Gene sequence screening for manganese poisoning-susceptible genes and analysis of gene interaction effects

2018 ◽  
Vol 64 ◽  
pp. 60-69 ◽  
Author(s):  
Yutian Tian ◽  
Shuhan Guo ◽  
Cengceng Chen ◽  
Li Zhao ◽  
Zhen Li ◽  
...  
Keyword(s):  
Gene Sequence ◽  
Gene Interaction ◽  
Interaction Effects ◽  
Manganese Poisoning

scholarly journals Single gene and gene interaction effects on fertilization and embryonic survival rates in cattle

2009 ◽  
Vol 92 (5) ◽  
pp. 2238-2247 ◽  
Author(s):  
H. Khatib ◽  
W. Huang ◽  
X. Wang ◽  
A.H. Tran ◽  
A.B. Bindrim ◽  
...  
Keyword(s):  
Single Gene ◽  
Survival Rates ◽  
Gene Interaction ◽  
Interaction Effects ◽  
Embryonic Survival

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.

scholarly journals Phenology and Dwarfing Gene Interaction Effects on the Adaptation of Selected Wheat (Triticum aestivum L.) Advanced Lines across Diverse Water-Limited Environments of Western Australia

Agriculture ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. 470
Author(s):  
Mirza A.N.N.U. Dowla ◽  
Shahidul Islam ◽  
Katia Stefanova ◽  
Graham O’ Hara ◽  
Wujun Ma ◽  
...  
Keyword(s):  
Triticum Aestivum ◽  
Grain Yield ◽  
Protein Content ◽  
Gene Interaction ◽  
Triticum Aestivum L ◽  
Interaction Effects ◽  
Dwarfing Genes ◽  
Heading Time ◽  
Dwarfing Gene ◽  
Photoperiod Sensitive

Photoperiod, vernalization, and plant height controlling genes are major developmental genes in wheat that govern environmental adaptation and hence, knowledge on the interaction effects among different alleles of these genes is crucial in breeding cultivars for target environments. The interaction effects among these genes were studied in nineteen Australian advanced lines from diverse germplasm pools and four commercial checks. Diagnostic markers for the Vrn-A1 locus revealed the presence of the spring allele Vrn-A1a in 10 lines and Vrn-A1c in one line. The dominant alleles of Vrn-B1a and Vrn-D1a were identified in 19 and 8 lines, respectively. The most common photoperiod-insensitive allele of Ppd-D1a was identified in 19 lines and three and four copy photoperiod-insensitive alleles (Ppd-B1a and Ppd-B1c) were present in five and one lines, respectively. All the lines were photoperiod-sensitive for the Ppd-A1 locus. All lines were semi-dwarf, having either of the two dwarfing alleles; 14 lines had the Rht-B1b (Rht-1) and the remaining had the Rht-D1b (Rht-2) dwarfing allele. The presence of the photoperiod-insensitive allele Ppd-D1a along with one or two spring alleles at the Vrn1 loci resulted in an earlier heading and better yield. Dwarfing genes were found to modify the heading time—the Rht-D1b allele advanced heading by three days and also showed superior effects on yield-contributing traits, indicating its beneficial role in yield under rain-fed conditions along with an appropriate combination of photoperiod and vernalization alleles. This study also identified the adaptability value of these allelic combinations for higher grain yield and protein content across the different the water-limited environments.

scholarly journals Exploration of gene–gene interaction effects using entropy-based methods

2007 ◽  
Vol 16 (2) ◽  
pp. 229-235 ◽  
Author(s):  
Changzheng Dong ◽  
Xun Chu ◽  
Ying Wang ◽  
Yi Wang ◽  
Li Jin ◽  
...  
Keyword(s):  
Gene Interaction ◽  
Interaction Effects
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