1
|
Anderson NW, Kirk L, Schraiber JG, Ragsdale AP. A Path Integral Approach for Allele Frequency Dynamics Under Polygenic Selection. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.14.599114. [PMID: 38915613 PMCID: PMC11195211 DOI: 10.1101/2024.06.14.599114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
Many phenotypic traits have a polygenic genetic basis, making it challenging to learn their genetic architectures and predict individual phenotypes. One promising avenue to resolve the genetic basis of complex traits is through evolve-and-resequence experiments, in which laboratory populations are exposed to some selective pressure and trait-contributing loci are identified by extreme frequency changes over the course of the experiment. However, small laboratory populations will experience substantial random genetic drift, and it is difficult to determine whether selection played a roll in a given allele frequency change. Predicting how much allele frequencies change under drift and selection had remained an open problem well into the 21st century, even those contributing to simple, monogenic traits. Recently, there have been efforts to apply the path integral, a method borrowed from physics, to solve this problem. So far, this approach has been limited to genic selection, and is therefore inadequate to capture the complexity of quantitative, highly polygenic traits that are commonly studied. Here we extend one of these path integral methods, the perturbation approximation, to selection scenarios that are of interest to quantitative genetics. In particular, we derive analytic expressions for the transition probability (i.e., the probability that an allele will change in frequency from x , to y in time t ) of an allele contributing to a trait subject to stabilizing selection, as well as that of an allele contributing to a trait rapidly adapting to a new phenotypic optimum. We use these expressions to characterize the use of allele frequency change to test for selection, as well as explore optimal design choices for evolve-and-resequence experiments to uncover the genetic architecture of polygenic traits under selection.
Collapse
Affiliation(s)
- Nathan W. Anderson
- Department of Integrative Biology, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Lloyd Kirk
- Department of Integrative Biology, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Joshua G. Schraiber
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, 90089, USA
| | - Aaron P. Ragsdale
- Department of Integrative Biology, University of Wisconsin-Madison, Madison, WI, 53706, USA
| |
Collapse
|
2
|
Grohmann CJ, Shull CM, Crum TE, Schwab C, Safranski TJ, Decker JE. Analysis of polygenic selection in purebred and crossbred pig genomes using generation proxy selection mapping. Genet Sel Evol 2023; 55:62. [PMID: 37710159 PMCID: PMC10500877 DOI: 10.1186/s12711-023-00836-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 08/25/2023] [Indexed: 09/16/2023] Open
Abstract
BACKGROUND Artificial selection on quantitative traits using breeding values and selection indices in commercial livestock breeding populations causes changes in allele frequency over time at hundreds or thousands of causal loci and the surrounding genomic regions. In population genetics, this type of selection is called polygenic selection. Researchers and managers of pig breeding programs are motivated to understand the genetic basis of phenotypic diversity across genetic lines, breeds, and populations using selection mapping analyses. Here, we applied generation proxy selection mapping (GPSM), a genome-wide association analysis of single nucleotide polymorphism (SNP) genotypes (38,294-46,458 markers) of birth date, in four pig populations (15,457, 15,772, 16,595 and 8447 pigs per population) to identify loci responding to artificial selection over a period of five to ten years. Gene-drop simulation analyses were conducted to provide context for the GPSM results. Selected loci within and across each population of pigs were compared in the context of swine breeding objectives. RESULTS The GPSM identified 49 to 854 loci as under selection (Q-values less than 0.10) across 15 subsets of pigs based on combinations of populations. The number of significant associations increased when data were pooled across populations. In addition, several significant associations were identified in more than one population. These results indicate concurrent selection objectives, similar genetic architectures, and shared causal variants responding to selection across these pig populations. Negligible error rates (less than or equal to 0.02%) of false-positive associations were found when testing GPSM on gene-drop simulated genotypes, suggesting that GPSM distinguishes selection from random genetic drift in actual pig populations. CONCLUSIONS This work confirms the efficacy and the negligible error rates of the GPSM method in detecting selected loci in commercial pig populations. Our results suggest shared selection objectives and genetic architectures across swine populations. The identified polygenic selection highlights loci that are important to swine production.
Collapse
|
3
|
Hoedjes KM, Kostic H, Keller L, Flatt T. Natural alleles at the Doa locus underpin evolutionary changes in Drosophila lifespan and fecundity. Proc Biol Sci 2022; 289:20221989. [PMID: 36350205 PMCID: PMC9653240 DOI: 10.1098/rspb.2022.1989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Abstract
'Evolve and resequence' (E&R) studies in Drosophila melanogaster have identified many candidate loci underlying the evolution of ageing and life history, but experiments that validate the effects of such candidates remain rare. In a recent E&R study we have identified several alleles of the LAMMER kinase Darkener of apricot (Doa) as candidates for evolutionary changes in lifespan and fecundity. Here, we use two complementary approaches to confirm a functional role of Doa in life-history evolution. First, we used transgenic RNAi to study the effects of Doa at the whole-gene level. Ubiquitous silencing of expression in adult flies reduced both lifespan and fecundity, indicating pleiotropic effects. Second, to characterize segregating variation at Doa, we examined four candidate single nucleotide polymorphisms (SNPs; Doa-1, -2, -3, -4) using a genetic association approach. Three candidate SNPs had effects that were qualitatively consistent with expectations based on our E&R study: Doa-2 pleiotropically affected both lifespan and late-life fecundity; Doa-1 affected lifespan (but not fecundity); and Doa-4 affected late-life fecundity (but not lifespan). Finally, the last candidate allele (Doa-3) also affected lifespan, but in the opposite direction from predicted.
Collapse
Affiliation(s)
- Katja M. Hoedjes
- Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland
| | - Hristina Kostic
- Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland
| | - Laurent Keller
- Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland
| | - Thomas Flatt
- Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland,Department of Biology, University of Fribourg, 1700 Fribourg, Switzerland
| |
Collapse
|
4
|
Burny C, Nolte V, Dolezal M, Schlötterer C. Highly Parallel Genomic Selection Response in Replicated Drosophila melanogaster Populations with Reduced Genetic Variation. Genome Biol Evol 2021; 13:6409861. [PMID: 34694407 PMCID: PMC8599828 DOI: 10.1093/gbe/evab239] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/21/2021] [Indexed: 12/12/2022] Open
Abstract
Many adaptive traits are polygenic and frequently more loci contributing to the phenotype are segregating than needed to express the phenotypic optimum. Experimental evolution with replicated populations adapting to a new controlled environment provides a powerful approach to study polygenic adaptation. Because genetic redundancy often results in nonparallel selection responses among replicates, we propose a modified evolve and resequence (E&R) design that maximizes the similarity among replicates. Rather than starting from many founders, we only use two inbred Drosophila melanogaster strains and expose them to a very extreme, hot temperature environment (29 °C). After 20 generations, we detect many genomic regions with a strong, highly parallel selection response in 10 evolved replicates. The X chromosome has a more pronounced selection response than the autosomes, which may be attributed to dominance effects. Furthermore, we find that the median selection coefficient for all chromosomes is higher in our two-genotype experiment than in classic E&R studies. Because two random genomes harbor sufficient variation for adaptive responses, we propose that this approach is particularly well-suited for the analysis of polygenic adaptation.
Collapse
Affiliation(s)
- Claire Burny
- Institut für Populationsgenetik, Vetmeduni Vienna, Austria.,Vienna Graduate School of Population Genetics, Vetmeduni Vienna, Austria
| | - Viola Nolte
- Institut für Populationsgenetik, Vetmeduni Vienna, Austria
| | - Marlies Dolezal
- Plattform Bioinformatik und Biostatistik, Vetmeduni Vienna, Wien, Austria
| | | |
Collapse
|
5
|
Olazcuaga L, Foucaud J, Gautier M, Deschamps C, Loiseau A, Leménager N, Facon B, Ravigné V, Hufbauer RA, Estoup A, Rode NO. Adaptation and correlated fitness responses over two time scales in Drosophila suzukii populations evolving in different environments. J Evol Biol 2021; 34:1225-1240. [PMID: 34097795 PMCID: PMC8457093 DOI: 10.1111/jeb.13878] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 04/23/2021] [Accepted: 05/31/2021] [Indexed: 01/09/2023]
Abstract
The process of local adaptation involves differential changes in fitness over time across different environments. Although experimental evolution studies have extensively tested for patterns of local adaptation at a single time point, there is relatively little research that examines fitness more than once during the time course of adaptation. We allowed replicate populations of the fruit pest Drosophila suzukii to evolve in one of eight different fruit media. After five generations, populations with the highest initial levels of maladaptation had mostly gone extinct, whereas experimental populations evolving on cherry, strawberry and cranberry media had survived. We measured the fitness of each surviving population in each of the three fruit media after five and after 26 generations of evolution. After five generations, adaptation to each medium was associated with increased fitness in the two other media. This was also true after 26 generations, except when populations that evolved on cranberry medium developed on cherry medium. These results suggest that, in the theoretical framework of a fitness landscape, the fitness optima of cherry and cranberry media are the furthest apart. Our results show that studying how fitness changes across several environments and across multiple generations provides insights into the dynamics of local adaptation that would not be evident if fitness were analysed at a single point in time. By allowing a qualitative mapping of an experimental fitness landscape, our approach will improve our understanding of the ecological factors that drive the evolution of local adaptation in D. suzukii.
