scholarly journals GWEHS: A Genome-Wide Effect Sizes and Heritability Screener

Genes ◽  
2019 ◽  
Vol 10 (8) ◽  
pp. 558
Author(s):  
López-Cortegano ◽  
Caballero
Keyword(s):  
Effect Size ◽  
Complex Traits ◽  
Association Studies ◽  
Meta Analysis ◽  
Effect Sizes ◽  
Genome Wide Association Studies ◽  
Online Application ◽  
Genome Wide ◽  
A Genome ◽  
Additive Contribution

During the last decade, there has been a huge development of Genome-Wide Association Studies (GWAS), and thousands of loci associated to complex traits have been detected. These efforts have led to the creation of public databases of GWAS results, making a huge source of information available on the genetic background of many diverse traits. Here we present GWEHS (Genome-Wide Effect size and Heritability Screener), an open-source online application to screen loci associated to human complex traits and diseases from the NHGRI-EBI GWAS Catalog. This application provides a way to explore the distribution of effect sizes of loci affecting these traits, as well as their contribution to heritability. Furthermore, it allows for making predictions on the change in the expected mean effect size, as well as in the heritability as new loci are found. The application enables inferences on whether the additive contribution of loci expected to be discovered in the future will be able to explain the estimates of familial heritability for the different traits. We illustrate the use of this tool, compare some of the results obtained with those from a previous meta-analysis, and discuss its uses and limitations.

scholarly journals Meta-analysis of genome-wide association studies for body fat distribution in 694 649 individuals of European ancestry

2018 ◽  
Vol 28 (1) ◽  
pp. 166-174 ◽  
Author(s):  
Sara L Pulit ◽  
Charli Stoneman ◽  
Andrew P Morris ◽  
Andrew R Wood ◽  
Craig A Glastonbury ◽  
...  
Keyword(s):  
Body Fat ◽  
Association Studies ◽  
Meta Analysis ◽  
Fat Distribution ◽  
Body Fat Distribution ◽  
Genome Wide Association ◽  
European Ancestry ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
A Genome

Abstract More than one in three adults worldwide is either overweight or obese. Epidemiological studies indicate that the location and distribution of excess fat, rather than general adiposity, are more informative for predicting risk of obesity sequelae, including cardiometabolic disease and cancer. We performed a genome-wide association study meta-analysis of body fat distribution, measured by waist-to-hip ratio (WHR) adjusted for body mass index (WHRadjBMI), and identified 463 signals in 346 loci. Heritability and variant effects were generally stronger in women than men, and we found approximately one-third of all signals to be sexually dimorphic. The 5% of individuals carrying the most WHRadjBMI-increasing alleles were 1.62 times more likely than the bottom 5% to have a WHR above the thresholds used for metabolic syndrome. These data, made publicly available, will inform the biology of body fat distribution and its relationship with disease.

Genome-wide association and genomic selection in animal breedingThis article is one of a selection of papers from the conference “Exploiting Genome-wide Association in Oilseed Brassicas: a model for genetic improvement of major OECD crops for sustainable farming”.

Genome ◽  
2010 ◽  
Vol 53 (11) ◽  
pp. 876-883 ◽  
Author(s):  
Ben Hayes ◽  
Mike Goddard
Keyword(s):  
Genomic Selection ◽  
Complex Traits ◽  
Association Studies ◽  
Genome Wide Association ◽  
Relationship Matrix ◽  
Genome Wide Association Studies ◽  
Simple Method ◽  
Breeding Values ◽  
Genome Wide ◽  
A Genome

Results from genome-wide association studies in livestock, and humans, has lead to the conclusion that the effect of individual quantitative trait loci (QTL) on complex traits, such as yield, are likely to be small; therefore, a large number of QTL are necessary to explain genetic variation in these traits. Given this genetic architecture, gains from marker-assisted selection (MAS) programs using only a small number of DNA markers to trace a limited number of QTL is likely to be small. This has lead to the development of alternative technology for using the available dense single nucleotide polymorphism (SNP) information, called genomic selection. Genomic selection uses a genome-wide panel of dense markers so that all QTL are likely to be in linkage disequilibrium with at least one SNP. The genomic breeding values are predicted to be the sum of the effect of these SNPs across the entire genome. In dairy cattle breeding, the accuracy of genomic estimated breeding values (GEBV) that can be achieved and the fact that these are available early in life have lead to rapid adoption of the technology. Here, we discuss the design of experiments necessary to achieve accurate prediction of GEBV in future generations in terms of the number of markers necessary and the size of the reference population where marker effects are estimated. We also present a simple method for implementing genomic selection using a genomic relationship matrix. Future challenges discussed include using whole genome sequence data to improve the accuracy of genomic selection and management of inbreeding through genomic relationships.

