HTreeQA: Using Semi-Perfect Phylogeny Trees in Quantitative Trait Loci Study on Genotype Data

G3: Genes, Genomes, Genetics

Xiang Zhang¹  Zhaojun Zhang²  Wei Wang² 

[1] Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, Ohio 44106Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, Ohio 44106Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, Ohio 44106;Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599

关键词: phylogeny;    quantitative trait loci (QTL);    Mouse Collaborative Cross;    Mouse Genetic Resource;   

DOI  :  10.1534/g3.111.001768

学科分类：生物科学（综合）

来源: Genetics Society of America

【 摘 要 】

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 phylogeny-based 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.

【 授权许可】

Unknown