Whole-genome sequencing identifies EN1 as a determinant of bone density and fracture.

The extent to which low-frequency (minor allele frequency (MAF) between 1-5%) and rare (MAF ≤ 1%) variants contribute to complex traits and disease in the general population is mainly unknown. Bone mineral density (BMD) is highly heritable, a major predictor of osteoporotic fractures, and has been previously associated with common genetic variants, as well as rare, population-specific, coding variants. Here we identify novel non-coding genetic variants with large effects on BMD (ntotal = 53,236) and fracture (ntotal = 508,253) in individuals of European ancestry from the general population. Associations for BMD were derived from whole-genome sequencing (n = 2,882 from UK10K (ref. 10); a population-based genome sequencing consortium), whole-exome sequencing (n = 3,549), deep imputation of genotyped samples using a combined UK10K/1000 Genomes reference panel (n = 26,534), and de novo replication genotyping (n = 20,271). We identified a low-frequency non-coding variant near a novel locus, EN1, with an effect size fourfold larger than the mean of previously reported common variants for lumbar spine BMD (rs11692564(T), MAF = 1.6%, replication effect size = +0.20 s.d., Pmeta = 2 × 10(-14)), which was also associated with a decreased risk of fracture (odds ratio = 0.85; P = 2 × 10(-11); ncases = 98,742 and ncontrols = 409,511). Using an En1(cre/flox) mouse model, we observed that conditional loss of En1 results in low bone mass, probably as a consequence of high bone turnover. We also identified a novel low-frequency non-coding variant with large effects on BMD near WNT16 (rs148771817(T), MAF = 1.2%, replication effect size = +0.41 s.d., Pmeta = 1 × 10(-11)). In general, there was an excess of association signals arising from deleterious coding and conserved non-coding variants. These findings provide evidence that low-frequency non-coding variants have large effects on BMD and fracture, thereby providing rationale for whole-genome sequencing and improved imputation reference panels to study the genetic architecture of complex traits and disease in the general population.

A genome-wide association study on medulloblastoma.

INTRODUCTION: Medulloblastoma is a malignant embryonal tumor of the cerebellum that occurs predominantly in children. To find germline genetic variants associated with medulloblastoma risk, we conducted a genome-wide association study (GWAS) including 244 medulloblastoma cases and 247 control subjects from Sweden and Denmark. METHODS: Genotyping was performed using Illumina BeadChips, and untyped variants were imputed using IMPUTE2. RESULTS: Fifty-nine variants in 11 loci were associated with increased medulloblastoma risk (p < 1 × 10-5), but none were statistically significant after adjusting for multiple testing (p < 5 × 10-8). Thirteen of these variants were genotyped, whereas 46 were imputed. Genotyped variants were further investigated in a validation study comprising 249 medulloblastoma cases and 629 control subjects. In the validation study, rs78021424 (18p11.23, PTPRM) was associated with medulloblastoma risk with OR in the same direction as in the discovery cohort (ORT = 1.59, pvalidation = 0.02). We also selected seven medulloblastoma predisposition genes for investigation using a candidate gene approach: APC, BRCA2, PALB2, PTCH1, SUFU, TP53, and GPR161. The strongest evidence for association was found for rs201458864 (PALB2, ORT = 3.76, p = 3.2 × 10-4) and rs79036813 (PTCH1, ORA = 0.42, p = 2.6 × 10-3). CONCLUSION: The results of this study, including a novel potential medulloblastoma risk loci at 18p11.23, are suggestive but need further validation in independent cohorts.

The association between longer relative leukocyte telomere length and risk of glioma is independent of the potentially confounding factors allergy, BMI, and smoking.

