scholarly journals Estimating the covariance structure of traits during growth and ageing, illustrated with lactation in dairy cattle

1994 ◽  
Vol 64 (1) ◽  
pp. 57-69 ◽  
Author(s):  
Mark Kirkpatrick ◽  
William G. Hill ◽  
Robin Thompson
Keyword(s):  
Genetic Variation ◽  
Dairy Cattle ◽  
Covariance Structure ◽  
Genetic Covariance ◽  
Additive Variance ◽  
Additive Genetic Variation ◽  
Covariance Functions ◽  
New Methods ◽  
A New Technique ◽  
Using Data

SummaryQuantitative variation in traits that change with age is important to both evolutionary biologists and breeders. We present three new methods for estimating the phenotypic and additive genetic covariance functions of a trait that changes with age, and illustrate them using data on daily lactation records from British Holstein—Friesian dairy cattle. First, a new technique is developed to fit a continuous covariance function to a covariance matrix. Secondly, this technique is used to estimate and correct for a bias that inflates estimates of phenotypic variances. Thirdly, we offer a numerical method for estimating the eigenvalues and eigenfunctions of covariance functions. Although the algorithms are moderately complex, they have been implemented in a software package that is made freely available.Analysis of lactation shows the advantages of the new methods over earlier ones. Results suggest that phenotypic variances are inflated by as much as 39 % above the underlying covariance structure by measurement error and short term environmental effects. Analysis of additive genetic variation indicates that about 90 % of the additive genetic variation for lactation during the first 10 months is associated with an eigenfunction that corresponds to increased (or decreased) production at all ages. Genetic tradeoffs between early and late milk yield are seen in the second eigenfunction, but it accounts for less than 8 % of the additive variance. This illustrates that selection is expected to increase production throughout lactation.

scholarly journals Genetic Parameters of Interspecific Hybrids of Eucalyptus grandis and E. urophylla Seedlings and Cuttings

2015 ◽  
Vol 64 (1-6) ◽  
pp. 291-308 ◽  
Author(s):  
G. J. van den Berg ◽  
S. D. Verryn ◽  
P. W. Chirwa ◽  
F. Van Deventer
Keyword(s):  
Genetic Variation ◽  
Growth Performance ◽  
Hybrid Material ◽  
Genetic Parameters ◽  
Breeding Strategy ◽  
Hybrid Breeding ◽  
Additive Variance ◽  
Breeding Values ◽  
Additive Genetic Variation ◽  
Hybrid Seedling

Abstract The current E. grandis × E. urophylla hybrid breeding strategy of South Africa’s Forestry Industry is to maintain large breeding populations of both parental species in which parents are selected based on their general combining ability (GCA) estimates or predicted individual tree breeding values and are used for interspecific hybrid crosses. The hybrid material is first screened in seedling progeny trials after which superior individuals are selected and tested as clones. Although this strategy has delivered superior clones for commercial production in South Africa, it is a time consuming strategy to follow and more cost effective strategies are being investigated. In order to review the current hybrid breeding strategy, information on the genetic control of the traits of interest is needed for E. grandis × E. urophylla seedling and clonal populations. The main objectives of this study were therefore to firstly estimate genetic parameters for E. grandis × E. urophylla hybrid seedling and clonal populations; secondly to investigate the correlation between E. grandis and E. urophylla parental (GCA) or individual breeding values and their general hybridising ability (GHA); and lastly to determine the correlation between E. grandis × E. urophylla hybrid seedling ortets and their ramets. Results of our study indicated that non-additive genetic variation explained the majority of the total genetic variation in E. grandis × E. urophylla seedling and clonal populations. Due to the pre-eminence of non-additive variance, the pure-hybrid correlations were weak, especially for clonal populations. It would therefore seem that GCA or predicted individual breeding values are not good predictors of GHA for growth performance in the observed populations. Our study also indicated a weak coefficient of correlation between the growth performance of seedling ortets and their ramets. These results suggest that: firstly a hybrid breeding strategy to capture non-additive genetic variation should be adopted; and secondly that the first phase of screening E. grandis × E. urophylla hybrid material as seedlings should be revisited.

