HTreeQA

Using semi-perfect phylogeny trees in quantitative trait loci study on genotype data

Zhaojun Zhang, Xiang Zhang, Wei Wang

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

With the advances in high-throughput genotyping technology, the study of quantitative trait loci (QTL) has emerged as a promising tool to understand the genetic basis of complex traits. Methodology development for the study of QTL recently has attracted significant research attention. Local phylogenybased methods have been demonstrated to be powerful tools for uncovering significant associations between phenotypes and single-nucleotide polymorphism markers. However, most existing methods are designed for homozygous genotypes, and a separate haplotype reconstruction step is often needed to resolve heterozygous genotypes. This approach has limited power to detect nonadditive genetic effects and imposes an extensive computational burden. In this article, we propose a new method, HTreeQA, that uses a tristate semi-perfect phylogeny tree to approximate the perfect phylogeny used in existing methods. The semi-perfect phylogeny trees are used as high-level markers for association study. HTreeQA uses the genotype data as direct input without phasing. HTreeQA can handle complex local population structures. It is suitable for QTL mapping on any mouse populations, including the incipient Collaborative Cross lines. Applied HTreeQA, significant QTLs are found for two phenotypes of the PreCC lines, white head spot and running distance at day 5/6. These findings are consistent with known genes and QTL discovered in independent studies. Simulation studies under three different genetic models show that HTreeQA can detect a wider range of genetic effects and is more efficient than existing phylogeny-based approaches. We also provide rigorous theoretical analysis to show that HTreeQA has a lower error rate than alternative methods.

Original languageEnglish (US)
Pages (from-to)175-189
Number of pages15
JournalG3: Genes, Genomes, Genetics
Volume2
Issue number2
DOIs
StatePublished - Feb 1 2012

Fingerprint

Quantitative Trait Loci
Phylogeny
Genotype
Phenotype
Genetic Models
Dental Caries
Running
Haplotypes
Population
Single Nucleotide Polymorphism
Head
Technology
Research

All Science Journal Classification (ASJC) codes

  • Molecular Biology
  • Genetics
  • Genetics(clinical)

Cite this

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abstract = "With the advances in high-throughput genotyping technology, the study of quantitative trait loci (QTL) has emerged as a promising tool to understand the genetic basis of complex traits. Methodology development for the study of QTL recently has attracted significant research attention. Local phylogenybased methods have been demonstrated to be powerful tools for uncovering significant associations between phenotypes and single-nucleotide polymorphism markers. However, most existing methods are designed for homozygous genotypes, and a separate haplotype reconstruction step is often needed to resolve heterozygous genotypes. This approach has limited power to detect nonadditive genetic effects and imposes an extensive computational burden. In this article, we propose a new method, HTreeQA, that uses a tristate semi-perfect phylogeny tree to approximate the perfect phylogeny used in existing methods. The semi-perfect phylogeny trees are used as high-level markers for association study. HTreeQA uses the genotype data as direct input without phasing. HTreeQA can handle complex local population structures. It is suitable for QTL mapping on any mouse populations, including the incipient Collaborative Cross lines. Applied HTreeQA, significant QTLs are found for two phenotypes of the PreCC lines, white head spot and running distance at day 5/6. These findings are consistent with known genes and QTL discovered in independent studies. Simulation studies under three different genetic models show that HTreeQA can detect a wider range of genetic effects and is more efficient than existing phylogeny-based approaches. We also provide rigorous theoretical analysis to show that HTreeQA has a lower error rate than alternative methods.",
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HTreeQA : Using semi-perfect phylogeny trees in quantitative trait loci study on genotype data. / Zhang, Zhaojun; Zhang, Xiang; Wang, Wei.

In: G3: Genes, Genomes, Genetics, Vol. 2, No. 2, 01.02.2012, p. 175-189.

Research output: Contribution to journalArticle

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