Estimating mobility using sparse data: Application to human genetic variation.

Mobility is one of the most important processes shaping spatiotemporal patterns of variation in genetic, morphological, and cultural traits. However, current approaches for inferring past migration episodes in the fields of archaeology and population genetics lack either temporal resolution or formal quantification of the underlying mobility, are poorly suited to spatially and temporally sparsely sampled data, and permit only limited systematic comparison between different time periods or geographic regions. Here we present an estimator of past mobility that addresses these issues by explicitly linking trait differentiation in space and time. We demonstrate the efficacy of this estimator using spatiotemporally explicit simulations and apply it to a large set of ancient genomic data from Western Eurasia. We identify a sequence of changes in human mobility from the Late Pleistocene to the Iron Age. We find that mobility among European Holocene farmers was significantly higher than among European hunter-gatherers both pre- and postdating the Last Glacial Maximum. We also infer that this Holocene rise in mobility occurred in at least three distinct stages: the first centering on the well-known population expansion at the beginning of the Neolithic, and the second and third centering on the beginning of the Bronze Age and the late Iron Age, respectively. These findings suggest a strong link between technological change and human mobility in Holocene Western Eurasia and demonstrate the utility of this framework for exploring changes in mobility through space and time.L.L. was supported by Natural Environment Research Council, UK Grants NE/K005243/1 and NE/K003259/1 and European Research Council Grant 339941-ADAPT. M.G.T. was supported by Wellcome Trust Senior Investigator Award Grant 100719/Z/12/Z and Leverhulme Trust Grant RP2011-R-045. A.M. and A.E. were supported by European Research Council Consolidator Grant 647787-LocalAdaptation. M.M.L. was supported by European Research Council Advanced Grant 295907, In-Africa. M.K. was funded by the Engineering and Physical Sciences Research Council through the Centre for Mathematics and Physics in the Life Sciences and Experimental Biology

Inferring Allele Frequency Trajectories from Ancient DNA Indicates That Selection on a Chicken Gene Coincided with Changes in Medieval Husbandry Practices

Ancient DNA provides an opportunity to infer the drivers of natural selection by linking allele frequency changes to temporal shifts in environment or cultural practices. However, analyses have often been hampered by uneven sampling and uncertainties in sample dating, as well as being confounded by demographic processes. Here, we present a Bayesian statistical framework for quantifying the timing and strength of selection using ancient DNA that explicitly addresses these challenges. We applied this method to time series data for two loci: TSHR and BCDO2, both hypothesised to have undergone strong and recent selection in domestic chickens. The derived variant in TSHR, associated with reduced aggression to conspecifics and faster onset of egg laying, shows strong selection beginning around 1,100 years ago, coincident with archaeological evidence for intensified chicken production and documented changes in egg and chicken consumption. To our knowledge, this is the first example of preindustrial domesticate trait selection in response to a historically attested cultural shift in food preference. For BCDO2, we find support for selection, but demonstrate that the recent rise in allele frequency could also have been driven by gene flow from imported Asian chickens during more recent breed formations. Our findings highlight that traits found ubiquitously in modern domestic species may not necessarily have originated during the early stages of domestication. In addition, our results demonstrate the importance of precise estimation of allele frequency trajectories through time for understanding the drivers of selection.The authors are grateful to Brian Follett for his comments on the biological functions of the TSHR gene. L.L., R.A., K.D., and G.L. were supported by Natural Environment Research Council, UK (grant numbers NE/K005243/1, NE/K003259/1). M.G.T. was supported by Wellcome Trust Senior Investigator Award (grant number 100719/Z/12/Z) and Leverhulme Trust (grant number RP2011-R-045). A.M. and A.E. were supported by the European Research Council Consolidator grant (grant number 647787-LocalAdaptation). R.A., N.S., and G.L. were supported by Arts and Humanities Research Council (grant number AH/L006979/1). R.A. and G.L. were supported by European Research Council (grant number ERC-2013-StG 337574-UNDEAD)

Parallel adaptation of rabbit populations to myxoma virus.

