Resolving sampling and population-size biases in domestication genomics supports a South Asian origin of walnuts

Resolving sampling and population-size biases in domestication genomics supports a South Asian origin of walnuts | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (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],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Resolving sampling and population-size biases in domestication genomics supports a South Asian origin of walnuts Cai-Jin Chen, Xiao-Xu Pang, Ya-Mei Ding, Wei-Ping Zhang, Yang Yang, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7127779/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Jan, 2026 Read the published version in Genome Biology → Version 1 posted 11 You are reading this latest preprint version Abstract (222 words) : Inference of population structure is central to domestication studies, yet population clustering algorithms are prone to biases when sampling is unbalanced and effective population sizes ( N e ) differ across populations. These confounding factors result in misclassification of large ancestral populations as admixed, rather than recognizing them as a distinct group, particularly in single-origin domestication scenarios. We propose a novel parameterization strategy for the STRUCTURE software, combining the F model and alternative ancestry prior (along with a smaller initial ALPHA value). Simulation analyses demonstrate that this combination of parameters works synergistically to mitigate biases arising from unbalanced sampling and unequal population sizes. To validate its empirical utility, we apply our parameter-setting strategy to the domestication history of the common walnut ( Juglans regia ), using whole-genome resequencing data from 399 individuals from across its range. The results support an origin of J. regia in South Asia, where walnut populations are characterized by high genetic diversity, extensive private allele content, low mutation load, and demographic stability. This finding clarifies long-standing questions about the center of walnut domestication and informs its global dispersal history. Building on this demographic framework, we further identified genomic regions under recent positive selection and detected candidate domestication genes involved in shell structure, pollen development, and lipid transport. These results underscore the utility of our approach for both domestication research and broader population-genetic studies. Crop domestication effective population size parameter optimization population clustering sampling bias STRUCTURE software Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Population-genetic clustering algorithms, such as those implemented in STRUCTURE (Pritchard et al. 2000 ) and ADMIXTURE (Alexander et al. 2009 ), are widely used to characterize individuals and populations using genetic data. A key application of these tools is reconstructing the domestication history of crops. In single-domestication scenarios, domesticated species typically consist of a large ancestral or wild population at the center of origin and multiple smaller, geographically dispersed populations derived from this source (Gutaker and Purugganan 2024 ). Under such conditions, clustering algorithms may misclassify individuals from the ancestral population as admixed, rather than recognizing them as a distinct group (Lawson et al. 2018 ; Hahn 2019 ). This misclassification is largely driven by unequal effective population size ( N e ) among populations: derived populations exhibit stronger drift due to founder effects or bottlenecks, quickly fixing alleles that were originally polymorphic in the ancestral gene pool. As a result, large source populations—retaining greater allelic diversity—falsely appear admixed (Lawson et al. 2018 ). Such misclassification—resulting from unequal N e between ancestral and derived populations—is a common, but often overlooked, source of bias in population-genetic analyses of domesticated species. Beyond misclassification due to unequal effective population size ( N e ), another challenge in population clustering arises from unbalanced sample sizes across populations. Simulation studies have demonstrated that clustering methods struggle with highly unbalanced sampling, placing underrepresented populations together even when they are not genetically close (Kalinowski 2011 ; Puechmaille 2016 ). However, uneven sampling is frequently unavoidable, as many biodiverse regions remain under-sampled due to geographic, political, and human-resource constraints. Unequal effective population size and unbalanced sample size together introduce compounding biases in population-clustering analyses. Among commonly used clustering algorithms, STRUCTURE remains the most popular due to its pioneering role and continuing refinement, offering high accuracy and flexibility (Novembre 2016 ; Pang and Zhang 2025 ). STRUCTURE supports a variety of prior models that allow users to account for demographic imbalance and uneven sampling across populations (Falush et al. 2003 ; Wang 2017 )—a level of user control that is limited or absent in other tools, such as ADMIXTURE. An evaluation of these prior models by Wang ( 2017 ) pointed out that the alternative ancestry prior (POPALPHA = 1 and a smaller initial ALPHA value), which allows unequal representations of the populations by the sample, can mitigate misclassification issues caused by unbalanced sampling under default ancestry prior (POPALPHA = 0). He also observed that this improvement would be further enhanced when used in conjunction with the uncorrelated allele frequency model. Another mitigation approach, the correlated allele frequency model (or F model) (Falush et al. 2003 ) can theoretically handle differences in N e among populations, though this capability is rarely appreciated. Importantly, these two strategies are rarely implemented together in practice, and each addresses only a single source of bias. This presents a challenge for crop domestication research, where both sampling imbalances and unequal population sizes presumably are common. To our knowledge, no prior study has systematically addressed both these sources of bias in STRUCTURE-based analyses. These methodological challenges in population-genetic clustering are particularly relevant when studying crops like the common walnut ( Juglans regia ), which has a large geographic distribution that includes several botanically underexplored regions, suggesting the presence of sampling biases in addition to likely unequal population sizes. Walnuts have been widely gathered and traded across Asia since the Late Neolithic, as evidenced by nut remains found in an Armenian grave (~ 6200 years ago) (Wilkinson et al. 2012 ), walnut shells from Kashmir (~ 4700–4000 years ago) (Pokharia et al. 2018 ), and shells from a former market site in Pakistan (~ 3200 years ago) (Spengler et al. 2021 ). Due to its extinction in much of Europe and parts of Asia during the Pleistocene glaciations—and botanical under-collecting in Central, South, and West Asia—the geographic origin of this crop has remained elusive. Competing hypotheses place its origin in the Irano-Anatolian region of West Asia (Zohary et al. 2012 ; Ding et al. 2022 ), the Tianshan Mountains of Central Asia (Molnar et al. 2011 ; Mapelli et al. 2016 ; Pollegioni et al. 2017 ), or a broader region encompassing Afghanistan, Bangladesh, Bhutan, India, Nepal, and Pakistan in South Asia (Aradhya et al. 2017 ; Roor et al. 2017 ; Yan et al. 2024 ). Here, we revisit the origin of the common walnut using a STRUCTURE-based approach, complemented by additional lines of genetic evidence—including chloroplast haplotypes, bottleneck signatures, inbreeding levels, and genetic load—leveraging an expanded sampling dataset. Crucially, we propose a new parameterization strategy for STRUCTURE that integrates the F model, an alternative ancestry prior, and a smaller initial ALPHA value, aiming to simultaneously address the effects of both unequal effective population size and sample size imbalance. Following clear definition of source and derived populations, we conduct genome-wide comparisons of each derived population against the source and identify, for the first time, genes under positive selection linked to walnut domestication. Results Genetic structure and phylogenetic relationship inferred from the nuclear genome Our nuclear dataset comprises 14,950 independent non-coding single nucleotide polymorphisms (SNPs) derived from 298 Juglans regia individuals (Supplementary Table S1 ). We investigated population clustering using two model configurations in STRUCTURE: ParamSet1, which implemented an alternative ancestry prior, a small ALPHA value, and the correlated allele frequency model ( F model); and ParamSet2, which applied the same ancestry prior and ALPHA but employed the uncorrelated allele frequency model as advised by Wang ( 2017 ). Under ParamSet1, clustering analysis identified K = 6 as the optimal number of populations based on the parsimony estimator (Wang 2019 ) (Fig. 1 A; Supplementary Table S2 ). Individuals were assigned to six genetically distinct groups ( q > 0.75): East Asia (73 individuals), Central Asia (48), Europe (26), West Asia (51), Tibet (40), and South Asia (16). When K = 5 was tested, individuals clustered into five regional groups (East Asia, Central Asia, Europe, West Asia, and Tibet), while South Asian samples appeared admixed across clusters (Fig. 1 A). In contrast, under ParamSet2, clustering analysis identified K = 5 as the optimal number of populations, supporting five distinct groups—East Asia (74), Central Asia (48), Europe (27), West Asia (36), and Tibet (40)—again with South Asian individuals showing evidence of admixture. Increasing to K = 6 did not result in the resolution of South Asia as an independent cluster (Supplementary Fig. S1 ). The F k parameter of F model quantifies the degree of genetic drift experienced by a population since its divergence from an ancestral population. Based on the estimated F k values under ParamSet1, the ranking of genetic differentiation from highest to lowest is as follows: East Asia (0.4964) > Europe (0.3917) > Tibet (0.2738) > Central Asia (0.2322) > West Asia (0.1647) > South Asia (0.0565), which suggests that South Asia has the largest population size. Principal component analysis (PCA) and a Neighbor-Joining (NJ) tree both supported the population structure inferred from STRUCTURE analyses under ParamSet1. PCA (PC1 = 8.10%, PC2 = 4.26%) clearly separated individuals into six geographic groups (Supplementary Fig. S2 ). The NJ tree, based on 11,803 independent SNPs from 298 J. regia and two outgroups, revealed three major clades: Tibet (Clade 1), Europe and West Asia (Clade 2), and Central-East Asia (Clade 3), with a subset of South Asian individuals positioned at the root of the tree (Fig. 1 B). This topology implies that the South Asian population may represent the ancestral lineage from which the other five regional populations diverged. Testing the effects of STRUCTURE parameter settings with real and simulated data To evaluate how two STRUCTURE parameter settings (see above) influence the accuracy of ancestral population inference under a domestication scenario, we selected the South Asian and the two other populations (West Asia and Tibet) to represent the possible ancestral and derived lineages in empirical datasets. Under ParamSet1, the optimal K was 3, revealing distinct genetic clusters for each population. In contrast, ParamSet2 yielded an optimal K of 2, with West Asia and Tibet forming separate clusters, while the South Asian population exhibited admixture from both (Fig. 1 D). To enable direct comparison, we designed a three-population simulation under a domestication scenario, modeling South Asia, Tibet, and West Asia (Fig. 1 C). The demographic histories (model history settings followed the demographic scenarios described below), sample size, and SNP count were matched to the empirical dataset. The simulation results closely paralleled the empirical findings: under ParamSet1, three distinct clusters were identified (Fig. 1 E), whereas under ParamSet2, two clusters were observed, with the source population showing genetic admixture from both bottlenecked populations (Fig. 1 E). These findings further support South Asia as the ancestral lineage and suggest that the ParamSet1 (alternative ancestry prior (POPALPHA = 1), a smaller ALPHA value, and the correlated allele frequency model ( F model)) may be more suitable for capturing the fine-scale genetic structure within these populations than the parameter combination, ParamSet2, proposed by Wang ( 2017 ). Genetic diversity and differentiation among the six geographic groups Accepting six geographic groups (East Asia, Central Asia, Europe, West Asia, Tibet, South Asia) as best matching our nuclear data, we next calculated standard genetic diversity, linkage disequilibrium (LD), nucleotide diversity (π), heterozygosity, and private SNPs across the six groups. Genome-wide LD analysis revealed substantial variation in r² decay, with South Asia showing the fastest decay among all STRUCTURE groups, followed by West Asia, Central Asia, Tibet, Europe, and East Asia (Fig. 2 A). Among the six groups, South Asia exhibited the highest nucleotide diversity, followed by West Asia, Tibet, Central Asia, Europe, and East Asia (Fig. 2 B). This pattern was also reflected in heterozygosity estimates, with South Asia displaying the highest values, followed by West Asia, Tibet, Central Asia, Europe, and East Asia (Fig. 2 C). Since private allele counts are influenced by sample size, we controlled for this effect by randomly selecting 16 individuals per group (the smallest group with q > 0.75) and repeating the analysis 20 times. The adjusted estimates showed the highest private SNP proportion in South Asia (21.4%-22.7%), followed by East Asia (8.6%-20.2%), West Asia (9.0%-14.3%), Tibet (8.2%-11.2%), Europe (5.9%-7.3%), and Central Asia (5.3%-6.5%) (Fig. 2 D). D XY values ranged from 0.140 to 0.165, with the South Asia group showing the highest genetic divergence from other populations (0.161–0.165). F ST values ranged from 0.074 to 0.306, with the highest between East Asia and Europe (0.306) and the lowest between West Asia and South Asia (0.074) (Fig. 2 E). These findings are consistent with the South Asian lineage being the earliest divergence among the groups and having experienced low genetic drift since its separation. Using OrientAGraph (Molloy et al. 2021 ), we analyzed population relationships and migration patterns among six geographic groups based on allele frequency data from STRUCTURE-defined gene pools. This analysis inferred a population topology that placed South Asia as the earliest-diverging lineage, with East Asia clustering with Central Asia, and Europe with West Asia. Among the migration events tested (m = 0–6), a single migration event (m = 1) best explained the sample covariance, with gene flow primarily observed from the East Asia group into the South Asia group (Fig. 2 F). Inbreeding and deleterious mutation load across the six geographic groups The F ROH values, representing the proportion of the genome within runs of homozygosity (ROH), varied significantly across the six geographic groups. East Asia exhibited the highest F ROH , followed by Europe, Tibet, Central Asia, West Asia, and South Asia (Fig. 3 A). Significance testing confirmed that South Asia had significantly lower F ROH values compared to all other groups. To compare patterns of mutation load across the six geographic groups, we categorized coding-sequence variants into three functional classes based on predicted effects: synonymous, deleterious, and loss-of-function (LoF). Ancestral and derived alleles for each variant were polarized using Juglans mandshurica and Juglans nigra as outgroups. Among the six groups, South Asia exhibited the lowest ratio of total derived deleterious variants to synonymous variants, followed by Central Asia, West Asia, Tibet, Europe, and East Asia (Fig. 3 B). Similarly, South Asia showed the lowest ratio of total derived loss-of-function (LoF) variants to synonymous variants, with the remaining regions ranked in the same ascending order (Fig. 3 C). Demographic history and inference of bottlenecks We inferred the demographic history of the six geographic groups by setting the maximum recombination rate to 0.05 in the software GONE (Santiago et al. 2020 ) and excluding inversion regions with frequencies between 0.15 and 0.85, and lengths greater than 10 Mb, as suggested by Novo et al. ( 2023 ). The East Asian and European groups underwent significant bottlenecks 60–20 generations ago, which, assuming a generation time of 50 years, correspond to approximately 3000–1000 years ago, with population sizes as low as 100 breeding individuals. The Central Asian and West Asian groups experienced bottlenecks between 40 and 10 generations ago (~ 2000–500 years ago), with minimum population sizes of 200. Tibet, on the other hand, underwent a rapid and severe population decline 30 generations ago (Fig. 4 A). Taking into account the shorter growing seasons and longer generation times at high altitudes, this decline likely occurred around the same time as those in the other regions. In contrast, South Asia displayed no such dramatic decline, consistent with the expectation of a stable population size, supporting the hypothesis of its role as the domestication origin of J. regia . To investigate differences in bottleneck histories among the six geographic populations, we conducted simulations. Based on geographic proximity, a NJ tree, and TreeMix-inferred clustering, the simulations assumed that populations in Europe and East Asia expanded from South Asia via West and Central Asia. Tibetan walnuts form a distinct lineage, separate from other East Asian samples (Fig. 1 B; Fig. 2 F), suggesting arrival from South Asia via a southern inner-plateau route ( Discussion ). We simulated scenarios with and without bottlenecks (A-E). Populations A, B, C, D, E, and F represent South Asia, Tibet, West Asia, Europe, Central Asia, and East Asia, respectively. Population A maintained a constant size with no bottlenecks. Population B underwent two bottlenecks, between generations 80 − 40 and 40 − 10. Populations C and E experienced a single bottleneck between generations 80 − 20, followed by recovery. Populations D and F had two bottlenecks, between 80 − 40 and 40 − 20, with recovery (Fig. 4 B). The results show that Population A maintained a constant size over 100 generations (Fig. 4 C). Population B had a bottleneck at generation 50, with recovery in recent generations. Populations C and E underwent a bottleneck between generations 60 − 20, followed by recovery, similar to West and Central Asia. Population D also had a bottleneck between generations 40 − 20, followed by recovery, and Population F experienced a bottleneck between generations 30 − 20, with recovery, similar to East Asia and Europe (Fig. 4 C). Phylogenetic relationships inferred from chloroplast genomes Chloroplast haplotype diversity can point to the domestication center of a species, and we therefore also analyzed chloroplast genomic data. We reconstructed a minimum of 160,537 base pairs of the chloroplast genome per sample, identifying 106 substitutions and defining 12 haplotypes. The addition of seven chloroplast genomes from the Western Himalayan region (Afghanistan (1), India (3), Nepal (1), Pakistan (2)) generated by Yan et al. ( 2024 ) —resulted in 19 haplotypes (Hap 1–19; see Supplementary Table S3). Europe (47 samples) and West Asia (Iran, Iraq, Armenia; 81 samples) predominantly harbor haplotypes 10 and 17, lacking any unique regional haplotypes. Central Asia (Kazakhstan, Tajikistan, Xinjiang; 39 samples) has two haplotypes (Hap 10 and 17); Tibet (51 samples) four haplotypes (Hap 7, 8, 10 and 17), with haplotype 7 being region-specific; East Asia (China, Korea, Japan; 111 samples) six haplotypes (Hap 8, 10, 15, 17, 18, and 19), three of them endemic (Hap 15, 18, and 19); and South Asia (Afghanistan, Pakistan, India, Nepal; 31 samples) harbors the highest diversity with 14 haplotypes (Hap 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, and 16), 12 of them region-specific (Hap 1, 2, 3, 4, 5, 6, 9, 11, 12, 13, 14, and 16). Geographically, haplotypes 10 and 17 are widely distributed in the Northern Hemisphere, while haplotype 8 is only found in samples from South Asia, Tibet, and a single sample from Qinghai in East Asia (Fig. 5 A). A maximum likelihood (ML) tree derived from whole-chloroplast genome sequences revealed a polytomy of three clades: the first contained four haplotypes (Hap 1, 2, 3, and 4) from South Asia, the second contained four haplotypes (Hap 5, 6, 7, and 8) from South Asia and Tibet, and the third contained 11 haplotypes from South Asia and other parts of the range (Hap 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, and 19) (Fig. 5 B). Of the haplotypes in the third clade, haplotypes 9, 11, 12, 13, 14, and 16 were all from South Asia. A Neighbor-Joining (NJ) tree from the same data also shows a polytomy of three main clades (Fig. 5 C). Cross-population selection signatures and candidate genes under positive selection As described above, we inferred five derived populations and one source population (South Asia). Given that walnut domestication likely occurred within the last ten thousand years (~ 200 generations), we applied the sensitive haplotype-based XP-EHH method (Sabeti et al. 2007 ), as implemented in selscan v.2.0.3 (Szpiech and Hernandez 2014 ; Szpiech 2024 ), to detect signatures of positive selection associated with domestication. After stringent filtering, we identified 45 genes under positive selection by intersecting significant SNPs showing consistent directional allele frequency changes across all derived populations relative to the source population, located within genes or in flanking regions (± 5 kb) (Methods, Supplementary Table S4). Among 29 functionally annotated candidates, three genes stood out as prime domestication candidates: JreChr11G12281 encodes the LRR receptor-like kinase FEI2, involved in cell wall remodeling; JreChr01G11061 encodes a pectinesterase implicated in pollen tube growth and fruit softening; and JreChr06G11221 encodes the ABC transporter ABCG7, which participates in lipid transport and likely contributes to kernel composition. Notably, two non-synonymous SNPs in the uncharacterized gene JreChr03G10963 show relatively pronounced allele frequency shifts—from 0.5937 and 0.7187 in the source population to near fixation in all derived populations—highlighting the possibility that currently unannotated genes may also contribute to domestication (Supplementary Table S4). Discussion Addressing unbalanced sample sizes and unequal population sizes in STRUCTURE analyses The use of clustering algorithms such as STRUCTURE in crop domestication studies is often challenged by two critical and distinct issues: unbalanced sample sizes and unequal effective population sizes ( N e ) among populations. While the effects of unbalanced sampling have been extensively discussed (Kalinowski 2011 ; Puechmaille 2016 ; Wang 2017 ), the impact of unequal effective population size ( N e ) among source populations has received comparatively little attention. This oversight is particularly consequential in the context of crop domestication, where ancestral populations often maintain large N e , while derived populations frequently experience bottlenecks and founder effects. The F model (the correlated allele frequency model) of STRUCTURE introduced by Falush et al. ( 2003 ) accounts for differences in N e by allowing population-specific genetic drift ( F k ) away from the ancestral allele frequencies. Although this model is the default setting in STRUCTURE now, it has rarely been explicitly employed in domestication studies for the purpose of addressing among-population differences in effective population size. In contrast, biases introduced by unbalanced sample sizes have received more targeted methodological attention. Wang ( 2017 ) proposed a strategy that combines an alternative ancestry prior, a small ALPHA value, and the uncorrelated allele frequency model to reduce misclassification caused by sampling imbalance. However, because the uncorrelated allele frequency model simply assumes allele frequencies across populations are independent, it is prone to erroneously classify populations with low drift levels (or large N e )—and thus preserved ancestral diversity—as admixed from populations that underwent stronger drift. Consequently, this approach alone is insufficient under conditions where both sample size and population size are unbalanced, leading to the misclassification of the ancestral population—such as the South Asian group in our study—as admixed with derived populations (Supplementary Fig. S1 ). To jointly address these two challenges, we propose an integrated parameterization strategy that combines Wang’s ( 2017 ) ancestry prior and ALPHA adjustment with the F -model's correlated allele frequency framework, enabling accurate identification of the ancestral population. This approach recovered South Asia as the ancestral origin of Juglans regia (Fig. 1 A, D-E; Supplementary Fig. S1 ). By simultaneously correcting for sampling imbalance and unequal N e , our strategy offers a robust and broadly applicable solution in study systems where unbalanced sample sizes and unequal effective population size among populations co-occur. Beyond STRUCTURE, other widely used clustering algorithms, such as ADMIXTURE (Alexander et al. 2009 ), face similar limitations. ADMIXTURE does not incorporate any mechanisms to address either unbalanced sampling or unequal population sizes, making it particularly vulnerable to erroneous inference in complex demographic settings (Alexander et al. 2009 ). The latest clustering algorithm, PopCluster (Wang 2022 ; Wang 2025 ), explicitly takes sampling imbalance into consideration through a weighted likelihood framework, but it so far lacks a mechanism to explicitly accommodate unequal population size. South Asia as the region where the common walnut was first domesticated The geographic origin of the common walnut ( Juglans regia ) has long been debated, largely due to limitations in earlier population-genetic studies that failed to account for demographic heterogeneity and botanical collecting imbalance, with entire relevant regions underrepresented. With an optimized STRUCTURE-based framework and multiple lines of genomic evidence, including from previously geographically under-sampled regions, our study now consistently identifies South Asia (western Himalayas and adjacent areas) as the initial center of walnut domestication. First, the nuclear genomic structure reveals that South Asian walnuts have the lowest F k value (i.e., low level of genetic drift), indicative of their ancestral status. Second, a Neighbor-Joining (NJ) tree based on nuclear genomic data places individuals from South Asia at basal phylogenetic positions (Fig. 1 B), consistent with early divergence. Third, this region harbors the highest number of private alleles and exhibits elevated heterozygosity among six groups (Fig. 2 D), reflecting genetic distinctiveness. Fourth, walnuts from South Asia exhibited the lowest mutation load (Fig. 3 B, C). Interestingly, when the Tibetan (Zhang et al. 2020 ) or Chandler 2.0 (Marrano et al. 2020 ) reference genomes were used for mutation load estimation, the lowest loads were observed in the Tibetan and European populations, respectively, rather than in South Asia. This discrepancy likely reflects reference bias, as reference genomes derived from bottlenecked populations with genomic erosion can substantially underestimate mutation load in those populations (Dussex et al. 2023 ). Fifth, demographic reconstruction indicates that the South Asian population has maintained a relatively stable effective population size, in contrast to repeated bottlenecks inferred in other regions (Fig. 4 A). Sixth and finally, chloroplast genome analysis reveals the highest diversity of haplotypes in South Asia (Fig. 5 ), suggesting deep historical lineage retention. Together, these six genomic patterns robustly support South Asia as the primary center of walnut domestication, aligning with previous insights from Aradhya et al. ( 2017 ), Roor et al. ( 2017 ), and Yan et al. ( 2024 ), while rejecting the Irano-Anatolian region of West Asia (Zohary et al. 2012 ; Ding et al. 2022 ) and the Tianshan Mountains of Central Asia (Molnar et al. 2011 ; Mapelli et al. 2016 ; Pollegioni et al. 2017 ). Based on our simulations, we speculate that the latter inferences were affected by geographically-biased sampling and unaccounted differences in N e . Clearly, the nuclear genomes of the West and Central Asian populations are not basal (Fig. 1 B), exhibit fewer private alleles, and have higher mutation loads compared to South Asian populations (Fig. 2 B–D; Fig. 3 B, C), suggesting reduced genetic distinctiveness and historical bottlenecks (Fig. 4 A). Chloroplast data further reveal fewer haplotypes in Central (3) and Western (2) Asia compared to South Asia (above). The GONE and STRUCTURE analyses revealed a large and stable effective population size in South Asia (Fig. 1 A; Fig. 4 A), providing partial support for the notion that the ‘domestication bottleneck’ may be a problematic concept (Allaby et al. 2022 )—particularly in woody perennial crops (Miller and Gross 2011 ; Gaut et al. 2015 ; Gaut et al. 2018 ; Allaby et al. 2022 ). In contrast, the significant bottlenecks observed in the five derived populations are more plausibly attributed to founder effects. Fossil pollen records match an initial domestication in South Asia. In Central and Western Asia, the earliest walnut pollen records date to the Holocene (Beer et al. 2008 ) with anthropogenic origins in Kyrgyzstan (2000 years BP) and Juglans pollen in northern Iran (2300–2350 years BP) (Ramezani et al. 2016 ). In contrast, South Asian records from Nepal and India indicate a longer presence, dating back to 18,000 and 30,000 years BP, respectively, suggesting refugia during the Last Glacial Maximum (Kotlia et al. 2000 ). The geographic spread of Juglans regia from its South Asian center of domestication Following domestication in South Asia, walnuts dispersed across the broader Eurasian landscape, shaping the present-day genetic structure of the species (Fig. 6 ). There is a deep genetic differentiation between eastern and western lineages (Roor et al. 2017 ; Ding et al. 2022 ; Kairova et al. 2025 ), a pattern previously interpreted as evidence of a Central Asian origin, particularly within the Irano-Anatolian region (Mapelli et al. 2016 ; Pollegioni et al. 2017 ; Ding et al. 2022 ). However, our new results, informed by expanded geographic sampling and improved STRUCTURE settings accounting for sampling and demographic biases, reveal that domesticated walnuts all trace their ancestry to South Asia. From South Asia, the domesticated form expanded eastward, as evidenced by the NJ tree (with Central Asia basal to East Asia within the same clade) and supported by TreeMix clustering results (Fig. 1 B; Fig. 2 F; Fig. 6 ). In Asia, domesticated walnuts underwent two sequential bottlenecks: the first during their human-mediated transfer out of South Asia and the second during their subsequent expansion into East Asia. Our GONE analysis of simulated data corroborates the occurrence of these bottleneck events (Fig. 4 B, C). There is a signal of gene flow from East Asia back into the South Asian source population (Fig. 2 F), possibly reflecting the deliberate reintroduction of desirable genotypes by humans. However, additional evidence is required to confirm this. Among the five derived groups, the East Asian population exhibits the highest proportion of private alleles (Fig. 2 D), likely due to introgression from Juglans sigillata , a species native to southwestern China that has contributed genetic material to eastern J. regia (Ding et al. 2022 ; Yan et al. 2024 ). Tibetan walnuts form a distinct lineage and may have been introduced from South Asia via the Southern Inner-Plateau Route (Fig. 6 ) (Zhao et al. 2023b ). In ancient Chinese, walnuts are sometimes called K’ang t’ao (meaning Tibetan walnut), with K’ang referring to Tibet (Laufer 1919 ). Westward dispersal occurred stepwise from South Asia through West Asia before reaching Europe, also involving two sequential bottlenecks: first during the transfer from South Asia to West Asia, and second during expansion into Europe (Fig. 6 ). Our GONE analysis corroborates the occurrence of these bottleneck events (Fig. 4 ). Notably, both F k values and the drift parameter from TreeMix (Fig. 2 F) consistently indicate stronger genetic drift in Europe compared to West Asia, suggesting a more pronounced reduction in genetic diversity during westward dispersal. These successive bottlenecks likely contributed to the low genetic diversity observed in modern European walnut populations. Identifying candidate domestication genes via XP-EHH Our demographic analyses position South Asia as the domestication center, with five derived populations experiencing severe bottlenecks during their geographic expansion. The XP-EHH approach (Sabeti et al. 2007 ) proved optimal for detecting recent selective sweeps in this context because it requires smaller sample sizes (n ≥ 10) than iHS (n ≥ 100), accommodates unphased genotypes, and effectively captures haplotype homozygosity differentials on very short time scales (Szpiech 2024 ). The observed allele frequency trajectories—moderate in source populations but nearly fixed in derived groups—support a multi-phase domestication model with recurrent selection (Gaut et al. 2018 ), mirroring patterns in rice (Jing et al. 2023 ), maize (Yang et al. 2023 ), and adzuki bean (Chien et al. 2025 ). Although XP-EHH is often interpreted as detecting positive selection in only one of the two compared populations (or in the present context, the derived population), it fundamentally measures differences in extended haplotype homozygosity between populations (Abondio et al. 2022 ), and thus captures differential selection intensities. In other words, the genes identified by XP-EHH might have already experienced a certain degree of human selection in the source population. It is also important to consider that some of the signals detected may reflect the effects of strong genetic drift following bottlenecks in the derived populations, rather than—or in addition to—artificial selection. Among the candidate loci, three annotated genes—JreChr11G12281, JreChr01G11061, and JreChr06G11221—are likely involved in domestication-related traits (Sharma and Kumar 1994 ; Khanal et al. 2023 ) (Supplementary Table S4). These genes participate in cell wall remodeling (Xu et al. 2008 ), pollen development (Leroux et al. 2015 ), and lipid transport (Borghi et al. 2019 ), respectively, and may have been targeted by selection for thinner shells, enhanced fertility, or increased oil accumulation—hallmark traits of cultivated walnut. Although these inferences rely on functional homology with Arabidopsis, they provide plausible hypotheses for future validation. In addition, strong selection signals were also observed in unannotated genes, such as JreChr03G10963, which contains non-synonymous SNPs with large allele frequency shifts (Supplementary Table S4). This suggests that currently uncharacterized loci may also contribute to domestication. Further studies integrating expression profiling, functional assays, and AI-based annotation will be essential to validate the roles of both known and novel genes in shaping key agronomic traits. Conclusion Running STRUCTURE is only a starting point; reliable inference requires proper use of the software and validation of the results with other kinds of data, such as ecological or fossil data. In this study, we show through simulations that an optimized STRUCTURE framework—combining the F -model with alternative ancestry