Male-driven admixture facilitated subsistence shift in northern China

Abstract

Agriculture was arguably the most profound innovation in human history, and while it eventually spread throughout the planet its specific origins and dispersals are unique across different regions. It is therefore important to take into account diverse geographic and cultural contexts when trying to understand independent transitions to farming in different regions. In recent years, there has been a significant increase in sequencing ancient human genomes, enabling more regionally specific questions to be asked regarding the genetic impact of the local adoption of agriculture. One particularly informative approach is to explore the imbalance of genetic admixture from male versus female sources, which offers valuable insights into the social structures and cultural interactions of prehistoric populations. Here, we utilize publicly available data of ancient genomes from northern China, an area that is well known for one of the earliest centres for agricultural revolution, to look at potential sex biases in genetic admixture from different time periods based on autosomal and sex-specifically inherited X chromosomal variation. Our analysis identifies a higher influx of males from the Yellow River basin to the West Liao River basin during the Late Neolithic period associated with an increase in the reliance on millet farming in this region. This result underscores a distinction in farming transitions in northern China, particularly when compared to agricultural transitions in Europe, where there is no evidence of sex-biased admixture.

Introduction

The shift to agriculture was a global phenomenon, yet its origins and dispersals were regionally specific (Barker, 2009). Therefore, it is imperative to investigate the independent processes of farming transitions that unfolded in diverse manners in different regions. An essential aspect that has intensified since the agricultural revolution is genetic admixture (Skoglund and Mathieson, 2018), defined as the exchange of genetic material between distinct populations. This process has transformed human history from a series of repeated episodes of population isolation to an era characterized by expansion and contact, with profound implications for human genetic, cultural, and linguistic diversity. These enduring effects continue to shape genomic variation in diverse human populations worldwide (Patterson et al., 2012; Hellenthal et al., 2014; Nielsen et al., 2017).

During admixture events between different communities or different geographic populations, males and females often undergo distinct demographic trajectories, due to several factors including population movement, social kinship, and family structure (Seielstad et al., 1998; Oota et al., 2001; Wilkins and Marlowe, 2006; Heyer et al., 2012; Rasteiro and Chikhi, 2013; Saag et al., 2017; Mathieson et al., 2018; Jeong et al., 2020; Korunes et al., 2022). Genetic and ethnographic studies consistently indicate a higher prevalence of patrilocality in farming communities compared with hunter-gatherer populations (Marlowe, 2004; Schroeder et al., 2019; Cassidy et al., 2020). The field of ancient genomics has experienced a surge in data, particularly from Europe (Marciniak and Perry, 2017; Liu et al., 2021), uncovering recurrent and extensive human migrations and replacements that characterize the history of Europe over the past several millennia (Haak et al., 2015; Mathieson et al., 2015; Cassidy et al., 2016). Contrary to the previous hypothesis, there is no evidence of sex-biased migration during the spread of farming in Europe (Goldberg et al., 2017a). However, the patterns observed in Europe may differ in other parts of the world.

East Asia, and in particular China, is one of the earliest independent centres for agricultural revolution, including the cultivation of rainfed rice in southern China (Fuller et al., 2009; Zuo et al., 2017) and dryland millet in the northern regions (Lu et al., 2009; Liu et al., 2012; Yang et al., 2012). Northern China holds particular significance as the origin of a well-known civilization that flourished in the middle and lower reaches of the Yellow River. This civilization witnessed the invention of agriculture and the subsequent formation of states from the Neolithic period onward. Furthermore, the West Liao River basin, a major river system within the same geographic area, is increasingly acknowledged for its critical role in the adoption and spread of millet farming by the Middle Neolithic (Peterson et al., 2010; Ma et al., 2016). Over the subsequent five millennia, millets domesticated in northern China spread across East Eurasia and beyond (Chen et al., 2015; Gao et al., 2020). However, in the West Liao River basin, this subsistence strategy underwent a partial replacement by nomadic pastoralism during the Bronze Age or Iron Age period (Jia et al., 2016). This shift may have reflected cultural interactions with the Amur River region, where people persisted in a lifestyle combining hunting, fishing, animal husbandry, and the cultivation of millets until the historic era (Kuzmin, 2013).

