Genetic Expectations in Inheritance: A Probabilistic Algebraic Framework

Abstract

This study introduces a formal framework for modeling inheritance patterns based on an algebraic representation of genotype distributions. The approach defines genotypes as probability measures rather than discrete states, enabling the computation of genetic expectations, a probabilistic analogue that captures the expected distribution of offspring genotypes under Mendelian inheritance rules. This formulation permits constant-time simulation of multi-locus inheritance processes and provides a means of incorporating uncertainty and partial information in genotype data. A series of eleven illustrative examples is presented, encompassing both Mendelian and selected non-Mendelian mechanisms, including polygenic inheritance, uniparental disomy, and haplodiploidy. While the current framework does not model recombination, linkage, or phenotypic traits, it is designed to accommodate extensions to some complex chromosomal structures, such as those encountered in polysomic inheritance. The model reproduces classical inheritance outcomes and is intended as a computational and theoretical tool for genetic analysis in both research and pedagogical settings. Future development will focus on increasing biological scope and integrating empirical data sources. HighlightsO_LIGenotypes modeled as probability distributions over allelic states. C_LIO_LIGenetic expectations summarize an individuals contribution to off-spring. C_LIO_LIExpectations are mating-independent, enabling modular inheritance logic. C_LIO_LIFramework supports constant-time, multi-locus inheritance simulation. C_LI