priors—can correct for biases from unequal population sizes and sampling imbalance, two major challenges in the study of domestication. Applying this strategy to Juglans regia , we identify South Asia as the center of domestication, supported by multiple lines of nuclear- and chloroplast-genomic evidence and demographic stability, and matching fossil pollen and nut shell remains. This correct identification of source and derived populations facilitates the detection of candidate genes under positive selection via sensitive cross-population comparisons. These findings resolve a long-standing debate on walnut origins and underscore the importance of model-aware clustering in evolutionary inference. The utility of the approach proposed here extends beyond crop domestication to a range of biological contexts that involve complex demography, such as ancient DNA analyses, post-glacial recolonization, and conservation genomics of fragmented or endangered taxa. Methods Sampling and sequencing We collected 33 mature J. regia individuals from Europe (3), Xinjiang (2), Yunnan (3), Beijing (2) and Tibet (23). Genomic DNA was extracted from dried leaf tissue using a plant total genomic DNA kit (Tiangen, Beijing, China) and was then sequenced using paired-end libraries with an insert size of 350 bp on Illumina HiSeq X-ten instruments by NovoGene (Beijing, China), with read lengths of 150 bp. Samples were sequenced to an average depth of 30×. Additionally, we downloaded whole-genome resequencing data of J. regia from various studies: Ji et al. ( 2021 ) (209 individuals), Luo et al. ( 2022 ) (49 individuals), Qi et al. ( 2023 ) (6 individuals), Steven et al. (2018) (20 individuals), Li et al. ( 2024 ) (5 individuals), Zhang et al. ( 2019 ) (6 individuals), Zhang et al. ( 2022 ) (29 individuals), and Ding et al. ( 2022 ) (42 J. regia individuals). These datasets encompass individuals from North America, Europe, Central Asia, Western Asia, South Asia, and East Asia, with an average depth higher than 10×. Mapping and variant calling for the nuclear genomes Raw reads from 399 J. regia individuals were trimmed of adapters and low-quality sequences using Trimmomatic v0.32 (Bolger et al. 2014 ) and then aligned to the J. regia reference genome (Zhang et al. 2020 ) using the BWA-MEM algorithm of BWA v0.7.15 (Li and Durbin 2009 ). Only uniquely mapped and properly paired reads were retained. SAMtools v1.19 (Li 2011 ) was used to convert SAM files to BAM format and remove PCR duplicates. Indel realignment and SNP calling were conducted using SENTIEON DNAseq software package v202308 (Weber et al. 2016 ) and SNPs were aggregated across samples. Stringent SNP filtration was applied via GATK's VariantFiltration (McKenna et al. 2010 ), using criteria including “QD > 2.0, QUAL > 30.0, SOR < 3.0, FS 40.0, MQRankSum >-12.5, and ReadPosRankSum >-8.0”. We excluded SNPs with mapping depths outside one-third to triple the individual’s average, non-biallelic sites, and those with missing data. Heterozygous genotypes were determined by the proportion of non-reference alleles, set at 20–80% for depths exceeding three times the average, and 10–90% for depths at least one-third of the average; all others were classified as homozygous. To minimize the introgression from J. sigillata , we excluded 48 samples with over 10% genetic contribution from this species, as determined by STRUCTURE v2.3.4 (Pritchard et al. 2000 ), reducing the dataset to 351 individuals. To ensure genealogical independence, we used King v.2.2.7 (Manichaikul et al. 2010 ) to identify related individuals, excluding one from each pair with a kinship coefficient exceeding 0.0442 (indicative of third-degree relations), favoring those with higher sequencing depths. This led to the exclusion of 53 J. regia individuals from Europe (2), East Asia (44), and North America (7), yielding a final dataset of 298 individuals for STRUCTURE analysis, PCA, and a Neighbor-Joining tree construction. To obtain neutral and independent SNPs, we excluded SNPs located within coding sequences and their 3-kb flanking regions, following Zhao et al. ( 2023a ). We further thinned the SNPs using a distance filter of greater than 20 kb between consecutive SNPs and removed singletons to minimize false positives due to sequencing errors, resulting in a data set of 14,950 SNPs for population structure analysis. Population structure and phylogenetic analysis To investigate the population structure of the 298 individuals, we performed principal component analysis (PCA) using the R package SNPRelate v1.6.2 (Zheng et al. 2012 ) with default settings. Additionally, we used STRUCTURE v2.3.4 (Pritchard et al. 2000 ) to cluster individuals based on the number of clusters ( K ) ranging from 1 to 8. Clustering was conducted under the admixture model with two distinct parameter settings: the first (ParamSet1) used the alternative ancestry prior (POPALPHA = 1) with a small ALPHA value (ALPHA = 0.25) and the correlated allele frequency model ( F model, FREOSCORR = 1), while the second (ParamSet2) used the alternative ancestry prior (POPALPHA = 1) with a small ALPHA value (0.25) and the uncorrelated allele frequency model (FREOSCORR = 0). Each parameter setting was run with 100,000 burn-in steps followed by 500,000 Markov Chain Monte Carlo (MCMC) steps, and 20 replicate runs were conducted for each value of K to assess the variation in likelihood. The optimal number of clusters ( K ) was determined using three criteria: Ln (D|K), the final posterior probability of K (Pritchard et al. 2000 ); Delta K , the rate of change in Ln (D|K) between successive K values (Evanno et al. 2005 ); and KFinder v1.0, based on the parsimony index (PI) proposed by Wang ( 2019 ). Additionally, we incorporated one individual each from J. nigra and J. mandshurica as outgroups. Using MEGA (Stecher et al. 2020 ), we constructed a Neighbor-Joining (NJ) tree based on the best-fit substitution model selected by the software and validated with 1,000 bootstrap replicates. To simplify the evaluation of how two STRUCTURE parameter settings (see above) influence the accuracy of ancestral population inference under a domestication scenario, we randomly selected two populations (West Asia and Tibet) from the five derived groups to represent the derived lineages. These two, together with the South Asian ancestral population, formed a three-population subset used for comparative analyses based on both empirical and simulated datasets. The sample sizes and the number of SNPs used were consistent with those in the empirical dataset representing three populations: Population A (South Asia), Population B (Tibet), and Population C (West Asia). Although the simulations were performed before demographic inference, the population history scenarios were set according to demographic history later inferred from GONE (see below). This ensured consistency between the simulated and empirical data. The simulated SNP datasets were analyzed using STRUCTURE under two parameter settings: ParamSet1 and ParamSet2. These simulations allowed us to assess the robustness of ancestry inference under alternative model assumptions informed by plausible demographic scenarios. Genetic diversity and differentiation analysis We used VCFtools v0.1.17 (Danecek et al. 2011 ) to calculate a suite of genetic diversity metrics based on datasets filtered to remove missing data. The analyses included linkage disequilibrium (LD) decay, nucleotide diversity (π), heterozygosity, genetic differentiation ( F ST ), absolute genetic divergence ( D XY ), and proportions of private SNPs. These metrics were assessed across six genetic groups defined by STRUCTURE and PCA analyses: Europe (26 individuals), West Asia (51 individuals), Central Asia (48 individuals), Tibet (40 individuals), East Asia (73 individuals), and South Asia (16 individuals). To account for sample size differences, we performed 20 replicates for each group by randomly subsampling 16 individuals per replicate. p -values were derived using t-tests comparing each group to the South Asia group. Significance levels are indicated as “***” p < 0.001, “**” p < 0.01, “*” p 0.05. Gene flow among populations was inferred using the OrientAGraph approach (Molloy et al. 2021 ), which optimizes Maximum Likelihood Network Orientation (MLNO) within the TreeMix framework (Pickrell and Pritchard 2012 ). Allele frequencies for the six groups (Central Asia, East Asia, Europe, Tibet, South Asia, and West Asia) were derived from STRUCTURE-based gene pools. Runs of homozygosity and mutation load of six groups To identify runs of homozygosity (ROH), we first converted the six populations’ filtered multi-individual vcf file into a .ped file and identified ROH in PLINK v.1.9 (Purcell et al. 2007 ). To assess the robustness of our results to the applied parameters and to potential sequencing errors, we used three sets of parameters where we varied the window size (homozyg-window-snp) and the number of heterozygous sites per window (homozyg-window-het): (1) homozyg-window-snp 100 and homozyg-window-het 1; (2) homozyg-window-snp 250 and homozyg-window-het 3 (reported in main text in Fig. 4 A); (3) homozyg-window-snp 500 and homozyg-window-het 5. All other parameters described hereafter were the same for each of the three parameter sets. If at least 5% of all windows that included a given SNP were defined as homozygous, the SNP was defined as being in a homozygous segment of a chromosome (homozyg-window-threshold 0.05). This threshold was chosen to ensure that the edges of a ROH are properly delimited. A homozygous segment was then defined as a ROH if all of the following conditions were met: the segment included ≥ 25 SNPs (homozyg-snp 25); the segment covered ≥ 100 kb (homozyg-kb 100); the minimum SNP density was one SNP per 50 kb (homozyg-density 50); the maximum distance between two neighbouring SNPs was ≤ 1,000 kb (homozyg-gap 1,000); the number of heterozygous sites within ROH was set to 750 (homozyg-het 750) in order to prevent sequencing errors from breaking ROH. We then calculated individual inbreeding coefficients ( F ROH ) (Kardos et al. 2015 ) by summing the proportion of the genome covered by ROHs (total length of ROHs/total length of genome assembly). p -values were derived using t-tests comparing each group to the South Asia group. Significance levels are indicated as “***” p < 0.001, “**” p < 0.01, “*” p 0.05. When calculating mutation load, if mapping to the Tibetan reference genome (Zhang et al. 2020 ), the Tibetan population exhibited the lowest ratio of derived deleterious and loss-of-function (LoF) variants to synonymous variants. Moreover, when mapping to the Chandler 2.0 reference genome (Marrano et al. 2020 ), which originated from France (Beede et al. 1998 ), the European population showed the lowest ratios (Supplementary Fig. S3). As Dussex et al. ( 2023 ) explicitly stated, using a reference genome that has suffered from genomic erosion (i.e., genetic threats to small populations) in a bottlenecked population can significantly underestimate genetic load of that population. This effect is corroborated by our results; to mitigate this bias, we followed Dussex et al. ( 2023 ) by using the genome of Juglans sigillata (Ning et al. 2025 ) as the reference for mutation load estimation. The effect of SNP variants on protein-coding gene sequences were further annotated and classified into loss-of-function (LoF), missense, and synonymous variants using SnpEff v5.0 (Cingolani et al. 2012 ). LoF variant denote those with gain and/or loss of a stop codon, or those with loss of a start codon. Missense SNPs were further predicted as deleterious (score ≤ 0.05) based on the SIFT score computed by the program SIFT 4G (Vaser et al. 2016 ). At each SNP position, we determined the derived versus ancestral allelic state using the est-sfs software through comparison with J. mandshurica and J. nigra sequences. The total derived alleles for LoF, deleterious and synonymous variants were estimated for each individual. p -values were derived using t-tests comparing each group to South Asia group. Significance levels are indicated as “***” p < 0.001, “**” p < 0.01, “*” p 0.05. Population demographic analysis To infer changes in effective population sizes ( N e ), we used GONE (Santiago et al. 2020 ) to analyzed six groups identified through STRUCTURE and PCA analysis: Central Asia, East Asia, Europe, South Asia, Tibet, and West Asia. We assumed a constant rate of recombination of 2.63 cM/Mb for the whole genome (Ding et al. 2023 ) and excluded LD data with recombination rates > 0.05 to reduce the effect of sampling on the estimates as well as artefacts from recent migrants, as recommended in the GONE User’s Guide. We performed 20 replicate analyses, each including 50,000 SNPs sampled randomly from each chromosome. To further investigate the impact of population bottlenecks on demographic inference, we simulated SNP data under the demographic models using Fastsimcoal2 (Excoffier and Foll 2011 ) based primarily on the empirical data from GONE: (1) a source population model without bottleneck (population A, represents South Asia), (2) a single bottleneck model (population C, represents West Asia; population E, represents Central Asia), and (3) a model incorporating two successive bottlenecks (population B, represents Tibet; population D, represents Europe; population F, represents East Asia). Parameter values for these models—including divergence times, historical N e changes, and bottleneck intensities—were informed by empirical estimates obtained from the GONE analysis of real data. Simulations were performed using the chromosome sizes of the Juglans regia reference genome (Zhang et al. 2020 ), assuming a mutation rate of 1.03 × 10⁻⁷ per site per generation (with 50 years per generation) and a recombination rate of 2.06 cM/Mb (Ding et al. 2023 ). The demographic model begins with an ancestral wild population ( N e = 10,000) that did not experience a bottleneck but reflects population changes associated with the initial phase of domestication. During this process, the effective population size decreased from N e = 10,000 to N e = 4,000, representing the transition to a managed population. This was followed by stabilization at N e = 2,000 (defined as population A), representing the core domesticated lineage. Population A then served as the source for all subsequent derived populations. Each subpopulation diverged from population A at specified time points and experienced distinct demographic trajectories. Population B (Tibet) split from A 100 generations ago and underwent a severe bottleneck, with effective population size reduced to 10, followed by partial recovery to 50 by 10 generations ago (sample size n = 40). Population C (West Asia) diverged 80 generations ago with an initial effective size of 50, expanding to 200 by 20 generations ago (n = 50). Similarly, population D (Europe) diverged 40 generations ago with an initial size of 20, increasing to 100 by 20 generations ago (n = 20). Population E (Central Asia) followed a trajectory analogous to that of population C, while population F (East Asia) diverged 40 generations ago with an initial size of 25, expanding to 100 by 20 generations ago (both with n = 50). Simulated datasets were subsequently analyzed using GONE with parameters set to hc = 0.05 and REPs = 40. Each scenario was replicated 20 times, and the geometric mean across replicates was taken as the final estimate of effective population size dynamics. Chloroplast genome analysis For the chloroplast analysis, we excluded 48 individuals with over 10% genetic contribution from J. sigillata from 399 samples based on STRUCTURE results from the nuclear data (below), and a total of 351 samples remained. We processed reads from the 351 J. regia individuals using Trimmomatic v0.32 (Bolger et al. 2014 ) to trim adapters and low-quality sequences. The cleaned reads of the 351 individuals were then aligned to the J. regia chloroplast genome (NC_028617.1) using the BWA-MEM algorithm of BWA v0.7.15 (Li and Durbin 2009 ). Variant calling was performed with SAMtools v1.19 (Li 2011 ), and the identified SNPs were formatted into the Variant Call Format (VCF). We distinguished plastid from nuclear sequences by accepting bases at positions where coverage exceeded five-fold the average of the nuclear genome and consensus was achieved in over 90% of reads. Positions not meeting these criteria were designated as missing data, and indels were excluded. In addition to the 351 chloroplast samples, seven chloroplast genomes from Yan et al. ( 2024 ) were included, resulting in a total of 358 chloroplast genomes of J. regia being obtained. We included chloroplast genomes of J. nigra (NC_035967.1) and J. mandshurica (NC_033892.1) as outgroups. Sequence alignment was conducted using MAFFT v7.475 (Katoh and Standley 2013 ). We constructed a Maximum Likelihood (ML) tree using IQ-TREE 2 (Minh et al. 2020 ), employing the ModelFinder Plus method and performing 1,000 bootstrap replicates. Simultaneously, a Neighbor-Joining (NJ) tree was generated using MEGA (Stecher et al. 2020 ), which used the best-fit substitution model selected by the software, also with 1,000 bootstrap replicates. Additionally, haplotypes were identified using DnaSP v6, including sites with two nucleotide types or two plus N (Rozas et al. 2017 ). Cross-population selection signatures and candidate genes under positive selection We employed the cross-population extended haplotype homozygosity (XP-EHH) method to detect signals of positive selection. XP-EHH scores were calculated using selscan (v2.0.3) (Szpiech and Hernandez 2014 ; Szpiech 2024 ), with each of the five derived populations independently compared to the South Asian population as the reference. The calculation followed the methodology described by Sabeti et al. ( 2007 ). The analysis was performed using the following parameters: --xpehh to specify XP-EHH calculation; --unphased to allow the use of unphased genotype data; --vcf and --vcf-ref to input VCF files for the test and reference populations, respectively; and –pmap to enable physical map-based computations. A maximum inter-SNP distance of 200 kb was set using --max-gap to reduce artifacts caused by long-range linkage disequilibrium due to missing data. Rare variants were filtered out using a minor allele frequency threshold of 0.05 (--maf 0.05). As the XP-EHH statistic approximately follows a normal distribution (Sabeti et al. 2007 ), we normalized the raw XP-EHH scores using the “norm" parameter in selscan. Significant SNPs were identified based on a normalized XP-EHH score threshold (normxpehh value ≥ 2) for each test population relative to the South Asian reference. We then intersected the significant SNPs across all five derived populations, yielding a set of shared loci. Allele frequencies of these SNPs were subsequently calculated across all six populations. We retained SNPs showing a consistent directional shift in allele frequency in all five derived populations relative to the South Asian reference (e.g., all increased or all decreased). Further filtering required these SNPs to be located within genes or within 5 kb upstream or downstream of gene boundaries. Gene function was annotated using eggNOG-mapper (v2.1.9) (Cantalapiedra et al. 2021 ) in combination with UniProt to obtain GO terms, KEGG pathways, and functional descriptions. Declarations Author Contribution W.N.B. and D.Y.Z. conceived and supervised the project; W.P.Z., A.N., and J.L. collected materials; C.J.C. and Y.Y. performed the analyses of chloroplast genome, C.J.C. and Y.M.D. performed the nuclear genome analyses, X.X.P. and C.J.C. conducted simulation analyses, W.N.B., C.J.C., S.S.R. wrote the paper; S.S.R., W.N.B., C.J.C., B.W.Z., and D.Y.Z. revised and proofed the paper. All authors approved the final version. Acknowledgments This work was supported by the National Natural Science Foundation of China (32370230), the “111” Program of Introducing Talents of Discipline to Universities (B13008), the Fundamental Research Funds for the Central Universities, and China Postdoctoral Science Foundation (GZB20240286 to XXP). 