easyGWAS: A Cloud-Based Platform for Comparing the Results of Genome-Wide Association Studies

Grimm, Dominik G.; Roqueiro, Damian; Salomé, Patrice A.; Kleeberger, Stefan; Greshake, Bastian; Zhu, Weiren; Liu, Chang; Lippert, Christoph; Stegle, Oliver; Schoelkopf, Bernhard; Weigel, Detlef; Borgwardt, Karsten

doi:10.1105/tpc.16.00551

Cited by 108 publications

(105 citation statements)

References 88 publications

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“…Although this is rarely done in the analysis of continuous traits, a few studies have reported associations of heterotic traits with heterozygosity (41)(42)(43)(44)(45). We selected two linear mixed models to search for associations between genotype and phenotype using FaSTLMM software in the easyGWAS framework (46,47). The first model used a standard linear additive SNP encoding, where the homozygous major allele was represented as 0, the heterozygous as 1, and the homozygous minor allele as 2; we refer to it as the "additive model."…”

Section: Resultsmentioning

confidence: 99%

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Genetic architecture of nonadditive inheritance inArabidopsis thalianahybrids

Seymour

Chae

Grimm

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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The ubiquity of nonparental hybrid phenotypes, such as hybrid vigor and hybrid inferiority, has interested biologists for over a century and is of considerable agricultural importance. Although examples of both phenomena have been subject to intense investigation, no general model for the molecular basis of nonadditive genetic variance has emerged, and prediction of hybrid phenotypes from parental information continues to be a challenge. Here we explore the genetics of hybrid phenotype in 435 Arabidopsis thaliana individuals derived from intercrosses of 30 parents in a half diallel mating scheme. We find that nonadditive genetic effects are a major component of genetic variation in this population and that the genetic basis of hybrid phenotype can be mapped using genome-wide association (GWA) techniques. Significant loci together can explain as much as 20% of phenotypic variation in the surveyed population and include examples that have both classical dominant and overdominant effects. One candidate region inherited dominantly in the half diallel contains the gene for the MADS-box transcription factor AGAMOUS-LIKE 50 (AGL50), which we show directly to alter flowering time in the predicted manner. Our study not only illustrates the promise of GWA approaches to dissect the genetic architecture underpinning hybrid performance but also demonstrates the contribution of classical dominance to genetic variance.T he often observed phenotypic superiority of progeny relative to their parents, or heterosis, is a universal phenomenon and of great importance to plant agriculture. The earliest description of heterosis (also known as hybrid vigor or superiority) dates to Darwin's studies of cross-fertilization in plants. He noticed that intercrossing distantly related individuals gave rise to larger, more vigorous progeny (1). Four decades later, George Shull coined the term "heterosis" (2) for this phenomenon, which he and Edward East had independently described for hybrids of inbred maize in 1908 (3, 4). Heterosis has long been of interest to evolutionary biologists as a potential explanation for the ubiquity of cross-fertilization in plants and animals, but it is also a central component of agricultural breeding programs. The combination of hybrid seed technology and inbred line improvement has driven an unprecedented improvement in maize yield over the past century (5). Despite the economic importance of heterosis and its intensive investigation in a wide spectrum of species, prediction of hybrid performance from parental information remains a major challenge (6).In the terms of quantitative genetics, hybrid vigor (and its opposite, inferiority) describes a deviation of progeny from the phenotypic mean of the parents. This means that heterosis cannot be explained by the addition of the effects of contributing alleles (7). Nonadditive genetic variance can result from a nonlinear phenotypic effect of alleles at one locus, as in the case of dominant/recessive allele pairs in classical genetics, or from epistatic interactions ...

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Section: Resultsmentioning

confidence: 99%

“…All GWA analyses were conducted using the easyGWAS framework (46). We used a local copy of easyGWAS and custom C/C++ and Python implementations of the FaSTLMM (47) algorithm.…”

Section: Methodsmentioning

confidence: 99%

Genetic architecture of nonadditive inheritance inArabidopsis thalianahybrids

Seymour

Chae

Grimm

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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“…Cloud platforms such as Amazon web services (AWS), Google Cloud Platform, and Microsoft Azure can be used to develop new tools or extend existing tools to perform large-scale PheWAS in a more efficient and less time-consuming manner. Currently, there are a handful of cloud-based tools to perform GWAS such as Google BigQuery[57], easyGWAS[58] and CloudAssoc[59] but there are none currently available for PheWAS. There are also platforms built upon different clouds that can be used to perform association testing such as DNAnexus[60].…”

Section: Resultsmentioning

confidence: 99%

Current Scope and Challenges in Phenome-Wide Association Studies

Verma

Ritchie

2017

Curr Epidemiol Rep

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Purpose of Review Over many decades, researchers have been designing studies to investigate the relationship between genotypes and phenotypes to gain an understanding about the effect of genetics on disease. Recently, a high-throughput approach called phenome-wide associations studies (PheWAS) have been extensively used to identify associations between genetic variants and many diseases and traits simultaneously. In this review, we describe the value of PheWAS along with methodological issues and challenges in interpretation for current applications of PheWAS. Recent findings PheWAS have uncovered a paradigm to identify new associations for genetic loci across many diseases. The application of PheWAS have been effective with phenotype data from electronic health records, epidemiological studies, and clinical trials data. Summary The key strength of a PheWAS is to identify the association of one or more genetic variants with multiple phenotypes, which can showcase interconnections among the phenotypes due to shared genetic associations. While the PheWAS approach appears promising, there are a number of challenges that need to be addressed to provide additional robustness to PheWAS findings.

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“…We calculated the broad-sense heritability (H) and variance (v) of each splice site using a custom R script. GWAS experiments were performed using the easyGWAS 43 and/or GWAPP 44 web portal using the EMMAX/AMM algorithm with a minimum minor allele frequency of 5%, with no transformation of phenotypes.…”

Section: Splise-qtl Analysismentioning

confidence: 99%

Splice-site Strength Estimation: A simple yet powerful approach to analyse RNA splicing

Dent¹,

Singh²,

Mishra

et al. 2020

Preprint

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Association Studies.RNA splicing, and variations in this process referred to as alternative splicing, are critical aspects of gene regulation in eukaryotes. From environmental responses in plants to being a primary link between genetic variation and disease in humans, splicing differences confer extensive phenotypic changes across diverse organisms 1-3 . Current approaches for analysing splicing rely on quantifying variant transcripts (i.e., isoforms) or splicing events (i.e., intron retention, exon skipping etc) 4,5 . However, regulation of splicing occurs at the level of selection of individual splice sites, which results in variation in the abundance of isoforms and/or splicing events. Here, we present a simple approach to quantify the strength of individual splice sites, which determines their selection in a splicing reaction.Splice-site strength, as a quantitative phenotype, allows us to analyse splicing precisely in unprecedented ways. We demonstrate the power of this approach in defining the genomic determinants of the strength of individual splice-sites through GWAS. Our pilot-GWAS with more than thousand splice sites hints that cis-sequence divergence and competition between splice-sites and are among the primary determinants of variation in splicing among natural accessions of Arabidopsis thaliana. This approach allows deciphering the principles of splicing, which in turn has implications that range from agriculture to medicine.

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easyGWAS: A Cloud-Based Platform for Comparing the Results of Genome-Wide Association Studies

Cited by 108 publications

References 88 publications

Genetic architecture of nonadditive inheritance inArabidopsis thalianahybrids

Genetic architecture of nonadditive inheritance inArabidopsis thalianahybrids

Current Scope and Challenges in Phenome-Wide Association Studies

Splice-site Strength Estimation: A simple yet powerful approach to analyse RNA splicing

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