scholarly journals Bayesian models with dominance effects for genomic evaluation of quantitative traits

2012 ◽  
Vol 94 (1) ◽  
pp. 21-37 ◽  
Author(s):  
ROBIN WELLMANN ◽  
JÖRN BENNEWITZ
Keyword(s):  
Genomic Selection ◽  
Bayesian Methods ◽  
Mate Selection ◽  
Quantitative Traits ◽  
Main Research ◽  
Genome Wide ◽  
Standard Tool ◽  
Additive Genetic Effects ◽  
Subsequent Selection ◽  
Selection Of

SummaryGenomic selection refers to the use of dense, genome-wide markers for the prediction of breeding values (BV) and subsequent selection of breeding individuals. It has become a standard tool in livestock and plant breeding for accelerating genetic gain. The core of genomic selection is the prediction of a large number of marker effects from a limited number of observations. Various Bayesian methods that successfully cope with this challenge are known. Until now, the main research emphasis has been on additive genetic effects. Dominance coefficients of quantitative trait loci (QTLs), however, can also be large, even if dominance variance and inbreeding depression are relatively small. Considering dominance might contribute to the accuracy of genomic selection and serve as a guide for choosing mating pairs with good combining abilities. A general hierarchical Bayesian model for genomic selection that can realistically account for dominance is introduced. Several submodels are proposed and compared with respect to their ability to predict genomic BV, dominance deviations and genotypic values (GV) by stochastic simulation. These submodels differ in the way the dependency between additive and dominance effects is modelled. Depending on the marker panel, the inclusion of dominance effects increased the accuracy of GV by about 17% and the accuracy of genomic BV by 2% in the offspring. Furthermore, it slowed down the decrease of the accuracies in subsequent generations. It was possible to obtain accurate estimates of GV, which enables mate selection programmes.

scholarly journals Autophagy as a mechanism for adaptive prediction-mediated emergence of drug resistance

2021 ◽  
Author(s):  
Nivedita Nivedita ◽  
John D. Aitchison ◽  
Nitin S. Baliga
Keyword(s):  
Drug Resistance ◽  
Stressful Environment ◽  
Adaptive Prediction ◽  
Genome Wide ◽  
Microbial Infections ◽  
Effective Therapeutic Strategy ◽  
Subsequent Selection ◽  
Tolerant State ◽  
Core Genes ◽  
Selection Of

ABSTRACTDrug resistance is a major problem in treatment of microbial infections and cancers. There is growing evidence that a transient drug tolerant state may precede and potentiate the emergence of drug resistance. Therefore, understanding the mechanisms leading to tolerance is critical for combating drug resistance and for the development of effective therapeutic strategy. Through laboratory evolution of yeast, we recently demonstrated that adaptive prediction (AP), a strategy employed by organisms to anticipate and prepare for a future stressful environment, can emerge within 100 generations by linking the response triggered by a neutral cue (caffeine) to a mechanism of protection against a lethal agent (5-FOA). Here, we demonstrate that mutations selected across multiple laboratory evolved lines had linked the neutral cue response to core genes of autophagy. Across these evolved lines, conditional activation of autophagy through AP conferred tolerance, and potentiated subsequent selection of mutations in genes specific to overcoming the toxicity of 5-FOA. We propose a model to explain how extensive genome-wide genetic interactions of autophagy facilitates emergence of AP over short evolutionary timescales to potentiate selection of resistance-conferring mutations.

scholarly journals Investigating a complex genotype-phenotype map for the development of methods to predict genetic values based on genome-wide marker data – a simulation study for the livestock perspective

2013 ◽  
Vol 56 (1) ◽  
pp. 380-398
Author(s):  
N. Melzer ◽  
D. Wittenburg ◽  
D. Repsilber
Keyword(s):  
Experimental Data ◽  
Genomic Selection ◽  
Prediction Method ◽  
Simulated Data ◽  
Conventional Approach ◽  
Data Simulation ◽  
Single Nucleotide ◽  
Genome Wide ◽  
Alternative Approach ◽  
Additive Genetic Effects

