Heritability Estimates of L*a*b* Color Space Values in Winter Squash (Cucurbita spp.)

Carotenoids serve as protective antioxidants, and function in normal vision, bone growth, cell division and differentiation, and reproduction. Winter squash (Cucurbita spp.) is an excellent dietary source of carotenoids. The range of colors from yellow to red in Cucurbita species indicates that increasing carotenoid levels through plant breeding is possible. The objective of this research was to determine the heritability of flesh color in winter squash in both Cucurbita moschata Duchesne and Cucurbita pepo L. Segregating families representing F2, BC1P1 and BC1P2 populations were created in two families of C. pepo (‘Table Gold Acorn’ × PI 314806 and ‘Table King Bush’ × PI 314806) and one family of C. moschata (‘Butterbush’ × ‘Sucrine DuBerry’). Broad-sense heritabilities were calculated for the F2, BC1P1, and BC1P2 populations within each of the three families. Heritabilities ranged from 0.19 to 0.82 for L*, 0.28 to 0.97 for chroma, and 0.12 to 0.87 for hue across all families. Transgressive segregation for color space values L* was identified in the ‘Table King Bush’ × PI 314806 C. pepo population. Our results indicate that it is possible to breed for improved flesh color in Cucurbita, but the population size and number of test locations for evaluation need to be increased to provide better heritability estimates. Cucurbita species are grown throughout the world and their availability and low price makes them an important potential source of carotenoids for human nutrition and health for all ages.

Organic dry pea (Pisum sativum L.) biofortification for better human health

A primary criticism of organic agriculture is its lower yield and nutritional quality compared to conventional systems. Nutritionally, dry pea (Pisum sativum L.) is a rich source of low digestible carbohydrates, protein, and micronutrients. This study aimed to evaluate dry pea cultivars and advanced breeding lines using on-farm field selections to inform the development of biofortified organic cultivars with increased yield and nutritional quality. A total of 44 dry pea entries were grown in two USDA-certified organic on-farm locations in South Carolina (SC), United States of America (USA) for two years. Seed yield and protein for dry pea ranged from 61 to 3833 kg ha-1 and 12.6 to 34.2 g/100 g, respectively, with low heritability estimates. Total prebiotic carbohydrate concentration ranged from 14.7 to 26.6 g/100 g. A 100-g serving of organic dry pea provides 73.5 to 133% of the recommended daily allowance (%RDA) of prebiotic carbohydrates. Heritability estimates for individual prebiotic carbohydrates ranged from 0.27 to 0.82. Organic dry peas are rich in minerals [iron (Fe): 1.9–26.2 mg/100 g; zinc (Zn): 1.1–7.5 mg/100 g] and have low to moderate concentrations of phytic acid (PA:18.8–516 mg/100 g). The significant cultivar, location, and year effects were evident for grain yield, thousand seed weight (1000-seed weight), and protein, but results for other nutritional traits varied with genotype, environment, and interactions. “AAC Carver,” “Jetset,” and “Mystique” were the best-adapted cultivars with high yield, and “CDC Striker,” “Fiddle,” and “Hampton” had the highest protein concentration. These cultivars are the best performing cultivars that should be incorporated into organic dry pea breeding programs to develop cultivars suitable for organic production. In conclusion, organic dry pea has potential as a winter cash crop in southern climates. Still, it will require selecting diverse genetic material and location sourcing to develop improved cultivars with a higher yield, disease resistance, and nutritional quality.

Genetic-Quantitative Uni- and Bi-Trait Analysis for Growth Traits in the Colombian Creole Breed Blanco Orejinegro (BON)

