Proportion Of Phenotypic Variance Research Articles

Bayesian analysis of additive and non-additive genetic variances of body weight gain traits in crossbred population of Japanese quail

Aim of study: To select the appropriate model for body weight gain (BWG) traits in different ages and estimation of additive and non-additive genetic variances based on the best model, of a crossbred population of quail. Area of study: Zabol, Iran Materials and methods: Four strains of Japanese quail, including Italian Speckled, Tuxedo, Pharaoh, and A&M Texas, were used to create a crossbred population in a partial diallel design over 4 generations. BWG traits were calculated as the average growth performance of the bird in a 5-day period from hatch to 45 days of age. Analyses were performed using the Bayesian method by fitting 24 models including the additive and non-additive genetic effects. The deviance information criteria (DIC) was used for the selection of an appropriate model for each trait. Main results: Based on DIC, the maternal genetic, maternal permanent environmental, dominance and epistasis effects had a significant contribution to the best model for BWG traits before 25 days of age, whereas these effects were not significant on BWG traits at the end of ages. With the best model, direct heritability of BWG traits in different ages ranged from 0.037 (BWG15-20) to 0.199 (BWG5-10). The maternal genetic and maternal permanent environmental as a proportion of phenotypic variance was less than 10% and 5%, respectively. The ratio of dominance and epistasis variance was in the range of 0.016-0.019, and 0.016-0.019, respectively. Research highlights: Non- additive genetic effects are important for the early BWG traits and must be included in the evaluation models to have accurate estimates.

Stability of polygenic scores across discovery genome-wide association studies.

SummaryPolygenic scores (PGS) are commonly evaluated in terms of their predictive accuracy at the population level by the proportion of phenotypic variance they explain. To be useful for precision medicine applications, they also need to be evaluated at the individual level when phenotypes are not necessarily already known. We investigated the stability of PGS in European American (EUR) and African American (AFR)-ancestry individuals from the Philadelphia Neurodevelopmental Cohort and the Adolescent Brain Cognitive Development study using different discovery genome-wide association study (GWAS) results for post-traumatic stress disorder (PTSD), type 2 diabetes (T2D), and height. We found that pairs of EUR-ancestry GWAS for the same trait had genetic correlations >0.92. However, PGS calculated from pairs of same-ancestry and different-ancestry GWAS had correlations that ranged from <0.01 to 0.74. PGS stability was greater for height than for PTSD or T2D. A series of height GWAS in the UK Biobank suggested that correlation between PGS is strongly dependent on the extent of sample overlap between the discovery GWAS. Focusing on the upper end of the PGS distribution, different discovery GWAS do not consistently identify the same individuals in the upper quantiles, with the best case being 60% of individuals above the 80th percentile of PGS overlapping from one height GWAS to another. The degree of overlap decreases sharply as higher quantiles, less heritable traits, and different-ancestry GWAS are considered. PGS computed from different discovery GWAS have only modest correlation at the individual level, underscoring the need to proceed cautiously with integrating PGS into precision medicine applications.

Leveraging host-genetics and gut microbiota to determine immunocompetence in pigs

BackgroundThe gut microbiota influences host performance playing a relevant role in homeostasis and function of the immune system. The aim of the present work was to identify microbial signatures linked to immunity traits and to characterize the contribution of host-genome and gut microbiota to the immunocompetence in healthy pigs.ResultsTo achieve this goal, we undertook a combination of network, mixed model and microbial-wide association studies (MWAS) for 21 immunity traits and the relative abundance of gut bacterial communities in 389 pigs genotyped for 70K SNPs. The heritability (h2; proportion of phenotypic variance explained by the host genetics) and microbiability (m2; proportion of variance explained by the microbial composition) showed similar values for most of the analyzed immunity traits, except for both IgM and IgG in plasma that was dominated by the host genetics, and the haptoglobin in serum which was the trait with larger m2 (0.275) compared to h2 (0.138). Results from the MWAS suggested a polymicrobial nature of the immunocompetence in pigs and revealed associations between pigs gut microbiota composition and 15 of the analyzed traits. The lymphocytes phagocytic capacity (quantified as mean fluorescence) and the total number of monocytes in blood were the traits associated with the largest number of taxa (6 taxa). Among the associations identified by MWAS, 30% were confirmed by an information theory network approach. The strongest confirmed associations were between Fibrobacter and phagocytic capacity of lymphocytes (r = 0.37), followed by correlations between Streptococcus and the percentage of phagocytic lymphocytes (r = -0.34) and between Megasphaera and serum concentration of haptoglobin (r = 0.26). In the interaction network, Streptococcus and percentage of phagocytic lymphocytes were the keystone bacterial and immune-trait, respectively.ConclusionsOverall, our findings reveal an important connection between gut microbiota composition and immunity traits in pigs, and highlight the need to consider both sources of information, host genome and microbial levels, to accurately characterize immunocompetence in pigs.

23 Exploring the Role of Gut Microbiota in Host Feeding Behavior Among Breeds in Swine

Abstract The present study aimed to explore gut microbiota’s overall influence on feeding behavior and identify specific microbes associated with the traits in three commercial swine breeds at different growth stages. Feeding behavior measures were obtained from 651 pigs of three breeds (Duroc, Landrace, and Large White) from 67 to 175 days of age. Seven feeding behavior traits cover the information of feed intake, feeder occupation time, feeding rate, and the number of visits to the feeder. Rectal swabs were collected from each pig at 73 ± 3, 123 ± 4, and 158 ± 4 days of age. DNA was extracted and subjected to 16S rRNA gene sequencing. Sequences were analyzed using the QIIME2. The amplicon sequence variant table was constructed using the DADA2. Mixed linear models were used to perform statistical analyses. Differences in feeding behavior traits among breeds and across time periods were found. The proportion of phenotypic variances explained by the microbiome ranged from 0.10 to 0.26, 0.14 to 0.25, and 0.12 to 0.28 in Duroc pigs, from 0.09 to 0.20, 0.10 to 0.23, and 0.12 to 0.23 in Landrace pigs, from 0.10 to 0.17, 0.11 to 0.19, and 0.10 to 0.31 in Large White pigs at three time points, respectively. A total of 21, 10, and 35 amplicon sequence variants were found to be significantly (q-value &amp;lt; 0.05) associated with feeding behavior traits for Duroc, Landrace, and Large White across three time points. This study demonstrated the importance of the gut microbiome composition in influencing the host feeding behavior and identified multiple microbes associated with the feeding behavior of pigs from either Duroc, Landrace, or Large White breeds at three growth stages. Our study provides insight into the interaction between gut microbiota and feeding behavior and highlights the breed effects in swine microbial studies.

Estimation of additive and non-additive genetic variances of average daily gain traits in Adani goats

• Dominance and epistasis effects are important for the average daily gain traits and could be added to evaluation models. • Adding the non-additive genetic effects to models led to increase the accuracy of estimated breeding values. • The adgbww3 and adgbww6 traits can be the basis of selection for the next generation. Considering the dominance and epistasis effects in the analysis can increase the accuracy of estimating breeding values. The objective of this study was to fit the best model for each average daily gain trait (average daily gain from birth to weaning (adgbwww), from birth to 3 months (adgbww3), from birth to 6 months (adgbww6), from weaning to 3 months (adgwww3), from 3 to 6 months (adgw3w6), from 6 to 9 months (adgw6w9), and from 9 to 12 months (adgw9w12)) and the estimation of the genetic parameters and variance components, especially non-additive genetic effects, in Adani goats. Analyses were carried out using the Bayesian method via the Gibbs sampler animal model by fitting 18 different models. With the best model, direct heritability estimates were 0.093, 0.250, 0.256, 0.084, 0.036, 0.048, and 0.151 for adgbwww, adgbww3, adgbww6, adgwww3, adgw3w6, adgw6w9 and adgw9w12 traits, respectively. Maternal genetic and maternal permanent environmental effects were significant only for adgbwww trait. Dominance and epistasis effects were significant almost for all traits and as a proportion of phenotypic variance was the range from 0.068 to 0.221 and 0.106 to 0.237, respectively. Adding dominance and epistasis effects to models reduced the error variance and the accuracy of estimating breeding values was increased. The accuracy of breeding values of these traits with the best models ranged from 0.456 to 0.674, 0.493 to 0.656, and 0.424 to 0.674 for all animals, 10 % of best males and 50 % of the best females, respectively. The result of the present study suggests that dominance and epistasis effect was important for average daily gain traits of Adani goats and should be included in evaluation models.

