Literature DB >> 28817678

Family-based exome-wide association study of childhood acute lymphoblastic leukemia among Hispanics confirms role of ARID5B in susceptibility.

Natalie P Archer1, Virginia Perez-Andreu2,3, Ulrik Stoltze4, Michael E Scheurer5, Anna V Wilkinson6, Ting-Nien Lin2, Maoxiang Qian2, Charnise Goodings2,3, Michael D Swartz7, Nalini Ranjit6, Karen R Rabin5, Erin C Peckham-Gregory5, Sharon E Plon5, Pedro A de Alarcon8, Ryan C Zabriskie5, Federico Antillon-Klussmann9,10, Cesar R Najera9, Jun J Yang2,3, Philip J Lupo5.   

Abstract

We conducted an exome-wide association study of childhood acute lymphoblastic leukemia (ALL) among Hispanics to confirm and identify novel variants associated with disease risk in this population. We used a case-parent trio study design; unlike more commonly used case-control studies, this study design is ideal for avoiding issues with population stratification bias among this at-risk ethnic group. Using 710 individuals from 323 Guatemalan and US Hispanic families, two inherited SNPs in ARID5B reached genome-wide level significance: rs10821936, RR = 2.31, 95% CI = 1.70-3.14, p = 1.7×10-8 and rs7089424, RR = 2.22, 95% CI = 1.64-3.01, p = 5.2×10-8. Similar results were observed when restricting our analyses to those with the B-ALL subtype: ARID5B rs10821936 RR = 2.22, 95% CI = 1.63-3.02, p = 9.63×10-8 and ARID5B rs7089424 RR = 2.13, 95% CI = 1.57-2.88, p = 2.81×10-7. Notably, effect sizes observed for rs7089424 and rs10821936 in our study were >20% higher than those reported among non-Hispanic white populations in previous genetic association studies. Our results confirmed the role of ARID5B in childhood ALL susceptibility among Hispanics; however, our assessment did not reveal any strong novel inherited genetic risks for acute lymphoblastic leukemia among this ethnic group.

Entities:  

Mesh:

Substances:

Year:  2017        PMID: 28817678      PMCID: PMC5560704          DOI: 10.1371/journal.pone.0180488

Source DB:  PubMed          Journal:  PLoS One        ISSN: 1932-6203            Impact factor:   3.240


Introduction

Acute lymphoblastic leukemia (ALL) is the most common malignancy among children, with B-cell ALL (B-ALL) accounting for the majority (80% to 85%) of cases [1,2]. Genome-wide association studies (GWAS) have identified several inherited genetic variants associated with childhood or adolescent ALL risk, including but not limited to single nucleotide polymorphisms (SNPs) in ARID5B, IKZF1, CEBPE, CDKN2A, PIP4K2A, GATA3, LHPP, and ELK3 [3-11]. Another important risk factor for childhood ALL is Hispanic ethnicity. Children of Hispanic ethnic background have a 10% to 30% higher incidence of ALL than do non-Hispanic whites, and a rate almost two times higher than among non-Hispanic blacks [12,13]. Hispanic children with ALL also have a lower 5-year survival rate and a higher incidence of relapse than do non-Hispanic whites [14,15]. These differences in ALL incidence and outcomes among Hispanics could be due to differences in the frequency of known or novel genetic risk factors that are unique to this population. Genetic risk factors associated with Amerindian ancestry could account for increased ALL incidence and decreased ALL survival among Hispanics [15]. Furthermore, risk alleles in ARID5B and GATA3 were found to be more frequent among Hispanic Americans than in European Americans [8,9], consistent with ancestry-related disparities in ALL susceptibility and treatment outcomes. However, there is much work remaining to identify the missing heritability of ALL among Hispanics. While a majority of GWAS rely on the case-control study design, this approach is subject to population stratification bias. This bias may result in spurious associations or the masking of associations due to subgroups within a population that have different genetic profiles and/or frequencies of disease [16,17]. Population stratification bias can be particularly important among Hispanic populations, where a three-way admixture among Native American, European, and West African ancestry populations is common [18,19]. However, the family-based case-parent trio design is immune to population stratification bias, as this approach relies on evaluating disequilibrium in the transmission of alleles among affected cases and their parents, which does not vary by race and ethnicity [16,20]. Thus, the case-parent trio study design has an advantage over the case-control study when analyzing data from highly admixed populations such as Hispanics. To determine if previous ALL GWAS findings in populations primarily of European descent are transferable to Hispanic populations, and to identify novel genetic risks for ALL for this ethnic group, we conducted an exome-wide association study (EXWAS) among Hispanics recruited in Guatemala and the Southwestern United States. We used a case-parent trio approach to attenuate the potential for population stratification bias due to admixture among this population.