Collapse
Affiliation(s)
- Laure Olazcuaga
- CBGP, INRAE, CIRAD, IRD, Institut Agro, Université Montpellier, Montpellier, France.,Department of Agricultural Biology and Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO, USA
| | - Julien Foucaud
- CBGP, INRAE, CIRAD, IRD, Institut Agro, Université Montpellier, Montpellier, France
| | - Mathieu Gautier
- CBGP, INRAE, CIRAD, IRD, Institut Agro, Université Montpellier, Montpellier, France
| | - Candice Deschamps
- CBGP, INRAE, CIRAD, IRD, Institut Agro, Université Montpellier, Montpellier, France
| | - Anne Loiseau
- CBGP, INRAE, CIRAD, IRD, Institut Agro, Université Montpellier, Montpellier, France
| | - Nicolas Leménager
- CBGP, INRAE, CIRAD, IRD, Institut Agro, Université Montpellier, Montpellier, France
| | - Benoit Facon
- INRAE, UMR Peuplements Végétaux et Bio-agresseurs en Milieu Tropical, La Réunion, France
| | | | - Ruth A Hufbauer
- CBGP, INRAE, CIRAD, IRD, Institut Agro, Université Montpellier, Montpellier, France.,Department of Agricultural Biology and Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO, USA
| | - Arnaud Estoup
- CBGP, INRAE, CIRAD, IRD, Institut Agro, Université Montpellier, Montpellier, France
| | - Nicolas O Rode
- CBGP, INRAE, CIRAD, IRD, Institut Agro, Université Montpellier, Montpellier, France
| |
Collapse
|
6
|
Langmüller AM, Dolezal M, Schlötterer C. Fine Mapping without Phenotyping: Identification of Selection Targets in Secondary Evolve and Resequence Experiments. Genome Biol Evol 2021; 13:6311659. [PMID: 34190980 PMCID: PMC8358229 DOI: 10.1093/gbe/evab154] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/24/2021] [Indexed: 12/19/2022] Open
Abstract
Evolve and Resequence (E&R) studies investigate the genomic selection response of populations in an Experimental Evolution setup. Despite the popularity of E&R, empirical studies in sexually reproducing organisms typically suffer from an excess of candidate loci due to linkage disequilibrium, and single gene or SNP resolution is the exception rather than the rule. Recently, so-called "secondary E&R" has been suggested as promising experimental follow-up procedure to confirm putatively selected regions from a primary E&R study. Secondary E&R provides also the opportunity to increase mapping resolution by allowing for additional recombination events, which separate the selection target from neutral hitchhikers. Here, we use computer simulations to assess the effect of different crossing schemes, population size, experimental duration, and number of replicates on the power and resolution of secondary E&R. We find that the crossing scheme and population size are crucial factors determining power and resolution of secondary E&R: A simple crossing scheme with few founder lines consistently outcompetes crossing schemes where evolved populations from a primary E&R experiment are mixed with a complex ancestral founder population. Regardless of the experimental design tested, a population size of at least 4,800 individuals, which is roughly five times larger than population sizes in typical E&R studies, is required to achieve a power of at least 75%. Our study provides an important step toward improved experimental designs aiming to characterize causative SNPs in Experimental Evolution studies.
Collapse
Affiliation(s)
- Anna Maria Langmüller
- Institut für Populationsgenetik, Vetmeduni Vienna, Vienna, Austria.,Vienna Graduate School of Population Genetics, Vetmeduni Vienna, Vienna, Austria
| | - Marlies Dolezal
- Plattform Bioinformatik und Biostatistik, Vetmeduni Vienna, Vienna, Austria
| | | |
Collapse
|
7
|
O’Connor CH, Sikkink KL, Nelson TC, Fierst JL, Cresko WA, Phillips PC. Complex pleiotropic genetic architecture of evolved heat stress and oxidative stress resistance in the nematode Caenorhabditis remanei. G3 (BETHESDA, MD.) 2021; 11:jkab045. [PMID: 33605401 PMCID: PMC8049431 DOI: 10.1093/g3journal/jkab045] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Accepted: 02/01/2021] [Indexed: 12/04/2022]
Abstract
The adaptation of complex organisms to changing environments has been a central question in evolutionary quantitative genetics since its inception. The structure of the genotype-phenotype maps is critical because pleiotropic effects can generate widespread correlated responses to selection and potentially restrict the extent of evolutionary change. In this study, we use experimental evolution to dissect the genetic architecture of natural variation for acute heat stress and oxidative stress response in the nematode Caenorhabiditis remanei. Previous work in the classic model nematode Caenorhabiditis elegans has found that abiotic stress response is controlled by a handful of genes of major effect and that mutations in any one of these genes can have widespread pleiotropic effects on multiple stress response traits. Here, we find that acute heat stress response and acute oxidative response in C. remanei are polygenic, complex traits, with hundreds of genomic regions responding to selection. In contrast to expectation from mutation studies, we find that evolved acute heat stress and acute oxidative stress response for the most part display independent genetic bases. This lack of correlation is reflected at the levels of phenotype, gene expression, and in the genomic response to selection. Thus, while these findings support the general view that rapid adaptation can be generated by changes at hundreds to thousands of sites in the genome, the architecture of segregating variation is likely to be determined by the pleiotropic structure of the underlying genetic networks.
Collapse
Affiliation(s)
- Christine H O’Connor
- Institute for Ecology and Evolution, University of Oregon, Eugene, OR 97403, USA
| | - Kristin L Sikkink
- Institute for Ecology and Evolution, University of Oregon, Eugene, OR 97403, USA
| | - Thomas C Nelson
- Institute for Ecology and Evolution, University of Oregon, Eugene, OR 97403, USA
| | - Janna L Fierst
- Department of Biological Sciences, University of Alabama, Tuscaloosa, AL 35487, USA
| | - William A Cresko
- Institute for Ecology and Evolution, University of Oregon, Eugene, OR 97403, USA
| | - Patrick C Phillips
- Institute for Ecology and Evolution, University of Oregon, Eugene, OR 97403, USA
| |
Collapse
|
8
|
Laghouaouta H, Sosa-Madrid BS, Zubiri-Gaitán A, Hernández P, Blasco A. Novel Genomic Regions Associated with Intramuscular Fatty Acid Composition in Rabbits. Animals (Basel) 2020; 10:ani10112090. [PMID: 33187110 PMCID: PMC7697864 DOI: 10.3390/ani10112090] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 11/08/2020] [Accepted: 11/09/2020] [Indexed: 12/19/2022] Open
Abstract
Intramuscular fat (IMF) content and its composition affect the quality of meat. Selection for IMF generated a correlated response on its fatty acid composition. The increase of IMF content is associated with an increase of its saturated (SFA) and monounsaturated (MUFA) fatty acids, and consequently a decrease of polyunsaturated fatty acids (PUFA). We carried out a genome wide association study (GWAS) for IMF composition on two rabbit lines divergently selected for IMF content, using a Bayes B procedure. Association analyses were performed using 475 individuals and 90,235 Single Nucleotide Polymorphisms (SNPs). The main objectives were to identify genomic regions associated with the IMF composition and to generate a list of candidate genes. Genomic regions associated with the intramuscular fatty acid composition were spread across different rabbit chromosomes (OCU). An important region at 34.0-37.9 Mb on OCU1 was associated with C14:0, C16:0, SFA, and C18:2n6, explaining 3.5%, 11.2%, 11.3%, and 3.2% of the genomic variance, respectively. Another relevant genomic region was found to be associated at 46.0-48.9 Mb on OCU18, explaining up to 8% of the genomic variance of MUFA/SFA. The associated regions harbor several genes related to lipid metabolism, such as SCD, PLIN2, and ERLIN1. The main genomic regions associated with the fatty acids were not previously associated with IMF content in rabbits. Nonetheless, MTMR2 is the only gene that was associated with both the IMF content and composition in rabbits. Our study highlighted the polygenic nature of the fatty acids in rabbits and elucidated its genetic background.
Collapse
|
9
|
Phillips MA, Kutch IC, Long AD, Burke MK. Increased time sampling in an evolve-and-resequence experiment with outcrossing Saccharomyces cerevisiae reveals multiple paths of adaptive change. Mol Ecol 2020; 29:4898-4912. [PMID: 33135198 DOI: 10.1111/mec.15687] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2020] [Revised: 09/29/2020] [Accepted: 09/30/2020] [Indexed: 11/28/2022]
Abstract
"Evolve and resequence" (E&R) studies combine experimental evolution and whole-genome sequencing to interrogate the genetics underlying adaptation. Due to ease of handling, E&R work with asexual organisms such as bacteria can employ optimized experimental design, with large experiments and many generations of selection. By contrast, E&R experiments with sexually reproducing organisms are more difficult to implement, and design parameters vary dramatically among studies. Thus, efforts have been made to assess how these differences, such as number of independent replicates, or size of experimental populations, impact inference. We add to this work by investigating the role of time sampling-the number of discrete time points sequence data are collected from evolving populations. Using data from an E&R experiment with outcrossing Saccharomyces cerevisiae in which populations were sequenced 17 times over ~540 generations, we address the following questions: (a) Do more time points improve the ability to identify candidate regions underlying selection? And (b) does high-resolution sampling provide unique insight into evolutionary processes driving adaptation? We find that while time sampling does not improve the ability to identify candidate regions, high-resolution sampling does provide valuable opportunities to characterize evolutionary dynamics. Increased time sampling reveals three distinct trajectories for adaptive alleles: one consistent with classic population genetic theory (i.e., models assuming constant selection coefficients), and two where trajectories suggest more context-dependent responses (i.e., models involving dynamic selection coefficients). We conclude that while time sampling has limited impact on candidate region identification, sampling eight or more time points has clear benefits for studying complex evolutionary dynamics.
