Higher delay discounting (DD) (i.e., propensity to devalue larger, delayed rewards over immediate, smaller rewards) is a transdiagnostic marker underpinning multiple health behaviors. Although genetic influences account for some of the variability in DD among adults, less is known about the genetic contributors to DD among preadolescents. We examined whether polygenic scores (PGS) for DD, educational attainment, and behavioral traits (i.e., impulsivity, inhibition, and externalizing behavior) were associated with phenotypic DD among preadolescents. Participants included youth (N = 8982, 53% male) from the Adolescent Brain Cognitive Development Study who completed an Adjusting Delay Discounting Task at the 1-year follow-up and had valid genetic data. PGS for DD, educational attainment, impulsivity, inhibition, and externalizing behaviors were created based on the largest GWAS available. Separate linear mixed effects models were conducted in individuals most genetically similar to European (EUR; n = 4972), African (AFR; n = 1769), and Admixed American (AMR; n = 2241) reference panels. After adjusting for age, sex, income, and the top ten genetic ancestry principal components, greater PGS for DD and lower educational attainment (but not impulsivity, inhibition, or externalizing) were associated with higher rates of DD (i.e., preference for sooner, smaller rewards) in participants most genetically similar to EUR reference panels. Findings provide insight into the influence of genetic propensity for DD and educational attainment on the discounting tendencies of preadolescents, particularly those most genetically similar to European reference samples, thereby advancing our understanding of the etiology of choice behaviors in this population.

Background Opioid use disorder (OUD) is a major public health crisis. Patients' initial exposure to opioids often comes from prescribed medications. Predicting which of these patients will develop OUD remains challenging. Prior evidence from various substances suggest that initial subjective responses influence addiction risk, however these studies have used relatively small cohorts and have not led to the development of widespread tools to predict OUD risk. Methods We used a cohort of 141,897 adult research participants to perform a retrospective observational study of self-reported subjective responses to prescription opioids. We collected demographics, subjective positive (e.g., euphoria), subjective negative (e.g., nausea), and analgesic responses as well as self-reported OUD. Results Positive subjective effects, particularly "Like Overall", "Euphoric", and "Energized", were the strongest predictors of OUD. For example, the odds-ratio for individuals responding "Extremely" for "Like Overall" was 36.5. The sensitivity and specificity of this single question was excellent (ROC=0.87). Negative effects and analgesic effects were much less predictive. We developed a two-question decision tree ("When you first took opioid pain medication, to what extent did you like the way they made you feel overall?" and "When you first took opioid pain medication, to what extent did you experience an unpleasant itchy feeling?"), that can identify a small high-risk subset with 78.5% prevalence of OUD and a much larger low-risk subset with 1.2% prevalence of OUD. Conclusions Screening for subjective responses can identify high-risk individuals who would benefit from tailored interventions.

Delay discounting (DD), a person’s preference for smaller immediate rewards over larger delayed rewards, is a heritable trait that is associated with psychiatric and physical outcomes, yet the biological mechanisms underlying these links are not known. We performed a GWAS of DD using 134,935 23andMe research participants and identified 11 genome-wide significant loci. We did not replicate our previously reported association with rs6528024 (chrXq13.3, GPM6B; P = 5.30 × 10−02). The SNP-heritability of DD was 9.85 ± 0.57%. We observed genetic correlations between DD and 73 behavioral, physical, and neuroimaging traits, many of which persisted even after accounting for educational attainment, intelligence, and executive function. Network analysis revealed that the associations between DD and certain traits were explained by both overlapping and trait-specific biological processes. In a hospital-based cohort (N = 66,917), DD polygenic scores were associated with 212 medical conditions. These results demonstrate that DD has a pleiotropic and polygenic common variant architecture, and is genetically associated with numerous outcomes, making it a promising endophenotype for psychiatric and physical health.