PURPOSE: Previous studies have suggested an association between relative leukocyte telomere length (rLTL) and glioma risk. This association may be influenced by several factors, including allergies, BMI, and smoking. Previous studies have shown that individuals with asthma and allergy have shortened relative telomere length, and decreased risk of glioma. Though, the details and the interplay between rLTL, asthma and allergies, and glioma molecular phenotype is largely unknown. METHODS: rLTL was measured by qPCR in a Swedish population-based glioma case-control cohort (421 cases and 671 controls). rLTL was related to glioma risk and health parameters associated with asthma and allergy, as well as molecular events in glioma including IDH1 mutation, 1p/19q co-deletion, and EGFR amplification. RESULTS: Longer rLTL was associated with increased risk of glioma (OR = 1.16; 95% CI 1.02-1.31). Similar to previous reports, there was an inverse association between allergy and glioma risk. Specific, allergy symptoms including watery eyes was most strongly associated with glioma risk. High body mass index (BMI) a year prior diagnosis was significantly protective against glioma in our population. Adjusting for allergy, asthma, BMI, and smoking did not markedly change the association between longer rLTL and glioma risk. rLTL among cases was not associated with IDH1 mutation, 1p/19q co-deletion, or EGFR amplification, after adjusting for age at diagnosis and sex. CONCLUSIONS: In this Swedish glioma case-control cohort, we identified that long rLTL increases the risk of glioma, an association not confounded by allergy, BMI, or smoking. This highlights the complex interplay of the immune system, rLTL and cancer risk.

Immune marker changes and risk of multiple myeloma: a nested case-control study using repeated pre-diagnostic blood samples.

Biomarkers reliably predicting progression to multiple myeloma (MM) are lacking. Myeloma risk has been associated with low blood levels of monocyte chemotactic protein-3 (MCP-3), macrophage inflammatory protein-1 alpha (MIP-1α), vascular endothelial growth factor (VEGF), fibroblast growth factor-2 (FGF-2), fractalkine, and transforming growth factor-alpha (TGF-α). In this study, we aimed to replicate these findings and study the individual dynamics of each marker in a prospective longitudinal cohort, thereby examining their potential as markers of myeloma progression. For this purpose, we identified 65 myeloma cases and 65 matched cancer-free controls each with two donated blood samples within the Northern Sweden Health and Disease Study. The first and repeated samples from myeloma cases were donated at a median 13 and 4 years, respectively, before the myeloma was diagnosed. Known risk factors for progression were determined by protein-, and immunofixation electrophoresis, and free light chain assays. We observed lower levels of MCP-3, VEGF, FGF-2, and TGF-α in myeloma patients than in controls, consistent with previous data. We also observed that these markers decreased among future myeloma patients while remaining stable in controls. Decreasing trajectories were noted for TGF-α (P=2.5 × 10-4) indicating progression to MM. Investigating this, we found that low levels of TGF-α assessed at the time of the repeated sample were independently associated with risk of progression in a multivariable model (hazard ratio = 3.5; P=0.003). TGF-α can potentially improve early detection of MM.

Pre-diagnostic serum levels of EGFR and ErbB2 and genetic glioma risk variants: a nested case-control study.

Genetic variants have been associated with the risk of developing glioma, but functional mechanisms on disease phenotypic traits remain to be investigated. One phenotypic trait of glioblastoma is the mutation and amplification of the epidermal growth factor receptor (EGFR) gene. We investigated associations between pre-diagnostic serum protein concentrations of EGFR and ErbB2, both members of the EGFR family, and future risk of glioma. Further, we studied if EGFR glioma risk variants were associated with EGFR and ErbB2 serum levels. We assessed the associations between genetic glioma risk variants and serum concentrations of EGFR and ErbB2, as measured in pre-diagnostic cohort serum samples of 593 glioma patients and 590 matched cancer-free controls. High serum EGFR and ErbB2 levels were associated with risk of developing glioblastoma (P = 0.008; OR = 1.58, 95 % CI = 1.13-2.22 and P = 0.017, OR = 1.63, 95 % CI = 1.09-2.44, respectively). High serum ErbB2 concentration was also associated with glioma risk overall (P = 0.049; OR = 1.39, 95 % CI = 1.00-1.93). Glioma risk variants were not associated with high serum protein abundance. In contrast, the EGFR risk variant rs4947986 (T) was correlated with decreased EGFR serum levels (study cohort P = 0.024 and controls P = 0.009). To our knowledge, this is the first study showing an association of EGFR and ErbB2 serum levels with glioma more than a decade before diagnosis, indicating that EGFR and ErbB2 serum proteins are important in early gliomagenesis. However, we did not find evidence that glioma risk variants were associated with high pre-diagnostic serum concentrations of EGFR and ErbB2.

Genetic risk variants in the CDKN2A/B, RTEL1 and EGFR genes are associated with somatic biomarkers in glioma.

