scholarly journals Genomics of Endometriosis: From Genome Wide Association Studies to Exome Sequencing

2021 ◽  
Vol 22 (14) ◽  
pp. 7297
Author(s):  
Imane Lalami ◽  
Carole Abo ◽  
Bruno Borghese ◽  
Charles Chapron ◽  
Daniel Vaiman
Keyword(s):  
Exome Sequencing ◽  
Complex Disease ◽  
Association Studies ◽  
Chromosome 9 ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Replication Studies ◽  
Genome Wide ◽  
Heritability Estimation ◽  
Functional Explanations

This review aims at better understanding the genetics of endometriosis. Endometriosis is a frequent feminine disease, affecting up to 10% of women, and characterized by pain and infertility. In the most accepted hypothesis, endometriosis is caused by the implantation of uterine tissue at ectopic abdominal places, originating from retrograde menses. Despite the obvious genetic complexity of the disease, analysis of sibs has allowed heritability estimation of endometriosis at ~50%. From 2010, large Genome Wide Association Studies (GWAS), aimed at identifying the genes and loci underlying this genetic determinism. Some of these loci were confirmed in other populations and replication studies, some new loci were also found through meta-analyses using pooled samples. For two loci on chromosomes 1 (near CCD42) and chromosome 9 (near CDKN2A), functional explanations of the SNP (Single Nucleotide Polymorphism) effects have been more thoroughly studied. While a handful of chromosome regions and genes have clearly been identified and statistically demonstrated as at-risk for the disease, only a small part of the heritability is explained (missing heritability). Some attempts of exome sequencing started to identify additional genes from families or populations, but are still scarce. The solution may reside inside a combined effort: increasing the size of the GWAS designs, better categorize the clinical forms of the disease before analyzing genome-wide polymorphisms, and generalizing exome sequencing ventures. We try here to provide a vision of what we have and what we should obtain to completely elucidate the genetics of this complex disease.

Jointly determining significance levels of primary and replication studies by controlling the false discovery rate in two-stage genome-wide association studies

2017 ◽  
Vol 27 (9) ◽  
pp. 2795-2808 ◽  
Author(s):  
Wei Jiang ◽  
Weichuan Yu
Keyword(s):  
False Discovery Rate ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Two Stage ◽  
Traditional Methods ◽  
Replication Studies ◽  
False Discovery ◽  
Genome Wide ◽  
Significance Levels

In genome-wide association studies, we normally discover associations between genetic variants and diseases/traits in primary studies, and validate the findings in replication studies. We consider the associations identified in both primary and replication studies as true findings. An important question under this two-stage setting is how to determine significance levels in both studies. In traditional methods, significance levels of the primary and replication studies are determined separately. We argue that the separate determination strategy reduces the power in the overall two-stage study. Therefore, we propose a novel method to determine significance levels jointly. Our method is a reanalysis method that needs summary statistics from both studies. We find the most powerful significance levels when controlling the false discovery rate in the two-stage study. To enjoy the power improvement from the joint determination method, we need to select single nucleotide polymorphisms for replication at a less stringent significance level. This is a common practice in studies designed for discovery purpose. We suggest this practice is also suitable in studies with validation purpose in order to identify more true findings. Simulation experiments show that our method can provide more power than traditional methods and that the false discovery rate is well-controlled. Empirical experiments on datasets of five diseases/traits demonstrate that our method can help identify more associations. The R-package is available at: http://bioinformatics.ust.hk/RFdr.html .

scholarly journals 026.Endometriosis — linkage, positional cloning and genome wide association

2004 ◽  
Vol 16 (9) ◽  
pp. 26
Author(s):  
G. W. Montgomery ◽  
J. Wicks ◽  
Z. Z. Zhao ◽  
D. R. Nyholt ◽  
N. G. Martin ◽  
...  
Keyword(s):  
High Throughput ◽  
Positional Cloning ◽  
Complex Disease ◽  
Association Studies ◽  
Snp Markers ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Single Experiment ◽  
Priority List ◽  
Genome Wide