Background Decades of research have identified a strong association between heavy cannabis use and schizophrenia (SCZ), with evidence of correlated genetic factors. However, many studies on the genetic relationship between cannabis use and psychosis have lacked data on both phenotypes within the same individuals, creating challenges due to unmeasured confounding. We aimed to address this by using multimodal data from the All of Us Research Program, which contains genetic data as well as information on SCZ diagnosis and cannabis use.Methods We tested the association between cannabis use disorder (CUD) and SCZ polygenic scores (PGSs) with SCZ and heavy cannabis use. We tested models where both CUD and SCZ PGSs were included as joint predictors of heavy cannabis use and SCZ case status. We defined three sets of cases based on comorbidities: relaxed (assessing for only the primary condition), strict (excluding comorbidity), and dual-comorbidity.Results CUD and SCZ polygenic liability were independently associated with heavy cannabis use; the SCZ PGS effect was very modest. In contrast, both SCZ and CUD PGSs were independently associated with SCZ, with independent significant effects of CUD PGS. Polygenic liability to CUD was associated with SCZ in individuals without a documented history of cannabis use, suggesting widespread pleiotropy.Conclusions These findings underscore the need for comprehensive models that integrate genetic risk factors for heavy cannabis use to advance our understanding of SCZ etiology.

Cannabis is one of the most widely used drugs globally. We performed genome-wide association studies (GWASs) of lifetime (N = 131,895) and frequency (N = 73,374) of cannabis use. For lifetime cannabis use, we identified two loci, one near CADM2 (rs35827242, p = 4.63E-12) and another near GRM3 (rs12673181, p = 6.90E-09). For frequency of cannabis use, we identified one locus near CADM2 (rs4856591, p = 8.10E-09; r2 = 0.76 with rs35827242). Lifetime and frequency of cannabis use were heritable (12.88 vs. 6.63%) and genetically correlated with previous GWASs of lifetime use and cannabis use disorder (CUD), as well as other substance use and cognitive traits. Polygenic scores (PGSs) for lifetime and frequency of cannabis use predicted cannabis use phenotypes in All of Us participants. A phenome-wide association study using a PGS for lifetime cannabis use to interrogate a hospital cohort replicated prior associations with substance use and mood disorders, and uncovered novel associations with celiac and infectious diseases. This work demonstrates the utility of pre-addiction phenotypes in cannabis use genomic discovery.

Tobacco use disorder (TUD) is the most prevalent substance use disorder in the world. Genetic factors influence smoking behaviours and although strides have been made using genome-wide association studies to identify risk variants, most variants identified have been for nicotine consumption, rather than TUD. Here we leveraged four US biobanks to perform a multi-ancestral meta-analysis of TUD (derived via electronic health records) in 653,790 individuals (495,005 European, 114,420 African American and 44,365 Latin American) and data from UK Biobank (ncombined = 898,680). We identified 88 independent risk loci; integration with functional genomic tools uncovered 461 potential risk genes, primarily expressed in the brain. TUD was genetically correlated with smoking and psychiatric traits from traditionally ascertained cohorts, externalizing behaviours in children and hundreds of medical outcomes, including HIV infection, heart disease and pain. This work furthers our biological understanding of TUD and establishes electronic health records as a source of phenotypic information for studying the genetics of TUD. In 653,790 individuals, this multi-ancestral meta-analysis of tobacco use disorder finds 461 potential risk genes and hundreds of associations with health outcomes, showcasing the utility of electronic health records for genetic research.

Coffee is one of the most widely consumed beverages. We performed a genome-wide association study (GWAS) of coffee intake in US-based 23andMe participants (N=130,153) and identified 7 significant loci, with many replicating in three multi-ancestral cohorts. We examined genetic correlations and performed a phenome-wide association study across thousands of biomarkers and health and lifestyle traits, then compared our results to the largest available GWAS of coffee intake from UK Biobank (UKB; N=334,659). The results of these two GWAS were highly discrepant. We observed positive genetic correlations between coffee intake and psychiatric illnesses, pain, and gastrointestinal traits in 23andMe that were absent or negative in UKB. Genetic correlations with cognition were negative in 23andMe but positive in UKB. The only consistent observations were positive genetic correlations with substance use and obesity. Our study shows that GWAS in different cohorts could capture cultural differences in the relationship between behavior and genetics.

