Variation in genetic identity within kinships

Rasmuson, Marianne

doi:10.1038/hdy.1993.38

Cited by 17 publications

(17 citation statements)

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“…The amount and distribution of meiotic recombination is species specific (Rasmuson, 1993) and consequently cannot be changed for a study organism. All else being equal, Pedigree F is a more precise estimate of GWIBD in species with more meiotic recombination (and a more uniform distribution of crossovers), whereas Marker F is losing precision.…”

Section: Discussionmentioning

confidence: 99%

“…Similarly, it has been predicted that the distribution of crossovers along chromosomes will influence the amount of variance in IBD (see, for example, Risch and Lange, 1979;Rasmuson, 1993;Guo, 1995;Forstmeier et al, 2012), but to our knowledge it has never received attention in a modeling framework. Here we show that the variance in IBD is much larger in zebra finches than in humans, because in the former almost half of the genome is inherited in only six segments (that is, the interiors of chromosomes Tgu1, Tgu1A, Tgu2, Tgu3, Tgu4 and Tgu5) that only rarely break up by crossovers (Backström et al, 2010).…”

Section: Mendelian Noise In Gwibdmentioning

confidence: 99%

“…For any given inbreeding constellation, the more segments in a genome segregate independently, the lower the variation in GWIBD between individuals of the same inbreeding constellation will be (law of large numbers; Rasmuson, 1993;Visscher, 2009). Consequently, because chromosomes get inherited as independent units in meiosis, the Mendelian noise for a given inbreeding constellation will be smaller in species with more chromosomes (Hill and Weir, 2011).…”

Section: Introductionmentioning

confidence: 99%

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Meiotic recombination shapes precision of pedigree- and marker-based estimates of inbreeding

2016

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The proportion of an individual's genome that is identical by descent (GWIBD) can be estimated from pedigrees (inbreeding coefficient 'Pedigree F') or molecular markers ('Marker F'), but both estimators come with error. Assuming unrelated pedigree founders, Pedigree F is the expected proportion of GWIBD given a specific inbreeding constellation. Meiotic recombination introduces variation around that expectation (Mendelian noise) and related pedigree founders systematically bias Pedigree F downward. Marker F is an estimate of the actual proportion of GWIBD but it suffers from the sampling error of markers plus the error that occurs when a marker is homozygous without reflecting common ancestry (identical by state). We here show via simulation of a zebra finch and a human linkage map that three aspects of meiotic recombination (independent assortment of chromosomes, number of crossovers and their distribution along chromosomes) contribute to variation in GWIBD and thus the precision of Pedigree and Marker F. In zebra finches, where the genome contains large blocks that are rarely broken up by recombination, the Mendelian noise was large (nearly twofold larger s.d. values compared with humans) and Pedigree F thus less precise than in humans, where crossovers are distributed more uniformly along chromosomes. Effects of meiotic recombination on Marker F were reversed, such that the same number of molecular markers yielded more precise estimates of GWIBD in zebra finches than in humans. As a consequence, in species inheriting large blocks that rarely recombine, even small numbers of microsatellite markers will often be more informative about inbreeding and fitness than large pedigrees.

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Section: Discussionmentioning

confidence: 99%

Section: Mendelian Noise In Gwibdmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Meiotic recombination shapes precision of pedigree- and marker-based estimates of inbreeding

2016

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“…The number of effective loci is similar to the recombination index for humans, assumed by Rasmusson (1993) to be the number of independently segregating units in the genome.…”

Section: (I) An Equivalent Model For Genomic Selectionmentioning

confidence: 99%

Increased accuracy of artificial selection by using the realized relationship matrix

