Rate Maps

It is often useful to express a variable rate of some sort along the chromosome. For example, most organisms have mutation rates and recombination rates which vary depending on genomic position. Rates of this sort can be stored as an instance of a RateMap, and then passed as a parameter to the relevant msprime method. For instance, sim_ancestry() accepts a RateMap for the recombination_rate parameter and sim_mutations() accepts a RateMap for the rate parameter.

A rate map is defined as a set of constant rates which apply piecewise over contiguous regions of the genome. For example, here we create a simple rate map over a 30kb genome:

rate_map = msprime.RateMap(
    position=[0, 4000, 9000, 11000, 20000, 30000],
    rate=[0, 0.1, 0.6, 0.2, 0.1]
)
rate_map
leftrightmidspanrate
04000200040000.00
40009000650050000.10
9000110001000020000.60
11000200001550090000.20
200003000025000100000.10

See the Working with RateMaps section for details on these columns. We can see how these rates vary along the genome by plotting:

plt.figure(figsize=(10, 4))
plt.stairs(rate_map.rate, rate_map.position)
plt.title("Example RateMap with 5 rates, and a hotspot around position 10000")
plt.xlabel("Genome position")
plt.ylabel("Rate")
plt.xlim(0, rate_map.sequence_length)
plt.show()
_images/rate_maps_4_0.png

Quick reference

RateMap

Represent the varying rate of processes along the genome as a piecewise function.

Creating rate maps

RateMap.uniform()

Create a RateMap with a single rate over the entire chromosome

RateMap.read_hapmap()

Read in a RateMap from a file in “HapMap” format (common in recombination maps)

RateMap.slice()

Create a new RateMap by slicing out a portion of an existing RateMap

Computing rates

RateMap.mean_rate

The weighted average rate

RateMap.total_mass

The cumulative sum of rates over the entire map

RateMap.get_rate()

Compute rate at a set of positions

RateMap.get_cumulative_mass()

Compute the cumulative rate at a set of positions

Working with RateMaps

In this section we illustrate the ways in which we can interact with a RateMap object. Throughout we’ll use the example map from the introduction, defined over a 30kb genome:

rate_map = msprime.RateMap(
    position=[0, 4000, 9000, 11000, 20000, 30000],
    rate=[0, 0.1, 0.6, 0.2, 0.1]
)
rate_map
leftrightmidspanrate
04000200040000.00
40009000650050000.10
9000110001000020000.60
11000200001550090000.20
200003000025000100000.10

See also

See the Creating rate maps section for different ways of creating RateMap instances with various properties.

To create the RateMap instance we called the constructor with two lists, position and rate. These define the intervals along the genome and the rates in each interval. Because we are interested in nonoverlapping contiguous intervals, we can define all the \(n\) intervals with \(n + 1\) positions and corresponding \(n\) rate values.

Important

All intervals are open-closed; that is, the left coordinate is inclusive and the right coordinate exclusive.

Information about each of the intervals is shown in the summary table above, one interval per row. So, the last interval has a left coordinate of 20000, a right coordinate of 30000, a midpoint of 25000 (mid), has a span of 10000 base pairs and maps to a rate of 0.1.

See also

See the Array interface for examples of how to access the columns efficiently as numpy arrays.

Mapping interface

The easiest way to access the rate information from a rate map is via the mapping interface. We can use the Python square bracket notation to get the rate at a particular point:

print("rate at position 4500 = ", rate_map[4500])
rate at position 4500 =  0.1

See also

The RateMap.get_rate() method is a more efficient way to compute the rate values at an array of locations along the genome.

We can also iterate over the intervals in the map, much as if it was a Python dictionary:

for midpoint, rate in rate_map.items():
    print(midpoint, rate, sep="\t")
2000.0	0.0
6500.0	0.1
10000.0	0.6
15500.0	0.2
25000.0	0.1

Note that the key here is the midpoint of each interval.

Operations such as in tell us whether the a give position is in the rate map:

print("Is position 10000 in the map?", 10000 in rate_map)
print("Is position -1 in the map?", -1 in rate_map)
Is position 10000 in the map? True
Is position -1 in the map? False

See also

See the Missing data section for information on how missing data is handled using this interface.

Array interface

We can also access the information from the rate map using numpy arrays. This is usually the preferred option when working with rate maps, since they can be quite large.

print("interval left position :", rate_map.left)
print("interval right position:", rate_map.right)
print("interval midpoint      :", rate_map.mid)
print("interval span          :", rate_map.span)
print("interval rate          :", rate_map.rate)
interval left position : [    0.  4000.  9000. 11000. 20000.]
interval right position: [ 4000.  9000. 11000. 20000. 30000.]
interval midpoint      : [ 2000.  6500. 10000. 15500. 25000.]
interval span          : [ 4000.  5000.  2000.  9000. 10000.]
interval rate          : [0.  0.1 0.6 0.2 0.1]

Please see the documentation for left, right, mid, span, and rate for more information.

The position array is also defined, which is equal to the left array with last element of the right array (i.e., the sequence_length) appended to it.

See also

See the Missing data section for information on how missing data is handled using this interface.

Todo

Give an example of how we might do something useful with these arrays, like plotting something, e.g.

Creating rate maps

A rate map can be created by initializing an instance of the RateMap class with a list of n+1 positions and n rates. Alternatively, uniform rate maps can be created as described below, or rates read in from a text file. The last position of a rate map is taken as the sequence length: it is your responsibility to ensure that this is the same length as the simulated chromosome to which the rate is applied.