Delay discounting (DD), a person's preference for smaller immediate rewards over larger delayed rewards, is a heritable trait that is associated with psychiatric and physical outcomes, yet the biological mechanisms underlying these links are not known. We performed a GWAS of DD using 134,935 adults and identified 14 genome-wide significant loci. We observed genetic correlations between DD and 73 behavioral, physical and neuroimaging traits, many of which persisted even after accounting for educational attainment, intelligence, and executive function. Network analysis revealed that the associations between DD and certain traits were explained by both overlapping and trait-specific biological processes. In a hospital-based cohort (N = 66,917), DD polygenic scores were associated with 212 medical conditions. These results demonstrate that DD has a pleiotropic and polygenic common variant architecture, and is genetically associated with numerous outcomes, making it a promising endophenotype for psychiatric and physical health.

Genome-wide association studies (GWAS) have identified hundreds of common variants associated with alcohol consumption. In contrast, rare variants have only begun to be studied for their role in alcohol consumption. No studies have examined whether common and rare variants implicate the same genes and molecular networks. To address this knowledge gap, we used publicly available alcohol consumption GWAS summary statistics (GSCAN, N=666,978) and whole exome sequencing data (Genebass, N=393,099) to identify a set of common and rare variants for alcohol consumption. Gene-based analysis of each dataset have implicated 294 (common variants) and 35 (rare variants) genes, including ethanol metabolizing genes ADH1B and ADH1C, which were identified by both analyses, and ANKRD12, GIGYF1, KIF21B, and STK31, which were identified only by rare variant analysis, but have been associated with related psychiatric traits. We then used a network colocalization procedure to propagate the common and rare gene sets onto a shared molecular network, revealing significant overlap. The shared network identified gene families that function in alcohol metabolism, including ADH, ALDH, CYP, and UGT. 74 of the genes in the network were previously implicated in comorbid psychiatric or substance use disorders, but had not previously been identified for alcohol-related behaviors, including EXOC2, EPM2A, CACNB3, and CACNG4. Differential gene expression analysis showed enrichment in the liver and several brain regions supporting the role of network genes in alcohol consumption. Thus, genes implicated by common and rare variants identify shared functions relevant to alcohol consumption, which also underlie psychiatric traits and substance use disorders that are comorbid with alcohol use.

Tobacco use disorder (TUD) is the most prevalent substance use disorder in the world. Genetic factors influence smoking behaviors, and although strides have been made using genome-wide association studies (GWAS) to identify risk variants, the majority of variants identified have been for nicotine consumption, rather than TUD. We leveraged five biobanks to perform a multi-ancestral meta-analysis of TUD (derived via electronic health records, EHR) in 898,680 individuals (739,895 European, 114,420 African American, 44,365 Latin American). We identified 72 independent risk loci; integration with functional genomic tools uncovered 330 potential risk genes, primarily expressed in the brain. TUD was genetically correlated with smoking and psychiatric traits from traditionally ascertained cohorts, externalizing behaviors in children, and hundreds of medical outcomes, including HIV infection, heart disease, and pain. This work furthers our biological understanding of TUD and establishes EHR as a source of phenotypic information for studying the genetics of TUD.

