A GENETIC STUDY OF BIOMASS IN TRIBOLIUM. I. PATH COEFFICIENT ANALYSIS OF BASE POPULATIONS

1972 ◽  
Vol 14 (1) ◽  
pp. 181-193 ◽  
Author(s):  
Eliot Krause ◽  
A. E. Bell
Keyword(s):  
Genetic Correlations ◽  
Black Population ◽  
Path Coefficient ◽  
Larval Weight ◽  
Maternal Influences ◽  
Fitness Traits ◽  
Egg Number ◽  
Fitness Trait ◽  
Additive Genetic Effects ◽  
Larval Number

Biomass, an aggregate trait combining the metric trait 13-day larval weight with the reproductive fitness trait number of larvae per mating, was investigated in two unrelated base populations of Tribolium castaneum, black and pearl.The black population was characterized by smaller 13-day larvae, more larvae per mating due to a higher rate of fecundity, and a smaller biomass than was observed for the pearl population. Both genetic and phenotypic variances were higher in black than in pearl. Heritability estimates for larval weight approached 0.20 in both populations while those for the fitness traits were smaller and seldom exceeded 10%. Non-additive genetic effects as well as maternal influences were observed in both populations. Positive estimates for the genetic correlations between larval weight and number of larvae per mating were observed for both populations (0.47 for black and 0.17 for pearl).Path coefficient analyses revealed that larval number in both populations had greater genetic and phenotypic influences on biomass than did larval weight. In turn, egg number had a greater influence on number of larvae than did larval viability.

scholarly journals Genetic correlations and path-coefficient analysis of yield and quality components in wheat, Triticum aestivum L.

Genetika ◽  
2004 ◽  
Vol 36 (1) ◽  
pp. 13-21 ◽  
Author(s):  
Veselinka Zecevic ◽  
Desimir Knezevic ◽  
Danica Micanovic
Keyword(s):  
Genetic Correlation ◽  
Harvest Index ◽  
Genetic Correlations ◽  
Grain Weight ◽  
Spike Length ◽  
Phenotypic Correlation ◽  
Path Coefficient ◽  
Thousand Grain Weight ◽  
Path Coefficient Analysis ◽  
Bread Making

The genetic and phenotypic correlations between yield components (productive tillering, plant height, spike length, number of spikelets per spike, number of grains per spike, grain weight per spike, grain weight per plant, harvest index, thousand grain weight) and quality components (grain protein content and sedimentation value) were investigated. The plant material was comprised of 50 genotypes of winter wheat grown during two years. Path- coefficient analysis of genetic correlation coefficients for grain mass/plant and other traits determined interrelationships among grain mass per plant and other yield and bread making quality components. The strongest positive genetic correlation was found between grain weight per spike and thousand grain weight and between spike length and number of spikelets per spike. Phenotypic correlation analysis indicated that grain weight per spike correlated positively and significantly with harvest index and thousand kernel weight. The strongest direct effect on grain weight per plant had harvest index and number of spikelets per spike. The spike length through number of spikelets per spike had the strongest indirect effect on grain weight per plant.

Genetic parameters for egg weight v. age curve, and other egg production and egg weight traits, in synthetic lines of chickens

1983 ◽  
Vol 34 (1) ◽  
pp. 85 ◽  
Author(s):  
BH Yoo ◽  
BL Sheldon ◽  
RN Podger
Keyword(s):  
Standard Deviation ◽  
Genetic Correlation ◽  
Egg Production ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Egg Mass ◽  
Control Line ◽  
Egg Weight ◽  
Egg Number ◽  
Selection For