During the last years, genome wide association studies have discovered common germline genetic variants associated with specific glioma subtypes. We aimed to study the association between these germline risk variants and tumor phenotypes, including copy number aberrations and protein expression. A total of 91 glioma patients were included. Thirteen well known genetic risk variants in TERT, EGFR, CCDC26, CDKN2A, CDKN2B, PHLDB1, TP53, and RTEL1 were selected for investigation of possible correlations with the glioma somatic markers: EGFR amplification, 1p/19q codeletion and protein expression of p53, Ki-67, and mutated IDH1. The CDKN2A/B risk variant, rs4977756, and the CDKN2B risk variant, rs1412829 were inversely associated (p = 0.049 and p = 0.002, respectively) with absence of a mutated IDH1, i.e., the majority of patients homozygous for the risk allele showed no or low expression of mutated IDH1. The RTEL1 risk variant, rs6010620 was associated (p = 0.013) with not having 1p/19q codeletion, i.e., the majority of patients homozygous for the risk allele did not show 1p/19q codeletion. In addition, the EGFR risk variant rs17172430 and the CDKN2B risk variant rs1412829, both showed a trend for association (p = 0.055 and p = 0.051, respectively) with increased EGFR copy number, i.e., the majority of patients homozygote for the risk alleles showed chromosomal gain or amplification of EGFR. Our findings indicate that CDKN2A/B risk genotypes are associated with primary glioblastoma without IDH mutation, and that there is an inverse association between RTEL1 risk genotypes and 1p/19q codeletion, suggesting that these genetic variants have a molecular impact on the genesis of high graded brain tumors. Further experimental studies are needed to delineate the functional mechanism of the association between genotype and somatic genetic aberrations.

CCND2, CTNNB1, DDX3X, GLI2, SMARCA4, MYC, MYCN, PTCH1, TP53, and MLL2 gene variants and risk of childhood medulloblastoma.

Recent studies have described a number of genes that are frequently altered in medulloblastoma tumors and that have putative key roles in the development of the disease. We hypothesized that common germline genetic variations in these genes may be associated with medulloblastoma development. Based on recent publications, we selected 10 genes that were frequently altered in medulloblastoma: CCND2, CTNNB1, DDX3X, GLI2, SMARCA4, MYC, MYCN, PTCH1, TP53, and MLL2 (now renamed as KMT2D). Common genetic variants (single nucleotide polymorphisms) annotating these genes (n = 221) were genotyped in germline DNA (neonatal dried blood spot samples) from 243 childhood medulloblastoma cases and 247 control subjects from Sweden and Denmark. Eight genetic variants annotating three genes in the sonic hedgehog signaling pathway; CCND2, PTCH1, and GLI2, were found to be associated with the risk of medulloblastoma (P(combined) < 0.05). The findings were however not statistically significant following correction for multiple testing by the very stringent Bonferroni method. The results do not support our hypothesis that common germline genetic variants in the ten studied genes are associated with the risk of developing medulloblastoma.

Low-grade glioma risk SNP rs11706832 is associated with type I interferon response pathway genes in cell lines.

Genome-wide association studies (GWAS) have contributed to our understanding of glioma susceptibility. To date, 25 risk loci for development of any of the glioma subtypes are known. However, GWAS studies reveal little about the molecular processes that lead to increased risk, especially for non-coding single nucleotide polymorphisms (SNP). A particular SNP in intron 2 of LRIG1, rs11706832, has been shown to increase the susceptibility for IDH1 mutated low-grade gliomas (LGG). Leucine-rich repeats and immunoglobulin-like domains protein 1 (LRIG1) is important in cancer development as it negatively regulates the epidermal growth factor receptor (EGFR); however, the mechanism responsible for this particular risk SNP and its potential effect on LRIG1 are not known. Using CRISPR-CAS9, we edited rs11706832 in HEK293T cells. Four HEK293T clones with the risk allele were compared to four clones with the non-risk allele for LRIG1 and SLC25A26 gene expression using RT-qPCR, for global gene expression using RNA-seq, and for metabolites using gas chromatography-mass spectrometry (GC-MS). The experiment did not reveal any significant effect of the SNP on the expression levels or splicing patterns of LRIG1 or SLC25A26. The global gene expression analysis revealed that the risk allele C was associated with upregulation of several mitochondrial genes. Gene enrichment analysis of 74 differentially expressed genes in the genome revealed a significant enrichment of type I interferon response genes, where many genes were downregulated for the risk allele C. Gene expression data of IDH1 mutated LGGs from the cancer genome atlas (TCGA) revealed a similar under expression of type I interferon genes associated with the risk allele. This study found the expression levels and splicing patterns of LRIG1 and SLC25A26 were not affected by the SNP in HEK293T cells. However, the risk allele was associated with a downregulation of genes involved in the innate immune response both in the HEK293T cells and in the LGG data from TCGA.

