scholarly journals Statistical power of QTL mapping methods applied to bacteria counts

2001 ◽  
Vol 78 (3) ◽  
pp. 303-316 ◽  
Author(s):  
P. TILQUIN ◽  
W. COPPIETERS ◽  
J. M. ELSEN ◽  
F. LANTIER ◽  
C. MORENO ◽  
...  
Keyword(s):  
Qtl Mapping ◽  
Statistical Power ◽  
Parametric Method ◽  
Mapping Method ◽  
Point Of View ◽  
Parametric Methods ◽  
Statistical Point ◽  
The Right ◽  
Non Parametric ◽  
Normally Distributed

Most QTL mapping methods assume that phenotypes follow a normal distribution, but many phenotypes of interest are not normally distributed, e.g. bacteria counts (or colony-forming units, CFU). Such data are extremely skewed to the right and can present a high amount of zero values, which are ties from a statistical point of view. Our objective is therefore to assess the efficiency of four QTL mapping methods applied to bacteria counts: (1) least-squares (LS) analysis, (2) maximum-likelihood (ML) analysis, (3) non-parametric (NP) mapping and (4) nested ANOVA (AN). A transformation based on quantiles is used to mimic observed distributions of bacteria counts. Single positions (1 marker, 1 QTL) as well as chromosome scans (11 markers, 1 QTL) are simulated. When compared with the analysis of a normally distributed phenotype, the analysis of raw bacteria counts leads to a strong decrease in power for parametric methods, but no decrease is observed for NP. However, when a mathematical transformation (MT) is applied to bacteria counts prior to analysis, parametric methods have the same power as NP. Furthermore, parametric methods, when coupled with MT, outperform NP when bacteria counts have a very high proportion of zeros (70·8%). Our results show that the loss of power is mainly explained by the asymmetry of the phenotypic distribution, for parametric methods, and by the existence of ties, for the non-parametric method. Therefore, mapping of QTL for bacterial diseases, as well as for other diseases assessed by a counting process, should focus on the occurrence of ties in phenotypes before choosing the appropriate QTL mapping method.

scholarly journals Non-parametric interval mapping in half-sib designs: use of midranks to account for ties

2003 ◽  
Vol 81 (3) ◽  
pp. 221-228 ◽  
Author(s):  
P. TILQUIN ◽  
I. VAN KEILEGOM ◽  
W. COPPIETERS ◽  
E. LE BOULENGÉ ◽  
P. V. BARET
Keyword(s):  
Disease Resistance ◽  
Infectious Diseases ◽  
Qtl Analysis ◽  
Statistical Power ◽  
Interval Mapping ◽  
Phenotypic Variance ◽  
Total Phenotypic Variance ◽  
Non Parametric ◽  
Normally Distributed

In QTL analysis of non-normally distributed phenotypes, non-parametric approaches have been proposed as an alternative to the use of parametric tests on mathematically transformed data. The non-parametric interval mapping test uses random ranking to deal with ties. Another approach is to assign to each tied individual the average of the tied ranks (midranks). This approach is implemented and compared to the random ranking approach in terms of statistical power and accuracy of the QTL position. Non-normal phenotypes such as bacteria counts showing high numbers of zeros are simulated (0–80% zeros). We show that, for low proportions of zeros, the power estimates are similar but, for high proportions of zeros, the midrank approach is superior to the random ranking approach. For example, with a QTL accounting for 8% of the total phenotypic variance, a gain from 8% to 11% of power can be obtained. Furthermore, the accuracy of the estimated QTL location is increased when using midranks. Therefore, if non-parametric interval mapping is chosen, the midrank approach should be preferred. This test might be especially relevant for the analysis of disease resistance phenotypes such as those observed when mapping QTLs for resistance to infectious diseases.

scholarly journals Bayesian mixture structural equation modelling in multiple-trait QTL mapping

2010 ◽  
Vol 92 (3) ◽  
pp. 239-250 ◽  
Author(s):  
XIAOJUAN MI ◽  
KENT ESKRIDGE ◽  
DONG WANG ◽  
P. STEPHEN BAENZIGER ◽  
B. TODD CAMPBELL ◽  
...  
Keyword(s):  
Qtl Mapping ◽  
Structural Equation ◽  
Statistical Power ◽  
Bayes Factor ◽  
Causal Structure ◽  
Mapping Method ◽  
Parameter Estimates ◽  
Detection Accuracy ◽  
Multiple Traits ◽  
Multiple Trait

