsis thaliana, Drosophila melanogaster, and

Pristionchus pacificus. Additional data for other species will be added and included in the

future.

For A. thaliana, datasets from various studies have been integrated (Atwell et al., 2010; Cao

et al., 2011; Horton et al., 2012; Long et al., 2013; Schmitz et al., 2013; The 1001 Genomes

Consortium, 2016; Seymour et al., 2016). The first dataset (AtPolyDB) includes 1307

worldwide A. thaliana accessions with a total of 214,051 SNPs genotyped with a 250k SNP

chip (Horton et al., 2012). In addition, 107 binary, continuous, and categorical phenotypes

have been integrated for a subset of these 1307 accessions (Atwell et al., 2010). The

phenotypic data comprises (1) flowering-time-related traits, (2) defense-related traits, (3)

ionomic traits and (4) developmental-related traits. The second dataset (80 genomes data)

includes 80 accessions from the first phase of the 1001 Genomes Project in A. thaliana (Cao

et al., 2011). SNP data were retrieved from the original genome matrix from the 1001

Genomes website. All singletons and SNPs with incomplete information were removed,

which resulted in a final subset of 1,438,752 SNPs. Third, we included 1135 samples and

6,973,565 non-singleton SNPs (1001 Genomes Data) from the final phase of the 1001

Genomes Project (The 1001 Genomes Consortium, 2016). Lastly, we included 372 in silico

F1 hybrid genotypes generated from parental genome sequences (Cao et al., 2011) with a

total of 204,753 SNPs (Seymour et al., 2016). We also integrated the TAIR9 and TAIR10

gene annotation sets.

For Drosophila melanogaster, we integrated the Drosophila Genetic Reference Panel

(DGRP) with a total number of 172 samples, 2,476,799 SNPs, and three phenotypes split

into male and female, making a total of six (Harbison et al., 2004; Morgan and Mackay,

2006; Jordan et al., 2007; Mackay et al., 2012). Missing SNPs in the Drosophila

17

melanogaster genome are imputed using a majority allele imputation. Gene annotations

were downloaded from the FlyBase website and integrated into easyGWAS (Attrill et al.,

2016).

For the species Pristionchus pacificus, a total of 149 samples with 2,135,350 SNPs were

integrated (McGaughran et al., 2016). The dataset comes with two binary and one

categorical phenotypes, as well as four categorical covariates.

Availability and Requirements of easyGWAS

The easyGWAS web application requires a modern internet browser that supports HTML5

and JavaScript (e.g., Google Chrome 47.0, Firefox 43.0). The web application can be

accessed online at: https://easygwas.ethz.ch.

Code for the algorithmically part of easyGWAS can be freely downloaded at:

https://github.com/dominikgrimm/easyGWASCore.

Data and Experimental Settings for the Arabidopsis thaliana Case Study

Phenotype Scoring. Seeds for 1135 Arabidopsis thaliana accessions (The 1001 Genomes

Consortium, 2016) were surface-sterilized in 95% ethanol for five minutes and allowed to air-

dry. After six days of stratification in the dark at 4ºC in 0.1% agarose, seeds were distributed

across 4800 pots as four replicates in a randomized block design, with each replicate

corresponding to one block. Plants were grown in controlled growth chambers with the

following settings: 16 hours light/eight hours darkness, 16ºC constant temperature, 65%

humidity. All trays within a block were moved to a new shelf and rotated 180ºC every other

day to minimize position effects. Flowering time was scored as days until the emergence of

visible flowering buds in the center of the rosette from time of sowing (DTF1), days until the

inflorescence stem elongated to 1 cm (DTF2), and days until first open flower (DTF3). For

some accessions, the inflorescence stem did not reach 1 cm before scoring of DTF3. In

addition, rosette leaf number (RL), cauline leaf number (CL), diameter of rosette (end point,

after flowering) (Diameter), rosette branch number (RBN), cauline leaf number (CBN), and

length of main flowering stem (Length) were recorded (Supplemental Table 2).

