A statistical framework for QTL hotspot detection. Issue 4 (26th February 2021)

• Record Type: Journal Article
• Authors: Wu, Po-Ya; Yang, Man-Hsia; Kao, Chen-Hung
• Editors: Koning, D-J de
• Is Part Of: G3. Volume 11:Issue 4(2021)
• Publication Date: 2021-02-26
• Subjects: QTL hotspots -- pleiotropic traits -- permutation tests -- genetical genomics -- genetic correlation -- LOD thresholds; Genetics -- Research -- Periodicals; Genomics -- Periodicals; Genetics; Genomics; Genes; Genetics -- Research; Genomics; Electronic journals; Periodical; Periodicals; Fulltext; Internet Resources; Periodicals; 572.8
• DOI: 10.1093/g3journal/jkab056
• Languages: English
• ISSNs: 2160-1836

Abstract

Abstract: Quantitative trait loci (QTL) hotspots (genomic locations enriched in QTL) are a common and notable feature when collecting many QTL for various traits in many areas of biological studies. The QTL hotspots are important and attractive since they are highly informative and may harbor genes for the quantitative traits. So far, the current statistical methods for QTL hotspot detection use either the individual-level data from the genetical genomics experiments or the summarized data from public QTL databases to proceed with the detection analysis. These methods may suffer from the problems of ignoring the correlation structure among traits, neglecting the magnitude of LOD scores for the QTL, or paying a very high computational cost, which often lead to the detection of excessive spurious hotspots, failure to discover biologically interesting hotspots composed of a small-to-moderate number of QTL with strong LOD scores, and computational intractability, respectively, during the detection process. In this article, we describe a statistical framework that can handle both types of data as well as address all the problems at a time for QTL hotspot detection. Our statistical framework directly operates on the QTL matrix and hence has a very cheap computational cost and is deployed to take advantage of the QTL mapping results for assisting the detection analysis. Two special devices, trait grouping and top γ n, α profile, are introduced into the framework. The trait grouping attempts to group the traits controlled by closely linked or pleiotropic QTL together into the same trait groups and randomly allocates these QTL together across the genomic positions separately by trait group to account for the correlation structure among traits, so as to have the ability to obtain much stricter thresholds and dismiss spurious hotspots. The top γ n, α profile is designed to outline the LOD-score pattern of QTL in a hotspot across the different hotspot architectures, so that it can serve to identify and characterize the types of QTL hotspots with varying sizes and LOD-score distributions. Real examples, numerical analysis, and simulation study are performed to validate our statistical framework, investigate the detection properties, and also compare with the current methods in QTL hotspot detection. The results demonstrate that the proposed statistical framework can effectively accommodate the correlation structure among traits, identify the types of hotspots, and still keep the notable features of easy implementation and fast computation for practical QTL hotspot detection.