@@ -55,11 +57,11 @@ The purpose of the `zalpha` package is to:
# Software and Methodology
The `zalpha` package examines correlations between SNPs (single nucleotide polymorphisms) along a chromosome. If SNPs are highly correlated in a region of a chromosome in relation to the rest of the genome, this could indicate the presence of a selective sweep [@Vitti:2013].
The `zalpha` package examines correlations between single nucleotide polymorphisms (SNPs) along a chromosome. If SNPs are highly correlated in a region of a chromosome in relation to the rest of the genome, this could indicate the presence of a selective sweep [@Vitti:2013].
Correlation, in the context of genetics, is the ability to predict the value of one SNP, given the value of another. An example is given in \autoref{fig:Figure1}A. The metric used to measure correlation is r^2^ [@Cutter:2019].
Correlation, in the context of genetics, is the ability to predict the value of one SNP, given the value of another. An example is given in \autoref{fig:Figure1}A. The metric used by these statistics to measure correlation is r^2^ [@Cutter:2019].
When a selective sweep occurs, the locus under selection becomes more frequent in the population, as individuals possessing the beneficial allele are more likely to survive and reproduce. When this happens, variants nearby the selected locus will also sweep, a phenomenon known as “hitchhiking” [@Maynard:1974]. This creates a region of the genome that is highly correlated. Eventually recombination will erode away these correlations.
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@@ -81,13 +83,13 @@ The package is designed to be as user-friendly as possible and is reflected in t
* vector of physical locations,
* a window size, and
* a matrix of SNP values where the rows are SNPs and the columns are chromosomes. This matrix could be binary, where the 0s represent ancestral alleles and the 1s derived, or it could be nucleotides (i.e. As, Cs, Gs, and Ts), or any other biallelic labelling system.
* a matrix of SNP values where the rows are SNPs and the columns are haplotypes. This matrix could be binary, where the 0s represent ancestral alleles and the 1s derived, or it could be nucleotides (i.e. As, Cs, Gs, and Ts), or any other biallelic labelling system.
One of the benefits of this package is the ability to calculate multiple statistics simultaneously. During a selective sweep, the correlations between alleles near to the selected locus increase. This means both $Z_{\alpha}$ and $Z_{\beta}$ should be higher than in other areas of the genome not experiencing selective pressure. Towards the end of a selective sweep however, the correlations between the sets of alleles on the left and the right of the target locus are expected to diminish [@Kim:2004]. This suggests at the end of a sweep, $Z_{\alpha}$ should remain high, but $Z_{\beta}$ will reduce. Thus, it is advantageous to calculate and combine the different statistics to ascertain the strength and stage of sweeps. $Z_{\alpha}$/$Z_{\beta}$ is a simple way to achieve this.
Recombination is a process that has the effect of breaking down the relationship between alleles. However, it is known that recombination does not occur uniformly across the genome. It is therefore imperative to consider recombination when calculating statistics based on LD measures. This package allows the user to supply a population LD profile, providing information on the expected relationships between alleles given the genetic distances between them. Supplying this data increases the power of the statistics and creates more opportunities for combinations and comparisons between statistics. Users can specify whatever units they wish for genetic distance (for example centimorgans (cM)), derived from an appropriate data source. The software contains a function for creating an LD profile from the data. Ideally, an LD profile would be created from a neutral data source without selection, for example from a simulation with relevant population parameters. However, this is not always possible, so creating an LD profile from the same data being analysed is sufficient.
Recombination is a process that has the effect of breaking down the relationship between alleles. However, it is known that recombination does not occur uniformly across the genome. It is therefore imperative to consider recombination when calculating statistics based on LD measures. This package allows the user to supply a population LD profile, providing information on the expected relationships between alleles given the genetic distances between them. Supplying these data increases the power of the statistics and creates more opportunities for combinations and comparisons between statistics. Users can specify whatever units they wish for genetic distance (for example centimorgans (cM)), derived from an appropriate data source. The software contains a function for creating an LD profile from the data. Ideally, an LD profile would be created from a neutral data source without selection, for example from a simulation with relevant population parameters. However, this is not always possible, so creating an LD profile from the same data being analysed is sufficient.
There are many statistics included in the package for adjusting for expected r^2^ using the LDprofile and genetic distances between SNPs. It is recommended the user runs all the statistics using the `Zalpha_all()` function and then picks and chooses the ones they are interested in, perhaps even creating their own. For example, $Z_{\alpha}$/${Z_{\alpha}^{E[r^2]}}$ performs well as a simple way to adjust for expected r^2^. if it is known that the r^2^ values for each genetic distance are normally distributed, ${Z_{\alpha}^{Zscore}}$ is appropriate, otherwise ${Z_{\alpha}^{BetaCDF}}$ may be useful.For more details of how they are derived see the paper by @Jacobs:2016. This paper also shows how the different statistics perform under a range of demographic scenarios.
There are many statistics included in the package for adjusting for expected r^2^ using the LDprofile and genetic distances between SNPs. It is recommended the user runs all the statistics using the `Zalpha_all()` function and then chooses the ones they are interested in, perhaps even creating their own. For example, $Z_{\alpha}$/${Z_{\alpha}^{E[r^2]}}$ performs well as a simple way to adjust for expected r^2^. if it is known that the r^2^ values for each genetic distance are normally distributed, ${Z_{\alpha}^{Zscore}}$ is appropriate, otherwise ${Z_{\alpha}^{BetaCDF}}$ may be useful.For more details of how they are derived see the paper by @Jacobs:2016. This paper also shows how the different statistics perform under a range of demographic scenarios.
The output of the functions is in list format. The SNP positions and the values of the statistic(s) are stored in vectors of equal length in the list. Users can then identify outlying SNPs in their data that are candidate regions for selection.