In the 1950s the myxoma virus was released into European rabbit populations in Australia and Europe, decimating populations and resulting in the rapid evolution of resistance. We investigated the genetic basis of resistance by comparing the exomes of rabbits collected before and after the pandemic. We found a strong pattern of parallel evolution, with selection on standing genetic variation favoring the same alleles in Australia, France, and the United Kingdom. Many of these changes occurred in immunity-related genes, supporting a polygenic basis of resistance. We experimentally validated the role of several genes in viral replication and showed that selection acting on an interferon protein has increased the protein's antiviral effect.This work was supported by grants from the Programa Operacional Potencial Humano–Quadro de Referência Estratégica Nacional funds from the European Social Fund and Portuguese Ministério da Ciência, Tecnologia e Ensino Superior to M.C. (IF/00283/2014/CP1256/CT0012), to P.J.E. (IF/00376/2015) and to J.M.A. (SFRH/BD/72381/2010). AM was supported by the European Research Council (grant 647787-LocalAdaptation). FJ was supported by the European Research Council (grant 281668). LL was supported by the European Research Council grant (339941-ADAPT). McFadden Lab is supported by National Institute of Health (NIH) grant R01 AI080607. S.C.G. holds a Sir Henry Dale Fellowship, co-funded by the Wellcome Trust and the Royal Society (098406/Z/12/Z)

Ancient DNA suggests modern wolves trace their origin to a late Pleistocene expansion from Beringia.

Grey wolves (Canis lupus) are one of the few large terrestrial carnivores that have maintained a wide geographic distribution across the Northern Hemisphere throughout the Pleistocene and Holocene. Recent genetic studies have suggested that, despite this continuous presence, major demographic changes occurred in wolf populations between the late Pleistocene and early Holocene, and that extant wolves trace their ancestry to a single late Pleistocene population. Both the geographic origin of this ancestral population and how it became widespread remain unknown. Here, we used a spatially and temporally explicit modelling framework to analyse a dataset of 90 modern and 45 ancient mitochondrial wolf genomes from across the Northern Hemisphere, spanning the last 50,000 years. Our results suggest that contemporary wolf populations trace their ancestry to an expansion from Beringia at the end of the Last Glacial Maximum, and that this process was most likely driven by Late Pleistocene ecological fluctuations that occurred across the Northern Hemisphere. This study provides direct ancient genetic evidence that long-range migration has played an important role in the population history of a large carnivore, and provides an insight into how wolves survived the wave of megafaunal extinctions at the end of the last glaciation. Moreover, because late Pleistocene grey wolves were the likely source from which all modern dogs trace their origins, the demographic history described in this study has fundamental implications for understanding the geographical origin of the dog.L.L., K.D. and G.L. were supported by the Natural Environment Research Council, UK (grant numbers NE/K005243/1, NE/K003259/1); LL was also supported by the European Research Council grant (339941‐ADAPT); A.M. and A.E. were supported by the European Research Council Consolidator grant (grant number 647787‐LocalAdaptation); L.F. and G.L. were supported by the European Research Council grant (ERC‐2013‐StG 337574‐UNDEAD); T.G. was supported by a European Research Council Consolidator grant (681396‐Extinction Genomics) & Lundbeck Foundation grant (R52‐5062); O.T. was supported by the National Science Center, Poland (2015/19/P/NZ7/03971), with funding from EU's Horizon 2020 programme under the Marie Skłodowska‐Curie grant agreement (665778) and Synthesys Project (BETAF 3062); V.P., E.P. and P.N. were supported by the Russian Science Foundation grant (N16‐18‐10265 RNF); A.P. was supported by the Max Planck Society; M.L‐G. was supported by a Czech Science Foundation grant (GAČR15‐06446S)

Ancient DNA suggests modern wolves trace their origin to a late Pleistocene expansion from Beringia.