A recent study of ancient genomics has provided compelling evidence linking the continuity or alteration of subsistence strategies to migration and admixture in northern China (Ning et al., 2020). The genetic ancestors of populations along the Yellow River and Amur River exhibit clearly distinguishable characteristics. The Amur River populations demonstrate a relatively stable genetic structure over time, possibly indicative of a consistent lifestyle over time. In contrast, the genetic make-up of Yellow River populations has been influenced by interactions with populations from southern China and Southeast Asia since the Late Neolithic period, which is potentially due to the northward expansion of rice farming. The West Liao River, situated between the Yellow River and Amur River, displays a dual genetic structure of Yellow River and Amur River ancestry. Importantly, the proportions of these two distinct ancestors vary over time, aligning with increased millet farming during the Neolithic and a partial transition to pastoralism in the Bronze Age. These migrations are also coincident with social, cultural, and even linguistic changes as proposed in a previous study (Robbeets, 2017). However, an intriguing question remains unexplored in the study, namely, whether the influx of immigrants was accompanied by potential sex bias.

Here, we utilize publicly available time transects of ancient genomes from the Yellow River, West Liao River, and Amur River regions (Ning et al., 2020). Our analysis then contrasts the genetic histories of these populations by looking at autosomal and sex-specifically inherited X chromosomal variation.

Materials and Methods

Data processing and preparation

We used ancient genomes that were previously processed and compiled (Cooke et al., 2021), with additional data from the Amur River region (Mao et al., 2021). We genotyped all ancient individuals for the biallelic sites present on the Simon Genome Diversity Project (Mallick et al., 2016) that have been filtered for transversion-only single-nucleotide polymorphisms (SNPs) on the autosomes and X chromosome with a minor allele frequency of 1%. This process resulted in 3867366 or 143265 SNP sites for the autosomes or X chromosome in our merged data. For each SNP site, we randomly called a high-quality single base (bq30) per position to create pseudo-haploid data using GATK v. 3.7-0 (McKenna et al., 2010).

Admixture modelling

Admixture events were modelled using qpAdm v1000 in the AdmixTools v. 6.0 package (Patterson et al., 2012; Haak et al., 2015) using the parameter option of ‘allsnps: YES.’ ‘Right’ populations included as outgroups in the analysis consisted of seven populations: Sardinian (n = 3), Kusunda (n = 2), Papuan (n = 14), Dai (n = 4), Ami (n = 2), Chokhopani (n = 1) (Jeong et al. 2016), and DevilsCave_N (n = 4) (Sikora et al., 2019). We modelled two-way admixture in the West Liao River populations using two distinct ancestral sources: the Yellow River and Amur River. To represent Yellow River ancestry, we incorporated Neolithic individuals from the central plain of the Yellow River Basin (Ning et al., 2020). For the Amur River ancestry, we included individuals from the Amur River region, dating between 9000 and 6000 years before the present (Ning et al., 2020; Mao et al., 2021).

To assess potential sex bias in admixture processes, we utilized the method developed in previous studies (Mathieson et al., 2018; Jeong et al., 2020). Briefly, we first estimated ancestry proportions using qpAdm with autosomal or X chromosomal data (adding ‘chrom:23’ to the parameter file) separately. We then calculated a Z-score based on the difference in the proportions between the autosomes (P_A) and X chromosome (P_X), divided by their standard errors, $Z=\left(P_{\mathrm{A}}-P_{\mathrm{X}}\right)/\sqrt{{\mathrm{\sigma }}_{\mathrm{A}}^2+{\mathrm{\sigma }}_{\mathrm{X}}^2}$ , where σ_A and σ_X are the jackknife standard errors of the ancestry proportions on the autosomes and X chromosome, respectively. A positive Z-score indicates that autosomes carry a particular ancestry more than X chromosomes, suggesting male-driven admixture. In contrast, a negative Z-score suggests female-driven admixture.

Results

Sex-specific behaviours leave signatures on the patterns of genetic variation that is differentially inherited between males and females within a population. To elucidate the sex-specific demography and its relationships with cultural transitions in northern China, we employ admixture modelling based on qpAdm to estimate the proportions of Yellow River and Amur River ancestry on both autosomes and X chromosome in individuals from the West Liao River region. Given that males exclusively inherit a maternal X chromosome, any disparities in these proportions between autosomes and the X chromosome suggest sex-biased admixture.