Collapse
Affiliation(s)
- Mark A Phillips
- Department of Integrative Biology, Oregon State University, Corvallis, OR, USA
| | - Ian C Kutch
- Department of Integrative Biology, Oregon State University, Corvallis, OR, USA
| | - Anthony D Long
- Department of Ecology and Evolutionary Biology, University of California, Irvine, CA, USA
| | - Molly K Burke
- Department of Integrative Biology, Oregon State University, Corvallis, OR, USA
| |
Collapse
|
10
|
Ruzicka F, Dutoit L, Czuppon P, Jordan CY, Li X, Olito C, Runemark A, Svensson EI, Yazdi HP, Connallon T. The search for sexually antagonistic genes: Practical insights from studies of local adaptation and statistical genomics. Evol Lett 2020; 4:398-415. [PMID: 33014417 PMCID: PMC7523564 DOI: 10.1002/evl3.192] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 07/13/2020] [Accepted: 07/28/2020] [Indexed: 12/16/2022] Open
Abstract
Sexually antagonistic (SA) genetic variation-in which alleles favored in one sex are disfavored in the other-is predicted to be common and has been documented in several animal and plant populations, yet we currently know little about its pervasiveness among species or its population genetic basis. Recent applications of genomics in studies of SA genetic variation have highlighted considerable methodological challenges to the identification and characterization of SA genes, raising questions about the feasibility of genomic approaches for inferring SA selection. The related fields of local adaptation and statistical genomics have previously dealt with similar challenges, and lessons from these disciplines can therefore help overcome current difficulties in applying genomics to study SA genetic variation. Here, we integrate theoretical and analytical concepts from local adaptation and statistical genomics research-including F ST and F IS statistics, genome-wide association studies, pedigree analyses, reciprocal transplant studies, and evolve-and-resequence experiments-to evaluate methods for identifying SA genes and genome-wide signals of SA genetic variation. We begin by developing theoretical models for between-sex F ST and F IS, including explicit null distributions for each statistic, and using them to critically evaluate putative multilocus signals of sex-specific selection in previously published datasets. We then highlight new statistics that address some of the limitations of F ST and F IS, along with applications of more direct approaches for characterizing SA genetic variation, which incorporate explicit fitness measurements. We finish by presenting practical guidelines for the validation and evolutionary analysis of candidate SA genes and discussing promising empirical systems for future work.
Collapse
Affiliation(s)
- Filip Ruzicka
- School of Biological SciencesMonash UniversityClaytonVIC 3800Australia
| | - Ludovic Dutoit
- Department of ZoologyUniversity of OtagoDunedin9054New Zealand
| | - Peter Czuppon
- Institute of Ecology and Environmental Sciences, UPEC, CNRS, IRD, INRASorbonne UniversitéParis75252France
- Center for Interdisciplinary Research in Biology, CNRS, Collège de FrancePSL Research UniversityParis75231France
| | - Crispin Y. Jordan
- School of Biomedical SciencesUniversity of EdinburghEdinburghEH8 9XDUnited Kingdom
| | - Xiang‐Yi Li
- Institute of BiologyUniversity of NeuchâtelNeuchatelCH‐2000Switzerland
| | - Colin Olito
- Department of BiologyLund UniversityLundSE‐22362Sweden
| | - Anna Runemark
- Department of BiologyLund UniversityLundSE‐22362Sweden
| | | | | | - Tim Connallon
- School of Biological SciencesMonash UniversityClaytonVIC 3800Australia
| |
Collapse
|
11
|
The Effects of Quantitative Trait Architecture on Detection Power in Short-Term Artificial Selection Experiments. G3-GENES GENOMES GENETICS 2020; 10:3213-3227. [PMID: 32646912 PMCID: PMC7466968 DOI: 10.1534/g3.120.401287] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Evolve and resequence (E&R) experiments, in which artificial selection is imposed on organisms in a controlled environment, are becoming an increasingly accessible tool for studying the genetic basis of adaptation. Previous work has assessed how different experimental design parameters affect the power to detect the quantitative trait loci (QTL) that underlie adaptive responses in such experiments, but so far there has been little exploration of how this power varies with the genetic architecture of the evolving traits. In this study, we use forward simulation to build a more realistic model of an E&R experiment in which a quantitative polygenic trait experiences a short, but strong, episode of truncation selection. We study the expected power for QTL detection in such an experiment and how this power is influenced by different aspects of trait architecture, including the number of QTL affecting the trait, their starting frequencies, effect sizes, clustering along a chromosome, dominance, and epistasis patterns. We show that all of these parameters can affect allele frequency dynamics at the QTL and linked loci in complex and often unintuitive ways, and thus influence our power to detect them. One consequence of this is that existing detection methods based on models of independent selective sweeps at individual QTL often have lower detection power than a simple measurement of allele frequency differences before and after selection. Our findings highlight the importance of taking trait architecture into account when designing and interpreting studies of molecular adaptation with temporal data. We provide a customizable modeling framework that will enable researchers to easily simulate E&R experiments with different trait architectures and parameters tuned to their specific study system, allowing for assessment of expected detection power and optimization of experimental design.
Collapse
|
12
|
Langmüller AM, Schlötterer C. Low concordance of short-term and long-term selection responses in experimental Drosophila populations. Mol Ecol 2020; 29:3466-3475. [PMID: 32762052 PMCID: PMC7540288 DOI: 10.1111/mec.15579] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Revised: 07/27/2020] [Accepted: 07/28/2020] [Indexed: 12/15/2022]
Abstract
Experimental evolution is becoming a popular approach to study the genomic selection response of evolving populations. Computer simulation studies suggest that the accuracy of the signature increases with the duration of the experiment. Since some assumptions of the computer simulations may be violated, it is important to scrutinize the influence of the experimental duration with real data. Here, we use a highly replicated Evolve and Resequence study in Drosophila simulans to compare the selection targets inferred at different time points. At each time point, approximately the same number of SNPs deviates from neutral expectations, but only 10% of the selected haplotype blocks identified from the full data set can be detected after 20 generations. Those haplotype blocks that emerge already after 20 generations differ from the others by being strongly selected at the beginning of the experiment and display a more parallel selection response. Consistent with previous computer simulations, our results demonstrate that only Evolve and Resequence experiments with a sufficient number of generations can characterize complex adaptive architectures.
Collapse
Affiliation(s)
- Anna Maria Langmüller
- Vienna Graduate School of Population GeneticsViennaAustria
- Institut für PopulationsgenetikVetmeduni ViennaViennaAustria
| | | |
Collapse
|
13
|
Wilson-Sánchez D, Lup SD, Sarmiento-Mañús R, Ponce MR, Micol JL. Next-generation forward genetic screens: using simulated data to improve the design of mapping-by-sequencing experiments in Arabidopsis. Nucleic Acids Res 2020; 47:e140. [PMID: 31544937 PMCID: PMC6868388 DOI: 10.1093/nar/gkz806] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2019] [Revised: 09/07/2019] [Accepted: 09/10/2019] [Indexed: 12/25/2022] Open
Abstract
Forward genetic screens have successfully identified many genes and continue to be powerful tools for dissecting biological processes in Arabidopsis and other model species. Next-generation sequencing technologies have revolutionized the time-consuming process of identifying the mutations that cause a phenotype of interest. However, due to the cost of such mapping-by-sequencing experiments, special attention should be paid to experimental design and technical decisions so that the read data allows to map the desired mutation. Here, we simulated different mapping-by-sequencing scenarios. We first evaluated which short-read technology was best suited for analyzing gene-rich genomic regions in Arabidopsis and determined the minimum sequencing depth required to confidently call single nucleotide variants. We also designed ways to discriminate mutagenesis-induced mutations from background Single Nucleotide Polymorphisms in mutants isolated in Arabidopsis non-reference lines. In addition, we simulated bulked segregant mapping populations for identifying point mutations and monitored how the size of the mapping population and the sequencing depth affect mapping precision. Finally, we provide the computational basis of a protocol that we already used to map T-DNA insertions with paired-end Illumina-like reads, using very low sequencing depths and pooling several mutants together; this approach can also be used with single-end reads as well as to map any other insertional mutagen. All these simulations proved useful for designing experiments that allowed us to map several mutations in Arabidopsis.
Collapse
Affiliation(s)
- David Wilson-Sánchez
- Instituto de Bioingeniería, Universidad Miguel Hernández, Campus de Elche, 03202 Elche, Spain
| | - Samuel Daniel Lup
- Instituto de Bioingeniería, Universidad Miguel Hernández, Campus de Elche, 03202 Elche, Spain
| | - Raquel Sarmiento-Mañús
- Instituto de Bioingeniería, Universidad Miguel Hernández, Campus de Elche, 03202 Elche, Spain
| | - María Rosa Ponce
- Instituto de Bioingeniería, Universidad Miguel Hernández, Campus de Elche, 03202 Elche, Spain
| | - José Luis Micol
- Instituto de Bioingeniería, Universidad Miguel Hernández, Campus de Elche, 03202 Elche, Spain
| |
Collapse
|
14
|
Pfenninger M, Foucault Q. Genomic processes underlying rapid adaptation of a natural
Chironomus riparius
population to unintendedly applied experimental selection pressures. Mol Ecol 2020; 29:536-548. [DOI: 10.1111/mec.15347] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Revised: 12/13/2019] [Accepted: 12/24/2019] [Indexed: 12/15/2022]
Affiliation(s)
- Markus Pfenninger
- Department of Molecular Ecology Senckenberg Biodiversity and Climate Research Centre Frankfurt am Main Germany
- Institute for Molecular and Organismic Evolution Johannes Gutenberg University Mainz Germany
- LOEWE Centre for Translational Biodiversity Genomics Senckenberg Biodiversity and Climate Research Centre Frankfurt am Main Germany
| | - Quentin Foucault
- Department of Molecular Ecology Senckenberg Biodiversity and Climate Research Centre Frankfurt am Main Germany
- Institute for Molecular and Organismic Evolution Johannes Gutenberg University Mainz Germany
| |
Collapse
|
15
|
Vlachos C, Kofler R. Optimizing the Power to Identify the Genetic Basis of Complex Traits with Evolve and Resequence Studies. Mol Biol Evol 2019; 36:2890-2905. [PMID: 31400203 PMCID: PMC6878953 DOI: 10.1093/molbev/msz183] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Evolve and resequence (E&R) studies are frequently used to dissect the genetic basis of quantitative traits. By subjecting a population to truncating selection for several generations and estimating the allele frequency differences between selected and nonselected populations using next-generation sequencing (NGS), the loci contributing to the selected trait may be identified. The role of different parameters, such as, the population size or the number of replicate populations has been examined in previous works. However, the influence of the selection regime, that is the strength of truncating selection during the experiment, remains little explored. Using whole genome, individual based forward simulations of E&R studies, we found that the power to identify the causative alleles may be maximized by gradually increasing the strength of truncating selection during the experiment. Notably, such an optimal selection regime comes at no or little additional cost in terms of sequencing effort and experimental time. Interestingly, we also found that a selection regime which optimizes the power to identify the causative loci is not necessarily identical to a regime that maximizes the phenotypic response. Finally, our simulations suggest that an E&R study with an optimized selection regime may have a higher power to identify the genetic basis of quantitative traits than a genome-wide association study, highlighting that E&R is a powerful approach for finding the loci underlying complex traits.
