Biology subjects

Fedotova, A. V.

Publications and source records attributed to Fedotova, A. V..

3 recordsLinked to original sources

bioRxiv2021

Complex fitness landscape shapes variation in a hyperpolymorphic species

It is natural to assume that patterns of genetic variation in hyperpolymorphic species can reveal large-scale properties of the fitness landscape that are hard to detect by studying species with ordinary levels of genetic variation1,2. Here, we study such patterns in a fungus Schizophyllum commune, the most polymorphic species known3. Throughout the genome, short-range linkage disequilibrium caused by attraction of rare alleles is higher between pairs of nonsynonymous than of synonymous sites. This effect is especially pronounced for pairs of sites that are located within the same gene, especially if a large fraction of the gene is covered by haploblocks, genome segments where the gene pool consists of two highly divergent haplotypes, which is a signature of balancing selection. Haploblocks are usually shorter than 1000 nucleotides, and collectively cover about 10% of the S. commune genome. LD tends to be substantially higher for pairs of nonsynonymous sites encoding amino acids that interact within the protein. There is a substantial correlation between LDs at the same pairs of nonsynonymous sites in the USA and the Russian populations. These patterns indicate that selection in S. commune involves positive epistasis due to compensatory interactions between nonsynonymous alleles. When less polymorphic species are studied, analogous patterns can be detected only through interspecific comparisons.

evolutionary biology↗

bioRxiv2019

Rapid accumulation of mutations in growing mycelia of a hypervariable fungus Schizophyllum commune

The number of mutations that occur per nucleotide per generation varies between species by several orders of magnitude. In multicellular eukaryotes, the per generation mutation rate depends both on the per cell division mutation rate and on the number of germline cell divisions per generation. In a range of species, from fungi to humans, the number of germline cell divisions is lower than that of somatic cells, reducing the mutation burden on the offspring. The basidiomycete Schizophyllum commune has the highest level of genetic polymorphism known among eukaryotes. In a previous study, it was also found to have a high per generation mutation rate, probably contributing to its high polymorphism. However, this rate has been measured only in a breeding experiment on Petri dishes, and it is unclear how this result translates to natural populations. Here, we used an experimental design that measures the rate of accumulation of de novo mutations in a linearly growing mycelium. We show that S. commune accumulates mutations at a uniform rate of 1.4{middle dot}10-7 substitutions per nucleotide per meter of growth, which is 3 orders of magnitude higher than the corresponding rates in the oak Quercus robur and the fungus Armillaria gallica. This figure is consistent with the estimate obtained before, and suggests the lack of a dedicated germline in this system. If so, even a low per cell division mutation rate can translate into a very high per generation mutation rate when the number of cell divisions between consecutive meioses is large.

evolutionary biology↗

bioRxiv2019

Accurate Fetal Variant Calling in the Presence of Maternal Cell Contamination

High-throughput sequencing of fetal DNA is a promising and increasingly common method for the discovery of all (or all coding) genetic variants in the fetus, either as part of prenatal screening or diagnosis, or for genetic diagnosis of spontaneous abortions. In many cases, the fetal DNA (from chorionic villi, amniotic fluid, or abortive tissue) can be contaminated with maternal cells, resulting in the mixture of fetal and maternal DNA. This maternal cell contamination (MCC) undermines the assumption, made by traditional variant callers, that each allele in a heterozygous site is covered, on average, by 50% of the reads, and therefore can lead to erroneous genotype calls. We present a panel of methods for reducing the genotyping error in the presence of MCC. All methods start with the output of GATK HaplotypeCaller on the sequencing data for the (contaminated) fetal sample and both of its parents, and additionally rely on information about the MCC fraction (which itself is readily estimated from the high-throughput sequencing data). The first of these methods uses a Bayesian probabilistic model to correct the fetal genotype calls produced by MCC-unaware HaplotypeCaller. The other two methods “learn” the genotype-correction model from examples. We use simulated contaminated fetal data to train and test the models. Using the test sets, we show that all three methods lead to substantially improved accuracy when compared with the original MCC-unaware HaplotypeCaller calls. We then apply the best-performing method to three chorionic villus samples from spontaneously terminated pregnancies.Code and training data availability https://github.com/bazykinlab/ML-maternal-cell-contaminationCompeting Interest StatementThe authors have declared no competing interest.View Full Text

bioinformatics↗