Our modelling, based on the autosomal variation, effectively reproduced the temporal shifts in the proportions of dual ancestry structure in the West Liao River populations (Figure 1, Table 1), as documented in a previous study (Ning et al., 2020). A Middle Neolithic individual from the Haminmangha site in Inner Mongolia (labelled ‘HMMH_MN’) carried a substantial proportion of Amur River ancestry (>80%). In contrast, individuals from a site located approximately 200–300 km closer to the Yellow River region than HMMH_MN, who are labelled as ‘WLR_MN,’ exhibited significantly higher Yellow River ancestry (~40%) than HMMH_MN (~20%), even though they were nearly contemporaneous. During the Late Neolithic period (‘WLR_LN’), Yellow River ancestry became predominant, indicating a substantial influx of individuals from the Yellow River basin, as supported by archaeological evidence of an increased reliance on millet farming. However, in the Bronze Age (‘WLR_BA’), the proportions of the two ancestors became balanced, possibly due to a partial replacement of millet farming with pastoralism.

Figure 1.

Sex-biased patterns of admixture based on autosomal and X chromosomal variation in the West Liao River basin. Bar plots illustrate the proportions of two distinct ancestral components, Yellow River (orange) and Amur River ancestry (blue), across spatially and temporally diverse populations from the West Liao River Basin. The populations examined include: HMMH_MN, a Middle Neolithic individual from the Haminmangha site in Inner Mongolia; WLR_MN, three Middle Neolithic individuals associated with the Hongshan culture spanning from 6500 years before present (BP) to 5000 years BP; WLR_LN, three Late Neolithic individuals linked to the Lower Xiajiadian culture; WLR_BA, two Bronze Age individuals associated with the Upper Xiajiadian culture. Vertical bars represent ±1 standard error estimated by qpAdm. Detailed admixture proportions and their standard errors, in addition to the number of individuals, are presented in Table 1. The admixture proportion of the HMMH_MN individual based on the X chromosome is not measurable due to insufficient SNP sites. Sex-biased admixture is assessed by differences in proportions of Yellow River ancestry between autosomes $\left(P_{\mathrm{A}}^{\text{Yellow River}}\right)$ and X chromosomes $\left(P_{\mathrm{X}}^{\text{Yellow River}}\right)$ (see the y-axis in the bottom plots). Bars represent three standard errors of the difference in ancestry proportions. Positive scores indicate more Yellow River ancestry on the autosomes than the X chromosome, which can be interpreted as a signature of male-driven admixture.

Table 1.

Admixture proportions of dual ancestors estimated from the autosomal and X chromosomal variation in the West Liao River populations.

Admixture model			Target populations from West Liao River
Chromosome	Dual ancestors		HMMH_MN (3665 BCE; n = 1)	WLR_MN (3300 BCE; n = 3)	WLR_LN (1697 BCE; n = 3)	WLR_BA (700 BCE; n = 2)
Autosomes	Yellow River		0.148 ± 0.051	0.368 ± 0.047	0.92 ± 0.081	0.567 ± 0.048
	Amur River		0.852 ± 0.051	0.632 ± 0.047	0.08 ± 0.081	0.433 ± 0.048
	Tail probability		0.0002	0.9154	0.2587	0.3941
X chromosome	Yellow River		NA	0.693 ± 0.219	0.353 ± 0.146	0.275 ± 0.179
	Amur River		NA	0.307 ± 0.219	0.647 ± 0.146	0.725 ± 0.179
	Tail probability		NA	0.2018	0.6109	0.1457

NA, not available.

The X chromosome shows distinct patterns of the shifts in the dual-ancestry structure over time (Figure 1). Due to a limited number of genetic variants available on this sex-linked chromosome, the estimated proportions have a considerably large variance. Nevertheless, by examining the disparity in the proportion of Yellow River ancestry between the autosomes and X chromosome, we can assess whether Yellow River ancestry is more enriched in the autosomes than the X chromosome (i.e. Z-score tending to be positive direction), indicative of male-biased admixture, or vice versa (i.e. female-biased admixture; Z-score tending to be negative).

