Expanding the repertoire reveals recurrent, cryptic, and hematopoietic HLA class I minor histocompatibility antigens.

ABSTRACT: Allogeneic stem cell transplantation (alloSCT) is a curative treatment for hematological malignancies. After HLA-matched alloSCT, antitumor immunity is caused by donor T cells recognizing polymorphic peptides, designated minor histocompatibility antigens (MiHAs), that are presented by HLA on malignant patient cells. However, T cells often target MiHAs on healthy nonhematopoietic tissues of patients, thereby inducing side effects known as graft-versus-host disease. Here, we aimed to identify the dominant repertoire of HLA-I-restricted MiHAs to enable strategies to predict, monitor or modulate immune responses after alloSCT. To systematically identify novel MiHAs by genome-wide association screening, T-cell clones were isolated from 39 transplanted patients and tested for reactivity against 191 Epstein-Barr virus transformed B cell lines of the 1000 Genomes Project. By discovering 81 new MiHAs, we more than doubled the antigen repertoire to 159 MiHAs and demonstrated that, despite many genetic differences between patients and donors, often the same MiHAs are targeted in multiple patients. Furthermore, we showed that one quarter of the antigens are cryptic, that is translated from unconventional open reading frames, for example long noncoding RNAs, showing that these antigen types are relevant targets in natural immune responses. Finally, using single cell RNA-seq data, we analyzed tissue expression of MiHA-encoding genes to explore their potential role in clinical outcome, and characterized 11 new hematopoietic-restricted MiHAs as potential targets for immunotherapy. In conclusion, we expanded the repertoire of HLA-I-restricted MiHAs and identified recurrent, cryptic and hematopoietic-restricted antigens, which are fundamental to predict, follow or manipulate immune responses to improve clinical outcome after alloSCT.

Serum proteomics reveals hemophagocytic lymphohistiocytosis-like phenotype in a subset of patients with multisystem inflammatory syndrome in children.

Children with Multisystem Inflammatory Syndrome in Children (MIS-C) can present with thrombocytopenia, which is a key feature of hemophagocytic lymphohistiocytosis (HLH). We hypothesized that thrombocytopenic MIS-C patients have more features of HLH. Clinical characteristics and routine laboratory parameters were collected from 228 MIS-C patients, of whom 85 (37%) were thrombocytopenic. Thrombocytopenic patients had increased ferritin levels; reduced leukocyte subsets; and elevated levels of ASAT and ALAT. Soluble IL-2RA was higher in thrombocytopenic children than in non-thrombocytopenic children. T-cell activation, TNF-alpha and IFN-gamma signaling markers were inversely correlated with thrombocyte levels, consistent with a more pronounced cytokine storm syndrome. Thrombocytopenia was not associated with severity of MIS-C and no pathogenic variants were identified in HLH-related genes. This suggests that thrombocytopenia in MIS-C is not a feature of a more severe disease phenotype, but the consequence of a distinct hyperinflammatory immunopathological process in a subset of children.

Metabolomic predictors of phenotypic traits can replace and complement measured clinical variables in population-scale expression profiling studies.

Population-scale expression profiling studies can provide valuable insights into biological and disease-underlying mechanisms. The availability of phenotypic traits is essential for studying clinical effects. Therefore, missing, incomplete, or inaccurate phenotypic information can make analyses challenging and prevent RNA-seq or other omics data to be reused. A possible solution are predictors that infer clinical or behavioral phenotypic traits from molecular data. While such predictors have been developed based on different omics data types and are being applied in various studies, metabolomics-based surrogates are less commonly used than predictors based on DNA methylation profiles.In this study, we inferred 17 traits, including diabetes status and exposure to lipid medication, using previously trained metabolomic predictors. We evaluated whether these metabolomic surrogates can be used as an alternative to reported information for studying the respective phenotypes using expression profiling data of four population cohorts. For the majority of the 17 traits, the metabolomic surrogates performed similarly to the reported phenotypes in terms of effect sizes, number of significant associations, replication rates, and significantly enriched pathways.The application of metabolomics-derived surrogate outcomes opens new possibilities for reuse of multi-omics data sets. In studies where availability of clinical metadata is limited, missing or incomplete information can be complemented by these surrogates, thereby increasing the size of available data sets. Additionally, the availability of such surrogates could be used to correct for potential biological confounding. In the future, it would be interesting to further investigate the use of molecular predictors across different omics types and cohorts.

