scholarly journals An Epigenetic Aging Clock for Cattle Using Portable Sequencing Technology

2021 ◽  
Vol 12 ◽  
Author(s):  
Ben J. Hayes ◽  
Loan T. Nguyen ◽  
Mehrnush Forutan ◽  
Bailey N. Engle ◽  
Harrison J. Lamb ◽  
...  
Keyword(s):  
Relationship Matrix ◽  
Epigenetic Clock ◽  
Absolute Deviation ◽  
Linear Unbiased Prediction ◽  
Methylation Of Dna ◽  
Epigenetic Aging ◽  
Oxford Nanopore ◽  
Best Linear Unbiased ◽  
Oxford Nanopore Technologies ◽  
Tail Hair

Extensively grazed cattle are often mustered only once a year. Therefore, birthdates are typically unknown or inaccurate. Birthdates would be useful for deriving important traits (growth rate; calving interval), breed registrations, and making management decisions. Epigenetic clocks use methylation of DNA to predict an individual’s age. An epigenetic clock for cattle could provide a solution to the challenges of industry birthdate recording. Here we derived the first epigenetic clock for tropically adapted cattle using portable sequencing devices from tail hair, a tissue which is widely used in industry for genotyping. Cattle (n = 66) with ages ranging from 0.35 to 15.7 years were sequenced using Oxford Nanopore Technologies MinION and methylation was called at CpG sites across the genome. Sites were then filtered and used to calculate a covariance relationship matrix based on methylation state. Best linear unbiased prediction was used with 10-fold cross validation to predict age. A second methylation relationship matrix was also calculated that contained sites associated with genes used in the dog and human epigenetic clocks. The correlation between predicted age and actual age was 0.71 for all sites and 0.60 for dog and human gene epigenetic clock sites. The mean absolute deviation was 1.4 years for animals aged less than 3 years of age, and 1.5 years for animals aged 3–10 years. This is the first reported epigenetic clock using industry relevant samples in cattle.

scholarly journals Core-dependent changes in genomic predictions using the Algorithm for Proven and Young in single-step genomic best linear unbiased prediction

2020 ◽  
Vol 98 (12) ◽  
Author(s):  
Ignacy Misztal ◽  
Shogo Tsuruta ◽  
Ivan Pocrnic ◽  
Daniela Lourenco
Keyword(s):  
Prediction Accuracy ◽  
Best Linear Unbiased Prediction ◽  
Single Step ◽  
Relationship Matrix ◽  
Linear Unbiased Prediction ◽  
Breeding Values ◽  
Best Linear Unbiased ◽  
The Impact ◽  
Genomic Predictions ◽  
Unbiased Prediction

Abstract Single-step genomic best linear unbiased prediction with the Algorithm for Proven and Young (APY) is a popular method for large-scale genomic evaluations. With the APY algorithm, animals are designated as core or noncore, and the computing resources to create the inverse of the genomic relationship matrix (GRM) are reduced by inverting only a portion of that matrix for core animals. However, using different core sets of the same size causes fluctuations in genomic estimated breeding values (GEBVs) up to one additive standard deviation without affecting prediction accuracy. About 2% of the variation in the GRM is noise. In the recursion formula for APY, the error term modeling the noise is different for every set of core animals, creating changes in breeding values. While average changes are small, and correlations between breeding values estimated with different core animals are close to 1.0, based on the normal distribution theory, outliers can be several times bigger than the average. Tests included commercial datasets from beef and dairy cattle and from pigs. Beyond a certain number of core animals, the prediction accuracy did not improve, but fluctuations decreased with more animals. Fluctuations were much smaller than the possible changes based on prediction error variance. GEBVs change over time even for animals with no new data as genomic relationships ties all the genotyped animals, causing reranking of top animals. In contrast, changes in nongenomic models without new data are small. Also, GEBV can change due to details in the model, such as redefinition of contemporary groups or unknown parent groups. In particular, increasing the fraction of blending of the GRM with a pedigree relationship matrix from 5% to 20% caused changes in GEBV up to 0.45 SD, with a correlation of GEBV > 0.99. Fluctuations in genomic predictions are part of genomic evaluation models and are also present without the APY algorithm when genomic evaluations are computed with updated data. The best approach to reduce the impact of fluctuations in genomic evaluations is to make selection decisions not on individual animals with limited individual accuracy but on groups of animals with high average accuracy.

