A graph based method for depicting population characteristics using Genome Wide Data

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Genome Wide Association (GWA) studies associate genetic variants to clinical phenotypes using statistical tests which are based on assumption of random mating. Populations having history of consanguineous marriages, especially from Middle East, can violate this assumption. Here we present, use of averaged weighted clustering coefficient of undirected graphs to quantify cryptic relatedness between individuals from a random cohort. This measure can be used to understand pattern of relatedness in populations to choose between removing related samples and using mixed linear models correcting for relatedness while performing GWA studies.

Original languageEnglish
Pages (from-to)11-17
Number of pages7
JournalJournal of Computational Science
Volume15
DOIs
Publication statusPublished - 1 Jul 2016

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Genome
Genes
Mixed Linear Model
Clustering Coefficient
Statistical tests
Violate
Graph in graph theory
Statistical test
Undirected Graph
Phenotype
Quantify
Choose
History

Keywords

  • Arab population
  • Averaged weighted clustering coefficient
  • Cryptic relatedness
  • Undirected graphs

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)
  • Modelling and Simulation

Cite this

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abstract = "Genome Wide Association (GWA) studies associate genetic variants to clinical phenotypes using statistical tests which are based on assumption of random mating. Populations having history of consanguineous marriages, especially from Middle East, can violate this assumption. Here we present, use of averaged weighted clustering coefficient of undirected graphs to quantify cryptic relatedness between individuals from a random cohort. This measure can be used to understand pattern of relatedness in populations to choose between removing related samples and using mixed linear models correcting for relatedness while performing GWA studies.",
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author = "Gaurav Thareja and Ziad Kronfol and Karsten Suhre and Pankaj Kumar",
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AU - Thareja, Gaurav

AU - Kronfol, Ziad

AU - Suhre, Karsten

AU - Kumar, Pankaj

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AB - Genome Wide Association (GWA) studies associate genetic variants to clinical phenotypes using statistical tests which are based on assumption of random mating. Populations having history of consanguineous marriages, especially from Middle East, can violate this assumption. Here we present, use of averaged weighted clustering coefficient of undirected graphs to quantify cryptic relatedness between individuals from a random cohort. This measure can be used to understand pattern of relatedness in populations to choose between removing related samples and using mixed linear models correcting for relatedness while performing GWA studies.

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KW - Averaged weighted clustering coefficient

KW - Cryptic relatedness

KW - Undirected graphs

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