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. 2024 Sep 26;6(1):100356. doi: 10.1016/j.xhgg.2024.100356

A phenome-wide association study of polygenic scores for selected childhood cancer: Results from the UK Biobank

Eun Mi Jung 1,∗, Andrew R Raduski 1, Lauren J Mills 1,2, Logan G Spector 1,2,3,∗∗
PMCID: PMC11538869  PMID: 39340156

Summary

The aim of this study was to scan phenotypes in adulthood associated with polygenic risk scores (PRS) for childhood cancers with well-articulated genetic architectures—acute lymphoblastic leukemia (ALL), Ewing sarcoma, and neuroblastoma—to examine genetic pleiotropy. Furthermore, we aimed to determine which SNPs could drive associations. Per-SNP summary statistics were extracted for PRS calculation. Participants with white British ancestry were exclusively included for analyses. SNPs were queried from the UK Biobank genotype imputation data. Records from the cancer registry, death registry, and inpatient diagnoses were abstracted for phenome-wide scans. Firth logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) alongside corresponding p values, adjusting for age at recruitment and sex. A total of 244,332 unrelated white British participants were included. We observed a significant association between ALL-PRS and ALL (OR: 1.20e+24, 95% CI: 9.08e+14–1.60e+33). In addition, we observed a significant association between high-risk neuroblastoma PRS and nonrheumatic aortic valve disorders (OR: 43.9, 95% CI: 7.42–260). There were no significant phenotype associations with Ewing sarcoma and neuroblastoma PRS. Regarding individual SNPs, rs17607816 increased the risk of ALL (OR: 6.40, 95% CI: 3.26–12.57). For high-risk neuroblastoma, rs80059929 elevated the risk of atrioventricular block (OR: 3.04, 95% CI: 1.85–4.99). Our findings suggest that individuals with genetic susceptibility to ALL may face a lifelong risk for developing ALL, along with a genetic pleiotropic association between high-risk neuroblastoma and circulatory diseases.

Keywords: childhood cancer, etiology, polygenic risk score, phenome-wide association study, pleiotropy, UK Biobank

Jung and colleagues found that individuals with a cumulative genetic risk for childhood acute lymphoblastic leukemia may face a lifelong risk of developing the disease. Additionally, those with a higher genetic risk for high-risk neuroblastoma may have an increased risk for circulatory diseases, suggesting shared biological mechanisms.

Introduction

Genome-wide association studies (GWASs) in large population-based studies have demonstrated the inherited genetic susceptibility to various cancer types.1 However, GWASs focusing on childhood cancer are limited due to the rarity of these patients. To date, multiple GWASs have been conducted for acute lymphoblastic leukemia (ALL [MIM: 613065]), Ewing sarcoma [MIM: 612219], and neuroblastoma [MIM: 256700],2 revealing sufficient germline SNPs. Compared to the 25,509 patients for ovarian cancer [MIM: 167000], 29,266 for lung cancer [MIM: 211980], 36,948 for colorectal cancer [MIM: 114500], and 81,318 for prostate cancer [MIM: 176807] reported in previous GWASs,3 childhood cancer patients included in GWAS are comparatively rarer. However, the effect sizes of SNPs in childhood cancers are generally larger.4 Specifically, the odds ratios (ORs) for adult cancers typically do not exceed 1.4,5 whereas ORs for childhood cancers frequently exceed 1.5. For instance, rs7089424 in ARID5B [MIM: 608538] and rs4132601 in IKZF1 [MIM: 603023] had ORs of 1.65 and 1.69, respectively, based on a study involving 907 ALL patients and 2,398 controls.6 Similarly, rs6939340 significantly increased the risk of neuroblastoma when present for both alleles, with an OR of 1.97, in a discovery set comprising 1,032 neuroblastoma patients and 2,043 controls.7 For Ewing sarcoma, ORs for risk loci located in TARDBP and EGR2 were 2.2 and 1.7, respectively, in a total of 733 Ewing sarcoma patients and 1,346 controls.8

Recently, individual genetic variants associated with childhood ALL have been assessed for pleiotropy and found to be associated with blood cell traits in the UK Biobank.9,10 However, investigating each SNP may present a potential limitation due to its failure to capture the polygenetic architecture of childhood cancer. A polygenic risk score (PRS), a collective estimation of genetic susceptibility of an individual, is a promising alternative to evaluate the genetic pleiotropy of childhood cancer. As a way of exploring genetic pleiotropy, various studies have employed a PRS-phenome-wide association study (PheWAS) approach, where multiple phenotypes were tested for association with PRS specific for diseases.11,12,13,14,15 However, few studies have performed PheWAS using PRS specific for childhood cancer. Large population-based biobank data, such as UK Biobank, offer rich genotype and phenotype information that is readily accessible, making them valuable resources for investigating the association between genetic susceptibility of childhood cancer and a broad range of phenotypes. The application of PRS-PheWAS approach in the UK Biobank can lead to the identification of novel phenotypes, with which childhood cancer share genetic architectures.

Studies have suggested that genetic susceptibility to childhood traits may be associated with phenotypes in adulthood.16,17,18 However, few studies have examined whether genetic susceptibility associated with childhood cancer is linked to phenotypes in adulthood, although genes involved the development of childhood cancer have been associated with other phenotypes in adulthood.19,20 Furthermore, childhood cancer survivors may be more susceptible to developing diseases that exhibit genetic pleiotropy with their initial cancer. This increased susceptibility can be attributable to the long-term adverse effects of treatments, such as chemotherapy and radiation therapy, as well as the potential influence of a shared genetic mechanism. The interplay between these factors highlights the need for ongoing research to better understand the long-term health outcomes in this population. Thus, the aim of this study was to scan phenotypes in adulthood using PRS for ALL, Ewing sarcoma, and neuroblastoma to determine genetic pleiotropy. Furthermore, we aimed to determine which individual SNPs could drive associations between PRS and phenotypes in adulthood.

Material and methods

Ethics statement

UK Biobank obtained approval as a Research Tissue Bank from the North West Multi-centre Research Ethics Committee. Thus, the present study is covered under Research Tissue Bank approval. All participants included consent to participate. This study was approved under application number 89902.

Study population

UK Biobank is a prospective cohort study comprising approximately 500,000 individuals aged between 40 and 69 years, recruited across the United Kingdom from 2006 to 2010.21 Genotyping was performed for all participants, using either the UK BiLEVE Axiom array or the UK Biobank Axiom array.21 Genotype imputation was performed using the merged UK10K and 1000 Genomes phase 3 reference panels.21 Variant IDs were assigned based on the Genome Reference Consortium Human Build 37 (GRCh37) reference genome.21 For the present study, we used the UK Biobank version 3 imputed data.

