Effects of Genotype, Environment and their Interaction on Quality Characteristics of Winter Bread Wheat

Grain quality is a complex character that depends on a number of traits, and the individual contribution of each trait varies depending on specific reaction to environmental conditions. The objective of this study was to assess the effects of genotype, environmental, and genotype x environmental interaction on quality characteristic of 16 wheat genotypes as well as to analyse the relationships between quality traits. The results of two-way analysis of variance showed that the effect of genotype, environment and genotype x environment interaction were significant (p≤0.001) for the investigated physical characteristics of grain. The strongest individual influence for thousand kernel weight, test weight and vitreousness had genotype. The interaction genotype x environment had stronger influence on the variance for the crude protein (44.98%) and the lysine (34.93%) than genotype and environment effects. Sources of variation genotype and genotype x environment interaction (year) had almost the equal influence on the variance of wet gluten content and bread making strength index. Genotype demonstrated the strongest influence on the sedimentation value and dry gluten content. The genotype x environment interaction influenced in the largest rate on the variance of gluten weakness. Protein content showed significant positive correlation with wet gluten content (0.676), gluten weakness (0.646) and dry gluten content. Vitreousness correlated positively with sedimentation value (0.541) while the test weight significantly correlated with dry gluten content. The results of this study can be used as selection criteria to increase grain quality in bread wheat in the region

Statistical analysis for genotype stability and adaptability in maize yield based on environment and genotype interaction models

Current analysis investigates genotype x environment interaction and stability performance of grain yield with nine maize genotypes in seven environments. ANOVA revealed highly significant (p-value<0.001) data for genotypes, environments and their interactions. Only PC1 (45.4%) and PC2 (35%) were significant (p ≤ 0.05). Genotype G7 had a specific adaptation to environment E7, whereas genotypes G2 and G3 were adapted to environment E1, and genotypes G8 and G9 to environment E5. Dataset was divided into group A, composed of E5 and E7, and group B composed of E1, E2, E3 and E6. Genotypes G1, G2, G3 and G6, belonging to group B, were the most productive. Further, no environment fell into the G4, G5, G7, G8 and G9 sectors, denoting these genotypes as the poorest ones across environments. GGE biplot indicated that genotype G4 was highly unstable, whereas G3 very stable. In addition, G2 was more desirable due to its small contribution to both G and GE. On the other hand, G4 and G9 were more undesirable due to large contribution to either G or GE. Finally, genotypes G2 and G9 were very different. Their dissimilarity may be due to difference in mean yield and/or in GEI

Evaluation of the Performance of Some White Seeded Sesame (Sesamum Indicum L.) Genotypes Using GGE Biplot in Northern Ethiopia

Sesame known as queen of oil seed crops is mainly grown for its oil of local consumption, sources of income and great contribution for the national economy of Ethiopia. However, the productivity and production is low due to environments, genotypes, Interaction and management variation. Four sesame genotypes were evaluated for their interactions with environments and seed yield stability analysis at three environments during the 2015 main cropping season. The objectives of the study were to estimate the magnitude and nature of GEI and to identify stable and/or high yielding white seeded sesame genotypes in Abergelle Agricultural Research Center mandate areas, Northern Ethiopia.  The study was conducted using a randomized complete block design with three replications at each environment. The combined analysis of variance revealed highly significant (P≤ 0.01) environment (E), genotype (G) and genotype × environment interaction (GEI). Environment explained 79.84% of the total (G + E +GE) variation, whereas G and GE explained 17.21% and 2.95% of the total variation, respectively. The magnitude of the environment was 4.6 times greater than the genotype, implying that most of the variation in seed yield was due to the environment. The significant genotype by environment interaction effects were further partitioned in to two significant interaction principal components by using the genotype main effect plus genotype x environment interaction (GGE) biplots model. The first two principal components for mean yield and stability of the GEI explained 96.81% with PC1 = 90.88 and PC2 = 5.93 of the GGE sum of squares, respectively, while 3.19% was attributed to noise. Thus, model diagnosis (fitting) showed that the first two PCs were significant and can be taken to interpret this data. The which-won-where biplot identified one winning genotype in one mega environment. The winning genotype across locations was Humera-1. Thus, the GGE (genotype and genotype by environment interaction) biplot analysis indicating that Humera-1 was considered as the most desirable and stable one’s, therefore can be recommended for wider cultivation due to better seed yield and stability performance across the test environments in the dry lowland areas of Southeast and Central zones of Tigray region, Northern Ethiopia. Keywords: Dry lowland, GEI, GGE biplot, Mega environment, Sesamum Indicum L

Isolating and Quantifying the Role of Developmental Noise in Generating Phenotypic Variation

Genotypic variation, environmental variation, and their interaction may produce variation in the developmental process and cause phenotypic differences among individuals. Developmental noise, which arises during development from stochasticity in cellular and molecular processes when genotype and environment are fixed, also contributes to phenotypic variation. While evolutionary biology has long focused on teasing apart the relative contribution of genes and environment to phenotypic variation, our understanding of the role of developmental noise has lagged due to technical difficulties in directly measuring the contribution of developmental noise. The influence of developmental noise is likely underestimated in studies of phenotypic variation due to intrinsic mechanisms within organisms that stabilize phenotypes and decrease variation. Since we are just beginning to appreciate the extent to which phenotypic variation due to stochasticity is potentially adaptive, the contribution of developmental noise to phenotypic variation must be separated and measured to fully understand its role in evolution. Here, we show that variation in the component of the developmental process corresponding to environmental and genetic factors (here treated together as a unit called the LALI-type) versus the contribution of developmental noise, can be distinguished for leopard gecko (Eublepharis macularius) head color patterns using mathematical simulations that model the role of random variation (corresponding to developmental noise) in patterning. Specifically, we modified the parameters of simulations corresponding to variation in the LALI-type to generate the full range of phenotypic variation in color pattern seen on the heads of eight leopard geckos. We observed that over the range of these parameters, variation in color pattern due to LALI-type variation exceeds that due to developmental noise in the studied gecko cohort. However, the effect of developmental noise on patterning is also substantial. Our approach addresses one of the major goals of evolutionary biology: to quantify the role of stochasticity in shaping phenotypic variation

