Concordance and dissonance: A genome-wide analysis of self-declared versus inferred ancestry in 10,250 participants from the HostSeq cohort

Concordance and dissonance: A genome-wide analysis of self-declared versus inferred ancestry in 10,250 participants from the HostSeq cohort | bioRxiv /* */ /* */ <!-- <!-- /*! * 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-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Concordance and dissonance: A genome-wide analysis of self-declared versus inferred ancestry in 10,250 participants from the HostSeq cohort View ORCID Profile René L Warren , View ORCID Profile Inanc Birol , CGEn HostSeq Initiative doi: https://doi.org/10.1101/2025.06.10.658783 René L Warren 1 BC Cancer , Vancouver, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for René L Warren For correspondence: rwarren{at}bcgsc.ca Inanc Birol 1 BC Cancer , Vancouver, Canada 3 Department of Medical Genetics, University of British Columbia , Vancouver, British Columbia, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Inanc Birol 1 BC Cancer , Vancouver, Canada 4 McGill Genome Centre, McGill University , Montreal, QC, Canada 5 The Hospital for Sick Children , Toronto, ON, Canada Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Accurate characterization of human diversity is foundational to equitable genomics. In this study, we analyzed self-declared and genome-derived ancestry in 10,250 participants from the pan-Canadian HostSeq cohort. Using the alignment-free ntRoot algorithm on whole genome sequencing data, we inferred global and local ancestry at the continental super-population level and compared these with self-reported sociocultural identity categories. We observed high concordance among individuals self-identifying as White (98.8%), Black (97.2%), East Asian (96.1%), and South Asian (89.9%). Concordance was lower among those self-identifying as Hispanic (74.6%), Middle Eastern / Central Asian (67.9%), or Indigenous (40.7%), reflecting greater admixture complexity. Agreement between expected and inferred ancestry labels was modest (Cohen’s kappa κ = −0.01 unweighted; 0.35 weighted), and ancestry discordance was strongly associated with higher Shannon entropy of ancestry fractions. Principal component analysis of ntRoot-derived ancestry composition revealed tightly clustered profiles in some groups and broader, overlapping distributions in others, illustrating how sociocultural identities and genomic data capture distinct but intersecting dimensions of human diversity. These findings support the complementary use of genome-derived continental ancestry fractions alongside self-identification, particularly in settings where sociocultural labels may be incomplete, heterogenous, or poorly aligned with genetic background. This approach can improve scientific rigor and enhance inclusion in population-scale genomics while respecting the social meaning of identity. We emphasize that genetic ancestry estimates are not proxies for race, which is a social construct with no biological basis. Introduction Accurately characterizing human diversity is critical for advancing equity in biomedical research and healthcare ( 1 ). Self-declared ancestry reflects complex sociocultural, historical, and geopolitical realities, while genome-derived ancestry summarizes patterns of biological inheritance shaped by demographic history. Although both frameworks aim to describe human diversity, they often diverge in practice with implications for clinical interpretation, genetic risk assessment, and participant trust. Previous studies have documented varying degrees of concordance between self-reported and inferred ancestry, particularly in admixed populations or those shaped by colonial histories ( 2 , 3 ). Such dissonance can reinforce structural inequities when race and ethnicity are used uncritically as population descriptors, rather than as context-dependent, socially constructed categories informed by lived experience. At the same time, genome-based ancestry estimation has become a standard tool in population genomics, where it is often treated as a technical covariate rather than explicitly interrogated in relation to sociocultural identity ( 4 – 6 ). Ancestry inference methodologies are typically classified as either global or local. Global Ancestry Inference (GAI) estimates genome-wide