
==== Front
Cancer Epidemiol Biomarkers Prev
Cancer Epidemiol Biomarkers Prev
Cancer Epidemiology, Biomarkers & Prevention
1055-9965
1538-7755
American Association for Cancer Research

38904462
EPI-24-0189
10.1158/1055-9965.EPI-24-0189
Version of Record
Genetics of Cancer Risk & Outcome
Carcinogen Metabolism Polymorphisms
Metabolism
Pediatric Cancers
Null Results in Brief
Folate Metabolism and Risk of Childhood Acute Lymphoblastic Leukemia: A Genetic Pathway Analysis from the Childhood Cancer and Leukemia International Consortium
Genetic Folate Pathway and Childhood Leukemia Risk
https://orcid.org/0000-0003-3467-4145
Metayer Catherine 1*
https://orcid.org/0000-0003-2516-0222
Spector Logan G. 2
https://orcid.org/0000-0002-8379-6088
Scheurer Michael E. 34
https://orcid.org/0000-0002-2916-6595
Jeon Soyoung 5
https://orcid.org/0000-0001-7724-3404
Scott Rodney J. 67
https://orcid.org/0000-0002-7580-9184
Takagi Masatoshi 8
https://orcid.org/0000-0002-3616-7676
Clavel Jacqueline 91011
https://orcid.org/0000-0002-6698-2348
Manabe Atsushi 12
https://orcid.org/0000-0001-9472-8032
Ma Xiaomei 13
https://orcid.org/0000-0002-0879-6176
Hailu Elleni M. 1
https://orcid.org/0000-0003-0978-5863
Lupo Philip J. 34
https://orcid.org/0000-0002-1360-9531
Urayama Kevin Y. 1415
https://orcid.org/0000-0001-6665-8145
Bonaventure Audrey 9
https://orcid.org/0000-0001-5145-1774
Kato Motohiro 16
https://orcid.org/0000-0001-6983-2364
Meirhaeghe Aline 17
https://orcid.org/0000-0002-0668-7865
Chiang Charleston W.K. 5
https://orcid.org/0000-0002-1509-739X
Morimoto Libby M. 1
https://orcid.org/0000-0003-4838-9951
Wiemels Joseph L. 5
1 Division of Epidemiology, School of Public Health, University of California, Berkeley, Berkeley, California.
2 Division of Epidemiology and Clinical Research, Department of Pediatrics, University of Minnesota, Minneapolis, Minnesota.
3 Division of Hematology-Oncology, Department of Pediatrics, Baylor College of Medicine, Houston, Texas.
4 Texas Children’s Cancer and Hematology Centers, Texas Children’s Hospital, Houston, Texas.
5 Center for Genetic Epidemiology, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, California.
6 Faculty of Medicine and Health, School of Biomedical Science and Pharmacy, Hunter Medical Research Institute, University of Newcastle, New Lambton, Australia.
7 Division of Molecular Medicine, NSW Health Pathology, John Hunter Hospital, Newcastle, Australia.
8 Department of Pediatrics and Developmental Biology, Tokyo Medical and Dental University, Tokyo, Japan.
9 Inserm UMR1153, Epidemiology of Childhood and Adolescent Cancers (EPICEA) Team, Université Paris Cité and Université Sorbonne Paris Nord, Inserm, INRAE, Center for Research in Epidemiology and Statistics (CRESS), Paris, France.
10 French National Registry of Childhood Cancers, RNHE, Hôpital Paul Brousse, Groupe Hospitalier Universitaire Paris-Sud, AP-HP, Villejuif, France.
11 RNTSE, CHRU de Nancy, Vandoeuvre-lès-Nancy, France.
12 Department of Pediatrics, Hokkaido University, Sapporo, Japan.
13 Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, Connecticut.
14 Department of Social Medicine, National Center for Child Health and Development, Tokyo, Japan.
15 Graduate School of Public Health, St. Luke’s International University, Tokyo, Japan.
16 Department of Pediatrics, The University of Tokyo, Tokyo, Japan.
17 Université de Lille, INSERM, Centre Hospitalier Universitaire de Lille, Institut Pasteur de Lille, U1167 - RID-AGE - Facteurs de Risque et Déterminants Moléculaires des Maladies Liées au Vieillissement, Lille, France.
* Corresponding Author: Catherine Metayer, School of Public Health, Epidemiology, University of California, Berkeley, 1995 University Avenue, Suite 265 Berkeley, CA 94704. E-mail: cmetayer@berkeley.edu
Cancer Epidemiol Biomarkers Prev 2024;33:1248–52

03 9 2024
21 6 2024
33 9 12481252
31 1 2024
29 4 2024
17 6 2024
©2024 The Authors; Published by the American Association for Cancer Research
2024
American Association for Cancer Research
https://creativecommons.org/licenses/by-nc-nd/4.0/ This open access article is distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license.