The Blanco Orejinegro (BON) is a Colombian creole cattle breed that is not genetically well characterized for growth traits. The aim of this work was to estimate genetic parameters for birth weight (BW), weaning weight (WW), yearling weight (YW), daily weight gain between birth and weaning (DWG), time to reach 120 kg of live weight (T120), and time to reach 60% of adult weight (T60%), and establish the selection criteria for growth traits in the BON population of Colombia. Genealogical and phenotypic information for BW, WW, YW, DWG, T120, and T60% traits of BON animals from 14 Colombian herds were used. These traits were analyzed with the AIREML method in a uni- and bi-trait animal model including the maternal effect for BW, WW, DWG, and T120. The direct heritability estimates values were 0.22 ± 0.059 (BW), 0.20 ± 0.057 (WW), 0.20 ± 0.153 (YW), 0.17 ± 0.07 (DWG), 0.26 (T120), and 0.44 ± 0.03 (T60%). The maternal heritability estimates values were 0.14 ± 0.040 (BW), 0.15 ± 0.039 (WW), 0.25 ± 0.06 (DWG), and 0.16 (T120). The direct genetic correlations were high (>|0.60|) among all the traits, except between T60% with BW, WW, YW, and DWG (ranged from -0.02 to -0.51), all in a favorable direction. The results showed that there is genetic variation in the growth traits associated with the additive genetic effect and they might respond to selection processes. Furthermore, genetic gains would improve through selection, especially for YW and T60% when WW is used as criterion.

Estimation the Genetic Parameters for Milk Yield in Romanian Spotted, Simmental Type Cattle Breed

The aim of this study was to estimate the genetic parameters for test-day traits milk yield and the breeding value in Romanian Spotted, Simmental type cattle. Random regression test-day animal model was used to estimate the genetic parameters. The number of records were 2062 test-day from 302 cows. The data were obtained from Romanian Breeding Association Romanian Spotted, Simmental type. The heritability estimates values for milk yield ranged between 0.377 and 0.417. The heritability for fat test-day yield in our study was low the values ranged between 0.117 and 0.236 and for protein test-day yield was medium, the values ranged between 0.308 and 0.372. The breeding value for the best ten cows for milk yield ranged from 1946.57 to 3250.38 kg, for fat yield were between 64.92 and 98.86 kg and for protein ranged from 67.26 to 102.21 kg. The correlations between test-day milk yields ranging from 0.28 to 1. Genetic correlations between test day fat and protein yields were high.

Prediction of the importance of auxiliary traits using computational intelligence and machine learning: A simulation study

The present study evaluated the importance of auxiliary traits of a principal trait based on phenotypic information and previously known genetic structure using computational intelligence and machine learning to develop predictive tools for plant breeding. Data of an F2 population represented by 500 individuals, obtained from a cross between contrasting homozygous parents, were simulated. Phenotypic traits were simulated based on previously established means and heritability estimates (30%, 50%, and 80%); traits were distributed in a genome with 10 linkage groups, considering two alleles per marker. Four different scenarios were considered. For the principal trait, heritability was 50%, and 40 control loci were distributed in five linkage groups. Another phenotypic control trait with the same complexity as the principal trait but without any genetic relationship with it and without pleiotropy or a factorial link between the control loci for both traits was simulated. These traits shared a large number of control loci with the principal trait, but could be distinguished by the differential action of the environment on them, as reflected in heritability estimates (30%, 50%, and 80%). The coefficient of determination were considered to evaluate the proposed methodologies. Multiple regression, computational intelligence, and machine learning were used to predict the importance of the tested traits. Computational intelligence and machine learning were superior in extracting nonlinear information from model inputs and quantifying the relative contributions of phenotypic traits. The R2 values ranged from 44.0% - 83.0% and 79.0% - 94.0%, for computational intelligence and machine learning, respectively. In conclusion, the relative contributions of auxiliary traits in different scenarios in plant breeding programs can be efficiently predicted using computational intelligence and machine learning.

Heritability of Sleep and Its Disorders in Childhood and Adolescence

Purpose of Review This review summarizes recent literature on the heritability of sleep and sleep disorders in childhood and adolescence. We also identify gaps in the literature and priorities for future research. Recent Findings Findings indicate that age, measurement method, reporter, and timing of sleep measurements can influence heritability estimates. Recent genome-wide association studies (GWAS) have identified differences in the heritability of sleep problems when ancestral differences are considered, but sample sizes are small compared to adult GWAS. Most studies focus on sleep variables in the full range rather than on disorder. Studies using objective measures of sleep typically comprised small samples. Summary Current evidence demonstrates a wide range of heritability estimates across sleep phenotypes in childhood and adolescence, but research in larger samples, particularly using objective sleep measures and GWAS, is needed. Further understanding of environmental mechanisms and the interaction between genes and environment is key for future research.

