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. 2022 Jan;601(7894):588-594.
doi: 10.1038/s41586-021-04287-4. Epub 2021 Dec 22.

Large-scale migration into Britain during the Middle to Late Bronze Age

Nick Patterson #  1   2 , Michael Isakov #  3 , Thomas Booth #  4 , Lindsey Büster #  5 , Claire-Elise Fischer #  5 , Iñigo Olalde  6   7   8 , Harald Ringbauer  1   2 , Ali Akbari  1   6 , Olivia Cheronet  9 , Madeleine Bleasdale  5 , Nicole Adamski  6   10 , Eveline Altena  11 , Rebecca Bernardos  6 , Selina Brace  4 , Nasreen Broomandkhoshbacht  6   10 , Kimberly Callan  6   10 , Francesca Candilio  12 , Brendan Culleton  13 , Elizabeth Curtis  6   10 , Lea Demetz  9 , Kellie Sara Duffett Carlson  9 , Ceiridwen J Edwards  14 , Daniel M Fernandes  9   15 , M George B Foody  15 , Suzanne Freilich  9 , Helen Goodchild  5 , Aisling Kearns  6 , Ann Marie Lawson  6   10 , Iosif Lazaridis  1   6 , Matthew Mah  2   6   10 , Swapan Mallick  2   6   10 , Kirsten Mandl  9 , Adam Micco  2   6 , Megan Michel  6   10 , Guillermo Bravo Morante  9 , Jonas Oppenheimer  6   10 , Kadir Toykan Özdoğan  9 , Lijun Qiu  6 , Constanze Schattke  9 , Kristin Stewardson  6   10 , J Noah Workman  6 , Fatma Zalzala  6   10 , Zhao Zhang  6 , Bibiana Agustí  16 , Tim Allen  17 , Katalin Almássy  18 , Luc Amkreutz  19   20 , Abigail Ash  21 , Christèle Baillif-Ducros  22 , Alistair Barclay  23 , László Bartosiewicz  24 , Katherine Baxter  25 , Zsolt Bernert  26 , Jan Blažek  27 , Mario Bodružić  28 , Philippe Boissinot  29 , Clive Bonsall  30 , Pippa Bradley  23 , Marcus Brittain  31 , Alison Brookes  32 , Fraser Brown  17 , Lisa Brown  33 , Richard Brunning  34 , Chelsea Budd  35 , Josip Burmaz  36 , Sylvain Canet  22 , Silvia Carnicero-Cáceres  37 , Morana Čaušević-Bully  38 , Andrew Chamberlain  39 , Sébastien Chauvin  22 , Sharon Clough  23 , Natalija Čondić  40 , Alfredo Coppa  6   9   41 , Oliver Craig  5 , Matija Črešnar  42   43 , Vicki Cummings  44 , Szabolcs Czifra  45 , Alžběta Danielisová  46 , Robin Daniels  47 , Alex Davies  17 , Philip de Jersey  48 , Jody Deacon  49 , Csilla Deminger  50 , Peter W Ditchfield  51 , Marko Dizdar  52 , Miroslav Dobeš  46 , Miluše Dobisíková  53 , László Domboróczki  54 , Gail Drinkall  55 , Ana Đukić  56 , Michal Ernée  46 , Christopher Evans  31 , Jane Evans  57 , Manuel Fernández-Götz  30 , Slavica Filipović  58 , Andrew Fitzpatrick  59 , Harry Fokkens  20 , Chris Fowler  60 , Allison Fox  61 , Zsolt Gallina  62 , Michelle Gamble  63 , Manuel R González Morales  64 , Borja González-Rabanal  65 , Adrian Green  66 , Katalin Gyenesei  67 , Diederick Habermehl  68 , Tamás Hajdu  26   69 , Derek Hamilton  70 , James Harris  32 , Chris Hayden  17 , Joep Hendriks  71 , Bénédicte Hernu  72 , Gill Hey  17 , Milan Horňák  42 , Gábor Ilon  73 , Eszter Istvánovits  74 , Andy M Jones  75 , Martina Blečić Kavur  76 , Kevin Kazek  77 , Robert A Kenyon  78 , Amal Khreisheh  34 , Viktória Kiss  79 , Jos Kleijne  80 , Mark Knight  31 , Lisette M Kootker  81 , Péter F Kovács  82 , Anita Kozubová  83 , Gabriella Kulcsár  84 , Valéria Kulcsár  85 , Christophe Le Pennec  86 , Michael Legge  87 , Matt Leivers  88 , Louise Loe  17 , Olalla López-Costas  89 , Tom Lord  90 , Dženi Los  36 , James Lyall  91 , Ana B Marín-Arroyo  65 , Philip Mason  43 , Damir Matošević  92 , Andy Maxted  93 , Lauren McIntyre  17 , Jacqueline McKinley  89 , Kathleen McSweeney  30 , Bernard Meijlink  94 , Balázs G Mende  84 , Marko Menđušić  95 , Milan Metlička  96 , Sophie Meyer  97 , Kristina Mihovilić  98 , Lidija Milasinovic  99 , Steve Minnitt  34 , Joanna Moore  100 , Geoff Morley  101 , Graham Mullan  102 , Margaréta Musilová  103 , Benjamin Neil  31 , Rebecca Nicholls  104 , Mario Novak  105 , Maria Pala  15 , Martin Papworth  106 , Cécile Paresys  22 , Ricky Patten  31 , Domagoj Perkić  107 , Krisztina Pesti  108 , Alba Petit  109 , Katarína Petriščáková  110 , Coline Pichon  111 , Catriona Pickard  30 , Zoltán Pilling  112 , T Douglas Price  113 , Siniša Radović  114 , Rebecca Redfern  115 , Branislav Resutík  103 , Daniel T Rhodes  116 , Martin B Richards  15 , Amy Roberts  117 , Jean Roefstra  118 , Pavel Sankot  119 , Alena Šefčáková  120 , Alison Sheridan  121 , Sabine Skae  122 , Miroslava Šmolíková  110 , Krisztina Somogyi  123 , Ágnes Somogyvári  124 , Mark Stephens  125 , Géza Szabó  126 , Anna Szécsényi-Nagy  84 , Tamás Szeniczey  26   69 , Jonathan Tabor  31 , Károly Tankó  127 , Clenis Tavarez Maria  128 , Rachel Terry  129 , Biba Teržan  42 , Maria Teschler-Nicola  9   130 , Jesús F Torres-Martínez  131 , Julien Trapp  77 , Ross Turle  132 , Ferenc Ujvári  108 , Menno van der Heiden  133 , Petr Veleminsky  53 , Barbara Veselka  134   135 , Zdeněk Vytlačil  53 , Clive Waddington  136 , Paula Ware  125 , Paul Wilkinson  137 , Linda Wilson  102 , Rob Wiseman  31 , Eilidh Young  138 , Joško Zaninović  139 , Andrej Žitňan  140 , Carles Lalueza-Fox  141 , Peter de Knijff  11 , Ian Barnes  4 , Peter Halkon  142 , Mark G Thomas  143 , Douglas J Kennett  144 , Barry Cunliffe  145 , Malcolm Lillie  35   146 , Nadin Rohland  2   6 , Ron Pinhasi  147   148 , Ian Armit  149 , David Reich  150   151   152   153
Affiliations

