Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences.

Humans vary substantially in their willingness to take risks. In a combined sample of over 1 million individuals, we conducted genome-wide association studies (GWAS) of general risk tolerance, adventurousness, and risky behaviors in the driving, drinking, smoking, and sexual domains. Across all GWAS, we identified hundreds of associated loci, including 99 loci associated with general risk tolerance. We report evidence of substantial shared genetic influences across risk tolerance and the risky behaviors: 46 of the 99 general risk tolerance loci contain a lead SNP for at least one of our other GWAS, and general risk tolerance is genetically correlated ([Formula: see text] ~ 0.25 to 0.50) with a range of risky behaviors. Bioinformatics analyses imply that genes near SNPs associated with general risk tolerance are highly expressed in brain tissues and point to a role for glutamatergic and GABAergic neurotransmission. We found no evidence of enrichment for genes previously hypothesized to relate to risk tolerance.

Author Correction: Pan-cancer subtyping in a 2D-map shows substructures that are driven by specific combinations of molecular characteristics.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper.

Functional mapping and annotation of genetic associations with FUMA.

A main challenge in genome-wide association studies (GWAS) is to pinpoint possible causal variants. Results from GWAS typically do not directly translate into causal variants because the majority of hits are in non-coding or intergenic regions, and the presence of linkage disequilibrium leads to effects being statistically spread out across multiple variants. Post-GWAS annotation facilitates the selection of most likely causal variant(s). Multiple resources are available for post-GWAS annotation, yet these can be time consuming and do not provide integrated visual aids for data interpretation. We, therefore, develop FUMA: an integrative web-based platform using information from multiple biological resources to facilitate functional annotation of GWAS results, gene prioritization and interactive visualization. FUMA accommodates positional, expression quantitative trait loci (eQTL) and chromatin interaction mappings, and provides gene-based, pathway and tissue enrichment results. FUMA results directly aid in generating hypotheses that are testable in functional experiments aimed at proving causal relations.

Genome-wide association analysis of insomnia complaints identifies risk genes and genetic overlap with psychiatric and metabolic traits.

Persistent insomnia is among the most frequent complaints in general practice. To identify genetic factors for insomnia complaints, we performed a genome-wide association study (GWAS) and a genome-wide gene-based association study (GWGAS) in 113,006 individuals. We identify three loci and seven genes associated with insomnia complaints, with the associations for one locus and five genes supported by joint analysis with an independent sample (n = 7,565). Our top association (MEIS1, P < 5 × 10-8) has previously been implicated in restless legs syndrome (RLS). Additional analyses favor the hypothesis that MEIS1 exhibits pleiotropy for insomnia and RLS and show that the observed association with insomnia complaints cannot be explained only by the presence of an RLS subgroup within the cases. Sex-specific analyses suggest that there are different genetic architectures between the sexes in addition to shared genetic factors. We show substantial positive genetic correlation of insomnia complaints with internalizing personality traits and metabolic traits and negative correlation with subjective well-being and educational attainment. These findings provide new insight into the genetic architecture of insomnia.

Genome-wide association meta-analysis of 78,308 individuals identifies new loci and genes influencing human intelligence.

Intelligence is associated with important economic and health-related life outcomes. Despite intelligence having substantial heritability (0.54) and a confirmed polygenic nature, initial genetic studies were mostly underpowered. Here we report a meta-analysis for intelligence of 78,308 individuals. We identify 336 associated SNPs (METAL P < 5 × 10-8) in 18 genomic loci, of which 15 are new. Around half of the SNPs are located inside a gene, implicating 22 genes, of which 11 are new findings. Gene-based analyses identified an additional 30 genes (MAGMA P < 2.73 × 10-6), of which all but one had not been implicated previously. We show that the identified genes are predominantly expressed in brain tissue, and pathway analysis indicates the involvement of genes regulating cell development (MAGMA competitive P = 3.5 × 10-6). Despite the well-known difference in twin-based heritability for intelligence in childhood (0.45) and adulthood (0.80), we show substantial genetic correlation (rg = 0.89, LD score regression P = 5.4 × 10-29). These findings provide new insight into the genetic architecture of intelligence.

