New Tool Treats Genetic Ancestry as a Continuum to Sharpen Disease Risk Prediction for All Populations

Polygenic risk scores, which aggregate the small effects of millions of genetic variants into a single estimate of a person’s inherited risk for a disease, have become one of the most promising tools in modern genomic medicine. Yet a persistent and well-documented problem has shadowed their clinical deployment: the scores work far better for people of European ancestry than for almost everyone else. A newly published method called SPLENDID, described in Nature Methods, offers a fresh statistical answer to this inequity by abandoning the practice of sorting people into discrete ancestry groups altogether and instead modeling genetic ancestry as the continuous spectrum that it truly is. The result, according to its developers at Harvard University, the National Cancer Institute and the Massachusetts Institute of Technology, is a single prediction model that performs significantly better across diverse populations, with the largest gains accruing to non-European and admixed individuals who have historically been shortchanged by genomic prediction.

The core difficulty that SPLENDID addresses stems from how human genetic variation is actually structured. Genetic ancestry does not come in neat, mutually exclusive boxes. Decades of population genetics have shown that human variation varies smoothly across geography and history, and many people, particularly those with recent admixture from multiple continental sources, sit at points in ancestry space that no pre-specified category can capture. When existing multi-ancestry prediction methods force every individual into a labeled group, they make two kinds of errors at once. Individuals whose ancestry blends several sources are assigned awkwardly to one group or another, discarding information, and the boundaries between groups themselves are arbitrary, changing with the reference panels and clustering algorithms used to define them. As the SPLENDID authors note, clinical decisions are rarely, if ever, made on the basis of an ancestry label, so a prediction framework that depends on such labels is poorly matched to real-world practice.

SPLENDID, whose name reflects its penalized-regression foundation, works directly with individual-level genotype data at biobank scale and treats ancestry as a continuum throughout the modeling pipeline. Rather than building separate ancestry-specific scores and then deciding which one to hand to each patient, the method fits a unified penalized regression model in which genetic effects are allowed to vary smoothly as a function of continuous ancestry information, typically captured through principal components of the genotype matrix. The framework uses modern sparse-regression machinery, including L0L1-type penalties that encourage the selection of a parsimonious set of variants, together with ensemble learning strategies that combine multiple candidate models tuned across the ancestry continuum. This produces one model, not many, and that model can be applied to any incoming individual without first asking which population bucket they belong to.

The mathematical machinery behind this idea is demanding, because biobank-scale genotyping data routinely contain hundreds of thousands of individuals and millions of variants. Fitting penalized models in which variant effects interact smoothly with ancestry dimensions requires efficient coordinate-descent and discrete-optimization algorithms that can operate on ultrahigh-dimensional data. The authors, including Tony Chen and Xihong Lin of Harvard T.H. Chan School of Public Health, Haoyu Zhang of the National Cancer Institute and Rahul Mazumder of MIT’s Sloan School of Management, built SPLENDID on computational tools of the kind developed for fast best-subset selection and large-scale sparse regression, making the method practical for datasets such as the UK Biobank and the All of Us Research Program. The software is implemented for R and released openly with tutorials, lowering the barrier for other groups to adopt the approach.

The empirical evaluation is extensive. In simulation studies designed to mimic the genetic architecture and demographic history of real human populations, SPLENDID outperformed a battery of existing polygenic prediction methods, including GWAS-based approaches and multi-ancestry tools such as PRS-CSx, CT-SLEB, PROSPER and iPGS. Notably, when the team forced other methods to produce a single pooled-ancestry score rather than ancestry-specific ones, prediction accuracy dropped sharply, particularly for the GWAS-based approaches. This comparison underscores the central argument of the paper: existing frameworks implicitly assume that ancestry labels are available and reliable, and when that assumption fails, their performance collapses in ways that SPLENDID’s continuum-based design avoids by construction.