SummaryQuantitative trait loci (QTLs) mapping often results in data on a number of traits that have well-established causal relationships. Many multi-trait QTL mapping methods that account for correlation among the multiple traits have been developed to improve the statistical power and the precision of QTL parameter estimation. However, none of these methods are capable of incorporating the causal structure among the traits. Consequently, genetic functions of the QTL may not be fully understood. In this paper, we developed a Bayesian multiple QTL mapping method for causally related traits using a mixture structural equation model (SEM), which allows researchers to decompose QTL effects into direct, indirect and total effects. Parameters are estimated based on their marginal posterior distribution. The posterior distributions of parameters are estimated using Markov Chain Monte Carlo methods such as the Gibbs sampler and the Metropolis–Hasting algorithm. The number of QTLs affecting traits is determined by the Bayes factor. The performance of the proposed method is evaluated by simulation study and applied to data from a wheat experiment. Compared with single trait Bayesian analysis, our proposed method not only improved the statistical power of QTL detection, accuracy and precision of parameter estimates but also provided important insight into how genes regulate traits directly and indirectly by fitting a more biologically sensible model.

scholarly journals PERBANDINGAN ANTARA METODE CART (CLASSIFICATION AND EGRESSION TREE) DAN REGRESI LOGISTIK (LOGISTIC REGRESSION) DALAM MENGKLASIFIKASIKAN PASIEN PENDERITA DBD (DEMAM BERDARAH DENGUE)

2018 ◽  
Vol 15 (1) ◽  
pp. 98-107
Author(s):  
R Lestawati ◽  
Rais Rais ◽  
I T Utami
Keyword(s):  
Logistic Regression ◽  
Statistical Methods ◽  
Parametric Method ◽  
Parametric Methods ◽  
Classification Methods ◽  
Classification Result ◽  
The Status ◽  
Classification Table ◽  
Non Parametric

Classification is one of statistical methods in grouping the data compiled systematically. The classification of an object can be done by two approaches, namely classification methods parametric and non-parametric methods. Non-parametric methods is used in this study is the method of CART to be compared to the classification result of the logistic regression as one of a parametric method. From accuracy classification table of CART method to classify the status of DHF patient into category of severe and non-severe exactly 76.3%, whereas the percentage of truth logistic regression was 76.7%, CART method to classify the status of DHF patient into categories of severe and non-severe exactly 76.3%, CART method yielded 4 significant variables that hepatomegaly, epitaksis, melena and diarrhea as well as the classification is divided into several segmens into a more accurate whereas the logistic regression produces only 1 significant variables that hepatomegaly

scholarly journals A comparison of bootstrap methods to construct confidence intervals in QTL mapping

1998 ◽  
Vol 71 (2) ◽  
pp. 171-180 ◽  
Author(s):  
GRANT A. WALLING ◽  
PETER M. VISSCHER ◽  
CHRIS S. HALEY
Keyword(s):  
Qtl Mapping ◽  
Confidence Intervals ◽  
Parametric Method ◽  
Parametric Bootstrap ◽  
Interval Mapping ◽  
Phenotypic Variance ◽  
Backcross Population ◽  
Mapping Procedure ◽  
Non Parametric

The determination of empirical confidence intervals for the location of quantitative trait loci (QTLs) by interval mapping was investigated using simulation. Confidence intervals were created using a non-parametric (resampling method) and parametric (resimulation method) bootstrap for a backcross population derived from inbred lines. QTLs explaining 1%, 5% and 10% of the phenotypic variance were tested in populations of 200 or 500 individuals. Results from the two methods were compared at all locations along one half of the chromosome. The non-parametric bootstrap produced results close to expectation at all non-marker locations, but confidence intervals when the QTL was located at the marker were conservative. The parametric method performed poorly; results varied from conservative confidence intervals at the location of the marker, to anti-conservative intervals midway between markers. The results were shown to be influenced by a bias in the mapping procedure and by the accumulation of type 1 errors at the location of the markers. The parametric bootstrap is not a suitable method for constructing confidence intervals in QTL mapping. The confidence intervals from the non-parametric bootstrap are accurate and suitable for practical use.

scholarly journals Comparison of Three Statistical Classification Techniques for Maser Identification

2016 ◽  
Vol 33 ◽  
Author(s):  
Ellen M. Manning ◽  
Barbara R. Holland ◽  
Simon P. Ellingsen ◽  
Shari L. Breen ◽  
Xi Chen ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Random Forests ◽  
Parametric Method ◽  
Parametric Models ◽  
Critical Parameters ◽  
Parametric Methods ◽  
Statistical Classification ◽  
Classification Techniques ◽  
Linear Discriminant ◽  
Non Parametric

