• Genomics & Informatics
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• v.18 no.4
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• pp.38.1-38.9
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• 2020

Chronotype is an important moderator of psychiatric illnesses, which seems to be controlled in some part by genetic factors. Clock genes are the most relevant genes for chronotype. In addition to the roles of individual genes, gene-gene interactions of clock genes substantially contribute to chronotype. We investigated genetic associations and gene-gene interactions of the clock genes BHLHB2, CLOCK, CSNK1E, NR1D1, PER1, PER2, PER3, and TIMELESS for chronotype in 1,293 healthy Korean individuals. Regression analysis was conducted to find associations between single nucleotide polymorphism (SNP) and chronotype. For gene-gene interaction analyses, the quantitative multifactor dimensionality reduction (QMDR) method, a nonparametric model-free method for quantitative phenotypes, were performed. No individual SNP or haplotype showed a significant association with chronotype by both regression analysis and single-locus model of QMDR. QMDR analysis identified NR1D1 rs2314339 and TIMELESS rs4630333 as the best SNP pairs among two-locus interaction models associated with chronotype (cross-validation consistency [CVC] = 8/10, p = 0.041). For the three-locus interaction model, the SNP combination of NR1D1 rs2314339, TIMELESS rs4630333, and PER3 rs228669 showed the best results (CVC = 4/10, p < 0.001). However, because the mean differences between genotype combinations were minor, the clinical roles of clock gene interactions are unlikely to be critical.

• The Plant Pathology Journal
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• v.31 no.2
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• pp.97-107
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• 2015

Alteration of genetic make-up of the isolates and mono-sporidial strains of Tilletia indica causing Karnal bunt (KB) disease in wheat was analyzed using DNA markers and SDS-PAGE. The generation of new variation with different growth characteristics is not a generalized feature and is not only dependant on the original genetic make up of the base isolate/monosporidial strains but also on interaction with host. Host determinant(s) plays a significant role in the generation of variability and the effect is much pronounced in monosporidial strains with narrow genetic base as compared to broad genetic base. The most plausible explanation of genetic variation in presence of host determinant(s) are the recombination of genetic material from two different mycelial/sporidia through sexual mating as well as through parasexual means. The morphological and development dependent variability further suggests that the variation in T. indica strains predominantly derived through the genetic rearrangements.

• Asian-Australasian Journal of Animal Sciences
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• v.16 no.1
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• pp.1-5
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• 2003

Genetic parameters of live weight at slaughter (LWT), quantity index (QIX), yield grade (YGD), quality grade (QGD), pH of meat, and boiled meat tenderness in terms of mastication (BMAS), shear force (BSFR) and penetration (BPEN) in Hanwoo steers were estimated. Effects of sire, location and their interaction on these traits were also evaluated. Sire effects were found to be significant on all the traits studied except for pH and BSFR. The LWT, QIX and QGD were also significantly affected both by location and by interaction effect between sire${\times}$location. The BSFR and BPEN were significantly (p<0.01) affected by location but not significantly by sire${\times}$location interaction. The boiled meat tenderness and pH were negatively correlated ($r_g$ and $r_p$) with LWT, QIX and QGD. All the other traits were positively correlated with each other. Positive and high genetic correlation (+0.56) between LWT and QGD was obtained indicating that selection for LWT would improve QGD. The $h^2$ estimates were 0.43, 0.37, 0.37, 0.35 and 0.32 for QGD, LWT, pH, BSFR and BPEN, respectively.

• Asian-Australasian Journal of Animal Sciences
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• v.19 no.6
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• pp.769-774
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• 2006

Fractions of herd-year-season, sire by herd interaction, additive genetic and dominance genetic variances were estimated for milk production traits in Holsteins of Japan using Method R. Inbreeding depressions for milk production traits were also estimated. Estimated fractions of herd-year-season variances ranged from 0.056 to 0.074 for yield traits and from 0.033 to 0.035 for content traits. Estimated fractions of additive genetic variances to phenotypic variances (heritabilities across a herd in the narrow sense) were 0.306, 0.287, 0.273, 0.255, 0.723, 0.697 and 0.663 for milk, fat, SNF and protein yields, and fat, SNF and protein contents, respectively. Estimated fractions of dominance genetic variances ranged from 0.019 to 0.022 for yield traits and from 0.014 to 0.018 for content traits. Fractions of variances for sire by herd interaction were estimated to range from 0.020 to 0.025 for yield traits and 0.011 to 0.012 for content traits. Estimates of inbreeding depression for milk, fat, SNF and protein yields were -36.16 kg, -1.42 kg, -3.24 kg and -1.15 kg per 1% inbreeding for milk, fat, SNF and protein yields, respectively. Estimates of depression per 1% inbreeding for content traits were positive at $0.39{\times}10^{-3}%$, $0.31{\times}10^{-3}%$ and $0.82{\times}10^{-3}%$ for fat, SNF and protein contents, respectively.

