The impact of Indigenous American-like ancestry on risk of acute lymphoblastic leukemia in Hispanic/Latino children

The impact of Indigenous American-like ancestry on risk of acute lymphoblastic leukemia in Hispanic/Latino children | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search The impact of Indigenous American-like ancestry on risk of acute lymphoblastic leukemia in Hispanic/Latino children View ORCID Profile Jalen Langie , View ORCID Profile Tsz Fung Chan , View ORCID Profile Wenjian Yang , View ORCID Profile Alice Y. Kang , View ORCID Profile Libby Morimoto , View ORCID Profile Daniel O. Stram , View ORCID Profile Nicholas Mancuso , View ORCID Profile Xiaomei Ma , View ORCID Profile Catherine Metayer , View ORCID Profile Philip J. Lupo , View ORCID Profile Karen R. Rabin , View ORCID Profile Michael E. Scheurer , View ORCID Profile Joseph L. Wiemels , View ORCID Profile Jun J. Yang , View ORCID Profile Adam J. de Smith , View ORCID Profile Charleston W.K. Chiang doi: https://doi.org/10.1101/2025.01.14.25320563 Jalen Langie 1 Center for Genetic Epidemiology, Department of Population and Public Health Sciences, University of Southern California Keck School of Medicine , Los Angeles, CA, USA 2 USC Norris Comprehensive Cancer Center, University of Southern California , Los Angeles, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jalen Langie Tsz Fung Chan 1 Center for Genetic Epidemiology, Department of Population and Public Health Sciences, University of Southern California Keck School of Medicine , Los Angeles, CA, USA 2 USC Norris Comprehensive Cancer Center, University of Southern California , Los Angeles, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Tsz Fung Chan Wenjian Yang 3 Department of Pharmacy and Pharmaceutical Sciences, St. Jude Children’s Research Hospital , Memphis, Tennessee, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Wenjian Yang Alice Y. Kang 4 School of Public Health, University of California Berkeley , Berkeley, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Alice Y. Kang Libby Morimoto 4 School of Public Health, University of California Berkeley , Berkeley, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Libby Morimoto Daniel O. Stram 5 Department of Population and Public Health Sciences, University of Southern California Keck School of Medicine , Los Angeles, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Daniel O. Stram Nicholas Mancuso 1 Center for Genetic Epidemiology, Department of Population and Public Health Sciences, University of Southern California Keck School of Medicine , Los Angeles, CA, USA 2 USC Norris Comprehensive Cancer Center, University of Southern California , Los Angeles, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Nicholas Mancuso Xiaomei Ma 6 Department of Chronic Disease Epidemiology, Yale School of Public Health , New Haven, CT, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Xiaomei Ma Catherine Metayer 4 School of Public Health, University of California Berkeley , Berkeley, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Catherine Metayer Philip J. Lupo 7 Division of Hematology-Oncology, Department of Pediatrics, Baylor College of Medicine , Houston, TX, USA 8 Texas Children’s Cancer and Hematology Centers, Texas Children’s Hospital , Houston, TX, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Philip J. Lupo Karen R. Rabin 7 Division of Hematology-Oncology, Department of Pediatrics, Baylor College of Medicine , Houston, TX, USA 8 Texas Children’s Cancer and Hematology Centers, Texas Children’s Hospital , Houston, TX, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Karen R. Rabin Michael E. Scheurer 7 Division of Hematology-Oncology, Department of Pediatrics, Baylor College of Medicine , Houston, TX, USA 8 Texas Children’s Cancer and Hematology Centers, Texas Children’s Hospital , Houston, TX, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Michael E. Scheurer Joseph L. Wiemels 1 Center for Genetic Epidemiology, Department of Population and Public Health Sciences, University of Southern California Keck School of Medicine , Los Angeles, CA, USA 2 USC Norris Comprehensive Cancer Center, University of Southern California , Los Angeles, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Joseph L. Wiemels Jun J. Yang 3 Department of Pharmacy and Pharmaceutical Sciences, St. Jude Children’s Research Hospital , Memphis, Tennessee, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jun J. Yang Adam J. de Smith 1 Center for Genetic Epidemiology, Department of Population and Public Health Sciences, University of Southern California Keck School of Medicine , Los Angeles, CA, USA 2 USC Norris Comprehensive Cancer Center, University of Southern California , Los Angeles, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Adam J. de Smith Charleston W.K. Chiang 1 Center for Genetic Epidemiology, Department of Population and Public Health Sciences, University of Southern California Keck School of Medicine , Los Angeles, CA, USA 2 USC Norris Comprehensive Cancer Center, University of Southern California , Los Angeles, CA, USA 9 Department of Quantitative and Computational Biology, University of Southern California , Los Angeles, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Charleston W.K. Chiang For correspondence: charleston.chiang{at}med.usc.edu Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Acute lymphoblastic leukemia (ALL) is the most common childhood cancer, with Hispanic/Latino children having a higher incidence of ALL than other racial/ethnic groups. Among genetic variants previously implicated in ALL risk, a number of them were found to be enriched in Indigenous American (IA)-like ancestries and inherited by many Hispanic/Latino individuals. However, due to potential confounding from environmental factors, the association between IA-like ancestry and risk for ALL has remained unclear. In this study, we characterized the impact of IA-like ancestry on overall ALL risk and on the frequency and effect size of known risk alleles, while accounting for non-genetic correlates of ancestry. Contrary to previous findings, we found that global IA-like ancestry was not significantly associated with ALL risk after adjusting for socio-economic indicators. On the other hand, locally at known ALL risk regions, we uncovered that increasing copies of the IA-like haplotype were positively and significantly associated with ALL risk ( e.g. the IA-like haplotype had ∼1.33 times the odds of harboring the risk allele compared to non-IA-like haplotypes), but we found no evidence of interaction between genotype and ancestry in relation to ALL. Admixture mapping identified replicable association signals at chr7p12.2 and chr10q21.2, consistent with the benefit of leveraging genetic ancestry in identifying genetic risk loci. Taken together, our results suggest that increased risk of ALL in Hispanic/Latino children may be conferred by higher frequency of risk alleles within IA-like ancestry, and that local ancestry-based analyses are robust stratagems to elucidate genetic etiology of disease. Introduction Acute lymphoblastic leukemia (ALL) is the most common childhood cancer and accounts for 25% of malignancies before the age of 20 (ref. 1 ). The long-term survival rate of the disease in children is upwards of 85-90% 2 , but survival rate decreases to approximately 40% after relapse 3 – 6 and 30-40% in individuals over the age of 35. Despite decades of investigation into ALL risk, the etiology of the disease is not well understood and disparities in risk persist. Children of Hispanic/Latino ethnicity have 1.4 and 2 times the risk of developing ALL compared to their Non-Latino White and Black counterparts, respectively 7 . This disparity in risk widens in margin for Hispanic/Latino adults 7 and is specific to the B-cell immunophenotype – which comprises approximately 85% of cases 8 . While environmental factors are involved in this increased risk, genetic factors, which have been historically understudied in this population 9 , 10 , likely contribute in part to this disparity 11 , 12 Genomic, migrational, and ecological data strongly suggest a complex genetic etiology of ALL, both broadly across humans and within diverse Latin American populations. Most directly, previous genome-wide association studies (GWAS) have estimated the heritability of ALL to be approximately 18-21% in both Hispanic/Latino and Non-Latino White populations, and approximately 24% of familial relative risk has been attributed to known ALL risk variants 13 – 15 . Consistent with these estimates, GWAS have identified a number of loci associated with ALL susceptibility, including risk alleles located near genes