TY - JOUR
T1 - Novel gene and network associations found for acute lymphoblastic leukemia using case–control and family-based studies in multiethnic populations
AU - Nakka, Priyanka
AU - Archer, Natalie P.
AU - Xu, Heng
AU - Lupo, Philip J.
AU - Raphael, Benjamin J.
AU - Yang, Jun J.
AU - Ramachandran, Sohini
N1 - Funding Information:
S. Ramachandran received grants from the U.S. National Science Foundation (NSF; CAREER Award DBI-1452622), and the NIH (R01GM118652 and COBRE award P20GM109035). S. Ramachandran is a Pew Scholar in the Biomedical Sciences, funded by the Pew Charitable Trust, and is an Alfred P. Sloan Research Fellow. B.J. Raphael was supported by a Career Award at the Scientific Interface from the Burroughs Wellcome Fund, an Alfred P. Sloan Research Fellowship, NSF grant IIS-1016648, an NSF CAREER Award (CCF-1053753), and NIH grants R01HG007069 and R01CA180776. P.J. Lupo was supported by Cancer Prevention Research Institute of Texas grant RP140258 and Alex's Lemonade Stand Foundation Epidemiology Grant. J.J. Yang was supported by NIH grant CA176063 and American Lebanese Syrian Associated Charities at St. Jude Children's Research Hospital.
Publisher Copyright:
©2017 AACR.
PY - 2017/10/1
Y1 - 2017/10/1
N2 - 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.
AB - 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.
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U2 - 10.1158/1055-9965.EPI-17-0360
DO - 10.1158/1055-9965.EPI-17-0360
M3 - Article
C2 - 28751478
AN - SCOPUS:85031691922
SN - 1055-9965
VL - 26
SP - 1531
EP - 1539
JO - Cancer Epidemiology Biomarkers and Prevention
JF - Cancer Epidemiology Biomarkers and Prevention
IS - 10
ER -