Abstract

Background:

Prenatal folate supplementation has been consistently associated with a reduced risk of childhood acute lymphoblastic leukemia (ALL). Previous germline genetic studies examining the one carbon (folate) metabolism pathway were limited in sample size, scope, and population diversity and led to inconclusive results.

Methods:

We evaluated whether ∼2,900 single-nucleotide polymorphisms (SNP) within 46 candidate genes involved in the folate metabolism pathway influence the risk of childhood ALL, using genome-wide data from nine case-control studies in the Childhood Cancer and Leukemia International Consortium (n = 9,058 cases including 4,510 children of European ancestry, 3,018 Latinx, and 1,406 Asians, and 92,364 controls). Each study followed a standardized protocol for quality control and imputation of genome-wide data and summary statistics were meta-analyzed for all children combined and by major ancestry group using METAL software.

Results:

None of the selected SNPs reached statistical significance, overall and for major ancestry groups (using adjusted Bonferroni P-value of 5 × 10−6 and less-stringent P-value of 3.5 × 10−5 accounting for the number of “independent” SNPs). None of the 10 top (nonsignificant) SNPs and corresponding genes overlapped across ancestry groups.

Conclusions:

This large meta-analysis of original data does not reveal associations between many common genetic variants in the folate metabolism pathway and childhood ALL in various ancestry groups.

Impact:

Genetic variants in the folate pathway alone do not appear to substantially influence childhood acute lymphoblastic leukemia risk. Other mechanisms such as gene–folate interaction, DNA methylation, or maternal genetic effects may explain the observed associations with self-reported prenatal folate intake.

CHILDREN with CANCER UK http://dx.doi.org/10.13039/501100001273 19-308 Metayer C. Wiemels J.L. Morimoto L.M. Hailu E.M.
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pmcIntroduction

Leukemia is the most common cancer in children comprised primarily of acute lymphoblastic leukemia (ALL). One-carbon micronutrients such as folic acid play an essential role in the maintenance of genomic integrity and epigenetic control. Pooled analyses of original data from the Childhood Cancer and Leukemia International Consortium (CLIC) have shown that self-reported prenatal folate and vitamin supplementation reduces the risk of childhood ALL (1). However, germline genetic studies investigating the role of the one carbon (folate) metabolism and childhood ALL risk mostly in European populations have been limited in size and scope focusing on single genes such as MTHFR, TS, MTR, and MTRR, and generally yielding inconsistent results (2). We conducted a meta-analysis of CLIC genetic data to investigate the role of ∼2,900 candidate single-nucleotide polymorphisms (SNP) in the folate metabolism pathway among diverse populations.

Materials and Methods

This study is based on genome-wide data from nine CLIC case-control studies in Europe, North America, Asia, and Oceania, including 9,058 childhood ALL cases and 92,364 study-specific and publicly available controls (Table 1). Each study was given standardized quality control (QC) guidelines for generating genome-wide data, as following: (i) pre-imputation QC (separately for cases and controls if genotyped separately) included filters for SNP call rate <98%, sample call-rate per person <95%, Hardy Weinberg Equilibrium P < 10−5 in controls, minor allele frequency (MAF) < 0.01; genome-wide identity by descent > 0.20, and genome heterozygosity rate within 6sd of mean; (ii) for populations with multiple ancestries, principal component analysis (PCA) was performed with known ancestral populations to identify racial and ethnic groups (Europeans, Asians, Latinx, and Black individuals), and exclude population outliers; (iii) PCAs were generated on post QC data for adjustment in association analyses; (iv) missing data were imputed to HRC reference panel, and (v) post-imputation QC thresholds included MAF < 0.01 and r2 < 0.5. Each study conducted their analyses independently, separately by race and ethnicity (if applicable) using SNPTEST or Plink2, adjusting for PC eigenvectors as appropriate. Prior to sharing summary statistics, each study was asked to assess for genomic inflation and adjust accordingly (lambda < 1.1 was considered sufficient). Summary results for each study, including snpID (chr:position), alleles, allele frequency, risk estimate, standard error, P-value, genome build, separately by race/ethnicity, were uploaded to a secure portal. Details on each study are published elsewhere (3–8).