High Levels of Canonical Wnt Signaling Lead to Loss of Stemness and Increased Differentiation in Hematopoietic Stem Cells.

Canonical Wnt signaling regulates the self-renewal of most if not all stem cell systems. In the blood system, the role of Wnt signaling has been the subject of much debate but there is consensus that high Wnt signals lead to loss of reconstituting capacity. To better understand this phenomenon, we have taken advantage of a series of hypomorphic mutant Apc alleles resulting in a broad range of Wnt dosages in hematopoietic stem cells (HSCs) and performed whole-genome gene expression analyses. Gene expression profiling and functional studies show that HSCs with APC mutations lead to high Wnt levels, enhanced differentiation, and diminished proliferation but have no effect on apoptosis, collectively leading to loss of stemness. Thus, we provide mechanistic insight into the role of APC mutations and Wnt signaling in HSC biology. As Wnt signals are explored in various in vivo and ex vivo expansion protocols for HSCs, our findings also have clinical ramifications.

Pan-cancer subtyping in a 2D-map shows substructures that are driven by specific combinations of molecular characteristics.

The use of genome-wide data in cancer research, for the identification of groups of patients with similar molecular characteristics, has become a standard approach for applications in therapy-response, prognosis-prediction, and drug-development. To progress in these applications, the trend is to move from single genome-wide measurements in a single cancer-type towards measuring several different molecular characteristics across multiple cancer-types. Although current approaches shed light on molecular characteristics of various cancer-types, detailed relationships between patients within cancer clusters are unclear. We propose a novel multi-omic integration approach that exploits the joint behavior of the different molecular characteristics, supports visual exploration of the data by a two-dimensional landscape, and inspection of the contribution of the different genome-wide data-types. We integrated 4,434 samples across 19 cancer-types, derived from TCGA, containing gene expression, DNA-methylation, copy-number variation and microRNA expression data. Cluster analysis revealed 18 clusters, where three clusters showed a complex collection of cancer-types, squamous-cell-carcinoma, colorectal cancers, and a novel grouping of kidney-cancers. Sixty-four samples were identified outside their tissue-of-origin cluster. Known and novel patient subgroups were detected for Acute Myeloid Leukemia's, and breast cancers. Quantification of the contributions of the different molecular types showed that substructures are driven by specific (combinations of) molecular characteristics.

2D Representation of Transcriptomes by t-SNE Exposes Relatedness between Human Tissues.

The GTEx Consortium reported that hierarchical clustering of RNA profiles from 25 unique tissue types among 1641 individuals accurately distinguished the tissue types, but a multidimensional scaling failed to generate a 2D projection of the data that separates tissue-subtypes. In this study we show that a projection by t-Distributed Stochastic Neighbor Embedding is in line with the cluster analysis which allows a more detailed examination and visualization of human tissue relationships.

An integrated approach of gene expression and DNA-methylation profiles of WNT signaling genes uncovers novel prognostic markers in acute myeloid leukemia.

BACKGROUND: The wingless-Int (WNT) pathway has an essential role in cell regulation of hematopoietic stem cells (HSC). For Acute Myeloid Leukemia (AML), the malignant counterpart of HSC, currently only a selective number of genes of the WNT pathway are analyzed by using either gene expression or DNA-methylation profiles for the identification of prognostic markers and potential candidate targets for drug therapy. It is known that mRNA expression is controlled by DNA-methylation and that specific patterns can infer the ability to differentiate biological differences, thus a combined analysis using all WNT annotated genes could provide more insight in the WNT signaling. APPROACH: We created a computational approach that integrates gene expression and DNA promoter methylation profiles. The approach represents the continuous gene expression and promoter methylation profiles with nine discrete mutually exclusive scenarios. The scenario representation allows for a refinement of patient groups by a more powerful statistical analysis, and the construction of a co-expression network. We focused on 268 WNT annotated signaling genes that are derived from the molecular signature database. RESULTS: Using the scenarios we identified seven prognostic markers for overall survival and event-free survival. Three genes are novel prognostic markers; two with favorable outcome (PSMD2, PPARD) and one with unfavorable outcome (XPNPEP). The remaining four genes (LEF1, SFRP2, RUNX1, and AXIN2) were previously identified but we could refine the patient groups. Three AML risk groups were further analyzed and the co-expression network showed that only the good risk group harbors frequent promoter hypermethylation and significantly correlated interactions with proteasome family members. CONCLUSION: Our results provide novel insights in WNT signaling in AML, we discovered new and previously identified prognostic markers and a refinement of the patient groups.

Integration of gene expression and DNA-methylation profiles improves molecular subtype classification in acute myeloid leukemia.

BACKGROUND: Acute Myeloid Leukemia (AML) is characterized by various cytogenetic and molecular abnormalities. Detection of these abnormalities is important in the risk-classification of patients but requires laborious experimentation. Various studies showed that gene expression profiles (GEP), and the gene signatures derived from GEP, can be used for the prediction of subtypes in AML. Similarly, successful prediction was also achieved by exploiting DNA-methylation profiles (DMP). There are, however, no studies that compared classification accuracy and performance between GEP and DMP, neither are there studies that integrated both types of data to determine whether predictive power can be improved. APPROACH: Here, we used 344 well-characterized AML samples for which both gene expression and DNA-methylation profiles are available. We created three different classification strategies including early, late and no integration of these datasets and used them to predict AML subtypes using a logistic regression model with Lasso regularization. RESULTS: We illustrate that both gene expression and DNA-methylation profiles contain distinct patterns that contribute to discriminating AML subtypes and that an integration strategy can exploit these patterns to achieve synergy between both data types. We show that concatenation of features from both data sets, i.e. early integration, improves the predictive power compared to classifiers trained on GEP or DMP alone. A more sophisticated strategy, i.e. the late integration strategy, employs a two-layer classifier which outperforms the early integration strategy. CONCLUSION: We demonstrate that prediction of known cytogenetic and molecular abnormalities in AML can be further improved by integrating GEP and DMP profiles.

Two splice-factor mutant leukemia subgroups uncovered at the boundaries of MDS and AML using combined gene expression and DNA-methylation profiling.

Mutations in splice factor (SF) genes occur more frequently in myelodysplastic syndromes (MDS) than in acute myeloid leukemias (AML). We sequenced complementary DNA from bone marrow of 47 refractory anemia with excess blasts (RAEB) patients, 29 AML cases with low marrow blast cell count, and 325 other AML patients and determined the presence of SF-hotspot mutations in SF3B1, U2AF35, and SRSF2. SF mutations were found in 10 RAEB, 12 AML cases with low marrow blast cell count, and 25 other AML cases. Our study provides evidence that SF-mutant RAEB and SF-mutant AML are clinically, cytologically, and molecularly highly similar. An integrated analysis of genomewide messenger RNA (mRNA) expression profiling and DNA-methylation profiling data revealed 2 unique patient clusters highly enriched for SF-mutant RAEB/AML. The combined genomewide mRNA expression profiling/DNA-methylation profiling signatures revealed 1 SF-mutant patient cluster with an erythroid signature. The other SF-mutant patient cluster was enriched for NRAS/KRAS mutations and showed an inferior survival. We conclude that SF-mutant RAEB/AML constitutes a related disorder overriding the artificial separation between AML and MDS, and that SF-mutant RAEB/AML is composed of 2 molecularly and clinically distinct subgroups. We conclude that SF-mutant disorders should be considered as myeloid malignancies that transcend the boundaries of AML and MDS.

