Common Variants Confer Susceptibility to Barrett’s Esophagus: Insights from the First Genome-Wide Association Studies

AbstractEstimating the polygenicity (proportion of causally associated single nucleotide polymorphisms (SNPs)) and discoverability (effect size variance) of causal SNPs for human traits is currently of considerable interest. SNP-heritability is proportional to the product of these quantities. We present a basic model, using detailed linkage disequilibrium structure from an extensive reference panel, to estimate these quantities from genome-wide association studies (GWAS) summary statistics. We apply the model to diverse phenotypes and validate the implementation with simulations. We find model polygenicities ranging from ≃ 2 × 10−5to ≃ 4 × 10−3, with discoverabilities similarly ranging over two orders of magnitude. A power analysis allows us to estimate the proportions of phenotypic variance explained additively by causal SNPs reaching genome-wide significance at current sample sizes, and map out sample sizes required to explain larger portions of additive SNP heritability. The model also allows for estimating residual inflation (or deflation from over-correcting of z-scores), and assessing compatibility of replication and discovery GWAS summary statistics.Author SummaryThere are ~10 million common variants in the genome of humans with European ancestry. For any particular phenotype a number of these variants will have some causal effect. It is of great interest to be able to quantify the number of these causal variants and the strength of their effect on the phenotype.Genome wide association studies (GWAS) produce very noisy summary statistics for the association between subsets of common variants and phenotypes. For any phenotype, these statistics collectively are difficult to interpret, but buried within them is the true landscape of causal effects. In this work, we posit a probability distribution for the causal effects, and assess its validity using simulations. Using a detailed reference panel of ~11 million common variants – among which only a small fraction are likely to be causal, but allowing for non-causal variants to show an association with the phenotype due to correlation with causal variants – we implement an exact procedure for estimating the number of causal variants and their mean strength of association with the phenotype. We find that, across different phenotypes, both these quantities – whose product allows for lower bound estimates of heritability – vary by orders of magnitude.

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Meta-analysis of genome-wide association studies identifies common variants associated with blood pressure variation in east Asians

Nature Genetics ◽

10.1038/ng.834 ◽

2011 ◽

Vol 43 (6) ◽

pp. 531-538 ◽

Cited By ~ 385

Author(s):

Norihiro Kato ◽

Fumihiko Takeuchi ◽

Yasuharu Tabara ◽

Tanika N Kelly ◽

Min Jin Go ◽

...

Keyword(s):

Blood Pressure ◽

Association Studies ◽

Meta Analysis ◽

Pressure Variation ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Common Variants ◽

East Asians ◽

Genome Wide ◽

Blood Pressure Variation

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The predictive power of the microbiome exceeds that of genome-wide association studies in the discrimination of complex human disease

10.1101/2019.12.31.891978 ◽

2020 ◽

Cited By ~ 5

Author(s):

Braden T Tierney ◽

Yixuan He ◽

George M Church ◽

Eran Segal ◽

Aleksandar D Kostic ◽

...

Keyword(s):

Association Study ◽

Human Disease ◽

Genome Wide Association Study ◽

Human Genetics ◽

Association Studies ◽

Human Microbiome ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Common Variants ◽

Genome Wide

AbstractOver the past decade, studies of the human genome and microbiome have deepened our understanding of the connections between human genes, environments, microbes, and disease. For example, the sheer number of indicators of the microbiome and human genetic common variants associated with disease has been immense, but clinical utility has been elusive. Here, we compared the predictive capabilities of the human microbiome versus human genomic common variants across 13 common diseases. We concluded that microbiomic indicators outperform human genetics in predicting host phenotype (overall Microbiome-Association-Study [MAS] area under the curve [AUC] = 0.79 [SE = 0.03], overall Genome-Wide-Association-Study [GWAS] AUC = 0.67 [SE = 0.02]). Our results, while preliminary and focused on a subset of the totality of disease, demonstrate the relative predictive ability of the microbiome, indicating that it may outperform human genetics in discriminating human disease cases and controls. They additionally motivate the need for population-level microbiome sequencing resources, akin to the UK Biobank, to further improve and reproduce metagenomic models of disease.

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