proportions of ancestry from broad reference groups, providing an overall summary of an individual’s genetic background. In contrast, Local Ancestry Inference (LAI) assigns ancestry to specific genomic segments by leveraging the persistence of ancestral haplotype blocks through recombination ( 7 ), thereby capturing admixture complexity at finer resolution and enabling locus-specific ancestry mapping. Recently, we introduced ntRoot, a scalable, reference-guided, alignment-free platform for both GAI and LAI ( 8 ). It leverages k-mer–based single nucleotide variant detection and integrated 1000 Genomes Project (1kGP) variant call sets to infer ancestry at the level of five continental super-population labels (AFR, AMR, EAS, EUR, SAS) ( 9 , 10 ). In this study, we applied ntRoot to whole genome sequencing (WGS) data from Canada’s HostSeq initiative, a national resource comprising over 10,000 individuals affected by COVID-19 with linked clinical and self-declared ancestry data ( 11 ). Using ntRoot we inferred both global and local ancestry at the 1kGP super-population level and compared these estimates with self-declared identity categories captured through standardized questionnaires. This study represents one of the largest efforts to date to examine concordance and discordance between sociocultural identity labels and super-population-level ancestry predictions in a Canadian cohort, providing a quantitative view of where these dimensions converge and where they diverge within a multicultural context. We emphasize that the ancestry labels used by ntRoot are geographically defined super-population categories derived from reference panels and are not proxies for race, which is a social construct without biological basis. Our use of genomic ancestry is intendeds to complement, not replace self-identification in efforts to improve representational equity and scientific validity in genomics research. Methods Cohort Description We analyzed data from 10,250 participants enrolled in the HostSeq project, representing a diverse cross-section of the Canadian population. Participants provided self-declared ancestry through structured questionnaires, selecting from categories aligned with national demographic standards. We acknowledge that pandemic-focused enrollment may have influenced participant diversity. For clarity, the total cohort comprises all 10,250 participants with available genomic data. Analyses requiring a direct comparison between self-declared identity and genome-derived global ancestry were restricted to an analyzed HostSeq cohort subset, defined as participants who provided a self-declared category that could be mapped to a continental-level expected ancestry ( Table 1 ). View this table: View inline View popup Download powerpoint Table 1. Concordance rate by self-declared ancestry category. Concordance is defined as the proportion of individuals whose genome-derived global ancestry (GAI, assigned based on their largest local ancestry fraction) matches the expected ancestry for their self-declared identity category. The five continental-level population groups include AFR (African), AMR (Admixed American), EAS (East Asian), EUR (European), and SAS (South Asian). The total number of participants (n) is shown for each self-declared category; together, these counts represent the ‘total cohort’. Only individuals with an assigned GAI label represent the ‘analyzed HostSeq cohort subset’, as defined in Methods. Genetic Ancestry Inference We used the ntRoot algorithm (v1.1.5) to perform ancestry prediction from WGS data, categorizing individuals into five continental-level population groups: AFR (African), AMR (Admixed American), EAS (East Asian), EUR (European), and SAS (South Asian). The ntRoot method employs a reference-guided framework optimized for WGS, targeting high-resolution classification. First, each individual read set in CRAM format was converted to fastq using the command: samtools fastq -@ 48 --reference GRCh38_full_analysis_set_plus_decoy_hla.fa −1 SR.1.fastq.gz −2 SR.2.fastq.gz sample.cram. Next, we executed ntRoot (v1.1.5, with parameters - k 55 --draft GRCh38.fa.gz --reads SR -t 48 -Y 0.55 −l 1000GP_integrated_snv_v2a_27022019.GRCh38.phased_gt1.vcf.gz --lai). A total of 10 batched jobs, each containing instructions to process 1,025 samples, ran for a total of 2 months and 3 weeks on