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Jake K. Byrnes

Researcher at Stanford University

Publications -  31
Citations -  19225

Jake K. Byrnes is an academic researcher from Stanford University. The author has contributed to research in topics: Population & Genome-wide association study. The author has an hindex of 21, co-authored 31 publications receiving 13755 citations. Previous affiliations of Jake K. Byrnes include University of Oxford & Wellcome Trust Centre for Human Genetics.

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A global reference for human genetic variation.

Adam Auton, +517 more
- 01 Oct 2015 - 
TL;DR: The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations, and has reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-generation sequencing, deep exome sequencing, and dense microarray genotyping.

A global reference for human genetic variation

Adam Auton, +479 more
TL;DR: The 1000 Genomes Project as mentioned in this paper provided a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations, and reported the completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole genome sequencing, deep exome sequencing and dense microarray genotyping.
Journal ArticleDOI

Genome-wide association study of CNVs in 16,000 cases of eight common diseases and 3,000 shared controls

Nicholas John Craddock, +235 more
- 01 Apr 2010 - 
TL;DR: A large, direct genome-wide study of association between CNVs and eight common human diseases concludes that common CNVs that can be typed on existing platforms are unlikely to contribute greatly to the genetic basis ofcommon human diseases.
Journal ArticleDOI

Bayesian refinement of association signals for 14 loci in 3 common diseases.

TL;DR: In this paper, the authors defined credible sets of SNPs that were 95% likely, based on posterior probability, to contain the causal disease-associated SNPs, and showed the value of more detailed mapping to target sequences for functional studies.