such as IKZF1 , ARID5B , GATA3 , and ERG that appear to have higher frequency or stronger effects in Hispanic/Latino populations 12 , 16 – 18 . Previous migration studies have indicated that non-native- and US-born Hispanic/Latino individuals have comparable rates of B-cell ALL to each other, and both have higher (∼2.13 times) incidence rates compared to their Non-Latino White counterparts 19 , 20 . Furthermore, ecological data has indicated that countries with the highest age standardized incidence rates of ALL were in Latin America ( i.e. Honduras and Mexico), while countries with the lowest incidence rates were in Africa, with European countries exhibiting intermediate incidence 21 , 22 . Notable amongst populations with differing incidence rates of ALL is the presence or dominance of a particular ancestry component. Due to the unique demographic history of Latin America, Hispanics/Latino populations are admixed - drawing ancestral components from the American, European, and African continents 23 , 24 . Previous literature has alluded to associations between Indigenous American (IA)-like ancestry and ALL, suggesting that every 20% increase in IA-like ancestry was associated with a 20% increase in odds of developing B-cell ALL 25 . Because Hispanics/Latinos inherited a significant proportion of their genetic ancestries from Indigenous Americans (though there is geographical heterogeneity across the Americas), this association was presumed to explain the migration and ecological findings above. However, global ancestry can be confounded by non-genetic factors such as socio-economic status 26 , which was not considered in previous analyses 25 . Further exploration of the association between IA-like ancestry and ALL risk would need to account for this potential confounding. Given the important role of genetic factors and genetic ancestry in ALL risk, we undertook a systematic admixture analysis in the largest ALL cohort of self-reported Hispanic/Latino children to date. Here, we sought to clarify the association between IA-like ancestry and ALL risk through a study design that would better account for non-genetic confounders and leveraged local ancestry analyses, which are less susceptible to confounding by environmental factors and have previously yielded promising results in examining ancestry-ALL relationships 30 . We investigated both inferred global ancestry across the genome and local ancestry at known ALL loci for associations with risk. Further, we employed admixture mapping 27 , which is best powered to detect loci that exhibit frequency differences between ancestries and may underlie the risk differences between populations 28 , 29 . We note that, given our goal to further understand the disparity in ALL risk epidemiologically observed among Hispanic/Latino children, we inferred the genetic ancestries and characterized their associations with ALL risk in self-reported Hispanic/Latino individuals. We defined genetic ancestries at the continental (e.g. Africa, Europe, and Americas) level, which represents patterns of genetic similarities across geographical space over the last ∼10-50k years due to human migrations and isolation-by-distance. These relatively distinct components of ancestries, though arbitrarily defined, are only recently reunited in admixed populations, such as in individuals of Hispanic/Latino origin. These components are inferred and defined relative to reference populations available to us and are leveraged to uncover associations with traits in this research setting but are invariably a simplified representation of the complex nature of genetic ancestries. We thus append the suffix “-like” to refer to these ancestries to remind the reader of this simplification and uncertainty 30 . Methods Study Subjects and Quality Control We use the term “Hispanics/Latinos” as a population descriptor of our study subjects to align with both the self-reported race/ethnicity categories and the definitions of the USA Office and Management Budget 31 , while acknowledging that we focus on individuals of Latin American origin. Our analysis utilized the following cohorts of self-reported Hispanic/Latino study participants: the California Cancer Linkage Project (CCRLP), the Kaiser Resource for Genetic Epidemiology Research on Aging Cohort (Kaiser-GERA), the California Childhood Leukemia Study (CCLS), Children’s Oncology Group (COG), Multi-Ethnic Study of Atherosclerosis (MESA), the Adolescent and Childhood Cancer Epidemiology and Susceptibility Service for Texas and Reducing Ethnic Disparities in Acute Leukemia (ACCESS/REDIAL), and the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). All relevant information on sample size, arrays, study design, and references/dbGaP accession numbers can be found in Table 1 . Following the practice in previous studies 12 , 13 , 32 , cases from CCRLP were augmented with external controls from Kaiser-GERA genotyped on a similar array. Cases from COG were paired with external controls from MESA and ACCESS/REDIAL cases were paired with external controls from HCHS/SOL. HCHS/SOL was a multi-center study, with study participants being ascertained from Bronx, New York; Chicago, Illinois; Miami, Florida; and San Diego, California. The SOL San Diego center was chosen as controls for ACCESS/REDIAL due to similarity in the first 2 principal components and estimated ancestry distributions ( Supplementary Figures 1-2). View this table: View inline View popup Download powerpoint Table 1: All cohorts included in the analysis. Information includes title, design, sample size, genotype array, dbGaP accession number or reference, and how each study was used in our analysis. All analyses were limited to autosomes. Within each study, SNPs and individuals were removed based on the following pre-defined quality control thresholds: SNP call rate < 0.98, MAF < 0.01, p HWE 6 standard deviations from the mean, or sample call rate < 0.95. Palindromic SNPs were also excluded as they could impact imputation accuracy due to unidentified strand flip or inversion between genome build 35 . The number of individuals and SNPs remaining can be found in Supplementary Table 1 . Local ancestry was inferred using RFMIX 36 . Reference haplotypes were obtained from the genome aggregation database (gnomAD) 37 version 3, including 708 African, 671 Non-Finnish European, and 94 Indigenous American individuals previously chosen for ancestry inference in Hispanics/Latinos 38 . After restricting to the intersection of SNPs across each paired dataset, each dataset was imputed separately using the TopMed Imputation Server 39 . We observed systemic differences in imputation quality as measured by the imputation R 2 between cases and external controls, presumably due to batch effects in data generation or subtle differences in ancestry compositions between cohorts and differential imputation accuracies across populations 40 . Consequently, for all comparisons of cases to controls, we imposed filters such that only SNPs with an imputation R 2 > 0.30 in both cases and controls and a R 2 difference < 0.15 between cases and controls were included in the analysis. Global Ancestry Analyses Global ancestry for each study participant was calculated by averaging local ancestry estimates across the genome. We performed logistic regression of ALL case-control status on global IA-like ancestry in CCRLP and CCLS, studies in which indicators of socio-economic status (SES) variables were available. Covariates included African-like ancestry, sex, and SES indicators (highest education obtained by a parent and annual household income). Income was categorized in 6 strata, in accordance with its treatment in CCLS 41 : $75,000. In CCLS, highest educational attainment of either parent was divided into 4 categories: Less than elementary school, high school, some college, and a bachelor’s degree or higher. In CCRLP, the strata were less than 9 th grade, 9 th -12 th grade, and some college or more. Both SES indicators were treated as ordinal categorical variables, in which the lowest category was used as a reference. Linearity assumptions were assessed using residual and QQ plots. Analysis was carried out using R v.4.3.1 (ref. 42 ). Enrichment of risk allele on IA-like ancestry haplotypes We performed local ancestry analyses at known ALL loci, based on the list provided in ref. 11 . We excluded the T-cell ALL-specific association locus at USP7 43 given that the increased risk of ALL in Hispanics/Latinos is limited to B-cell ALL. We restricted analyses to remaining SNPs that were independent at the local ancestry level ( i.e. choosing SNPs that were not in admixture linkage disequilibrium; N=20) using PLINK’s clumping algorithm 44 . SNPs were clumped if the diploid local IA-like ancestry assignments covering the SNPs were 90% correlated and within 10 Mb of each other. A total of three analyses were performed using local ancestry estimates. First, across known ALL loci 11 , we cross-tabulated local genetic ancestry (number of copies of the IA-like, African-like, or European-like haplotypes) and risk allele dosage to identify association of risk alleles with ancestry. To establish a null distribution to which we would compare our observed ancestry-risk allele association, we randomly selected 1000 sets of 20 frequency-matched alleles from across the genome, without replacement. Differences between the null and known loci distributions were evaluated via a Chi-Squared Goodness of Fit test. We restricted this analysis to CCRLP and CCLS controls, to approximate the frequency of the risk allele in the general population. Second, we performed a logistic regression of ALL case-control status on IA-like ancestry dosage at each locus, including as covariates global African-like and European-like ancestry, and sex, to evaluate how likely is the IA-like ancestry associated with increasing ALL risk. Third, to test for presence of heterogeneous effect sizes across ancestries, we performed logistic regression of ALL case-control status on risk allele genotype, IA-like ancestry dosage, sex, the first 20 principal components, and an interaction term of local ancestry and risk allele dosage. Analyses were carried out either using PLINK v2.0 (ref. 45 ) or R v.4.3.1 (ref. 42 ). Admixture mapping, regional association testing, and replication We performed a genome-wide scan for loci in which IA-like ancestry was associated with ALL risk. We regressed ALL case-control status on the local IA ancestry dosage, global African-like and European-like ancestry, sex, and the first 20 principal components. We performed this analysis in CCRLP/Kaiser as our discovery dataset. The significance threshold for genome-wide admixture mapping was 5.7 x 10 -5 , as suggested in literature previously for Hispanic/Latino populations 46 , 47 . We fine-mapped putative admixture association region through single-variant association testing in the same dataset. The admixture peak region 48 was defined as a range of positions that contains all SNPs with a -log10( P ) between -log10( P am ) and -log10( P am ) – 1, with P am representing the p-value associated with the lead SNP from admixture mapping. Genotypes of all imputed SNPs within this region passing QC filtering were tested via logistic regression, with sex and the first 20 principal components as covariates. Significance threshold for each region were computed using 1000 rounds of permutation of case-control labels, choosing the threshold that controls for the 5% false discovery rate. We attempted to replicate the association at the lead variants from regional association testing and conditional analysis in additional Hispanic/Latino cohorts: CCLS, COG/MESA, and SOL-SD/REDIAL. The replication cohorts were meta-analyzed together by inverse variance weighting in the software METAL 49 . Evaluating the impact of using self-reported race/ethnicity labels versus genetic inferred ancestry We opted to perform admixture mapping using all self-reported Hispanic/Latino individuals to be inclusive and avoid forming study exclusion criteria based on estimated genetic ancestry. However, alternative approaches could choose to include participants based on inferred genetic ancestries. To examine, in our context, the impact of informing study inclusion based on genetically inferred ancestry, we implemented a software, Harmonizing Ancestry and Race Ethnicity 50 (HARE; Fang et. al, 2019) on genetic data from CCRLP/Kaiser-GERA ( Supplementary Figure 3. HARE is a two-step machine learning-based algorithm that identifies individuals who may have discordant self-identified race/ethnicity (SIRE) and genetically inferred ancestry (GIA) information, given the mapping of the two learned from the rest of the dataset. The first step is a training phase in which genetically inferred ancestry based on principal components are used to predict available self-reported race/ethnicity in a support vector machine (SVM). The second step is HARE strata assignment, in which the highest probability of being assigned to a stratum is contrasted with the probability that the individual belongs to their self-reported stratum using preset thresholds. If the ratio of the probabilities exceeds a pre-defined threshold, an individual with existing SIRE could be assigned as missing ( i.e. removed from analysis), while an individual without SIRE would be assigned into the top predicted strata. We applied HARE with the default parameters and thresholds 50 , training a multi-class SVM with a radial kernel on the first 30 principal components (calculated using PLINK 45 v2.0) of genotype data from individuals across all self-reported race/ethnicity groups from CCRLP/Kaiser-GERA (10,450 Hispanic/Latino, 2,191 Non-Latino African, 5,335 Non-Latino East Asian, 58,503 Non-Latino European individuals). We first evaluated if the HARE algorithm would exclude any study participants due to discordance between SIRE and GIA. We also assessed the rate of discordance between SIRE and GIA by performing 10-fold cross validation in which we randomly masked the SIRE information of 10% of study participants for each race/ethnicity group in each trial. This assessment simulates a scenario where one may define study exclusion criteria based on comparing the GIA to a known reference panel. Percent discordance was defined as the number of individuals classified as missing or as a stratum not corresponding to the masked SIRE label. We then investigated the impact of this study exclusion criteria on our admixture mapping result. IRB Statement This study was approved by the institutional review boards of the California Health and Human Services Agency, the University of Southern California, Children’s Oncology Group, Baylor College of Medicine, Yale University, and University of California, Berkeley. All study participants were de-identified and newborn bloodspots from the CCRLP were obtained via a consent waiver from Committee for the Protection of Human Subjects of the State of California. Results The association between global Indigenous American-like ancestries, ALL risk, and SES indicators Previous studies have identified a significant and positive association between global IA-like ancestries and ALL risk 25 (Walsh et al., 2013) but did not adjust for socioeconomic status (SES) variables that often confound ancestry-disease associations. To quantify the effects of IA-like ancestry on ALL risk, we tested the association between ALL case-control status and global IA-like ancestry, while adjusting for African-like ancestry, sex, as well as covariates that are proxies for SES, such as highest parental education and annual household income. We performed these assessments for all self-reported LAT individuals in both CCRLP (excluding Kaiser-GERA controls; N = 4033) and CCLS (N = 605), as both of these cohorts have available SES variables, contained internal controls and are less likely to be confounded due to their study design. In CCLS, IA-like ancestry was significantly associated with ALL risk, prior to including SES indicators in the model, as previously reported (Walsh et al., 2013). In the base model adjusting for sex and African-like ancestry, a 20% increase in IA-like ancestry was associated with a 23% (OR: 1.23, 95% CI: 1.07-1.42; P = 0.003) increase in odds of developing ALL. However, this association appears confounded, as inclusion of SES covariates strongly attenuated the strength of association (OR: 1.12; 95% CI: 0.96-1.31) and case/control status is no longer significantly associated with IA-like ancestry ( P = 0. 