We screened genotype data to include individuals identified as white in genetic ethnic grouping and White British in ethnic background based on the information available in the UK Biobank data, thereby ensuring the inclusion of only those with White British ancestry. We decided to include only participants with white British ancestry, because Europeans broadly have the highest incidence rate of childhood cancer and the most GWAS data.22,23 Additionally, we included participants who were identified as either female or male in genetic sex and had no kinship found in the dataset, ensuring the inclusion of individuals without sex discordance or kinship. Finally, we included only individuals used in the calculation of principal components to ensure they met the internal quality control standards. Thus, individuals who had autosomal missing rates of 0.02 or less, were not classified as extreme missingness or heterozygosity, were in a maximal set of unrelated individuals, and were not sex discordant were included.24 An unrelated individual was defined as someone with no relatives within the third degree of kinship or closer.21

SNP curation and PRS calculation

We curated the list of SNPs associated with ALL, Ewing sarcoma, and neuroblastoma by reviewing the National Human Genome Research Institute-European Bioinformatics Institute GWAS Catalog and prior GWASs.25,26,27,28,29,30 For neuroblastoma, SNPs associated with the high-risk group were curated separately, as several SNPs have been specifically identified for high-risk neuroblastoma,7,31,32,33,34,35,36,37,38 allowing for the creation of subtype-specific PRS. Because SNPs associated with high-risk neuroblastoma were extracted from several different papers,28,29,30 linkage disequilibrium (LD) was checked using the British population in England and Scotland in the LDpop Tool.39 The threshold for LD was set at R2 < 0.2. When in LD, SNPs that overlapped between review papers were selected over those that appeared in one paper. In total, 23 SNPs were identified for ALL, 6 SNPs were identified for Ewing sarcoma, 22 SNPs were identified for neuroblastoma, and 6 SNPs were identified for high-risk neuroblastoma (Table S1).

SNPs were queried from the UK Biobank genotype imputation data through bgenix.40 Per-SNP summary statistics, including rsIDs, genomic ranges, effect alleles, non-effect alleles, and effect sizes, were extracted from either the National Human Genome Research Institute-European Bioinformatics Institute GWAS Catalog or prior GWASs for PRS calculation. Quality control was performed following previously published methods.24 For multi-allelic SNPs, only the alleles of interest were included. Additionally, if the effect allele and alternative allele in UK Biobank did not match, the sign of beta was flipped. SNPs were removed if they were ambiguous or had an imputation information score <0.4.

PRS were calculated using PLINK 2.0 alpha.41 They were computed by weighting the sum of risk alleles for each variant by the risk allele effect sizes. PRS were standardized by the number of SNPs included for calculating the score without imputing missing SNPs using no-mean-imputation option.24

Phenome-wide association

We utilized records from the cancer registry, death registry, and inpatient diagnoses. All diagnoses were coded according to the International Classification of Diseases, 10th revision (ICD-10). Diagnoses categorized into “Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere,” “Injury, poisoning and certain other consequences of external causes,” “External causes of morbidity and mortality,” “Factors influencing health status and contact with health services,” and “Codes for special purposes” were removed. Thus, ICD-10 codes starting with the letters R, S, T, U, V, W, X, Y, and Z were excluded.42

To map ICD-10 codes to Phecodes, identify patient and control groups, perform analyses, and plot the results, we used PheWAS package (R version 4.3.1, R Foundation for Statistical Computing, Vienna, Austria).43 More detailed descriptions of algorithms can be found in the software documentation.43 In short, the package automatically converts ICD-10 codes into Phecodes. Each PheWAS code has a set of phenotypes that cannot serve as controls due to their similarities in diseases. To be eligible as patients, the same diagnosis needs to be recorded on at least two different days. Due to the significant imbalances in the numbers of patients and controls, we used multivariable Firth logistic regression to mitigate bias,44 adjusting for age at recruitment and sex. Model convergence was confirmed through generalized linear regression analysis using a binomial distribution. To investigate the association between individual SNP and phenotypes, we also performed PheWAS for individual SNPs using multivariable Firth logistic regression, adjusting for age at recruitment and sex. The results were presented as ORs and 95% confidence intervals (CIs) alongside corresponding p values. Additionally, significance was assessed using Bonferroni correction and false discovery rate in the PheWAS package.43

Results

Study population characteristics

A total of 286,378 unrelated White British participants were included after sample quality control. Among them, 244,332 participants had records in the registry data. 54.3% of the study participants were female, and the median age at recruitment was 59 years old (Table 1).

Table 1.

Characteristics of the participants

No. of participants 244,332
Age at recruitment, y, median (25th; 75th) 59.0 (52.0; 64.0)
Sex, n (%) Male: 111,729 (45.7)
Female: 132,603 (54.3)
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A total of 6,265 distinct ICD-10 codes from inpatient diagnoses, cancer registry, and death registry data were mapped to 1,705 Phecodes. Phecodes with at least 20 patients or controls were included in the analysis.42 Although we excluded diseases categorized as “Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere,” “Injury, poisoning and certain other consequences of external causes,” “External causes of morbidity and mortality,” “Factors influencing health status and contact with health services,” and “Codes for special purposes,” some ICD-10 codes were mapped to Phecodes that fell into one of those categories. For instance, ICD-10 codes for sepsis started with the letter A. However, in the PheWAS analysis group, they fell into the category called “Injuries & poisonings.” Therefore, in the final Manhattan plots, some diseases were categorized into “Injuries & poisonings” and “Symptoms.”

PRS results

Table 2 presents the descriptive statistics of the PRS for each cancer type. Figures 1, 2, 3, and 4 show Manhattan plots to visualize the association between PRS and phenotypes in adulthood for each childhood cancer type. The top line indicates the threshold for Bonferroni significance.43 A list of the top 10 phenotypes for each childhood cancer type, regardless of statistical significance, can be found in Tables S2–S5.

Table 2.

Descriptive statistics of the PRS for each cancer type

Cancer type Median (minimum; maximum)
ALL −0.020 (−0.076; 0.070)
Ewing sarcoma 0.0021 (−0.22; 0.20)
Neuroblastoma 0.021 (−0.060; 0.12)
High-risk neuroblastoma −0.016 (−0.14; 0.40)
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Figure 1.

