scholarly journals Should gene interactions be included in genomic evaluation in animal breeding?

2014 ◽  
pp. 1-6
Author(s):  
Asko Mäki-Tanila ◽  
William G. Hill
Keyword(s):  
Allele Frequency ◽  
Gene Interaction ◽  
Interaction Effects ◽  
Animal Breeding ◽  
Additive Effects ◽  
Additive Variance ◽  
Epistatic Effects ◽  
Genome Wide ◽  
Genomic Tools ◽  
Epistatic Variance

The genetic comparison of animals is based on their own performance and that of animals sharing genetic factors with them. Their expected genetic similarity is deduced from pedigree information and also now directly using a large number of molecular genetic markers over the genome (genomic breeding values). Quantitative trait analyses may also include gene interaction or epistatic effects. Additive x additive interaction effects have been found, particularly in crosses of inbred and widely diverse selected lines. These and gene functional studies have generated much interest in including the interaction effects in genome-wide analyses within populations, including animal breeding stocks. Several issues need consideration before incorporating them in genetic models: influence of gene interaction on the genetic evaluation and on the gains produced by selection, proportion of epistatic variance with multiple genes, expectations with common allele frequency distributions, and probability of finding interaction effects with the genomic tools. - The average effect of an allele already includes interaction effects with other loci, but with magnitude dependent on their frequencies. If a major epistatic effect is favourable, selection may fix the respective allele quickly. With milder effects the frequencies of interacting favourable alleles at both loci of pair will increase. - Even with additive effects in an underlying genotype, the relationship between phenotypes and genotypes may be non-linear and there is epistasis on the observed scale. An example is a categorical trait (diseased or not), where the analysis on the observed scale using an approximating model can be transformed to the underlying additive scale. In the multiplicative model the amount of epistasis increases with the coefficient of variation (CV), but the proportion never exceeds 1- ln(1+CV2)/CV2, and most of the epistatic variance is due to two-locus interactions. - The additive variance is directly proportional to heterozygosity (H), with a maximum at allele frequency ½ in a biallelic case. Additive x additive variance requires segregation in both the interacting loci A and B and is proportional to HAHB, and correspondingly for more loci. Hence epistatic variance can reach high values only when allele frequencies near ½. - As the number of loci (n) is increased, average effects at individual loci decline with 1/√n (i.e. variance as 1/n). Similarly additive x additive effects must decline as 1/n. In genome-wide analyses, the number of effects to be estimated is the square of that for individual loci. With many thousands of markers very stringent test criteria have to be used so the power is very low. It has become obvious that the genomic tools cannot harvest all the existing genetic variation. In particular the variation due to rare alleles is often undetected. Such problems are even more likely in considering interaction effects. In summary, gene interaction effects are automatically utilized in selection using additive models while most epistatic effects are expected to be very small and difficult to detect in genome-wide analyses.

scholarly journals Hypothesis-free detection of gene-interaction effects on biomarker concentration in UK Biobank using variance prioritisation

2022 ◽  
Author(s):  
Matthew S Lyon ◽  
Louise Amanda Claire Millard ◽  
George Davey Smith ◽  
Tom R Gaunt ◽  
Kate Tilling
Keyword(s):  
Gene Interaction ◽  
Interaction Effects ◽  
Effect Estimate ◽  
Uk Biobank ◽  
Blood Biomarkers ◽  
Gene Environment ◽  
Genome Wide ◽  
Trait Variance ◽  
Preclinical Drug Development ◽  
Genetic And Environmental Factors

Blood biomarkers include disease intervention targets that may interact with genetic and environmental factors resulting in subgroups of individuals who respond differently to treatment. Such interactions may be observed in genetic effects on trait variance. Variance prioritisation is an approach to identify genetic loci with interaction effects by estimating their association with trait variance, even where the modifier is unknown or unmeasured. Here, we develop and evaluate a regression-based Brown-Forsythe test and variance effect estimate to detect such interactions. We provide scalable open-source software (varGWAS) for genome-wide association analysis of SNP-variance effects (https://github.com/MRCIEU/varGWAS) and apply our software to 30 blood biomarkers in UK Biobank. We find 468 variance quantitative trait loci across 24 biomarkers and follow up findings to detect 82 gene-environment and six gene-gene interactions independent of strong scale or phantom effects. Our results replicate existing findings and identify novel epistatic effects of TREH rs12225548 x FUT2 rs281379 and TREH rs12225548 x ABO rs635634 on alkaline phosphatase and ZNF827 rs4835265 x NEDD4L rs4503880 on gamma glutamyltransferase. These data could be used to discover possible subgroup effects for a given biomarker during preclinical drug development.