• Animal Bioscience
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• v.37 no.4
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• pp.622-630
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• 2024

Objective: Pig breeders cannot obtain phenotypic information at the time of selection for sow lifetime productivity (SLP). They would benefit from obtaining genetic information of candidate sows. Genomic data interpreted using deep learning (DL) techniques could contribute to the genetic improvement of SLP to maximize farm profitability because DL models capture nonlinear genetic effects such as dominance and epistasis more efficiently than conventional genomic prediction methods based on linear models. This study aimed to investigate the usefulness of DL for the genomic prediction of two SLP-related traits; lifetime number of litters (LNL) and lifetime pig production (LPP). Methods: Two bivariate DL models, convolutional neural network (CNN) and local convolutional neural network (LCNN), were compared with conventional bivariate linear models (i.e., genomic best linear unbiased prediction, Bayesian ridge regression, Bayes A, and Bayes B). Phenotype and pedigree data were collected from 40,011 sows that had husbandry records. Among these, 3,652 pigs were genotyped using the PorcineSNP60K BeadChip. Results: The best predictive correlation for LNL was obtained with CNN (0.28), followed by LCNN (0.26) and conventional linear models (approximately 0.21). For LPP, the best predictive correlation was also obtained with CNN (0.29), followed by LCNN (0.27) and conventional linear models (approximately 0.25). A similar trend was observed with the mean squared error of prediction for the SLP traits. Conclusion: This study provides an example of a CNN that can outperform against the linear model-based genomic prediction approaches when the nonlinear interaction components are important because LNL and LPP exhibited strong epistatic interaction components. Additionally, our results suggest that applying bivariate DL models could also contribute to the prediction accuracy by utilizing the genetic correlation between LNL and LPP.

• Journal of the Korean Wood Science and Technology
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• v.51 no.3
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• pp.222-237
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• 2023

Eucalyptus pellita is one of the fast-growing tree species and has become predominant in Indonesian forest plantations. Meanwhile, tree breeding programs with clone development are the best way to provide greater genetic advantages. A better understanding of genetic control on growth and basic density in E. pellita is important for increasing wood productivity and quality. In this study, growth characteristics (tree height, diameter, and volume), basic density and its genetic parameters (heritability, genetic gain and genetic correlation) were determined. The number of clones tested in both trials was 50, divided into 5 blocks, and 5 trees/plot. The results showed that there were significant differences in growth and basic density among clones. There was an interaction between genetics and the environment further indicating the existence of unstable clones. The high heritability was found in tree height (0.82-0.86), diameter (0.82-0.90), and basic density (0.91-0.93). This implies that E. pellita has good opportunities for genetic improvement to increase wood productivity and quality. In addition, the results of genetic correlations among growth characteristics (height, diameter, and volume) and basic density showed positive moderate to highly significant value. It is suggested that these characters may be used to the advantage of the breeder for bringing improvement in these traits simultaneously. Therefore, this study provides important information of the genetic improvement of wood quality in E. pellita in Indonesia.

• Journal of Korean Biological Nursing Science
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• v.25 no.1
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• pp.73-85
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• 2023

Purpose: This study focused on identifying the interaction effects of genetic and lifestyle-environmental factors on the development of type 2 diabetes mellitus (T2D). Methods: Study subjects were selected from the Korean Genome and Epidemiology Study (KoGES) from 2001 to 2014. Data on genetic variations, anthropometric measurements, biochemical data, and seven lifestyle factors (diet, physical activity, alcohol drinking, smoking, sleep, depression, and stress) were obtained from 4,836 Koreans aged between 40 and 59 years, including those with T2D at baseline (n = 1,209), newly developed T2D (n= 1,298) and verified controls (n = 3,538). The genetic risk score (GRS) was calculated by using 11 single-nucleotide polymorphisms (SNPs) related to T2D development and the second quartile was used as the reference category. A Cox proportional hazards regression model was used to evaluate the associations of GRS and lifestyle factors with T2D risk, controlling for covariates. Results: Multivariate regression analysis revealed that GRS was the strongest risk factor for T2D, and body mass index (BMI), smoking, drinking, and spicy food preference also increased the risk. Lifestyle/environmental factors that showed significant interactions with GRS were BMI, current smoking, current drinking, fatty food preference, and spicy food preference. Conclusions: Interactions between genetic factors and lifestyle/environmental factors were associated with an increased risk of T2D. The results will be useful to provide a new perspective on genetic profiling for the earlier detection of T2D risk and clues for personalized interventions, which might be more effective prevention strategies or therapies in individuals with a genetic predisposition to T2D.

