The research team, led by J. A. Woolliams and Gregor Gorjanc of The Roslin Institute at the University of Edinburgh, together with colleagues at the University of Oxford and Forest Research, analysed 1295 elite spruce trees drawn from 50 full-sib families, along with 73 open-pollinated control trees from a Haida Gwaii provenance. The trees came from two genetically linked field trials established in 1996 and 1997 and replicated at two contrasting sites: Brecon in Wales at 375 metres above sea level, and Kintyre in Scotland at just 60 metres. Nine traits were measured, spanning early growth traits such as height at six years and diameter at eleven years, visual scores for branching and stem straightness, a pilodyn penetration reading used as a proxy for wood density, and, on felled trees, merchantable volume, modulus of rupture, modulus of elasticity and wood density. The wood quality traits were assessed on small clear specimens cut from boards spanning the diameter of each log, stabilised to 12 percent moisture content and tested in three-point flexural bending, with cross-sectional averages weighted by ring area to reflect how European softwood grading actually values timber.

The conceptual heart of the study lies in the distinction between two kinds of genetic information. Pedigree-based evaluation, the industry standard since the work of Sewall Wright in the 1920s, relies on identity by descent: the expected probability that two relatives share copies of the same ancestral gene, derived purely from recorded family relationships. Genomic evaluation, pioneered in livestock by Meuwissen and colleagues in 2001, instead typically uses identity by state, in which thousands of DNA markers scattered across the genome capture the actual sharing of chromosome segments between individuals, exploiting population-wide linkage disequilibrium between markers and the causal variants underlying traits. The team compared three relationship matrices within a single statistical framework: the classical pedigree matrix, a VanRaden genomic matrix built from identity by state, and a third, less common option that uses dense markers in linkage analysis to track the realised inheritance of genome segments from offspring back through the pedigree to its founders, yielding a genomic identity-by-descent matrix.

Genotyping was carried out on a SNP chip containing 12,911 markers, of which 4123 could be assigned to the 12 linkage groups of a recently published Sitka spruce genetic map spanning 21.42 Morgans. Missing genotypes were filled in by multi-locus peeling using the AlphaPeel software, raising the proportion of trees with call rates above 95 percent from 84 to 96 percent, with an estimated residual genotype error probability of 0.022. The pedigree itself was audited against the genomic data, revealing inconsistencies for two parents that required dummy parents to be introduced into the pedigree for four families, a quiet reminder of how often recorded pedigrees in tree breeding diverge from biological reality.

The critical methodological innovation was in how accuracy was judged. Model-based accuracies, calculated from prediction error variances under the fitted mixed model, assume that both the model structure and its estimated parameters are correct. Cross-validation makes no such assumption: the researchers withheld phenotypes from roughly one-twelfth of the elite trees at a time in a 12-fold scheme, re-estimated breeding values without those records, and then correlated the predictions with the withheld phenotypes block by block across the two sites, pooling the results through a meta-analytic framework that accounted for heterogeneity among blocks. This distinction proved decisive. When accuracies were read directly from the fitted models, pedigree and genomic approaches looked almost indistinguishable. But under the more objective cross-validation, genomic information delivered the highest accuracy for all but one of the nine traits, and the pedigree-based model showed a characteristic weakness: without genomic data, two full siblings in the validation set receive identical breeding values, because the only source of information about an individual’s Mendelian sampling, its own phenotype, has been deliberately withheld.

That last point gets to the biological and statistical core of why genomics helps. Within a full-sib family, each offspring inherits a unique random shuffle of its parents’ chromosomes, and this Mendelian sampling term is the engine of within-family genetic variation. Pedigree can only assign siblings the same expected relationship; markers can observe which sibling actually inherited which segments. The team exploited this with a fourth model, IIIA, that separated the between-family component, captured by parental transmitting abilities, from the within-family Mendelian sampling component, estimated directly from the markers. This model produced the best cross-validation accuracies, suggesting that in breeding programmes with large sib families, as is typical in conifers, genomic selection can be made to work efficiently even with a relatively sparse marker panel, because recombination events are rare enough along a chromosome that localising them does not require very high marker density.

The study was also candid about the limitations of its genomic data. Several independent lines of evidence indicated that the 4123 markers failed to capture the full genetic variance. Genomic models yielded lower heritability estimates than the pedigree model for traits with high heritability, with the unaccounted genetic covariance among siblings leaking into the residual variance. More tellingly, the ratio of Mendelian sampling variance to the classical expectation under random mating was estimated at 0.666, implying that the markers captured only about 83 percent of the true genetic variance. For context, livestock breeders routinely work with around 2500 markers per Morgan, roughly ten times the density used here. Yet despite this handicap, the genomic models still matched or beat pedigree accuracy, a result the authors attribute to the rich sib structure of the population and the value of estimating Mendelian sampling directly.

