Genetic Variance Research Articles

A Holistic Approach to the Selection of Soybean (Glycine max) Cultivars for Shade Environments Based on Morphological, Yield and Genetic Traits

ABSTRACTThe reduction of photosynthetically active radiation impacts the growth and productivity of soybean in agroforestry and intercropping systems. Thus, this report explored the responses of 16 soybean cultivars submitted to shade levels in field conditions. Multi‐faceted and relative importance analyses revealed that the steam diameter and plant height are fundamental morphological markers for selecting shade‐resilient cultivars, both were high and positively correlated to yield components. Moreover, the responses to shade varied among soybean cultivars, with certain genotypes demonstrating distinct tolerance levels, which allowed also the estimative of genetic variance that revealed strong participation of genetic components in responses to shade. Multivariate and clustering analysis using steam diameter and plant height in combination with two yield components resulted in the identification of four soybean cultivars more tolerant to shade environments and two sensible. Therefore, this report provides insights into soybean cultivation under varying light conditions, provides a robust foundation for the integration of morphological and yield markers in breeding programmes focused on shade tolerance and guides future endeavours in crop improvement for optimal and sustainable yield and resilience in the climate change context.

PSX-5 Repeatability of overreach, term and acuteness in Peruvian Paso horse

Abstract The Peruvian Paso Horse is considered one of the best saddle horses worldwide due to the gait that makes this horse perform one of the smoothest walks. The overreach, term, and acuteness are the most characteristic movements in the gait, making them the most desirable attributes in its breeding and reproduction. Repeatability of overreach, term, and acuteness in Peruvian Paso horses (PPH) have not been determined to date. It is important to estimate this parameter for application in PPH breeding, therefore, the aim of this study was to estimate the repeatability of overreach, term, and acuteness of PPHs. The study included records of 137 stallions and mares. All measurements were recorded in MP4 video format with a resolution of 1920 x 1080 megapixels and at 60 frames per second. All traits were measured three times (once per stride), and each trait was analyzed. KINOVEA software version 0.9.5 was used to analyze the measurements. A multivariate repeated measures animal model with sex effect was used to estimate the variance components for each trait using WOMBAT software. Repeatability was high in all traits (> 0.78; Table 1). The low standard error values close to 0.02 indicate high precision in all traits (0.904, 0.855, and 0.903 for overreach, term, and acuteness, respectively), being defined as the variance of the mean of “n” measurements as a percentage of the variance of one measurement, indicating that this parameter has little effect on the temporal environment of the three traits. These results demonstrate that previous training can influence the regularity of the gait, in agreement with the present study, in which only adult animals that have already had previous training were evaluated. Furthermore, our results corroborate that traits with high repeatability require few measurements (3 in this study) to obtain greater precision, and an increase in the number of measurements may be irrelevant for parameter estimation. In a breeding plan, this high precision may be due to an increase in the additive genetic variance, indicating that two or more measurements have greater heritability than a single measurement, allowing a better breeding value estimate to be obtained. Considering the high repeatability values found, this parameter can be used as an indicator of how effective the selection process can be, considering its relationship with heritability, as repeatability values are higher than heritability, due to the inclusion of permanent environmental variance (within-individual variance) in its estimation. High values also indicate that individuals tend to have consistent performance, and therefore, obtaining several measures for evaluation may be impractical. It can be concluded that overreach, term, and acuteness have high repeatability values, which allows these traits to respond better in a selection process and performance evaluation for subsequent competitions.