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Supplementary Files ChenCaiJinWalnutdomesticationSuppinfo.docx ChenCaiJinWalnutdomesticationSuppTable.xlsx Cite Share Download PDF Status: Published Journal Publication published 30 Jan, 2026 Read the published version in Genome Biology → Version 1 posted Editorial decision: Revision requested 19 Sep, 2025 Reviews received at journal 18 Sep, 2025 Reviews received at journal 26 Aug, 2025 Reviews received at journal 07 Aug, 2025 Reviewers agreed at journal 04 Aug, 2025 Reviewers agreed at journal 04 Aug, 2025 Reviewers agreed at journal 30 Jul, 2025 Reviewers invited by journal 30 Jul, 2025 Editor assigned by journal 21 Jul, 2025 Submission checks completed at journal 16 Jul, 2025 First submitted to journal 15 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7127779","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":494382194,"identity":"6670427b-dee2-4cb1-a7a8-2e3926cc83ce","order_by":0,"name":"Cai-Jin Chen","email":"","orcid":"","institution":"Beijing Normal University","correspondingAuthor":false,"prefix":"","firstName":"Cai-Jin","middleName":"","lastName":"Chen","suffix":""},{"id":494382196,"identity":"dc52b3d4-c73a-4d0e-9249-80bf885e42d1","order_by":1,"name":"Xiao-Xu Pang","email":"","orcid":"","institution":"Beijing Normal University","correspondingAuthor":false,"prefix":"","firstName":"Xiao-Xu","middleName":"","lastName":"Pang","suffix":""},{"id":494382197,"identity":"cc5149e4-7afa-4f59-86c6-c7ddf665d201","order_by":2,"name":"Ya-Mei Ding","email":"","orcid":"","institution":"Beijing Normal University","correspondingAuthor":false,"prefix":"","firstName":"Ya-Mei","middleName":"","lastName":"Ding","suffix":""},{"id":494382202,"identity":"f9e829d3-cb5d-47e2-b0d1-0aee7a61e029","order_by":3,"name":"Wei-Ping Zhang","email":"","orcid":"","institution":"Beijing Normal University","correspondingAuthor":false,"prefix":"","firstName":"Wei-Ping","middleName":"","lastName":"Zhang","suffix":""},{"id":494382203,"identity":"804ca774-5c13-4130-bfed-883407487e85","order_by":4,"name":"Yang Yang","email":"","orcid":"","institution":"Beijing Normal University","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Yang","suffix":""},{"id":494382204,"identity":"7f8ce673-d936-4102-8bd1-768517610507","order_by":5,"name":"Jie Liu","email":"","orcid":"","institution":"Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Liu","suffix":""},{"id":494382205,"identity":"53bed429-e936-40c1-87ee-e4a7fa37c0ca","order_by":6,"name":"Anush Nersesyan","email":"","orcid":"","institution":"A. 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The y-axis represents the \u003cem\u003eq\u003c/em\u003e value, and the x-axis shows each individual. Color coding: Europe (EUR, blue), West Asia (WA, yellow), Central Asia (CA, orange), East Asia (EA, pink), Tibet (TIB, green), and South Asia (SA, purple). Color coding: Europe (blue, \u003cem\u003eq\u003c/em\u003e \u0026gt; 0.75), West Asia (yellow, \u003cem\u003eq\u003c/em\u003e \u0026gt; 0.75), Central Asia (orange, \u003cem\u003eq\u003c/em\u003e \u0026gt; 0.75), East Asia (pink, \u003cem\u003eq\u003c/em\u003e \u0026gt; 0.75), Tibet (green, \u003cem\u003eq\u003c/em\u003e \u0026gt; 0.75), South Asia (purple), and black (indicating admixture groups). (\u003cem\u003eB\u003c/em\u003e) A phylogeny of 298 unrelated individuals inferred using Neighbor-Joining (NJ) tree and rooted on \u003cem\u003eJ. mandshurica\u003c/em\u003e and \u003cem\u003eJ. nigra\u003c/em\u003e. Three major clades were identified: Clade 1 (green bar), Clade 2 (pink bar), and Clade 3 (orange bar). Clades with bootstrap values \u0026lt; 70 were collapsed. (\u003cem\u003eC\u003c/em\u003e) Schematic representation of the models used to simulate SNP data with fastsimcoal2. PopA, popB, and popC represent simulated populations. (\u003cem\u003eD\u003c/em\u003e) STRUCTURE clusters obtained from real data under two model settings (ParamSet1: POPALPHA = 1, ALPHA = 0.25 with \u003cem\u003eF\u003c/em\u003e model (the correlated allele frequency model) and ParamSet2: POPALPHA = 1, ALPHA = 0.25 with theuncorrelated allele frequency model) for three populations (107 individuals including 16 South Asian, 51 West Asian, and 40 Tibetan) at \u003cem\u003eK \u003c/em\u003e= 2 and \u003cem\u003eK \u003c/em\u003e= 3. (\u003cem\u003eE\u003c/em\u003e) STRUCTURE clusters obtained from simulated data and analyzed with two structure parameter settings for popA, popB, and popC (with sample sizes and SNP quantities matching the real dataset) at \u003cem\u003eK \u003c/em\u003e= 2 and \u003cem\u003eK \u003c/em\u003e= 3.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7127779/v1/a059df39dd6af99d57a9765a.png"},{"id":88228265,"identity":"b486bedc-6840-4a24-a86b-077e1b3cd10f","added_by":"auto","created_at":"2025-08-04 09:07:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":254512,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGenetic diversity and differentiation analysis of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eJuglans regia\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e across its geographic range.\u003c/strong\u003e (\u003cem\u003eA\u003c/em\u003e) Linkage disequilibrium (LD) decay. (\u003cem\u003eB\u003c/em\u003e) Nucleotide diversity (π). (\u003cem\u003eC\u003c/em\u003e) Heterozygosity. (\u003cem\u003eD\u003c/em\u003e) Proportions of private SNPs. (\u003cem\u003eE\u003c/em\u003e) Matrix of relative (\u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e) and absolute (\u003cem\u003eD\u003c/em\u003e\u003csub\u003eXY\u003c/sub\u003e) divergence for pairwise groups comparisons (Upper triangle: \u003cem\u003eD\u003c/em\u003e\u003csub\u003eXY\u003c/sub\u003e; Lower triangle: \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e). (\u003cem\u003eF\u003c/em\u003e) Population relationship and migration among populations inferred by OrientAGraph, which incorporates an exhaustive search for Maximum Likelihood Network Orientation (MLNO) into TreeMix. Allele frequencies for Central Asia, East Asia, Europe, Tibet, West Asia and South Asia were obtained from the STRUCTURE-inferred gene pools. The orange arrow indicates an inferred migration event from the source (here East Asia) to the recipient population (South Asia). For panels B, C, and D, the boxplots indicate the minimum (the lower hinge), maximum (the upper hinge), and median (the middle hinge). \u003cem\u003ep\u003c/em\u003e-values were derived using t-tests comparing each group to South Asia group. Significance levels are indicated as “****” \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001, “***” \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, “**” \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, “*” \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, “ns” \u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7127779/v1/5df705f81ba81f14bb94026d.png"},{"id":88228263,"identity":"78130465-06f8-4c5f-a711-5c2e067c7e56","added_by":"auto","created_at":"2025-08-04 09:07:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":117510,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInbreeding, mutational load, and loss-of-function (LoF) variants were analyzed in six groups of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eJuglans regia\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e.\u003c/strong\u003e (\u003cem\u003eA\u003c/em\u003e) Inbreeding coefficients were estimated as the average proportion of the genome in runs of homozygosity (\u003cem\u003eF\u003c/em\u003e\u003csub\u003eROH\u003c/sub\u003e). (\u003cem\u003eB\u003c/em\u003e) The ratios of derived deleterious variants to synonymous variants were calculated for each individual. (\u003cem\u003eC\u003c/em\u003e) The ratios of derived LoF variants to synonymous variants were calculated per individual. All the boxplots indicate the minimum (the lower hinge), maximum (the upper hinge), and median (the middle hinge). \u003cem\u003ep\u003c/em\u003e-values were derived using t-tests comparing each group to South Asia group. Significance levels are indicated as “***” \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, “**” \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, “*” \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, “ns” \u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7127779/v1/32232eeeac3997ee8af5f9c5.png"},{"id":88228266,"identity":"4c39297a-f79e-4d44-b318-a4a2119ef676","added_by":"auto","created_at":"2025-08-04 09:07:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":357864,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePopulation demographic history of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eJuglans regia\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e.\u003c/strong\u003e (A) Demographic history of six populations—South Asia, Tibet, West Asia, Europe, Central Asia, and East Asia—inferred using GONE after excluding inversion regions with frequencies ranging from 0.15 to 0.85 and lengths exceeding 10 megabases (Mb). Colored lines represent distinct populations: South Asia (purple, popA), Tibet (green, popB), West Asia (yellow, popC), Europe (blue, popD), Central Asia (orange, popE), and East Asia (pink, popF). (B) Schematic of the demographic model used for simulating SNP data in \u003cem\u003efastsimcoal2\u003c/em\u003e, mirroring the historical dynamics of the six populations (popA–popF) analyzed in panel A. (C) Each subpanel (C1–C6) corresponds to a simulated population (popA–popF), showing temporal changes in \u003cem\u003eN\u003c/em\u003e\u003csub\u003ee\u003c/sub\u003e inferred from SNP datasets generated under the model in panel B.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7127779/v1/67ac4f683f9e8736ce267883.png"},{"id":88230151,"identity":"c05e5004-203f-42ec-a8c8-5a2403510380","added_by":"auto","created_at":"2025-08-04 09:23:41","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":808151,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eChloroplast haplotype distribution and phylogenetic relationships of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eJuglans regia\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e.\u003c/strong\u003e (\u003cem\u003eA\u003c/em\u003e)\u003cstrong\u003e \u003c/strong\u003eMap showing the geographical distribution of 19 chloroplast haplotypes. For published studies, exact coordinates were used if available; otherwise, the capital’s coordinates or those extracted from maps were used. (\u003cem\u003eB\u003c/em\u003e) A rooted maximum likelihood (ML) tree: the red branch represents the clade 1, and the orange branch represents clade 2, and the blue branch represents clade 3. Bootstrap support values ≥ 90% are shown as a red dot at nodes. (\u003cem\u003eC\u003c/em\u003e) A rooted Neighbor-Joining (NJ) tree: the red branch represents the clade 1, and the orange branch represents clade 2, and the blue branch represents clade 3. Bootstrap support values ≥ 0.9 are shown as a red dot at nodes.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7127779/v1/b813ff73e4a2108e6cbb756a.png"},{"id":88231278,"identity":"883b04f3-4853-4f7f-8674-a505f0784838","added_by":"auto","created_at":"2025-08-04 09:31:41","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":947908,"visible":true,"origin":"","legend":"\u003cp\u003eHypothesized domestication center and dispersal routes of the common walnut.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7127779/v1/5a5d16e5bfcd9e4febde5744.png"},{"id":101690760,"identity":"15ec180a-7bbb-4f84-ad02-d6f11a4aa868","added_by":"auto","created_at":"2026-02-02 16:08:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4113746,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7127779/v1/89cc7bdb-1b3d-4989-a34e-f27a02c4e864.pdf"},{"id":88228268,"identity":"40744fec-573e-4f85-bdf6-8d12d7ead0a9","added_by":"auto","created_at":"2025-08-04 09:07:41","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":399140,"visible":true,"origin":"","legend":"","description":"","filename":"ChenCaiJinWalnutdomesticationSuppinfo.docx","url":"https://assets-eu.researchsquare.com/files/rs-7127779/v1/15b099f5fd9982cd69ae56bb.docx"},{"id":88229549,"identity":"bec56049-14e0-4f5e-9b94-cd5ff779a43d","added_by":"auto","created_at":"2025-08-04 09:15:41","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":89796,"visible":true,"origin":"","legend":"","description":"","filename":"ChenCaiJinWalnutdomesticationSuppTable.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7127779/v1/73f357ed070b79099ed4ed6f.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Resolving sampling and population-size biases in domestication genomics supports a South Asian origin of walnuts","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePopulation-genetic clustering algorithms, such as those implemented in STRUCTURE (Pritchard et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) and ADMIXTURE (Alexander et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), are widely used to characterize individuals and populations using genetic data. A key application of these tools is reconstructing the domestication history of crops. In single-domestication scenarios, domesticated species typically consist of a large ancestral or wild population at the center of origin and multiple smaller, geographically dispersed populations derived from this source (Gutaker and Purugganan \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Under such conditions, clustering algorithms may misclassify individuals from the ancestral population as admixed, rather than recognizing them as a distinct group (Lawson et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Hahn \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This misclassification is largely driven by unequal effective population size (\u003cem\u003eN\u003c/em\u003e\u003csub\u003ee\u003c/sub\u003e) among populations: derived populations exhibit stronger drift due to founder effects or bottlenecks, quickly fixing alleles that were originally polymorphic in the ancestral gene pool. As a result, large source populations\u0026mdash;retaining greater allelic diversity\u0026mdash;falsely appear admixed (Lawson et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Such misclassification\u0026mdash;resulting from unequal \u003cem\u003eN\u003c/em\u003e\u003csub\u003ee\u003c/sub\u003e between ancestral and derived populations\u0026mdash;is a common, but often overlooked, source of bias in population-genetic analyses of domesticated species.\u003c/p\u003e\u003cp\u003eBeyond misclassification due to unequal effective population size (\u003cem\u003eN\u003c/em\u003e\u003csub\u003ee\u003c/sub\u003e), another challenge in population clustering arises from unbalanced sample sizes across populations. Simulation studies have demonstrated that clustering methods struggle with highly unbalanced sampling, placing underrepresented populations together even when they are not genetically close (Kalinowski \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Puechmaille \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, uneven sampling is frequently unavoidable, as many biodiverse regions remain under-sampled due to geographic, political, and human-resource constraints. Unequal effective population size and unbalanced sample size together introduce compounding biases in population-clustering analyses.