Additive and Dominance Genomic Analysis for Litter Size in Purebred and Crossbred Iberian Pigs

INGA FOOD S. A., as a Spanish company that produces and commercializes fattened pigs, has produced a hybrid Iberian sow called CASTÚA by crossing the Retinto and Entrepelado varieties. The selection of the parental populations is based on selection criteria calculated from purebred information, under the assumption that the genetic correlation between purebred and crossbred performance is high; however, these correlations can be less than one because of a GxE interaction or the presence of non-additive genetic effects. This study estimated the additive and dominance variances of the purebred and crossbred populations for litter size, and calculated the additive genetic correlations between the purebred and crossbred performances. The dataset consisted of 2030 litters from the Entrepelado population, 1977 litters from the Retinto population, and 1958 litters from the crossbred population. The individuals were genotyped with a GeneSeek® GGP Porcine70K HDchip. The model of analysis was a ‘biological’ multivariate mixed model that included additive and dominance SNP effects. The estimates of the additive genotypic variance for the total number born (TNB) were 0.248, 0.282 and 0.546 for the Entrepelado, Retinto and Crossbred populations, respectively. The estimates of the dominance genotypic variances were 0.177, 0.172 and 0.262 for the Entrepelado, Retinto and Crossbred populations. The results for the number born alive (NBA) were similar. The genetic correlations between the purebred and crossbred performance for TNB and NBA—between the brackets—were 0.663 in the Entrepelado and 0.881 in Retinto poplulations. After backsolving to obtain estimates of the SNP effects, the additive genetic variance associated with genomic regions containing 30 SNPs was estimated, and we identified four genomic regions that each explained > 2% of the additive genetic variance in chromosomes (SSC) 6, 8 and 12: one region in SSC6, two regions in SSC8, and one region in SSC12.

Multi-information analysis for recommendation of flooded-irrigated rice for adaptability and phenotypic stability

The GxE interaction is one of the major difficulties of plant breeding programs, both in the selection phase and in the recommendation of cultivars. To assess adaptability and stability, various statistical methods are used. The simultaneous use of some methodologies, using multi-information criteria for cultivar’s recommendation, can extract information that cannot be observed using each methodology separately. The aim of this work was to perform a large description of the behavior of flooded-irrigated rice genotypes, responding to environmental variations, using methods already established in the literature, but exploring the particularities of each methodology that together establish an information criterion for cultivar recommendation. To this end, 18 rice genotypes belonging to flood-irrigated rice breeding program were evaluated over four agricultural years, 2012/2013 to 2015/2016, totaling 12 environments (3 sites × 4 years). Multi-information estimates were performed to adaptability and stability analysis. There was no sign for the effect of the genotypes, and there was the significance of the effects of environment and GxE interaction. The aggregation of information and the large description of the behavior of the flooded rice genotypes demonstrated to be an efficient tool for studies of adaptability and stability.

Phenotypic Stability Analysis for Seed Yield and its Associated Traits in Advanced Lines of Indian Mustard (Brassica juncea L. Czern and Coss)

Phenotypic Stability Analysis for Seed Yield and its Associated Traits In advanced lines of Indian Mustard (Brassica juncea L. Czern and Coss)” was carried out to study the effects of different environments on seed yield and its associated traits, to estimate the magnitude of Genotype x Environment interaction and to find out the most stable and high yielding genotype among the selected lines of Indian mustard under different environments of different topography, soil texture and prevailing climatic conditions of Manipur. Fifteen genotypes were evaluated in 3 different locations (Andro, Iroisemba and Senapati) in RBD with 3 replications over two seasons viz; Rabi 2018-19 and 2019-20. The environment wise ANOVA revealed highly significant differences among all the genotypes studied for all the 11 characters of seed yield and its associated traits. The pooled ANOVA also indicated significant differences among the environments, genotypes as well as genotype by environment (GXE) interaction for all the traits. Genotype X Location interaction was observed as the main component for GXE interaction. Genotypes performed better in 2019-20 rabi season as compared to rabi 2018-19. Environment (E-5) i.e. Iroisemba, valley area was the best for expression of most of the characters studied. Stability Analysis using Eberhart and Russell indicated the significance of GXE (linear) for no. of siliqua/plant, no. of seeds/siliqua, siliqua length, no. of primary branches, no. of secondary branches, days to first flowering, days to 50% flowering, days to 80% maturity and 1000 seed weight except for plant height and seed yield per plot which shows the substantial amount of predictable G X E interaction for the expression of these characters in the selected genotypes. All the 15 genotypes were tested for 3 stability parameters, viz mean, bi and S2 di. The genotypes CAURMM-3, CAURMM-4, CAURM- 5, CAURM-4, PM- 25, CAURMM-1 and JM-1 were identified to be the high yielding and stable, hence they can be recommended for general cultivation under varied environments of Manipur. CAURM-1 CAURM-2, CAURM-3 had more stable characters although their yield was below the population mean, Therefore, proposed as promising genotypes for general cultivation under intensive input supply as they performed best in favourable environments, while NRCHB 101 found to be suitable for cultivation under poor environments.

