PSVII-28 Genetic analysis of frame score and performance traits in Nellore cattle–growth, carcass and efficiency traits

We evaluated a novel frame score system for Nellore cattle raised on pasture (Guimarães et al., 2020 J, Anim. Sci. 98 (Suppl. 4)). Growth traits included age-adjusted weights (kg) at birth (W0), 120 (W120), 210 (W210) and 450 (W450) days of age, and adult weight (AW). Carcass traits (by ultrasound) included ribeye area (REA, cm2), 12th-13th rib backfat (BF, mm), rump fat (RF, mm), and intramuscular fat (IMF, %). Feed efficiency traits included residual feed intake (RFI, kg of DM/d) and DM intake (DMI, kg/d). We applied the previously developed equations (that also include hip height (HH, cm) and age to 12,049 records from the National Association of Breeders and Researchers (ANCP) database:FSmales= -20.35 + 0.1305*REA + 0.2633*BF - 0.5901*RF+ 0.1139*HH + 0.0056*AGEFSfemales = -11.87 + 0.1316*REA - 0.2457*BF - 0.6218*RF + 0.1139*HH + 0.0009507*AGEThe (co)variance components and genetic parameters were estimated using a linear animal model or a threshold animal model depending on the trait. The heritability estimate for frame score was moderate (0.30). Estimated genetic correlations were moderate for growth traits (W0, 0.51; W120, 0.41; W210, 0.35; W450, 0.29; AW, 0.39). Estimated genetic correlations for carcass and efficiency traits ranged from low to high (REA, 0.50; BF, -0.25; RF, -0.84; IMF, -0.15; DMI, 0.29; RFI, 0.10). The estimates support the notion that larger frame animals are heavier, leaner and later maturing, and less feed efficient. The new frame score may be a useful tool for genetic selection of animals that are best suited to their environment.

PSVII-29 Genetic analysis of frame score and performance traits in Nellore cattle – reproduction and productivity traits

Frame score systems developed for Bos taurus breeds do not translate well for Bos indicus breeds, as these tend to be taller. We evaluated a novel frame score system for Nellore cattle raised on pasture [Guimaraes et al., 2020 J, Anim. Sci. 98 (Suppl. 4)]. Traits included adjusted scrotal circumference (cm) at 365 (SC365) and 450 (SC450) days of age, age at first calving (AFC, d), early heifer pregnancy (EHP, % calving as two-year-olds), stayability (STAY, % of cows remaining in the herd over 76 months of age, calving at least three times), and accumulated productivity (AP, kg of calf weaned per cow per year). In this study, we applied the previously developed equations (that also include hip height (HH, cm) and age to 12,049 records from the National Association of Breeders and Researchers (ANCP) database:FSmales= -20.35 + 0.1305*REA + 0.2633*BF - 0.5901*RF + 0.1139*HH + 0.0056*AGEFSfemales = -11.87 + 0.1316*REA - 0.2457*BF - 0.6218*RF + 0.1139*HH + 0.0009507*AGEThe (co)variance components and genetic parameters were estimated using a linear animal model or a threshold animal model depending on the trait. The heritability estimate for frame score was moderate (0.30). Estimated genetic correlations for reproductive traits were low to moderate (SC365, 0.06; SC450, 0.04; AFC, 0.18; EHP, -0.24; STAY, 0.06; AP, 0.25). The estimates support the notion that larger frame animals are later maturing but have greater lifetime productivity. The new frame score may be a useful tool for genetic selection of animals that are best suited to their environment.

Assessment of Genetic Variability and Correlation of Yield Related Traits in Chickpea (Cicer arietinum L.)

