Abstract
Quantitative trait locus (QTL) mapping using deep DNA sequencing data is a challenging task. In this study we performed region-based and pathway-based QTL mappings using a p-value combination method to analyze the simulated quantitative traits Q1 and Q4 and the exome sequencing data. The aims were to evaluate the performance of the QTL mapping approaches that were used and to suggest plausible strategies for QTL mapping of DNA sequencing data. We conducted single-locus QTL mappings using a linear regression model with adjustments for age and smoking status, and we also conducted region-based and pathway-based QTL mappings using a truncated product method for combining p-values from the single-locus QTL mapping. To account for the features of rare variants and common single-nucleotide polymorphisms (SNPs), we considered independently rare-variant-only, common-SNP-only, and combined analyses. An analysis of 200 simulated replications showed that the three region-based methods reasonably controlled type I error, whereas the combined analysis yielded the greatest statistical power. Rare-variant-only, common-SNP-only, and combined analyses were also applied to pathway-based QTL mappings. We found that pathway-based QTL mappings had a power of approximately 100% when the significance of the vascular endothelial growth factor pathway was evaluated, but type I errors were slightly inflated. Our approach complements single-locus QTL mapping. An integrated approach using single-locus, combined region-based, and combined pathway-based analyses should yield promising results for QTL mapping of DNA sequencing data.
Highlights
Deep DNA sequencing technology provides a vast number of common single-nucleotide polymorphisms (SNPs) and a vast number of rare variants for genetic and genomic research
Mapping a quantitative trait locus (QTL) using this kind of genomic data remains a challenging task. p-Value combination methods have been used in genetic association studies in which common SNPs were analyzed, but rare variants were excluded from those studies [1,2,3,4,5,6]
In order to evaluate type I error associated with the QTL mapping approaches, we examined the association between the nine Q1-associated genes and the simulated quantitative trait Q4
Summary
Deep DNA sequencing technology provides a vast number of common single-nucleotide polymorphisms (SNPs) and a vast number of rare variants for genetic and genomic research. P-Value combination methods have been used in genetic association studies in which common SNPs were analyzed, but rare variants were excluded from those studies [1,2,3,4,5,6]. In this study we conduct region-based and pathwaybased QTL mappings using a p-value combination method to identify genetic loci, regions, and pathways responsible for a quantitative trait using exome DNA sequencing data provided by Genetic Analysis Workshop 17 (GAW17). We propose and evaluate several analytical strategies for QTL mapping of common SNPs and rare variants in deep DNA sequencing data. The aims of this study are to evaluate the type I error and statistical power associated with the proposed analytical strategies for region-based and pathway-based QTL mappings and to suggest plausible analytical strategies for QTL mapping of deep DNA sequencing data
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
