↓ Skip to main content

PLOS

Pooled Sample-Based GWAS: A Cost-Effective Alternative for Identifying Colorectal and Prostate Cancer Risk Variants in the Polish Population

Overview of attention for article published in PLOS ONE, April 2012
Altmetric Badge

Mentioned by

twitter
1 X user

Citations

dimensions_citation
34 Dimensions

Readers on

mendeley
65 Mendeley
Title
Pooled Sample-Based GWAS: A Cost-Effective Alternative for Identifying Colorectal and Prostate Cancer Risk Variants in the Polish Population
Published in
PLOS ONE, April 2012
DOI 10.1371/journal.pone.0035307
Pubmed ID
Authors

Pawel Gaj, Natalia Maryan, Ewa E. Hennig, Joanna K. Ledwon, Agnieszka Paziewska, Aneta Majewska, Jakub Karczmarski, Monika Nesteruk, Jan Wolski, Artur A. Antoniewicz, Krzysztof Przytulski, Andrzej Rutkowski, Alexander Teumer, Georg Homuth, Teresa Starzyńska, Jaroslaw Regula, Jerzy Ostrowski

Abstract

Prostate cancer (PCa) and colorectal cancer (CRC) are the most commonly diagnosed cancers and cancer-related causes of death in Poland. To date, numerous single nucleotide polymorphisms (SNPs) associated with susceptibility to both cancer types have been identified, but their effect on disease risk may differ among populations.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 65 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Lithuania 1 2%
United States 1 2%
Russia 1 2%
Unknown 62 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 16 25%
Student > Ph. D. Student 12 18%
Student > Master 8 12%
Professor 4 6%
Student > Bachelor 4 6%
Other 11 17%
Unknown 10 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 24 37%
Medicine and Dentistry 12 18%
Biochemistry, Genetics and Molecular Biology 6 9%
Computer Science 3 5%
Unspecified 2 3%
Other 6 9%
Unknown 12 18%