Genome-Wide Association Mapping. All phenotypes were uploaded for the 1001

Genomes A. thaliana dataset (Supplemental Text 1 or online FAQ). GWAS were

conducted for all phenotypes and the 1001 Genomes dataset using the easyGWAS wizard

(Supplemental Text 2 or online FAQ). All phenotypes were Box-Cox transformed (Box and

18

Cox, 1964) to improve the measurements’ normality, except for RBN (no transformation) and

CBN (square root transformation) (Supplemental Table 6). A minor allele frequency (MAF)

filter of 5% was applied for all experiments and a standard additive genotype encoding was

chosen. To account for confounding due to population stratification and cryptic relatedness,

we used FaST-LMM (Lippert et al., 2011), estimating the genetic similarity between all

genotypes by computing the realized relationship kinship matrix (Hayes et al., 2009). All

results are stored in a publicly accessible GWAS project and can be found at:

https://easygwas.ethz.ch/gwas/myhistory/public/14/.

Intersection Analysis of Genome-Wide Association Experiments. Results of all nine

conducted GWAS experiments were compared using the easyGWAS comparison wizard to

identify shared associations between phenotypes. Results are stored in a publicly available

project and can be accessed via:

https://easygwas.ethz.ch/comparison/results/manhattan/view/2c8da231-96ff-4f28-a17e-

fd0e3510d8e1/.

Accession Numbers The following Arabidopsis Genome Initiative locus identifiers are reported: DOG1 (At5g45830), FLC

(At5G10140), FRIGIDA (At4G00650), ANL2 (At4G00730), FT (AT1G65480), ATPS1 (AT1G34355),

and PINOID (AT2G34650). Sample information can be found online:

https://easygwas.ethz.ch/data/public/samples/1/7/

All performed GWAS for the case study can be found:

https://easygwas.ethz.ch/gwas/myhistory/public/14/

Supplemental Data

Supplemental Figure 1. Public and Private Data Repository.

Supplemental Figure 2. Data Repository and Detailed Species View.

Supplemental Figure 3. Detailed Sample View.

Supplemental Figure 4. Detailed Phenotype View.

Supplemental Figure 5. Download Manager.

Supplemental Figure 6. Upload Manager.

Supplemental Figure 7. GWAS Wizard, Step to Normalize Phenotypes.

Supplemental Figure 8. Temporary History.

Supplemental Figure 9. Save Experiments Permanently into GWAS Projects.

Supplemental Figure 10. easyGWAS Data Sharing Dialog.

Supplemental Figure 11. GWAS Project Publishing Inquiry Form.

19

Supplemental Figure 12: Schematics of the easyGWAS Architecture.

Supplemental Figure 13: Runtime Comparison between State-of-the-Art Tools and

easyGWAS.

Supplemental Figure 14: Non-coding RNAs downstream of FT (AT1G65480).

Supplemental Table 1. Case Study Results in Arabidopsis thaliana using

Bonferroni.

Supplemental Table 2. Phenotype Information for the Case Study.

Supplemental Table 3. Pitfalls when conducting intersection analyses or metaanalyses

of GWAS results

Supplemental Table 4. Available Genotype Encodings.

Supplemental Table 5. Transformation Methods.

Supplemental Table 6. Phenotype Information for the Case Study.

Supplemental Text 1: easyGWAS Data Repository

Supplemental Text 2: easyGWAS Wizard

Supplemental Text 3: easyGWAS GWAS History

Supplemental Text 4: Step-by-Step Procedures to Reproduce the

Content of Figures

Supplemental Text 5: Tips on how to Access Certain Panels

Supplemental Text 6: Variance Explained for Linear Mixed Models

Supplemental Text 7: Procedure to Measure Dependence Between

Phenotype and Population Structure

Supplemental Text 8: Transformation Methods

Supplemental Text 9: Meta-Analysis

Supplemental Text 10: Correction methods for multiple hypothesis

testing

Supplemental Text 11: REST Interface

Supplemental Data Set 1: Significantly Associated Phenotypes

AcknowledgmentsThis study was funded by the DFG (SPP1529 ADAPTOMICS, DW), ERC (AdG IMMUNEMESIS,

DW), the Max Planck Society (DW, KB), and by the Alfried Krupp Prize for Young University Teachers

of the Alfried Krupp von Bohlen und Halbach-Stiftung (KB). The authors wish to thank the IT

departments at the Max Planck Campus Tübingen and the ETH Zürich for hosting easyGWAS and for

their continued support.

Author Contributions

20

DGG and KMB conceived and designed the study. DGG implemented the back- and front-end of the

web application. DR, SK and BG contributed to the development of various modules. PAS measured

the phenotypes and contributed to the data analysis. WZ and C. Liu contributed the analysis of the

biological results. OS and C. Lippert contributed to the development of the statistical analysis pipeline.