server-class systems with Intel(R) Xeon(R) CPU E7-8867 v3 @ 2.50GHz and 2.6TB RAM. Concordance Rate Estimation We defined an expected global ancestry for each self-declared category based on Canadian census categories, prior literature on population structure in these groups, and 1kGP continental label conventions (e.g., “Ashkenazi Jewish” → EUR, “East Asian / Pacific Islander” → EAS, etc.) ( Table 1 ) ( 12 – 14 ). These mappings are heuristic and intended to reflect broad, historically grounded expectations rather than precise genetic truths. Concordance was defined as a match between the expected global ancestry and the ancestry inferred from ntRoot, assigned based on the largest predicted LAI fraction. All data handling and analysis were performed using R (v4.3.1) with the dplyr (v1.1.2) package. Cohen’s Kappa Agreement Analysis To evaluate categorical agreement beyond chance, we used the cohen.kappa() function from the psych package (v2.3.9) on a contingency table comparing expected versus inferred ancestry labels (alpha level = 0.05). We reported both unweighted and weighted kappa scores, the latter accounting for partial similarity across related continental superpopulation groups. Shannon Entropy (Admixture Complexity) To assess genomic admixture per individual, we calculated Shannon entropy from continental ancestry fractions (AFR, AMR, EAS, EUR, SAS) using the entropy package (v1.3.1). Entropy scores were then aggregated by self-declared category to identify which groups exhibit more mixed ancestry profiles. In this five-ancestry framework, entropy near 0 reflects effective ‘single-ancestry’ profiles, while entropy values above 1.5 correspond to substantial contributions from at least three continental ancestries. Principal Component Analysis (PCA) We conducted PCA on the ntRoot-predicted ancestry fractions to visualize similarities and differences in genomic ancestry composition across self-declared identity groups. Principal components were plotted and colored by self-declared category to observe clustering and spread. PCA was computed using the base prcomp() function in R, and visualized using ggplot2 (v3.4.0). Inclusion/Exclusion in Analyses Individuals selecting ‘Prefer not to answer / do not know’ were retained for completeness in descriptive summaries but excluded from concordance, entropy, and PCA analyses, as their self-identification could not be meaningfully compared to an expected ancestry label. Results The HostSeq cohort comprises 10,250 individuals recruited across Canada through COVID-19 pandemic-related studies. While the largest proportion of participants (26.8%) self-identified as White (n = 2,743), smaller proportions identified as South Asian (n = 358; 3.5%), East Asian / Pacific Islander (n = 358; 3.5%), Black (n = 213; 2.1%), Hispanic (n = 185; 1.8%), Middle Eastern or Central Asian (n = 162; 1.6%), and Indigenous (First Nations, Métis, Inuit; n = 27; 0.3%). A notable fraction of individuals did not disclose their ancestry (n = 4,723; 46.1%), or selected “Prefer not to answer / do not know” (n = 1,168; 11.4%), “More than one race” (n = 171; 1.7%), or “Other” (n = 78; 0.8%) ( Table 1 ). These uncategorized responses, which together represented the largest fraction (i.e., ∼60%) of individuals, mapped across diverse ancestries, suggesting complex identity constructs or reluctance to engage with constrained identity categories, highlighting the limits of categorical identity labels. This composition does not necessarily mirror the broader demographic diversity of Canada and likely reflects a combination of cohort recruitment patterns, barriers to participation among some communities, and the constrained nature of categorical identity labels used in biomedical research. Participants who selected ‘Prefer not to answer’ were retained in the dataset for completeness but excluded from identity-discordance calculations and ancestry visualizations, as described in Methods. Patterns of Discordance Substantial discordance between expected and inferred continental labels was observed in certain categories, including individuals of self-declared Hispanic, Indigenous and Middle Eastern / Central Asian backgrounds. Among participants identifying as Hispanic, most were assigned global ancestry labels of AMR (74.6%), EUR (15.1%), or AFR (8.6%) ( Fig. 1 ), and showed appreciable