scholarly journals MultiGWAS: An integrative tool for Genome Wide Association Studies (GWAS) in tetraploid organisms

2020 ◽  
Author(s):  
L. Garreta ◽  
I. Cerón-Souza ◽  
M.R. Palacio ◽  
P.H. Reyes-Herrera
Keyword(s):  
Current Practice ◽  
Association Studies ◽  
Research Question ◽  
Genome Wide Association ◽  
Model Organisms ◽  
Venn Diagram ◽  
Genome Wide Association Studies ◽  
Chord Diagrams ◽  
Genome Wide ◽  
Action Model

AbstractSummaryThe Genome-Wide Association Studies (GWAS) are essential to determine the genetic bases of either ecological or economic phenotypic variation across individuals within populations of model and non-model organisms. For this research question, current practice is the replication of the GWAS testing different parameters and models to validate the reproducibility of results. However, straightforward methodologies that manage both replication and tetraploid data are still missing. To solve this problem, we designed the MultiGWAS, a tool that does GWAS for diploid and tetraploid organisms by executing in parallel four software, two for polyploid data (GWASpoly and SHEsis) and two for diploids data (PLINK and TASSEL). MultiGWAS has several advantages. It runs either in the command line or in an interface. It manages different genotype formats, including VCF. It executes both the full and naïve models using several quality filters. Besides, it calculates a score to choose the best gene action model across GWASPoly and TASSEL. Finally, it generates several reports that facilitate the identification of false associations from both the significant and the best-ranked association SNP among the four software. We tested MultiGWAS with tetraploid potato data. The execution demonstrated that the Venn diagram and the other companion reports (i.e., Manhattan and QQ plots, heatmaps for associated SNP profiles, and chord diagrams to trace associated SNP by chromosomes) were useful to identify associated SNP shared among different models and parameters. Therefore, we confirmed that MultiGWAS is a suitable wrapping tool that successfully handles GWAS replication in both diploid and tetraploid [email protected]

scholarly journals Genome-wide association studies on endometriosis and endometriosis-related infertility

2018 ◽  
Author(s):  
Geneviève Galarneau ◽  
Pierre Fontanillas ◽  
Caterina Clementi ◽  
Tina Hu-Seliger ◽  
David-Emlyn Parfitt ◽  
...  
Keyword(s):  
Genome Wide Association Study ◽  
Association Studies ◽  
Reproductive Age ◽  
Genome Wide Association ◽  
European Ancestry ◽  
Model Organisms ◽  
Genome Wide Association Studies ◽  
Gwas Study ◽  
Genome Wide ◽  
Genome Wide Significance

AbstractEndometriosis affects ∼10% of women of reproductive age. It is characterized by the growth of endometrial-like tissue outside the uterus and is frequently associated with severe pain and infertility. We performed the largest endometriosis genome-wide association study (GWAS) to date, with 37,183 cases and 251,258 controls. All women were of European ancestry. We also performed the first GWAS of endometriosis-related infertility, including 2,969 cases and 3,770 controls. Our endometriosis GWAS study replicated, at genome-wide significance, seven loci identified in previous endometriosis GWASs (CELA3A-CDC42, SYNE1, KDR, FSHB-ARL14EP, GREB1, ID4, and CEP112) and identified seven new candidate loci with genome-wide significance (NGF, ATP1B1-F5, CD109, HEY2, OSR2-VPS13B, WT1, and TEX11-SLC7A3). No loci demonstrated genome-wide significance for endometriosis-related infertility, however, the three most strongly associated loci (MCTP1, EPS8L3-CSF1, and LPIN1) were in or near genes associated with female fertility or embryonic lethality in model organisms. These results reveal new candidate genes with potential involvement in the pathophysiology of endometriosis and endometriosis-related infertility.

scholarly journals Cross-species alcohol dependence-associated gene networks: Co-analysis of mouse brain gene expression and human genome-wide association data

2018 ◽  
Author(s):  
Kristin M. Mignogna ◽  
Silviu A. Bacanu ◽  
Brien P. Riley ◽  
Aaron R. Wolen ◽  
Michael F. Miles
Keyword(s):  
Gene Expression ◽  
Alcohol Dependence ◽  
Mouse Brain ◽  
Gene Networks ◽  
Association Studies ◽  
Complex Trait ◽  
Genome Wide Association ◽  
Model Organisms ◽  
Genome Wide Association Studies ◽  
Genome Wide