An exponential curve, W = P-Qexp(- Rt), where W is egg weight at age t, was fitted to egg weights of individual pullets, and genetic parameters were estimated for P, Q and R, the residual standard deviation and other egg weight and egg production characters. The data consisted of records collected over six generations on more than 4000 pullets in two selection lines and a control line which originated from a synthetic gene pool of White Leghorn x Australorp crosses. The half-sib and offspring-on-parent regression estimates of heritability pooled over the lines were 0.23 and 0.33 for P, 0.14 and 0.20 for Q, and 0.14 and 0.25 for R. Genetic correlations were estimated to be -0.10 between P and Q, -0.46 between P and R, and 0.90 between Q and R. These estimates suggest that the egg weight v. age curve may be modified to increase the proportion of eggs in desirable weight grades and reduce the incidence of oversized eggs later in the production year. The genetic correlation between mean weight of first 10 eggs and egg weight at 62 weeks of age was estimated to be 0.68, further suggesting that early egg weight may be improved partly independently of late egg weight. The heritability estimates of egg mass output were not higher than those of egg number in spite of the highly heritable average egg weight being an important component of egg mass, probably because of the negative genetic correlation (r = -0.49) between egg number and average egg weight. The standard deviation of individual pullet's egg weights was moderately heritable and genetically correlated positively with egg weight characters and negatively with egg production; these estimates were consistent with the responses to selection for reduced egg weight variability observed elsewhere

scholarly journals Path analysis of yield components in tropical pumpkin

1969 ◽  
Vol 90 (3-4) ◽  
pp. 183-191
Author(s):  
Miguel Espitia-Camacho ◽  
Franco A. Vallejo-Cabrera ◽  
Diosdado Baena-Garcia ◽  
Linda Wessel-Beaver
Keyword(s):  
Path Analysis ◽  
Indirect Effects ◽  
Block Design ◽  
Genetic Correlations ◽  
Fruit Weight ◽  
Residual Effects ◽  
Path Coefficient ◽  
Direct Effects ◽  
Randomized Complete Block Design ◽  
Effect On Yield

Path coefficient analysis was carried out in tropical pumpkin (Cucurbita moschata Duchesne) for yield per plant as a function of number of fruit per plant and weight per fruit, using phenotypic and genetic correlations from two diallels: one using five varieties (variety diallel) and a second using five S1 lines derived from the varieties (line diallel). A randomized complete block design with five replications was used to evaluate 15 genotypes from each diallel, not including reciprocals. Direct effects were 300 to 500% greater than indirect effects in the line diallel. In contrast, direct effects were only 17 to 68% greater than indirect effects in the variety diallel. Effects (direct and indirect) were greater (17 to 500%) when genetic correlations rather than phenotype correlations (42 to 440%) were used in the path analysis. Fruit weight showed a greater effect on yield than did number of fruit, with values between 3.2 to 4.9 times the residual effects. Fruit weight and number of fruit can be used as selection criteria to increase yield in tropical pumpkin.  

scholarly journals Partitioning variation in measurements of beef carcass traits using ultrasound1

2020 ◽  
Vol 4 (3) ◽  
Author(s):  
Bradie M Schmidt ◽  
Michael G Gonda ◽  
Michael D MacNeil
Keyword(s):  
Animal Models ◽  
Carcass Traits ◽  
Genetic Correlations ◽  
Subcutaneous Fat ◽  
Genetic Effects ◽  
Residual Variance ◽  
Phenotypic Variance ◽  
Contemporary Group ◽  
Genetic Evaluations ◽  
Additive Genetic Effects

