scholarly journals Relationships between body reserve dynamics and rearing performances in meat ewes1

2019 ◽  
Vol 97 (10) ◽  
pp. 4076-4084
Author(s):  
Tiphaine Macé ◽  
Dominique Hazard ◽  
Fabien Carrière ◽  
Sebastien Douls ◽  
Didier Foulquié ◽  
...  
Keyword(s):  
Genetic Relationships ◽  
Genetic Correlations ◽  
Repeated Measurements ◽  
Production Cycle ◽  
Data Sets ◽  
Data Set ◽  
Litter Weight ◽  
Significant Relationships ◽  
Physiological Stage ◽  
Changes Over Time

Abstract The main objective of this work was to study the relationships between body reserve (BR) dynamics and rearing performance (PERF) traits in ewes from a Romane meat sheep flock managed extensively on “Causse” rangelands in the south of France. Flock records were used to generate data sets covering 14 lambing years (YR). The data set included 1,146 ewes with 2 ages of first lambing (AGE), 3 parities (PAR), and 4 litter sizes (LS). Repeated measurements of the BW and BCS were used as indicators of BR. The ewe PERF traits recorded were indirect measurements for maternal abilities and included prolificacy, litter weight and lamb BW at lambing and weaning, ADG at 1, 2, and 3 mo after lambing, and litter survival from lambing to weaning. The effects of different BW and BCS trajectories (e.g., changes in BW and BCS across the production cycle), previously been characterized in the same animals, on PERF traits were investigated. Such trajectories reflected different profiles at the intraflock level in the dynamics of BR mobilization–accretion cycles. Genetic relationships between BR and PERF traits were assessed. All the fixed variables considered (i.e., YR, AGE, PAR, LS, and SEX ratio of the litter) have significant effects on the PERF traits. Similarly, BW trajectories had an effect on the PERF traits across the 3 PARs studied, particularly during the first cycle (PAR 1). The BCS trajectories only affected prolificacy, lamb BW at birth, and litter survival. Most of the PERF traits considered here showed moderate heritabilities (0.17–0.23) except for prolificacy, the lamb growth rate during the third month and litter survival which showed very low heritabilities. With exception of litter survival and prolificacy, ewe PERF traits were genetically, strongly, and positively correlated with BW whatever the physiological stage. A few weak genetic correlations were found between BCS and PERF traits. As illustrated by BW and BCS changes over time, favorable genetic correlations were found, even if few and moderate, between BR accretion or mobilization and PERF traits, particularly for prolificacy and litter weight at birth. In conclusion, our results show significant relationships between BR dynamics and PERF traits in ewes, which could be considered in future sheep selection programs aiming to improve robustness.

scholarly journals A New Method for Landmark-Based Studies of the Dynamic Stability of Growth, with Implications for Evolutionary Analyses

2021 ◽  
Author(s):  
Fred L. Bookstein
Keyword(s):  
Growth Regulation ◽  
Pattern Analysis ◽  
Developmental Time ◽  
Dynamical Stability ◽  
New Method ◽  
Data Sets ◽  
Data Set ◽  
Landmark Data ◽  
Study Designs ◽  
Changes Over Time

AbstractA matrix manipulation new to the quantitative study of develomental stability reveals unexpected morphometric patterns in a classic data set of landmark-based calvarial growth. There are implications for evolutionary studies. Among organismal biology’s fundamental postulates is the assumption that most aspects of any higher animal’s growth trajectories are dynamically stable, resilient against the types of small but functionally pertinent transient perturbations that may have originated in genotype, morphogenesis, or ecophenotypy. We need an operationalization of this axiom for landmark data sets arising from longitudinal data designs. The present paper introduces a multivariate approach toward that goal: a method for identification and interpretation of patterns of dynamical stability in longitudinally collected landmark data. The new method is based in an application of eigenanalysis unfamiliar to most organismal biologists: analysis of a covariance matrix of Boas coordinates (Procrustes coordinates without the size standardization) against their changes over time. These eigenanalyses may yield complex eigenvalues and eigenvectors (terms involving $$i=\sqrt{-1}$$ i = - 1 ); the paper carefully explains how these are to be scattered, gridded, and interpreted by their real and imaginary canonical vectors. For the Vilmann neurocranial octagons, the classic morphometric data set used as the running example here, there result new empirical findings that offer a pattern analysis of the ways perturbations of growth are attenuated or otherwise modified over the course of developmental time. The main finding, dominance of a generalized version of dynamical stability (negative autoregressions, as announced by the negative real parts of their eigenvalues, often combined with shearing and rotation in a helpful canonical plane), is surprising in its strength and consistency. A closing discussion explores some implications of this novel pattern analysis of growth regulation. It differs in many respects from the usual way covariance matrices are wielded in geometric morphometrics, differences relevant to a variety of study designs for comparisons of development across species.