\u003c/p\u003e\u003cp\u003eAmong commonly used clustering algorithms, STRUCTURE remains the most popular due to its pioneering role and continuing refinement, offering high accuracy and flexibility (Novembre \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Pang and Zhang \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). STRUCTURE supports a variety of prior models that allow users to account for demographic imbalance and uneven sampling across populations (Falush et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Wang \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u0026mdash;a level of user control that is limited or absent in other tools, such as ADMIXTURE. An evaluation of these prior models by Wang (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) pointed out that the alternative ancestry prior (POPALPHA\u0026thinsp;=\u0026thinsp;1 and a smaller initial ALPHA value), which allows unequal representations of the populations by the sample, can mitigate misclassification issues caused by unbalanced sampling under default ancestry prior (POPALPHA\u0026thinsp;=\u0026thinsp;0). He also observed that this improvement would be further enhanced when used in conjunction with the uncorrelated allele frequency model. Another mitigation approach, the correlated allele frequency model (or \u003cem\u003eF\u003c/em\u003e model) (Falush et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) can theoretically handle differences in \u003cem\u003eN\u003c/em\u003e\u003csub\u003ee\u003c/sub\u003e among populations, though this capability is rarely appreciated. Importantly, these two strategies are rarely implemented together in practice, and each addresses only a single source of bias. This presents a challenge for crop domestication research, where both sampling imbalances and unequal population sizes presumably are common. To our knowledge, no prior study has systematically addressed both these sources of bias in STRUCTURE-based analyses.\u003c/p\u003e\u003cp\u003eThese methodological challenges in population-genetic clustering are particularly relevant when studying crops like the common walnut (\u003cem\u003eJuglans regia\u003c/em\u003e), which has a large geographic distribution that includes several botanically underexplored regions, suggesting the presence of sampling biases in addition to likely unequal population sizes. Walnuts have been widely gathered and traded across Asia since the Late Neolithic, as evidenced by nut remains found in an Armenian grave (~\u0026thinsp;6200 years ago) (Wilkinson et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), walnut shells from Kashmir (~\u0026thinsp;4700\u0026ndash;4000 years ago) (Pokharia et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and shells from a former market site in Pakistan (~\u0026thinsp;3200 years ago) (Spengler et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Due to its extinction in much of Europe and parts of Asia during the Pleistocene glaciations\u0026mdash;and botanical under-collecting in Central, South, and West Asia\u0026mdash;the geographic origin of this crop has remained elusive. Competing hypotheses place its origin in the Irano-Anatolian region of West Asia (Zohary et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Ding et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), the Tianshan Mountains of Central Asia (Molnar et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Mapelli et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Pollegioni et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), or a broader region encompassing Afghanistan, Bangladesh, Bhutan, India, Nepal, and Pakistan in South Asia (Aradhya et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Roor et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Yan et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHere, we revisit the origin of the common walnut using a STRUCTURE-based approach, complemented by additional lines of genetic evidence\u0026mdash;including chloroplast haplotypes, bottleneck signatures, inbreeding levels, and genetic load\u0026mdash;leveraging an expanded sampling dataset. Crucially, we propose a new parameterization strategy for STRUCTURE that integrates the \u003cem\u003eF\u003c/em\u003e model, an alternative ancestry prior, and a smaller initial ALPHA value, aiming to simultaneously address the effects of both unequal effective population size and sample size imbalance. Following clear definition of source and derived populations, we conduct genome-wide comparisons of each derived population against the source and identify, for the first time, genes under positive selection linked to walnut domestication.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eGenetic structure and phylogenetic relationship inferred from the nuclear genome\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOur nuclear dataset comprises 14,950 independent non-coding single nucleotide polymorphisms (SNPs) derived from 298 \u003cem\u003eJuglans regia\u003c/em\u003e individuals (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). We investigated population clustering using two model configurations in STRUCTURE: ParamSet1, which implemented an alternative ancestry prior, a small ALPHA value, and the correlated allele frequency model (\u003cem\u003eF\u003c/em\u003e model); and ParamSet2, which applied the same ancestry prior and ALPHA but employed the uncorrelated allele frequency model as advised by Wang (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Under ParamSet1, clustering analysis identified \u003cem\u003eK\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6 as the optimal number of populations based on the parsimony estimator (Wang \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA; Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Individuals were assigned to six genetically distinct groups (\u003cem\u003eq\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.75): East Asia (73 individuals), Central Asia (48), Europe (26), West Asia (51), Tibet (40), and South Asia (16). When \u003cem\u003eK\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5 was tested, individuals clustered into five regional groups (East Asia, Central Asia, Europe, West Asia, and Tibet), while South Asian samples appeared admixed across clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). In contrast, under ParamSet2, clustering analysis identified \u003cem\u003eK\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5 as the optimal number of populations, supporting five distinct groups\u0026mdash;East Asia (74), Central Asia (48), Europe (27), West Asia (36), and Tibet (40)\u0026mdash;again with South Asian individuals showing evidence of admixture. Increasing to \u003cem\u003eK\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6 did not result in the resolution of South Asia as an independent cluster (Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe \u003cem\u003eF\u003c/em\u003e\u003csub\u003ek\u003c/sub\u003e parameter of \u003cem\u003eF\u003c/em\u003e model quantifies the degree of genetic drift experienced by a population since its divergence from an ancestral population. Based on the estimated \u003cem\u003eF\u003c/em\u003e\u003csub\u003ek\u003c/sub\u003e values under ParamSet1, the ranking of genetic differentiation from highest to lowest is as follows: East Asia (0.4964)\u0026thinsp;\u0026gt;\u0026thinsp;Europe (0.3917)\u0026thinsp;\u0026gt;\u0026thinsp;Tibet (0.2738)\u0026thinsp;\u0026gt;\u0026thinsp;Central Asia (0.2322)\u0026thinsp;\u0026gt;\u0026thinsp;West Asia (0.1647)\u0026thinsp;\u0026gt;\u0026thinsp;South Asia (0.0565), which suggests that South Asia has the largest population size. Principal component analysis (PCA) and a Neighbor-Joining (NJ) tree both supported the population structure inferred from STRUCTURE analyses under ParamSet1. PCA (PC1\u0026thinsp;=\u0026thinsp;8.10%, PC2\u0026thinsp;=\u0026thinsp;4.26%) clearly separated individuals into six geographic groups (Supplementary Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). The NJ tree, based on 11,803 independent SNPs from 298 \u003cem\u003eJ. regia\u003c/em\u003e and two outgroups, revealed three major clades: Tibet (Clade 1), Europe and West Asia (Clade 2), and Central-East Asia (Clade 3), with a subset of South Asian individuals positioned at the root of the tree (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). This topology implies that the South Asian population may represent the ancestral lineage from which the other five regional populations diverged.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTesting the effects of STRUCTURE parameter settings with real and simulated data\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo evaluate how two STRUCTURE parameter settings (see above) influence the accuracy of ancestral population inference under a domestication scenario, we selected the South Asian and the two other populations (West Asia and Tibet) to represent the possible ancestral and derived lineages in empirical datasets. Under ParamSet1, the optimal \u003cem\u003eK\u003c/em\u003e was 3, revealing distinct genetic clusters for each population. In contrast, ParamSet2 yielded an optimal \u003cem\u003eK\u003c/em\u003e of 2, with West Asia and Tibet forming separate clusters, while the South Asian population exhibited admixture from both (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003eTo enable direct comparison, we designed a three-population simulation under a domestication scenario, modeling South Asia, Tibet, and West Asia (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). The demographic histories (model history settings followed the demographic scenarios described below), sample size, and SNP count were matched to the empirical dataset. The simulation results closely paralleled the empirical findings: under ParamSet1, three distinct clusters were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE), whereas under ParamSet2, two clusters were observed, with the source population showing genetic admixture from both bottlenecked populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). These findings further support South Asia as the ancestral lineage and suggest that the ParamSet1 (alternative ancestry prior (POPALPHA\u0026thinsp;=\u0026thinsp;1), a smaller ALPHA value, and the correlated allele frequency model (\u003cem\u003eF\u003c/em\u003e model)) may be more suitable for capturing the fine-scale genetic structure within these populations than the parameter combination, ParamSet2, proposed by Wang (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eGenetic diversity and differentiation among the six geographic groups\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAccepting six geographic groups (East Asia, Central Asia, Europe, West Asia, Tibet, South Asia) as best matching our nuclear data, we next calculated standard genetic diversity, linkage disequilibrium (LD), nucleotide diversity (π), heterozygosity, and private SNPs across the six groups. Genome-wide LD analysis revealed substantial variation in r\u0026sup2; decay, with South Asia showing the fastest decay among all STRUCTURE groups, followed by West Asia, Central Asia, Tibet, Europe, and East Asia (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Among the six groups, South Asia exhibited the highest nucleotide diversity, followed by West Asia, Tibet, Central Asia, Europe, and East Asia (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). This pattern was also reflected in heterozygosity estimates, with South Asia displaying the highest values, followed by West Asia, Tibet, Central Asia, Europe, and East Asia (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Since private allele counts are influenced by sample size, we controlled for this effect by randomly selecting 16 individuals per group (the smallest group with \u003cem\u003eq\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.75) and repeating the analysis 20 times. The adjusted estimates showed the highest private SNP proportion in South Asia (21.4%-22.7%), followed by East Asia (8.6%-20.2%), West Asia (9.0%-14.3%), Tibet (8.2%-11.2%), Europe (5.9%-7.3%), and Central Asia (5.3%-6.5%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eD\u003c/em\u003e\u003csub\u003eXY\u003c/sub\u003e values ranged from 0.140 to 0.165, with the South Asia group showing the highest genetic divergence from other populations (0.161\u0026ndash;0.165). \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e values ranged from 0.074 to 0.306, with the highest between East Asia and Europe (0.306) and the lowest between West Asia and South Asia (0.074) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). These findings are consistent with the South Asian lineage being the earliest divergence among the groups and having experienced low genetic drift since its separation.\u003c/p\u003e\u003cp\u003eUsing OrientAGraph (Molloy et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), we analyzed population relationships and migration patterns among six geographic groups based on allele frequency data from STRUCTURE-defined gene pools. This analysis inferred a population topology that placed South Asia as the earliest-diverging lineage, with East Asia clustering with Central Asia, and Europe with West Asia. Among the migration events tested (m\u0026thinsp;=\u0026thinsp;0\u0026ndash;6), a single migration event (m\u0026thinsp;=\u0026thinsp;1) best explained the sample covariance, with gene flow primarily observed from the East Asia group into the South Asia group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF).\u003c/p\u003e\u003cp\u003e\u003cb\u003eInbreeding and deleterious mutation load across the six geographic groups\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe \u003cem\u003eF\u003c/em\u003e\u003csub\u003eROH\u003c/sub\u003e values, representing the proportion of the genome within runs of homozygosity (ROH), varied significantly across the six geographic groups. East Asia exhibited the highest \u003cem\u003eF\u003c/em\u003e\u003csub\u003eROH\u003c/sub\u003e, followed by Europe, Tibet, Central Asia, West Asia, and South Asia (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Significance testing confirmed that South Asia had significantly lower \u003cem\u003eF\u003c/em\u003e\u003csub\u003eROH\u003c/sub\u003e values compared to all other groups.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo compare patterns of mutation load across the six geographic groups, we categorized coding-sequence variants into three functional classes based on predicted effects: synonymous, deleterious, and loss-of-function (LoF). Ancestral and derived alleles for each variant were polarized using \u003cem\u003eJuglans mandshurica\u003c/em\u003e and \u003cem\u003eJuglans nigra\u003c/em\u003e as outgroups. Among the six groups, South Asia exhibited the lowest ratio of total derived deleterious variants to synonymous variants, followed by Central Asia, West Asia, Tibet, Europe, and East Asia (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Similarly, South Asia showed the lowest ratio of total derived loss-of-function (LoF) variants to synonymous variants, with the remaining regions ranked in the same ascending order (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003e\u003cb\u003eDemographic history and inference of bottlenecks\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe inferred the demographic history of the six geographic groups by setting the maximum recombination rate to 0.05 in the software GONE (Santiago et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and excluding inversion regions with frequencies between 0.15 and 0.85, and lengths greater than 10 Mb, as suggested by Novo et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The East Asian and European groups underwent significant bottlenecks 60\u0026ndash;20 generations ago, which, assuming a generation time of 50 years, correspond to approximately 3000\u0026ndash;1000 years ago, with population sizes as low as 100 breeding individuals. The Central Asian and West Asian groups experienced bottlenecks between 40 and 10 generations ago (~\u0026thinsp;2000\u0026ndash;500 years ago), with minimum population sizes of 200. Tibet, on the other hand, underwent a rapid and severe population decline 30 generations ago (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Taking into account the shorter growing seasons and longer generation times at high altitudes, this decline likely occurred around the same time as those in the other regions. In contrast, South Asia displayed no such dramatic decline, consistent with the expectation of a stable population size, supporting the hypothesis of its role as the domestication origin of \u003cem\u003eJ. regia\u003c/em\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo investigate differences in bottleneck histories among the six geographic populations, we conducted simulations. Based on geographic proximity, a NJ tree, and TreeMix-inferred clustering, the simulations assumed that populations in Europe and East Asia expanded from South Asia via West and Central Asia. Tibetan walnuts form a distinct lineage, separate from other East Asian samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF), suggesting arrival from South Asia via a southern inner-plateau route (\u003cem\u003eDiscussion\u003c/em\u003e). We simulated scenarios with and without bottlenecks (A-E). Populations A, B, C, D, E, and F represent South Asia, Tibet, West Asia, Europe, Central Asia, and East Asia, respectively. Population A maintained a constant size with no bottlenecks. Population B underwent two bottlenecks, between generations 80\u0026thinsp;\u0026minus;\u0026thinsp;40 and 40\u0026thinsp;\u0026minus;\u0026thinsp;10. Populations C and E experienced a single bottleneck between generations 80\u0026thinsp;\u0026minus;\u0026thinsp;20, followed by recovery. Populations D and F had two bottlenecks, between 80\u0026thinsp;\u0026minus;\u0026thinsp;40 and 40\u0026thinsp;\u0026minus;\u0026thinsp;20, with recovery (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). The results show that Population A maintained a constant size over 100 generations (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Population B had a bottleneck at generation 50, with recovery in recent generations. Populations C and E underwent a bottleneck between generations 60\u0026thinsp;\u0026minus;\u0026thinsp;20, followed by recovery, similar to West and Central Asia. Population D also had a bottleneck between generations 40\u0026thinsp;\u0026minus;\u0026thinsp;20, followed by recovery, and Population F experienced a bottleneck