Abstract Ultrasound technology provides cattle breeders with a quick, noninvasive, and inexpensive way to measure carcass data on live animals. Ultrasound data are used as indicator traits in cattle genetic evaluations for economically relevant carcass traits. Ultrasound cattle genetic evaluations assume homogeneous additive genetic and residual variance. Thus, the objective was to partition phenotypic variance in ultrasound carcass measurements into components for additive genetic effects, technicians, contemporary groups within technicians, and residual and to examine the homogeneity of these variances among image interpretation laboratories. Records of longissimus muscle area (LMA), percentage of intramuscular fat (IMF), and subcutaneous fat depth (SFD), measured using ultrasound, were provided by the American Angus Association (n = 65,967), American Hereford Association (n = 43,182), and American Simmental Association (n = 48,298). The data also included contemporary group, technician, imaging lab, and a three-generation pedigree for each animal. Variance components for ultrasound carcass measurements were first estimated with univariate animal models for each breed and imaging laboratory using derivative-free restricted maximum likelihood. Then, treating data from each imaging laboratory as separate traits, genetic correlations between laboratories for LMA, percentage of IMF, and subcutaneous fat were estimated with trivariate animal models. The technician explained 12–27%, 5–23%, and 4–26% of the variance for IMF, SFD, and LMA, respectively, across all three breeds. Variance due to technician was often greater than variance due to additive genetic effects but almost always less than that explained by the contemporary group. Within breeds, estimates of additive genetic variance for LMA, SFD, and IMF differed (range divided by mean) among laboratories by 4.5%, 21.5%, and 39.4 % (Angus); 31.6%, 15.0%, and 49.1% (Hereford); and 19.9%, 46.6%, and 55.3% (Simmental), respectively. Likewise, estimates of residual variance for LMA, SFD, and IMF differed among laboratories by 43.4%, 22.9%, and 43.3% (Angus); 24.9%, 15.2%, and 79.2% (Hereford); and 26.4%, 32.5%, and 46.2% (Simmental), respectively. Genetic correlations between labs across breeds ranged from 0.79 to 0.95 for IMF, 0.26 to 0.94 for SFD, and 0.78 to 0.98 for LMA. The impact of the observed heterogeneity of variance between labs on genetic evaluation requires further study.

scholarly journals Genetic parameters for egg and related characteristics of white Leghorn hens in a subtropical environment

1999 ◽  
Vol 22 (2) ◽  
pp. 183-186 ◽  
Author(s):  
Hani M. Sabri ◽  
Henry R. Wilson ◽  
Robert H. Harms ◽  
Charles J. Wilcox
Keyword(s):  
Body Weight ◽  
Specific Gravity ◽  
Weight Change ◽  
Sexual Maturity ◽  
Genetic Correlations ◽  
Metabolizable Energy ◽  
White Leghorn ◽  
Shell Weight ◽  
Egg Number ◽  
Metabolizable Energy Intake

Estimates of heritability and phenotypic and genetic correlations between egg number, weight, specific gravity, mass, and estimated shell weight were obtained, along with phenotypic and genetic correlations of specific gravity and weight with body weight, weight change, metabolizable energy intake, residual feed consumption, and weight and age at sexual maturity. Data were from 350 White Leghorn hens by 50 sires and 175 dams. Heritabilities of the egg traits ranged from 0.20 to 0.55, increasing with age of bird from 26 to 54 weeks of age. Their standard errors ranged from 0.07 (all data) to 0.17 (26 to 29 weeks). Phenotypic correlations ranged from 0.80 to -0.13, and genetic correlations from 0.91 to -0.27, depending on egg trait. The highest phenotypic and genetic correlations were between egg number and mass. Genetic correlations for specific gravity and estimated shell weight were, with body weight, -0.02 and 0.56; weight change, 0.29 and 0.44; daily metabolizable energy intake, -0.10 and 0.33; residual consumption, -0.16 and 0.11; age at sexual maturity, -0.61 and -0.46, and weight at sexual maturity, 0.02 and 0.63. Results should contribute to the design of efficient selection programs for economically important traits in hens.

scholarly journals Changes in genetic parameters for fitness and growth traits in pigs under genomic selection

2020 ◽  
Vol 98 (2) ◽  
Author(s):  
Jorge Hidalgo ◽  
Shogo Tsuruta ◽  
Daniela Lourenco ◽  
Yutaka Masuda ◽  
Yijian Huang ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Genetic Parameters ◽  
Growth Traits ◽  
Genetic Relationships ◽  
Genetic Correlations ◽  
Genomic Analysis ◽  
Data Set ◽  
Genetic Changes ◽  
Fitness Traits ◽  
Bayesian Implementation

