Impact of fertility transmission and other sociodemographic factors on reproductive success and coalescent trees
Authored by Jean-Tristan Brandenburg, Frederic Austerlitz, Bruno Toupance
Date Published: 2012
DOI: 10.1017/s0016672312000298
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Abstract
Fertility transmission (FT) is a phenomenon with a cultural and/or
genetic basis, whereby a positive correlation exists between the number
of offspring of an individual and that of his/her parents. Theoretical
studies using a haploid individual-based model have shown that FT
increases the variance and intergenerational correlation in reproductive
success and results in an imbalance in the coalescent tree of sampled
genes. This phenomenon has been documented in several demographic
studies conducted on the correlation in fertility between generations, or through the reconstruction of the genealogical trees of mitochondria!
DNA sequences. However, as mtDNA is a single locus, potentially subject
to other forces (e.g. natural selection), it is of interest to extend
the theory of FT to nuclear loci. We show that because random mating
between individuals leads to a mixing of their fertility profiles, FT in
these cases will have less influence on the variance and
intergenerational correlation of reproductive success. This, in turn, results in less impact on the shape of the coalescent trees.
Nevertheless, in the presence of FT, high heterogeneity in reproductive
success and homogamy for family size will increase the imbalance in the
coalescent tree. Thus, FT should be easier to detect when occurring in
conjunction with these other factors. We also show the utility of
analysing different kinds of loci (X-linked, Y-linked, mitochondrial and
autosomal) to assess whether FT is matrilineal, patrilineal or
biparental. Finally, we demonstrate that the shape of the coalescent
tree depends upon population size, in contrast to the classical
Kingman's model.
Tags
Evolution
Genetic diversity
selection
Mortality
fitness
Heritability
Effective population-size
Human mitochondrial-dna
Danish twin
Human mtdna