local ancestry contributions from these same groups ( Fig. 2 ). Many individuals identifying as First Nations, Métis, or Inuit clustered within either the AMR (40.7%), or EUR (59.3%) GAI predictions. For Middle Eastern or Central Asian, a similar dispersion across EUR (67.9%), SAS (19.8%), and AFR (12.3%) continental labels suggests diverse ancestral backgrounds and limited resolution of these identities within current 1kGP-based reference panels ( Fig. 1A and 1B ). Download figure Open in new tab Fig. 1. Inferred versus self-declared ancestry in the HostSeq cohort. A) by ntRoot predictions of the main continental ancestry (1000 Genomes Project continental ancestry labels ( 12 ) AMR:American; AFR:African; EUR:European; SAS:South Asian; EAS:East Asian. Percentages 30% or higher indicated) and B) by self-declared ancestry categories in the HostSeq study (percentages above 5% indicated). Participants who selected ‘Prefer not to answer’ were excluded to avoid visual stratification or interpretation of inferred ancestry in relation to an explicitly withheld identity label. Download figure Open in new tab Fig. 2. ntRoot-predicted ancestry fractions based on LAI (x-axis, 1000 Genomes Project continental ancestry labels ( 12 ) AMR:American; AFR:African; EUR:European; SAS:South Asian; EAS:East Asian), for each HostSeq individual sample (y-axis) in the cohort, organized by self-declared identity category and normalized by sample count. Participants who selected ‘Prefer not to answer’ were excluded to avoid visual stratification or interpretation of inferred ancestry in relation to an explicitly withheld identity label. Overall, unweighted Cohen’s kappa comparing expected and inferred continental labels was –0.01, indicating minimal agreement beyond chance across all categories ( Table 2 , Supplementary Fig. 1), whereas weighted kappa was 0.35, reflecting moderate agreement when accounting for partial similarity across ancestry types. Shannon entropy, a measure of admixture complexity where higher values reflect greater ancestry heterogeneity, revealed marked differences across groups. Individuals identifying as Sephardic (mean entropy [m.e.]=2.04) and Ashkenazi Jewish (m.e.=1.91), Middle Eastern or Central Asian (m.e.=2.00), Indigenous (m.e.=1.78), White (m.e.=1.72), Hispanic (m.e.=1.71), and “More than one race” (m.e.=1.72) exhibited the highest levels of admixture, consistent with ancestry contributions from multiple continental groups in the ntRoot-derived fractions, whereas South Asian (m.e.=1.36), Black (m.e.=0.474), and East Asian / Pacific Islander (m.e.=0.471) showed the lowest entropy values ( Table 3 , Fig. 2 ). Principal component analysis of ntRoot-derived ancestry fractions revealed distinct clustering among individuals self-identifying as White, East Asian, South Asian, and Black (Supplementary Fig. 2), consistent with lower admixture entropy and high concordance with inferred ancestry. In contrast, individuals identifying as Indigenous, Hispanic, or Middle Eastern / Central Asian exhibited broader, overlapping distributions in PCA space, reflecting more complex ancestry profiles and lower overall concordance between self-declared and genome-derived ancestry. These observations are consistent with the concordance table, which shows higher match rates for White (98.8%), Black (97.2%), East Asian / Pacific Islander (96.1%), and South Asian (89.9%) individuals, and lower rates for Hispanic (74.6%), Middle Eastern or Central Asian (67.9%) and Indigenous (40.7%) populations, according to their respective assigned global ancestry ( Table 1 ). View this table: View inline View popup Download powerpoint Table 2. Cohen’s Kappa agreement (Interrater agreement). Analyses were performed on the analyzed HostSeq cohort subset, as defined in Methods. The five continental-level population groups include AFR (African), AMR (Admixed American), EAS (East Asian), EUR (European), and SAS (South Asian). View this table: View inline View popup Download powerpoint Table 3. Shannon entropy and admixture by self-declared ancestry category for the HostSeq cohort, excluding participants who selected “Prefer not to answer”*. Mean entropy represents the average genomic admixture complexity for individuals within each self-declared category, based on the distribution of genome-wide ancestry fractions. Standard deviation (SD) reflects the variability of entropy within each group. Higher entropy indicates greater heterogeneity in continental ancestry proportions, and thus more admixture. Proportional Ancestry Patterns Figure 2 and Supplementary Fig. 3 present the ancestry composition of each self-declared group using stacked ancestry fractions. In Supplementary Figure 3, all individuals are shown in full cohort proportions whereas Fig. 2 normalizes by sample count per group to eliminate size bias. In those figures, the ancestry fractions tallied from LAI for each sample reveal that while categorical self-declared groups including Black, South Asian, and East Asian display tightly clustered and more homogenous genetic profiles, groups such as Hispanic, Indigenous, Ashkenazi and Sephardic Jewish, Middle Eastern and White contain a wider range and appreciable fractions of continental ancestries. This indicates that admixture is a common feature of these identities in this cohort, rather than an artifact of sample size or distribution. Discussion Despite relatively high overall concordance between self-declared identity categories and genome-derived global ancestry predictions, entropy calculations and local ancestry fractions revealed high admixture complexity in several groups. These included individuals identifying as Ashkenazi and Sephardic Jewish, Middle Eastern or Central Asian, Indigenous, White, and Hispanic, as well as those selecting “More than one race”, “Other”, or who did not self-declare ancestry, mirroring the broad clustering patterns observed in PCA. These results reinforce the association between entropy, discordance, and generally less distinct PCA clustering in groups with more complex admixture histories. The analysis presented here affirms the general reliability of self-declared ancestry in broad continental contexts, while also highlighting the need for more nuanced interpretation in admixed or historically complex populations. Our findings are consistent with previous work ( 2 , 15 ) showing that discordance between self-identified and inferred ancestry is most pronounced in groups shaped by colonial histories or recent admixture. Importantly, we emphasize that neither genome-derived ancestry nor self-identification should be treated as inherently more valid. Each reflects a different lens on identity, biological and sociocultural, and their intersection offers a richer and more complete picture of human diversity. For genomic research, these findings reinforce the need for flexible, multidimensional approaches to participant categorization, and call for a deliberate commitment to equity-informed study design ( 4 ). The inclusion of proportional ancestry analysis underscores that identity categories such as Ashkenazi and Sephardic Jewish, Middle Eastern or Central Asian, Indigenous, White and Hispanic are not genetically homogeneous groups but socially meaningful designations encompassing diverse ancestry profiles. These insights have critical implications for how populations are stratified in biomedical research, particularly in precision medicine frameworks that rely heavily on genetic clustering and inference. Moreover, the incorporation of local ancestry inference (LAI) using whole genome sequencing (WGS) via ntRoot provides resolution that global ancestry inference (GAI) alone cannot achieve. While GAI summarizes continental-scale proportions, LAI identifies the likely super-population origin of specific genomic segments, enabling locus-level insights into admixture and genomic architecture. This is particularly valuable in large cohort association studies, pharmacogenomics, and polygenic risk score development, especially in admixed or underrepresented populations ( 16 – 18 ). Fine-grained ancestry analysis at the continental level helps mitigate biases introduced by majority-European training datasets ( 19 ) and allows for the identification of ancestry-specific associations with complex traits. As such, LAI represents an essential complement to GAI in efforts to make genomic medicine more equitable and mechanistically precise. This study also has several limitations. First, it relies on categorical mapping assumptions that assign self-declared identities to expected genomic ancestry groups, which may oversimplify complex sociocultural constructs. Second, while ntRoot offers both