Computational disease-risk prediction: Tools and statistical approaches

Emile R. Chimusa*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

It has become clear that a mixture between diverged populations (admixture) has been a recurrent feature in human evolution. Whole or genome-wide association studies (GWAS) have become a fundamental method for dissecting genetic variations and architecture of human conditions based on common polymorphisms. It is hoped that there will be several opportunities to use identified associated variants to comprehend the pathogenesis of human conditions, discover novel biomarkers, and protein targets, as well as predict clinical drugs and treatments for worldwide global health. Technological, statistical, and computational advances keep fostering the development of genomics tools ranging from GWAS to post-GWAS including polygenic risk scores and functional GWAS. We summarize the concepts of several computational genetic ancestries, polygenic risk scores, and GWAS approaches. In addition, we outline the implications, challenges, and opportunities, these approaches present and summarize with brief discussions of future research directions.

Original languageEnglish
Title of host publicationPopulation Genomics in the Developing World
Subtitle of host publicationConcepts, Applications, and Challenges
EditorsMarlo Möller, Caitlin Uren
Place of PublicationLondon, United Kingdom
PublisherAcademic Press
Pages91-106
Number of pages16
Edition1st
ISBN (Electronic)9780443185465
ISBN (Print)9780443185472
DOIs
Publication statusPublished - 15 Nov 2024

Publication series

NameTranslational and Applied Genomics
PublisherAcademic Press

Keywords

  • Genome-wide association studies
  • Genomics
  • Human genetics
  • Missing heritability
  • Polygenic risk scores
  • Risk prediction
  • Statistical computing

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