159; Supplementary Table 2 ). In CCRLP, IA-like ancestry was not significantly associated with ALL risk in the base model adjusting for only African-like ancestry and sex, and did not qualitatively change when SES covariates were included ( Supplementary Table 2 ). Regression analysis between the SES variables and ALL case-control status and ancestry, respectively, further supported the suggestion that socio-cultural factors can act as confounders in ancestry-trait relations. For instance, we observed higher income to be associated with lower risk of disease ( e.g. 0.27 – 0.76 times the odds of developing ALL; Supplementary Tables 3-4 ), and higher European-like ancestry is associated with higher socio-economic status ( Supplementary Tables 5-6 ). Together, because SES measures can correlate with both ancestry and disease risk, the previously observed association between IA-like ancestry and ALL risk (Walsh et al., 2013) may have been confounded. Known ALL risk loci are enriched among Indigenous American-like ancestral haplotypes While global ancestry association analyses can be confounded by socioeconomic indicators, use of local ancestry to identify associations at risk loci may be more robust. Further, the absence of an association between IA-like ancestry and ALL risk globally would not necessarily preclude an association locally at known risk loci. Thus, we performed three analyses leveraging the pattern of local ancestry at known B-cell ALL risk loci 11 ( Supplementary Table 7 ) to examine if the risk alleles are found more frequently or show larger effects on IA-like ancestry haplotypes. First, we tabulated the proportion of times the risk allele at known ALL loci is associated with the African-like, IA-like, or European-like ancestry haplotypes, and compared these to the proportions found across 1000 sets of SNPs randomly selected across the genome and matched by the risk allele frequencies. Across a total of 20 quasi-independent ALL SNPs ( Methods ), the risk alleles were found on the African-like, IA-like, and European-like ancestry haplotypes 5.76%, 45.35%, and 48.88% of the time, respectively, in CCRLP. This significantly deviates from the genome-wide expectation (4.83%, 43.33%, and 51.85% for African-like, IA-like, and European-like ancestry haplotypes; p-value = 1.04x10 -36 by a chi-square goodness-of-fit test), with IA-like haplotypes showing a significant enrichment in association with the risk alleles. We also replicated this observation in CCLS, with IA-like haplotypes enriched with ALL risk alleles (p-value = 1.629x10 - 9 ). Consistent with this finding, the IA-like haplotypes have 1.32-1.33 (95% CI: 1.29 - 1.36 in CCRLP, 1.26-1.41 in CCLS) times the odds of carrying the risk allele compared to Non-IA-like haplotypes (p = 5.61x10 -88 and 2.61x10 -23 in CCRLP and CCLS, respectively; Supplementary Table 8 ). In the second local ancestry analysis, we regressed ALL case-control status on the number of copies of IA-like ancestry segments locally at each known ALL locus, while adjusting for sex and global African and European ancestry proportions. Because we anticipate that risk alleles for ALL are found disproportionately on IA-like haplotypes across these loci, we would expect that IA-like ancestries show an enrichment of association increasing the risk of ALL in the setting of admixture mapping studies. Indeed, we found a positive association of IA-like ancestry with ALL at 14 out of 20 known B-cell ALL loci. At all loci in which IA-like ancestry was nominally significantly (p < 0.05) associated with ALL, IA ancestry was positively associated with risk (Binomial test, p = 0.008 Table 2 ). To confirm that the known risk alleles were in fact driving the ancestry association, we included risk genotype as an additional covariate in each model. At each locus, upon adjustment for risk genotype, the association between IA-ancestry and ALL risk reduced in significance ( Supplementary Table 9 ). Taken together, our results suggest that IA-like haplotypes are enriched for association with increased ALL risk at known loci, driven by increased frequency of risk allele. View this table: View inline View popup Table 2: Indigenous American-like ancestry is positively associated with risk at known ALL loci. ALL case-control status was regressed on local Indigenous ancestry dosage, African-like and European-like global ancestries, and sex a . Loci that reach nominal significance (P < 0.05) are in bold ) In addition to ALL risk alleles being found more prevalently on IA-like haplotypes, risk alleles may also exert larger (or smaller) effects on ALL when they are found on the IA-like background. Therefore, for the third local ancestry analysis, we tested for effect size heterogeneity by including a SNPxAncestry interaction term in the standard association testing model. After Bonferroni correction, no significant interactions between genotype and local IA-like ancestry were observed in either CCRLP or CCLS, or when the two cohorts were combined ( Supplementary Table 10 ). We additionally found no evidence of interaction between global IA ancestry and genotype ( Supplementary Table 11 ). Therefore, we found no evidence that ALL risk alleles may exert larger effects on an IA haplotype than on other haplotypic backgrounds. Admixture mapping proposes putative novel ALL loci associated with Indigenous American-like ancestry . Although results from our global ancestry analysis showed minimal association between IA-like ancestry and ALL risk after adjusting for SES indicators, it does not preclude the possibility that some risk alleles show drastic differences in frequency between ancestries, and thus are amenable to being discovered through admixture mapping, particularly since we observed that IA-like ancestries are enriched for association with risk alleles for ALL ( Table 2 above). We thus performed admixture mapping using the CCRLP and Kaiser/GERA Hispanic/Latino cohort as our discovery panel (1930 cases and 6417 controls). Our discovery admixture mapping analyses identified genome-wide significant (threshold = 5.7 x 10 -5 ; see Methods ) signals at 2q21.2, 7p12.2, 10q21.2, and 15q22.31. The loci on chromosomes 7 ( IKZF1 ) and 10 ( ARID5B ) are known, while those on chromosomes 2 and 15 are potentially novel ( Figure 2 ). Download figure Open in new tab Figure 2: Manhattan plot of the admixture mapping analysis. The analysis identified signals at 2q21.2, 7p12.2, 10q21.2, and 15q22.31. The significance threshold is 5.7e-5. We then fine-mapped each of the four admixture loci via single-variant regional association testing ( Methods ), which uncovered the putative lead SNPs in each region to be 2:133605881 (p = 4.209 x 10 -5 ), 7:50395237 (p = 2.262 x 10 -12 ), 10:61959980 (p = 2.245 x 10 -44 ), and 15:66111790 (p = 3.011 x 10 -4 ; Table 3 ). SNPs on chr2, 7, and 10 surpassed the regional significance threshold after accounting the number of tests performed in each region via permutation ( Methods ), and chr15 SNP was slightly below the threshold. These SNPs exhibit allele frequency differences by ancestry group ( Table 3 ), and conditioning on these SNPs also generally attenuated the admixture mapping association ( Table 3 ) . We sought replication of these associations in additional Hispanic/Latino cohorts, including cases from CCLS, COG, and REDIAL (see Methods ), totaling 1,951 cases and 3,918 controls. In the meta-analyzed replication cohorts, only the lead SNPs or other SNPs in LD with the lead SNPs on chromosomes 7 and 10 were statistically significant ( Supplementary Table 12-13 ). Through power calculations 51 , we found that, given observed odds ratios in the discovery cohort and combined allele frequencies in the replication datasets, our replication analysis had only 1.2% and 0.5% power to detect the associations on chromosomes 2 and 15, respectively; in contrast, we had 17.8% power to identify the signal on chromosome 7 and 100% power to detect the locus on chromosome 10. View this table: View inline View popup Download powerpoint Table 3: Odds Ratios, 95% CI’s, and p-values of top SNPs from regional association testing, under the admixture peak, as well as the crude and adjusted p-values of the admixture mapping tag SNP. The regional significance thresholds, determined via permutation to account for the regional multiple testing burden, are 7.906x10 -5 , 6.256x10 -4 , 1.905x10 -3 , and 1.270x10 -4 for 2q21.2, 7p12.2, 10q21.2, and 15q22.31, respectively.