313 Many farmers want a prediction of future performance, not necessarily breeding potential

Abstract High heritability is a characteristic of many performance traits in cattle translating to a relatively strong correlation between an (accurate) estimated breeding value for an animal and its (future) production potential. However, the broad sense heritability (i.e., numerator in the formula includes both additive and non-additive genetic variance) is often larger than the narrow sense heritability (i.e., numerator includes only the additive genetic variance) especially for low heritability traits like reproductive performance. Moreover, the repeatability of a trait can be greater again, the difference between the heritability and repeatability is the inclusion of a permanent environmental variance in the numerator. Accurate and pertinent omic-based animal-level features can contribute to an increase in narrow sense heritability owing to a possible reduction in the residual variance; such data can also provide insights into both non-additive genetic variance and the permanent environmental variance. Even at the level of the contemporary group, omic-level data from the likes of bulk blood/saliva/milk sample, can improve how systematic environmental effects are modeled in the mixed model equations thus also reducing the residual variance. The greater heritability will contribute to a strengthening of the correlation between future phenotypic performance potential and estimated additive genetic merit. Importantly, omic measures for individual animals can help predict the permanent environmental effect of that animal capturing the yet untapped contributor to phenotypic variance. Irrespective, predictions need to be replaced by prescriptions; end users are inundated with data yet crave actionable information. Animal breeders have a lush (pardon the pun!) history of distilling estimates of genetic merit for a plethora of traits into a single value per animal (i.e., breeding indexes); this is the first step in transitioning from predictions to prescriptions. Given the familiarity of end users with breeding indexes, the potential exists to develop production indexes to rank commercial animals on future expected performance taking cognizance of genetic and non-genetic effects. Such production indexes align well with the associated breeding index but where the estimates of genetic merit for the traits in question are replaced by estimates of production merit; these estimated production values are the sum of the additive genetic, non-additive genetic and permanent environmental effects. The weighting factors in the production indexes are more short-term using current costs and prices as opposed to breeding indexes which are longer term. In comparison with the breeding indexes, the production indexes can include ancillary information like gender, age, and month of calving/birth. Like breeding indexes, they are updated in real time. Examples of the application of genome-enabled production indexes are guides for valuing animals for purchase or culling.

347 Quantifying phenotypic and genetic variation for cow fertility phenotypes in American Simmentals using total herd reporting data

Abstract Inefficiencies in reproduction are the largest drivers of loss in United States beef production. Nearly all reproductive traits are at least modestly heritable, making genetic selection and improvement possible over the long term. Cow-centric reproductive traits present unique challenges, as they are not expressed in male animals and will not be observed until later in the productive life of a female, if at all. This makes cow-centric fertility traits ideal candidates for selection with Expected Progeny Differences (EPDs). We used a commercially generated dataset from the American Simmental Association (ASA) to explore the phenotypic and genetic variation in these traits. The dataset comprised Total Herd Enrollment (THE) records, which required unbiased reporting of enrollment, calving, and disposal for 303,158 females (132,403 cows and 170,755 heifers). Using the THE data, we calculated three continuous and two discrete traits that are indicators of heifer and cow fertility and rebreeding ability. These traits were: 1) calving date (calving date of the cow relative to the start of the calving season), 2) calving interval (days between calves), 3) first calving interval (calving interval observation between the first and second calving record for a female), 4) heifer pregnancy (did the animal calve as 2-yr-old?), and 5) discrete early calving (did animal calve in the first 30 d of the calving season?). We observed a significant difference in calving date in mature cows that calved in the first 30 d of the calving season of their contemporary group as heifers compared with those that calved later (Table 1). Early calving heifers averaged a nearly 10 d sooner calving date throughout their productive lives compared with those that calved after 30 d as heifers (29.9 d vs. 40.2 d, P-value < 0.01). We used a combined pedigree and genomic approach, implemented in GIBBSF90+, to estimate genetic, environmental, and permanent environmental variance components. We estimated heritabilities of 0.24 (± 0.037215) for heifer pregnancy, 0.06 (± 0.009265) for calving date, 0.07 (± 0.015451) for discrete early calving, 0.04 (± 0.003954) for calving interval, and 0.04 (± 0.009604) for first calving interval. We observed positive phenotypic and genetic correlations between these traits, ranging from 0.01 to 0.96 (Table 2). This work provides an extensive population survey of phenotypic trends in reproductive performance and management in a large seedstock population. The heritable nature of the inspected traits motivates further work in understanding their genetic architectures and how they are affected by heterosis in crossbreeding programs.