between generations 30\u0026thinsp;\u0026minus;\u0026thinsp;20, with recovery, similar to East Asia and Europe (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003e\u003cb\u003ePhylogenetic relationships inferred from chloroplast genomes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eChloroplast haplotype diversity can point to the domestication center of a species, and we therefore also analyzed chloroplast genomic data. We reconstructed a minimum of 160,537 base pairs of the chloroplast genome per sample, identifying 106 substitutions and defining 12 haplotypes. The addition of seven chloroplast genomes from the Western Himalayan region (Afghanistan (1), India (3), Nepal (1), Pakistan (2)) generated by Yan et al. (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) \u0026mdash;resulted in 19 haplotypes (Hap 1\u0026ndash;19; see Supplementary Table S3). Europe (47 samples) and West Asia (Iran, Iraq, Armenia; 81 samples) predominantly harbor haplotypes 10 and 17, lacking any unique regional haplotypes. Central Asia (Kazakhstan, Tajikistan, Xinjiang; 39 samples) has two haplotypes (Hap 10 and 17); Tibet (51 samples) four haplotypes (Hap 7, 8, 10 and 17), with haplotype 7 being region-specific; East Asia (China, Korea, Japan; 111 samples) six haplotypes (Hap 8, 10, 15, 17, 18, and 19), three of them endemic (Hap 15, 18, and 19); and South Asia (Afghanistan, Pakistan, India, Nepal; 31 samples) harbors the highest diversity with 14 haplotypes (Hap 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, and 16), 12 of them region-specific (Hap 1, 2, 3, 4, 5, 6, 9, 11, 12, 13, 14, and 16). Geographically, haplotypes 10 and 17 are widely distributed in the Northern Hemisphere, while haplotype 8 is only found in samples from South Asia, Tibet, and a single sample from Qinghai in East Asia (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eA maximum likelihood (ML) tree derived from whole-chloroplast genome sequences revealed a polytomy of three clades: the first contained four haplotypes (Hap 1, 2, 3, and 4) from South Asia, the second contained four haplotypes (Hap 5, 6, 7, and 8) from South Asia and Tibet, and the third contained 11 haplotypes from South Asia and other parts of the range (Hap 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, and 19) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Of the haplotypes in the third clade, haplotypes 9, 11, 12, 13, 14, and 16 were all from South Asia. A Neighbor-Joining (NJ) tree from the same data also shows a polytomy of three main clades (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003e\u003cb\u003eCross-population selection signatures and candidate genes under positive selection\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAs described above, we inferred five derived populations and one source population (South Asia). Given that walnut domestication likely occurred within the last ten thousand years (~\u0026thinsp;200 generations), we applied the sensitive haplotype-based XP-EHH method (Sabeti et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), as implemented in selscan v.2.0.3 (Szpiech and Hernandez \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Szpiech \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), to detect signatures of positive selection associated with domestication.\u003c/p\u003e\u003cp\u003eAfter stringent filtering, we identified 45 genes under positive selection by intersecting significant SNPs showing consistent directional allele frequency changes across all derived populations relative to the source population, located within genes or in flanking regions (\u0026plusmn;\u0026thinsp;5 kb) (Methods, Supplementary Table S4). Among 29 functionally annotated candidates, three genes stood out as prime domestication candidates: JreChr11G12281 encodes the LRR receptor-like kinase FEI2, involved in cell wall remodeling; JreChr01G11061 encodes a pectinesterase implicated in pollen tube growth and fruit softening; and JreChr06G11221 encodes the ABC transporter ABCG7, which participates in lipid transport and likely contributes to kernel composition. Notably, two non-synonymous SNPs in the uncharacterized gene JreChr03G10963 show relatively pronounced allele frequency shifts\u0026mdash;from 0.5937 and 0.7187 in the source population to near fixation in all derived populations\u0026mdash;highlighting the possibility that currently unannotated genes may also contribute to domestication (Supplementary Table S4).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cb\u003eAddressing unbalanced sample sizes and unequal population sizes in STRUCTURE analyses\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe use of clustering algorithms such as STRUCTURE in crop domestication studies is often challenged by two critical and distinct issues: unbalanced sample sizes and unequal effective population sizes (\u003cem\u003eN\u003c/em\u003e\u003csub\u003ee\u003c/sub\u003e) among populations. While the effects of unbalanced sampling have been extensively discussed (Kalinowski \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Puechmaille \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wang \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), the impact of unequal effective population size (\u003cem\u003eN\u003c/em\u003e\u003csub\u003ee\u003c/sub\u003e) among source populations has received comparatively little attention. This oversight is particularly consequential in the context of crop domestication, where ancestral populations often maintain large \u003cem\u003eN\u003c/em\u003e\u003csub\u003ee\u003c/sub\u003e, while derived populations frequently experience bottlenecks and founder effects. The \u003cem\u003eF\u003c/em\u003e model (the correlated allele frequency model) of STRUCTURE introduced by Falush et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) accounts for differences in \u003cem\u003eN\u003c/em\u003e\u003csub\u003ee\u003c/sub\u003e by allowing population-specific genetic drift (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003ek\u003c/em\u003e\u003c/sub\u003e) away from the ancestral allele frequencies. Although this model is the default setting in STRUCTURE now, it has rarely been explicitly employed in domestication studies for the purpose of addressing among-population differences in effective population size.\u003c/p\u003e\u003cp\u003eIn contrast, biases introduced by unbalanced sample sizes have received more targeted methodological attention. Wang (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) proposed a strategy that combines an alternative ancestry prior, a small ALPHA value, and the uncorrelated allele frequency model to reduce misclassification caused by sampling imbalance. However, because the uncorrelated allele frequency model simply assumes allele frequencies across populations are independent, it is prone to erroneously classify populations with low drift levels (or large \u003cem\u003eN\u003c/em\u003e\u003csub\u003e\u003cem\u003ee\u003c/em\u003e\u003c/sub\u003e)\u0026mdash;and thus preserved ancestral diversity\u0026mdash;as admixed from populations that underwent stronger drift. Consequently, this approach alone is insufficient under conditions where both sample size and population size are unbalanced, leading to the misclassification of the ancestral population\u0026mdash;such as the South Asian group in our study\u0026mdash;as admixed with derived populations (Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo jointly address these two challenges, we propose an integrated parameterization strategy that combines Wang\u0026rsquo;s (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) ancestry prior and ALPHA adjustment with the \u003cem\u003eF\u003c/em\u003e-model's correlated allele frequency framework, enabling accurate identification of the ancestral population. This approach recovered South Asia as the ancestral origin of \u003cem\u003eJuglans regia\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, D-E; Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). By simultaneously correcting for sampling imbalance and unequal \u003cem\u003eN\u003c/em\u003e\u003csub\u003ee\u003c/sub\u003e, our strategy offers a robust and broadly applicable solution in study systems where unbalanced sample sizes and unequal effective population size among populations co-occur.\u003c/p\u003e\u003cp\u003eBeyond STRUCTURE, other widely used clustering algorithms, such as ADMIXTURE (Alexander et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), face similar limitations. ADMIXTURE does not incorporate any mechanisms to address either unbalanced sampling or unequal population sizes, making it particularly vulnerable to erroneous inference in complex demographic settings (Alexander et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The latest clustering algorithm, PopCluster (Wang \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wang \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), explicitly takes sampling imbalance into consideration through a weighted likelihood framework, but it so far lacks a mechanism to explicitly accommodate unequal population size.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSouth Asia as the region where the common walnut was first domesticated\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe geographic origin of the common walnut (\u003cem\u003eJuglans regia\u003c/em\u003e) has long been debated, largely due to limitations in earlier population-genetic studies that failed to account for demographic heterogeneity and botanical collecting imbalance, with entire relevant regions underrepresented. With an optimized STRUCTURE-based framework and multiple lines of genomic evidence, including from previously geographically under-sampled regions, our study now consistently identifies South Asia (western Himalayas and adjacent areas) as the initial center of walnut domestication. First, the nuclear genomic structure reveals that South Asian walnuts have the lowest \u003cem\u003eF\u003c/em\u003e\u003csub\u003ek\u003c/sub\u003e value (i.e., low level of genetic drift), indicative of their ancestral status. Second, a Neighbor-Joining (NJ) tree based on nuclear genomic data places individuals from South Asia at basal phylogenetic positions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB), consistent with early divergence. Third, this region harbors the highest number of private alleles and exhibits elevated heterozygosity among six groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD), reflecting genetic distinctiveness. Fourth, walnuts from South Asia exhibited the lowest mutation load (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, C). Interestingly, when the Tibetan (Zhang et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) or Chandler 2.0 (Marrano et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) reference genomes were used for mutation load estimation, the lowest loads were observed in the Tibetan and European populations, respectively, rather than in South Asia. This discrepancy likely reflects reference bias, as reference genomes derived from bottlenecked populations with genomic erosion can substantially underestimate mutation load in those populations (Dussex et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Fifth, demographic reconstruction indicates that the South Asian population has maintained a relatively stable effective population size, in contrast to repeated bottlenecks inferred in other regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Sixth and finally, chloroplast genome analysis reveals the highest diversity of haplotypes in South Asia (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), suggesting deep historical lineage retention.\u003c/p\u003e\u003cp\u003eTogether, these six genomic patterns robustly support South Asia as the primary center of walnut domestication, aligning with previous insights from Aradhya et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), Roor et al. (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and Yan et al. (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), while rejecting the Irano-Anatolian region of West Asia (Zohary et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Ding et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and the Tianshan Mountains of Central Asia (Molnar et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Mapelli et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Pollegioni et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Based on our simulations, we speculate that the latter inferences were affected by geographically-biased sampling and unaccounted differences in \u003cem\u003eN\u003c/em\u003e\u003csub\u003ee\u003c/sub\u003e. Clearly, the nuclear genomes of the West and Central Asian populations are not basal (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB), exhibit fewer private alleles, and have higher mutation loads compared to South Asian populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB\u0026ndash;D; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, C), suggesting reduced genetic distinctiveness and historical bottlenecks (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Chloroplast data further reveal fewer haplotypes in Central (3) and Western (2) Asia compared to South Asia (above).\u003c/p\u003e\u003cp\u003eThe GONE and STRUCTURE analyses revealed a large and stable effective population size in South Asia (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), providing partial support for the notion that the \u0026lsquo;domestication bottleneck\u0026rsquo; may be a problematic concept (Allaby et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u0026mdash;particularly in woody perennial crops (Miller and Gross \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Gaut et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Gaut et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Allaby et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In contrast, the significant bottlenecks observed in the five derived populations are more plausibly attributed to founder effects.\u003c/p\u003e\u003cp\u003eFossil pollen records match an initial domestication in South Asia. In Central and Western Asia, the earliest walnut pollen records date to the Holocene (Beer et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) with anthropogenic origins in Kyrgyzstan (2000 years BP) and \u003cem\u003eJuglans\u003c/em\u003e pollen in northern Iran (2300\u0026ndash;2350 years BP) (Ramezani et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In contrast, South Asian records from Nepal and India indicate a longer presence, dating back to 18,000 and 30,000 years BP, respectively, suggesting refugia during the Last Glacial Maximum (Kotlia et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eThe geographic spread of\u003c/b\u003e \u003cb\u003eJuglans regia\u003c/b\u003e \u003cb\u003efrom its South Asian center of domestication\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFollowing domestication in South Asia, walnuts dispersed across the broader Eurasian landscape, shaping the present-day genetic structure of the species (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). There is a deep genetic differentiation between eastern and western lineages (Roor et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ding et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kairova et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), a pattern previously interpreted as evidence of a Central Asian origin, particularly within the Irano-Anatolian region (Mapelli et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Pollegioni et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ding et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, our new results, informed by expanded geographic sampling and improved STRUCTURE settings accounting for sampling and demographic biases, reveal that domesticated walnuts all trace their ancestry to South Asia.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFrom South Asia, the domesticated form expanded eastward, as evidenced by the NJ tree (with Central Asia basal to East Asia within the same clade) and supported by TreeMix clustering results (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). In Asia, domesticated walnuts underwent two sequential bottlenecks: the first during their human-mediated transfer out of South Asia and the second during their subsequent expansion into East Asia. Our GONE analysis of simulated data corroborates the occurrence of these bottleneck events (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, C). There is a signal of gene flow from East Asia back into the South Asian source population (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF), possibly reflecting the deliberate reintroduction of desirable genotypes by humans. However, additional evidence is required to confirm this. Among the five derived groups, the East Asian population exhibits the highest proportion of private alleles (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD), likely due to introgression from \u003cem\u003eJuglans sigillata\u003c/em\u003e, a species native to southwestern China that has contributed genetic material to eastern \u003cem\u003eJ. regia\u003c/em\u003e (Ding et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Yan et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTibetan walnuts form a distinct lineage and may have been introduced from South Asia via the Southern Inner-Plateau Route (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) (Zhao et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e). In ancient Chinese, walnuts are sometimes called K\u0026rsquo;ang t\u0026rsquo;ao (meaning Tibetan walnut), with K\u0026rsquo;ang referring to Tibet (Laufer \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1919\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWestward dispersal occurred stepwise from South Asia through West Asia before reaching Europe, also involving two sequential bottlenecks: first during the transfer from South Asia to West Asia, and second during expansion into Europe (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Our GONE analysis corroborates the occurrence of these bottleneck events (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Notably, both \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003ek\u003c/em\u003e\u003c/sub\u003e values and the drift parameter from TreeMix (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF) consistently indicate stronger genetic drift in Europe compared to West Asia, suggesting a more pronounced reduction in genetic diversity during westward dispersal. These successive bottlenecks likely contributed to the low genetic diversity observed in modern European walnut populations.