Optimizing pedigrees: Using a biasing system to determine likely inheritance systems
Justin Ang
Abstract
Justin Ang
Abstract
Pedigrees, though straightforward and versatile, lack the ability to tell us information about many individuals. Though numerical systems have been developed, there is currently no system to quantify the probability of a pedigree following certain inheritance systems. My system intends to fulfill that chasm by creating a flexible numerical system and testing it for variance. First, my system attempts to adapt inheritance system to known pedigree data. Then, it calculates the difference between the calculated values and the known pedigree data. It aggregates these values, then it uses a chi-squared analysis in order to determine the likelihood of said inheritance system. This is done for many different systems, until we have a general idea of which systems are probable and which are not.
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Pedigrees, though straightforward and versatile, lack the ability to tell us information about many individuals. Though numerical systems have been developed, there is currently no system to quantify the probability of a pedigree following certain inheritance systems. My system intends to fulfill that chasm by creating a flexible numerical system and testing it for variance. First, my system attempts to adapt inheritance system to known pedigree data. Then, it calculates the difference between the calculated values and the known pedigree data. It aggregates these values, then it uses a chi-squared analysis in order to determine the likelihood of said inheritance system. This is done for many different systems, until we have a general idea of which systems are probable and which are not.
Key concepts: Pedigree chart, Inheritance (genetic algorithm), Computer science, Variance (accounting), Theoretical computer science, Data mining, Statistics, Mathematics