T and NK Cells in IL2RG-Deficient Patient 50 Years After Hematopoietic Stem Cell Transplantation.

The first successful European hematopoietic stem cell transplantation (HSCT) was performed in 1968 as treatment in a newborn with IL2RG deficiency using an HLA-identical sibling donor. Because of declining naive T and natural killer (NK) cells, and persistent human papilloma virus (HPV)-induced warts, the patient received a peripheral stem cell boost at the age of 37 years. NK and T cells were assessed before and up to 14 years after the boost by flow cytometry. The boost induced renewed reconstitution of functional NK cells that were 14 years later enriched for CD56dimCD27+ NK cells. T-cell phenotype and T-cell receptor (TCR) repertoire were simultaneously analyzed by including TCR Vß antibodies in the cytometry panel. Naive T-cell numbers with a diverse TCR Vß repertoire were increased by the boost. Before and after the boost, clonal expansions with a homogeneous TIGIT and PD-1 phenotype were identified in the CD27- and/or CD28- memory population in the patient, but not in the donor. TRB sequencing was applied on sorted T-cell subsets from blood and on T cells from skin biopsies. Abundant circulating CD8 memory clonotypes with a chronic virus-associated CD57+KLRG1+CX3CR1+ phenotype were also present in warts, but not in healthy skin of the patient, suggesting a link with HPV. In conclusion, we demonstrate in this IL2RG-deficient patient functional NK cells, a diverse and lasting naive T-cell compartment, supported by a stem cell boost, and an oligoclonal memory compartment half a century after HSCT.

A Comprehensive Workflow for Applying Single-Cell Clustering and Pseudotime Analysis to Flow Cytometry Data.

The introduction of single-cell platforms inspired the development of high-dimensional single-cell analysis tools to comprehensively characterize the underlying cellular heterogeneity. Flow cytometry data are traditionally analyzed by (subjective) gating of subpopulations on two-dimensional plots. However, the increasing number of parameters measured by conventional and spectral flow cytometry reinforces the need to apply many of the recently developed tools for single-cell analysis on flow cytometry data, as well. However, the myriads of analysis options offered by the continuously released novel packages can be overwhelming to the immunologist with limited computational background. In this article, we explain the main concepts of such analyses and provide a detailed workflow to illustrate their implications and additional prerequisites when applied on flow cytometry data. Moreover, we provide readily applicable R code covering transformation, normalization, dimensionality reduction, clustering, and pseudotime analysis that can serve as a template for future analyses. We demonstrate the merit of our workflow by reanalyzing a public human dataset. Compared with standard gating, the results of our workflow provide new insights in cellular subsets, alternative classifications, and hypothetical trajectories. Taken together, we present a well-documented workflow, which utilizes existing high-dimensional single-cell analysis tools to reveal cellular heterogeneity and intercellular relationships in flow cytometry data.

Population matched (pm) germline allelic variants of immunoglobulin (IG) loci: Relevance in infectious diseases and vaccination studies in human populations.

Immunoglobulin (IG) loci harbor inter-individual allelic variants in many different germline IG variable, diversity and joining genes of the IG heavy (IGH), kappa (IGK) and lambda (IGL) loci, which together form the genetic basis of the highly diverse antigen-specific B-cell receptors. These allelic variants can be shared between or be specific to human populations. The current immunogenetics resources gather the germline alleles, however, lack the population specificity of the alleles which poses limitations for disease-association studies related to immune responses in different human populations. Therefore, we systematically identified germline alleles from 26 different human populations around the world, profiled by "1000 Genomes" data. We identified 409 IGHV, 179 IGKV, and 199 IGLV germline alleles supported by at least seven haplotypes. The diversity of germline alleles is the highest in Africans. Remarkably, the variants in the identified novel alleles show strikingly conserved patterns, the same as found in other IG databases, suggesting over-time evolutionary selection processes. We could relate the genetic variants to population-specific immune responses, e.g. IGHV1-69 for flu in Africans. The population matched IG (pmIG) resource will enhance our understanding of the SHM-related B-cell receptor selection processes in (infectious) diseases and vaccination within and between different human populations.

A data-driven methodology reveals novel myofiber clusters in older human muscles.

