Genomic imputation of ancient Asian populations contrasts local adaptation in pre- and post-agricultural Japan.

Early modern humans lived as hunter-gatherers for millennia before agriculture, yet the genetic adaptations of these populations remain a mystery. Here, we investigate selection in the ancient hunter-gatherer-fisher Jomon and contrast pre- and post-agricultural adaptation in the Japanese archipelago. Building on the successful validation of imputation with ancient Asian genomes, we identify selection signatures in the Jomon, particularly robust signals from KITLG variants, which may have influenced dark pigmentation evolution. The Jomon lacks well-known adaptive variants (EDAR, ADH1B, and ALDH2), marking their emergence after the advent of farming in the archipelago. Notably, the EDAR and ADH1B variants were prevalent in the archipelago 1,300 years ago, whereas the ALDH2 variant could have emerged later due to its absence in other ancient genomes. Overall, our study underpins local adaptation unique to the Jomon population, which in turn sheds light on post-farming selection that continues to shape contemporary Asian populations.

Genomic insights into a tripartite ancestry in the Southern Ryukyu Islands.

A tripartite structure for the genetic origin of Japanese populations states that present-day populations are descended from three main ancestors: (1) the indigenous Jomon hunter-gatherers; (2) a Northeast Asian component that arrived during the agrarian Yayoi period; and (3) a major influx of East Asian ancestry in the imperial Kofun period. However, the genetic heterogeneity observed in different regions of the Japanese archipelago highlights the need to assess the applicability and suitability of this model. Here, we analyse historic genomes from the southern Ryukyu Islands, which have unique cultural and historical backgrounds compared with other parts of Japan. Our analysis supports the tripartite structure as the best fit in this region, with significantly higher estimated proportions of Jomon ancestry than mainland Japanese. Unlike the main islands, where each continental ancestor was directly brought by immigrants from the continent, those who already possessed the tripartite ancestor migrated to the southern Ryukyu Islands and admixed with the prehistoric people around the eleventh century AD, coinciding with the emergence of the Gusuku period. These results reaffirm the tripartite model in the southernmost extremes of the Japanese archipelago and show variability in how the structure emerged in diverse geographic regions.

Ancient genomics reveals tripartite origins of Japanese populations.

Prehistoric Japan underwent rapid transformations in the past 3000 years, first from foraging to wet rice farming and then to state formation. A long-standing hypothesis posits that mainland Japanese populations derive dual ancestry from indigenous Jomon hunter-gatherer-fishers and succeeding Yayoi farmers. However, the genomic impact of agricultural migration and subsequent sociocultural changes remains unclear. We report 12 ancient Japanese genomes from pre- and postfarming periods. Our analysis finds that the Jomon maintained a small effective population size of ~1000 over several millennia, with a deep divergence from continental populations dated to 20,000 to 15,000 years ago, a period that saw the insularization of Japan through rising sea levels. Rice cultivation was introduced by people with Northeast Asian ancestry. Unexpectedly, we identify a later influx of East Asian ancestry during the imperial Kofun period. These three ancestral components continue to characterize present-day populations, supporting a tripartite model of Japanese genomic origins.

Fine-tuning of Approximate Bayesian Computation for human population genomics.

Approximate Bayesian Computation (ABC) is a flexible statistical tool widely applied to addressing a variety of questions regarding the origin and evolution of humans. The significant growth of genomic scale data from diverse geographic populations has facilitated the use of ABC in modelling the complex processes that underlie human demography and local adaptation. However, a fundamental issue still remains in how to efficiently capture patterns of genetic variation with a set of summary statistics in order to achieve better approximation of Bayesian inference. Here, we review recent advances in ABC methodology and its applications for human population genomics, with a particular focus on optimal tuning of ABC approaches for different types of genetic data and different sets of evolutionary parameters.