scholarly journals Across-cohort QC analyses of genome-wide association study summary statistics from complex traits

2015 ◽  
Author(s):  
Guo-Bo Chen ◽  
Sang Hong Lee ◽  
Matthew R Robinson ◽  
Maciej Trzaskowski ◽  
Zhi-Xiang Zhu ◽  
...  
Keyword(s):  
Complex Traits ◽  
Statistical Power ◽  
Association Studies ◽  
False Negative ◽  
Genome Wide Association ◽  
Effect Sizes ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Unknown Sample ◽  
Genome Wide

Genome-wide association studies (GWASs) have been successful in discovering replicable SNP-trait associations for many quantitative traits and common diseases in humans. Typically the effect sizes of SNP alleles are very small and this has led to large genome-wide association meta-analyses (GWAMA) to maximize statistical power. A trend towards ever-larger GWAMA is likely to continue, yet dealing with summary statistics from hundreds of cohorts increases logistical and quality control problems, including unknown sample overlap, and these can lead to both false positive and false negative findings. In this study we propose a new set of metrics and visualization tools for GWAMA, using summary statistics from cohort-level GWASs. We proposed a pair of methods in examining the concordance between demographic information and summary statistics. In method I, we use the population genetics Fststatistic to verify the genetic origin of each cohort and their geographic location, and demonstrate using GWAMA data from the GIANT Consortium that geographic locations of cohorts can be recovered and outlier cohorts can be detected. In method II, we conduct principal component analysis based on reported allele frequencies, and is able to recover the ancestral information for each cohort. In addition, we propose a new statistic that uses the reported allelic effect sizes and their standard errors to identify significant sample overlap or heterogeneity between pairs of cohorts. Finally, to quantify unknown sample overlap across all pairs of cohorts we propose a method that uses randomly generated genetic predictors that does not require the sharing of individual-level genotype data and does not breach individual privacy.

scholarly journals Host genetics and microbiome associations from the lens of genome wide association studies

2018 ◽  
Author(s):  
Omer Weissbrod ◽  
Daphna Rothschild ◽  
Elad Barkan ◽  
Eran Segal
Keyword(s):  
Gut Microbiome ◽  
Complex Traits ◽  
Association Studies ◽  
Meta Analysis ◽  
Lessons Learned ◽  
Host Genome ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
History Of

Recent studies indicate that the gut microbiome is partially heritable, motivating the need to investigate microbiome-host genome associations via microbial genome-wide association studies (mGWAS). Existing mGWAS demonstrate that microbiome-host genotypes associations are typically weak and are spread across multiple variants, similar to associations often observed in genome-wide association studies (GWAS) of complex traits. Here we reconsider mGWAS by viewing them through the lens of GWAS, and demonstrate that there are striking similarities between the challenges and pitfalls faced by the two study designs. We further advocate the mGWAS community to adopt three key lessons learned over the history of GWAS: (a) Adopting uniform data and reporting formats to facilitate replication and meta-analysis efforts; (b) enforcing stringent statistical criteria to reduce the number of false positive findings; and (c) considering the microbiome and the host genome as distinct entities, rather than studying different taxa and single nucleotide polymorphism (SNPs) separately. Finally, we anticipate that mGWAS sample sizes will have to increase by orders of magnitude to reproducibly associate the host genome with the gut microbiome.

scholarly journals Genetic modifiers of menopausal hormone replacement therapy and breast cancer risk: a genome–wide interaction study

2013 ◽  
Vol 20 (6) ◽  
pp. 875-887 ◽  
Author(s):  
Anja Rudolph ◽  
Rebecca Hein ◽  
Sara Lindström ◽  
Lars Beckmann ◽  
Sabine Behrens ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Fixed Effects ◽  
Association Studies ◽  
Meta Analysis ◽  
Case Control ◽  
Genome Wide Association Studies ◽  
Genetic Modifiers ◽  
Genome Wide ◽  
A Genome ◽  
Increased Risk