DGG performed all computational experiments for the case-study. DW and BS gave statistical and

biological advice for the development of the web application and the conducted experiments. DGG,

DR and KMB wrote the paper with input from all authors.

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Figure Legends

Figure 1. Illustration of the functionalities of easyGWAS in comparison with other

current online GWAS tools. Squares illustrate supported functionalities and ovals illustrate

supported data types that can be uploaded to easyGWAS. White objects are supported by

all available web servers but hatched objects are only partially supported. Blue objects are

only supported by easyGWAS.

Figure 2. Screenshot of the easyGWAS Result Layout.

The screenshot shows the general layout of the easyGWAS result view. The top left panel

(A) is the “GWAS Center” menu with links to different wizards and experiment tables, e.g. to

create new GWAS, meta-analysis or comparative intersection experiments. The top right

27

panel (B) is a sub-menu for the GWAS results to navigate between Manhattan plots, QQ-

plots, SNP annotations or an experiment summary. (C) Shows general options to

dynamically adjust the multiple hypothesis testing method or various different plotting

options. The main results of a GWAS, meta- or intersection analysis are displayed in panel

(D). In this screenshot, Manhattan plots are shown. In panel (E), a brief summary of the most

important experimental parameters is shown. This can be either a summary of a regular

GWAS experiment, a meta-analysis or a comparison of several GWAS. If available, the top

10 associated hits of a GWAS are shown in panel (F).

Figure 3. Screenshot of the easyGWAS Detailed SNP View.

The “Detailed SNP” view of a SNP gives more detailed information and annotation about the

selected SNP and its close neighborhood, illustrated in this screenshot. Panel (A) shows a

donut diagram with the allele distribution of the selected SNP. In panel (B) a boxplot with the

trait values for each allele is shown. The distribution of genes and the linkage disequilibrium

(LD) pattern around the focal SNP is shown in panel (C). The panel at the bottom shows

more detailed annotations for the focal SNP, e.g. if a SNP is a missense variant, frameshift,

or stop-codon.

Figure 4. Screenshot of the easyGWAS Pairwise Comparison View.

The screenshot illustrates the general layout of the pairwise comparison view of different

GWAS. In panel (A) a phenotype-phenotype correlation plot is shown, while phenotype

names highlighted in red are significantly associated with population structure. Hovering over

any correlation point in the plot will dynamically update the phenotype-phenotype scatter plot

in panel (B). The phenotype-phenotype scatter diagram plots the measurements of both

phenotypes against each other. In panel (C) a Venn diagram is shown to illustrate the

sample overlap between the two phenotypes. The bottom panel (D) shows the Manhattan

plots for both GWAS on top of each other.

Figure 5. Phenotype-Phenotype Correlation Plot for Case Study

Phenotype-phenotype correlation plot showing the pairwise Pearson’s correlation

coefficients between all phenotypes for the case study in Arabidopsis thaliana. Five of the

phenotypes are highly correlated to each other: flowering time as days until emergence of

visible flowering buds in the center of the rosette from time of sowing (DTF1); flowering time

as days until the inflorescence stem elongated to 1 cm (DTF2); flowering time as days until

first open flower (DTF3); rosette leaf number (RL); and cauline leaf number (CL).

Phenotypes highlighted in red are significantly associated with population structure.

28

Figure 6. Linkage Disequilibrium Plot for SNP Chr1:24338990 for Phenotype RL

Three SNPs for the phenotype RL are significantly associated using Storey and Tibshirani’s

correction for multiple hypothesis testing. These hits are in close proximity to the FT gene.

easyGWASConduct GWAS

Meta-analysis of GWAS

Comparison of GWAS

Result & Data Sharing

Multiple species

Phenotype data

Genotype data (PLINK Format)

Covariate data

Results from offline tools (e.g. PLINK)

Gene annotation files (e.g. GFF)

Public and Private data repository

Supported functionality Supports data upload

Only supported by easyGWASPartially supported by other web-servers

Various correction methods for multiple hypothesis testing

Figure 1. Illustration of the functionalities of easyGWAS in comparison with other current online GWAS tools.

