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• W1598501967 abstract "The field of statistical genetics has been the area of a great deal of active research in recent years; due in part to dramatic advances in sequencing technology which has led to vast amounts of genomic data becoming available at lower and lower costs. The data from these sequencing efforts is not only copious, but is also characterized by significant levels of experimental noise. Processing data of this nature to draw statistically significant inferences requires dealing with a number of challenges, both statistical and algorithmic. In this chapter we will not discuss the very substantial statistical issues which arise in extracting genome sequences, but rather will focus on computational and algorithmic issues which arise in drawing biological inferences once the sequence is known. Event though our discussion will be oriented more towards applications of graph theory, it is worth keeping in mind that statistical considerations will still play a role due the intrinsically probabilistic nature of Mendelian Genetics as well as finite sample sizes. We will first begin by reviewing earlier well known work which will serve to illustrate the utility of graph theoretical concepts in dealing with genomic data. The data at our disposal are assumed to consist of observations of at a large number of locations (tens of thousands or possibly much more) on multiple chromosomes for a collections of individuals, or plants or animals; for now we assume that these individuals are related with known parent offpsring information. We restrict our attention to species which have just two chromosomes, but much of what we will discuss can be generalized to species with more than two chromosomes although the computational implementation could be challenging. At any locus (precise location on a specified chromosome) we assume that there are two or more possible alleles in the population, the precise number is assumed to differ from locus to locus. The number of possible observable genotypes at each locus will thus also vary from locus to locus. In a population of related individuals with known parent offspring relations between individuals (i.e. pedigree) it is possible to predict the probability for an offspring to receive a given allele from a parent based on Mendelian Genetics. If we represent a given locus for a given individual by a vertex, we can assign multiple possible states to each vertex depending on how many genotypes are possible. Since genetic information flows from parents to offspring" @default.
• W1598501967 created "2016-06-24" @default.
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• W1598501967 date "2012-03-02" @default.
• W1598501967 modified "2023-10-17" @default.
• W1598501967 title "Applications of Graphical Clustering Algorithms in Genome Wide Association Mapping" @default.
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• W1598501967 doi "https://doi.org/10.5772/36314" @default.
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