Skeletal muscles control posture, mobility and strength, and influence whole-body metabolism. Muscles are built of different types of myofibers, each having specific metabolic, molecular, and contractile properties. Fiber classification is, therefore, regarded the key for understanding muscle biology, (patho-) physiology. The expression of three myosin heavy chain (MyHC) isoforms, MyHC-1, MyHC-2A, and MyHC-2X, marks myofibers in humans. Typically, myofiber classification is performed by an eye-based histological analysis. This classical approach is insufficient to capture complex fiber classes, expressing more than one MyHC-isoform. We, therefore, developed a methodological procedure for high-throughput characterization of myofibers on the basis of multiple isoforms. The mean fluorescence intensity of the three most abundant MyHC isoforms was measured per myofiber in muscle biopsies of 56 healthy elderly adults, and myofiber classes were identified using computational biology tools. Unsupervised clustering revealed the existence of six distinct myofiber clusters. A comparison with the visual assessment of myofibers using the same images showed that some of these myofiber clusters could not be detected or were frequently misclassified. The presence of these six clusters was reinforced by RNA expressions levels of sarcomeric genes. In addition, one of the clusters, expressing all three MyHC isoforms, correlated with histological measures of muscle health. To conclude, this methodological procedure enables deep characterization of the complex muscle heterogeneity. This study opens opportunities to further investigate myofiber composition in comparative studies.

Human Plasma N-glycosylation as Analyzed by Matrix-Assisted Laser Desorption/Ionization-Fourier Transform Ion Cyclotron Resonance-MS Associates with Markers of Inflammation and Metabolic Health.

Glycosylation is an abundant co- and post-translational protein modification of importance to protein processing and activity. Although not template-defined, glycosylation does reflect the biological state of an organism and is a high-potential biomarker for disease and patient stratification. However, to interpret a complex but informative sample like the total plasma N-glycome, it is important to establish its baseline association with plasma protein levels and systemic processes. Thus far, large-scale studies (n >200) of the total plasma N-glycome have been performed with methods of chromatographic and electrophoretic separation, which, although being informative, are limited in resolving the structural complexity of plasma N-glycans. MS has the opportunity to contribute additional information on, among others, antennarity, sialylation, and the identity of high-mannose type species.Here, we have used matrix-assisted laser desorption/ionization (MALDI)-Fourier transform ion cyclotron resonance (FTICR)-MS to study the total plasma N-glycome of 2144 healthy middle-aged individuals from the Leiden Longevity Study, to allow association analysis with markers of metabolic health and inflammation. To achieve this, N-glycans were enzymatically released from their protein backbones, labeled at the reducing end with 2-aminobenzoic acid, and following purification analyzed by negative ion mode intermediate pressure MALDI-FTICR-MS. In doing so, we achieved the relative quantification of 61 glycan compositions, ranging from Hex4HexNAc2 to Hex7HexNAc6dHex1Neu5Ac4, as well as that of 39 glycosylation traits derived thereof. Next to confirming known associations of glycosylation with age and sex by MALDI-FTICR-MS, we report novel associations with C-reactive protein (CRP), interleukin 6 (IL-6), body mass index (BMI), leptin, adiponectin, HDL cholesterol, triglycerides (TG), insulin, gamma-glutamyl transferase (GGT), alanine aminotransferase (ALT), and smoking. Overall, the bisection, galactosylation, and sialylation of diantennary species, the sialylation of tetraantennary species, and the size of high-mannose species proved to be important plasma characteristics associated with inflammation and metabolic health.

Genome-wide association meta-analysis of human longevity identifies a novel locus conferring survival beyond 90 years of age.

The genetic contribution to the variation in human lifespan is ∼ 25%. Despite the large number of identified disease-susceptibility loci, it is not known which loci influence population mortality. We performed a genome-wide association meta-analysis of 7729 long-lived individuals of European descent (≥ 85 years) and 16 121 younger controls (<65 years) followed by replication in an additional set of 13 060 long-lived individuals and 61 156 controls. In addition, we performed a subset analysis in cases aged ≥ 90 years. We observed genome-wide significant association with longevity, as reflected by survival to ages beyond 90 years, at a novel locus, rs2149954, on chromosome 5q33.3 (OR = 1.10, P = 1.74 × 10(-8)). We also confirmed association of rs4420638 on chromosome 19q13.32 (OR = 0.72, P = 3.40 × 10(-36)), representing the TOMM40/APOE/APOC1 locus. In a prospective meta-analysis (n = 34 103), the minor allele of rs2149954 (T) on chromosome 5q33.3 associates with increased survival (HR = 0.95, P = 0.003). This allele has previously been reported to associate with low blood pressure in middle age. Interestingly, the minor allele (T) associates with decreased cardiovascular mortality risk, independent of blood pressure. We report on the first GWAS-identified longevity locus on chromosome 5q33.3 influencing survival in the general European population. The minor allele of this locus associates with low blood pressure in middle age, although the contribution of this allele to survival may be less dependent on blood pressure. Hence, the pleiotropic mechanisms by which this intragenic variation contributes to lifespan regulation have to be elucidated.