AbstractWe applied three statistical classification techniques—linear discriminant analysis (LDA), logistic regression, and random forests—to three astronomical datasets associated with searches for interstellar masers. We compared the performance of these methods in identifying whether specific mid-infrared or millimetre continuum sources are likely to have associated interstellar masers. We also discuss the interpretability of the results of each classification technique. Non-parametric methods have the potential to make accurate predictions when there are complex relationships between critical parameters. We found that for the small datasets the parametric methods logistic regression and LDA performed best, for the largest dataset the non-parametric method of random forests performed with comparable accuracy to parametric techniques, rather than any significant improvement. This suggests that at least for the specific examples investigated here accuracy of the predictions obtained is not being limited by the use of parametric models. We also found that for LDA, transformation of the data to match a normal distribution led to a significant improvement in accuracy. The different classification techniques had significant overlap in their predictions; further astronomical observations will enable the accuracy of these predictions to be tested.

scholarly journals Usefulness of the DETECT program for assessing the internal structure of dimensionality in simulated data and results of the Korean nursing licensing examination

2017 ◽  
Vol 14 ◽  
pp. 32 ◽  
Author(s):  
Dong Gi Seo ◽  
Younyoung Choi ◽  
Sun Huh
Keyword(s):  
Internal Structure ◽  
Parametric Method ◽  
Simulated Data ◽  
Parametric Methods ◽  
Simulation Data ◽  
Content Areas ◽  
Proper Number ◽  
Examination Methods ◽  
Licensing Examination ◽  
Non Parametric

Purpose: The dimensionality of examinations provides empirical evidence of the internal test structure underlying the responses to a set of items. In turn, the internal structure is an important piece of evidence of the validity of an examination. Thus, the aim of this study was to investigate the performance of the DETECT program and to use it to examine the internal structure of the Korean nursing licensing examination. Methods: Non-parametric methods of dimensional testing, such as the DETECT program, have been proposed as ways of overcoming the limitations of traditional parametric methods. A non-parametric method (the DETECT program) was investigated using simulation data under several conditions and applied to the Korean nursing licensing examination. Results: The DETECT program performed well in terms of determining the number of underlying dimensions under several different conditions in the simulated data. Further, the DETECT program correctly revealed the internal structure of the Korean nursing licensing examination, meaning that it detected the proper number of dimensions and appropriately clustered the items within each dimension.Conclusion: The DETECT program performed well in detecting the number of dimensions and in assigning items for each dimension. This result implies that the DETECT method can be useful for examining the internal structure of assessments, such as licensing examinations, that possess relatively many domains and content areas.

scholarly journals Resampling Approach for Determination of the Method for Reference Interval Calculation in Clinical Laboratory Practice

2010 ◽  
Vol 17 (8) ◽  
pp. 1217-1222 ◽  
Author(s):  
Igor Y. Pavlov ◽  
Andrew R. Wilson ◽  
Julio C. Delgado
Keyword(s):  
Clinical Laboratory ◽  
Parametric Method ◽  
Reference Interval ◽  
Reference Intervals ◽  
Complement Factor ◽  
Factor B ◽  
Laboratory Test Results ◽  
Resampling Procedure ◽  
The Right ◽  
Normally Distributed

ABSTRACT Reference intervals (RI) play a key role in clinical interpretation of laboratory test results. Numerous articles are devoted to analyzing and discussing various methods of RI determination. The two most widely used approaches are the parametric method, which assumes data normality, and a nonparametric, rank-based procedure. The decision about which method to use is usually made arbitrarily. The goal of this study was to demonstrate that using a resampling approach for the comparison of RI determination techniques could help researchers select the right procedure. Three methods of RI calculation—parametric, transformed parametric, and quantile-based bootstrapping—were applied to multiple random samples drawn from 81 values of complement factor B observations and from a computer-simulated normally distributed population. It was shown that differences in RI between legitimate methods could be up to 20% and even more. The transformed parametric method was found to be the best method for the calculation of RI of non-normally distributed factor B estimations, producing an unbiased RI and the lowest confidence limits and interquartile ranges. For a simulated Gaussian population, parametric calculations, as expected, were the best; quantile-based bootstrapping produced biased results at low sample sizes, and the transformed parametric method generated heavily biased RI. The resampling approach could help compare different RI calculation methods. An algorithm showing a resampling procedure for choosing the appropriate method for RI calculations is included.

scholarly journals Geographical Modules of COVID-19 Pandemic in Bangladesh: Social Response and Action Taken

2021 ◽  
Vol 3 ◽  
pp. 24-35
Author(s):  
Mohammad Tareq Alam ◽  
Md. Ashrafuzzaman ◽  
Nazmir Nur Showva ◽  
Mansura Ahmed ◽  
Faruq Ahmed Jewel ◽  
...  
Keyword(s):  
Social Distance ◽  
Health Care Professionals ◽  
Point Of View ◽  
Decision Makers ◽  
Social Response ◽  
Statistical Point ◽  
The Social ◽  
The Government ◽  
Public Audience ◽  
The Right

