Gene action, genetic variation, and GWAS: A user-friendly web tool

Hivert, Valentin; Wray, Naomi R.; Visscher, Peter M.

doi:10.1371/journal.pgen.1009548

Cited by 8 publications

(7 citation statements)

References 21 publications

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“…Specifically, we are interested in the average effect of an allele substitution, or allele substitution effect for short, (α) at each locus, which is estimated by regressing phenotypes onto allele dosages (Falconer, 1985; Fisher, 1941; Price, 1972). In a randomly mating population, allele substitution effect is a function of additive effect α, dominance effect d , and allele frequencies p and q at a locus: α = a + d( q – p ) (Falconer, 1985; Falconer et al., 1996; Hivert et al., 2021; Lynch & Walsh, 1998). The allele substitution is inferred for a population at hand.…”

Section: Methodsmentioning

confidence: 99%

Comparison of genomic prediction models for general combining ability in early stages of hybrid breeding programs

de Jong,

Powell,

Gorjanc

et al. 2023

Crop Science

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This study evaluates the impact of genomic prediction models on selecting inbred lines as parents in hybrid breeding programs. New parents in a hybrid breeding program are typically selected from early‐stage yield trials based on general combining ability (GCA) from testcrosses. Genomic studies have largely focused on predicting hybrid performance in the late stages of the breeding pipeline and largely ignored the selection of inbred lines as parents of the subsequent breeding cycles. Here we used stochastic simulations of a maize (Zea mays L.) hybrid breeding program for 20 years to evaluate the performance of genomic prediction models for selecting parents based on their predicted GCA. Five genomic prediction models were evaluated in terms of achieved genetic gain and heterosis under two different SNP marker densities and the true QTL genotypes. The results show that using high‐density SNP markers generated more genetic gain and heterosis than the low‐density SNP markers. The relative performance of genomic prediction models differed across marker scenarios. For genetic gain, we observed more differences between the models at low than high marker density. For heterosis, we observed the opposite, more differences between the models at high than low marker density. Overall, models that fitted the average or additive effects specific to each heterotic pool and dominance effects provide a better fit and hence higher genetic gain in hybrid breeding programs.This article is protected by copyright. All rights reserved

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Section: Methodsmentioning

confidence: 99%

Comparison of genomic prediction models for general combining ability in early stages of hybrid breeding programs

de Jong,

Powell,

Gorjanc

et al. 2023

Crop Science

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“…GWASs essentially make use of the Fisher method of partitioning genotypic values by performing a linear regression of phenotype on marker allelic dosage (7). Regression coefficients estimate the average allele effect size, and the regression variance is the additive genetic variance due to the locus (8). However, an ongoing debate exists over whether the present analysis paradigm in quantitative genetics is at its limits for truly understanding complex traits, namely traits resulting from many genes each with very small effect size (9).…”

Section: Introductionmentioning

confidence: 99%

Investigative power of Genomic Informational Field Theory (GIFT) relative to GWAS for genotype-phenotype mapping

Kyratzi,

Matika,

Brassington

et al. 2024

Preprint

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Identifying associations between phenotype and genotype is the fundamental basis of genetic analyses. Inspired by frequentist probability and the work of R.A. Fisher, genome-wide association studies (GWAS) extract information using averages and variances from genotype-phenotype datasets. Averages and variances are legitimated upon creating distribution density functions obtained through the grouping of data into categories. However, as data from within a given category cannot be differentiated, the investigative power of such methodologies is limited. Genomic Informational Field Theory (GIFT) is a method specifically designed to circumvent this issue. The way GIFT proceeds is opposite to that of GWAS. Whilst GWAS determines the extent to which genes are involved in phenotype formation (bottom-up approach), GIFT determines the degree to which the phenotype can select microstates (genes) for its subsistence (top-down approach). Doing so requires dealing with new genetic concepts, a.k.a. genetic paths, upon which significance levels for genotype-phenotype associations can be determined. By using different datasets obtained in ovis aries related to bone growth (Dataset-1) and to a series of linked metabolic and epigenetic pathways (Dataset-2), we demonstrate that removing the informational barrier linked to categories enhances the investigative and discriminative powers of GIFT, namely that GIFT extracts more information than GWAS. We conclude by suggesting that GIFT is an adequate tool to study how phenotypic plasticity and genetic assimilation are linked.

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“…Using probability density functions (PDFs) and in particular the normal distribution, Fisher's method partitions genotypic values by performing a linear regression of the phenotype on marker allelic dosage [6]. The regression coefficient estimates the average allele effect size, and the regression variance is the additive genetic variance due to the locus [7]. While Fisher's method has been improved, for example using conditional probability linked to potential prior knowledge of genetic systems (Bayes' method) [8,9], the overall determination of genotype-phenotype mapping is still grounded on PDFs.…”

Section: Introductionmentioning

confidence: 99%

GIFT: new method for the genetic analysis of small gene effects involving small sample sizes

Rauch

Kyratzi²,

Blott³

et al. 2022

Phys. Biol.

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Small gene effects involved in complex/omnigenic traits remain costly to analyse using current genome-wide association methods (GWAS) because of the number of individuals required to return meaningful association(s), a.k.a. study power. Inspired by field theory in physics, we provide a different method called Genomic Informational Field Theory (GIFT). In contrast to GWAS, GIFT assumes that the phenotype is measured precisely enough and/or the number of individuals in the population is too small to permit the creation of categories. To extract information, GIFT uses the information contained in the cumulative sums difference of gene microstates between two configurations: (i) when the individuals are taken at random without information on phenotype values, and (ii) when individuals are ranked as a function of their phenotypic value. The difference in the cumulative sum is then attributed to the emergence of a phenotypic field. We demonstrate that GIFT recovers GWAS, that is, Fisher’s theory, when the phenotypic field is linear (first order). However, unlike GWAS, GIFT demonstrates how the variance of microstate distribution density functions can also be involved in genotype-phenotype associations when the phenotypic field is quadratic (second order). Using genotype-phenotype simulations based on Fisher’s theory as a toy model, we illustrate the application of the method with a small sample size of 1000 individuals.

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Gene action, genetic variation, and GWAS: A user-friendly web tool

Cited by 8 publications

References 21 publications

Comparison of genomic prediction models for general combining ability in early stages of hybrid breeding programs

Comparison of genomic prediction models for general combining ability in early stages of hybrid breeding programs

Investigative power of Genomic Informational Field Theory (GIFT) relative to GWAS for genotype-phenotype mapping

GIFT: new method for the genetic analysis of small gene effects involving small sample sizes

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