116 Contribution of X-chromosome to the genetic variance of litter size and stillbirth in pigs

Abstract Genes with important roles in reproduction traits have been described in pig X-chromosome (SSCX), which may contribute considerably to genetic variance and could impact the accuracy of genomic prediction. The phenotypic data set includes information on 82,580 litter size records from 34,346 sows. Additionally, there were 108,006 in the pedigree, and genotypes for 41,492 SNPs, of which 1,435 in SSCX, were available for 30,000 animals. Three different approaches were used to estimate variance components for the total number born and number of stillborn. In the first approach, only SNPs in the autosomes were used and the model included the additive genetic (u), permanent environment (p), and residual (e) random effects. In the second approach, the same model was adopted; however, the data included SNPs in the autosomes and in the X chromosome. For both models, it was assumed that u~N(0,H⊗Vu), e~N(0,I⊗Ve) and p~N(0,I⊗Vp), wherein H is a relationship matrix that combines pedigree and genomic information; and, Vu, Vp, and Ve are the (co)variance matrices for the random additive genetic, permanent environment, and residual effects, respectively. In the third approach, the random additive genetic effects associated with the X-chromosome (s) were included in the model described before. Then, it was assumed that s~N(0,Hx⊗Vs), where Hx is the genomic relationship matrix for the X-chromosome and Vs is the additive genetic variance associated with the SSCX. All analyses were carried out using GIBBSF90+. Table 1 shows variance components for the three approaches. For both traits, additive genetic variance and heritabilities were similar across the approaches. The additive genetic variance for the SSCX was small, accounting for approximately two percent of the total additive genetic variance. Including the X chromosome as a separate effect and using Hx as a covariance matrix captured only a small part of the additive genetic variance. The additive variance across approaches did not differ. For total number born and number of stillborn, using the SSCX SNPs to build Hx did not provide extra benefit compared with fitting those SNP in the regular H matrix.

121 Copy number variation: From mapping to variance components in Holsteins

Abstract Copy number variations (CNVs) modify transcription levels mainly through changing genes and regulatory elements dosages. Hence, CNVs can contribute to the additive genetic variance of quantitative traits, which might not be fully captured by SNPs. Here, CNVs were mapped from 3,601 Holsteins and their contribution to complex traits was explored by i) quantitative trait loci (QTL) enrichment analysis, and ii) variance component analysis for metritis (1 = disease, 0 = no disease). The 4,113 CNVs derived from high-density (777k) genotypic information were compiled into 1,184 CNV regions (CNVRs) overlapping 7,832 QTLs reported in dairy cattle and available in the AnimalQTLdb. Fisher’s exact test was applied to perform the QTL enrichment analysis followed by False Discovery Rate (FDR) correction. Then, QTLs overlapping CNVRs were enriched for traits when the corrected P-value (PFDR) was lower than 0.05. For variance components estimation, genomic information from all CNVs loci was used to build the CNV-derived genomic relationship matrix (CNV_GRM). CNV loci showing double deletions, single deletions, and normal state in the population were coded as 0, 1, and 2, respectively, whereas CNV loci presenting normal state, single duplications, and double duplications were coded as 0, 1, and 2, respectively. Given that some CNVs presented both deletions and duplications at the same position in the population, these mixed-CNVs were treated as two distinct CNVs loci, one to represent deletions and the other to represent duplications. The variance components for metritis were estimated by fitting either only a SNP-derived GRM (SNP_GRM) or jointly SNP_GRM and CNV_GRM considering 3,272 cows in models that included the female category and farm-year-season as fixed effects. QTLs overlapped with CNVRs were enriched for milk (e.g., milk fat yield, milk yield, and milk protein percentage), reproduction (e.g., calving ease, non-return rate, and heat intensity), exterior (e.g., teat length, udder depth, and udder height), production (e.g., length of productive life, lifetime profit index, and net merit), and health traits (e.g., immune globulin G level, somatic cell, and bovine respiratory susceptibility). Regarding variance components, copy number variations were able to capture a fraction of the additive genetic variance not captured by SNPs alone, increasing the heritability estimate for metritis. In summary, our findings suggest that CNVs overlap QTLs associated with economically important traits more than only by chance and CNVs can capture additional additive genetic variance not fully captured by SNPs alone.

Genetic Variance in Heparan Sulfation Is Associated With Salt Sensitivity.

