Novel Gene and Network Associations Found for Acute Lymphoblastic Leukemia Using Case–Control and Family-Based Studies in Multiethnic Populations

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 analyze two published datasets of case-control GWA summary statistics along with germline data from ALL case-parent trios. We use the gene-level association method PEGASUS to identify genes with multiple variants associated with ALL. We then use PEGASUS gene scores as input to the network analysis algorithm HotNet2 to characterize the genomic architecture of ALL. Results Using PEGASUS, we confirm associations previously observed at genes such as ARID5B, IKZF1, CDKN2A/2B, and PIP4K2A, and we identify novel candidate gene associations. Using HotNet2, we uncover 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 uncovers loci associated with ALL that are missed by GWA studies such as MEIS1. Further, 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.

Enrichment analyses identify shared associations for 25 quantitative traits in over 600,000 individuals from seven diverse ancestries

“…We tested for shared and divergent enrichment of association with a trait of interest at the gene-level among interacting genes using network propagation of gene- ε gene-level association statistics as input to Hierarchical HotNet 61 . Hierarchical HotNet identifies significantly altered subnetworks using gene-level scores as input; in this study, these gene scores were set to − log 10 -transformed p -values of gene- ε gene-level association test statistics (see also Nakka et al 56, 105 ). For each ancestry-trait combination, we assigned p -values of 1 to genes with p -values greater than 0.1 to make the resulting networks both sparse and more interpretable (again see Nakka et al 56, 105 ).…”

“…Hierarchical HotNet identifies significantly altered subnetworks using gene-level scores as input; in this study, these gene scores were set to − log 10 -transformed p -values of gene- ε gene-level association test statistics (see also Nakka et al 41, 43 ). For each ancestry-trait combination, we assigned p -values of 1 to genes with p -values greater than 0.1 to make the resulting networks both sparse and more interpretable (again see Nakka et al 41, 43 ). In addition, ancestry-trait pairs in which less than 25 genes produced gene- ε p -values less than 0.1 were discarded as there were an insufficient number of gene-level statistics to populate the protein-protein interaction networks.…”

Detecting Shared Genetic Architecture Among Multiple Phenotypes by Hierarchical Clustering of Gene-Level Association Statistics

“…PEGASUS, developed by our group (Nakka et al 2016(Nakka et al , 2017, models correlation among genotypes in a region using linkage disequilibrium, the same model as VEGAS (Liu et al 2010) and SKAT without weighting rare variants (Wu et al 2011). PEGA-SUS, by contrast, achieves up to machine precision in gene-level association statistic computations via numerical integration.…”

“…To identify genetic architectures shared across a set of phenotypes, we aggregate SNP-level association statistics using PEGASUS (Nakka et al 2016(Nakka et al , 2017. PEGASUS can calculate a region-level association p-value for any set of the user-defined genomic region or compute gene-level association statistics.…”

Pathway Analysis within Multiple Human Ancestries Reveals Novel Signals for Epistasis in Complex Traits

“…To overcome this limitation, more recent computational approaches have expanded the additive GWA framework to aggregate across multiple SNP-level association signals and test for the enrichment of genes and pathways [50][51][52][53][54][55][56][57][58][59][60][61]. In Nakka et al [62] we showed that enrichment analyses applied to multiple ancestries can identify genes and gene networks contributing to disease risk that ancestry-specific enrichment analyses fail to find. Recent multiethnic GWA studies have also found that using non-European populations offer new insights into additive genetic architecture [63][64][65][66][67][68][69][70].…”