Genetic relationships between seasonal tissue levels in Scottish Blackface ewes and lamb growth traits

2005 ◽  
Vol 81 (1) ◽  
pp. 11-21 ◽  
Author(s):  
N. R. Lambe ◽  
S. Brotherstone ◽  
M. J. Young ◽  
J. Conington ◽  
G. Simm
Keyword(s):  
Weight Gain ◽  
Birth Weight ◽  
Litter Size ◽  
Growth Traits ◽  
Genetic Relationships ◽  
Genetic Correlations ◽  
Muscle Weight ◽  
Litter Weight ◽  
Weight Gains ◽  
Lamb Growth

AbstractScottish Blackface ewes (no. = 308) were scanned four times per year using X-ray computed tomography (CT scanning) (pre-mating, pre-lambing, mid lactation and weaning), from 18 months to 5 years of age, giving a maximum of 16 scanning events per ewe. Total weights of carcass fat, internal fat and carcass muscle were estimated from the CT images at each scanning event. Lambs produced by these ewes were weighed at birth, mid lactation and weaning to calculate litter growth traits: litter birth weight; litter weight gain from birth until mid lactation; and litter weight gain from birth until weaning. Genetic (rg) and phenotypic (rp) correlations were estimated between ewe CT tissue traits and litter growth traits. Correlations between ewe CT tissue traits and litter size (LS) were also estimated. Ewe CT tissue traits were either unadjusted or adjusted for total soft tissue weight (sum of weights of carcass fat, internal fat and carcass muscle) to investigate relationships with either absolute tissue weights of carcass fat (CFWT), internal fat (IFWT), and carcass muscle (CMWT), or relative proportions of carcass fat (CFP), internal fat (IFP), and carcass muscle (CMP). Litter growth traits were either unadjusted or adjusted for litter size, to investigate relationships with total lamb burden (total litter birth weight (TBW), total litter weight gain from birth until mid lactation (TWGM), total litter weight gain from birth until weaning (TWGW)) or average lamb performance (average lamb birth weight (ABW), average lamb weight gain from birth until mid lactation (AWGM), average lamb weight gain from birth until weaning (AWGW)).Moderate to large positive genetic correlations were estimated between absolute weights of all three ewe tissues (CFWT, IFWT, CMWT), or muscle proportion (CMP), and litter size (LS). Significant positive genetic correlations were also estimated between weight (CMWT) or proportion (CMP) of muscle carried by the ewe pre-mating and total birth weight (TBW) and weight gains (TWGM, TWGW) of her litter, largely due to the associated increase in litter size. Muscle proportion (CMP) was not significantly correlated to average lamb weights or weight gains (ABW, AWGM, AWGW). Pre-lambing carcass fat weight (CFWT) and proportion (CFP) in the ewe showed positive genetic correlations with average lamb weights and weight gains (ABW, AWGM, AWGW), whereas, after lambing, CFP was negatively correlated with these lamb traits. Internal fat weight (IFWT) pre-mating showed positive genetic correlations with all litter growth traits (TBW, TWGM, TWGW, ABW, AWGM, AWGW). Average lamb growth traits were negatively correlated with pre-lambing internal fat proportion (IFP), but positively correlated to IFP at mid lactation and weaning.Correlations were also estimated between each pair of CT traits. Total internal fat weight and total carcass fat weight were very highly correlated (rp= 0·75,rg= 0·96). Correlations with total carcass muscle weight were smaller and positive for both carcass fat weight (rp= 0·48,rg= 0·12) and internal fat weight (rp= 0·42,rg= 0·20).The results suggest that selection for increased carcass muscle weight or proportion in a Scottish Blackface hill flock would have a positive effect on total weights of litters reared, but that selection against carcass fat weight or proportion in a breeding programme for Blackface sheep may have an impact on the maternal ability of the ewe. However, maintaining fat in internal depots may reduce the depletion of carcass fat during pregnancy, allowing this depot to provide energy for lactation, and may have a positive impact on lamb growth.