High heritability of salt sensitivity suggests an essential role for genetics in the relationship between sodium intake and blood pressure (BP). The role of glycosaminoglycan genes, which are crucial for salinity tolerance, remains to be elucidated. Interactions between 54 126 variants in 130 glycosaminoglycan genes and daily sodium excretion on BP were explored in 20 420 EPIC-Norfolk (European Prospective Investigation Into Cancer in Norfolk) subjects. The UK Biobank (n=414 132) and the multiethnic HELIUS study (Healthy Life in an Urban Setting; n=2239) were used for validation. Afterward, the urinary glycosaminoglycan composition was studied in HELIUS participants (n=57) stratified by genotype and upon dietary sodium loading in a time-controlled crossover intervention study (n=12). rs2892799 in NDST3 (heparan sulfate N-deacetylase/N-sulfotransferase 3) showed the strongest interaction with sodium on mean arterial pressure (false discovery rate 0.03), with higher mean arterial pressure for the C allele in high sodium conditions. Also, rs9654628 in HS3ST5 (heparan sulfate-glucosamine 3-sulfotransferase 5) showed an interaction with sodium on systolic BP (false discovery rate 0.03). These interactions were multiethnically validated. Stratifying for the rs2892799 genotype showed higher urinary expression of N-sulfated heparan sulfate epitope D0S0 for the T allele. Conversely, upon dietary sodium loading, urinary D0S0 expression was higher in participants with stable BP after sodium loading, and sodium-induced effects on this epitope were opposite in individuals with and without BP response to sodium. The C allele of rs2892799 in NDST3 exhibits higher BP in high sodium conditions when compared with low sodium conditions, whereas no differences were detected for the T allele. Concomitantly, both alleles demonstrate distinct expressions of D0S0, which, in turn, correlates with sodium-mediated BP elevation. These findings underscore the potential significance of genetic glycosaminoglycan variation in human BP regulation.

Scouting rice (Oryza sativa L.) landraces for moisture stress tolerance using morphometric diversity analysis

Climate-change is a major threat to sustainable agriculture. Rice is one of the staple food grains that sustain 2/3rd of global population. Therefore, developing climate-resilient rice genotypes to combat moisture stress and other climate change factors are most important to address the challenges posed by climate change. The vast availability of genetic variance among the rice genotypes for different traits favours the selection of desirable donor parents. A field investigation was carried out to characterize 21 traditional rice landraces under induced moisture stress in 2 environments including induced moisture stress (S) and non-stress (NS). The response to the varied level of environments was observed in terms of biometric and yield parameters followed by multivariate analysis and variability analysis. The study revealed higher phenotypic-coefficient of variation (PCV) and genotypic-coefficient of variation (GCV) for plant height and single plant yield in both environments. High heritability was also recorded in the moisture stressed environment for plant height (98.63 %), single plant yield (98.19 %) and productive tillers (95.12 %), whereas under NS, the heritability is more for single plant yield only (99.56 %). The genotypes formed nine clusters under the S environment, based on the Euclidean distance, of which the three CMS lines segregated in cluster III distinctly. The principal component analysis exhibited three principal components with a cumulative variance of 86.13 % and 86.40 % in the NS and S environments respectively. The traits such as days to 50 % flowering, panicle height, panicle length, grains per panicle, productive tillers, thousand-grain weight and single plant yield were the positive contributors. Spikelet fertility (%) and harvest index were positively correlated with seed yield per plant. Path-coefficient analysis was significant for grain yield, harvest index, spikelet fertility, pollen fertility and plant height and positively influenced by productive tillers plant-1. The study concluded the prospect of using Mattaikar, Rajalakshmi, Mallikar and Kuliyadichan as potential donors for developing upland rice with moisture stress resilience.

Genomic-inferred cross-selection methods for multi-trait improvement in a recurrent selection breeding program

The major drawback to the implementation of genomic selection in a breeding program lies in long-term decrease in additive genetic variance, which is a trade-off for rapid genetic improvement in short term. Balancing increase in genetic gain with retention of additive genetic variance necessitates careful optimization of this trade-off. In this study, we proposed an integrated index selection approach within the genomic inferred cross-selection (GCS) framework to maximize genetic gain across multiple traits. With this method, we identified optimal crosses that simultaneously maximize progeny performance and maintain genetic variance for multiple traits. Using a stochastic simulated recurrent breeding program over a 40-years period, we evaluated different GCS methods along with other factors, such as the number of parents, crosses, and progeny per cross, that influence genetic gain in a pulse crop breeding program. Across all breeding scenarios, the posterior mean variance consistently enhances genetic gain when compared to other methods, such as the usefulness criterion, optimal haploid value, mean genomic estimated breeding value, and mean index selection value of the superior parents. In addition, we provide a detailed strategy to optimize the number of parents, crosses, and progeny per cross that can potentially maximize short- and long-term genetic gain in a public breeding program.

