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Genome-wide association analysis of chronic lymphocytic leukaemia, Hodgkin lymphoma and multiple myeloma identifies pleiotropic risk loci

Overview of attention for article published in Scientific Reports, January 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (82nd percentile)
  • Good Attention Score compared to outputs of the same age and source (74th percentile)

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16 X users

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Title
Genome-wide association analysis of chronic lymphocytic leukaemia, Hodgkin lymphoma and multiple myeloma identifies pleiotropic risk loci
Published in
Scientific Reports, January 2017
DOI 10.1038/srep41071
Pubmed ID
Authors

Philip J. Law, Amit Sud, Jonathan S. Mitchell, Marc Henrion, Giulia Orlando, Oleg Lenive, Peter Broderick, Helen E. Speedy, David C. Johnson, Martin Kaiser, Niels Weinhold, Rosie Cooke, Nicola J. Sunter, Graham H. Jackson, Geoffrey Summerfield, Robert J. Harris, Andrew R. Pettitt, David J. Allsup, Jonathan Carmichael, James R. Bailey, Guy Pratt, Thahira Rahman, Chris Pepper, Chris Fegan, Elke Pogge von Strandmann, Andreas Engert, Asta Försti, Bowang Chen, Miguel Inacio da Silva Filho, Hauke Thomsen, Per Hoffmann, Markus M. Noethen, Lewin Eisele, Karl-Heinz Jöckel, James M. Allan, Anthony J. Swerdlow, Hartmut Goldschmidt, Daniel Catovsky, Gareth J. Morgan, Kari Hemminki, Richard S. Houlston

Abstract

B-cell malignancies (BCM) originate from the same cell of origin, but at different maturation stages and have distinct clinical phenotypes. Although genetic risk variants for individual BCMs have been identified, an agnostic, genome-wide search for shared genetic susceptibility has not been performed. We explored genome-wide association studies of chronic lymphocytic leukaemia (CLL, N = 1,842), Hodgkin lymphoma (HL, N = 1,465) and multiple myeloma (MM, N = 3,790). We identified a novel pleiotropic risk locus at 3q22.2 (NCK1, rs11715604, P = 1.60 × 10(-9)) with opposing effects between CLL (P = 1.97 × 10(-8)) and HL (P = 3.31 × 10(-3)). Eight established non-HLA risk loci showed pleiotropic associations. Within the HLA region, Ser37 + Phe37 in HLA-DRB1 (P = 1.84 × 10(-12)) was associated with increased CLL and HL risk (P = 4.68 × 10(-12)), and reduced MM risk (P = 1.12 × 10(-2)), and Gly70 in HLA-DQB1 (P = 3.15 × 10(-10)) showed opposing effects between CLL (P = 3.52 × 10(-3)) and HL (P = 3.41 × 10(-9)). By integrating eQTL, Hi-C and ChIP-seq data, we show that the pleiotropic risk loci are enriched for B-cell regulatory elements, as well as an over-representation of binding of key B-cell transcription factors. These data identify shared biological pathways influencing the development of CLL, HL and MM. The identification of these risk loci furthers our understanding of the aetiological basis of BCMs.

X Demographics

X Demographics

The data shown below were collected from the profiles of 16 X users 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 71 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 71 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 21%
Student > Ph. D. Student 13 18%
Student > Bachelor 6 8%
Student > Master 5 7%
Student > Doctoral Student 4 6%
Other 9 13%
Unknown 19 27%
Readers by discipline Count As %
Medicine and Dentistry 14 20%
Biochemistry, Genetics and Molecular Biology 14 20%
Agricultural and Biological Sciences 9 13%
Immunology and Microbiology 5 7%
Pharmacology, Toxicology and Pharmaceutical Science 1 1%
Other 5 7%
Unknown 23 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 August 2019.
All research outputs
#3,978,658
of 24,323,543 outputs
Outputs from Scientific Reports
#31,761
of 132,258 outputs
Outputs of similar age
#76,403
of 426,766 outputs
Outputs of similar age from Scientific Reports
#992
of 3,846 outputs
Altmetric has tracked 24,323,543 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 132,258 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.6. This one has done well, scoring higher than 75% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 426,766 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 82% of its contemporaries.
We're also able to compare this research output to 3,846 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 74% of its contemporaries.