scholarly journals Detection of hard and soft selective sweeps from Drosophila melanogaster population genomic data

PLoS Genetics ◽  
2021 ◽  
Vol 17 (2) ◽  
pp. e1009373 ◽  
Author(s):  
Nandita R. Garud ◽  
Philipp W. Messer ◽  
Dmitri A. Petrov
Keyword(s):  
Drosophila Melanogaster ◽  
Natural Population ◽  
Genomic Data ◽  
Selective Sweeps ◽  
Demographic Models ◽  
Population Genomic ◽  
Free Parameters ◽  
Candidate Loci ◽  
Soft Selective Sweeps ◽  
Positive Controls

Whether hard sweeps or soft sweeps dominate adaptation has been a matter of much debate. Recently, we developed haplotype homozygosity statistics that (i) can detect both hard and soft sweeps with similar power and (ii) can classify the detected sweeps as hard or soft. The application of our method to population genomic data from a natural population ofDrosophila melanogaster(DGRP) allowed us to rediscover three known cases of adaptation at the lociAce,Cyp6g1, andCHKov1known to be driven by soft sweeps, and detected additional candidate loci for recent and strong sweeps. Surprisingly, all of the top 50 candidates showed patterns much more consistent with soft rather than hard sweeps. Recently, Harriset al. 2018 criticized this work, suggesting that all the candidate loci detected by our haplotype statistics, including the positive controls, are unlikely to be sweeps at all and that instead these haplotype patterns can be more easily explained by complex neutral demographic models. They also claim that these neutral non-sweeps are likely to be hard instead of soft sweeps. Here, we reanalyze the DGRP data using a range of complex admixture demographic models and reconfirm our original published results suggesting that the majority of recent and strong sweeps inD.melanogasterare first likely to be true sweeps, and second, that they do appear to be soft. Furthermore, we discuss ways to take this work forward given that most demographic models employed in such analyses are necessarily too simple to capture the full demographic complexity, while more realistic models are unlikely to be inferred correctly because they require a large number of free parameters.

scholarly journals Detection of hard and soft selective sweeps from Drosophila melanogaster population genomic data

2020 ◽  
Author(s):  
Nandita Garud ◽  
Philipp W. Messer ◽  
Dmitri Petrov
Keyword(s):  
Drosophila Melanogaster ◽  
Natural Population ◽  
Genomic Data ◽  
Selective Sweeps ◽  
Demographic Models ◽  
Population Genomic ◽  
Free Parameters ◽  
Candidate Loci ◽  
Soft Selective Sweeps ◽  
Positive Controls

AbstractWhether hard sweeps or soft sweeps dominate adaptation has been a matter of much debate. Recently, we developed haplotype homozygosity statistics that (i) can detect both hard and soft sweeps with similar power and (ii) can classify the detected sweeps as hard or soft. The application of our method to population genomic data from a natural population of Drosophila melanogaster (DGRP) allowed us to rediscover three known cases of adaptation at the loci Ace, Cyp6g1, and CHKov1 known to be driven by soft sweeps, and detected additional candidate loci for recent and strong sweeps. Surprisingly, all of the top 50 candidates showed patterns much more consistent with soft rather than hard sweeps. Recently, Harris et al. 2018 criticized this work, suggesting that all the candidate loci detected by our haplotype statistics, including the positive controls, are unlikely to be sweeps at all and instead these haplotype patterns can be more easily explained by complex neutral demographic models. They also claim, confusingly, that these neutral non-sweeps are likely to be hard instead of soft sweeps. Here, we reanalyze the DGRP data using a range of complex admixture demographic models and reconfirm our original published results suggesting that the majority of recent and strong sweeps in D. melanogaster are first likely to be true sweeps, and second, that they do appear to be soft. Furthermore, we discuss ways to take this work forward given that the demographic models employed in such analyses are generally necessarily too simple to capture the full demographic complexity, while more realistic models are unlikely to be inferred correctly because they require fitting a very large number of free parameters.

scholarly journals Evaluating the performance of selection scans to detect selective sweeps in domestic dogs

2015 ◽  
Author(s):  
Florencia Schlamp ◽  
Julian van der Made ◽  
Rebecca Stambler ◽  
Lewis Chesebrough ◽  
Adam R Boyko ◽  
...  
Keyword(s):  
Artificial Selection ◽  
Population Sample ◽  
Population Bottlenecks ◽  
Selective Sweeps ◽  
Nucleotide Polymorphism ◽  
Genomic Signatures ◽  
Population Genomic ◽  
Dog Domestication ◽  
Soft Selective Sweeps ◽  
Positive Controls