scholarly journals Emerging issues in genomic selection

2021 ◽  
Author(s):  
I Misztal ◽  
I Aguilar ◽  
D Lourenco ◽  
L Ma ◽  
J Steibel ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Sparse Matrices ◽  
Large Datasets ◽  
Single Step ◽  
Genomic Information ◽  
Long Term Effects ◽  
Additive Variance ◽  
Recent Developments ◽  
Emerging Issues ◽  
Using Data

Abstract Genomic selection is now practiced successfully across many species. However, many questions remain such as long-term effects, estimations of genomic parameters, robustness of GWAS with small and large datasets, and stability of genomic predictions. This study summarizes presentations from at the 2020 ASAS symposium. The focus of many studies until now is on linkage disequilibrium (LD) between two loci. Ignoring higher level equilibrium may lead to phantom dominance and epistasis. The Bulmer effect leads to a reduction of the additive variance; however, selection for increased recombination rate can release anew genetic variance. With genomic information, estimates of genetic parameters may be biased by genomic preselection, but costs of estimation can increase drastically due to the dense form of the genomic information. To make computation of estimates feasible, genotypes could be retained only for the most important animals, and methods of estimation should use algorithms that can recognize dense blocks in sparse matrices. GWAS studies using small genomic datasets frequently find many marker-trait associations whereas studies using much bigger datasets find only a few. Most current tools use very simple models for GWAS, possibly causing artifacts. These models are adequate for large datasets where pseudo-phenotypes such as deregressed proofs indirectly account for important effects for traits of interest. Artifacts arising in GWAS with small datasets can be minimized by using data from all animals (whether genotyped or not), realistic models, and methods that account for population structure. Recent developments permit computation of p-values from GBLUP, where models can be arbitrarily complex but restricted to genotyped animals only, and to single-step GBLUP that also uses phenotypes from ungenotyped animals. Stability was an important part of nongenomic evaluations, where genetic predictions were stable in the absence of new data even with low prediction accuracies. Unfortunately, genomic evaluations for such animals change because all animals with genotypes are connected. A top ranked animal can easily drop in the next evaluation, causing a crisis of confidence in genomic evaluations. While correlations between consecutive genomic evaluations are high, outliers can have differences as high as one SD. A solution to fluctuating genomic evaluations is to base selection decisions on groups of animals. While many issues in genomic selection have been solved, many new issues that require additional research continue to surface.

scholarly journals Pleiotropy and multilocus polymorphisms.

Genetics ◽  
1992 ◽  
Vol 130 (1) ◽  
pp. 223-227
Author(s):  
A Gimelfarb
Keyword(s):  
Genetic Variation ◽  
Linkage Disequilibrium ◽  
Stabilizing Selection ◽  
Additive Genetic Variation ◽  
Very High

Abstract It is demonstrated that systems of two pleiotropically related characters controlled by additive diallelic loci can maintain under Gaussian stabilizing selection a stable polymorphism in more than two loci. It is also shown that such systems may have multiple stable polymorphic equilibria. Stabilizing selection generates negative linkage disequilibrium, as a result of which the equilibrium phenotypic variances are quite low, even though the level of allelic polymorphisms can be very high. Consequently, large amounts of additive genetic variation can be hidden in populations at equilibrium under stabilizing selection on pleiotropically related characters.

scholarly journals The cross-sectional average inequality in lifespan (CAL†): A lifespan variation measure that reflects the mortality histories of cohorts