Abstract Genomic selection increases accuracy and decreases generation interval, speeding up genetic changes in the populations. However, intensive changes caused by selection can reduce the genetic variation and can strengthen undesirable genetic correlations. The purpose of this study was to investigate changes in genetic parameters for fitness traits related with prolificacy (FT1) and litter survival (FT2 and FT3), and for growth (GT1 and GT2) traits in pigs over time. The data set contained 21,269 (FT1), 23,246 (FT2), 23,246 (FT3), 150,492 (GT1), and 150,493 (GT2) phenotypic records obtained from 2009 to 2018. The pedigree file included 369,776 animals born between 2001 and 2018, of which 39,103 were genotyped. Genetic parameters were estimated with bivariate models (FT1-GT1, FT1-GT2, FT2-GT1, FT2-GT2, FT3-GT1, and FT3-GT2) using 3-yr sliding subsets. With a Bayesian implementation using the GIBBS3F90 program computations were performed as genomic analysis (GEN) or pedigree-based analysis (PED), that is, with or without genotypes, respectively. For GEN (PED), the changes in heritability from the first to the last year interval, that is, from 2009–2011 to 2015–2018 were 8.6 to 5.6 (7.9 to 8.8) for FT1, 7.8 to 7.2 (7.7 to 10.8) for FT2, 11.4 to 7.6 (10.1 to 7.5) for FT3, 35.1 to 16.5 (32.5 to 23.7) for GT1, and 35.9 to 16.5 (32.6 to 24.1) for GT2. Differences were also observed for genetic correlations as they changed from −0.31 to −0.58 (−0.28 to −0.73) for FT1-GT1, −0.32 to −0.50 (−0.29 to −0.74) for FT1-GT2, −0.27 to −0.45 (−0.30 to −0.65) for FT2-GT1, −0.28 to −0.45 (−0.32 to −0.66) for FT2-GT2, 0.14 to 0.17 (0.11 to 0.04) for FT3-GT1, and 0.14 to 0.18 (0.11 to 0.05) for FT3-GT2. Strong selection in pigs reduced heritabilities and emphasized the antagonistic genetic relationships between fitness and growth traits. With genotypes considered, heritability estimates were smaller and genetic correlations were greater than estimates with only pedigree and phenotypes. When selection is based on genomic information, genetic parameters estimated without this information can be biased because preselection is not accounted for by the model.

scholarly journals Environment Dependence of Mutational Parameters for Viability in Drosophila melanogaster

Genetics ◽  
2002 ◽  
Vol 161 (3) ◽  
pp. 1155-1167 ◽  
Author(s):  
James D Fry ◽  
Stefanie L Heinsohn
Keyword(s):  
Drosophila Melanogaster ◽  
Genetic Correlations ◽  
High Density ◽  
Low Density ◽  
Fitness Traits ◽  
Parental Density ◽  
The Difference ◽  
Environment Dependence ◽  
Using Data ◽  
Assay Conditions

Abstract The genomic rate of mildly deleterious mutations (U) figures prominently in much evolutionary and ecological theory. In Drosophila melanogaster, estimates of U have varied widely, from <0.1 to nearly 1 per zygote. The source of this variation is unknown, but could include differences in the conditions used for assaying fitness traits. We examined how assay conditions affect estimates of the rates and effects of viability-depressing mutations in two sets of lines with accumulated spontaneous mutations on the second chromosome. In each set, the among-line variance in egg-to-adult viability was significantly greater when viability was assayed using a high parental density than when it was assayed using a low density. In contrast, the proportional decline in viability due to new mutations did not differ between densities. Two other manipulations, lowering the temperature and adding ethanol to the medium, had no significant effects on either the mean decline or among-line variance. Cross-environment genetic correlations in viability were generally close to one, implying that most mutations reduced viability in all environments. Using data from the low-density, lower-bound estimates of U approached the classic, high values of Mukai and Ohnishi; at the high density, U estimates were similar to recently reported low values. The difference in estimated mutation rates, taken at face value, would imply that many mutations affected fitness at low density but not at high density, but this is shown to be incompatible with the observed high cross-environment correlations. Possible reasons for this discrepancy are discussed. Regardless of the interpretation, the results show that assay conditions can have a large effect on estimates of mutational parameters for fitness traits.