Figure 1

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Manhattan plot for phenotypes associated with ALL-PRS

Figure 2.

Figure 2

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Manhattan plot for phenotypes associated with Ewing sarcoma PRS

Figure 3.

Figure 3

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Manhattan plot for phenotypes associated with neuroblastoma PRS

Figure 4.

Figure 4

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Manhattan plot for phenotypes associated with high-risk neuroblastoma PRS

We observed a significant association between ALL PRS and ALL in adults, with an OR of 1.20e+24 (95% CI: 9.08e+14–1.60e+33; Figure 1; Table 3). The PRS for patients appeared higher compared to those for controls (Figure S1). No adulthood phenotypes were enriched for Ewing sarcoma PRS (Figure 2). For neuroblastoma PRS, no enriched phenotypes were identified (Figure 3). However, when PRS for high-risk neuroblastoma was examined, we observed a significant association with nonrheumatic aortic valve disorders (OR: 43.9, 95% CI:7.42–260; Figure 4; Table 3).

Table 3.

Significant results of phenome-wide scan of the PRS by cancer type

Cancer type Phecode Description Group No. of cases No. of controls OR (95% CI) p
ALL 204.11 lymphoid leukemia, acute neoplasms 31 238,761 1.20e+24 (9.08e+14–1.60e+33) 1.12e−6
High-risk neuroblastoma 395.2 nonrheumatic aortic valve disorders circulatory system 262 241,260 4.39e+1 (7.42e+0–2.60e+2) 5.06e−5
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Individual SNP results

Individual SNPs were also tested for phenome-wide scans. The results of phenome-wide scan for individual SNPs associated with each cancer type are shown in Tables S6–S9.

Figure 5 presents Manhattan plots for phenotypes associated with SNPs in ALL. Out of the 23 SNPs, those that showed significant results were included in Figure 5. rs11741255, located within the intron of the IRF1-AS1 gene, was inversely associated with the risks of electrolyte imbalance (OR: 0.44, 95% CI: 0.29–0.65; Table 4) and skin cancer (OR: 0.94, 95% CI: 0.91–0.97; Table 4). rs9415680, located within the intron of the JMJD1C gene [MIM: 604503], increased the risk of atrioventricular block (OR: 2.18, 95% CI: 1.52–3.13; Table 4). rs17607816, located within the intron of the IKZF3 gene [MIM: 606221], was associated with an increased risk of ALL (OR: 6.40, 95% CI: 3.26–12.57; Table 4).

Figure 5.

Figure 5

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Manhattan plots for phenotypes associated with SNPs in ALL

Table 4.

Significant results of phenome-wide scan of the individual SNPs associated with ALL

rsID Gene: consequence Phecode Description Group No. of cases No. of controls OR (95% CI) p
rs11741255 IRF1-AS1: intron variant 276.1 electrolyte imbalance endocrine/metabolic 50 241,570 0.44 (0.29–0.65) 4.81e−5
rs11741255 IRF1-AS1: intron variant 172 skin cancer neoplasms 9,109 221,552 0.94 (0.91–0.97) 5.04e−5
rs9415680 JMJD1C: intron variant 426.2 atrioventricular block circulatory system 73 231,797 2.18 (1.52–3.13) 8.97e−5
rs17607816 IKZF3: intron variant 204.11 ALL neoplasms 31 238,761 6.40 (3.26–12.57) 2.06e−5
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For SNPs associated with Ewing sarcoma, all associations were null (Table S7). Osteoarthrosis appeared twice in the associations for rs10822056. Additionally, spinal stenosis was one of the top phenotypes for rs113663169 and rs6106336, even though the directions of their effect sizes were different.

No significant associations were found for SNPs associated with neuroblastoma. Out of 22 SNPs, nine SNPs were associated with circulatory system diseases as the primary phenotype. Among these nine SNPs, rs6912869 and rs9348500 were found to have congestive heart failure (nonhypertensive) as the primary phenotype (Table S8).

Figure 6 presents Manhattan plots for phenotypes associated with SNPs in high-risk neuroblastoma. For high-risk neuroblastoma, rs80059929, located within the intron of the KIF15 gene [MIM: 617569], was associated with an elevated risk of atrioventricular block (OR: 3.04, 95% CI: 1.85–4.99; Table 5).

Figure 6.

Figure 6

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Manhattan plots for phenotypes associated with SNPs in high-risk neuroblastoma

Table 5.

Result of phenome-wide scan of the individual SNPs associated with high-risk neuroblastoma

rsID Gene: consequence Phecode Description Group No. of cases No. of controls OR (95% CI) p
rs80059929 KIF15: intron variant 426.2 atrioventricular block circulatory system 73 231,797 3.04 (1.85–4.99) 0.00015
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Discussion

This study aimed to identify phenotypes that were associated with polygenetic predisposition to ALL, Ewing sarcoma, and neuroblastoma in the UK Biobank cohort using a phenome-wide association framework. We found that the increased childhood ALL PRS was associated with an elevated risk of ALL. The same association was observed for an individual SNP, rs17607816. In addition, we observed that the increased high-risk neuroblastoma PRS was associated with an elevated risk of nonrheumatic aortic valve disorders. At the individual SNP level, rs80059929 conferred an elevated risk of atrioventricular block.

It has been reported that children in the top 1% of PRS for B cell ALL have a 4.7 times higher relative risk compared to those with median PRS.45 Due to the lack of immunophenotypic information in the registry data, we were not able to confirm the immunophenotype of ALL patients. However, since B cell ALL accounts for about 80% of patients in children and 75% of patients in adults,46,47 it is plausible to assume that most of the patients in the UK Biobank data were B cell ALL. Thus, this finding suggests the potential collective impacts of genetic variants on the risk of ALL throughout life.

Moreover, a previous study has demonstrated that childhood leukemia survivors face nearly a 5-fold increased risk of developing subsequent primary leukemia.48 Additionally, research has indicated that the latency period between the primary diagnosis and the subsequent diagnosis can extend more than 15 years.49 While late effects of treatment such as chemotherapy and radiation therapy may contribute to this risk,49,50 genetic predispositions to ALL may also elevate the likelihood of developing subsequent hematopoietic malignancies. Our findings support long-term effects of genetic variants on the risks of ALL.

