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Exploring whole-genome duplicate gene retention with complex genetic interaction analysis.

ABSTRACT: Whole-genome duplication has played a central role in the genome evolution of many organisms, including the human genome. Most duplicated genes are eliminated, and factors that influence the retention of persisting duplicates remain poorly understood. We describe a systematic complex genetic interaction analysis with yeast paralogs derived from the whole-genome duplication event. Mapping of digenic interactions for a deletion mutant of each paralog, and of trigenic interactions for the double mutant, provides insight into their roles and a quantitative measure of their functional redundancy. Trigenic interaction analysis distinguishes two classes of paralogs: a more functionally divergent subset and another that retained more functional overlap. Gene feature analysis and modeling suggest that evolutionary trajectories of duplicated genes are dictated by combined functional and structural entanglement factors.

SUBMITTER: Kuzmin E

PROVIDER: S-EPMC7539174 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Exploring whole-genome duplicate gene retention with complex genetic interaction analysis.

Kuzmin Elena E VanderSluis Benjamin B Nguyen Ba Alex N AN Wang Wen W Koch Elizabeth N EN Usaj Matej M Khmelinskii Anton A Usaj Mojca Mattiazzi MM van Leeuwen Jolanda J Kraus Oren O Tresenrider Amy A Pryszlak Michael M Hu Ming-Che MC Varriano Brenda B Costanzo Michael M Knop Michael M Moses Alan A Myers Chad L CL Andrews Brenda J BJ Boone Charles C

Science (New York, N.Y.) 20200601 6498

Whole-genome duplication has played a central role in the genome evolution of many organisms, including the human genome. Most duplicated genes are eliminated, and factors that influence the retention of persisting duplicates remain poorly understood. We describe a systematic complex genetic interaction analysis with yeast paralogs derived from the whole-genome duplication event. Mapping of digenic interactions for a deletion mutant of each paralog, and of trigenic interactions for the double mu ...[more]

PMID: 32586993

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Project description:BACKGROUND:A critical goal in biology is to relate the phenotype to the genotype, that is, to find the genetic determinants of various traits. However, while simple monofactorial determinants are relatively easy to identify, the underpinnings of complex phenotypes are harder to predict. While traditional approaches rely on genome-wide association studies based on Single Nucleotide Polymorphism data, the ability of machine learning algorithms to find these determinants in whole proteome data is still not well known. RESULTS:To better understand the applicability of machine learning in this case, we implemented two such algorithms, adaptive boosting (AB) and repeated random forest (RRF), and developed a chunking layer that facilitates the analysis of whole proteome data. We first assessed the performance of these algorithms and tuned them on an influenza data set, for which the determinants of three complex phenotypes (infectivity, transmissibility, and pathogenicity) are known based on experimental evidence. This allowed us to show that chunking improves runtimes by an order of magnitude. Based on simulations, we showed that chunking also increases sensitivity of the predictions, reaching 100% with as few as 20 sequences in a small proteome as in the influenza case (5k sites), but may require at least 30 sequences to reach 90% on larger alignments (500k sites). While RRF has less specificity than random forest, it was never <50%, and RRF sensitivity was significantly higher at smaller chunk sizes. We then used these algorithms to predict the determinants of three types of drug resistance (to Ciprofloxacin, Ceftazidime, and Gentamicin) in a bacterium, Pseudomonas aeruginosa. While both algorithms performed well in the case of the influenza data, results were more nuanced in the bacterial case, with RRF making more sensible predictions, with smaller errors rates, than AB. CONCLUSIONS:Altogether, we demonstrated that ML algorithms can be used to identify genetic determinants in small proteomes (viruses), even when trained on small numbers of individuals. We further showed that our RRF algorithm may deserve more scrutiny, which should be facilitated by the decreasing costs of both sequencing and phenotyping of large cohorts of individuals.

Project description:Ferns are the second largest clade of vascular plants with over 10,000 species, yet the generation of genomic resources for the group has lagged behind other major clades of plants. Transcriptomic data have proven to be a powerful tool to assess phylogenetic relationships, using thousands of markers that are largely conserved across the genome, and without the need to sequence entire genomes. We assembled the largest nuclear phylogenetic dataset for ferns to date, including 2884 single-copy nuclear loci from 247 transcriptomes (242 ferns, five outgroups), and investigated phylogenetic relationships across the fern tree, the placement of whole genome duplications (WGDs), and gene retention patterns following WGDs. We generated a well-supported phylogeny of ferns and identified several regions of the fern phylogeny that demonstrate high levels of gene tree–species tree conflict, which largely correspond to areas of the phylogeny that have been difficult to resolve. Using a combination of approaches, we identified 27 WGDs across the phylogeny, including 18 large-scale events (involving more than one sampled taxon) and nine small-scale events (involving only one sampled taxon). Most inferred WGDs occur within single lineages (e.g., orders, families) rather than on the backbone of the phylogeny, although two inferred events are shared by leptosporangiate ferns (excluding Osmundales) and Polypodiales (excluding Lindsaeineae and Saccolomatineae), clades which correspond to the majority of fern diversity. We further examined how retained duplicates following WGDs compared across independent events and found that functions of retained genes were largely convergent, with processes involved in binding, responses to stimuli, and certain organelles over-represented in paralogs while processes involved in transport, organelles derived from endosymbiotic events, and signaling were under-represented. To date, our study is the most comprehensive investigation of the nuclear fern phylogeny, though several avenues for future research remain unexplored.

Dataset Information

Exploring whole-genome duplicate gene retention with complex genetic interaction analysis.

Publications

Exploring whole-genome duplicate gene retention with complex genetic interaction analysis.

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