Grey wolves (Canis lupus) are one of the few large terrestrial carnivores that have maintained a wide geographic distribution across the Northern Hemisphere throughout the Pleistocene and Holocene. Recent genetic studies have suggested that, despite this continuous presence, major demographic changes occurred in wolf populations between the late Pleistocene and early Holocene, and that extant wolves trace their ancestry to a single late Pleistocene population. Both the geographic origin of this ancestral population and how it became widespread remain unknown. Here, we used a spatially and temporally explicit modelling framework to analyse a dataset of 90 modern and 45 ancient mitochondrial wolf genomes from across the Northern Hemisphere, spanning the last 50,000 years. Our results suggest that contemporary wolf populations trace their ancestry to an expansion from Beringia at the end of the Last Glacial Maximum, and that this process was most likely driven by Late Pleistocene ecological fluctuations that occurred across the Northern Hemisphere. This study provides direct ancient genetic evidence that long-range migration has played an important role in the population history of a large carnivore, and provides an insight into how wolves survived the wave of megafaunal extinctions at the end of the last glaciation. Moreover, because late Pleistocene grey wolves were the likely source from which all modern dogs trace their origins, the demographic history described in this study has fundamental implications for understanding the geographical origin of the dog.L.L., K.D. and G.L. were supported by the Natural Environment Research Council, UK (grant numbers NE/K005243/1, NE/K003259/1); LL was also supported by the European Research Council grant (339941‐ADAPT); A.M. and A.E. were supported by the European Research Council Consolidator grant (grant number 647787‐LocalAdaptation); L.F. and G.L. were supported by the European Research Council grant (ERC‐2013‐StG 337574‐UNDEAD); T.G. was supported by a European Research Council Consolidator grant (681396‐Extinction Genomics) & Lundbeck Foundation grant (R52‐5062); O.T. was supported by the National Science Center, Poland (2015/19/P/NZ7/03971), with funding from EU's Horizon 2020 programme under the Marie Skłodowska‐Curie grant agreement (665778) and Synthesys Project (BETAF 3062); V.P., E.P. and P.N. were supported by the Russian Science Foundation grant (N16‐18‐10265 RNF); A.P. was supported by the Max Planck Society; M.L‐G. was supported by a Czech Science Foundation grant (GAČR15‐06446S)

Sometimes hidden but always there: the assumptions underlying genetic inference of demographic histories.

Demographic processes directly affect patterns of genetic variation within contemporary populations as well as future generations, allowing for demographic inference from patterns of both present-day and past genetic variation. Advances in laboratory procedures, sequencing and genotyping technologies in the past decades have resulted in massive increases in high-quality genome-wide genetic data from present-day populations and allowed retrieval of genetic data from archaeological material, also known as ancient DNA. This has resulted in an explosion of work exploring past changes in population size, structure, continuity and movement. However, as genetic processes are highly stochastic, patterns of genetic variation only indirectly reflect demographic histories. As a result, past demographic processes need to be reconstructed using an inferential approach. This usually involves comparing observed patterns of variation with model expectations from theoretical population genetics. A large number of approaches have been developed based on different population genetic models that each come with assumptions about the data and underlying demography. In this article I review some of the key models and assumptions underlying the most commonly used approaches for past demographic inference and their consequences for our ability to link the inferred demographic processes to the archaeological and climate records. This article is part of the theme issue 'Cross-disciplinary approaches to prehistoric demography'.Herchel Smith Fellowship (University of Cambridge