DNA-repair gene variants are associated with glioblastoma survival.

Patient outcome from glioma may be influenced by germline variation. Considering the importance of DNA repair in cancer biology as well as in response to treatment, we studied the relationship between 1458 SNPs, which captured the majority of the common genetic variation in 136 DNA repair genes, in 138 glioblastoma samples from Sweden and Denmark. We confirmed our findings in an independent cohort of 121 glioblastoma patients from the UK. Our analysis revealed nine SNPs annotating MSH2, RAD51L1 and RECQL4 that were significantly (p < 0.05) associated with glioblastoma survival.

Pre-diagnostic levels of sVEGFR2, sTNFR2, sIL-2Rα and sIL-6R are associated with glioma risk: A nested case-control study of repeated samples.

No strong aetiological factors have been established for glioma aside from genetic mutations and variants, ionising radiation and an inverse relationship with asthmas and allergies. Our aim was to investigate the association between pre-diagnostic immune protein levels and glioma risk. We conducted a case-control study nested in the Northern Sweden Health and Disease Study cohort. We analysed 133 glioma cases and 133 control subjects matched by age, sex and date of blood donation. ELISA or Luminex bead-based multiplex assays were used to measure plasma levels of 19 proteins. Conditional logistic regression models were used to estimate the odds ratios and 95% CIs. To further model the protein trajectories over time, the linear mixed-effects models were conducted. We found that the levels of sVEGFR2, sTNFR2, sIL-2Rα and sIL-6R were associated with glioma risk. After adjusting for the time between blood sample collection and glioma diagnosis, the odds ratios were 1.72 (95% CI = 1.01-2.93), 1.48 (95% CI = 1.01-2.16) and 1.90 (95% CI = 1.14-3.17) for sTNFR2, sIL-2Rα and sIL-6R, respectively. The trajectory of sVEGFR2 concentrations over time was different between cases and controls (p-value = 0.031), increasing for cases (0.8% per year) and constant for controls. Our findings suggest these proteins play important roles in gliomagenesis.

Prediagnostic biomarkers for early detection of glioma-using case-control studies from cohorts as study approach.

Background: Understanding the trajectory and development of disease is important and the knowledge can be used to find novel targets for therapy and new diagnostic tools for early diagnosis. Methods: Large cohorts from different parts of the world are unique assets for research as they have systematically collected plasma and DNA over long-time periods in healthy individuals, sometimes even with repeated samples. Over time, the population in the cohort are diagnosed with many different diseases, including brain tumors. Results: Recent studies have detected genetic variants that are associated with increased risk of glioblastoma and lower grade gliomas specifically. The impact for genetic markers to predict disease in a healthy population has been deemed low, and a relevant question is if the genetic variants for glioma are associated with risk of disease or partly consist of genes associated to survival. Both metabolite and protein spectra are currently being explored for early detection of cancer. Conclusions: We here present a focused review of studies of genetic variants, metabolomics, and proteomics studied in prediagnostic glioma samples and discuss their potential in early diagnostics.

Distinct metabolic hallmarks of WHO classified adult glioma subtypes.