*Risk allele frequencies (RAF) per ancestry group were obtained from European (Orcadian, Russian, Tuscan, French, Basque, Bergamo, Sardinian, Adygei), African (Bantu, Mbuti, Biaka, San, Yoruba, Mandenka), and Indigenous American (Surui, Karitiana, Maya, Pima, Colombian) superpopulation data from the Human Genome Diversity Project (accessed from the Genome Aggregation Database 38 v4). While local ancestry is not likely to be associated with socio-cultural variables, such analyses can still be confounded by population stratification (Mersha 2015). In our study, we opted to include all self-reported Hispanic/Latino individuals in our admixture mapping analysis and adjusted for potential confounding by including global ancestries as covariates in our admixture mapping model. Nevertheless, use of genetically inferred ancestry is pervasive and often heralded as a convenient, albeit arbitrary way of identifying racial/ethnic strata to better control for confounding by population structure (Fang et al., 2019) To evaluate whether our use of self-reported data may lead to starkly different results from using genetically inferred ancestry, we employed the Harmonizing Ancestry Race Ethnicity algorithm (HARE; Fang et al., 2019) to identify individuals whose self-reported race ethnicity labelled may not be consistent with genetically inferred ancestry compared to a reference. Consistent with the goals of our study and best practices in using population labels in human genetic studies 30 , HARE does not attempt to re-assign individuals to strata; rather, individuals with discordant self-report and genetically inferred data are indicated and thus can be excluded from analysis. Using all available self-reported race/ethnicity labels in the multi-ethnic CCRLP + Kaiser cohort, we observed very low rates of discordance between HARE-predicted labels and the self-reported labels (percent discordance = 0.47% overall, and 0.68%, 3.37%, 0.18%, and 0.54% for AFR, EAS, EUR, and LAT study participants, respectively. Supplementary Table 14 ), suggesting that in general there is not enough genetic evidence to exclude participants from analysis (nor do we advocate for removing individuals from analysis on the basis of genetic make-up alone). In a scenario where individuals might be missing self-reported race/ethnicity labels, HARE can be used to predict the label based on a reference. We simulated such a scenario using 10-fold cross validations, randomly masking in turn 10% of the cohort while using the remaining 90% as the reference for the HARE algorithm to learn the relationship between self-reported labels and genetically inferred ancestry ( Methods ). In this case, we observed the algorithm to be less confident, recalling 61.5% of LAT individuals, compared to 77.5%, 82.6%, and 86.9% of NLW, AFR, and EAS individuals, respectively. Of those not recalled back as Latino, 63.5% were assigned as missing, 35.3% were assigned as Non-Latino White, 0.4% were assigned as Non-Latino East Asian, and 0.8% were assigned as Non-Latino Black ( Supplementary Table 15 ). As a sensitivity analysis, we restricted our admixture mapping analysis to the set of Hispanic/Latino individuals inferred by HARE across 10 cross-validation trials, which produced a panel of 1,729 cases and 4,696 controls ( Supplementary Table 16 ). Overall, we observed high correlation in odds ratios (Pearson’s correlation coefficient, r = 0. 913) and log P -values (r = 0.853), with a modest decrease in significance among variants with P -values < 1 x 10 -3 , including the four putative loci ( Supplementary Table 12; Supplementary Figures 5-6), likely reflecting the smaller sample size. Discussion Motivated by the increased risk of ALL in Hispanic/Latino children, we carried out a systematic investigation into the impact of IA-like ancestry on childhood ALL risk, both at the local and global level. We found that, socio-economic status indicators confound the relationship between global IA-like ancestry and ALL risk, and adjustment for these variables attenuates the association. The lack of global ancestry association, however, did not preclude an association at the local level, as IA-like ancestry was significantly associated with ALL risk across known loci and risk alleles segregated with the IA haplotype more often than expected. On the other hand, we found no interaction between IA-like ancestry and the risk genotype at either the local or global levels, suggesting that there is little evidence the risk alleles exert different effects in different ancestry contexts. Together, these results suggest that, in addition to environmental factors, risk alleles present in higher frequencies in IA populations may contribute to Hispanic/Latino individuals’ excess risk of B-cell ALL. Further, our in-depth investigation into inferred genetic ancestry also adds to and clarifies previous findings. A previous study found a positive, statistically significant association between global IA-like ancestry and ALL risk 25 . This result may have been affected by selection bias, due to the lower participation of controls than cases in lower SES areas 52 . Associations between global ancestries and disease risks are often reported, but these associations are often attenuated once non-genetic factors are included in the model or when a family-based design is used 53 , 54 . Indeed, observed associations between ancestry and type-2 diabetes risk in Hispanic/Latinos 26 and between ancestry and hypertension and lung cancer in African Americans 55 , 56 were attenuated once socio-economic covariates were accounted for. In our analysis of the CCLS dataset, we found that the association of ALL risk with global IA-like ancestry was attenuated and no longer statistically significant (though trending in the positive direction) after adjusting for income and education level. We acknowledge that disentangling the association between global genetic ancestry and ALL risk is complex, especially given the observed correlations between SES indicators, ALL risk, and genetic ancestry reported in this study and elsewhere 57 , 58 . In the CCRLP, however, in which controls were ascertained through statewide birth records 32 and thus should not be impacted by selection bias, we found no significant association between genetic ancestry and ALL risk. Our admixture mapping identified associations on chromosomes 2, 7, 10, and 15, but SNPs at the putatively novel loci on chromosomes 2 and 15 did not replicate in additional Hispanic/Latino cohorts. The chromosome 2 locus (2q21.2) is a relative gene desert but overlaps MGAT5 , which has been reported to be involved in cancer metastasis and tumor suppression in mice 59 . Further, transcriptional regulation of the gene is driven by the Ras-Raf-Ets pathway, which is oncogenic 60 – 62 . On chromosome 15, the gene closest to the putative risk SNP is RAB11A , a member of the RAS oncogene family, and has been previously associated with risk of lung squamous cell carcinoma 63 and ovarian cancer 64 . Further, this region contains multiple SNP associations with blood cell phenotypes 65 , 66 , suggesting a possible connection to the etiology of childhood ALL 15 . Adjusting for the lead SNP did not strongly attenuate the chromosome 15 association identified by admixture mapping, suggesting that more complex genomic variation such as structural variants, which may be in low LD with imputed SNPs 67 , could underlie this association and would require further validation. While there may be biological plausibility for these potentially novel loci, the lack of replication could also be due to lack of statistical power, heterogeneity of Hispanic/Latino populations across the United States, or that our initial findings were statistical artefacts. Within the genetic epidemiology literature, careful consideration has been given to the use of self-reported race/ethnicity or genetically inferred ancestry as strata in performing GWAS. For instance, it is common to define “White British” based on demographic data complemented by principal components of the genotype data when analyzing UK Biobank to remove concerns of stratification 68 – 70 . However, analysis in a relatively homogenous cohort such as the UKB is not completely free from the effect of stratification 71 , and the notion of defining a homogenous subset of the cohort becomes even more difficult when performing analysis in multi-ancestry cohorts or admixed populations. In those cases, one can pool samples across all ancestries for a “mega-analysis” 72 , or perform stratified analysis based on either (1) self-reported demographic labels 13 , 32 , 73 or (2) some arbitrary heuristics using genetically inferred ancestry 14 , 18 , 74 , 75 . The downside of using genetically inferred ancestry is the prescription of arbitrary thresholds to define ancestry strata. Across studies, the inclusion criteria vary, with individuals of self-reported White, Black and Asian race/ethnicity being defined as study participants with over 75-95% European-like, 50-90% African-like, and 75-90% Asian-like ancestries, respectively 14 , 18 , 74 – 77 . Previous genetic studies involving Hispanic/Latino participants have defined their strata as individuals whose IA ancestry was greater or equal to 10% and greater than the proportion of African ancestry 75 , 78 , 79 . Given the heterogeneity of Hispanics/Latinos as a population, this method of categorization may prove untenable. The admixture proportions of Hispanic/Latino individuals vary by country of origin with Caribbean and mainland Hispanics/Latinos exhibiting the highest levels of African-like and IA-like ancestry, respectively 80 , 81 . Moreover, the estimated admixture proportions can vary depending on the reference samples available, as