global and local inference, its resolution is currently limited to continental-scale super-population labels defined using 1kGP reference data and may obscure the continuous and temporally deep nature of human population structure ( 8 , 20 ). As others have noted, these classifications can conflate distinct concepts such as race, ethnicity, geography, and genetic ancestry ( 21 – 24 ), which may limit their interpretability, particularly in diverse or historically admixed populations. Additionally, the self-declared data reflect heterogeneous interpretations of ancestry, race, and ethnicity by participants. Because individuals who did not provide a specific ancestry label constituted a large fraction of the cohort, concordance and entropy estimates were necessarily restricted to the subset who did self-identify. These results should therefore be interpreted as characterizing patterns among declarative respondents rather than the entire HostSeq cohort. However, comparison of inferred global ancestry between responders and non-responders revealed statistically detectable differences driven by the large sample size, with small absolute shifts in ancestry proportions (typically <3%; data not shown). Systematic differences in identity disclosure therefore cannot be excluded. Finally, reference panels used for ancestry inference may underrepresent certain populations, particularly Middle Eastern and Indigenous groups, potentially reducing accuracy in these categories ( 25 , 26 ). It is important to note that the Indigenous category in this Canadian cohort may encompass a range of identities, including First Nations, Inuit, and particularly Métis individuals, whose genetic ancestry often reflects historical admixture with European populations, which may partly explain our observations. These findings, therefore, may not generalize to Indigenous populations in other national contexts, and highlight the importance of region-specific considerations, including documented colonial and post-colonial histories, when interpreting genomic diversity within socially defined groups ( 27 – 29 ). Conclusion This work represents a large-scale effort to compare self-declared and genome-derived ancestry predictions in a nationally diverse cohort. While self-declared identity may reflect lived experience and social determinants, ntRoot provides standardized whole genome–based estimates of genomic ancestry, with advantages in scalability, reproducibility, and population-level harmonization. By combining whole genome-based ancestry inference with participant-reported identity data, we provide a picture of where genomic and sociocultural understandings of ancestry converge and where they diverge. These findings underscore the importance of integrating both biological and self-identified dimensions of identity in genomic research. Doing so will be critical for developing more inclusive, precise, and equitable research in population genomics. Author Contributions Study concept: RLW, IB. Software implementation: RLW. Data analysis: RLW. Manuscript development: RLW. Manuscript editing: RLW, IB. Funding acquisition: IB, RLW. Data and Code Availability The data used in this study is available for download at CGEn ( https://www.cgen.ca/hostseq-application-form ). ntRoot is freely available on GitHub ( https://github.com/bcgsc/ntroot ). Funding This study is supported by the Canadian Institutes of Health Research (CIHR) [PJT-183608, I.B., and PJT-203692, I.B./R.L.W.]. The research presented here has been conducted using CGEn’s HostSeq Databank, funded by the Government of Canada through Genome Canada, under Project ID ‘DACO-5’. Funder Information Declared Canadian Institutes of Health Research, https://ror.org/01gavpb45 , PJT-183608 , PJT-203692 Footnotes https://www.cgen.ca/hostseq-contributing-studies-implementation-committee Revised the manuscript narrative. All display items were revised, and supplemental file updated https://www.cgen.ca/hostseq-application-form References 1. ↵ Bonham VL , Green ED , Pérez-Stable EJ . Examining How Race, Ethnicity, and Ancestry Data Are Used in Biomedical Research . JAMA . 2018 Oct 16 ; 320 ( 15 ): 1533 . OpenUrl CrossRef PubMed 2. ↵ Bryc K , Durand EY , Macpherson JM , Reich D , Mountain JL . The Genetic Ancestry of African Americans, Latinos, and European Americans across the United States . Am J Hum Genet . 