Collapse
Affiliation(s)
- Christos Vlachos
- Institute für Populationsgenetik, Vetmeduni Vienna, Wien, Austria
- Vienna Graduate School of Population Genetics, Wien, Austria
| | - Robert Kofler
- Institute für Populationsgenetik, Vetmeduni Vienna, Wien, Austria
| |
Collapse
|
16
|
Sosa-Madrid BS, Santacreu MA, Blasco A, Fontanesi L, Pena RN, Ibáñez-Escriche N. A genomewide association study in divergently selected lines in rabbits reveals novel genomic regions associated with litter size traits. J Anim Breed Genet 2019; 137:123-138. [PMID: 31657065 DOI: 10.1111/jbg.12451] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2019] [Revised: 10/02/2019] [Accepted: 10/03/2019] [Indexed: 12/28/2022]
Abstract
Uterine capacity (UC), defined as the total number of kits from unilaterally ovariectomized does at birth, has a high genetic correlation with litter size. The aim of our research was to identify genomic regions associated with litter size traits through a genomewide association study using rabbits from a divergent selection experiment for UC. A high-density SNP array (200K) was used to genotype 181 does from a control population, high and low UC lines. Traits included total number born (TNB), number born alive (NBA), number born dead, ovulation rate (OR), implanted embryos (IE) and embryo, foetal and prenatal survivals at second parity. We implemented the Bayes B method and the associations were tested by Bayes factors and the percentage of genomic variance (GV) explained by windows. Different genomic regions associated with TNB, NBA, IE and OR were found. These regions explained 7.36%, 1.27%, 15.87% and 3.95% of GV, respectively. Two consecutive windows on chromosome 17 were associated with TNB, NBA and IE. This genomic region accounted for 6.32% of GV of TNB. In this region, we found the BMP4, PTDGR, PTGER2, STYX and CDKN3 candidate genes which presented functional annotations linked to some reproductive processes. Our findings suggest that a genomic region on chromosome 17 has an important effect on litter size traits. However, further analyses are needed to validate this region in other maternal rabbit lines.
Collapse
Affiliation(s)
| | - María Antonia Santacreu
- Institute for Animal Science and Technology, Universitat Politècnica de València, Valencia, Spain
| | - Agustín Blasco
- Institute for Animal Science and Technology, Universitat Politècnica de València, Valencia, Spain
| | - Luca Fontanesi
- Department of Agricultural and Food Sciences, Division of Animal Sciences, University of Bologna, Bologna, Italy
| | - Romi Natacha Pena
- Departament de Ciència Animal, Universitat de Lleida-Agrotecnio Center, Lleida, Spain
| | - Noelia Ibáñez-Escriche
- Institute for Animal Science and Technology, Universitat Politècnica de València, Valencia, Spain
| |
Collapse
|
17
|
Accurate Allele Frequencies from Ultra-low Coverage Pool-Seq Samples in Evolve-and-Resequence Experiments. G3 (BETHESDA, MD.) 2019; 9:4159-4168. [PMID: 31636085 PMCID: PMC6893198 DOI: 10.1534/g3.119.400755] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Evolve-and-resequence (E+R) experiments leverage next-generation sequencing technology to track the allele frequency dynamics of populations as they evolve. While previous work has shown that adaptive alleles can be detected by comparing frequency trajectories from many replicate populations, this power comes at the expense of high-coverage (>100x) sequencing of many pooled samples, which can be cost-prohibitive. Here, we show that accurate estimates of allele frequencies can be achieved with very shallow sequencing depths (<5x) via inference of known founder haplotypes in small genomic windows. This technique can be used to efficiently estimate frequencies for any number of bi-allelic SNPs in populations of any model organism founded with sequenced homozygous strains. Using both experimentally-pooled and simulated samples of Drosophila melanogaster, we show that haplotype inference can improve allele frequency accuracy by orders of magnitude for up to 50 generations of recombination, and is robust to moderate levels of missing data, as well as different selection regimes. Finally, we show that a simple linear model generated from these simulations can predict the accuracy of haplotype-derived allele frequencies in other model organisms and experimental designs. To make these results broadly accessible for use in E+R experiments, we introduce HAF-pipe, an open-source software tool for calculating haplotype-derived allele frequencies from raw sequencing data. Ultimately, by reducing sequencing costs without sacrificing accuracy, our method facilitates E+R designs with higher replication and resolution, and thereby, increased power to detect adaptive alleles.
Collapse
|
18
|
Vlachos C, Burny C, Pelizzola M, Borges R, Futschik A, Kofler R, Schlötterer C. Benchmarking software tools for detecting and quantifying selection in evolve and resequencing studies. Genome Biol 2019; 20:169. [PMID: 31416462 PMCID: PMC6694636 DOI: 10.1186/s13059-019-1770-8] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2019] [Accepted: 07/22/2019] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND The combination of experimental evolution with whole-genome resequencing of pooled individuals, also called evolve and resequence (E&R) is a powerful approach to study the selection processes and to infer the architecture of adaptive variation. Given the large potential of this method, a range of software tools were developed to identify selected SNPs and to measure their selection coefficients. RESULTS In this benchmarking study, we compare 15 test statistics implemented in 10 software tools using three different scenarios. We demonstrate that the power of the methods differs among the scenarios, but some consistently outperform others. LRT-1, CLEAR, and the CMH test perform best despite LRT-1 and the CMH test not requiring time series data. CLEAR provides the most accurate estimates of selection coefficients. CONCLUSION This benchmark study will not only facilitate the analysis of already existing data, but also affect the design of future data collections.
Collapse
Affiliation(s)
- Christos Vlachos
- Institut für Populationsgenetik, Vetmeduni Vienna, Veterinärplatz 1, Wien, 1210, Austria
- Vienna Graduate School of Population Genetics, Vienna, Austria
| | - Claire Burny
- Institut für Populationsgenetik, Vetmeduni Vienna, Veterinärplatz 1, Wien, 1210, Austria
- Vienna Graduate School of Population Genetics, Vienna, Austria
| | - Marta Pelizzola
- Institut für Populationsgenetik, Vetmeduni Vienna, Veterinärplatz 1, Wien, 1210, Austria
- Vienna Graduate School of Population Genetics, Vienna, Austria
| | - Rui Borges
- Institut für Populationsgenetik, Vetmeduni Vienna, Veterinärplatz 1, Wien, 1210, Austria
| | - Andreas Futschik
- Institute of Applied Statistics, Johannes Kepler University, Linz, 4040, Austria
- Plattform Bioinformatik und Biostatistik, Vetmeduni Vienna, Veterinärplatz 1, Wien, 1210, Austria
| | - Robert Kofler
- Institut für Populationsgenetik, Vetmeduni Vienna, Veterinärplatz 1, Wien, 1210, Austria.
| | - Christian Schlötterer
- Institut für Populationsgenetik, Vetmeduni Vienna, Veterinärplatz 1, Wien, 1210, Austria.
| |
Collapse
|
19
|
Brennan RS, Garrett AD, Huber KE, Hargarten H, Pespeni MH. Rare genetic variation and balanced polymorphisms are important for survival in global change conditions. Proc Biol Sci 2019; 286:20190943. [PMID: 31185858 PMCID: PMC6571474 DOI: 10.1098/rspb.2019.0943] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Accepted: 05/17/2019] [Indexed: 12/14/2022] Open
Abstract
Standing genetic variation is important for population persistence in extreme environmental conditions. While some species may have the capacity to adapt to predicted average future global change conditions, the ability to survive extreme events is largely unknown. We used single-generation selection experiments on hundreds of thousands of Strongylocentrotus purpuratus sea urchin larvae generated from wild-caught adults to identify adaptive genetic variation responsive to moderate (pH 8.0) and extreme (pH 7.5) low-pH conditions. Sequencing genomic DNA from pools of larvae, we identified consistent changes in allele frequencies across replicate cultures for each pH condition and observed increased linkage disequilibrium around selected loci, revealing selection on recombined standing genetic variation. We found that loci responding uniquely to either selection regime were at low starting allele frequencies while variants that responded to both pH conditions (11.6% of selected variants) started at high frequencies. Loci under selection performed functions related to energetics, pH tolerance, cell growth and actin/cytoskeleton dynamics. These results highlight that persistence in future conditions will require two classes of genetic variation: common, pH-responsive variants maintained by balancing selection in a heterogeneous environment, and rare variants, particularly for extreme conditions, that must be maintained by large population sizes.