2021 ◽  
Author(s):  
Marília Nepomuceno ◽  
Qi Cui ◽  
Alyson A van Raalte ◽  
José Manuel Aburto ◽  
Vladimir Canudas-Romo
Keyword(s):  
Temporal Dimension ◽  
Cross Sectional ◽  
New Methods ◽  
Country Rankings ◽  
Lifespan Variation ◽  
The Cross ◽  
Using Data ◽  
Length Of Life ◽  
Timing Of Death ◽  
Cross Sectional Average

Lifespan variation is a key metric of mortality that describes both individual uncertaintyabout the length of life and heterogeneity in population health. We propose a novel andtimely lifespan variation measure, which we call the Cross-sectional Average Inequality in Lifespan. This new index provides an alternative perspective on the analysis of lifespan inequality by combining the mortality histories of all cohorts present in a cross-sectional approach. We demonstrate how differences in the Cross-sectional Average Inequality in Lifespan measure can be decomposed between populations by age and cohort to explore the compression or expansion of mortality in a cohort perspective. We apply these new methods using data from ten low-mortality countries from 1879 to 2013. The Cross-sectional Average Inequality in Lifespan measure reveals greater uncertainty in the timing of death than the period life table-based indices of variation indicate. Also, country rankings of lifespan inequality vary considerably between period and cross-sectional measures. These differences open intriguing questions as to which temporal dimension is the most relevant to individuals when considering the uncertainty in the timing of death in planning their life courses.

scholarly journals Investigation of the genetic variation in ACE2 on the structural recognition by the novel coronavirus (SARS-CoV-2)

2020 ◽  
Author(s):  
Xingyi Guo ◽  
Zhishan Chen ◽  
Yumin Xia ◽  
Weiqiang Lin ◽  
Hongzhi Li
Keyword(s):  
Genetic Variation ◽  
The Novel ◽  
S Protein ◽  
Angiotensin Converting Enzyme 2 ◽  
Missense Variants ◽  
Catalytic Metal ◽  
Using Data ◽  
Novel Coronavirus ◽  
Insight Into

Abstract Background: The outbreak of coronavirus disease (COVID-19) was caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), through its surface spike glycoprotein (S-protein) recognition on the receptor Angiotensin-converting enzyme 2 (ACE2) in humans. However, it remains unclear how genetic variations in ACE2 may affect its function and structure, and consequently alter the recognition by SARS-CoV-2. Methods: We have systemically characterized missense variants in the gene ACE2 using data from the Genome Aggregation Database (gnomAD; N = 141,456). To investigate the putative deleterious role of missense variants, six existing functional prediction tools were applied to evaluate their impact. We further analyzed the structural flexibility of ACE2 and its protein-protein interface with the S-protein of SARS-CoV-2 using our developed Legion Interfaces Analysis (LiAn) program.Results: Here, we characterized a total of 12 ACE2 putative deleterious missense variants. Of those 12 variants, we further showed that p.His378Arg could directly weaken the binding of catalytic metal atom to decrease ACE2 activity and p.Ser19Pro could distort the most important helix to the S-protein. Another seven missense variants may affect secondary structures (i.e. p.Gly211Arg; p.Asp206Gly; p.Arg219Cys; p.Arg219His, p.Lys341Arg, p.Ile468Val, and p.Ser547Cys), whereas p.Ile468Val with AF = 0.01 is only present in Asian.Conclusions: We provide strong evidence of putative deleterious missense variants in ACE2 that are present in specific populations, which could disrupt the function and structure of ACE2. These findings provide novel insight into the genetic variation in ACE2 which may affect the SARS-CoV-2 recognition and infection, and COVID-19 susceptibility and treatment.

scholarly journals Genetic variation of maize silage ingestibility in dairy cattle

2003 ◽  
Vol 52 (6) ◽  
pp. 489-500 ◽  
Author(s):  
Yves Barrière ◽  
Jean-Claude Emile ◽  
Fabien Surault
Keyword(s):  
Genetic Variation ◽  
Dairy Cattle ◽  
Maize Silage
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