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A sibling method for identifying vQTLs.

The propensity of a trait to vary within a population may have evolutionary, ecological, or clinical significance. In the present study we deploy sibling models to offer a novel and unbiased way to ascertain loci associated with the extent to which phenotypes vary (variance-controlling quantitative... Full description

Journal Title: PloS one 2018, Vol.13(4), p.e0194541
Main Author: Conley, Dalton
Other Authors: Johnson, Rebecca , Domingue, Ben , Dawes, Christopher , Boardman, Jason , Siegal, Mark L
Format: Electronic Article Electronic Article
Language: English
Subjects:
ID: E-ISSN: 1932-6203 ; DOI: 10.1371/journal.pone.0194541
Link: http://search.proquest.com/docview/2022129360/?pq-origsite=primo
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title: A sibling method for identifying vQTLs.
format: Article
creator:
  • Conley, Dalton
  • Johnson, Rebecca
  • Domingue, Ben
  • Dawes, Christopher
  • Boardman, Jason
  • Siegal, Mark L
subjects:
  • Body Height–Genetics
  • Body Mass Index–Methods
  • Chromosome Mapping–Methods
  • Computer Simulation–Methods
  • Genotype–Methods
  • Humans–Methods
  • Models, Genetic–Methods
  • Phenotype–Methods
  • Polymorphism, Single Nucleotide–Methods
  • Quantitative Trait Loci–Methods
  • Siblings–Methods
ispartof: PloS one, 2018, Vol.13(4), p.e0194541
description: The propensity of a trait to vary within a population may have evolutionary, ecological, or clinical significance. In the present study we deploy sibling models to offer a novel and unbiased way to ascertain loci associated with the extent to which phenotypes vary (variance-controlling quantitative trait loci, or vQTLs). Previous methods for vQTL-mapping either exclude genetically related individuals or treat genetic relatedness among individuals as a complicating factor addressed by adjusting estimates for non-independence in phenotypes. The present method uses genetic relatedness as a tool to obtain unbiased estimates of variance effects rather than as a nuisance. The family-based approach, which utilizes random variation between siblings in minor allele counts at a locus, also allows controls for parental genotype, mean effects, and non-linear (dominance) effects that may spuriously appear to generate variation. Simulations show that the approach performs equally well as two existing methods (squared Z-score and DGLM) in controlling type I error rates when there is no unobserved confounding, and performs significantly better than these methods in the presence of small degrees of confounding. Using height and BMI as empirical applications, we investigate SNPs that alter within-family variation in height and BMI, as well as pathways that appear to be enriched. One significant SNP for BMI variability, in the MAST4 gene, replicated. Pathway analysis revealed one gene set, encoding members of several signaling pathways related to gap junction function, which appears significantly enriched for associations with within-family height variation in both datasets (while not enriched in analysis of mean levels). We recommend approximating laboratory random assignment of genotype using family data and more careful attention to the possible conflation of mean and variance effects.
language: eng
source:
identifier: E-ISSN: 1932-6203 ; DOI: 10.1371/journal.pone.0194541
fulltext: fulltext
issn:
  • 19326203
  • 1932-6203
url: Link


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titleA sibling method for identifying vQTLs.
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subjectBody Height–Genetics ; Body Mass Index–Methods ; Chromosome Mapping–Methods ; Computer Simulation–Methods ; Genotype–Methods ; Humans–Methods ; Models, Genetic–Methods ; Phenotype–Methods ; Polymorphism, Single Nucleotide–Methods ; Quantitative Trait Loci–Methods ; Siblings–Methods
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descriptionThe propensity of a trait to vary within a population may have evolutionary, ecological, or clinical significance. In the present study we deploy sibling models to offer a novel and unbiased way to ascertain loci associated with the extent to which phenotypes vary (variance-controlling quantitative trait loci, or vQTLs). Previous methods for vQTL-mapping either exclude genetically related individuals or treat genetic relatedness among individuals as a complicating factor addressed by adjusting estimates for non-independence in phenotypes. The present method uses genetic relatedness as a tool to obtain unbiased estimates of variance effects rather than as a nuisance. The family-based approach, which utilizes random variation between siblings in minor allele counts at a locus, also allows controls for parental genotype, mean effects, and non-linear (dominance) effects that may spuriously appear to generate variation. Simulations show that the approach performs equally well as two existing methods (squared Z-score and DGLM) in controlling type I error rates when there is no unobserved confounding, and performs significantly better than these methods in the presence of small degrees of confounding. Using height and BMI as empirical applications, we investigate SNPs that alter within-family variation in height and BMI, as well as pathways that appear to be enriched. One significant SNP for BMI variability, in the MAST4 gene, replicated. Pathway analysis revealed one gene set, encoding members of several signaling pathways related to gap junction function, which appears significantly enriched for associations with within-family height variation in both datasets (while not enriched in analysis of mean levels). We recommend approximating laboratory random assignment of genotype using family data and more careful attention to the possible conflation of mean and variance effects.
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