Hypergeometric analysis of tiling-array and sequence data: detection and interpretation of peaks.

Probing protein-deoxyribonucleic acid (DNA) is gaining popularity as it sheds light on molecular mechanisms that regulate the expression of genes. Currently, tiling-arrays and next-generation sequencing technology can be used to measure these interactions. Both methods generate a signal over the genome in which contiguous regions of peaks on the genome represent the presence of an interacting molecule. Many methods do exist to identify functional regions of interest (ROIs) on the genome. However the detection of ROIs are often not an end-point in research questions and it therefore requires data dragging between tools to relate the ROIs to information present in databases, such as gene-ontology, pathway information, or enrichment of certain genomic content. We introduce hypergeometric analysis of tiling-array and sequence data (HATSEQ), a powerful tool that accurately identifies functional ROIs on the genome where a genomic signal significantly deviates from the general genome-wide behavior. HATSEQ also includes a number of built-in post-analyses with which biological meaning can be attached to the detected ROIs in terms of gene pathways and de-novo motif analysis, and provides different visualizations and statistical summaries for the detected ROIs. In addition, HATSEQ has an intuitive graphic user interface that lowers the barrier for researchers to analyze their data without the need of scripting languages. We compared the results of HATSEQ against two other popular chromatin immunoprecipitation sequencing (ChIP-Seq) methods and observed overlap in the detected ROIs but HATSEQ is more specific in delineating the peak boundaries. We also discuss the versatility of HATSEQ by using a Signal Transducer and Activator of Transcription 1 (STAT1) ChIP-Seq data-set, and show that the detected ROIs are highly specific for the expected STAT1 binding motif. HATSEQ is freely available at: http://hema13.erasmusmc.nl/index.php/HATSEQ.

HAT: a novel statistical approach to discover functional regions in the genome.

Tiling arrays are useful for exploring local functions of regions of the genome in an unbiased fashion. The exact determination of those genomic regions based on tiling-array data, e.g., generated by means of hybridization with immunopreciptated DNA-fragments to the arrays is a challenge. Many different statistical methodologies have been developed to find biological relevant regions-of-interest (ROI) by using the quantitative signal intensity of each probe. We previously developed a method called Hypergeometric Analysis of Tiling arrays (HAT) for the analysis of tiling-array data, but it is developed such that it can also be used to study data derived by genome-wide deep sequencing approaches. Here we applied HAT to analyze two publicly available tiling-array data sets. After the detection of statistically significant ROI, these are often used in additional analysis for hypothesis testing. We therefore discuss, by using the results of the tiling-array experiment, pathway and motif analyses.

The value of allogeneic and autologous hematopoietic stem cell transplantation in prognostically favorable acute myeloid leukemia with double mutant CEBPA.

The clinical value of allogeneic hematopoietic stem cell transplantation (alloHSCT) and autologous hematopoietic stem cell transplantation (autoHSCT) in the subtype of acute myeloid leukemia (AML) with double mutant CEBPA (CEBPAdm) has remained unsettled. Among 2983 patients analyzed for CEBPA mutational status (age 18-60 years) treated on 4 published Dutch-Belgian-Swiss Hemato-Oncology Cooperative Group (HOVON/SAKK) and 3 German-Austrian AML Study Group (AMLSG) protocols (2 published, 1 registered, clinicaltrials.gov NCT00151255), 124 had AML with CEBPAdm and achieved first complete remission (CR1). Evaluation of the clinical impact of alloHSCT and autoHSCT vs chemotherapy was performed by addressing time dependency in the statistical analyses. Thirty-two patients proceeded to alloHSCT from a matched related (MRD, n = 29) or a matched unrelated donor (MUD, n = 3), 20 to autoHSCT in CR1 and 72 received chemotherapy. Relapse-free survival was significantly superior in patients receiving an alloHSCT or autoHSCT in CR1 as compared with chemotherapy (P < .001), whereas overall survival was not different (P < .12). Forty-five patients relapsed. Of 42 patients treated with reinduction therapy, 35 achieved a second CR (83%) and most patients (n = 33) received an alloHSCT MRD, n = 11; MUD, n = 19; haplo-identical donor, n = 3). Survival of relapsed patients measured from date of relapse was 46% after 3 years. Adult AML patients with CEBPAdm benefit from alloHSCT and autoHSCT; relapsed patients still have a favorable outcome after reinduction followed by alloHSCT.