The genetic foundation of fitness and reproduction traits in Australian pig populations. 2. Relationships between weaning to conception interval, farrowing interval, stayability, and other common reproduction and production traits.

1996 ◽  
Vol 47 (8) ◽  
pp. 1275 ◽  
Author(s):  
E Tholen ◽  
KL Bunter ◽  
S Hermesch ◽  
HU Graser
Keyword(s):  
Genetic Parameters ◽  
Performance Test ◽  
Genetic Relationships ◽  
Average Daily Gain ◽  
Genetic Correlations ◽  
Data Sets ◽  
Production Traits ◽  
Breeding Programs ◽  
Reproduction Traits ◽  
Daily Gain

Data sets from 2 large Australian piggeries were used to estimate genetic parameters for the traits weaning to conception interval (WCIi-l,i) and farrowing interval (FIi-l,i), number born alive (NBAI), average piglet birthweight (BWi), 21-day litter weight (W21i), and sow stayability (STAYli) recorded for each ith parity, as well as sow average daily gain (ADG) and backfat (BF) recorded at the end of performance test. Over parities and herds, heritabilities for each trait were in the ranges: WCI/FI, 0.0-0.10; NBA, 0.09-0.16; BW, 0.11-0.35; W21, 0.12-0.23; STAYli, 0.02-0.09; ADG, 0.35-0.37; BF, 0.36-0.45. Genetic correlations between NBAl and NBA from later parities were significantly different from 1. In addition, in 1 herd negative genetic correlations (rg = -0.04 to -0.25) were found between sow stayability traits and NBA1, but not NBA recorded in later parities. Stayability was Unfavourably correlated with ADG and BF, and favourably correlated with WCI12. However, WCI12 was unfavourably correlated genetically with BF (rg = -0.24) but uncorrelated with ADG. Antagonistic relationships also existed between NBA and BW, NBA and W21, and BW and STAY. In addition to the traditional traits currently included in pig-breeding programs (e.g. ADG, BF, and NBA), traits such as WCI, BW, and STAY should also be considered as selection criteria to minimise the detrimental effects of antagonistic genetic relationships between traits.

scholarly journals A Method for Identifying Environmental Stimuli and Genes Responsible for Genotype-by-Environment Interactions From a Large-Scale Multi-Environment Data Set

2021 ◽  
Vol 12 ◽  
Author(s):  
Akio Onogi ◽  
Daisuke Sekine ◽  
Akito Kaga ◽  
Satoshi Nakano ◽  
Tetsuya Yamada ◽  
...  
Keyword(s):  
Large Scale ◽  
Genetic Correlations ◽  
Data Driven ◽  
Data Sets ◽  
Data Set ◽  
Environmental Stimuli ◽  
Genotype By Environment ◽  
Genome Wide ◽  
Sowing Dates ◽  
Data Driven Approach

It has not been fully understood in real fields what environment stimuli cause the genotype-by-environment (G × E) interactions, when they occur, and what genes react to them. Large-scale multi-environment data sets are attractive data sources for these purposes because they potentially experienced various environmental conditions. Here we developed a data-driven approach termed Environmental Covariate Search Affecting Genetic Correlations (ECGC) to identify environmental stimuli and genes responsible for the G × E interactions from large-scale multi-environment data sets. ECGC was applied to a soybean (Glycine max) data set that consisted of 25,158 records collected at 52 environments. ECGC illustrated what meteorological factors shaped the G × E interactions in six traits including yield, flowering time, and protein content and when these factors were involved in the interactions. For example, it illustrated the relevance of precipitation around sowing dates and hours of sunshine just before maturity to the interactions observed for yield. Moreover, genome-wide association mapping on the sensitivities to the identified stimuli discovered candidate and known genes responsible for the G × E interactions. Our results demonstrate the capability of data-driven approaches to bring novel insights on the G × E interactions observed in fields.

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 Better models by discarding data?