We examined problematic alcohol use using both genome-wide association studies for common alleles (alcohol intake (drinks per week) and problematic alcohol use), and association and burden tests for rare variants. We compared several methods for mapping variants to genes, including MAGMA, PASCAL, H-MAGMA, S-PrediXcan and S-MultiXcan. From these genesets, we examined the overlap between the genes identified using the common and rare approaches, then subsequently built gene networks using network propagation built upon the PCNet interactome. We explored these shared molecular networks using various means, including differentially tissue expression, functional enrichment, and hierarchical clustering. We examined problematic alcohol use using both genome-wide association studies for common alleles (alcohol intake (drinks per week) and problematic alcohol use), and association and burden tests for rare variants. We compared several methods for mapping variants to genes, including MAGMA, PASCAL, H-MAGMA, S-PrediXcan and S-MultiXcan. From these genesets, we examined the overlap between the genes identified using the common and rare approaches, then subsequently built gene networks using network propagation built upon the PCNet interactome. We explored these shared molecular networks using various means, including differentially tissue expression, functional enrichment, and hierarchical clustering. We observed limited overlap when considering the lists of genes identified by our alcohol intake rare variants and both common variant approaches (alcohol intake n=1, p=0.55; problematic alcohol use n=2, p=0.063). However, after network propagation, we identified significant network overlap between the alcohol intake rare variant network and both common variant networks (alcohol intake p=5.7 × 10-3, problematic alcohol use p=1.7 × 10-5). These shared networks included genes associated with alcohol metabolism, such as those in the alcohol dehydrogenase family and cytochrome P-450 enzymes, as well as gene networks not previously implicated in alcohol use-related phenotypes. Both networks also identified genes associated with various alcohol use traits identified through GWAS and showed enrichment for genes differentially expressed in alcohol metabolism and neurological tissues. Together, this work illustrates the use of network biology to integrate rare and common-variants to investigate mechanisms of problematic alcohol use. We are currently pursuing a similar approach for other substance use disorder phenotypes.

Delay discounting (DD) is a heritable transdiagnostic trait, or endophenotype, that has been implicated in multiple psychiatric diseases, including substance use disorders and attention deficit hyperactivity disorder. A prior genome-wide association study (GWAS) of DD identified genetic correlations with these and other traits but was underpowered for genome-wide discovery. In collaboration with 23 and Me, Inc., we collected responses to a 30-item delay discounting questionnaire from 134,945 research participants, and performed a GWAS assuming an additive genetic model that included age, sex, the first five genetic principal components, and indicator variables for genotype platforms as covariates. We further explored the genetic architecture of DD and the pleiotropic mechanisms with other outcomes using an array of genomic tools, such as MAGMA, H-MAGMA, S-MultiXcan and S-PrediXcan, LDSC and LAVA (Local Analysis of [co]Variant Annotation). We identified 14 significant loci associated with DD, with an estimated SNP-heritability of 9.86% (± 0.57%). Most of these loci (e.g., rs34645063, chr6q16.1, p=3.20E-13; rs3020805, chr16p11.2, p=6.50E-10) have been previously associated with various other behavioral traits, including risk-taking, alcohol consumption, educational variables and cognitive ability, and psychiatric disorders, as well as obesity and BMI. Genetic correlation analyses revealed significant associations with 27 traits, such as educational variables (e.g., years of education rg=-0.57, SE=0.03; intelligence rg=-0.39, SE=0.03), smoking behaviors (e.g., smoking initiation rg=0.32, SE=0.02; tobacco use disorder (TUD) rg=0.33, SE=0.03), risky behaviors (e.g., externalizing rg=0.30, SE=0.03), and health-related outcomes (BMI rg=0.28, SE=0.03). Local genetic correlation analysis revealed 20 significant bivariate loci between DD and these 27 other traits. Among them, the locus comprising the NCAM1-TTC12-ANKK1-DRD2 gene cluster was positively correlated with 12 traits, including substance use traits (i.e., drinks per week rg = 0.61; problematic alcohol use rg = 0.47; cannabis initiation rg = 0.63; TUD rg = 0.46; smoking initiation rg = 0.31; cigarettes per day rg = 0.34), and psychiatric disorders (schizophrenia rg = 0.52). Polygenic analyses in a hospital-based cohort (BioVU, N=69,447) showed that the DD polygenic risk score (PGS) was significantly associated with 127 medical traits across 16 categories, the strongest association being with TUD (p=1.21E-16) and mood disorders (p=5.26E-10). Beyond psychiatric disorders, the PGS for DD was also positively associated with medical phenotypes, such as Diabetes Mellitus (p=1.75e-06), ischemic heart disease (p=8.51e-06), and hypertension (p=1.63e-05). Our results support the polygenic architecture of DD, identifying novel significant loci and highlighting common genetic factors between DD and other psychiatric and somatic health outcomes. This work further establishes DD as a valuable endophenotype.