Collapse
|
20
|
Castro JPL, Yancoskie MN, Marchini M, Belohlavy S, Hiramatsu L, Kučka M, Beluch WH, Naumann R, Skuplik I, Cobb J, Barton NH, Rolian C, Chan YF. An integrative genomic analysis of the Longshanks selection experiment for longer limbs in mice. eLife 2019; 8:e42014. [PMID: 31169497 PMCID: PMC6606024 DOI: 10.7554/elife.42014] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2018] [Accepted: 05/19/2019] [Indexed: 12/30/2022] Open
Abstract
Evolutionary studies are often limited by missing data that are critical to understanding the history of selection. Selection experiments, which reproduce rapid evolution under controlled conditions, are excellent tools to study how genomes evolve under selection. Here we present a genomic dissection of the Longshanks selection experiment, in which mice were selectively bred over 20 generations for longer tibiae relative to body mass, resulting in 13% longer tibiae in two replicates. We synthesized evolutionary theory, genome sequences and molecular genetics to understand the selection response and found that it involved both polygenic adaptation and discrete loci of major effect, with the strongest loci tending to be selected in parallel between replicates. We show that selection may favor de-repression of bone growth through inactivating two limb enhancers of an inhibitor, Nkx3-2. Our integrative genomic analyses thus show that it is possible to connect individual base-pair changes to the overall selection response.
Collapse
Affiliation(s)
- João PL Castro
- Friedrich Miescher Laboratory of the Max Planck SocietyTübingenGermany
| | | | | | | | - Layla Hiramatsu
- Friedrich Miescher Laboratory of the Max Planck SocietyTübingenGermany
| | - Marek Kučka
- Friedrich Miescher Laboratory of the Max Planck SocietyTübingenGermany
| | - William H Beluch
- Friedrich Miescher Laboratory of the Max Planck SocietyTübingenGermany
| | - Ronald Naumann
- Max Planck Institute for Molecular Cell Biology and GeneticsDresdenGermany
| | | | | | - Nicholas H Barton
- Institute of Science and Technology (IST) AustriaKlosterneuburgAustria
| | | | | |
Collapse
|
21
|
VanRaden PM, Bickhart DM, O'Connell JR. Calling known variants and identifying new variants while rapidly aligning sequence data. J Dairy Sci 2019; 102:3216-3229. [PMID: 30772032 DOI: 10.3168/jds.2018-15172] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2018] [Accepted: 12/10/2018] [Indexed: 12/30/2022]
Abstract
Whole-genome sequencing studies can identify causative mutations for subsequent use in genomic evaluations. Speed and accuracy of sequence alignment can be improved by accounting for known variant locations during alignment instead of calling the variants after alignment as in previous programs. The new programs Findmap and Findvar were compared with alignment using Burrows-Wheeler alignment (BWA) or SNAP and variant identification using Genome Analysis ToolKit (GATK) or SAMtools. Findmap stores the reference map and any known variant locations while aligning reads and counting reference and alternate alleles for each DNA source. Findmap also outputs potential new single nucleotide variant, insertion, and deletion alleles. Findvar separates likely true variants from read errors and outputs genotype probabilities. Strategies were tested using cattle, human, and a completely random reference map and simulated or actual data. Most tests simulated 10 bulls, each with 10× simulated sequence reads containing 39 million variants from the 1000 Bull Genomes Project. With 10 processors, clock times for processing 100× data were 105 h for BWA, 25 h for GATK, and 11 h for SAMtools but only about 4 h for SNAP, 3 h for Findmap, and 1 h for Findvar. Alignment programs required about the same total memory; BWA used 46 GB (4.6 GB/processor), whereas >10 processors can share the same memory in SNAP and Findmap, which used 40 and 46 GB, respectively. Findmap correctly mapped 92.9% of reads (compared with 92.6% from SNAP and 90.5% from BWA) and had high accuracy of calling alleles for known variants. For new variants, Findvar found 99.8% of single nucleotide variants, 79% of insertions, and 67% of deletions; GATK found 99.4, 95, and 90%, respectively; and SAMtools found 99.8, 12, and 16%, respectively. False positives (as percentages of true variants) were 11% of single nucleotide variants, 0.4% of insertions, and 0.3% of deletions from Findvar; 12, 8.4, and 2.9%, respectively, from GATK; and 37, 1.3, and 0.4%, respectively, from SAMtools. Advantages of Findmap and Findvar are fast processing, precise alignment, more useful data summaries, more compact output, and fewer steps. Calling known variants during alignment allows more efficient and accurate sequence-based genotyping.
Collapse
Affiliation(s)
- P M VanRaden
- USDA, Agricultural Research Service, Animal Genomics and Improvement Laboratory, Beltsville, MD 20705-2350.
| | - D M Bickhart
- USDA, Agricultural Research Service, Animal Genomics and Improvement Laboratory, Beltsville, MD 20705-2350
| | - J R O'Connell
- University of Maryland School of Medicine, Baltimore 21201
| |
Collapse
|
22
|
Johnsson M. Integrating Selection Mapping With Genetic Mapping and Functional Genomics. Front Genet 2018; 9:603. [PMID: 30619447 PMCID: PMC6295561 DOI: 10.3389/fgene.2018.00603] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2018] [Accepted: 11/19/2018] [Indexed: 01/23/2023] Open
Abstract
Genomic scans for signatures of selection allow us to, in principle, detect variants and genes that underlie recent adaptations. By combining selection mapping with genetic mapping of traits known to be relevant to adaptation, we can simultaneously investigate whether genes and variants show signals of recent selection and whether they impact traits that have likely been selected. There are three ways to integrate selection mapping with genetic mapping or functional genomics: (1) To use genetic mapping data from other populations as a form of genome annotation. (2) To perform experimental evolution or artificial selection to be able to study selected variants when they segregate, either by performing genetic mapping before selection or by crossing the selected individuals to some reference population. (3) To perform a comparative study of related populations facing different selection regimes. This short review discusses these different ways of integrating selection mapping with genetic mapping and functional genomics, with examples of how each has been done.
Collapse
Affiliation(s)
- Martin Johnsson
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Edinburgh, United Kingdom.,Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Uppsala, Sweden
| |
Collapse
|
23
|
Michalak P, Kang L, Schou MF, Garner HR, Loeschcke V. Genomic signatures of experimental adaptive radiation in Drosophila. Mol Ecol 2018; 28:600-614. [PMID: 30375065 DOI: 10.1111/mec.14917] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2018] [Revised: 10/03/2018] [Accepted: 10/17/2018] [Indexed: 12/12/2022]
Abstract
Abiotic environmental factors play a fundamental role in determining the distribution, abundance and adaptive diversification of species. Empowered by new technologies enabling rapid and increasingly accurate examination of genomic variation in populations, researchers may gain new insights into the genomic background of adaptive radiation and stress resistance. We investigated genomic variation across generations of large-scale experimental selection regimes originating from a single founder population of Drosophila melanogaster, diverging in response to ecologically relevant environmental stressors: heat shock, heat knock down, cold shock, desiccation and starvation. When compared to the founder population, and to parallel unselected controls, there were more than 100,000 single nucleotide polymorphisms (SNPs) displaying consistent allelic changes in response to selective pressures across generations. These SNPs were found in both coding and noncoding sequences, with the highest density in promoter regions, and involved a broad range of functionalities, including molecular chaperoning by heat-shock proteins. The SNP patterns were highly stressor-specific despite considerable variation among line replicates within each selection regime, as reflected by a principal component analysis, and co-occurred with selective sweep regions. Only ~15% of SNPs with putatively adaptive changes were shared by at least two selective regimes, while less than 1% of SNPs diverged in opposite directions. Divergent stressors driving evolution in the experimental system of adaptive radiation left distinct genomic signatures, most pronounced in starvation and heat-shock selection regimes.
Collapse
Affiliation(s)
- Pawel Michalak
- Edward Via College of Osteopathic Medicine, Blacksburg, Virginia.,One Health Research Center, Virginia-Maryland College of Veterinary Medicine, Blacksburg, Virginia.,Institute of Evolution, University of Haifa, Haifa, Israel
| | - Lin Kang
- Edward Via College of Osteopathic Medicine, Blacksburg, Virginia
| | - Mads F Schou
- Department of Bioscience, Aarhus University, Aarhus, Denmark
| | - Harold R Garner
- Edward Via College of Osteopathic Medicine, Blacksburg, Virginia.,The Gibbs Cancer Center and Research Institute, Spartanburg, SC, USA
| | | |
Collapse
|
24
|
Vlachos C, Kofler R. MimicrEE2: Genome-wide forward simulations of Evolve and Resequencing studies. PLoS Comput Biol 2018; 14:e1006413. [PMID: 30114186 PMCID: PMC6112681 DOI: 10.1371/journal.pcbi.1006413] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2018] [Revised: 08/28/2018] [Accepted: 08/02/2018] [Indexed: 11/18/2022] Open
Abstract
Evolve and Resequencing (E&R) studies allow us to monitor adaptation at the genomic level. By sequencing evolving populations at regular time intervals, E&R studies promise to shed light on some of the major open questions in evolutionary biology such as the repeatability of evolution and the molecular basis of adaptation. However, data interpretation, statistical analysis and the experimental design of E&R studies increasingly require simulations of evolving populations, a task that is difficult to accomplish with existing tools, which may i) be too slow, ii) require substantial reformatting of data, iii) not support an adaptive scenario of interest or iv) not sufficiently capture the biology of the used model organism. Therefore we developed MimicrEE2, a multi-threaded Java program for genome-wide forward simulations of evolving populations. MimicrEE2 enables the convenient usage of available genomic resources, supports biological particulars of model organism frequently used in E&R studies and offers a wide range of different adaptive models (selective sweeps, polygenic adaptation, epistasis). Due to its user-friendly and efficient design MimicrEE2 will facilitate simulations of E&R studies even for small labs with limited bioinformatics expertise or computational resources. Additionally, the scripts provided for executing MimicrEE2 on a computer cluster permit the coverage even of a large parameter space. MimicrEE2 runs on any computer with Java installed. It is distributed under the GPLv3 license at https://sourceforge.net/projects/mimicree2/.
