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LaBonte NR, Zhao P, Woeste K. Signatures of Selection in the Genomes of Chinese Chestnut ( Castanea mollissima Blume): The Roots of Nut Tree Domestication. FRONTIERS IN PLANT SCIENCE 2018; 9:810. [PMID: 29988533 PMCID: PMC6026767 DOI: 10.3389/fpls.2018.00810] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Accepted: 05/25/2018] [Indexed: 05/18/2023]
Abstract
Chestnuts (Castanea) are major nut crops in East Asia and southern Europe, and are unique among temperate nut crops in that the harvested seeds are starchy rather than oily. Chestnut species have been cultivated for three millennia or more in China, so it is likely that artificial selection has affected the genome of orchard-grown chestnuts. The genetics of Chinese chestnut (Castanea mollissima Blume) domestication are also of interest to breeders of hybrid American chestnut, especially if the low-growing, branching habit of Chinese chestnut, an impediment to American chestnut restoration, is partly the result of artificial selection. We resequenced genomes of wild and orchard-derived Chinese chestnuts and identified selective sweeps based on pooled whole-genome SNP datasets. We present candidate gene loci for chestnut domestication and discuss the potential phenotypic effects of candidate loci, some of which may be useful genes for chestnut improvement in Asia and North America. Selective sweeps included predicted genes potentially related to flower phenology and development, fruit maturation, and secondary metabolism, and included some genes homologous to domestication candidates in other woody plants.
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Affiliation(s)
- Nicholas R. LaBonte
- Department of Crop Sciences, University of Illinois Urbana-Champaign, Urbana, IL, United States
- *Correspondence: Nicholas R. LaBonte
| | - Peng Zhao
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, College of Life Sciences, Northwest University, Xi'an, China
| | - Keith Woeste
- Hardwood Tree Improvement and Regeneration Center, Northern Research Station, USDA Forest Service, West Lafayette, IN, United States
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Luikart G, Kardos M, Hand BK, Rajora OP, Aitken SN, Hohenlohe PA. Population Genomics: Advancing Understanding of Nature. POPULATION GENOMICS 2018. [DOI: 10.1007/13836_2018_60] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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Detecting Recent Positive Selection with a Single Locus Test Bipartitioning the Coalescent Tree. Genetics 2017; 208:791-805. [PMID: 29217523 DOI: 10.1534/genetics.117.300401] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2017] [Accepted: 12/01/2017] [Indexed: 01/09/2023] Open
Abstract
Many population genomic studies have been conducted in the past to search for traces of recent events of positive selection. These traces, however, can be obscured by temporal variation of population size or other demographic factors. To reduce the confounding impact of demography, the coalescent tree topology has been used as an additional source of information for detecting recent positive selection in a population or a species. Based on the branching pattern at the root, we partition the hypothetical coalescent tree, inferred from a sequence sample, into two subtrees. The reasoning is that positive selection could impose a strong impact on branch length in one of the two subtrees while demography has the same effect on average on both subtrees. Thus, positive selection should be detectable by comparing statistics calculated for the two subtrees. Simulations demonstrate that the proposed test based on these principles has high power to detect recent positive selection even when DNA polymorphism data from only one locus is available, and that it is robust to the confounding effect of demography. One feature is that all components in the summary statistics ([Formula: see text]) can be computed analytically. Moreover, misinference of derived and ancestral alleles is seen to have only a limited effect on the test, and it therefore avoids a notorious problem when searching for traces of recent positive selection.
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Abstract
Relatively little is known about the evolutionary history of the African green monkey (genus Chlorocebus) due to the lack of sampled polymorphism data from wild populations. Yet, this characterization of genetic diversity is not only critical for a better understanding of their own history, but also for human biomedical research given that they are one of the most widely used primate models. Here, I analyze the demographic and selective history of the African green monkey, utilizing one of the most comprehensive catalogs of wild genetic diversity to date, consisting of 1,795,643 autosomal single nucleotide polymorphisms in 25 individuals, representing all five major populations: C. a. aethiops, C. a. cynosurus, C. a. pygerythrus, C. a. sabaeus, and C. a tantalus. Assuming a mutation rate of 5.9 × 10-9 per base pair per generation and a generation time of 8.5 years, divergence time estimates range from 523 to 621 kya for the basal split of C. a. aethiops from the other four populations. Importantly, the resulting tree characterizing the relationship and split-times between these populations differs significantly from that presented in the original genome paper, owing to their neglect of within-population variation when calculating between population-divergence. In addition, I find that the demographic history of all five populations is well explained by a model of population fragmentation and isolation, rather than novel colonization events. Finally, utilizing these demographic models as a null, I investigate the selective history of the populations, identifying candidate regions potentially related to adaptation in response to pathogen exposure.
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Affiliation(s)
- Susanne P Pfeifer
- School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.,Swiss Institute of Bioinformatics, Lausanne, Switzerland.,School of Life Sciences, Arizona State University, Tempe, AZ
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55
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Mostafavi H, Berisa T, Day FR, Perry JRB, Przeworski M, Pickrell JK. Identifying genetic variants that affect viability in large cohorts. PLoS Biol 2017; 15:e2002458. [PMID: 28873088 PMCID: PMC5584811 DOI: 10.1371/journal.pbio.2002458] [Citation(s) in RCA: 49] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2017] [Accepted: 08/03/2017] [Indexed: 12/20/2022] Open
Abstract
A number of open questions in human evolutionary genetics would become tractable if we were able to directly measure evolutionary fitness. As a step towards this goal, we developed a method to examine whether individual genetic variants, or sets of genetic variants, currently influence viability. The approach consists in testing whether the frequency of an allele varies across ages, accounting for variation in ancestry. We applied it to the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort and to the parents of participants in the UK Biobank. Across the genome, we found only a few common variants with large effects on age-specific mortality: tagging the APOE ε4 allele and near CHRNA3. These results suggest that when large, even late-onset effects are kept at low frequency by purifying selection. Testing viability effects of sets of genetic variants that jointly influence 1 of 42 traits, we detected a number of strong signals. In participants of the UK Biobank of British ancestry, we found that variants that delay puberty timing are associated with a longer parental life span (P~6.2 × 10−6 for fathers and P~2.0 × 10−3 for mothers), consistent with epidemiological studies. Similarly, variants associated with later age at first birth are associated with a longer maternal life span (P~1.4 × 10−3). Signals are also observed for variants influencing cholesterol levels, risk of coronary artery disease (CAD), body mass index, as well as risk of asthma. These signals exhibit consistent effects in the GERA cohort and among participants of the UK Biobank of non-British ancestry. We also found marked differences between males and females, most notably at the CHRNA3 locus, and variants associated with risk of CAD and cholesterol levels. Beyond our findings, the analysis serves as a proof of principle for how upcoming biomedical data sets can be used to learn about selection effects in contemporary humans. Our global understanding of adaptation in humans is limited to indirect statistical inferences from patterns of genetic variation, which are sensitive to past selection pressures. We introduced a method that allowed us to directly observe ongoing selection in humans by identifying genetic variants that affect survival to a given age (i.e., viability selection). We applied our approach to the GERA cohort and parents of the UK Biobank participants. We found viability effects of variants near the APOE and CHRNA3 genes, which are associated with the risk of Alzheimer disease and smoking behavior, respectively. We also tested for the joint effect of sets of genetic variants that influence quantitative traits. We uncovered an association between longer life span and genetic variants that delay puberty timing and age at first birth. We also detected detrimental effects of higher genetically predicted cholesterol levels, body mass index, risk of coronary artery disease (CAD), and risk of asthma on survival. Some of the observed effects differ between males and females, most notably those at the CHRNA3 gene and variants associated with risk of CAD and cholesterol levels. Beyond this application, our analysis shows how large biomedical data sets can be used to study natural selection in humans.
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Affiliation(s)
- Hakhamanesh Mostafavi
- Department of Biological Sciences, Columbia University, New York, New York, United States of America
- * E-mail: (HM); (MP); (JKP)
| | - Tomaz Berisa
- New York Genome Center, New York, New York, United States of America
| | - Felix R. Day
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom
| | - John R. B. Perry
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom
| | - Molly Przeworski
- Department of Biological Sciences, Columbia University, New York, New York, United States of America
- Department of Systems Biology, Columbia University, New York, New York, United States of America
- * E-mail: (HM); (MP); (JKP)
| | - Joseph K. Pickrell
- Department of Biological Sciences, Columbia University, New York, New York, United States of America
- New York Genome Center, New York, New York, United States of America
- * E-mail: (HM); (MP); (JKP)
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56
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Hanson D, Hu J, Hendry AP, Barrett RDH. Heritable gene expression differences between lake and stream stickleback include both parallel and antiparallel components. Heredity (Edinb) 2017; 119:339-348. [PMID: 28832577 PMCID: PMC5637370 DOI: 10.1038/hdy.2017.50] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2017] [Revised: 07/05/2017] [Accepted: 07/13/2017] [Indexed: 12/19/2022] Open
Abstract
The repeated phenotypic patterns that characterize populations undergoing parallel evolution provide support for a deterministic role of adaptation by natural selection. Determining the level of parallelism also at the genetic level is thus central to our understanding of how natural selection works. Many studies have looked for repeated genomic patterns in natural populations, but work on gene expression is less common. The studies that have examined gene expression have found some support for parallelism, but those studies almost always used samples collected from the wild that potentially confounds the effects of plasticity with heritable differences. Here we use two independent pairs of lake and stream threespine stickleback (Gasterosteus aculeatus) raised in common garden conditions to assess both parallel and antiparallel (that is, similar versus different directions of lake–stream expression divergence in the two watersheds) heritable gene expression differences as measured by total RNA sequencing. We find that more genes than expected by chance show either parallel (22 genes, 0.18% of expressed genes) or antiparallel (24 genes, 0.20% of expressed genes) lake–stream expression differences. These results correspond well with previous genomic studies in stickleback ecotype pairs that found similar levels of parallelism. We suggest that parallelism might be similarly constrained at the genomic and transcriptomic levels.
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Affiliation(s)
- D Hanson
- Redpath Museum and Department of Biology, McGill University, Montreal, Quebec, Canada
| | - J Hu
- Redpath Museum and Department of Biology, McGill University, Montreal, Quebec, Canada
| | - A P Hendry
- Redpath Museum and Department of Biology, McGill University, Montreal, Quebec, Canada
| | - R D H Barrett
- Redpath Museum and Department of Biology, McGill University, Montreal, Quebec, Canada
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Byars SG, Huang QQ, Gray LA, Bakshi A, Ripatti S, Abraham G, Stearns SC, Inouye M. Genetic loci associated with coronary artery disease harbor evidence of selection and antagonistic pleiotropy. PLoS Genet 2017; 13:e1006328. [PMID: 28640878 PMCID: PMC5480811 DOI: 10.1371/journal.pgen.1006328] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2016] [Accepted: 05/02/2017] [Indexed: 12/18/2022] Open
Abstract
Traditional genome-wide scans for positive selection have mainly uncovered selective sweeps associated with monogenic traits. While selection on quantitative traits is much more common, very few signals have been detected because of their polygenic nature. We searched for positive selection signals underlying coronary artery disease (CAD) in worldwide populations, using novel approaches to quantify relationships between polygenic selection signals and CAD genetic risk. We identified new candidate adaptive loci that appear to have been directly modified by disease pressures given their significant associations with CAD genetic risk. These candidates were all uniquely and consistently associated with many different male and female reproductive traits suggesting selection may have also targeted these because of their direct effects on fitness. We found that CAD loci are significantly enriched for lifetime reproductive success relative to the rest of the human genome, with evidence that the relationship between CAD and lifetime reproductive success is antagonistic. This supports the presence of antagonistic-pleiotropic tradeoffs on CAD loci and provides a novel explanation for the maintenance and high prevalence of CAD in modern humans. Lastly, we found that positive selection more often targeted CAD gene regulatory variants using HapMap3 lymphoblastoid cell lines, which further highlights the unique biological significance of candidate adaptive loci underlying CAD. Our study provides a novel approach for detecting selection on polygenic traits and evidence that modern human genomes have evolved in response to CAD-induced selection pressures and other early-life traits sharing pleiotropic links with CAD.