Estimation of Combining Ability Effect for Hybrid Breeding in Bottle Gourd [Lagenaria siceraria (Molina) Standl.] Using Diallel Mating Design

A half diallel mating design was employed to cross eight parental lines of bottle gourd, resulting in 28 F1 hybrids (excluding reciprocals). These F1 hybrids, along with the parental lines and a commercial check (Pant Lauki-3), were evaluated during the summer of 2024 at Govind Ballabh Pant University of Agriculture and Technology, Pantnagar. The study aimed to assess the combining ability for yield and yield-related traits using a randomized complete block design with three replications. The genetic variance ratio being less than unity suggested that non-additive gene action was dominant for all the characters. The analysis indicated that none of the parents consistently exhibited good general combining ability across all traits. However, the cross combination NDBG-104 × Pusa Sandesh followed by Pant Lauki-4 × Kashi Kirti emerged as the best for most of the traits analyzed. The results suggest that it may be possible to further enhance fruit yield and earliness through hybridization and selection in transgressive segregants.

Recombination fraction in pre-recombinant inbred lines (PRERIL) - revisiting a century old problem in genetics

BackgroundTraditional recombinant inbred lines (RILs) are generated from repeated self-fertilization or brother-sister mating from the F1 hybrid of two inbred parents. Compared with the F2 population, RILs cumulate more crossovers between loci and thus increase the number of recombinants, resulting in an increased resolution of genetic mapping. Since they are inbred to the isogenic stage, another consequence of the heterozygosity reduction is the increased genetic variance and thus the increased power of QTL detection. Self-fertilization is the primary form of developing RILs in plants. Brother-sister mating is another way to develop RILs but in small laboratory animals. To ensure that the RILs have at least 98% of homozygosity, we need about seven generations of self-fertilization or 20 generations of brother-sister mating. Prior to homozygosity, these lines are called pre-recombinant inbred lines (PRERIL). Phenotypic values of traits in PRERILs are often collected but not used in QTL mapping. To perform QTL mapping in PRERILs, we need the recombination fraction between two markers at generation t for t < 7 (selfing) or t < 20 (brother-sister mating) so that the genotypes of QTL flanked by the markers can be inferred.ResultsIn this study, we developed formulas to calculate the recombination fractions of PRERILs at generation t in self-fertilization, brother-sister mating, and random mating. In contrast to existing works in this topic, we used computer code to construct the transition matrix to form the Markov chain of genotype array between consecutive generations, the so-called recurrent equations.ConclusionsWe provide R functions to calculate the recombination fraction using the newly developed recurrent equations of ordered genotype array. With the recurrent equations and the R code, users can perform QTL mapping in PRERILs. Substantial time and effort can be saved compared with QTL mapping in RILs.

Improved Tomato Breeding Lines Adapted to Organic Farming Systems Have Enhanced Flavor, Yield, and Disease Resistance

Most of the tomato (Solanum lycopersicum) varieties currently used in organic farming were bred for conventional farming, often characterized by high-input use. These varieties do not perform as well in low-input organic systems, generating the need to develop varieties that are adapted to organic management systems. This project focused on improving flavor, disease resistance, and yield, all identified as key traits by organic tomato farmers in the Upper Midwest, USA. Ten advanced tomato breeding lines and two check varieties were developed and evaluated for 16 traits in organic high tunnel systems in 2020 and 2021. The line CSDE-F6.47 averaged 6.32 kg/plant and obtained high flavor intensity and overall flavor scores (3.78 and 3.69 out of 5, respectively). The line JBDE-F5.31 was another outstanding line, with a yield of 5.18 kg/plant, with good flavor intensity (3.32) and overall flavor (2.92) scores. Broad-sense heritability of marketable weight per plant was high (0.91), and the genetic variance was also high, which shows the opportunity to continue to increase the marketable weight in lines with excellent flavor. A significant positive correlation was found between overall flavor and °Brix (0.56), and titratable acidity (0.70), indicating that both measurements can be good predictors of overall flavor. The most promising lines will be further evaluated on-farm to evaluate their potential as releasable varieties.