Collapse
Affiliation(s)
- Christos Vlachos
- Institut für Populationsgenetik, Vetmeduni Vienna, Veterinärplatz, Wien, Austria
| | - Robert Kofler
- Institut für Populationsgenetik, Vetmeduni Vienna, Veterinärplatz, Wien, Austria
| |
Collapse
|
25
|
Abstract
Allele frequency time series data constitute a powerful resource for unraveling mechanisms of adaptation, because the temporal dimension captures important information about evolutionary forces. In particular, Evolve and Resequence (E&R), the whole-genome sequencing of replicated experimentally evolving populations, is becoming increasingly popular. Based on computer simulations several studies proposed experimental parameters to optimize the identification of the selection targets. No such recommendations are available for the underlying parameters selection strength and dominance. Here, we introduce a highly accurate method to estimate selection parameters from replicated time series data, which is fast enough to be applied on a genome scale. Using this new method, we evaluate how experimental parameters can be optimized to obtain the most reliable estimates for selection parameters. We show that the effective population size (Ne) and the number of replicates have the largest impact. Because the number of time points and sequencing coverage had only a minor effect, we suggest that time series analysis is feasible without major increase in sequencing costs. We anticipate that time series analysis will become routine in E&R studies.
Collapse
Affiliation(s)
- Thomas Taus
- Institut für Populationsgenetik, Vetmeduni Vienna, Vienna, Austria.,Vienna Graduate School of Population Genetics, Vienna, Austria
| | - Andreas Futschik
- Department of Applied Statistics, Johannes Kepler Universität Linz, Linz, Austria
| | | |
Collapse
|
26
|
Hardy CM, Burke MK, Everett LJ, Han MV, Lantz KM, Gibbs AG. Genome-Wide Analysis of Starvation-Selected Drosophila melanogaster-A Genetic Model of Obesity. Mol Biol Evol 2018; 35:50-65. [PMID: 29309688 PMCID: PMC5850753 DOI: 10.1093/molbev/msx254] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Experimental evolution affords the opportunity to investigate adaptation to stressful environments. Studies combining experimental evolution with whole-genome resequencing have provided insight into the dynamics of adaptation and a new tool to uncover genes associated with polygenic traits. Here, we selected for starvation resistance in populations of Drosophila melanogaster for over 80 generations. In response, the starvation-selected lines developed an obese condition, storing nearly twice the level of total lipids than their unselected controls. Although these fats provide a ∼3-fold increase in starvation resistance, the imbalance in lipid homeostasis incurs evolutionary cost. Some of these tradeoffs resemble obesity-associated pathologies in mammals including metabolic depression, low activity levels, dilated cardiomyopathy, and disrupted sleeping patterns. To determine the genetic basis of these traits, we resequenced genomic DNA from the selected lines and their controls. We found 1,046,373 polymorphic sites, many of which diverged between selection treatments. In addition, we found a wide range of genetic heterogeneity between the replicates of the selected lines, suggesting multiple mechanisms of adaptation. Genome-wide heterozygosity was low in the selected populations, with many large blocks of SNPs nearing fixation. We found candidate loci under selection by using an algorithm to control for the effects of genetic drift. These loci were mapped to a set of 382 genes, which associated with many processes including nutrient response, catabolic metabolism, and lipid droplet function. The results of our study speak to the evolutionary origins of obesity and provide new targets to understand the polygenic nature of obesity in a unique model system.
Collapse
Affiliation(s)
- Christopher M Hardy
- School of Life Sciences, University of Nevada Las Vegas, Las Vegas, NV
- Nevada Institute of Personalized Medicine, University of Nevada Las Vegas, Las Vegas, NV
| | - Molly K Burke
- Department of Integrative Biology, Oregon State University, Corvallis, OR
| | - Logan J Everett
- Department of Biological Sciences, North Carolina State University, Raleigh, NC
| | - Mira V Han
- School of Life Sciences, University of Nevada Las Vegas, Las Vegas, NV
- Nevada Institute of Personalized Medicine, University of Nevada Las Vegas, Las Vegas, NV
| | - Kathryn M Lantz
- School of Life Sciences, University of Nevada Las Vegas, Las Vegas, NV
- Nevada Institute of Personalized Medicine, University of Nevada Las Vegas, Las Vegas, NV
| | - Allen G Gibbs
- School of Life Sciences, University of Nevada Las Vegas, Las Vegas, NV
- Nevada Institute of Personalized Medicine, University of Nevada Las Vegas, Las Vegas, NV
| |
Collapse
|
27
|
Drosophila simulans: A Species with Improved Resolution in Evolve and Resequence Studies. G3-GENES GENOMES GENETICS 2017; 7:2337-2343. [PMID: 28546383 PMCID: PMC5499140 DOI: 10.1534/g3.117.043349] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The combination of experimental evolution with high-throughput sequencing of pooled individuals—i.e., evolve and resequence (E&R)—is a powerful approach to study adaptation from standing genetic variation under controlled, replicated conditions. Nevertheless, E&R studies in Drosophila melanogaster have frequently resulted in inordinate numbers of candidate SNPs, particularly for complex traits. Here, we contrast the genomic signature of adaptation following ∼60 generations in a novel hot environment for D. melanogaster and D. simulans. For D. simulans, the regions carrying putatively selected loci were far more distinct, and thus harbored fewer false positives, than those in D. melanogaster. We propose that species without segregating inversions and higher recombination rates, such as D. simulans, are better suited for E&R studies that aim to characterize the genetic variants underlying the adaptive response.
Collapse
|
28
|
Wiberg RAW, Gaggiotti OE, Morrissey MB, Ritchie MG. Identifying consistent allele frequency differences in studies of stratified populations. Methods Ecol Evol 2017; 8:1899-1909. [PMID: 29263778 PMCID: PMC5726381 DOI: 10.1111/2041-210x.12810] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2017] [Accepted: 05/02/2017] [Indexed: 12/02/2022]
Abstract
With increasing application of pooled‐sequencing approaches to population genomics robust methods are needed to accurately quantify allele frequency differences between populations. Identifying consistent differences across stratified populations can allow us to detect genomic regions under selection and that differ between populations with different histories or attributes. Current popular statistical tests are easily implemented in widely available software tools which make them simple for researchers to apply. However, there are potential problems with the way such tests are used, which means that underlying assumptions about the data are frequently violated. These problems are highlighted by simulation of simple but realistic population genetic models of neutral evolution and the performance of different tests are assessed. We present alternative tests (including Generalised Linear Models [GLMs] with quasibinomial error structure) with attractive properties for the analysis of allele frequency differences and re‐analyse a published dataset. The simulations show that common statistical tests for consistent allele frequency differences perform poorly, with high false positive rates. Applying tests that do not confound heterogeneity and main effects significantly improves inference. Variation in sequencing coverage likely produces many false positives and re‐scaling allele frequencies to counts out of a common value or an effective sample size reduces this effect. Many researchers are interested in identifying allele frequencies that vary consistently across replicates to identify loci underlying phenotypic responses to selection or natural variation in phenotypes. Popular methods that have been suggested for this task perform poorly in simulations. Overall, quasibinomial GLMs perform better and also have the attractive feature of allowing correction for multiple testing by standard procedures and are easily extended to other designs.
Collapse
Affiliation(s)
- R Axel W Wiberg
- Centre for Biological Diversity Sir Harold Mitchell Building University of St Andrews St Andrews, Scotland United Kingdom
| | - Oscar E Gaggiotti
- Scottish Oceans Institute Gatty Marine Laboratory University of St Andrews East Sands St Andrews, Scotland United Kingdom
| | - Michael B Morrissey
- Centre for Biological Diversity Sir Harold Mitchell Building University of St Andrews St Andrews, Scotland United Kingdom
| | - Michael G Ritchie
- Centre for Biological Diversity Sir Harold Mitchell Building University of St Andrews St Andrews, Scotland United Kingdom
| |
Collapse
|
29
|
Chen S, Lin Z, Zhou D, Wang C, Li H, Yu R, Deng H, Tang X, Zhou S, Wang Deng X, He H. Genome-wide study of an elite rice pedigree reveals a complex history of genetic architecture for breeding improvement. Sci Rep 2017; 7:45685. [PMID: 28374863 PMCID: PMC5379486 DOI: 10.1038/srep45685] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2017] [Accepted: 03/01/2017] [Indexed: 01/19/2023] Open
Abstract
Improving breeding has been widely utilized in crop breeding and contributed to yield and quality improvement, yet few researches have been done to analyze genetic architecture underlying breeding improvement comprehensively. Here, we collected genotype and phenotype data of 99 cultivars from the complete pedigree including Huanghuazhan, an elite, high-quality, conventional indica rice that has been grown over 4.5 million hectares in southern China and from which more than 20 excellent cultivars have been derived. We identified 1,313 selective sweeps (SSWs) revealing four stage-specific selection patterns corresponding to improvement preference during 65 years, and 1113 conserved Huanghuazhan traceable blocks (cHTBs) introduced from different donors and conserved in >3 breeding generations were the core genomic regions for superior performance of Huanghuazhan. Based on 151 quantitative trait loci (QTLs) identified for 13 improved traits in the pedigree, we reproduced their improvement process in silico, highlighting improving breeding works well for traits controlled by major/major + minor effect QTLs, but was inefficient for traits controlled by QTLs with complex interactions or explaining low levels of phenotypic variation. These results indicate long-term breeding improvement is efficient to construct superior genetic architecture for elite performance, yet molecular breeding with designed genotype of QTLs can facilitate complex traits improvement.
