diplo-locus: A lightweight toolkit for inference and simulation of time-series genetic data under general diploid selection

Cheng, Xiaoheng; Steinrücken, Matthias

doi:10.1101/2023.10.12.562101

Cited by 2 publications

(1 citation statement)

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“…We furthermore develop a novel framework for identifying the best-fitting mode of selection for a given temporal dataset. While some methods to estimate general diploid selection have been presented in the literature (Steinrücken et al 2014; Foll et al 2015; Schraiber et al 2016; Iranmehr et al 2017; Taus et al 2017; Cheng and Steinrücken 2023), they do not explicitly address the statistical problem of using the data to distinguish between different modes of selection. Furthermore, none of these methods have been applied to genome-wide data from human populations.…”

Section: Introductionmentioning

confidence: 99%

A novel expectation-maximization approach to infer general diploid selection from time-series genetic data

Fine,

Steinrücken

2024

Preprint

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Detecting and quantifying the strength of selection is a main objective in population genetics. Since selection acts over multiple generations, many approaches have been developed to detect and quantify selection using genetic data sampled at multiple points in time. Such time series genetic data is commonly analyzed using Hidden Markov Models, but in most cases, under the assumption of additive selection. However, many examples of genetic variation exhibiting non-additive mechanisms exist, making it critical to develop methods that can characterize selection in more general scenarios. Thus, we extend a previously introduced expectation-maximization algorithm for the inference of additive selection coefficients to the case of general diploid selection, in which heterozygote and homozygote fitnesses are parameterized independently. We furthermore introduce a framework to identify bespoke modes of diploid selection from given data, as well as a procedure for aggregating data across linked loci to increase power and robustness. Using extensive simulation studies, we find that our method accurately and efficiently estimates selection coefficients for different modes of diploid selection across a wide range of scenarios; however, power to classify the mode of selection is low unless selection is very strong. We apply our method to ancient DNA samples from Great Britain in the last 4,450 years, and detect evidence for selection in six genomic regions, including the well-characterized LCT locus. Our work is the first genome-wide scan characterizing signals of general diploid selection.

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Section: Introductionmentioning

confidence: 99%

A novel expectation-maximization approach to infer general diploid selection from time-series genetic data

Fine,

Steinrücken

2024

Preprint

Self Cite

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Identification and validation of an m7G-related lncRNAs signature for predicting prognosis, immune response and therapy landscapes in ovarian cancer

Li,

Wang,

Zhang

et al. 2024

Front. Genet.

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BackgroundOvarian cancer is the most mortality malignancy in gynecology. N7-methylguanosine (m7G) is one of the most prevalent RNA modifications in the development and progression of cancer. The aim of this study is to investigate the effect of m7G-related lncRNA on ovarian cancer in terms of instruction prognosis and immunotherapy.MethodsAfter integrating and processing the RNA expression profiles with the clinical sample information in the TCGA database, we initially screened to the m7G-related lncRNAs by Spearman correlation analysis, and subsequently obtained a prognostic model constructed by five m7G-related lncRNAs with Univariate Cox analysis, LASSO regression analysis, and Multivariate Cox regression analysis, after which we further evaluated and validated the prognostic value of the model using Kaplan-Meier survival analysis, Principal component analysis, Nomogram, and ROC curve. In addition, based on this risk model, we explored the differentially enriched pathways and functions of the high and low risk groups, and characterized the immune cells, immune functions, gene mutations, and drug sensitivity between the two groups.ResultsAfter a series of rigorous filtering, we finally attained a prognostic risk model consisting of KRT7-AS, USP30-AS1, ZFHX4-AS1, ACAP2-IT1, and TWSG1-DT which is excellent in predicting the prognostic survival of ovarian cancer patients as well as existing as an independent prognostic factor. Moreover, the model has certain relevance in the immune cells and functions between high and low risk groups, and simultaneously, the signature has the role of guiding the option of immunotherapy and chemotherapeutic drugs.ConclusionAltogether, our study established a tight connection between m7G-associated lncRNAs and ovarian cancer, with potential that the prognostic patterns contribute to steering the prognosis of ovarian cancer patients, measuring the efficacy of immunotherapeutic approaches, and detecting effective chemotherapeutic agents.

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diplo-locus: A lightweight toolkit for inference and simulation of time-series genetic data under general diploid selection

Cited by 2 publications

References 15 publications

A novel expectation-maximization approach to infer general diploid selection from time-series genetic data

A novel expectation-maximization approach to infer general diploid selection from time-series genetic data

Identification and validation of an m7G-related lncRNAs signature for predicting prognosis, immune response and therapy landscapes in ovarian cancer

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