Novel gene and network associations found for acute lymphoblastic leukemia using case–control and family-based studies in multiethnic populations

Priyanka Nakka, Natalie P. Archer, Heng Xu, Philip J. Lupo, Benjamin J. Raphael, Jun J. Yang, Sohini Ramachandran

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

Background: Acute lymphoblastic leukemia (ALL) is the most common childhood cancer, suggesting that germline variants influence ALL risk. Although multiple genome-wide association (GWA) studies have identified variants predisposing children to ALL, it remains unclear whether genetic heterogeneity affects ALL susceptibility and how interactions within and among genes containing ALL-associated variants influence ALL risk. Methods: Here, we jointly analyzed two published datasets of case–control GWA summary statistics along with germline data from ALL case–parent trios. We used the gene-level association method PEGASUS to identify genes with multiple variants associated with ALL. We then used PEGASUS gene scores as input to the network analysis algorithm HotNet2 to characterize the genomic architecture of ALL. Results: Using PEGASUS, we confirmed associations previously observed at genes such as ARID5B, IKZF1, CDKN2A/2B, and PIP4K2A, and we identified novel candidate gene associations. Using HotNet2, we uncovered significant gene subnetworks that may underlie inherited ALL risk: a subnetwork involved in B-cell differentiation containing the ALL-associated gene CEBPE, and a subnetwork of homeobox genes, including MEIS1. Conclusions: Gene and network analysis uncovered loci associated with ALL that are missed by GWA studies, such as MEIS1. Furthermore, ALL-associated loci do not appear to interact directly with each other to influence ALL risk, and instead appear to influence leukemogenesis through multiple, complex pathways. Impact: We present a new pipeline for post hoc analysis of association studies that yields new insight into the etiology of ALL and can be applied in future studies to shed light on the genomic underpinnings of cancer.

Original languageEnglish (US)
Pages (from-to)1531-1539
Number of pages9
JournalCancer Epidemiology Biomarkers and Prevention
Volume26
Issue number10
DOIs
StatePublished - Oct 1 2017

All Science Journal Classification (ASJC) codes

  • Epidemiology
  • Oncology

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