Consistent with autosomal data, a higher proportion of Yellow River ancestry relative to Amur River ancestry is evident on the X chromosome among the Middle Neolithic individuals associated with the Hongshan culture from the West Liao River basin. These results suggest that the admixture during this period may have been unbiased with respect to sexes, as indicated with Z = –1.451 (Figure 1). However, this trend shifted during the Late Neolithic period; while the proportion of Yellow River ancestry is high on the autosomes, it is less prominent on the X chromosome. Indeed, we observe a significant signal of male-biased Yellow River admixture (Z = 3.396). It is important to note that within the WLR_LN group, two males are included, one of whom carries the Y chromosome lineage O2a, a predominant haplogroup among ancient Yellow River individuals (Ning et al., 2020). The later Bronze Age period marks an increase in Amur River ancestry, without any evidence of sex bias (Z = 1.576).

Discussion

Our findings uncover sex-specific dynamics in the agricultural expansion of northern China. The substantial rise in Yellow River ancestry observed in the autosomes of WLR_LN, compared to the X chromosome, serves as compelling evidence for a higher influx of males migrating from the Yellow River region. This migration led to admixture with the local population in the West Liao River area. This male-biased pattern aligns with a previous discovery indicating that Y chromosomal data, rather than mitochondrial DNA, lends more support to a south-to-north migration in contemporary Korean populations (Pan and Xu, 2020). Importantly, our study highlights a regional distinction in the farming transition processes, particularly when compared to agricultural transitions in Europe where there is no evidence of sex-biased admixture during the early Neolithic (Goldberg et al., 2017a).

As indicated in this study, estimating admixture proportions using ancient genomic data often involves certain degrees of uncertainty due to various factors, such as small sample sizes and low coverage (Lazaridis and Reich, 2017; Goldberg et al., 2017b). These factors should be carefully taken into account when interpreting results for sex-biased admixture, as they directly impact a number of analysable SNPs on the X chromosome and can pose challenges in observing statistical significance. Nevertheless, the growing number of higher-resolution ancient genomes holds promise for unravelling the diverse processes of sex-biased demography, which may mirror sex differences in cultural practices and social structures (Brielle et al., 2023; Gnecchi-Ruscone et al., 2024).

This study serves as a proof-of-concept by concurrently leveraging autosomal and X chromosomal variation among ancient individuals to infer crucial processes in human history. Future research, incorporating larger and more densely sampled time transects, will further enhance our understanding of the mechanisms by which farming spread across a broader area of East Asia through interactions between immigrants and local populations.

Acknowledgments

This work was supported by the Wellcome Trust ISSF Award (to S.N.) and grants from the Japan Society for the Promotion of Science KAKENHI (nos. 20H05822 and 22H02711 to S.N.). We would like to thank the anonymous reviewers for their constructive feedback.

Author contributions

S.N. conceived the study. N.P.C. processed data. S.N. analysed data. S.N. wrote the manuscript with input from N.P.C.

