Common genetic variation and human traits.

By David B. Goldstein. New England Journal of Medicine, 2009

966 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/5c524afc74b2d8846563635da026c57feaf12f85

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Citations in the corpus

Uncovering the roles of rare variants in common disease through whole-genome sequencing (2010, background): “In this case, the causal variants explaining the association are common, perhaps because the trait relates to a drug respons”
Molecular networks as sensors and drivers of common human diseases (2009, background): “Interestingly, in the span of just a few years, the realization that tractable drug targets and clinically useful biomarkers of disease are not immediately apparent from GWAS data has, for some, reduced enthusiasm for the GWAS approac”
Defining the role of common variation in the genomic and biological architecture of adult human height (2014, positive): “We combined height summary association statistics from 37 independent studies genotyped using Illumina’s Metabochip array 9 in a meta-analysis of 80,067 individuals of European ancestry (Supplementary Tables 20–22).”
Causality : Objectives and Assessment (2010, background): “” And, according to Goldstein (Goldstein, 2009): “More than 100 genomewide association studies have been conducted for scores of human diseases, identifying hundreds of polymorphisms that are widely seen to influence disease risk.”
Trait-Associated SNPs Are More Likely to Be eQTLs: Annotation to Enhance Discovery from GWAS (2010, background): “Although there are important caveats to these grim statistics – most published studies have conducted discovery research in populations of European descent, and some important disorders have not even been examined using GWAS yet – the collective experience has led to sharp disagreement as to whether”
An expanded view of complex traits: from polygenic to omnigenic (2017, background): “This was taken to imply that there must be many causal loci, each with small effect sizes (Goldstein, 2009).”
Consistent Association of Type 2 Diabetes Risk Variants Found in Europeans in Diverse Racial and Ethnic Groups (2010, background): “It has recently been argued that single rare causal variants and/or collections of multiple different rare variants on unrelated haplotypes may create “synthetic associations” of common variants with disease risk”
Rare de novo variants associated with autism implicate a large functional network of genes involved in formation and function of synapses (2011, background): “Although these studies have successfully identified hundreds of significant associations, common polymorphisms reaching genome-wide significance usually explain a relatively small fraction of disease heritability (Goldstein, 2009).”
Network Medicine: A Network-based Approach to Human Disease (2011, background): “Therefore, an understanding of a gene's network context is essential in determining the phenotypic impact of defects that affect i”
Prioritizing GWAS results: A review of statistical methods and recommendations for their application. (2010, background): “3.(1) Thus, most of the significantly associated SNPs are in linkage disequilibrium with predisposing variants that increase the carriers’ disease risks by between 10% and 30% over the risk in noncarriers.”
Genome-wide association studies in diverse populations (2010, neutral): “These results have understandably triggered a series of reflections on the magnitude of the contributions of GWA studies in genera”
Metabolic Reconstruction for Metagenomic Data and Its Application to the Human Microbiome (2012, background): “As has been shown for genotype [58] and gene expression [59] data, phenotype can be difficult to reproducibly associate with high-dimensional genomic or metagenomic fea-Metabolic tures, particularly in large cohorts with complex population structure or when using multivariate models [60].”
Estimation of effect size distribution from genome-wide association studies and implications for future discoveries (2010, neutral): “The debate over the value of conducting more GWASs with current genotyping platforms has contrasted the benefits of discovering new regions for understanding biology with the diminishing returns of identifying new loci that have progressively smaller estimated effect sizes and thus marginal value fo”