Underlying molecular mechanisms of DIO2 susceptibility in symptomatic osteoarthritis.

OBJECTIVES: To investigate how the genetic susceptibility gene DIO2 confers risk to osteoarthritis (OA) onset in humans and to explore whether counteracting the deleterious effect could contribute to novel therapeutic approaches. METHODS: Epigenetically regulated expression of DIO2 was explored by assessing methylation of positional CpG-dinucleotides and the respective DIO2 expression in OA-affected and macroscopically preserved articular cartilage from end-stage OA patients. In a human in vitro chondrogenesis model, we measured the effects when thyroid signalling during culturing was either enhanced (excess T3 or lentiviral induced DIO2 overexpression) or decreased (iopanoic acid). RESULTS: OA-related changes in methylation at a specific CpG dinucleotide upstream of DIO2 caused significant upregulation of its expression (ß=4.96; p=0.0016). This effect was enhanced and appeared driven specifically by DIO2 rs225014 risk allele carriers (ß=5.58, p=0.0006). During in vitro chondrogenesis, DIO2 overexpression resulted in a significant reduced capacity of chondrocytes to deposit extracellular matrix (ECM) components, concurrent with significant induction of ECM degrading enzymes (ADAMTS5, MMP13) and markers of mineralisation (ALPL, COL1A1). Given their concurrent and significant upregulation of expression, this process is likely mediated via HIF-2α/RUNX2 signalling. In contrast, we showed that inhibiting deiodinases during in vitro chondrogenesis contributed to prolonged cartilage homeostasis as reflected by significant increased deposition of ECM components and attenuated upregulation of matrix degrading enzymes. CONCLUSIONS: Our findings show how genetic variation at DIO2 could confer risk to OA and raised the possibility that counteracting thyroid signalling may be a novel therapeutic approach.

IL7R gene expression network associates with human healthy ageing.

BACKGROUND: The level of expression of the interleukin 7 receptor (IL7R) gene in blood has recently been found to be associated with familial longevity and healthy ageing. IL7R is crucial for T cell development and important for immune competence. To further investigate the IL7R pathway in ageing, we identified the closest interacting genes to construct an IL7R gene network that consisted of IL7R and six interacting genes: IL2RG, IL7, TSLP, CRLF2, JAK1 and JAK3. This network was explored for association with chronological age, familial longevity and immune-related diseases (type 2 diabetes, chronic obstructive pulmonary disease and rheumatoid arthritis) in 87 nonagenarians, 337 of their middle-aged offspring and 321 middle-aged controls from the Leiden Longevity Study (LLS). RESULTS: We observed that expression levels within the IL7R gene network were significantly different between the nonagenarians and middle-aged controls (P = 4.6 × 10(-4)), being driven by significantly lower levels of expression in the elderly of IL7, IL2RG and IL7R. After adjustment for multiple testing and white blood cell composition and in comparison with similarly aged controls, middle-aged offspring of nonagenarian siblings exhibit a lower expression level of IL7R only (P = 0.006). Higher IL7R gene expression in the combined group of middle-aged offspring and controls is associated with a higher prevalence of immune-related disease (P = 0.001). On the one hand, our results indicate that lower IL7R expression levels, as exhibited by the members of long-lived families that can be considered as 'healthy agers', are beneficial in middle age. This is augmented by the observation that higher IL7R gene expression associates with immune-related disease. On the other hand, IL7R gene expression in blood is lower in older individuals, indicating that low IL7R gene expression might associate with reduced health. Interestingly, this contradictory result is supported by the observation that a higher IL7R gene expression level is associated with better prospective survival, both in the nonagenarians (Hazard ratio (HR) = 0.63, P = 0.037) and the middle-aged individuals (HR = 0.33, P = 1.9 × 10(-4)). CONCLUSIONS: Overall, we conclude that the IL7R network reflected by gene expression levels in blood may be involved in the rate of ageing and health status of elderly individuals.