• Asian-Australasian Journal of Animal Sciences
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• v.13 no.2
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• pp.149-154
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• 2000

The objectives of this study of Hanwoo (Korean Cattle) were 1) to estimate genetic parameters for direct and maternal genetic effects for birth weight, weaning weight, and six months weight which can be used for genetic evaluations and 2) to compare models with and without grandmatemal effects. Data were obtained from the National Livestock Research Institute in Rural Development Administration (RDA) of Korea and were used to estimate genetic parameters for birth weight (BW, n=10,889), weaning weight at 120-d (WW, n=8,637), and six month weight (W6, n=8,478) in Hanwoo. Total number of animals in pedigrees was 14,949. A single-trait animal model was initially used to obtain starting values for multiple-trait animal models. Estimates of genetic parameters were obtained with MTDFREML using animal models and derivative-free REML (Boldman et al., 1995). Estimates of direct heritability for BW, WW, and W6 analyzed as single-traits were 0.09, 0.03, and 0.02 from Model 3 which included direct and maternal genetic, maternal permanental environmental effects, and effects due to sire ${\times}$ region ${\times}$ year-season interaction, respectively. Ignoring sire ${\times}$ region ${\times}$ year-season interaction effect in the model (Model 2) resulted in larger estimates for direct heritability than for Model 3. Estimates of maternal heritability for BW, WW and W6 were 0.04, 0.05, and 0.07 from Model 3, respectively. The estimates of direct-maternal genetic correlation were positive for BW, WW, and W6 with Model 3 but were negative with Model 2 for WW and W6. Estimates of direct genetic correlations between BW and WW, BW and W6, and WW and W6 were large: 0.52, 0.45, and 0.90, respectively. Genetic correlations were also large and positive for maternal effects for BW with maternal effects for WW and W6 (0.69 and 0.74), and even larger for WW with W6 (0.97). The log likelihood values were the same for models including grandmatemal effects as for models including maternal effects for all traits. These results indicate that grandmatemal effects are not important for these traits for Hanwoo or that the data structure was not adequate for estimating parameters for a grandmatemal model.

• Preventive Nutrition and Food Science
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• v.20 no.1
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• pp.1-7
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• 2015

Obesity has become one of the major public health problems all over the world. Recent novel eras of research are opening for the effective management of obesity though gene and nutrient intake interactions because the causes of obesity are complex and multifactorial. Through GWASs (genome-wide association studies) and genetic variations (SNPs, single nucleotide polymorphisms), as the genetic factors are likely to determine individuals' obesity predisposition. The understanding of genetic approaches in nutritional sciences is referred as "nutrigenomics". Nutrigenomics explores the interaction between genetic factors and dietary nutrient intake on various disease phenotypes such as obesity. Therefore, this novel approach might suggest a solution for the effective prevention and treatment of obesity through individual genetic profiles and help improve health conditions.

• Genomics & Informatics
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• v.9 no.2
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• pp.45-51
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• 2011

How personality forms and whether personality genes exist are long-studied questions. Various concepts and theories have been presented for centuries. Personality is a complex trait and is developed through the interaction of genes and the environment. Twin and family studies have found that there are critical genetic and environmental components in the inheritance of personality traits, and modern advances in genetics are making it possible to identify specific variants for personality traits. Although genes that were found in studies on personality have not provided replicable association between genetic and personality variability, more and more genetic variants associated with personality traits are being discovered. Here, we present the current state of the art on genetic research in the personality field and finally list several of the recently published research highlights. First, we briefly describe the commonly used self-reported measures that define personality traits. Then, we summarize the characteristics of the candidate genes for personality traits and investigate gene variants that have been suggested to be associated with personality traits.