Abstract. Phenotypic variation can partly be explained by genetic variation, such as variation in single nucleotide polymorphism (SNP) genotypes. Genomic selection methods seek to predict genetic values (breeding values) based on SNP genotypes. To develop and to optimize these methods, simulated data are often used, which follow a rather simple genotype-phenotype map. Is the conventional approach for data simulation in this field an appropriate basis to optimize such methods in view of experimental data? Here, we present an alternative approach, striving to simulate more realistic data based on a genotype-phenotype map which includes a simulated metabolome level. This level was used to simulate genetic values, implicitly including additive and non-additive genetic effects, whereas in a conventional approach additive and dominance effects were explicitly simulated and assembled to genetic values. For both simulation approaches, different scenarios regarding numbers of quantitative trait loci (QTLs) and SNPs were analysed using fastBayesB as prediction method. We observed that our alternative map showed a smaller prediction precision (at least 3.75 %) compared to the conventional approach in all investigated scenarios. The observed degree of linearity is at least 94.12 % of the conventional approach or less. Additionally, we present results for different simulated data and experimental data to allow a comparison on a purely conceptual level. Concluding, simulating a more complex genotype-phenotype map including a molecular level, allows to study processing of variation from the genetic to the phenotype level in more detail and may prepare the ground for modern methods of genomic selection.

scholarly journals Analysis of the inheritance of quantitative traits in the rice hybrid Kuboyar × Gagat

2020 ◽  
Vol 202 (11) ◽  
pp. 64-75
Author(s):  
Pavel Kostylev ◽  
E. Krasnova ◽  
A. Aksenov ◽  
E. Balyukova
Keyword(s):  
Genetic Analysis ◽  
Quantitative Traits ◽  
Partial Dominance ◽  
Allelic State ◽  
Grain Productivity ◽  
Mathematical Processing ◽  
Grain Width ◽  
Negative Dominance ◽  
Subsequent Selection ◽  
Selection Of

Abstract. Rice is one of the main food items in the world. White rice is mainly used, but there are also varieties with red, brown and black pericarp grains. This rice is much healthier. The article is devoted to the creation of new lines of rice with black pericarp. The purpose of the work is to study the inheritance of varying quantitative traits in an interspecific hybrid of rice Kuboyar × Gagat, with subsequent selection of isolated samples. Methods. Hybridization of these varieties was carried out in 2017. Mathematical processing of research data was performed Using the program Statistica 6. For genetic analysis, the program “Polygen A” was used by A. F. Merezhko (2005). The research was conducted in 2018–2019 on the basis of a Separate division “Proletarskoe” of the Rostov region. Scientific novelty. A genetic analysis of varying quantitative traits that affect the grain productivity of rice was performed, and a number of new patterns were established. Results. Inheritance of plant height in F2 hybrids was based on the type of overdomination of large trait values. The parent forms differed in the allelic state of the two pairs of genes. Along the length of the panicle, there was a partial negative dominance and monogenic differences in crossed varieties. According to the number of spikelets on the panicle, overdomination of a larger trait value and the interaction of two pairs of genes were established. By the mass of 1000 grains, partial dominance of large values of the trait and monogenic differences in the initial parent forms were established. There was no dominance in the length of the grain, and the parent forms differed by two pairs of genes. The grain width was dominated by smaller values of the trait, splitting was performed according to the monohybrid scheme. The selection of isolated samples forming a compact erect panicle and grains with a black pericarp was carried out for further selection work.