Abstract 72: Genetic variance in heparan sulfation is associated with salt-sensitivity: a gene-environment analysis in EPIC-Norfolk, UK biobank, and HELIUS

Background: High heritability of salt sensitivity suggests an essential role for genetics in the relationship between sodium intake and blood pressure (BP). The role of glycosaminoglycan (GAG) genes, which are crucial for salinity tolerance througout evolution, remains to be elucidated. Methods: Interactions between 54,126 variants in 130 GAG genes and daily sodium excretion on BP were explored in 20,420 EPIC-Norfolk subjects. UK Biobank (n=414,132) and the multi-ethnic HELIUS study (n=2,239, comprising a balanced representation of people from European, Subsaharan, South Asian, Turkish, and Moroccan descent) were used for validation. Afterwards, urinary GAG composition was studied in HELIUS participants (n=57) stratified by genotype, and upon dietary sodium loading in a time-controlled crossover intervention study (n=12). Results: rs2892799 (NDST3) showed the strongest interaction with sodium on mean arterial pressure (MAP) (FDR 0.03), with higher MAP for the C-allele in high sodium conditions. Also, rs9654628 (HS3ST5) showed an interaction with sodium on systolic BP (FDR 0.03). These interactions were multi-ethnically validated (figures A and B, respectively). Stratifying for rs2892799 genotype, showed higher urinary expression of N-sulfated heparan sulfate epitope D0S0 for the T-allele. Conversely, upon dietary sodium loading, urinary D0S0 expression was higher in participants with stable BP after sodium loading, and sodium-induced effects on this epitope were opposite in individuals with and without BP response to sodium. Conclusion: The C-allele of rs2892799 (NDST3) exhibits higher BP in high sodium conditions when compared to low sodium conditions, whereas no differences were detected for the T-allele. Concomitantly, both alleles demonstrate distinct expressions of D0S0, which, in turn, correlates with sodium-mediated BP elevation. These findings underscore the potential significance of genetic GAG variation in human BP regulation.

Mapping rust resistance in European winter wheat: many QTLs for yellow rust resistance, but only a few well characterized genes for stem rust resistance

Key messageStem rust resistance was mainly based on a few, already known resistance genes; for yellow rust resistance there was a combination of designated genes and minor QTLs.Yellow rust (YR) caused by Puccinia striiformis f. sp. tritici (Pst) and stem rust (SR) caused by Puccinia graminis f. sp. tritici (Pgt) are among the most damaging wheat diseases. Although, yellow rust has occurred regularly in Europe since the advent of the Warrior race in 2011, damaging stem rust epidemics are still unusual. We analyzed the resistance of seven segregating populations at the adult growth stage with the parents being selected for YR and SR resistances across three to six environments (location–year combinations) following inoculation with defined Pst and Pgt races. In total, 600 progenies were phenotyped and 563 were genotyped with a 25k SNP array. For SR resistance, three major resistance genes (Sr24, Sr31, Sr38/Yr17) were detected in different combinations. Additional QTLs provided much smaller effects except for a gene on chromosome 4B that explained much of the genetic variance. For YR resistance, ten loci with highly varying percentages of explained genetic variance (pG, 6–99%) were mapped. Our results imply that introgression of new SR resistances will be necessary for breeding future rust resistant cultivars, whereas YR resistance can be achieved by genomic selection of many of the detected QTLs.

Measurement Invariance Testing Works.

Psychometricians have argued that measurement invariance (MI) testing is needed to know if the same psychological constructs are measured in different groups. Data from five experiments allowed that position to be tested. In the first, participants answered questionnaires on belief in free will and either the meaning of life or the meaning of a nonsense concept called "gavagai." Since the meaning of life and the meaning of gavagai conceptually differ, MI should have been violated when groups were treated like their measurements were identical. MI was severely violated, indicating the questionnaires were interpreted differently. In the second and third experiments, participants were randomized to watch treatment videos explaining figural matrices rules or task-irrelevant control videos. Participants then took intelligence and figural matrices tests. The intervention worked and the experimental group had an additional influence on figural matrix performance in the form of knowing matrix rules, so their performance on the matrices tests violated MI and was anomalously high for their intelligence levels. In both experiments, MI was severely violated. In the fourth and fifth experiments, individuals were exposed to growth mindset interventions that a twin study revealed changed the amount of genetic variance in the target mindset measure without affecting other variables. When comparing treatment and control groups, MI was attainable before but not after treatment. Moreover, the control group showed longitudinal invariance, but the same was untrue for the treatment group. MI testing is likely able to show if the same things are measured in different groups.