References

•  Barker  G. (2009) The Agricultural Revolution in Prehistory: Why Did Foragers Become Farmers? Oxford University Press, Oxford.
•  Brielle  E.S.,  Fleisher  J.,  Wynne-Jones  S.,  Sirak  K.,  Broomandkhoshbacht  N.,  Callan  K.,  Curtis  E.,  Iliev  L.,  Lawson  A.M.,  Oppenheimer  J., et al (2023) Entwined African and Asian genetic roots of medieval peoples of the Swahili coast. Nature, 615: 866–873.
•  Cassidy  L.M.,  Martiniano  R.,  Murphy  E.M.,  Teasdale  M.D.,  Mallory  J.,  Hartwell  B., and  Bradley  D.G. (2016) Neolithic and Bronze Age migration to Ireland and establishment of the insular Atlantic genome. Proceedings of the National Academy of Sciences of the United States of America, 113: 368–373.
•  Cassidy  L.M.,  Maoldúin  R.Ó.,  Kador  T.,  Lynch  A.,  Jones  C.,  Woodman  P.C.,  Murphy  E.,  Ramsey  G.,  Dowd  M.,  Noonan  A., et al (2020) A dynastic elite in monumental Neolithic society. Nature, 582: 384–388.
•  Chen  F.H.,  Dong  G.H.,  Zhang  D.J.,  Liu  X.Y.,  Jia  X.,  An  C.B.,  Ma  M.M.,  Xie  Y.W.,  Barton  L.,  Ren  X.Y. et al (2015) Agriculture facilitated permanent human occupation of the Tibetan Plateau after 3600 BP. Science, 347: 248–250.
•  Cooke  N.P.,  Mattiangeli  V.,  Cassidy  L.M.,  Okazaki  K.,  Stokes  C.A.,  Onbe  S.,  Hatakeyama  S.,  Machida  K.,  Kasai  K.,  Tomioka  N. et al (2021) Ancient genomics reveals tripartite origins of Japanese populations. Science Advances, 7: eabh2419.
•  Fuller  D.Q.,  Qin  L.,  Zheng  Y.,  Zhao  Z.,  Chen  X.,  Hosoya  L.A., and  Sun  G.-P. (2009) The domestication process and domestication rate in rice: spikelet bases from the Lower Yangtze. Science, 323: 1607–1610.
•  Gao  Y.,  Dong  G.,  Yang  X., and  Chen  F. (2020) A review on the spread of prehistoric agriculture from southern China to mainland Southeast Asia. Science China Earth Sciences, 63: 615–625.
•  Gnecchi-Ruscone  G.A.,  Rácz  Z.,  Samu  L.,  Szeniczey  T.,  Faragó  N.,  Knipper  C.,  Friedrich  R.,  Zlámalová  D.,  Traverso  L.,  Liccardo  S. et al (2024) Network of large pedigrees reveals social practices of Avar communities. Nature, 629: 376–383.
•  Goldberg  A.,  Günther  T.,  Rosenberg  N.A., and  Jakobsson  M. (2017a) Ancient X chromosomes reveal contrasting sex bias in Neolithic and Bronze Age Eurasian migrations. Proceedings of the National Academy of Sciences of the United States of America, 114: 2657–2662.
•  Goldberg  A.,  Günther  T.,  Rosenberg  N.A., and  Jakobsson  M. (2017b) Reply to Lazaridis and Reich: robust model-based inference of male-biased admixture during Bronze Age migration from the Pontic-Caspian Steppe. Proceedings of the National Academy of Sciences of the United States of America, 114: E3875–E3877.
•  Haak  W.,  Lazaridis  I.,  Patterson  N.,  Rohland  N.,  Mallick  S.,  Llamas  B.,  Brandt  G.,  Nordenfelt  S.,  Harney  E.,  Stewardson  K. et al (2015) Massive migration from the steppe was a source for Indo-European languages in Europe. Nature, 522: 207–211.
•  Hellenthal  G.,  Busby  G.B.J.,  Band  G.,  Wilson  J.F.,  Capelli  C.,  Falush  D., and  Myers  S. (2014) A genetic atlas of human admixture history. Science, 343: 747–751.
•  Heyer  E.,  Chaix  R.,  Pavard  S., and  Austerlitz  F. (2012) Sex-specific demographic behaviours that shape human genomic variation. Molecular Ecology, 21: 597–612.
•  Jeong  C.,  Ozga  A.T.,  Witonsky  D.B.,  Malmström  H.,  Edlund  H.,  Hofman  C.A.,  Hagan  R.W.,  Jakobsson  M.,  Lewis  C.M.,  Aldenderfer  M.S. et al (2016) Long-term genetic stability and a high-altitude East Asian origin for the peoples of the high valleys of the Himalayan arc. Proceedings of the National Academy of Sciences of the United States of America, 113: 7485–7490.
