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Crossing Over

Crossing Over

Crossing over is the exchange of genetic information between homologous chromosomes during prophase I of meiosis I. Genetic recombination gives rise to allelic diversity in the newly formed daughter cells. In humans, crossing over produces genetically distinct haploid egg and sperm cells that undergo fertilization to produce unique offspring. Before cell division starts, the germ cell’s chromosome(s) undergo duplication in the S phase of the cell cycle. As the cells enter prophase I,...

Crossing Over

Crossing Over

Unlike mitosis, meiosis aims for genetic diversity in its creation of haploid gametes. Dividing germ cells first begin this process in prophase I, where each chromosome—replicated in S phase—is now composed of two sister chromatids (identical copies) joined centrally.
The homologous pairs of sister chromosomes—one from the maternal and one from the paternal genome—then begin to align alongside each other lengthwise, matching corresponding DNA positions in a process...

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Trihybrid Crosses
Some of Mendel’s crosses examined three pairs of contrasting characteristics. Such a cross is called a trihybrid cross. A trihybrid cross is a combination of three individual monohybrid crosses. For example, plant height (tall vs. short), seed shape (round vs. wrinkled), and seed color (yellow vs. green).
The F1 generation plants of a trihybrid cross are heterozygous for all three traits and produce eight gametes. Upon self-fertilization, these gametes have an equal...

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Nonparametric causal inference for bivariate time series.

Physical review. E·2016

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Frequency and Distribution of Crossovers in Caenorhabditis elegans Meiosis by SNP Genotyping using Real-time PCR

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Convergent cross-mapping and pairwise asymmetric inference.

James M McCracken¹, Robert S Weigel¹

¹School of Physics, Astronomy, and Computational Sciences, George Mason University, 4400 University Drive, MS 3F3, Fairfax, Virginia 22030-4444, USA.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics

|January 24, 2015

Summary

Convergent cross-mapping (CCM) may not reliably indicate causality. A new method, pairwise asymmetric inference (PAI), offers a more intuitive approach to understanding time series relationships and identifying causal drivers.

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Area of Science:

Complex systems analysis
Nonlinear dynamics
Causality inference

Background:

Convergent cross-mapping (CCM) is a method to infer causality from time series data.
CCM claims to identify causal relationships by detecting correlations between dynamical systems.
The reliability of CCM in determining causality has been questioned in certain contexts.

Purpose of the Study:

To critically evaluate the causality claims of the Convergent Cross-Mapping (CCM) technique.
To investigate the limitations of CCM in identifying causal drivers in simple systems.
To introduce a modified algorithm, Pairwise Asymmetric Inference (PAI), for more intuitive causality assessment.

Main Methods:

Analysis of time series correlations using the CCM algorithm.
Simulation of simple linear and nonlinear systems (e.g., RLC circuits).
Development and application of the Pairwise Asymmetric Inference (PAI) method.

Main Results:

CCM's causality attributions were found to be dependent on system parameters and not always intuitive.
In an RLC circuit example, CCM identified both voltage and current as drivers depending on frequency.
The proposed PAI method provided more intuitive driver identifications consistent with system behavior.

Conclusions:

The standard CCM algorithm's causality claims require careful consideration and may not align with intuitive notions of driving.
CCM's performance is sensitive to system parameters, potentially leading to non-intuitive results.
Pairwise Asymmetric Inference (PAI) offers a promising alternative for robust and intuitive causality inference in time series analysis.