The present investigation was undertaken with 21 genotypes of Chickpea, (including one check) during Rabi 2020-21 in a randomized block design with three replications at field experimentation centre of Department of Genetics and Plant Breeding. Sam Higginbottom University of Agriculture, Technology and Sciences. The data were recorded for 13 characters to study genetic variability, correlation and path analysis. Analysis of variance among 21 genotypes showed highly significant differences for all the characters indicated the presence of substantial amount of genetic variability. On the basis of mean performance, the highest seed yield was observed in genotype NBEG-1121 and RVS-5949. The estimates of GCV and PCV were moderate for plant height, number of primary branches. Low GCV and PCV was recorded for days to 50% flowering. The difference between PCV and GCV was very low for all 13 yield contributing characters. This indicates that the influence of environment factor was low on these characters. High heritability estimate was recorded for number of seeds per plant, number of secondary branches per plant. A high magnitude of genetic advance expressed as a percent of mean was observed in respect of number of seeds per plant, number of secondary branches per plant. Seed yield per plant exhibited positive and highly significant correlations with days to 50% Flowering, no of secondary branches at both genotypic and phenotypic level. The positive non-significant correlations of seed yield per plant were found with number of secondary branches and days to maturity. Negative non- significant correlation was being observed with the plant height, number of primary branches per plant and number of pods per plant. Path analysis at both genotypic and phenotypic level showed positive direct effects by days to 50% flowering, number of secondary branches per plant. The characters identified above as important components merit due to consideration in the formulation of effective selection strategy in chickpea for developing high yielding varieties.

Development of Improved Mutants for Grain Yield and Related Traits in Finger Millet (Eleusine coracana L Gaertn.) Through Gamma Mutagenesis

Recombination breeding is limited in finger millet due to tiny florets and irregular anthesis. Induced mutagenesis using gamma irradiation was attempted to improve grain yield and related traits in three elite cultivars (GPU28, GPU 67 and MR 6). Mean performance and estimates of BLUP and BLUE of 61 mutants showed variability among the mutants for grain yield in comparison to parental genotypes. Grain yield being a complex trait and influenced by high G × E interaction recorded low heritability estimate sand therefore BLUP provided more reliable estimates for grain yield. Stability analysis was conducted using GGE biplot techniques and also by plotting the trait means with their respective coefficient of variation values(CVs). From mean performance and stability analysis, the mutants GPU28-2212, GPU28-2086, GPU28-2082 derived from GPU 28 variety, GPU67-3358, GPU67-3359, GPU67-2094 from GPU67 variety and MR6-3393 from MR6 variety were superior for grain yield and related traits. Combined association analysis revealed tillers per plant as highly correlated trait with grain yield indicating key trait for grain yield improvement in finger millet. Mutants with improved finger traits and blast resistance are also discussed.

PERFORMANCE EVALUATION AND VARIABILITY ANALYSIS FOR MAJOR GROWTH AND FLOWERING TRAITS OF Lilium longiflorum THUNB. GENOTYPES

The performance evaluation and variability estimation are some of the most important basic steps in any breeding scheme and are usually taken as a pre-breeding task. This experiment was carried out to evaluate the performance and estimate the variability for major growth and flowering traits of Sixteen Lilium longiflorum Thunb genotypes in a randomized complete block design (RCBD) with three replications. The estimated analysis of variance (ANOVA) demonstrated prevailing significant variability for studied ten growth and flowering traits of all 16 genotypes. The experiment was conducted to quantify the genetic variability, heritability, and genetic advance as a percentage of the mean (GAM), for understanding the breeding potentiality of L. longiflorum genotypes based on their performance for growth and flowering traits, so that pre-breeding scheme could be executed properly. The estimated genotypic coefficient of variation (GCV) and phenotypic coefficient of variation (PCV) value, along with heritability estimate and GAM, revealed that almost all studied traits besides flower diameter were controlled by additive gene action. For these traits, improvement would be possible through the selection after cross-breeding among these genotypes. The choice of genotypes for the hybridization scheme could be made based on the mean performance of those genotypes for the particular traits of the breeding scheme.