\u003c/p\u003e\u003cp\u003e\u003cb\u003eIdentifying candidate domestication genes via XP-EHH\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOur demographic analyses position South Asia as the domestication center, with five derived populations experiencing severe bottlenecks during their geographic expansion. The XP-EHH approach (Sabeti et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) proved optimal for detecting recent selective sweeps in this context because it requires smaller sample sizes (n\u0026thinsp;\u0026ge;\u0026thinsp;10) than iHS (n\u0026thinsp;\u0026ge;\u0026thinsp;100), accommodates unphased genotypes, and effectively captures haplotype homozygosity differentials on very short time scales (Szpiech \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe observed allele frequency trajectories\u0026mdash;moderate in source populations but nearly fixed in derived groups\u0026mdash;support a multi-phase domestication model with recurrent selection (Gaut et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), mirroring patterns in rice (Jing et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), maize (Yang et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and adzuki bean (Chien et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Although XP-EHH is often interpreted as detecting positive selection in only one of the two compared populations (or in the present context, the derived population), it fundamentally measures differences in extended haplotype homozygosity between populations (Abondio et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and thus captures differential selection intensities. In other words, the genes identified by XP-EHH might have already experienced a certain degree of human selection in the source population. It is also important to consider that some of the signals detected may reflect the effects of strong genetic drift following bottlenecks in the derived populations, rather than\u0026mdash;or in addition to\u0026mdash;artificial selection.\u003c/p\u003e\u003cp\u003eAmong the candidate loci, three annotated genes\u0026mdash;JreChr11G12281, JreChr01G11061, and JreChr06G11221\u0026mdash;are likely involved in domestication-related traits (Sharma and Kumar \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Khanal et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) (Supplementary Table S4). These genes participate in cell wall remodeling (Xu et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), pollen development (Leroux et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and lipid transport (Borghi et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), respectively, and may have been targeted by selection for thinner shells, enhanced fertility, or increased oil accumulation\u0026mdash;hallmark traits of cultivated walnut. Although these inferences rely on functional homology with Arabidopsis, they provide plausible hypotheses for future validation. In addition, strong selection signals were also observed in unannotated genes, such as JreChr03G10963, which contains non-synonymous SNPs with large allele frequency shifts (Supplementary Table S4). This suggests that currently uncharacterized loci may also contribute to domestication. Further studies integrating expression profiling, functional assays, and AI-based annotation will be essential to validate the roles of both known and novel genes in shaping key agronomic traits.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eRunning STRUCTURE is only a starting point; reliable inference requires proper use of the software and validation of the results with other kinds of data, such as ecological or fossil data. In this study, we show through simulations that an optimized STRUCTURE framework—combining the \u003cem\u003eF\u003c/em\u003e-model with alternative ancestry priors—can correct for biases from unequal population sizes and sampling imbalance, two major challenges in the study of domestication. Applying this strategy to \u003cem\u003eJuglans regia\u003c/em\u003e, we identify South Asia as the center of domestication, supported by multiple lines of nuclear- and chloroplast-genomic evidence and demographic stability, and matching fossil pollen and nut shell remains. This correct identification of source and derived populations facilitates the detection of candidate genes under positive selection via sensitive cross-population comparisons. These findings resolve a long-standing debate on walnut origins and underscore the importance of model-aware clustering in evolutionary inference. The utility of the approach proposed here extends beyond crop domestication to a range of biological contexts that involve complex demography, such as ancient DNA analyses, post-glacial recolonization, and conservation genomics of fragmented or endangered taxa.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eSampling and sequencing\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe collected 33 mature \u003cem\u003eJ. regia\u003c/em\u003e individuals from Europe (3), Xinjiang (2), Yunnan (3), Beijing (2) and Tibet (23). Genomic DNA was extracted from dried leaf tissue using a plant total genomic DNA kit (Tiangen, Beijing, China) and was then sequenced using paired-end libraries with an insert size of 350 bp on Illumina HiSeq X-ten instruments by NovoGene (Beijing, China), with read lengths of 150 bp. Samples were sequenced to an average depth of 30×. Additionally, we downloaded whole-genome resequencing data of \u003cem\u003eJ. regia\u003c/em\u003e from various studies: Ji et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) (209 individuals), Luo et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) (49 individuals), Qi et al. (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) (6 individuals), Steven et al. (2018) (20 individuals), Li et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) (5 individuals), Zhang et al. (\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) (6 individuals), Zhang et al. (\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) (29 individuals), and Ding et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) (42 \u003cem\u003eJ. regia\u003c/em\u003e individuals). These datasets encompass individuals from North America, Europe, Central Asia, Western Asia, South Asia, and East Asia, with an average depth higher than 10×.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMapping and variant calling for the nuclear genomes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eRaw reads from 399 \u003cem\u003eJ. regia\u003c/em\u003e individuals were trimmed of adapters and low-quality sequences using Trimmomatic v0.32 (Bolger et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and then aligned to the \u003cem\u003eJ. regia\u003c/em\u003e reference genome (Zhang et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) using the BWA-MEM algorithm of BWA v0.7.15 (Li and Durbin \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Only uniquely mapped and properly paired reads were retained. SAMtools v1.19 (Li \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) was used to convert SAM files to BAM format and remove PCR duplicates. Indel realignment and SNP calling were conducted using SENTIEON DNAseq software package v202308 (Weber et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and SNPs were aggregated across samples. Stringent SNP filtration was applied via GATK's VariantFiltration (McKenna et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), using criteria including “QD \u0026gt; 2.0, QUAL \u0026gt; 30.0, SOR \u0026lt; 3.0, FS \u0026lt; 60.0, MQ \u0026gt; 40.0, MQRankSum \u0026gt;-12.5, and ReadPosRankSum \u0026gt;-8.0”. We excluded SNPs with mapping depths outside one-third to triple the individual’s average, non-biallelic sites, and those with missing data. Heterozygous genotypes were determined by the proportion of non-reference alleles, set at 20–80% for depths exceeding three times the average, and 10–90% for depths at least one-third of the average; all others were classified as homozygous.\u003c/p\u003e\u003cp\u003eTo minimize the introgression from \u003cem\u003eJ. sigillata\u003c/em\u003e, we excluded 48 samples with over 10% genetic contribution from this species, as determined by STRUCTURE v2.3.4 (Pritchard et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), reducing the dataset to 351 individuals. To ensure genealogical independence, we used King v.2.2.7 (Manichaikul et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) to identify related individuals, excluding one from each pair with a kinship coefficient exceeding 0.0442 (indicative of third-degree relations), favoring those with higher sequencing depths. This led to the exclusion of 53 \u003cem\u003eJ. regia\u003c/em\u003e individuals from Europe (2), East Asia (44), and North America (7), yielding a final dataset of 298 individuals for STRUCTURE analysis, PCA, and a Neighbor-Joining tree construction.\u003c/p\u003e\u003cp\u003eTo obtain neutral and independent SNPs, we excluded SNPs located within coding sequences and their 3-kb flanking regions, following Zhao et al. (\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). We further thinned the SNPs using a distance filter of greater than 20 kb between consecutive SNPs and removed singletons to minimize false positives due to sequencing errors, resulting in a data set of 14,950 SNPs for population structure analysis.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePopulation structure and phylogenetic analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo investigate the population structure of the 298 individuals, we performed principal component analysis (PCA) using the R package SNPRelate v1.6.2 (Zheng et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) with default settings. Additionally, we used STRUCTURE v2.3.4 (Pritchard et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) to cluster individuals based on the number of clusters (\u003cem\u003eK\u003c/em\u003e) ranging from 1 to 8. Clustering was conducted under the admixture model with two distinct parameter settings: the first (ParamSet1) used the alternative ancestry prior (POPALPHA = 1) with a small ALPHA value (ALPHA = 0.25) and the correlated allele frequency model (\u003cem\u003eF\u003c/em\u003e model, FREOSCORR = 1), while the second (ParamSet2) used the alternative ancestry prior (POPALPHA = 1) with a small ALPHA value (0.25) and the uncorrelated allele frequency model (FREOSCORR = 0). Each parameter setting was run with 100,000 burn-in steps followed by 500,000 Markov Chain Monte Carlo (MCMC) steps, and 20 replicate runs were conducted for each value of \u003cem\u003eK\u003c/em\u003e to assess the variation in likelihood. The optimal number of clusters (\u003cem\u003eK\u003c/em\u003e) was determined using three criteria: Ln (D|K), the final posterior probability of \u003cem\u003eK\u003c/em\u003e (Pritchard et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2000\u003c/span\u003e); Delta \u003cem\u003eK\u003c/em\u003e, the rate of change in Ln (D|K) between successive \u003cem\u003eK\u003c/em\u003e values (Evanno et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2005\u003c/span\u003e); and KFinder v1.0, based on the parsimony index (PI) proposed by Wang (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAdditionally, we incorporated one individual each from \u003cem\u003eJ. nigra\u003c/em\u003e and \u003cem\u003eJ. mandshurica\u003c/em\u003e as outgroups. Using MEGA (Stecher et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), we constructed a Neighbor-Joining (NJ) tree based on the best-fit substitution model selected by the software and validated with 1,000 bootstrap replicates.\u003c/p\u003e\u003cp\u003eTo simplify the evaluation of how two STRUCTURE parameter settings (see above) influence the accuracy of ancestral population inference under a domestication scenario, we randomly selected two populations (West Asia and Tibet) from the five derived groups to represent the derived lineages. These two, together with the South Asian ancestral population, formed a three-population subset used for comparative analyses based on both empirical and simulated datasets. The sample sizes and the number of SNPs used were consistent with those in the empirical dataset representing three populations: Population A (South Asia), Population B (Tibet), and Population C (West Asia). Although the simulations were performed before demographic inference, the population history scenarios were set according to demographic history later inferred from GONE (see below). This ensured consistency between the simulated and empirical data. The simulated SNP datasets were analyzed using STRUCTURE under two parameter settings: ParamSet1 and ParamSet2. These simulations allowed us to assess the robustness of ancestry inference under alternative model assumptions informed by plausible demographic scenarios.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGenetic diversity and differentiation analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe used VCFtools v0.1.17 (Danecek et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) to calculate a suite of genetic diversity metrics based on datasets filtered to remove missing data. The analyses included linkage disequilibrium (LD) decay, nucleotide diversity (π), heterozygosity, genetic differentiation (\u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e), absolute genetic divergence (\u003cem\u003eD\u003c/em\u003e\u003csub\u003eXY\u003c/sub\u003e), and proportions of private SNPs. These metrics were assessed across six genetic groups defined by STRUCTURE and PCA analyses: Europe (26 individuals), West Asia (51 individuals), Central Asia (48 individuals), Tibet (40 individuals), East Asia (73 individuals), and South Asia (16 individuals). To account for sample size differences, we performed 20 replicates for each group by randomly subsampling 16 individuals per replicate. \u003cem\u003ep\u003c/em\u003e-values were derived using t-tests comparing each group to the South Asia group. Significance levels are indicated as “***” \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, “**” \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, “*” \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, “ns” \u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05.\u003c/p\u003e\u003cp\u003eGene flow among populations was inferred using the OrientAGraph approach (Molloy et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which optimizes Maximum Likelihood Network Orientation (MLNO) within the TreeMix framework (Pickrell and Pritchard \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Allele frequencies for the six groups (Central Asia, East Asia, Europe, Tibet, South Asia, and West Asia) were derived from STRUCTURE-based gene pools.\u003c/p\u003e\u003cp\u003e\u003cb\u003eRuns of homozygosity and mutation load of six groups\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo identify runs of homozygosity (ROH), we first converted the six populations’ filtered multi-individual vcf file into a .ped file and identified ROH in PLINK v.1.9 (Purcell et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). To assess the robustness of our results to the applied parameters and to potential sequencing errors, we used three sets of parameters where we varied the window size (homozyg-window-snp) and the number of heterozygous sites per window (homozyg-window-het): (1) homozyg-window-snp 100 and homozyg-window-het 1; (2) homozyg-window-snp 250 and homozyg-window-het 3 (reported in main text in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA); (3) homozyg-window-snp 500 and homozyg-window-het 5.\u003c/p\u003e\u003cp\u003eAll other parameters described hereafter were the same for each of the three parameter sets. If at least 5% of all windows that included a given SNP were defined as homozygous, the SNP was defined as being in a homozygous segment of a chromosome (homozyg-window-threshold 0.05). This threshold was chosen to ensure that the edges of a ROH are properly delimited. A homozygous segment was then defined as a ROH if all of the following conditions were met: the segment included ≥ 25 SNPs (homozyg-snp 25); the segment covered ≥ 100 kb (homozyg-kb 100); the minimum SNP density was one SNP per 50 kb (homozyg-density 50); the maximum distance between two neighbouring SNPs was ≤ 1,000 kb (homozyg-gap 1,000); the number of heterozygous sites within ROH was set to 750 (homozyg-het 750) in order to prevent sequencing errors from breaking ROH. We then calculated individual inbreeding coefficients (\u003cem\u003eF\u003c/em\u003e\u003csub\u003eROH\u003c/sub\u003e) (Kardos et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) by summing the proportion of the genome covered by ROHs (total length of ROHs/total length of genome assembly). \u003cem\u003ep\u003c/em\u003e-values were derived using t-tests comparing each group to the South Asia group. Significance levels are indicated as “***” \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, “**” \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, “*” \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, “ns” \u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05.