Genetic analysis of three maize husk traits by QTL mapping in a maize-teosinte population

BackgroundThe maize husk consists of numerous leafy layers and plays vital roles in protecting the ear from pathogen infection and dehydration. Teosinte, the wild ancestor of maize, has about three layers of small husk outer covering the ear. Although several quantitative trait loci (QTL) underlying husk morphology variation have been reported, the genetic basis of husk traits between teosinte and maize remains unclear.ResultsA linkage population including 191 BC2F8 inbred lines generated from the maize line Mo17 and the teosinte line X26–4 was used to identify QTL associated with three husk traits: i.e., husk length (HL), husk width (HW) and the number of husk layers (HN). The best linear unbiased predictor (BLUP) depicted wide phenotypic variation and high heritability of all three traits. The HL exhibited greater correlation with HW than HN. A total of 4 QTLs were identified including 1, 1, 2, which are associated with HL, HW and HN, respectively. The proportion of phenotypic variation explained by these QTLs was 9.6, 8.9 and 8.1% for HL, HN and HW, respectively.ConclusionsThe QTLs identified in this study will pave a path to explore candidate genes regulating husk growth and development, and benefit the molecular breeding program based on molecular marker-assisted selection to cultivate maize varieties with an ideal husk morphology.

The heritability of body composition

BackgroundPhysical growth during childhood and adolescence is influenced by both genetic and environmental factors. Heritability, the proportion of phenotypic variance explained by genetic factors, has been demonstrated for stature and weight status. The aim of this study was to explore the heritability of body composition.MethodsA real-life, observational study of the children and adolescents referred to the Endocrine Unit in a tertiary medical center. In January 2018, body composition by means of bioimpedance analysis (BIA) was implemented as part of the standard intake assessment of subjects referred for endocrine consultation. The clinic BIA database was searched for subjects with the term “observation of growth” as the sole reason for referral. BIA of 114 triads of healthy subjects aged 5–18 years and their parents were analyzed. The BIA report included the following data: fat mass, fat percentage, truncal fat percentage and muscle mass. Calculated variables included: appendicular skeletal muscle mass (ASMM = the sum of muscle mass of four limbs), muscle-to-fat ratio [MFR = ASMM (kg)/fat mass (kg)] and sarcopenic index [(SI = ASMM(kg)/height (meter)²]. Data collection from medical files included pubertal stage and home address for socioeconomic position grading.ResultsThere were sex differences in body composition parameters in both the prepubertal and pubertal subjects. The boys among the prepubertal subjects had a lower fat percentage on average than girls (p = 0.020). Among the adolescents, boys on average had lower fat percentage (p = 0.011), higher sarcopenic index (p = 0.021), and higher muscle-to-fat ratio (p < 0.001), than adolescent girls. Correlation analyses between body composition parameters of all participants revealed significant correlations in the sarcopenic index of prepubertal children and their parents (boys-fathers: r = 0.380, p = 0.050; boys-mothers: r = 0.435, p = 0.026; girls-fathers: r = 0.462, p = 0.012; girls-mothers: r = 0.365, p = 0.050) and adiposity indices (fat percentage, truncal fat percentage and muscle-to-fat ratio) of prepubertal boys and their mothers (r = 0.438, p = 0.025; r = 0.420, p = 0.033, and r = 0.478, p = 0.014, respectively). There were no associations between body composition parameters of adolescents and their parents. Socioeconomic position adversely affected fat percentage in adolescent girls and mothers.ConclusionsHeritable body composition traits were demonstrated in childhood but not in adolescence, suggesting that environmental influence has a more telling effect during teenage years.