•  Jeong  C.,  Wang  K.,  Wilkin  S.,  Taylor  W.T.T.,  Miller  B.K.,  Bemmann  J.H.,  Stahl  R.,  Chiovelli  C.,  Knolle  F.,  Ulziibayar  S. et al (2020) A Dynamic 6000-Year Genetic History of Eurasia’s Eastern Steppe. Cell, 183: 890–904.e29.
•  Jia  X.,  Sun  Y.,  Wang  L.,  Sun  W.,  Zhao  Z.,  Lee  H.F.,  Huang  W.,  Wu  S., and  Lu  H. (2016) The transition of human subsistence strategies in relation to climate change during the Bronze Age in the West Liao River Basin, Northeast China. Holocene, 26: 781–789.
•  Korunes  K.L.,  Soares-Souza  G.B.,  Bobrek  K.,  Tang  H.,  Araújo  I.I.,  Goldberg  A., and  Beleza  S. (2022) Sex-biased admixture and assortative mating shape genetic variation and influence demographic inference in admixed Cabo Verdeans. G3 (Bethesda, Md), 12(10): jkac183. http://dx.doi.org/10.1093/g3journal/jkac183
•  Kuzmin  Y.V. (2013) The beginnings of prehistoric agriculture in the Russian Far East: current evidence and concepts. Documenta Praehistorica, 40: 1–12.
•  Lazaridis  I. and  Reich  D. (2017) Failure to replicate a genetic signal for sex bias in the steppe migration into central Europe. Proceedings of the National Academy of Sciences of the United States of America, 114: E3873–E3874.
•  Liu  X.,  Jones  M.K.,  Zhao  Z.,  Liu  G., and  O’Connell  T.C. (2012) The earliest evidence of millet as a staple crop: new light on Neolithic foodways in North China. American Journal of Biological Anthropology, 149: 283–290.
•  Liu  Y.,  Mao  X.,  Krause  J., and  Fu  Q. (2021) Insights into human history from the first decade of ancient human genomics. Science, 373: 1479–1484.
•  Lu  H.,  Zhang  J.,  Liu  K.-B.,  Wu  N.,  Li  Y.,  Zhou  K.,  Ye  M.,  Zhang  T.,  Zhang  H.,  Yang  X. et al (2009) Earliest domestication of common millet (Panicum miliaceum) in East Asia extended to 10000 years ago. Proceedings of the National Academy of Sciences of the United States of America, 106: 7367–7372.
•  Ma  Z.,  Yang  X.,  Zhang  C.,  Sun  Y., and  Jia  X. (2016) Early millet use in West Liaohe area during Early–Middle Holocene. Science China Earth Sciences, 59: 1554–1561.
•  Mallick  S.,  Li  H.,  Lipson  M.,  Mathieson  I.,  Gymrek  M.,  Racimo  F.,  Zhao  M.,  Chennagiri  N.,  Nordenfelt  S.,  Tandon  A. et al (2016) The Simons Genome Diversity Project: 300 genomes from 142 diverse populations. Nature, 538: 201–206.
•  Mao  X.,  Zhang  H.,  Qiao  S.,  Liu  Y.,  Chang  F.,  Xie  P.,  Zhang  M.,  Wang  T.,  Li  M.,  Cao  P. et al (2021) The deep population history of northern East Asia from the Late Pleistocene to the Holocene. Cell, 184: 3256–3266.e13.
•  Marciniak  S. and  Perry  G.H. (2017) Harnessing ancient genomes to study the history of human adaptation. Nature Reviews Genetics, 18: 659–674.
•  Marlowe  F.W. (2004) Marital residence among foragers. Current Anthropology, 45: 277–284.
•  Mathieson  I.,  Lazaridis  I.,  Rohland  N.,  Mallick  S.,  Patterson  N.,  Roodenberg  S.A.,  Harney  E.,  Stewardson  K.,  Fernandes  D.,  Novak  M. et al (2015) Genome-wide patterns of selection in 230 ancient Eurasians. Nature, 528: 499–503.
•  Mathieson  I.,  Alpaslan-Roodenberg  S.,  Posth  C.,  Szécsényi-Nagy  A.,  Rohland  N.,  Mallick  S.,  Olalde  I.,  Broomandkhoshbacht  N.,  Candilio  F.,  Cheronet  O. et al (2018) The genomic history of southeastern Europe. Nature, 555: 197–203.
