Genetic and Functional Investigation of Germline JAK2 Alleles That Predispose to Myeloproliferative Neoplasms

Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 124-124
Author(s):  
Alison M Schram ◽  
Xing Xu ◽  
Outi Kilpivaara ◽  
Semanti Mukherjee ◽  
Aaron D Viny ◽  
...  

Abstract Abstract 124 A somatic activating mutation in the pseudokinase domain of JAK2 (JAK2V617F) is found in the majority of patients with myeloproliferative neoplams (MPN). Using a genome-wide approach, we and others identified a germline haplotype in the JAK2 locus (rs10974944) that predisposes to the development of JAK2V617F-positive MPN. Importantly, this haplotype is associated with in cis acquisition of the somatic JAK2 mutation. An extended linkage disequilibrium block of 300kb is observed at this locus and others have reported an association between single nucleotide polymorphisms (SNPs) within this haplotype and risk of inflammatory bowel disease consistent with increased JAK-STAT signaling in patients who carry this risk haplotype. The mechanism by which this germline locus contributes to MPN pathogenesis has not been delineated. We hypothesized that the identified allele heightens the risk of developing MPN by either a) increasing the mutational rate at the JAK2 locus, or b) imparting a selective advantage on cells that acquire the somatic mutation through increased JAK2 expression. To address the mutational hypothesis, we performed targeted, high coverage, next-generation sequencing of the entire haplotype and of the entire JAK2 locus in 12 patients homozygous for the risk allele, and in 12 patients without the risk allele. Importantly we did not note an increased rate of somatic mutations (coding or noncoding) in patients homozygous for the risk haplotype. In addition, we expanded our GWAS to include 200 additional cases genotyped using the Illumina 1,000,000 SNP genotyping array. The number of SNPs did not significantly differ between the risk haplotype and non-risk haplotype, further suggesting that there is no increase in mutability attributable to the risk genotype. By constructing a phylogenetic tree, we found that the risk haplotype is ancestral to modern humans and demonstrates evidence of ancestral positive selection, although there was no evidence of recent selection at this locus. Taken together these data suggest that the JAK2 MPN risk hapolotype does not increase the mutational rate at this locus. We next investigated whether the risk allele affects JAK2 expression in hematopoietic cells. We compared the relative abundance of an exonic SNP within the haplotype using matched genomic DNA and cDNA from 8 MPN patients heterozygous for the risk allele. In each case we found that the risk allele was more highly expressed in cDNA compared to the non-risk allele despite similar allelic ratios in genomic DNA. The results suggest an increase in allele-specific expression of JAK2 associated with the JAK2 risk haplotype. We annotated all germline variants in cis with the JAK2 risk haplotype using next generation sequencing data of the entire JAK2 haplotype from MPN patients and from the 1000 Genomes project. We then used Encode ChIP-seq data and the ConSite web-based transcription factor binding prediction model to identify SNPs within the JAK2 haplotype that affect transcription factor binding. We identified a SNP within the JAK2 promoter region, rs1887428, as a potential causative allele because it is significantly associated with MPN (p=9.11E-11) and c-Fos/c-Jun is predicted to preferentially bind to the risk allele. In order to determine if this preferential transcription factor binding leads to a haplotype-specific increase in expression of JAK2, we performed luciferase assays in cells expressing reporter constructs with the two different alleles at rs1887428. Importantly, this demonstrated increased transcriptional activity in cells containing the risk allele at rs1887428, suggesting that enhanced transcription factor binding at rs1887428 may lead to increased JAK2 expression and confer a selective advantage on cells containing the risk haplotype. The effects of allelic variation at rs1887428 on JAK2 expression in hematopoietic cells will be presented. Taken together, our data suggests that the JAK2 MPN risk haplotype contributes to MPN pathogenesis through allele-specific transcription factor binding and JAK2 expression, which increases the selective advantage of JAK2 mutations arising on the risk haplotype. This study provides insight into how predisposing loci increase the predisposition to MPN and to other hematopoietic malignancies. Disclosures: No relevant conflicts of interest to declare.

PLoS Genetics ◽  
2012 ◽  
Vol 8 (9) ◽  
pp. e1002982 ◽  
Author(s):  
Falk Butter ◽  
Lucy Davison ◽  
Tar Viturawong ◽  
Marion Scheibe ◽  
Michiel Vermeulen ◽  
...  