Women using menopausal hormone therapy (MHT) are at increased risk of developing breast cancer (BC). To detect genetic modifiers of the association between current use of MHT and BC risk, we conducted a meta-analysis of four genome-wide case-only studies followed by replication in 11 case–control studies. We used a case-only design to assess interactions between single-nucleotide polymorphisms (SNPs) and current MHT use on risk of overall and lobular BC. The discovery stage included 2920 cases (541 lobular) from four genome-wide association studies. The top 1391 SNPs showing P values for interaction (Pint) <3.0×10−3 were selected for replication using pooled case–control data from 11 studies of the Breast Cancer Association Consortium, including 7689 cases (676 lobular) and 9266 controls. Fixed-effects meta-analysis was used to derive combined Pint. No SNP reached genome-wide significance in either the discovery or combined stage. We observed effect modification of current MHT use on overall BC risk by two SNPs on chr13 near POMP (combined Pint≤8.9×10−6), two SNPs in SLC25A21 (combined Pint≤4.8×10−5), and three SNPs in PLCG2 (combined Pint≤4.5×10−5). The association between lobular BC risk was potentially modified by one SNP in TMEFF2 (combined Pint≤2.7×10−5), one SNP in CD80 (combined Pint≤8.2×10−6), three SNPs on chr17 near TMEM132E (combined Pint≤2.2×10−6), and two SNPs on chr18 near SLC25A52 (combined Pint≤4.6×10−5). In conclusion, polymorphisms in genes related to solute transportation in mitochondria, transmembrane signaling, and immune cell activation are potentially modifying BC risk associated with current use of MHT. These findings warrant replication in independent studies.

scholarly journals Epigenome-wide association study of serum urate reveals insights into urate co-regulation and the SLC2A9 locus

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Adrienne Tin ◽  
Pascal Schlosser ◽  
Pamela R. Matias-Garcia ◽  
Chris H. L. Thio ◽  
Roby Joehanes ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Complex Traits ◽  
Cardiometabolic Risk ◽  
Association Studies ◽  
Meta Analysis ◽  
Elevated Serum ◽  
Complex Trait ◽  
Serum Urate ◽  
Genome Wide Association Studies ◽  
Genome Wide

AbstractElevated serum urate levels, a complex trait and major risk factor for incident gout, are correlated with cardiometabolic traits via incompletely understood mechanisms. DNA methylation in whole blood captures genetic and environmental influences and is assessed in transethnic meta-analysis of epigenome-wide association studies (EWAS) of serum urate (discovery, n = 12,474, replication, n = 5522). The 100 replicated, epigenome-wide significant (p < 1.1E–7) CpGs explain 11.6% of the serum urate variance. At SLC2A9, the serum urate locus with the largest effect in genome-wide association studies (GWAS), five CpGs are associated with SLC2A9 gene expression. Four CpGs at SLC2A9 have significant causal effects on serum urate levels and/or gout, and two of these partly mediate the effects of urate-associated GWAS variants. In other genes, including SLC7A11 and PHGDH, 17 urate-associated CpGs are associated with conditions defining metabolic syndrome, suggesting that these CpGs may represent a blood DNA methylation signature of cardiometabolic risk factors. This study demonstrates that EWAS can provide new insights into GWAS loci and the correlation of serum urate with other complex traits.

scholarly journals The role of gene expression on human sexual dimorphism: too early to call

2020 ◽  
Author(s):  
Eleonora Porcu ◽  
Annique Claringbould ◽  
Kaido Lepik ◽  
Tom G. Richardson ◽  
Federico A. Santoni ◽  
...  
Keyword(s):  
Sexual Dimorphism ◽  
Complex Traits ◽  
Causal Effect ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
A Genome ◽  
Specific Trait ◽  
Trait Associations

AbstractThe genetic underpinning of sexual dimorphism is very poorly understood. The prevalence of many diseases differs between men and women, which could be in part caused by sex-specific genetic effects. Nevertheless, only a few published genome-wide association studies (GWAS) were performed separately in each sex. The reported enrichment of expression quantitative trait loci (eQTLs) among GWAS–associated SNPs suggests a potential role of sex-specific eQTLs in the sex-specific genetic mechanism underlying complex traits.To explore this scenario, we performed a genome-wide analysis of sex-specific whole blood RNA-seq eQTLs from 3,447 individuals. Among 9 million SNP-gene pairs showing sex-combined associations, we found 18 genes with significant sex-specific cis-eQTLs (FDR 5%). Our phenome-wide association study of the 18 top sex-specific eQTLs on >700 traits unraveled that these eQTLs do not systematically translate into detectable sex-specific trait-associations. Power analyses using real eQTL- and causal effect sizes showed that millions of samples would be necessary to observe sex-specific trait associations that are fully driven by sex-specific cis-eQTLs. Compensatory effects may further hamper their detection. In line with this observation, we confirmed that the sex-specific trait-associations detected so far are not driven by sex-specific cis-eQTLs.

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