Materials and methods

Study subjects and samples

The study population has been described previously [21]. Briefly, the population under study included Guatemalan ALL case-parent trios recruited during the period 2012–2015 from Unidad Nacional de Oncología Pediátrica (UNOP), a pediatric cancer treatment center in Guatemala City, as well as Hispanic trios recruited during the period 2003–2011 from the Texas Children’s Cancer Center (part of Texas Children’s Hospital (TCH) in Houston, Texas). Children and adolescents (ages 1 to 19 years) diagnosed with ALL and treated at either UNOP or TCH during their respective recruitment timeframes were eligible for the study. All Guatemalan ALL cases were either of indigenous Native American ancestry (Mayan; primarily K’iche’, Mam, and Q’anjob’al) or Ladino (Mestizo, with a mixed Native American and European background) ancestry. Written informed consent was obtained from parents, guardians, or patients, as appropriate. For all ALL cases, germline genomic DNA was extracted from peripheral blood samples obtained during clinical remission. Peripheral blood samples were also obtained from participating Guatemalan parents, while saliva samples were collected from parents of TCH cases. DNA extraction was performed using a Puregene DNA isolation kit by Gentra Systems. This study was approved by the Institutional Review Boards at University of Texas Health Science Center (UTHSC), Baylor College of Medicine, and St. Jude Children’s Research Hospital, as well as by the Bioethics Committee at Facultad de Medicina, Universidad Francisco Marroquín in Guatemala.

Genotyping and quality control (QC)

Germline genomic DNA for all samples was submitted to exome-wide genotyping using the Illumina Human Exome BeadChip (Illumina, San Diego CA). SNPs genotyped (N = 237,436) were mostly from exonic regions. Genotype calls were made using Illumina GenomeStudio Software; genotype was coded as 0, 1, or 2, indicating the number of minor alleles (an additive genetic model was assumed). Individuals with a genotype call rate <95% and case-parent trios or duos with a Mendelian error rate (i.e., Mendelian inconsistencies) >0.5% were excluded from analyses. In addition, non-autosomal SNPs, SNPs with a minor allele frequency (MAF) <1%, and SNPs with poor genotyping quality (<95% call rate) were excluded. For SNPs with a MAF of 1%-5%, a more stringent set of QC criteria determined by call rate was used (S1 Fig), and SNPs that did not meet these criteria were also excluded from analyses.

Statistical analysis

For each SNP, associations between the inherited genotype and risk of childhood ALL were assessed with multinomial modeling, using the EMIM program [22]. This analysis compares observed case genotype distributions to expected distributions, assuming Mendelian transmission of the minor allele [20,22,23]. Multinomial modeling is mathematically equivalent to a log-linear approach [24], which has been used in several case-parent trio GWAS [25-27]. The multinomial modeling approach allows for the direct estimation of inherited effects even when one or more individuals are missing from a case-parent trio [24]. Case-parent trios, case-mother or case-father duos, parents alone, or cases alone can all be analyzed simultaneously in EMIM to estimate inherited genetic effects. We used EMIM to estimate relative risks (RR) for each SNP, as well as corresponding 95% confidence intervals (CIs) and chi-squared values, assuming a log-additive model of inheritance. EMIM analyses stratified the data by parental mating type, inherently adjusting RR estimates for effects of population stratification [23,24]. We calculated two-tailed p-values from chi-squared values for each SNP using R, version 3.2.2. We also created Manhattan plots and Q-Q plots using R; and regional association plots using LocusZoom [28]. We used a threshold of P<1.0 × 10−6 to denote statistical significance in our analyses. The majority of ALL cases in the study cohort were of the B-ALL subtype; therefore, in addition to examining inherited variants associated with ALL in the entire study cohort, we also conducted a subgroup analysis with only B-ALL cases and families, to assess inherited genotype effects for B-ALL in particular. In order to select cases for the TCH population, we determined genetic ancestry using STRUCTURE [15,29] on the basis of genotypes at 30,000 randomly selected SNPs, using HapMap samples (CEU, YRI, CHB/JPT) and indigenous Amerindian references [30] as ancestry populations. Hispanic Americans were defined as individuals for whom the proportion of Native American genetic ancestry was ≥10% and was also greater than the proportion of African ancestry [8].