Formal methods for evolutionary inference using ancient DNA

Ancient DNA has revolutionised our ability to study past evolutionary processes by enabling direct observation of genetic variation in past populations. However, current formal methods for evolutionary inference are challenged by the sparse and heterogeneous distribution of data in space and in time, typical of ancient DNA datasets, as well as sample age uncertainty. In this thesis, I introduce analytical approaches that explicitly address these problems and show how inference from ancient DNA can greatly benefit from analysis that formally compares different evolutionary and demographic scenarios. I apply these approaches to three different cases that represent a range of demographic histories and evolutionary questions spanning different time- scales, species and types of data. The first approach combines space and time into a single matrix that can be used to estimate levels of past within population mobility. I apply this method to human ancient DNA data spanning the last 35 thousand years to recover changes in mobility over this time period. The second approach combines spatially and temporally explicit simulations with ancient mitochondrial genome data, spanning the Northern hemisphere and the last 50 thousand years to reconstruct large- scale population dynamics in Grey wolves over this time period. The third approach uses likelihood-based analysis to reconstruct episodes of past selection from time series of ancient DNA data. I apply this method to genotype data spanning two thousand years from two loci in domestic chicken to estimate a number of selection parameters including the starting time of the selection. These case studies all illustrate how formal hypothesis testing in spatially and temporally explicit frameworks make it possible to directly link evolutionary histories to the climatic and archaeological records as well as historical sources and show how identifying potential drivers of evolution allow building a more detailed and complete picture of the past.</p

Formal methods for evolutionary inference using ancient DNA

Ancient DNA has revolutionised our ability to study past evolutionary processes by enabling direct observation of genetic variation in past populations. However, current formal methods for evolutionary inference are challenged by the sparse and heterogeneous distribution of data in space and in time, typical of ancient DNA datasets, as well as sample age uncertainty. In this thesis, I introduce analytical approaches that explicitly address these problems and show how inference from ancient DNA can greatly benefit from analysis that formally compares different evolutionary and demographic scenarios. I apply these approaches to three different cases that represent a range of demographic histories and evolutionary questions spanning different time- scales, species and types of data. The first approach combines space and time into a single matrix that can be used to estimate levels of past within population mobility. I apply this method to human ancient DNA data spanning the last 35 thousand years to recover changes in mobility over this time period. The second approach combines spatially and temporally explicit simulations with ancient mitochondrial genome data, spanning the Northern hemisphere and the last 50 thousand years to reconstruct large- scale population dynamics in Grey wolves over this time period. The third approach uses likelihood-based analysis to reconstruct episodes of past selection from time series of ancient DNA data. I apply this method to genotype data spanning two thousand years from two loci in domestic chicken to estimate a number of selection parameters including the starting time of the selection. These case studies all illustrate how formal hypothesis testing in spatially and temporally explicit frameworks make it possible to directly link evolutionary histories to the climatic and archaeological records as well as historical sources and show how identifying potential drivers of evolution allow building a more detailed and complete picture of the past.</p

Induced Pluripotent Stem Cells as a Possible Approach for Exploring the Pathophysiology of Polycystic Ovary Syndrome (PCOS).

Funder: University of Helsinki including Helsinki University Central HospitalPolycystic ovary syndrome (PCOS) is the most prevalent endocrine condition among women with pleiotropic sequelae possessing reproductive, metabolic, and psychological characteristics. Although the exact origin of PCOS is elusive, it is known to be a complex multigenic disorder with a genetic, epigenetic, and environmental background. However, the pathogenesis of PCOS, and the role of genetic variants in increasing the risk of the condition, are still unknown due to the lack of an appropriate study model. Since the debut of induced pluripotent stem cell (iPSC) technology, the ability of reprogrammed somatic cells to self-renew and their potential for multidirectional differentiation have made them excellent tools to study different disease mechanisms. Recently, researchers have succeeded in establishing human in vitro PCOS disease models utilizing iPSC lines from heterogeneous PCOS patient groups (iPSCPCOS). The current review sets out to summarize, for the first time, our current knowledge of the implications and challenges of iPSC technology in comprehending PCOS pathogenesis and tissue-specific disease mechanisms. Additionally, we suggest that the analysis of polygenic risk prediction based on genome-wide association studies (GWAS) could, theoretically, be utilized when creating iPSC lines as an additional research tool to identify women who are genetically susceptible to PCOS. Taken together, iPSCPCOS may provide a new paradigm for the exploration of PCOS tissue-specific disease mechanisms