well as the geographic and temporal definitions of an ancestry component one wishes to estimate. Given these nuances, we have tended to favor relying on self-reported demographic labels as strata when performing multi-ancestry GWAS 32 , 82 or when limiting our analyses to Hispanics/Latinos 12 . Further, the results of our HARE analysis showed that the algorithm rarely has enough evidence to exclude an individual from analysis when self-reported labels are present, and imputing missing labels have low recall rate for a heterogenous population like the Latinos. In cases where self-reported labels are missing at high proportions, it may be preferable to conduct a mega-analysis instead of stratified analysis. Indeed, even if overt categorizations impact little of the statistical power of the study design and may provide a bit more robustness to the study, the underlying message conveyed by explicit categorization of people based on estimated genetic ancestry may bring more harm than benefits. Finally, the results of our study have implications on future investigations of the genetic architecture of ALL and designs of ancestry-based analyses. As reflected in our findings, the elevated ALL risk in Hispanic/Latino children is to some extent driven by the higher frequencies of known ALL risk alleles on IA-like haplotypes, which may underlie the shift towards higher mean polygenic risk scores for ALL in Hispanic/Latino individuals compared to in non-Latino White individuals 82 , 83 . Moreover, these consistent directional associations with risk could be indicative of a signature of past adaptation. There is some evidence that ALL risk loci, such as the IKZF1 locus that we found to be genome-wide significant in admixture mapping, may be under positive selection in Hispanic/Latino populations 12 . Given the overall enrichment of risk alleles on IA haplotypes, the selection may be more oligo-genic or polygenic, though careful scrutiny is needed to rule out other potential confounding 68 , 84 . Irrespective of whether ALL risk loci are under transient directional selection, the increased prevalence of risk alleles on IA-like haplotypes suggest that continued discovery in larger cohorts with IA-like ancestries and/or utilizing mapping approaches that incorporate signals from both ancestry and allelic associations may be fruitful in discovering more ALL risk loci and in understanding the genetic contributions to risk disparities. Data Availability Genetic data from CCRLP and CCLS were obtained from the California Biobank. Because samples and data are property of the State of California, researchers who would like to request to use the data should establish a protocol with the California Department of Public Health Committee for the Protection of Human Subjects and other relevant agencies to understand if approval to share data are acceptable. The State has provided the following guidance on data sharing, as noted in the following statement: "California has determined that researchers requesting the use of California Biobank biospecimens for their studies will need to seek an exemption from NIH or other granting or funder requirements regarding the uploading of study results into an external bank or repository (including into the NIH dbGaP or other bank or repository). This applies to any uploading of genomic data and/or sharing of these biospecimens or individual data derived from these biospecimens. Such activities have been determined to violate the statutory scheme at California Health and Safety Code Section 124980 (j), 124991 (b), (g), (h) and 103850 (a) and (d), which protect the confidential nature of biospecimens and individual data derived from biospecimens. Investigators may agree to share aggregate data on SNP frequency and their associated p values with other investigators and may upload such frequencies into repositories including the NIH dbGaP repository providing: a) the denominator from which the data is derived includes no fewer than 20,000 individuals; b) no cell count is for <5 individuals; and c) no correlations or linkage probabilities between SNPs are provided." Our data, from CCRLP and CCLS, are each comprised of less than 20,000 subjects and therefore cannot be uploaded to a repository such as dbGaP. Data from Children's Oncology Group and REDIAL were obtained by special request (Yang et al., 2011; Harris et al., 2023). Data from MESA (phs000209.v13.p3) and HCHS/SOL (phs000810.v2.p2.c1, phs000810.v2.p2.c2) were obtained from dbGaP. Data Availability Statement Genetic data from CCRLP and CCLS were obtained from the California Biobank. Because samples and data are property of the State of California, researchers who would like to request to use the data should establish a protocol with the California Department of Public Health Committee for the Protection of Human Subjects and other relevant agencies to understand if approval to share data are acceptable. The State has provided the following guidance on data sharing, as noted in the following statement: “California has determined that researchers requesting the use of California Biobank biospecimens for their studies will need to seek an exemption from NIH or other granting or funder requirements regarding the uploading of study results into an external bank or repository (including into the NIH dbGaP or other bank or repository). This applies to any uploading of genomic data and/or sharing of these biospecimens or individual data derived from these biospecimens. Such activities have been determined to violate the statutory scheme at California Health and Safety Code Section 124980 (j), 124991 (b), (g), (h) and 103850 (a) and (d), which protect the confidential nature of biospecimens and individual data derived from biospecimens. Investigators may agree to share aggregate data on SNP frequency and their associated p values with other investigators and may upload such frequencies into repositories including the NIH dbGaP repository providing: a) the denominator from which the data is derived includes no fewer than 20,000 individuals; b) no cell count is for <5 individuals; and c) no correlations or linkage probabilities between SNPs are provided.‒ Our data, from CCRLP and CCLS, are each comprised of less than 20,000 subjects and therefore cannot be uploaded to a repository such as dbGaP. Data from Children’s Oncology Group and REDIAL were obtained by special request (Yang et al., 2011; Harris et al., 2023). Data from MESA (phs000209.v13.p3) and HCHS/SOL (phs000810.v2.p2.c1, phs000810.v2.p2.c2) were obtained from dbGaP. Acknowledgements We would like to thank the families and patients who participated in each study for ALL, and Soyoung Jeon for helpful conversations contributing to this research. Data used in this study were generated with supports from multiple grants from the National Institutes of Health (R01CA155461, R01CA175737, R01ES009137, P42ES004705, P50ES018172, P42ES0470518, R24ES028524, P20CA262733, U24ES028524, and R01CA262263), the Environmental Protection Agency (RD83615901), the Cancer Prevention and Research Institute of Texas (RP160771, RP210064), and St. Baldrick’s Foundation (consortium grant 522277 with support from the Micaela’s Army Foundation). Analysis performed in this study was supported by F31CA278359 to J.L., R01CA262263 to A.J.D., and R35GM142783 to C.W.K.C. A.J.D. is a Scholar of the Leukemia & Lymphoma Society. The content and conclusions from this work do not reflect the official views of the sponsors. The collection of cancer incidence data used in this study was supported by the California Department of Public Health pursuant to California Health and Safety Code Section 103885; Centers for Disease Control and Prevention’s (CDC) National Program of Cancer Registries, under cooperative agreement 1NU58DP007156; the National Cancer Institute’s Surveillance, Epidemiology and End Results Program under contract HHSN261201800032I awarded to the University of California, San Francisco, contract HHSN261201800015I awarded to the University of Southern California, and contract HHSN261201800009I awarded to the Public Health Institute. The ideas and opinions expressed herein are those of the authors and do not reflect the opinions of the State of California, Department of Public Health, the National Cancer Institute, and the Centers for Disease Control and Prevention or their Contractors and Subcontractors. The biospecimens and/or data used in this study were obtained from the California Biobank Program (CBP request #26 and #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. Computation of this work is supported by USC’s Center for Advanced Research Computing ( https://www.carc.usc.edu/ ). Footnotes ↵ 10 Co-senior authors Edits include: clarification of the methods section, an additional analysis added to preliminary admixture mapping analysis (supplemental), and updates of citations to the manuscript and supplementary files. References 1. ↵ Bhojwani , D. , Yang , J. J. & Pui , C.