Deciphering novel QTL for spectral reflectance indices in spring wheat

Drought is a common abiotic stress factor that seriously affects wheat production in many parts of the world. To identify stable QTLs associated with three morphophysiological traits and four spectral reflectance indices (SRIs) in F6 (previous research) and F7 generations, recombinant inbred line (RIL) population from the cross between drought-sensitive (Yecora Rojo) and drought-tolerant (Pavon 76) cultivars was used in a study conducted under two irrigation regimes at the King Saud University Agricultural, Research Station. The parents and RILs were genotyped using a set of microsatellite loci. The composite interval mapping (CIM) analysis detected 53 QTLs for the three morphophysiological traits and four SRIs. The phenotypic variance (PV) explained varied from 3 to 44%. Twelve co-located QTLs were identified on chromosomes: 1B, 1D, 2A, 2D, 3A, 3B, 4B, 5B and 5D. The highest number of co-located QTLs was identified on chromosomes 5B. The QTL on chromosome 5B flanked by barc109–barc59 showed significant effects for the morphophysiological traits and SRIs under the two irrigation regimes. This QTL explained the highest proportion of PV (14.0–44.0%) with LOD score (3.2–13.2). Seven stable QTLs were identified in both F6 and F7 generations. Of these QTLs, QWc.li-5B co-localized with QNdvi.li-5B and flanked by markers Barc109 and Barc59 was identified in F6 and F7 generations under reduced irrigation. QWc.li-5B explained the highest PV (43 and 44%) for relative water content in F6 and F7 RIL, respectively. The novel and consistent QTLs obtained can be used in marker-assisted selection to improve drought tolerance in wheat.

Genetic and nongenetic factors influencing ewe prolificacy and lamb body weight in a closed Romanov flock.

The U.S. Meat Animal Research Center was the first entity in the United States to import the Romanov breed and it has been maintained as a closed flock for over 30 yr. Incorporating this super-prolific breed into crossbred and composite populations has resulted in large improvements in ewe productivity. However, few have quantified factors contributing to genetic and nongenetic variation in ewe reproduction and lamb growth within purebred Romanov populations, which were the objectives of this study. The pedigree contained a total of 8,683 lambs born to 218 and 1,600 unique sires and dams, respectively. Number of lambs born on a per ewe exposed (NLBE) and lambing (NLBL) basis were analyzed in univariate repeatability animal models. As expected, the proportion of phenotypic variance (σP2) in litter size attributable to additive genetic (0.06 to 0.08) and permanent environmental (0.05 to 0.07) effects of the ewe was low. The service sire permanent environmental effect contributed to a small but significant amount of σP2 in NLBE (0.03) but not NLBL. However, the service sire additive genetic effect did not influence σP2 in NLBE or NLBL. Lamb body weight was recorded at birth (BWB) and upon weaning from either milk replacer (~30 d; BWW-N) or their dam (~60 d; BWW-D) and were analyzed in a three-trait model with random additive direct and maternal effects. Estimated direct heritabilities were low for all body weight (BW) traits (0.07 to 0.10). Maternal heritability was moderate for BWB (0.34) but low for weaning BW (0.11 to 0.18). This was the first to report direct and maternal genetic correlations between BW of nursery- and dam-reared lambs, and both were estimated to be moderate (0.43 to 0.47). Additionally, the direct and maternal effects of BWB were more strongly correlated with BWW-N (0.74 to 0.82) than BWW-D (0.17 to 0.33). Despite inbreeding coefficients having increased at a rate of 0.33% per birth year (1986 to 2019) in this flock, they were not consistently associated with reductions in ewe or lamb performance. Parameter estimates generally agreed with those from less-prolific breeds, and results indicate that selection can be an effective means of improving subcomponents of ewe productivity.

Abstract 2121: Genetic correlation between lung cancer and environmental exposures