We observed the largest effect size in rs17607816 among individual SNPs. Thus, it is likely that rs17607816 drove the association between childhood ALL PRS and the risk of ALL in adulthood. rs17607816 is located in IKZF3, a hematopoietic-specific transcription factor that plays a crucial role in B cell development and regulation of immune cells.51,52 Given that IKFZ3 upregulation has been associated with an increased risk of chronic lymphocytic leukemia,53,54 it is reasonable to infer that persistent dysregulation of IKFZ3 may predispose individuals to ALL throughout life. However, further investigations are warranted to support our findings.

It is not surprising to see that no phenotypes were significantly enriched for Ewing sarcoma PRS and SNPs because, besides EWS/FLI-1 fusion, the etiology of Ewing sarcoma is enigmatic. A few investigations have identified clues to Ewing sarcoma etiology. Notably, Yang et al.55 observed a causal relationship between Ewing sarcoma and diaphragmatic hernia using genetic approaches. While we were not able to observe similar findings from PRS and individual SNPs, osteoarthrosis was more frequently shown in the top phenotypes of rs10822056 than those of other SNPs. However, the associations were not significant, and given that degenerative musculoskeletal diseases are common in the elderly, rs10822056 may not necessarily influence the risk of osteoarthrosis.

Circulatory diseases have been linked to PRS and germline variants associated with neuroblastoma and high-risk neuroblastoma. In particular, nonrheumatic aortic valve disorders, which encompass regurgitations and stenosis,56 were significantly enriched for high-risk neuroblastoma PRS. Both aortic stenosis and regurgitation become more prevalent with age.56 The two most common causes of aortic stenosis are calcification and congenital anomalies.56 Causes of regurgitation vary depending on their onset, the causes of acute aortic regurgitation may arise from dissection or endocarditis, while chronic aortic regurgitation may result from aortic stenosis or congenital anomalies.56 Thus, patients with congenital anomalies may have an increased risk of developing aortic stenosis and regurgitation over time. Furthermore, given that neuroblastoma survivors are at an excess risk of developing cardiac-related diseases due to anthracyclines and radiation therapy to the chest or upper abdomen,57 the presence of genetic predispositions along with the late effects of treatment may make high-risk neuroblastoma survivors more susceptible to circulatory diseases.

Epidemiologic studies have noted the association between neuroblastoma and congenital cardiac anomalies. A case-control study using data from the Children’s Oncology Group showed that congenital cardiac anomalies significantly elevated the risk of neuroblastoma in children aged 0–19 years (OR: 4.27, 95% CI: 1.22–15.0).58 Another case-control study in Washington state found that infants with congenital cardiac anomalies had a nearly 6-fold increased risk of neuroblastoma (OR: 5.84, 95% CI: 1.93–17.66).59 Additionally, a study including over 10 million live births supported these findings, reporting an OR of 7.8 (95% CI: 3.5–17.3) for left ventricular outflow tract defects, 3.6 (2.6–5.1) between atrial septal defects, and 3.9 (95% CI: 2.5–6.2) between patent ductus arteriosus.60

A possible biological mechanism linking cardiac anomalies and neuroblastoma is their shared developmental origin, as neural crest cells are essential in cardiogenesis.61,62 Case series further supported these findings, revealing that the prevalence of cardiac anomalies among patients with neuroblastoma could be as high as 20%.63,64

Among individual SNPs, rs80059929 was found to elevate the risk of atrioventricular block. Even though atrioventricular block affects different parts of the heart and has distinct pathological mechanisms compared to nonrheumatic aortic valve disorders, atrioventricular block can occur postoperatively following aortic valve replacement.65 rs80059929 is located within KIF15, which plays a significant role in neuronal development and spindle separation during mitosis.66,67 The KIF15 gene has been implicated in pathologic cardiac remodeling, acting downstream of Forkhead box protein O6.68 The specific association between rs80059929 and MYCN-amplified neuroblastoma further underscores potential genetic mechanisms that circulatory diseases and high-risk neuroblastoma may share,38 because MYCN is also known for its influence on heart development and its residual expression in the adult heart.69,70,71 However, because the regulatory pathways through which rs80059929 and MYCN-amplification predispose individuals to neuroblastoma and circulatory diseases are unclear, additional investigation is necessary.

UK Biobank has high-quality genotype and phenotype data that allowed us to link PRS to diseases recorded from national cancer registry, death registry, and hospital inpatient diagnoses. We used the most recent GWAS to construct PRS to investigate the cumulative risk of SNPs, which reflect a complex genetic architecture of ALL, Ewing sarcoma, and neuroblastoma. Methodologically, this study is robust because we employed Firth’s logistic regression to control type I error due to small patient counts. Furthermore, we adopted a hypothesis-free approach to overcome limitations imposed by the incomplete understanding of etiologies. In addition to the association between childhood cancer PRS and phenotypes in adulthood, we conducted additional analyses using individual SNPs to assess the consistency of the findings with previous studies and potential biological mechanisms. Our analyses of individual SNPs are more refined than those available publicly, as we tailored our datasets specifically for our study.

This study has several limitations. First, because childhood cancer history was not available in the UK Biobank data, we were not able to verify which participants were childhood cancer survivors. However, considering that childhood cancer is exceptionally rare, with incidence rates of 50.2/1 million for lymphoid leukemia, 3.1/1 million for Ewing tumor and related sarcomas of bone, and 8.1/1 million for neuroblastoma among children aged 0 to 19 years,72 only a handful of participants would be survivors. Regardless of cancer history, we compiled the list of germline variants to genetic pleiotropy for PRS and examined which SNPs drive the association across phenotypes. Second, although individuals with congenital anomalies or genetic diseases may visit the hospital for specific conditions, the ICD code is typically recorded based on their current presenting condition. Therefore, without access to their medical history, it may be difficult to accurately identify individuals with these underlying medical conditions. Third, since we used inpatient diagnoses, diagnoses or conditions that did not require hospital admission were missed. However, primary care data are available for only about 45% of the UK Biobank cohort.73 Given the already small number of patients in our analyses, incorporating these additional data could further reduce the number of patients, potentially leading to underestimation in our analysis. Finally, due to the restriction of our study population to White British individuals, the generalizability of our findings to other populations is uncertain.

Conclusion

In summary, our findings suggest that individuals with genetic susceptibility to ALL may face a lifelong risk of developing ALL, along with a genetic pleiotropic association between high-risk neuroblastoma risk and circulatory diseases. Continued research to evaluate pleiotropy for childhood cancer genetic predispositions will improve our understanding and shared genetic mechanisms between diseases and their etiologies.

Data and code availability

The UK Biobank data are available following a process outlined at https://www.ukbiobank.ac.uk/enable-your-research. The original code for PRS calculation is available at https://doi.org/10.3389/fgene.2022.818574, and the original code for PheWAS can be found in R documentation (https://rdrr.io/github/PheWAS/PheWAS/man/phewas-package.html).