-H . Biology of Childhood Acute Lymphoblastic Leukemia . Pediatr. Clin. North Am . 62 , 47 – 60 ( 2015 ). OpenUrl CrossRef PubMed 2. ↵ Rheingold , S. R. et al. Determinants of survival after first relapse of acute lymphoblastic leukemia: a Children’s Oncology Group study . Leukemia 38 , 2382 – 2394 ( 2024 ). OpenUrl CrossRef PubMed 3. ↵ Devilli , L. et al. Long-term and quality of survival in patients treated for acute lymphoblastic leukemia during the pediatric age . Hematol. Rep . 13 , 8847 ( 2021 ). OpenUrl PubMed 4. Pui , C.-H. , Robison , L. L. & Look , A. T . Acute lymphoblastic leukaemia . The Lancet 371 , 1030 – 1043 ( 2008 ). OpenUrl CrossRef 5. Sasaki , K. et al. Acute lymphoblastic leukemia: A population-based study of outcome in the United States based on the surveillance, epidemiology, and end results (SEER) database, 1980–2017. Am. J. Hematol . 96 , 650 – 658 ( 2021 ). OpenUrl PubMed 6. ↵ American Cancer Society . Key Statistics for Acute Lymphocytic Leukemia (ALL). American Cancer Society https://www.cancer.org/cancer/types/acute-lymphocytic-leukemia/about/key-statistics.html ( 2024 ). 7. ↵ Feng , Q. et al. Trends in Acute Lymphoblastic Leukemia Incidence in the United States by Race/Ethnicity From 2000 to 2016 . Am. J. Epidemiol . 190 , 519 – 527 ( 2021 ). OpenUrl PubMed 8. ↵ Quiroz , E. et al. Immunophenotype of acute lymphoblastic leukemia in minorities-analysis from the SEER database . Hematol. Oncol . 40 , 105 – 110 ( 2022 ). OpenUrl PubMed 9. ↵ Popejoy , A. B. & Fullerton , S. M . Genomics is failing on diversity . Nature 538 , 161 – 164 ( 2016 ). OpenUrl CrossRef PubMed 10. ↵ Mills , M. C. & Rahal , C . The GWAS Diversity Monitor tracks diversity by disease in real time . Nat. Genet . 52 , 242 – 243 ( 2020 ). OpenUrl CrossRef PubMed 11. ↵ de Smith , A. J. , Jiménez-Morales , S. & Mejía-Aranguré , J. M . The genetic risk of acute lymphoblastic leukemia and its implications for children of Latin American origin . Front. Oncol . 13 , 1299355 ( 2024 ). 12. ↵ de Smith , A. J. et al. A noncoding regulatory variant in IKZF1 increases acute lymphoblastic leukemia risk in Hispanic/Latino children . Cell Genomics 4 , ( 2024 ). 13. ↵ Jeon , S. , Smith , A. J. & Li , S . Genome-wide trans-ethnic meta-analysis identifies novel susceptibility loci for childhood acute lymphoblastic leukemia . Leukemia 36 , 865 – 868 ( 2022 ). OpenUrl PubMed 14. ↵ Vijayakrishnan , J. et al. Identification of four novel associations for B-cell acute lymphoblastic leukaemia risk . Nat. Commun . 10 , 5348 ( 2019 ). OpenUrl CrossRef PubMed 15. ↵ Kachuri , L. et al. Genetic determinants of blood-cell traits influence susceptibility to childhood acute lymphoblastic leukemia . Am. J. Hum. Genet . 108 , 1823 – 1835 ( 2021 ). OpenUrl CrossRef PubMed 16. ↵ Qian , M. et al. Novel susceptibility variants at the ERG locus for childhood acute lymphoblastic leukemia in Hispanics . Blood 133 , 724 – 729 ( 2019 ). OpenUrl Abstract / FREE Full Text 17. Xu , H. et al. ARID5B genetic polymorphisms contribute to racial disparities in the incidence and treatment outcome of childhood acute lymphoblastic leukemia . J. Clin. Oncol. Off. J. Am. Soc. Clin. Oncol . 30 , 751 – 757 ( 2012 ). OpenUrl 18. ↵ Perez-Andreu , V. et al. Inherited GATA3 variants are associated with Ph-like childhood acute lymphoblastic leukemia and risk of relapse . Nat. Genet . 45 , 1494 – 1498 ( 2013 ). OpenUrl CrossRef PubMed 19. ↵ Swords , R. et al. Acute leukemia in adult Hispanic Americans: a large-population study . Blood Cancer J . 6 , e484 ( 2016 ). OpenUrl CrossRef PubMed 20. ↵ Pollyea , D. A. et al. Acute Leukemia in Hispanic Americans: Incidence and Incidence Rate Differences by Nativity . Blood 120 , 3160 ( 2012 ). OpenUrl 21. ↵ Dong , Y. et al. Leukemia incidence trends at the global, regional, and national level between 1990 and 2017 . Exp. Hematol. Oncol . 9 , 14 ( 2020 ). 22. ↵ Yi , M. , Zhou , L. , Li , A. , Luo , S. & Wu , K . Global burden and trend of acute lymphoblastic leukemia from 1990 to 2017 . Aging 12 , 22869 – 22891 ( 2020 ). OpenUrl CrossRef PubMed 23. ↵ Bryc , K. , Durand , E. Y. , Macpherson , J. M. , Reich , D. & Mountain , J. L . The genetic ancestry of african americans, latinos, and european Americans across the United States . Am. J. Hum. Genet . 96 , 37 – 53 ( 2015 ). OpenUrl CrossRef PubMed 24. ↵ Homburger , J. R. et al. Genomic Insights into the Ancestry and Demographic History of South America . PLOS Genet . 11 , e1005602 ( 2015 ). OpenUrl CrossRef PubMed 25. ↵ Walsh , K. et al. Associations between genome-wide Native American ancestry, known risk alleles and B-cell ALL risk in Hispanic children . Leukemia 27 , doi: 10.1038/leu.2013.130 ( 2013 ). OpenUrl CrossRef PubMed 26. ↵ Florez , J. C. et al. Strong association of socioeconomic status with genetic ancestry in Latinos: implications for admixture studies of type 2 diabetes . Diabetologia 52 , 1528 – 1536 ( 2009 ). OpenUrl CrossRef PubMed Web of Science 27. ↵ Shriver , M. D. et al. Skin pigmentation, biogeographical ancestry and admixture mapping . Hum. Genet . 112 , 387 – 399 ( 2003 ). OpenUrl CrossRef PubMed Web of Science 28. ↵ Qin , H. & Zhu , X . Power comparison of admixture mapping and direct association analysis in genome-wide association studies . Genet. Epidemiol . 36 , 235 – 243 ( 2012 ). OpenUrl CrossRef PubMed 29. ↵ McKeigue , P. M . Prospects for Admixture Mapping of Complex Traits . Am. J. Hum. Genet . 76 , 1 – 7 ( 2005 ). OpenUrl CrossRef PubMed Web of Science 30. ↵ Committee on the Use of Race, Ethnicity, and Ancestry as Population Descriptors in Genomics Research et al. Using Population Descriptors in Genetics and Genomics Research: A New Framework for an Evolving Field. 26902 (National Academies Press, Washington, D.C ., 2023 ). doi: 10.17226/26902 . OpenUrl CrossRef PubMed 31. ↵ Zavala , V. A. et al. Cancer health disparities in racial/ethnic minorities in the United States . Br. J. Cancer 124 , 315 – 332 ( 2021 ). OpenUrl CrossRef PubMed 32. ↵ Wiemels , J. L. et al. GWAS in childhood acute lymphoblastic leukemia reveals novel genetic associations at chromosomes 17q12 and 8q24.21 . Nat. Commun . 9 , 286 ( 2018 ). 33. Ma , X. , Buffler , P. A. , Layefsky , M. , Does , M. B. & Reynolds , P . Control Selection Strategies in Case-Control Studies of Childhood Diseases . Am. J. Epidemiol . 159 , 915 – 921 ( 2004 ). OpenUrl CrossRef PubMed Web of Science 34. Harris , R. D. et al. Ethnic-Specific Predictors of Neurotoxicity Among Pediatric Acute Lymphoblastic Leukemia Patients Following High-Dose Methotrexate . Cancer 129 , 1287 – 1294 ( 2023 ). OpenUrl PubMed 35. ↵ Sheng , X. et al. Inverted genomic regions between reference genome builds in humans impact imputation accuracy and decrease the power of association testing . Hum. Genet. Genomics Adv . 4 , ( 2023 ). 36. ↵ Maples , B. K. , Gravel , S. , Kenny , E. E. & Bustamante , C. D . RFMix: a discriminative modeling approach for rapid and robust local-ancestry inference . Am. J. Hum. Genet . 93 , 278 – 288 ( 2013 ). OpenUrl CrossRef PubMed 37. ↵ Karczewski , K. J. et al. The mutational constraint spectrum quantified from variation in 141,456 humans . Nature 581 , 434 – 443 ( 2020 ). OpenUrl CrossRef PubMed 38. ↵ Li , S. et al. Localized variation in ancestral admixture identifies pilocytic astrocytoma risk loci among Latino children . PLoS Genet . 18 , e1010388 ( 2022 ). OpenUrl PubMed 39. ↵ Kowalski , M. H. et al. Use of 100,000 NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium whole genome sequences improves imputation quality and detection of rare variant associations in admixed African and Hispanic/Latino populations . PLoS Genet . 15 , 1008500 ( 2019 ). 40. ↵ Cahoon , J. L. et al. Imputation accuracy across global human populations . Am. J. Hum. Genet . 111 , 979 – 989 ( 2024 ). OpenUrl CrossRef PubMed 41. ↵ Metayer , C. et al. Tobacco Smoke Exposure and the Risk of Childhood Acute Lymphoblastic and Myeloid Leukemias by Cytogenetic Subtype . Cancer Epidemiol. Biomarkers Prev . 22 , 1600 – 1611 ( 2013 ). OpenUrl Abstract / FREE Full Text 42. ↵ R Core Team . R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing ( 2023 ). 43. ↵ Qian , M. et al. Genome-Wide Association Study of Susceptibility Loci for T-Cell Acute Lymphoblastic Leukemia in Children . J. Natl. Cancer Inst . 111 , 1350 – 1357 ( 2019 ). OpenUrl PubMed 44. ↵ Purcell , S. et al. PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses . Am. J. Hum. Genet . 