Hyun Min Kang

Genetics · 2008–2022

A researcher in the Science corpus: 21 papers; trusts 2 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

Whom Hyun Min Kang trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

  1. Jinliang Wang
  2. Andrey A. Shabalin
  3. Olivier J. Hardy
  4. Nick Patterson
  5. John A. Nelder
  6. Ciprian M. Crainiceanu
  7. J. K. Pritchard
  8. S. Picelli
  9. Heng Li
  10. Alkes L Price

Who trusts Hyun Min Kang overall

  1. Xiaoping Zhou
  2. Barbara Rakitsch
  3. Johann A. Gagnon-Bartsch
  4. G. Covarrubias-Pazaran
  5. Jeffrey B. Endelman
  6. Matthew Stephens
  7. Yang Xu
  8. Jiabo Wang
  9. Xiaolei Liu
  10. J. Rutkoski

Papers from Hyun Min Kang's perspective

Ordered by how strongly the researchers Hyun Min Kang trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility
  2. spagedi: a versatile computer program to analyse spatial genetic structure at the individual or population levels
  3. A framework for variation discovery and genotyping using next-generation DNA sequencing data
  4. Supporting Online Material Materials and Methods Figs. S1 to S3 Tables S1 to S5 References Worldwide Human Relationships Inferred from Genome-wide Patterns of Variation
  5. Statistical significance for genomewide studies (Proceedings of the National Academy of Sciences of the United States of America)
  6. Next-generation genotype imputation service and methods (Nature Genetics)
  7. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits (Proceedings of the National Academy of Sciences of the United States of America)
  8. The Human Gene Mutation Database: 2008 update
  9. Systematic Localization of Common Disease-Associated Variation in Regulatory DNA (Science)
  10. Mapping copy number variation by population scale genome sequencing (Nature)

Their papers