Hayes¹,

2009

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SummaryDense marker genotypes allow the construction of the realized relationship matrix between individuals, with elements the realized proportion of the genome that is identical by descent (IBD) between pairs of individuals. In this paper, we demonstrate that by replacing the average relationship matrix derived from pedigree with the realized relationship matrix in best linear unbiased prediction (BLUP) of breeding values, the accuracy of the breeding values can be substantially increased, especially for individuals with no phenotype of their own. We further demonstrate that this method of predicting breeding values is exactly equivalent to the genomic selection methodology where the effects of quantitative trait loci (QTLs) contributing to variation in the trait are assumed to be normally distributed. The accuracy of breeding values predicted using the realized relationship matrix in the BLUP equations can be deterministically predicted for known family relationships, for example half sibs. The deterministic method uses the effective number of independently segregating loci controlling the phenotype that depends on the type of family relationship and the length of the genome. The accuracy of predicted breeding values depends on this number of effective loci, the family relationship and the number of phenotypic records. The deterministic prediction demonstrates that the accuracy of breeding values can approach unity if enough relatives are genotyped and phenotyped. For example, when 1000 full sibs per family were genotyped and phenotyped, and the heritability of the trait was 0 . 5, the reliability of predicted genomic breeding values (GEBVs) for individuals in the same full sib family without phenotypes was 0 . 82. These results were verified by simulation. A deterministic prediction was also derived for random mating populations, where the effective population size is the key parameter determining the effective number of independently segregating loci. If the effective population size is large, a very large number of individuals must be genotyped and phenotyped in order to accurately predict breeding values for unphenotyped individuals from the same population. If the heritability of the trait is 0 . 3, and N e =1000, approximately 5750 individuals with genotypes and phenotypes are required in order to predict GEBVs of un-phenotyped individuals in the same population with an accuracy of 0 . 7.

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“…

In a recent paper of this journal, Rasmuson (1993) computed the standard deviation (s.d.) of the genetic identity of relatives, i.e.

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confidence: 99%

Variation in genetic identity within kinships

Hill

1993

Heredity

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In a recent paper of this journal, Rasmuson (1993) computed the standard deviation (s.d.) of the genetic identity of relatives, i.e. of the proportion of the genome which is identical by descent. For example, for parent and offspring the mean identity is 50 per cent and its s.d. is 0 per cent because the offspring inherits exactly one-half of the autosomal genes from each parent; whereas for grandparent and grandoffspring or for half-sibs, the mean is 25 per cent and, as there is segregation and recombination, the s.d. is not zero, Rasmuson giving a value of 2.5 per cent for each case. The variance of identity was computed byRasmuson on the assumption that the genotype comprised a number of independently segregating discrete units, the segregation index, which was set at 100, a value considered appropriate for human females (equal to the haploid chromosome number plus the total number of chiasmata). In her model it is, in effect, assumed that the genome comprises 100 chromosomes in each of which no recombination occurs, whereas recombination events are essentially continuously distributed along the chromosomes and occur at different locations in each generation. An equivalent problem to that considered byRasmuson for the case of unilateral descendants is the variance of the contribution of the non-recurrent parental line in a backcrossing programme. Coincidentally, I have recently derived a formula to compute this variance for a model that takes account of the distribution of recombinations (Hill, 1993) The variances are not the same for collateral relatives of corresponding mean identity. The reason is that, for two loci with recombination fraction c, the probability that a grandoffspring inherits genes at both loci from a grandparent is (1 -c)/2, whereas the probability that two haif-sibs inherit the same pair of genes is (1 -c)2/2 + c2/2. Only for c =0 (complete linkage) and c = 1/2 (no linkage) are these quantities equal. For halfsibs, the mean identity is E(PHS) = 1/4 and the variance isusing results of Franklin (1977). In our example s.d. (PHS) = 2.82 per cent, lower than s.d. (P2) for grandoffspring-grandparent. For full-sibs the variance is 2V(PHS), because the two parents' contributions are independent. Formulae can be computed for other relationships, but are more complicated and beyond the scope of this note. The methods of Weir and Cockerham (1969) could be employed, and also means and variances of identity of genotype derived for fullsibs, for example, rather than of genes as here.

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Variation in genetic identity within kinships

Cited by 17 publications

References 6 publications

Meiotic recombination shapes precision of pedigree- and marker-based estimates of inbreeding

Meiotic recombination shapes precision of pedigree- and marker-based estimates of inbreeding

Increased accuracy of artificial selection by using the realized relationship matrix

Variation in genetic identity within kinships

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