Results

The study cohort consists of a total of 733 individuals from 332 families: 287 families (628 individuals) from Unidad Nacional de Oncología Pediátrica (UNOP) in Guatemala, and 45 families (105 individuals) from Texas Children’s Hospital (TCH) in the United States. We excluded nine families from analysis because they did not meet Mendelian error rate criteria. The full cohort used in analyses included 323 families with 710 individuals. For 24 of these families, parental genetic data were used in the analysis, but genetic information was missing for the ALL cases themselves. Demographic and clinical characteristics of the 299 ALL cases included in this study are shown in Table 1. Of the 237,436 exonic SNPs available for analysis from the Illumina chip, 32,175 SNPs met the MAF and call rate criteria and were subsequently used in analyses (S1 File). The vast majority (99.6%) of excluded SNPs were omitted from analyses because they had a MAF of <1%.
Table 1

Characteristics of ALL cases used in EXWAS (N = 299).

CharacteristicAll ALL casesGuatemalan casesUS Hispanic cases
N%N%N%
Race/Ethnicity
 Indigenous (Native American)9632.19662.800.0
 Ladino (Hispanic Mestizo)16254.216237.200.0
 Hispanic (Unknown type)4113.700.041100
Sex
 Male17257.514957.82356.1
 Female12742.510942.21843.9
Age
 1–1021471.617768.63790.2
 >108528.48131.449.8
ALL subtype
 B-ALL28896.324795.741100
 T-ALL113.7114.300.0
Total29910025810041100
In our exome-wide association analysis of the ALL cohort, we identified two SNPs on chromosome 10 within the ARID5B gene that were significantly associated with ALL risk: rs10821936 (RR = 2.31, 95% CI = 1.70–3.14, p = 1.70 × 10−8) and rs7089424 (RR = 2.22, 95% CI = 1.64–3.01, p = 5.19 × 10−8) (Fig 1 and Table 2). These two SNPs were in linkage disequilibrium (LD; r2 = 0.84, D’ = 0.92) in the 1000 Genomes version hg19 Amerindian (AMR) reference population [31] (Fig 2). A subcohort analysis of only B-ALL cases and families (312 families, 683 individuals) yielded very similar effect estimates for rs10821936 (RR = 2.22, 95% CI = 1.63–3.02) and rs7089424 (RR = 2.13, 95% CI = 1.57–2.88), which were also significantly associated with B-ALL susceptibility (p = 9.63 × 10−8 and p = 2.81 × 10−7, respectively) (Table 2). Results for all SNPs that were nominally significant in our analysis (p<0.05) among the entire study cohort can be found in S2 Table.
Fig 1

Manhattan plot of inherited genetic effects in the full study cohort.

Table 2

Inherited effects results for ARID5B SNPs rs10821936 and rs7089424 (10q21.2), both for the entire ALL study cohort and for B-ALL cases and families alone.

SNP Name(Gene, Chromosome)RAFPRR95% CI
CasesParents
rs10821936 (ARID5B,10)
 Entire study cohort(n = 32 families)0.730.641.70 x 10−82.31(1.70, 3.14)
 B-ALL subcohort(n = 312 families)0.730.649.63 x 10−82.22(1.63, 3.02)
rs7089424 (ARID5B,10)
 Entire study cohort(n = 323 families)0.645.19 x 10−82.22(1.64, 3.01)
 B-ALL subcohort(n = 312 families)0.730.642.81 x 10−72.13(1.57, 2.88)
Fig 2

Regional association plot for ARID5B SNPs rs10821936 and rs7089424.

The left y-axis shows the multinomial model’s association probabilities (-log10p) for the SNP of interest (rs10821936; purple diamond) as well as for nearby SNPs used in the analysis. Red shading indicates linkage disequilibrium (r2) between the SNP of interest and nearby markers. Recombination rates for each region, obtained from 1000 Genomes build hg19 Amerindian (AMR) data, are indicated by blue lines measured on the right y-axis. The x-axis shows the position of the SNP of interest and other markers on the chromosome, relative to the position of nearby genes.

Regional association plot for ARID5B SNPs rs10821936 and rs7089424.