Externalizing (EXT) refers to a group of psychiatric disorders and behaviors related to self-regulation, such as substance use disorders, aggression, and antisocial behaviors. Externalizing behaviors often vary by sex (male/female) and co-occur with health-related outcomes. These associations may reflect common genetic factors that influence both externalizing behavior and other health-related outcomes. Using electronic health records (EHR) obtained from a US-based biobank from the Vanderbilt University Medical Center biorepository (BioVU; N = 72,225), we performed a phenome-wide association study (PheWAS) to assess the sex-stratified associations between a polygenic scores (PGS) for EXT and 1,817 clinical EHR-based diagnoses. The sex-stratified PGS for EXT was based on the latest EXT genome-wide association study (N = 1,492,085). We first prioritized phenotypes with a main effect in at least one sex (i.e., male or female) and then returned to the sex-combined analysis to test whether there were significant differences in the odds ratios between males and females by performing an interaction test (Sex*PGS). We identified 418 medical conditions associated with PGS-EXT (FDR 5%); 154 traits were associated in both males and females; 172 and 92 traits were associated in only females or males respectively. The sex‐combined EXT PGS interaction analysis revealed 9 FDR p < 0.01 significant interactions. Traits such as HIV (males: B = 0.33, p = 1.97E-15; females: B = 0.54, p = 2.25E-08), alcohol-related disorders (males: B = 0.33, p = 3.16E-24; females: B = 0.40, p = 9.85E-17), and viral hepatitis (males: B = 0.36, p = 4.39E-26; females: B = 0.44, p = 4.63E-22) were more strongly associated with the EXT PGS in males, whereas traits pertaining to cardiovascular health, such as ischemic heart disease (males: B = 0.08, p = 1.87E-07; females: B = 0.15, p = 4.23E-16) and coronary atherosclerosis (males: B = 0.08, p = 1.36E-06; females: B = 0.14, p = 7.85E-13), were more strongly associated in females. Overall our findings indicate a sex-specific increased risk of medical conditions in individuals with high genetic liability for EXT. The effect sizes for the substance use and infectious disease traits were greater in males, while the effect sizes for cardiovascular conditions were greater in females, indicating that males and females may be particularly vulnerable to these specific conditions. These results highlight the benefits of sex-specific PheWAS of externalizing symptoms and other behavioral and somatic traits.

