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SUMMARYMultiple sclerosis (MS) is a modern neuro-inflammatory and -degenerative disease, which is most prevalent in Northern Europe. Whilst it is known that inherited risk to MS is located within or within close proximity to immune genes it is unknown when, where and how this genetic risk originated. By using the largest ancient genome dataset from the Stone Age, along with new Medieval and post-Medieval genomes, we show that many of the genetic risk variants for MS rose to higher frequency among pastoralists located on the Pontic Steppe, and were brought into Europe by the Yamnaya-related migration approximately 5,000 years ago. We further show that these MS-associated immunogenetic variants underwent positive selection both within the Steppe population, and later in Europe, likely driven by pathogenic challenges coinciding with dietary and lifestyle environmental changes. This study highlights the critical importance of this period as a determinant of modern immune responses and its subsequent impact on the risk of developing MS in a changing environment.

Summary The Eurasian Holocene (beginning c. 12 thousand years ago) encompassed some of the most significant changes in human evolution, with far-reaching consequences for the dietary, physical and mental health of present-day populations. Using an imputed dataset of >1600 complete ancient genome sequences, and new computational methods for locating selection in time and space, we reconstructed the selection landscape of the transition from hunting and gathering, to farming and pastoralism across West Eurasia. We identify major selection signals related to metabolism, possibly associated with the dietary shift occurring in this period. We show that the selection on loci such as the FADS cluster, associated with fatty acid metabolism, and the lactase persistence locus, began earlier than previously thought. A substantial amount of selection is also found in the HLA region and other loci associated with immunity, possibly due to the increased exposure to pathogens during the Neolithic, which may explain the current high prevalence of auto-immune disease, such as psoriasis, due to genetic trade-offs. By using ancient populations to infer local ancestry tracks in hundreds of thousands of samples from the UK Biobank, we find strong genetic differentiation among ancient Europeans in loci associated with anthropometric traits and susceptibility to several diseases that contribute to present-day disease burden. These were previously thought to be caused by local selection, but in fact can be attributed to differential genetic contributions from various source populations that are ancestral to present-day Europeans. Thus, alleles associated with increased height seem to have increased in frequency following the Yamnaya migration into northwestern Europe around 5,000 years ago. Alleles associated with increased risk of some mood-related phenotypes are overrepresented in the farmer ancestry component entering Europe from Anatolia around 11,000 years ago, while western hunter-gatherers show a strikingly high contribution of alleles conferring risk of traits related to diabetes. Our results paint a picture of the combined contributions of migration and selection in shaping the phenotypic landscape of present-day Europeans that suggests a combination of ancient selection and migration, rather than recent local selection, is the primary driver of present-day phenotypic differences in Europe.

Integrating datasets from different disciplines is hard because the data are often qualitatively different in meaning, scale, and reliability. When two datasets describe the same entities, many scientific questions can be phrased around whether the (dis)similarities between entities are conserved across such different data. Our method, CLARITY, quantifies consistency across datasets, identifies where inconsistencies arise, and aids in their interpretation. We illustrate this using three diverse comparisons: gene methylation vs expression, evolution of language sounds vs word use, and country-level economic metrics vs cultural beliefs. The non-parametric approach is robust to noise and differences in scaling, and makes only weak assumptions about how the data were generated. It operates by decomposing similarities into two components: a `structural' component analogous to a clustering, and an underlying `relationship' between those structures. This allows a `structural comparison' between two similarity matrices using their predictability from `structure'. Significance is assessed with the help of re-sampling appropriate for each dataset. The software, CLARITY, is available as an R package from https://github.com/danjlawson/CLARITY., R package available from https://github.com/danjlawson/CLARITY . 30 pages, 8 Figures

Genetic clustering algorithms, implemented in programs such as STRUCTURE and ADMIXTURE, have been used extensively in the characterisation of individuals and populations based on genetic data. A successful example is the reconstruction of the genetic history of African Americans as a product of recent admixture between highly differentiated populations. Histories can also be reconstructed using the same procedure for groups that do not have admixture in their recent history, where recent genetic drift is strong or that deviate in other ways from the underlying inference model. Unfortunately, such histories can be misleading. We have implemented an approach, badMIXTURE, to assess the goodness of fit of the model using the ancestry “palettes” estimated by CHROMOPAINTER and apply it to both simulated data and real case studies. Combining these complementary analyses with additional methods that are designed to test specific hypotheses allows a richer and more robust analysis of recent demographic history., Clustering methods such as STRUCTURE and ADMIXTURE are widely used in population genetic studies to investigate ancestry. Here, the authors provide a tutorial on how to interpret results of these analyses and a tool to test the goodness of fit of the model.