Equally important was what the study did not find. Some researchers have hypothesised that pedigree-based breeding values are systematically biased relative to genomic ones, but across all trait and information combinations, the regression slopes of phenotype on breeding value fell within 1.5 standard errors of one, indicating no evidence of over- or under-estimation in any model. The authors note that theory supports this: with the correct model, unbiased estimates should follow, and both pedigree and genomic approaches remain vulnerable to unmodelled sources of field variation such as maternal effects or environmental gradients. The absence of bias strengthens confidence that the accuracy gains from genomics are genuine rather than artefacts of miscalibration.

The forward-looking implications are where the study becomes genuinely striking. With the current training set of roughly 1341 genotyped and phenotyped individuals, cross-validation accuracies of genomic breeding values ranged from about 0.6 to 0.7 across traits. Projections using established theory suggest that an eightfold increase in the training population would push accuracies to 0.8 or 0.9 for all traits studied, approaching the ceiling imposed by the current marker density. Pedigree-based accuracy, by contrast, is fundamentally capped: an eightfold increase in records per parent would lift validation accuracy only from about 0.5 to 0.667, because predictions for unphenotyped individuals rest entirely on parental averages. In other words, genomics is already competitive at a scale where pedigree has nearly exhausted its potential, and every additional investment in training data and marker density compounds the genomic advantage rather than hitting a wall.

For forestry, the practical consequences could be transformative. Genomic breeding values can be computed for seedlings or even seeds before any field trial, enabling early selection decisions that shave years off breeding cycles, and for traits such as wood strength that require felling a tree to measure properly, genomics offers selection without destruction. The authors argue that realising this potential in conifers will require denser SNP panels to capture the full genetic variance in less structured populations, and training sets numbering in the tens of thousands rather than thousands. But the direction of travel is now clear. Sitka spruce, with a genome larger than ten billion base pairs and a breeding history written more in field notebooks than in sequence data, has just demonstrated that the genomic era of tree breeding is no longer a promise for the future. It is a working tool, ready to be scaled.

Subject of Research: Genomic versus pedigree-based estimation of breeding values in Sitka spruce

Article Title: Genomic models for accurate estimation of breeding values in Sitka spruce (Picea sitchensis Bong. Carr)

Article References: Genomic models for accurate estimation of breeding values in Sitka spruce (Picea sitchensis Bong. Carr). (n.d.). https://doi.org/10.1038/s41437-026-00894-x

Image Credits: AI Generated

DOI: 10.1038/s41437-026-00894-x

Keywords: genomic selection, Sitka spruce, breeding values, identity by descent, identity by state, conifer breeding, wood quality traits, heritability, cross-validation, Mendelian sampling, SNP markers, forest genetics

The study focused on the Late Iron Age and Early Medieval period, an era that Finnish archaeologists have traditionally called the “Crusade Period,” reflecting the long-held assumption that Swedish kings launched violent military campaigns to convert the local population to Christianity. Modern scholars largely regard these so-called crusades as later literary constructions, and the researchers instead use the term Early Medieval Period to describe the gradual emergence of Christian institutions and Swedish political influence. During this time, Finland was not a unified state but a patchwork of culturally distinct regions, including a coastal zone with strong Scandinavian ties and an inland zone known as Häme, whose material culture blended western and eastern traditions.

Recovering ancient DNA from Finland is notoriously difficult. The country’s acidic soil rapidly destroys unburnt human remains, and before this study only 26 ancient genomes with sufficient genome-wide coverage had ever been published from the entire country. The team, led by researchers at the University of Turku and the Max Planck Institute for Evolutionary Anthropology, generated new target-capture data from 38 individuals buried at 17 archaeological sites, carefully screening each genome for contamination and excluding four that could not be reliably authenticated. The resulting dataset, analysed alongside biobank genomes from thousands of present-day Finns, provides the most detailed genomic portrait of pre-modern Finland ever assembled.

The first major finding concerns the relationship between the coastal and inland zones. Despite their documented differences in economy, burial practice, and material culture, the ancient populations of the two zones were genetically indistinguishable. Statistical analyses of allele frequencies showed that the coastal and inland groups formed a single genetic clade, excluding both modern and other ancient comparison populations. Proximal ancestry modelling revealed that both groups could be described as a three-way mixture of Scandinavian, Baltic Iron Age, and Sámi-related ancestry, with the inland group showing a slight, though not statistically significant, tilt toward the Siberian-associated component that characterises Uralic-speaking populations further east.

That genetic unity, however, did not last. When the researchers compared the ancient genomes with modern inhabitants of the same regions, a striking asymmetry emerged. People living in the inland zone today are the direct descendants of their Early Medieval predecessors: rare identity-by-descent segments shared between ancient and modern genomes map onto a broad regional pattern of connectedness across the inland zone, demonstrating nearly a millennium of population continuity. This extends earlier work on medieval cemeteries in Pirkanmaa from a local phenomenon to a regional one, showing that the persistence is a feature of the whole inland gene pool rather than an artefact of a few closely related burial sites.