Epistatic Effects and QTL×Environment Interaction Effects of QTLs for Yield and Agronomic Traits in Soybean

2014 ◽  
Vol 40 (1) ◽  
pp. 37
Author(s):  
Hui-Zhen LIANG ◽  
Yong-Liang YU ◽  
Hong-Qi YANG ◽  
Hai-Yang ZHANG ◽  
Wei DONG ◽  
...  
Keyword(s):  
Agronomic Traits ◽  
Interaction Effects ◽  
Environment Interaction ◽  
Epistatic Effects

Multiplicative vs. arbitrary gene action in heterosis.

Genetics ◽  
1992 ◽  
Vol 131 (2) ◽  
pp. 461-469 ◽  
Author(s):  
F W Schnell ◽  
C C Cockerham
Keyword(s):  
Gene Action ◽  
Extensive Literature ◽  
Additive Effects ◽  
Nonlinear Functions ◽  
Epistatic Effects ◽  
Genetic Significance ◽  
The Mean ◽  
The Difference ◽  
Multiplicative Models ◽  
Multiplicative Effects

Abstract In this article we investigate multiplicative effects between genes in relation to heterosis. The extensive literature on heterosis due to multiplicative effects between characters is reviewed, as is earlier work on the genetic description of heterosis. A two-locus diallelic model of arbitrary gene action is used to derive linear parameters for two multiplicative models. With multiplicative action between loci, epistatic effects are nonlinear functions of one-locus effects and the mean. With completely multiplicative action, the mean and additive effects form similar restrictions for all the rest of the effects. Extensions to more than two loci are indicated. The linear parameters of various models are then used to describe heterosis, which is taken as the difference between respective averages of a cross (F1) and its two parent populations (P). The difference (F2 - P) is also discussed. Two parts of heterosis are distinguished: part I arising from dominance, and part II due to additive x additive (a x a)-epistasis. Heterosis with multiplicative action between loci implies multiplicative accumulation of heterosis present at individual loci in part I, in addition to multiplicative (a x a)-interaction in part II. Heterosis with completely multiplicative action can only be negative (i.e., the F1 values must be less than the midparent), but the difference (F2 - P) can be positive under certain conditions. Heterosis without dominance can arise from multiplicative as well as any other nonadditive action between loci, as is exemplified by diminishing return interaction. The discussion enlarges the scope in various directions: the genetic significance of multiplicative models is considered.(ABSTRACT TRUNCATED AT 250 WORDS)

scholarly journals Genomic prediction with non-additive effects in beef cattle: stability of variance component and genetic effect estimates against population size

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Akio Onogi ◽  
Toshio Watanabe ◽  
Atsushi Ogino ◽  
Kazuhito Kurogi ◽  
Kenji Togashi
Keyword(s):  
Population Size ◽  
Genomic Prediction ◽  
Variance Components ◽  
Variance Component ◽  
Predictive Accuracy ◽  
Genetic Effect ◽  
Genetic Effects ◽  
Additive Effects ◽  
Additive Variance ◽  
Additive Genetic Effects