The left y-axis shows the multinomial model’s association probabilities (-log10p) for the SNP of interest (rs10821936; purple diamond) as well as for nearby SNPs used in the analysis. Red shading indicates linkage disequilibrium (r2) between the SNP of interest and nearby markers. Recombination rates for each region, obtained from 1000 Genomes build hg19 Amerindian (AMR) data, are indicated by blue lines measured on the right y-axis. The x-axis shows the position of the SNP of interest and other markers on the chromosome, relative to the position of nearby genes. Because this study included Hispanic childhood ALL cases and parents from two distinct geographic locations (Guatemala and Texas), analyses were, in effect, conducted on a pooled study population. We explored results for the ARID5B SNPs found to be of genome-wide significance in the overall study population in both of these populations separately, and effect sizes were consistent (rs10821936: RR = 2.32 for Guatemala, RR = 2.50 for Texas; rs7089424: RR = 2.27 for Guatemala, RR = 2.35 for Texas).

Discussion

In this family-based EXWAS of childhood ALL among those of Hispanic ancestry, we observed two inherited SNPs in ARID5B, rs10821936 and rs7089424, that were associated with childhood ALL. These two SNPs are in strong linkage disequilibrium with one another (r2 = 0.84, D’ = 0.92), most likely representing a single susceptibility locus (Fig 2). Both ARID5B rs7089424 and rs10821936 have been identified in previous ALL GWAS [3-5,7,8]. These two SNPs also have had some of the strongest and most statistically significant associations with ALL in GWAS analyses [4,7,8]. We reviewed whole-exome sequence data from a subset of the cases (n = 41 from TCH) described here and did not identify any recurring ARID5B coding variants in patients with the risk allele. We also compared findings from our analysis to susceptibility SNPs identified in previous GWAS/EXWAS of childhood ALL (Table 3). Notably, the magnitude of the effects of rs7089424 and rs10821936 in our study were over 20% higher than estimates calculated in previous genetic association studies among primarily European (non-Hispanic white) populations.
Table 3

Inherited effects reported in other ALL GWAS and EXWAS analyses, compared to our study results.

SNPChrGeneResults—other studiesaOur study resultsb
OR (95% CI)PReferencesRR (95% CI)P
rs41326017p12.2IKZF11.69 (1.58, 1.81)1.2 x 10−19Papaemmanuil et al. 20091.48 (1.09, 2.02)0.01
1.59 (1.34, 1.89)1.2 x 10−7Orsi et al. 2012
