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Gaurav Bhatia

Researcher at Harvard University

Publications -  92
Citations -  23900

Gaurav Bhatia is an academic researcher from Harvard University. The author has contributed to research in topics: Controller (computing) & Population. The author has an hindex of 35, co-authored 88 publications receiving 16846 citations. Previous affiliations of Gaurav Bhatia include Delphi Automotive & Mercer University.

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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

Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores

Bjarni J. Vilhjálmsson, +394 more
TL;DR: LDpred is introduced, a method that infers the posterior mean effect size of each marker by using a prior on effect sizes and LD information from an external reference panel, and outperforms the approach of pruning followed by thresholding, particularly at large sample sizes.
Journal ArticleDOI

Leveraging Polygenic Functional Enrichment to Improve GWAS Power

TL;DR: Leveraging polygenic functional enrichment to incorporate coding, conserved, regulatory, and LD-related genomic annotations into association analyses robustly increases GWAS power is introduced.