2013 ◽  
Vol 69 (7) ◽  
pp. 1215-1222 ◽  
Author(s):  
K. Diederichs ◽  
P. A. Karplus
Keyword(s):  
Data Quality ◽  
Practical Importance ◽  
Repeated Measurements ◽  
Data Sets ◽  
Model Quality ◽  
Data Set ◽  
X Ray Crystallography ◽  
Typical Data ◽  
The One ◽  
Assess Data Quality

In macromolecular X-ray crystallography, typical data sets have substantial multiplicity. This can be used to calculate the consistency of repeated measurements and thereby assess data quality. Recently, the properties of a correlation coefficient, CC1/2, that can be used for this purpose were characterized and it was shown that CC1/2has superior properties compared with `merging'Rvalues. A derived quantity, CC*, links data and model quality. Using experimental data sets, the behaviour of CC1/2and the more conventional indicators were compared in two situations of practical importance: merging data sets from different crystals and selectively rejecting weak observations or (merged) unique reflections from a data set. In these situations controlled `paired-refinement' tests show that even though discarding the weaker data leads to improvements in the mergingRvalues, the refined models based on these data are of lower quality. These results show the folly of such data-filtering practices aimed at improving the mergingRvalues. Interestingly, in all of these tests CC1/2is the one data-quality indicator for which the behaviour accurately reflects which of the alternative data-handling strategies results in the best-quality refined model. Its properties in the presence of systematic error are documented and discussed.

Automated identification of changes in electrode contact properties for long-term permanent ERT monitoring experiments

Geophysics ◽  
2013 ◽  
Vol 78 (2) ◽  
pp. E79-E94 ◽  
Author(s):  
John Deceuster ◽  
Olivier Kaufmann ◽  
Michel Van Camp
Keyword(s):  
Repeated Measurements ◽  
Cumulative Distribution ◽  
Time Slice ◽  
Data Sets ◽  
Automated Identification ◽  
Weighting Method ◽  
Data Set ◽  
Electrode Contact ◽  
The Given

Electrical resistivity tomography (ERT) monitoring experiments are being conducted more often to image spatiotemporal changes in soil properties. When conducting long-term ERT monitoring, the identification of suspicious electrodes in a permanent spread is of major importance because changes in electrode contact properties of a single electrode may affect the quality of many measurements on each time-slice. An automated methodology was developed to detect these temporal changes in electrode contact properties, based on a Bayesian approach called “weights of evidence.” Contrasts [Formula: see text] and studentized contrasts [Formula: see text] are estimators of the influence of each electrode in the global data quality. A consolidated studentized contrast [Formula: see text] is introduced to consider the proportion of rejected quadripoles which contain a single electrode. These estimators are computed for each time-slice using [Formula: see text]-factor (coefficient of variation of repeated measurements) threshold values, from 0 to 10%, to discriminate between selected and rejected quadripoles. An automated detection strategy is proposed to identify suspicious electrodes by comparing the [Formula: see text] to the [Formula: see text] (maximum expected [Formula: see text] values when every electrode is good for the given data set). These [Formula: see text] are computed using Monte-Carlo simulations of a hundred random draws where the distribution of [Formula: see text]-factor values follows a Weibull cumulative distribution, with [Formula: see text] and [Formula: see text], fitted on a background data set filtered using a 5% threshold on absolute reciprocal errors. The efficiency of the methodology and its sensitivity to the selected reciprocal error threshold are assessed on synthetic and field data. Our approach is suitable to detect suspicious electrodes and slowly changing conditions affecting the galvanic contact resistances where classical approaches are shown to be inadequate except when the faulty electrode is disconnected. A data-weighting method is finally proposed to ensure that only good data will be used in the inversion of ERT monitoring data sets.

scholarly journals A method for identifying environmental stimuli and genes responsible for genotype-by-environment interactions from a large-scale multi-environment data set

2021 ◽  
Author(s):  
Akio Onogi ◽  
Daisuke Sekine ◽  
Akito Kaga ◽  
Satoshi Nakano ◽  
Tetsuya Yamada ◽  
...  
Keyword(s):  
Large Scale ◽  
Genetic Correlations ◽  
Data Driven ◽  
Data Sets ◽  
Data Set ◽  
Environmental Stimuli ◽  
Genotype By Environment ◽  
Genome Wide ◽  
Sowing Dates ◽  
Data Driven Approach

It has not been fully understood in real fields what environment stimuli cause the genotype-by-environment (G x E) interactions, when they occur, and what genes react to them. Large-scale multi-environment data sets are attractive data sources for these purposes because they potentially experienced various environmental conditions. Here we developed a data-driven approach termed Environmental Covariate Search Affecting Genetic Correlations (ECGC) to identify environmental stimuli and genes responsible for the G x E interactions from large-scale multi-environment data sets. ECGC was applied to a soybean (Glycine max) data set that consisted of 25,158 records collected at 52 environments. ECGC illustrated what meteorological factors shaped the G x E interactions in six traits including yield, flowering time, and protein content and when they were involved. For example, it illustrated the relevance of precipitation around sowing dates and hours of sunshine just before maturity to the interactions observed for yield. Moreover, genome-wide association mapping on the sensitivities to the identified stimuli discovered candidate and known genes responsible for the G x E interactions. Our results demonstrate the capability of data-driven approaches to bring novel insights on the G x E interactions observed in fields.