2015 Jan ; 96 ( 1 ): 37 – 53 . OpenUrl CrossRef PubMed 3. ↵ Wojcik GL , Graff M , Nishimura KK , Tao R , Haessler J , Gignoux CR , et al. Genetic analyses of diverse populations improves discovery for complex traits . Nature . 2019 June ; 570 ( 7762 ): 514 – 8 . OpenUrl CrossRef PubMed 4. ↵ Committee on the Use of Race , Ethnicity, and Ancestry as Population Descriptors in Genomics Research, Board on Health Sciences Policy, Committee on Population, Health and Medicine Division, Division of Behavioral and Social Sciences and Education, National Academies of Sciences, Engineering, and Medicine . Using Population Descriptors in Genetics and Genomics Research: A New Framework for an Evolving Field [Internet] . Washington, D.C .: National Academies Press ; 2023 [cited 2024 Aug 27 ]. Available from: https://www.nap.edu/catalog/26902 5. Morning A. The Nature of RaceHow Scientists Think and Teach about Human Difference [Internet] . University of California Press ; 2011 [cited 2025 May 21 ]. Available from: https://academic.oup.com/california-scholarship-online/book/13767 6. ↵ Roth WD . Racial Mismatch: The Divergence Between Form and Function in Data for Monitoring Racial Discrimination of Hispanics* . Soc Sci Q . 2010 Dec ; 91 ( 5 ): 1288 – 311 . OpenUrl CrossRef 7. ↵ Tang H , Peng J , Wang P , Risch NJ . Estimation of individual admixture: analytical and study design considerations . Genet Epidemiol Off Publ Int Genet Epidemiol Soc . 2005 ; 28 ( 4 ): 289 – 301 . OpenUrl 8. ↵ Warren RL , Coombe L , Wong J , Kazemi P , Birol I. ntRoot: computational inference of human ancestry at scale from genomic data . DeGiorgio M, editor. Bioinforma Adv . 2025 Nov 9 ; 5 ( 1 ): vbaf287 . OpenUrl 9. ↵ Zheng-Bradley X , Streeter I , Fairley S , Richardson D , Clarke L , Flicek P , et al. Alignment of 1000 Genomes Project reads to reference assembly GRCh38 . GigaScience [Internet] . 2017 July 1 [cited 2024 Aug 26 ]; 6 ( 7 ). Available from: https://academic.oup.com/gigascience/article/doi/10.1093/gigascience/gix038/3836916 10. ↵ Lowy-Gallego E , Fairley S , Zheng-Bradley X , Ruffier M , Clarke L , Flicek P , et al. Variant calling on the GRCh38 assembly with the data from phase three of the 1000 Genomes Project . Wellcome Open Res . 2019 Dec 30 ; 4 : 50 . 11. ↵ Yoo S , Garg E , Elliott LT , Hung RJ , Halevy AR , Brooks JD , et al. HostSeq: a Canadian whole genome sequencing and clinical data resource . BMC Genomic Data . 2023 May 2 ; 24 ( 1 ): 26 . OpenUrl PubMed 12. ↵ Auton A , The 1000 Genomes Project Consortium, Corresponding authors , Abecasis GR , Steering committee , Altshuler DM , et al. A global reference for human genetic variation . Nature . 2015 Oct 1 ; 526 ( 7571 ): 68 – 74 . OpenUrl CrossRef PubMed 13. Rocha ZL , Aspinall PJ Kwan-Lafond D , Winterstein S . The Canadian Census and Mixed Race: Tracking Mixed Race Through Ancestry, Visible Minority Status, and Métis Population Groups in Canada . In: Rocha ZL , Aspinall PJ , editors. The Palgrave International Handbook of Mixed Racial and Ethnic Classification [Internet] . Cham : Springer International Publishing ; 2020 [cited 2026 Jan 20 ]. p. 75 – 94 . Available from: https://link.springer.com/10.1007/978-3-030-22874-3_4 14. ↵ Aspinall PJ . What kind of mixed race/ethnicity data is needed for the 2020/21 global population census round: the cases of the UK, USA, and Canada . Ethn Racial Stud . 2018 Sept 2 ; 41 ( 11 ): 1990 – 2008 . OpenUrl 15. ↵ Relethford J Via M , Gignoux CR , Roth LA , Fejerman L , Galanter J , Choudhry S , et al. History Shaped the Geographic Distribution of Genomic Admixture on the Island of Puerto Rico . Relethford J , editor. PLoS ONE . 2011 Jan 31 ; 6 ( 1 ): e16513 . OpenUrl CrossRef PubMed Web of Science 16. ↵ Marnetto D , Pärna K , Läll K , Molinaro L , Montinaro F , Haller T , et al. Ancestry deconvolution and partial polygenic score can improve susceptibility predictions in recently admixed individuals . Nat Commun . 2020 Apr 2 ; 11 ( 1 ): 1628 . OpenUrl CrossRef PubMed 17. Martin AR , Kanai M , Kamatani Y , Okada Y , Neale BM , Daly MJ . Clinical use of current polygenic risk scores may exacerbate health disparities . Nat Genet . 2019 Apr ; 51 ( 4 ): 584 – 91 . OpenUrl CrossRef PubMed 18. ↵ Flint J Mathieson I , Scally A. What is ancestry? Flint J , editor. PLOS Genet . 