Acknowledgments

The Children's Cancer Research Fund (Minneapolis, MN) provided fellowship support for E.M.J.’s training.

Declaration of interests

The authors declare no competing interests.

Published: September 26, 2024

Footnotes

Supplemental information can be found online at https://doi.org/10.1016/j.xhgg.2024.100356.

Contributor Information

Eun Mi Jung, Email: jung0305@umn.edu.

Logan G. Spector, Email: spect012@umn.edu.

Web resources

http://www.omim.org

https://biobank.ndph.ox.ac.uk/ukb/label.cgi?id=3000

Supplemental information

Document S1. Figure S1
mmc1.pdf (141.6KB, pdf)
Tables S1–S9
mmc2.xlsx (100.9KB, xlsx)
Document S2. Article plus supplemental information
mmc3.pdf (3.2MB, pdf)

References

  • 1.Sud A., Kinnersley B., Houlston R.S. Genome-wide association studies of cancer: current insights and future perspectives. Nat. Rev. Cancer. 2017;17:692–704. doi: 10.1038/nrc.2017.82. [DOI] [PubMed] [Google Scholar]
  • 2.Plon S.E., Lupo P.J. Genetic predisposition to childhood cancer in the genomic era. Annu. Rev. Genomics Hum. Genet. 2019;20:241–263. doi: 10.1146/annurev-genom-083118-015415. [DOI] [PubMed] [Google Scholar]
  • 3.Park S.L., Cheng I., Haiman C.A. Genome-wide association studies of cancer in diverse populations. Cancer Epidemiol. Biomarkers Prev. 2018;27:405–417. doi: 10.1158/1055-9965.EPI-17-0169. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Raynor L.a., Pankratz N., Spector L.G. An analysis of measures of effect size by age of onset in cancer genomewide association studies. Genes Chromosomes Cancer. 2013;52:855–859. doi: 10.1002/gcc.22081. [DOI] [PubMed] [Google Scholar]
  • 5.Chung C.C., Chanock S.J. Current status of genome-wide association studies in cancer. Hum. Genet. 2011;130:59–78. doi: 10.1007/s00439-011-1030-9. [DOI] [PubMed] [Google Scholar]
  • 6.Papaemmanuil E., Hosking F.J., Vijayakrishnan J., Price A., Olver B., Sheridan E., Kinsey S.E., Lightfoot T., Roman E., Irving J.A.E., et al. Loci on 7p12.2, 10q21.2 and 14q11.2 are associated with risk of childhood acute lymphoblastic leukemia. Nat. Genet. 2009;41:1006–1010. doi: 10.1038/ng.430. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Maris J.M., Mosse Y.P., Bradfield J.P., Hou C., Monni S., Scott R.H., Asgharzadeh S., Attiyeh E.F., Diskin S.J., Laudenslager M., et al. A genome-wide association study identifies a susceptibility locus to clinically aggressive neuroblastoma at 6p22. N. Engl. J. Med. 2008;358:2585–2593. doi: 10.1056/NEJMoa0708698. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Postel-Vinay S., Véron A.S., Tirode F., Pierron G., Reynaud S., Kovar H., Oberlin O., Lapouble E., Ballet S., Lucchesi C., et al. Common variants near TARDBP and EGR2 are associated with susceptibility to Ewing sarcoma. Nat. Genet. 2012;44:323–327. doi: 10.1038/ng.1085. [DOI] [PubMed] [Google Scholar]
  • 9.Semmes E.C., Vijayakrishnan J., Zhang C., Hurst J.H., Houlston R.S., Walsh K.M. Leveraging Genome and Phenome-Wide Association Studies to Investigate Genetic Risk of Acute Lymphoblastic Leukemia. Cancer Epidemiol Prev Biomark. 2020;29:1606–1614. doi: 10.1158/1055-9965.EPI-20-0113. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Kachuri L., Jeon S., DeWan A.T., Metayer C., Ma X., Witte J.S., Chiang C.W.K., Wiemels J.L., de Smith A.J. Genetic determinants of blood-cell traits influence susceptibility to childhood acute lymphoblastic leukemia. Am. J. Hum. Genet. 2021;108:1823–1835. doi: 10.1016/j.ajhg.2021.08.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Fritsche L.G., Gruber S.B., Wu Z., Schmidt E.M., Zawistowski M., Moser S.E., Blanc V.M., Brummett C.M., Kheterpal S., Abecasis G.R., Mukherjee B. Association of Polygenic Risk Scores for Multiple Cancers in a Phenome-wide Study: Results from The Michigan Genomics Initiative. Am. J. Hum. Genet. 2018;102:1048–1061. doi: 10.1016/j.ajhg.2018.04.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Leppert B., Millard L.A.C., Riglin L., Davey Smith G., Thapar A., Tilling K., Walton E., Stergiakouli E. A cross-disorder PRS-pheWAS of 5 major psychiatric disorders in UK Biobank. PLoS Genet. 2020;16:e1008185. doi: 10.1371/journal.pgen.1008185. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Zhang X., Li X., He Y., Law P.J., Farrington S.M., Campbell H., Tomlinson I.P.M., Houlston R.S., Dunlop M.G., Timofeeva M., Theodoratou E. Phenome-wide association study (PheWAS) of colorectal cancer risk SNP effects on health outcomes in UK Biobank. Br. J. Cancer. 2022;126:822–830. doi: 10.1038/s41416-021-01655-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Fu M., UCLA Precision Health Data Discovery Repository Working Group, UCLA Precision Health ATLAS Working Group. Chang T.S. UCLA Precision Health ATLAS Working Group, Chang TS. Phenome-Wide Association Study of Polygenic Risk Score for Alzheimer’s Disease in Electronic Health Records. Front. Aging Neurosci. 2022;14:800375. doi: 10.3389/fnagi.2022.800375. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Jain P., Miller-Fleming T., Topaloudi A., Yu D., Drineas P., Georgitsi M., Yang Z., Rizzo R., Müller-Vahl K.R., Tumer Z., et al. Polygenic risk score-based phenome-wide association study identifies novel associations for Tourette syndrome. Transl. Psychiatry. 2023;13:69. doi: 10.1038/s41398-023-02341-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Hohenadel M.G., Baier L.J., Piaggi P., Muller Y.L., Hanson R.L., Krakoff J., Thearle M.S. The impact of genetic variants on BMI increase during childhood versus adulthood. Int. J. Obes. 2016;40:1301–1309. doi: 10.1038/ijo.2016.53. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Vogelezang S., Bradfield J.P., Ahluwalia T.S., Curtin J.A., Lakka T.A., Grarup N., Scholz M., van der Most P.J., Monnereau C., Stergiakouli E., et al. Novel loci for childhood body mass index and shared heritability with adult cardiometabolic traits. PLoS Genet. 2020;16:e1008718. doi: 10.1371/journal.pgen.1008718. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Akingbuwa W.A., Hammerschlag A.R., Jami E.S., Allegrini A.G., Karhunen V., Sallis H., Ask H., Askeland R.B., Baselmans B., Diemer E., et al. Genetic Associations Between Childhood Psychopathology and Adult Depression and Associated Traits in 42 998 Individuals: A Meta-analysis. JAMA Psychiatr. 2020;77:715–728. doi: 10.1001/jamapsychiatry.2020.0527. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Gao X., Zhu L., Lu X., Wang Y., Li R., Jiang G. KIF15 contributes to cell proliferation and migration in breast cancer. Hum. Cell. 2020;33:1218–1228. doi: 10.1007/s13577-020-00392-0. [DOI] [PubMed] [Google Scholar]
  • 20.Li L., Ding X., Wang X., Yao Q., Shao X., An X., Yan N., Jiang Y., Wang W., Shi L., et al. Polymorphisms of IKZF3 Gene and Autoimmune Thyroid Diseases: Associated with Graves’ Disease but Not with Hashimoto’s Thyroiditis. Cell. Physiol. Biochem. 2018;45:1787–1796. doi: 10.1159/000487870. [DOI] [PubMed] [Google Scholar]
  • 21.Bycroft C., Freeman C., Petkova D., Band G., Elliott L.T., Sharp K., Motyer A., Vukcevic D., Delaneau O., O'Connell J., et al. The UK Biobank resource with deep phenotyping and genomic data. Nature. 2018;562:203–209. doi: 10.1038/s41586-018-0579-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Popejoy A.B., Fullerton S.M. Genomics is failing on diversity. Nature. 2016;538:161–164. doi: 10.1038/538161a. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Lupo P.J., Spector L.G. Cancer Progress and Priorities: Childhood Cancer. Cancer Epidemiol. Biomarkers Prev. 2020;29:1081–1094. doi: 10.1158/1055-9965.EPI-19-0941. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Collister J.A., Liu X., Clifton L. Calculating Polygenic Risk Scores (PRS) in UK Biobank: A Practical Guide for Epidemiologists. Front. Genet. 2022;13:818574. doi: 10.3389/fgene.2022.818574. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Buniello A., MacArthur J.A.L., Cerezo M., Harris L.W., Hayhurst J., Malangone C., McMahon A., Morales J., Mountjoy E., Sollis E., et al. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 2019;47:D1005–D1012. doi: 10.1093/nar/gky1120. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Jeon S., de Smith A.J., Li S., Chen M., Chan T.F., Muskens I.S., Morimoto L.M., DeWan A.T., Mancuso N., Metayer C., et al. Genome-wide trans-ethnic meta-analysis identifies novel susceptibility loci for childhood acute lymphoblastic leukemia. Leukemia. 2022;36:865–868. doi: 10.1038/s41375-021-01465-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Machiela M.J., Grünewald T.G.P., Surdez D., Reynaud S., Mirabeau O., Karlins E., Rubio R.A., Zaidi S., Grossetete-Lalami S., Ballet S., et al. Genome-wide association study identifies multiple new loci associated with Ewing sarcoma susceptibility. Nat. Commun. 2018;9:3184. doi: 10.1038/s41467-018-05537-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Tolbert V.P., Coggins G.E., Maris J.M. Genetic susceptibility to neuroblastoma. Curr. Opin. Genet. Dev. 2017;42:81–90. doi: 10.1016/j.gde.2017.03.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Ritenour L.E., Randall M.P., Bosse K.R., Diskin S.J. Genetic susceptibility to neuroblastoma: current knowledge and future directions. Cell Tissue Res. 2018;372:287–307. doi: 10.1007/s00441-018-2820-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Tonini G.P., Capasso M. Genetic predisposition and chromosome instability in neuroblastoma. Cancer Metastasis Rev. 2020;39:275–285. doi: 10.1007/s10555-020-09843-4. [DOI] [PubMed] [Google Scholar]
  • 31.Capasso M., Devoto M., Hou C., Asgharzadeh S., Glessner J.T., Attiyeh E.F., Mosse Y.P., Kim C., Diskin S.J., Cole K.A., et al. Common variations in BARD1 influence susceptibility to high-risk neuroblastoma. Nat. Genet. 2009;41:718–723. doi: 10.1038/ng.374. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Wang K., Diskin S.J., Zhang H., Attiyeh E.F., Winter C., Hou C., Schnepp R.W., Diamond M., Bosse K., Mayes P.A., et al. Integrative genomics identifies LMO1 as a neuroblastoma oncogene. Nature. 2011;469:216–220. doi: 10.1038/nature09609. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Capasso M., Diskin S.J., Totaro F., Longo L., De Mariano M., Russo R., Cimmino F., Hakonarson H., Tonini G.P., Devoto M., et al. Replication of GWAS-identified neuroblastoma risk loci strengthens the role of BARD1 and affirms the cumulative effect of genetic variations on disease susceptibility. Carcinogenesis. 2013;34:605–611. doi: 10.1093/carcin/bgs380. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Oldridge D.A., Wood A.C., Weichert-Leahey N., Crimmins I., Sussman R., Winter C., McDaniel L.D., Diamond M., Hart L.S., Zhu S., et al. Genetic predisposition to neuroblastoma mediated by a LMO1 super-enhancer polymorphism. Nature. 2015;528:418–421. doi: 10.1038/nature15540. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Chang X., Zhao Y., Hou C., Glessner J., McDaniel L., Diamond M.A., Thomas K., Li J., Wei Z., Liu Y., et al. Common variants in MMP20 at 11q22.2 predispose to 11q deletion and neuroblastoma risk. Nat. Commun. 2017;8:569. doi: 10.1038/s41467-017-00408-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Cimmino F., Avitabile M., Diskin S.J., Vaksman Z., Pignataro P., Formicola D., Cardinale A., Testori A., Koster J., de Torres C., et al. Fine mapping of 2q35 high-risk neuroblastoma locus reveals independent functional risk variants and suggests full-length BARD1 as tumor-suppressor. Int. J. Cancer. 