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Affiliation(s)
- Sean G. Byars
- Centre for Systems Genomics, School of BioSciences, The University of Melbourne, Parkville, Victoria, Australia
- Department of Pathology, The University of Melbourne, Parkville, Victoria, Australia
| | - Qin Qin Huang
- Centre for Systems Genomics, School of BioSciences, The University of Melbourne, Parkville, Victoria, Australia
- Department of Pathology, The University of Melbourne, Parkville, Victoria, Australia
- Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Lesley-Ann Gray
- Centre for Systems Genomics, School of BioSciences, The University of Melbourne, Parkville, Victoria, Australia
- Department of Pathology, The University of Melbourne, Parkville, Victoria, Australia
| | - Andrew Bakshi
- Centre for Systems Genomics, School of BioSciences, The University of Melbourne, Parkville, Victoria, Australia
| | - Samuli Ripatti
- Institute of Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
- Department of Public Health, University of Helsinki, Helsinki, Finland
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Gad Abraham
- Centre for Systems Genomics, School of BioSciences, The University of Melbourne, Parkville, Victoria, Australia
- Department of Pathology, The University of Melbourne, Parkville, Victoria, Australia
- Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Stephen C. Stearns
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, United States of America
| | - Michael Inouye
- Centre for Systems Genomics, School of BioSciences, The University of Melbourne, Parkville, Victoria, Australia
- Department of Pathology, The University of Melbourne, Parkville, Victoria, Australia
- Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
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58
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Li Y, Hansson B, Ghatnekar L, Prentice HC. Contrasting patterns of nucleotide polymorphism suggest different selective regimes within different parts of the PgiC1 gene in Festuca ovina L. Hereditas 2017; 154:11. [PMID: 28529468 PMCID: PMC5437402 DOI: 10.1186/s41065-017-0032-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2016] [Accepted: 04/13/2017] [Indexed: 01/11/2023] Open
Abstract
Background Phosphoglucose isomerase (PGI, EC 5.3.1.9) is an essential metabolic enzyme in all eukaryotes. An earlier study of the PgiC1 gene, which encodes cytosolic PGI in the grass Festuca ovina L., revealed a marked difference in the levels of nucleotide polymorphism between the 5’ and 3’ portions of the gene. Methods In the present study, we characterized the sequence polymorphism in F. ovina PgiC1 in more detail and examined possible explanations for the non-uniform pattern of nucleotide polymorphism across the gene. Results Our study confirms that the two portions of the PgiC1 gene show substantially different levels of DNA polymorphism and also suggests that the peptide encoded by the 3’ portion of PgiC1 is functionally and structurally more important than that encoded by the 5’ portion. Although there was some evidence of purifying selection (dN/dS test) on the 5’ portion of the gene, the signature of purifying selection was considerably stronger on the 3’ portion of the gene (dN/dS and McDonald–Kreitman tests). Several tests support the action of balancing selection within the 5’ portion of the gene. Wall’s B and Q tests were significant only for the 5’ portion of the gene. There were also marked peaks of nucleotide diversity, Tajima’s D and the dN/dS ratio at or around a PgiC1 codon site (within the 5’ portion of the gene) that a previous study had suggested was subject to positive diversifying selection. Conclusions Our results suggest that the two portions of the gene have been subject to different selective regimes. Purifying selection appears to have been the main force contributing to the relatively low level of polymorphism within the 3’ portion of the sequence. In contrast, it is possible that balancing selection has contributed to the maintenance of the polymorphism within the 5’ portion of the gene. Electronic supplementary material The online version of this article (doi:10.1186/s41065-017-0032-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Yuan Li
- Department of Biology, Lund University, Lund, Sweden
| | - Bengt Hansson
- Department of Biology, Lund University, Lund, Sweden
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59
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Yoder JB, Tiffin P. Effects of Gene Action, Marker Density, and Timing of Selection on the Performance of Landscape Genomic Scans of Local Adaptation. J Hered 2017; 109:16-28. [DOI: 10.1093/jhered/esx042] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2017] [Accepted: 05/05/2017] [Indexed: 11/13/2022] Open
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Abstract
Molecular population genetics aims to explain genetic variation and molecular evolution from population genetics principles. The field was born 50 years ago with the first measures of genetic variation in allozyme loci, continued with the nucleotide sequencing era, and is currently in the era of population genomics. During this period, molecular population genetics has been revolutionized by progress in data acquisition and theoretical developments. The conceptual elegance of the neutral theory of molecular evolution or the footprint carved by natural selection on the patterns of genetic variation are two examples of the vast number of inspiring findings of population genetics research. Since the inception of the field, Drosophila has been the prominent model species: molecular variation in populations was first described in Drosophila and most of the population genetics hypotheses were tested in Drosophila species. In this review, we describe the main concepts, methods, and landmarks of molecular population genetics, using the Drosophila model as a reference. We describe the different genetic data sets made available by advances in molecular technologies, and the theoretical developments fostered by these data. Finally, we review the results and new insights provided by the population genomics approach, and conclude by enumerating challenges and new lines of inquiry posed by increasingly large population scale sequence data.
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61
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Narum SR, Gallardo P, Correa C, Matala A, Hasselman D, Sutherland BJG, Bernatchez L. Genomic patterns of diversity and divergence of two introduced salmonid species in Patagonia, South America. Evol Appl 2017; 10:402-416. [PMID: 28352299 PMCID: PMC5367078 DOI: 10.1111/eva.12464] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2016] [Accepted: 01/26/2017] [Indexed: 12/25/2022] Open
Abstract
Invasive species have become widespread in aquatic environments throughout the world, yet there are few studies that have examined genomic variation of multiple introduced species in newly colonized environments. In this study, we contrast genomic variation in two salmonid species (anadromous Chinook Salmon, Oncorhynchus tshawytscha, 11,579 SNPs and resident Brook Charr Salvelinus fontinalis, 13,522 SNPs) with differing invasion success after introduction to new environments in South America relative to populations from their native range in North America. Estimates of genetic diversity were not significantly different between introduced and source populations for either species, indicative of propagule pressure that has been shown to maintain diversity in founding populations relative to their native range. Introduced populations also demonstrated higher connectivity and gene flow than those in their native range. Evidence for candidate loci under divergent selection was observed, but was limited to specific introduced populations and was not widely evident. Patterns of genomic variation were consistent with general dispersal potential of each species and therefore also the notion that life history variation may contribute to both invasion success and subsequent genetic structure of these two salmonids in Patagonia.
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Affiliation(s)
- Shawn R. Narum
- Hagerman Fish Culture Experiment StationColumbia River Inter‐Tribal Fish CommissionHagermanIDUSA
| | - Pablo Gallardo
- Centro de Cultivos Marinos Bahía LaredoUniversity of MagallanesPunta ArenasChile
| | - Cristian Correa
- Facultad de Ciencias Forestales y Recursos NaturalesInstituto de Conservación Biodiversidad y TerritorioUniversidad Austral de ChileValdiviaChile
- Facultad de CienciasInstituto de Ciencias Marinas y LimnológicasUniversidad Austral de ChileValdiviaChile
| | - Amanda Matala
- Hagerman Fish Culture Experiment StationColumbia River Inter‐Tribal Fish CommissionHagermanIDUSA
| | - Daniel Hasselman
- Hagerman Fish Culture Experiment StationColumbia River Inter‐Tribal Fish CommissionHagermanIDUSA
| | - Ben J. G. Sutherland
- Institut de Biologie Intégrative et des Systèmes (IBIS)Université LavalQuébecQCCanada
| | - Louis Bernatchez
- Institut de Biologie Intégrative et des Systèmes (IBIS)Université LavalQuébecQCCanada
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Kim J, Hanotte O, Mwai OA, Dessie T, Bashir S, Diallo B, Agaba M, Kim K, Kwak W, Sung S, Seo M, Jeong H, Kwon T, Taye M, Song KD, Lim D, Cho S, Lee HJ, Yoon D, Oh SJ, Kemp S, Lee HK, Kim H. The genome landscape of indigenous African cattle. Genome Biol 2017; 18:34. [PMID: 28219390 PMCID: PMC5319050 DOI: 10.1186/s13059-017-1153-y] [Citation(s) in RCA: 148] [Impact Index Per Article: 21.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2016] [Accepted: 01/11/2017] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND The history of African indigenous cattle and their adaptation to environmental and human selection pressure is at the root of their remarkable diversity. Characterization of this diversity is an essential step towards understanding the genomic basis of productivity and adaptation to survival under African farming systems. RESULTS We analyze patterns of African cattle genetic variation by sequencing 48 genomes from five indigenous populations and comparing them to the genomes of 53 commercial taurine breeds. We find the highest genetic diversity among African zebu and sanga cattle. Our search for genomic regions under selection reveals signatures of selection for environmental adaptive traits. In particular, we identify signatures of selection including genes and/or pathways controlling anemia and feeding behavior in the trypanotolerant N'Dama, coat color and horn development in Ankole, and heat tolerance and tick resistance across African cattle especially in zebu breeds. CONCLUSIONS Our findings unravel at the genome-wide level, the unique adaptive diversity of African cattle while emphasizing the opportunities for sustainable improvement of livestock productivity on the continent.
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Affiliation(s)
- Jaemin Kim
- C&K genomics, Seoul National University Research Park, Seoul, 151-919, Republic of Korea
| | - Olivier Hanotte
- The University of Nottingham, School of Life Sciences, Nottingham, NG7 2RD, UK
- International Livestock Research institute (ILRI), P. O. Box 5689, Addis Ababa, Ethiopia
| | - Okeyo Ally Mwai
- International Livestock Research Institute (ILRI), Box 30709 -00100, Nairobi, Kenya
| | - Tadelle Dessie
- International Livestock Research institute (ILRI), P. O. Box 5689, Addis Ababa, Ethiopia
| | - Salim Bashir
- Department of Parasitology, Faculty of Veterinary Medicine, University of Khartoum, 13314, Khartoum North, Sudan
| | - Boubacar Diallo
- National Coordinateur RGA, Ministère Elevage - Productions Animales, B.P. 559, Conakry, Guinea
| | - Morris Agaba
- Nelson Mandela African Institution of Science and Technology, Nelson Mandela Road. P. O. Box 447, Arusha, Tanzania
| | - Kwondo Kim
- C&K genomics, Seoul National University Research Park, Seoul, 151-919, Republic of Korea
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 151-741, Republic of Korea
| | - Woori Kwak
- C&K genomics, Seoul National University Research Park, Seoul, 151-919, Republic of Korea
| | - Samsun Sung
- C&K genomics, Seoul National University Research Park, Seoul, 151-919, Republic of Korea
| | - Minseok Seo
- C&K genomics, Seoul National University Research Park, Seoul, 151-919, Republic of Korea
| | - Hyeonsoo Jeong
- Department of Animal Sciences, University of Illinois, Urbana, IL, 61801, USA
| | - Taehyung Kwon
- Department of Agricultural Biotechnology and Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul, 151-742, Republic of Korea
| | - Mengistie Taye
- Department of Agricultural Biotechnology and Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul, 151-742, Republic of Korea
- College of Agriculture and Environmental Sciences, Bahir Dar University, P. O. Box 79, Bahir Dar, Ethiopia
| | - Ki-Duk Song
- The Animal Molecular Genetics and Breeding Center, Chonbuk National University, Jeonju, 54896, Republic of Korea
- Department of Animal Biotechnology, Chonbuk National University, Jeonju, 561-756, Republic of Korea
| | - Dajeong Lim
- Division of Animal Genomics & Bioinformatics, National Institute of Animal Science, RDA, Jeonju, 565-851, Republic of Korea
| | - Seoae Cho
- C&K genomics, Seoul National University Research Park, Seoul, 151-919, Republic of Korea
| | - Hyun-Jeong Lee
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 151-741, Republic of Korea
- Animal Nutritional & Physiology Team, National Institute of Animal Science, RDA, Jeonju, 565-851, Republic of Korea
| | - Duhak Yoon
- Department of Animal Science, Kyungpook National University, Sangju, 742-711, Republic of Korea
| | - Sung Jong Oh
- National Institute of Animal Science, RDA, Jeonju, 565-851, Republic of Korea
| | - Stephen Kemp
- International Livestock Research Institute (ILRI), Box 30709 -00100, Nairobi, Kenya
- The Centre for Tropical Livestock Genetics and Health, The Roslin Institute, The University of Edinburgh, Easter Bush Campus, Midlothian, EH25 9RG, UK
| | - Hak-Kyo Lee
- The Animal Molecular Genetics and Breeding Center, Chonbuk National University, Jeonju, 54896, Republic of Korea.
- Department of Animal Biotechnology, Chonbuk National University, Jeonju, 561-756, Republic of Korea.
| | - Heebal Kim
- C&K genomics, Seoul National University Research Park, Seoul, 151-919, Republic of Korea.
- Department of Agricultural Biotechnology and Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul, 151-742, Republic of Korea.
- Institute for Biomedical Sciences, Shinshu University, Nagano, Japan.
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Gittelman RM, Schraiber JG, Vernot B, Mikacenic C, Wurfel MM, Akey JM. Archaic Hominin Admixture Facilitated Adaptation to Out-of-Africa Environments. Curr Biol 2016; 26:3375-3382. [PMID: 27839976 DOI: 10.1016/j.cub.2016.10.041] [Citation(s) in RCA: 104] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2016] [Revised: 09/26/2016] [Accepted: 10/19/2016] [Indexed: 12/31/2022]
Abstract
As modern humans dispersed from Africa throughout the world, they encountered and interbred with archaic hominins, including Neanderthals and Denisovans [1, 2]. Although genome-scale maps of introgressed sequences have been constructed [3-6], considerable gaps in knowledge remain about the functional, phenotypic, and evolutionary significance of archaic hominin DNA that persists in present-day individuals. Here, we describe a comprehensive set of analyses that identified 126 high-frequency archaic haplotypes as putative targets of adaptive introgression in geographically diverse populations. These loci are enriched for immune-related genes (such as OAS1/2/3, TLR1/6/10, and TNFAIP3) and also encompass genes (including OCA2 and BNC2) that influence skin pigmentation phenotypes. Furthermore, we leveraged existing and novel large-scale gene expression datasets to show many positively selected archaic haplotypes act as expression quantitative trait loci (eQTLs), suggesting that modulation of transcript abundance was a common mechanism facilitating adaptive introgression. Our results demonstrate that hybridization between modern and archaic hominins provided an important reservoir of advantageous alleles that enabled adaptation to out-of-Africa environments.
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Affiliation(s)
- Rachel M Gittelman
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
| | - Joshua G Schraiber
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
| | - Benjamin Vernot
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
| | - Carmen Mikacenic
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, University of Washington, Seattle, WA 98195, USA
| | - Mark M Wurfel
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, University of Washington, Seattle, WA 98195, USA
| | - Joshua M Akey
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.
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Freedman AH, Lohmueller KE, Wayne RK. Evolutionary History, Selective Sweeps, and Deleterious Variation in the Dog. ANNUAL REVIEW OF ECOLOGY EVOLUTION AND SYSTEMATICS 2016. [DOI: 10.1146/annurev-ecolsys-121415-032155] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The dog is our oldest domesticate and has experienced a wide variety of demographic histories, including a bottleneck associated with domestication and individual bottlenecks associated with the formation of modern breeds. Admixture with gray wolves, and among dog breeds and populations, has also occurred throughout its history. Likewise, the intensity and focus of selection have varied, from an initial focus on traits enhancing cohabitation with humans, to more directed selection on specific phenotypic characteristics and behaviors. In this review, we summarize and synthesize genetic findings from genome-wide and complete genome studies that document the genomic consequences of demography and selection, including the effects on adaptive and deleterious variation. Consistent with the evolutionary history of the dog, signals of natural and artificial selection are evident in the dog genome. However, conclusions from studies of positive selection are fraught with the problem of false positives given that demographic history is often not taken into account.