Endometriosis is a complex disease which affects up to 10% of women in their reproductive years. Common symptoms include severe dysmenorrhea and pelvic pain. The disease is associated with subfertility and some malignancies. Genetic and environmental factors both influence endometriosis. The aim of our studies is to identify genetic variation contributing to endometriosis and define pathways leading to disease. We recruited a large cohort of affected sister pair (ASP) families where two sisters have had surgically confirmed disease and conducted a 10�cM genome scan. The results of the linkage analysis identified one chromosomal region with significant linkage and one region of suggestive linkage. The regions implicated by these studies are generally of the order of 20–30�cM and include several hundred genes. Locating the gene or genes contributing to disease within the region is a challenging task. The best approach to the problem is association studies using a high density of SNP markers. The recent development of human SNP maps and high throughput SNP genotyping platforms makes this task easier. We have developed high throughput SNP typing at QIMR using the Sequenom MassARRAY platform. The method allows multiple SNP assays to be genotyped on the same sample in a single experiment. Throughput and genotyping costs depend critically on this level of multiplexing and we routinely genotype 6–8 SNPs in a single assay. We are using bioinformatics and functional approaches to develop a priority list of genes to screen early in the project. SNP markers in these genes are being genotyped using the MassARRAY platform to search for genes contributing to endometriosis. In the future, genome wide association studies with our families may locate additional genes contributing to endometriosis.

scholarly journals Accurate modeling of replication rates in genome-wide association studies by accounting for winner’s curse and study-specific heterogeneity

2019 ◽  
Author(s):  
Jennifer Zou ◽  
Jinjing Zhou ◽  
Sarah Faller ◽  
Robert Brown ◽  
Eleazar Eskin
Keyword(s):  
Association Studies ◽  
Genome Wide Association ◽  
Data Sets ◽  
Genome Wide Association Studies ◽  
Significant Finding ◽  
Winner's Curse ◽  
Winner’S Curse ◽  
Replication Studies ◽  
Genome Wide ◽  
Complex Human Traits

AbstractGenome-wide association studies (GWAS) have identified thousands of genetic variants associated with complex human traits, but only a fraction of variants identified in discovery studies achieve significance in replication studies. Replication in GWAS studies has been well-studied in the context of winner’s curse, which is the inflation of effect size estimates for significant variants in a study. Multiple methods have been proposed to correct for the effects of winner’s curse. However, winner’s curse is often not sufficient to explain lack of replication. Another reason why studies fail to replicate is that there are fundamental differences between the discovery and replication studies. A confounding factor can create the appearance of a significant finding while actually being an artifact that will not replicate in future studies. We propose a statistical framework that utilizes GWAS replication studies to model winner’s curse and study-specific heterogeneity due to confounders and correct for these effects. We show through simulations and application to 100 human GWAS data sets that modeling both winner’s curse and study-specific heterogeneity explains observed patterns of replication in GWAS studies better than modeling winner’s curse alone.

scholarly journals The druggable genome and support for target identification and validation in drug development

2016 ◽  
Author(s):  
Chris Finan ◽  
Anna Gaulton ◽  
Felix Kruger ◽  
Tom Lumbers ◽  
Tina Shah ◽  
...  
Keyword(s):  
Drug Development ◽  
Drug Target ◽  
Complex Disease ◽  
Target Identification ◽  
Association Studies ◽  
Target Selection ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Druggable Genome

Target identification (identifying the correct drug targets for each disease) and target validation (demonstrating the effect of target perturbation on disease biomarkers and disease end-points) are essential steps in drug development. We showed previously that biomarker and disease endpoint associations of single nucleotide polymorphisms (SNPs) in a gene encoding a drug target accurately depict the effect of modifying the same target with a pharmacological agent; others have shown that genomic support for a target is associated with a higher rate of drug development success. To delineate drug development (including repurposing) opportunities arising from this paradigm, we connected complex disease- and biomarker-associated loci from genome wide association studies (GWAS) to an updated set of genes encoding druggable human proteins, to compounds with bioactivity against these targets and, where these were licensed drugs, to clinical indications. We used this set of genes to inform the design of a new genotyping array, to enable druggable genome-wide association studies for drug target selection and validation in human disease.

scholarly journals The genetic aetiology of cannabis use initiation: a meta-analysis of genome-wide association studies and a SNP-based heritability estimation

2012 ◽  
Vol 18 (5) ◽  
pp. 846-850 ◽  
Author(s):  
Karin J. H. Verweij ◽  
Anna A. E. Vinkhuyzen ◽  
Beben Benyamin ◽  
Michael T. Lynskey ◽  
Lydia Quaye ◽  
...  
Keyword(s):  
Association Studies ◽  
Meta Analysis ◽  
Cannabis Use ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Heritability Estimation
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