Exome and whole genome sequencing in aging and longevity.

Calendar age is the major risk factor for common disease. It is therefore expected that understanding the aging process will eventually lead to promotion of better health conditions in elderly populations. Such insight may be obtained by identifying the genetic determinants of familial and exceptional longevity and age-related disease. Research of these determinants has been performed in candidate gene, genome-wide association and linkage studies. Because exploration of the common variation in the genome did not explain much of the variation in the rate of aging and longevity, researchers in the field have only recently started to investigate the contribution of rare genetic variants to these traits. The increased throughput and decreased costs of next generation sequencing (NGS) have resulted in a great deal of novel applications for sequencing sets of candidate genes, whole exomes, and whole genomes of individuals. Most of the successful NGS applications are as yet those focused on genetic syndromes and cancers for which causal mutations are readily being identified. In this chapter, we discuss the genetic and phenomic aspects of human aging research and the use of NGS data to identify genes relevant for age-related diseases and lifespan regulation, and to investigate the accumulation of somatic genetic variation during the course of life.

NMR metabolomics-guided DNA methylation mortality predictors.

BACKGROUND: 1H-NMR metabolomics and DNA methylation in blood are widely known biomarkers predicting age-related physiological decline and mortality yet exert mutually independent mortality and frailty signals. METHODS: Leveraging multi-omics data in four Dutch population studies (N = 5238, ∼40% of which male) we investigated whether the mortality signal captured by 1H-NMR metabolomics could guide the construction of DNA methylation-based mortality predictors. FINDINGS: We trained DNA methylation-based surrogates for 64 metabolomic analytes and found that analytes marking inflammation, fluid balance, or HDL/VLDL metabolism could be accurately reconstructed using DNA-methylation assays. Interestingly, a previously reported multi-analyte score indicating mortality risk (MetaboHealth) could also be accurately reconstructed. Sixteen of our derived surrogates, including the MetaboHealth surrogate, showed significant associations with mortality, independent of relevant covariates. INTERPRETATION: The addition of our metabolic analyte-derived surrogates to the well-established epigenetic clock GrimAge demonstrates that our surrogates potentially represent valuable mortality signal. FUNDING: BBMRI-NL, X-omics, VOILA, Medical Delta, NWO, ERC.

Mapping AML heterogeneity - multi-cohort transcriptomic analysis identifies novel clusters and divergent ex-vivo drug responses.

Subtyping of acute myeloid leukaemia (AML) is predominantly based on recurrent genetic abnormalities, but recent literature indicates that transcriptomic phenotyping holds immense potential to further refine AML classification. Here we integrated five AML transcriptomic datasets with corresponding genetic information to provide an overview (n = 1224) of the transcriptomic AML landscape. Consensus clustering identified 17 robust patient clusters which improved identification of CEBPA-mutated patients with favourable outcomes, and uncovered transcriptomic subtypes for KMT2A rearrangements (2), NPM1 mutations (5), and AML with myelodysplasia-related changes (AML-MRC) (5). Transcriptomic subtypes of KMT2A, NPM1 and AML-MRC showed distinct mutational profiles, cell type differentiation arrests and immune properties, suggesting differences in underlying disease biology. Moreover, our transcriptomic clusters show differences in ex-vivo drug responses, even when corrected for differentiation arrest and superiorly capture differences in drug response compared to genetic classification. In conclusion, our findings underscore the importance of transcriptomics in AML subtyping and offer a basis for future research and personalised treatment strategies. Our transcriptomic compendium is publicly available and we supply an R package to project clusters to new transcriptomic studies.

A transcriptomic based deconvolution framework for assessing differentiation stages and drug responses of AML.