Combinatorial G x G x E CRISPR screening and functional analysis highlights SLC25A39 in mitochondrial GSH transport

The SLC25 carrier family consists of 53 transporters that shuttle nutrients and co-factors across mitochondrial membranes1-3. The family is highly redundant and their transport activities are coupled to metabolic state. Here, we introduce a pooled, dual CRISPR screening strategy that knocks out pairs of transporters in four metabolic states — glucose, galactose, OXPHOS inhibition, and absence of pyruvate — designed to unmask the inter-dependence of these genes. In total, we screened 63 genes in four metabolic states, corresponding to 2016 single and pair-wise genetic perturbations. We recovered 19 gene-by-environment (GxE) interactions and 9 gene-by-gene (GxG) interactions. One GxE interaction hit illustrated that the fitness defect in the mitochondrial folate carrier (SLC25A32) KO cells were genetically buffered in galactose due to a lack of substrate in de novo purine biosynthesis. Another GxE interaction hit revealed non-equivalence of the paralogous ATP/ADP exchangers (ANTs) with ANT2 specifically required during OXPHOS inhibition. GxG analysis highlighted a buffering interaction between the iron transporter SLC25A37 and the poorly characterized SLC25A39. Mitochondrial metabolite profiling, organelle transport assays, and structure-guided mutagenesis suggest SLC25A39 is critical for mitochondrial glutathione (GSH) transport. Our work underscores the importance of systematically investigating family-wide genetic interactions between mitochondrial transporters across many metabolic environments.

Biomass production and nutritional properties of promising genotypes of Tithonia diversifolia (Hemsl.) A. Gray under different environments

Tithonia diversifolia is a shrub with excellent forage characteristics that has shown a wide genetic and phenotypic diversity. The objective of this study was to determine the biomass production and nutritional quality of seven genotypes of T. diversifolia with outstanding characteristics for ruminant nutrition, to analyze the Genotype x Environment (GxE) interaction of biomass production and to compare the performance of these genotypes with grasses offered normally in tropical conditions. For the GxE interaction the AMMI and SREG models were used, and evaluations were made in three environments. In the GxE analysis, the interaction was significant and effects of the environment on biomass productivity were observed with differences between genotypes (p <0.05). In the three environments, the chemical composition was adequate to be offered to ruminants. It is worth noting the high content of crude protein (28.89 g/100 g of dry matter - DM), the low fiber content (30.95 g of neutral detergent fiber - NDF/100 g of DM) and the high percentages of in vitrodegradation of DM for all the genotypes. It is possible to conclude that there are superior genotypes of T. diversifolia with the ability to have good productive and adaptive performance for high-altitude and low-altitude zones with low fertility soils.

Combinatorial G x G x E CRISPR screening and functional analysis highlights SLC25A39 in mitochondrial GSH transport

The SLC25 carrier family consists of 53 transporters that shuttle nutrients and co-factors across mitochondrial membranes. The family is highly redundant and their transport activities coupled to metabolic state. Here, we introduce a pooled, dual CRISPR screening strategy that knocks out pairs of transporters in four metabolic states- glucose, galactose, OXPHOS inhibition, and absence of pyruvate-designed to unmask the inter-dependence of these genes. In total, we screened 63 genes in four metabolic states, corresponding to 2016 single and pair-wise genetic perturbations. We recovered 19 gene-by-environment (GxE) interactions and 9 gene-by-gene (GxG) interactions. One GxE interaction hit illustrated that the fitness defect in the mitochondrial folate carrier (SLC25A32) KO cells was genetically buffered in galactose due to a lack of substrate in de novo purine biosynthesis. Another GxE interaction hit revealed non-equivalence of the paralogous ATP/ADP exchangers (ANTs) with ANT2 specifically required during OXPHOS inhibition. GxG analysis highlighted a buffering interaction between the iron transporter SLC25A37 and the poorly characterized SLC25A39. Mitochondrial metabolite profiling, organelle transport assays, and structure-guided mutagenesis suggests SLC25A39 is critical for mitochondrial glutathione (GSH) transport. Our work underscores the importance of systemetically investigating family-wide genetic interactions between mitochondrial transporters across many metabolic environments.