Large-scale migration into Britain during the Middle to Late Bronze Age

Nick Patterson et al. Nature. 2022 Jan.

Abstract

Present-day people from England and Wales have more ancestry derived from early European farmers (EEF) than did people of the Early Bronze Age1. To understand this, here we generated genome-wide data from 793 individuals, increasing data from the Middle to the Late Bronze Age and Iron Age in Britain by 12-fold, and western and central Europe by 3.5-fold. Between 1000 and 875 BC, EEF ancestry increased in southern Britain (England and Wales) but not northern Britain (Scotland) due to incorporation of migrants who arrived at this time and over previous centuries, and who were genetically most similar to ancient individuals from France. These migrants contributed about half the ancestry of people of England and Wales from the Iron Age, thereby creating a plausible vector for the spread of early Celtic languages into Britain. These patterns are part of a broader trend of EEF ancestry becoming more similar across central and western Europe in the Middle to the Late Bronze Age, coincident with archaeological evidence of intensified cultural exchange2-6. There was comparatively less gene flow from continental Europe during the Iron Age, and the independent genetic trajectory in Britain is also reflected in the rise of the allele conferring lactase persistence to approximately 50% by this time compared to approximately 7% in central Europe where it rose rapidly in frequency only a millennium later. This suggests that dairy products were used in qualitatively different ways in Britain and in central Europe over this period.

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Conflict of interest statement

Competing interests The authors declare no competing interests.

