Confidence intervals obtained from different methods using simulated data and their evaluation through artificial neural network

KARIM NOBAR; ALI ASGHAR ASLAMINEJAD; MOHAMMAD REZA NASSIRY; MOJTABA TAHMOORESPUR; ALI K ESMAILIZADEH

doi:10.56093/ijans.v82i11.25180

Authors

KARIM NOBAR Ferdowsi University of Mashhad, Mashhad, and Shahid Bahonar University of Kerman, Kerman, Iran
ALI ASGHAR ASLAMINEJAD Ferdowsi University of Mashhad, Mashhad, and Shahid Bahonar University of Kerman, Kerman, Iran
MOHAMMAD REZA NASSIRY Ferdowsi University of Mashhad, Mashhad, and Shahid Bahonar University of Kerman, Kerman, Iran
MOJTABA TAHMOORESPUR Ferdowsi University of Mashhad, Mashhad, and Shahid Bahonar University of Kerman, Kerman, Iran
ALI K ESMAILIZADEH Ferdowsi University of Mashhad, Mashhad, and Shahid Bahonar University of Kerman, Kerman, Iran

https://doi.org/10.56093/ijans.v82i11.25180

Keywords:

Artificial Neural Network, F2 Design, QTL mapping, Regression method

Abstract

Determination of confidence intervals (CI) using different methods at different levels of population size (Ps), marker space (Ms), standard deviation of QTL effect (SDQ), ratio of additive to dominance SD (Rad) and QTL position relative to flanking markers (rpQ) were investigated by simulation. The simulation conducted by F₂ design and analyzed with Haley and Knott (HK) method. Moreover an ANN model trained by backprobagation algorithm obtained to predict CIs of different methods at combinations of simulated parameters. After obtain of best ANN model with optimal adequacy parameters we used the artifitial neural network (ANN) model to prediction of CIs at very large-scale combination of simulated parameters comparing actual simulation study. Bootstrap method had more per cent of acurate intervals but average size of the intervals was very high in more scenarios. 1 LOD support interval and bayesian credible interval resulted to be preferable with high per cent of acurate and small confidence intervals, moreover they weekly affected by parameters such as population size and SD of QTL. This study investigated that we can predict CIs for more combination of simulated parameters using best trained ANN. By this study it is sugestive to consideration of more combinations of simulated parameters using the model obtained by best structured ANN to expanding of original study.

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Author Biographies

KARIM NOBAR, Ferdowsi University of Mashhad, Mashhad, and Shahid Bahonar University of Kerman, Kerman, Iran
ALI ASGHAR ASLAMINEJAD, Ferdowsi University of Mashhad, Mashhad, and Shahid Bahonar University of Kerman, Kerman, Iran
MOHAMMAD REZA NASSIRY, Ferdowsi University of Mashhad, Mashhad, and Shahid Bahonar University of Kerman, Kerman, Iran
MOJTABA TAHMOORESPUR, Ferdowsi University of Mashhad, Mashhad, and Shahid Bahonar University of Kerman, Kerman, Iran
ALI K ESMAILIZADEH, Ferdowsi University of Mashhad, Mashhad, and Shahid Bahonar University of Kerman, Kerman, Iran

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