\u003c/p\u003e\u003cp\u003eWhen calculating mutation load, if mapping to the Tibetan reference genome (Zhang et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), the Tibetan population exhibited the lowest ratio of derived deleterious and loss-of-function (LoF) variants to synonymous variants. Moreover, when mapping to the Chandler 2.0 reference genome (Marrano et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), which originated from France (Beede et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), the European population showed the lowest ratios (Supplementary Fig. S3). As Dussex et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) explicitly stated, using a reference genome that has suffered from genomic erosion (i.e., genetic threats to small populations) in a bottlenecked population can significantly underestimate genetic load of that population. This effect is corroborated by our results; to mitigate this bias, we followed Dussex et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) by using the genome of \u003cem\u003eJuglans sigillata\u003c/em\u003e (Ning et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) as the reference for mutation load estimation. The effect of SNP variants on protein-coding gene sequences were further annotated and classified into loss-of-function (LoF), missense, and synonymous variants using SnpEff v5.0 (Cingolani et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). LoF variant denote those with gain and/or loss of a stop codon, or those with loss of a start codon. Missense SNPs were further predicted as deleterious (score ≤ 0.05) based on the SIFT score computed by the program SIFT 4G (Vaser et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). At each SNP position, we determined the derived versus ancestral allelic state using the est-sfs software through comparison with \u003cem\u003eJ. mandshurica\u003c/em\u003e and \u003cem\u003eJ. nigra\u003c/em\u003e sequences. The total derived alleles for LoF, deleterious and synonymous variants were estimated for each individual. \u003cem\u003ep\u003c/em\u003e-values were derived using t-tests comparing each group to South Asia group. Significance levels are indicated as “***” \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, “**” \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, “*” \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, “ns” \u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePopulation demographic analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo infer changes in effective population sizes (\u003cem\u003eN\u003c/em\u003e\u003csub\u003ee\u003c/sub\u003e), we used GONE (Santiago et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) to analyzed six groups identified through STRUCTURE and PCA analysis: Central Asia, East Asia, Europe, South Asia, Tibet, and West Asia. We assumed a constant rate of recombination of 2.63 cM/Mb for the whole genome (Ding et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and excluded LD data with recombination rates \u0026gt; 0.05 to reduce the effect of sampling on the estimates as well as artefacts from recent migrants, as recommended in the GONE User’s Guide. We performed 20 replicate analyses, each including 50,000 SNPs sampled randomly from each chromosome.\u003c/p\u003e\u003cp\u003eTo further investigate the impact of population bottlenecks on demographic inference, we simulated SNP data under the demographic models using Fastsimcoal2 (Excoffier and Foll \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) based primarily on the empirical data from GONE: (1) a source population model without bottleneck (population A, represents South Asia), (2) a single bottleneck model (population C, represents West Asia; population E, represents Central Asia), and (3) a model incorporating two successive bottlenecks (population B, represents Tibet; population D, represents Europe; population F, represents East Asia). Parameter values for these models—including divergence times, historical \u003cem\u003eN\u003c/em\u003e\u003csub\u003ee\u003c/sub\u003e changes, and bottleneck intensities—were informed by empirical estimates obtained from the GONE analysis of real data. Simulations were performed using the chromosome sizes of the \u003cem\u003eJuglans regia\u003c/em\u003e reference genome (Zhang et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), assuming a mutation rate of 1.03 × 10⁻⁷ per site per generation (with 50 years per generation) and a recombination rate of 2.06 cM/Mb (Ding et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe demographic model begins with an ancestral wild population (\u003cem\u003eN\u003c/em\u003e\u003csub\u003ee\u003c/sub\u003e = 10,000) that did not experience a bottleneck but reflects population changes associated with the initial phase of domestication. During this process, the effective population size decreased from \u003cem\u003eN\u003c/em\u003e\u003csub\u003ee\u003c/sub\u003e = 10,000 to \u003cem\u003eN\u003c/em\u003e\u003csub\u003ee\u003c/sub\u003e = 4,000, representing the transition to a managed population. This was followed by stabilization at \u003cem\u003eN\u003c/em\u003e\u003csub\u003ee\u003c/sub\u003e = 2,000 (defined as population A), representing the core domesticated lineage. Population A then served as the source for all subsequent derived populations.\u003c/p\u003e\u003cp\u003eEach subpopulation diverged from population A at specified time points and experienced distinct demographic trajectories. Population B (Tibet) split from A 100 generations ago and underwent a severe bottleneck, with effective population size reduced to 10, followed by partial recovery to 50 by 10 generations ago (sample size n = 40). Population C (West Asia) diverged 80 generations ago with an initial effective size of 50, expanding to 200 by 20 generations ago (n = 50). Similarly, population D (Europe) diverged 40 generations ago with an initial size of 20, increasing to 100 by 20 generations ago (n = 20). Population E (Central Asia) followed a trajectory analogous to that of population C, while population F (East Asia) diverged 40 generations ago with an initial size of 25, expanding to 100 by 20 generations ago (both with n = 50). Simulated datasets were subsequently analyzed using GONE with parameters set to hc = 0.05 and REPs = 40. Each scenario was replicated 20 times, and the geometric mean across replicates was taken as the final estimate of effective population size dynamics.\u003c/p\u003e\u003cp\u003e\u003cb\u003eChloroplast genome analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFor the chloroplast analysis, we excluded 48 individuals with over 10% genetic contribution from \u003cem\u003eJ. sigillata\u003c/em\u003e from 399 samples based on STRUCTURE results from the nuclear data (below), and a total of 351 samples remained. We processed reads from the 351 \u003cem\u003eJ. regia\u003c/em\u003e individuals using Trimmomatic v0.32 (Bolger et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) to trim adapters and low-quality sequences. The cleaned reads of the 351 individuals were then aligned to the \u003cem\u003eJ. regia\u003c/em\u003e chloroplast genome (NC_028617.1) using the BWA-MEM algorithm of BWA v0.7.15 (Li and Durbin \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Variant calling was performed with SAMtools v1.19 (Li \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), and the identified SNPs were formatted into the Variant Call Format (VCF). We distinguished plastid from nuclear sequences by accepting bases at positions where coverage exceeded five-fold the average of the nuclear genome and consensus was achieved in over 90% of reads. Positions not meeting these criteria were designated as missing data, and indels were excluded.\u003c/p\u003e\u003cp\u003eIn addition to the 351 chloroplast samples, seven chloroplast genomes from Yan et al. (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) were included, resulting in a total of 358 chloroplast genomes of \u003cem\u003eJ. regia\u003c/em\u003e being obtained. We included chloroplast genomes of \u003cem\u003eJ. nigra\u003c/em\u003e (NC_035967.1) and \u003cem\u003eJ. mandshurica\u003c/em\u003e (NC_033892.1) as outgroups. Sequence alignment was conducted using MAFFT v7.475 (Katoh and Standley \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). We constructed a Maximum Likelihood (ML) tree using IQ-TREE 2 (Minh et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), employing the ModelFinder Plus method and performing 1,000 bootstrap replicates. Simultaneously, a Neighbor-Joining (NJ) tree was generated using MEGA (Stecher et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), which used the best-fit substitution model selected by the software, also with 1,000 bootstrap replicates. Additionally, haplotypes were identified using DnaSP v6, including sites with two nucleotide types or two plus N (Rozas et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eCross-population selection signatures and candidate genes under positive selection\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe employed the cross-population extended haplotype homozygosity (XP-EHH) method to detect signals of positive selection. XP-EHH scores were calculated using selscan (v2.0.3) (Szpiech and Hernandez \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Szpiech \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), with each of the five derived populations independently compared to the South Asian population as the reference. The calculation followed the methodology described by Sabeti et al. (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The analysis was performed using the following parameters: --xpehh to specify XP-EHH calculation; --unphased to allow the use of unphased genotype data; --vcf and --vcf-ref to input VCF files for the test and reference populations, respectively; and –pmap to enable physical map-based computations. A maximum inter-SNP distance of 200 kb was set using --max-gap to reduce artifacts caused by long-range linkage disequilibrium due to missing data. Rare variants were filtered out using a minor allele frequency threshold of 0.05 (--maf 0.05).\u003c/p\u003e\u003cp\u003eAs the XP-EHH statistic approximately follows a normal distribution (Sabeti et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), we normalized the raw XP-EHH scores using the “norm\" parameter in selscan. Significant SNPs were identified based on a normalized XP-EHH score threshold (normxpehh value ≥ 2) for each test population relative to the South Asian reference. We then intersected the significant SNPs across all five derived populations, yielding a set of shared loci. Allele frequencies of these SNPs were subsequently calculated across all six populations. We retained SNPs showing a consistent directional shift in allele frequency in all five derived populations relative to the South Asian reference (e.g., all increased or all decreased). Further filtering required these SNPs to be located within genes or within 5 kb upstream or downstream of gene boundaries. Gene function was annotated using eggNOG-mapper (v2.1.9) (Cantalapiedra et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) in combination with UniProt to obtain GO terms, KEGG pathways, and functional descriptions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eW.N.B. and D.Y.Z. conceived and supervised the project; W.P.Z., A.N., and J.L. collected materials; C.J.C. and Y.Y. performed the analyses of chloroplast genome, C.J.C. and Y.M.D. performed the nuclear genome analyses, X.X.P. and C.J.C. conducted simulation analyses, W.N.B., C.J.C., S.S.R. wrote the paper; S.S.R., W.N.B., C.J.C., B.W.Z., and D.Y.Z. revised and proofed the paper. All authors approved the final version.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (32370230), the \u0026ldquo;111\u0026rdquo; Program of Introducing Talents of Discipline to Universities (B13008), the Fundamental Research Funds for the Central Universities, and China Postdoctoral Science Foundation (GZB20240286 to XXP).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe entire genome resequencing data generated in this study are available on GenBank under accession number PRJNA356989 and at the National Genomics Data Center under BioProject number PRJCA010540 (https://ngdc.cncb.ac.cn/bioproject/browse/PRJCA010540). The assembled genomes are available at the National Genomics Data Center under BioProject number PRJCA010540 and can also be accessed at our website (http://cmb.bnu.edu.cn/juglans)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbondio P, Cilli E, Luiselli D. 2022. Inferring signatures of positive selection in whole-genome sequencing data: an overview of haplotype-based methods. Genes (Basel) 13.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlexander DH, Novembre J, Lange K. Fast model-based estimation of ancestry in unrelated individuals. Genome Resource. 2009;19:1655\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAllaby RG, Stevens CJ, Kistler L, Fuller DQ. Emerging evidence of plant domestication as a landscape-level process. 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Oxford: Oxford University Press; 2012. p. 149.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"genome-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gbio","sideBox":"Learn more about [Genome Biology](https://genomebiology.biomedcentral.com/)","snPcode":"13059","submissionUrl":"https://submission.springernature.com/new-submission/13059/3","title":"Genome Biology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Crop domestication, effective population size, parameter optimization, population clustering, sampling bias, STRUCTURE software","lastPublishedDoi":"10.21203/rs.3.rs-7127779/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7127779/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003e(222 words)\u003c/b\u003e: Inference of population structure is central to domestication studies, yet population clustering algorithms are prone to biases when sampling is unbalanced and effective population sizes (\u003cem\u003eN\u003c/em\u003e\u003csub\u003ee\u003c/sub\u003e) differ across populations. These confounding factors result in misclassification of large ancestral populations as admixed, rather than recognizing them as a distinct group, particularly in single-origin domestication scenarios. We propose a novel parameterization strategy for the STRUCTURE software, combining the \u003cem\u003eF\u003c/em\u003e model and alternative ancestry prior (along with a smaller initial ALPHA value). Simulation analyses demonstrate that this combination of parameters works synergistically to mitigate biases arising from unbalanced sampling and unequal population sizes. To validate its empirical utility, we apply our parameter-setting strategy to the domestication history of the common walnut (\u003cem\u003eJuglans regia\u003c/em\u003e), using whole-genome resequencing data from 399 individuals from across its range. The results support an origin of \u003cem\u003eJ. regia\u003c/em\u003e in South Asia, where walnut populations are characterized by high genetic diversity, extensive private allele content, low mutation load, and demographic stability. This finding clarifies long-standing questions about the center of walnut domestication and informs its global dispersal history. Building on this demographic framework, we further identified genomic regions under recent positive selection and detected candidate domestication genes involved in shell structure, pollen development, and lipid transport. These results underscore the utility of our approach for both domestication research and broader population-genetic studies.\u003c/p\u003e","manuscriptTitle":"Resolving sampling and population-size biases in domestication genomics supports a South Asian origin of walnuts","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-04 09:07:36","doi":"10.21203/rs.3.rs-7127779/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-19T08:04:56+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-18T14:58:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-26T14:56:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-07T15:32:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"184818309482970493087851725699008054757","date":"2025-08-04T13:10:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"207756110978922082461459876245466881174","date":"2025-08-04T11:02:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"259935995104433909572916088218173801071","date":"2025-07-30T11:38:20+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-30T06:40:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-21T09:35:47+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-16T04:40:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"Genome Biology","date":"2025-07-15T07:38:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"genome-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gbio","sideBox":"Learn more about [Genome Biology](https://genomebiology.biomedcentral.com/)","snPcode":"13059","submissionUrl":"https://submission.springernature.com/new-submission/13059/3","title":"Genome Biology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2e9ac035-91ad-4489-95ed-e794831fd2e1","owner":[],"postedDate":"August 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-02T16:05:05+00:00","versionOfRecord":{"articleIdentity":"rs-7127779","link":"https://doi.org/10.1186/s13059-026-03959-6","journal":{"identity":"genome-biology","isVorOnly":false,"title":"Genome Biology"},"publishedOn":"2026-01-30 15:58:10","publishedOnDateReadable":"January 30th, 2026"},"versionCreatedAt":"2025-08-04 09:07:36","video":"","vorDoi":"10.1186/s13059-026-03959-6","vorDoiUrl":"https://doi.org/10.1186/s13059-026-03959-6","workflowStages":[]},"version":"v1","identity":"rs-7127779","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7127779","identity":"rs-7127779","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}