Evaluating dairy sires for conformation of their daughters: use of first classification records

1985 ◽  
Vol 36 (3) ◽  
pp. 509 ◽  
Author(s):  
K Meyer ◽  
EB Burnside ◽  
K Hammond ◽  
AE McClintock
Keyword(s):  
Breeding Value ◽  
Relationship Matrix ◽  
Linear Unbiased Prediction ◽  
Maximum Likelihood Procedure ◽  
Holstein Friesian ◽  
Genetic And Phenotypic Correlations ◽  
Best Linear Unbiased ◽  
Covariance Components ◽  
The Relationship ◽  
Type Classification

Type classification records for 18 132 Australian Holstein-Friesian heifers were analysed. These consisted of 27 traits scored in three or six categories, from three rounds of classification between 1981 and 1983. Only first lactation, first classification records were considered. The model of analysis included herd-round-classifier subclasses as fixed and sires as random effects, fitting age at classification as a linear and quadratic covariable within subclasses. Herd-round-classifier effects explained between 18% and 37% of total sums of squares. Age accounted for 5.8-2.4% for traits related to body size and for 1.6% or less for the other traits. Heritability estimates obtained using a Restricted Maximum Likelihood procedure ranged from 0.44 for total score, 0.42 for stature and 0.40 for dairy character, to 0.10 or less for feet and legs, rear heel, rear legs set, bone quality and rear teat placement. On average, values were higher than corresponding 'all-lactation' estimates. Covariance components between all traits were obtained. The resulting genetic variance/covariance matrix was then forced to be positive semi-definite before calculating genetic and phenotypic correlations. Breeding value estimates for all sires and traits were determined using a univariate Best Linear Unbiased Prediction procedure for the above model. In addition, the relationship matrix between males was incorporated. There were 2 597 sires with an average of 23.2 effective daughters, and 474 sires without daughter records included as male ancestors. The association between breeding value estimates for different traits was examined by multiple regression. Sire-son regressions were determined and compared with their expectations. Australian Breeding Values for production were obtained for a subset of sires and contrasted to the type proofs. There seemed to be little correlation between genetic merit for type and milk production.

scholarly journals Computing the condensed conditional gametic QTL relationship matrix and its inverse

2007 ◽  
Vol 50 (3) ◽  
pp. 294-308 ◽  
Author(s):  
C. Baes ◽  
N. Reinsch
Keyword(s):  
Sparse Matrix ◽  
Relationship Matrix ◽  
Computer Memory ◽  
Pedigree Data ◽  
Linear Unbiased Prediction ◽  
Marker Data ◽  
Covariance Between Relatives ◽  
Best Linear Unbiased ◽  
Fortran 90 ◽  
Trait Locus

Abstract. The inverse of the conditional gametic relationship matrix (G-1) for a marked quantitative trait locus (MQTL) is required for estimation of gametic effects in best linear unbiased prediction (BLUP) of breeding values if marker data are available. Calculation of the "condensed" gametic relationship matrix G* – a version of G where linear dependencies have been removed – and its inverse G*-1 is described using a series of simplified equations following a known algorithm. The software program COBRA (covariance between relatives for a marked QTL) is introduced, and techniques for storing and computing the condensed gametic relationship matrix G* and the non-zero elements of its inverse are discussed. The program operates with both simple pedigrees and those augmented by transmission probabilities derived from marker data. Using sparse matrix storage techniques, G* and its inverse can be efficiently stored in computer memory. COBRA is written in FORTRAN 90/95 and runs on a variety of computers. Pedigree data and information for a single MQTL in the German Holstein population are used to test the efficiency of the program.

scholarly journals Improving the Efficiency of Genomic Selection in Chinese Simmental beef cattle

2015 ◽  
Author(s):  
Jiangwei Xia ◽  
Yang Wu ◽  
Huizhong Fang ◽  
Wengang Zhang ◽  
Yuxin Song ◽  
...  
Keyword(s):  
Beef Cattle ◽  
Genomic Relationship Matrix ◽  
Relationship Matrix ◽  
Nucleotide Polymorphisms ◽  
Linear Unbiased Prediction ◽  
Single Nucleotide ◽  
Kinship Matrix ◽  
Genome Wide ◽  
Best Linear Unbiased ◽  
Selection Of

Genomic selection is an accurate and efficient method of estimating genetic merits by using high-density genome-wide single nucleotide polymorphisms (SNPs).In this study, we investigate an approach to increase the efficiency of genomic prediction by using genome-wide markers. The approach is a feature selection based on genomic best linear unbiased prediction (GBLUP),which is a statistical method used to predict breeding values using SNPs for selection in animal and plant breeding. The objective of this study is the choice of kinship matrix for genomic best linear unbiased prediction (GBLUP).The G-matrix is using the information of genome-wide dense markers. We compare three kinds of kinships based on different combinations of centring and scaling of marker genotypes.And find a suitable kinship approach that adjusts for the resource population of Chinese Simmental beef cattle.Single nucleotide polymorphism (SNPs) can be used to estimate kinship matrix and individual inbreeding coefficients more accurately. So in our research a genomic relationship matrix was developed for 1059 Chinese Simmental beef cattle using 640000 single nucleotide polymorphisms and breeding values were estimated using phenotypes about Carcass weight and Sirloin weight. The number of SNPs needed to accurately estimate a genomic relationship matrix was evaluated in this population. Another aim of this study was to optimize the selection of markers and determine the required number of SNPs for estimation of kinship in the Chinese Simmental beef cattle. We find that the feature selection of GBLUP using Xu’s and the Astle and Balding’s kinships model performed similarly well, and were the best-performing methods in our study. Inbreeding and kinship matrix can be estimated with high accuracy using ≥12,000s in Chinese Simmental beef cattle.

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