Hidden Clusters Beyond Ethnic Boundaries

Hidden cluster problems can manifest when broad ethnic categories are used as proxies for cultural traits, especially when traits are assumed to encode cultural distances between groups. We suggest a granular understanding of cultural trait distributions within and between ethnic categories is fundamental to the interpretation of heritability estimates as well as general behavioral outcomes.

PSXI-8 Heritability estimates of antibody- and cell-mediated immune response in north American angus beef cattle

Including immune response traits in breeding programs has been suggested as a solution to improve overall animal health and enhance disease resistance but is not yet available for Angus cattle in North America. One way to measure immune response is to use the High Immune Response (HIR™) technology, which identifies animals with superior heritable immunity by evaluating antibody-mediated (AMIR) and cell-mediated (CMIR) immune response, allowing for the calculation of an estimated breeding value for total adaptive immune response for each animal. Before HIR™ can be included in beef cattle breeding programs, genetic parameters, including heritability, must be estimated for the traits involved. The objective of this study was to estimate a preliminary heritability for AMIR and CMIR in North American Angus beef cattle. On day 0 of the phenotyping protocol, cattle were immunized intramuscularly with a preparation of type 1 (CMIR) and type 2 (AMIR) antigens with adjuvant. On day 14, cattle received an intradermal injection of 0.1 mL of the CMIR test antigen in the right tail fold, and 0.1 mL PBS in the left tail fold. Change in cutaneous double skinfold thickness after 24 hours was used to assess CMIR. Blood was collected on days 0 and 14 to evaluate serum antibody to the type 2 antigen by ELISA to quantify AMIR. Heritability estimates were calculated for Canada and the USA separately, and then for both countries combined using single-trait animal models in ASReml and are presented in Table 1. The results of this study indicate that the heritabilities of AMIR and CMIR are moderate and emphasize the potential for its inclusion into Angus breeding schemes.

PSV-14 Using Bayesian Hamiltonian Monte Carlo and a nonlinear model for the estimation of genetic parameters for lactation curves in goats

Hamiltonian Monte Carlo (HMC) is an algorithm of the Markov Chain Monte Carlo (MCMC) method that uses dynamics to propose samples that follow a target distribution. This algorithm enables more effective and consistent exploration of the probability interval and is more sensitive to correlated parameters. Therefore, Bayesian-HMC is a promising alternative to estimate individual parameters of complex functions such as nonlinear models, especially when using small datasets. Our objective was to estimate genetic parameters for milk traits defined based on nonlinear model parameters predicted using the Bayesian-HMC algorithm. A total of 64,680 milk yield test-day records from 2,624 first, second, and third lactations of Saanen and Alpine goats were used. First, the Wood model was fitted to the data. Second, lactation persistency (LP), peak time (PT), peak yield (PY), and total milk yield [estimated from zero to 50 (TMY50), 100(TMY100), 150(TMY150), 200(TMY200), 250(TMY250), and 300(TMY300) days-in-milk] were predicted for each animal and parity based on the output of the first step (the individual phenotypic parameters of the Wood model). Thereafter, these predicted phenotypes were used for estimating genetic parameters for each trait. In general, the heritability estimates across lactations ranged from 0.10 to 0.20 for LP, 0.04 to 0.07 for PT, 0.26 to 0.27 for PY, and 0.21 to 0.28 for TMY (considering the different intervals). Lower heritabilities were obtained for the nonlinear function parameters (A, b and l) compared to its predicted traits (except PT), especially for the first and second lactations (range: 0.09 to 0.18). Higher heritability estimates were obtained for the third lactation traits. To our best knowledge, this study is the first attempt to use the HMC algorithm to fit a nonlinear model in animal breeding. The two-step method proposed here allowed us to estimate genetic parameters for all traits evaluated.