1.43 (1.30, 1.58)8.3 x 10−13Ellinghaus et al. 2012
rs119782677p12.2IKZF11.59 (1.45, 1.74)5.3 x 10−24Xu et al. 20131.52 (1.11, 2.07)0.007
(Total study pop.)
1.31 (1.07, 1.61)0.01
(Hispanics only)
1.69 (1.40, 1.90)8.8 x 10−11Trevino et al. 2009
1.44 (1.30, 1.59)1.1 x 10−12Ellinghaus et al. 2012
rs69648237p12.2IKZF11.52 (1.41, 1.64)6.0 x 10−14Papaemmanuil et al. 2009Not available to evaluate
rs69446027p12.2IKZF11.64 (1.37, 2.07)3.4 x 10−15Papaemmanuil et al. 2009Not available to evaluate
1.42 (1.27, 1.60)3.1 x 10−9Ellinghaus et al. 2012
rs708942410q21.2ARID5B1.65 (1.54, 1.76)6.7 x 10−19Papaemmanuil et al. 20092.22 (1.64, 3.01)5.19 x 10−8
1.42 (1.30, 1.56)2.0 x 10−13Ellinghaus et al. 2012
1.83 (1.55, 2.15)6.1 x 10−13Orsi et al. 2012
rs1082193610q21.2ARID5B1.86 (1.71, 2.03)5.9 x 10−46Xu et al. 20132.31 (1.70, 3.14)1.70 x 10−8
(Total study pop.)
1.95 (1.60, 2.38)3.78 x 10−11
(Hispanics only)
1.91 (1.60, 2.20)1.4 x 10−15Trevino et al. 2009
1.46 (1.33, 1.60)4.1 x 10−15Ellinghaus et al. 2012
rs707383710q21.2ARID5B1.58 (1.35, 1.89)4.7 x 10−16Papaemmanuil et al. 2009Not available to evaluate
1.64 (1.40, 1.92)1.0 x 10−9Orsi et al. 2012
1.30 (1.19, 1.43)1.5 x 10−8Ellinghaus et al. 2012
rs1074005510q21.2ARID5B1.53 (1.41, 1.64)5.4 x 10−14Papaemmanuil et al. 2009Not available to evaluate
1.75 (1.49, 2.06)1.8 x 10−11Orsi et al. 2012
0.76 (0.70, 0.84)9.0 x 10−9Ellinghaus et al. 2012c
SNPChrGeneResults—other studiesaOur study resultsb
OR (95% CI)PReferencesRR (95% CI)P
rs223963314q11.2CEBPE1.34 (1.22, 1.45)2.9 x 10−7Papaemmanuil et al. 2009Not available to evaluate
0.74 (0.68, 0.82)4.0 x 10−10Ellinghaus et al. 2012c
rs498273114q11.2CEBPE1.36 (1.24, 1.48)9.0 x 10−12Xu et al. 2013Not available to evaluate
(Total study pop.)
1.58 (1.31, 1.91)2.32 x 10−6
(Hispanics only)
rs37312179p21.3CDKN2A0.71 (0.64, 0.78)3.0 x 10−11Sherborne et al. 2010dNot available to evaluate
rs37312499p21.3CDKN2A2.23 (1.90, 2.61)9.0 x 10−23Xu et al. 2015Not available to evaluate
rs177563119p21.3CDKN2A/B1.36 (1.18, 1.56)1.4 x 10−5Xu et al. 2013eNot available to evaluate
(Total study pop.)
1.36 (0.94, 1.97)0.1
(Hispanics only)
rs1082831710p12.2PIP4K2A1.23 (1.15, 1.32)2.3 x 10−9Migliorini et al. 20131.16 (0.75, 1.79)0.50
rs708831810p12.2PIP4K2A1.40 (1.28, 1.53)1.1 x 10−11Xu et al. 20131.04 (0.70, 1.56)0.83
(Total study pop.)
1.42 (1.12, 1.80)0.009
(Hispanics only)
rs382466210p14GATA33.85 (2.71, 5.47)2.2 x 10−14Perez-Andreu et al. 2013fNot available to evaluate
1.31 (1.21, 1.41)8.6 x 10−12Migliorini et al. 2013
1.77 (1.48, 2.12)2.8 x 10−10Perez-Andreu et al. 2015

