Chapter 2 A brief introduction to AlphaSimR
2.2 Creating founder haplotypes
In creating a population for simulation you need to first develop the founder haplotypes. In creating the founder haplotypes, the genomic architecture of the simulated organism is defined. In AlphaSimR, haplotypes are binary strings of 0’s and 1’s. Strings are arranged in pre-defined chromosome numbers and lengths in units of segregation sites. Given that the custom development of haplotypes can be a bit overwhelming to first time users, the function quickHaplo() can initially be used to get started. quickHaplo() makes it easy to define the number of individuals, number of chromosomes per individual, number of segregation sites per chromosome, ploidy level, and the genetic length of chromosomes in units of Morgans. An option for creating inbred founders is also available for more advanced users. The code to create haplotypes for 10 individuals having extremely simple single chromosome diploid genomes is shown below.
pop_haplos <- quickHaplo(nInd = 10, # number of individuals
nChr = 1, # number of chromosomes
segSites = 10, # number of segregation sites
genLen = 1, # genetic length in Morgans
ploidy = 2L, # ploidy level of organism
inbred = FALSE) # are founders inbred?quickHaplo() saves haplotypes as a MapPop class object. Each slot can be accessed using an @ symbol. Of course, slots in the form of lists require additional designations within []. The MapPop object provides a record of important genome characteristics. Knowing how to access these characteristics is important to the analysis of breeding simulations. The following information can be obtained and/or modified through the MapPop object.
pop_haplos@genMap[1] # a vector of segregation sites locations (M) per [chromosome]
pop_haplos@centromere[1] # a vector of centromere location (M) per [chromosome]
pop_haplos@nInd # the number of individuals
pop_haplos@nChr # the number of chromosomes
pop_haplos@ploidy # the ploidy of individuals
pop_haplos@nLoci[1] # a vector of loci number per [chromosome]
pop_haplos@geno[1] # matrices of haplotypes per [individual]As suggested through the name, MapPop class objects contain information about the physical map, or genomic architecture, of the simulated organisms. Chromosome number, segregation site location, ploidy, loci number, and centromere location are all central to the physical mapping of a genome. We will discuss all of these items in greater detail in Chapter 5, Physical mapping of simulated genomes. Here, it is important to note that genome length in AlphaSimR is not defined in base pairs, but rather Morgans. Being a measure of genetic linkage, chromosome length interacts directly with MapPop information in ‘guiding’ the formation of gametes (NB: meiosis) in the simulated organism. Being of great importance to breeding simulations, genetic length is discussed in more detail in Chapter 3, An overview of recombination.
When applied to a MapPop class object, the function pullSegSiteHaplo() returns the simulated haplotypes. The code and first few lines of the output are shown below.
haplos <- pullSegSiteHaplo(pop = pop_haplos, # Pop-class object
haplo = "all", # "all" or use 1 for males and 2 for females
chr = 1) # chromosome number, NULL = all
head(haplos)## SITE_1 SITE_2 SITE_3 SITE_4 SITE_5 SITE_6 SITE_7 SITE_8 SITE_9 SITE_10
## 1_1 1 1 0 0 1 0 0 0 1 1
## 1_2 0 0 0 1 0 1 1 1 1 1
## 2_1 0 1 1 1 1 0 0 1 0 0
## 2_2 1 0 1 1 0 1 0 1 0 0
## 3_1 1 1 0 1 0 0 1 0 1 0
## 3_2 1 1 1 1 1 1 1 1 1 1pullSegSiteHaplo() returns a m x n matrix where each row equates to a haplotype (m) and each column to a loci (n). Haplotypes are labeled using an “individual ID number” and a “haplotype number”, separated by an underscore. Loci are simply labeled in ascending order as “SITE_1”, “SITE_2”, etc. For each haplotype x loci combination the allele has been randomly defined as either 0 or 1. As the developers of AlphaSimR clearly state in a vignette associated with the package (NB: vignette("intro", package = "AlphaSimR")), the quickHaplo() approach to haplotype development, “is equivalent to modeling a population in linkage and Hardy-Weinberg equilibrium with allele frequencies of 0.5. This approach is very rapid but does not generate realistic haplotypes. This makes the approach great for prototyping code, but ill-suited for some types of simulations”. We use quickHaplo() throughout the introductory genetic lessons but will discuss the alternatives such as runMacs and newMapPop in Chapter 7, Simulating a tilapia genome.
2.3 Setting simulation parameters
After defining the founder population haplotypes, simulation parameters are set. As their name implies, simulation parameters define the conditions through which a simulation operates. AlphaSimR possesses a myriad of simulation parameters – far more than can be covered here. A review of the most important parameters to the breeding simulations covered throughout the book are illustrated here. The first step in defining simulation parameters is to create a SimParam object. After this is done, simulation parameters can be added sequentially.
2.3.1 Adding traits to a breeding simulation
When paired in diploid individuals, the binary haplotypes become genotypes. At a given locus an individual can be 00, 01, or 11. While it is possible to simply simulate and evaluate genotypic changes in AlphaSimR, most breeding simulations aim to also explore phenotypic change. The first step in simulating phenotypic change is define linkages between the genotypes of specific loci and traits of interest for simulation. Traits of any complexity can be added.
SP$addTraitA(nQtlPerChr = 3, # number of QTL for trait
mean = 454, # mean genetic value of the trait
var = 50, # variance of trait in population
corA = NULL, # matrix of correlations between additive effects
gamma = FALSE, # to use a gamma distribution in place of a normal distribution
shape = 1, # shape parameter for gamma distribution only
force = FALSE) # keep false until this is understood!