The coastal zone tells a completely different story. Present-day coastal Finns carry roughly 25 percentage points more Scandinavian-derived ancestry than their Early Medieval counterparts, and their Baltic-derived ancestry has all but vanished. Haplotype-based analyses found no discernible pattern of shared DNA segments between the ancient coastal population and modern residents of the same municipalities, indicating a genuine population turnover rather than gradual drift. The researchers attribute this shift primarily to large-scale emigration from Sweden, most likely concentrated in the late thirteenth and early fourteenth centuries, when the expanding Kingdom of Sweden consolidated its rule over what medieval documents called Österland, the eastern lands. Crucially, the genetic data show that this immigration was confined to the coast: Swedish settlement did not penetrate the hinterland, and the consolidation of Swedish authority inland appears to have proceeded through administrative and ecclesiastical structures rather than through mass movement of people.

The study also reconstructed the demographic trajectory of the region using a technique called time-transect effective population size estimation, which exploits shared DNA segments among individuals who lived at different times. The results show a population in rapid expansion: the effective population size of southwestern Finland grew from roughly 2,500 individuals around 800 CE to approximately 13,000 by 1250 CE, a fivefold increase in four and a half centuries. This boom coincides with the introduction of new plough technologies from Scandinavia and the Baltics, which allowed farmers to break the heavy clay soils that dominate the region, as well as with pollen and macrofossil evidence for expanding grasslands from the mid-1100s. Notably, the researchers found no evidence of consanguinity: while many individuals carried short runs of homozygosity consistent with distant parental relatedness in a small population, none showed the long stretches that would indicate mating between close relatives, suggesting that social mechanisms to avoid inbreeding were already in place.

Perhaps the most visually compelling result is a network map of shared DNA segments connecting 103 ancient individuals from around the Baltic Sea, from the Viking Age to the Early Modern period. The network resolves into three clusters, Scandinavian, Slavic, and Finnish, linked by a single Viking Age individual from Gotland whose ancestry was previously modelled as more than 90 percent Finnish-like. When adjusted for sample size, Finland shared its strongest connections with Gotland, Latvia, and Russia, mirroring the archaeological record of trade, diplomacy, and intermarriage that tied coastal Finland into the exchange systems of the medieval North. The Finnish word for money, raha, originally meant fur pelt, a linguistic fossil of the fur trade that likely drove much of this connectivity.

Some of the individual connections are remarkably specific. A genetically female individual buried at Laukskola on the Daugava river in Latvia, dated to 1031 to 1147 CE and likely a speaker of the nearly extinct Liv language, shares DNA segments with two individuals from Early Medieval Finland consistent with a relationship as close as sixth cousins. Because the three were largely contemporaneous, their shared ancestry points to a common forebear who moved between the regions within living memory of the burials. Even more striking, the researchers recovered IBD links between Early Medieval Finns and two crew members of the Kronan, a Swedish warship that sank at the Battle of Öland in 1675. Historical records attest that officers for the Kronan were recruited from Finland, and the genetic evidence corroborates that these sailors were either Finnish themselves or recent descendants of Finnish immigrants to Sweden.

The study does have limits. Because all the sampled burials predate the main wave of Swedish immigration, the analysis can only establish that the coastal shift occurred after roughly 1300 CE, and the researchers note that genomes from the fourteenth to sixteenth centuries, particularly from sites along the axis between the two zones, will be needed to trace the migration in finer detail. The Siberian ancestry cline that runs from northeast to southwest across Finland, likely reflecting ancient admixture with Sámi-related groups, also remains only partially resolved, since no ancient genomes from eastern Finland were available. Nevertheless, the big picture is clear. Early Medieval southwestern Finland was not a remote periphery waiting to be conquered, but a genetically homogeneous and rapidly growing population firmly embedded in the circum-Baltic world. When Swedish power arrived, it reshaped the coast through settlement while leaving the inland gene pool untouched, a dichotomy whose imprint is still visible in the DNA of Finns today, nearly eight centuries later.

Subject of Research: Population genomics and demographic history of Early Medieval southwestern Finland using ancient DNA

Article Title: Population genomics, demography, and circum-Baltic connectivity of Early Medieval southwestern Finland

Article References: O’Sullivan, R. J., Nordfors, U., Saari, N.-J., Majander, K., Taavitsainen, J.-P., Salo, K., Rohrlach, A. B., Salmela, E., Lamnidis, T. C., Traverso, L., Krause, J., Peltola, S., & Onkamo, P. (2026). Population genomics, demography, and circum-Baltic connectivity of Early Medieval southwestern Finland. Genome Biology, 27(1), Article 288. https://doi.org/10.1186/s13059-026-04277-7

Image Credits: AI Generated

DOI: 10.1186/s13059-026-04277-7

Keywords: ancient DNA, Finland, Early Medieval period, population genomics, identity-by-descent, Scandinavian ancestry, Viking Age, Baltic Sea, effective population size, Swedish migration, Sámi ancestry, archaeogenetics