Title

A Comparison on Some Interval Mapping Approaches for QTL Detection

Authors

Zobaer Akond^{1, 2, 3,*,} Md. Jahangir Alam¹, Mohammad Nazmol Hasan^1,5 , Md. Shalim Uddin⁶, Munirul Alam⁴, Md. Nurul Haque Mollah⁵

Affiliation

¹Bioinformatics Lab, Department of Statistics, University of Rajshahi, Rajshahi-6205, Bangladesh; ²Institute of Environmental Science, University of Rajshahi-6205, Bangladesh; ³Agricultural Statistics and Information & Communication Technology (ASICT) Division, Bangladesh Agricultural Research Institute (BARI), Joydebpur, Gazipur-1701, Bangladesh; ⁴Emerging Infections, Infectious Diseases Division, International Centre for Diarrheal Disease Research, Bangladesh (icddr,b); ⁵Bangabandhu Sheikh Mujibur Rahaman Agricultural University, Joydebpur, Gazipur-1706, Bangladesh, ⁶Plant Genetic Resources Center, Bangladesh Agricultural Research Institute, Joydebpur, Gazipur-1701

Email

Zobaer Akond – E-mail: akond25@yahoo.com; *Corresponding Author

Article Type

Research Article

Date

Received January 17, 2019; Revised February 2, 2019; Accepted February 2, 2019; Published February 28, 2019

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.

Keywords

Quantitative trait locus (QTL), Simple Interval Mapping, Composite Interval Mapping, Logarithm-Of-Odds (LOD)

Citation

Akond et al. Bioinformation 15(2): 90-94 (2019)

Edited by

P Kangueane

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.