Figures

Extended Data Fig. 1 |
Extended Data Fig. 1 |. Post-MBA Britain was not a mix of earlier British populations.
(A) qpAdm p-values for modeling British groups as a mix of Neolithic and Chalcolithic/EBA populations from England and Wales or Scotland (outgroups OldAfrica, OldSteppe, Turkey_N, CzechRepublic.Slovakia.Germany_3800.to.2700BP, Netherlands_C.EBA, Poland_Globular_Amphora, Spain.Portugal_4425.to.3800BP, CzechRepublic.Slovakia.Germany_4465.to.3800.BP, Sardinia_4100.to.2700BP, Sardinia_8100.to.4100BP, Spain.Portugal_6500.to.4425BP). We highlight p<0.05 (yellow) or p<0.005 (red). Both sources and target populations in this analysis remove outlier individuals (“Filter 2” in Supplementary Table 5); we obtain qualitatively similar results when outlier individuals are not removed (not shown). (B) To obtain insight into the source of the new ancestry in Britain in the IA, we computed f4(England.and.Wales_IA, α(England.and.Wales_N) + (1-α)(England.Wales_C.EBA); R1, R2) for different (R1, R2) population pairs. If England.and.Wales_IA is a simple mixture of England.and.Wales_N and England.and.Wales_C.EBA without additional ancestry, then for some mixture proportion the statistic will be consistent with zero for all (R1, R2 pairs). When (R1, R2) = (OldAfrica, OldSteppe) feasible Z-scores (Z1 in the plot) are observed when α∼0.85, showing that ~85% ancestry from England.and.Wales_C.EBA ancestry is needed to contribute the observed proportion of Steppe ancestry in England.and.Wales_IA. However, when (R1, R2) is (Balkan_N, Sardinian_8100.to.4100BP), we get infeasible Z-scores (Z2) of <−6 across the range where Z1 is remotely feasible. Thus, Iron Age people from England and Wales must have ancestry from an additional population deeply related to Sardinian Early Neolithic groups.
Extended Data Fig. 2 |
Extended Data Fig. 2 |. By-individual analysis of the British time transect.
Version of Figure 3 with the time transect extended into the Neolithic, and adding in individuals from Scotland. We plot mean estimates of EEF ancestry and one standard error bars from a Block Jackknife for all individuals in the time transect that pass basic quality control, that fit to a three-way admixture model (EEF + WHG + Yamnaya) at p>0.01 using qpAdm, and for the Neolithic period that fit a two-way admixture model (EEF + WHG) at p>0.01. Individuals that fit the main cluster of their time are shown in blue (southern Britain) and green (Scotland), while red and orange respectively show outliers at the ancestry tails (identified either as p<0.005 based on a qpWave test from the main cluster of individuals from their period and |Z|>3 for a difference in their EEF ancestry proportion from the period, or alternatively p<0.1 and |Z|>3.5). The averages for the main clusters in both southern Britain and Scotland in each archaeological period (Neolithic, C/EBA, MBA, LBA and IA) are shown in dashed lines.
Extended Data Fig. 3 |
Extended Data Fig. 3 |. Changes in the size of the mate pool over time.
Close kin unions were rare at all periods as reflected in the paucity of individuals harbouring >50 centimorgans (cM) of their genome in runs of homozygosity (ROH) of >12 cM (red dots in top panel). The number of ROH of size 4–8 cM per individual (bottom panel) reflects the rate at which distant relatives have children, providing information about the sizes of mate pools (Ne) averaged over the hundreds of years prior to when individuals lived; thus, the broad trend of an approximately four-fold drop in Ne from the Neolithic to the IA is robust, but we may miss fluctuations on a time scale of centuries. The thick black lines represent the mean Ne obtained by fitting a mathematical model of Gaussian process with a 600-year smoothing kernel (gray area 95% confidence interval). The horizontal grey lines show period averages from maximum likelihood which can differ from the mean obtained through the mathematical modeling if the counts do not confirm well to a Gaussian process. We interrupt the fitted line for periods with too little data for accurate inference (
Extended Data Fig. 4 |
Extended Data Fig. 4 |. Frequency change over time at two phenotypically important alleles.
Present-day frequencies are shown by the red dashed lines; sample sizes for each epoch are labeled at the bottom of each plot; and we show means along with 95% confidence intervals (Supplementary Table 8). (A-D/Top) Lactase persistence allele at rs4988235. (E-H/Bottom) Light skin pigmentation allele at rs16891982. In Britain the rise in frequency of the lactase persistence allele occurred earlier than in Central Europe. This analysis is based on direct observation of alleles; imputation results are qualitatively consistent (Figure 4B).
Extended Data Fig. 5 |
Extended Data Fig. 5 |. Y chromosome haplogroup frequency changes over time.
Estimated frequency of the characteristically British Y chromosome haplogroup R1b-P312 L21/M529 in all individuals for which we are able to make a determination and which are not first-degree relatives of a higher coverage individual in the dataset. Sample sizes for each epoch are labeled at the bottom, and we show means and one standard error bars from a binominal distribution. The frequency increases significantly from ~0% in the whole island Neolithic, to 89±4% in the whole island C/EBA. It declines non-significantly to 79±9% in the MBA and LBA (from this time onward restricting to England and Wales because of the autosomal evidence of a change in EEF ancestry in the south but not the north). It further declines to 68±4% in the IA, a significant reduction relative to the C/EBA (P=0.014 by a two-sided chi-square contingency test). There is additional reduction from this time to the present, when the proportion is 43±3% in Wales and the west of England (P=5×10−6 for a reduction relative to the IA), and 14±2% in the center and east of England (P=3×10−32 for a reduction relative to the IA).
