A novel haplotype-based eQTL approach identifies genetic associations not detected through conventional SNP-based methods

MotivationThe high accuracy of current haplotype phasing tools has enabled the interrogation of haplotype (or phase) information more widely in genetic investigations. Including such information in eQTL analysis complements SNP-based approaches as it has the potential to detect associations that may otherwise be missed.ResultsWe have developed a haplotype-based eQTL approach called eQTLHap to investigate associations between gene expression and haplotype blocks. Using simulations, we demonstrate that eQTLHap significantly outperforms typical SNP-based eQTL methods when the causal genetic architecture involves multiple SNPs. We show that phasing errors slightly impact the sensitivity of the proposed method (< 4%). Finally, the application of eQTLHap to real GEUVADIS and GTEx datasets finds 22 associations that replicated in larger studies or other tissues and could not be detected using a single-SNP approach.Availabilityhttps://github.com/ziadbkh/eQTLHap.

Structural equation modeling for hypertension and type 2 diabetes based on multiple SNPs and multiple phenotypes

Genome-wide association studies (GWAS) have been successful in identifying genetic variants associated with complex diseases. However, association analyses between genotypes and phenotypes are not straightforward due to the complex relationships between genetic and environmental factors. Moreover, multiple correlated phenotypes further complicate such analyses.To resolve this complexity, we present an analysis using structural equation modeling (SEM). Unlike current methods that focus only on identifying direct associations between diseases and genetic variants such as single-nucleotide polymorphisms (SNPs), our method introduces the effects of intermediate phenotypes, which are related phenotypes distinct from the target, into the systematic genetic study of diseases. Moreover, we consider multiple diseases simultaneously in a single model. The procedure can be summarized in four steps: 1) selection of informative SNPs, 2) extraction of latent variables from the selected SNPs, 3) investigation of the relationships among intermediate phenotypes and diseases, and 4) construction of an SEM. As a result, a quantitative map can be drawn that simultaneously shows the relationship among multiple SNPs, phenotypes, and diseases.In this study, we considered two correlated diseases, hypertension and type 2 diabetes (T2D), which are known to have a substantial overlap in their disease mechanism and have significant public health implications. As intermediate phenotypes for these diseases, we considered three obesity-related phenotypes—subscapular skin fold thickness, body mass index, and waist circumference—as traits representing subcutaneous adiposity, overall adiposity, and abdominal adiposity, respectively. Using GWAS data collected from the Korea Association Resource (KARE) project, we applied the proposed SEM process. Among 327,872 SNPs, 24 informative SNPs were selected in the first step (p<1.0E-05). Ten latent variables were generated in step 2. After an exploratory analysis, we established a path diagram among phenotypes and diseases in step 3. Finally, in step 4, we produced a quantitative map with paths moving from specific SNPs to hypertension through intermediate phenotypes and T2D. The resulting model had high goodness-of fit measures (χ2= 536.52, NFI=0.997, CFI=0.998).

Integrated Genome Wide Association Study (GWAS) Identifies SNPs Associated with Outcome in Pediatric AML

Acute myeloid leukemia (AML) treatment response remains poorly understood. Although multiple studies have focused on understanding the transcriptomic and epigenetic landscape of AML, a genome-wide analysis of SNPs in pediatric AML has not yet been investigated in depth. Thus, we sought to identify genetic variants predictive of AML response, relapse, and survival in pediatric AML patients. For this study, we generated genome-wide SNP data patients (n=160) treated on the multicenter AML02 clinical trial (ClinicalTrials.gov Identifier: NCT00136084) using Infinium Omni 2.5M Exome Beadchip. Standard GWAS QC procedure was followed in order to remove SNPs with call rate < 95%, monomorphic SNPs, SNPs with MAF<5% and samples with call rate<95%. Following QC, a risk-adjusted multi-outcome integrative GWAS was performed to identify SNPs associated with minimal residual disease (MRD) following induction I, relapse-free survival (RFS) and overall survival (OS). We performed a risk-adjusted analysis to identify 21 SNPs mapping to 14 genes at an endpoint-integrative p value <2x10-5. Table 1 provides list of genes with SNPs significantly associated with MRD, RFS, OS as well as in the integrated analysis at <2x10-5. Of interest multiple SNPs in DICER1, which is a key enzyme required for the biogenesis of microRNAs and small interfering RNAs were significantly associated with clinical outcome with promise integrated analysis at p = 0.000011, supported by associations with MRD, RFS and OS at p <0.002 (Figure 1A). DICER1 is over-expressed in AML with its expression under the influence of hematopoietic transcript factor, GATA1. RAI14, a retinoic acid induced 14 is a prognostic marker of poor response in solid tumors and has been associated with development of drug resistance. Multiple SNPs in RAI14 were significantly associated with clinical endpoints. Figure 1B shows RAI14 SNP rs336474 with C allele significantly associated with better RFS (p= 0.027) and OS (p=0.007), with an integrated p= 0.000004. SNP in upstream of RBFOX1, a RNA binding fox-1 homolog 1 and within intron of GRIN2A, glutamate ionotropic receptor NMDA type subunit 2A were significantly associated with MRD, RFS and OS (all p<0.005) and integrated p =0.00001 (Figure 1C). SNPs within genes involved in pyrimidine metabolism such as UPP2, a uridine phosphorylase; tumor suppressor genes such as JPH3, which codes for junctophilin; LILRB4 which encodes for a Leukocyte Immunoglobulin Like Receptor B4, that regulates inflammatory responses and cytotoxicity; HACE1 a potential tumor suppressor involved in the solid tumors pathophysiology; ANK2, an ankyrin family protein with role in cell proliferation and motility; BIRC8, which is implicated in CML disease progression etc. In conclusion, our results demonstrate significance of genome-wide investigation of SNPs to identify novel and clinically relevant SNPs of prognostic significance in childhood AML. We will present the in depth results of our integrated GWAS analysis as well as validation in independent patient cohorts. In summary, our results constitute one of the first integrated GWAS analyses to identify SNPs of prognostic significance in pediatric AML. Acknowledgments: We are thankful for funding from NIH R01-CA139246 and ALSAC. Disclosures No relevant conflicts of interest to declare.

Haplotype-based eQTL mapping finds evidence for complex gene regulatory regions poorly tagged by marginal SNPs

MotivationExpression quantitative trait loci (eQTLs), variations in the genome that impact gene expression, are identified through eQTL studies that test for a relationship between single nucleotide polymorphisms (SNPs) and gene expression levels. These studies typically assume an underlying additive model. Non-additive tests have been proposed, but are limited due to the increase in the multiple testing burden and are potentially biased by filtering criteria that relies on marginal association data. Here we propose using combinations of short haplotypes instead of SNPs as predictors for gene expression. Essentially, this method looks for genomic regions where haplotypes have different effect sizes. The differences in effect can be due to multiple genetic architectures such as a single SNP, a burden of rare SNPs, multiple SNPs with independent effect or multiple SNPs with an interaction effect occurring on the same haplotype.ResultsSimulations show that when haplotypes, rather than SNPs, are assigned non-zero effect sizes, our method has increased power compared to the marginal SNP method. In the GEUVADIS gene expression data, our method finds 101 more eGenes than the marginal method (5,202 vs. 5,101). The methods do not have full overlap in the eGenes that they find. Of the 5,202 eGenes found by our method, 707 are not found by the marginal method—even though it has a lower significance threshold. This indicates that many genes have regulatory architectures that are not well tagged by marginal SNPs and demonstrates the need to better model alternative archi-tectures.