scholarly journals Heritability, selection, and the response to selection in the presence of phenotypic measurement error: effects, cures, and the role of repeated measurements

2018 ◽  
Author(s):  
Erica Ponzi ◽  
Lukas F. Keller ◽  
Timothée Bonnet ◽  
Stefanie Muff
Keyword(s):  
Measurement Error ◽  
Repeated Measurements ◽  
Phenotypic Traits ◽  
Response To Selection ◽  
Transient Effects ◽  
Genetic Analyses ◽  
Selection Gradients ◽  
Quantitative Genetic ◽  
Selection Processes ◽  
Unbiased Estimates

Quantitative genetic analyses require extensive measurements of phenotypic traits, a task that is often not trivial, especially in wild populations. On top of instrumental measurement error, some traits may undergo transient (i.e. non-persistent) fluctuations that are biologically irrelevant for selection processes. These two sources of variability, which we denote here as measurement error in a broad sense, are possible causes for bias in the estimation of quantitative genetic parameters. We illustrate how in a continuous trait transient effects with a classical measurement error structure may bias estimates of heritability, selection gradients, and the predicted response to selection. We propose strategies to obtain unbiased estimates with the help of repeated measurements taken at an appropriate temporal scale. However, the fact that in quantitative genetic analyses repeated measurements are also used to isolate permanent environmental instead of transient effects, requires a re-assessment of the information content of repeated measurements. To do so, we propose to distinguish “short-term” from “long-term” repeats, where the former capture transient variability and the latter the permanent effects. We show how the inclusion of the corresponding variance components in quantitative genetic models yields unbiased estimates of all quantities of interest, and we illustrate the application of the method to data from a Swiss snow vole population.

Towards evolutionary agroecology

2018 ◽  
Vol 6 (14) ◽  
pp. 51 ◽  
Author(s):  
Kristin L. Mercer
Keyword(s):  
Social Dynamics ◽  
Agricultural Systems ◽  
Short Term ◽  
Ecological Processes ◽  
Genetic Analyses ◽  
Holistic Perspective ◽  
Evolutionary Study ◽  
Quantitative Genetic ◽  
Field Evolution

Agroecology derives much of its strength from interactions between disciplines that produce a holistic perspective on agricultural systems and issues.  Although ongoing integration of social dynamics into agroecology has strengthened the field, evolution and genetics have not been embraced to the same degree, despite the fact that they have been are discussed in some common agroecology texts.  I argue that the field of agroecology could extend its reach and depth by embracing the evolutionary study of agroecosystems.  Areas of evolutionary inquiry with relevance to agriculture focus on long or short term processes, encompass a range of scales, incorporate molecular or quantitative genetic analyses, and explore ecological processes to differing degrees.

scholarly journals ON THE UNIFORM CONVERGENCE OF DECONVOLUTION ESTIMATORS FROM REPEATED MEASUREMENTS

2021 ◽  
pp. 1-22
Author(s):  
Daisuke Kurisu ◽  
Taisuke Otsu
Keyword(s):  
Multivariate Analysis ◽  
Measurement Error ◽  
Uniform Convergence ◽  
Measurement Errors ◽  
Convergence Rates ◽  
Free Variable ◽  
Error Model ◽  
Repeated Measurements ◽  
Empirical Characteristic Function ◽  
Measurement Error Model

This paper studies the uniform convergence rates of Li and Vuong’s (1998, Journal of Multivariate Analysis 65, 139–165; hereafter LV) nonparametric deconvolution estimator and its regularized version by Comte and Kappus (2015, Journal of Multivariate Analysis 140, 31–46) for the classical measurement error model, where repeated noisy measurements on the error-free variable of interest are available. In contrast to LV, our assumptions allow unbounded supports for the error-free variable and measurement errors. Compared to Bonhomme and Robin (2010, Review of Economic Studies 77, 491–533) specialized to the measurement error model, our assumptions do not require existence of the moment generating functions of the square and product of repeated measurements. Furthermore, by utilizing a maximal inequality for the multivariate normalized empirical characteristic function process, we derive uniform convergence rates that are faster than the ones derived in these papers under such weaker conditions.

scholarly journals HAPPI GWAS: Holistic Analysis with Pre and Post Integration GWAS

2020 ◽  
Author(s):  
Marianne L. Slaten ◽  
Yen On Chan ◽  
Vivek Shrestha ◽  
Alexander E. Lipka ◽  
Ruthie Angelovici
Keyword(s):  
Association Studies ◽  
Phenotypic Traits ◽  
Genome Wide Association Studies ◽  
Sequencing Data ◽  
Gwas Analysis ◽  
Genome Wide ◽  
Large Populations ◽  
Unbiased Estimates ◽  
Best Linear Unbiased ◽  
Automated Pipeline

