Revised Calculation of Kalinowski’s Ancestral and New Inbreeding Coefficients

To test for the presence of purging in populations, the classical pedigree-based inbreeding coefficient (F) can be decomposed into Kalinowski’s ancestral (FANC) and new (FNEW) inbreeding coefficients. The FANC and FNEW can be calculated by a stochastic approach known as gene dropping. However, the only publicly available algorithm for the calculation of FANC and FNEW, implemented in GRain v 2.1 (and also incorporated in the PEDIG software package), has produced biased estimates. The FANC was systematically underestimated and consequently, FNEW was overestimated. To illustrate this bias, we calculated FANC and FNEW by hand for simple example pedigrees. We revised the GRain program so that it now provides unbiased estimates. Correlations between the biased and unbiased estimates of FANC and FNEW, obtained for example data sets of Hungarian Pannon White rabbits (22,781 individuals) and Dutch Holstein Friesian cattle (37,061 individuals), were high, i.e., >0.96. Although the magnitude of bias appeared to be small, results from studies based on biased estimates should be interpreted with caution. The revised GRain program (v 2.2) is now available online and can be used to calculate unbiased estimates of FANC and FNEW.

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ESTIMATES OF INBREEDING IN A NATURAL POPULATION: A COMPARISON OF SAMPLING PROPERTIES

Genetics ◽

10.1093/genetics/100.2.339 ◽

1982 ◽

Vol 100 (2) ◽

pp. 339-358 ◽

Cited By ~ 3

Author(s):

Martin Curie-Cohen

Keyword(s):

Maximum Likelihood ◽

Natural Population ◽

Inbreeding Coefficient ◽

Single Locus ◽

Maximum Likelihood Estimates ◽

Gene Frequencies ◽

Chi Square ◽

Inbreeding Coefficients ◽

Good For ◽

Biased Estimates

ABSTRACT The average inbreeding coefficient f of a population can be estimated in several different ways based solely on the genotypic frequencies at a single locus. The means and variances of four different estimates have been compared. While the four estimates are equivalent when there are two alleles, the best estimates when there are three or more alleles are based upon total heterozygosity (see PDF) where x and y are the expected and observed number of heterozygotes) and the proportion of alleles that are homozygous (see PDF) where k = the number of alleles, aii = the number of AiAi homozygotes, and 2aij = the number of AiAj heterozygotes). Both are minimally biased estimates of f and have identical sampling variances when all alleles are equally frequent. However, when alleles have different frequencies, the choice between these two estimates depends on the gene frequencies and the true inbreeding coefficient of a population; f 2 is the best estimate when the true average inbreeding coefficient is suspected to be low or f = 0, while f 1 is best in populations with large average inbreeding coefficients. Approximate sampling variances of these two estimates are given for any f and any number of alleles with arbitrary gene frequencies; these approximations are accurate for samples as small as n = 100. The chi-square and maximum likelihood estimates of f are not as good for realistic sample sizes.

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Bayesian Inference of Recent Migration Rates Using Multilocus Genotypes

Genetics ◽

10.1093/genetics/163.3.1177 ◽

2003 ◽

Vol 163 (3) ◽

pp. 1177-1191 ◽

Cited By ~ 13

Author(s):

Gregory A Wilson ◽

Bruce Rannala

Keyword(s):

Probability Distributions ◽

Gray Wolf ◽

Data Sets ◽

Genotype Data ◽

Migration Rates ◽

Multilocus Genotypes ◽

Monte Carlo Techniques ◽

Inbreeding Coefficients ◽

Genotype Frequencies ◽

Marker Loci

Abstract A new Bayesian method that uses individual multilocus genotypes to estimate rates of recent immigration (over the last several generations) among populations is presented. The method also estimates the posterior probability distributions of individual immigrant ancestries, population allele frequencies, population inbreeding coefficients, and other parameters of potential interest. The method is implemented in a computer program that relies on Markov chain Monte Carlo techniques to carry out the estimation of posterior probabilities. The program can be used with allozyme, microsatellite, RFLP, SNP, and other kinds of genotype data. We relax several assumptions of early methods for detecting recent immigrants, using genotype data; most significantly, we allow genotype frequencies to deviate from Hardy-Weinberg equilibrium proportions within populations. The program is demonstrated by applying it to two recently published microsatellite data sets for populations of the plant species Centaurea corymbosa and the gray wolf species Canis lupus. A computer simulation study suggests that the program can provide highly accurate estimates of migration rates and individual migrant ancestries, given sufficient genetic differentiation among populations and sufficient numbers of marker loci.

