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

(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.