81 , 559 – 575 ( 2007 ). OpenUrl CrossRef PubMed 45. ↵ Chang , C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets . GigaScience 4 , 7 ( 2015 ). 46. ↵ Brown , L. A. et al. Admixture Mapping Identifies an Amerindian Ancestry Locus Associated with Albuminuria in Hispanics in the United States . J. Am. Soc. Nephrol. JASN 28 , 2211 – 2220 ( 2017 ). OpenUrl PubMed 47. ↵ Schick , U. M. et al. Genome-wide Association Study of Platelet Count Identifies Ancestry-Specific Loci in Hispanic/Latino Americans . Am. J. Hum. Genet . 98 , 229 – 242 ( 2016 ). OpenUrl CrossRef PubMed 48. ↵ Qin , H. & Zhu , X . Power Comparison of Admixture Mapping and Direct Association Analysis in Genome-Wide Association Studies . Genet. Epidemiol . 36 , 235 – 243 ( 2012 ). OpenUrl CrossRef PubMed 49. ↵ Willer , C. J. , Li , Y. & Abecasis , G. R . METAL: fast and efficient meta-analysis of genomewide association scans . Bioinformatics 26 , 2190 – 2191 ( 2010 ). OpenUrl CrossRef PubMed Web of Science 50. ↵ Fang , H. et al. Harmonizing Genetic Ancestry and Self-identified Race/Ethnicity in Genome-wide Association Studies . Am. J. Hum. Genet . 105 , 763 – 772 ( 2019 ). OpenUrl CrossRef PubMed 51. ↵ Johnson , J. L. & Abecasis , G. R . GAS Power Calculator: web-based power calculator for genetic association studies . 164343 Preprint at doi: 10.1101/164343 ( 2017 ). OpenUrl Abstract / FREE Full Text 52. ↵ Wheeler , D. C. , Boyle , J. , Carli , M. , Ward , M. H. & Metayer , C . Neighborhood Deprivation, Indoor Chemical Concentrations, and Spatial Risk for Childhood Leukemia . Int. J. Environ. Res. Public. Health 20 , 3582 ( 2023 ). OpenUrl PubMed 53. ↵ Sun , H. et al. The impact of global and local Polynesian genetic ancestry on complex traits in Native Hawaiians . PLOS Genet . 17 , e1009273 ( 2021 ). OpenUrl PubMed 54. ↵ Asgari , S. et al. A positively selected FBN1 missense variant reduces height in Peruvians . Nature 582 , 234 – 239 ( 2020 ). OpenUrl CrossRef PubMed 55. ↵ Aldrich , M. C. et al. Socioeconomic Status and Lung Cancer: Unraveling the Contribution of Genetic Admixture . Am. J. Public Health 103 , e73 – e80 ( 2013 ). OpenUrl 56. ↵ Non , A. L. , Gravlee , C. C. & Mulligan , C. J . Education, Genetic Ancestry, and Blood Pressure in African Americans and Whites . Am. J. Public Health 102 , 1559 – 1565 ( 2012 ). OpenUrl CrossRef PubMed 57. ↵ Wang , L. , Gomez , S. L. & Yasui , Y . Racial and Ethnic Differences in Socioeconomic Position and Risk of Childhood Acute Lymphoblastic Leukemia . Am. J. Epidemiol . 185 , 1263 – 1271 ( 2017 ). OpenUrl PubMed 58. ↵ Spector , L. G ., et al. RE: ‘RACIAL AND ETHNIC DIFFERENCES IN SOCIOECONOMIC POSITION AND RISK OF CHILDHOOD ACUTE LYMPHOBLASTIC LEUKEMIA’. Am. J. Epidemiol . 188 , 1192 – 1193 ( 2019 ). OpenUrl PubMed 59. ↵ Granovsky , M. et al. Suppression of tumor growth and metastasis in Mgat5-deficient mice . Nat. Med . 6 , 306 – 312 ( 2000 ). OpenUrl CrossRef PubMed Web of Science 60. ↵ Hirata , A. et al. Increased risk of metastasis in patients with incidental use of renin-angiotensin system inhibitors: a retrospective analysis for multiple types of cancer based on electronic medical records . Hypertens. Res . 45 , 1869 – 1881 ( 2022 ). OpenUrl PubMed 61. Kang , R. et al. Transcriptional Regulation of the N-Acetylglucosaminyltransferase V Gene in Human Bile Duct Carcinoma Cells (HuCC-T1) Is Mediated by Ets-1 * . J. Biol. Chem . 271 , 26706 – 26712 ( 1996 ). OpenUrl Abstract / FREE Full Text 62. ↵ Buckhaults , P. , Chen , L. , Fregien , N. & Pierce , M . Transcriptional Regulation ofN-Acetylglucosaminyltransferase V by the srcOncogene * . J. Biol. Chem . 272 , 19575 – 19581 ( 1997 ). OpenUrl Abstract / FREE Full Text 63. ↵ Gombodorj , N. et al. RAB11A Expression Is Associated With Cancer Aggressiveness Through Regulation of FGFR-Signaling in Lung Squamous Cell Carcinoma . Ann. Surg. Oncol . 29 , 7149 – 7162 ( 2022 ). OpenUrl PubMed 64. ↵ Wang , Y. , Ren , Y. , Li , N. , Zhao , J. & Zhao , S . Rab11a promotes the malignant progression of ovarian cancer by inducing autophagy . Genes Genomics 44 , 1375 – 1384 ( 2022 ). OpenUrl PubMed 65. ↵ Vuckovic , D. et al. The Polygenic and Monogenic Basis of Blood Traits and Diseases . Cell 182 , 1214 – 1231 .e11 ( 2020 ). OpenUrl CrossRef PubMed 66. ↵ Chen , M.-H. et al. Trans-ethnic and ancestry-specific blood-cell genetics in 746,667 individuals from 5 global populations . Cell 182 , 1198 – 1213 .e14 ( 2020 ). OpenUrl CrossRef PubMed 67. ↵ Sudmant , P. H. et al. An integrated map of structural variation in 2,504 human genomes . Nature 526 , 75 – 81 ( 2015 ). OpenUrl CrossRef PubMed 68. ↵ Sohail , M. et al. Polygenic adaptation on height is overestimated due to uncorrected stratification in genome-wide association studies . eLife 8 , e39702 ( 2019 ). OpenUrl CrossRef PubMed 69. Bycroft , C. et al. The UK Biobank resource with deep phenotyping and genomic data . Nature 562 , 203 – 209 ( 2018 ). OpenUrl CrossRef PubMed 70. ↵ Astle , W. J. et al. The Allelic Landscape of Human Blood Cell Trait Variation and Links to Common Complex Disease . Cell 167 , 1415 – 1429 .e19 ( 2016 ). OpenUrl CrossRef PubMed 71. ↵ Chen , M. et al. Evidence of Polygenic Adaptation in Sardinia at Height-Associated Loci Ascertained from the Biobank Japan . Am. J. Hum. Genet . 107 , 60 – 71 ( 2020 ). OpenUrl CrossRef PubMed 72. ↵ Wojcik , G. L. et al. Genetic analyses of diverse populations improves discovery for complex traits . Nature 570 , 514 – 518 ( 2019 ). OpenUrl CrossRef PubMed 73. ↵ Kachuri , L. et al. Genetically adjusted PSA levels for prostate cancer screening . Nat. Med . 29 , 1412 – 1423 ( 2023 ). OpenUrl CrossRef PubMed 74. ↵ Klarin , D. et al. Genetics of blood lipids among ∼300,000 multi-ethnic participants of the Million Veteran Program . Nat. Genet . 50 , 1514 – 1523 ( 2018 ). OpenUrl CrossRef PubMed 75. ↵ Xu , H. et al. Novel susceptibility variants at 10p12.31-12.2 for childhood acute lymphoblastic leukemia in ethnically diverse populations . J. Natl. Cancer Inst . 105 , 733 – 742 ( 2013 ). OpenUrl CrossRef PubMed Web of Science 76. Gautam , Y. et al. Genome-wide admixture and association analysis identifies African ancestry specific risk loci of eosinophilic esophagitis in African American . J. Allergy Clin. Immunol . 151 , 1337 – 1350 ( 2023 ). OpenUrl 77. ↵ Shrine , N. et al. Multi-ancestry genome-wide association analyses improve resolution of genes and pathways influencing lung function and chronic obstructive pulmonary disease risk . Nat. Genet . 55 , 410 – 422 ( 2023 ). OpenUrl CrossRef PubMed 78. ↵ Archer , N. P. et al. Family-based exome-wide association study of childhood acute lymphoblastic leukemia among Hispanics confirms role of ARID5B in susceptibility . PLOS ONE 12 , e0180488 ( 2017 ). OpenUrl PubMed 79. ↵ Karol , S. E. et al. Genetics of ancestry-specific risk for relapse in acute lymphoblastic leukemia . Leukemia 31 , 1325 – 1332 ( 2017 ). OpenUrl CrossRef PubMed 80. ↵ Conomos , M. P. et al. Genetic diversity and association studies in US Hispanic/Latino populations: applications in the Hispanic Community Health Study/Study of Latinos . Am. J. Hum. Genet . 98 , 165 – 184 ( 2016 ). OpenUrl CrossRef PubMed 81. ↵ Bryc , K. et al. Genome-wide patterns of population structure and admixture among Hispanic/Latino populations . Proc. Natl. Acad. Sci . 107 , 8954 – 8961 ( 2010 ). OpenUrl Abstract / FREE Full Text 82. ↵ Jeon , S. et al. Genome-wide trans-ethnic meta-analysis identifies novel susceptibility loci for childhood acute lymphoblastic leukemia . Leukemia 36 , 865 – 868 ( 2022 ). OpenUrl PubMed 83. ↵ Jeon , S. et al. Evaluating Genomic Polygenic Risk Scores for Childhood Acute Lymphoblastic Leukemia in Latinos . MedRxiv Prepr. Serv. Health Sci . 2023.06.08.23291167 ( 2023 ) doi: 10.1101/2023.06.08.23291167 . OpenUrl Abstract / FREE Full Text 84. ↵ Berg , J. J. et al. Reduced signal for polygenic adaptation of height in UK Biobank . eLife 8 , e39725 ( 2019 ). OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted April 10, 2025. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. 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Chiang medRxiv 2025.01.14.25320563; doi: https://doi.org/10.1101/2025.01.14.25320563 Share This Article: Copy Citation Tools The impact of Indigenous American-like ancestry on risk of acute lymphoblastic leukemia in Hispanic/Latino children Jalen Langie , Tsz Fung Chan , Wenjian Yang , Alice Y. Kang , Libby Morimoto , Daniel O. Stram , Nicholas Mancuso , Xiaomei Ma , Catherine Metayer , Philip J. Lupo , Karen R. Rabin , Michael E. Scheurer , Joseph L. Wiemels , Jun J. Yang , Adam J. de Smith , Charleston W.K. 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