Abstract Background Genomic prediction is now an essential technology for genetic improvement in animal and plant breeding. Whereas emphasis has been placed on predicting the breeding values, the prediction of non-additive genetic effects has also been of interest. In this study, we assessed the potential of genomic prediction using non-additive effects for phenotypic prediction in Japanese Black, a beef cattle breed. In addition, we examined the stability of variance component and genetic effect estimates against population size by subsampling with different sample sizes. Results Records of six carcass traits, namely, carcass weight, rib eye area, rib thickness, subcutaneous fat thickness, yield rate and beef marbling score, for 9850 animals were used for analyses. As the non-additive genetic effects, dominance, additive-by-additive, additive-by-dominance and dominance-by-dominance effects were considered. The covariance structures of these genetic effects were defined using genome-wide SNPs. Using single-trait animal models with different combinations of genetic effects, it was found that 12.6–19.5 % of phenotypic variance were occupied by the additive-by-additive variance, whereas little dominance variance was observed. In cross-validation, adding the additive-by-additive effects had little influence on predictive accuracy and bias. Subsampling analyses showed that estimation of the additive-by-additive effects was highly variable when phenotypes were not available. On the other hand, the estimates of the additive-by-additive variance components were less affected by reduction of the population size. Conclusions The six carcass traits of Japanese Black cattle showed moderate or relatively high levels of additive-by-additive variance components, although incorporating the additive-by-additive effects did not improve the predictive accuracy. Subsampling analysis suggested that estimation of the additive-by-additive effects was highly reliant on the phenotypic values of the animals to be estimated, as supported by low off-diagonal values of the relationship matrix. On the other hand, estimates of the additive-by-additive variance components were relatively stable against reduction of the population size compared with the estimates of the corresponding genetic effects.

scholarly journals Agrobacterium tumefaciens Gene Transfer: How a Plant Pathogen Hacks the Nuclei of Plant and Nonplant Organisms

2015 ◽  
Vol 105 (10) ◽  
pp. 1288-1301 ◽  
Author(s):  
Salim Bourras ◽  
Thierry Rouxel ◽  
Michel Meyer
Keyword(s):  
Genetic Transformation ◽  
Host Cell ◽  
Dna Integration ◽  
Genome Wide ◽  
Genomic Tools ◽  
Genome Features ◽  
Integration Mechanisms ◽  
Dna Fragment ◽  
Eukaryotic Genomes

Agrobacterium species are soilborne gram-negative bacteria exhibiting predominantly a saprophytic lifestyle. Only a few of these species are capable of parasitic growth on plants, causing either hairy root or crown gall diseases. The core of the infection strategy of pathogenic Agrobacteria is a genetic transformation of the host cell, via stable integration into the host genome of a DNA fragment called T-DNA. This genetic transformation results in oncogenic reprogramming of the host to the benefit of the pathogen. This unique ability of interkingdom DNA transfer was largely used as a tool for genetic engineering. Thus, the artificial host range of Agrobacterium is continuously expanding and includes plant and nonplant organisms. The increasing availability of genomic tools encouraged genome-wide surveys of T-DNA tagged libraries, and the pattern of T-DNA integration in eukaryotic genomes was studied. Therefore, data have been collected in numerous laboratories to attain a better understanding of T-DNA integration mechanisms and potential biases. This review focuses on the intranuclear mechanisms necessary for proper targeting and stable expression of Agrobacterium oncogenic T-DNA in the host cell. More specifically, the role of genome features and the putative involvement of host’s transcriptional machinery in relation to the T-DNA integration and effects on gene expression are discussed. Also, the mechanisms underlying T-DNA integration into specific genome compartments is reviewed, and a theoretical model for T-DNA intranuclear targeting is presented.

Abstract P359: A Sickle Cell Variant Associates With Urine Albumin Excretion in Genome Wide Association Study of Hispanics: The HCHS/SOL Study

Circulation ◽  
2015 ◽  
Vol 131 (suppl_1) ◽  
Author(s):  
Nora Franceschini ◽  
Adrienne Stilp ◽  
Christina L Wassel ◽  
Holly J Mattix-Kramer ◽  
Michael F Flessner ◽  
...  
Keyword(s):  
Allele Frequency ◽  
Sickle Cell ◽  
Missense Variant ◽  
Genome Wide Association ◽  
European Ancestry ◽  
Minority Population ◽  
Genome Wide Association Studies ◽  
Urine Albumin ◽  
Genome Wide ◽  
A Genome