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Affiliation(s)
- Adam H. Freedman
- Informatics Group, Faculty of Arts and Sciences, Harvard University, Cambridge, Massachusetts 02138
| | - Kirk E. Lohmueller
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, California 90095
| | - Robert K. Wayne
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, California 90095
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Schrider DR, Shanku AG, Kern AD. Effects of Linked Selective Sweeps on Demographic Inference and Model Selection. Genetics 2016; 204:1207-1223. [PMID: 27605051 PMCID: PMC5105852 DOI: 10.1534/genetics.116.190223] [Citation(s) in RCA: 93] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2016] [Accepted: 09/02/2016] [Indexed: 01/06/2023] Open
Abstract
The availability of large-scale population genomic sequence data has resulted in an explosion in efforts to infer the demographic histories of natural populations across a broad range of organisms. As demographic events alter coalescent genealogies, they leave detectable signatures in patterns of genetic variation within and between populations. Accordingly, a variety of approaches have been designed to leverage population genetic data to uncover the footprints of demographic change in the genome. The vast majority of these methods make the simplifying assumption that the measures of genetic variation used as their input are unaffected by natural selection. However, natural selection can dramatically skew patterns of variation not only at selected sites, but at linked, neutral loci as well. Here we assess the impact of recent positive selection on demographic inference by characterizing the performance of three popular methods through extensive simulation of data sets with varying numbers of linked selective sweeps. In particular, we examined three different demographic models relevant to a number of species, finding that positive selection can bias parameter estimates of each of these models-often severely. We find that selection can lead to incorrect inferences of population size changes when none have occurred. Moreover, we show that linked selection can lead to incorrect demographic model selection, when multiple demographic scenarios are compared. We argue that natural populations may experience the amount of recent positive selection required to skew inferences. These results suggest that demographic studies conducted in many species to date may have exaggerated the extent and frequency of population size changes.
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Affiliation(s)
- Daniel R Schrider
- Department of Genetics, Rutgers University, Piscataway, New Jersey 08854
- Human Genetics Institute of New Jersey, Rutgers University, Piscataway, New Jersey 08554
| | - Alexander G Shanku
- Department of Genetics, Rutgers University, Piscataway, New Jersey 08854
- Institute for Quantitative Biomedicine, Rutgers University, Piscataway, New Jersey 08554
| | - Andrew D Kern
- Department of Genetics, Rutgers University, Piscataway, New Jersey 08854
- Human Genetics Institute of New Jersey, Rutgers University, Piscataway, New Jersey 08554
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66
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Genetic hitchhiking and resistance evolution to transgenic Bt toxins: insights from the African stalk borer Busseola fusca (Noctuidae). Heredity (Edinb) 2016; 118:330-339. [PMID: 27782120 DOI: 10.1038/hdy.2016.104] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2016] [Revised: 07/29/2016] [Accepted: 09/08/2016] [Indexed: 11/09/2022] Open
Abstract
Since transgenic crops expressing Bacillus thuringiensis (Bt) toxins were first released, resistance evolution leading to failure in control of pests populations has been observed in a number of species. Field resistance of the moth Busseola fusca was acknowledged 8 years after Bt maize was introduced in South Africa. Since then, field resistance of this corn borer has been observed at several locations, raising questions about the nature, distribution and dynamics of the resistance trait. Using genetic markers, our study identified four outlier loci clearly associated with resistance. In addition, genetic structure at neutral loci reflected extensive gene flow among populations. A realistically parameterised model suggests that resistance could travel in space at speed of several kilometres a year. Markers at outlier loci delineated a geographic region associated with resistance spread. This was an area of approximately 100 km radius, including the location where resistance was first reported. Controlled crosses corroborated these findings and showed significant differences of progeny survival on Bt plants depending on the origin of the resistant parent. Last, our study suggests diverse resistance mutations, which would explain the widespread occurrence of resistant larvae in Bt fields across the main area of maize production in South Africa.
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67
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Song Z, Zhang M, Li F, Weng Q, Zhou C, Li M, Li J, Huang H, Mo X, Gan S. Genome scans for divergent selection in natural populations of the widespread hardwood species Eucalyptus grandis (Myrtaceae) using microsatellites. Sci Rep 2016; 6:34941. [PMID: 27748400 PMCID: PMC5066178 DOI: 10.1038/srep34941] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2016] [Accepted: 09/20/2016] [Indexed: 11/09/2022] Open
Abstract
Identification of loci or genes under natural selection is important for both understanding the genetic basis of local adaptation and practical applications, and genome scans provide a powerful means for such identification purposes. In this study, genome-wide simple sequence repeats markers (SSRs) were used to scan for molecular footprints of divergent selection in Eucalyptus grandis, a hardwood species occurring widely in costal areas from 32° S to 16° S in Australia. High population diversity levels and weak population structure were detected with putatively neutral genomic SSRs. Using three FST outlier detection methods, a total of 58 outlying SSRs were collectively identified as loci under divergent selection against three non-correlated climatic variables, namely, mean annual temperature, isothermality and annual precipitation. Using a spatial analysis method, nine significant associations were revealed between FST outlier allele frequencies and climatic variables, involving seven alleles from five SSR loci. Of the five significant SSRs, two (EUCeSSR1044 and Embra394) contained alleles of putative genes with known functional importance for response to climatic factors. Our study presents critical information on the population diversity and structure of the important woody species E. grandis and provides insight into the adaptive responses of perennial trees to climatic variations.
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Affiliation(s)
- Zhijiao Song
- State Key Laboratory of Tree Genetics and Breeding, Chinese Academy of Forestry, Xiangshan Road, Beijing 100091, China
- Key Laboratory of State Forestry Administration on Tropical Forestry Research, Research Institute of Tropical Forestry, Chinese Academy of Forestry, Longdong, Guangzhou 510520, China
- Baoshan University, Yuanzheng Road, Baoshan 678000, China
| | - Miaomiao Zhang
- Key Laboratory of State Forestry Administration on Tropical Forestry Research, Research Institute of Tropical Forestry, Chinese Academy of Forestry, Longdong, Guangzhou 510520, China
- College of Forestry, South China Agricultural University, 284 Block, Wushan Street, Guangzhou 510642, China
| | - Fagen Li
- Key Laboratory of State Forestry Administration on Tropical Forestry Research, Research Institute of Tropical Forestry, Chinese Academy of Forestry, Longdong, Guangzhou 510520, China
| | - Qijie Weng
- Key Laboratory of State Forestry Administration on Tropical Forestry Research, Research Institute of Tropical Forestry, Chinese Academy of Forestry, Longdong, Guangzhou 510520, China
| | - Chanpin Zhou
- Key Laboratory of State Forestry Administration on Tropical Forestry Research, Research Institute of Tropical Forestry, Chinese Academy of Forestry, Longdong, Guangzhou 510520, China
| | - Mei Li
- Key Laboratory of State Forestry Administration on Tropical Forestry Research, Research Institute of Tropical Forestry, Chinese Academy of Forestry, Longdong, Guangzhou 510520, China
| | - Jie Li
- Key Laboratory of State Forestry Administration on Tropical Forestry Research, Research Institute of Tropical Forestry, Chinese Academy of Forestry, Longdong, Guangzhou 510520, China
| | - Huanhua Huang
- Guangdong Academy of Forestry, Longdong, Guangzhou 510520, China
| | - Xiaoyong Mo
- College of Forestry, South China Agricultural University, 284 Block, Wushan Street, Guangzhou 510642, China
| | - Siming Gan
- State Key Laboratory of Tree Genetics and Breeding, Chinese Academy of Forestry, Xiangshan Road, Beijing 100091, China
- Key Laboratory of State Forestry Administration on Tropical Forestry Research, Research Institute of Tropical Forestry, Chinese Academy of Forestry, Longdong, Guangzhou 510520, China
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Johnsson M, Gering E, Willis P, Lopez S, Van Dorp L, Hellenthal G, Henriksen R, Friberg U, Wright D. Feralisation targets different genomic loci to domestication in the chicken. Nat Commun 2016; 7:12950. [PMID: 27686863 PMCID: PMC5056458 DOI: 10.1038/ncomms12950] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2015] [Accepted: 08/18/2016] [Indexed: 12/03/2022] Open
Abstract
Feralisation occurs when a domestic population recolonizes the wild, escaping its previous restricted environment, and has been considered as the reverse of domestication. We have previously shown that Kauai Island's feral chickens are a highly variable and admixed population. Here we map selective sweeps in feral Kauai chickens using whole-genome sequencing. The detected sweeps were mostly unique to feralisation and distinct to those selected for during domestication. To ascribe potential phenotypic functions to these genes we utilize a laboratory-controlled equivalent to the Kauai population-an advanced intercross between Red Junglefowl and domestic layer birds that has been used previously for both QTL and expression QTL studies. Certain sweep genes exhibit significant correlations with comb mass, maternal brooding behaviour and fecundity. Our analyses indicate that adaptations to feral and domestic environments involve different genomic regions and feral chickens show some evidence of adaptation at genes associated with sexual selection and reproduction.
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Affiliation(s)
- M. Johnsson
- AVIAN Behavioural Genomics and Physiology Group, IFM Biology, Department of Zoology, Linköping University, 58183 Linköping, Sweden
| | - E. Gering
- Department of Zoology, Michigan University, Michigan 48824, USA
| | - P. Willis
- Department of Biology, University of Victoria, Victoria, British Columbia, Canada V8P 5C2
| | - S. Lopez
- UCL Genetics Institute, Department of Genetics, Evolution and Environment, University College London, London WC1E 6BT, UK
| | - L. Van Dorp
- UCL Genetics Institute, Department of Genetics, Evolution and Environment, University College London, London WC1E 6BT, UK
- Centre for Mathematics, Physics and Engineering in the Life Sciences and EXperimental Biology (CoMPLEX), University College London, London WC1E 6BT, UK
| | - G. Hellenthal
- UCL Genetics Institute, Department of Genetics, Evolution and Environment, University College London, London WC1E 6BT, UK
| | - R. Henriksen
- AVIAN Behavioural Genomics and Physiology Group, IFM Biology, Department of Zoology, Linköping University, 58183 Linköping, Sweden
| | - U. Friberg
- AVIAN Behavioural Genomics and Physiology Group, IFM Biology, Department of Zoology, Linköping University, 58183 Linköping, Sweden
| | - D. Wright
- AVIAN Behavioural Genomics and Physiology Group, IFM Biology, Department of Zoology, Linköping University, 58183 Linköping, Sweden
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Eyres I, Duvaux L, Gharbi K, Tucker R, Hopkins D, Simon JC, Ferrari J, Smadja CM, Butlin RK. Targeted re-sequencing confirms the importance of chemosensory genes in aphid host race differentiation. Mol Ecol 2016; 26:43-58. [PMID: 27552184 PMCID: PMC6849616 DOI: 10.1111/mec.13818] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2016] [Revised: 08/18/2016] [Accepted: 08/22/2016] [Indexed: 01/01/2023]
Abstract
Host‐associated races of phytophagous insects provide a model for understanding how adaptation to a new environment can lead to reproductive isolation and speciation, ultimately enabling us to connect barriers to gene flow to adaptive causes of divergence. The pea aphid (Acyrthosiphon pisum) comprises host races specializing on legume species and provides a unique system for examining the early stages of diversification along a gradient of genetic and associated adaptive divergence. As host choice produces assortative mating, understanding the underlying mechanisms of choice will contribute directly to understanding of speciation. As host choice in the pea aphid is likely mediated by smell and taste, we use capture sequencing and SNP genotyping to test for the role of chemosensory genes in the divergence between eight host plant species across the continuum of differentiation and sampled at multiple locations across western Europe. We show high differentiation of chemosensory loci relative to control loci in a broad set of pea aphid races and localities, using a model‐free approach based on principal component analysis. Olfactory and gustatory receptors form the majority of highly differentiated genes and include loci that were already identified as outliers in a previous study focusing on the three most closely related host races. Consistent indications that chemosensory genes may be good candidates for local adaptation and barriers to gene flow in the pea aphid open the way to further investigations aiming to understand their impact on gene flow and to determine their precise functions in response to host plant metabolites.
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Affiliation(s)
- Isobel Eyres
- Department of Animal and Plant Sciences, University of Sheffield, Western Bank, Alfred Denny Building, Sheffield, S10 2TN, UK
| | - Ludovic Duvaux
- Department of Animal and Plant Sciences, University of Sheffield, Western Bank, Alfred Denny Building, Sheffield, S10 2TN, UK
| | - Karim Gharbi
- Edinburgh Genomics, Ashworth Laboratories, University of Edinburgh, EH9 3JT, Edinburgh, UK
| | - Rachel Tucker
- Department of Animal and Plant Sciences, University of Sheffield, Western Bank, Alfred Denny Building, Sheffield, S10 2TN, UK
| | - David Hopkins
- Department of Animal and Plant Sciences, University of Sheffield, Western Bank, Alfred Denny Building, Sheffield, S10 2TN, UK
| | - Jean-Christophe Simon
- Institut de Génétique, Environnement et Protection des Plantes, UMR 1349 IGEPP, Domaine de la Motte, INRA, 35653, Le Rheu Cedex, France
| | - Julia Ferrari
- Department of Biology, University of York, York YO10 5DD, UK
| | - Carole M Smadja
- Institut des Sciences de l'Evolution (UMR 5554 CNRS-IRD-CIRAD-Université de Montpellier), Université de Montpellier, cc065, Place Bataillon, Campus Triolet, 34095, Montpellier Cedex 05, France
| | - Roger K Butlin
- Department of Animal and Plant Sciences, University of Sheffield, Western Bank, Alfred Denny Building, Sheffield, S10 2TN, UK
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70
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Gould BA, Stinchcombe JR. Population genomic scans suggest novel genes underlie convergent flowering time evolution in the introduced range of Arabidopsis thaliana. Mol Ecol 2016; 26:92-106. [PMID: 27064998 DOI: 10.1111/mec.13643] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2016] [Revised: 04/06/2016] [Accepted: 04/08/2016] [Indexed: 01/08/2023]
Abstract
A long-standing question in evolutionary biology is whether the evolution of convergent phenotypes results from selection on the same heritable genetic components. Using whole-genome sequencing and genome scans, we tested whether the evolution of parallel longitudinal flowering time clines in the native and introduced ranges of Arabidopsis thaliana has a similar genetic basis. We found that common variants of large effect on flowering time in the native range do not appear to have been under recent strong selection in the introduced range. We identified a set of 38 new candidate genes that are putatively linked to the evolution of flowering time. A high degree of conditional neutrality of flowering time variants between the native and introduced range may preclude parallel evolution at the level of genes. Overall, neither gene pleiotropy nor available standing genetic variation appears to have restricted the evolution of flowering time to high-frequency variants from the native range or to known flowering time pathway genes.