Selective breeding of dogs has resulted in repeated artificial selection on breed-specific morphological phenotypes. A number of quantitative trait loci associated with these phenotypes have been identified in genetic mapping studies. We analyzed the population genomic signatures observed around the causal mutations for 12 of these loci in 25 dog breeds, for which we genotyped 25 individuals in each breed. By measuring the population frequencies of the causal mutations in each breed, we identified those breeds in which specific mutations most likely experienced positive selection. These instances were then used as positive controls for assessing the performance of popular statistics to detect selection from population genomic data. We found that artificial selection during dog domestication has left characteristic signatures in the haplotype and nucleotide polymorphism patterns around selected loci that can be detected in the genotype data from a single population sample. However, the sensitivity and accuracy at which such signatures were detected varied widely between loci, the particular statistic used, and the choice of analysis parameters. We observed examples of both hard and soft selective sweeps and detected strong selective events that removed genetic diversity almost entirely over regions >10 Mbp. Our study demonstrates the power and limitations of selection scans in populations with high levels of linkage disequilibrium due to severe founder effects and recent population bottlenecks.

scholarly journals Identifying loci under positive selection in complex population histories

2018 ◽  
Author(s):  
Alba Refoyo-Martínez ◽  
Rute R. da Fonseca ◽  
Katrín Halldórsdóttir ◽  
Einar Árnason ◽  
Thomas Mailund ◽  
...  
Keyword(s):  
Natural Selection ◽  
Positive Selection ◽  
Human Population ◽  
Genomic Data ◽  
Human Populations ◽  
Selective Sweeps ◽  
Population Genomic ◽  
Complex Population ◽  
History Of ◽  
Over Time

AbstractDetailed modeling of a species’ history is of prime importance for understanding how natural selection operates over time. Most methods designed to detect positive selection along sequenced genomes, however, use simplified representations of past histories as null models of genetic drift. Here, we present the first method that can detect signatures of strong local adaptation across the genome using arbitrarily complex admixture graphs, which are typically used to describe the history of past divergence and admixture events among any number of populations. The method—called Graph-aware Retrieval of Selective Sweeps (GRoSS)—has good power to detect loci in the genome with strong evidence for past selective sweeps and can also identify which branch of the graph was most affected by the sweep. As evidence of its utility, we apply the method to bovine, codfish and human population genomic data containing multiple population panels related in complex ways. We find new candidate genes for important adaptive functions, including immunity and metabolism in under-studied human populations, as well as muscle mass, milk production and tameness in specific bovine breeds. We are also able to pinpoint the emergence of large regions of differentiation due to inversions in the history of Atlantic codfish.

scholarly journals Microbiome composition shapes rapid genomic adaptation of Drosophila melanogaster

2019 ◽  
Author(s):  
Seth M. Rudman ◽  
Sharon Greenblum ◽  
Rachel C. Hughes ◽  
Subhash Rajpurohit ◽  
Ozan Kiratli ◽  
...  
Keyword(s):  
Drosophila Melanogaster ◽  
Rapid Evolution ◽  
Natural Populations ◽  
Genomic Data ◽  
Allele Frequencies ◽  
Frequency Change ◽  
Microbiome Composition ◽  
Population Genomic ◽  
Genome Wide ◽  
Microbial Group

AbstractPopulation genomic data has revealed patterns of genetic variation associated with adaptation in many taxa. Yet understanding the adaptive process that drives such patterns is challenging - it requires disentangling the ecological agents of selection, determining the relevant timescales over which evolution occurs, and elucidating the genetic architecture of adaptation. Doing so for the adaptation of hosts to their microbiome is of particular interest with growing recognition of the importance and complexity of host-microbe interactions. Here, we track the pace and genomic architecture of adaptation to an experimental microbiome manipulation in replicate populations of Drosophila melanogaster in field mesocosms. Manipulation of the microbiome altered population dynamics and increased divergence between treatments in allele frequencies genome-wide, with regions showing strong divergence found on all chromosomes. Moreover, at divergent loci previously associated with adaptation across natural populations, we found that the more common allele in fly populations experimentally enriched for a certain microbial group was also more common in natural populations with high relative abundance of that microbial group. These results suggest that microbiomes may be an agent of selection that shapes the pattern and process of adaptation and, more broadly, that variation in a single ecological factor within a complex environment can drive rapid, polygenic adaptation over short timescales.Significance statementNatural selection can drive evolution over short timescales. However, there is little understanding of which ecological factors are capable of driving rapid evolution and how this rapid evolution alters allele frequencies across the genome. Here we combine a field experiment with population genomic data from natural populations across a latitudinal gradient to assess whether and how microbiome composition drives rapid genomic evolution of host populations. We find that differences in microbiome composition cause divergence in allele frequencies genome-wide, including in genes previously associated with local adaptation. Moreover, we observed concordance between experimental and natural populations in terms of the direction of allele frequency change, suggesting that microbiome composition may be an agent of selection that drives adaptation in the wild.

scholarly journals diploS/HIC: an updated approach to classifying selective sweeps

2018 ◽  
Author(s):  
Andrew D. Kern ◽  
Daniel R. Schrider
Keyword(s):  
Machine Learning ◽  
Genomic Data ◽  
Difficult Problem ◽  
Small Sample ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Selective Sweeps ◽  
Machine Learning Approach ◽  
Small Sample Sizes ◽  
Soft Selective Sweeps