scholarly journals 296 Awardee Talk: The Genetics of Sow Longevity

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 28-28
Author(s):  
Kenneth J Stalder
Keyword(s):  
Genomic Selection ◽  
Genetic Parameter ◽  
Genetic Correlations ◽  
Parameter Estimate ◽  
Production Traits ◽  
Sow Longevity ◽  
Future Technologies ◽  
Indicator Traits ◽  
Additive Genetic Effects ◽  
The Impact

Abstract Sow longevity is a key productivity indicator trait that has real economic and welfare importance for commercial swine farms globally. The average parity at culling is 3.8 parities. Reports indicate that it takes 3 to 4 parities before a sow “pays for herself.” Research groups around the world have reported heritabilities estimates for sow longevity traits ranging from 0.05 to 0.35. Estimate differences result from the animal population under evaluation, the trait being evaluated, and the methodology employed to obtain the genetic parameter estimate. Because sow longevity is measured at the end of the sow’s productive life, indicator traits like age at first farrowing, leg conformation, and other traits are utilized in gilt selection programs. The genetic correlations between sow longevity and lifetime production traits range have been reported to range from 0.64 to 0.94, suggesting that selection will improve sow longevity. Genetic markers have been identified that affect both sow longevity and other indicator traits. Selection to improved sow longevity still requires phenotypes. Future technologies, e.g. CT scans, digital images, and automated disease detection, will provide additional phenotypes. Continued hardware, software, and molecular developments will improve selection accuracy for sow longevity traits and related traits. Research is needed to evaluate the impact that non-additive genetic effects have on sow longevity and other fitness-related traits. Sow longevity seems to be an ideal trait to employ genomic selection in order to make more rapid trait improvements because it is measured late in life, it is sex-limited, and the trait is not directly measured on nucleus animals. In conclusion, sow longevity and related traits have sufficient heritability and variation to improve through traditional and genomic enhanced selection methods. Selection programs employing effective genomic selection programs will be more effective in improving sow longevity trait and related traits and ultimately economical return.

REML estimates of variance and covariance components for production traits in Australian Merino sheep, using an animal model. 1. Body weight from birth to 22 months

1996 ◽  
Vol 47 (8) ◽  
pp. 1235 ◽  
Author(s):  
Torshizi R Vaez ◽  
FW Nicolas ◽  
HW Raadsma
Keyword(s):  
Animal Model ◽  
Body Weight ◽  
Birth Weight ◽  
Genetic Correlations ◽  
Genetic Effects ◽  
Production Traits ◽  
Weaning Weight ◽  
Merino Sheep ◽  
Additive Genetic Effects ◽  
Australian Merino

Variance components for direct additive genetic, maternal additive genetic, and maternal environmental effects, and the covariance between direct and maternal additive genetic effects, were estimated by restricted maximum likelihood (REML) procedures, using an animal model, for body weight between birth and 22 months of age in Australian Merino sheep. Direct heritability was estimated to be 0.30 for birth weight, 0.28 for weaning weight, 0.24 for body weight at 10 months, 0.34 for body weight at 16 months, and 0.34 for body weight at 22 months. Maternal heritability estimates were 0.29, 0.41, 0.14, 0.07, and 0.07 for the same performances, respectively. Our results suggested that for birth weight and weaning weight, maternal additive genetic effects and the covariance between direct and maternal additive genetic effects were important. Following weaning, maternal additive genetic effects were the only significant maternal effects. Genetic correlations between direct and maternal additive effects were -0.43, -0.59, and -0.29 for birth weight, weaning weight, and body weight at 10 months, respectively. Direct and maternal additive genetic correlations between birth weight and body weight performances at later ages were positive and moderate, ranging from 0.17 to 0.52 and from 0.06 to 0.65, respectively, whereas they were positive and high between weaning weight and later weights, ranging from 0.59 to 0.77 and from 0.61 to 0.85, respectively. A carry-over of maternal influence after weaning was shown. Early (indirect) selection for body weight at weaning or 10 months will achieve a substantial proportion (between 53 and 81%) of direct response for performance at later ages (16 and 22 months).

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