Table 1. Participants by country/study and ancestry: CLIC.

Countrya	Study name (period)	Overall	Cases	Controls	
Australia	Aus-ALL (1998–2006)	1,550	358	1,192	
France	ESCALE (2003–2004)e	1,983	441	1,542b	
	ESTELLE (2010–2011)e	1,758	343	1,415c	
Japan	TCCSG (1990–2011)	4,254	540	3,714	
	JPLSG (2012–2018)	2,149	548	1,601	
United States	ACCESS, Texas (2005–ongoing)e	6,965	658	6,307	
	CCLS, California (1995–2009)	2,011	1,184	827	
	CCRLP, California (1988–2011)	76,317	3,482	72,835d	
	COG, US-wide (2000–2014)	4,435	1,504	2,931	
Total					
 All combined		101,422	9,058	92,364	
 Major ancestry groups					
  European		74,521	4,510	70,011	
  Latinx		12,972	3,018	9,954	
  Asian		11,738	1,406	10,332	
Abbreviations: CCLS, California Childhood Leukemia Study; CCRLP, California Childhood Cancer Record Linkage Project, which does not overlap with CCLS; COG, Children Oncology Group; JPLSG, Japanese Pediatric Leukemia/Lymphoma Study Group; TCCSG, Tokyo Children Cancer Study Group.

a Alphabetical order.

b Generic controls from the SU.VI.Max study, France.

c Generic controls from the MONALISA Lille study, France.

d Includes publicly available controls from the Wellcome Trust Case–Control Consortium and Resource for Genetic Epidemiology Research in Adult Health and Aging awarded to the Kaiser Permanente Research Program on Genes, Environment, and Health and the University of California San Francisco Institute for Human Genetics, United States.

e Estimated proportion of B-cell/T-cell for studies with available subtype information: ESCALE (84%/16%), ESTELLE (80%/20%), ACCESS (89%/11%).

We identified 46 genes in the folate metabolism pathway by curating biological pathways in Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, gene set enrichment analysis/MSigDB (Broad Institute), USC Genome Browser, and Bioconductor (R) databases and by reviewing published literature (Table 2). Each selected gene was annotated from the Genome Assembly GRCh37/hg19 using the Bioconductor R package, and SNPs were extracted within 5 kb upstream and downstream from each gene location using UCSC genome table browser, leading to 7,979 candidate SNPs. Genome-wide meta-analyses were conducted using METAL software (version March 2011) for 9,058 ALL cases combined and for the major ancestry subgroups separately i.e., European (n = 4,510 cases), Latinx (n = 3,018 cases), and Asian (n = 1,406 cases). SNPs were included in the meta-analysis if (i) they were available in at least two studies and among >50,000 subjects overall or of European ancestry and >10,000 subjects of Asian or Latinx ancestry, and (ii) the allele frequency difference across studies was <0.5 among controls (as a quality control check), resulting in ∼2,900 SNPs available for analysis [total and European (n = 2,855), Latinx (n = 2,930), Asian (n = 2,230)]. To account for multiple testing, we applied Bonferroni correction (adjusted P-value = 5 × 10−6) and a less-stringent correction defined by the number of “independent” SNPs (based upon 1,000 Genomes, calculating the pairwise genotypic correlation using a 100-SNP window, a 10-SNP shift, and a r2 threshold of 0.2, which average to 350 independent SNPs) and the number of test for each four group examined (total, and Europeans, Latinx, and Asian ancestries) resulting in an adjusted P-value of 3.5 × 10−5 (0.05/350/4).

Table 2. Selected genes in the folate metabolism pathway.