a Other study results shown are largely from GWAS conducted among individuals of European ancestry. However, Xu et al. 2013 included European-American, African-American, and Hispanic-American participants; results for this study are shown for the total study population as well as for Hispanic-Americans alone. Perez-Andreu et al. 2013 also included European-American and Hispanic participants, among others; results shown are adjusted for genetic ancestry.

b Effect sizes for our entire study cohort are shown (both B-ALL and T-ALL case and parents).

c These results reflect associations for the minor allele, which is not the risk allele; therefore, these results show a protective odds ratio.

d OR results are for the T allele, which is not the risk allele, since results are protective. The T allele is the major (more common) allele.

e Although this result has a larger p-value than the cutoff specified (1x10-6), it is included in the table as an additional study that has found an association between CDKN2A and ALL.

f OR results are for a high-risk ALL subtype (Ph-like ALL), compared to non-ALL controls.

ARID5B SNPs have also been evaluated among Hispanics in particular in a small subset of studies. Our results are consistent with one other study, which observed a similar effect size for rs7089424 among Hispanics with childhood B-ALL (OR = 2.33, 95%CI: 1.85–2.92) [13]. a Other study results shown are largely from GWAS conducted among individuals of European ancestry. However, Xu et al. 2013 included European-American, African-American, and Hispanic-American participants; results for this study are shown for the total study population as well as for Hispanic-Americans alone. Perez-Andreu et al. 2013 also included European-American and Hispanic participants, among others; results shown are adjusted for genetic ancestry. b Effect sizes for our entire study cohort are shown (both B-ALL and T-ALL case and parents). c These results reflect associations for the minor allele, which is not the risk allele; therefore, these results show a protective odds ratio. d OR results are for the T allele, which is not the risk allele, since results are protective. The T allele is the major (more common) allele. e Although this result has a larger p-value than the cutoff specified (1x10-6), it is included in the table as an additional study that has found an association between CDKN2A and ALL. f OR results are for a high-risk ALL subtype (Ph-like ALL), compared to non-ALL controls. It has been observed that these ARID5B variants appear to be more common among Hispanics [8,13]. As would be expected, the risk allele frequencies (RAFs) observed for both rs10821936 and rs7089424 among both cases and parents in our study were higher than the risk allele frequencies reported among Hispanic and Native American reference populations (S1 Table). However, a GWAS by Xu et al. (2013) observed a RAF of 0.63 for rs10821936 among Hispanic ALL cases [8], which was closer to the RAF observed among Hispanic ALL cases for this SNP in our study (0.73). It is possible that the increased frequency of the risk allele for both rs10821936 and rs7089424 among Hispanics and Native Americans could be a factor in the increased risk of childhood ALL among these populations. The higher magnitude of effect observed among Hispanics in this study (as compared to studies among predominantly non-Hispanic whites) could be due to a stronger effect within this population for biological reasons, which are not apparent through this study. It is also possible that the larger effect size could be due to the study design used. The case-parent trio design may be a more unbiased approach in evaluating these effects, because it produces estimates that are inherently adjusted for effects of population stratification. ARID5B regulates the transcription of certain genes during embryonic development, and plays a role in the differentiation of B-lymphocyte progenitor cells [7,32]. Therefore, germline variation at this locus could alter B-lymphocyte development, thus playing a part in susceptibility to B-ALL [7,33]. Furthermore, both rs7089424 and rs10821936 may have a role in transcriptional regulation. The risk allele for rs10821936, in particular, is thought to eliminate the binding site of transcriptional factor NIT2, which could alter gene expression in cis [34,35]. While SNPs in ARID5B were the only variants to reach genome-wide level significance in our study, previously-reported SNPs IKZF1 rs4132601 and rs11978267 were nominally significant (P<0.05), and their effect sizes were similar to previous assessments [3-5,7,8]. The only other SNPs previously found to be associated with ALL that were directly genotyped on the platform included PIP4K2A rs10828317 and rs7088318. Although PIP4K2A rs10828317 was not statistically significant, the effect size was comparable to that reported in Migliorini et al. (RR = 1.16 vs. 1.23) [11]. However, this was not the case for PIP4K2A rs7088318 (RR = 1.04 vs. 1.40) [8]. It is possible that variations in risk allele frequency among Hispanics could contribute to these differences, in part because prior assessments have largely focused on those of European ancestry. Because of the nature of the variants contained on the SNP array used in this study (i.e., the majority were less common exonic SNPs, with 86.1% of the SNPs on the chip having a MAF ≤1%), most of the SNPs associated with ALL in the literature were not available and therefore were not evaluated in this analysis. This study should be considered in the light of certain limitations. As noted, because of the rare exonic content of the SNP chip, as well as the admixed study population, we did not perform SNP imputation. Additionally, because of the sample size, imputation of additional rare variants (and subsequent association testing) would have provided limited information. More specifically, our sample size did not allow for the detection of novel variants that are moderately associated with ALL. However, an important strength of our study was that it focused on Hispanic childhood ALL cases, in order to increase our knowledge of genetic risk factors among this higher-risk group. Populations of non-European ancestry are severely under-represented in GWAS; indeed, more than 95% of subjects in previous GWAS have been of European descent [19,36]. Because Hispanic children carry the burden of both a higher incidence of childhood ALL and worse outcomes of the disease than non-Hispanic whites or blacks [37,38], it is especially important to conduct exome- and genome-wide assessments for this disease among this particular ethnic group. Furthermore, a case-parent trio study design was used; unlike the case-control design employed by many GWAS, this approach is not subject to population stratification bias and is thus more likely to yield valid conclusions when assessing inherited genetic effects among Hispanic or other genetically diverse populations [16,17]. In summary, this is the first family-based study to evaluate inherited genetic variations associated with childhood ALL among those with Hispanic ancestry. These results confirm that ARID5B plays an important role in childhood ALL susceptibility in this high-risk population, and that the risk of childhood ALL associated with ARID5B rs10821936 and rs7089424 variants may be greater for Hispanics than among European populations. The study’s focus on Hispanic populations addresses an important gap in information about childhood ALL genetic risk factors for Hispanics.

Risk allele frequencies (RAFs) of ARID5B rs10821936 and rs7089424 for each of the groups used in the analysis, as well as for selected reference populations.

(DOCX) Click here for additional data file.

Results for the 100 most highly significant SNPs in the inherited effects analysis, entire ALL study cohort.

(DOCX) Click here for additional data file.