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Affiliation(s)
- Billie A Gould
- Department of Plant Biology, Michigan State University, 612 Wilson Rd, East Lansing, MI, 48823, USA
| | - John R Stinchcombe
- Department of Plant Biology, Michigan State University, 612 Wilson Rd, East Lansing, MI, 48823, USA
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71
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Brousseau L, Postolache D, Lascoux M, Drouzas AD, Källman T, Leonarduzzi C, Liepelt S, Piotti A, Popescu F, Roschanski AM, Zhelev P, Fady B, Vendramin GG. Local Adaptation in European Firs Assessed through Extensive Sampling across Altitudinal Gradients in Southern Europe. PLoS One 2016; 11:e0158216. [PMID: 27392065 PMCID: PMC4938419 DOI: 10.1371/journal.pone.0158216] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2015] [Accepted: 06/13/2016] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Local adaptation is a key driver of phenotypic and genetic divergence at loci responsible for adaptive traits variations in forest tree populations. Its experimental assessment requires rigorous sampling strategies such as those involving population pairs replicated across broad spatial scales. METHODS A hierarchical Bayesian model of selection (HBM) that explicitly considers both the replication of the environmental contrast and the hierarchical genetic structure among replicated study sites is introduced. Its power was assessed through simulations and compared to classical 'within-site' approaches (FDIST, BAYESCAN) and a simplified, within-site, version of the model introduced here (SBM). RESULTS HBM demonstrates that hierarchical approaches are very powerful to detect replicated patterns of adaptive divergence with low false-discovery (FDR) and false-non-discovery (FNR) rates compared to the analysis of different sites separately through within-site approaches. The hypothesis of local adaptation to altitude was further addressed by analyzing replicated Abies alba population pairs (low and high elevations) across the species' southern distribution range, where the effects of climatic selection are expected to be the strongest. For comparison, a single population pair from the closely related species A. cephalonica was also analyzed. The hierarchical model did not detect any pattern of adaptive divergence to altitude replicated in the different study sites. Instead, idiosyncratic patterns of local adaptation among sites were detected by within-site approaches. CONCLUSION Hierarchical approaches may miss idiosyncratic patterns of adaptation among sites, and we strongly recommend the use of both hierarchical (multi-site) and classical (within-site) approaches when addressing the question of adaptation across broad spatial scales.
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Affiliation(s)
- Louise Brousseau
- INRA, UR629 URFM Ecologie des Forêts Méditerranéennes, Domaine Saint Paul, Site Agroparc CS 40509, 84914 Avignon Cedex 9, France
- Institute of Biosciences and BioResources, National Research Council (IBBR-CNR), Division of Florence, Via Madonna del Piano 10, 50019 Sesto Fiorentino (FI), Italy
| | - Dragos Postolache
- Institute of Biosciences and BioResources, National Research Council (IBBR-CNR), Division of Florence, Via Madonna del Piano 10, 50019 Sesto Fiorentino (FI), Italy
- Scuola Superiore Sant'Anna, Piazza Martiri della Libertà 33, 56127 Pisa, Italy
- National Institute of Forest Research and Development (INCDS), Research Station Simeria, Str. Biscaria 1, 335900 Simeria, Romania
| | - Martin Lascoux
- Department of Ecology and Genetics, Evolutionary Biology Center and Science for Life Laboratory, Uppsala University, 75236 Uppsala, Sweden
| | - Andreas D. Drouzas
- School of Biology, Aristotle University of Thessaloniki, GR-54124, Thessaloniki, Greece
| | - Thomas Källman
- Department of Ecology and Genetics, Evolutionary Biology Center and Science for Life Laboratory, Uppsala University, 75236 Uppsala, Sweden
| | - Cristina Leonarduzzi
- Institute of Biosciences and BioResources, National Research Council (IBBR-CNR), Division of Florence, Via Madonna del Piano 10, 50019 Sesto Fiorentino (FI), Italy
- Institute of Biosciences and BioResources, National Research Council (IBBR-CNR), Division of Palermo, National 3. Research Council—Corso Calatafimi, 414—I-90129, Palermo (PA), Italy
| | - Sascha Liepelt
- University of Marburg, Faculty of Biology, Conservation Biology, Karl-von-Frisch-Straße 35032 Marburg, Germany
| | - Andrea Piotti
- Institute of Biosciences and BioResources, National Research Council (IBBR-CNR), Division of Florence, Via Madonna del Piano 10, 50019 Sesto Fiorentino (FI), Italy
| | - Flaviu Popescu
- National Institute of Forest Research and Development (INCDS), Research Station Simeria, Str. Biscaria 1, 335900 Simeria, Romania
| | - Anna M. Roschanski
- University of Marburg, Faculty of Biology, Conservation Biology, Karl-von-Frisch-Straße 35032 Marburg, Germany
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Genebank Collections North, Inselstrasse 9, D-23999 Malchow/Poel, Germany
| | - Peter Zhelev
- University of Forestry, 10, Kl. Ohridsky Blvd., 1797 Sofia, Bulgaria
| | - Bruno Fady
- INRA, UR629 URFM Ecologie des Forêts Méditerranéennes, Domaine Saint Paul, Site Agroparc CS 40509, 84914 Avignon Cedex 9, France
| | - Giovanni Giuseppe Vendramin
- Institute of Biosciences and BioResources, National Research Council (IBBR-CNR), Division of Florence, Via Madonna del Piano 10, 50019 Sesto Fiorentino (FI), Italy
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72
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Wei C, Wang H, Liu G, Zhao F, Kijas JW, Ma Y, Lu J, Zhang L, Cao J, Wu M, Wang G, Liu R, Liu Z, Zhang S, Liu C, Du L. Genome-wide analysis reveals adaptation to high altitudes in Tibetan sheep. Sci Rep 2016; 6:26770. [PMID: 27230812 PMCID: PMC4882523 DOI: 10.1038/srep26770] [Citation(s) in RCA: 75] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2015] [Accepted: 05/09/2016] [Indexed: 02/07/2023] Open
Abstract
Tibetan sheep have lived on the Tibetan Plateau for thousands of years; however, the process and consequences of adaptation to this extreme environment have not been elucidated for important livestock such as sheep. Here, seven sheep breeds, representing both highland and lowland breeds from different areas of China, were genotyped for a genome-wide collection of single-nucleotide polymorphisms (SNPs). The FST and XP-EHH approaches were used to identify regions harbouring local positive selection between these highland and lowland breeds, and 236 genes were identified. We detected selection events spanning genes involved in angiogenesis, energy production and erythropoiesis. In particular, several candidate genes were associated with high-altitude hypoxia, including EPAS1, CRYAA, LONP1, NF1, DPP4, SOD1, PPARG and SOCS2. EPAS1 plays a crucial role in hypoxia adaption; therefore, we investigated the exon sequences of EPAS1 and identified 12 mutations. Analysis of the relationship between blood-related phenotypes and EPAS1 genotypes in additional highland sheep revealed that a homozygous mutation at a relatively conserved site in the EPAS1 3' untranslated region was associated with increased mean corpuscular haemoglobin concentration and mean corpuscular volume. Taken together, our results provide evidence of the genetic diversity of highland sheep and indicate potential high-altitude hypoxia adaptation mechanisms, including the role of EPAS1 in adaptation.
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Affiliation(s)
- Caihong Wei
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, National Center for Molecular Genetics and Breeding of Animal, Beijing, People's Republic of China
| | - Huihua Wang
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, National Center for Molecular Genetics and Breeding of Animal, Beijing, People's Republic of China.,National Animal Husbandry Service, National Center of Preservation &Utilization of Animal Genetic Resources, Beijing, People's Republic of China.,Institute of apicultural research, Chinese Academy of Agricultural Sciences, Beijing, People's Republic of China
| | - Gang Liu
- National Animal Husbandry Service, National Center of Preservation &Utilization of Animal Genetic Resources, Beijing, People's Republic of China
| | - Fuping Zhao
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, National Center for Molecular Genetics and Breeding of Animal, Beijing, People's Republic of China
| | | | - Youji Ma
- College of Animal Science and Technology, Gansu Agriculture University, Lanzhou 730070, People's Republic of China
| | - Jian Lu
- National Animal Husbandry Service, National Center of Preservation &Utilization of Animal Genetic Resources, Beijing, People's Republic of China
| | - Li Zhang
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, National Center for Molecular Genetics and Breeding of Animal, Beijing, People's Republic of China
| | - Jiaxue Cao
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, National Center for Molecular Genetics and Breeding of Animal, Beijing, People's Republic of China
| | - Mingming Wu
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, National Center for Molecular Genetics and Breeding of Animal, Beijing, People's Republic of China
| | - Guangkai Wang
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, National Center for Molecular Genetics and Breeding of Animal, Beijing, People's Republic of China
| | - Ruizao Liu
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, National Center for Molecular Genetics and Breeding of Animal, Beijing, People's Republic of China
| | - Zhen Liu
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, National Center for Molecular Genetics and Breeding of Animal, Beijing, People's Republic of China
| | - Shuzhen Zhang
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, National Center for Molecular Genetics and Breeding of Animal, Beijing, People's Republic of China
| | - Chousheng Liu
- National Animal Husbandry Service, National Center of Preservation &Utilization of Animal Genetic Resources, Beijing, People's Republic of China
| | - Lixin Du
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, National Center for Molecular Genetics and Breeding of Animal, Beijing, People's Republic of China
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73
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Uricchio LH, Zaitlen NA, Ye CJ, Witte JS, Hernandez RD. Selection and explosive growth alter genetic architecture and hamper the detection of causal rare variants. Genome Res 2016; 26:863-73. [PMID: 27197206 PMCID: PMC4937562 DOI: 10.1101/gr.202440.115] [Citation(s) in RCA: 52] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2015] [Accepted: 05/16/2016] [Indexed: 12/20/2022]
Abstract
The role of rare alleles in complex phenotypes has been hotly debated, but most rare variant association tests (RVATs) do not account for the evolutionary forces that affect genetic architecture. Here, we use simulation and numerical algorithms to show that explosive population growth, as experienced by human populations, can dramatically increase the impact of very rare alleles on trait variance. We then assess the ability of RVATs to detect causal loci using simulations and human RNA-seq data. Surprisingly, we find that statistical performance is worst for phenotypes in which genetic variance is due mainly to rare alleles, and explosive population growth decreases power. Although many studies have attempted to identify causal rare variants, few have reported novel associations. This has sometimes been interpreted to mean that rare variants make negligible contributions to complex trait heritability. Our work shows that RVATs are not robust to realistic human evolutionary forces, so general conclusions about the impact of rare variants on complex traits may be premature.
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Affiliation(s)
- Lawrence H Uricchio
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, California 94143, USA; Graduate Program in Bioinformatics, University of California, San Francisco, San Francisco, California 94143, USA
| | - Noah A Zaitlen
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, California 94143, USA; Institute for Human Genetics, University of California, San Francisco, San Francisco, California 94143, USA; Institute for Quantitative Biosciences (QB3), University of California, San Francisco, San Francisco, California 94143, USA
| | - Chun Jimmie Ye
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, California 94143, USA; Institute for Human Genetics, University of California, San Francisco, San Francisco, California 94143, USA; Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California 94143, USA
| | - John S Witte
- Institute for Human Genetics, University of California, San Francisco, San Francisco, California 94143, USA; Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California 94143, USA
| | - Ryan D Hernandez
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, California 94143, USA; Institute for Human Genetics, University of California, San Francisco, San Francisco, California 94143, USA; Institute for Quantitative Biosciences (QB3), University of California, San Francisco, San Francisco, California 94143, USA
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Schlamp F, van der Made J, Stambler R, Chesebrough L, Boyko AR, Messer PW. Evaluating the performance of selection scans to detect selective sweeps in domestic dogs. Mol Ecol 2016; 25:342-56. [PMID: 26589239 DOI: 10.1111/mec.13485] [Citation(s) in RCA: 50] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2015] [Revised: 11/10/2015] [Accepted: 11/11/2015] [Indexed: 01/11/2023]
Abstract
Selective breeding of dogs has resulted in repeated artificial selection on breed-specific morphological phenotypes. A number of quantitative trait loci associated with these phenotypes have been identified in genetic mapping studies. We analysed the population genomic signatures observed around the causal mutations for 12 of these loci in 25 dog breeds, for which we genotyped 25 individuals in each breed. By measuring the population frequencies of the causal mutations in each breed, we identified those breeds in which specific mutations most likely experienced positive selection. These instances were then used as positive controls for assessing the performance of popular statistics to detect selection from population genomic data. We found that artificial selection during dog domestication has left characteristic signatures in the haplotype and nucleotide polymorphism patterns around selected loci that can be detected in the genotype data from a single population sample. However, the sensitivity and accuracy at which such signatures were detected varied widely between loci, the particular statistic used and the choice of analysis parameters. We observed examples of both hard and soft selective sweeps and detected strong selective events that removed genetic diversity almost entirely over regions >10 Mbp. Our study demonstrates the power and limitations of selection scans in populations with high levels of linkage disequilibrium due to severe founder effects and recent population bottlenecks.
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Affiliation(s)
- Florencia Schlamp
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY, 14853, USA.,Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY, 14853, USA
| | - Julian van der Made
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY, 14853, USA
| | - Rebecca Stambler
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY, 14853, USA
| | - Lewis Chesebrough
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY, 14853, USA
| | - Adam R Boyko
- Department of Biomedical Sciences, Cornell University, Ithaca, NY, 14853, USA
| | - Philipp W Messer
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY, 14853, USA
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75
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Mazzarella AB, Boessenkool S, Østbye K, Vøllestad LA, Trucchi E. Genomic signatures of the plateless phenotype in the threespine stickleback. Ecol Evol 2016; 6:3161-73. [PMID: 27096077 PMCID: PMC4829042 DOI: 10.1002/ece3.2072] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2015] [Revised: 02/17/2016] [Accepted: 02/22/2016] [Indexed: 12/15/2022] Open
Abstract
Understanding the genetic basis of traits involved in adaptive divergence and speciation is one of the most fundamental objectives in evolutionary biology. Toward that end, we look for signatures of extreme plate loss in the genome of freshwater threespine sticklebacks (Gasterosteus aculeatus). Plateless stickleback have been found in only a few lakes and streams across the world; they represent the far extreme of a phenotypic continuum (plate number) that has been studied for years, although plateless individuals have not yet been the subject of much investigation. We use a dense single nucleotide polymorphism dataset made using RADseq to study fish from three freshwater populations containing plateless and low plated individuals, as well as fish from full plated marine populations. Analyses were performed using FastStructure, sliding windows FST, Bayescan and latent factor mixed models to search for genomic differences between the low plated and plateless phenotypes both within and among the three lakes. At least 18 genomic regions which may contribute to within‐morph plate number variation were detected in our low plated stickleback populations. We see no evidence of a selective sweep between low and plateless fish; rather reduction of plate number within the low plated morph seems to be polygenic.