Interaksi Genotipe X Lingkungan Untuk Hasil Gabah Padi Sawah

Grain yield of rice is determined by genotype (G), environment (E), and interaction between genotype x environment (G x E). Variety can achieve its maximum yield potential if it is grown in suitable environments. This study was aimed to determine the adaptability and the yield stability of rice genotypes grown in different environments. Sixteen rice genotypes were tested using RBD in 16 sites during the wet season of 2010/2011, and dry season of 2011. The tested rice lines were developed for resistance to pest and diseases. The experiment unit was 4 m x 5 m of plot, plants were fertilized with urea, SP36, and KCl at rates of 250 kg/ha, 100 kg/ha, and 100 kg/ha, respectively. Variable observed was grain yield per plot. Combined analyses of variance showed that there was no lines yielded higher than did check variety Conde. The AMMI analysis showed that the largest variation was contributed by the environment factors (76.49%), genotype x environment interactions (17.55%), and the smallest was contributed by the genotypes (5.97%). Data exploration using boxplot method indicated that the low contribution of the genotype x environment interaction variance in this study was due to the high degree of similarity of yield potentials among the genotypes, and due to high similarity of environmental conditions of the sites.Based on the analysis of AMMI 2, lines B12743 - MR-18-2-3-8, IPB107-F-82-2-1, and Conde was each classified as widely adapted genotypes, while G8, IPB107-F-27-6-1, and BIO111-2-BC-PIR-3714, each was considered as genotype having a specific adaptation

Genome-wide contribution of genotype by environment interaction to variation of diabetes-related traits

While genome-wide association studies (GWAS) and candidate gene approaches have identified many genetic variants that contribute to disease risk as main effects, the impact of genotype by environment (GxE) interactions remains rather under-surveyed. To explore the importance of GxE interactions for diabetes-related traits, a tool for Genome-wide Complex Trait Analysis (GCTA) was used to examine GxE variance contribution of 15 macronutrients and lifestyle to the total phenotypic variance of diabetes-related traits at the genome-wide level in a European American population. GCTA identified two key environmental factors making significant contributions to the GxE variance for diabetes-related traits: carbohydrate for fasting insulin (25.1% of total variance, P-nominal = 0.032) and homeostasis model assessment of insulin resistance (HOMA-IR) (24.2% of total variance, P-nominal = 0.035), n-6 polyunsaturated fatty acid (PUFA) for HOMA-β-cell-function (39.0% of total variance, P-nominal = 0.005). To demonstrate and support the results from GCTA, a GxE GWAS was conducted with each of the significant dietary factors and a control E factor (dietary protein), which contributed a non-significant GxE variance. We observed that GxE GWAS for the environmental factor contributing a significant GxE variance yielded more significant SNPs than the control factor. For each trait, we selected all significant SNPs produced from GxE GWAS, and conducted anew the GCTA to estimate the variance they contributed. We noted the variance contributed by these SNPs is higher than that of the control. In conclusion, we utilized a novel method that demonstrates the importance of genome-wide GxE interactions in explaining the variance of diabetes-related traits

High Heritability Is Compatible with the Broad Distribution of Set Point Viral Load in HIV Carriers.

Set point viral load in HIV patients ranges over several orders of magnitude and is a key determinant of disease progression in HIV. A number of recent studies have reported high heritability of set point viral load implying that viral genetic factors contribute substantially to the overall variation in viral load. The high heritability is surprising given the diversity of host factors associated with controlling viral infection. Here we develop an analytical model that describes the temporal changes of the distribution of set point viral load as a function of heritability. This model shows that high heritability is the most parsimonious explanation for the observed variance of set point viral load. Our results thus not only reinforce the credibility of previous estimates of heritability but also shed new light onto mechanisms of viral pathogenesis

Genotype-environment associations support a mosaic hybrid zone between two tidal marsh birds

Local environmental features can shape hybrid zone dynamics when hybrids are bounded by ecotones or when patchily distributed habitat types lead to a corresponding mosaic of genotypes. We investigated the role of marsh-level characteristics in shaping a hybrid zone between two recently diverged avian taxa – Saltmarsh (Ammodramus caudacutus) and Nelson\u27s (A. nelsoni) sparrows. These species occupy different niches where allopatric, with caudacutus restricted to coastal marshes and nelsoni found in a broader array of wetland and grassland habitats and co-occur in tidal marshes in sympatry. We determined the influence of habitat types on the distribution of pure and hybrid sparrows and assessed the degree of overlap in the ecological niche of each taxon. To do this, we sampled and genotyped 305 sparrows from 34 marshes across the hybrid zone and from adjacent regions. We used linear regression to test for associations between marsh characteristics and the distribution of pure and admixed sparrows. We found a positive correlation between genotype and environmental variables with a patchy distribution of genotypes and habitats across the hybrid zone. Ecological niche models suggest that the hybrid niche was more similar to that of A. nelsoni and habitat suitability was influenced strongly by distance from coastline. Our results support a mosaic model of hybrid zone maintenance, suggesting a role for local environmental features in shaping the distribution and frequency of pure species and hybrids across space