​Enhancement of Production Performance of Tharparkar Cattle using Lactation Persistency as A Selection Tool

Background: Lactation persistency of cattle is the ability to maintain constant milk production after the peak milk yield. This work was conducted using a total of 372 daily milk yield records of all parities of 190 Tharparkar cattle sired by 38 bulls from 1990 to 2019 (29 years), collected from Animal Genetics and Breeding Division, maintained in Livestock Research centre of NDRI, Karnal. Methods: The lactation persistency was estimated using Mahadevan’s method (1951) based on the ratio of milk production in first 28 weeks to milk production in 10 weeks. Standard error as percentage of mean was used to test the efficiency of persistency indices. To study the effect of non-genetic factors Least-squares analysis was carried out by Harvey (1990) model. Result: The least squares mean of total milk yield (TMY), lactation length (LL), peak yield (PY), days to attain peak yield (DAPY) and lactation persistency (LP) were 1633.40±45.79Kg, 272.55±4.64 days, 10.83±0.17kg, 41.48±2.34 days and 1.27±0.02 respectively. First calvers had highest LL, DAPY and persistency. Rainy calvers had more TMY, LL, DAPY and LP and winter calvers were the low performers. Correlation of persistency with TMY, LL and DAPY were highly significant and positive. There was negative correlation with PY and LP. The estimated heritability for TMY, LL and PY was 0.16±0.34, 0.49±0.38 and 0.84±0.41 respectively. The heritability estimate of lactation persistency was very low with high standard error which revealed the significance of environmental effect in persistency.

Eleven genomic loci affect plasma levels of chronic inflammation marker soluble urokinase-type plasminogen activator receptor

Soluble urokinase-type plasminogen activator receptor (suPAR) is a chronic inflammation marker associated with the development of a range of diseases, including cancer and cardiovascular disease. The genetics of suPAR remain unexplored but may shed light on the biology of the marker and its connection to outcomes. We report a heritability estimate of 60% for the variation in suPAR and performed a genome-wide association meta-analysis on suPAR levels measured in Iceland (N = 35,559) and in Denmark (N = 12,177). We identified 13 independently genome-wide significant sequence variants associated with suPAR across 11 distinct loci. Associated variants were found in and around genes encoding uPAR (PLAUR), its ligand uPA (PLAU), the kidney-disease-associated gene PLA2R1 as well as genes with relations to glycosylation, glycoprotein biosynthesis, and the immune response. These findings provide new insight into the causes of variation in suPAR plasma levels, which may clarify suPAR’s potential role in associated diseases, as well as the underlying mechanisms that give suPAR its prognostic value as a unique marker of chronic inflammation.

Genetic parameters and trends for reproductive traits in Blanco Orejinegro cattle from Colombia

The Colombian creole cattle breed Blanco Orejinegro (BON) is an important zoogenetic resource, but there is little knowledge about the genetic parameters and trends of its reproductive traits. Therefore, the aim of this study was to estimate parameters for the reproductive traits calving interval (CI), age at first calving (AFC), gestation duration (GD) and genetic trends for CI in the BON breed. Genealogy information from 7,799 animals was used, and employing the MTDFREML program, the components of the variance, heritability (h2), repeatability (rep), and estimated breeding values (EBV) for CI (n=3308), AFC (n=729), and GD (n=306) were estimated, in addition to the inbreeding coefficient (F) of the population. Genetic trends were established through linear regression using R software. Finally, the animals were classified as inbred (F > 0) and noninbred (F=0), and the effect of inbreeding on reproductive performance was established through a generalized linear model using the R program. An average F value of 4.41%±0.06 was observed. The h2 for CI was 0.11±0.03 with a rep of 0.15±0.04; for AFC, h2 was 0.00±0.05; and for GD, h2 was 0.00±0.08. The genetic trend for CI was -0.01 days/year. Finally, for CI, inbreeding depression was evident; this trait increased when inbreeding increased. These results indicate an important environmental influence on reproductive traits. The heritability estimate for CI suggests that little genetic progress could be achieved through selection. The evidence of inbreeding depression raises the need to control inbreeding to conserve this genetic resource.