QC criteria based upon a combination of MAF and call rate (for those SNPs with a MAF <5%).

(TIF) Click here for additional data file.

Compressed file folder containing allele frequencies for genotyped subjects and raw statistical results for SNPs included in analysis.

(ZIP) Click here for additional data file.
  38 in total

1.  Inference of population structure using multilocus genotype data.

Authors:  J K Pritchard; M Stephens; P Donnelly
Journal:  Genetics       Date:  2000-06       Impact factor: 4.562

2.  Variation in CDKN2A at 9p21.3 influences childhood acute lymphoblastic leukemia risk.

Authors:  Amy L Sherborne; Fay J Hosking; Rashmi B Prasad; Rajiv Kumar; Rolf Koehler; Jayaram Vijayakrishnan; Elli Papaemmanuil; Claus R Bartram; Martin Stanulla; Martin Schrappe; Andreas Gast; Sara E Dobbins; Yussanne Ma; Eamonn Sheridan; Malcolm Taylor; Sally E Kinsey; Tracey Lightfoot; Eve Roman; Julie A E Irving; James M Allan; Anthony V Moorman; Christine J Harrison; Ian P Tomlinson; Sue Richards; Martin Zimmermann; Csaba Szalai; Agnes F Semsei; Daniel J Erdelyi; Maja Krajinovic; Daniel Sinnett; Jasmine Healy; Anna Gonzalez Neira; Norihiko Kawamata; Seishi Ogawa; H Phillip Koeffler; Kari Hemminki; Mel Greaves; Richard S Houlston
Journal:  Nat Genet       Date:  2010-05-09       Impact factor: 38.330

3.  Variation at 10p12.2 and 10p14 influences risk of childhood B-cell acute lymphoblastic leukemia and phenotype.

Authors:  Gabriele Migliorini; Bettina Fiege; Fay J Hosking; Yussanne Ma; Rajiv Kumar; Amy L Sherborne; Miguel Inacio da Silva Filho; Jayaram Vijayakrishnan; Rolf Koehler; Hauke Thomsen; Julie A Irving; James M Allan; Tracy Lightfoot; Eve Roman; Sally E Kinsey; Eamonn Sheridan; Pamela Thompson; Per Hoffmann; Markus M Nöthen; Thomas W Mühleisen; Lewin Eisele; Martin Zimmermann; Claus R Bartram; Martin Schrappe; Mel Greaves; Martin Stanulla; Kari Hemminki; Richard S Houlston
Journal:  Blood       Date:  2013-08-30       Impact factor: 22.113

4.  A genomewide admixture mapping panel for Hispanic/Latino populations.

Authors:  Xianyun Mao; Abigail W Bigham; Rui Mei; Gerardo Gutierrez; Ken M Weiss; Tom D Brutsaert; Fabiola Leon-Velarde; Lorna G Moore; Enrique Vargas; Paul M McKeigue; Mark D Shriver; Esteban J Parra
Journal:  Am J Hum Genet       Date:  2007-04-20       Impact factor: 11.025

5.  A log-linear approach to case-parent-triad data: assessing effects of disease genes that act either directly or through maternal effects and that may be subject to parental imprinting.

Authors:  C R Weinberg; A J Wilcox; R T Lie
Journal:  Am J Hum Genet       Date:  1998-04       Impact factor: 11.025

6.  Intron 3 of the ARID5B gene: a hot spot for acute lymphoblastic leukemia susceptibility.

Authors:  Ángela Gutiérrez-Camino; Elixabet López-López; Idoia Martín-Guerrero; José Sánchez-Toledo; Nagore García de Andoin; Ana Carboné Bañeres; Purificación García-Miguel; Aurora Navajas; África García-Orad
Journal:  J Cancer Res Clin Oncol       Date:  2013-09-08       Impact factor: 4.553

7.  Genome wide study of maternal and parent-of-origin effects on the etiology of orofacial clefts.

Authors:  Min Shi; Jeffrey C Murray; Mary L Marazita; Ronald G Munger; Ingo Ruczinski; Jacqueline B Hetmanski; Tao Wu; Tanda Murray; Richard J Redett; Allen J Wilcox; Rolv T Lie; Ethylin Wang Jabs; Yah Huei Wu-Chou; Philip K Chen; Hong Wang; Xiaoqian Ye; Vincent Yeow; Samuel S Chong; Bing Shi; Kaare Christensen; Alan F Scott; Poorav Patel; Felicia Cheah; Terri H Beaty
Journal:  Am J Med Genet A       Date:  2012-03-14       Impact factor: 2.802