Abstract Introduction: Lung cancer is the leading cause of cancer-related deaths. 10% of never-smokers develop lung cancer. Currently, no genetic-based lung cancer screening tool exists. As a substitute, phenotypic traits can serve as surrogate markers for lung cancer risk. Our study focuses on identifying novel phenotypes associated with lung cancer. Genome-wide association studies (GWAS) are useful in elucidating complex inheritance patterns and genetic architecture. Cross-trait linkage disequilibrium score regression (LDSC) specifically allows for use of GWAS summary statistics to identify genetic correlations between phenotypes of interest. This method allows for accurate calculations of genetic correlation, as it neutralizes effects from population stratification or cryptic relatedness. Methods: We used LDSC to (1) confirm prior phenotypic trait associations with lung cancer and to (2) identify novel associations. We measured pairwise genetic correlation (rg) and SNP heritability (h2) (the proportion of phenotypic variance observed in a population that can be explained genetically) between multiple phenotypes and lung cancer using summary statistics from the UK Biobank and OncoArray lung consortium. In addition, we conducted analysis after removal of genome regions related to smoking effects that enables us to correct the potential confounding effect in lung cancer. Results: Significant negative genetic correlations were found to exist between lung cancer and environmental factors. Overall alcohol use was significantly correlated with lung cancers. The effect observed was split, with a positive correlation for beer and cider intake (rg = 0.2957, p = 3.936 × 10-8) and a negative correlation with wine intake (rg = -0.3281, p = 2.251 × 10-14) for overall lung. Significant correlations existed between lung cancer and health metrics. A positive correlation was found between lung cancer and increased BMI (rg = 0.1986, p = 3.57 × 10-9). This finding was consistent across other BMI related metrics and within histological subtypes of lung cancer, including for lung adenocarcinoma (rg = 0.1059, p = 3.688 × 10-3) and lung small cell carcinoma (rg = 0.2393, p = 6.463 × 10-7). In comparison, physical conditioning metrics such as cycling to work had a negative correlation with lung cancer (rg = -0.2714, p = 5.690 × 10-5). Further, negative correlations were observed between being breastfed as a baby and lung cancer (rg = -0.320, p = 1.554 × 10-6). Each of these associations maintained its significance even after the removal of smoking-related SNPs. Conclusions: This analysis demonstrates a genetic basis for the shared genetic architecture between environmental factors and lung oncogenesis. We identify several novel associations, including a correlation between breastfeeding and lung cancer. Further studies are necessary in order to confirm these associations and investigate driving genetic mechanisms. Citation Format: Rowland West Pettit, Jinyoung Byun, Younghun Han, Jacob Edelson, Quinn Ostrom, Kyle Walsh, Melissa Bondy, James McKay, Christopher Amos, INTEGRAL Consortium. Genetic correlation between lung cancer and environmental exposures [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2121.

The Genetic Architecture for Phenotypic Plasticity of the Rice Grain Ionome.

The ionome of the rice grain is crucial for the health of populations that consume rice as a staple food. However, the contribution of phenotypic plasticity to the variation of rice grain ionome and the genetic architecture of phenotypic plasticity are poorly understood. In this study, we investigated the rice grain ionome of a rice diversity panel in up to eight environments. A considerable proportion of phenotypic variance can be attributed to phenotypic plasticity. Then, phenotypic plasticity and mean phenotype were quantified using Bayesian Finlay-Wilkinson regression, and a significant correlation between them was observed. However, the genetic architecture of mean phenotype was distinct from that of phenotypic plasticity. Also, the correlation between them was mainly attributed to the phenotypic divergence between rice subspecies. Furthermore, the results of whole-genome regression analysis showed that the genetic loci related to phenotypic plasticity can explain a considerable proportion of the phenotypic variance in some environments, especially for Cd, Cu, Mn, and Zn. Our study not only sheds light on the genetic architecture of phenotypic plasticity of the rice grain ionome but also suggests that the genetic loci which related to phenotypic plasticity are valuable in rice grain ionome improvement breeding.

QTL mapping for plant height and fruit branch number based on RIL population of upland cotton