Combining Ability and Heterosis for Endosperm Carotenoids and Agronomic Traits in Tropical Maize Lines

Provitamin A enrichment of staple crops through biofortification breeding is a powerful approach to mitigate the public health problem of vitamin A deficiency in developing countries. Twenty-four genetically diverse yellow and orange endosperm maize inbred lines with differing levels of provitamin A content were used for the analysis of their combining ability. Each inbred line was developed from crosses and backcrosses between temperate and tropical germplasm. The inbred lines were grouped into different sets according to their provitamin A levels and were then intercrossed in a factorial mating scheme to generate 80 different single-cross hybrids. The hybrids were evaluated in field trials across a range of agroecological zones in Nigeria. The effect of hybrids was significant on all the measured provitamin A and non-provitamin A carotenoids and agronomic traits. While the effect of genotype-by-environment (GxE) interaction was significant for almost all traits, it was a non-crossover-type interaction for carotenoid content. Partitioning of the variances associated with the carotenoid and agronomic traits into their respective components revealed the presence of significant positive and negative estimates of general combining ability (GCA) and specific combining ability (SCA) effects for both carotenoid content and agronomic traits. The preponderance of GCA effects indicates the importance of additive gene effects in the inheritance of carotenoid content. We found F1 hybrids displaying high parent heterosis for both provitamin A content and agronomic performance. Our study demonstrates that provitamin A biofortification can be effectively implemented in maize breeding programs without adverse effects on important agronomic traits, including grain yield.

Genotype-Dependent Variation of Nutritional Quality-Related Traits in Quinoa Seeds

Exploiting the relationship between the nutritional properties of seeds and the environment (E), genotype (G) and the GXE interaction, constitutes an essential analysis which contributes to broaden our knowledge regarding the control of the nutritional quality of seeds or of any other edible plant structure. This constitutes an important aspect when aiming at improving the nutri-tional characteristics properties of plant species, including those of Chenopodium quinoa Willd (quinoa) which is intended to be one of the main nutrient sources ensuring food security worldwide. This crop has gained popularity in the last decade achieving a fast-global expansion due to its excellent nutritional and agronomical properties together with the excellent adaptation shown to a wide diversity of agroclimatic conditions. Changes in the nutritional properties of quinoa seeds due to the influence exerted by the environment, the genotype, or their interaction, have been already described in previous works, but there is an important limitation in the anal-yses carried out, including the outcomes that can be translated into agronomical practices by which quality can be improved selecting the most adequate genotype. In here, several seed nu-tritional-related parameters from fifteen quinoa cultivars grown in a particular environmental context were analysed aiming at targeting compounds that can be determinants of seed quality varying with the genetic background. Important nutritional and agronomical differences were found among quinoa varieties highlighting the importance of choosing a proper genotype when cultivating quinoa.

Smooth-threshold multivariate genetic prediction incorporating gene–environment interactions

We propose a genetic prediction modeling approach for genome-wide association study (GWAS) data that can include not only marginal gene effects but also gene-environment (GxE) interaction effects—i.e., multiplicative effects of environmental factors with genes rather than merely additive effects of each. The proposed approach is a straightforward extension of our previous multiple-regression-based method, STMGP (smooth-threshold multivariate genetic prediction), with the new feature being that genome-wide test statistics from a GxE interaction analysis are used to weight the corresponding variants. We develop a simple univariate regression approximation to the GxE interaction effect that allows a direct fit of the STMGP framework without modification. The sparse nature of our model automatically removes irrelevant predictors (including variants and GxE combinations), and the model is able to simultaneously incorporate multiple environmental variables. Simulation studies to evaluate the proposed method in comparison with other modeling approaches demonstrate its superior performance under the presence of GxE interaction effects. We illustrate the usefulness of our prediction model through application to real GWAS data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI).

Wheat genotypes evaluated for GxE interactions in Central Zone of the country by AMMI analysis

Highly significant effects of environments, GxE interaction and genotypes were observed for cropping years 2017-18 and 2018-19. Further analysis of interactions sum of squares bifurcated into seven significant multiplicative interactions principal components to assess the performances of genotypes as per AMMI based measures. For the first year of study wheat genotypes (G5, G6, G7) had top ranked by EV2, D2, ASV, ASV1 and ASTAB2 measures. MASV & MASV1 pointed towards G7, G8, G6 wheat genotypes. Association among these measures displayed graphically in a biplot analysis. Largest cluster comprised of D2, D3, D5, D7, ASV, ASV1, ASTAB2, EV2, EV3, EV5, ASTAB3, ASTAB5, ASTAB7 measures. Wheat genotypes (G1, G11, G3) pointed by EV2, D2, ASV, ASV1 and ASTAB2 values for the second year. MASV settled for G11, G7, G13 whereas MASV1 pointed towards G11, G7, G2. Biplot analysis based on first two PC’s observed largest group had clubbed measures D2, ASV, ASTAB2, EV5, MASV, MASV1, EV3, D3, D5, D7, EV7, ASTAB3 ASTAB5, ASTAB7. AMMI based measures would be useful to identify and recommend genotypes with high, stable and predictable yield across environments.