EVI1 is critical for the pathogenesis of a subset of MLL-AF9-rearranged AMLs.

The proto-oncogene EVI1 (ecotropic viral integration site-1), located on chromosome band 3q26, is aberrantly expressed in human acute myeloid leukemia (AML) with 3q26 rearrangements. In the current study, we showed, in a large AML cohort carrying 11q23 translocations, that ∼ 43% of all mixed lineage leukemia (MLL)-rearranged leukemias are EVI1(pos). High EVI1 expression occurs in AMLs expressing the MLL-AF6, -AF9, -AF10, -ENL, or -ELL fusion genes. In addition, we present evidence that EVI1(pos) MLL-rearranged AMLs differ molecularly, morphologically, and immunophenotypically from EVI1(neg) MLL-rearranged leukemias. In mouse bone marrow cells transduced with MLL-AF9, we show that MLL-AF9 fusion protein maintains Evi1 expression on transformation of Evi1(pos) HSCs. MLL-AF9 does not activate Evi1 expression in MLL-AF9-transformed granulocyte macrophage progenitors (GMPs) that were initially Evi1(neg). Moreover, shRNA-mediated knockdown of Evi1 in an Evi1(pos) MLL-AF9 mouse model inhibits leukemia growth both in vitro and in vivo, suggesting that Evi1 provides a growth-promoting signal. Using the Evi1(pos) MLL-AF9 mouse leukemia model, we demonstrate increased sensitivity to chemotherapeutic agents on reduction of Evi1 expression. We conclude that EVI1 is a critical player in tumor growth in a subset of MLL-rearranged AMLs.

Ribosomal deficiencies in Diamond-Blackfan anemia impair translation of transcripts essential for differentiation of murine and human erythroblasts.

Diamond-Blackfan anemia (DBA) is associated with developmental defects and profound anemia. Mutations in genes encoding a ribosomal protein of the small (e.g., RPS19) or large (e.g., RPL11) ribosomal subunit are found in more than half of these patients. The mutations cause ribosomal haploinsufficiency, which reduces overall translation efficiency of cellular mRNAs. We reduced the expression of Rps19 or Rpl11 in mouse erythroblasts and investigated mRNA polyribosome association, which revealed deregulated translation initiation of specific transcripts. Among these were Bag1, encoding a Hsp70 cochaperone, and Csde1, encoding an RNA-binding protein, and both were expressed at increased levels in erythroblasts. Their translation initiation is cap independent and starts from an internal ribosomal entry site, which appeared sensitive to knockdown of Rps19 or Rpl11. Mouse embryos lacking Bag1 die at embryonic day 13.5, with reduced erythroid colony forming cells in the fetal liver, and low Bag1 expression impairs erythroid differentiation in vitro. Reduced expression of Csde1 impairs the proliferation and differentiation of erythroid blasts. Protein but not mRNA expression of BAG1 and CSDE1 was reduced in erythroblasts cultured from DBA patients. Our data suggest that impaired internal ribosomal entry site-mediated translation of mRNAs expressed at increased levels in erythroblasts contributes to the erythroid phenotype of DBA.

Retroviral integration mutagenesis in mice and comparative analysis in human AML identify reduced PTP4A3 expression as a prognostic indicator.