BackgroundPlant height (PH) and fruit branch number (FBN) are important traits for improving yield and mechanical harvesting of cotton. In order to identify genes of PH and FBN in cotton germplasms to develop superior cultivars, quantitative trait loci (QTLs) for these traits were detected based on the phenotypic evaluation data in nine environments across four locations and 4 years and a previously reported genetic linkage map of an recombinant inbred line (RIL) population of upland cotton.ResultsIn total, 53 QTLs of PH and FBN, were identified on 21 chromosomes of the cotton genome except chromosomes c02, c09-c11, and c22. For PH, 27 QTLs explaining 3.81%–8.54% proportions of phenotypic variance were identified on 18 chromosomes except c02, c08-c12, c15, and c22. For FBN, 26 QTLs explaining 3.23%–11.00% proportions of phenotypic variance were identified on 16 chromosomes except c02-c03, c06, c09-c11, c17, c22-c23, and c25. Eight QTLs were simultaneously identified in at least two environments. Three QTL clusters containing seven QTLs were identified on three chromosomes (c01, c18 and c21). Eleven QTLs were the same as previously reported ones, while the rest were newly identified.ConclusionsThe QTLs and QTL clusters identified in the current study will be helpful to further understand the genetic mechanism of PH and FBN development of cotton and will enhance the development of excellent cultivars for mechanical managements in cotton production.

Estimation of additive and non-additive genetic variance component for growth traits in Adani goats.

Non-additive genetic effects are important to increase the accuracy of estimating genetic parameters for growth traits. The aim of this study was to estimate genetic parameters and variance components, specially dominance and epistasis genetic effects, for growth traits (birth weight (BW), weaning weight (WW), 3 (W3), 6 (W6), 9 (W9), and 12 (W12) month weight) in Adani goats. Analyses were carried out using Bayesian method via Gibbs sampler animal model by fitting of 18 different models. All fixed effects (sex, type of birth, age of dam, and year) showed significant effects on BW, WW, W3, and W6, whereas the type of birth and age of dam were not significant on W9 and W12. With the best model, direct heritability estimates were 0.347, 0.178, 0.158, 0.359, 0.278, and 0.281 for BW, WW, W3, W6, W9, and W12 traits, respectively. Maternal permanent environmental effect was significant for BW and WW, but maternal genetic effect was significant only for W3. Dominance and epitasis effects were significant almost for all traits and as a proportion of phenotypic variance were ranged from 0.115 to 0.258 and 0.107 to 0.218, respectively. The range of accuracy of breeding values estimated for growth traits with appropriate evaluation models was from 0.521 to 0.652, 0.616 to 0.694, and 0.548 to 0.684 for the all animals, 10% of the best males and 50% of the best females, respectively. When dominance and epistasis effects added to models, the error variance was reduced and the accuracy of estimated breeding values increased. The accuracy of the best model showed a significant difference with the accuracy of other models (p < 0.01). The result of the present study suggests that non-additive genetic effects should be in genetic evaluation models for goat growth traits because of its effect on accuracy of estimated breeding values.

Genetic Correlations Between Diabetes and Glaucoma: An Analysis of Continuous and Dichotomous Phenotypes

A genetic correlation is the proportion of phenotypic variance between traits that is shared on a genetic basis. Here we explore genetic correlations between diabetes- and glaucoma-related traits. Cross-sectional study. We assembled genome-wide association study summary statistics from European-derived participants regarding diabetes-related traits like fasting blood sugar (FBS) and type 2 diabetes (T2D) and glaucoma-related traits (intraocular pressure [IOP], central corneal thickness [CCT], corneal hysteresis [CH], corneal resistance factor [CRF], cup-to-disc ratio [CDR], and primary open-angle glaucoma [POAG]). We included data from the National Eye Institute Glaucoma Human Genetics Collaboration Heritable Overall Operational Database, the UK Biobank, and the International Glaucoma Genetics Consortium. We calculated genetic correlation (rg) between traits using linkage disequilibrium score regression. We also calculated genetic correlations between IOP, CCT, and select diabetes-related traits based on individual level phenotype data in 2 Northern European population-based samples using pedigree information and Sequential Oligogenic Linkage Analysis Routines. Overall, there was little rg between diabetes- and glaucoma-related traits. Specifically, we found a nonsignificant negative correlation between T2D and POAG (rg=-0.14; P= .16). Using Sequential Oligogenic Linkage Analysis Routines, the genetic correlations between measured IOP, CCT, FBS, fasting insulin, and hemoglobin A1c were null. In contrast, genetic correlations between IOP and POAG (rg ≥ 0.45; P≤ 3.0 × 10-4) and between CDR and POAG were high (rg= 0.57; P= 2.8 × 10-10). However, genetic correlations between corneal properties (CCT, CRF, and CH) and POAG were low (rg range-0.18 to 0.11) and nonsignificant (P ≥ .07). These analyses suggest that there is limited genetic correlation between diabetes- and glaucoma-related traits.