Collapse
Affiliation(s)
- Shaoxia Chen
- School of Life Sciences, State Key Laboratory of Protein and Plant Gene Research, Peking-Tsinghua Center for Life Sciences, and School of Advanced Agriculture Sciences, Peking University, Beijing 100871, China.,Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program (PTN), Peking University, China
| | - Zechuan Lin
- School of Life Sciences, State Key Laboratory of Protein and Plant Gene Research, Peking-Tsinghua Center for Life Sciences, and School of Advanced Agriculture Sciences, Peking University, Beijing 100871, China
| | - Degui Zhou
- Guangdong Provincial Key Laboratory of New Technology in Rice Breeding, Rice Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China
| | - Chongrong Wang
- Guangdong Provincial Key Laboratory of New Technology in Rice Breeding, Rice Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China
| | - Hong Li
- Guangdong Provincial Key Laboratory of New Technology in Rice Breeding, Rice Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China
| | - Renbo Yu
- School of Life Sciences, State Key Laboratory of Protein and Plant Gene Research, Peking-Tsinghua Center for Life Sciences, and School of Advanced Agriculture Sciences, Peking University, Beijing 100871, China
| | - Hanchao Deng
- Shenzhen Institute of Molecular Crop Design, Shenzhen 518107, China
| | - Xiaoyan Tang
- Shenzhen Institute of Molecular Crop Design, Shenzhen 518107, China.,Guangdong Key Lab of Biotechnology for Plant Development, College of Life Sciences, South China Normal University, Guangzhou 510631, China
| | - Shaochuan Zhou
- Guangdong Provincial Key Laboratory of New Technology in Rice Breeding, Rice Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China
| | - Xing Wang Deng
- School of Life Sciences, State Key Laboratory of Protein and Plant Gene Research, Peking-Tsinghua Center for Life Sciences, and School of Advanced Agriculture Sciences, Peking University, Beijing 100871, China.,Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program (PTN), Peking University, China
| | - Hang He
- School of Life Sciences, State Key Laboratory of Protein and Plant Gene Research, Peking-Tsinghua Center for Life Sciences, and School of Advanced Agriculture Sciences, Peking University, Beijing 100871, China.,Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program (PTN), Peking University, China
| |
Collapse
|
30
|
Hughes KA, Leips J. Pleiotropy, constraint, and modularity in the evolution of life histories: insights from genomic analyses. Ann N Y Acad Sci 2017; 1389:76-91. [PMID: 27936291 PMCID: PMC5318229 DOI: 10.1111/nyas.13256] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2016] [Revised: 08/10/2016] [Accepted: 08/22/2016] [Indexed: 12/20/2022]
Abstract
Multicellular organisms display an enormous range of life history (LH) strategies and present an evolutionary conundrum; despite strong natural selection, LH traits are characterized by high levels of genetic variation. To understand the evolution of life histories and maintenance of this variation, the specific phenotypic effects of segregating alleles and the genetic networks in which they act need to be elucidated. In particular, the extent to which LH evolution is constrained by the pleiotropy of alleles contributing to LH variation is generally unknown. Here, we review recent empirical results that shed light on this question, with an emphasis on studies employing genomic analyses. While genome-scale analyses are increasingly practical and affordable, they face limitations of genetic resolution and statistical power. We describe new research approaches that we believe can produce new insights and evaluate their promise and applicability to different kinds of organisms. Two approaches seem particularly promising: experiments that manipulate selection in multiple dimensions and measure phenotypic and genomic response and analytical approaches that take into account genome-wide associations between markers and phenotypes, rather than applying a traditional marker-by-marker approach.
Collapse
Affiliation(s)
- Kimberly A. Hughes
- Department of Biological Science, Florida State University, Tallahassee, Florida
| | - Jeff Leips
- Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, Maryland
| |
Collapse
|
31
|
Genomic Trajectories to Desiccation Resistance: Convergence and Divergence Among Replicate Selected Drosophila Lines. Genetics 2016; 205:871-890. [PMID: 28007884 DOI: 10.1534/genetics.116.187104] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2016] [Accepted: 12/05/2016] [Indexed: 12/20/2022] Open
Abstract
Adaptation to environmental stress is critical for long-term species persistence. With climate change and other anthropogenic stressors compounding natural selective pressures, understanding the nature of adaptation is as important as ever in evolutionary biology. In particular, the number of alternative molecular trajectories available for an organism to reach the same adaptive phenotype remains poorly understood. Here, we investigate this issue in a set of replicated Drosophila melanogaster lines selected for increased desiccation resistance-a classical physiological trait that has been closely linked to Drosophila species distributions. We used pooled whole-genome sequencing (Pool-Seq) to compare the genetic basis of their selection responses, using a matching set of replicated control lines for characterizing laboratory (lab-)adaptation, as well as the original base population. The ratio of effective population size to census size was high over the 21 generations of the experiment at 0.52-0.88 for all selected and control lines. While selected SNPs in replicates of the same treatment (desiccation-selection or lab-adaptation) tended to change frequency in the same direction, suggesting some commonality in the selection response, candidate SNP and gene lists often differed among replicates. Three of the five desiccation-selection replicates showed significant overlap at the gene and network level. All five replicates showed enrichment for ovary-expressed genes, suggesting maternal effects on the selected trait. Divergence between pairs of replicate lines for desiccation-candidate SNPs was greater than between pairs of control lines. This difference also far exceeded the divergence between pairs of replicate lines for neutral SNPs. Overall, while there was overlap in the direction of allele frequency changes and the network and functional categories affected by desiccation selection, replicates showed unique responses at all levels, likely reflecting hitchhiking effects, and highlighting the challenges in identifying candidate genes from these types of experiments when traits are likely to be polygenic.
Collapse
|
32
|
Uncovering the genetic signature of quantitative trait evolution with replicated time series data. Heredity (Edinb) 2016; 118:42-51. [PMID: 27848948 DOI: 10.1038/hdy.2016.98] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2016] [Revised: 08/18/2016] [Accepted: 08/24/2016] [Indexed: 01/04/2023] Open
Abstract
The genetic architecture of adaptation in natural populations has not yet been resolved: it is not clear to what extent the spread of beneficial mutations (selective sweeps) or the response of many quantitative trait loci drive adaptation to environmental changes. Although much attention has been given to the genomic footprint of selective sweeps, the importance of selection on quantitative traits is still not well studied, as the associated genomic signature is extremely difficult to detect. We propose 'Evolve and Resequence' as a promising tool, to study polygenic adaptation of quantitative traits in evolving populations. Simulating replicated time series data we show that adaptation to a new intermediate trait optimum has three characteristic phases that are reflected on the genomic level: (1) directional frequency changes towards the new trait optimum, (2) plateauing of allele frequencies when the new trait optimum has been reached and (3) subsequent divergence between replicated trajectories ultimately leading to the loss or fixation of alleles while the trait value does not change. We explore these 3 phase characteristics for relevant population genetic parameters to provide expectations for various experimental evolution designs. Remarkably, over a broad range of parameters the trajectories of selected alleles display a pattern across replicates, which differs both from neutrality and directional selection. We conclude that replicated time series data from experimental evolution studies provide a promising framework to study polygenic adaptation from whole-genome population genetics data.
Collapse
|
33
|
Franssen SU, Barton NH, Schlötterer C. Reconstruction of Haplotype-Blocks Selected during Experimental Evolution. Mol Biol Evol 2016; 34:174-184. [PMID: 27702776 DOI: 10.1093/molbev/msw210] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The genetic analysis of experimentally evolving populations typically relies on short reads from pooled individuals (Pool-Seq). While this method provides reliable allele frequency estimates, the underlying haplotype structure remains poorly characterized. With small population sizes and adaptive variants that start from low frequencies, the interpretation of selection signatures in most Evolve and Resequencing studies remains challenging. To facilitate the characterization of selection targets, we propose a new approach that reconstructs selected haplotypes from replicated time series, using Pool-Seq data. We identify selected haplotypes through the correlated frequencies of alleles carried by them. Computer simulations indicate that selected haplotype-blocks of several Mb can be reconstructed with high confidence and low error rates, even when allele frequencies change only by 20% across three replicates. Applying this method to real data from D. melanogaster populations adapting to a hot environment, we identify a selected haplotype-block of 6.93 Mb. We confirm the presence of this haplotype-block in evolved populations by experimental haplotyping, demonstrating the power and accuracy of our haplotype reconstruction from Pool-Seq data. We propose that the combination of allele frequency estimates with haplotype information will provide the key to understanding the dynamics of adaptive alleles.
Collapse
Affiliation(s)
| | - Nicholas H Barton
- Institute of Science and Technology Austria (IST Austria), Klosterneuburg, Austria
| | | |
Collapse
|
34
|
Hoang KL, Morran LT, Gerardo NM. Experimental Evolution as an Underutilized Tool for Studying Beneficial Animal-Microbe Interactions. Front Microbiol 2016; 7:1444. [PMID: 27679620 PMCID: PMC5020044 DOI: 10.3389/fmicb.2016.01444] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2016] [Accepted: 08/30/2016] [Indexed: 11/29/2022] Open
Abstract
Microorganisms play a significant role in the evolution and functioning of the eukaryotes with which they interact. Much of our understanding of beneficial host–microbe interactions stems from studying already established associations; we often infer the genotypic and environmental conditions that led to the existing host–microbe relationships. However, several outstanding questions remain, including understanding how host and microbial (internal) traits, and ecological and evolutionary (external) processes, influence the origin of beneficial host–microbe associations. Experimental evolution has helped address a range of evolutionary and ecological questions across different model systems; however, it has been greatly underutilized as a tool to study beneficial host–microbe associations. In this review, we suggest ways in which experimental evolution can further our understanding of the proximate and ultimate mechanisms shaping mutualistic interactions between eukaryotic hosts and microbes. By tracking beneficial interactions under defined conditions or evolving novel associations among hosts and microbes with little prior evolutionary interaction, we can link specific genotypes to phenotypes that can be directly measured. Moreover, this approach will help address existing puzzles in beneficial symbiosis research: how symbioses evolve, how symbioses are maintained, and how both host and microbe influence their partner’s evolutionary trajectories. By bridging theoretical predictions and empirical tests, experimental evolution provides us with another approach to test hypotheses regarding the evolution of beneficial host–microbe associations.