Squares illustrate supported functionalities and ovals illustrate supported data types that can be uploaded to easyGWAS. White

objects are supported by all available web servers while hatched objects are only partially supported. Blue objects are only

supported by easyGWAS.

A B

C

D

E

F

Figure 2. Screenshot of the easyGWAS Result Layout.

The screenshot shows the general layout of the easyGWAS result view. The top left panel (A) is the “GWAS Center” menu with links to

different wizards and experiment tables, e.g. to create new GWAS, meta-analysis or comparative intersection experiments. The top right

panel (B) is a sub-menu for the GWAS results to navigate between Manhattan plots, QQ-plots, SNP annotations or an experiment

summary. (C) shows general options to dynamically adjust the multiple hypothesis testing method or various different plotting options.

The main results of a GWAS, meta- or intersection analysis are displayed in panel (D). In this screenshot, Manhattan plots are shown. In

panel (E), a brief summary of the most important experimental settings is shown. This can be either a summary of a regular GWAS

experiment, a meta-analysis or a comparison of several GWAS. If available, the top 10 associated hits of a GWAS are shown in panel

(F).

Figure 3. Screenshot of the easyGWAS Detailed SNP View.

The “Detailed SNP” view of a SNP gives more detailed information and annotation about the selected SNP and its close neighborhood,

illustrated in this screenshot. Panel (A) shows a donut diagram with the allele distribution of the selected SNP. In panel (B) a boxplot with

the trait values for each allele is shown. The distribution of genes and the linkage disequilibrium (LD) pattern around the focal SNP is

shown in panel (C). The panel at the bottom shows more detailed annotations for the focal SNP, e.g. if a SNP is a missense variant,

frameshift, or stop-codon.

A B

C

D

A B

C

D

Figure 4. Screenshot of the easyGWAS Pairwise Comparison View.

The screenshot illustrates the general layout of the pairwise comparison view of different GWAS. In panel (A) a phenotype-phenotype

correlation plot is shown, while phenotype names highlighted in red are significantly associated with population structure. Hovering over

any correlation point in the plot will dynamically update the phenotype-phenotype scatter plot in panel (B). The phenotype-phenotype

scatter diagram plots the measurements of both phenotypes against each other. In panel (C) a venn diagram is shown to illustrate the

sample overlap between the two phenotypes. The bottom panel (D) shows the Manhattan plots for both GWAS on top of each other.

DTF2DTF3

DTF1CBN

RBNCL

RL

Diamet

er

Length

DTF2DTF3DTF1CBNRBNCL RL Diam

eter

Length

Figure 5. Phenotype-Phenotype Correlation Plot for Case Study

Phenotype-phenotype correlation plot showing the the pairwise Pearson’s correlation

coefficients between all phenotypes for the case study in Arabidopsis thaliana. Five of

the phenotypes are highly correlated to each other: : flowering time as days until

emergence of visible flowering buds in the center of the rosette from time of sowing

(DTF1); flowering time as days until the inflorescence stem elongated to 1 cm (DTF2);

flowering time as days until first open flower (DTF3); rosette leaf number (RL); and

cauline leaf number (CL). Phenotypes highlighted in red are significantly associated with

population structure.

Pea

rson

Cor

rela

tion

Figure 6. Linkage Disequilibrium Plot for SNP Chr1:24338990 for Phenotype RL

Three SNPs for the phenotype RL are significantly associated using Storey and Tibshirani’s correction for multiple hypothesis testing.

These hits are in close proximity to the FT gene.

FTR

2 [c

orre

latio

n]

Position in bp

R2

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DOI 10.1105/tpc.16.00551; originally published online December 16, 2016;Plant Cell

Karsten BorgwardtWangsheng Zhu, Chang Liu, Christoph Lippert, Oliver Stegle, Bernhard Schölkopf, Detlef Weigel and

Dominik G. Grimm, Damian Roqueiro, Patrice Salome, Stefan Kleeberger, Bastian Greshake,Studies

easyGWAS: A Cloud-based Platform for Comparing the Results of Genome-wide Association

This information is current as of December 22, 2020

Supplemental Data /content/suppl/2017/02/06/tpc.16.00551.DC2.html /content/suppl/2016/12/16/tpc.16.00551.DC1.html

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easyGWAS: A Cloud-based Platform for Comparing the Results of … · 2016. 12. 16. · information in species such as Arabidopsis thaliana (The 1001 Genomes Consortium, 2016). The