AHCY	DHFRL1	MPST	RTBDN	
ALDH1L1	DPEP1	MTHFD1	SARDH	
ALDH1L2	FOLH1	MTHFD1L	SHMT1	
AMT	FOLR1	MTHFD2	SHMT2	
ATIC	FOLR2	MTHFD2L	SLC19A1	
ATPIF1	FOLR3	MTHFR	SLC19A2	
BHMT	FPGS	MTHFS	SLC19A3	
C2orf83	FTCD	MTR	SLC25A32	
CBS	GART	MTRR	SLC46A1	
CPS1	GCH1	MUT	TYMS	
CTH	GGH	NOX4		
DHFR	LRP2	PIPOX		

The study was approved by Institutional Review Boards for the California Health and Human Services and the University of California, Berkeley, and was conducted according to the U.S Common Rule.

Data availability

Only summary statistics were shared by participating studies and no new data were generated as part of this analysis. Original study-specific data may be available at the discretion of the individual study principal investigators (information may be requested from the corresponding author).

Results

None of the selected SNPs in the folate metabolism pathway reached the levels of significance defined above, overall and for the three major ancestry groups. Table 3 presents the top 10 SNPs for all groups combined and by ancestry, with crude P-values. None of the 10 top SNPs (and corresponding genes) in each ancestry group overlapped (i.e., C2orf83, MTHFD1L, NXPH4, SHMT2, and SLC19A3 in Europeans; CBS, GCH1, and LRP2 in Latinx; and CTH, FOLH1, and NOX4 in Asians).

Table 3. Top 10 SNPs and corresponding genes, sorted by crude P-value of the meta-risk estimate for all subjects combined and by ancestry group: CLIC.

Rs#	Symbol	Gene	Reference allele frequency	Beta coefficient	P-value	
Total						
rs2239910	SLC46A1	Solute carrier family 46 (folate transporter), member 1/sterile alpha and TIR motif containing 1	0.3643	0.0788	2.65E−04	
rs9371202	MTHFD1L	Methylenetetrahydrofolate dehydrogenase (NADP+ dependent) 1-like	0.8455	0.1103	4.35E−04	
rs12947270	SLC46A1	Solute carrier family 46 (folate transporter), member 1/H3 histone, family 3B (H3.3B) pseudogene 2	0.675	−0.0781	5.28E−04	
rs9322291	MTHFD1L	Methylenetetrahydrofolate dehydrogenase (NADP+ dependent) 1-like	0.865	0.1397	6.31E−04	
rs34449727	CPS1	Carbamoyl-phosphate synthase 1, mitochondrial	0.3292	−0.078	7.61E−04	
rs11679391	SLC19A3	Solute carrier family 19 member 3	0.3726	0.0777	8.36E−04	
rs2268369	LRP2	Low-density lipoprotein receptor-related protein 2	0.5444	−0.0645	1.09E−03	
rs2268367	LRP2	Low-density lipoprotein receptor-related protein 2	0.5445	−0.0643	1.12E−03	