2020 Mar 9 ; 16 ( 3 ): e1008624 . OpenUrl CrossRef PubMed 19. ↵ Popejoy AB , Fullerton SM . Genomics is failing on diversity . Nature . 2016 Oct 13 ; 538 ( 7624 ): 161 – 4 . OpenUrl CrossRef PubMed 20. ↵ Greater Middle East Variome Consortium , Scott EM , Halees A , Itan Y , Spencer EG , He Y , et al. Characterization of Greater Middle Eastern genetic variation for enhanced disease gene discovery . Nat Genet . 2016 Sept ; 48 ( 9 ): 1071 – 6 . OpenUrl CrossRef PubMed 21. ↵ Cerdeña JP , Grubbs V , Non AL . Genomic supremacy: the harm of conflating genetic ancestry and race . Hum Genomics . 2022 May 19 ; 16 ( 1 ): 18 . OpenUrl CrossRef PubMed 22. Gouveia MH , Meeks KAC , Borda V , Leal TP , Kehdy FSG , Mogire R , et al. Subcontinental genetic variation in the All of Us Research Program: Implications for biomedical research . Am J Hum Genet . 2025 June 5 ; 112 ( 6 ): 1286 – 301 . OpenUrl CrossRef PubMed 23. Grundler MC , Terhorst J , Bradburd GS . A geographic history of human genetic ancestry . Science . 2025 Mar 28 ; 387 ( 6741 ): 1391 – 7 . OpenUrl CrossRef PubMed 24. ↵ Lewis ACF , Molina SJ , Appelbaum PS , Dauda B , Di Rienzo A , Fuentes A , et al. Getting genetic ancestry right for science and society . Science . 2022 Apr 15 ; 376 ( 6590 ): 250 – 2 . OpenUrl CrossRef PubMed 25. ↵ Greater Middle East Variome Consortium , Scott EM , Halees A , Itan Y , Spencer EG , He Y , et al. Characterization of Greater Middle Eastern genetic variation for enhanced disease gene discovery . Nat Genet . 2016 Sept ; 48 ( 9 ): 1071 – 6 . OpenUrl CrossRef PubMed 26. ↵ Nievergelt CM , Maihofer AX , Shekhtman T , Libiger O , Wang X , Kidd KK , et al. Inference of human continental origin and admixture proportions using a highly discriminative ancestry informative 41-SNP panel . Investig Genet . 2013 ; 4 ( 1 ): 13 . OpenUrl CrossRef PubMed 27. ↵ Ellison LK , Ng C , Kewayosh A , Troke N , Elias B , Tjepkema M , et al. Ancestry or identity? The importance of Indigenous engagement in articulating First Nations, Inuit and Métis peoples in the 2006 Canadian Census Health and Environmental Cohort (CanCHEC) . Int J Circumpolar Health . 2025 Dec ; 84 ( 1 ): 2495378 . OpenUrl CrossRef PubMed 28. Moreau C , Vézina H , Jomphe M , Lavoie ÈM , Roy-Gagnon MH , Labuda D . When Genetics and Genealogies Tell Different Stories-Maternal Lineages in Gaspesia: Amerindian Admixture in New France . Ann Hum Genet . 2010 Nov ;no-no. 29. ↵ Long JC Verdu P , Pemberton TJ , Laurent R , Kemp BM , Gonzalez-Oliver A , Gorodezky C , et al. Patterns of Admixture and Population Structure in Native Populations of Northwest North America . Long JC , editor. PLoS Genet . 2014 Aug 14 ; 10 ( 8 ): e1004530 . OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted January 20, 2026. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about bioRxiv. 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. You are going to email the following Concordance and dissonance: A genome-wide analysis of self-declared versus inferred ancestry in 10,250 participants from the HostSeq cohort Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Concordance and dissonance: A genome-wide analysis of self-declared versus inferred ancestry in 10,250 participants from the HostSeq cohort René L Warren , Inanc Birol , CGEn HostSeq Initiative bioRxiv 2025.06.10.658783; doi: https://doi.org/10.1101/2025.06.10.658783 Share This Article: Copy Citation Tools Concordance and dissonance: A genome-wide analysis of self-declared versus inferred ancestry in 10,250 participants from the HostSeq cohort René L Warren , Inanc Birol , CGEn HostSeq Initiative bioRxiv 2025.06.10.658783; doi: https://doi.org/10.1101/2025.06.10.658783 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Genomics Subject Areas All Articles Animal Behavior and Cognition (7635) Biochemistry (17690) Bioengineering (13892) Bioinformatics (41936) Biophysics (21451) Cancer Biology (18588) Cell Biology (25499) Clinical Trials (138) Developmental Biology (13378) Ecology (19899) Epidemiology (2067) Evolutionary Biology (24320) Genetics (15609) Genomics (22506) Immunology (17736) Microbiology (40394) Molecular Biology (17181) Neuroscience (88603) Paleontology (666) Pathology (2832) Pharmacology and Toxicology (4824) Physiology (7641) Plant Biology (15152) Scientific Communication and Education (2045) Synthetic Biology (4294) Systems Biology (9825) Zoology (2271)