Marker-Assisted Selection Efficiency in Populations of Finite Size

Genetics ◽  
1998 ◽  
Vol 148 (3) ◽  
pp. 1353-1365
Author(s):  
Laurence Moreau ◽  
Alain Charcosset ◽  
Frédéric Hospital ◽  
André Gallais
Keyword(s):  
Relative Efficiency ◽  
Marker Assisted Selection ◽  
Analytical Approach ◽  
Type I Error ◽  
Genetic Model ◽  
Finite Size ◽  
Type I ◽  
Selection Efficiency ◽  
Molecular Information ◽  
Selection Of

Abstract The efficiency of marker-assisted selection (MAS) based on an index incorporating both phenotypic and molecular information is evaluated with an analytical approach that takes into account the size of the experiment. We consider the case of a population derived from a cross between two homozygous lines, which is commonly used in plant breeding, and we study the relative efficiency of MAS compared with selection based only on phenotype in the first cycle of selection. It is shown that the selection of the markers included in the index leads to an overestimation of the effects associated with these markers. Taking this bias into account, we study the influence of several parameters, including experiment size and heritability, on MAS efficiency. Even if MAS appears to be most interesting for low heritabilities, we point out the existence of an optimal heritability (~0.2) below which the low power of quantitative trait loci detection and the bias caused by the selection of markers reduce the efficiency. In this situation, increasing the power of detection by using a higher probability of type I error can improve MAS efficiency. This approach, validated by simulations, gives results that are generally consistent with those previously obtained by simulations using a more sophisticated biological model than ours. Thus, though developed from a simple genetic model, our approach may be a useful tool to optimize the experimental means for more complex genetic situations.

Detection of SNP epistasis effects of quantitative traits using an extended Kempthorne model

2006 ◽  
Vol 28 (1) ◽  
pp. 46-52 ◽  
Author(s):  
Yongcai Mao ◽  
Nicole R. London ◽  
Li Ma ◽  
Daniel Dvorkin ◽  
Yang Da
Keyword(s):  
Sample Size ◽  
Candidate Gene ◽  
Complex Traits ◽  
Statistical Power ◽  
Type I Error ◽  
Genetic Model ◽  
Genetic Effects ◽  
Type I ◽  
Nucleotide Polymorphisms ◽  
Epistasis Effect

Epistasis effects (gene interactions) have been increasingly recognized as important genetic factors underlying complex traits. The existence of a large number of single nucleotide polymorphisms (SNPs) provides opportunities and challenges to screen DNA variations affecting complex traits using a candidate gene analysis. In this article, four types of epistasis effects of two candidate gene SNPs with Hardy-Weinberg disequilibrium (HWD) and linkage disequilibrium (LD) are considered: additive × additive, additive × dominance, dominance × additive, and dominance × dominance. The Kempthorne genetic model was chosen for its appealing genetic interpretations of the epistasis effects. The method in this study consists of extension of Kempthorne's definitions of 35 individual genetic effects to allow HWD and LD, genetic contrasts of the 35 extended individual genetic effects to define the 4 epistasis effects, and a linear model method for testing epistasis effects. Formulas to predict statistical power (as a function of contrast heritability, sample size, and type I error) and sample size (as a function of contrast heritability, type I error, and type II error) for detecting each epistasis effect were derived, and the theoretical predictions agreed well with simulation studies. The accuracy in estimating each epistasis effect and rates of false positives in the absence of all or three epistasis effects were evaluated using simulations. The method for epistasis testing can be a useful tool to understand the exact mode of epistasis, to assemble genome-wide SNPs into an epistasis network, and to assemble all SNP effects affecting a phenotype using pairwise epistasis tests.

Evaluating and Implementing Data and Safety Monitoring Plans

2004 ◽  
Vol 52 (7) ◽  
pp. 459-463 ◽  
Author(s):  
Michael L. Terrin
Keyword(s):  
Clinical Trial ◽  
Type I Error ◽  
Safety Monitoring ◽  
Study Data ◽  
Phase Iii ◽  
Type I ◽  
Independent Data ◽  
Willingness To Participate ◽  
Unexpected Events ◽  
Selection Of

Data and safety monitoring plans are essential for fulfilling assurances that are given to patients enrolling in clinical trials that they will not be exposed to avoidable risks and that any information that may influence their willingness to participate in the clinical trial will be made available to them whether the clinical trial is a phase I, phase II, or phase III effort. In addition to the often explicitly detailed nomination of members of an independent data and safety monitoring board and selection of interim analysis plans for control of type I error and avoidance of conduct of a futile study, data and safety monitoring requires organization with regard to schedules for data collection, active surveillance for adverse events, with special emphasis on serious or unexpected events, and the ability to bring all information within a study and newly accruing information external to the research project together as needed for assessment of patient safety.