rs11886318	LRP2	Low-density lipoprotein receptor-related protein 2	0.5349	−0.0635	1.34E−03	
rs28785011	MTHFD1L	Methylenetetrahydrofolate dehydrogenase (NADP+ dependent) 1-like	0.8654	0.1345	1.40E−03	
European						
rs11679391	SLC19A3	Solute carrier family 19 (thiamine transporter), member 3	0.4029	0.1107	3.55E−04	
rs9371202	MTHFD1L	Methylenetetrahydrofolate dehydrogenase (NADP+ dependent) 1-like	0.8636	0.1576	5.33E−04	
rs2138406	C2orf83	Chromosome 2 open reading frame 83	0.1873	0.1185	1.21E−03	
rs7601819	SLC19A3	Solute carrier family 19 (thiamine transporter), member 3	0.8777	0.1626	1.24E−03	
rs7583413	C2orf83	Chromosome 2 open reading frame 83	0.8086	−0.1156	1.32E−03	
rs76758508	SHMT2	Serine hydroxymethyltransferase 2	0.315	0.0958	1.63E−03	
rs68176600	NXPH4	Neurexophilin 4	0.6767	−0.0949	1.69E−03	
rs11679339	SLC19A3	Solute carrier family 19 (thiamine transporter), member 3	0.7727	−0.1108	1.74E−03	
rs4973234	SLC19A3	Solute carrier family 19 (thiamine transporter), member 3	0.7727	−0.1093	1.96E−03	
rs803456	MTHFD1L	Methylenetetrahydrofolate dehydrogenase (NADP+ dependent) 1-like	0.5117	0.0907	2.24E−03	
Latinx						
rs8018688	GCH1	GTP cyclohydrolase 1	0.7902	0.1384	1.38E−03	
rs9980564	CBS	cystathionine-beta-synthase	0.6155	0.1202	1.60E−03	
rs7147201	GCH1	GTP cyclohydrolase 1	0.7875	0.1298	2.73E−03	
rs9671455	GCH1	GTP cyclohydrolase 1	0.7462	0.1212	2.78E−03	
rs56213135	GCH1	GTP cyclohydrolase 1	0.2014	−0.1308	2.79E−03	
rs3759664	GCH1	GTP cyclohydrolase 1	0.1988	−0.13	3.07E−03	
rs11886318	LRP2	Low density lipoprotein receptor-related protein 2	0.5423	−0.1056	3.24E−03	
rs6433109	LRP2	Low density lipoprotein receptor-related protein 2	0.5391	−0.1047	3.37E−03	
rs7600336	LRP2	Low density lipoprotein receptor-related protein 3	0.4182	0.1047	3.73E−03	
rs113100590	GCH1	GTP cyclohydrolase 1	0.8052	0.1302	3.74E−03	
Asian						
rs11018581	NOX4	NADPH oxidase 4	0.2848	0.2081	7.74E−05	
rs11821838	NOX4	NADPH oxidase 4	0.2103	0.196	7.09E−04	
rs6677781	CTH	Cystathionase	0.2337	0.1782	1.43E−03	
rs7925419	FOLH1	Folate hydrolase 1	0.4587	0.1463	3.79E−03	
rs609054	FOLH1	Folate hydrolase 2	0.5818	0.135	6.76E−03	
rs2734002	FOLH1	Folate hydrolase 3	0.5818	0.1348	6.82E−03	
rs10839236	FOLH1	Folate hydrolase 4	0.5658	0.1326	8.20E−03	
rs3872578	FOLH1	Folate hydrolase 5	0.5659	0.1326	8.22E−03	
rs9651571	FOLH1	Folate hydrolase 6	0.5658	0.1325	8.27E−03	
rs7120943	FOLH1	Folate hydrolase 7	0.4342	−0.1321	8.44E−03	