BACKGROUND: Gliomas are complex tumors with several genetic aberrations and diverse metabolic programs contributing to their aggressive phenotypes and poor prognoses. This study defines key metabolic features that can be used to differentiate between glioma subtypes, with potential for improved diagnostics and subtype targeted therapy. METHODS: Cross-platform global metabolomic profiling coupled with clinical, genetic, and pathological analysis of glioma tissue from 224 tumors-oligodendroglioma (n = 31), astrocytoma (n = 31) and glioblastoma (n = 162)-were performed. Identified metabolic phenotypes were evaluated in accordance with the WHO classification, IDH-mutation, 1p/19q-codeletion, WHO-grading 2-4, and MGMT promoter methylation. RESULTS: Distinct metabolic phenotypes separate all six analyzed glioma subtypes. IDH-mutated subtypes, expressing 2-hydroxyglutaric acid, were clearly distinguished from IDH-wildtype subtypes. Considerable metabolic heterogeneity outside of the mutated IDH pathway were also evident, with key metabolites being high expression of glycerophosphates, inositols, monosaccharides, and sugar alcohols and low levels of sphingosine and lysoglycerophospholipids in IDH-mutants. Among the IDH-mutated subtypes, we observed high levels of amino acids, especially glycine and 2-aminoadipic acid, in grade 4 glioma, and N-acetyl aspartic acid in low-grade astrocytoma and oligodendroglioma. Both IDH-wildtype and mutated oligodendroglioma and glioblastoma were characterized by high levels of acylcarnitines, likely driven by rapid cell growth and hypoxic features. We found elevated levels of 5-HIAA in gliosarcoma and a subtype of oligodendroglioma not yet defined as a specific entity, indicating a previously not described role for the serotonin pathway linked to glioma with bimorphic tissue. CONCLUSION: Key metabolic differences exist across adult glioma subtypes.

Assessment of the genetic and clinical determinants of hip fracture risk: Genome-wide association and Mendelian randomization study.

Hip fracture is the clinically most important fracture, but the genetic architecture of hip fracture is unclear. Here, we perform a large-scale hip fracture genome-wide association study meta-analysis and Mendelian randomization study using five cohorts from European biobanks. The results show that five genetic signals associate with hip fractures. Among these, one signal associates with falls, but not with bone mineral density (BMD), while four signals are in loci known to be involved in bone biology. Mendelian randomization analyses demonstrate a strong causal effect of decreased femoral neck BMD and moderate causal effects of Alzheimer's disease and having ever smoked regularly on risk of hip fractures. The substantial causal effect of decreased femoral neck BMD on hip fractures in both young and old subjects and in both men and women supports the use of change in femoral neck BMD as a surrogate outcome for hip fractures in clinical trials.

Analysis of DNA repair gene polymorphisms and survival in low-grade and anaplastic gliomas.

The purpose of this study was to explore the variation in DNA repair genes in adults with WHO grade II and III gliomas and their relationship to patient survival. We analysed a total of 1,458 tagging single-nucleotide polymorphisms (SNPs) that were selected to cover DNA repair genes, in 81 grade II and grade III gliomas samples, collected in Sweden and Denmark. The statistically significant genetic variants from the first dataset (P < 0.05) were taken forward for confirmation in a second dataset of 72 grade II and III gliomas from northern UK. In this dataset, eight gene variants mapping to five different DNA repair genes (ATM, NEIL1, NEIL2, ERCC6 and RPA4) which were associated with survival. Finally, these eight genetic variants were adjusted for treatment, malignancy grade, patient age and gender, leaving one variant, rs4253079, mapped to ERCC6, with a significant association to survival (OR 0.184, 95% CI 0.054-0.63, P = 0.007). We suggest a possible novel association between rs4253079 and survival in this group of patients with low-grade and anaplastic gliomas that needs confirmation in larger datasets.

Intraindividual Long-term Immune Marker Stability in Plasma Samples Collected in Median 9.4 Years Apart in 304 Adult Cancer-free Individuals.