The diagnostic spectrum for AML patients is increasingly based on genetic abnormalities due to their prognostic and predictive value. However, information on the AML blast phenotype regarding their maturational arrest has started to regain importance due to its predictive power for drug responses. Here, we deconvolute 1350 bulk RNA-seq samples from five independent AML cohorts on a single-cell healthy BM reference and demonstrate that the morphological differentiation stages (FAB) could be faithfully reconstituted using estimated cell compositions (ECCs). Moreover, we show that the ECCs reliably predict ex-vivo drug resistances as demonstrated for Venetoclax, a BCL-2 inhibitor, resistance specifically in AML with CD14+ monocyte phenotype. We validate these predictions using LUMC proteomics data by showing that BCL-2 protein abundance is split into two distinct clusters for NPM1-mutated AML at the extremes of CD14+ monocyte percentages, which could be crucial for the Venetoclax dosing patients. Our results suggest that Venetoclax resistance predictions can also be extended to AML without recurrent genetic abnormalities and possibly to MDS-related and secondary AML. Lastly, we show that CD14+ monocytic dominated Ven/Aza treated patients have significantly lower overall survival. Collectively, we propose a framework for allowing a joint mutation and maturation stage modeling that could be used as a blueprint for testing sensitivity for new agents across the various subtypes of AML.

The performance of metabolomics-based prediction scores for mortality in older patients with solid tumors.

Prognostic information is needed to balance benefits and risks of cancer treatment in older patients. Metabolomics-based scores were previously developed to predict 5- and 10-year mortality (MetaboHealth) and biological age (MetaboAge). This study aims to investigate the association of MetaboHealth and MetaboAge with 1-year mortality in older patients with solid tumors, and to study their predictive value for mortality in addition to established clinical predictors. This prospective cohort study included patients aged ≥ 70 years with a solid malignant tumor, who underwent blood sampling and a geriatric assessment before treatment initiation. The outcome was all-cause 1-year mortality. Of the 192 patients, the median age was 77 years. With each SD increase of MetaboHealth, patients had a 2.32 times increased risk of mortality (HR 2.32, 95% CI 1.59-3.39). With each year increase in MetaboAge, there was a 4% increased risk of mortality (HR 1.04, 1.01-1.07). MetaboHealth and MetaboAge showed an AUC of 0.66 (0.56-0.75) and 0.60 (0.51-0.68) for mortality prediction accuracy, respectively. The AUC of a predictive model containing age, primary tumor site, distant metastasis, comorbidity, and malnutrition was 0.76 (0.68-0.83). Addition of MetaboHealth increased AUC to 0.80 (0.74-0.87) (p = 0.09) and AUC did not change with MetaboAge (0.76 (0.69-0.83) (p = 0.89)). Higher MetaboHealth and MetaboAge scores were associated with 1-year mortality. The addition of MetaboHealth to established clinical predictors only marginally improved mortality prediction in this cohort with various types of tumors. MetaboHealth may potentially improve identification of older patients vulnerable for adverse events, but numbers were too small for definitive conclusions. The TENT study is retrospectively registered at the Netherlands Trial Register (NTR), trial number NL8107. Date of registration: 22-10-2019.

A genome-wide association study identifies a region at chromosome 12 as a potential susceptibility locus for restenosis after percutaneous coronary intervention.

Percutaneous coronary intervention (PCI) has become an effective therapy to treat obstructive coronary artery diseases (CAD). However, one of the major drawbacks of PCI is the occurrence of restenosis in 5-25% of all initially treated patients. Restenosis is defined as the re-narrowing of the lumen of the blood vessel, resulting in renewed symptoms and the need for repeated intervention. To identify genetic variants that are associated with restenosis, a genome-wide association study (GWAS) was conducted in 295 patients who developed restenosis (cases) and 571 who did not (controls) from the GENetic Determinants of Restenosis (GENDER) study. Analysis of ~550 000 single nucleotide polymorphisms (SNPs) in GENDER was followed by a replication phase in three independent case-control populations (533 cases and 3067 controls). A potential susceptibility locus for restenosis at chromosome 12, including rs10861032 (P(combined) = 1.11 × 10(-7)) and rs9804922 (P(combined) = 1.45 × 10(-6)), was identified in the GWAS and replication phase. In addition, both SNPs were also associated with coronary events (rs10861032, P(additive) = 0.005; rs9804922, P(additive) = 0.023) in a trial based cohort set of elderly patients with (enhanced risk of) CAD (PROSPER) and all-cause mortality in PROSPER (rs10861032, P(additive) = 0.007; rs9804922, P(additive) = 0.013) and GENDER (rs10861032, P(additive) = 0.005; rs9804922, P(additive) = 0.023). Further analysis suggests that this locus could be involved in regulatory functions.