Comparative evolutionary genetics of deleterious load in sorghum and maize

Lozano, Roberto; Gazave, Élodie; Santos, José Fortunato Ferreira; Stetter, Markus G; Valluru, Ravi; Bandillo, Nonoy; Fernandes, Samuel B.; Brown, Patrick J.; Shakoor, Nadia; Mockler, Todd C.; Cooper, Elizabeth A.; Perkins, M. Taylor; Buckler, Edward S.; Ross‐Ibarra, Jeffrey; Gore, Michael A.

doi:10.1038/s41477-020-00834-5

Cited by 72 publications

(74 citation statements)

References 68 publications

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“…Shifts in demography are known to complicate inferences of the strength of selection and genetic load (Brandvain and Wright 2016); for example, even in one of the best studied demographic shifts, the Out of Africa migration in humans, several papers (Lohmueller et al 2008; Gazave et al 2013; Simons et al 2014; Henn et al 2016; Simons and Sella 2016) have reached seemingly contradictory conclusions on whether genetic load has increased as a result of these shifts in demography (but see (Lohmueller 2014)). The pattern of deleterious mutation accumulation has also been well-documented in bottlenecks and population growth associated with domestication in crops such as maize (Wang et al 2017), soybean and barley (Kono et al 2016), sorghum (Lozano et al 2021), cassava (Ramu et al 2017), and rice (Liu et al 2017).…”

Section: Resultsmentioning

confidence: 99%

Deleterious Mutations Accumulate Faster in Allopolyploid than Diploid Cotton (Gossypium) and Unequally Between Subgenomes

Conover

Wendel

2021

Preprint

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Whole genome duplication (polyploidization) is among the most dramatic mutational processes in nature, so understanding how natural selection differs in polyploids relative to diploids is an important goal. Population genetics theory predicts that recessive deleterious mutations accumulate faster in allopolyploids than diploids due to the masking effect of redundant gene copies, but this prediction is hitherto unconfirmed. Here, we use the cotton genus (Gossypium), which contains seven allopolyploids derived from a single polyploidization event 1-2 million years ago, to investigate deleterious mutation accumulation. We use two methods of identifying deleterious mutations at the nucleotide and amino acid level, along with whole-genome resequencing of 43 individuals spanning six allopolyploid species and their two diploid progenitors, to demonstrate that deleterious mutations accumulate faster in allopolyploids than in their diploid progenitors. We find that, unlike what would be expected under models of demographic changes alone, strongly deleterious mutations show the biggest difference between ploidy levels, and this effect diminishes for moderately and mildly deleterious mutations. We further show that the proportion of nonsynonymous mutations that are deleterious differs between the two co-resident subgenomes in the allopolyploids, suggesting that homoeologous masking acts unequally between subgenomes. Our results provide a genome-wide perspective on classic notions of the significance of gene duplication that likely are broadly applicable to allopolyploids, with implications for our understanding of the evolutionary fate of deleterious mutations. Finally, we note that some measures of selection (e.g. dN/dS, 𝛑N/𝛑S) may be biased when species of different ploidy levels are compared.

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Section: Resultsmentioning

confidence: 99%

Deleterious Mutations Accumulate Faster in Allopolyploid than Diploid Cotton (Gossypium) and Unequally Between Subgenomes

Conover

Wendel

2021

Preprint

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“…The impressive performance of deep learning for population genetics encouraged recent development of userfriendly tools for inference from empirical data, including selective sweep classification (Kern and Schrider, 2018), quantifying selection strength (Torada et al, 2019), jointly inferring selection and population size change (Sheehan and Song, 2016), and inferring recombination landscapes (Adrion et al, 2020). Other studies relied on custom approaches to identifying deleterious variants in sorghum (Lozano et al, 2021) and positive selection in SARS-CoV-2 (Ouellette et al, 2021). An emerging approach involves combining deep learning with approximate Bayesian computation (ABC) (Beaumont et al, 2002;Bertorelle et al, 2010).…”

Section: Genomics Population Genetics and Phylogeneticsmentioning

confidence: 99%

Deep learning as a tool for ecology and evolution

Borowiec¹,

Frandsen²,

Dikow³

et al. 2021

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Deep learning is driving recent advances behind many everyday technologies, including those relying on speech and image recognition, natural language processing, and autonomous driving. It is also gaining popularity in biology, where it has been used for automated species identification, environmental monitoring, behavioral studies, DNA sequencing, and population genetics and phylogenetics, among other applications. Deep learning relies on artificial neural networks for predictive modeling and excels at recognizing complex patterns. Operating within the machine learning paradigm, deep learning can be viewed as an alternative to likelihood-based inference methods. It has desirable properties, including good performance and scaling with increasing complexity, while posing unique challenges such as sensitivity to bias in input data. In this review we provide a gentle introduction to deep learning, review its applications in ecology and evolution, and discuss its limitations and efforts to overcome them. We also provide a practical primer for biologists interested in including deep learning in their toolkit and identify its possible future applications.

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“…In this study, we deep-sequenced an additional 445 EMS pools to further boost the coverage of genes with significant mutations. We also compared the statistics of EMS data with recently released large natural population data (Lozano et al 2021). Genes with significant mutations from both populations were integrated and are available through the SciApps platform.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Sorghum EMS Mutants and Natural Populations

Wang

Lipzen

et al. 2021

Preprint

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To build a large-scale genomic resource for functional validation of sorghum genes through EMS-mutagenized BTx623 seeds, we deep sequenced (30-60X) an additional 445 phenotyped EMS mutant lines. 4.2 million EMS mutations are called with nearly 36,800 mutations that could have a disruptive effect on functions of over 15,500 genes. Combining variants carried by both the natural population and previous EMS efforts, over 69% of sorghum coding genes (23644) are now presented with one or more mutations that are, or are predicted to be, disruptive to their functions. Our results show that the EMS population carries more significant mutations but less in each sample than the natural population, which makes it more powerful in elucidating sorghum gene functions on a large scale and requiring less work in the validation of candidate causal genes. We have made the data available through two ways, one is the integration with the BSAseq workflow that supports retrieving independent EMS samples carrying the same genes with significant mutation for complementary testing, and the other is a web application for directly querying genes with significant mutations on SciApps.org.

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Comparative evolutionary genetics of deleterious load in sorghum and maize

Cited by 72 publications

References 68 publications

Deleterious Mutations Accumulate Faster in Allopolyploid than Diploid Cotton (Gossypium) and Unequally Between Subgenomes

Deleterious Mutations Accumulate Faster in Allopolyploid than Diploid Cotton (Gossypium) and Unequally Between Subgenomes

Deep learning as a tool for ecology and evolution

Comparative Analysis of Sorghum EMS Mutants and Natural Populations

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