Statistical analyses of repeated measures in physiological research: a tutorial

2004 ◽  
Vol 28 (1) ◽  
pp. 2-14 ◽  
Author(s):  
Michael Kristensen ◽  
Thomas Hansen
Keyword(s):  
Repeated Measures ◽  
Repeated Measurements ◽  
Experimental Unit ◽  
Data Sets ◽  
Data Set ◽  
Distributed Software ◽  
Software Products ◽  
Anova Model ◽  
Nonlinear Features ◽  
Physiological Research

Experimental designs involving repeated measurements on experimental units are widely used in physiological research. Often, relatively many consecutive observations on each experimental unit are involved and the data may be quite nonlinear. Yet evidently, one of the most commonly used statistical methods for dealing with such data sets in physiological research is the repeated-measurements ANOVA model. The problem herewith is that it is not well suited for data sets with many consecutive measurements; it does not deal with nonlinear features of the data, and the interpretability of the model may be low. The use of inappropriate statistical models increases the likelihood of drawing wrong conclusions. The aim of this article is to illustrate, for a reasonably typical repeated-measurements data set, how fundamental assumptions of the repeated-measurements ANOVA model are inappropriate and how researchers may benefit from adopting different modeling approaches using a variety of different kinds of models. We emphasize intuitive ideas rather than mathematical rigor. We illustrate how such models represent alternatives that 1) can have much higher interpretability, 2) are more likely to meet underlying assumptions, 3) provide better fitted models, and 4) are readily implemented in widely distributed software products.

Genetic aspects of condition score, heart girth and milk-yield traits in Italian Friesian cows

1999 ◽  
Vol 24 ◽  
pp. 159-164
Author(s):  
L. Gallo ◽  
P. Carnier ◽  
M. Cassandro ◽  
R. Dal Zotto ◽  
G. Bittante
Keyword(s):  
Milk Yield ◽  
Genetic Relationships ◽  
Genetic Correlations ◽  
Repeated Measurements ◽  
Protein Yield ◽  
Yield Traits ◽  
Dry Period ◽  
Condition Score ◽  
Heritability Estimates ◽  
Friesian Cows

AbstractFunctional traits related to costs are currently of interest for selection and management of dairy cattle. The present study was aimed to estimate heritability for body condition score (BCS) and heart girth (HG), to investigate the genetic relationships between BCS, HG and milk-yield traits using a test-day model and to analyse the consistency of the estimates in different lactation stages. Cows from 25 dairy herds were scored for BCS and measured for HG at 3-month intervals for 2 years. Approximately 5000 test-day observations on BCS, HG and milk fat and protein yield from 1429 Italian Friesian cows were analysed using two approaches: (1) repeated observations were treated as repeated measurements of the same trait, both within and across lactations; (2) observations collected in different stages of lactation (dry period, 1 to 75 days in milk (DIM), 76 to 130 DIM, 131 to 210 DIM, 211 to 300 DIM) were treated as different traits. (Co)variance components and related parameters were estimated using REML multiple-trait procedures and unequal design animal models.Heritability estimates (approach 1) for fat and protein test-day yield, BCS and HG were 0.22, 0.18, 0.29 and 0.33, respectively. BCS was negatively correlated with yield traits (-0.43 and -0.48 for fat and protein yield, respectively) but positively correlated (0.33) with HG. Genetic relationships between HG and milk-yield traits were negligible. Heritability estimates (approach 2) were 0.28 and 0.27 for BCS recorded in the first half of lactation (1 to 75 and 76 to 130 DIM, respectively), 0.36 for BCS measured on cows in the second half of lactation and 0.32 for BCS recorded on dry cows. Heritability estimates for HG in different lactation stages ranged from 0.31 to 0.40. Genetic correlations between BCS measured in different lactation stages were generally high (0.85 or more), with the exception of the correlation between the first and the last stage of lactation (0.74) and of the relationships between the beginning of lactation and the dry period (0.7). Genetic correlations between HG measured in different lactation stages were mostly higher than 0.80.

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