Estimating Broad‐Sense Heritability with Unbalanced Data from Agricultural Cultivar Trials

Broad‐sense heritability is defined as the proportion of phenotypic variance that is attributable to an overall variance for the genotype. It is often calculated as a measure (i) to quantify and eventually compare the precision of agricultural cultivar trials, and/or (ii) to estimate the response to selection in plant breeding trials. In practice, most such trials are conducted at multiple environments (i.e., year–location combinations) resulting in a multienvironment trial (MET) with unbalanced data, as, for example, not all cultivars are tested at each environment. However, the standard method for estimating heritability implicitly assumes balanced data, independent genotype effects, and homogeneous variances. Therefore, we compared the estimates for broad‐sense heritability computed via the standard method to those obtained via six alternative estimation methods (example codes: https://github.com/PaulSchmidtGit/Heritability). We did so by analyzing four cultivar METs, which all displayed a typically unbalanced data structure but differed in the genetic frameworks of their cultivars. Results indicate that the standard method may overestimate heritability for all datasets, whereas alternative methods show similar estimates per dataset and thus seem better able to handle this kind of unbalanced data. Finally, we show that to compare heritability estimates between different METs, genetic variance component estimates should be fixed to common values for both datasets.

Identification of core locations for soybean breeding in Southern Brazil

Abstract: The objective of this work was to identify core locations in two soybean macroregions in Brazil for the evaluation and selection of soybean (Glycine max) lineages. Twenty-two cultivars were tested in 23 locations in these two macroregions (MR1 and MR2), during four years, from 2012 to 2015. Trials were conducted in a randomized complete block design with three replicates. All analyses were performed using the GGEbiplot software. The genotype main effects plus genotype × location interaction + genotype main effects plus genotype × environment interaction and genotypic effect vs. genotype x environment effect analyses were used to identify core locations, i.e., locations with high representativeness and consistency of results. Chapada, in the state of Rio Grande do Sul, and Maracaju, in the state of Mato Grosso do Sul, were the core locations in MR1 and MR2, respectively. These locations were the most representative and consistent over the years, and the genotypic effect explained a high proportion of phenotypic variance.

QTL mapping for Q-enzyme activity during grain development in rice

Q-enzyme is important for the synthesis of rice grain starch and vital for grain filling. In this study, QTL (Quantitative trait locus) analysis for Q-enzyme activity during grain filling was carried out using recombinant inbred line population derived from the super rice Xieyou 9308. The activity of Qenzyme was greatest at 15 days after heading. In addition, the Q-enzyme activity in early stage (Q10 and Q15) showed significant correlation with the grain filling rate of each stage as well as the average grain filling rate. Nine maineffect QTLs for grain Q-enzyme activity were identified through QTL analysis, they were distributed on chromosome 2, 3, 5, 6 and 7, and the proportion of phenotypic variation explained (R2) by individual QTLs ranged from 5.68% to 11.59%. Among them, qQ10-3 and qQ15-3-2 are located in the same region of RM282-RM6283 on chromosome 3. qQ10-7-1 was detected in two years with an average R2 of 8.86%, and significant epistasis effect with environment was also identified. These results enhance our understanding of the genetic bases of Q-enzyme activity in rice grain.

QTL Mapping of Grain Zn and Fe Concentrations in Two Hexaploid Wheat RIL Populations with Ample Transgressive Segregation.