PSIII-7 Relationship of Age and Genetics with the Methylation Profile of Beef Cattle

This study aimed to compare models for the prediction of cow age from DNA methylation profiles and estimate the heritability of the proportion of methylated sites (PM) and methylation status at each site (MS). Methylation data from blood samples of cows (n=136) were generated from the HorvathMammalMethylChip40 array that consists of 34,324 CpG sites that mapped to the bovine genome. Methylation status was determined by the distribution of the methylation values, with values above, within and below 2 standard deviations classified as methylated (2), intermediately methylated (1) and unmethylated (0), respectively. Principal component analysis (PCA) was applied to a (co)variance methylation status matrix. The first and second PC accounted for 25.65% and 9% of the total variance, respectively. Five Bayesian models (Bayesian ridge regression, BayesA, BayesB, BayesCπ and Bayesian LASSO) were implemented with the BGLR package in R. Bootstrapping validation (n=400) was used to evaluate the tested models, with 102 and 34 individuals in the training and validation sets, respectively. The correlation between the predicted and true age was high (r = 0.97 to 0.99). A BayesA model performed the best (r = 0.99, MSE = 0.11 and slope = 0.93), while Bayesian LASSO was the least accurate (r = 0.97, MSE = 0.26 and slope = 0.88). Heritability was estimated using GBLUP implemented in the BGLR package. The mean (SD) heritability estimate for PM was 0.46 ± 0.10 and the heritability of MS ranged from 0.18 to 0.73 (mean = 0.33). The 10% of sites with the highest heritability (343 sites; mean = 0.62) were located in exon (91), intron (84), intergenic (152), and promoter (16) regions. The largest number of these top sites (31) were located on chromosome 3 in genetic or intergenic regions close to transcription factor binding sites (i.e., FOXO6, ELAV4 and LMO4).

An exploration of the genetic epidemiology of non-suicidal self-harm and suicide attempt

BackgroundEmpirical evidence supporting the distinction between suicide attempt (SA) and non-suicidal self-harm (NSSH) is lacking. Although NSSH is a risk factor for SA, we do not currently know whether these behaviours lie on a continuum of severity, or whether they are discrete outcomes with different aetiologies. We conducted this exploratory genetic epidemiology study to investigate this issue further.MethodsWe explored the extent of genetic overlap between NSSH and SA in a large, richly-phenotyped cohort (the Avon Longitudinal Study of Parents and Children;N = 4959), utilising individual-level genetic and phenotypic data to conduct analyses of genome-wide complex traits and polygenic risk scores (PRS).ResultsThe single nucleotide polymorphism heritability of NSSH was estimated to be 13% (SE 0.07) and that of SA to be 0% (SE 0.07). Of the traits investigated, NSSH was most strongly correlated with higher IQ (rG = 0.31, SE = 0.22), there was little evidence of high genetic correlation between NSSH and SA (rG = − 0.1, SE = 0.54), likely due to the low heritability estimate for SA. The PRS for depression differentiated between those with NSSH and SA in multinomial regression. The optimal PRS prediction model for SA (NagelkerkeR20.022,p < 0.001) included ADHD, depression, income, anorexia and neuroticism and explained more variance than the optimal prediction model for NSSH (Nagelkerke R20.010,p < 0.001) which included ADHD, alcohol consumption, autism spectrum conditions, depression, IQ, neuroticism and suicide attempt.ConclusionsOur findings suggest that SA does not have a large genetic component, and that although NSSH and SA are not discrete outcomes there appears to be little genetic overlap between the two. The relatively small sample size and resulting low heritability estimate for SA was a limitation of the study. Combined with low heritability estimates, this implies that family or population structures in SA GWASs may contribute to signals detected.