2018;143:2828–2837. doi: 10.1002/ijc.31822. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Nguyen L.B., Diskin S.J., Capasso M., Wang K., Diamond M.A., Glessner J., Kim C., Attiyeh E.F., Mosse Y.P., Cole K., et al. Phenotype restricted genome-wide association study using a gene-centric approach identifies three low-risk neuroblastoma susceptibility Loci. PLoS Genet. 2011;7:e1002026. doi: 10.1371/journal.pgen.1002026. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Hungate E.A., Applebaum M.A., Skol A.D., Vaksman Z., Diamond M., McDaniel L., Volchenboum S.L., Stranger B.E., Maris J.M., Diskin S.J., et al. Evaluation of Genetic Predisposition for MYCN-Amplified Neuroblastoma. J. Natl. Cancer Inst. 2017;109:djx093. doi: 10.1093/jnci/djx093. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Alexander T.A., Machiela M.J. LDpop: an interactive online tool to calculate and visualize geographic LD patterns. BMC Bioinf. 2020;21:14. doi: 10.1186/s12859-020-3340-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.BGEN: Documentation. 2023. https://enkre.net/cgi-bin/code/bgen/doc/trunk/doc/wiki/bgenix.md
  • 41.Purcell S., Neale B., Todd-Brown K., Thomas L., Ferreira M.A.R., Bender D., Maller J., Sklar P., de Bakker P.I.W., Daly M.J., Sham P.C. PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses. Am. J. Hum. Genet. 2007;81:559–575. doi: 10.1086/519795. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Denny J.C., Ritchie M.D., Basford M.A., Pulley J.M., Bastarache L., Brown-Gentry K., Wang D., Masys D.R., Roden D.M., Crawford D.C. PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene–disease associations. Bioinformatics. 2010;26:1205–1210. doi: 10.1093/bioinformatics/btq126. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Carroll R.J., Bastarache L., Denny J.C. R PheWAS: data analysis and plotting tools for phenome-wide association studies in the R environment. Bioinformatics. 2014;30:2375–2376. doi: 10.1093/bioinformatics/btu197. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Wang X. Firth logistic regression for rare variant association tests. Front. Genet. 2014;5:187. doi: 10.3389/fgene.2014.00187. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Vijayakrishnan J., Qian M., Studd J.B., Yang W., Kinnersley B., Law P.J., Broderick P., Raetz E.A., Allan J., Pui C.H., et al. Identification of four novel associations for B-cell acute lymphoblastic leukaemia risk. Nat. Commun. 2019;10:5348. doi: 10.1038/s41467-019-13069-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Jackson T.R., Ling R.E., Roy A. The Origin of B-cells: Human Fetal B Cell Development and Implications for the Pathogenesis of Childhood Acute Lymphoblastic Leukemia. Front. Immunol. 2021;12:637975. doi: 10.3389/fimmu.2021.637975. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Terwilliger T., Abdul-Hay M. Acute lymphoblastic leukemia: a comprehensive review and 2017 update. Blood Cancer J. 2017;7:e577. doi: 10.1038/bcj.2017.53. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Allodji R.S., Hawkins M.M., Bright C.J., Fidler-Benaoudia M.M., Winter D.L., Alessi D., Fresneau B., Journy N., Morsellino V., Bárdi E., et al. Risk of subsequent primary leukaemias among 69,460 five-year survivors of childhood cancer diagnosed from 1940 to 2008 in Europe: A cohort study within PanCareSurFup. Eur. J. Cancer. 2019;117:71–83. doi: 10.1016/j.ejca.2019.05.013. [DOI] [PubMed] [Google Scholar]
  • 49.Nottage K., Lanctot J., Li Z., Neglia J.P., Bhatia S., Hammond S., Leisenring W., Meadows A., Srivastava D., Robison L.L., Armstrong G.T. Long-term risk for subsequent leukemia after treatment for childhood cancer: a report from the Childhood Cancer Survivor Study. Blood. 2011;117:6315–6318. doi: 10.1182/blood-2011-02-335158. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Tallman M.S., Gray R., Bennett J.M., Variakojis D., Robert N., Wood W.C., Rowe J.M., Wiernik P.H. Leukemogenic potential of adjuvant chemotherapy for early-stage breast cancer: the Eastern Cooperative Oncology Group experience. J. Clin. Oncol. 1995;13:1557–1563. doi: 10.1200/JCO.1995.13.7.1557. [DOI] [PubMed] [Google Scholar]
  • 51.Ferreirós-Vidal I., Carroll T., Taylor B., Terry A., Liang Z., Bruno L., Dharmalingam G., Khadayate S., Cobb B.S., Smale S.T., et al. Genome-wide identification of Ikaros targets elucidates its contribution to mouse B-cell lineage specification and pre-B-cell differentiation. Blood. 2013;121:1769–1782. doi: 10.1182/blood-2012-08-450114. [DOI] [PubMed] [Google Scholar]
  • 52.Meng C., Chen S., He Q., Tan J., Wu J., Zhao J. IKZF3 modulates cerebral ischemia/reperfusion injury by inhibiting neuroinflammation. Int. Immunopharmacol. 2023;114:109480. doi: 10.1016/j.intimp.2022.109480. [DOI] [PubMed] [Google Scholar]
  • 53.Nückel H., Frey U.H., Sellmann L., Collins C.H., Dührsen U., Siffert W. The IKZF3 (Aiolos) transcription factor is highly upregulated and inversely correlated with clinical progression in chronic lymphocytic leukaemia. Br. J. Haematol. 2009;144:268–270. doi: 10.1111/j.1365-2141.2008.07442.x. [DOI] [PubMed] [Google Scholar]
  • 54.Billot K., Soeur J., Chereau F., Arrouss I., Merle-Béral H., Huang M.E., Mazier D., Baud V., Rebollo A. Deregulation of Aiolos expression in chronic lymphocytic leukemia is associated with epigenetic modifications. Blood. 2011;117:1917–1927. doi: 10.1182/blood-2010-09-307140. [DOI] [PubMed] [Google Scholar]
