Author Correction: Genome-wide association study of classical Hodgkin lymphoma identifies key regulators of disease susceptibility.

The original version of this Article contained an error in the spelling of a member of the PRACTICAL Consortium, Manuela Gago-Dominguez, which was incorrectly given as Manuela Gago Dominguez. This has now been corrected in both the PDF and HTML versions of the Article.

Genome-wide association study of classical Hodgkin lymphoma identifies key regulators of disease susceptibility.

Several susceptibility loci for classical Hodgkin lymphoma have been reported. However, much of the heritable risk is unknown. Here, we perform a meta-analysis of two existing genome-wide association studies, a new genome-wide association study, and replication totalling 5,314 cases and 16,749 controls.

Neutral tumor evolution in myeloma is associated with poor prognosis.

Recent studies suggest that the evolutionary history of a cancer is important in forecasting clinical outlook. To gain insight into the clonal dynamics of multiple myeloma (MM) and its possible influence on patient outcomes, we analyzed whole exome sequencing tumor data for 333 patients from Myeloma XI, a UK phase 3 trial and 434 patients from the CoMMpass study, all of which had received immunomodulatory drug (IMiD) therapy. By analyzing mutant allele frequency distributions in tumors, we found that 17% to 20% of MM is under neutral evolutionary dynamics.

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

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).

Inferring extrinsic noise from single-cell gene expression data using approximate Bayesian computation.

Background: Gene expression is known to be an intrinsically stochastic process which can involve single-digit numbers of mRNA molecules in a cell at any given time. The modelling of such processes calls for the use of exact stochastic simulation methods, most notably the Gillespie algorithm. However, this stochasticity, also termed "intrinsic noise", does not account for all the variability between genetically identical cells growing in a homogeneous environment.

Genome-wide association study identifies multiple susceptibility loci for multiple myeloma.

Multiple myeloma (MM) is a plasma cell malignancy with a significant heritable basis. Genome-wide association studies have transformed our understanding of MM predisposition, but individual studies have had limited power to discover risk loci. Here we perform a meta-analysis of these GWAS, add a new GWAS and perform replication analyses resulting in 9,866 cases and 239,188 controls.

Information processing by simple molecular motifs and susceptibility to noise.

Biological organisms rely on their ability to sense and respond appropriately to their environment. The molecular mechanisms that facilitate these essential processes are however subject to a range of random effects and stochastic processes, which jointly affect the reliability of information transmission between receptors and, for example, the physiological downstream response. Information is mathematically defined in terms of the entropy; and the extent of information flowing across an information channel or signalling system is typically measured by the 'mutual information', or the reduction in the uncertainty about the output once the input signal is known.