BACKGROUND: Changes in immune marker levels in the blood could be used to improve the early detection of tumor-associated inflammatory processes. To increase predictiveness and utility in cancer detection, intraindividual long-term stability in cancer-free individuals is critical for biomarker candidates as to facilitate the detection of deviation from the norm. METHODS: We assessed intraindividual long-term stability for 19 immune markers (IL10, IL13, TNFα, CXCL13, MCP-3, MIP-1α, MIP-1ß, fractalkine, VEGF, FGF-2, TGFα, sIL2Rα, sIL6R, sVEGF-R2, sTNF-R1, sTNF-R2, sCD23, sCD27, and sCD30) in 304 cancer-free individuals. Repeated blood samples were collected up to 20 years apart. Intraindividual reproducibility was assessed by calculating intraclass correlation coefficients (ICC) using a linear mixed model. RESULTS: ICCs indicated fair to good reproducibility (ICCs ≥ 0.40 and < 0.75) for 17 of 19 investigated immune markers, including IL10, IL13, TNFα, CXCL13, MCP-3, MIP-1α, MIP-1ß, fractalkine, VEGF, FGF-2, TGFα, sIL2Rα, sIL6R, sTNF-R1, sTNF-R2, sCD27, and sCD30. Reproducibility was strong (ICC ≥ 0.75) for sCD23, while reproducibility was poor (ICC < 0.40) for sVEGF-R2. Using a more stringent criterion for reproducibility (ICC ≥ 0.55), we observed either acceptable or better reproducibility for IL10, IL13, CXCL13, MCP-3, MIP-1α, MIP-1ß, VEGF, FGF-2, sTNF-R1, sCD23, sCD27, and sCD30. CONCLUSIONS: IL10, IL13, CXCL13, MCP-3, MIP-1α, MIP-1ß, VEGF, FGF-2, sTNF-R1, sCD23, sCD27, and sCD30 displayed ICCs consistent with intraindividual long-term stability in cancer-free individuals. IMPACT: Our data support using these markers in prospective longitudinal studies seeking early cancer detection biomarkers.

Metabolomic patterns in glioblastoma and changes during radiotherapy: a clinical microdialysis study.

We employed stereotactic microdialysis to sample extracellular fluid intracranially from glioblastoma patients, before and during the first five days of conventional radiotherapy treatment. Microdialysis catheters were implanted in the contrast enhancing tumor as well as in the brain adjacent to tumor (BAT). Reference samples were collected subcutaneously from the patients' abdomen. The samples were analyzed by gas chromatography-time-of-flight mass spectrometry (GC-TOF MS), and the acquired data was processed by hierarchical multivariate curve resolution (H-MCR) and analyzed with orthogonal partial least-squares (OPLS). To enable detection of treatment-induced alterations, the data was processed by individual treatment over time (ITOT) normalization. One-hundred fifty-one metabolites were reliably detected, of which 67 were identified. We found distinct metabolic differences between the intracranially collected samples from tumor and the BAT region. There was also a marked difference between the intracranially and the subcutaneously collected samples. Furthermore, we observed systematic metabolic changes induced by radiotherapy treatment among both tumor and BAT samples. The metabolite patterns affected by treatment were different between tumor and BAT, both containing highly discriminating information, ROC values of 0.896 and 0.821, respectively. Our findings contribute to increased molecular knowledge of basic glioblastoma pathophysiology and point to the possibility of detecting metabolic marker patterns associated to early treatment response.

Proteomic profiles differ between bone invasive and noninvasive benign meningiomas of fibrous and meningothelial subtype.

Meningiomas of WHO grade I can usually be cured by surgical resection. However, some tumors may, despite their benign appearance, display invasive growth behavior. These tumors constitute a difficult clinical problem to handle. By histology alone, bone invasive meningiomas may be indistinguishable from their noninvasive counterparts. In this study we have examined the protein spectra in a series of meningiomas in search of protein expression patterns that may distinguish between bone invasive and noninvasive meningiomas. Tumor tissue from 13 patients with fibrous (6 invasive and 7 noninvasive) and 29 with meningothelial (10 invasive and 19 noninvasive) grade I meningiomas were analyzed by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI). Multivariate statistical methods were applied for data analyses. Comparing the protein spectra from invasive and noninvasive fibrous meningioma we found 22 peaks whose intensities were significantly different between the two groups (P < 0.001). Based on the expression pattern of these peaks we were able to perfectly separate the two entities (area under ROC curve = 1.0). In meningothelial meningioma the same comparison yielded six significantly differentially expressed peaks (P < 0.001), which to a large degree separated the invasive from noninvasive tissue (area under ROC curve = 0.873). By analyzing the protein spectra in benign meningiomas we could differentiate between invasive and noninvasive growth behavior in both fibrous and meningothelial meningiomas of grade I. A possibility for early identification of invasive grade I meningiomas may have a strong influence on the follow-up policy and the issue of early or late radiotherapy.

Genetic Variants in the 9p21.3 Locus Associated with Glioma Risk in Children, Adolescents, and Young Adults: A Case-Control Study.