Objectives: This research attempts to figure out a comparative pattern of the social response from the peoples of Bangladesh as well as different communities and regarding actions taken by the government during the COVID-19 pandemic in Bangladesh. Else, this study also investigates the shortcomings of the different wing authorities of the Bangladesh Government to reach people of every corner of this country. Methods: In this research, different facts are analyzed from a statistical point of view. Authentic sources like reputed national and international newspapers, governmental release documents, release notes of WHO, etc. were considered for data collection to realize this country’s preparations against the COVID-19 pandemic. Results: From the statistical analysis, this study found that different government authorities partially failed to communicate the measures of the government to the wider public audience. This study has shown that having more tests could have prevented the spread of the virus in Bangladesh. The strong lockdown measures taken by the government were not enough as the population of this country is huge and it is really difficult to maintain social distance in a densely populated country like Bangladesh. Conclusions: The socio-economic condition and decision-makers’ shortcomings could have been overcome if the right plan and action had been taken at the right time. Until the vaccine is available it is recommended that people should keep social distance while going outside, using masks and protection should be mandatory. Furthermore, more health care professionals should be hired and trained to fight this virus. Doi: 10.28991/SciMedJ-2021-03-SI-4 Full Text: PDF

scholarly journals Airborne SAR Autofocus Based on Blurry Imagery Classification

2021 ◽  
Vol 13 (19) ◽  
pp. 3872
Author(s):  
Jianlai Chen ◽  
Hanwen Yu ◽  
Gang Xu ◽  
Junchao Zhang ◽  
Buge Liang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Community ◽  
Parametric Method ◽  
Measured Data ◽  
Parametric Methods ◽  
Phase Gradient ◽  
Automatic Batch ◽  
Dominant Point ◽  
Airborne Sar ◽  
Non Parametric

Existing airborne SAR autofocus methods can be classified as parametric and non-parametric. Generally, non-parametric methods, such as the widely used phase gradient autofocus (PGA) algorithm, are only suitable for scenes with many dominant point targets, while the parametric ones are suitable for all types of scenes, in theory, but their efficiency is generally low. In practice, whether many dominant point targets are present in the scene is usually unknown, so determining what kind of algorithm should be selected is not straightforward. To solve this issue, this article proposes an airborne SAR autofocus approach combined with blurry imagery classification to improve the autofocus efficiency for ensuring autofocus precision. In this approach, we embed the blurry imagery classification based on a typical VGGNet in a deep learning community into the traditional autofocus framework as a preprocessing step before autofocus processing to analyze whether dominant point targets are present in the scene. If many dominant point targets are present in the scene, the non-parametric method is used for autofocus processing. Otherwise, the parametric one is adopted. Therefore, the advantage of the proposed approach is the automatic batch processing of all kinds of airborne measured data.

A novel rank-based non-parametric method for longitudinal ordinal data

2017 ◽  
Vol 27 (9) ◽  
pp. 2775-2794 ◽  
Author(s):  
Yan Zhuang ◽  
Ying Guan ◽  
Libin Qiu ◽  
Meisheng Lai ◽  
Ming T Tan ◽  
...  
Keyword(s):  
Analysis Of Variance ◽  
Ordinal Data ◽  
Statistical Power ◽  
Parametric Method ◽  
Type I ◽  
Test Statistic ◽  
Transform Method ◽  
Rank Transform ◽  
Aligned Rank ◽  
Non Parametric

Longitudinal ordinal data are common in biomedical research. Although various methods for the analysis of such data have been proposed in the past few decades, they are limited in several ways. For instance, the constraints on parameters in the proportional odds model may result in convergence problems; the rank-based aligned rank transform method imposes constraints on other parameters and the distributional assumptions with parametric model. We propose a novel rank-based non-parametric method that models the profile rather than the distribution of the data to make an effective statistical inference without the constraint conditions. We construct the test statistic of the interaction first, and then construct the test statistics of the main effects separately with or without the interaction, while “adjusted coefficient” for the case of ties is derived. A simulation study is conducted for comparison between rank-based non-parametric and rank-transformed analysis of variance. The results show that type I errors of the two methods are both maintained closer to the priori level, but the statistical power of rank-based non-parametric is greater than that of rank-transformed analysis of variance, suggesting higher efficiency of the former. We then apply rank-based non-parametric to two real studies on acne and osteoporosis, and the results also illustrate the effectiveness of rank-based non-parametric, particularly when the distribution is skewed.

Sign in / Sign up

Export Citation Format

Share Document