Extended Data Fig. 6 |
Extended Data Fig. 6 |. Version of Fig. 3A contrasting Kent to the rest of southern Britain.
We show the period 2450–1 BCE. Each point corresponds to a single individual and we show means and one standard error bars from a Block Jackknife. All the high EEF outliers at the M-LBA are from Kent—the part of the island closest to France—and in addition all the individuals from 1000–875 BCE from the group of samples showing the ramp-up from MBA to IA levels of EEF ancestry are from Kent (5 from Cliffs End Farm and 3 from East Kent Access Road). This suggests the possibility that this small region was the gateway for migration to Britain at the M-LBA. Further sampling from the rest of Britain at the M-LBA is critical in order to understand the dynamics of how this ancestry spread more broadly. However, the fact that only sample from the second half of the LBA that is not from Kent—I12624 from Blackberry Field in Potterne in Wiltshire at 950–750 BCE—already has a proportion of EEF ancestry typical of the IA in southern Britain—suggests that this ancestry began spreading more broadly by the second half of the LBA.
Fig. 1:
Fig. 1:. Ancient DNA Dataset.
Geographic distribution of sites and temporal distribution of individuals 4000 BCE-43 CE. Newly reported in black; published in orange. Base maps made with Natural Earth; elevation data Copernicus, European Digital Elevation Model v1.1. The Britain map labels sites harbouring ancestry outliers relative to others of the same period. The timeline shows archaeological periods in the British chronology: Neolithic (3950–2450 BCE), Chalcolithic and Early Bronze Age (C/EBA, 2450–1550 BCE), Middle Bronze Age (MBA, 1550–1150 BCE), Late Bronze Age (LBA, 1150–750 BCE), and pre-Roman Iron Age (IA, 750 BCE-43 CE). We add jitter on the Y axis and sample dates from their probability distributions (Supplementary Table 1).
Fig. 2:
Fig. 2:. Increase in EEF ancestry during the Middle to Late Bronze Age.
EEF ancestry increased in southern Britain beginning with the Margetts Pit MBA outliers but hardly in the north. Estimates from qpAdm are binned into four archaeological periods. We plot means and one standard error from a Block Jackknife. Sample sizes in the C-EBA/MBA/LBA/IA are 69/26/23/273 in England and Wales and 10/5/4/18 in Scotland.
Fig. 3:
Fig. 3:. By-individual analysis of the southern Britain time transect.
(A) Estimates of EEF ancestry and one standard error for all individuals fitting a three-way admixture model (EEF + WHG + Yamnaya) at p>0.01 using qpAdm; we restrict to 2450 BCE-43 CE using the best date estimate from Supplementary Table 5. Most individuals are in blue, while significant outliers at the ancestry tails are in red (outliers are identified as p<0.005 based on a qpWave test from the main cluster from their period and |Z|>3 for a difference in EEF proportion, or p<0.1 and |Z|>3.5). We use a horizontal bar to show one standard error for the date (Supplementary Table 5). The black line shows population-wide EEF ancestry at each time obtained by weighting each individual’s EEF estimate by the inverse square of their standard error and the probability that their date falls at that time (based on the mean and standard error in Supplementary Table 5 assuming normality; we filter out individuals with standard errors >120 years). The incorporation of increased EEF ancestry into the majority of individuals occurred ~1000–875 BCE. (B) Proportion of outliers over 300-year sliding windows centered on each point, based on randomly sampling dates of all individuals 100 times assuming normality and their mean and standard deviation in Supplementary Table 5 (removing individuals with EEF errors >0.022 and date errors >120 years). Major epochs of migration into Britain are periods with elevated proportions of outliers: between 2450–1800 BCE (17% outliers) and 1300–750 BCE (17% again). The fact that there was an elevated rate of outliers prior to the 1000–875 BCE population-wide rise in EEF ancestry may reflect a delay between the time of arrival of migrants and their full incorporation into the population.
Fig. 4:
Fig. 4:. Genetic change in Britain in the context of Europe-wide trends.
(A) Eight ancient DNA time transects for up to four periods, plotting the mean of the EEF inference on the y-axis and on the x-axis using the average of dates of individuals in periods defined for each region as in Supplementary Table 5. Sample sizes used to compute each point are given in Supplementary Table 7. Dotted lines connecting points should not be interpreted as implying a smooth change over time and instead are meant to help in visual discernment of which groups of points come from the same time transects. (B) The allele conferring lactase persistence experienced its major rise about a frequency millennium earlier in Britain than in Central Europe suggesting different selection regimes and possibly cultural differences in the use of dairy products in the two regions in the IA. This analysis based on imputed data includes 459 ancient individuals from Britain and 468 from Central Europe (Czech Republic, Slovakia, Croatia, Hungary, Austria, Germany and Slovenia) (we then co-analyzed with present-day individuals; Methods). Each vertical bar represents the derived allele frequency for each individual with values [0, 0.5, 1]; we use jitter on the x-axis, and show in shading the inferred 95% confidence interval for the allele frequency at each time point.
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