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Affiliation(s)
- Anna B Mazzarella
- Department of Biosciences Centre for Ecological and Evolutionary Synthesis University of Oslo PO Box 1066 Blindern Norway
| | - Sanne Boessenkool
- Department of Biosciences Centre for Ecological and Evolutionary Synthesis University of Oslo PO Box 1066 Blindern Norway
| | - Kjartan Østbye
- Department of Biosciences Centre for Ecological and Evolutionary Synthesis University of Oslo PO Box 1066 Blindern Norway; Faculty of Applied Ecology and Agricultural Sciences Hedmark University College Campus Evenstad No-2480 Koppang Norway
| | - Leif Asbjørn Vøllestad
- Department of Biosciences Centre for Ecological and Evolutionary Synthesis University of Oslo PO Box 1066 Blindern Norway
| | - Emiliano Trucchi
- Department of Biosciences Centre for Ecological and Evolutionary Synthesis University of Oslo PO Box 1066 Blindern Norway; Department of Botany and Biodiversity Research University of Vienna Rennweg 14A-1030 Vienna Austria
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76
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Allaby RG, Kitchen JL, Fuller DQ. Surprisingly Low Limits of Selection in Plant Domestication. Evol Bioinform Online 2016; 11:41-51. [PMID: 27081302 PMCID: PMC4822723 DOI: 10.4137/ebo.s33495] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2015] [Revised: 12/10/2015] [Accepted: 12/13/2015] [Indexed: 11/22/2022] Open
Abstract
Current debate concerns the pace at which domesticated plants emerged from cultivated wild populations and how many genes were involved. Using an individual-based model, based on the assumptions of Haldane and Maynard Smith, respectively, we estimate that a surprisingly low number of 50–100 loci are the most that could be under selection in a cultivation regime at the selection strengths observed in the archaeological record. This finding is robust to attempts to rescue populations from extinction through selection from high standing genetic variation, gene flow, and the Maynard Smith-based model of threshold selection. Selective sweeps come at a cost, reducing the capacity of plants to adapt to new environments, which may contribute to the explanation of why selective sweeps have not been detected more frequently and why expansion of the agrarian package during the Neolithic was so frequently associated with collapse.
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Affiliation(s)
- Robin G Allaby
- School of Life Sciences, Gibbet Hill Campus, University of Warwick, Coventry, UK
| | - James L Kitchen
- School of Life Sciences, Gibbet Hill Campus, University of Warwick, Coventry, UK
| | - Dorian Q Fuller
- Institute of Archaeology, University College London, London, UK
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77
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Evidence of recent signatures of selection during domestication in an Atlantic salmon population. Mar Genomics 2016; 26:41-50. [DOI: 10.1016/j.margen.2015.12.007] [Citation(s) in RCA: 44] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2015] [Revised: 11/25/2015] [Accepted: 12/16/2015] [Indexed: 11/17/2022]
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78
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Schrider DR, Kern AD. S/HIC: Robust Identification of Soft and Hard Sweeps Using Machine Learning. PLoS Genet 2016; 12:e1005928. [PMID: 26977894 PMCID: PMC4792382 DOI: 10.1371/journal.pgen.1005928] [Citation(s) in RCA: 109] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2015] [Accepted: 02/21/2016] [Indexed: 12/17/2022] Open
Abstract
Detecting the targets of adaptive natural selection from whole genome sequencing data is a central problem for population genetics. However, to date most methods have shown sub-optimal performance under realistic demographic scenarios. Moreover, over the past decade there has been a renewed interest in determining the importance of selection from standing variation in adaptation of natural populations, yet very few methods for inferring this model of adaptation at the genome scale have been introduced. Here we introduce a new method, S/HIC, which uses supervised machine learning to precisely infer the location of both hard and soft selective sweeps. We show that S/HIC has unrivaled accuracy for detecting sweeps under demographic histories that are relevant to human populations, and distinguishing sweeps from linked as well as neutrally evolving regions. Moreover, we show that S/HIC is uniquely robust among its competitors to model misspecification. Thus, even if the true demographic model of a population differs catastrophically from that specified by the user, S/HIC still retains impressive discriminatory power. Finally, we apply S/HIC to the case of resequencing data from human chromosome 18 in a European population sample, and demonstrate that we can reliably recover selective sweeps that have been identified earlier using less specific and sensitive methods.
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Affiliation(s)
- Daniel R. Schrider
- Department of Genetics, Rutgers University, Piscataway, New Jersey, United States of America
- * E-mail:
| | - Andrew D. Kern
- Department of Genetics, Rutgers University, Piscataway, New Jersey, United States of America
- Human Genetics Institute of New Jersey, Rutgers University, Piscataway, New Jersey, United States of America
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79
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Freedman AH, Schweizer RM, Ortega-Del Vecchyo D, Han E, Davis BW, Gronau I, Silva PM, Galaverni M, Fan Z, Marx P, Lorente-Galdos B, Ramirez O, Hormozdiari F, Alkan C, Vilà C, Squire K, Geffen E, Kusak J, Boyko AR, Parker HG, Lee C, Tadigotla V, Siepel A, Bustamante CD, Harkins TT, Nelson SF, Marques-Bonet T, Ostrander EA, Wayne RK, Novembre J. Demographically-Based Evaluation of Genomic Regions under Selection in Domestic Dogs. PLoS Genet 2016; 12:e1005851. [PMID: 26943675 PMCID: PMC4778760 DOI: 10.1371/journal.pgen.1005851] [Citation(s) in RCA: 65] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2015] [Accepted: 01/18/2016] [Indexed: 12/31/2022] Open
Abstract
Controlling for background demographic effects is important for accurately identifying loci that have recently undergone positive selection. To date, the effects of demography have not yet been explicitly considered when identifying loci under selection during dog domestication. To investigate positive selection on the dog lineage early in the domestication, we examined patterns of polymorphism in six canid genomes that were previously used to infer a demographic model of dog domestication. Using an inferred demographic model, we computed false discovery rates (FDR) and identified 349 outlier regions consistent with positive selection at a low FDR. The signals in the top 100 regions were frequently centered on candidate genes related to brain function and behavior, including LHFPL3, CADM2, GRIK3, SH3GL2, MBP, PDE7B, NTAN1, and GLRA1. These regions contained significant enrichments in behavioral ontology categories. The 3rd top hit, CCRN4L, plays a major role in lipid metabolism, that is supported by additional metabolism related candidates revealed in our scan, including SCP2D1 and PDXC1. Comparing our method to an empirical outlier approach that does not directly account for demography, we found only modest overlaps between the two methods, with 60% of empirical outliers having no overlap with our demography-based outlier detection approach. Demography-aware approaches have lower-rates of false discovery. Our top candidates for selection, in addition to expanding the set of neurobehavioral candidate genes, include genes related to lipid metabolism, suggesting a dietary target of selection that was important during the period when proto-dogs hunted and fed alongside hunter-gatherers.
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Affiliation(s)
- Adam H. Freedman
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, California, United States of America
| | - Rena M. Schweizer
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, California, United States of America
| | - Diego Ortega-Del Vecchyo
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, California, United States of America
| | - Eunjung Han
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, California, United States of America
| | - Brian W. Davis
- National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Ilan Gronau
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, United States of America
| | | | | | - Zhenxin Fan
- Key Laboratory of Bioresources and Ecoenvironment, Sichuan University, Chengdu, China
| | - Peter Marx
- Department of Measurement and Information Systems, Budapest University of Technology and Economics, Budapest, Hungary
| | - Belen Lorente-Galdos
- ICREA at Institut de Biologia Evolutiva (CSIC-Universitat Pompeu Fabra), Barcelona, Spain
| | - Oscar Ramirez
- ICREA at Institut de Biologia Evolutiva (CSIC-Universitat Pompeu Fabra), Barcelona, Spain
| | - Farhad Hormozdiari
- Department of Computer Science, University of California, Los Angeles, Los Angeles, California, United States of America
| | | | - Carles Vilà
- Estación Biológia de Doñana EBD-CSIC, Sevilla, Spain
| | - Kevin Squire
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, California, United States of America
| | - Eli Geffen
- Department of Zoology, Tel Aviv University, Tel Aviv, Israel
| | - Josip Kusak
- Department of Biology, University of Zagreb, Zagreb, Croatia
| | - Adam R. Boyko
- Department of Biomedical Sciences, Cornell University, Ithaca, New York, United States of America
| | - Heidi G. Parker
- ICREA at Institut de Biologia Evolutiva (CSIC-Universitat Pompeu Fabra), Barcelona, Spain
| | - Clarence Lee
- Life Technologies, Foster City, California, United States of America
| | - Vasisht Tadigotla
- Life Technologies, Foster City, California, United States of America
| | - Adam Siepel
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America
| | | | | | - Stanley F. Nelson
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, California, United States of America
| | - Tomas Marques-Bonet
- ICREA at Institut de Biologia Evolutiva (CSIC-Universitat Pompeu Fabra), Barcelona, Spain
- Centro Nacional de Analisis Genomico (CNAG/PCB), Baldiri Reixach 4–8, Barcelona, Spain
| | - Elaine A. Ostrander
- National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Robert K. Wayne
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, California, United States of America
| | - John Novembre
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, California, United States of America
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80
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Hsieh P, Veeramah KR, Lachance J, Tishkoff SA, Wall JD, Hammer MF, Gutenkunst RN. Whole-genome sequence analyses of Western Central African Pygmy hunter-gatherers reveal a complex demographic history and identify candidate genes under positive natural selection. Genome Res 2016; 26:279-90. [PMID: 26888263 PMCID: PMC4772011 DOI: 10.1101/gr.192971.115] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2015] [Accepted: 01/07/2016] [Indexed: 12/20/2022]
Abstract
African Pygmies practicing a mobile hunter-gatherer lifestyle are phenotypically and genetically diverged from other anatomically modern humans, and they likely experienced strong selective pressures due to their unique lifestyle in the Central African rainforest. To identify genomic targets of adaptation, we sequenced the genomes of four Biaka Pygmies from the Central African Republic and jointly analyzed these data with the genome sequences of three Baka Pygmies from Cameroon and nine Yoruba famers. To account for the complex demographic history of these populations that includes both isolation and gene flow, we fit models using the joint allele frequency spectrum and validated them using independent approaches. Our two best-fit models both suggest ancient divergence between the ancestors of the farmers and Pygmies, 90,000 or 150,000 yr ago. We also find that bidirectional asymmetric gene flow is statistically better supported than a single pulse of unidirectional gene flow from farmers to Pygmies, as previously suggested. We then applied complementary statistics to scan the genome for evidence of selective sweeps and polygenic selection. We found that conventional statistical outlier approaches were biased toward identifying candidates in regions of high mutation or low recombination rate. To avoid this bias, we assigned P-values for candidates using whole-genome simulations incorporating demography and variation in both recombination and mutation rates. We found that genes and gene sets involved in muscle development, bone synthesis, immunity, reproduction, cell signaling and development, and energy metabolism are likely to be targets of positive natural selection in Western African Pygmies or their recent ancestors.
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Affiliation(s)
- PingHsun Hsieh
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, Arizona 85721, USA
| | - Krishna R Veeramah
- Arizona Research Laboratories Division of Biotechnology, University of Arizona, Tucson, Arizona 85721, USA; Department of Ecology and Evolution, Stony Brook University, Stony Brook, New York 11794, USA
| | - Joseph Lachance
- Department of Biology and Genetics, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA; Department of Biology, Georgia Institute of Technology, Atlanta, Georgia 30332, USA
| | - Sarah A Tishkoff
- Department of Biology and Genetics, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Jeffrey D Wall
- Institute for Human Genetics, University of California, San Francisco, California 94143, USA
| | - Michael F Hammer
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, Arizona 85721, USA; Arizona Research Laboratories Division of Biotechnology, University of Arizona, Tucson, Arizona 85721, USA
| | - Ryan N Gutenkunst
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, Arizona 85721, USA; Department of Molecular and Cellular Biology, University of Arizona, Tucson, Arizona 85721, USA
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81
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François O, Martins H, Caye K, Schoville SD. Controlling false discoveries in genome scans for selection. Mol Ecol 2016; 25:454-69. [PMID: 26671840 DOI: 10.1111/mec.13513] [Citation(s) in RCA: 138] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2015] [Revised: 11/23/2015] [Accepted: 11/25/2015] [Indexed: 02/06/2023]
Abstract
Population differentiation (PD) and ecological association (EA) tests have recently emerged as prominent statistical methods to investigate signatures of local adaptation using population genomic data. Based on statistical models, these genomewide testing procedures have attracted considerable attention as tools to identify loci potentially targeted by natural selection. An important issue with PD and EA tests is that incorrect model specification can generate large numbers of false-positive associations. Spurious association may indeed arise when shared demographic history, patterns of isolation by distance, cryptic relatedness or genetic background are ignored. Recent works on PD and EA tests have widely focused on improvements of test corrections for those confounding effects. Despite significant algorithmic improvements, there is still a number of open questions on how to check that false discoveries are under control and implement test corrections, or how to combine statistical tests from multiple genome scan methods. This tutorial study provides a detailed answer to these questions. It clarifies the relationships between traditional methods based on allele frequency differentiation and EA methods and provides a unified framework for their underlying statistical tests. We demonstrate how techniques developed in the area of genomewide association studies, such as inflation factors and linear mixed models, benefit genome scan methods and provide guidelines for good practice while conducting statistical tests in landscape and population genomic applications. Finally, we highlight how the combination of several well-calibrated statistical tests can increase the power to reject neutrality, improving our ability to infer patterns of local adaptation in large population genomic data sets.