scholarly journals Hello again, ANOVA: rethinking ANOVA in the context of confirmatory data analysis

2021 ◽  
Author(s):  
Haiyang Jin
Keyword(s):  
Data Analysis ◽  
Error Rate ◽  
Statistical Power ◽  
Type I Error ◽  
Interaction Analysis ◽  
Type I ◽  
Type I Error Rate ◽  
Exploratory Approach ◽  
Confirmatory Data Analysis ◽  
Selection Of

Analysis of variance (ANOVA) is one of the most popular statistical methods employed for data analysis in psychology and other fields. Nevertheless, ANOVA is frequently used as an exploratory approach, even in confirmatory studies with explicit hypotheses. Such misapplication may invalidate ANOVA conventions, resulting in reduced statistical power, and even threatening the validity of conclusions. This paper evaluates the appropriateness of ANOVA conventions, discusses the potential motivations possibly misunderstood by researchers, and provides practical suggestions. Moreover, this paper proposes to control the Type I error rate with Hypothesis-based Type I Error Rate to consider both the number of tests and their logical relationships in rejecting the null hypothesis. Furthermore, this paper introduces the simple interaction analysis, which can employ the most straightforward interaction to test a hypothesis of interest. Finally, pre-registration is recommended to provide clarity for the selection of appropriate ANOVA tests in both confirmatory and exploratory studies.

scholarly journals Distribution-based comprehensive evaluation of methods for differential expression analysis in metatranscriptomics

2021 ◽  
Author(s):  
Hunyong Cho ◽  
Chuwen Liu ◽  
Bridget Mengshan Lin ◽  
Boyang Tang ◽  
Jeffrey Roach ◽  
...  
Keyword(s):  
Differential Expression ◽  
Expression Analysis ◽  
Statistical Power ◽  
Type I Error ◽  
Comprehensive Evaluation ◽  
Differential Expression Analysis ◽  
Preschool Age ◽  
Type I ◽  
Analysis Methods ◽  
Selection Of

Background: Measuring and understanding the function of the human microbiome is key for several aspects of health; however, the development of statistical methods specifically for the analysis of microbial gene expression (i.e., metatranscriptomics) is in its infancy. Many currently employed differential expression analysis methods have been designed for different data types and have not been evaluated in metatranscriptomics settings. To address this knowledge gap, we undertook a comprehensive evaluation and benchmarking of eight differential analysis methods for metatranscriptomics data. Results: We used a combination of real and simulated metatranscriptomics data to evaluate the performance (i.e., model fit, Type-I error, and statistical power) of eights methods: log-normal (LN), logistic-beta (LB), MAST, Kruskal-Wallis, two-part Kruskal-Wallis, DESeq2, and ANCOM-BC and metagenomeSeq. The simulation was informed by supragingival biofilm microbiome data from about 300 preschool-age children enrolled in a study of early childhood caries (ECC), whereas validations were sought in two additional datasets, including an ECC and an inflammatory bowel disease one. The LB test showed the highest power in both small and large sample sizes and reasonably controlled Type-I error. Contrarily, MAST was hampered by inflated Type-I error. Using LN and LB tests, we found that genes C8PHV7 and C8PEV7, harbored by the lactate-producing Campylobacter gracilis, had the strongest association with ECC. Conclusion: This comprehensive model evaluation findings offer practical guidance for the selection of appropriate methods for rigorous analyses of differential expression in metatranscriptomics data. Selection of an optimal method is likely to increase the possibility of detecting true signals while minimizing the chance of claiming false ones.

scholarly journals Multinomial Propensity Score for Ternary Exposure for Genetic Study

2020 ◽  
Author(s):  
Cathy S. J. Fann ◽  
Thai Son Dinh ◽  
Yu-Hsien Chang ◽  
Jia Jyun Sie ◽  
Ie-Bin Lian
Keyword(s):  
Propensity Score ◽  
Error Rate ◽  
Type I Error ◽  
Type I ◽  
Matched Cohort ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Type I Error Rate ◽  
Regression Adjustment ◽  
Selection Of