2016 ◽  
Vol 135 (5) ◽  
pp. 485-497 ◽  
Author(s):  
Marco Cavalli ◽  
Gang Pan ◽  
Helena Nord ◽  
Ola Wallerman ◽  
Emelie Wallén Arzt ◽  
...  

Genomics ◽  
2016 ◽  
Vol 107 (6) ◽  
pp. 248-254 ◽  
Author(s):  
Marco Cavalli ◽  
Gang Pan ◽  
Helena Nord ◽  
Emelie Wallén Arzt ◽  
Ola Wallerman ◽  
...  

2017 ◽  
Vol 18 (1) ◽  
Author(s):  
Ines de Santiago ◽  
Wei Liu ◽  
Ke Yuan ◽  
Martin O’Reilly ◽  
Chandra Sekhar Reddy Chilamakuri ◽  
...  

Author(s):  
Sergey Abramov ◽  
Alexandr Boytsov ◽  
Dariia Bykova ◽  
Dmitry D. Penzar ◽  
Ivan Yevshin ◽  
...  

AbstractSequence variants in gene regulatory regions alter gene expression and contribute to phenotypes of individual cells and the whole organism, including disease susceptibility and progression. Single-nucleotide variants in enhancers or promoters may affect gene transcription by altering transcription factor binding sites. Differential transcription factor binding in heterozygous genomic loci provides a natural source of information on such regulatory variants. We present a novel approach to call the allele-specific transcription factor binding events at single-nucleotide variants in ChIP-Seq data, taking into account the joint contribution of aneuploidy and local copy number variation, that is estimated directly from variant calls. We have conducted a meta-analysis of more than 7 thousand ChIP-Seq experiments and assembled the database of allele-specific binding events listing more than half a million entries at nearly 270 thousand single-nucleotide polymorphisms for several hundred human transcription factors and cell types. These polymorphisms are enriched for associations with phenotypes of medical relevance and often overlap eQTLs, making candidates for causality by linking variants with molecular mechanisms. Specifically, there is a special class of switching sites, where different transcription factors preferably bind alternative alleles, thus revealing allele-specific rewiring of molecular circuitry.


2018 ◽  
Author(s):  
Omar Wagih ◽  
Daniele Merico ◽  
Andrew Delong ◽  
Brendan J Frey

ABSTRACTGenetic variation has long been known to alter transcription factor binding sites, resulting in sometimes major phenotypic consequences. While the performance for current binding site predictors is well characterised, little is known on how these models perform at predicting impact of variants. We collected and curated over 132,000 potential allele-specific binding (ASB) ChIP-seq variants across 101 transcription factors (TFs). We then assessed the accuracy of TF binding models from five different methods on these high-confidence measurements, finding that deep learning methods were best performing yet still have room for improvement. Importantly, machine learning methods were consistently better than the venerable position weight matrix (PWM). Finally, predictions for certain TFs were consistently poor, and our investigation supports efforts to use features beyond sequence, such as methylation, DNA shape, and post-translational modifications. We submit that ASB data is a valuable benchmark for variant impact on TF binding.


DNA Sequence ◽  
2001 ◽  
Vol 12 (5-6) ◽  
pp. 385-395 ◽  
Author(s):  
Ae Ran Moon ◽  
Goo Taeg Oh ◽  
Jae Wha Kim ◽  
Young Jin Cho ◽  
In Seong Choe

2016 ◽  
Author(s):  
Ines de Santiago ◽  
Wei Liu ◽  
Martin O’Reilly ◽  
Ke Yuan ◽  
Chandra Sekhar Reddy Chilamakuri ◽  
...  

AbstractAllele-specific measurements of transcription factor binding from ChIP-seq data are key to dissecting the allelic effects of non-coding variants and their contribution to phenotypic diversity. However, most methods to detect allelic imbalance assume diploid genomes. This assumption severely limits their applicability to cancer samples with frequent DNA copy number changes. Here we present a Bayesian statistical approach called BaalChIP to correct for the effect of background allele frequency on the observed ChIP-seq read counts. BaalChIP allows the joint analysis of multiple ChIP-seq samples across a single variant and outperforms competing approaches in simulations. Using 548 ENCODE ChIP-seq and 6 targeted FAIRE-seq samples we show that BaalChIP effectively corrects allele-specific analysis for copy number variation and increases the power to detect putative cis-acting regulatory variants in cancer genomes.


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