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Affiliation(s)
- Olivier François
- Centre National de la Recherche Scientifique, Université Grenoble-Alpes, TIMC-IMAG UMR 5525, Grenoble, 38042, France
| | - Helena Martins
- Centre National de la Recherche Scientifique, Université Grenoble-Alpes, TIMC-IMAG UMR 5525, Grenoble, 38042, France
| | - Kevin Caye
- Centre National de la Recherche Scientifique, Université Grenoble-Alpes, TIMC-IMAG UMR 5525, Grenoble, 38042, France
| | - Sean D Schoville
- Department of Entomology, 637 Russell Laboratories, University of Wisconsin-Madison, 1630 Linden Drive, Madison, WI, 53706, USA
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82
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Storz JF, Cheviron ZA. Functional Genomic Insights into Regulatory Mechanisms of High-Altitude Adaptation. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2016; 903:113-28. [PMID: 27343092 DOI: 10.1007/978-1-4899-7678-9_8] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Recent studies of indigenous human populations at high altitude have provided proof-of-principle that genome scans of DNA polymorphism can be used to identify candidate loci for hypoxia adaptation. When integrated with experimental analyses of physiological phenotypes, genome-wide surveys of DNA polymorphism and tissue-specific transcriptional profiles can provide insights into actual mechanisms of adaptation. It has been suggested that adaptive phenotypic evolution is largely mediated by cis-regulatory changes in genes that are located at integrative control points in regulatory networks. This hypothesis can be tested by conducting transcriptomic analyses of hypoxic signaling pathways in conjunction with experimental measures of vascular oxygen supply and metabolic pathway flux. Such studies may reveal whether the architecture of gene regulatory networks can be used to predict which loci (and which types of loci) are likely to be "hot spots" for adaptive physiological evolution. Functional genomic studies of deer mice (Peromyscus maniculatus) demonstrate how the integrated analysis of variation in tissue-specific transcriptomes, whole-animal physiological performance, and various subordinate traits can yield insights into the mechanistic underpinnings of high-altitude adaptation.
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Affiliation(s)
- Jay F Storz
- School of Biological Sciences, University of Nebraska, Lincoln, NE, USA.
| | - Zachary A Cheviron
- Division of Biological Sciences, University of Montana, Missoula, MT, USA
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83
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Jensen JD, Foll M, Bernatchez L. The past, present and future of genomic scans for selection. Mol Ecol 2016; 25:1-4. [PMID: 26745554 DOI: 10.1111/mec.13493] [Citation(s) in RCA: 65] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2015] [Accepted: 11/16/2015] [Indexed: 12/18/2022]
Affiliation(s)
- Jeffrey D Jensen
- School of Life Sciences, École Polytechnique Fédérale de Lausanne, 1015, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland
| | - Matthieu Foll
- Genetic Cancer Susceptibility, International Agency for Research on Cancer, Lyon, France
| | - Louis Bernatchez
- IBIS (Institut de Biologie Intégrative et des Systèmes), Université Laval, Québec, Québec, Canada
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84
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Welch JJ, Jiggins CD. Standing and flowing: the complex origins of adaptive variation. Mol Ecol 2015; 23:3935-7. [PMID: 25088550 DOI: 10.1111/mec.12859] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2014] [Revised: 07/01/2014] [Accepted: 07/10/2014] [Indexed: 01/17/2023]
Abstract
A population faced with a new selection pressure can only adapt if appropriate genetic variation is available. This genetic variation might come from new mutations or from gene exchange with other populations or species, or it might already segregate in the population as standing genetic variation (which might itself have arisen from either mutation or gene flow). Understanding the relative importance of these sources of adaptive variation is a fundamental issue in evolutionary genetics (Orr & Betancourt ; Barrett & Schluter ; Gladyshev et al. ) and has practical implications for conservation, plant and animal breeding, biological control and infectious disease prevention (e.g. Robertson ; Soulé & Wilcox ; Prentis et al. ; Pennings ). In this issue of Molecular Ecology, Roesti et al. () make an important contribution to this longstanding debate.
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Affiliation(s)
- John J Welch
- Department of Genetics, University of Cambridge, Downing St., Cambridge, CB23EH, UK
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85
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He Y, Wang M, Huang X, Li R, Xu H, Xu S, Jin L. A probabilistic method for testing and estimating selection differences between populations. Genome Res 2015; 25:1903-9. [PMID: 26463656 PMCID: PMC4665011 DOI: 10.1101/gr.192336.115] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2015] [Accepted: 10/13/2015] [Indexed: 01/18/2023]
Abstract
Human populations around the world encounter various environmental challenges and, consequently, develop genetic adaptations to different selection forces. Identifying the differences in natural selection between populations is critical for understanding the roles of specific genetic variants in evolutionary adaptation. Although numerous methods have been developed to detect genetic loci under recent directional selection, a probabilistic solution for testing and quantifying selection differences between populations is lacking. Here we report the development of a probabilistic method for testing and estimating selection differences between populations. By use of a probabilistic model of genetic drift and selection, we showed that logarithm odds ratios of allele frequencies provide estimates of the differences in selection coefficients between populations. The estimates approximate a normal distribution, and variance can be estimated using genome-wide variants. This allows us to quantify differences in selection coefficients and to determine the confidence intervals of the estimate. Our work also revealed the link between genetic association testing and hypothesis testing of selection differences. It therefore supplies a solution for hypothesis testing of selection differences. This method was applied to a genome-wide data analysis of Han and Tibetan populations. The results confirmed that both the EPAS1 and EGLN1 genes are under statistically different selection in Han and Tibetan populations. We further estimated differences in the selection coefficients for genetic variants involved in melanin formation and determined their confidence intervals between continental population groups. Application of the method to empirical data demonstrated the outstanding capability of this novel approach for testing and quantifying differences in natural selection.
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Affiliation(s)
- Yungang He
- Chinese Academy of Sciences Key Laboratory of Computational Biology, Chinese Academy of Sciences-Max Planck Society Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Minxian Wang
- Chinese Academy of Sciences Key Laboratory of Computational Biology, Chinese Academy of Sciences-Max Planck Society Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Xin Huang
- Chinese Academy of Sciences Key Laboratory of Computational Biology, Chinese Academy of Sciences-Max Planck Society Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Ran Li
- Chinese Academy of Sciences Key Laboratory of Computational Biology, Chinese Academy of Sciences-Max Planck Society Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Hongyang Xu
- Chinese Academy of Sciences Key Laboratory of Computational Biology, Chinese Academy of Sciences-Max Planck Society Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Shuhua Xu
- Chinese Academy of Sciences Key Laboratory of Computational Biology, Chinese Academy of Sciences-Max Planck Society Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Li Jin
- Chinese Academy of Sciences Key Laboratory of Computational Biology, Chinese Academy of Sciences-Max Planck Society Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China; State Key Laboratory of Genetic Engineering and Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai 200433, China
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86
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Abstract
Darwin's theory of evolution by natural selection is the foundation of modern biology. However, it has proven remarkably difficult to demonstrate at the genetic, genomic, and population level exactly how wild species adapt to their natural environments. We discuss how one can use large sets of multiple genome sequences from wild populations to understand adaptation, with an emphasis on the small herbaceous plant Arabidopsis thaliana. We present motivation for such studies; summarize progress in describing whole-genome, species-wide sequence variation; and then discuss what insights have emerged from these resources, either based on sequence information alone or in combination with phenotypic data. We conclude with thoughts on opportunities with other plant species and the impact of expected progress in sequencing technology and genome engineering for studying adaptation in nature.
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Affiliation(s)
- Detlef Weigel
- Max Planck Institute for Developmental Biology, 72076 Tübingen, Germany;
| | - Magnus Nordborg
- Gregor Mendel Institute, Austrian Academy of Sciences, Vienna Biocenter, 1030 Vienna, Austria;
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87
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Ravinet M, Westram A, Johannesson K, Butlin R, André C, Panova M. Shared and nonshared genomic divergence in parallel ecotypes of Littorina saxatilis at a local scale. Mol Ecol 2015. [PMID: 26222268 DOI: 10.1111/mec.13332] [Citation(s) in RCA: 111] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Parallel speciation occurs when selection drives repeated, independent adaptive divergence that reduces gene flow between ecotypes. Classical examples show parallel speciation originating from shared genomic variation, but this does not seem to be the case in the rough periwinkle (Littorina saxatilis) that has evolved considerable phenotypic diversity across Europe, including several distinct ecotypes. Small 'wave' ecotype snails inhabit exposed rocks and experience strong wave action, while thick-shelled, 'crab' ecotype snails are larger and experience crab predation on less exposed shores. Crab and wave ecotypes appear to have arisen in parallel, and recent evidence suggests only marginal sharing of molecular variation linked to evolution of similar ecotypes in different parts of Europe. However, the extent of genomic sharing is expected to increase with gene flow and more recent common ancestry. To test this, we used de novo RAD-sequencing to quantify the extent of shared genomic divergence associated with phenotypic similarities amongst ecotype pairs on three close islands (<10 km distance) connected by weak gene flow (Nm ~ 0.03) and with recent common ancestry (<10 000 years). After accounting for technical issues, including a large proportion of null alleles due to a large effective population size, we found ~8-28% of positive outliers were shared between two islands and ~2-9% were shared amongst all three islands. This low level of sharing suggests that parallel phenotypic divergence in this system is not matched by shared genomic divergence despite a high probability of gene flow and standing genetic variation.
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Affiliation(s)
- Mark Ravinet
- Department of Marine Sciences-Tjärnö, University of Gothenburg, Hättesbäckvägen 8, 452 96, Strömstad, Sweden.,Ecological Genetics Laboratory, National Institute of Genetics, Yata 1111, Mishima, 411-8540, Japan
| | - Anja Westram
- Department of Animal and Plant Sciences, University of Sheffield, Western Bank, Sheffield, S10 2TN, UK
| | - Kerstin Johannesson
- Department of Marine Sciences-Tjärnö, University of Gothenburg, Hättesbäckvägen 8, 452 96, Strömstad, Sweden
| | - Roger Butlin
- Department of Marine Sciences-Tjärnö, University of Gothenburg, Hättesbäckvägen 8, 452 96, Strömstad, Sweden.,Department of Animal and Plant Sciences, University of Sheffield, Western Bank, Sheffield, S10 2TN, UK
| | - Carl André
- Department of Marine Sciences-Tjärnö, University of Gothenburg, Hättesbäckvägen 8, 452 96, Strömstad, Sweden
| | - Marina Panova
- Department of Marine Sciences-Tjärnö, University of Gothenburg, Hättesbäckvägen 8, 452 96, Strömstad, Sweden
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88
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He SL, Yang Y, Morrell PL, Yi TS. Nucleotide Sequence Diversity and Linkage Disequilibrium of Four Nuclear Loci in Foxtail Millet (Setaria italica). PLoS One 2015; 10:e0137088. [PMID: 26325578 PMCID: PMC4556640 DOI: 10.1371/journal.pone.0137088] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2015] [Accepted: 08/13/2015] [Indexed: 11/18/2022] Open
Abstract
Foxtail millet (Setaria italica (L.) Beauv) is one of the earliest domesticated grains, which has been cultivated in northern China by 8,700 years before present (YBP) and across Eurasia by 4,000 YBP. Owing to a small genome and diploid nature, foxtail millet is a tractable model crop for studying functional genomics of millets and bioenergy grasses. In this study, we examined nucleotide sequence diversity, geographic structure, and levels of linkage disequilibrium at four nuclear loci (ADH1, G3PDH, IGS1 and TPI1) in representative samples of 311 landrace accessions across its cultivated range. Higher levels of nucleotide sequence and haplotype diversity were observed in samples from China relative to other sampled regions. Genetic assignment analysis classified the accessions into seven clusters based on nucleotide sequence polymorphisms. Intralocus LD decayed rapidly to half the initial value within ~1.2 kb or less.
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Affiliation(s)
- Shui-lian He
- China Southwestern Germplasm Bank of Wild Species, the Key Laboratory of Biodiversity and Biogeography, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming, Yunnan, China
- Landscape and Horticulture College, Yunnan Agriculture University, Kunming, Yunnan, China
| | - Yang Yang
- China Southwestern Germplasm Bank of Wild Species, the Key Laboratory of Biodiversity and Biogeography, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Peter L. Morrell
- Department of Agronomy & Plant genetics, 411 Borlaug Hall, 1991 Upper Buford Circle, University of Minnesota, Saint Paul, Minnesota, United States of America
| | - Ting-shuang Yi
- China Southwestern Germplasm Bank of Wild Species, the Key Laboratory of Biodiversity and Biogeography, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming, Yunnan, China
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89
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Pybus M, Luisi P, Dall'Olio GM, Uzkudun M, Laayouni H, Bertranpetit J, Engelken J. Hierarchical boosting: a machine-learning framework to detect and classify hard selective sweeps in human populations. Bioinformatics 2015; 31:3946-52. [PMID: 26315912 DOI: 10.1093/bioinformatics/btv493] [Citation(s) in RCA: 49] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2015] [Accepted: 08/17/2015] [Indexed: 12/15/2022] Open
Abstract
MOTIVATION Detecting positive selection in genomic regions is a recurrent topic in natural population genetic studies. However, there is little consistency among the regions detected in several genome-wide scans using different tests and/or populations. Furthermore, few methods address the challenge of classifying selective events according to specific features such as age, intensity or state (completeness). RESULTS We have developed a machine-learning classification framework that exploits the combined ability of some selection tests to uncover different polymorphism features expected under the hard sweep model, while controlling for population-specific demography. As a result, we achieve high sensitivity toward hard selective sweeps while adding insights about their completeness (whether a selected variant is fixed or not) and age of onset. Our method also determines the relevance of the individual methods implemented so far to detect positive selection under specific selective scenarios. We calibrated and applied the method to three reference human populations from The 1000 Genome Project to generate a genome-wide classification map of hard selective sweeps. This study improves detection of selective sweep by overcoming the classical selection versus no-selection classification strategy, and offers an explanation to the lack of consistency observed among selection tests when applied to real data. Very few signals were observed in the African population studied, while our method presents higher sensitivity in this population demography. AVAILABILITY AND IMPLEMENTATION The genome-wide results for three human populations from The 1000 Genomes Project and an R-package implementing the 'Hierarchical Boosting' framework are available at http://hsb.upf.edu/.