Caffeinated coffee is one of the most consumed beverages; its habitual consumption is heritable but is also influenced by environmental factors such as cultural norms surrounding the consumption of caffeinated beverages. While some epidemiological studies suggest that habitual coffee intake may have health benefits, others indicate that it increases the risk of certain cancers, metabolic disorders, and substance use. Previous genetic studies have investigated the biological basis of habitual coffee intake and its relationship with select health outcomes, but no studies have examined if these associations are subject to cultural or cohort influences. We conducted a genome-wide association study (GWAS) of coffee intake in 23 and Me research participants of European ancestry (N=130,135). Secondary analyses were performed using Multimarker Analysis of Genomic Annotation (MAGMA), Hi-C-coupled-MAGMA, and Summary-MultiXcan to explore associations between genes, transcriptomics, and tissues with coffee intake. To investigate genetic associations between health and coffee intake in two culturally unique cohorts, summary statistics of coffee intake from 23 and Me and the UK Biobank (UKB; N=334,659) were used to calculate genetic correlations with 316 health, psychiatric, and anthropomorphic traits using Linkage Disequilibrium Score Regression (LDSC). Polygenic scores (PGS) of coffee intake were calculated in a hospital-based cohort (BioVU; N=72,225) to conduct phenome- and lab-wide association analyses across 2,137 medical phenotypes and biomarkers, respectively. We identified seven loci, all of which replicated associations from prior coffee GWAS. Gene-based analyses supported transcriptional regulation of coffee intake genes within the nervous and digestive systems. Comparisons across top loci (p < 5E-08) between 23 and Me and UKB cohorts revealed that only 52% of the 29 significant variants shared the same direction of effect on coffee intake. Comparisons across 23 and Me and UKB summary statistics of habitual coffee intake revealed predominantly discrepant or inconsistent genetic associations - 83% of correlations with 23 and Me summary statistics indicated greater health risk by coffee intake versus 54% in the UKB. Elevated risk for substance use and obesity were consistent in both cohorts. We expanded previous GWAS of coffee intake to 23 and Me research participants based in the US, replicating previous associations and uncovering biological relationships with tissues and gene sets important for digestive and nervous system function. Aside from consistent positive associations with substance use and obesity, we observed significant differences in genetic associations between two large cohorts of European ancestry, suggesting that genetic predisposition for coffee intake and associations with other health traits may be influenced by cultural differences. Our study provides a cautionary perspective on combining large cohort datasets from populations of similar ancestries that are exposed to different environments.

Background Maintenance of telomere length has long been established to play a role in the biology of cancer, and several lines of evidence suggest that it may be especially important in myeloid malignancy. Measuring leukocyte telomere length (LTL) directly in case control studies is problematic because telomere length is likely to be influenced by the disease process. Recent studies have led to the identification of a robust genetic predictor of LTL, which we have used to compare differences in predicted telomere length in case control studies of myeloid leukemia and myelodysplastic syndromes (MDS) using Mendelian randomization (MR). MR is a method used to measure the association between a trait (e.g. telomere length) and an outcome (e.g. myeloid malignancy) that can't be measured directly by utilizing genetic variants known to be associated with the trait as instrumental variables. The use of genetic predictors of telomere length reduces the possibility that the association is explained by reverse causation and confounding likely to be relevant in case-control studies where telomere length is measured directly because genotypes in the general population should be randomly distributed. Additionally, predicted telomere length has the added advantage of representing telomere length over the lifetime of the individual while measuring LTL directly will represent only one time point in an individual's life. Methods Myeloid leukemia (AML and CML) and MDS cases were identified by rapid case ascertainment through the Minnesota Cancer Surveillance System (MCSS). Centralized pathology and cytogenetics reviews were conducted to confirm diagnosis and classify by subtypes. Controls were identified through the Minnesota State driver's license/identification card list. We used the Sequenom platform to genotype seven SNPs that were validated predictors of LTL in a recently published meta-analysis. We estimated associations between individual SNPs and myeloid malignancy using multivariable logistic regression. Mendelian randomization was used to evaluate the association between predicted LTL and risk of myeloid malignancy. Results We included 253 AML cases, 353 MDS cases, 145 CML cases, and 1,042 controls. We observed a significant association between longer predicted telomere length and AML in analyses adjusted for sex and age (OR 4.82 per additional kilobase of average telomere length, 95% CI 1.32-17.63). The individual SNP analyses suggest that the association is largely driven by rs10936599, located in TERC (OR 1.47 per allele, 95% CI 1.14-1.90; Figure). We did not observe statistically significant associations between predicted LTL and MDS or CML. Discussion In this analysis of predicted telomere length and myeloid malignancy, we identified a significant association between longer predicted leukocyte telomere length and AML. In contrast, no significant association was observed for MDS or CML although we did observe an elevated OR for longer predicted telomere length and MDS that did not reach statistical significance. Figure Disclosures No relevant conflicts of interest to declare.