AbstractMotivationAdvanced publicly available sequencing data from large populations have enabled in-formative genome-wide association studies (GWAS) that associate SNPs with phenotypic traits of interest. Many publicly available tools able to perform GWAS have been developed in response to increased demand. However, these tools lack a comprehensive pipeline that includes both pre-GWAS analysis such as outlier removal, data transformation, and calculation of Best Linear Unbiased Predictions (BLUPs) or Best Linear Unbiased Estimates (BLUEs). In addition, post-GWAS analysis such as haploblock analysis and candidate gene identification are lacking.ResultsHere, we present HAPPI GWAS, an open-source GWAS tool able to perform pre-GWAS, GWAS, and post-GWAS analysis in an automated pipeline using the command-line interface.AvailabilityHAPPI GWAS is written in R for any Unix-like operating systems and is available on GitHub (https://github.com/Angelovici-Lab/HAPPI.GWAS.git)[email protected]

Measuring natural selection on multivariate phenotypic traits: a protocol for verifiable and reproducible analyses of natural selection

2019 ◽  
Vol 65 (3-4) ◽  
pp. 130-136 ◽  
Author(s):  
Facundo Xavier Palacio ◽  
Mariano Ordano ◽  
Santiago Benitez-Vieyra
Keyword(s):  
Regression Analysis ◽  
Natural Selection ◽  
Evolutionary Biology ◽  
Specific Model ◽  
Quadratic Model ◽  
Standard Errors ◽  
Phenotypic Traits ◽  
Linear Gradient ◽  
Selection Gradients ◽  
Model Residuals

The use of multiple regression analysis to quantify the regime and strength of natural selection in nature has been an influential approach in evolutionary biology over the last 36 years. However, many studies fail to report the protocol of estimation of selection coefficients (selection gradients) and the specific model assumptions, thus failing to verify and reproduce the estimation of selection coefficients. We present a brief overview of the Lande and Arnold’s approach and a step-by-step R routine to aid researchers to perform a verifiable and reproducible regression analysis of natural selection. The steps involved in the analysis include: (1) assessing collinearity between phenotypic traits, (2) testing normality of model residuals, and (3) testing multivariate normality of phenotypic traits. We also performed a series of simulations to test the effect of non-symmetrical (skewed) phenotypic traits on the estimation of linear selection gradients. These showed that the bias in the linear gradient increased with increased skewness in phenotypic traits for the quadratic model, whereas the linear gradient of a model with only linear terms was nearly independent of trait skewness. If none of the above assumptions are met, selection gradients need to be estimated from two separate equations, whereas standard errors must be computed using other methods (e.g. bootstrapping). We expect that the procedure outlined here and the availability of analytical codes motivate the verifiability and reproducibility of the Lande and Arnold’s approach in the study of microevolution.

Genetic Susceptibility of Myocardial Infarction

2007 ◽  
Vol 10 (6) ◽  
pp. 848-852 ◽  
Author(s):  
Slobodan Zdravkovic ◽  
Andreas Wienke ◽  
Nancy L. Pedersen ◽  
Ulf de Faire
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Genetic Factors ◽  
First Episode ◽  
Phenotypic Variance ◽  
Genetic Analyses ◽  
Quantitative Genetic ◽  
Equal Variance ◽  
First Occurrence ◽  
Mz Twins

AbstractThe aim of this study was to determine the influence of genetic factors on the first episode of acute myocardial infarction. Probandwise concordances, tetrachoric correlations and quantitative genetic analyses of liability were applied to data drawn from the Swedish Twin Registry and the Swedish Acute Myocardial Infarction Register. All same-sexed twin pairs born between 1886 and 1958 who were alive in 1987 were included in the analyses. Our results show that concordance rates for acute myocardial infarction in monozygotic (MZ) twins were similar across sexes (among males .26 and females .27). For dizygotic (DZ) twins the concordances were .20 for males and .16 for females, yielding a greater MZ–DZ concordance differential for females than males. Tetrachoric correlations were greater for MZ than DZ twins for both sexes (.49 for male MZ and .34 for male DZ-twins and .56 and .35 for female MZ and DZ twins respectively). Quantitative genetic analyses of liability resulted in equal variance components for males and females (.36) but significantly different thresholds (prevalences). In conclusion, liability to first occurrence of acute myocardial infarction is moderately influenced by genetic variants in both sexes. The familial influence on phenotypic variance is exclusively explained by additive genetic factors.

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