Introduction: Genome wide association studies have identified genetic variants in the Cubillin gene ( CUBN ) that explain inter-individual variation in urine albumin-to-creatinine excretion (UACR) in populations. These studies have not included Hispanics/Latinos, the fast growing minority population in the U.S., who has also high prevalence of chronic kidney disease and its risk factors. Hypothesis: By leveraging on population admixture of Hispanics and using a genome wide association approach, we hypothesized that novel loci associated with UACR would be identified. Methods: We used data from 12,212 self-identified Hispanic individuals recruited in a community-based study, aged 18-74 years at screening (2008-2011), and randomly selected from households in four U.S. field centers (Chicago, IL; Miami, FL; Bronx, NY; San Diego, CA). Urine albumin (mg/dl) and creatinine (g/dl) were measured at the baseline exam. UACR was log-transformed for analysis. Individuals were excluded if reporting to have end-stage renal disease. Genotyping was performed using a custom Illumina Omni2.5M array. Imputation of variants was performed using 1000 Genome Project data from cosmopolitan HapMap samples. After quality control of imputed data, we performed mixed linear regression analyses that accounted for the sampling strategy and family relatedness, for variants with minor allele frequency (MAF) > 0.01 and imputation quality > 0.3. We used additive genetic models and adjusted for age, sex, and principal components which were estimated from the data. In a secondary analysis, we also examine the association of significant variants with kidney function using estimated glomerular filtration rate (eGFR) equations. Results: Among 12,212 participants, 41% were men, and mean age was 46 (SD =13). There was little evidence for genome wide inflation (lambda =1.024). We identified significant associations of single nucleotide polymorphisms (SNPs) with UACR at two loci: CUBN and HBB . The CUBN SNP (chr10:16966414, p=2.1x10-8) is an indel variant with MAF of 0.14, which was not in linkage disequilibrium with previously reported SNP rs1801239 (rsq=0.38, p=1.3x10-4) identified in individuals of European ancestry. The HBB SNP is a missense variant which results in an E [Glu] ⇒ A [Ala] substitution in the beta-globin chain of hemoglobin and a cause of the Mendelian disorder sickle cell anemia (rs334, T allele frequency =0.01, beta=0.44, SE=0.06, p=7.6x10-12). rs344 was not associated with eGFR in our data (p>0.05). Conclusion: This study identified a novel association of a sickle cell missense variant with UACR in Hispanics, and provided evidence for allelic heterogeneity at the CUBN locus. Our findings suggest a role for Mendelian gene variants in increased albuminuria in Hispanic populations with admixture.

scholarly journals Analysis of genetic dominance in the UK Biobank

2021 ◽  
Author(s):  
Duncan S Palmer ◽  
Wei Zhou ◽  
Liam Abbott ◽  
Nik Baya ◽  
Claire Churchhouse ◽  
...  
Keyword(s):  
Complex Traits ◽  
Multiple Testing ◽  
Model Organisms ◽  
Systematic Evaluation ◽  
Hair Color ◽  
Phenotypic Variance ◽  
Additive Effects ◽  
Uk Biobank ◽  
Genome Wide ◽  
The Uk

In classical statistical genetic theory, a dominance effect is defined as the deviation from a purely additive genetic effect for a biallelic variant. Dominance effects are well documented in model organisms. However, evidence in humans is limited to a handful of traits, particularly those with strong single locus effects such as hair color. We carried out the largest systematic evaluation of dominance effects on phenotypic variance in the UK Biobank. We curated and tested over 1,000 phenotypes for dominance effects through GWAS scans, identifying 175 loci at genome-wide significance correcting for multiple testing (P < 4.7 × 10-11). Power to detect non-additive loci is much lower than power to detect additive effects for complex traits: based on the relative effect sizes at genome-wide significant additive loci, we estimate a factor of 20-30 increase in sample size will be necessary to capture clear evidence of dominance similar to those currently observed for additive effects. However, these localised dominance hits do not extend to a significant aggregate contribution to phenotypic variance genome-wide. By deriving a version of LD-score regression to detect dominance effects tagged by common variation genome-wide (minor allele frequency > 0.05), we found no strong evidence of a contribution to phenotypic variance when accounting for multiple testing. Across the 267 continuous and 793 binary traits the median contribution was 5.73 × 10-4, with unbiased point estimates ranging from -0.261 to 0.131. Finally, we introduce dominance fine-mapping to explore whether the more rapid decay of dominance LD can be leveraged to find causal variants. These results provide the most comprehensive assessment of dominance trait variation in humans to date.

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