  • 55.Yang T., Mills L.J., Hubbard A.K., Cao R., Raduski A., Machiela M.J., Spector L.G. Genetic analyses identify evidence for a causal relationship between Ewing sarcoma and hernias. HGG Adv. 2024;5:100254. doi: 10.1016/j.xhgg.2023.100254. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Wenn P., Zeltser R. StatPearls. StatPearls Publishing; 2024. Aortic Valve Disease.http://www.ncbi.nlm.nih.gov/books/NBK542205/ [PubMed] [Google Scholar]
  • 57.DuBois S.G., Macy M.E., Henderson T.O. High-Risk and Relapsed Neuroblastoma: Toward More Cures and Better Outcomes. Am. Soc. Clin. Oncol. Educ. Book. 2022;42:1–13. doi: 10.1200/EDBK_349783. [DOI] [PubMed] [Google Scholar]
  • 58.Menegaux F., Olshan A.F., Reitnauer P.J., Blatt J., Cohn S.L. Positive association between congenital anomalies and risk of neuroblastoma. Pediatr. Blood Cancer. 2005;45:649–655. doi: 10.1002/pbc.20263. [DOI] [PubMed] [Google Scholar]
  • 59.Chow E.J., Friedman D.L., Mueller B.A. Maternal and perinatal characteristics in relation to neuroblastoma. Cancer. 2007;109:983–992. doi: 10.1002/cncr.22486. [DOI] [PubMed] [Google Scholar]
  • 60.Lupo P.J., Schraw J.M., Desrosiers T.A., Nembhard W.N., Langlois P.H., Canfield M.A., Copeland G., Meyer R.E., Brown A.L., Chambers T.M., et al. Association Between Birth Defects and Cancer Risk Among Children and Adolescents in a Population-Based Assessment of 10 Million Live Births. JAMA Oncol. 2019;5:1150–1158. doi: 10.1001/jamaoncol.2019.1215. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 61.Hutson M.R., Kirby M.L. Neural crest and cardiovascular development: a 20-year perspective. Birth Defects Res. C Embryo Today. 2003;69:2–13. doi: 10.1002/bdrc.10002. [DOI] [PubMed] [Google Scholar]
  • 62.Botto L.D., Lin A.E., Riehle-Colarusso T., Malik S., Correa A., National Birth Defects Prevention Study National Birth Defects Prevention Study. Seeking causes: Classifying and evaluating congenital heart defects in etiologic studies. Birt Defects Res A Clin Mol Teratol. 2007;79:714–727. doi: 10.1002/bdra.20403. [DOI] [PubMed] [Google Scholar]
  • 63.Foulkes W.D., Buu P.N., Filiatrault D., Leclerc J.M., Narod S.A. Excess of congenital abnormalities in French-Canadian children with neuroblastoma: a case series study from Montréal. Med. Pediatr. Oncol. 1997;29:272–279. doi: 10.1002/(sici)1096-911x(199710)29:4&#x0003c;272::aid-mpo7&#x0003e;3.0.co;2-j. [DOI] [PubMed] [Google Scholar]
  • 64.George R.E., Lipshultz S.E., Lipsitz S.R., Colan S.D., Diller L. Association between congenital cardiovascular malformations and neuroblastoma. J. Pediatr. 2004;144:444–448. doi: 10.1016/j.jpeds.2003.12.032. [DOI] [PubMed] [Google Scholar]
  • 65.Gill R., Meghrajani V., Ali S., Riasat M. Delayed Onset Atrioventricular Block After Surgical Aortic Valve Replacement: A Rare Entity. Cureus. 2022;14:e25606. doi: 10.7759/cureus.25606. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 66.Liu M., Nadar V.C., Kozielski F., Kozlowska M., Yu W., Baas P.W. Kinesin-12, a mitotic microtubule-associated motor protein, impacts axonal growth, navigation, and branching. J. Neurosci. 2010;30:14896–14906. doi: 10.1523/JNEUROSCI.3739-10.2010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 67.Zhang D., Povysil G., Kobeissy P.H., Li Q., Wang B., Amelotte M., Jaouadi H., Newton C.A., Maher T.M., Molyneaux P.L., et al. Rare and Common Variants in KIF15 Contribute to Genetic Risk of Idiopathic Pulmonary Fibrosis. Am. J. Respir. Crit. Care Med. 2022;206:56–69. doi: 10.1164/rccm.202110-2439OC. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 68.Zhang B., Shi L., Tan Y., Zhou Y., Cui J., Song Y., Liu Y., Zhang M., Duan W., Jin Z., et al. Forkhead box O6 (FoxO6) promotes cardiac pathological remodeling and dysfunction by activating Kif15–TGF-β1 under aggravated afterload. MedComm. 2023;4:e383. doi: 10.1002/mco2.383. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 69.Zimmerman K.A., Yancopoulos G.D., Collum R.G., Smith R.K., Kohl N.E., Denis K.A., Nau M.M., Witte O.N., Toran-Allerand D., Gee C.E. Differential expression of myc family genes during murine development. Nature. 1986;319:780–783. doi: 10.1038/319780a0. [DOI] [PubMed] [Google Scholar]
  • 70.Charron J., Malynn B.A., Fisher P., Stewart V., Jeannotte L., Goff S.P., Robertson E.J., Alt F.W. Embryonic lethality in mice homozygous for a targeted disruption of the N-myc gene. Genes Dev. 1992;6:2248–2257. doi: 10.1101/gad.6.12a.2248. [DOI] [PubMed] [Google Scholar]
  • 71.Sawai S., Shimono A., Wakamatsu Y., Palmes C., Hanaoka K., Kondoh H. Defects of embryonic organogenesis resulting from targeted disruption of the N-myc gene in the mouse. Dev. Camb. Engl. 1993;117:1445–1455. doi: 10.1242/dev.117.4.1445. [DOI] [PubMed] [Google Scholar]
  • 72.Howlader N., Noone A.M., Krapcho M., et al. Natl Cancer Inst; 2021. SEER Cancer Statistics Review, 1975–2018. [Google Scholar]
  • 73.Category 3000. 2024. https://biobank.ndph.ox.ac.uk/ukb/label.cgi?id=3000

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Document S1. Figure S1
mmc1.pdf (141.6KB, pdf)
Tables S1–S9
mmc2.xlsx (100.9KB, xlsx)
Document S2. Article plus supplemental information
mmc3.pdf (3.2MB, pdf)

Data Availability Statement

The UK Biobank data are available following a process outlined at https://www.ukbiobank.ac.uk/enable-your-research. The original code for PRS calculation is available at https://doi.org/10.3389/fgene.2022.818574, and the original code for PheWAS can be found in R documentation (https://rdrr.io/github/PheWAS/PheWAS/man/phewas-package.html).

Articles from Human Genetics and Genomics Advances are provided here courtesy of Elsevier

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