Empirical Sampling Estimates of Genetic Correlations

VanVleck, L. D.; Henderson, Charles

doi:10.2307/2527831

Cited by 43 publications

(22 citation statements)

References 5 publications

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“…However, this upward bias, which is most pronounced when heritabilities are low and genetic correlations are high, is not unique to the methods introduced herein, as it even occurs in the ideal case in which all pairs consist of related individuals. Although such bias has been noticed (Van Vleck & Henderson, 1961 ;Brown, 1969), it has received little attention in previous studies of the statistical behaviour of the genetic correlation, and given its magnitude relative to the sampling variance of estimates, however, it will not generally be an overwhelming concern.…”

Section: Discussionmentioning

confidence: 99%

“…Thus, contrary to the situation with the well-known productmoment correlation, estimates of the genetic correlation can fall outside of the parametric limits (p1) or can be undefined when one of the genetic-variance estimates is negative (Hill & Thompson, 1978). Sample sizes of a few thousand pairs of relatives are often necessary to achieve estimates that can confidently be interpreted at the level of even single significant digits (Van Vleck & Henderson, 1961 ;Brown, 1969 ;Klein, 1974 ;Visscher, 1998), although a simple knowledge of the sign of the genetic correlation can be achieved with less, but still substantial, effort.…”

Section: Introductionmentioning

confidence: 99%

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Estimating genetic correlations in natural populations

Lynch

1999

Genet. Res.

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SummaryInformation on the genetic correlation between traits provides fundamental insight into the constraints on the evolutionary process. Estimates of such correlations are conventionally obtained by raising individuals of known relatedness in artificial environments. However, many species are not readily amenable to controlled breeding programmes, and considerable uncertainty exists over the extent to which estimates derived under benign laboratory conditions reflect the properties of populations in natural settings. Here, non-invasive methods that allow the estimation of genetic correlations from phenotypic measurements derived from individuals of unknown relatedness are introduced. Like the conventional approach, these methods demand large sample sizes in order to yield reasonably precise estimates, and special precautions need to be taken to eliminate bias from shared environmental effects. Provided the sample consists of at least 20 % or so relatives, informative estimates of the genetic correlation are obtainable with sample sizes of several hundred individuals, particularly if supplemental information on relatedness is available from polymorphic molecular markers.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimating genetic correlations in natural populations

Lynch

1999

Genet. Res.

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“…Genetic correlations often have large sampling errors, especially when heritability estimates are low (e.g. Robertson, 1959;Van Vleck and Henderson, 1961); thus there can be uncertainty about calculation of expected correlated responses.…”

Section: General Considerationmentioning

confidence: 99%

Genetic parameters of reproductive traits in sheep

Rosati¹,

Mousa

Vleck

et al. 2002

Small Ruminant Research

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Reproductive traits from 7642 ewes were recorded from 1975 to 1983. The ewes were of five breeds (Dorset (D), Finnsheep (F), Rambouillet (R), Suffolk (S) and Targhee (T)) and two composite lines [C1 (1=2F þ 1=4R þ 1=4D) and C2 (1=2F þ 1=4S þ 1=4T)]. Genetic parameters were estimated for six basic and seven composite traits. The basic traits were conception rate (CR), total number of lamb born (NLB), number of lambs born alive (NLBA), number of lambs alive at weaning (NLAW), litter mean weight per lamb born (LMWLB) and litter mean weight per lamb weaned (LMWLW). The composite traits were ratio of lambs surviving to weaning relative to NLB (LSW ¼ NLAW=NLB), number of lambs born per ewe exposed (NLBEE ¼ CR Â NLB), number of lambs weaned per ewe exposed (NLWEE ¼ CR Â NLAW), total litter weight at birth (TLWB ¼ NLB Â LMWLB), total litter weight at weaning (TLWW ¼ NLAW Â LMWLW), total litter weight at birth per ewe exposed (TLWBEE ¼ CR Â NLB Â LMWLB) and total litter weight at weaning per ewe exposed (TLWWEE ¼ CR Â NLAW Â LMWLW). Year, age of ewe, breed of ewe, hormone treatment and season of breeding were used as fixed effects. Direct and maternal genetic effects, permanent environmental effects of ewe and mate of ewe were considered to be random effects. A derivative-free algorithm was used to obtain REML estimates of genetic and environmental parameters. Estimates of heritabilities for animal genetic and permanent environmental and maternal genetic effects were mainly small due to the typical high influence of environmental factors on reproductive traits and to non-normal distributions of traits. Mate of ewe effects were not important for any trait. Important genetic correlations were found between some traits. Some estimates of genetic correlations do not seem to have a biological explanation. Nevertheless, these estimates of genetic correlations among traits may provide a basis for deriving selection indexes for reproductive traits. #

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“…In any estimation procedure it is necessary to estimate both the value of the statistic itself and also the associated confidence limits. The estimation of confidence limits for the genetic correlation is difficult and even in the restricted cases where estimation methods have been worked out the statistical behaviour is not well understood (Robertson, 1959(Robertson, , 1960Van Vleck & Henderson, 1961;Van Vleck, 1968;Hammond & Nicholas, 1972;Grossman & Norton, 1974;Becker, 1985). An alternative approach suggested by Via (1984) is to use the Pearson product-moment correlation between family means, for which the usual methods of estimating confidence intervals on correlations can be applied.…”

Section: Introductionmentioning

confidence: 99%

The estimation of the genetic correlation: the use of the jackknife

1994

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To understand fully the process of evolution of quantitative traits it is necessary to be able to estimate the genetic correlation and its associated standard error. At present, estimation methods are available only for relatively simple designs. An alternative procedure is to use the correlation of family means as an estimate of the genetic correlation. We evaluate the utility of the family mean method and that of the more general procedure, the jackknife. The family mean method is shown to be potentially very biased unless family sizes are very large ( 20), and therefore its general utility is questionable. However, the jackknife method does provide valid estimates of both the correlations (phenotypic and genetic) and their standard errors.

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Empirical Sampling Estimates of Genetic Correlations

Cited by 43 publications

References 5 publications

Estimating genetic correlations in natural populations

Estimating genetic correlations in natural populations

Genetic parameters of reproductive traits in sheep

The estimation of the genetic correlation: the use of the jackknife

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