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Using Quantitative Genetics to Predict Behavior ofMarkov Brains
Dalarna University, School of Information and Engineering.
2022 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Genome-wide association studies (GWAS) are an instrumental tool in genetics to determinewhich genes control what phenotypic traits. Beyond understanding what those genes do, thismethod is used to identify genes critically involved in diseases, genetic predispositions likeAlzheimer's, obesity, and cancer.GWAS is capable of identifying simple scenarios, such as galactosemia, where a single geneis responsible for this metabolic disorder. However, for more complex traits, which arebelieved to be controlled by many genes, such as obesity, GWAS faces a challenge inidentifying the responsible genes.Testing the accuracy of GWAS also raises questions. It is impossible to test the accuracy usingbiological data because the genes the GWAS is trying to identify are not known in the firstplace, meaning that no positive control exists to verify the results. Consequently, here, acomputational model evolving artificial genomes and their affiliated traits is used to create suchpositive control data set. When conducting a GWAS on this control data set, only a few genescould be found, while other genes remained undetected, given the size of the experiment. Someof the phenotypes were highly heritable as compared to others. However, predicting thephenotypic traits based on the genes variation was also attempted, but no significant resultcould be produced.This work is a pilot study to establish if this approach can be used to test the efficiency ofGWAS using artificially created data, and creates the foundation to perform future studiesusing more complex and larger dataset

Place, publisher, year, edition, pages
2022.
Keywords [en]
Genes, Genomes, Quantitative Genetics, Genotypes, Phenotypes, Markov Brains, Alleles, Heritability, Prediction, Behavior, Evolutionary Neural Network, Agent Based Evolution, Genome-Wide Association Study, Single Nucleotide Polymorphism, Statistical Analysis, Genes Knockout, Machine Learning.
National Category
Social Sciences Interdisciplinary
Identifiers
URN: urn:nbn:se:du-39629OAI: oai:DiVA.org:du-39629DiVA, id: diva2:1639492
Subject / course
Microdata Analysis
Available from: 2022-02-21 Created: 2022-02-21

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf