A Comparison on Some Interval Mapping Approaches for QTL Detection.

Zobaer Akond,Md Jahangir Alam,Munirul Alam,Md Shalim Uddin,Mohammad Nazmol Hasan,Nurul Haque Mollah

doi:10.6026/97320630015090

Abstract

Quantitative trait locus (QTL) analysis is a statistical method that links two types of information such as phenotypic data (trait measurements) and genotypic data (usually molecular markers). There a number of QTL tools have been developed for gene linkage mapping. Standard Interval Mapping (SIM) or Simple Interval Mapping or Interval Mapping (IM), Haley Knott, Extended Haley Knott and Multiple Imputation (IMP) method when the single-QTL is unlinked and Composite Interval Mapping (CIM) is designed to map the genetic linkage for both linked and unlinked genes in the chromosome. Performance of these methods is measured based on calculated LOD score. The QTLs are considered significant above the threshold LOD score 3.0. For backcross-simulated data, the CIM method performs significantly in detecting QTLs compare to other SIM mapping methods. CIM detected three QTLs in chromosome 1 and 4 whereas the other methods were unable to detect any significant marker positions for simulated data. For a real rice dataset, CIM also showed performance considerably in detecting marker positions compared to other four interval mapping methods. CIM finally detected 12 QTL positions while each of the other four SIM methods detected only six positions.

Highlights

Phenotypic variations in living creature are observed due to the variation of molecular genetic factor that is called DNA or gene or biomarker
Simulation Study: To calculate the performance of the Standard Interval Mapping (SIM)/Interval Mapping (IM), Haley and Knott (HK), Extended Haley and Knott (eHK) and IMP in comparison of the Composite Interval Mapping (CIM) approach for Quantitative trait locus (QTL) analysis, we consider backcross population for simulation study
Comparison Analysis Based on Real Data: To investigate the performance of the Composite Interval Mapping (CIM) in comparison of other four simple interval methods for QTL analysis in the scenario of real data, we considered a rice mapping population derived from the parent variety of IR64, an irrigated indica variety and Azucena, a traditional upland japonica variety [9]

Summary

Introduction

Phenotypic variations in living creature are observed due to the variation of molecular genetic factor that is called DNA or gene or biomarker. Variation in such quantitative traits is often due to the effects of multiple genetic loci and for environmental factors. A QTL is defined as a region of the genome that is associated with an effect on a quantitative trait [1]. QTL analysis is specialized techniques that construct the genetic linkage maps to locate loci (QTLs) that affect a quantitative trait and estimate the effect of QTLs on the trait [11]. QTL analysis allows researchers in fields as diverse as agriculture, evolution, and medicine to link certain complex phenotypes to specific regions of chromosomes. The goal of QTL analysis is to identify the action, interaction, number, and precise location of these regions [8].

Methods

Results

Conclusion