Discussion

This CLIC study is the largest and most comprehensive to date to investigate the role of genetic variants in the folate metabolism pathway and childhood ALL risk among populations of diverse ancestries. We did not observe statistically significant associations with ∼2,900 SNPs. Inherited genetic variants in the folate pathway alone do not appear to substantially influence childhood ALL risk. Alternatively, gene–folate interaction, epigenetic mechanisms, or maternal genetic effects may contribute to the risk.

Acknowledgments

This study was funded by the UK Children with Cancer grant # 19-308 (C. Metayer, L.M. Morimoto, E. Hailu, J.L. Wiemels). Funding for acquisition of original data in each participating study is listed below by alphabetical order:ACCESS study (Texas, US): Cancer Prevention and Research Institute of Texas RP160771 and RP210064 (M.E. Schuerer). Aus-ALL study (Australia): The collection of samples and data from the patients with childhood ALL was funded by the National Health and Medical Research Council of Australia (https://www.rgms.nhmrc.gov.au) Grant number APP254534. Genotyping was funded by the Hunter Medical Research Institute—the Lawrie Bequest Paediatric Oncology Grant, and the Hunter Children’s Research Foundation. CCLS study (California, US): The National Institute of Health grants #P42ES004705 (C. Metayer), R01ES009137 (C. Metayer, L.M. Morimoto), and R24ES028524 (C. Metayer, L.M. Morimoto). Biospecimens and/or data used in this study were obtained from the California Biobank Program, (CBP requests #26 and #1531), Section 6555(b), 17 CCR. The California Department of Public Health is not responsible for the results or conclusions drawn by the authors of this publication. CCRPL study (California, US): National Institutes of Health grants #R01CA155461 (J.L. Wiemels and X. Ma).The collection of cancer incidence data used in this study was supported by the California Department of Public Health as part of the statewide cancer reporting program mandated by California Health and Safety Code Section 103885; the National Cancer Institute’s Surveillance, Epidemiology and End Results Program under contract HHSN261201000140C awarded to the Cancer Prevention Institute of California, contract HHSN261201000035C awarded to the University of Southern California, and contract HHSN261201000034C awarded to the Public Health Institute; and the Centers for Disease Control and Prevention’s National Program of Cancer Registries, under agreement U58DP003862-01 awarded to the California Department of Public Health. This study makes use of data generated by the Wellcome Trust Case–Control Consortium. A full list of the investigators who contributed to the generation of the data is available from www.wtccc.org.uk. Funding for the project was provided by the Wellcome Trust under award 076113 and 085475. Data came from a grant, the Resource for Genetic Epidemiology Research in Adult Health and Aging (RC2 AG033067; Schaefer and Risch, PIs) awarded to the Kaiser Permanente Research Program on Genes, Environment, and Health (RPGEH) and the UCSF Institute for Human Genetics. The RPGEH was supported by grants from the Robert Wood Johnson Foundation, the Wayne and Gladys Valley Foundation, the Ellison Medical Foundation, Kaiser Permanente Northern California, and the Kaiser Permanente National and Northern California Community Benefit Programs. The RPGEH and the Resource for Genetic Epidemiology Research in Adult Health and Aging are described here: https://divisionofresearch.kaiserpermanente.org/genetics/rpgeh/rpgehhome. Biospecimens and/or data used in this study were obtained from the California Biobank Program, (CBP request #1380), Section 6555(b), 17 CCR. The California Department of Public Health is not responsible for the results or conclusions drawn by the authors of this publication. COG study (US): dbGAP accession number: phs000638.v1.p. ESCALE study (France): Fondation de France, Fondation ARC pour la recherche sur le cancer, AFSSAPS, Cent pour Sang la Vie, Inserm, AFSSET, ANR, Institut National du Cancer INCa, Cancéropôle Ile de France and the Agence Française de Sécurité Sanitaire du Médicament et des Produits de Santé (ANSM). ESTELLE study (France): INCa, Ligue Nationale contre le Cancer, association Enfants et Santé, ANSES, the Agence Nationale de Sécurité Sanitaire de l’alimentation, de l’Environnement et du Travail (PNREST Anses, Cancer TMOI AVIESAN, 2013/1/248), INCa-DHOS, Cancéropôle Ile de France, ANR (Grant id: ANR-10-COHO-0009), Fondation ARC pour la recherche sur le cancer. We are grateful to the SFCE (Société Française de lutte contre les Cancers et leucémies de l’Enfant et l’adolescent), all pediatric oncologists and biologists involved in the two studies, Claire Mulot and the CRB Epigenetec, the Fondation Jean Dausset-CEPH (Centre d’Etude du Polymorphisme Humain), the CEA/CNRGH (Centre National de Recherche en Génomique Humaine), and the SU.VI.Max and the MONALISA Lille studies. JPLSG and TCCSG studies (Japan): St. Luke’s Life Science Institute [(Tokyo, Japan), Japan Society for the Promotion of Science (JSPS) KAKENHI grant number 26253041], Japan Agency for Medical Research and Development (grant numbers 15km0305013h0101, 16km0405107h0004, 21kk0305014), the Children’s Cancer Association of Japan, and the Japan Leukemia Research Fund.

Authors’ Disclosures

C. Metayer reports grants from UK Children with Cancer Foundation during the conduct of the study; grants from NIH and TRDRP outside the submitted work. X. Ma reports other support from BMS outside the submitted work. M. Kato reports grants and personal fees from a commercial sponsor outside the submitted work. No disclosures were reported by the other authors.

Authors’ Contributions

C. Metayer: Conceptualization, resources, data curation, formal analysis, supervision, funding acquisition, writing–original draft, project administration, writing–review and editing. L.G. Spector: Resources, writing–review and editing. M.E. Scheurer: resources, writing–review and editing. S. Jeon: Formal analysis, writing–review and editing. R.J. Scott: Resources, writing–review and editing. M. Takagi: Resources, writing–review and editing. J. Clavel: Resources, writing–review and editing. A. Manabe: Resources, writing–review and editing. X. Ma: Resources, writing–review and editing. E.M. Hailu: Data curation, writing–review and editing. P.J. Lupo: Resources, writing–review and editing. K.Y. Urayama: Resources, writing–review and editing. A. Bonaventure: Resources, writing–review and editing. M. Kato: Resources, writing–review and editing. A. Meirhaeghe: Resources, writing–review and editing. C.W. Chiang: Formal analysis, writing–review and editing. L.M. Morimoto: Data curation, formal analysis, writing–original draft, writing–review and editing. J.L. Wiemels: Resources, formal analysis, writing–review and editing.
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