•  McKenna  A.,  Hanna  M.,  Banks  E.,  Sivachenko  A.,  Cibulskis  K.,  Kernytsky  A.,  Garimella  K.,  Altshuler  D.,  Gabriel  S.,  Daly  M. et al (2010) The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Research, 20: 1297–1303.
•  Nielsen  R.,  Akey  J.M.,  Jakobsson  M.,  Pritchard  J.K.,  Tishkoff  S., and  Willerslev  E. (2017) Tracing the peopling of the world through genomics. Nature, 541: 302–310.
•  Ning  C.,  Li  T.,  Wang  K.,  Zhang  F.,  Li  T.,  Wu  X.,  Gao  S.,  Zhang  Q.,  Zhang  H.,  Hudson  M.J. et al (2020) Ancient genomes from northern China suggest links between subsistence changes and human migration. Nature Communications, 11: 2700.
•  Oota  H.,  Settheetham-Ishida  W.,  Tiwawech  D.,  Ishida  T., and  Stoneking  M. (2001) Human mtDNA and Y-chromosome variation is correlated with matrilocal versus patrilocal residence. Nature Genetics, 29: 20–21.
•  Pan  Z. and  Xu  S. (2020) Population genomics of East Asian ethnic groups. Hereditas, 157: 49.
•  Patterson  N.,  Moorjani  P.,  Luo  Y.,  Mallick  S.,  Rohland  N.,  Zhan  Y.,  Genschoreck  T.,  Webster  T., and  Reich  D. (2012) Ancient admixture in human history. Genetics, 192: 1065–1093.
•  Peterson  C.E.,  Lu  X.,  Drennan  R.D., and  Zhu  D. (2010) Hongshan chiefly communities in Neolithic northeastern China. Proceedings of the National Academy of Sciences of the United States of America, 107: 5756–5761.
•  Rasteiro  R. and  Chikhi  L. (2013) Female and male perspectives on the Neolithic transition in Europe: clues from ancient and modern genetic data. PLoS One, 8: e60944.
•  Robbeets  M. (2017) Proto-Transeurasian: where and when? Man in India: An International Journal of Anthropology, 97: 19–46.
•  Saag  L.,  Varul  L.,  Scheib  C.L.,  Stenderup  J.,  Allentoft  M.E.,  Saag  L.,  Pagani  L.,  Reidla  M.,  Tambets  K.,  Metspalu  E. et al (2017) Extensive farming in Estonia started through a sex-biased migration from the steppe. Current Biology, 27: 2185–2193.e6.
•  Schroeder  H.,  Margaryan  A.,  Szmyt  M.,  Theulot  B.,  Włodarczak  P.,  Rasmussen  S.,  Gopalakrishnan  S.,  Szczepanek  A.,  Konopka  T.,  Jensen  T.Z.T. et al (2019) Unraveling ancestry, kinship, and violence in a Late Neolithic mass grave. Proceedings of the National Academy of Sciences of the United States of America, 116: 10705–10710.
•  Seielstad  M.T.,  Minch  E., and  Cavalli-Sforza  L.L. (1998) Genetic evidence for a higher female migration rate in humans. Nature Genetics, 20: 278–280.
•  Sikora  M.,  Pitulko  V.V.,  Sousa  V.C.,  Allentoft  M.E.,  Vinner  L.,  Rasmussen  S.,  Margaryan  A.,  de Barros Damgaard  P.,  de la Fuente  C.,  Renaud  G. et al (2019) The population history of northeastern Siberia since the Pleistocene. Nature, 570: 182–188.
•  Skoglund  P. and  Mathieson  I. (2018) Ancient genomics of modern humans: the first decade. Annual Review of Genomics and Human Genetics, 19: 381–404.
•  Wilkins  J.F. and  Marlowe  F.W. (2006) Sex-biased migration in humans: what should we expect from genetic data? Bioessays, 28: 290–300.
•  Yang  X.,  Wan  Z.,  Perry  L.,  Lu  H.,  Wang  Q.,  Zhao  C.,  Li  J.,  Xie  F.,  Yu  J.,  Cui  T. et al (2012) Early millet use in northern China. Proceedings of the National Academy of Sciences of the United States of America, 109: 3726–3730.
•  Zuo  X.,  Lu  H.,  Jiang  L.,  Zhang  J.,  Yang  X.,  Huan  X.,  He  K.,  Wang  C., and  Wu  N. (2017) Dating rice remains through phytolith carbon-14 study reveals domestication at the beginning of the Holocene. Proceedings of the National Academy of Sciences of the United States of America, 114: 6486–6491.