AbstractIdentifying selective sweeps in populations that have complex demographic histories remains a difficult problem in population genetics. We previously introduced a supervised machine learning approach, S/HIC, for finding both hard and soft selective sweeps in genomes on the basis of patterns of genetic variation surrounding a window of the genome. While S/HIC was shown to be both powerful and precise, the utility of S/HIC was limited by the use of phased genomic data as input. In this report we describe a deep learning variant of our method, diploS/HIC, that uses unphased genotypes to accurately classify genomic windows. diploS/HIC is shown to be quite powerful even at moderate to small sample sizes

scholarly journals Population genomic data in spider mites point to a role for local adaptation in shaping range shifts

2020 ◽  
Vol 13 (10) ◽  
pp. 2821-2835
Author(s):  
Lei Chen ◽  
Jing‐Tao Sun ◽  
Peng‐Yu Jin ◽  
Ary A. Hoffmann ◽  
Xiao‐Li Bing ◽  
...  
Keyword(s):  
Local Adaptation ◽  
Genomic Data ◽  
Spider Mites ◽  
Range Shifts ◽  
Population Genomic

scholarly journals GENETIC LOAD IN NATURAL POPULATIONS: IS IT COMPATIBLE WITH THE HYPOTHESIS THAT MANY POLYMORPHISMS ARE MAINTAINED BY NATURAL SELECTION?

Genetics ◽  
1974 ◽  
Vol 77 (3) ◽  
pp. 569-589
Author(s):  
Martin L Tracey ◽  
Francisco J Ayala
Keyword(s):  
Drosophila Melanogaster ◽  
Natural Selection ◽  
Natural Population ◽  
Natural Populations ◽  
Genetic Load ◽  
Structural Genes ◽  
Invertebrate Species ◽  
Average Proportion ◽  
Mean Fitness ◽  
The Mean

ABSTRACT Recent studies of genetically controlled enzyme variation lead to an estimation that at least 30 to 60% of the structural genes are polymorphic in natural populations of many vertebrate and invertebrate species. Some authors have argued that a substantial proportion of these polymorphisms cannot be maintained by natural selection because this would result in an unbearable genetic load. If many polymorphisms are maintained by heterotic natural selection, individuals with much greater than average proportion of homozygous loci should have very low fitness. We have measured in Drosophila melanogaster the fitness of flies homozygous for a complete chromosome relative to normal wild flies. A total of 37 chromosomes from a natural population have been tested using 92 experimental populations. The mean fitness of homozygous flies is 0.12 for second chromosomes, and 0.13 for third chromosomes. These estimates are compatible with the hypothesis that many (more than one thousand) loci are maintained by heterotic selection in natural populations of D. melanogaster.

Seasonal changes in recombination characteristics in a natural population of Drosophila melanogaster

Heredity ◽  
2021 ◽  
Author(s):  
Dau Dayal Aggarwal ◽  
Sviatoslav Rybnikov ◽  
Shaul Sapielkin ◽  
Eugenia Rashkovetsky ◽  
Zeev Frenkel ◽  
...  
Keyword(s):  
Drosophila Melanogaster ◽  
Natural Population ◽  
Seasonal Changes

scholarly journals Inferring Adaptive Introgression Using Hidden Markov Models

2020 ◽  
Author(s):  
Jesper Svedberg ◽  
Vladimir Shchur ◽  
Solomon Reinman ◽  
Rasmus Nielsen ◽  
Russell Corbett-Detig
Keyword(s):  
Markov Models ◽  
Hidden Markov ◽  
Genomic Data ◽  
Diverse Populations ◽  
Powerful Method ◽  
Adaptive Genetic Variation ◽  
Adaptive Introgression ◽  
Population Genomic ◽  
Significant Interest ◽  
Selection Parameters

AbstractAdaptive introgression - the flow of adaptive genetic variation between species or populations - has attracted significant interest in recent years and it has been implicated in a number of cases of adaptation, from pesticide resistance and immunity, to local adaptation. Despite this, methods for identification of adaptive introgression from population genomic data are lacking. Here, we present Ancestry_HMM-S, a Hidden Markov Model based method for identifying genes undergoing adaptive introgression and quantifying the strength of selection acting on them. Through extensive validation, we show that this method performs well on moderately sized datasets for realistic population and selection parameters. We apply Ancestry_HMM-S to a dataset of an admixed Drosophila melanogaster population from South Africa and we identify 17 loci which show signatures of adaptive introgression, four of which have previously been shown to confer resistance to insecticides. Ancestry_HMM-S provides a powerful method for inferring adaptive introgression in datasets that are typically collected when studying admixed populations. This method will enable powerful insights into the genetic consequences of admixture across diverse populations. Ancestry_HMM-S can be downloaded from https://github.com/jesvedberg/Ancestry_HMM-S/.

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