AbstractGenome-wide association studies on alcohol dependence, by themselves, have yet to account for the estimated heritability of the disorder and provide incomplete mechanistic understanding of this complex trait. Integrating brain ethanol-responsive gene expression networks from model organisms with human genetic data on alcohol dependence could aid in identifying dependence-associated genes and functional networks in which they are involved. This study used a modification of the Edge-Weighted Dense Module Searching for genome-wide association studies (EW-dmGWAS) approach to co-analyze whole-genome gene expression data from ethanol-exposed mouse brain tissue, human protein-protein interaction databases and alcohol dependence-related genome-wide association studies. Results revealed novel ethanol-regulated and alcohol dependence-associated gene networks in prefrontal cortex, nucleus accumbens, and ventral tegmental area. Three of these networks were overrepresented with genome-wide association signals from an independent dataset. These networks were significantly overrepresented for gene ontology categories involving several mechanisms, including actin filament-based activity, transcript regulation, Wnt and Syndecan-mediated signaling, and ubiquitination. Together, these studies provide novel insight for brain mechanisms contributing to alcohol dependence.

scholarly journals Genome-Wide Association Studies of CKD and Related Traits

2020 ◽  
Vol 15 (11) ◽  
pp. 1643-1656
Author(s):  
Adrienne Tin ◽  
Anna Köttgen
Keyword(s):  
Kidney Function ◽  
Complex Traits ◽  
Kidney Diseases ◽  
Association Studies ◽  
Genome Wide Association ◽  
Model Organisms ◽  
Genome Wide Association Studies ◽  
Genetic Loci ◽  
Genome Wide ◽  
Causal Genes

The past few years have seen major advances in genome-wide association studies (GWAS) of CKD and kidney function–related traits in several areas: increases in sample size from >100,000 to >1 million, enabling the discovery of >250 associated genetic loci that are highly reproducible; the inclusion of participants not only of European but also of non-European ancestries; and the use of advanced computational methods to integrate additional genomic and other unbiased, high-dimensional data to characterize the underlying genetic architecture and prioritize potentially causal genes and variants. Together with other large-scale biobank and genetic association studies of complex traits, these GWAS of kidney function–related traits have also provided novel insight into the relationship of kidney function to other diseases with respect to their genetic associations, genetic correlation, and directional relationships. A number of studies also included functional experiments using model organisms or cell lines to validate prioritized potentially causal genes and/or variants. In this review article, we will summarize these recent GWAS of CKD and kidney function–related traits, explain approaches for downstream characterization of associated genetic loci and the value of such computational follow-up analyses, and discuss related challenges along with potential solutions to ultimately enable improved treatment and prevention of kidney diseases through genetics.

scholarly journals Genome-wide association study in two cohorts from a multi-generational mouse advanced intercross line highlights the difficulty of replication

2018 ◽  
Author(s):  
Xinzhu Zhou ◽  
Celine L. St. Pierre ◽  
Natalia M. Gonzales ◽  
Riyan Cheng ◽  
Apurva Chitre ◽  
...  
Keyword(s):  
Body Weight ◽  
Locomotor Activity ◽  
Coat Color ◽  
Association Studies ◽  
Genome Wide Association ◽  
Model Organisms ◽  
Genome Wide Association Studies ◽  
Advanced Intercross Line ◽  
Genome Wide ◽  
Two Samples

AbstractReplication is considered to be critical for genome-wide association studies (GWAS) in humans, but is not routinely performed in model organisms. We explored replication using an advanced intercross line (AIL) which is the simplest possible multigenerational intercross. We re-genotyped a previously published cohort of LG/J x SM/J AIL mice (F34; n=428) using a denser marker set and also genotyped a novel cohort of AIL mice (F39-43; n=600) for the first time. We identified 110 significant loci in the F34 cohort, 36 of which were new discoveries attributable to the denser marker set; we also identified 27 novel significant loci in the F39-43 cohort. For traits measured in both cohorts (locomotor activity, body weight, and coat color), the genetic correlations were high, although, the F39-43 cohort showed systematically lower SNP-heritability estimates. We then attempted to replicate loci identified in either F34 or F39-43 in the other cohort. Albino coat color was robustly replicated; we observed only partial replication of associations for locomotor activity and body weight. Finally, we performed a mega-analysis of locomotor activity and body weight by combining F34 and F39-43 cohorts (n=1,028), which identified four novel loci. The incomplete replication was inconsistent with simulations we performed to estimate our power to replicate. This may reflect: 1) false positives errors in the discovery cohort, 2) environmental or genetic heterogeneity between the two samples, or 3) the systematic over estimation of the effect sizes at significant loci (“Winner’s Curse”). Our results demonstrate that it is difficult to replicate GWAS results even when using similarly sized discovery and replication cohorts drawn from the same population.

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