More than 50% of undernourished children live in Asia and more than 25% live in Africa. Coupled with an inadequate food supply, mineral deficiencies are widespread in these populations; particularly zinc (Zn) and iron (Fe) deficiencies that lead to retarded growth, adverse effects on both the immune system and an individual's cognitive abilities. Biofortification is one solution aimed at reducing the incidence of these deficiencies. To efficiently breed a biofortified wheat variety, it is important to generate knowledge of the genomic regions associated with grain Zn (GZn) and Fe (GFe) concentration. This allows for the introgression of favorable alleles into elite germplasm. In this study we evaluated two bi-parental populations of 188 recombinant inbred lines (RILs) displaying a significant range of transgressive segregation for GZn and GFe during three crop cycles in CIMMYT, Mexico. Parents of the RILs were derived from Triticum spelta L. and synthetic hexaploid wheat crosses. QTL analysis identified a number of significant QTL with a region denominated as QGZn.cimmyt-7B_1P2 on chromosome 7B explaining the largest (32.7%) proportion of phenotypic variance (PVE) for GZn and leading to an average additive effect of −1.3. The QTL with the largest average additive effect for GFe (−0.161) was found on chromosome 4A (QGFe.cimmyt-4A_P2), with 21.14% of the PVE. The region QGZn.cimmyt-7B_1P2 co-localized closest to the region QGZn.cimmyt-7B_1P1 in a consensus map built from the linkage maps of both populations. Pleiotropic or tightly linked QTL were also found on chromosome 3B, however of minor effects and PVE between 4.3 and 10.9%. Further efforts are required to utilize the QTL information in marker assisted backcrossing schemes for wheat biofortification. A strategy to follow is to intercross the transgressive individuals from both populations and then utilize them as sources in biofortification breeding pipelines.

Genetic analysis of average daily gain in Baluchi sheep

The objective of the present study was to estimate genetic parameters and genetic trends for average daily gain (growth rate) and to investigate the genetic effect of body weight at different ages (birth (BW), weaning (WW), 6months (W6) and 9months (W9)) on growth rate of Baluchi sheep. Data were collected at the Abbasabad Baluchi Sheep Breeding Station (flock 2) from 1983 to 2009. Traits were average daily gain from birth to weaning (ADGa), from weaning to 6months of age (ADGb) and from weaning to 9months of age (ADGc). (Co) variance components and genetic parameters were estimated by univariate and multivariate animal models using Restricted Maximum Likelihood (REML) procedure. Genetic improvements over analyze were calculated as the differences between the means of estimated breeding values at the beginning and at the end of analyze. Genetic trends were obtained by regressing the means of predicted breeding values on year of birth for each trait. Direct heritability estimates were, correspondingly, 0.03, 0.11 and 0.05 for ADGa, ADGb and ADGc. Estimate of maternal heritability (m2) for ADGc was 0.11. Estimated value of maternal permanent environment variance as a proportion of phenotypic variance (c2) was 0.14 for ADGa. Negative genetic correlations were observed for BW-ADGb and BW-ADGc. Direct additive genetic correlations between traits ranged from −0.63 (BW-ADGc) to 0.91 (W9-ADGc). Phenotypic correlations ranged from −0.49 (BW-ADGb) to 0.88 (WW-ADGa). Maternal permanent environmental correlations ranged between 0.13 (W9-ADGa) to 0.99 (WW-ADGa). Genetic improvements were 0.033g, 0.36g and 0.31g for ADGa, ADGb and ADGc, respectively. Annual genetic gains (g/year) for ADGa, ADGb and ADGc, were 0.02, 0.03 and 0.04, respectively.

Genomic variance estimates: With or without disequilibrium covariances?

Whole-genome regression methods are often used for estimating genomic heritability: the proportion of phenotypic variance that can be explained by regression on marker genotypes. Recently, there has been an intensive debate on whether and how to account for the contribution of linkage disequilibrium (LD) to genomic variance. Here, we investigate two different methods for genomic variance estimation that differ in their ability to account for LD. By analysing flowering time in a data set on 1,057 fully sequenced Arabidopsis lines with strong evidence for diversifying selection, we observed a large contribution of covariances between quantitative trait loci (QTL) to the genomic variance. The classical estimate of genomic variance that ignores covariances underestimated the genomic variance in the data. The second method accounts for LD explicitly and leads to genomic variance estimates that when added to error variance estimates match the sample variance of phenotypes. This method also allows estimating the covariance between sets of markers when partitioning the genome into subunits. Large covariance estimates between the five Arabidopsis chromosomes indicated that the population structure in the data led to strong LD also between physically unlinked QTL. By consecutively removing population structure from the phenotypic variance using principal component analysis, we show how population structure affects the magnitude of LD contribution and the genomic variance estimates obtained with the two methods.