Background Genetically predicted leukocyte telomere length (LTL) has been evaluated in several studies of childhood and adult cancer. We test whether genetically predicted longer LTL is associated with germ cell tumours (GCT) in children and adults. Methods Paediatric GCT samples were obtained from a Children’s Oncology Group study and state biobank programs in California and Michigan ( N = 1413 cases, 1220 biological parents and 1022 unrelated controls). Replication analysis included 396 adult testicular GCTs (TGCT) and 1589 matched controls from the UK Biobank. Mendelian randomisation was used to look at the association between genetically predicted LTL and GCTs and TERT variants were evaluated within GCT subgroups. Results We identified significant associations between TERT variants reported in previous adult TGCT GWAS in paediatric GCT: TERT /rs2736100-C (OR = 0.82; P = 0.0003), TERT /rs2853677-G (OR = 0.80; P = 0.001), and TERT /rs7705526-A (OR = 0.81; P = 0.003). We also extended these findings to females and tumours outside the testes. In contrast, we did not observe strong evidence for an association between genetically predicted LTL by other variants and GCT risk in children or adults. Conclusion While TERT is a known susceptibility locus for GCT, our results suggest that LTL predicted by other variants is not strongly associated with risk in either children or adults.

Pediatric malignant germ cell tumors (GCTs) are heterogeneous but are grouped together due to the presumed common cell of origin, the primordial germ cell (PGC). Little is known about etiology; however, evidence supporting in utero origins of pediatric and adult GCT suggests that disruptions in normal germ cell development are likely to be highly relevant. Germline copy number variants (CNVs) are a plausible source of inherited genetic variation and have not been thoroughly evaluated to date. CNVs on the X chromosome are of particular interest because individuals with both Klinefelter syndrome (47, XXY) and Turner syndrome (45, X) are at increased risk of pediatric GCT, suggesting that alterations in X chromosome dosage contribute to etiology. A family study of adult testicular cancer where at least one member had bilateral cancer identified a significant linkage peak at Xq27 (hLOD=4.7). In addition, somatic copy number gains of chromosome X are frequently seen in adult testicular germ cell tumors. Given these findings in adult GCT, similar aberrations may also be relevant risk factors for the pediatric group. Pediatric GCT samples were obtained from a Children’s Oncology Group study and state biobank programs in Michigan and California. Genotyping array data were generated using the Illumina HumanCoreExome Beadchip (Illumina, San Diego). CNV calls were made for 2,002 GCT cases and 1,402 controls with high quality intensity data using Genvisis, which has specialized algorithms to make CNV calls on the X chromosome. Klinefelter syndrome cases (n=27) were excluded from the analyses. No study sample had classic Turner syndrome; however, one female case had a giant 51Mb deletion of Xq. Analyses were performed for females-only, males-only, and everyone together. We identified two regions that are study-wide significant on chromosome X at Xq27.1 and Xp11.2 and one nominally significant near Xq28. The most significant finding was identified in females-only and is located within a region that encodes genes within the SPANX family. Evidence suggests these genes affect germ cells; however, supporting data is reported in spermatozoa rather than oocytes. The events in our study were large (240kb), had similar breakpoints, and appear to be flanked by repetitive DNA that might lead to recurrent events at the same region. The variant was present in the control samples within gnomAD at a low allele frequency (0.18%). In our study, both deletions and duplications were noted: 11 female cases (1 or 2 expected based on gnomAD allele frequencies) and 0 female controls (0-1 expected). The finding was not replicated in males (6 male cases and 5 male controls harbored similar CNVs). We are in the process of validating these in silico and potentially via quantitative PCR. Findings may shed light on disruptions in normal development that may increase risk of GCT. Citation Format: Shannon S. Cigan, John J. Meredith, John Zaharick, Erica Langer, Anthony J. Hooten, John A. Lane, Nathan Pankratz, Jenny N. Poynter. Inherited copy number variants that increase risk of developing pediatric germ cell tumors with a particular focus on the X chromosome [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2497.