A Phenome-Wide Association Study (PheWAS) of Genetic Risk for C-Reactive Protein in Children of European Ancestry: Results From the ABCD Study.

C-reactive protein (CRP) is a moderately heritable marker of systemic inflammation that is associated with adverse physical and mental health outcomes. Identifying factors associated with genetic liability to elevated CRP in childhood may inform our understanding of variability in CRP that could be targeted to prevent and/or delay the onset of related health outcomes. We conducted a phenome-wide association study (PheWAS) of genetic risk for elevated CRP (i.e. CRP polygenic risk score [PRS]) among children genetically similar to European ancestry reference populations (median analytic n = 5,509) from the Adolescent Brain and Cognitive Development℠ (ABCD) Study. Associations between CRP PRS and 2,377 psychosocial and neuroimaging phenotypes were estimated using independent mixed effects models. Post hoc analyses examined whether: (1) covarying for measured body mass index (BMI) or removing the shared genetic architecture between CRP and BMI altered phenotypic associations, (2) sex moderated CRP PRS associations, and (3) associations are unconfounded by assortative mating or passive gene-environment correlations (using a within-family analyses). Multiple testing was adjusted for using Bonferroni and false discovery rate (FDR) correction. Nine phenotypes were positively associated with CRP PRS after multiple testing correction: five weight- and eating-related phenotypes (e.g. BMI, overeating), three phenotypes related to caregiver somatic problems (e.g. caregiver somatic complaints), as well as weekday video watching (all ps = 1.2 × 10-7 - 2.5 × 10-4, all p FDR s = 0.0002 - 0.05). No neuroimaging phenotypes were associated with CRP PRS (all ps = 0.0003 - 0.998; all p FDR s = 0.08 - 0.998) after correction for multiple testing. Eating and weight-related phenotypes remained associated with CRP PRS in within-family analyses. Covarying for BMI resulted in largely consistent results, and sex did not moderate any CRP PRS associations. Removing the shared genetic variance between CRP and BMI attenuated all relationships; associations with weekday video watching, caregiver somatic problems and caregiver report that the child is overweight remained significant while associations with waist circumference, weight, and caregiver report that child overeats did not. Genetic liability to elevated CRP is associated with higher weight, eating, and weekday video watching during childhood as well as caregiver somatic problems. These associations were consistent with direct genetic effects (i.e., not solely due to confounding factors like passive gene-environment correlations) and were independent of measured BMI. The majority of associations with weight and eating phenotypes were attributable to shared genetic architecture between BMI and inflammation. The relationship between genetics and heightened inflammation in later life may be partially attributable to modifiable behaviors (e.g. weight and activity levels) that are expressed as early as childhood.

Genetic Analysis of Vitamin C Content in Rapeseed Seedlings by the Major Gene Plus Polygene Mixed Effect Model.

Rapeseed (Brassica napus L.) seedlings are rich in vitamin C (Vc), which is beneficial for humans. Understanding the genetic variance in Vc content has practical significance for the breeding of "oil-vegetable dual-purpose" rapeseed. In this study, the joint segregation analysis of a mixed genetic model of the major gene plus polygene was conducted on the Vc content in rapeseed seedlings. Six generations, including two parents, P1 (high Vc content) and P2 (low Vc content), F1, and the populations of F2, BC1P1, and BC1P2 from two crosses were investigated. Genetic analysis revealed that the genetic model MX2-A-AD was the most fitting genetic model, which indicates that Vc content is controlled by two additive major genes plus additive and dominance polygenes. In addition, the whole heritability in F2 and BC1P1 was higher than that in BC1P2. The largest coefficient of variation for Vc content appeared in the F2 generation. Therefore, for Vc content, the method of single cross recross or single backcross are suggested to transfer major genes, and the selection in F2 would be more efficient than that in other generations. Our findings provide a theoretical basis for the quantitative trait locus (QTL) mapping and breeding of Vc content in rapeseed seedlings.