Alcohol use is correlated within spouse-pairs, but it is difficult to disentangle effects of alcohol consumption on mate-selection from social factors or the shared spousal environment. We hypothesised that genetic variants related to alcohol consumption may, via their effect on alcohol behaviour, influence mate selection. Here, we find strong evidence that an individual’s self-reported alcohol consumption and their genotype at rs1229984, a missense variant in ADH1B, are associated with their partner’s self-reported alcohol use. Applying Mendelian randomization, we estimate that a unit increase in an individual’s weekly alcohol consumption increases partner’s alcohol consumption by 0.26 units (95% C.I. 0.15, 0.38; P = 8.20 × 10−6). Furthermore, we find evidence of spousal genotypic concordance for rs1229984, suggesting that spousal concordance for alcohol consumption existed prior to cohabitation. Although the SNP is strongly associated with ancestry, our results suggest some concordance independent of population stratification. Our findings suggest that alcohol behaviour directly influences mate selection.

Alcohol use is correlated within spouse-pairs, but it is difficult to disentangle effects of alcohol consumption on mate-selection from social factors or the shared spousal environment. We hypothesised that genetic variants related to alcohol consumption may, via their effect on alcohol behaviour, influence mate selection. Here, we find strong evidence that an individual’s self-reported alcohol consumption and their genotype at rs1229984, a missense variant in ADH1B, are associated with their partner’s self-reported alcohol use. Applying Mendelian randomization, we estimate that a unit increase in an individual’s weekly alcohol consumption increases partner’s alcohol consumption by 0.26 units (95% C.I. 0.15, 0.38; P = 8.20 × 10−6). Furthermore, we find evidence of spousal genotypic concordance for rs1229984, suggesting that spousal concordance for alcohol consumption existed prior to cohabitation. Although the SNP is strongly associated with ancestry, our results suggest some concordance independent of population stratification. Our findings suggest that alcohol behaviour directly influences mate selection., From observational studies, alcohol consumption behaviours are known to be correlated in spouses. Here, Howe et al. use partners’ genotypic information in a Mendelian randomization framework and show that a SNP in the ADH1B gene associates with partner’s alcohol consumption, suggesting that alcohol consumption affects mate choice.

The mid-p-value is a proposed improvement on the ordinary p-value for the case where the test statistic is partially or completely discrete. In this case, the ordinary p-value is conservative, meaning that its null distribution is larger than a uniform distribution on the unit interval, in the usual stochastic order. The mid-p-value is not conservative. However, its null distribution is dominated by the uniform distribution in a different stochastic order, called the convex order. The property leads us to discover some new finite-sample and asymptotic bounds on functions of mid-p-values, which can be used to combine results from different hypothesis tests conservatively, yet more powerfully, using mid-p-values rather than p-values. Our methodology is demonstrated on real data from a cyber-security application.

SummarySeveral major migrations and population turnover events during the later Stone Age (after c. 11,000 cal. BP) are believed to have shaped the contemporary population genetic diversity in Eurasia. While the genetic impacts of these migrations have been investigated on regional scales, a detailed understanding of their spatiotemporal dynamics both within and between major geographic regions across Northern Eurasia remains largely elusive. Here, we present the largest shotgun-sequenced genomic dataset from the Stone Age to date, representing 317 primarily Mesolithic and Neolithic individuals from across Eurasia, with associated radiocarbon dates, stable isotope data, and pollen records. Using recent advances, we imputed >1,600 ancient genomes to obtain accurate diploid genotypes, enabling previously unachievable fine-grained population structure inferences. We show that 1) Eurasian Mesolitic hunter-gatherers were more genetically diverse than previously known, and deeply divergent between the west and the east; 2) Hitherto genetically undescribed hunter-gatherers from the Middle Don region contributed significant ancestry to the later Yamnaya steppe pastoralists; 3) The genetic impact of the transition from Mesolithic hunter-gatherers to Neolithic farmers was highly distinct, east and west of a “Great Divide” boundary zone extending from the Black Sea to the Baltic, with large-scale shifts in genetic ancestry to the west. This include an almost complete replacement of hunter-gatherers in Denmark, but no substantial shifts during the same period further to the east; 4) Within-group relatedness changes substantially during the Neolithic transition in the west, where clusters of Neolithic farmer-associated individuals show overall reduced relatedness, while genetic relatedness remains high until ~4,000 BP in the east, consistent with a much longer persistence of smaller localised hunter-gatherer groups; 5) A fast-paced second major genetic transformation beginning around 5,000 BP, with Steppe-related ancestry reaching most parts of Europe within a 1,000 years span. Local Neolithic farmers admixed with incoming pastoralists in most parts of Europe, whereas Scandinavia experienced another near-complete population replacement, with similar dramatic turnover-patterns also evident in western Siberia; 6) Extensive regional differences in the ancestry components related to these early events remain visible to this day, even within countries (research conducted using the UK Biobank resource). Neolithic farmer ancestry is highest in southern and eastern England while Steppe-related ancestry is highest in the Celtic populations of Scotland, Wales, and Cornwall. Overall, our findings show that although the Stone-Age migrations have been important in shaping contemporary genetic diversity in Eurasia, their dynamics and impact were geographically highly heterogeneous.