The genetic consequences of the subdivision of populations are regarded as significant to long‐term evolution, and research has shown that the scale and speed at which this is now occurring is critically reducing the adaptive potential of most species which inhabit human‐impacted landscapes. Here, we provide a rare and, to our knowledge, the first analysis of this process while it is happening and demonstrate a method of evaluating the effect of mitigation measures such as fauna crossings. We did this by using an extensive genetic data set collected from a koala population which was intensely monitored during the construction of linear transport infrastructure which resulted in the subdivision of their population. First, we found that both allelic richness and effective population size decreased through the process of population subdivision. Second, we predicted the extent to which genetic drift could impact genetic diversity over time and showed that after only 10 generations the resulting two subdivided populations could experience between 12% and 69% loss in genetic diversity. Lastly, using forward simulations we estimated that a minimum of eight koalas would need to disperse from each side of the subdivision per generation to maintain genetic connectivity close to zero but that 16 koalas would ensure that both genetic connectivity and diversity remained unchanged. These results have important consequences for the genetic management of species in human‐impacted landscapes by showing which genetic metrics are best to identify immediate loss in genetic diversity and how to evaluate the effectiveness of any mitigation measures.
1. The fixation index, FIS has been a staple measure to detect selection or departures from random mating in populations. However, current Next Generation Sequencing (NGS) cannot easily estimate Fis, in multi-locus gene families, which contain multiple loci having similar or identical arrays of variant sequences of ≥1 kilobase, which differ at multiple positions. In these families, high-quality short-read NGS data typically identify variants, but not the genomic location, which is required to calculate Fis (based on locus-specific observed and expected heterozygosity). Thus, to assess assortative mating, or selection on heterozygotes, from NGS of multi-locus gene families, we need a method that does not require knowledge of which variants are allelic at which locus in the genome. 2. We developed such a method. Like Fis, our novel measure, 1His, is based on the principle that positive assortative mating, or selection against heterozygotes, reduces within-individual variability relative to the population. 3. We demonstrate high accuracy of 1His on a wide-range of simulated scenarios, and two datasets from natural populations of penguins and dolphins. 4. 1His is important because multi-locus gene families are often involved in assortative mating, or selection on heterozygotes. 1His is particularly useful for multi-locus gene families such as toll-like receptors, the major-histocompatibility-complex in animals, homeobox genes in fungi and self-incompatibility genes in plants.
New sequencing technologies have opened the door to many new research opportunities, but these advances in data collection are not always compatible with some important methods for data analysis. Fis has been a staple calculation in the field of population genetics. Fis can be used to measure either a departure from random mating, or measure underlying selective pressures for or against heterozygote genotypes. However, when using Next Generation Sequencing (NGS) technology on multi-locus gene families it is often impossible to discern which allelic variants are present at each locus. Some important multi-locus gene families are: the major histocompatibility complex (MHC) in animals; homeobox genes in fungi; or the self-incompatibility genes in plants. This in turn makes it impossible to calculate either locus-specific expected heterozygosity, or observed heterozygosity, both of which are required to calculate Fis. Without the ability to calculate Fis from NGS of multi-locus gene families, we need a new multi-locus measure that will allow us to detect the underlining mating, and selective patterns present in such multi-locus genes. This paper provides such a novel multi-locus measure, called 1His. We demonstrate the accuracy of the 1His equation using simulated data, and two datasets taken from natural populations of dolphins and penguins. The introduction of this new measure is particularly important because of the great interest in mating patterns and selection of multi-locus gene families, such as MHC.
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