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Affiliation(s)
- Marc Pybus
- Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Barcelona 08003, Spain
| | - Pierre Luisi
- Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Barcelona 08003, Spain, Department of Biology, Stanford University, Stanford, CA 94305, USA
| | - Giovanni Marco Dall'Olio
- Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Barcelona 08003, Spain, Division of Cancer Studies, King's College of London, London SE1 1UL, UK and
| | - Manu Uzkudun
- Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Barcelona 08003, Spain
| | - Hafid Laayouni
- Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Barcelona 08003, Spain, Departament de Genètica i de Microbiologia, Universitat Autonòma de Barcelona, Bellaterra 8193, Spain
| | - Jaume Bertranpetit
- Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Barcelona 08003, Spain
| | - Johannes Engelken
- Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Barcelona 08003, Spain
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90
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Mathew LA, Jensen JD. Evaluating the ability of the pairwise joint site frequency spectrum to co-estimate selection and demography. Front Genet 2015; 6:268. [PMID: 26347771 PMCID: PMC4538300 DOI: 10.3389/fgene.2015.00268] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2015] [Accepted: 08/03/2015] [Indexed: 12/23/2022] Open
Abstract
The ability to infer the parameters of positive selection from genomic data has many important implications, from identifying drug-resistance mutations in viruses to increasing crop yield by genetically integrating favorable alleles. Although it has been well-described that selection and demography may result in similar patterns of diversity, the ability to jointly estimate these two processes has remained elusive. Here, we use simulation to explore the utility of the joint site frequency spectrum to estimate selection and demography simultaneously, including developing an extension of the previously proposed Jaatha program (Mathew et al., 2013). We evaluate both complete and incomplete selective sweeps under an isolation-with-migration model with and without population size change (both population growth and bottlenecks). Results suggest that while it may not be possible to precisely estimate the strength of selection, it is possible to infer the presence of selection while estimating accurate demographic parameters. We further demonstrate that the common assumption of selective neutrality when estimating demographic models may lead to severe biases. Finally, we apply the approach we have developed to better characterize the within-host demographic and selective history of human cytomegalovirus (HCMV) infection using published next generation sequencing data.
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Affiliation(s)
- Lisha A Mathew
- School of Life Sciences, École Polytechnique Fédérale de Lausanne Lausanne, Switzerland
| | - Jeffrey D Jensen
- School of Life Sciences, École Polytechnique Fédérale de Lausanne Lausanne, Switzerland
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91
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Zanetti D, Carreras-Torres R, Esteban E, Via M, Moral P. Potential Signals of Natural Selection in the Top Risk Loci for Coronary Artery Disease: 9p21 and 10q11. PLoS One 2015; 10:e0134840. [PMID: 26252781 PMCID: PMC4529309 DOI: 10.1371/journal.pone.0134840] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2015] [Accepted: 07/15/2015] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Coronary artery disease (CAD) is a complex disease and the leading cause of death in the world. Populations of different ancestry do not always share the same risk markers. Natural selective processes may be the cause of some of the population differences detected for specific risk mutations. OBJECTIVE In this study, 384 single nucleotide polymorphisms (SNPs) located in four genomic regions associated with CAD (1p13, 1q41, 9p21 and 10q11) are analysed in a set of 19 populations from Europe, Middle East and North Africa and also in Asian and African samples from the 1000 Genomes Project. The aim of this survey is to explore for the first time whether the genetic variability in these genomic regions is better explained by demography or by natural selection. RESULTS The results indicate significant differences in the structure of genetic variation and in the LD patterns among populations that probably explain the population disparities found in markers of susceptibility to CAD. CONCLUSIONS The results are consistent with potential signature of positive selection in the 9p21 region and of balancing selection in the 9p21 and 10q11. Specifically, in Europe three CAD risk markers in the 9p21 region (rs9632884, rs1537371 and rs1333042) show consistent signals of positive selection. The results of this study are consistent with a potential selective role of CAD in the configuration of genetic diversity in current human populations.
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Affiliation(s)
- Daniela Zanetti
- Department of Animal Biology-Anthropology, University of Barcelona, Barcelona, Spain
| | | | - Esther Esteban
- Department of Animal Biology-Anthropology, University of Barcelona, Barcelona, Spain
- Biodiversity Research Institute, University of Barcelona, Spain
| | - Marc Via
- Department of Psychiatry and Clinical Psychobiology and Institute for Brain, Cognition and Behavior (IR3C), University of Barcelona, Barcelona, Spain
| | - Pedro Moral
- Department of Animal Biology-Anthropology, University of Barcelona, Barcelona, Spain
- Biodiversity Research Institute, University of Barcelona, Spain
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92
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Storz JF, Bridgham JT, Kelly SA, Garland T. Genetic approaches in comparative and evolutionary physiology. Am J Physiol Regul Integr Comp Physiol 2015; 309:R197-214. [PMID: 26041111 PMCID: PMC4525326 DOI: 10.1152/ajpregu.00100.2015] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2015] [Accepted: 05/23/2015] [Indexed: 01/04/2023]
Abstract
Whole animal physiological performance is highly polygenic and highly plastic, and the same is generally true for the many subordinate traits that underlie performance capacities. Quantitative genetics, therefore, provides an appropriate framework for the analysis of physiological phenotypes and can be used to infer the microevolutionary processes that have shaped patterns of trait variation within and among species. In cases where specific genes are known to contribute to variation in physiological traits, analyses of intraspecific polymorphism and interspecific divergence can reveal molecular mechanisms of functional evolution and can provide insights into the possible adaptive significance of observed sequence changes. In this review, we explain how the tools and theory of quantitative genetics, population genetics, and molecular evolution can inform our understanding of mechanism and process in physiological evolution. For example, lab-based studies of polygenic inheritance can be integrated with field-based studies of trait variation and survivorship to measure selection in the wild, thereby providing direct insights into the adaptive significance of physiological variation. Analyses of quantitative genetic variation in selection experiments can be used to probe interrelationships among traits and the genetic basis of physiological trade-offs and constraints. We review approaches for characterizing the genetic architecture of physiological traits, including linkage mapping and association mapping, and systems approaches for dissecting intermediary steps in the chain of causation between genotype and phenotype. We also discuss the promise and limitations of population genomic approaches for inferring adaptation at specific loci. We end by highlighting the role of organismal physiology in the functional synthesis of evolutionary biology.
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Affiliation(s)
- Jay F Storz
- School of Biological Sciences, University of Nebraska, Lincoln, Nebraska;
| | - Jamie T Bridgham
- Institute of Ecology and Evolution, University of Oregon, Eugene, Oregon
| | - Scott A Kelly
- Department of Zoology, Ohio Wesleyan University, Delaware, Ohio; and
| | - Theodore Garland
- Department of Biology, University of California, Riverside, Riverside, California
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93
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Frichot E, Schoville SD, de Villemereuil P, Gaggiotti OE, François O. Detecting adaptive evolution based on association with ecological gradients: orientation matters! Heredity (Edinb) 2015; 115:22-8. [PMID: 25690180 PMCID: PMC4815498 DOI: 10.1038/hdy.2015.7] [Citation(s) in RCA: 57] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2014] [Revised: 11/03/2014] [Accepted: 01/08/2015] [Indexed: 01/10/2023] Open
Abstract
Population genetic signatures of local adaptation are frequently investigated by identifying loci with allele frequencies that exhibit high correlation with ecological variables. One difficulty with this approach is that ecological associations might be confounded by geographic variation at selectively neutral loci. Here, we consider populations that underwent spatial expansion from their original range, and for which geographical variation of adaptive allele frequency coincides with habitat gradients. Using range expansion simulations, we asked whether our ability to detect genomic regions involved in adaptation could be impacted by the orientation of the ecological gradients. For three ecological association methods tested, we found, counter-intuitively, fewer false-positive associations when ecological gradients aligned along the main axis of expansion than when they aligned along any other direction. This result has important consequences for the analysis of genomic data under non-equilibrium population genetic models. Alignment of gradients with expansion axes is likely to be common in scenarios in which expanding species track their ecological niche during climate change while adapting to changing environments at their rear edge.
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Affiliation(s)
- E Frichot
- Université Joseph Fourier Grenoble, Centre National de la Recherche Scientifique, TIMC-IMAG UMR 5525, Grenoble, France
| | - S D Schoville
- University of Wisconsin-Madison, Department of Entomology, Madison, WI, USA
| | - P de Villemereuil
- Université Joseph Fourier Grenoble, Centre National de la Recherche Scientifique, Laboratoire d'Ecologie Alpine UMR 5553, Grenoble, France
| | - O E Gaggiotti
- Université Joseph Fourier Grenoble, Centre National de la Recherche Scientifique, Laboratoire d'Ecologie Alpine UMR 5553, Grenoble, France
- University of St Andrews, Scottish Oceans Institute, East Sands, St Andrews, Fife, UK
| | - O François
- Université Joseph Fourier Grenoble, Centre National de la Recherche Scientifique, TIMC-IMAG UMR 5525, Grenoble, France
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94
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Kim H, Song KD, Kim HJ, Park W, Kim J, Lee T, Shin DH, Kwak W, Kwon YJ, Sung S, Moon S, Lee KT, Kim N, Hong JK, Eo KY, Seo KS, Kim G, Park S, Yun CH, Kim H, Choi K, Kim J, Lee WK, Kim DK, Oh JD, Kim ES, Cho S, Lee HK, Kim TH, Kim H. Exploring the genetic signature of body size in Yucatan miniature pig. PLoS One 2015; 10:e0121732. [PMID: 25885114 PMCID: PMC4401510 DOI: 10.1371/journal.pone.0121732] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2014] [Accepted: 02/18/2015] [Indexed: 01/09/2023] Open
Abstract
Since being domesticated about 10,000-12,000 years ago, domestic pigs (Sus scrofa domesticus) have been selected for traits of economic importance, in particular large body size. However, Yucatan miniature pigs have been selected for small body size to withstand high temperature environment and for laboratory use. This renders the Yucatan miniature pig a valuable model for understanding the evolution of body size. We investigate the genetic signature for selection of body size in the Yucatan miniature pig. Phylogenetic distance of Yucatan miniature pig was compared to other large swine breeds (Yorkshire, Landrace, Duroc and wild boar). By estimating the XP-EHH statistic using re-sequencing data derived from 70 pigs, we were able to unravel the signatures of selection of body size. We found that both selections at the level of organism, and at the cellular level have occurred. Selection at the higher levels include feed intake, regulation of body weight and increase in mass while selection at the molecular level includes cell cycle and cell proliferation. Positively selected genes probed by XP-EHH may provide insight into the docile character and innate immunity as well as body size of Yucatan miniature pig.
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Affiliation(s)
- Hyeongmin Kim
- Department of Agricultural Biotechnology, Animal Biotechnology Major, and Research Institute for Agriculture and Life Sciences, Seoul National University, Seoul, 151-921, Republic of Korea
| | - Ki Duk Song
- Genomic Informatics Center, Hankyong National University, Anseong, 456-749, Republic of Korea
| | - Hyeon Jeong Kim
- CHO & KIM Genomics, Seoul National University Research Park, Seoul, 151-919, Republic of Korea
| | - WonCheoul Park
- Department of Agricultural Biotechnology, Animal Biotechnology Major, and Research Institute for Agriculture and Life Sciences, Seoul National University, Seoul, 151-921, Republic of Korea
| | - Jaemin Kim
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 151-742, Republic of Korea
| | - Taeheon Lee
- Department of Agricultural Biotechnology, Animal Biotechnology Major, and Research Institute for Agriculture and Life Sciences, Seoul National University, Seoul, 151-921, Republic of Korea
| | - Dong-Hyun Shin
- Department of Agricultural Biotechnology, Animal Biotechnology Major, and Research Institute for Agriculture and Life Sciences, Seoul National University, Seoul, 151-921, Republic of Korea
| | - Woori Kwak
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 151-742, Republic of Korea
| | - Young-jun Kwon
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 151-742, Republic of Korea
| | - Samsun Sung
- CHO & KIM Genomics, Seoul National University Research Park, Seoul, 151-919, Republic of Korea
| | - Sunjin Moon
- Department of Agricultural Biotechnology, Animal Biotechnology Major, and Research Institute for Agriculture and Life Sciences, Seoul National University, Seoul, 151-921, Republic of Korea
| | - Kyung-Tai Lee
- National Institute of Animal Science, RDA, Suwon, 441-706, Republic of Korea
| | - Namshin Kim
- Korean Bioinformation Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, 305-806, Republic of Korea
| | - Joon Ki Hong
- Swine Science Division, National Institute of Animal Science, RDA, Cheonan, 331-801, Republic of Korea
| | - Kyung Yeon Eo
- Animal Research Division, Seoul Zoo, Seoul, 427-702, Republic of Korea
| | - Kang Seok Seo
- Department of Animal Science and Technology, College of Life Science and Natural Resources, Sunchon National University, Suncheon, 540-950, Republic of Korea
| | - Girak Kim
- Department of Agricultural Biotechnology, Animal Biotechnology Major, and Research Institute for Agriculture and Life Sciences, Seoul National University, Seoul, 151-921, Republic of Korea
| | - Sungmoo Park
- Department of Agricultural Biotechnology, Animal Biotechnology Major, and Research Institute for Agriculture and Life Sciences, Seoul National University, Seoul, 151-921, Republic of Korea
| | - Cheol-Heui Yun
- Department of Agricultural Biotechnology, Animal Biotechnology Major, and Research Institute for Agriculture and Life Sciences, Seoul National University, Seoul, 151-921, Republic of Korea
| | - Hyunil Kim
- Optipharm, Inc., 63, Osongsangmyeong 6-ro, Osong-eup, Chengwon-gun, Chungcheongbuk-do, 363-954, Republic of Korea
| | - Kimyung Choi
- Optipharm, Inc., 63, Osongsangmyeong 6-ro, Osong-eup, Chengwon-gun, Chungcheongbuk-do, 363-954, Republic of Korea
| | - Jiho Kim
- Optipharm, Inc., 63, Osongsangmyeong 6-ro, Osong-eup, Chengwon-gun, Chungcheongbuk-do, 363-954, Republic of Korea
| | - Woon Kyu Lee
- Laboratory of Developmental Genetics, College of Medicine, Inha University, Incheon, 400-103, Republic of Korea
| | - Duk-Kyung Kim
- Genomic Informatics Center, Hankyong National University, Anseong, 456-749, Republic of Korea
| | - Jae-Don Oh
- Genomic Informatics Center, Hankyong National University, Anseong, 456-749, Republic of Korea
| | - Eui-Soo Kim
- Department of Animal Sciencs, Iowa State University, Ames, Iowa, 50011, United States of America
| | - Seoae Cho
- CHO & KIM Genomics, Seoul National University Research Park, Seoul, 151-919, Republic of Korea
| | - Hak-Kyo Lee
- Genomic Informatics Center, Hankyong National University, Anseong, 456-749, Republic of Korea
| | - Tae-Hun Kim
- National Institute of Animal Science, RDA, Suwon, 441-706, Republic of Korea
| | - Heebal Kim
- Department of Agricultural Biotechnology, Animal Biotechnology Major, and Research Institute for Agriculture and Life Sciences, Seoul National University, Seoul, 151-921, Republic of Korea; CHO & KIM Genomics, Seoul National University Research Park, Seoul, 151-919, Republic of Korea
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95
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Garud NR, Rosenberg NA. Enhancing the mathematical properties of new haplotype homozygosity statistics for the detection of selective sweeps. Theor Popul Biol 2015; 102:94-101. [PMID: 25891325 DOI: 10.1016/j.tpb.2015.04.001] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2015] [Revised: 04/01/2015] [Accepted: 04/05/2015] [Indexed: 10/23/2022]
Abstract
Soft selective sweeps represent an important form of adaptation in which multiple haplotypes bearing adaptive alleles rise to high frequency. Most statistical methods for detecting selective sweeps from genetic polymorphism data, however, have focused on identifying hard selective sweeps in which a favored allele appears on a single haplotypic background; these methods might be underpowered to detect soft sweeps. Among exceptions is the set of haplotype homozygosity statistics introduced for the detection of soft sweeps by Garud et al. (2015). These statistics, examining frequencies of multiple haplotypes in relation to each other, include H12, a statistic designed to identify both hard and soft selective sweeps, and H2/H1, a statistic that conditional on high H12 values seeks to distinguish between hard and soft sweeps. A challenge in the use of H2/H1 is that its range depends on the associated value of H12, so that equal H2/H1 values might provide different levels of support for a soft sweep model at different values of H12. Here, we enhance the H12 and H2/H1 haplotype homozygosity statistics for selective sweep detection by deriving the upper bound on H2/H1 as a function of H12, thereby generating a statistic that normalizes H2/H1 to lie between 0 and 1. Through a reanalysis of resequencing data from inbred lines of Drosophila, we show that the enhanced statistic both strengthens interpretations obtained with the unnormalized statistic and leads to empirical insights that are less readily apparent without the normalization.