Uniform rate maps

The most basic rate map is a uniform rate over the entire sequence. This can simply be generated by RateMap.uniform():

msprime.RateMap.uniform(sequence_length=1e8, rate=0.5)
leftrightmidspanrate
0100000000500000001000000000.50

Reading from a text file

Todo

Give an example of working with the RateMap.read_hapmap() method

Hotspots

Todo

Give an example of making a RateMap with some recurrent hotspots.

Missing data

There are often times when we do not know what the rate of a particular process is along the genome. For example, in telomeric regions, we don’t really know what the rate of recombination is, and we need some way of encoding this information.

See also

See the Missing data section for an example of running an ancestry simulation with unknown recombination rates, and the properties of the resulting tree sequence.

Following the standard convention in Python numerical libraries, we encode missing data using NaN values. Here, for example we create a rate map in which we have a uniform rate except for the 10 base pairs at either end, which are unknown:

rate_map = msprime.RateMap(position=[0, 10, 90, 100], rate=[np.nan, 0.1, np.nan])
rate_map
leftrightmidspanrate
010510nan
109050800.10
901009510nan

Values that we compute such as mean_rate correctly ignore the regions of missing data:

print("mean rate = ", rate_map.mean_rate)
mean rate =  0.1

The Mapping interface and Array interface have different semantics for dealing with missing data: the mapping interface filters out intervals that are missing, whereas the array interface leaves them in.

Array interface

The array interface in the RateMap is an efficient way of directly accessing the underlying array data. To give the user the maximum flexibility, the per-interval arrays like left are always defined over both missing and non-missing intervals:

print("left  = ", rate_map.left)
print("right = ", rate_map.right)
print("mid   = ", rate_map.mid)
print("span  = ", rate_map.span)
left  =  [ 0. 10. 90.]
right =  [ 10.  90. 100.]
mid   =  [ 5. 50. 95.]
span  =  [10. 80. 10.]

We can use the missing and non_missing arrays to get access to the missing and non-missing portions of the map. These are numpy boolean arrays, which we can use to index into the other per-interval arrays. For example, we can compare the total span of missing and non-missing intervals:

print("Total missing span      = ", np.sum(rate_map.span[rate_map.missing]))
print("Total non-missing span  = ", np.sum(rate_map.span[rate_map.non_missing]))
Total missing span      =  20.0
Total non-missing span  =  80.0

Mapping interace

The Mapping interface takes the opposite approach to missing data to the Array interface: missing intervals are automatically filtered out. Consider what we get when we iterate over the items() in the map:

for key, value in rate_map.items():
    print(f"{key} -> {value}")
50.0 -> 0.1

As far as the mapping interface is concerned, we have one interval in this map, not three. This logic is reflected by the in operation: we only consider a position to be “in” the map if it’s in a non-missing interval:

print("Is position 5 in the map? ", 5 in rate_map)
print("Is position 50 in the map? ", 50 in rate_map)
Is position 5 in the map?  False
Is position 50 in the map?  True

An important consequence of this definition is that the len() of a RateMap is the number of non-empty intervals:

print("len                       = ", len(rate_map))
print("num_intervals             = ", rate_map.num_intervals)
print("num_non_missing_intervals = ", rate_map.num_non_missing_intervals)
len                       =  1
num_intervals             =  3
num_non_missing_intervals =  1

Important

Because the len() of a RateMap is defined as the number of non-empty intervals we recommend using the num_intervals and num_non_missing_intervals for clarity.

Slicing

Sometimes we are interested in running a simulation for a small portion of a map, for example when we want to focus on a small region of a much larger chromosome. We can do this using the slice() method, which extracts a target region from a given map and marks the flanking regions as missing data, so that the original coordinate system is maintained.

Todo

Example of the slice method and slice notation on small maps so we can illustrate them.

Simulation example

For example, if you wish to simulate a 40 kb section of human chromosome 1 from position 0.7Mb, but keep the standard chromosome 1 genomic coordinates, you could do this:

import io
# First and last sections of human chr 1 build 36 genetic map taken from
# https://ftp.ncbi.nlm.nih.gov/hapmap/recombination/latest/rates/
hapmap_chr1_snippet = io.StringIO("""\
chr position COMBINED_rate(cM/Mb) Genetic_Map(cM)
1 72434 0.0015000000 0
1 78032 0.0015000000 0.0000083970
1 554461 0.0015000000 0.0007230405
1 554484 0.0015000000 0.0007230750
1 555296 0.0015000000 0.0007242930
1 558185 0.0015000000 0.0007286265
1 558390 0.0010000000 0.0007289340
1 711153 1.9757156611 0.0008816970
1 713682 2.0415081787 0.0058782819
1 713754 2.1264889217 0.0060252705
1 718105 2.1260252999 0.0152776238
1 719811 2.1911540342 0.0189046230
1 728873 2.1938678585 0.0387608608
1 730720 2.1951341342 0.0428129347
1 740098 1.3377804252 0.0633989027
1 742429 0.9035885389 0.0665172688
1 743132 0.9037201735 0.0671524916  # Next 256440 lines snipped
1 247184904 0.6238867396 278.0908415443
1 247185273 0 278.0910717585""")
large_ratemap = msprime.RateMap.read_hapmap(hapmap_chr1_snippet)
sliced_ratemap = large_ratemap.slice(700e3, 740e3)
ts = msprime.sim_ancestry(10, recombination_rate=sliced_ratemap, random_seed=1)

Because the recombination rate is marked as unknown in regions below position 700 000 and above position 740 000, msprime has not had to put effort into simulating recombination (and its effect on ancestry) in these regions, so the simulation completes very quickly, even though the resulting tree sequence has a sequence_length of 247 megabases.

See also

See the Missing data section for more information about how ancestry simulations with missing rate data are performed.