Dataset Information

Somatic deleterious mutation rate in a woody plant: estimation from phenotypic data.

ABSTRACT: We conducted controlled crosses in populations of the long-lived clonal shrub, Vaccinium angustifolium (lowbush blueberry) to estimate inbreeding depression and mutation parameters associated with somatic deleterious mutation. Inbreeding depression level was high, with many plants failing to set fruit after self-pollination. We also compared fruit set from autogamous pollinations (pollen collected from within the same inflorescence) with fruit set from geitonogamous pollinations (pollen collected from the same plant but from inflorescences separated by several meters of branch growth). The difference between geitonogamous versus autogamous fitness within single plants is referred to as 'autogamy depression' (AD). AD can be caused by somatic deleterious mutation. AD was significantly different from zero for fruit set. We developed a maximum-likelihood procedure to estimate somatic mutation parameters from AD, and applied it to geitonogamous and autogamous fruit set data from this experiment. We infer that, on average, approximately three sublethal, partially dominant somatic mutations exist within the crowns of the plants studied. We conclude that somatic mutation in this woody plant results in an overall genomic deleterious mutation rate that exceeds the rate measured to date for annual plants. Some implications of this result for evolutionary biology and agriculture are discussed.

SUBMITTER: Bobiwash K

PROVIDER: S-EPMC3807267 | biostudies-literature | 2013 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Somatic deleterious mutation rate in a woody plant: estimation from phenotypic data.

Bobiwash K K Schultz S T ST Schoen D J DJ

Heredity 20130619 4

We conducted controlled crosses in populations of the long-lived clonal shrub, Vaccinium angustifolium (lowbush blueberry) to estimate inbreeding depression and mutation parameters associated with somatic deleterious mutation. Inbreeding depression level was high, with many plants failing to set fruit after self-pollination. We also compared fruit set from autogamous pollinations (pollen collected from within the same inflorescence) with fruit set from geitonogamous pollinations (pollen collecte ...[more]

PMID: 23778990

Similar Datasets

Project description:Detailed modelling of the neutral mutational process in cancer cells is crucial for identifying driver mutations and understanding the mutational mechanisms that act during cancer development. The neutral mutational process is very complex: whole-genome analyses have revealed that the mutation rate differs between cancer types, between patients and along the genome depending on the genetic and epigenetic context. Therefore, methods that predict the number of different types of mutations in regions or specific genomic elements must consider local genomic explanatory variables. A major drawback of most methods is the need to average the explanatory variables across the entire region or genomic element. This procedure is particularly problematic if the explanatory variable varies dramatically in the element under consideration.To take into account the fine scale of the explanatory variables, we model the probabilities of different types of mutations for each position in the genome by multinomial logistic regression. We analyse 505 cancer genomes from 14 different cancer types and compare the performance in predicting mutation rate for both regional based models and site-specific models. We show that for 1000 randomly selected genomic positions, the site-specific model predicts the mutation rate much better than regional based models. We use a forward selection procedure to identify the most important explanatory variables. The procedure identifies site-specific conservation (phyloP), replication timing, and expression level as the best predictors for the mutation rate. Finally, our model confirms and quantifies certain well-known mutational signatures.We find that our site-specific multinomial regression model outperforms the regional based models. The possibility of including genomic variables on different scales and patient specific variables makes it a versatile framework for studying different mutational mechanisms. Our model can serve as the neutral null model for the mutational process; regions that deviate from the null model are candidates for elements that drive cancer development.

Dataset Information

Somatic deleterious mutation rate in a woody plant: estimation from phenotypic data.

Publications

Somatic deleterious mutation rate in a woody plant: estimation from phenotypic data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets