Attachment | Size |
---|---|
9th_World_Congress_on_Genetics_Applied_to_Livestock_Production,_Leipzig,_Germany.pdf | 343.17 KB |
The development of methods aiming at detecting molecular signatures of
selection is one of the major concerns of modern population genetics. Broadly,
such methods can be classified into four groups: methods focusing on (i) the
interspecific comparison of gene substitution pat- terns, (ii) the frequency
spectrum and models of selective sweeps, (iii) linkage disequilibrium (LD) and
haplotype structure, and (iv) patterns of genetic differentiation among
populations (for a review see Nielsen (2005)).
One approach of detecting loci under selection (outliers) with population
genetic data is based on the genetic differentiation between loci only
influenced by neutral processes (genetic drift, mutation, migration) and loci
influenced by selection. Lewontin and Krakauer’s test - LK test - for the
heterogeneity of the inbreeding coefficient ( F ) across loci was the first to
be developed with regard to this concept (Lewontin and Krakauer, 1973). The
neutral model was a star-like evolution of populations with the same pattern of
evolution. This has led to some criticisms in the literature, as well as
improvements such as more sophisticated neutral models (Excoffier et al.,
2009), conditional distribution on heterozygosity (Beaumont and Nichols, 1996),
Bayesian models with McMC estimation (Beaumont and Balding, 2004; Foll and
Gaggiotti, 2008).
The key point is a good specification of the neutral model under which the null
hypothesis will be based. It should be as close as possible to the real
evolutionary history of the set of populations under study. In livestock, the
divergence time between breeds in intraspecific studies is small, the history
has a complex tree-like pattern, with negligible migrations between breeds. We
propose here to take account of this kind of complex demographic history
specific to livestock in a new test inspired from the original LK approach. Its
application is very fast, making it a method of choice for massive SNP data.