Abstract Background: Propensity score (PS) is a popular method for reducing multiple confounding effects in observational studies. It is applicable mainly for situations wherein the exposure/treatment of interest is dichotomous and the PS can be estimated through logistic regression. However, multinomial exposures with 3 or more levels are not rare, e.g., when considering genetic variants, such as single nucleotide polymorphisms (SNPs), which have 3 levels (aa/aA/AA), as an exposure. Conventional PS is inapplicable for this situation unless the 3 levels are collapsed into 2 classes first. Methods: A simulation study was conducted to compare the performance of the proposed multinomial propensity score (MPS) method under various contrast codings and approaches, including regression adjustment and matching.Results: MPS methods had more reasonable type I error rate than the non-MPS methods, of which the latter could be as high as 30~50%. Compared with MPS-direct adjusted methods, MPS-matched cohort methods have better power but larger type I error rate. Performance of contrast codings depend on the selection of MPS models. Conclusions: In general, two combinations had relatively better performance in our simulation of ternary exposure: MPS-matched cohort method with Helmert contrast and MPS-direct adjusted regression with treatment contrasts. Compared with the latter, the former had better power but larger type I error rate as a trade-off.

Smoothing Facilitates the Detection of Coupled Responses in Psychophysiological Time Series

2000 ◽  
Vol 14 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Joni Kettunen ◽  
Niklas Ravaja ◽  
Liisa Keltikangas-Järvinen
Keyword(s):  
Time Series ◽  
Type I Error ◽  
Electrodermal Activity ◽  
Moving Average ◽  
Type I ◽  
Facial Electromyography ◽  
Response Characteristics ◽  
Smoothing Methods ◽  
Response Systems ◽  
Different Response

Abstract We examined the use of smoothing to enhance the detection of response coupling from the activity of different response systems. Three different types of moving average smoothers were applied to both simulated interbeat interval (IBI) and electrodermal activity (EDA) time series and to empirical IBI, EDA, and facial electromyography time series. The results indicated that progressive smoothing increased the efficiency of the detection of response coupling but did not increase the probability of Type I error. The power of the smoothing methods depended on the response characteristics. The benefits and use of the smoothing methods to extract information from psychophysiological time series are discussed.

One Size Fits All?

Methodology ◽  
2012 ◽  
Vol 8 (1) ◽  
pp. 23-38 ◽  
Author(s):  
Manuel C. Voelkle ◽  
Patrick E. McKnight
Keyword(s):  
Repeated Measures ◽  
Structural Equation ◽  
Type I Error ◽  
Equation Modeling ◽  
Type I ◽  
Finite Sample ◽  
Traditional Methods ◽  
Repeated Measures Anova ◽  
Special Cases ◽  
Latent Curve

The use of latent curve models (LCMs) has increased almost exponentially during the last decade. Oftentimes, researchers regard LCM as a “new” method to analyze change with little attention paid to the fact that the technique was originally introduced as an “alternative to standard repeated measures ANOVA and first-order auto-regressive methods” (Meredith & Tisak, 1990, p. 107). In the first part of the paper, this close relationship is reviewed, and it is demonstrated how “traditional” methods, such as the repeated measures ANOVA, and MANOVA, can be formulated as LCMs. Given that latent curve modeling is essentially a large-sample technique, compared to “traditional” finite-sample approaches, the second part of the paper addresses the question to what degree the more flexible LCMs can actually replace some of the older tests by means of a Monte-Carlo simulation. In addition, a structural equation modeling alternative to Mauchly’s (1940) test of sphericity is explored. Although “traditional” methods may be expressed as special cases of more general LCMs, we found the equivalence holds only asymptotically. For practical purposes, however, no approach always outperformed the other alternatives in terms of power and type I error, so the best method to be used depends on the situation. We provide detailed recommendations of when to use which method.

Assessing Person Fit With the Information Matrix Test

Methodology ◽  
2015 ◽  
Vol 11 (1) ◽  
pp. 3-12 ◽  
Author(s):  
Jochen Ranger ◽  
Jörg-Tobias Kuhn
Keyword(s):  
Simulation Study ◽  
Type I Error ◽  
Information Matrix ◽  
Small Samples ◽  
Type I ◽  
Person Fit ◽  
Power Of The Test ◽  
Order Expansion ◽  
Trait Stability ◽  
Information Matrix Test

In this manuscript, a new approach to the analysis of person fit is presented that is based on the information matrix test of White (1982) . This test can be interpreted as a test of trait stability during the measurement situation. The test follows approximately a χ2-distribution. In small samples, the approximation can be improved by a higher-order expansion. The performance of the test is explored in a simulation study. This simulation study suggests that the test adheres to the nominal Type-I error rate well, although it tends to be conservative in very short scales. The power of the test is compared to the power of four alternative tests of person fit. This comparison corroborates that the power of the information matrix test is similar to the power of the alternative tests. Advantages and areas of application of the information matrix test are discussed.

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