8.  Patterns of leukemia incidence in the United States by subtype and demographic characteristics, 1997-2002.

Authors:  Jennifer F Yamamoto; Marc T Goodman
Journal:  Cancer Causes Control       Date:  2007-12-07       Impact factor: 2.506

9.  PREMIM and EMIM: tools for estimation of maternal, imprinting and interaction effects using multinomial modelling.

Authors:  Richard Howey; Heather J Cordell
Journal:  BMC Bioinformatics       Date:  2012-06-27       Impact factor: 3.169

10.  Identification of germline susceptibility loci in ETV6-RUNX1-rearranged childhood acute lymphoblastic leukemia.

Authors:  E Ellinghaus; M Stanulla; G Richter; D Ellinghaus; G te Kronnie; G Cario; G Cazzaniga; M Horstmann; R Panzer Grümayer; H Cavé; J Trka; O Cinek; A Teigler-Schlegel; A ElSharawy; R Häsler; A Nebel; B Meissner; T Bartram; F Lescai; C Franceschi; M Giordan; P Nürnberg; B Heinzow; M Zimmermann; S Schreiber; M Schrappe; A Franke
Journal:  Leukemia       Date:  2011-11-11       Impact factor: 11.528

View more
  7 in total

1.  Novel Gene and Network Associations Found for Acute Lymphoblastic Leukemia Using Case-Control and Family-Based Studies in Multiethnic Populations.

Authors:  Priyanka Nakka; Natalie P Archer; Heng Xu; Philip J Lupo; Benjamin J Raphael; Jun J Yang; Sohini Ramachandran
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2017-07-27       Impact factor: 4.254

2.  The role of parental and perinatal characteristics on Langerhans cell histiocytosis: characterizing increased risk among Hispanics.

Authors:  Erin C Peckham-Gregory; Kenneth L McClain; Carl E Allen; Michael E Scheurer; Philip J Lupo
Journal:  Ann Epidemiol       Date:  2018-04-17       Impact factor: 3.797

3.  Cancer Progress and Priorities: Childhood Cancer.

Authors:  Philip J Lupo; Logan G Spector
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2020-06       Impact factor: 4.090

4.  Reduced-dose intensity therapy for pediatric lymphoblastic leukemia: long-term results of the Recife RELLA05 pilot study.

Authors:  Francisco Pedrosa; Elaine Coustan-Smith; Yinmei Zhou; Cheng Cheng; Arli Pedrosa; Mecneide Mendes Lins; Marcia Pedrosa; Norma Lucena-Silva; Alessandra Maria de Luna Ramos; Ester Vinhas; Gaston K Rivera; Dario Campana; Raul C Ribeiro
Journal:  Blood       Date:  2020-04-23       Impact factor: 25.476

5.  Elucidating the role of maternal environmental exposures on offspring health and disease using two-sample Mendelian randomization.

Authors:  David M Evans; Gunn-Helen Moen; Liang-Dar Hwang; Debbie A Lawlor; Nicole M Warrington
Journal:  Int J Epidemiol       Date:  2019-06-01       Impact factor: 7.196

6.  ZBTB24 is a transcriptional regulator that coordinates with DNMT3B to control DNA methylation.

Authors:  Joyce J Thompson; Rupinder Kaur; Carlos P Sosa; Jeong-Heon Lee; Katsunobu Kashiwagi; Dan Zhou; Keith D Robertson
Journal:  Nucleic Acids Res       Date:  2018-11-02       Impact factor: 16.971

7.  Association of genes ARID5B, CEBPE and folate pathway with acute lymphoblastic leukemia in a population from the Brazilian Amazon region.

Authors:  Darlen Cardoso Carvalho; Alayde Vieira Wanderley; Fernando Augusto Rodrigues Mello Junior; André Mauricio Ribeiro Dos Santos; Luciana Pereira Colares Leitão; Tatiane Piedade de Souza; Amanda de Nazaré Cohen Lima de Castro; Leandro Lopes de Magalhães; Marianne Rodrigues Fernandes; João Augusto Nunes de Carvalho Junior; André Salim Khayat; Sidney Santos; Paulo Pimentel de Assumpção; Ney Pereira Carneiro Dos Santos
Journal:  Leuk Res Rep       Date:  2019-11-27
  7 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.