The real-world tests were conducted on two of the largest and most demographically diverse genetic resources in existence. In the All of Us Research Program, whose more than 224,000 genotyped participants include substantial representation of African, Hispanic, Asian and Indigenous American ancestries, and in the UK Biobank, with roughly 340,000 analyzed participants, SPLENDID delivered significantly higher prediction accuracy than competing methods across nine continuous traits examined. The improvements were most pronounced for non-European and admixed individuals, exactly the groups for whom conventional scores perform worst. In admixed UK Biobank samples, identified through probabilistic classification against the 1000 Genomes Project reference panel, SPLENDID maintained accuracy where label-dependent methods faltered, validating the premise that treating ancestry as a continuum captures genuine genetic heterogeneity that discrete labels erase.

One striking illustration of why continuous modeling matters comes from the biology of blood cell traits. The paper examines individual-level effects of a variant, rs1213375, on mean corpuscular hemoglobin across the genetic ancestry spectrum, an example that connects to well-known ancestry-linked variation in hematological parameters such as the influence of Duffy status on neutrophil counts in people of African ancestry. Effect sizes of genetic variants are known from recent work to be broadly conserved across human groups, but fine-scale population structure and local ancestry still modulate how variants express themselves. A model that can represent these gradual shifts, rather than jumping between group-specific estimates at arbitrary boundaries, is better positioned to translate genomic discovery into accurate predictions for everyone.

The broader significance of this work lies in health equity. Polygenic risk scores are beginning to enter clinical workflows, with validated assays for conditions ranging from cardiovascular disease to breast cancer and with large consortia such as PRIMED dedicated to reducing disparities in polygenic risk assessment. If these tools systematically underperform in non-European populations, the benefits of genomic medicine will flow disproportionately to people already advantaged by existing research infrastructure, widening health disparities rather than narrowing them. Analyses spanning the Global Biobank Meta-analysis Initiative and studies of portability across nine ancestry groups have repeatedly documented the scope of this problem. SPLENDID’s contribution is to show that part of the solution does not require waiting for larger non-European datasets alone; it also requires statistical frameworks that use the full richness of ancestry information already present in biobanks.

There are practical implications as well. Because SPLENDID produces a single model, health systems deploying it do not need to maintain separate score pipelines for separate populations, nor do they need to assign patients an ancestry label before applying a score, a step that raises both logistical and ethical concerns given the fraught relationship between genetic ancestry and social identity. Scholars have cautioned repeatedly that genetic ancestry must be handled carefully in science and society, and a method that renders explicit labeling unnecessary sidesteps many of those pitfalls. The method’s demonstrated scalability also means it can be retrained as biobanks grow and diversify, and its dependence on individual-level data makes it well suited to trusted-access environments such as All of Us and the UK Biobank, where controlled analyses are the norm.

The path from a Nature Methods paper to routine clinical use is never short, and the authors are careful to frame SPLENDID as a tool for robust risk prediction across diverse populations rather than a finished clinical product. Validation for additional traits, disease endpoints and health systems will be needed, and continued investment in diversifying genomic datasets remains essential, because no statistical method can fully compensate for reference data that under-represent much of humanity. Still, the results reported across more than half a million participants in two flagship biobanks represent a meaningful advance: evidence that acknowledging the continuous, blended nature of human genetic ancestry, rather than forcing it into categories, yields measurably better predictions for the people who need them most. In a field where each incremental gain in accuracy can translate into earlier screening and better prevention, SPLENDID’s continuum-based approach offers a template for building genomic risk tools that work equitably across the full breadth of human diversity.

Subject of Research: A penalized regression framework, SPLENDID, that treats genetic ancestry as a continuum to improve polygenic risk prediction across diverse biobank populations.

Article Title: SPLENDID incorporates continuous genetic ancestry in biobank-scale data to improve polygenic risk prediction across diverse populations

Article References: Chen, T., Zhang, H., Mazumder, R., & Lin, X. (2026). SPLENDID incorporates continuous genetic ancestry in biobank-scale data to improve polygenic risk prediction across diverse populations. Nature Methods. https://doi.org/10.1038/s41592-026-03235-2

Image Credits: AI Generated

DOI: 10.1038/s41592-026-03235-2

Keywords: polygenic risk scores, genetic ancestry, SPLENDID, biobank-scale data, All of Us Research Program, UK Biobank, penalized regression, health disparities, admixed populations, Nature Methods, precision medicine, statistical genetics