Collapse
Affiliation(s)
- Kim L Hoang
- Department of Biology, O. Wayne Rollins Research Center, Emory University Atlanta, GA, USA
| | - Levi T Morran
- Department of Biology, O. Wayne Rollins Research Center, Emory University Atlanta, GA, USA
| | - Nicole M Gerardo
- Department of Biology, O. Wayne Rollins Research Center, Emory University Atlanta, GA, USA
| |
Collapse
|
35
|
Konczal M, Koteja P, Orlowska-Feuer P, Radwan J, Sadowska ET, Babik W. Genomic Response to Selection for Predatory Behavior in a Mammalian Model of Adaptive Radiation. Mol Biol Evol 2016; 33:2429-40. [PMID: 27401229 DOI: 10.1093/molbev/msw121] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
If genetic architectures of various quantitative traits are similar, as studies on model organisms suggest, comparable selection pressures should produce similar molecular patterns for various traits. To test this prediction, we used a laboratory model of vertebrate adaptive radiation to investigate the genetic basis of the response to selection for predatory behavior and compare it with evolution of aerobic capacity reported in an earlier work. After 13 generations of selection, the proportion of bank voles (Myodes [=Clethrionomys] glareolus) showing predatory behavior was five times higher in selected lines than in controls. We analyzed the hippocampus and liver transcriptomes and found repeatable changes in allele frequencies and gene expression. Genes with the largest differences between predatory and control lines are associated with hunger, aggression, biological rhythms, and functioning of the nervous system. Evolution of predatory behavior could be meaningfully compared with evolution of high aerobic capacity, because the experiments and analyses were performed in the same methodological framework. The number of genes that changed expression was much smaller in predatory lines, and allele frequencies changed repeatably in predatory but not in aerobic lines. This suggests that more variants of smaller effects underlie variation in aerobic performance, whereas fewer variants of larger effects underlie variation in predatory behavior. Our results thus contradict the view that comparable selection pressures for different quantitative traits produce similar molecular patterns. Therefore, to gain knowledge about molecular-level response to selection for complex traits, we need to investigate not only multiple replicate populations but also multiple quantitative traits.
Collapse
Affiliation(s)
- Mateusz Konczal
- Institute of Environmental Sciences, Jagiellonian University, Kraków, Poland Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Paweł Koteja
- Institute of Environmental Sciences, Jagiellonian University, Kraków, Poland
| | - Patrycja Orlowska-Feuer
- Department of Neurophysiology and Chronobiology, Institute of Zoology, Jagiellonian University, Kraków, Poland
| | - Jacek Radwan
- Faculty of Biology, Institute of Environmental Biology, Adam Mickiewicz University, Poznań, Poland
| | - Edyta T Sadowska
- Institute of Environmental Sciences, Jagiellonian University, Kraków, Poland
| | - Wiesław Babik
- Institute of Environmental Sciences, Jagiellonian University, Kraków, Poland
| |
Collapse
|
36
|
Versace E, Reisenberger J. Large-scale assessment of olfactory preferences and learning in Drosophila melanogaster: behavioral and genetic components. PeerJ 2015; 3:e1214. [PMID: 26357595 PMCID: PMC4562235 DOI: 10.7717/peerj.1214] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2015] [Accepted: 08/05/2015] [Indexed: 01/08/2023] Open
Abstract
In the Evolve and Resequence method (E&R), experimental evolution and genomics are combined to investigate evolutionary dynamics and the genotype-phenotype link. As other genomic approaches, this methods requires many replicates with large population sizes, which imposes severe restrictions on the analysis of behavioral phenotypes. Aiming to use E&R for investigating the evolution of behavior in Drosophila, we have developed a simple and effective method to assess spontaneous olfactory preferences and learning in large samples of fruit flies using a T-maze. We tested this procedure on (a) a large wild-caught population and (b) 11 isofemale lines of Drosophila melanogaster. Compared to previous methods, this procedure reduces the environmental noise and allows for the analysis of large population samples. Consistent with previous results, we show that flies have a preference for orange vs. apple odor. With our procedure wild-derived flies exhibit olfactory learning in the absence of previous laboratory selection. Furthermore, we find genetic differences in the olfactory learning with relatively high heritability. We propose this large-scale method as an effective tool for E&R and genome-wide association studies on olfactory preferences and learning.
Collapse
Affiliation(s)
- Elisabetta Versace
- Institut für Populationsgenetik, Vetmeduni, Vienna, Austria
- Center for Mind/Brain Sciences, University of Trento, Rovereto, Italy
| | | |
Collapse
|
37
|
Gaut BS. Evolution Is an Experiment: Assessing Parallelism in Crop Domestication and Experimental Evolution. Mol Biol Evol 2015; 32:1661-71. [DOI: 10.1093/molbev/msv105] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
|
38
|
Schlötterer C, Kofler R, Versace E, Tobler R, Franssen SU. Combining experimental evolution with next-generation sequencing: a powerful tool to study adaptation from standing genetic variation. Heredity (Edinb) 2015; 114:431-40. [PMID: 25269380 PMCID: PMC4815507 DOI: 10.1038/hdy.2014.86] [Citation(s) in RCA: 158] [Impact Index Per Article: 17.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2014] [Revised: 07/01/2014] [Accepted: 07/14/2014] [Indexed: 12/20/2022] Open
Abstract
Evolve and resequence (E&R) is a new approach to investigate the genomic responses to selection during experimental evolution. By using whole genome sequencing of pools of individuals (Pool-Seq), this method can identify selected variants in controlled and replicable experimental settings. Reviewing the current state of the field, we show that E&R can be powerful enough to identify causative genes and possibly even single-nucleotide polymorphisms. We also discuss how the experimental design and the complexity of the trait could result in a large number of false positive candidates. We suggest experimental and analytical strategies to maximize the power of E&R to uncover the genotype-phenotype link and serve as an important research tool for a broad range of evolutionary questions.
Collapse
Affiliation(s)
- C Schlötterer
- Institut für Populationsgenetik, Vetmeduni Vienna, Vienna, Austria
| | - R Kofler
- Institut für Populationsgenetik, Vetmeduni Vienna, Vienna, Austria
| | - E Versace
- Institut für Populationsgenetik, Vetmeduni Vienna, Vienna, Austria
- Center for Mind/Brain Sciences, University of Trento, Rovereto, Italy
| | - R Tobler
- Institut für Populationsgenetik, Vetmeduni Vienna, Vienna, Austria
- Vienna Graduate School of Population Genetics, Vienna, Austria
| | - S U Franssen
- Institut für Populationsgenetik, Vetmeduni Vienna, Vienna, Austria
| |
Collapse
|
39
|
Franssen SU, Nolte V, Tobler R, Schlötterer C. Patterns of linkage disequilibrium and long range hitchhiking in evolving experimental Drosophila melanogaster populations. Mol Biol Evol 2015; 32:495-509. [PMID: 25415966 PMCID: PMC4298179 DOI: 10.1093/molbev/msu320] [Citation(s) in RCA: 67] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
Whole-genome resequencing of experimental populations evolving under a specific selection regime has become a popular approach to determine genotype-phenotype maps and understand adaptation to new environments. Despite its conceptual appeal and success in identifying some causative genes, it has become apparent that many studies suffer from an excess of candidate loci. Several explanations have been proposed for this phenomenon, but it is clear that information about the linkage structure during such experiments is needed. Until now only Pool-Seq (whole-genome sequencing of pools of individuals) data were available, which do not provide sufficient information about the correlation between linked sites. We address this problem in two complementary analyses of three replicate Drosophila melanogaster populations evolving to a new hot temperature environment for almost 70 generations. In the first analysis, we sequenced 58 haploid genomes from the founder population and evolved flies at generation 67. We show that during the experiment linkage disequilibrium (LD) increased almost uniformly over much greater distances than typically seen in Drosophila. In the second analysis, Pool-Seq time series data of the three replicates were combined with haplotype information from the founder population to follow blocks of initial haplotypes over time. We identified 17 selected haplotype-blocks that started at low frequencies in the base population and increased in frequency during the experiment. The size of these haplotype-blocks ranged from 0.082 to 4.01 Mb. Moreover, between 42% and 46% of the top candidate single nucleotide polymorphisms from the comparison of founder and evolved populations fell into the genomic region covered by the haplotype-blocks. We conclude that LD in such rising haplotype-blocks results in long range hitchhiking over multiple kilobase-sized regions. LD in such haplotype-blocks is therefore a major factor contributing to an excess of candidate loci. Although modifications of the experimental design may help to reduce the hitchhiking effect and allow for more precise mapping of causative variants, we also note that such haplotype-blocks might be well suited to study the dynamics of selected genomic regions during experimental evolution studies.
Collapse
Affiliation(s)
| | - Viola Nolte
- Institut für Populationsgenetik, Vetmeduni Vienna, Vienna, Austria
| | - Ray Tobler
- Institut für Populationsgenetik, Vetmeduni Vienna, Vienna, Austria
| | | |
Collapse
|