BACKGROUND: Genome-wide association studies have identified germline genetic variants in 25 genetic loci that increase the risk of developing glioma in adulthood. It is not known if these variants increase the risk of developing glioma in children and adolescents and young adults (AYA). To date, no studies have performed genome-wide analyses to find novel genetic variants associated with glioma risk in children and AYA. METHODS: We investigated the association between 8,831,628 genetic variants and risk of glioma in 854 patients diagnosed up to the age of 29 years and 3,689 controls from Sweden and Denmark. Recruitment of patients and controls was population based. Genotyping was performed using Illumina BeadChips, and untyped variants were imputed with IMPUTE2. We selected 41 established adult glioma risk variants for detailed investigation. RESULTS: Three adult glioma risk variants, rs634537, rs2157719, and rs145929329, all mapping to the 9p21.3 (CDKN2B-AS1) locus, were associated with glioma risk in children and AYA. The strongest association was seen for rs634537 (odds ratioG = 1.21; 95% confidence interval = 1.09-1.35; P = 5.8 × 10-4). In genome-wide analysis, an association with risk was suggested for 129 genetic variants (P <1 × 10-5). CONCLUSIONS: Carriers of risk alleles in the 9p21.3 locus have an increased risk of glioma throughout life. The results from genome-wide association analyses require validation in independent cohorts. IMPACT: Our findings line up with existing evidence that some, although not all, established adult glioma risk variants are associated with risk of glioma in children and AYA. Validation of results from genome-wide analyses may reveal novel susceptibility loci for glioma in children and AYA.

The Genetic Architecture of Gliomagenesis-Genetic Risk Variants Linked to Specific Molecular Subtypes.

Genome-wide association studies have identified 25 germline genetic loci that increase the risk of glioma. The somatic tumor molecular alterations, including IDH-mutation status and 1p/19q co-deletion, have been included into the WHO 2016 classification system for glioma. To investigate how the germline genetic risk variants correlate with the somatic molecular subtypes put forward by WHO, we performed a meta-analysis that combined findings from 330 Swedish cases and 876 controls with two other recent studies. In total, 5,103 cases and 10,915 controls were included. Three categories of associations were found. First, variants in TERT and TP53 were associated with increased risk of all glioma subtypes. Second, variants in CDKN2B-AS1, EGFR, and RTEL1 were associated with IDH-wildtype glioma. Third, variants in CCDC26 (the 8q24 locus), C2orf80 (close to IDH), LRIG1, PHLDB1, ETFA, MAML2 and ZBTB16 were associated with IDH-mutant glioma. We therefore propose three etiopathological pathways in gliomagenesis based on germline variants for future guidance of diagnosis and potential functional targets for therapies. Future prospective clinical trials of patients with suspicion of glioma diagnoses, using the genetic variants as biomarkers, are necessary to disentangle how strongly they can predict glioma diagnosis.

A geographically matched control population efficiently limits the number of candidate disease-causing variants in an unbiased whole-genome analysis.

Whole-genome sequencing is a promising approach for human autosomal dominant disease studies. However, the vast number of genetic variants observed by this method constitutes a challenge when trying to identify the causal variants. This is often handled by restricting disease studies to the most damaging variants, e.g. those found in coding regions, and overlooking the remaining genetic variation. Such a biased approach explains in part why the genetic causes of many families with dominantly inherited diseases, in spite of being included in whole-genome sequencing studies, are left unsolved today. Here we explore the use of a geographically matched control population to minimize the number of candidate disease-causing variants without excluding variants based on assumptions on genomic position or functional predictions. To exemplify the benefit of the geographically matched control population we apply a typical disease variant filtering strategy in a family with an autosomal dominant form of colorectal cancer. With the use of the geographically matched control population we end up with 26 candidate variants genome wide. This is in contrast to the tens of thousands of candidates left when only making use of available public variant datasets. The effect of the local control population is dual, it (1) reduces the total number of candidate variants shared between affected individuals, and more importantly (2) increases the rate by which the number of candidate variants are reduced as additional affected family members are included in the filtering strategy. We demonstrate that the application of a geographically matched control population effectively limits the number of candidate disease-causing variants and may provide the means by which variants suitable for functional studies are identified genome wide.