Evolution and Selection of Quantitative Traits

2018 ◽  
Author(s):  
Bruce Walsh ◽  
Michael Lynch
Keyword(s):  
Mathematical Models ◽  
Quantitative Genetics ◽  
Complex Traits ◽  
Evolutionary Biology ◽  
Quantitative Traits ◽  
Specific Gene ◽  
Real World Data ◽  
Holistic Treatment ◽  
Genetics And Genomics ◽  
Selection Of

Quantitative traits—be they morphological or physiological characters, aspects of behavior, or genome-level features such as the amount of RNA or protein expression for a specific gene—usually show considerable variation within and among populations. Quantitative genetics, also referred to as the genetics of complex traits, is the study of such characters and is based on mathematical models of evolution in which many genes influence the trait and in which non-genetic factors may also be important. Evolution and Selection of Quantitative Traits presents a holistic treatment of the subject, showing the interplay between theory and data with extensive discussions on statistical issues relating to the estimation of the biologically relevant parameters for these models. Quantitative genetics is viewed as the bridge between complex mathematical models of trait evolution and real-world data, and the authors have clearly framed their treatment as such. This is the second volume in a planned trilogy that summarizes the modern field of quantitative genetics, informed by empirical observations from wide-ranging fields (agriculture, evolution, ecology, and human biology) as well as population genetics, statistical theory, mathematical modeling, genetics, and genomics. Whilst volume 1 (1998) dealt with the genetics of such traits, the main focus of volume 2 is on their evolution, with a special emphasis on detecting selection (ranging from the use of genomic and historical data through to ecological field data) and examining its consequences. This extensive work of reference is suitable for graduate level students as well as professional researchers (both empiricists and theoreticians) in the fields of evolutionary biology, genetics, and genomics. It will also be of particular relevance and use to plant and animal breeders, human geneticists, and statisticians.

scholarly journals Genome-Wide Association and Prediction of Male and Female Floral Hybrid Potential Traits in Elite Spring Bread Wheat Genotypes

Plants ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 895
Author(s):  
Samira El Hanafi ◽  
Souad Cherkaoui ◽  
Zakaria Kehel ◽  
Ayed Al-Abdallat ◽  
Wuletaw Tadesse
Keyword(s):  
Genomic Selection ◽  
Bread Wheat ◽  
Genome Wide Association ◽  
Hybrid Breeding ◽  
Floral Traits ◽  
Phenotypic Variance ◽  
Spring Bread Wheat ◽  
Genome Wide ◽  
Anther Length ◽  
Anther Extrusion

Hybrid wheat breeding is one of the most promising technologies for further sustainable yield increases. However, the cleistogamous nature of wheat displays a major bottleneck for a successful hybrid breeding program. Thus, an optimized breeding strategy by developing appropriate parental lines with favorable floral trait combinations is the best way to enhance the outcrossing ability. This study, therefore, aimed to dissect the genetic basis of various floral traits using genome-wide association study (GWAS) and to assess the potential of genome-wide prediction (GP) for anther extrusion (AE), visual anther extrusion (VAE), pollen mass (PM), pollen shedding (PSH), pollen viability (PV), anther length (AL), openness of the flower (OPF), duration of floret opening (DFO) and stigma length. To this end, we employed 196 ICARDA spring bread wheat lines evaluated for three years and genotyped with 10,477 polymorphic SNP. In total, 70 significant markers were identified associated to the various assessed traits at FDR ≤ 0.05 contributing a minor to large proportion of the phenotypic variance (8–26.9%), affecting the traits either positively or negatively. GWAS revealed multi-marker-based associations among AE, VAE, PM, OPF and DFO, most likely linked markers, suggesting a potential genomic region controlling the genetic association of these complex traits. Of these markers, Kukri_rep_c103359_233 and wsnp_Ex_rep_c107911_91350930 deserve particular attention. The consistently significant markers with large effect could be useful for marker-assisted selection. Genomic selection revealed medium to high prediction accuracy ranging between 52% and 92% for the assessed traits with the least and maximum value observed for stigma length and visual anther extrusion, respectively. This indicates the feasibility to implement genomic selection to predict the performance of hybrid floral traits with high reliability.

Sign in / Sign up

Export Citation Format

Share Document