Prediction of additive genetic variances of descendants for complex families based on Mendelian sampling variances.

The ability to predict the outcome of selection and mating decisions enables breeders to make strategically better selection decisions. To improve genetic progress, those individuals need to be selected whose offspring can be expected to show high genetic variance next to high breeding values. Previously published approaches enable to predict the variance of descendants of two future generations for up to 4 founding haplotypes, or 2 outbred individuals, based on phased genotypes, allele effects and recombination frequencies. The purpose of this study was to develop a general approach for the analytical calculation of the genetic variance in any future generation. The core development is an equation for the prediction of the variance of double haploid lines, under the assumption of no selection and negligible drift, stemming from an arbitrary number of founder haplotypes. This double haploid variance can be decomposed into gametic Mendelian sampling variances (MSV) of ancestors of the double haploid lines allowing usage for non-double haploid genotypes which enables application in animal breeding programs as well as in plant breeding programs. Together with the breeding values of the founders, the gametic MSV may be used in new selection criteria. We present our idea of such a criterion that describes the genetic level of selected individuals in four generations. Since breeding programs do select, the assumption made for predicting variances is clearly violated which decreases the accuracy of predicted gametic MSV caused by changes in allele frequency and linkage disequilibrium. Despite violating the assumption, we found high predictive correlations of our criterion to the true genetic level which was obtained by means of simulation for the "corn" and "cattle" genome models tested in this study (0.90 and 0.97). In practice, the genotype phases, genetic map and allele effects all need to be estimated meaning inaccuracies in their estimation will lead to inaccurate variance prediction. Investigation of variance prediction accuracy when input parameters are estimated was not part of this study.

Systematic discovery of gene-environment interactions underlying the human plasma proteome in UK Biobank

Understanding how gene-environment interactions (GEIs) influence the circulating proteome could aid in biomarker discovery and validation. The presence of GEIs can be inferred from single nucleotide polymorphisms that associate with phenotypic variability - termed variance quantitative trait loci (vQTLs). Here, vQTL association studies are performed on plasma levels of 1463 proteins in 52,363 UK Biobank participants. A set of 677 independent vQTLs are identified across 568 proteins. They include 67 variants that lack conventional additive main effects on protein levels. Over 1100 GEIs are identified between 101 proteins and 153 environmental exposures. GEI analyses uncover possible mechanisms that explain why 13/67 vQTL-only sites lack corresponding main effects. Additional analyses also highlight how age, sex, epistatic interactions and statistical artefacts may underscore associations between genetic variation and variance heterogeneity. This study establishes the most comprehensive database yet of vQTLs and GEIs for the human proteome.

Experimental unbalance: reflections on genetic parameters and corn selection gain

Genetic progress depends on the components of variance and genetic parameters. In this context, the objective of this work was to identify the maximum allowed percentage of experimental imbalance in the estimation of variance components, genetic parameters and selection gain in intervarietal corn hybrids. The experimental design used was randomized blocks, with 60 corn hybrids with three replications. Random subsets of data were created in order to express unbalance scenarios of the phenotypic matrix, we obtained 9 scenarios (0, 10, 20, 30, 40, 50, 60, 70 and 80% unbalance).Thus, the genetic variance of the paternal genitor, the genetic variance of the maternal genitor, additive variance, individual phenotypic variance, narrow-sense heritability of intervarietal additive effects for the paternal genitor were estimated, narrow-sense heritability of intervarietal additive effects for the maternal genitor, narrow-sense heritability, broad-sense heritability, genotypic variation coefficient between progenies and variation coefficient residual. Narrow sense heritability reveals high magnitude for the characters cob mass and hundred grain mass. Maternal effect is expressed through ear insertion height, ear grain mass, cob mass and diameter, number of rows, ear mass and length, and grain yield. The imbalance inflates the variance components and genetic parameters for the highlighted characters. It supports 10% imbalance in the data matrix without compromising the reliability of the estimates. Pronounceable selection gains are obtained for grain yield based on pressures of 1 and 5%. An unbalance of 10% is supported in the selection gain for cob mass, ear length and hundred grain mass.