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Visual identification of individual Holstein-Friesian cattle via deep metric learning

Computers and Electronics in Agriculture ◽

10.1016/j.compag.2021.106133 ◽

2021 ◽

Vol 185 ◽

pp. 106133

Author(s):

William Andrew ◽

Jing Gao ◽

Siobhan Mullan ◽

Neill Campbell ◽

Andrew W. Dowsey ◽

...

Keyword(s):

Metric Learning ◽

Visual Identification ◽

Holstein Friesian ◽

Deep Metric Learning ◽

Friesian Cattle

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Meat quality and carcass characteristics of Norwegian dairy and Holstein-Friesian cattle

Proceedings of the British Society of Animal Science ◽

10.1017/s1752756200020135 ◽

2007 ◽

Vol 2007 ◽

pp. 110-110

Author(s):

R.M. Kirkland ◽

D.C. Patterson ◽

B.W. Moss ◽

T.W.J. Keady ◽

R.W.J. Steen

Keyword(s):

Meat Quality ◽

Production Systems ◽

Carcass Characteristics ◽

Quality Parameters ◽

Quality Characteristics ◽

Production Data ◽

Dairy Traits ◽

Holstein Friesian ◽

Friesian Cattle ◽

Norwegian Dairy

Any evaluation of breeds or production systems for beef must consider effects on production, carcass and meat quality characteristics. Holstein-Friesian (HF) cattle are bred for dairy traits only, while Norwegian dairy cattle (NOR) have been selected with some emphasis on beef characteristics. A comparison of production data from bulls of these two breeds has been presented previously (Kirkland et al., 2005). The objective of the present study was to evaluate specific carcass and meat quality parameters of HF and NOR bulls.

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The Effect of Polymorphism in PGLYRP1 Gene on the Productivity and Health Traits in Holstein-Friesian Cattle

Russian Journal of Genetics ◽

10.1134/s1022795420030138 ◽

2020 ◽

Vol 56 (3) ◽

pp. 333-338

Author(s):

P. Sablik ◽

A. Klenowicz ◽

M. Szewczuk ◽

A. Olszewski ◽

A. Dybus

Keyword(s):

Holstein Friesian ◽

Friesian Cattle

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Recent and Planned Developments of the Program OxCal

Radiocarbon ◽

10.1017/s0033822200057878 ◽

2013 ◽

Vol 55 (2) ◽

pp. 720-730 ◽

Cited By ~ 641

Author(s):

Christopher Bronk Ramsey ◽

Sharen Lee

Keyword(s):

Statistical Analysis ◽

Statistical Methods ◽

Software Package ◽

Model Averaging ◽

Data Sets ◽

Radiocarbon Dates ◽

New Approach ◽

New Models ◽

Multiphase Models ◽

Deposition Models

OxCal is a widely used software package for the calibration of radiocarbon dates and the statistical analysis of 14C and other chronological information. The program aims to make statistical methods easily available to researchers and students working in a range of different disciplines. This paper will look at the recent and planned developments of the package. The recent additions to the statistical methods are primarily aimed at providing more robust models, in particular through model averaging for deposition models and through different multiphase models. The paper will look at how these new models have been implemented and explore the implications for researchers who might benefit from their use. In addition, a new approach to the evaluation of marine reservoir offsets will be presented. As the quantity and complexity of chronological data increase, it is also important to have efficient methods for the visualization of such extensive data sets and methods for the presentation of spatial and geographical data embedded within planned future versions of OxCal will also be discussed.

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