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Affiliation(s)
- Nandita R Garud
- Department of Genetics, Stanford University, Stanford, CA 94305, USA.
| | - Noah A Rosenberg
- Department of Biology, Stanford University, Stanford, CA 94305, USA.
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96
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Luisi P, Alvarez-Ponce D, Pybus M, Fares MA, Bertranpetit J, Laayouni H. Recent positive selection has acted on genes encoding proteins with more interactions within the whole human interactome. Genome Biol Evol 2015; 7:1141-54. [PMID: 25840415 PMCID: PMC4419801 DOI: 10.1093/gbe/evv055] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Genes vary in their likelihood to undergo adaptive evolution. The genomic factors that determine adaptability, however, remain poorly understood. Genes function in the context of molecular networks, with some occupying more important positions than others and thus being likely to be under stronger selective pressures. However, how positive selection distributes across the different parts of molecular networks is still not fully understood. Here, we inferred positive selection using comparative genomics and population genetics approaches through the comparison of 10 mammalian and 270 human genomes, respectively. In agreement with previous results, we found that genes with lower network centralities are more likely to evolve under positive selection (as inferred from divergence data). Surprisingly, polymorphism data yield results in the opposite direction than divergence data: Genes with higher centralities are more likely to have been targeted by recent positive selection during recent human evolution. Our results indicate that the relationship between centrality and the impact of adaptive evolution highly depends on the mode of positive selection and/or the evolutionary time-scale.
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Affiliation(s)
- Pierre Luisi
- Institute of Evolutionary Biology, Universitat Pompeu Fabra-CSIC, CEXS-UPF-PRBB, Barcelona, Catalonia, Spain
| | - David Alvarez-Ponce
- Integrative Systems Biology Group, Instituto de Biología Molecular y Celular de Plantas, Consejo Superior de Investigaciones Científicas (CSIC)-Universidad Politécnica de Valencia (UPV), Spain Biology Department, University of Nevada, Reno Institute of Evolutionary Biology, Universitat Pompeu Fabra-CSIC, CEXS-UPF-PRBB, Barcelona, Catalonia, Spain
| | - Marc Pybus
- Institute of Evolutionary Biology, Universitat Pompeu Fabra-CSIC, CEXS-UPF-PRBB, Barcelona, Catalonia, Spain
| | - Mario A Fares
- Integrative Systems Biology Group, Instituto de Biología Molecular y Celular de Plantas, Consejo Superior de Investigaciones Científicas (CSIC)-Universidad Politécnica de Valencia (UPV), Spain Smurfit Institute of Genetics, University of Dublin, Trinity College, Ireland
| | - Jaume Bertranpetit
- Institute of Evolutionary Biology, Universitat Pompeu Fabra-CSIC, CEXS-UPF-PRBB, Barcelona, Catalonia, Spain
| | - Hafid Laayouni
- Institute of Evolutionary Biology, Universitat Pompeu Fabra-CSIC, CEXS-UPF-PRBB, Barcelona, Catalonia, Spain Departament de Genètica i de Microbiologia, Grup de Biologia Evolutiva (GBE), Universitat Autonòma de Barcelona, Bellaterra, Spain
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97
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Wollstein A, Stephan W. Inferring positive selection in humans from genomic data. INVESTIGATIVE GENETICS 2015; 6:5. [PMID: 25834723 PMCID: PMC4381672 DOI: 10.1186/s13323-015-0023-1] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 11/21/2014] [Accepted: 02/23/2015] [Indexed: 01/06/2023]
Abstract
Adaptation can be described as an evolutionary process that leads to an adjustment of the phenotypes of a population to their environment. In the classical view, new mutations can introduce novel phenotypic features into a population that leave footprints in the genome after fixation, such as selective sweeps. Alternatively, existing genetic variants may become beneficial after an environmental change and increase in frequency. Although they may not reach fixation, they may cause a shift of the optimum of a phenotypic trait controlled by multiple loci. With the availability of polymorphism data from various organisms, including humans and chimpanzees, it has become possible to detect molecular evidence of adaptation and to estimate the strength and target of positive selection. In this review, we discuss the two competing models of adaptation and suitable approaches for detecting the footprints of positive selection on the molecular level.
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Affiliation(s)
- Andreas Wollstein
- Section of Evolutionary Biology, Department of Biology II, University of Munich, Großhaderner Str. 2, 82152 Planegg-Martinsried, Germany
| | - Wolfgang Stephan
- Section of Evolutionary Biology, Department of Biology II, University of Munich, Großhaderner Str. 2, 82152 Planegg-Martinsried, Germany
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98
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Haasl RJ, Johnson RC, Payseur BA. The effects of microsatellite selection on linked sequence diversity. Genome Biol Evol 2015; 6:1843-61. [PMID: 25115009 PMCID: PMC4122932 DOI: 10.1093/gbe/evu134] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
The genome-wide scan for selection is an important method for identifying loci involved in adaptive evolution. However, theory that underlies standard scans for selection assumes a simple mutation model. In particular, recurrent mutation of the selective target is not considered. Although this assumption is reasonable for single-nucleotide variants (SNVs), a microsatellite targeted by selection will reliably violate this assumption due to high mutation rate. Moreover, the mutation rate of microsatellites is generally high enough to ensure that recurrent mutation is pervasive rather than occasional. It is therefore unclear if positive selection targeting microsatellites can be detected using standard scanning statistics. Examples of functional variation at microsatellites underscore the significance of understanding the genomic effects of microsatellite selection. Here, we investigate the joint effects of selection and complex mutation on linked sequence diversity, comparing simulations of microsatellite selection and SNV-based selective sweeps. We find that selection on microsatellites is generally difficult to detect using popular summaries of the site frequency spectrum, and, under certain conditions, using popular methods such as the integrated haplotype statistic and SweepFinder. However, comparisons of the number of haplotypes (K) and segregating sites (S) often provide considerable power to detect selection on microsatellites. We apply this knowledge to a scan of autosomes in the human CEU population (CEPH population sampled from Utah). In addition to the most commonly reported targets of selection in European populations, we identify numerous novel genomic regions that bear highly anomalous haplotype configurations. Using one of these regions—intron 1 of MAGI2—as an example, we show that the anomalous configuration is coincident with a perfect CA repeat of length 22. We conclude that standard genome-wide scans will commonly fail to detect mutationally complex targets of selection but that comparisons of K and S will, in many cases, facilitate their identification.
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Affiliation(s)
- Ryan J. Haasl
- Laboratory of Genetics, University of Wisconsin – Madison
- Present address: Department of Biology, University of Wisconsin – Platteville, Platteville, WI
- *Corresponding author: E-mail:
| | - Ross C. Johnson
- Department of Biology, University of Wisconsin – Platteville
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99
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Soft shoulders ahead: spurious signatures of soft and partial selective sweeps result from linked hard sweeps. Genetics 2015; 200:267-84. [PMID: 25716978 DOI: 10.1534/genetics.115.174912] [Citation(s) in RCA: 61] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2015] [Accepted: 02/20/2015] [Indexed: 11/18/2022] Open
Abstract
Characterizing the nature of the adaptive process at the genetic level is a central goal for population genetics. In particular, we know little about the sources of adaptive substitution or about the number of adaptive variants currently segregating in nature. Historically, population geneticists have focused attention on the hard-sweep model of adaptation in which a de novo beneficial mutation arises and rapidly fixes in a population. Recently more attention has been given to soft-sweep models, in which alleles that were previously neutral, or nearly so, drift until such a time as the environment shifts and their selection coefficient changes to become beneficial. It remains an active and difficult problem, however, to tease apart the telltale signatures of hard vs. soft sweeps in genomic polymorphism data. Through extensive simulations of hard- and soft-sweep models, here we show that indeed the two might not be separable through the use of simple summary statistics. In particular, it seems that recombination in regions linked to, but distant from, sites of hard sweeps can create patterns of polymorphism that closely mirror what is expected to be found near soft sweeps. We find that a very similar situation arises when using haplotype-based statistics that are aimed at detecting partial or ongoing selective sweeps, such that it is difficult to distinguish the shoulder of a hard sweep from the center of a partial sweep. While knowing the location of the selected site mitigates this problem slightly, we show that stochasticity in signatures of natural selection will frequently cause the signal to reach its zenith far from this site and that this effect is more severe for soft sweeps; thus inferences of the target as well as the mode of positive selection may be inaccurate. In addition, both the time since a sweep ends and biologically realistic levels of allelic gene conversion lead to errors in the classification and identification of selective sweeps. This general problem of "soft shoulders" underscores the difficulty in differentiating soft and partial sweeps from hard-sweep scenarios in molecular population genomics data. The soft-shoulder effect also implies that the more common hard sweeps have been in recent evolutionary history, the more prevalent spurious signatures of soft or partial sweeps may appear in some genome-wide scans.
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100
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Garud NR, Messer PW, Buzbas EO, Petrov DA. Recent selective sweeps in North American Drosophila melanogaster show signatures of soft sweeps. PLoS Genet 2015; 11:e1005004. [PMID: 25706129 PMCID: PMC4338236 DOI: 10.1371/journal.pgen.1005004] [Citation(s) in RCA: 257] [Impact Index Per Article: 28.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2014] [Accepted: 01/14/2015] [Indexed: 11/18/2022] Open
Abstract
Adaptation from standing genetic variation or recurrent de novo mutation in large populations should commonly generate soft rather than hard selective sweeps. In contrast to a hard selective sweep, in which a single adaptive haplotype rises to high population frequency, in a soft selective sweep multiple adaptive haplotypes sweep through the population simultaneously, producing distinct patterns of genetic variation in the vicinity of the adaptive site. Current statistical methods were expressly designed to detect hard sweeps and most lack power to detect soft sweeps. This is particularly unfortunate for the study of adaptation in species such as Drosophila melanogaster, where all three confirmed cases of recent adaptation resulted in soft selective sweeps and where there is evidence that the effective population size relevant for recent and strong adaptation is large enough to generate soft sweeps even when adaptation requires mutation at a specific single site at a locus. Here, we develop a statistical test based on a measure of haplotype homozygosity (H12) that is capable of detecting both hard and soft sweeps with similar power. We use H12 to identify multiple genomic regions that have undergone recent and strong adaptation in a large population sample of fully sequenced Drosophila melanogaster strains from the Drosophila Genetic Reference Panel (DGRP). Visual inspection of the top 50 candidates reveals that in all cases multiple haplotypes are present at high frequencies, consistent with signatures of soft sweeps. We further develop a second haplotype homozygosity statistic (H2/H1) that, in combination with H12, is capable of differentiating hard from soft sweeps. Surprisingly, we find that the H12 and H2/H1 values for all top 50 peaks are much more easily generated by soft rather than hard sweeps. We discuss the implications of these results for the study of adaptation in Drosophila and in species with large census population sizes. Evolutionary adaptation is a process in which beneficial mutations increase in frequency in response to selective pressures. If these mutations were previously rare or absent from the population, adaptation should generate a characteristic signature in the genetic diversity around the adaptive locus, known as a selective sweep. Such selective sweeps can be distinguished into hard selective sweeps, where only a single adaptive mutation rises in frequency, or soft selective sweeps, where multiple adaptive mutations at the same locus sweep through the population simultaneously. Here we design a new statistical method that can identify both hard and soft sweeps in population genomic data and apply this method to a Drosophila melanogaster population genomic dataset consisting of 145 sequenced strains collected in North Carolina. We find that selective sweeps were abundant in the recent history of this population. Interestingly, we also find that practically all of the strongest and most recent sweeps show patterns that are more consistent with soft rather than hard sweeps. We discuss the implications of these findings for the discovery and quantification of adaptation from population genomic data in Drosophila and other species with large population sizes.
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Affiliation(s)
- Nandita R. Garud
- Department of Genetics, Stanford University, Stanford, California, United States of America
- Department of Biology, Stanford University, Stanford, California, United States of America
- * E-mail: (NRG); (DAP)
| | - Philipp W. Messer
- Department of Biology, Stanford University, Stanford, California, United States of America
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, United States of America
| | - Erkan O. Buzbas
- Department of Biology, Stanford University, Stanford, California, United States of America
- Department of Statistical Science, University of Idaho, Moscow, Idaho, United States of America
| | - Dmitri A. Petrov
- Department of Biology, Stanford University, Stanford, California, United States of America
- * E-mail: (NRG); (DAP)
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