Acute myeloid leukemia (AML) results from multiple genetic and epigenetic aberrations, many of which remain unidentified. Frequent loss of large chromosomal regions marks haplo-insufficiency as one of the major mechanisms contributing to leukemogenesis. However, which haplo-insufficient genes (HIGs) are involved in leukemogenesis is largely unknown and powerful experimental strategies aimed at their identification are currently lacking. Here, we present a new approach to discover HIGs, using retroviral integration mutagenesis in mice in which methylated viral integration sites and neighbouring genes were identified. In total we mapped 6 genes which are flanked by methylated viral integration sites (mVIS). Three of these, i.e., Lrmp, Hcls1 and Prkrir, were up regulated and one, i.e., Ptp4a3, was down regulated in the affected tumor. Next, we investigated the role of PTP4A3 in human AML and we show that PTP4A3 expression is a negative prognostic indicator, independent of other prognostic parameters. In conclusion, our novel strategy has identified PTP4A3 to potentially have a role in AML, on one hand as a candidate HIG contributing to leukemogenesis in mice and on the other hand as a prognostic indicator in human AML.

Functional crosstalk between Bmi1 and MLL/Hoxa9 axis in establishment of normal hematopoietic and leukemic stem cells.

Bmi1 is required for efficient self-renewal of hematopoietic stem cells (HSCs) and leukemic stem cells (LSCs). In this study, we investigated whether leukemia-associated fusion proteins, which differ in their ability to activate Hox expression, could initiate leukemia in the absence of Bmi1. AML1-ETO and PLZF-RARα, which do not activate Hox, triggered senescence in Bmi1(-/-) cells. In contrast, MLL-AF9, which drives expression of Hoxa7 and Hoxa9, readily transformed Bmi1(-/-) cells. MLL-AF9 could not initiate leukemia in Bmi1(-/-)Hoxa9(-/-) mice, which have further compromised HSC functions. But either gene could restore the ability of MLL-AF9 to establish LSCs in the double null background. As reported for Bmi1, Hoxa9 regulates expression of p16(Ink4a)/p19(ARF) locus and could overcome senescence induced by AML1-ETO. Together, these results reveal an important functional interplay between MLL/Hox and Bmi1 in regulating cellular senescence for LSC development, suggesting that a synergistic targeting of both molecules is required to eradicate a broader spectrum of LSCs.

MiR-17/20/93/106 promote hematopoietic cell expansion by targeting sequestosome 1-regulated pathways in mice.

MicroRNAs (miRNAs) are pivotal for regulation of hematopoiesis but their critical targets remain largely unknown. Here, we show that ectopic expression of miR-17, -20,-93 and -106, all AAAGUGC seed-containing miRNAs, increases proliferation, colony outgrowth and replating capacity of myeloid progenitors and results in enhanced P-ERK levels. We found that these miRNAs are endogenously and abundantly expressed in myeloid progenitors and down-regulated in mature neutrophils. Quantitative proteomics identified sequestosome 1 (SQSTM1), an ubiquitin-binding protein and regulator of autophagy-mediated protein degradation, as a major target for these miRNAs in myeloid progenitors. In addition, we found increased expression of Sqstm1 transcripts during CSF3-induced neutrophil differentiation of 32D-CSF3R cells and an inverse correlation of SQSTM1 protein levels and miR-106 expression in AML samples. ShRNA-mediated silencing of Sqstm1 phenocopied the effects of ectopic miR-17/20/93/106 expression in hematopoietic progenitors in vitro and in mice. Further, SQSTM1 binds to the ligand-activated colony-stimulating factor 3 receptor (CSF3R) mainly in the late endosomal compartment, but not in LC3 positive autophagosomes. SQSTM1 regulates CSF3R stability and ligand-induced mitogen-activated protein kinase signaling. We demonstrate that AAAGUGC seed-containing miRNAs promote cell expansion, replating capacity and signaling in hematopoietic cells by interference with SQSTM1-regulated pathways.